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"""simple docstring""" import argparse import json import logging import os import shutil import sys import tempfile import unittest from unittest import mock import torch from accelerate.utils import write_basic_config from transformers.testing_utils import TestCasePlus, get_gpu_count, run_command, slow, torch_device from transformers.utils import is_apex_available logging.basicConfig(level=logging.DEBUG) _a = logging.getLogger() def __a ( ): UpperCAmelCase_ : Tuple = argparse.ArgumentParser() parser.add_argument("-f" ) UpperCAmelCase_ : List[Any] = parser.parse_args() return args.f def __a ( __lowerCamelCase ): UpperCAmelCase_ : List[str] = {} UpperCAmelCase_ : Optional[int] = os.path.join(__lowerCamelCase, "all_results.json" ) if os.path.exists(__lowerCamelCase ): with open(__lowerCamelCase, "r" ) as f: UpperCAmelCase_ : List[Any] = json.load(__lowerCamelCase ) else: raise ValueError(f"""can't find {path}""" ) return results def __a ( ): UpperCAmelCase_ : str = torch.cuda.is_available() and torch_device == "cuda" return is_using_cuda and is_apex_available() _a = logging.StreamHandler(sys.stdout) logger.addHandler(stream_handler) class A_ (lowercase__ ): '''simple docstring''' @classmethod def UpperCamelCase__ ( cls ): """simple docstring""" # Write Accelerate config, will pick up on CPU, GPU, and multi-GPU UpperCAmelCase_ : Any = tempfile.mkdtemp() UpperCAmelCase_ : Dict = os.path.join(cls.tmpdir , "default_config.yml" ) write_basic_config(save_location=cls.configPath ) UpperCAmelCase_ : int = ["accelerate", "launch", "--config_file", cls.configPath] @classmethod def UpperCamelCase__ ( cls ): """simple docstring""" shutil.rmtree(cls.tmpdir ) @mock.patch.dict(os.environ , {"WANDB_MODE": "offline"} ) def UpperCamelCase__ ( self ): """simple docstring""" UpperCAmelCase_ : List[Any] = self.get_auto_remove_tmp_dir() UpperCAmelCase_ : Union[str, Any] = F""" {self.examples_dir}/pytorch/text-classification/run_glue_no_trainer.py --model_name_or_path distilbert-base-uncased --output_dir {tmp_dir} --train_file ./tests/fixtures/tests_samples/MRPC/train.csv --validation_file ./tests/fixtures/tests_samples/MRPC/dev.csv --per_device_train_batch_size=2 --per_device_eval_batch_size=1 --learning_rate=1e-4 --seed=42 --checkpointing_steps epoch --with_tracking """.split() if is_cuda_and_apex_available(): testargs.append("--fp16" ) run_command(self._launch_args + testargs ) UpperCAmelCase_ : int = get_results(lowercase_ ) self.assertGreaterEqual(result["eval_accuracy"] , 0.75 ) self.assertTrue(os.path.exists(os.path.join(lowercase_ , "epoch_0" ) ) ) self.assertTrue(os.path.exists(os.path.join(lowercase_ , "glue_no_trainer" ) ) ) @mock.patch.dict(os.environ , {"WANDB_MODE": "offline"} ) def UpperCamelCase__ ( self ): """simple docstring""" UpperCAmelCase_ : List[str] = self.get_auto_remove_tmp_dir() UpperCAmelCase_ : Optional[Any] = F""" {self.examples_dir}/pytorch/language-modeling/run_clm_no_trainer.py --model_name_or_path distilgpt2 --train_file ./tests/fixtures/sample_text.txt --validation_file ./tests/fixtures/sample_text.txt --block_size 128 --per_device_train_batch_size 5 --per_device_eval_batch_size 5 --num_train_epochs 2 --output_dir {tmp_dir} --checkpointing_steps epoch --with_tracking """.split() if torch.cuda.device_count() > 1: # Skipping because there are not enough batches to train the model + would need a drop_last to work. return run_command(self._launch_args + testargs ) UpperCAmelCase_ : List[str] = get_results(lowercase_ ) self.assertLess(result["perplexity"] , 100 ) self.assertTrue(os.path.exists(os.path.join(lowercase_ , "epoch_0" ) ) ) self.assertTrue(os.path.exists(os.path.join(lowercase_ , "clm_no_trainer" ) ) ) @mock.patch.dict(os.environ , {"WANDB_MODE": "offline"} ) def UpperCamelCase__ ( self ): """simple docstring""" UpperCAmelCase_ : str = self.get_auto_remove_tmp_dir() UpperCAmelCase_ : str = F""" {self.examples_dir}/pytorch/language-modeling/run_mlm_no_trainer.py --model_name_or_path distilroberta-base --train_file ./tests/fixtures/sample_text.txt --validation_file ./tests/fixtures/sample_text.txt --output_dir {tmp_dir} --num_train_epochs=1 --checkpointing_steps epoch --with_tracking """.split() run_command(self._launch_args + testargs ) UpperCAmelCase_ : int = get_results(lowercase_ ) self.assertLess(result["perplexity"] , 42 ) self.assertTrue(os.path.exists(os.path.join(lowercase_ , "epoch_0" ) ) ) self.assertTrue(os.path.exists(os.path.join(lowercase_ , "mlm_no_trainer" ) ) ) @mock.patch.dict(os.environ , {"WANDB_MODE": "offline"} ) def UpperCamelCase__ ( self ): """simple docstring""" # with so little data distributed training needs more epochs to get the score on par with 0/1 gpu UpperCAmelCase_ : Union[str, Any] = 7 if get_gpu_count() > 1 else 2 UpperCAmelCase_ : int = self.get_auto_remove_tmp_dir() UpperCAmelCase_ : str = F""" {self.examples_dir}/pytorch/token-classification/run_ner_no_trainer.py --model_name_or_path bert-base-uncased --train_file tests/fixtures/tests_samples/conll/sample.json --validation_file tests/fixtures/tests_samples/conll/sample.json --output_dir {tmp_dir} --learning_rate=2e-4 --per_device_train_batch_size=2 --per_device_eval_batch_size=2 --num_train_epochs={epochs} --seed 7 --checkpointing_steps epoch --with_tracking """.split() run_command(self._launch_args + testargs ) UpperCAmelCase_ : List[Any] = get_results(lowercase_ ) self.assertGreaterEqual(result["eval_accuracy"] , 0.75 ) self.assertLess(result["train_loss"] , 0.5 ) self.assertTrue(os.path.exists(os.path.join(lowercase_ , "epoch_0" ) ) ) self.assertTrue(os.path.exists(os.path.join(lowercase_ , "ner_no_trainer" ) ) ) @unittest.skip(reason="Fix me @muellerzr" ) @mock.patch.dict(os.environ , {"WANDB_MODE": "offline"} ) def UpperCamelCase__ ( self ): """simple docstring""" UpperCAmelCase_ : Optional[int] = self.get_auto_remove_tmp_dir() UpperCAmelCase_ : Tuple = F""" {self.examples_dir}/pytorch/question-answering/run_qa_no_trainer.py --model_name_or_path bert-base-uncased --version_2_with_negative --train_file tests/fixtures/tests_samples/SQUAD/sample.json --validation_file tests/fixtures/tests_samples/SQUAD/sample.json --output_dir {tmp_dir} --seed=42 --max_train_steps=10 --num_warmup_steps=2 --learning_rate=2e-4 --per_device_train_batch_size=2 --per_device_eval_batch_size=1 --checkpointing_steps epoch --with_tracking """.split() run_command(self._launch_args + testargs ) UpperCAmelCase_ : int = get_results(lowercase_ ) # Because we use --version_2_with_negative the testing script uses SQuAD v2 metrics. self.assertGreaterEqual(result["eval_f1"] , 28 ) self.assertGreaterEqual(result["eval_exact"] , 28 ) self.assertTrue(os.path.exists(os.path.join(lowercase_ , "epoch_0" ) ) ) self.assertTrue(os.path.exists(os.path.join(lowercase_ , "qa_no_trainer" ) ) ) @mock.patch.dict(os.environ , {"WANDB_MODE": "offline"} ) def UpperCamelCase__ ( self ): """simple docstring""" UpperCAmelCase_ : Union[str, Any] = self.get_auto_remove_tmp_dir() UpperCAmelCase_ : Any = F""" {self.examples_dir}/pytorch/multiple-choice/run_swag_no_trainer.py --model_name_or_path bert-base-uncased --train_file tests/fixtures/tests_samples/swag/sample.json --validation_file tests/fixtures/tests_samples/swag/sample.json --output_dir {tmp_dir} --max_train_steps=20 --num_warmup_steps=2 --learning_rate=2e-4 --per_device_train_batch_size=2 --per_device_eval_batch_size=1 --with_tracking """.split() run_command(self._launch_args + testargs ) UpperCAmelCase_ : Optional[int] = get_results(lowercase_ ) self.assertGreaterEqual(result["eval_accuracy"] , 0.8 ) self.assertTrue(os.path.exists(os.path.join(lowercase_ , "swag_no_trainer" ) ) ) @slow @mock.patch.dict(os.environ , {"WANDB_MODE": "offline"} ) def UpperCamelCase__ ( self ): """simple docstring""" UpperCAmelCase_ : Dict = self.get_auto_remove_tmp_dir() UpperCAmelCase_ : List[str] = F""" {self.examples_dir}/pytorch/summarization/run_summarization_no_trainer.py --model_name_or_path t5-small --train_file tests/fixtures/tests_samples/xsum/sample.json --validation_file tests/fixtures/tests_samples/xsum/sample.json --output_dir {tmp_dir} --max_train_steps=50 --num_warmup_steps=8 --learning_rate=2e-4 --per_device_train_batch_size=2 --per_device_eval_batch_size=1 --checkpointing_steps epoch --with_tracking """.split() run_command(self._launch_args + testargs ) UpperCAmelCase_ : str = get_results(lowercase_ ) self.assertGreaterEqual(result["eval_rouge1"] , 10 ) self.assertGreaterEqual(result["eval_rouge2"] , 2 ) self.assertGreaterEqual(result["eval_rougeL"] , 7 ) self.assertGreaterEqual(result["eval_rougeLsum"] , 7 ) self.assertTrue(os.path.exists(os.path.join(lowercase_ , "epoch_0" ) ) ) self.assertTrue(os.path.exists(os.path.join(lowercase_ , "summarization_no_trainer" ) ) ) @slow @mock.patch.dict(os.environ , {"WANDB_MODE": "offline"} ) def UpperCamelCase__ ( self ): """simple docstring""" UpperCAmelCase_ : List[str] = self.get_auto_remove_tmp_dir() UpperCAmelCase_ : List[str] = F""" {self.examples_dir}/pytorch/translation/run_translation_no_trainer.py --model_name_or_path sshleifer/student_marian_en_ro_6_1 --source_lang en --target_lang ro --train_file tests/fixtures/tests_samples/wmt16/sample.json --validation_file tests/fixtures/tests_samples/wmt16/sample.json --output_dir {tmp_dir} --max_train_steps=50 --num_warmup_steps=8 --num_beams=6 --learning_rate=3e-3 --per_device_train_batch_size=2 --per_device_eval_batch_size=1 --source_lang en_XX --target_lang ro_RO --checkpointing_steps epoch --with_tracking """.split() run_command(self._launch_args + testargs ) UpperCAmelCase_ : Any = get_results(lowercase_ ) self.assertGreaterEqual(result["eval_bleu"] , 30 ) self.assertTrue(os.path.exists(os.path.join(lowercase_ , "epoch_0" ) ) ) self.assertTrue(os.path.exists(os.path.join(lowercase_ , "translation_no_trainer" ) ) ) @slow def UpperCamelCase__ ( self ): """simple docstring""" UpperCAmelCase_ : Optional[Any] = logging.StreamHandler(sys.stdout ) logger.addHandler(lowercase_ ) UpperCAmelCase_ : Union[str, Any] = self.get_auto_remove_tmp_dir() UpperCAmelCase_ : int = F""" {self.examples_dir}/pytorch/semantic-segmentation/run_semantic_segmentation_no_trainer.py --dataset_name huggingface/semantic-segmentation-test-sample --output_dir {tmp_dir} --max_train_steps=10 --num_warmup_steps=2 --learning_rate=2e-4 --per_device_train_batch_size=2 --per_device_eval_batch_size=1 --checkpointing_steps epoch """.split() run_command(self._launch_args + testargs ) UpperCAmelCase_ : Tuple = get_results(lowercase_ ) self.assertGreaterEqual(result["eval_overall_accuracy"] , 0.10 ) @mock.patch.dict(os.environ , {"WANDB_MODE": "offline"} ) def UpperCamelCase__ ( self ): """simple docstring""" UpperCAmelCase_ : Tuple = self.get_auto_remove_tmp_dir() UpperCAmelCase_ : Optional[int] = F""" {self.examples_dir}/pytorch/image-classification/run_image_classification_no_trainer.py --model_name_or_path google/vit-base-patch16-224-in21k --dataset_name hf-internal-testing/cats_vs_dogs_sample --learning_rate 1e-4 --per_device_train_batch_size 2 --per_device_eval_batch_size 1 --max_train_steps 2 --train_val_split 0.1 --seed 42 --output_dir {tmp_dir} --with_tracking --checkpointing_steps 1 """.split() if is_cuda_and_apex_available(): testargs.append("--fp16" ) run_command(self._launch_args + testargs ) UpperCAmelCase_ : Optional[Any] = get_results(lowercase_ ) # The base model scores a 25% self.assertGreaterEqual(result["eval_accuracy"] , 0.6 ) self.assertTrue(os.path.exists(os.path.join(lowercase_ , "step_1" ) ) ) self.assertTrue(os.path.exists(os.path.join(lowercase_ , "image_classification_no_trainer" ) ) )
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'''simple docstring''' import argparse import json import os import fairseq import torch from fairseq.data import Dictionary from transformers import ( WavaVecaConfig, WavaVecaCTCTokenizer, WavaVecaFeatureExtractor, WavaVecaForCTC, WavaVecaForPreTraining, WavaVecaProcessor, logging, ) from transformers.models.wavaveca.modeling_wavaveca import WavaVecaForSequenceClassification logging.set_verbosity_info() UpperCamelCase__: int = logging.get_logger(__name__) UpperCamelCase__: Dict = { "post_extract_proj": "feature_projection.projection", "encoder.pos_conv.0": "encoder.pos_conv_embed.conv", "self_attn.k_proj": "encoder.layers.*.attention.k_proj", "self_attn.v_proj": "encoder.layers.*.attention.v_proj", "self_attn.q_proj": "encoder.layers.*.attention.q_proj", "self_attn.out_proj": "encoder.layers.*.attention.out_proj", "self_attn_layer_norm": "encoder.layers.*.layer_norm", "fc1": "encoder.layers.*.feed_forward.intermediate_dense", "fc2": "encoder.layers.*.feed_forward.output_dense", "final_layer_norm": "encoder.layers.*.final_layer_norm", "encoder.layer_norm": "encoder.layer_norm", "adapter_layer": "encoder.layers.*.adapter_layer", "w2v_model.layer_norm": "feature_projection.layer_norm", "quantizer.weight_proj": "quantizer.weight_proj", "quantizer.vars": "quantizer.codevectors", "project_q": "project_q", "final_proj": "project_hid", "w2v_encoder.proj": "lm_head", "mask_emb": "masked_spec_embed", "pooling_layer.linear": "projector", "pooling_layer.projection": "classifier", } UpperCamelCase__: Optional[Any] = [ "lm_head", "quantizer.weight_proj", "quantizer.codevectors", "project_q", "project_hid", "projector", "classifier", ] def snake_case_ ( _lowerCAmelCase : str ) -> Union[str, Any]: UpperCAmelCase : Optional[int] = {} with open(_lowerCAmelCase , '''r''' ) as file: for line_number, line in enumerate(_lowerCAmelCase ): UpperCAmelCase : List[str] = line.strip() if line: UpperCAmelCase : str = line.split() UpperCAmelCase : Union[str, Any] = line_number UpperCAmelCase : List[Any] = words[0] UpperCAmelCase : Union[str, Any] = value return result def snake_case_ ( _lowerCAmelCase : int , _lowerCAmelCase : Optional[Any] , _lowerCAmelCase : Optional[Any] , _lowerCAmelCase : Tuple , _lowerCAmelCase : str ) -> int: for attribute in key.split('''.''' ): UpperCAmelCase : Dict = getattr(_lowerCAmelCase , _lowerCAmelCase ) UpperCAmelCase : Dict = None for param_key in PARAM_MAPPING.keys(): if full_name.endswith(_lowerCAmelCase ): UpperCAmelCase : Any = PARAM_MAPPING[full_name.split('''.''' )[-1]] UpperCAmelCase : Dict = '''param''' if weight_type is not None and weight_type != "param": UpperCAmelCase : Optional[int] = getattr(_lowerCAmelCase , _lowerCAmelCase ).shape elif weight_type is not None and weight_type == "param": UpperCAmelCase : List[Any] = hf_pointer for attribute in hf_param_name.split('''.''' ): UpperCAmelCase : Optional[Any] = getattr(_lowerCAmelCase , _lowerCAmelCase ) UpperCAmelCase : int = shape_pointer.shape # let's reduce dimension UpperCAmelCase : Union[str, Any] = value[0] else: UpperCAmelCase : List[Any] = hf_pointer.shape if hf_shape != value.shape: raise ValueError( f"""Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be""" f""" {value.shape} for {full_name}""" ) if weight_type == "weight": UpperCAmelCase : int = value elif weight_type == "weight_g": UpperCAmelCase : str = value elif weight_type == "weight_v": UpperCAmelCase : Dict = value elif weight_type == "bias": UpperCAmelCase : str = value elif weight_type == "param": for attribute in hf_param_name.split('''.''' ): UpperCAmelCase : int = getattr(_lowerCAmelCase , _lowerCAmelCase ) UpperCAmelCase : Optional[int] = value else: UpperCAmelCase : Tuple = value logger.info(f"""{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.""" ) def snake_case_ ( _lowerCAmelCase : Optional[int] , _lowerCAmelCase : Any , _lowerCAmelCase : List[Any] , _lowerCAmelCase : Dict , _lowerCAmelCase : List[Any] ) -> List[Any]: UpperCAmelCase : List[str] = None for param_key in PARAM_MAPPING.keys(): if full_name.endswith(_lowerCAmelCase ): UpperCAmelCase : List[str] = PARAM_MAPPING[full_name.split('''.''' )[-1]] UpperCAmelCase : Any = '''param''' if weight_type is not None and weight_type != "param": UpperCAmelCase : Optional[int] = '''.'''.join([key, weight_type] ) elif weight_type is not None and weight_type == "param": UpperCAmelCase : Optional[int] = '''.'''.join([key, hf_param_name] ) else: UpperCAmelCase : List[Any] = key UpperCAmelCase : Tuple = value if '''lm_head''' in full_key else value[0] UpperCamelCase__: Tuple = { "W_a": "linear_1.weight", "W_b": "linear_2.weight", "b_a": "linear_1.bias", "b_b": "linear_2.bias", "ln_W": "norm.weight", "ln_b": "norm.bias", } def snake_case_ ( _lowerCAmelCase : str , _lowerCAmelCase : List[str] , _lowerCAmelCase : Any=None , _lowerCAmelCase : Optional[Any]=None ) -> int: UpperCAmelCase : List[Any] = False for key, mapped_key in MAPPING.items(): UpperCAmelCase : int = '''wav2vec2.''' + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key if key in name or key.split('''w2v_model.''' )[-1] == name.split('''.''' )[0]: UpperCAmelCase : Optional[Any] = True if "*" in mapped_key: UpperCAmelCase : Tuple = name.split(_lowerCAmelCase )[0].split('''.''' )[-2] UpperCAmelCase : List[Any] = mapped_key.replace('''*''' , _lowerCAmelCase ) if "weight_g" in name: UpperCAmelCase : str = '''weight_g''' elif "weight_v" in name: UpperCAmelCase : int = '''weight_v''' elif "bias" in name: UpperCAmelCase : int = '''bias''' elif "weight" in name: # TODO: don't match quantizer.weight_proj UpperCAmelCase : List[str] = '''weight''' else: UpperCAmelCase : Dict = None if hf_dict is not None: rename_dict(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ) else: set_recursively(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ) return is_used return is_used def snake_case_ ( _lowerCAmelCase : Union[str, Any] , _lowerCAmelCase : Optional[Any] , _lowerCAmelCase : Any ) -> Any: UpperCAmelCase : Dict = [] UpperCAmelCase : Dict = fairseq_model.state_dict() UpperCAmelCase : Union[str, Any] = hf_model.wavaveca.feature_extractor for name, value in fairseq_dict.items(): UpperCAmelCase : Dict = False if "conv_layers" in name: load_conv_layer( _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , hf_model.config.feat_extract_norm == '''group''' , ) UpperCAmelCase : Any = True else: UpperCAmelCase : Optional[Any] = load_wavaveca_layer(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ) if not is_used: unused_weights.append(_lowerCAmelCase ) logger.warning(f"""Unused weights: {unused_weights}""" ) def snake_case_ ( _lowerCAmelCase : Optional[int] , _lowerCAmelCase : Tuple , _lowerCAmelCase : Any , _lowerCAmelCase : Optional[int] , _lowerCAmelCase : Optional[int] ) -> Union[str, Any]: UpperCAmelCase : Any = full_name.split('''conv_layers.''' )[-1] UpperCAmelCase : Optional[int] = name.split('''.''' ) UpperCAmelCase : Tuple = int(items[0] ) UpperCAmelCase : Tuple = int(items[1] ) if type_id == 0: if "bias" in name: if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape: raise ValueError( f"""{full_name} has size {value.shape}, but""" f""" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.""" ) UpperCAmelCase : Tuple = value logger.info(f"""Feat extract conv layer {layer_id} was initialized from {full_name}.""" ) elif "weight" in name: if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape: raise ValueError( f"""{full_name} has size {value.shape}, but""" f""" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.""" ) UpperCAmelCase : Union[str, Any] = value logger.info(f"""Feat extract conv layer {layer_id} was initialized from {full_name}.""" ) elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm): if "bias" in name: if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape: raise ValueError( f"""{full_name} has size {value.shape}, but""" f""" {feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape} was found.""" ) UpperCAmelCase : Union[str, Any] = value logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.""" ) elif "weight" in name: if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape: raise ValueError( f"""{full_name} has size {value.shape}, but""" f""" {feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape} was found.""" ) UpperCAmelCase : List[str] = value logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.""" ) else: unused_weights.append(_lowerCAmelCase ) @torch.no_grad() def snake_case_ ( _lowerCAmelCase : Union[str, Any] , _lowerCAmelCase : List[Any] , _lowerCAmelCase : Dict=None , _lowerCAmelCase : List[Any]=None , _lowerCAmelCase : int=True , _lowerCAmelCase : Optional[int]=False ) -> Dict: if config_path is not None: UpperCAmelCase : List[str] = WavaVecaConfig.from_pretrained(_lowerCAmelCase ) else: UpperCAmelCase : List[Any] = WavaVecaConfig() if is_seq_class: UpperCAmelCase : Optional[Any] = read_txt_into_dict(_lowerCAmelCase ) UpperCAmelCase : Optional[int] = idalabel UpperCAmelCase : Optional[Any] = WavaVecaForSequenceClassification(_lowerCAmelCase ) UpperCAmelCase : Dict = WavaVecaFeatureExtractor( feature_size=1 , sampling_rate=16000 , padding_value=0 , do_normalize=_lowerCAmelCase , return_attention_mask=_lowerCAmelCase , ) feature_extractor.save_pretrained(_lowerCAmelCase ) elif is_finetuned: if dict_path: UpperCAmelCase : Dict = Dictionary.load(_lowerCAmelCase ) # important change bos & pad token id since CTC symbol is <pad> and # not <s> as in fairseq UpperCAmelCase : Any = target_dict.pad_index UpperCAmelCase : Tuple = target_dict.bos_index UpperCAmelCase : Optional[int] = target_dict.eos_index UpperCAmelCase : Union[str, Any] = len(target_dict.symbols ) UpperCAmelCase : Dict = os.path.join(_lowerCAmelCase , '''vocab.json''' ) if not os.path.isdir(_lowerCAmelCase ): logger.error('''--pytorch_dump_folder_path ({}) should be a directory'''.format(_lowerCAmelCase ) ) return os.makedirs(_lowerCAmelCase , exist_ok=_lowerCAmelCase ) UpperCAmelCase : List[Any] = target_dict.indices # fairseq has the <pad> and <s> switched UpperCAmelCase : List[str] = 0 UpperCAmelCase : List[str] = 1 with open(_lowerCAmelCase , '''w''' , encoding='''utf-8''' ) as vocab_handle: json.dump(_lowerCAmelCase , _lowerCAmelCase ) UpperCAmelCase : Optional[int] = WavaVecaCTCTokenizer( _lowerCAmelCase , unk_token=target_dict.unk_word , pad_token=target_dict.pad_word , bos_token=target_dict.bos_word , eos_token=target_dict.eos_word , word_delimiter_token='''|''' , do_lower_case=_lowerCAmelCase , ) UpperCAmelCase : int = True if config.feat_extract_norm == '''layer''' else False UpperCAmelCase : int = WavaVecaFeatureExtractor( feature_size=1 , sampling_rate=16000 , padding_value=0 , do_normalize=_lowerCAmelCase , return_attention_mask=_lowerCAmelCase , ) UpperCAmelCase : str = WavaVecaProcessor(feature_extractor=_lowerCAmelCase , tokenizer=_lowerCAmelCase ) processor.save_pretrained(_lowerCAmelCase ) UpperCAmelCase : Union[str, Any] = WavaVecaForCTC(_lowerCAmelCase ) else: UpperCAmelCase : Dict = WavaVecaForPreTraining(_lowerCAmelCase ) if is_finetuned or is_seq_class: UpperCAmelCase , UpperCAmelCase , UpperCAmelCase : List[str] = fairseq.checkpoint_utils.load_model_ensemble_and_task( [checkpoint_path] , arg_overrides={'''data''': '''/'''.join(dict_path.split('''/''' )[:-1] )} ) else: UpperCAmelCase : Optional[Any] = argparse.Namespace(task='''audio_pretraining''' ) UpperCAmelCase : List[Any] = fairseq.tasks.setup_task(_lowerCAmelCase ) UpperCAmelCase , UpperCAmelCase , UpperCAmelCase : int = fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path] , task=_lowerCAmelCase ) UpperCAmelCase : Optional[int] = model[0].eval() recursively_load_weights(_lowerCAmelCase , _lowerCAmelCase , not is_finetuned ) hf_wavavec.save_pretrained(_lowerCAmelCase ) if __name__ == "__main__": UpperCamelCase__: Dict = argparse.ArgumentParser() parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.") parser.add_argument("--checkpoint_path", default=None, type=str, help="Path to fairseq checkpoint") parser.add_argument("--dict_path", default=None, type=str, help="Path to dict of fine-tuned model") parser.add_argument("--config_path", default=None, type=str, help="Path to hf config.json of model to convert") parser.add_argument( "--not_finetuned", action="store_true", help="Whether the model to convert is a fine-tuned model or not" ) parser.add_argument( "--is_seq_class", action="store_true", help="Whether the model to convert is a fine-tuned sequence classification model or not", ) UpperCamelCase__: Any = parser.parse_args() UpperCamelCase__: int = not args.not_finetuned and not args.is_seq_class convert_wavaveca_checkpoint( args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, is_finetuned, args.is_seq_class, )
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def _UpperCAmelCase ( SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : int ): if not isinstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ): raise ValueError('iterations must be defined as integers' ) if not isinstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) or not number >= 1: raise ValueError( 'starting number must be\n and integer and be more than 0' ) if not iterations >= 1: raise ValueError('Iterations must be done more than 0 times to play FizzBuzz' ) __UpperCamelCase ='' while number <= iterations: if number % 3 == 0: out += "Fizz" if number % 5 == 0: out += "Buzz" if 0 not in (number % 3, number % 5): out += str(SCREAMING_SNAKE_CASE__ ) # print(out) number += 1 out += " " return out if __name__ == "__main__": import doctest doctest.testmod()
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'''simple docstring''' import os import tempfile import unittest import numpy as np from diffusers.utils import is_flax_available from diffusers.utils.testing_utils import require_flax, slow if is_flax_available(): import jax import jax.numpy as jnp from flax.jax_utils import replicate from flax.training.common_utils import shard from diffusers import FlaxDDIMScheduler, FlaxDiffusionPipeline, FlaxStableDiffusionPipeline @require_flax class SCREAMING_SNAKE_CASE( unittest.TestCase ): """simple docstring""" def A ( self : str ) -> int: with tempfile.TemporaryDirectory() as tmpdirname: # pipeline has Flax weights UpperCAmelCase : Optional[Any] = FlaxDiffusionPipeline.from_pretrained( '''hf-internal-testing/tiny-stable-diffusion-pipe''' , safety_checker=__snake_case , cache_dir=__snake_case ) UpperCAmelCase : str = [t[-1] for t in os.walk(os.path.join(__snake_case , os.listdir(__snake_case )[0] , '''snapshots''' ) )] UpperCAmelCase : str = [item for sublist in all_root_files for item in sublist] # None of the downloaded files should be a PyTorch file even if we have some here: # https://huggingface.co/hf-internal-testing/tiny-stable-diffusion-pipe/blob/main/unet/diffusion_pytorch_model.bin assert not any(f.endswith('''.bin''' ) for f in files ) @slow @require_flax class SCREAMING_SNAKE_CASE( unittest.TestCase ): """simple docstring""" def A ( self : List[str] ) -> Dict: UpperCAmelCase , UpperCAmelCase : str = FlaxStableDiffusionPipeline.from_pretrained( '''hf-internal-testing/tiny-stable-diffusion-pipe''' , safety_checker=__snake_case ) UpperCAmelCase : List[Any] = ( '''A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of''' ''' field, close up, split lighting, cinematic''' ) UpperCAmelCase : List[str] = jax.random.PRNGKey(0 ) UpperCAmelCase : Optional[Any] = 4 UpperCAmelCase : Optional[Any] = jax.device_count() UpperCAmelCase : Tuple = num_samples * [prompt] UpperCAmelCase : int = pipeline.prepare_inputs(__snake_case ) # shard inputs and rng UpperCAmelCase : Tuple = replicate(__snake_case ) UpperCAmelCase : Any = jax.random.split(__snake_case , __snake_case ) UpperCAmelCase : Optional[Any] = shard(__snake_case ) UpperCAmelCase : Optional[int] = pipeline(__snake_case , __snake_case , __snake_case , __snake_case , jit=__snake_case ).images assert images.shape == (num_samples, 1, 64, 64, 3) if jax.device_count() == 8: assert np.abs(np.abs(images[0, 0, :2, :2, -2:] , dtype=np.floataa ).sum() - 4.1_51_47_45 ) < 1E-3 assert np.abs(np.abs(__snake_case , dtype=np.floataa ).sum() - 4_99_47.8_75 ) < 5E-1 UpperCAmelCase : Union[str, Any] = pipeline.numpy_to_pil(np.asarray(images.reshape((num_samples,) + images.shape[-3:] ) ) ) assert len(__snake_case ) == num_samples def A ( self : List[Any] ) -> List[str]: UpperCAmelCase , UpperCAmelCase : Union[str, Any] = FlaxStableDiffusionPipeline.from_pretrained( '''CompVis/stable-diffusion-v1-4''' , revision='''flax''' , safety_checker=__snake_case ) UpperCAmelCase : Dict = ( '''A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of''' ''' field, close up, split lighting, cinematic''' ) UpperCAmelCase : Optional[Any] = jax.random.PRNGKey(0 ) UpperCAmelCase : Any = 50 UpperCAmelCase : Union[str, Any] = jax.device_count() UpperCAmelCase : int = num_samples * [prompt] UpperCAmelCase : Union[str, Any] = pipeline.prepare_inputs(__snake_case ) # shard inputs and rng UpperCAmelCase : Dict = replicate(__snake_case ) UpperCAmelCase : int = jax.random.split(__snake_case , __snake_case ) UpperCAmelCase : Tuple = shard(__snake_case ) UpperCAmelCase : Tuple = pipeline(__snake_case , __snake_case , __snake_case , __snake_case , jit=__snake_case ).images assert images.shape == (num_samples, 1, 512, 512, 3) if jax.device_count() == 8: assert np.abs((np.abs(images[0, 0, :2, :2, -2:] , dtype=np.floataa ).sum() - 0.05_65_24_01) ) < 1E-3 assert np.abs((np.abs(__snake_case , dtype=np.floataa ).sum() - 2_38_38_08.2) ) < 5E-1 def A ( self : int ) -> Dict: UpperCAmelCase , UpperCAmelCase : List[str] = FlaxStableDiffusionPipeline.from_pretrained( '''CompVis/stable-diffusion-v1-4''' , revision='''bf16''' , dtype=jnp.bfloataa , safety_checker=__snake_case ) UpperCAmelCase : Dict = ( '''A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of''' ''' field, close up, split lighting, cinematic''' ) UpperCAmelCase : Union[str, Any] = jax.random.PRNGKey(0 ) UpperCAmelCase : List[str] = 50 UpperCAmelCase : Union[str, Any] = jax.device_count() UpperCAmelCase : List[Any] = num_samples * [prompt] UpperCAmelCase : int = pipeline.prepare_inputs(__snake_case ) # shard inputs and rng UpperCAmelCase : Tuple = replicate(__snake_case ) UpperCAmelCase : List[Any] = jax.random.split(__snake_case , __snake_case ) UpperCAmelCase : Optional[int] = shard(__snake_case ) UpperCAmelCase : Optional[Any] = pipeline(__snake_case , __snake_case , __snake_case , __snake_case , jit=__snake_case ).images assert images.shape == (num_samples, 1, 512, 512, 3) if jax.device_count() == 8: assert np.abs((np.abs(images[0, 0, :2, :2, -2:] , dtype=np.floataa ).sum() - 0.04_00_39_06) ) < 1E-3 assert np.abs((np.abs(__snake_case , dtype=np.floataa ).sum() - 2_37_35_16.75) ) < 5E-1 def A ( self : int ) -> Any: UpperCAmelCase , UpperCAmelCase : Dict = FlaxStableDiffusionPipeline.from_pretrained( '''CompVis/stable-diffusion-v1-4''' , revision='''bf16''' , dtype=jnp.bfloataa ) UpperCAmelCase : List[str] = ( '''A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of''' ''' field, close up, split lighting, cinematic''' ) UpperCAmelCase : List[str] = jax.random.PRNGKey(0 ) UpperCAmelCase : Union[str, Any] = 50 UpperCAmelCase : Optional[int] = jax.device_count() UpperCAmelCase : List[str] = num_samples * [prompt] UpperCAmelCase : Dict = pipeline.prepare_inputs(__snake_case ) # shard inputs and rng UpperCAmelCase : Tuple = replicate(__snake_case ) UpperCAmelCase : Any = jax.random.split(__snake_case , __snake_case ) UpperCAmelCase : str = shard(__snake_case ) UpperCAmelCase : Optional[int] = pipeline(__snake_case , __snake_case , __snake_case , __snake_case , jit=__snake_case ).images assert images.shape == (num_samples, 1, 512, 512, 3) if jax.device_count() == 8: assert np.abs((np.abs(images[0, 0, :2, :2, -2:] , dtype=np.floataa ).sum() - 0.04_00_39_06) ) < 1E-3 assert np.abs((np.abs(__snake_case , dtype=np.floataa ).sum() - 2_37_35_16.75) ) < 5E-1 def A ( self : Tuple ) -> Optional[Any]: UpperCAmelCase : int = FlaxDDIMScheduler( beta_start=0.0_00_85 , beta_end=0.0_12 , beta_schedule='''scaled_linear''' , set_alpha_to_one=__snake_case , steps_offset=1 , ) UpperCAmelCase , UpperCAmelCase : Tuple = FlaxStableDiffusionPipeline.from_pretrained( '''CompVis/stable-diffusion-v1-4''' , revision='''bf16''' , dtype=jnp.bfloataa , scheduler=__snake_case , safety_checker=__snake_case , ) UpperCAmelCase : Tuple = scheduler.create_state() UpperCAmelCase : Dict = scheduler_state UpperCAmelCase : str = ( '''A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of''' ''' field, close up, split lighting, cinematic''' ) UpperCAmelCase : int = jax.random.PRNGKey(0 ) UpperCAmelCase : Union[str, Any] = 50 UpperCAmelCase : Optional[Any] = jax.device_count() UpperCAmelCase : Any = num_samples * [prompt] UpperCAmelCase : Dict = pipeline.prepare_inputs(__snake_case ) # shard inputs and rng UpperCAmelCase : str = replicate(__snake_case ) UpperCAmelCase : List[str] = jax.random.split(__snake_case , __snake_case ) UpperCAmelCase : Optional[int] = shard(__snake_case ) UpperCAmelCase : Dict = pipeline(__snake_case , __snake_case , __snake_case , __snake_case , jit=__snake_case ).images assert images.shape == (num_samples, 1, 512, 512, 3) if jax.device_count() == 8: assert np.abs((np.abs(images[0, 0, :2, :2, -2:] , dtype=np.floataa ).sum() - 0.0_45_04_39_45) ) < 1E-3 assert np.abs((np.abs(__snake_case , dtype=np.floataa ).sum() - 2_34_76_93.5) ) < 5E-1 def A ( self : Any ) -> Tuple: UpperCAmelCase : List[Any] = ( '''A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of''' ''' field, close up, split lighting, cinematic''' ) UpperCAmelCase : Union[str, Any] = jax.device_count() UpperCAmelCase : List[Any] = num_samples * [prompt] UpperCAmelCase : str = jax.random.split(jax.random.PRNGKey(0 ) , __snake_case ) UpperCAmelCase , UpperCAmelCase : Tuple = FlaxStableDiffusionPipeline.from_pretrained( '''CompVis/stable-diffusion-v1-4''' , revision='''bf16''' , dtype=jnp.bfloataa , safety_checker=__snake_case , ) UpperCAmelCase : Dict = replicate(__snake_case ) UpperCAmelCase : Optional[Any] = pipeline.prepare_inputs(__snake_case ) UpperCAmelCase : List[str] = shard(__snake_case ) UpperCAmelCase : Any = pipeline(__snake_case , __snake_case , __snake_case , jit=__snake_case ).images assert images.shape == (num_samples, 1, 512, 512, 3) UpperCAmelCase : Optional[int] = images[2, 0, 256, 10:17, 1] # With memory efficient attention UpperCAmelCase , UpperCAmelCase : Any = FlaxStableDiffusionPipeline.from_pretrained( '''CompVis/stable-diffusion-v1-4''' , revision='''bf16''' , dtype=jnp.bfloataa , safety_checker=__snake_case , use_memory_efficient_attention=__snake_case , ) UpperCAmelCase : int = replicate(__snake_case ) UpperCAmelCase : int = pipeline.prepare_inputs(__snake_case ) UpperCAmelCase : List[Any] = shard(__snake_case ) UpperCAmelCase : Optional[Any] = pipeline(__snake_case , __snake_case , __snake_case , jit=__snake_case ).images assert images_eff.shape == (num_samples, 1, 512, 512, 3) UpperCAmelCase : int = images[2, 0, 256, 10:17, 1] # I checked the results visually and they are very similar. However, I saw that the max diff is `1` and the `sum` # over the 8 images is exactly `256`, which is very suspicious. Testing a random slice for now. assert abs(slice_eff - slice ).max() < 1E-2
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'''simple docstring''' from . import ( albert, align, altclip, audio_spectrogram_transformer, auto, autoformer, bark, bart, barthez, bartpho, beit, bert, bert_generation, bert_japanese, bertweet, big_bird, bigbird_pegasus, biogpt, bit, blenderbot, blenderbot_small, blip, blip_a, bloom, bridgetower, byta, camembert, canine, chinese_clip, clap, clip, clipseg, codegen, conditional_detr, convbert, convnext, convnextva, cpm, cpmant, ctrl, cvt, dataavec, deberta, deberta_va, decision_transformer, deformable_detr, deit, deprecated, deta, detr, dialogpt, dinat, distilbert, dit, donut, dpr, dpt, efficientformer, efficientnet, electra, encodec, encoder_decoder, ernie, ernie_m, esm, falcon, flaubert, flava, fnet, focalnet, fsmt, funnel, git, glpn, gpta, gpt_bigcode, gpt_neo, gpt_neox, gpt_neox_japanese, gpt_swa, gptj, gptsan_japanese, graphormer, groupvit, herbert, hubert, ibert, imagegpt, informer, instructblip, jukebox, layoutlm, layoutlmva, layoutlmva, layoutxlm, led, levit, lilt, llama, longformer, longta, luke, lxmert, mam_aaa, marian, markuplm, maskaformer, maskformer, mbart, mbartaa, mega, megatron_bert, megatron_gpta, mgp_str, mluke, mobilebert, mobilenet_va, mobilenet_va, mobilevit, mobilevitva, mpnet, mra, mta, musicgen, mvp, nat, nezha, nllb, nllb_moe, nystromformer, oneformer, open_llama, openai, opt, owlvit, pegasus, pegasus_x, perceiver, phobert, pixastruct, plbart, poolformer, prophetnet, qdqbert, rag, realm, reformer, regnet, rembert, resnet, roberta, roberta_prelayernorm, roc_bert, roformer, rwkv, sam, segformer, sew, sew_d, speech_encoder_decoder, speech_to_text, speech_to_text_a, speechta, splinter, squeezebert, swiftformer, swin, swinasr, swinva, switch_transformers, ta, table_transformer, tapas, time_series_transformer, timesformer, timm_backbone, transfo_xl, trocr, tvlt, umta, unispeech, unispeech_sat, upernet, videomae, vilt, vision_encoder_decoder, vision_text_dual_encoder, visual_bert, vit, vit_hybrid, vit_mae, vit_msn, vivit, wavaveca, wavaveca_conformer, wavaveca_phoneme, wavaveca_with_lm, wavlm, whisper, x_clip, xglm, xlm, xlm_prophetnet, xlm_roberta, xlm_roberta_xl, xlnet, xmod, yolos, yoso, )
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'''simple docstring''' import random from .binary_exp_mod import bin_exp_mod def snake_case_ ( _lowerCAmelCase : Tuple , _lowerCAmelCase : Optional[Any]=1000 ) -> int: if n < 2: return False if n % 2 == 0: return n == 2 # this means n is odd UpperCAmelCase : str = n - 1 UpperCAmelCase : List[Any] = 0 while d % 2 == 0: d /= 2 exp += 1 # n - 1=d*(2**exp) UpperCAmelCase : List[str] = 0 while count < prec: UpperCAmelCase : int = random.randint(2 , n - 1 ) UpperCAmelCase : List[str] = bin_exp_mod(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ) if b != 1: UpperCAmelCase : int = True for _ in range(_lowerCAmelCase ): if b == n - 1: UpperCAmelCase : Dict = False break UpperCAmelCase : str = b * b b %= n if flag: return False count += 1 return True if __name__ == "__main__": UpperCamelCase__: Optional[int] = abs(int(input("Enter bound : ").strip())) print("Here's the list of primes:") print(", ".join(str(i) for i in range(n + 1) if is_prime_big(i)))
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"""simple docstring""" import shutil import tempfile import unittest from transformers import SPIECE_UNDERLINE, BatchEncoding, MBartaaTokenizer, MBartaaTokenizerFast, is_torch_available from transformers.testing_utils import ( get_tests_dir, nested_simplify, require_sentencepiece, require_tokenizers, require_torch, slow, ) from ...test_tokenization_common import TokenizerTesterMixin A_ = get_tests_dir('''fixtures/test_sentencepiece.model''') if is_torch_available(): from transformers.models.mbart.modeling_mbart import shift_tokens_right A_ = 25_00_04 A_ = 25_00_20 @require_sentencepiece @require_tokenizers class lowercase( __a , unittest.TestCase ): '''simple docstring''' lowercase__ = MBartaaTokenizer lowercase__ = MBartaaTokenizerFast lowercase__ = True lowercase__ = True def UpperCamelCase_ ( self: str ): '''simple docstring''' super().setUp() # We have a SentencePiece fixture for testing _snake_case : List[str] = MBartaaTokenizer(a_, src_lang="""en_XX""", tgt_lang="""ro_RO""", keep_accents=a_ ) tokenizer.save_pretrained(self.tmpdirname ) def UpperCamelCase_ ( self: Optional[Any] ): '''simple docstring''' _snake_case : Any = """<s>""" _snake_case : List[str] = 0 self.assertEqual(self.get_tokenizer()._convert_token_to_id(a_ ), a_ ) self.assertEqual(self.get_tokenizer()._convert_id_to_token(a_ ), a_ ) def UpperCamelCase_ ( self: List[str] ): '''simple docstring''' _snake_case : Any = list(self.get_tokenizer().get_vocab().keys() ) self.assertEqual(vocab_keys[0], """<s>""" ) self.assertEqual(vocab_keys[1], """<pad>""" ) self.assertEqual(vocab_keys[-1], """<mask>""" ) self.assertEqual(len(a_ ), 1_054 ) def UpperCamelCase_ ( self: str ): '''simple docstring''' self.assertEqual(self.get_tokenizer().vocab_size, 1_054 ) def UpperCamelCase_ ( self: Tuple ): '''simple docstring''' _snake_case : List[Any] = MBartaaTokenizer(a_, src_lang="""en_XX""", tgt_lang="""ro_RO""", keep_accents=a_ ) _snake_case : List[Any] = tokenizer.tokenize("""This is a test""" ) self.assertListEqual(a_, ["""▁This""", """▁is""", """▁a""", """▁t""", """est"""] ) self.assertListEqual( tokenizer.convert_tokens_to_ids(a_ ), [value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]], ) _snake_case : List[Any] = tokenizer.tokenize("""I was born in 92000, and this is falsé.""" ) self.assertListEqual( a_, [SPIECE_UNDERLINE + """I""", SPIECE_UNDERLINE + """was""", SPIECE_UNDERLINE + """b""", """or""", """n""", SPIECE_UNDERLINE + """in""", SPIECE_UNDERLINE + """""", """9""", """2""", """0""", """0""", """0""", """,""", SPIECE_UNDERLINE + """and""", SPIECE_UNDERLINE + """this""", SPIECE_UNDERLINE + """is""", SPIECE_UNDERLINE + """f""", """al""", """s""", """é""", """."""], ) _snake_case : Any = tokenizer.convert_tokens_to_ids(a_ ) self.assertListEqual( a_, [ value + tokenizer.fairseq_offset for value in [8, 21, 84, 55, 24, 19, 7, 2, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 2, 4] ], ) _snake_case : Any = tokenizer.convert_ids_to_tokens(a_ ) self.assertListEqual( a_, [SPIECE_UNDERLINE + """I""", SPIECE_UNDERLINE + """was""", SPIECE_UNDERLINE + """b""", """or""", """n""", SPIECE_UNDERLINE + """in""", SPIECE_UNDERLINE + """""", """<unk>""", """2""", """0""", """0""", """0""", """,""", SPIECE_UNDERLINE + """and""", SPIECE_UNDERLINE + """this""", SPIECE_UNDERLINE + """is""", SPIECE_UNDERLINE + """f""", """al""", """s""", """<unk>""", """."""], ) @slow def UpperCamelCase_ ( self: Any ): '''simple docstring''' _snake_case : Union[str, Any] = {"""input_ids""": [[250_004, 11_062, 82_772, 7, 15, 82_772, 538, 51_529, 237, 17_198, 1_290, 206, 9, 215_175, 1_314, 136, 17_198, 1_290, 206, 9, 56_359, 42, 122_009, 9, 16_466, 16, 87_344, 4_537, 9, 4_717, 78_381, 6, 159_958, 7, 15, 24_480, 618, 4, 527, 22_693, 5_428, 4, 2_777, 24_480, 9_874, 4, 43_523, 594, 4, 803, 18_392, 33_189, 18, 4, 43_523, 24_447, 12_399, 100, 24_955, 83_658, 9_626, 144_057, 15, 839, 22_335, 16, 136, 24_955, 83_658, 83_479, 15, 39_102, 724, 16, 678, 645, 2_789, 1_328, 4_589, 42, 122_009, 115_774, 23, 805, 1_328, 46_876, 7, 136, 53_894, 1_940, 42_227, 41_159, 17_721, 823, 425, 4, 27_512, 98_722, 206, 136, 5_531, 4_970, 919, 17_336, 5, 2], [250_004, 20_080, 618, 83, 82_775, 47, 479, 9, 1_517, 73, 53_894, 333, 80_581, 110_117, 18_811, 5_256, 1_295, 51, 152_526, 297, 7_986, 390, 124_416, 538, 35_431, 214, 98, 15_044, 25_737, 136, 7_108, 43_701, 23, 756, 135_355, 7, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [250_004, 581, 63_773, 119_455, 6, 147_797, 88_203, 7, 645, 70, 21, 3_285, 10_269, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], """attention_mask""": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]} # noqa: E501 # fmt: on self.tokenizer_integration_test_util( expected_encoding=a_, model_name="""facebook/mbart-large-50""", revision="""d3913889c59cd5c9e456b269c376325eabad57e2""", ) def UpperCamelCase_ ( self: Any ): '''simple docstring''' if not self.test_slow_tokenizer: # as we don't have a slow version, we can't compare the outputs between slow and fast versions return _snake_case : Dict = (self.rust_tokenizer_class, """hf-internal-testing/tiny-random-mbart50""", {}) for tokenizer, pretrained_name, kwargs in self.tokenizers_list: with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})" ): _snake_case : Dict = self.rust_tokenizer_class.from_pretrained(a_, **a_ ) _snake_case : List[str] = self.tokenizer_class.from_pretrained(a_, **a_ ) _snake_case : List[str] = tempfile.mkdtemp() _snake_case : Tuple = tokenizer_r.save_pretrained(a_ ) _snake_case : Tuple = tokenizer_p.save_pretrained(a_ ) # Checks it save with the same files + the tokenizer.json file for the fast one self.assertTrue(any("""tokenizer.json""" in f for f in tokenizer_r_files ) ) _snake_case : List[Any] = tuple(f for f in tokenizer_r_files if """tokenizer.json""" not in f ) self.assertSequenceEqual(a_, a_ ) # Checks everything loads correctly in the same way _snake_case : List[Any] = tokenizer_r.from_pretrained(a_ ) _snake_case : Any = tokenizer_p.from_pretrained(a_ ) # Check special tokens are set accordingly on Rust and Python for key in tokenizer_pp.special_tokens_map: self.assertTrue(hasattr(a_, a_ ) ) # self.assertEqual(getattr(tokenizer_rp, key), getattr(tokenizer_pp, key)) # self.assertEqual(getattr(tokenizer_rp, key + "_id"), getattr(tokenizer_pp, key + "_id")) shutil.rmtree(a_ ) # Save tokenizer rust, legacy_format=True _snake_case : Any = tempfile.mkdtemp() _snake_case : List[str] = tokenizer_r.save_pretrained(a_, legacy_format=a_ ) _snake_case : List[str] = tokenizer_p.save_pretrained(a_ ) # Checks it save with the same files self.assertSequenceEqual(a_, a_ ) # Checks everything loads correctly in the same way _snake_case : Any = tokenizer_r.from_pretrained(a_ ) _snake_case : List[Any] = tokenizer_p.from_pretrained(a_ ) # Check special tokens are set accordingly on Rust and Python for key in tokenizer_pp.special_tokens_map: self.assertTrue(hasattr(a_, a_ ) ) shutil.rmtree(a_ ) # Save tokenizer rust, legacy_format=False _snake_case : List[str] = tempfile.mkdtemp() _snake_case : Dict = tokenizer_r.save_pretrained(a_, legacy_format=a_ ) _snake_case : Optional[Any] = tokenizer_p.save_pretrained(a_ ) # Checks it saved the tokenizer.json file self.assertTrue(any("""tokenizer.json""" in f for f in tokenizer_r_files ) ) # Checks everything loads correctly in the same way _snake_case : Dict = tokenizer_r.from_pretrained(a_ ) _snake_case : int = tokenizer_p.from_pretrained(a_ ) # Check special tokens are set accordingly on Rust and Python for key in tokenizer_pp.special_tokens_map: self.assertTrue(hasattr(a_, a_ ) ) shutil.rmtree(a_ ) @require_torch @require_sentencepiece @require_tokenizers class lowercase( unittest.TestCase ): '''simple docstring''' lowercase__ = "facebook/mbart-large-50-one-to-many-mmt" lowercase__ = [ " UN Chief Says There Is No Military Solution in Syria", " Secretary-General Ban Ki-moon says his response to Russia's stepped up military support for Syria is that \"there is no military solution\" to the nearly five-year conflict and more weapons will only worsen the violence and misery for millions of people.", ] lowercase__ = [ "Şeful ONU declară că nu există o soluţie militară în Siria", "Secretarul General Ban Ki-moon declară că răspunsul său la intensificarea sprijinului militar al Rusiei" " pentru Siria este că \"nu există o soluţie militară\" la conflictul de aproape cinci ani şi că noi arme nu vor" " face decât să înrăutăţească violenţele şi mizeria pentru milioane de oameni.", ] lowercase__ = [EN_CODE, 82_74, 12_78_73, 2_59_16, 7, 86_22, 20_71, 4_38, 6_74_85, 53, 18_78_95, 23, 5_17_12, 2] @classmethod def UpperCamelCase_ ( cls: Optional[Any] ): '''simple docstring''' _snake_case : MBartaaTokenizer = MBartaaTokenizer.from_pretrained( cls.checkpoint_name, src_lang="""en_XX""", tgt_lang="""ro_RO""" ) _snake_case : Union[str, Any] = 1 return cls def UpperCamelCase_ ( self: int ): '''simple docstring''' self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["""ar_AR"""], 250_001 ) self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["""en_EN"""], 250_004 ) self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["""ro_RO"""], 250_020 ) self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["""mr_IN"""], 250_038 ) def UpperCamelCase_ ( self: List[str] ): '''simple docstring''' _snake_case : str = self.tokenizer.batch_encode_plus(self.src_text ).input_ids[0] self.assertListEqual(self.expected_src_tokens, a_ ) def UpperCamelCase_ ( self: Any ): '''simple docstring''' self.assertIn(a_, self.tokenizer.all_special_ids ) _snake_case : Dict = [RO_CODE, 884, 9_019, 96, 9, 916, 86_792, 36, 18_743, 15_596, 5, 2] _snake_case : Dict = self.tokenizer.decode(a_, skip_special_tokens=a_ ) _snake_case : Tuple = self.tokenizer.decode(generated_ids[1:], skip_special_tokens=a_ ) self.assertEqual(a_, a_ ) self.assertNotIn(self.tokenizer.eos_token, a_ ) def UpperCamelCase_ ( self: Tuple ): '''simple docstring''' _snake_case : Tuple = ["""this is gunna be a long sentence """ * 20] assert isinstance(src_text[0], a_ ) _snake_case : Any = 10 _snake_case : List[Any] = self.tokenizer(a_, max_length=a_, truncation=a_ ).input_ids[0] self.assertEqual(ids[0], a_ ) self.assertEqual(ids[-1], 2 ) self.assertEqual(len(a_ ), a_ ) def UpperCamelCase_ ( self: Tuple ): '''simple docstring''' self.assertListEqual(self.tokenizer.convert_tokens_to_ids(["""<mask>""", """ar_AR"""] ), [250_053, 250_001] ) def UpperCamelCase_ ( self: Dict ): '''simple docstring''' _snake_case : List[str] = tempfile.mkdtemp() _snake_case : Optional[int] = self.tokenizer.fairseq_tokens_to_ids self.tokenizer.save_pretrained(a_ ) _snake_case : Any = MBartaaTokenizer.from_pretrained(a_ ) self.assertDictEqual(new_tok.fairseq_tokens_to_ids, a_ ) @require_torch def UpperCamelCase_ ( self: Optional[Any] ): '''simple docstring''' _snake_case : int = self.tokenizer(self.src_text, text_target=self.tgt_text, padding=a_, return_tensors="""pt""" ) _snake_case : Any = shift_tokens_right(batch["""labels"""], self.tokenizer.pad_token_id ) # fairseq batch: https://gist.github.com/sshleifer/cba08bc2109361a74ac3760a7e30e4f4 assert batch.input_ids[1][0] == EN_CODE assert batch.input_ids[1][-1] == 2 assert batch.labels[1][0] == RO_CODE assert batch.labels[1][-1] == 2 assert batch.decoder_input_ids[1][:2].tolist() == [2, RO_CODE] @require_torch def UpperCamelCase_ ( self: int ): '''simple docstring''' _snake_case : Any = self.tokenizer( self.src_text, text_target=self.tgt_text, padding=a_, truncation=a_, max_length=len(self.expected_src_tokens ), return_tensors="""pt""", ) _snake_case : List[Any] = shift_tokens_right(batch["""labels"""], self.tokenizer.pad_token_id ) self.assertIsInstance(a_, a_ ) self.assertEqual((2, 14), batch.input_ids.shape ) self.assertEqual((2, 14), batch.attention_mask.shape ) _snake_case : Optional[Any] = batch.input_ids.tolist()[0] self.assertListEqual(self.expected_src_tokens, a_ ) self.assertEqual(2, batch.decoder_input_ids[0, 0] ) # decoder_start_token_id # Test that special tokens are reset self.assertEqual(self.tokenizer.prefix_tokens, [EN_CODE] ) self.assertEqual(self.tokenizer.suffix_tokens, [self.tokenizer.eos_token_id] ) def UpperCamelCase_ ( self: Union[str, Any] ): '''simple docstring''' _snake_case : int = self.tokenizer(self.src_text, padding=a_, truncation=a_, max_length=3, return_tensors="""pt""" ) _snake_case : int = self.tokenizer( text_target=self.tgt_text, padding=a_, truncation=a_, max_length=10, return_tensors="""pt""" ) _snake_case : Union[str, Any] = targets["""input_ids"""] _snake_case : Optional[int] = shift_tokens_right(a_, self.tokenizer.pad_token_id ) self.assertEqual(batch.input_ids.shape[1], 3 ) self.assertEqual(batch.decoder_input_ids.shape[1], 10 ) @require_torch def UpperCamelCase_ ( self: Dict ): '''simple docstring''' _snake_case : Tuple = self.tokenizer._build_translation_inputs( """A test""", return_tensors="""pt""", src_lang="""en_XX""", tgt_lang="""ar_AR""" ) self.assertEqual( nested_simplify(a_ ), { # en_XX, A, test, EOS """input_ids""": [[250_004, 62, 3_034, 2]], """attention_mask""": [[1, 1, 1, 1]], # ar_AR """forced_bos_token_id""": 250_001, }, )
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'''simple docstring''' import argparse import json from pathlib import Path import requests import torch from huggingface_hub import cached_download, hf_hub_url from PIL import Image from transformers import DPTConfig, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTImageProcessor from transformers.utils import logging logging.set_verbosity_info() UpperCamelCase__: Optional[int] = logging.get_logger(__name__) def snake_case_ ( _lowerCAmelCase : Optional[int] ) -> Optional[int]: UpperCAmelCase : Tuple = DPTConfig(embedding_type='''hybrid''' ) if "large" in checkpoint_url: UpperCAmelCase : Tuple = 1024 UpperCAmelCase : List[Any] = 4096 UpperCAmelCase : str = 24 UpperCAmelCase : List[Any] = 16 UpperCAmelCase : str = [5, 11, 17, 23] UpperCAmelCase : List[Any] = [256, 512, 1024, 1024] UpperCAmelCase : Tuple = (1, 384, 384) if "nyu" or "midas" in checkpoint_url: UpperCAmelCase : Optional[Any] = 768 UpperCAmelCase : Tuple = [1, 1, 1, 0.5] UpperCAmelCase : int = [256, 512, 768, 768] UpperCAmelCase : Any = 150 UpperCAmelCase : Tuple = 16 UpperCAmelCase : Any = (1, 384, 384) UpperCAmelCase : Optional[Any] = False UpperCAmelCase : Tuple = '''project''' if "ade" in checkpoint_url: UpperCAmelCase : Any = True UpperCAmelCase : str = 768 UpperCAmelCase : Optional[int] = [1, 1, 1, 0.5] UpperCAmelCase : List[Any] = 150 UpperCAmelCase : List[Any] = 16 UpperCAmelCase : str = '''huggingface/label-files''' UpperCAmelCase : Tuple = '''ade20k-id2label.json''' UpperCAmelCase : Any = json.load(open(cached_download(hf_hub_url(_lowerCAmelCase , _lowerCAmelCase , repo_type='''dataset''' ) ) , '''r''' ) ) UpperCAmelCase : Optional[Any] = {int(_lowerCAmelCase ): v for k, v in idalabel.items()} UpperCAmelCase : List[Any] = idalabel UpperCAmelCase : Optional[int] = {v: k for k, v in idalabel.items()} UpperCAmelCase : Union[str, Any] = [1, 150, 480, 480] return config, expected_shape def snake_case_ ( _lowerCAmelCase : Union[str, Any] ) -> int: UpperCAmelCase : List[str] = ['''pretrained.model.head.weight''', '''pretrained.model.head.bias'''] for k in ignore_keys: state_dict.pop(_lowerCAmelCase , _lowerCAmelCase ) def snake_case_ ( _lowerCAmelCase : Tuple ) -> Any: if ( "pretrained.model" in name and "cls_token" not in name and "pos_embed" not in name and "patch_embed" not in name ): UpperCAmelCase : Tuple = name.replace('''pretrained.model''' , '''dpt.encoder''' ) if "pretrained.model" in name: UpperCAmelCase : Union[str, Any] = name.replace('''pretrained.model''' , '''dpt.embeddings''' ) if "patch_embed" in name: UpperCAmelCase : int = name.replace('''patch_embed''' , '''''' ) if "pos_embed" in name: UpperCAmelCase : Tuple = name.replace('''pos_embed''' , '''position_embeddings''' ) if "attn.proj" in name: UpperCAmelCase : Any = name.replace('''attn.proj''' , '''attention.output.dense''' ) if "proj" in name and "project" not in name: UpperCAmelCase : str = name.replace('''proj''' , '''projection''' ) if "blocks" in name: UpperCAmelCase : Any = name.replace('''blocks''' , '''layer''' ) if "mlp.fc1" in name: UpperCAmelCase : Optional[int] = name.replace('''mlp.fc1''' , '''intermediate.dense''' ) if "mlp.fc2" in name: UpperCAmelCase : Optional[Any] = name.replace('''mlp.fc2''' , '''output.dense''' ) if "norm1" in name and "backbone" not in name: UpperCAmelCase : Dict = name.replace('''norm1''' , '''layernorm_before''' ) if "norm2" in name and "backbone" not in name: UpperCAmelCase : Tuple = name.replace('''norm2''' , '''layernorm_after''' ) if "scratch.output_conv" in name: UpperCAmelCase : Tuple = name.replace('''scratch.output_conv''' , '''head''' ) if "scratch" in name: UpperCAmelCase : str = name.replace('''scratch''' , '''neck''' ) if "layer1_rn" in name: UpperCAmelCase : Dict = name.replace('''layer1_rn''' , '''convs.0''' ) if "layer2_rn" in name: UpperCAmelCase : int = name.replace('''layer2_rn''' , '''convs.1''' ) if "layer3_rn" in name: UpperCAmelCase : Tuple = name.replace('''layer3_rn''' , '''convs.2''' ) if "layer4_rn" in name: UpperCAmelCase : int = name.replace('''layer4_rn''' , '''convs.3''' ) if "refinenet" in name: UpperCAmelCase : List[str] = int(name[len('''neck.refinenet''' ) : len('''neck.refinenet''' ) + 1] ) # tricky here: we need to map 4 to 0, 3 to 1, 2 to 2 and 1 to 3 UpperCAmelCase : str = name.replace(f"""refinenet{layer_idx}""" , f"""fusion_stage.layers.{abs(layer_idx-4 )}""" ) if "out_conv" in name: UpperCAmelCase : List[str] = name.replace('''out_conv''' , '''projection''' ) if "resConfUnit1" in name: UpperCAmelCase : Union[str, Any] = name.replace('''resConfUnit1''' , '''residual_layer1''' ) if "resConfUnit2" in name: UpperCAmelCase : Any = name.replace('''resConfUnit2''' , '''residual_layer2''' ) if "conv1" in name: UpperCAmelCase : Optional[int] = name.replace('''conv1''' , '''convolution1''' ) if "conv2" in name: UpperCAmelCase : Tuple = name.replace('''conv2''' , '''convolution2''' ) # readout blocks if "pretrained.act_postprocess1.0.project.0" in name: UpperCAmelCase : Dict = name.replace('''pretrained.act_postprocess1.0.project.0''' , '''neck.reassemble_stage.readout_projects.0.0''' ) if "pretrained.act_postprocess2.0.project.0" in name: UpperCAmelCase : int = name.replace('''pretrained.act_postprocess2.0.project.0''' , '''neck.reassemble_stage.readout_projects.1.0''' ) if "pretrained.act_postprocess3.0.project.0" in name: UpperCAmelCase : Any = name.replace('''pretrained.act_postprocess3.0.project.0''' , '''neck.reassemble_stage.readout_projects.2.0''' ) if "pretrained.act_postprocess4.0.project.0" in name: UpperCAmelCase : Optional[Any] = name.replace('''pretrained.act_postprocess4.0.project.0''' , '''neck.reassemble_stage.readout_projects.3.0''' ) # resize blocks if "pretrained.act_postprocess1.3" in name: UpperCAmelCase : List[Any] = name.replace('''pretrained.act_postprocess1.3''' , '''neck.reassemble_stage.layers.0.projection''' ) if "pretrained.act_postprocess1.4" in name: UpperCAmelCase : Any = name.replace('''pretrained.act_postprocess1.4''' , '''neck.reassemble_stage.layers.0.resize''' ) if "pretrained.act_postprocess2.3" in name: UpperCAmelCase : Optional[int] = name.replace('''pretrained.act_postprocess2.3''' , '''neck.reassemble_stage.layers.1.projection''' ) if "pretrained.act_postprocess2.4" in name: UpperCAmelCase : str = name.replace('''pretrained.act_postprocess2.4''' , '''neck.reassemble_stage.layers.1.resize''' ) if "pretrained.act_postprocess3.3" in name: UpperCAmelCase : List[str] = name.replace('''pretrained.act_postprocess3.3''' , '''neck.reassemble_stage.layers.2.projection''' ) if "pretrained.act_postprocess4.3" in name: UpperCAmelCase : Tuple = name.replace('''pretrained.act_postprocess4.3''' , '''neck.reassemble_stage.layers.3.projection''' ) if "pretrained.act_postprocess4.4" in name: UpperCAmelCase : int = name.replace('''pretrained.act_postprocess4.4''' , '''neck.reassemble_stage.layers.3.resize''' ) if "pretrained" in name: UpperCAmelCase : Optional[int] = name.replace('''pretrained''' , '''dpt''' ) if "bn" in name: UpperCAmelCase : Dict = name.replace('''bn''' , '''batch_norm''' ) if "head" in name: UpperCAmelCase : Any = name.replace('''head''' , '''head.head''' ) if "encoder.norm" in name: UpperCAmelCase : Optional[int] = name.replace('''encoder.norm''' , '''layernorm''' ) if "auxlayer" in name: UpperCAmelCase : Union[str, Any] = name.replace('''auxlayer''' , '''auxiliary_head.head''' ) if "backbone" in name: UpperCAmelCase : List[Any] = name.replace('''backbone''' , '''backbone.bit.encoder''' ) if ".." in name: UpperCAmelCase : Optional[int] = name.replace('''..''' , '''.''' ) if "stem.conv" in name: UpperCAmelCase : Optional[Any] = name.replace('''stem.conv''' , '''bit.embedder.convolution''' ) if "blocks" in name: UpperCAmelCase : Optional[int] = name.replace('''blocks''' , '''layers''' ) if "convolution" in name and "backbone" in name: UpperCAmelCase : List[Any] = name.replace('''convolution''' , '''conv''' ) if "layer" in name and "backbone" in name: UpperCAmelCase : List[str] = name.replace('''layer''' , '''layers''' ) if "backbone.bit.encoder.bit" in name: UpperCAmelCase : List[Any] = name.replace('''backbone.bit.encoder.bit''' , '''backbone.bit''' ) if "embedder.conv" in name: UpperCAmelCase : List[Any] = name.replace('''embedder.conv''' , '''embedder.convolution''' ) if "backbone.bit.encoder.stem.norm" in name: UpperCAmelCase : Tuple = name.replace('''backbone.bit.encoder.stem.norm''' , '''backbone.bit.embedder.norm''' ) return name def snake_case_ ( _lowerCAmelCase : List[str] , _lowerCAmelCase : List[Any] ) -> Optional[Any]: for i in range(config.num_hidden_layers ): # read in weights + bias of input projection layer (in timm, this is a single matrix + bias) UpperCAmelCase : Optional[int] = state_dict.pop(f"""dpt.encoder.layer.{i}.attn.qkv.weight""" ) UpperCAmelCase : Tuple = state_dict.pop(f"""dpt.encoder.layer.{i}.attn.qkv.bias""" ) # next, add query, keys and values (in that order) to the state dict UpperCAmelCase : Tuple = in_proj_weight[: config.hidden_size, :] UpperCAmelCase : int = in_proj_bias[: config.hidden_size] UpperCAmelCase : List[str] = in_proj_weight[ config.hidden_size : config.hidden_size * 2, : ] UpperCAmelCase : List[str] = in_proj_bias[ config.hidden_size : config.hidden_size * 2 ] UpperCAmelCase : str = in_proj_weight[ -config.hidden_size :, : ] UpperCAmelCase : Union[str, Any] = in_proj_bias[-config.hidden_size :] def snake_case_ ( ) -> List[str]: UpperCAmelCase : Optional[int] = '''http://images.cocodataset.org/val2017/000000039769.jpg''' UpperCAmelCase : Optional[int] = Image.open(requests.get(_lowerCAmelCase , stream=_lowerCAmelCase ).raw ) return im @torch.no_grad() def snake_case_ ( _lowerCAmelCase : Optional[Any] , _lowerCAmelCase : Union[str, Any] , _lowerCAmelCase : str , _lowerCAmelCase : Union[str, Any] , _lowerCAmelCase : List[str] ) -> Any: UpperCAmelCase , UpperCAmelCase : int = get_dpt_config(_lowerCAmelCase ) # load original state_dict from URL # state_dict = torch.hub.load_state_dict_from_url(checkpoint_url, map_location="cpu") UpperCAmelCase : List[Any] = torch.load(_lowerCAmelCase , map_location='''cpu''' ) # remove certain keys remove_ignore_keys_(_lowerCAmelCase ) # rename keys for key in state_dict.copy().keys(): UpperCAmelCase : Any = state_dict.pop(_lowerCAmelCase ) UpperCAmelCase : List[Any] = val # read in qkv matrices read_in_q_k_v(_lowerCAmelCase , _lowerCAmelCase ) # load HuggingFace model UpperCAmelCase : Optional[Any] = DPTForSemanticSegmentation(_lowerCAmelCase ) if '''ade''' in checkpoint_url else DPTForDepthEstimation(_lowerCAmelCase ) model.load_state_dict(_lowerCAmelCase ) model.eval() # Check outputs on an image UpperCAmelCase : int = 480 if '''ade''' in checkpoint_url else 384 UpperCAmelCase : List[Any] = DPTImageProcessor(size=_lowerCAmelCase ) UpperCAmelCase : Dict = prepare_img() UpperCAmelCase : Optional[int] = image_processor(_lowerCAmelCase , return_tensors='''pt''' ) # forward pass UpperCAmelCase : Any = model(**_lowerCAmelCase ).logits if '''ade''' in checkpoint_url else model(**_lowerCAmelCase ).predicted_depth if show_prediction: UpperCAmelCase : Dict = ( torch.nn.functional.interpolate( outputs.unsqueeze(1 ) , size=(image.size[1], image.size[0]) , mode='''bicubic''' , align_corners=_lowerCAmelCase , ) .squeeze() .cpu() .numpy() ) Image.fromarray((prediction / prediction.max()) * 255 ).show() if pytorch_dump_folder_path is not None: Path(_lowerCAmelCase ).mkdir(exist_ok=_lowerCAmelCase ) print(f"""Saving model to {pytorch_dump_folder_path}""" ) model.save_pretrained(_lowerCAmelCase ) print(f"""Saving image processor to {pytorch_dump_folder_path}""" ) image_processor.save_pretrained(_lowerCAmelCase ) if push_to_hub: model.push_to_hub('''ybelkada/dpt-hybrid-midas''' ) image_processor.push_to_hub('''ybelkada/dpt-hybrid-midas''' ) if __name__ == "__main__": UpperCamelCase__: Dict = argparse.ArgumentParser() # Required parameters parser.add_argument( "--checkpoint_url", default="https://github.com/intel-isl/DPT/releases/download/1_0/dpt_large-midas-2f21e586.pt", type=str, help="URL of the original DPT checkpoint you'd like to convert.", ) parser.add_argument( "--pytorch_dump_folder_path", default=None, type=str, required=False, help="Path to the output PyTorch model directory.", ) parser.add_argument( "--push_to_hub", action="store_true", ) parser.add_argument( "--model_name", default="dpt-large", type=str, help="Name of the model, in case you're pushing to the hub.", ) parser.add_argument( "--show_prediction", action="store_true", ) UpperCamelCase__: Tuple = parser.parse_args() convert_dpt_checkpoint( args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name, args.show_prediction )
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0
import unittest from pathlib import Path from tempfile import TemporaryDirectory from transformers import AutoConfig, TFAutoModel, is_tensorflow_text_available, is_tf_available from transformers.models.bert.tokenization_bert import BertTokenizer from transformers.testing_utils import require_tensorflow_text, require_tf, slow if is_tf_available(): import tensorflow as tf if is_tensorflow_text_available(): from transformers.models.bert import TFBertTokenizer UpperCamelCase__ = ['bert-base-uncased', 'bert-base-cased'] UpperCamelCase__ = 'hf-internal-testing/tiny-bert-tf-only' if is_tf_available(): class A ( tf.keras.Model ): def __init__(self : Any , __UpperCAmelCase : Tuple ) -> Optional[Any]: """simple docstring""" super().__init__() UpperCAmelCase__ = tokenizer UpperCAmelCase__ = AutoConfig.from_pretrained(__UpperCAmelCase ) UpperCAmelCase__ = TFAutoModel.from_config(__UpperCAmelCase ) def lowercase_ (self : int , __UpperCAmelCase : Union[str, Any] ) -> Optional[int]: """simple docstring""" UpperCAmelCase__ = self.tokenizer(__UpperCAmelCase ) UpperCAmelCase__ = self.bert(**__UpperCAmelCase ) return out["pooler_output"] @require_tf @require_tensorflow_text class A ( unittest.TestCase ): def lowercase_ (self : List[str] ) -> List[str]: """simple docstring""" super().setUp() UpperCAmelCase__ = [ BertTokenizer.from_pretrained(__UpperCAmelCase ) for checkpoint in (TOKENIZER_CHECKPOINTS * 2) ] # repeat for when fast_bert_tokenizer=false UpperCAmelCase__ = [TFBertTokenizer.from_pretrained(__UpperCAmelCase ) for checkpoint in TOKENIZER_CHECKPOINTS] + [ TFBertTokenizer.from_pretrained(__UpperCAmelCase , use_fast_bert_tokenizer=__UpperCAmelCase ) for checkpoint in TOKENIZER_CHECKPOINTS ] assert len(self.tokenizers ) == len(self.tf_tokenizers ) UpperCAmelCase__ = [ "This is a straightforward English test sentence.", "This one has some weird characters\rto\nsee\r\nif those\u00E9break things.", "Now we're going to add some Chinese: 一 二 三 一二三", "And some much more rare Chinese: 齉 堃 齉堃", "Je vais aussi écrire en français pour tester les accents", "Classical Irish also has some unusual characters, so in they go: Gaelaċ, ꝼ", ] UpperCAmelCase__ = list(zip(self.test_sentences , self.test_sentences[::-1] ) ) def lowercase_ (self : Any ) -> int: """simple docstring""" for tokenizer, tf_tokenizer in zip(self.tokenizers , self.tf_tokenizers ): for test_inputs in (self.test_sentences, self.paired_sentences): UpperCAmelCase__ = tokenizer(__UpperCAmelCase , return_tensors="tf" , padding="longest" ) UpperCAmelCase__ = tf_tokenizer(__UpperCAmelCase ) for key in python_outputs.keys(): self.assertTrue(tf.reduce_all(python_outputs[key].shape == tf_outputs[key].shape ) ) self.assertTrue(tf.reduce_all(tf.cast(python_outputs[key] , tf.intaa ) == tf_outputs[key] ) ) @slow def lowercase_ (self : List[Any] ) -> List[Any]: """simple docstring""" for tf_tokenizer in self.tf_tokenizers: UpperCAmelCase__ = tf_tokenizer(self.paired_sentences ) UpperCAmelCase__ = tf_tokenizer( text=[sentence[0] for sentence in self.paired_sentences] , text_pair=[sentence[1] for sentence in self.paired_sentences] , ) for key in merged_outputs.keys(): self.assertTrue(tf.reduce_all(tf.cast(merged_outputs[key] , tf.intaa ) == separated_outputs[key] ) ) @slow def lowercase_ (self : Union[str, Any] ) -> Optional[int]: """simple docstring""" for tf_tokenizer in self.tf_tokenizers: UpperCAmelCase__ = tf.function(__UpperCAmelCase ) for test_inputs in (self.test_sentences, self.paired_sentences): UpperCAmelCase__ = tf.constant(__UpperCAmelCase ) UpperCAmelCase__ = compiled_tokenizer(__UpperCAmelCase ) UpperCAmelCase__ = tf_tokenizer(__UpperCAmelCase ) for key in eager_outputs.keys(): self.assertTrue(tf.reduce_all(eager_outputs[key] == compiled_outputs[key] ) ) @slow def lowercase_ (self : str ) -> List[str]: """simple docstring""" for tf_tokenizer in self.tf_tokenizers: UpperCAmelCase__ = ModelToSave(tokenizer=__UpperCAmelCase ) UpperCAmelCase__ = tf.convert_to_tensor(self.test_sentences ) UpperCAmelCase__ = model(__UpperCAmelCase ) # Build model with some sample inputs with TemporaryDirectory() as tempdir: UpperCAmelCase__ = Path(__UpperCAmelCase ) / "saved.model" model.save(__UpperCAmelCase ) UpperCAmelCase__ = tf.keras.models.load_model(__UpperCAmelCase ) UpperCAmelCase__ = loaded_model(__UpperCAmelCase ) # We may see small differences because the loaded model is compiled, so we need an epsilon for the test self.assertLessEqual(tf.reduce_max(tf.abs(out - loaded_output ) ) , 1E-5 )
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'''simple docstring''' # coding=utf-8 # Copyright 2020 The HuggingFace Inc. team. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # this script dumps information about the environment import os import sys import transformers UpperCamelCase__: Optional[int] = "3" print("Python version:", sys.version) print("transformers version:", transformers.__version__) try: import torch print("Torch version:", torch.__version__) print("Cuda available:", torch.cuda.is_available()) print("Cuda version:", torch.version.cuda) print("CuDNN version:", torch.backends.cudnn.version()) print("Number of GPUs available:", torch.cuda.device_count()) print("NCCL version:", torch.cuda.nccl.version()) except ImportError: print("Torch version:", None) try: import deepspeed print("DeepSpeed version:", deepspeed.__version__) except ImportError: print("DeepSpeed version:", None) try: import tensorflow as tf print("TensorFlow version:", tf.__version__) print("TF GPUs available:", bool(tf.config.list_physical_devices("GPU"))) print("Number of TF GPUs available:", len(tf.config.list_physical_devices("GPU"))) except ImportError: print("TensorFlow version:", None)
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0
"""simple docstring""" def A_ ( _lowercase ): '''simple docstring''' snake_case_ :Tuple = [int(_lowercase ) for i in ip_va_address.split(""".""" ) if i.isdigit()] return len(_lowercase ) == 4 and all(0 <= int(_lowercase ) <= 254 for octet in octets ) if __name__ == "__main__": __a = input().strip() __a = "valid" if is_ip_va_address_valid(ip) else "invalid" print(F"""{ip} is a {valid_or_invalid} IP v4 address.""")
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'''simple docstring''' from __future__ import annotations def snake_case_ ( _lowerCAmelCase : list[int | float] , _lowerCAmelCase : int , _lowerCAmelCase : int ) -> int | float: if len(_lowerCAmelCase ) == 0: raise ValueError('''find_max() arg is an empty sequence''' ) if ( left >= len(_lowerCAmelCase ) or left < -len(_lowerCAmelCase ) or right >= len(_lowerCAmelCase ) or right < -len(_lowerCAmelCase ) ): raise IndexError('''list index out of range''' ) if left == right: return nums[left] UpperCAmelCase : List[Any] = (left + right) >> 1 # the middle UpperCAmelCase : Optional[Any] = find_max(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ) # find max in range[left, mid] UpperCAmelCase : Dict = find_max(_lowerCAmelCase , mid + 1 , _lowerCAmelCase ) # find max in range[mid + 1, right] return left_max if left_max >= right_max else right_max if __name__ == "__main__": import doctest doctest.testmod(verbose=True)
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'''simple docstring''' import torch from torch import nn from torch.nn import CrossEntropyLoss, MSELoss from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward from transformers.models.bert.modeling_bert import ( BERT_INPUTS_DOCSTRING, BERT_START_DOCSTRING, BertEmbeddings, BertLayer, BertPooler, BertPreTrainedModel, ) def __lowerCAmelCase ( UpperCamelCase__ ) -> int: __lowerCamelCase = torch.exp(UpperCamelCase__ ) __lowerCamelCase = torch.sum(UpperCamelCase__ , dim=1 ) # sum of exp(x_i) __lowerCamelCase = torch.sum(x * exp_x , dim=1 ) # sum of x_i * exp(x_i) return torch.log(UpperCamelCase__ ) - B / A class a__ ( nn.Module ): def __init__( self : Union[str, Any] , a : List[Any] ): """simple docstring""" super().__init__() __lowerCamelCase = config.output_attentions __lowerCamelCase = config.output_hidden_states __lowerCamelCase = nn.ModuleList([BertLayer(a ) for _ in range(config.num_hidden_layers )] ) __lowerCamelCase = nn.ModuleList([BertHighway(a ) for _ in range(config.num_hidden_layers )] ) __lowerCamelCase = [-1 for _ in range(config.num_hidden_layers )] def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] , a : str ): """simple docstring""" if (type(a ) is float) or (type(a ) is int): for i in range(len(self.early_exit_entropy ) ): __lowerCamelCase = x else: __lowerCamelCase = x def SCREAMING_SNAKE_CASE__ ( self : Tuple , a : Tuple ): """simple docstring""" __lowerCamelCase = pooler.state_dict() for highway in self.highway: for name, param in highway.pooler.state_dict().items(): param.copy_(loaded_model[name] ) def SCREAMING_SNAKE_CASE__ ( self : Union[str, Any] , a : Optional[int] , a : Tuple=None , a : Optional[Any]=None , a : str=None , a : str=None , ): """simple docstring""" __lowerCamelCase = () __lowerCamelCase = () __lowerCamelCase = () for i, layer_module in enumerate(self.layer ): if self.output_hidden_states: __lowerCamelCase = all_hidden_states + (hidden_states,) __lowerCamelCase = layer_module( a , a , head_mask[i] , a , a ) __lowerCamelCase = layer_outputs[0] if self.output_attentions: __lowerCamelCase = all_attentions + (layer_outputs[1],) __lowerCamelCase = (hidden_states,) if self.output_hidden_states: __lowerCamelCase = current_outputs + (all_hidden_states,) if self.output_attentions: __lowerCamelCase = current_outputs + (all_attentions,) __lowerCamelCase = self.highway[i](a ) # logits, pooled_output if not self.training: __lowerCamelCase = highway_exit[0] __lowerCamelCase = entropy(a ) __lowerCamelCase = highway_exit + (highway_entropy,) # logits, hidden_states(?), entropy __lowerCamelCase = all_highway_exits + (highway_exit,) if highway_entropy < self.early_exit_entropy[i]: __lowerCamelCase = (highway_logits,) + current_outputs[1:] + (all_highway_exits,) raise HighwayException(a , i + 1 ) else: __lowerCamelCase = all_highway_exits + (highway_exit,) # Add last layer if self.output_hidden_states: __lowerCamelCase = all_hidden_states + (hidden_states,) __lowerCamelCase = (hidden_states,) if self.output_hidden_states: __lowerCamelCase = outputs + (all_hidden_states,) if self.output_attentions: __lowerCamelCase = outputs + (all_attentions,) __lowerCamelCase = outputs + (all_highway_exits,) return outputs # last-layer hidden state, (all hidden states), (all attentions), all highway exits @add_start_docstrings( "The Bert Model transformer with early exiting (DeeBERT). " , UpperCAmelCase__ , ) class a__ ( UpperCAmelCase__ ): def __init__( self : List[Any] , a : Optional[Any] ): """simple docstring""" super().__init__(a ) __lowerCamelCase = config __lowerCamelCase = BertEmbeddings(a ) __lowerCamelCase = DeeBertEncoder(a ) __lowerCamelCase = BertPooler(a ) self.init_weights() def SCREAMING_SNAKE_CASE__ ( self : int ): """simple docstring""" self.encoder.init_highway_pooler(self.pooler ) def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] ): """simple docstring""" return self.embeddings.word_embeddings def SCREAMING_SNAKE_CASE__ ( self : List[Any] , a : Optional[Any] ): """simple docstring""" __lowerCamelCase = value def SCREAMING_SNAKE_CASE__ ( self : Optional[int] , a : Dict ): """simple docstring""" for layer, heads in heads_to_prune.items(): self.encoder.layer[layer].attention.prune_heads(a ) @add_start_docstrings_to_model_forward(a ) def SCREAMING_SNAKE_CASE__ ( self : int , a : Optional[Any]=None , a : Union[str, Any]=None , a : Optional[int]=None , a : Optional[int]=None , a : Any=None , a : Optional[Any]=None , a : int=None , a : Optional[int]=None , ): """simple docstring""" if input_ids is not None and inputs_embeds is not None: raise ValueError('''You cannot specify both input_ids and inputs_embeds at the same time''' ) elif input_ids is not None: __lowerCamelCase = input_ids.size() elif inputs_embeds is not None: __lowerCamelCase = inputs_embeds.size()[:-1] else: raise ValueError('''You have to specify either input_ids or inputs_embeds''' ) __lowerCamelCase = input_ids.device if input_ids is not None else inputs_embeds.device if attention_mask is None: __lowerCamelCase = torch.ones(a , device=a ) if encoder_attention_mask is None: __lowerCamelCase = torch.ones(a , device=a ) if token_type_ids is None: __lowerCamelCase = torch.zeros(a , dtype=torch.long , device=a ) # We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length] # ourselves in which case we just need to make it broadcastable to all heads. __lowerCamelCase = self.get_extended_attention_mask(a , a , a ) # If a 2D ou 3D attention mask is provided for the cross-attention # we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length] if encoder_attention_mask.dim() == 3: __lowerCamelCase = encoder_attention_mask[:, None, :, :] if encoder_attention_mask.dim() == 2: __lowerCamelCase = encoder_attention_mask[:, None, None, :] __lowerCamelCase = encoder_extended_attention_mask.to( dtype=next(self.parameters() ).dtype ) # fp16 compatibility __lowerCamelCase = (1.0 - encoder_extended_attention_mask) * -1_00_00.0 # Prepare head mask if needed # 1.0 in head_mask indicate we keep the head # attention_probs has shape bsz x n_heads x N x N # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads] # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length] __lowerCamelCase = self.get_head_mask(a , self.config.num_hidden_layers ) __lowerCamelCase = self.embeddings( input_ids=a , position_ids=a , token_type_ids=a , inputs_embeds=a ) __lowerCamelCase = self.encoder( a , attention_mask=a , head_mask=a , encoder_hidden_states=a , encoder_attention_mask=a , ) __lowerCamelCase = encoder_outputs[0] __lowerCamelCase = self.pooler(a ) __lowerCamelCase = ( sequence_output, pooled_output, ) + encoder_outputs[ 1: ] # add hidden_states and attentions if they are here return outputs # sequence_output, pooled_output, (hidden_states), (attentions), highway exits class a__ ( UpperCAmelCase__ ): def __init__( self : str , a : Dict , a : List[Any] ): """simple docstring""" __lowerCamelCase = message __lowerCamelCase = exit_layer # start from 1! class a__ ( nn.Module ): def __init__( self : Any , a : List[Any] ): """simple docstring""" super().__init__() __lowerCamelCase = BertPooler(a ) __lowerCamelCase = nn.Dropout(config.hidden_dropout_prob ) __lowerCamelCase = nn.Linear(config.hidden_size , config.num_labels ) def SCREAMING_SNAKE_CASE__ ( self : Optional[int] , a : int ): """simple docstring""" __lowerCamelCase = encoder_outputs[0] __lowerCamelCase = self.pooler(a ) # "return" pooler_output # BertModel __lowerCamelCase = (pooler_input, pooler_output) + encoder_outputs[1:] # "return" bmodel_output # Dropout and classification __lowerCamelCase = bmodel_output[1] __lowerCamelCase = self.dropout(a ) __lowerCamelCase = self.classifier(a ) return logits, pooled_output @add_start_docstrings( "Bert Model (with early exiting - DeeBERT) with a classifier on top,\n also takes care of multi-layer training. " , UpperCAmelCase__ , ) class a__ ( UpperCAmelCase__ ): def __init__( self : Optional[Any] , a : Tuple ): """simple docstring""" super().__init__(a ) __lowerCamelCase = config.num_labels __lowerCamelCase = config.num_hidden_layers __lowerCamelCase = DeeBertModel(a ) __lowerCamelCase = nn.Dropout(config.hidden_dropout_prob ) __lowerCamelCase = nn.Linear(config.hidden_size , self.config.num_labels ) self.init_weights() @add_start_docstrings_to_model_forward(a ) def SCREAMING_SNAKE_CASE__ ( self : Tuple , a : int=None , a : List[Any]=None , a : Optional[Any]=None , a : List[str]=None , a : List[str]=None , a : Tuple=None , a : List[str]=None , a : Any=-1 , a : List[Any]=False , ): """simple docstring""" __lowerCamelCase = self.num_layers try: __lowerCamelCase = self.bert( a , attention_mask=a , token_type_ids=a , position_ids=a , head_mask=a , inputs_embeds=a , ) # sequence_output, pooled_output, (hidden_states), (attentions), highway exits __lowerCamelCase = outputs[1] __lowerCamelCase = self.dropout(a ) __lowerCamelCase = self.classifier(a ) __lowerCamelCase = (logits,) + outputs[2:] # add hidden states and attention if they are here except HighwayException as e: __lowerCamelCase = e.message __lowerCamelCase = e.exit_layer __lowerCamelCase = outputs[0] if not self.training: __lowerCamelCase = entropy(a ) __lowerCamelCase = [] __lowerCamelCase = [] if labels is not None: if self.num_labels == 1: # We are doing regression __lowerCamelCase = MSELoss() __lowerCamelCase = loss_fct(logits.view(-1 ) , labels.view(-1 ) ) else: __lowerCamelCase = CrossEntropyLoss() __lowerCamelCase = loss_fct(logits.view(-1 , self.num_labels ) , labels.view(-1 ) ) # work with highway exits __lowerCamelCase = [] for highway_exit in outputs[-1]: __lowerCamelCase = highway_exit[0] if not self.training: highway_logits_all.append(a ) highway_entropy.append(highway_exit[2] ) if self.num_labels == 1: # We are doing regression __lowerCamelCase = MSELoss() __lowerCamelCase = loss_fct(highway_logits.view(-1 ) , labels.view(-1 ) ) else: __lowerCamelCase = CrossEntropyLoss() __lowerCamelCase = loss_fct(highway_logits.view(-1 , self.num_labels ) , labels.view(-1 ) ) highway_losses.append(a ) if train_highway: __lowerCamelCase = (sum(highway_losses[:-1] ),) + outputs # exclude the final highway, of course else: __lowerCamelCase = (loss,) + outputs if not self.training: __lowerCamelCase = outputs + ((original_entropy, highway_entropy), exit_layer) if output_layer >= 0: __lowerCamelCase = ( (outputs[0],) + (highway_logits_all[output_layer],) + outputs[2:] ) # use the highway of the last layer return outputs # (loss), logits, (hidden_states), (attentions), (highway_exits)
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'''simple docstring''' from typing import List, Optional, Tuple, Union import torch from ...models import UNetaDModel from ...schedulers import ScoreSdeVeScheduler from ...utils import randn_tensor from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput class SCREAMING_SNAKE_CASE( A__ ): """simple docstring""" lowerCamelCase__ = 42 lowerCamelCase__ = 42 def __init__( self : Union[str, Any] , __snake_case : UNetaDModel , __snake_case : ScoreSdeVeScheduler ) -> int: super().__init__() self.register_modules(unet=__snake_case , scheduler=__snake_case ) @torch.no_grad() def __call__( self : Optional[int] , __snake_case : int = 1 , __snake_case : int = 2000 , __snake_case : Optional[Union[torch.Generator, List[torch.Generator]]] = None , __snake_case : Optional[str] = "pil" , __snake_case : bool = True , **__snake_case : Optional[int] , ) -> Union[ImagePipelineOutput, Tuple]: UpperCAmelCase : str = self.unet.config.sample_size UpperCAmelCase : Union[str, Any] = (batch_size, 3, img_size, img_size) UpperCAmelCase : int = self.unet UpperCAmelCase : Any = randn_tensor(__snake_case , generator=__snake_case ) * self.scheduler.init_noise_sigma UpperCAmelCase : List[Any] = sample.to(self.device ) self.scheduler.set_timesteps(__snake_case ) self.scheduler.set_sigmas(__snake_case ) for i, t in enumerate(self.progress_bar(self.scheduler.timesteps ) ): UpperCAmelCase : Any = self.scheduler.sigmas[i] * torch.ones(shape[0] , device=self.device ) # correction step for _ in range(self.scheduler.config.correct_steps ): UpperCAmelCase : Union[str, Any] = self.unet(__snake_case , __snake_case ).sample UpperCAmelCase : Optional[Any] = self.scheduler.step_correct(__snake_case , __snake_case , generator=__snake_case ).prev_sample # prediction step UpperCAmelCase : Optional[Any] = model(__snake_case , __snake_case ).sample UpperCAmelCase : List[str] = self.scheduler.step_pred(__snake_case , __snake_case , __snake_case , generator=__snake_case ) UpperCAmelCase , UpperCAmelCase : Optional[Any] = output.prev_sample, output.prev_sample_mean UpperCAmelCase : int = sample_mean.clamp(0 , 1 ) UpperCAmelCase : Union[str, Any] = sample.cpu().permute(0 , 2 , 3 , 1 ).numpy() if output_type == "pil": UpperCAmelCase : Optional[Any] = self.numpy_to_pil(__snake_case ) if not return_dict: return (sample,) return ImagePipelineOutput(images=__snake_case )
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from ..utils import DummyObject, requires_backends class a__ ( metaclass=snake_case ): """simple docstring""" __lowerCamelCase = ['note_seq'] def __init__( self , *lowercase , **lowercase ) -> List[str]: '''simple docstring''' requires_backends(self , ["note_seq"] ) @classmethod def UpperCamelCase ( cls , *lowercase , **lowercase ) -> Optional[int]: '''simple docstring''' requires_backends(cls , ["note_seq"] ) @classmethod def UpperCamelCase ( cls , *lowercase , **lowercase ) -> int: '''simple docstring''' requires_backends(cls , ["note_seq"] )
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'''simple docstring''' import warnings from contextlib import contextmanager from ....processing_utils import ProcessorMixin class SCREAMING_SNAKE_CASE( A__ ): """simple docstring""" lowerCamelCase__ = """MCTCTFeatureExtractor""" lowerCamelCase__ = """AutoTokenizer""" def __init__( self : Dict , __snake_case : Optional[int] , __snake_case : List[str] ) -> str: super().__init__(__snake_case , __snake_case ) UpperCAmelCase : List[Any] = self.feature_extractor UpperCAmelCase : Union[str, Any] = False def __call__( self : Any , *__snake_case : List[str] , **__snake_case : Any ) -> List[Any]: # For backward compatibility if self._in_target_context_manager: return self.current_processor(*__snake_case , **__snake_case ) if "raw_speech" in kwargs: warnings.warn('''Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead.''' ) UpperCAmelCase : int = kwargs.pop('''raw_speech''' ) else: UpperCAmelCase : Union[str, Any] = kwargs.pop('''audio''' , __snake_case ) UpperCAmelCase : Optional[Any] = kwargs.pop('''sampling_rate''' , __snake_case ) UpperCAmelCase : Dict = kwargs.pop('''text''' , __snake_case ) if len(__snake_case ) > 0: UpperCAmelCase : Any = args[0] UpperCAmelCase : Optional[int] = args[1:] if audio is None and text is None: raise ValueError('''You need to specify either an `audio` or `text` input to process.''' ) if audio is not None: UpperCAmelCase : List[str] = self.feature_extractor(__snake_case , *__snake_case , sampling_rate=__snake_case , **__snake_case ) if text is not None: UpperCAmelCase : int = self.tokenizer(__snake_case , **__snake_case ) if text is None: return inputs elif audio is None: return encodings else: UpperCAmelCase : str = encodings['''input_ids'''] return inputs def A ( self : List[Any] , *__snake_case : List[Any] , **__snake_case : List[Any] ) -> str: return self.tokenizer.batch_decode(*__snake_case , **__snake_case ) def A ( self : List[Any] , *__snake_case : int , **__snake_case : Optional[int] ) -> Any: # For backward compatibility if self._in_target_context_manager: return self.current_processor.pad(*__snake_case , **__snake_case ) UpperCAmelCase : List[Any] = kwargs.pop('''input_features''' , __snake_case ) UpperCAmelCase : Optional[Any] = kwargs.pop('''labels''' , __snake_case ) if len(__snake_case ) > 0: UpperCAmelCase : List[str] = args[0] UpperCAmelCase : List[Any] = args[1:] if input_features is not None: UpperCAmelCase : Tuple = self.feature_extractor.pad(__snake_case , *__snake_case , **__snake_case ) if labels is not None: UpperCAmelCase : Optional[int] = self.tokenizer.pad(__snake_case , **__snake_case ) if labels is None: return input_features elif input_features is None: return labels else: UpperCAmelCase : List[str] = labels['''input_ids'''] return input_features def A ( self : Union[str, Any] , *__snake_case : Optional[Any] , **__snake_case : Optional[int] ) -> Optional[Any]: return self.tokenizer.decode(*__snake_case , **__snake_case ) @contextmanager def A ( self : Any ) -> Optional[int]: warnings.warn( '''`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your ''' '''labels by using the argument `text` of the regular `__call__` method (either in the same call as ''' '''your audio inputs, or in a separate call.''' ) UpperCAmelCase : Dict = True UpperCAmelCase : List[Any] = self.tokenizer yield UpperCAmelCase : Tuple = self.feature_extractor UpperCAmelCase : List[Any] = False
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"""simple docstring""" def UpperCAmelCase ( UpperCAmelCase ) -> bool: if not all(x.isalpha() for x in string ): raise ValueError('String must only contain alphabetic characters.' ) snake_case_ = sorted(string.lower() ) return len(UpperCAmelCase ) == len(set(UpperCAmelCase ) ) if __name__ == "__main__": __UpperCamelCase = input('''Enter a string ''').strip() __UpperCamelCase = is_isogram(input_str) print(F"""{input_str} is {"an" if isogram else "not an"} isogram.""")
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'''simple docstring''' from math import isclose, sqrt def snake_case_ ( _lowerCAmelCase : float , _lowerCAmelCase : float , _lowerCAmelCase : float ) -> tuple[float, float, float]: UpperCAmelCase : Optional[int] = point_y / 4 / point_x UpperCAmelCase : str = 2 * normal_gradient / (1 + normal_gradient * normal_gradient) UpperCAmelCase : Any = (1 - normal_gradient * normal_gradient) / ( 1 + normal_gradient * normal_gradient ) UpperCAmelCase : Union[str, Any] = (sa - ca * incoming_gradient) / (ca + sa * incoming_gradient) # to find the next point, solve the simultaeneous equations: # y^2 + 4x^2 = 100 # y - b = m * (x - a) # ==> A x^2 + B x + C = 0 UpperCAmelCase : Union[str, Any] = outgoing_gradient**2 + 4 UpperCAmelCase : Dict = 2 * outgoing_gradient * (point_y - outgoing_gradient * point_x) UpperCAmelCase : List[Any] = (point_y - outgoing_gradient * point_x) ** 2 - 100 UpperCAmelCase : List[str] = ( -linear_term - sqrt(linear_term**2 - 4 * quadratic_term * constant_term ) ) / (2 * quadratic_term) UpperCAmelCase : Optional[int] = ( -linear_term + sqrt(linear_term**2 - 4 * quadratic_term * constant_term ) ) / (2 * quadratic_term) # two solutions, one of which is our input point UpperCAmelCase : Optional[Any] = x_minus if isclose(_lowerCAmelCase , _lowerCAmelCase ) else x_plus UpperCAmelCase : Union[str, Any] = point_y + outgoing_gradient * (next_x - point_x) return next_x, next_y, outgoing_gradient def snake_case_ ( _lowerCAmelCase : float = 1.4 , _lowerCAmelCase : float = -9.6 ) -> int: UpperCAmelCase : int = 0 UpperCAmelCase : float = first_x_coord UpperCAmelCase : float = first_y_coord UpperCAmelCase : float = (1_0.1 - point_y) / (0.0 - point_x) while not (-0.0_1 <= point_x <= 0.0_1 and point_y > 0): UpperCAmelCase , UpperCAmelCase , UpperCAmelCase : str = next_point(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ) num_reflections += 1 return num_reflections if __name__ == "__main__": print(F"{solution() = }")
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'''simple docstring''' from typing import TYPE_CHECKING from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available A__ : str ={ '''configuration_longt5''': ['''LONGT5_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''LongT5Config''', '''LongT5OnnxConfig'''], } try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: A__ : Optional[int] =[ '''LONGT5_PRETRAINED_MODEL_ARCHIVE_LIST''', '''LongT5EncoderModel''', '''LongT5ForConditionalGeneration''', '''LongT5Model''', '''LongT5PreTrainedModel''', ] try: if not is_flax_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: A__ : int =[ '''FlaxLongT5ForConditionalGeneration''', '''FlaxLongT5Model''', '''FlaxLongT5PreTrainedModel''', ] if TYPE_CHECKING: from .configuration_longta import LONGT5_PRETRAINED_CONFIG_ARCHIVE_MAP, LongTaConfig, LongTaOnnxConfig try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_longta import ( LONGT5_PRETRAINED_MODEL_ARCHIVE_LIST, LongTaEncoderModel, LongTaForConditionalGeneration, LongTaModel, LongTaPreTrainedModel, ) try: if not is_flax_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_flax_longta import ( FlaxLongTaForConditionalGeneration, FlaxLongTaModel, FlaxLongTaPreTrainedModel, ) else: import sys A__ : Any =_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
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'''simple docstring''' from typing import TYPE_CHECKING from ...utils import ( OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_tokenizers_available, is_torch_available, ) UpperCamelCase__: str = { "configuration_lxmert": ["LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "LxmertConfig"], "tokenization_lxmert": ["LxmertTokenizer"], } try: if not is_tokenizers_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: UpperCamelCase__: int = ["LxmertTokenizerFast"] try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: UpperCamelCase__: Union[str, Any] = [ "LxmertEncoder", "LxmertForPreTraining", "LxmertForQuestionAnswering", "LxmertModel", "LxmertPreTrainedModel", "LxmertVisualFeatureEncoder", "LxmertXLayer", ] try: if not is_tf_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: UpperCamelCase__: int = [ "TF_LXMERT_PRETRAINED_MODEL_ARCHIVE_LIST", "TFLxmertForPreTraining", "TFLxmertMainLayer", "TFLxmertModel", "TFLxmertPreTrainedModel", "TFLxmertVisualFeatureEncoder", ] if TYPE_CHECKING: from .configuration_lxmert import LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP, LxmertConfig from .tokenization_lxmert import LxmertTokenizer try: if not is_tokenizers_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .tokenization_lxmert_fast import LxmertTokenizerFast try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_lxmert import ( LxmertEncoder, LxmertForPreTraining, LxmertForQuestionAnswering, LxmertModel, LxmertPreTrainedModel, LxmertVisualFeatureEncoder, LxmertXLayer, ) try: if not is_tf_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_tf_lxmert import ( TF_LXMERT_PRETRAINED_MODEL_ARCHIVE_LIST, TFLxmertForPreTraining, TFLxmertMainLayer, TFLxmertModel, TFLxmertPreTrainedModel, TFLxmertVisualFeatureEncoder, ) else: import sys UpperCamelCase__: Tuple = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
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import math from collections import defaultdict from typing import List, Optional, Tuple, Union import numpy as np import torch from ..configuration_utils import ConfigMixin, register_to_config from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput def A ( a_ ,a_=0.999 ,a_="cosine" ,) -> Optional[Any]: if alpha_transform_type == "cosine": def alpha_bar_fn(a_ ): return math.cos((t + 0.008) / 1.008 * math.pi / 2 ) ** 2 elif alpha_transform_type == "exp": def alpha_bar_fn(a_ ): return math.exp(t * -12.0 ) else: raise ValueError(F'Unsupported alpha_tranform_type: {alpha_transform_type}' ) __UpperCamelCase : str =[] for i in range(a_ ): __UpperCamelCase : Optional[int] =i / num_diffusion_timesteps __UpperCamelCase : Optional[Any] =(i + 1) / num_diffusion_timesteps betas.append(min(1 - alpha_bar_fn(a_ ) / alpha_bar_fn(a_ ) ,a_ ) ) return torch.tensor(a_ ,dtype=torch.floataa ) class __A ( a , a ): """simple docstring""" UpperCamelCase__ : Tuple =[e.name for e in KarrasDiffusionSchedulers] UpperCamelCase__ : Optional[Any] =2 @register_to_config def __init__( self , lowerCamelCase__ = 1000 , lowerCamelCase__ = 0.00_085 , lowerCamelCase__ = 0.012 , lowerCamelCase__ = "linear" , lowerCamelCase__ = None , lowerCamelCase__ = "epsilon" , lowerCamelCase__ = "linspace" , lowerCamelCase__ = 0 , ): """simple docstring""" if trained_betas is not None: __UpperCamelCase : Optional[int] =torch.tensor(lowerCamelCase__ , dtype=torch.floataa ) elif beta_schedule == "linear": __UpperCamelCase : Tuple =torch.linspace(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , dtype=torch.floataa ) elif beta_schedule == "scaled_linear": # this schedule is very specific to the latent diffusion model. __UpperCamelCase : int =( torch.linspace(beta_start**0.5 , beta_end**0.5 , lowerCamelCase__ , dtype=torch.floataa ) ** 2 ) elif beta_schedule == "squaredcos_cap_v2": # Glide cosine schedule __UpperCamelCase : Dict =betas_for_alpha_bar(lowerCamelCase__ ) else: raise NotImplementedError(f'{beta_schedule} does is not implemented for {self.__class__}' ) __UpperCamelCase : Dict =1.0 - self.betas __UpperCamelCase : List[Any] =torch.cumprod(self.alphas , dim=0 ) # set all values self.set_timesteps(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ) def __lowercase ( self , lowerCamelCase__ , lowerCamelCase__=None ): """simple docstring""" if schedule_timesteps is None: __UpperCamelCase : Dict =self.timesteps __UpperCamelCase : List[Any] =(schedule_timesteps == timestep).nonzero() # The sigma index that is taken for the **very** first `step` # is always the second index (or the last index if there is only 1) # This way we can ensure we don't accidentally skip a sigma in # case we start in the middle of the denoising schedule (e.g. for image-to-image) if len(self._index_counter ) == 0: __UpperCamelCase : List[str] =1 if len(lowerCamelCase__ ) > 1 else 0 else: __UpperCamelCase : int =timestep.cpu().item() if torch.is_tensor(lowerCamelCase__ ) else timestep __UpperCamelCase : Optional[Any] =self._index_counter[timestep_int] return indices[pos].item() @property def __lowercase ( self ): """simple docstring""" if self.config.timestep_spacing in ["linspace", "trailing"]: return self.sigmas.max() return (self.sigmas.max() ** 2 + 1) ** 0.5 def __lowercase ( self , lowerCamelCase__ , lowerCamelCase__ , ): """simple docstring""" __UpperCamelCase : Union[str, Any] =self.index_for_timestep(lowerCamelCase__ ) if self.state_in_first_order: __UpperCamelCase : str =self.sigmas[step_index] else: __UpperCamelCase : Optional[int] =self.sigmas_interpol[step_index] __UpperCamelCase : Any =sample / ((sigma**2 + 1) ** 0.5) return sample def __lowercase ( self , lowerCamelCase__ , lowerCamelCase__ = None , lowerCamelCase__ = None , ): """simple docstring""" __UpperCamelCase : Dict =num_inference_steps __UpperCamelCase : Optional[int] =num_train_timesteps or self.config.num_train_timesteps # "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891 if self.config.timestep_spacing == "linspace": __UpperCamelCase : Optional[int] =np.linspace(0 , num_train_timesteps - 1 , lowerCamelCase__ , dtype=lowerCamelCase__ )[::-1].copy() elif self.config.timestep_spacing == "leading": __UpperCamelCase : int =num_train_timesteps // self.num_inference_steps # creates integer timesteps by multiplying by ratio # casting to int to avoid issues when num_inference_step is power of 3 __UpperCamelCase : List[Any] =(np.arange(0 , lowerCamelCase__ ) * step_ratio).round()[::-1].copy().astype(lowerCamelCase__ ) timesteps += self.config.steps_offset elif self.config.timestep_spacing == "trailing": __UpperCamelCase : List[str] =num_train_timesteps / self.num_inference_steps # creates integer timesteps by multiplying by ratio # casting to int to avoid issues when num_inference_step is power of 3 __UpperCamelCase : Optional[int] =(np.arange(lowerCamelCase__ , 0 , -step_ratio )).round().copy().astype(lowerCamelCase__ ) timesteps -= 1 else: raise ValueError( f'{self.config.timestep_spacing} is not supported. Please make sure to choose one of \'linspace\', \'leading\' or \'trailing\'.' ) __UpperCamelCase : Any =np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5 ) __UpperCamelCase : Optional[Any] =torch.from_numpy(np.log(lowerCamelCase__ ) ).to(lowerCamelCase__ ) __UpperCamelCase : Any =np.interp(lowerCamelCase__ , np.arange(0 , len(lowerCamelCase__ ) ) , lowerCamelCase__ ) __UpperCamelCase : List[Any] =np.concatenate([sigmas, [0.0]] ).astype(np.floataa ) __UpperCamelCase : Dict =torch.from_numpy(lowerCamelCase__ ).to(device=lowerCamelCase__ ) # interpolate sigmas __UpperCamelCase : Tuple =sigmas.log().lerp(sigmas.roll(1 ).log() , 0.5 ).exp() __UpperCamelCase : int =torch.cat([sigmas[:1], sigmas[1:].repeat_interleave(2 ), sigmas[-1:]] ) __UpperCamelCase : Optional[Any] =torch.cat( [sigmas_interpol[:1], sigmas_interpol[1:].repeat_interleave(2 ), sigmas_interpol[-1:]] ) if str(lowerCamelCase__ ).startswith('mps' ): # mps does not support float64 __UpperCamelCase : Tuple =torch.from_numpy(lowerCamelCase__ ).to(lowerCamelCase__ , dtype=torch.floataa ) else: __UpperCamelCase : List[str] =torch.from_numpy(lowerCamelCase__ ).to(lowerCamelCase__ ) # interpolate timesteps __UpperCamelCase : Union[str, Any] =self.sigma_to_t(lowerCamelCase__ ).to(lowerCamelCase__ , dtype=timesteps.dtype ) __UpperCamelCase : List[Any] =torch.stack((timesteps_interpol[1:-1, None], timesteps[1:, None]) , dim=-1 ).flatten() __UpperCamelCase : List[Any] =torch.cat([timesteps[:1], interleaved_timesteps] ) __UpperCamelCase : Optional[int] =None # for exp beta schedules, such as the one for `pipeline_shap_e.py` # we need an index counter __UpperCamelCase : List[Any] =defaultdict(lowerCamelCase__ ) def __lowercase ( self , lowerCamelCase__ ): """simple docstring""" __UpperCamelCase : Dict =sigma.log() # get distribution __UpperCamelCase : Optional[int] =log_sigma - self.log_sigmas[:, None] # get sigmas range __UpperCamelCase : List[str] =dists.ge(0 ).cumsum(dim=0 ).argmax(dim=0 ).clamp(max=self.log_sigmas.shape[0] - 2 ) __UpperCamelCase : List[Any] =low_idx + 1 __UpperCamelCase : int =self.log_sigmas[low_idx] __UpperCamelCase : Tuple =self.log_sigmas[high_idx] # interpolate sigmas __UpperCamelCase : str =(low - log_sigma) / (low - high) __UpperCamelCase : List[Any] =w.clamp(0 , 1 ) # transform interpolation to time range __UpperCamelCase : List[str] =(1 - w) * low_idx + w * high_idx __UpperCamelCase : Dict =t.view(sigma.shape ) return t @property def __lowercase ( self ): """simple docstring""" return self.sample is None def __lowercase ( self , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ = True , ): """simple docstring""" __UpperCamelCase : int =self.index_for_timestep(lowerCamelCase__ ) # advance index counter by 1 __UpperCamelCase : str =timestep.cpu().item() if torch.is_tensor(lowerCamelCase__ ) else timestep self._index_counter[timestep_int] += 1 if self.state_in_first_order: __UpperCamelCase : List[str] =self.sigmas[step_index] __UpperCamelCase : str =self.sigmas_interpol[step_index + 1] __UpperCamelCase : Any =self.sigmas[step_index + 1] else: # 2nd order / KDPM2's method __UpperCamelCase : int =self.sigmas[step_index - 1] __UpperCamelCase : Tuple =self.sigmas_interpol[step_index] __UpperCamelCase : List[Any] =self.sigmas[step_index] # currently only gamma=0 is supported. This usually works best anyways. # We can support gamma in the future but then need to scale the timestep before # passing it to the model which requires a change in API __UpperCamelCase : Any =0 __UpperCamelCase : Tuple =sigma * (gamma + 1) # Note: sigma_hat == sigma for now # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise if self.config.prediction_type == "epsilon": __UpperCamelCase : List[str] =sigma_hat if self.state_in_first_order else sigma_interpol __UpperCamelCase : Union[str, Any] =sample - sigma_input * model_output elif self.config.prediction_type == "v_prediction": __UpperCamelCase : Any =sigma_hat if self.state_in_first_order else sigma_interpol __UpperCamelCase : Optional[int] =model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + ( sample / (sigma_input**2 + 1) ) elif self.config.prediction_type == "sample": raise NotImplementedError('prediction_type not implemented yet: sample' ) else: raise ValueError( f'prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`' ) if self.state_in_first_order: # 2. Convert to an ODE derivative for 1st order __UpperCamelCase : List[Any] =(sample - pred_original_sample) / sigma_hat # 3. delta timestep __UpperCamelCase : List[str] =sigma_interpol - sigma_hat # store for 2nd order step __UpperCamelCase : Tuple =sample else: # DPM-Solver-2 # 2. Convert to an ODE derivative for 2nd order __UpperCamelCase : Optional[Any] =(sample - pred_original_sample) / sigma_interpol # 3. delta timestep __UpperCamelCase : Optional[Any] =sigma_next - sigma_hat __UpperCamelCase : List[Any] =self.sample __UpperCamelCase : int =None __UpperCamelCase : Union[str, Any] =sample + derivative * dt if not return_dict: return (prev_sample,) return SchedulerOutput(prev_sample=lowerCamelCase__ ) def __lowercase ( self , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , ): """simple docstring""" __UpperCamelCase : int =self.sigmas.to(device=original_samples.device , dtype=original_samples.dtype ) if original_samples.device.type == "mps" and torch.is_floating_point(lowerCamelCase__ ): # mps does not support float64 __UpperCamelCase : int =self.timesteps.to(original_samples.device , dtype=torch.floataa ) __UpperCamelCase : Union[str, Any] =timesteps.to(original_samples.device , dtype=torch.floataa ) else: __UpperCamelCase : Optional[Any] =self.timesteps.to(original_samples.device ) __UpperCamelCase : Tuple =timesteps.to(original_samples.device ) __UpperCamelCase : Any =[self.index_for_timestep(lowerCamelCase__ , lowerCamelCase__ ) for t in timesteps] __UpperCamelCase : List[Any] =sigmas[step_indices].flatten() while len(sigma.shape ) < len(original_samples.shape ): __UpperCamelCase : int =sigma.unsqueeze(-1 ) __UpperCamelCase : Dict =original_samples + noise * sigma return noisy_samples def __len__( self ): """simple docstring""" return self.config.num_train_timesteps
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'''simple docstring''' import gc import unittest import numpy as np import torch from transformers import CLIPTextConfig, CLIPTextModel, XLMRobertaTokenizer from diffusers import AltDiffusionPipeline, AutoencoderKL, DDIMScheduler, PNDMScheduler, UNetaDConditionModel from diffusers.pipelines.alt_diffusion.modeling_roberta_series import ( RobertaSeriesConfig, RobertaSeriesModelWithTransformation, ) from diffusers.utils import slow, torch_device from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin enable_full_determinism() class SCREAMING_SNAKE_CASE( A__ , A__ , A__ , unittest.TestCase ): """simple docstring""" lowerCamelCase__ = AltDiffusionPipeline lowerCamelCase__ = TEXT_TO_IMAGE_PARAMS lowerCamelCase__ = TEXT_TO_IMAGE_BATCH_PARAMS lowerCamelCase__ = TEXT_TO_IMAGE_IMAGE_PARAMS lowerCamelCase__ = TEXT_TO_IMAGE_IMAGE_PARAMS def A ( self : Dict ) -> int: torch.manual_seed(0 ) UpperCAmelCase : str = UNetaDConditionModel( block_out_channels=(32, 64) , layers_per_block=2 , sample_size=32 , in_channels=4 , out_channels=4 , down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''') , up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''') , cross_attention_dim=32 , ) UpperCAmelCase : Dict = DDIMScheduler( beta_start=0.0_00_85 , beta_end=0.0_12 , beta_schedule='''scaled_linear''' , clip_sample=__snake_case , set_alpha_to_one=__snake_case , ) torch.manual_seed(0 ) UpperCAmelCase : Union[str, Any] = AutoencoderKL( block_out_channels=[32, 64] , in_channels=3 , out_channels=3 , down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D'''] , up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D'''] , latent_channels=4 , ) # TODO: address the non-deterministic text encoder (fails for save-load tests) # torch.manual_seed(0) # text_encoder_config = RobertaSeriesConfig( # hidden_size=32, # project_dim=32, # intermediate_size=37, # layer_norm_eps=1e-05, # num_attention_heads=4, # num_hidden_layers=5, # vocab_size=5002, # ) # text_encoder = RobertaSeriesModelWithTransformation(text_encoder_config) torch.manual_seed(0 ) UpperCAmelCase : Dict = CLIPTextConfig( bos_token_id=0 , eos_token_id=2 , hidden_size=32 , projection_dim=32 , intermediate_size=37 , layer_norm_eps=1E-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=5002 , ) UpperCAmelCase : List[Any] = CLIPTextModel(__snake_case ) UpperCAmelCase : Optional[Any] = XLMRobertaTokenizer.from_pretrained('''hf-internal-testing/tiny-xlm-roberta''' ) UpperCAmelCase : Optional[int] = 77 UpperCAmelCase : Optional[int] = { '''unet''': unet, '''scheduler''': scheduler, '''vae''': vae, '''text_encoder''': text_encoder, '''tokenizer''': tokenizer, '''safety_checker''': None, '''feature_extractor''': None, } return components def A ( self : Optional[Any] , __snake_case : Dict , __snake_case : List[str]=0 ) -> Union[str, Any]: if str(__snake_case ).startswith('''mps''' ): UpperCAmelCase : str = torch.manual_seed(__snake_case ) else: UpperCAmelCase : Tuple = torch.Generator(device=__snake_case ).manual_seed(__snake_case ) UpperCAmelCase : Dict = { '''prompt''': '''A painting of a squirrel eating a burger''', '''generator''': generator, '''num_inference_steps''': 2, '''guidance_scale''': 6.0, '''output_type''': '''numpy''', } return inputs def A ( self : Union[str, Any] ) -> List[str]: super().test_attention_slicing_forward_pass(expected_max_diff=3E-3 ) def A ( self : Tuple ) -> List[str]: super().test_inference_batch_single_identical(expected_max_diff=3E-3 ) def A ( self : Tuple ) -> Optional[int]: UpperCAmelCase : List[str] = '''cpu''' # ensure determinism for the device-dependent torch.Generator UpperCAmelCase : Any = self.get_dummy_components() torch.manual_seed(0 ) UpperCAmelCase : Optional[int] = RobertaSeriesConfig( hidden_size=32 , project_dim=32 , intermediate_size=37 , layer_norm_eps=1E-05 , num_attention_heads=4 , num_hidden_layers=5 , vocab_size=5002 , ) # TODO: remove after fixing the non-deterministic text encoder UpperCAmelCase : List[str] = RobertaSeriesModelWithTransformation(__snake_case ) UpperCAmelCase : str = text_encoder UpperCAmelCase : Optional[int] = AltDiffusionPipeline(**__snake_case ) UpperCAmelCase : str = alt_pipe.to(__snake_case ) alt_pipe.set_progress_bar_config(disable=__snake_case ) UpperCAmelCase : Optional[int] = self.get_dummy_inputs(__snake_case ) UpperCAmelCase : Optional[int] = '''A photo of an astronaut''' UpperCAmelCase : List[Any] = alt_pipe(**__snake_case ) UpperCAmelCase : Optional[Any] = output.images UpperCAmelCase : str = image[0, -3:, -3:, -1] assert image.shape == (1, 64, 64, 3) UpperCAmelCase : List[str] = np.array( [0.5_74_81_62, 0.60_44_71_45, 0.48_82_12_17, 0.50_10_06_36, 0.5_43_11_85, 0.45_76_36_83, 0.49_65_76_96, 0.48_13_27_33, 0.47_57_30_93] ) assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2 def A ( self : int ) -> Union[str, Any]: UpperCAmelCase : Optional[int] = '''cpu''' # ensure determinism for the device-dependent torch.Generator UpperCAmelCase : int = self.get_dummy_components() UpperCAmelCase : int = PNDMScheduler(skip_prk_steps=__snake_case ) torch.manual_seed(0 ) UpperCAmelCase : int = RobertaSeriesConfig( hidden_size=32 , project_dim=32 , intermediate_size=37 , layer_norm_eps=1E-05 , num_attention_heads=4 , num_hidden_layers=5 , vocab_size=5002 , ) # TODO: remove after fixing the non-deterministic text encoder UpperCAmelCase : Union[str, Any] = RobertaSeriesModelWithTransformation(__snake_case ) UpperCAmelCase : Union[str, Any] = text_encoder UpperCAmelCase : Optional[int] = AltDiffusionPipeline(**__snake_case ) UpperCAmelCase : Dict = alt_pipe.to(__snake_case ) alt_pipe.set_progress_bar_config(disable=__snake_case ) UpperCAmelCase : int = self.get_dummy_inputs(__snake_case ) UpperCAmelCase : Optional[int] = alt_pipe(**__snake_case ) UpperCAmelCase : Optional[int] = output.images UpperCAmelCase : int = image[0, -3:, -3:, -1] assert image.shape == (1, 64, 64, 3) UpperCAmelCase : Optional[int] = np.array( [0.51_60_50_93, 0.5_70_72_41, 0.47_36_55_07, 0.50_57_88_86, 0.5_63_38_77, 0.4_64_25_03, 0.5_18_20_81, 0.48_76_34_84, 0.49_08_42_37] ) assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2 @slow @require_torch_gpu class SCREAMING_SNAKE_CASE( unittest.TestCase ): """simple docstring""" def A ( self : str ) -> Any: # clean up the VRAM after each test super().tearDown() gc.collect() torch.cuda.empty_cache() def A ( self : List[Any] ) -> Any: # make sure here that pndm scheduler skips prk UpperCAmelCase : List[Any] = AltDiffusionPipeline.from_pretrained('''BAAI/AltDiffusion''' , safety_checker=__snake_case ) UpperCAmelCase : Tuple = alt_pipe.to(__snake_case ) alt_pipe.set_progress_bar_config(disable=__snake_case ) UpperCAmelCase : List[Any] = '''A painting of a squirrel eating a burger''' UpperCAmelCase : Any = torch.manual_seed(0 ) UpperCAmelCase : Optional[int] = alt_pipe([prompt] , generator=__snake_case , guidance_scale=6.0 , num_inference_steps=20 , output_type='''np''' ) UpperCAmelCase : Dict = output.images UpperCAmelCase : Optional[Any] = image[0, -3:, -3:, -1] assert image.shape == (1, 512, 512, 3) UpperCAmelCase : List[str] = np.array([0.10_10, 0.08_00, 0.07_94, 0.08_85, 0.08_43, 0.07_62, 0.07_69, 0.07_29, 0.05_86] ) assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2 def A ( self : Tuple ) -> int: UpperCAmelCase : int = DDIMScheduler.from_pretrained('''BAAI/AltDiffusion''' , subfolder='''scheduler''' ) UpperCAmelCase : Tuple = AltDiffusionPipeline.from_pretrained('''BAAI/AltDiffusion''' , scheduler=__snake_case , safety_checker=__snake_case ) UpperCAmelCase : Dict = alt_pipe.to(__snake_case ) alt_pipe.set_progress_bar_config(disable=__snake_case ) UpperCAmelCase : Tuple = '''A painting of a squirrel eating a burger''' UpperCAmelCase : Optional[int] = torch.manual_seed(0 ) UpperCAmelCase : List[Any] = alt_pipe([prompt] , generator=__snake_case , num_inference_steps=2 , output_type='''numpy''' ) UpperCAmelCase : Dict = output.images UpperCAmelCase : Optional[Any] = image[0, -3:, -3:, -1] assert image.shape == (1, 512, 512, 3) UpperCAmelCase : Union[str, Any] = np.array([0.40_19, 0.40_52, 0.38_10, 0.41_19, 0.39_16, 0.39_82, 0.46_51, 0.41_95, 0.53_23] ) assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
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"""simple docstring""" from __future__ import annotations def snake_case_ ( A_ : list[int] ): '''simple docstring''' if len(A_ ) == 0: return array _lowerCamelCase , _lowerCamelCase : List[str] = min(A_ ), max(A_ ) # Compute the variables _lowerCamelCase : int = _max - _min + 1 _lowerCamelCase , _lowerCamelCase : Dict = [0] * holes_range, [0] * holes_range # Make the sorting. for i in array: _lowerCamelCase : Optional[int] = i - _min _lowerCamelCase : Union[str, Any] = i holes_repeat[index] += 1 # Makes the array back by replacing the numbers. _lowerCamelCase : List[Any] = 0 for i in range(A_ ): while holes_repeat[i] > 0: _lowerCamelCase : Tuple = holes[i] index += 1 holes_repeat[i] -= 1 # Returns the sorted array. return array if __name__ == "__main__": import doctest doctest.testmod() lowerCAmelCase__ = input('''Enter numbers separated by comma:\n''') lowerCAmelCase__ = [int(x) for x in user_input.split(''',''')] print(pigeon_sort(unsorted))
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'''simple docstring''' import io import json import fsspec import pytest from datasets import Dataset, DatasetDict, Features, NamedSplit, Value from datasets.io.json import JsonDatasetReader, JsonDatasetWriter from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases def snake_case_ ( _lowerCAmelCase : List[str] , _lowerCAmelCase : Optional[Any] ) -> Optional[int]: assert isinstance(_lowerCAmelCase , _lowerCAmelCase ) assert dataset.num_rows == 4 assert dataset.num_columns == 3 assert dataset.column_names == ["col_1", "col_2", "col_3"] for feature, expected_dtype in expected_features.items(): assert dataset.features[feature].dtype == expected_dtype @pytest.mark.parametrize('''keep_in_memory''' , [False, True] ) def snake_case_ ( _lowerCAmelCase : int , _lowerCAmelCase : Dict , _lowerCAmelCase : Optional[int] ) -> Dict: UpperCAmelCase : Dict = tmp_path / '''cache''' UpperCAmelCase : List[Any] = {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''} with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase(): UpperCAmelCase : str = JsonDatasetReader(_lowerCAmelCase , cache_dir=_lowerCAmelCase , keep_in_memory=_lowerCAmelCase ).read() _check_json_dataset(_lowerCAmelCase , _lowerCAmelCase ) @pytest.mark.parametrize( '''features''' , [ None, {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}, {'''col_1''': '''string''', '''col_2''': '''string''', '''col_3''': '''string'''}, {'''col_1''': '''int32''', '''col_2''': '''int32''', '''col_3''': '''int32'''}, {'''col_1''': '''float32''', '''col_2''': '''float32''', '''col_3''': '''float32'''}, ] , ) def snake_case_ ( _lowerCAmelCase : Optional[int] , _lowerCAmelCase : Dict , _lowerCAmelCase : int ) -> Optional[int]: UpperCAmelCase : Any = tmp_path / '''cache''' UpperCAmelCase : Tuple = {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''} UpperCAmelCase : Any = features.copy() if features else default_expected_features UpperCAmelCase : List[Any] = ( Features({feature: Value(_lowerCAmelCase ) for feature, dtype in features.items()} ) if features is not None else None ) UpperCAmelCase : Dict = JsonDatasetReader(_lowerCAmelCase , features=_lowerCAmelCase , cache_dir=_lowerCAmelCase ).read() _check_json_dataset(_lowerCAmelCase , _lowerCAmelCase ) @pytest.mark.parametrize( '''features''' , [ None, {'''col_3''': '''float64''', '''col_1''': '''string''', '''col_2''': '''int64'''}, ] , ) def snake_case_ ( _lowerCAmelCase : Optional[Any] , _lowerCAmelCase : List[Any] , _lowerCAmelCase : Tuple ) -> Tuple: UpperCAmelCase : Optional[Any] = tmp_path / '''cache''' UpperCAmelCase : Optional[int] = {'''col_3''': '''float64''', '''col_1''': '''string''', '''col_2''': '''int64'''} UpperCAmelCase : int = features.copy() if features else default_expected_features UpperCAmelCase : Any = ( Features({feature: Value(_lowerCAmelCase ) for feature, dtype in features.items()} ) if features is not None else None ) UpperCAmelCase : Tuple = JsonDatasetReader(_lowerCAmelCase , features=_lowerCAmelCase , cache_dir=_lowerCAmelCase ).read() assert isinstance(_lowerCAmelCase , _lowerCAmelCase ) assert dataset.num_rows == 2 assert dataset.num_columns == 3 assert dataset.column_names == ["col_3", "col_1", "col_2"] for feature, expected_dtype in expected_features.items(): assert dataset.features[feature].dtype == expected_dtype def snake_case_ ( _lowerCAmelCase : List[str] , _lowerCAmelCase : Dict ) -> Union[str, Any]: # jsonl_312_path features are {"col_3": "float64", "col_1": "string", "col_2": "int64"} UpperCAmelCase : Tuple = {'''col_2''': '''int64''', '''col_3''': '''float64''', '''col_1''': '''string'''} UpperCAmelCase : List[str] = features.copy() UpperCAmelCase : Union[str, Any] = ( Features({feature: Value(_lowerCAmelCase ) for feature, dtype in features.items()} ) if features is not None else None ) UpperCAmelCase : Tuple = tmp_path / '''cache''' UpperCAmelCase : List[str] = JsonDatasetReader(_lowerCAmelCase , features=_lowerCAmelCase , cache_dir=_lowerCAmelCase ).read() assert isinstance(_lowerCAmelCase , _lowerCAmelCase ) assert dataset.num_rows == 2 assert dataset.num_columns == 3 assert dataset.column_names == ["col_2", "col_3", "col_1"] for feature, expected_dtype in expected_features.items(): assert dataset.features[feature].dtype == expected_dtype @pytest.mark.parametrize('''split''' , [None, NamedSplit('''train''' ), '''train''', '''test'''] ) def snake_case_ ( _lowerCAmelCase : Union[str, Any] , _lowerCAmelCase : Tuple , _lowerCAmelCase : List[str] ) -> Optional[Any]: UpperCAmelCase : Any = tmp_path / '''cache''' UpperCAmelCase : List[Any] = {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''} UpperCAmelCase : List[Any] = JsonDatasetReader(_lowerCAmelCase , cache_dir=_lowerCAmelCase , split=_lowerCAmelCase ).read() _check_json_dataset(_lowerCAmelCase , _lowerCAmelCase ) assert dataset.split == split if split else "train" @pytest.mark.parametrize('''path_type''' , [str, list] ) def snake_case_ ( _lowerCAmelCase : List[Any] , _lowerCAmelCase : str , _lowerCAmelCase : Any ) -> Dict: if issubclass(_lowerCAmelCase , _lowerCAmelCase ): UpperCAmelCase : str = jsonl_path elif issubclass(_lowerCAmelCase , _lowerCAmelCase ): UpperCAmelCase : Dict = [jsonl_path] UpperCAmelCase : int = tmp_path / '''cache''' UpperCAmelCase : Dict = {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''} UpperCAmelCase : Optional[int] = JsonDatasetReader(_lowerCAmelCase , cache_dir=_lowerCAmelCase ).read() _check_json_dataset(_lowerCAmelCase , _lowerCAmelCase ) def snake_case_ ( _lowerCAmelCase : List[str] , _lowerCAmelCase : Dict , _lowerCAmelCase : Dict=("train",) ) -> Union[str, Any]: assert isinstance(_lowerCAmelCase , _lowerCAmelCase ) for split in splits: UpperCAmelCase : List[str] = dataset_dict[split] assert dataset.num_rows == 4 assert dataset.num_columns == 3 assert dataset.column_names == ["col_1", "col_2", "col_3"] for feature, expected_dtype in expected_features.items(): assert dataset.features[feature].dtype == expected_dtype @pytest.mark.parametrize('''keep_in_memory''' , [False, True] ) def snake_case_ ( _lowerCAmelCase : int , _lowerCAmelCase : Optional[Any] , _lowerCAmelCase : str ) -> Any: UpperCAmelCase : Optional[Any] = tmp_path / '''cache''' UpperCAmelCase : List[str] = {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''} with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase(): UpperCAmelCase : Optional[int] = JsonDatasetReader({'''train''': jsonl_path} , cache_dir=_lowerCAmelCase , keep_in_memory=_lowerCAmelCase ).read() _check_json_datasetdict(_lowerCAmelCase , _lowerCAmelCase ) @pytest.mark.parametrize( '''features''' , [ None, {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}, {'''col_1''': '''string''', '''col_2''': '''string''', '''col_3''': '''string'''}, {'''col_1''': '''int32''', '''col_2''': '''int32''', '''col_3''': '''int32'''}, {'''col_1''': '''float32''', '''col_2''': '''float32''', '''col_3''': '''float32'''}, ] , ) def snake_case_ ( _lowerCAmelCase : Optional[Any] , _lowerCAmelCase : Optional[int] , _lowerCAmelCase : Union[str, Any] ) -> int: UpperCAmelCase : Dict = tmp_path / '''cache''' UpperCAmelCase : Dict = {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''} UpperCAmelCase : Optional[int] = features.copy() if features else default_expected_features UpperCAmelCase : Union[str, Any] = ( Features({feature: Value(_lowerCAmelCase ) for feature, dtype in features.items()} ) if features is not None else None ) UpperCAmelCase : Tuple = JsonDatasetReader({'''train''': jsonl_path} , features=_lowerCAmelCase , cache_dir=_lowerCAmelCase ).read() _check_json_datasetdict(_lowerCAmelCase , _lowerCAmelCase ) @pytest.mark.parametrize('''split''' , [None, NamedSplit('''train''' ), '''train''', '''test'''] ) def snake_case_ ( _lowerCAmelCase : Any , _lowerCAmelCase : Optional[int] , _lowerCAmelCase : Dict ) -> Union[str, Any]: if split: UpperCAmelCase : Optional[int] = {split: jsonl_path} else: UpperCAmelCase : Any = '''train''' UpperCAmelCase : Any = {'''train''': jsonl_path, '''test''': jsonl_path} UpperCAmelCase : Tuple = tmp_path / '''cache''' UpperCAmelCase : int = {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''} UpperCAmelCase : Optional[Any] = JsonDatasetReader(_lowerCAmelCase , cache_dir=_lowerCAmelCase ).read() _check_json_datasetdict(_lowerCAmelCase , _lowerCAmelCase , splits=list(path.keys() ) ) assert all(dataset[split].split == split for split in path.keys() ) def snake_case_ ( _lowerCAmelCase : List[str] ) -> str: return json.load(_lowerCAmelCase ) def snake_case_ ( _lowerCAmelCase : Dict ) -> str: return [json.loads(_lowerCAmelCase ) for line in buffer] class SCREAMING_SNAKE_CASE: """simple docstring""" @pytest.mark.parametrize('''lines, load_json_function''' , [(True, load_json_lines), (False, load_json)] ) def A ( self : Union[str, Any] , __snake_case : Tuple , __snake_case : Optional[Any] , __snake_case : Optional[int] ) -> Dict: with io.BytesIO() as buffer: JsonDatasetWriter(__snake_case , __snake_case , lines=__snake_case ).write() buffer.seek(0 ) UpperCAmelCase : Union[str, Any] = load_json_function(__snake_case ) assert isinstance(__snake_case , __snake_case ) assert isinstance(exported_content[0] , __snake_case ) assert len(__snake_case ) == 10 @pytest.mark.parametrize( '''orient, container, keys, len_at''' , [ ('''records''', list, {'''tokens''', '''labels''', '''answers''', '''id'''}, None), ('''split''', dict, {'''columns''', '''data'''}, '''data'''), ('''index''', dict, set('''0123456789''' ), None), ('''columns''', dict, {'''tokens''', '''labels''', '''answers''', '''id'''}, '''tokens'''), ('''values''', list, None, None), ('''table''', dict, {'''schema''', '''data'''}, '''data'''), ] , ) def A ( self : Optional[int] , __snake_case : Optional[Any] , __snake_case : str , __snake_case : Optional[Any] , __snake_case : List[str] , __snake_case : Optional[Any] ) -> List[Any]: with io.BytesIO() as buffer: JsonDatasetWriter(__snake_case , __snake_case , lines=__snake_case , orient=__snake_case ).write() buffer.seek(0 ) UpperCAmelCase : Union[str, Any] = load_json(__snake_case ) assert isinstance(__snake_case , __snake_case ) if keys: if container is dict: assert exported_content.keys() == keys else: assert exported_content[0].keys() == keys else: assert not hasattr(__snake_case , '''keys''' ) and not hasattr(exported_content[0] , '''keys''' ) if len_at: assert len(exported_content[len_at] ) == 10 else: assert len(__snake_case ) == 10 @pytest.mark.parametrize('''lines, load_json_function''' , [(True, load_json_lines), (False, load_json)] ) def A ( self : str , __snake_case : str , __snake_case : str , __snake_case : int ) -> Any: with io.BytesIO() as buffer: JsonDatasetWriter(__snake_case , __snake_case , lines=__snake_case , num_proc=2 ).write() buffer.seek(0 ) UpperCAmelCase : Any = load_json_function(__snake_case ) assert isinstance(__snake_case , __snake_case ) assert isinstance(exported_content[0] , __snake_case ) assert len(__snake_case ) == 10 @pytest.mark.parametrize( '''orient, container, keys, len_at''' , [ ('''records''', list, {'''tokens''', '''labels''', '''answers''', '''id'''}, None), ('''split''', dict, {'''columns''', '''data'''}, '''data'''), ('''index''', dict, set('''0123456789''' ), None), ('''columns''', dict, {'''tokens''', '''labels''', '''answers''', '''id'''}, '''tokens'''), ('''values''', list, None, None), ('''table''', dict, {'''schema''', '''data'''}, '''data'''), ] , ) def A ( self : Any , __snake_case : int , __snake_case : Union[str, Any] , __snake_case : Optional[int] , __snake_case : Optional[Any] , __snake_case : List[str] ) -> Any: with io.BytesIO() as buffer: JsonDatasetWriter(__snake_case , __snake_case , lines=__snake_case , orient=__snake_case , num_proc=2 ).write() buffer.seek(0 ) UpperCAmelCase : List[str] = load_json(__snake_case ) assert isinstance(__snake_case , __snake_case ) if keys: if container is dict: assert exported_content.keys() == keys else: assert exported_content[0].keys() == keys else: assert not hasattr(__snake_case , '''keys''' ) and not hasattr(exported_content[0] , '''keys''' ) if len_at: assert len(exported_content[len_at] ) == 10 else: assert len(__snake_case ) == 10 def A ( self : List[Any] , __snake_case : str ) -> Dict: with pytest.raises(__snake_case ): with io.BytesIO() as buffer: JsonDatasetWriter(__snake_case , __snake_case , num_proc=0 ) @pytest.mark.parametrize('''compression, extension''' , [('''gzip''', '''gz'''), ('''bz2''', '''bz2'''), ('''xz''', '''xz''')] ) def A ( self : Optional[int] , __snake_case : Any , __snake_case : str , __snake_case : Union[str, Any] , __snake_case : Union[str, Any] , __snake_case : Dict ) -> Union[str, Any]: UpperCAmelCase : List[str] = tmp_path_factory.mktemp('''data''' ) / F"""test.json.{extension}""" UpperCAmelCase : List[Any] = str(shared_datadir / F"""test_file.json.{extension}""" ) JsonDatasetWriter(__snake_case , __snake_case , compression=__snake_case ).write() with fsspec.open(__snake_case , '''rb''' , compression='''infer''' ) as f: UpperCAmelCase : str = f.read() with fsspec.open(__snake_case , '''rb''' , compression='''infer''' ) as f: UpperCAmelCase : Optional[int] = f.read() assert exported_content == original_content
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import tempfile import torch from diffusers import ( DEISMultistepScheduler, DPMSolverMultistepScheduler, DPMSolverSinglestepScheduler, UniPCMultistepScheduler, ) from .test_schedulers import SchedulerCommonTest class A_ ( SCREAMING_SNAKE_CASE ): _UpperCAmelCase : Any = (UniPCMultistepScheduler,) _UpperCAmelCase : List[Any] = (('''num_inference_steps''', 25),) def lowerCAmelCase ( self : Optional[Any] ,**SCREAMING_SNAKE_CASE__ : int): __lowerCamelCase : str = { 'num_train_timesteps': 1_0_0_0, 'beta_start': 0.0001, 'beta_end': 0.02, 'beta_schedule': 'linear', 'solver_order': 2, 'solver_type': 'bh2', } config.update(**SCREAMING_SNAKE_CASE__) return config def lowerCAmelCase ( self : Union[str, Any] ,SCREAMING_SNAKE_CASE__ : Union[str, Any]=0 ,**SCREAMING_SNAKE_CASE__ : List[Any]): __lowerCamelCase : Union[str, Any] = dict(self.forward_default_kwargs) __lowerCamelCase : List[Any] = kwargs.pop('num_inference_steps' ,SCREAMING_SNAKE_CASE__) __lowerCamelCase : Any = self.dummy_sample __lowerCamelCase : List[Any] = 0.1 * sample __lowerCamelCase : Optional[Any] = [residual + 0.2, residual + 0.15, residual + 0.10] for scheduler_class in self.scheduler_classes: __lowerCamelCase : Any = self.get_scheduler_config(**SCREAMING_SNAKE_CASE__) __lowerCamelCase : Tuple = scheduler_class(**SCREAMING_SNAKE_CASE__) scheduler.set_timesteps(SCREAMING_SNAKE_CASE__) # copy over dummy past residuals __lowerCamelCase : Optional[Any] = dummy_past_residuals[: scheduler.config.solver_order] with tempfile.TemporaryDirectory() as tmpdirname: scheduler.save_config(SCREAMING_SNAKE_CASE__) __lowerCamelCase : Optional[int] = scheduler_class.from_pretrained(SCREAMING_SNAKE_CASE__) new_scheduler.set_timesteps(SCREAMING_SNAKE_CASE__) # copy over dummy past residuals __lowerCamelCase : Union[str, Any] = dummy_past_residuals[: new_scheduler.config.solver_order] __lowerCamelCase , __lowerCamelCase : Union[str, Any] = sample, sample for t in range(SCREAMING_SNAKE_CASE__ ,time_step + scheduler.config.solver_order + 1): __lowerCamelCase : Any = scheduler.step(SCREAMING_SNAKE_CASE__ ,SCREAMING_SNAKE_CASE__ ,SCREAMING_SNAKE_CASE__ ,**SCREAMING_SNAKE_CASE__).prev_sample __lowerCamelCase : Tuple = new_scheduler.step(SCREAMING_SNAKE_CASE__ ,SCREAMING_SNAKE_CASE__ ,SCREAMING_SNAKE_CASE__ ,**SCREAMING_SNAKE_CASE__).prev_sample assert torch.sum(torch.abs(output - new_output)) < 1E-5, "Scheduler outputs are not identical" def lowerCAmelCase ( self : Tuple ,SCREAMING_SNAKE_CASE__ : Optional[Any]=0 ,**SCREAMING_SNAKE_CASE__ : Dict): __lowerCamelCase : List[Any] = dict(self.forward_default_kwargs) __lowerCamelCase : Any = kwargs.pop('num_inference_steps' ,SCREAMING_SNAKE_CASE__) __lowerCamelCase : Optional[int] = self.dummy_sample __lowerCamelCase : Dict = 0.1 * sample __lowerCamelCase : Union[str, Any] = [residual + 0.2, residual + 0.15, residual + 0.10] for scheduler_class in self.scheduler_classes: __lowerCamelCase : Optional[int] = self.get_scheduler_config() __lowerCamelCase : List[str] = scheduler_class(**SCREAMING_SNAKE_CASE__) scheduler.set_timesteps(SCREAMING_SNAKE_CASE__) # copy over dummy past residuals (must be after setting timesteps) __lowerCamelCase : Union[str, Any] = dummy_past_residuals[: scheduler.config.solver_order] with tempfile.TemporaryDirectory() as tmpdirname: scheduler.save_config(SCREAMING_SNAKE_CASE__) __lowerCamelCase : List[Any] = scheduler_class.from_pretrained(SCREAMING_SNAKE_CASE__) # copy over dummy past residuals new_scheduler.set_timesteps(SCREAMING_SNAKE_CASE__) # copy over dummy past residual (must be after setting timesteps) __lowerCamelCase : Optional[Any] = dummy_past_residuals[: new_scheduler.config.solver_order] __lowerCamelCase : Optional[int] = scheduler.step(SCREAMING_SNAKE_CASE__ ,SCREAMING_SNAKE_CASE__ ,SCREAMING_SNAKE_CASE__ ,**SCREAMING_SNAKE_CASE__).prev_sample __lowerCamelCase : Tuple = new_scheduler.step(SCREAMING_SNAKE_CASE__ ,SCREAMING_SNAKE_CASE__ ,SCREAMING_SNAKE_CASE__ ,**SCREAMING_SNAKE_CASE__).prev_sample assert torch.sum(torch.abs(output - new_output)) < 1E-5, "Scheduler outputs are not identical" def lowerCAmelCase ( self : List[Any] ,SCREAMING_SNAKE_CASE__ : List[str]=None ,**SCREAMING_SNAKE_CASE__ : List[Any]): if scheduler is None: __lowerCamelCase : Any = self.scheduler_classes[0] __lowerCamelCase : Optional[int] = self.get_scheduler_config(**SCREAMING_SNAKE_CASE__) __lowerCamelCase : List[str] = scheduler_class(**SCREAMING_SNAKE_CASE__) __lowerCamelCase : int = self.scheduler_classes[0] __lowerCamelCase : Optional[Any] = self.get_scheduler_config(**SCREAMING_SNAKE_CASE__) __lowerCamelCase : Union[str, Any] = scheduler_class(**SCREAMING_SNAKE_CASE__) __lowerCamelCase : int = 1_0 __lowerCamelCase : Any = self.dummy_model() __lowerCamelCase : List[Any] = self.dummy_sample_deter scheduler.set_timesteps(SCREAMING_SNAKE_CASE__) for i, t in enumerate(scheduler.timesteps): __lowerCamelCase : Any = model(SCREAMING_SNAKE_CASE__ ,SCREAMING_SNAKE_CASE__) __lowerCamelCase : List[str] = scheduler.step(SCREAMING_SNAKE_CASE__ ,SCREAMING_SNAKE_CASE__ ,SCREAMING_SNAKE_CASE__).prev_sample return sample def lowerCAmelCase ( self : Union[str, Any]): __lowerCamelCase : List[Any] = dict(self.forward_default_kwargs) __lowerCamelCase : Union[str, Any] = kwargs.pop('num_inference_steps' ,SCREAMING_SNAKE_CASE__) for scheduler_class in self.scheduler_classes: __lowerCamelCase : Any = self.get_scheduler_config() __lowerCamelCase : int = scheduler_class(**SCREAMING_SNAKE_CASE__) __lowerCamelCase : int = self.dummy_sample __lowerCamelCase : Any = 0.1 * sample if num_inference_steps is not None and hasattr(SCREAMING_SNAKE_CASE__ ,'set_timesteps'): scheduler.set_timesteps(SCREAMING_SNAKE_CASE__) elif num_inference_steps is not None and not hasattr(SCREAMING_SNAKE_CASE__ ,'set_timesteps'): __lowerCamelCase : Optional[Any] = num_inference_steps # copy over dummy past residuals (must be done after set_timesteps) __lowerCamelCase : int = [residual + 0.2, residual + 0.15, residual + 0.10] __lowerCamelCase : List[Any] = dummy_past_residuals[: scheduler.config.solver_order] __lowerCamelCase : Optional[Any] = scheduler.timesteps[5] __lowerCamelCase : Optional[int] = scheduler.timesteps[6] __lowerCamelCase : Any = scheduler.step(SCREAMING_SNAKE_CASE__ ,SCREAMING_SNAKE_CASE__ ,SCREAMING_SNAKE_CASE__ ,**SCREAMING_SNAKE_CASE__).prev_sample __lowerCamelCase : Optional[int] = scheduler.step(SCREAMING_SNAKE_CASE__ ,SCREAMING_SNAKE_CASE__ ,SCREAMING_SNAKE_CASE__ ,**SCREAMING_SNAKE_CASE__).prev_sample self.assertEqual(output_a.shape ,sample.shape) self.assertEqual(output_a.shape ,output_a.shape) def lowerCAmelCase ( self : Tuple): # make sure that iterating over schedulers with same config names gives same results # for defaults __lowerCamelCase : Dict = UniPCMultistepScheduler(**self.get_scheduler_config()) __lowerCamelCase : Dict = self.full_loop(scheduler=SCREAMING_SNAKE_CASE__) __lowerCamelCase : Any = torch.mean(torch.abs(SCREAMING_SNAKE_CASE__)) assert abs(result_mean.item() - 0.2464) < 1E-3 __lowerCamelCase : Optional[int] = DPMSolverSinglestepScheduler.from_config(scheduler.config) __lowerCamelCase : Optional[Any] = DEISMultistepScheduler.from_config(scheduler.config) __lowerCamelCase : List[str] = DPMSolverMultistepScheduler.from_config(scheduler.config) __lowerCamelCase : Tuple = UniPCMultistepScheduler.from_config(scheduler.config) __lowerCamelCase : Optional[int] = self.full_loop(scheduler=SCREAMING_SNAKE_CASE__) __lowerCamelCase : List[str] = torch.mean(torch.abs(SCREAMING_SNAKE_CASE__)) assert abs(result_mean.item() - 0.2464) < 1E-3 def lowerCAmelCase ( self : int): for timesteps in [2_5, 5_0, 1_0_0, 9_9_9, 1_0_0_0]: self.check_over_configs(num_train_timesteps=SCREAMING_SNAKE_CASE__) def lowerCAmelCase ( self : Tuple): self.check_over_configs(thresholding=SCREAMING_SNAKE_CASE__) for order in [1, 2, 3]: for solver_type in ["bh1", "bh2"]: for threshold in [0.5, 1.0, 2.0]: for prediction_type in ["epsilon", "sample"]: self.check_over_configs( thresholding=SCREAMING_SNAKE_CASE__ ,prediction_type=SCREAMING_SNAKE_CASE__ ,sample_max_value=SCREAMING_SNAKE_CASE__ ,solver_order=SCREAMING_SNAKE_CASE__ ,solver_type=SCREAMING_SNAKE_CASE__ ,) def lowerCAmelCase ( self : List[Any]): for prediction_type in ["epsilon", "v_prediction"]: self.check_over_configs(prediction_type=SCREAMING_SNAKE_CASE__) def lowerCAmelCase ( self : List[str]): for solver_type in ["bh1", "bh2"]: for order in [1, 2, 3]: for prediction_type in ["epsilon", "sample"]: self.check_over_configs( solver_order=SCREAMING_SNAKE_CASE__ ,solver_type=SCREAMING_SNAKE_CASE__ ,prediction_type=SCREAMING_SNAKE_CASE__ ,) __lowerCamelCase : Union[str, Any] = self.full_loop( solver_order=SCREAMING_SNAKE_CASE__ ,solver_type=SCREAMING_SNAKE_CASE__ ,prediction_type=SCREAMING_SNAKE_CASE__ ,) assert not torch.isnan(SCREAMING_SNAKE_CASE__).any(), "Samples have nan numbers" def lowerCAmelCase ( self : Optional[Any]): self.check_over_configs(lower_order_final=SCREAMING_SNAKE_CASE__) self.check_over_configs(lower_order_final=SCREAMING_SNAKE_CASE__) def lowerCAmelCase ( self : str): for num_inference_steps in [1, 2, 3, 5, 1_0, 5_0, 1_0_0, 9_9_9, 1_0_0_0]: self.check_over_forward(num_inference_steps=SCREAMING_SNAKE_CASE__ ,time_step=0) def lowerCAmelCase ( self : Optional[int]): __lowerCamelCase : List[Any] = self.full_loop() __lowerCamelCase : Union[str, Any] = torch.mean(torch.abs(SCREAMING_SNAKE_CASE__)) assert abs(result_mean.item() - 0.2464) < 1E-3 def lowerCAmelCase ( self : Optional[Any]): __lowerCamelCase : Optional[int] = self.full_loop(prediction_type='v_prediction') __lowerCamelCase : Optional[Any] = torch.mean(torch.abs(SCREAMING_SNAKE_CASE__)) assert abs(result_mean.item() - 0.1014) < 1E-3 def lowerCAmelCase ( self : int): __lowerCamelCase : Tuple = self.scheduler_classes[0] __lowerCamelCase : Optional[Any] = self.get_scheduler_config(thresholding=SCREAMING_SNAKE_CASE__ ,dynamic_thresholding_ratio=0) __lowerCamelCase : Union[str, Any] = scheduler_class(**SCREAMING_SNAKE_CASE__) __lowerCamelCase : int = 1_0 __lowerCamelCase : List[Any] = self.dummy_model() __lowerCamelCase : Any = self.dummy_sample_deter.half() scheduler.set_timesteps(SCREAMING_SNAKE_CASE__) for i, t in enumerate(scheduler.timesteps): __lowerCamelCase : Optional[Any] = model(SCREAMING_SNAKE_CASE__ ,SCREAMING_SNAKE_CASE__) __lowerCamelCase : Tuple = scheduler.step(SCREAMING_SNAKE_CASE__ ,SCREAMING_SNAKE_CASE__ ,SCREAMING_SNAKE_CASE__).prev_sample assert sample.dtype == torch.floataa def lowerCAmelCase ( self : List[str] ,**SCREAMING_SNAKE_CASE__ : Union[str, Any]): for scheduler_class in self.scheduler_classes: __lowerCamelCase : Any = self.get_scheduler_config(**SCREAMING_SNAKE_CASE__) __lowerCamelCase : str = scheduler_class(**SCREAMING_SNAKE_CASE__) scheduler.set_timesteps(scheduler.config.num_train_timesteps) assert len(scheduler.timesteps.unique()) == scheduler.num_inference_steps
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'''simple docstring''' import os from shutil import copyfile from typing import Any, Dict, List, Optional, Tuple import sentencepiece as spm from ...tokenization_utils import AddedToken, PreTrainedTokenizer from ...utils import logging UpperCamelCase__: Tuple = logging.get_logger(__name__) UpperCamelCase__: Optional[int] = {"vocab_file": "sentencepiece.bpe.model"} UpperCamelCase__: Optional[int] = { "vocab_file": { "moussaKam/mbarthez": "https://huggingface.co/moussaKam/mbarthez/resolve/main/sentencepiece.bpe.model", "moussaKam/barthez": "https://huggingface.co/moussaKam/barthez/resolve/main/sentencepiece.bpe.model", "moussaKam/barthez-orangesum-title": ( "https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/sentencepiece.bpe.model" ), }, } UpperCamelCase__: Dict = { "moussaKam/mbarthez": 1024, "moussaKam/barthez": 1024, "moussaKam/barthez-orangesum-title": 1024, } UpperCamelCase__: Tuple = "▁" class SCREAMING_SNAKE_CASE( A__ ): """simple docstring""" lowerCamelCase__ = VOCAB_FILES_NAMES lowerCamelCase__ = PRETRAINED_VOCAB_FILES_MAP lowerCamelCase__ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES lowerCamelCase__ = ["""input_ids""", """attention_mask"""] def __init__( self : List[Any] , __snake_case : List[Any] , __snake_case : Tuple="<s>" , __snake_case : List[Any]="</s>" , __snake_case : int="</s>" , __snake_case : Any="<s>" , __snake_case : Optional[int]="<unk>" , __snake_case : Union[str, Any]="<pad>" , __snake_case : Union[str, Any]="<mask>" , __snake_case : Optional[Dict[str, Any]] = None , **__snake_case : Dict , ) -> None: # Mask token behave like a normal word, i.e. include the space before it UpperCAmelCase : int = AddedToken(__snake_case , lstrip=__snake_case , rstrip=__snake_case ) if isinstance(__snake_case , __snake_case ) else mask_token UpperCAmelCase : Union[str, Any] = {} if sp_model_kwargs is None else sp_model_kwargs super().__init__( bos_token=__snake_case , eos_token=__snake_case , unk_token=__snake_case , sep_token=__snake_case , cls_token=__snake_case , pad_token=__snake_case , mask_token=__snake_case , sp_model_kwargs=self.sp_model_kwargs , **__snake_case , ) UpperCAmelCase : Optional[int] = vocab_file UpperCAmelCase : int = spm.SentencePieceProcessor(**self.sp_model_kwargs ) self.sp_model.Load(str(__snake_case ) ) UpperCAmelCase : int = {'''<s>''': 0, '''<pad>''': 1, '''</s>''': 2, '''<unk>''': 3} UpperCAmelCase : Optional[Any] = len(self.sp_model ) - 1 UpperCAmelCase : Optional[int] = {v: k for k, v in self.fairseq_tokens_to_ids.items()} def A ( self : Tuple , __snake_case : List[int] , __snake_case : Optional[List[int]] = None ) -> List[int]: if token_ids_a is None: return [self.cls_token_id] + token_ids_a + [self.sep_token_id] UpperCAmelCase : Union[str, Any] = [self.cls_token_id] UpperCAmelCase : Optional[Any] = [self.sep_token_id] return cls + token_ids_a + sep + sep + token_ids_a + sep def A ( self : int , __snake_case : List[int] , __snake_case : Optional[List[int]] = None , __snake_case : bool = False ) -> List[int]: if already_has_special_tokens: return super().get_special_tokens_mask( token_ids_a=__snake_case , token_ids_a=__snake_case , already_has_special_tokens=__snake_case ) if token_ids_a is None: return [1] + ([0] * len(__snake_case )) + [1] return [1] + ([0] * len(__snake_case )) + [1, 1] + ([0] * len(__snake_case )) + [1] def A ( self : Optional[int] , __snake_case : List[int] , __snake_case : Optional[List[int]] = None ) -> List[int]: UpperCAmelCase : Tuple = [self.sep_token_id] UpperCAmelCase : Tuple = [self.cls_token_id] if token_ids_a is None: return len(cls + token_ids_a + sep ) * [0] return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0] @property def A ( self : Dict ) -> Optional[int]: return len(self.sp_model ) def A ( self : List[str] ) -> Dict: UpperCAmelCase : Optional[Any] = {self.convert_ids_to_tokens(__snake_case ): i for i in range(self.vocab_size )} vocab.update(self.added_tokens_encoder ) return vocab def A ( self : Optional[Any] , __snake_case : str ) -> List[str]: return self.sp_model.encode(__snake_case , out_type=__snake_case ) def A ( self : int , __snake_case : int ) -> int: if token in self.fairseq_tokens_to_ids: return self.fairseq_tokens_to_ids[token] UpperCAmelCase : Optional[Any] = self.sp_model.PieceToId(__snake_case ) return spm_id if spm_id else self.unk_token_id def A ( self : int , __snake_case : Any ) -> List[Any]: if index in self.fairseq_ids_to_tokens: return self.fairseq_ids_to_tokens[index] return self.sp_model.IdToPiece(__snake_case ) def A ( self : List[Any] , __snake_case : Union[str, Any] ) -> List[str]: UpperCAmelCase : Optional[Any] = [] UpperCAmelCase : int = '''''' UpperCAmelCase : Union[str, Any] = False for token in tokens: # make sure that special tokens are not decoded using sentencepiece model if token in self.all_special_tokens: if not prev_is_special: out_string += " " out_string += self.sp_model.decode(__snake_case ) + token UpperCAmelCase : str = True UpperCAmelCase : List[str] = [] else: current_sub_tokens.append(__snake_case ) UpperCAmelCase : Optional[int] = False out_string += self.sp_model.decode(__snake_case ) return out_string.strip() def __getstate__( self : Union[str, Any] ) -> Union[str, Any]: UpperCAmelCase : Optional[Any] = self.__dict__.copy() UpperCAmelCase : Any = None return state def __setstate__( self : Optional[int] , __snake_case : Union[str, Any] ) -> List[Any]: UpperCAmelCase : Dict = d # for backward compatibility if not hasattr(self , '''sp_model_kwargs''' ): UpperCAmelCase : Optional[Any] = {} UpperCAmelCase : Any = spm.SentencePieceProcessor(**self.sp_model_kwargs ) self.sp_model.Load(self.vocab_file ) def A ( self : Optional[int] , __snake_case : str , __snake_case : Optional[str] = None ) -> Tuple[str]: if not os.path.isdir(__snake_case ): logger.error(F"""Vocabulary path ({save_directory}) should be a directory""" ) return UpperCAmelCase : Union[str, Any] = os.path.join( __snake_case , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] ) if os.path.abspath(self.vocab_file ) != os.path.abspath(__snake_case ) and os.path.isfile(self.vocab_file ): copyfile(self.vocab_file , __snake_case ) elif not os.path.isfile(self.vocab_file ): with open(__snake_case , '''wb''' ) as fi: UpperCAmelCase : Any = self.sp_model.serialized_model_proto() fi.write(__snake_case ) return (out_vocab_file,)
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0
"""simple docstring""" from dataclasses import dataclass, field from typing import Tuple from ..utils import cached_property, is_tf_available, logging, requires_backends from .benchmark_args_utils import BenchmarkArguments if is_tf_available(): import tensorflow as tf _lowercase = logging.get_logger(__name__) @dataclass class lowerCAmelCase_ ( _lowercase ): '''simple docstring''' _lowerCamelCase: Optional[int] = [ '''no_inference''', '''no_cuda''', '''no_tpu''', '''no_speed''', '''no_memory''', '''no_env_print''', '''no_multi_process''', ] def __init__( self : int ,**A_ : Any ) -> Any: for deprecated_arg in self.deprecated_args: if deprecated_arg in kwargs: A = deprecated_arg[3:] A = not kwargs.pop(A_ ) logger.warning( F'{deprecated_arg} is depreciated. Please use --no-{positive_arg} or' F' {positive_arg}={kwargs[positive_arg]}' ) A = kwargs.pop('tpu_name' ,self.tpu_name ) A = kwargs.pop('device_idx' ,self.device_idx ) A = kwargs.pop('eager_mode' ,self.eager_mode ) A = kwargs.pop('use_xla' ,self.use_xla ) super().__init__(**A_ ) _lowerCamelCase: str = field( default=_lowercase , metadata={'''help''': '''Name of TPU'''} , ) _lowerCamelCase: int = field( default=0 , metadata={'''help''': '''CPU / GPU device index. Defaults to 0.'''} , ) _lowerCamelCase: bool = field(default=_lowercase , metadata={'''help''': '''Benchmark models in eager model.'''} ) _lowerCamelCase: bool = field( default=_lowercase , metadata={ '''help''': '''Benchmark models using XLA JIT compilation. Note that `eager_model` has to be set to `False`.''' } , ) @cached_property def _SCREAMING_SNAKE_CASE ( self : List[Any] ) -> Tuple["tf.distribute.cluster_resolver.TPUClusterResolver"]: requires_backends(self ,['tf'] ) A = None if self.tpu: try: if self.tpu_name: A = tf.distribute.cluster_resolver.TPUClusterResolver(self.tpu_name ) else: A = tf.distribute.cluster_resolver.TPUClusterResolver() except ValueError: A = None return tpu @cached_property def _SCREAMING_SNAKE_CASE ( self : Tuple ) -> Tuple["tf.distribute.Strategy", "tf.distribute.cluster_resolver.TPUClusterResolver"]: requires_backends(self ,['tf'] ) if self.is_tpu: tf.config.experimental_connect_to_cluster(self._setup_tpu ) tf.tpu.experimental.initialize_tpu_system(self._setup_tpu ) A = tf.distribute.TPUStrategy(self._setup_tpu ) else: # currently no multi gpu is allowed if self.is_gpu: # TODO: Currently only single GPU is supported tf.config.set_visible_devices(self.gpu_list[self.device_idx] ,'GPU' ) A = tf.distribute.OneDeviceStrategy(device=F'/gpu:{self.device_idx}' ) else: tf.config.set_visible_devices([] ,'GPU' ) # disable GPU A = tf.distribute.OneDeviceStrategy(device=F'/cpu:{self.device_idx}' ) return strategy @property def _SCREAMING_SNAKE_CASE ( self : Optional[Any] ) -> bool: requires_backends(self ,['tf'] ) return self._setup_tpu is not None @property def _SCREAMING_SNAKE_CASE ( self : Union[str, Any] ) -> "tf.distribute.Strategy": requires_backends(self ,['tf'] ) return self._setup_strategy @property def _SCREAMING_SNAKE_CASE ( self : int ) -> str: requires_backends(self ,['tf'] ) return tf.config.list_physical_devices('GPU' ) @property def _SCREAMING_SNAKE_CASE ( self : List[str] ) -> int: requires_backends(self ,['tf'] ) if self.cuda: return len(self.gpu_list ) return 0 @property def _SCREAMING_SNAKE_CASE ( self : str ) -> bool: return self.n_gpu > 0
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'''simple docstring''' import tempfile import unittest import numpy as np from huggingface_hub import HfFolder, delete_repo from requests.exceptions import HTTPError from transformers import BertConfig, is_flax_available from transformers.testing_utils import TOKEN, USER, is_staging_test, require_flax if is_flax_available(): import os from flax.core.frozen_dict import unfreeze from flax.traverse_util import flatten_dict from transformers import FlaxBertModel UpperCamelCase__: Tuple = "0.12" # assumed parallelism: 8 @require_flax @is_staging_test class SCREAMING_SNAKE_CASE( unittest.TestCase ): """simple docstring""" @classmethod def A ( cls : Union[str, Any] ) -> int: UpperCAmelCase : Optional[Any] = TOKEN HfFolder.save_token(__snake_case ) @classmethod def A ( cls : List[str] ) -> Tuple: try: delete_repo(token=cls._token , repo_id='''test-model-flax''' ) except HTTPError: pass try: delete_repo(token=cls._token , repo_id='''valid_org/test-model-flax-org''' ) except HTTPError: pass def A ( self : int ) -> Tuple: UpperCAmelCase : List[Any] = BertConfig( vocab_size=99 , hidden_size=32 , num_hidden_layers=5 , num_attention_heads=4 , intermediate_size=37 ) UpperCAmelCase : Dict = FlaxBertModel(__snake_case ) model.push_to_hub('''test-model-flax''' , use_auth_token=self._token ) UpperCAmelCase : Tuple = FlaxBertModel.from_pretrained(F"""{USER}/test-model-flax""" ) UpperCAmelCase : List[Any] = flatten_dict(unfreeze(model.params ) ) UpperCAmelCase : Tuple = flatten_dict(unfreeze(new_model.params ) ) for key in base_params.keys(): UpperCAmelCase : Union[str, Any] = (base_params[key] - new_params[key]).sum().item() self.assertLessEqual(__snake_case , 1E-3 , msg=F"""{key} not identical""" ) # Reset repo delete_repo(token=self._token , repo_id='''test-model-flax''' ) # Push to hub via save_pretrained with tempfile.TemporaryDirectory() as tmp_dir: model.save_pretrained(__snake_case , repo_id='''test-model-flax''' , push_to_hub=__snake_case , use_auth_token=self._token ) UpperCAmelCase : str = FlaxBertModel.from_pretrained(F"""{USER}/test-model-flax""" ) UpperCAmelCase : Any = flatten_dict(unfreeze(model.params ) ) UpperCAmelCase : str = flatten_dict(unfreeze(new_model.params ) ) for key in base_params.keys(): UpperCAmelCase : Optional[Any] = (base_params[key] - new_params[key]).sum().item() self.assertLessEqual(__snake_case , 1E-3 , msg=F"""{key} not identical""" ) def A ( self : Optional[Any] ) -> Union[str, Any]: UpperCAmelCase : Dict = BertConfig( vocab_size=99 , hidden_size=32 , num_hidden_layers=5 , num_attention_heads=4 , intermediate_size=37 ) UpperCAmelCase : Optional[Any] = FlaxBertModel(__snake_case ) model.push_to_hub('''valid_org/test-model-flax-org''' , use_auth_token=self._token ) UpperCAmelCase : Union[str, Any] = FlaxBertModel.from_pretrained('''valid_org/test-model-flax-org''' ) UpperCAmelCase : List[Any] = flatten_dict(unfreeze(model.params ) ) UpperCAmelCase : int = flatten_dict(unfreeze(new_model.params ) ) for key in base_params.keys(): UpperCAmelCase : Any = (base_params[key] - new_params[key]).sum().item() self.assertLessEqual(__snake_case , 1E-3 , msg=F"""{key} not identical""" ) # Reset repo delete_repo(token=self._token , repo_id='''valid_org/test-model-flax-org''' ) # Push to hub via save_pretrained with tempfile.TemporaryDirectory() as tmp_dir: model.save_pretrained( __snake_case , repo_id='''valid_org/test-model-flax-org''' , push_to_hub=__snake_case , use_auth_token=self._token ) UpperCAmelCase : str = FlaxBertModel.from_pretrained('''valid_org/test-model-flax-org''' ) UpperCAmelCase : Any = flatten_dict(unfreeze(model.params ) ) UpperCAmelCase : Optional[Any] = flatten_dict(unfreeze(new_model.params ) ) for key in base_params.keys(): UpperCAmelCase : int = (base_params[key] - new_params[key]).sum().item() self.assertLessEqual(__snake_case , 1E-3 , msg=F"""{key} not identical""" ) def snake_case_ ( _lowerCAmelCase : Optional[Any] , _lowerCAmelCase : Any ) -> Union[str, Any]: UpperCAmelCase : str = True UpperCAmelCase : int = flatten_dict(modela.params ) UpperCAmelCase : Dict = flatten_dict(modela.params ) for key in flat_params_a.keys(): if np.sum(np.abs(flat_params_a[key] - flat_params_a[key] ) ) > 1e-4: UpperCAmelCase : Dict = False return models_are_equal @require_flax class SCREAMING_SNAKE_CASE( unittest.TestCase ): """simple docstring""" def A ( self : Tuple ) -> Union[str, Any]: UpperCAmelCase : List[Any] = BertConfig.from_pretrained('''hf-internal-testing/tiny-bert-flax-only''' ) UpperCAmelCase : Dict = FlaxBertModel(__snake_case ) UpperCAmelCase : int = '''bert''' with tempfile.TemporaryDirectory() as tmp_dir: model.save_pretrained(os.path.join(__snake_case , __snake_case ) ) with self.assertRaises(__snake_case ): UpperCAmelCase : Tuple = FlaxBertModel.from_pretrained(__snake_case ) UpperCAmelCase : str = FlaxBertModel.from_pretrained(__snake_case , subfolder=__snake_case ) self.assertTrue(check_models_equal(__snake_case , __snake_case ) ) def A ( self : List[str] ) -> Dict: UpperCAmelCase : Dict = BertConfig.from_pretrained('''hf-internal-testing/tiny-bert-flax-only''' ) UpperCAmelCase : Dict = FlaxBertModel(__snake_case ) UpperCAmelCase : Optional[int] = '''bert''' with tempfile.TemporaryDirectory() as tmp_dir: model.save_pretrained(os.path.join(__snake_case , __snake_case ) , max_shard_size='''10KB''' ) with self.assertRaises(__snake_case ): UpperCAmelCase : Any = FlaxBertModel.from_pretrained(__snake_case ) UpperCAmelCase : Union[str, Any] = FlaxBertModel.from_pretrained(__snake_case , subfolder=__snake_case ) self.assertTrue(check_models_equal(__snake_case , __snake_case ) ) def A ( self : Optional[int] ) -> str: UpperCAmelCase : Dict = '''bert''' UpperCAmelCase : int = '''hf-internal-testing/tiny-random-bert-subfolder''' with self.assertRaises(__snake_case ): UpperCAmelCase : Optional[Any] = FlaxBertModel.from_pretrained(__snake_case ) UpperCAmelCase : Tuple = FlaxBertModel.from_pretrained(__snake_case , subfolder=__snake_case ) self.assertIsNotNone(__snake_case ) def A ( self : Dict ) -> List[Any]: UpperCAmelCase : Optional[int] = '''bert''' UpperCAmelCase : int = '''hf-internal-testing/tiny-random-bert-sharded-subfolder''' with self.assertRaises(__snake_case ): UpperCAmelCase : Dict = FlaxBertModel.from_pretrained(__snake_case ) UpperCAmelCase : Union[str, Any] = FlaxBertModel.from_pretrained(__snake_case , subfolder=__snake_case ) self.assertIsNotNone(__snake_case )
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'''simple docstring''' def a_ ( __snake_case : List[Any] ) -> Optional[Any]: """simple docstring""" lowerCamelCase_ =len(__snake_case ) while cur > 1: # Find the maximum number in arr lowerCamelCase_ =arr.index(max(arr[0:cur] ) ) # Reverse from 0 to mi lowerCamelCase_ =arr[mi::-1] + arr[mi + 1 : len(__snake_case )] # Reverse whole list lowerCamelCase_ =arr[cur - 1 :: -1] + arr[cur : len(__snake_case )] cur -= 1 return arr if __name__ == "__main__": a_ : Union[str, Any] = input("""Enter numbers separated by a comma:\n""").strip() a_ : Any = [int(item) for item in user_input.split(""",""")] print(pancake_sort(unsorted))
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'''simple docstring''' import unittest from transformers import MraConfig, is_torch_available from transformers.testing_utils import require_torch, slow, torch_device from ...test_configuration_common import ConfigTester from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask if is_torch_available(): import torch from transformers import ( MraForMaskedLM, MraForMultipleChoice, MraForQuestionAnswering, MraForSequenceClassification, MraForTokenClassification, MraModel, ) from transformers.models.mra.modeling_mra import MRA_PRETRAINED_MODEL_ARCHIVE_LIST class SCREAMING_SNAKE_CASE: """simple docstring""" def __init__( self : Optional[int] , __snake_case : str , __snake_case : Union[str, Any]=2 , __snake_case : Optional[int]=8 , __snake_case : Any=True , __snake_case : Union[str, Any]=True , __snake_case : Dict=True , __snake_case : int=True , __snake_case : List[Any]=99 , __snake_case : str=16 , __snake_case : Tuple=5 , __snake_case : Tuple=2 , __snake_case : str=36 , __snake_case : Dict="gelu" , __snake_case : str=0.0 , __snake_case : Optional[int]=0.0 , __snake_case : Optional[int]=512 , __snake_case : Optional[Any]=16 , __snake_case : int=2 , __snake_case : int=0.02 , __snake_case : str=3 , __snake_case : Dict=4 , __snake_case : str=None , ) -> Optional[int]: UpperCAmelCase : Optional[int] = parent UpperCAmelCase : Tuple = batch_size UpperCAmelCase : List[str] = seq_length UpperCAmelCase : List[Any] = is_training UpperCAmelCase : int = use_input_mask UpperCAmelCase : Any = use_token_type_ids UpperCAmelCase : str = use_labels UpperCAmelCase : Union[str, Any] = vocab_size UpperCAmelCase : List[str] = hidden_size UpperCAmelCase : Optional[Any] = num_hidden_layers UpperCAmelCase : Union[str, Any] = num_attention_heads UpperCAmelCase : Optional[Any] = intermediate_size UpperCAmelCase : Union[str, Any] = hidden_act UpperCAmelCase : int = hidden_dropout_prob UpperCAmelCase : Optional[int] = attention_probs_dropout_prob UpperCAmelCase : Union[str, Any] = max_position_embeddings UpperCAmelCase : str = type_vocab_size UpperCAmelCase : List[str] = type_sequence_label_size UpperCAmelCase : Tuple = initializer_range UpperCAmelCase : Optional[Any] = num_labels UpperCAmelCase : Optional[int] = num_choices UpperCAmelCase : Any = scope def A ( self : Tuple ) -> Union[str, Any]: UpperCAmelCase : Optional[int] = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size ) UpperCAmelCase : Optional[int] = None if self.use_input_mask: UpperCAmelCase : Dict = random_attention_mask([self.batch_size, self.seq_length] ) UpperCAmelCase : Dict = None if self.use_token_type_ids: UpperCAmelCase : Union[str, Any] = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size ) UpperCAmelCase : str = None UpperCAmelCase : Tuple = None UpperCAmelCase : int = None if self.use_labels: UpperCAmelCase : str = ids_tensor([self.batch_size] , self.type_sequence_label_size ) UpperCAmelCase : Dict = ids_tensor([self.batch_size, self.seq_length] , self.num_labels ) UpperCAmelCase : str = ids_tensor([self.batch_size] , self.num_choices ) UpperCAmelCase : List[str] = self.get_config() return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels def A ( self : int ) -> Tuple: return MraConfig( vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , is_decoder=__snake_case , initializer_range=self.initializer_range , ) def A ( self : Optional[Any] ) -> Any: UpperCAmelCase : Optional[Any] = self.get_config() UpperCAmelCase : int = 300 return config def A ( self : Optional[Any] ) -> Any: ( ( UpperCAmelCase ) , ( UpperCAmelCase ) , ( UpperCAmelCase ) , ( UpperCAmelCase ) , ( UpperCAmelCase ) , ( UpperCAmelCase ) , ( UpperCAmelCase ) , ) : Tuple = self.prepare_config_and_inputs() UpperCAmelCase : Dict = True UpperCAmelCase : Tuple = floats_tensor([self.batch_size, self.seq_length, self.hidden_size] ) UpperCAmelCase : str = ids_tensor([self.batch_size, self.seq_length] , vocab_size=2 ) return ( config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels, encoder_hidden_states, encoder_attention_mask, ) def A ( self : Union[str, Any] , __snake_case : Union[str, Any] , __snake_case : int , __snake_case : Optional[int] , __snake_case : int , __snake_case : Dict , __snake_case : Tuple , __snake_case : Optional[Any] ) -> List[str]: UpperCAmelCase : int = MraModel(config=__snake_case ) model.to(__snake_case ) model.eval() UpperCAmelCase : Tuple = model(__snake_case , attention_mask=__snake_case , token_type_ids=__snake_case ) UpperCAmelCase : Optional[int] = model(__snake_case , token_type_ids=__snake_case ) UpperCAmelCase : Dict = model(__snake_case ) self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) ) def A ( self : Tuple , __snake_case : Optional[Any] , __snake_case : List[str] , __snake_case : List[str] , __snake_case : int , __snake_case : Union[str, Any] , __snake_case : Optional[Any] , __snake_case : Any , __snake_case : List[Any] , __snake_case : Optional[Any] , ) -> Tuple: UpperCAmelCase : str = True UpperCAmelCase : Tuple = MraModel(__snake_case ) model.to(__snake_case ) model.eval() UpperCAmelCase : Optional[int] = model( __snake_case , attention_mask=__snake_case , token_type_ids=__snake_case , encoder_hidden_states=__snake_case , encoder_attention_mask=__snake_case , ) UpperCAmelCase : Optional[Any] = model( __snake_case , attention_mask=__snake_case , token_type_ids=__snake_case , encoder_hidden_states=__snake_case , ) UpperCAmelCase : str = model(__snake_case , attention_mask=__snake_case , token_type_ids=__snake_case ) self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) ) def A ( self : Tuple , __snake_case : str , __snake_case : Optional[Any] , __snake_case : List[str] , __snake_case : Tuple , __snake_case : Optional[Any] , __snake_case : List[str] , __snake_case : int ) -> Any: UpperCAmelCase : Dict = MraForMaskedLM(config=__snake_case ) model.to(__snake_case ) model.eval() UpperCAmelCase : Optional[int] = model(__snake_case , attention_mask=__snake_case , token_type_ids=__snake_case , labels=__snake_case ) self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) ) def A ( self : Tuple , __snake_case : Tuple , __snake_case : Dict , __snake_case : Dict , __snake_case : Any , __snake_case : int , __snake_case : Optional[Any] , __snake_case : Tuple ) -> Optional[int]: UpperCAmelCase : List[str] = MraForQuestionAnswering(config=__snake_case ) model.to(__snake_case ) model.eval() UpperCAmelCase : List[Any] = model( __snake_case , attention_mask=__snake_case , token_type_ids=__snake_case , start_positions=__snake_case , end_positions=__snake_case , ) self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) ) self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) ) def A ( self : str , __snake_case : Optional[int] , __snake_case : List[str] , __snake_case : str , __snake_case : int , __snake_case : Optional[Any] , __snake_case : Union[str, Any] , __snake_case : List[Any] ) -> int: UpperCAmelCase : int = self.num_labels UpperCAmelCase : Union[str, Any] = MraForSequenceClassification(__snake_case ) model.to(__snake_case ) model.eval() UpperCAmelCase : List[str] = model(__snake_case , attention_mask=__snake_case , token_type_ids=__snake_case , labels=__snake_case ) self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) ) def A ( self : str , __snake_case : Dict , __snake_case : Optional[Any] , __snake_case : Dict , __snake_case : Union[str, Any] , __snake_case : Union[str, Any] , __snake_case : Union[str, Any] , __snake_case : Dict ) -> int: UpperCAmelCase : Tuple = self.num_labels UpperCAmelCase : List[str] = MraForTokenClassification(config=__snake_case ) model.to(__snake_case ) model.eval() UpperCAmelCase : str = model(__snake_case , attention_mask=__snake_case , token_type_ids=__snake_case , labels=__snake_case ) self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) ) def A ( self : str , __snake_case : int , __snake_case : Any , __snake_case : Tuple , __snake_case : Optional[int] , __snake_case : List[str] , __snake_case : str , __snake_case : Union[str, Any] ) -> Optional[Any]: UpperCAmelCase : Tuple = self.num_choices UpperCAmelCase : int = MraForMultipleChoice(config=__snake_case ) model.to(__snake_case ) model.eval() UpperCAmelCase : str = input_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous() UpperCAmelCase : List[Any] = token_type_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous() UpperCAmelCase : List[str] = input_mask.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous() UpperCAmelCase : List[str] = model( __snake_case , attention_mask=__snake_case , token_type_ids=__snake_case , labels=__snake_case , ) self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) ) def A ( self : str ) -> Dict: UpperCAmelCase : Any = self.prepare_config_and_inputs() ( ( UpperCAmelCase ) , ( UpperCAmelCase ) , ( UpperCAmelCase ) , ( UpperCAmelCase ) , ( UpperCAmelCase ) , ( UpperCAmelCase ) , ( UpperCAmelCase ) , ) : List[str] = config_and_inputs UpperCAmelCase : Any = {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask} return config, inputs_dict @require_torch class SCREAMING_SNAKE_CASE( A__ , unittest.TestCase ): """simple docstring""" lowerCamelCase__ = ( ( MraModel, MraForMaskedLM, MraForMultipleChoice, MraForQuestionAnswering, MraForSequenceClassification, MraForTokenClassification, ) if is_torch_available() else () ) lowerCamelCase__ = False lowerCamelCase__ = False lowerCamelCase__ = False lowerCamelCase__ = False lowerCamelCase__ = () def A ( self : int ) -> Union[str, Any]: UpperCAmelCase : List[str] = MraModelTester(self ) UpperCAmelCase : Optional[int] = ConfigTester(self , config_class=__snake_case , hidden_size=37 ) def A ( self : Optional[Any] ) -> str: self.config_tester.run_common_tests() def A ( self : Tuple ) -> Optional[Any]: UpperCAmelCase : Optional[int] = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_model(*__snake_case ) def A ( self : List[Any] ) -> Optional[Any]: UpperCAmelCase : Tuple = self.model_tester.prepare_config_and_inputs() for type in ["absolute", "relative_key", "relative_key_query"]: UpperCAmelCase : List[Any] = type self.model_tester.create_and_check_model(*__snake_case ) def A ( self : Tuple ) -> Dict: UpperCAmelCase : List[Any] = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_masked_lm(*__snake_case ) def A ( self : Tuple ) -> List[str]: UpperCAmelCase : Tuple = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_multiple_choice(*__snake_case ) def A ( self : int ) -> Dict: UpperCAmelCase : Optional[int] = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_question_answering(*__snake_case ) def A ( self : Dict ) -> Optional[int]: UpperCAmelCase : Any = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_sequence_classification(*__snake_case ) def A ( self : Any ) -> Optional[int]: UpperCAmelCase : List[str] = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_token_classification(*__snake_case ) @slow def A ( self : Dict ) -> Any: for model_name in MRA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: UpperCAmelCase : str = MraModel.from_pretrained(__snake_case ) self.assertIsNotNone(__snake_case ) @unittest.skip(reason='''MRA does not output attentions''' ) def A ( self : str ) -> Optional[Any]: return @require_torch class SCREAMING_SNAKE_CASE( unittest.TestCase ): """simple docstring""" @slow def A ( self : Tuple ) -> List[Any]: UpperCAmelCase : int = MraModel.from_pretrained('''uw-madison/mra-base-512-4''' ) UpperCAmelCase : Optional[Any] = torch.arange(256 ).unsqueeze(0 ) with torch.no_grad(): UpperCAmelCase : List[Any] = model(__snake_case )[0] UpperCAmelCase : Optional[Any] = torch.Size((1, 256, 768) ) self.assertEqual(output.shape , __snake_case ) UpperCAmelCase : Any = torch.tensor( [[[-0.01_40, 0.08_30, -0.03_81], [0.15_46, 0.14_02, 0.02_20], [0.11_62, 0.08_51, 0.01_65]]] ) self.assertTrue(torch.allclose(output[:, :3, :3] , __snake_case , atol=1E-4 ) ) @slow def A ( self : Optional[Any] ) -> Any: UpperCAmelCase : Optional[int] = MraForMaskedLM.from_pretrained('''uw-madison/mra-base-512-4''' ) UpperCAmelCase : Dict = torch.arange(256 ).unsqueeze(0 ) with torch.no_grad(): UpperCAmelCase : List[Any] = model(__snake_case )[0] UpperCAmelCase : int = 50265 UpperCAmelCase : int = torch.Size((1, 256, vocab_size) ) self.assertEqual(output.shape , __snake_case ) UpperCAmelCase : Union[str, Any] = torch.tensor( [[[9.25_95, -3.60_38, 11.88_19], [9.38_69, -3.26_93, 11.09_56], [11.85_24, -3.49_38, 13.12_10]]] ) self.assertTrue(torch.allclose(output[:, :3, :3] , __snake_case , atol=1E-4 ) ) @slow def A ( self : str ) -> List[Any]: UpperCAmelCase : List[Any] = MraForMaskedLM.from_pretrained('''uw-madison/mra-base-4096-8-d3''' ) UpperCAmelCase : List[Any] = torch.arange(4096 ).unsqueeze(0 ) with torch.no_grad(): UpperCAmelCase : Tuple = model(__snake_case )[0] UpperCAmelCase : Optional[int] = 50265 UpperCAmelCase : Tuple = torch.Size((1, 4096, vocab_size) ) self.assertEqual(output.shape , __snake_case ) UpperCAmelCase : Optional[int] = torch.tensor( [[[5.47_89, -2.35_64, 7.50_64], [7.90_67, -1.33_69, 9.96_68], [9.07_12, -1.81_06, 7.03_80]]] ) self.assertTrue(torch.allclose(output[:, :3, :3] , __snake_case , atol=1E-4 ) )
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import unittest from accelerate import debug_launcher from accelerate.test_utils import require_cpu, test_ops, test_script @require_cpu class _UpperCamelCase ( unittest.TestCase ): '''simple docstring''' def __UpperCamelCase ( self : Optional[int] ) -> Optional[Any]: """simple docstring""" debug_launcher(test_script.main ) def __UpperCamelCase ( self : int ) -> str: """simple docstring""" debug_launcher(test_ops.main )
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'''simple docstring''' import unittest import numpy as np from transformers.testing_utils import require_flax, require_tf, require_torch from transformers.utils import ( expand_dims, flatten_dict, is_flax_available, is_tf_available, is_torch_available, reshape, squeeze, transpose, ) if is_flax_available(): import jax.numpy as jnp if is_tf_available(): import tensorflow as tf if is_torch_available(): import torch class SCREAMING_SNAKE_CASE( unittest.TestCase ): """simple docstring""" def A ( self : Any ) -> str: UpperCAmelCase : Any = { '''task_specific_params''': { '''summarization''': {'''length_penalty''': 1.0, '''max_length''': 128, '''min_length''': 12, '''num_beams''': 4}, '''summarization_cnn''': {'''length_penalty''': 2.0, '''max_length''': 142, '''min_length''': 56, '''num_beams''': 4}, '''summarization_xsum''': {'''length_penalty''': 1.0, '''max_length''': 62, '''min_length''': 11, '''num_beams''': 6}, } } UpperCAmelCase : int = { '''task_specific_params.summarization.length_penalty''': 1.0, '''task_specific_params.summarization.max_length''': 128, '''task_specific_params.summarization.min_length''': 12, '''task_specific_params.summarization.num_beams''': 4, '''task_specific_params.summarization_cnn.length_penalty''': 2.0, '''task_specific_params.summarization_cnn.max_length''': 142, '''task_specific_params.summarization_cnn.min_length''': 56, '''task_specific_params.summarization_cnn.num_beams''': 4, '''task_specific_params.summarization_xsum.length_penalty''': 1.0, '''task_specific_params.summarization_xsum.max_length''': 62, '''task_specific_params.summarization_xsum.min_length''': 11, '''task_specific_params.summarization_xsum.num_beams''': 6, } self.assertEqual(flatten_dict(__snake_case ) , __snake_case ) def A ( self : int ) -> str: UpperCAmelCase : Any = np.random.randn(3 , 4 ) self.assertTrue(np.allclose(transpose(__snake_case ) , x.transpose() ) ) UpperCAmelCase : str = np.random.randn(3 , 4 , 5 ) self.assertTrue(np.allclose(transpose(__snake_case , axes=(1, 2, 0) ) , x.transpose((1, 2, 0) ) ) ) @require_torch def A ( self : str ) -> Union[str, Any]: UpperCAmelCase : Any = np.random.randn(3 , 4 ) UpperCAmelCase : List[Any] = torch.tensor(__snake_case ) self.assertTrue(np.allclose(transpose(__snake_case ) , transpose(__snake_case ).numpy() ) ) UpperCAmelCase : Tuple = np.random.randn(3 , 4 , 5 ) UpperCAmelCase : Any = torch.tensor(__snake_case ) self.assertTrue(np.allclose(transpose(__snake_case , axes=(1, 2, 0) ) , transpose(__snake_case , axes=(1, 2, 0) ).numpy() ) ) @require_tf def A ( self : List[str] ) -> Optional[Any]: UpperCAmelCase : int = np.random.randn(3 , 4 ) UpperCAmelCase : Optional[int] = tf.constant(__snake_case ) self.assertTrue(np.allclose(transpose(__snake_case ) , transpose(__snake_case ).numpy() ) ) UpperCAmelCase : Union[str, Any] = np.random.randn(3 , 4 , 5 ) UpperCAmelCase : str = tf.constant(__snake_case ) self.assertTrue(np.allclose(transpose(__snake_case , axes=(1, 2, 0) ) , transpose(__snake_case , axes=(1, 2, 0) ).numpy() ) ) @require_flax def A ( self : Tuple ) -> Any: UpperCAmelCase : List[Any] = np.random.randn(3 , 4 ) UpperCAmelCase : List[str] = jnp.array(__snake_case ) self.assertTrue(np.allclose(transpose(__snake_case ) , np.asarray(transpose(__snake_case ) ) ) ) UpperCAmelCase : Dict = np.random.randn(3 , 4 , 5 ) UpperCAmelCase : int = jnp.array(__snake_case ) self.assertTrue(np.allclose(transpose(__snake_case , axes=(1, 2, 0) ) , np.asarray(transpose(__snake_case , axes=(1, 2, 0) ) ) ) ) def A ( self : Optional[Any] ) -> Any: UpperCAmelCase : Union[str, Any] = np.random.randn(3 , 4 ) self.assertTrue(np.allclose(reshape(__snake_case , (4, 3) ) , np.reshape(__snake_case , (4, 3) ) ) ) UpperCAmelCase : Union[str, Any] = np.random.randn(3 , 4 , 5 ) self.assertTrue(np.allclose(reshape(__snake_case , (12, 5) ) , np.reshape(__snake_case , (12, 5) ) ) ) @require_torch def A ( self : Union[str, Any] ) -> int: UpperCAmelCase : Dict = np.random.randn(3 , 4 ) UpperCAmelCase : Optional[Any] = torch.tensor(__snake_case ) self.assertTrue(np.allclose(reshape(__snake_case , (4, 3) ) , reshape(__snake_case , (4, 3) ).numpy() ) ) UpperCAmelCase : List[str] = np.random.randn(3 , 4 , 5 ) UpperCAmelCase : List[Any] = torch.tensor(__snake_case ) self.assertTrue(np.allclose(reshape(__snake_case , (12, 5) ) , reshape(__snake_case , (12, 5) ).numpy() ) ) @require_tf def A ( self : int ) -> List[str]: UpperCAmelCase : List[Any] = np.random.randn(3 , 4 ) UpperCAmelCase : List[str] = tf.constant(__snake_case ) self.assertTrue(np.allclose(reshape(__snake_case , (4, 3) ) , reshape(__snake_case , (4, 3) ).numpy() ) ) UpperCAmelCase : List[Any] = np.random.randn(3 , 4 , 5 ) UpperCAmelCase : Optional[Any] = tf.constant(__snake_case ) self.assertTrue(np.allclose(reshape(__snake_case , (12, 5) ) , reshape(__snake_case , (12, 5) ).numpy() ) ) @require_flax def A ( self : Any ) -> Dict: UpperCAmelCase : Tuple = np.random.randn(3 , 4 ) UpperCAmelCase : Union[str, Any] = jnp.array(__snake_case ) self.assertTrue(np.allclose(reshape(__snake_case , (4, 3) ) , np.asarray(reshape(__snake_case , (4, 3) ) ) ) ) UpperCAmelCase : Any = np.random.randn(3 , 4 , 5 ) UpperCAmelCase : Optional[Any] = jnp.array(__snake_case ) self.assertTrue(np.allclose(reshape(__snake_case , (12, 5) ) , np.asarray(reshape(__snake_case , (12, 5) ) ) ) ) def A ( self : List[Any] ) -> List[Any]: UpperCAmelCase : Union[str, Any] = np.random.randn(1 , 3 , 4 ) self.assertTrue(np.allclose(squeeze(__snake_case ) , np.squeeze(__snake_case ) ) ) UpperCAmelCase : str = np.random.randn(1 , 4 , 1 , 5 ) self.assertTrue(np.allclose(squeeze(__snake_case , axis=2 ) , np.squeeze(__snake_case , axis=2 ) ) ) @require_torch def A ( self : Optional[int] ) -> Union[str, Any]: UpperCAmelCase : Optional[int] = np.random.randn(1 , 3 , 4 ) UpperCAmelCase : List[str] = torch.tensor(__snake_case ) self.assertTrue(np.allclose(squeeze(__snake_case ) , squeeze(__snake_case ).numpy() ) ) UpperCAmelCase : Any = np.random.randn(1 , 4 , 1 , 5 ) UpperCAmelCase : str = torch.tensor(__snake_case ) self.assertTrue(np.allclose(squeeze(__snake_case , axis=2 ) , squeeze(__snake_case , axis=2 ).numpy() ) ) @require_tf def A ( self : Optional[Any] ) -> Dict: UpperCAmelCase : int = np.random.randn(1 , 3 , 4 ) UpperCAmelCase : Optional[int] = tf.constant(__snake_case ) self.assertTrue(np.allclose(squeeze(__snake_case ) , squeeze(__snake_case ).numpy() ) ) UpperCAmelCase : List[str] = np.random.randn(1 , 4 , 1 , 5 ) UpperCAmelCase : Optional[int] = tf.constant(__snake_case ) self.assertTrue(np.allclose(squeeze(__snake_case , axis=2 ) , squeeze(__snake_case , axis=2 ).numpy() ) ) @require_flax def A ( self : List[Any] ) -> Dict: UpperCAmelCase : Optional[int] = np.random.randn(1 , 3 , 4 ) UpperCAmelCase : int = jnp.array(__snake_case ) self.assertTrue(np.allclose(squeeze(__snake_case ) , np.asarray(squeeze(__snake_case ) ) ) ) UpperCAmelCase : str = np.random.randn(1 , 4 , 1 , 5 ) UpperCAmelCase : int = jnp.array(__snake_case ) self.assertTrue(np.allclose(squeeze(__snake_case , axis=2 ) , np.asarray(squeeze(__snake_case , axis=2 ) ) ) ) def A ( self : Optional[Any] ) -> int: UpperCAmelCase : Optional[Any] = np.random.randn(3 , 4 ) self.assertTrue(np.allclose(expand_dims(__snake_case , axis=1 ) , np.expand_dims(__snake_case , axis=1 ) ) ) @require_torch def A ( self : List[str] ) -> Tuple: UpperCAmelCase : Tuple = np.random.randn(3 , 4 ) UpperCAmelCase : Tuple = torch.tensor(__snake_case ) self.assertTrue(np.allclose(expand_dims(__snake_case , axis=1 ) , expand_dims(__snake_case , axis=1 ).numpy() ) ) @require_tf def A ( self : List[str] ) -> Union[str, Any]: UpperCAmelCase : Union[str, Any] = np.random.randn(3 , 4 ) UpperCAmelCase : Any = tf.constant(__snake_case ) self.assertTrue(np.allclose(expand_dims(__snake_case , axis=1 ) , expand_dims(__snake_case , axis=1 ).numpy() ) ) @require_flax def A ( self : Any ) -> List[Any]: UpperCAmelCase : List[str] = np.random.randn(3 , 4 ) UpperCAmelCase : str = jnp.array(__snake_case ) self.assertTrue(np.allclose(expand_dims(__snake_case , axis=1 ) , np.asarray(expand_dims(__snake_case , axis=1 ) ) ) )
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"""simple docstring""" from math import loga def a_ ( _lowerCAmelCase : int ): '''simple docstring''' if a < 0: raise ValueError('Input value must be a positive integer' ) elif isinstance(_lowerCAmelCase , _lowerCAmelCase ): raise TypeError('Input value must be a \'int\' type' ) return 0 if (a == 0) else int(loga(a & -a ) ) if __name__ == "__main__": import doctest doctest.testmod()
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'''simple docstring''' import argparse import os import re import packaging.version UpperCamelCase__: Union[str, Any] = "examples/" UpperCamelCase__: Optional[Any] = { "examples": (re.compile(r"^check_min_version\(\"[^\"]+\"\)\s*$", re.MULTILINE), "check_min_version(\"VERSION\")\n"), "init": (re.compile(r"^__version__\s+=\s+\"([^\"]+)\"\s*$", re.MULTILINE), "__version__ = \"VERSION\"\n"), "setup": (re.compile(r"^(\s*)version\s*=\s*\"[^\"]+\",", re.MULTILINE), r"\1version=\"VERSION\","), "doc": (re.compile(r"^(\s*)release\s*=\s*\"[^\"]+\"$", re.MULTILINE), "release = \"VERSION\"\n"), } UpperCamelCase__: Optional[int] = { "init": "src/diffusers/__init__.py", "setup": "setup.py", } UpperCamelCase__: List[Any] = "README.md" def snake_case_ ( _lowerCAmelCase : Optional[Any] , _lowerCAmelCase : List[Any] , _lowerCAmelCase : int ) -> Optional[int]: with open(_lowerCAmelCase , '''r''' , encoding='''utf-8''' , newline='''\n''' ) as f: UpperCAmelCase : Optional[int] = f.read() UpperCAmelCase , UpperCAmelCase : List[Any] = REPLACE_PATTERNS[pattern] UpperCAmelCase : List[Any] = replace.replace('''VERSION''' , _lowerCAmelCase ) UpperCAmelCase : Optional[Any] = re_pattern.sub(_lowerCAmelCase , _lowerCAmelCase ) with open(_lowerCAmelCase , '''w''' , encoding='''utf-8''' , newline='''\n''' ) as f: f.write(_lowerCAmelCase ) def snake_case_ ( _lowerCAmelCase : Any ) -> Optional[int]: for folder, directories, fnames in os.walk(_lowerCAmelCase ): # Removing some of the folders with non-actively maintained examples from the walk if "research_projects" in directories: directories.remove('''research_projects''' ) if "legacy" in directories: directories.remove('''legacy''' ) for fname in fnames: if fname.endswith('''.py''' ): update_version_in_file(os.path.join(_lowerCAmelCase , _lowerCAmelCase ) , _lowerCAmelCase , pattern='''examples''' ) def snake_case_ ( _lowerCAmelCase : Any , _lowerCAmelCase : str=False ) -> List[str]: for pattern, fname in REPLACE_FILES.items(): update_version_in_file(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ) if not patch: update_version_in_examples(_lowerCAmelCase ) def snake_case_ ( ) -> Optional[Any]: UpperCAmelCase : Optional[int] = '''🤗 Transformers currently provides the following architectures''' UpperCAmelCase : Optional[int] = '''1. Want to contribute a new model?''' with open(_lowerCAmelCase , '''r''' , encoding='''utf-8''' , newline='''\n''' ) as f: UpperCAmelCase : Optional[Any] = f.readlines() # Find the start of the list. UpperCAmelCase : List[Any] = 0 while not lines[start_index].startswith(_start_prompt ): start_index += 1 start_index += 1 UpperCAmelCase : Optional[Any] = start_index # Update the lines in the model list. while not lines[index].startswith(_end_prompt ): if lines[index].startswith('''1.''' ): UpperCAmelCase : Optional[int] = lines[index].replace( '''https://huggingface.co/docs/diffusers/main/model_doc''' , '''https://huggingface.co/docs/diffusers/model_doc''' , ) index += 1 with open(_lowerCAmelCase , '''w''' , encoding='''utf-8''' , newline='''\n''' ) as f: f.writelines(_lowerCAmelCase ) def snake_case_ ( ) -> Optional[Any]: with open(REPLACE_FILES['''init'''] , '''r''' ) as f: UpperCAmelCase : Union[str, Any] = f.read() UpperCAmelCase : int = REPLACE_PATTERNS['''init'''][0].search(_lowerCAmelCase ).groups()[0] return packaging.version.parse(_lowerCAmelCase ) def snake_case_ ( _lowerCAmelCase : List[str]=False ) -> Any: UpperCAmelCase : Optional[Any] = get_version() if patch and default_version.is_devrelease: raise ValueError('''Can\'t create a patch version from the dev branch, checkout a released version!''' ) if default_version.is_devrelease: UpperCAmelCase : Optional[int] = default_version.base_version elif patch: UpperCAmelCase : Union[str, Any] = f"""{default_version.major}.{default_version.minor}.{default_version.micro + 1}""" else: UpperCAmelCase : Union[str, Any] = f"""{default_version.major}.{default_version.minor + 1}.0""" # Now let's ask nicely if that's the right one. UpperCAmelCase : Dict = input(f"""Which version are you releasing? [{default_version}]""" ) if len(_lowerCAmelCase ) == 0: UpperCAmelCase : Tuple = default_version print(f"""Updating version to {version}.""" ) global_version_update(_lowerCAmelCase , patch=_lowerCAmelCase ) def snake_case_ ( ) -> Any: UpperCAmelCase : List[Any] = get_version() UpperCAmelCase : List[str] = f"""{current_version.major}.{current_version.minor + 1}.0.dev0""" UpperCAmelCase : List[Any] = current_version.base_version # Check with the user we got that right. UpperCAmelCase : Optional[int] = input(f"""Which version are we developing now? [{dev_version}]""" ) if len(_lowerCAmelCase ) == 0: UpperCAmelCase : Dict = dev_version print(f"""Updating version to {version}.""" ) global_version_update(_lowerCAmelCase ) # print("Cleaning main README, don't forget to run `make fix-copies`.") # clean_main_ref_in_model_list() if __name__ == "__main__": UpperCamelCase__: Union[str, Any] = argparse.ArgumentParser() parser.add_argument("--post_release", action="store_true", help="Whether this is pre or post release.") parser.add_argument("--patch", action="store_true", help="Whether or not this is a patch release.") UpperCamelCase__: Optional[Any] = parser.parse_args() if not args.post_release: pre_release_work(patch=args.patch) elif args.patch: print("Nothing to do after a patch :-)") else: post_release_work()
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"""simple docstring""" from .glue import GlueDataset, GlueDataTrainingArguments from .language_modeling import ( LineByLineTextDataset, LineByLineWithRefDataset, LineByLineWithSOPTextDataset, TextDataset, TextDatasetForNextSentencePrediction, ) from .squad import SquadDataset, SquadDataTrainingArguments
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'''simple docstring''' from __future__ import annotations import matplotlib.pyplot as plt # type: ignore import numpy # initial triangle of Koch snowflake UpperCamelCase__: Tuple = numpy.array([0, 0]) UpperCamelCase__: Union[str, Any] = numpy.array([0.5, 0.8660254]) UpperCamelCase__: Dict = numpy.array([1, 0]) UpperCamelCase__: int = [VECTOR_1, VECTOR_2, VECTOR_3, VECTOR_1] def snake_case_ ( _lowerCAmelCase : list[numpy.ndarray] , _lowerCAmelCase : int ) -> list[numpy.ndarray]: UpperCAmelCase : Union[str, Any] = initial_vectors for _ in range(_lowerCAmelCase ): UpperCAmelCase : Union[str, Any] = iteration_step(_lowerCAmelCase ) return vectors def snake_case_ ( _lowerCAmelCase : list[numpy.ndarray] ) -> list[numpy.ndarray]: UpperCAmelCase : Tuple = [] for i, start_vector in enumerate(vectors[:-1] ): UpperCAmelCase : List[str] = vectors[i + 1] new_vectors.append(_lowerCAmelCase ) UpperCAmelCase : Optional[Any] = end_vector - start_vector new_vectors.append(start_vector + difference_vector / 3 ) new_vectors.append( start_vector + difference_vector / 3 + rotate(difference_vector / 3 , 60 ) ) new_vectors.append(start_vector + difference_vector * 2 / 3 ) new_vectors.append(vectors[-1] ) return new_vectors def snake_case_ ( _lowerCAmelCase : numpy.ndarray , _lowerCAmelCase : float ) -> numpy.ndarray: UpperCAmelCase : List[str] = numpy.radians(_lowerCAmelCase ) UpperCAmelCase , UpperCAmelCase : Tuple = numpy.cos(_lowerCAmelCase ), numpy.sin(_lowerCAmelCase ) UpperCAmelCase : Union[str, Any] = numpy.array(((c, -s), (s, c)) ) return numpy.dot(_lowerCAmelCase , _lowerCAmelCase ) def snake_case_ ( _lowerCAmelCase : list[numpy.ndarray] ) -> None: UpperCAmelCase : List[Any] = plt.gca() axes.set_aspect('''equal''' ) # matplotlib.pyplot.plot takes a list of all x-coordinates and a list of all # y-coordinates as inputs, which are constructed from the vector-list using # zip() UpperCAmelCase , UpperCAmelCase : str = zip(*_lowerCAmelCase ) plt.plot(_lowerCAmelCase , _lowerCAmelCase ) plt.show() if __name__ == "__main__": import doctest doctest.testmod() UpperCamelCase__: List[Any] = iterate(INITIAL_VECTORS, 5) plot(processed_vectors)
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'''simple docstring''' from typing import List, Optional, TypeVar from .arrow_dataset import Dataset, _concatenate_map_style_datasets, _interleave_map_style_datasets from .dataset_dict import DatasetDict, IterableDatasetDict from .info import DatasetInfo from .iterable_dataset import IterableDataset, _concatenate_iterable_datasets, _interleave_iterable_datasets from .splits import NamedSplit from .utils import logging from .utils.py_utils import Literal lowerCamelCase_ = logging.get_logger(__name__) lowerCamelCase_ = TypeVar('''DatasetType''', Dataset, IterableDataset) def __lowercase ( __lowercase , __lowercase = None , __lowercase = None , __lowercase = None , __lowercase = None , __lowercase = "first_exhausted" , ) -> DatasetType: '''simple docstring''' from .arrow_dataset import Dataset from .iterable_dataset import IterableDataset if not datasets: raise ValueError("Unable to interleave an empty list of datasets." ) for i, dataset in enumerate(__lowercase ): if not isinstance(__lowercase , (Dataset, IterableDataset) ): if isinstance(__lowercase , (DatasetDict, IterableDatasetDict) ): if not dataset: raise ValueError( F'''Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} ''' "is an empty dataset dictionary." ) raise ValueError( F'''Dataset at position {i} has at least one split: {list(__lowercase )}\n''' F'''Please pick one to interleave with the other datasets, for example: dataset[\'{next(iter(__lowercase ) )}\']''' ) raise ValueError( F'''Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} is a {type(__lowercase ).__name__}.''' ) if i == 0: _A , _A = ( (Dataset, IterableDataset) if isinstance(__lowercase , __lowercase ) else (IterableDataset, Dataset) ) elif not isinstance(__lowercase , __lowercase ): raise ValueError( F'''Unable to interleave a {dataset_type.__name__} (at position 0) with a {other_type.__name__} (at position {i}). Expected a list of Dataset objects or a list of IterableDataset objects.''' ) if stopping_strategy not in ["first_exhausted", "all_exhausted"]: raise ValueError(F'''{stopping_strategy} is not supported. Please enter a valid stopping_strategy.''' ) if dataset_type is Dataset: return _interleave_map_style_datasets( __lowercase , __lowercase , __lowercase , info=__lowercase , split=__lowercase , stopping_strategy=__lowercase ) else: return _interleave_iterable_datasets( __lowercase , __lowercase , __lowercase , info=__lowercase , split=__lowercase , stopping_strategy=__lowercase ) def __lowercase ( __lowercase , __lowercase = None , __lowercase = None , __lowercase = 0 , ) -> DatasetType: '''simple docstring''' if not dsets: raise ValueError("Unable to concatenate an empty list of datasets." ) for i, dataset in enumerate(__lowercase ): if not isinstance(__lowercase , (Dataset, IterableDataset) ): if isinstance(__lowercase , (DatasetDict, IterableDatasetDict) ): if not dataset: raise ValueError( F'''Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} ''' "is an empty dataset dictionary." ) raise ValueError( F'''Dataset at position {i} has at least one split: {list(__lowercase )}\n''' F'''Please pick one to interleave with the other datasets, for example: dataset[\'{next(iter(__lowercase ) )}\']''' ) raise ValueError( F'''Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} is a {type(__lowercase ).__name__}.''' ) if i == 0: _A , _A = ( (Dataset, IterableDataset) if isinstance(__lowercase , __lowercase ) else (IterableDataset, Dataset) ) elif not isinstance(__lowercase , __lowercase ): raise ValueError( F'''Unable to interleave a {dataset_type.__name__} (at position 0) with a {other_type.__name__} (at position {i}). Expected a list of Dataset objects or a list of IterableDataset objects.''' ) if dataset_type is Dataset: return _concatenate_map_style_datasets(__lowercase , info=__lowercase , split=__lowercase , axis=__lowercase ) else: return _concatenate_iterable_datasets(__lowercase , info=__lowercase , split=__lowercase , axis=__lowercase )
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'''simple docstring''' from manim import * class SCREAMING_SNAKE_CASE( A__ ): """simple docstring""" def A ( self : Union[str, Any] ) -> List[str]: UpperCAmelCase : Optional[Any] = Rectangle(height=0.5 , width=0.5 ) UpperCAmelCase : str = Rectangle(height=0.46 , width=0.46 ).set_stroke(width=0 ) UpperCAmelCase : Tuple = [mem.copy() for i in range(6 )] UpperCAmelCase : Optional[int] = [mem.copy() for i in range(6 )] UpperCAmelCase : Dict = VGroup(*__snake_case ).arrange(__snake_case , buff=0 ) UpperCAmelCase : Any = VGroup(*__snake_case ).arrange(__snake_case , buff=0 ) UpperCAmelCase : Union[str, Any] = VGroup(__snake_case , __snake_case ).arrange(__snake_case , buff=0 ) UpperCAmelCase : Optional[Any] = Text('''CPU''' , font_size=24 ) UpperCAmelCase : Union[str, Any] = Group(__snake_case , __snake_case ).arrange(__snake_case , buff=0.5 , aligned_edge=__snake_case ) cpu.move_to([-2.5, -0.5, 0] ) self.add(__snake_case ) UpperCAmelCase : Optional[Any] = [mem.copy() for i in range(4 )] UpperCAmelCase : Union[str, Any] = VGroup(*__snake_case ).arrange(__snake_case , buff=0 ) UpperCAmelCase : List[str] = Text('''GPU''' , font_size=24 ) UpperCAmelCase : Dict = Group(__snake_case , __snake_case ).arrange(__snake_case , buff=0.5 , aligned_edge=__snake_case ) gpu.move_to([-1, -1, 0] ) self.add(__snake_case ) UpperCAmelCase : int = [mem.copy() for i in range(6 )] UpperCAmelCase : Union[str, Any] = VGroup(*__snake_case ).arrange(__snake_case , buff=0 ) UpperCAmelCase : List[str] = Text('''Model''' , font_size=24 ) UpperCAmelCase : Tuple = Group(__snake_case , __snake_case ).arrange(__snake_case , buff=0.5 , aligned_edge=__snake_case ) model.move_to([3, -1.0, 0] ) self.add(__snake_case ) UpperCAmelCase : Any = [] for i, rect in enumerate(__snake_case ): rect.set_stroke(__snake_case ) # target = fill.copy().set_fill(YELLOW, opacity=0.7) # target.move_to(rect) # self.add(target) UpperCAmelCase : Dict = Rectangle(height=0.46 / 4 , width=0.46 / 3 ).set_stroke(width=0.0 ).set_fill(__snake_case , opacity=0.7 ) if i == 0: cpu_target.next_to(cpu_left_col_base[0].get_corner(DOWN + LEFT ) , buff=0.02 , direction=__snake_case ) cpu_target.set_x(cpu_target.get_x() + 0.1 ) elif i == 3: cpu_target.next_to(cpu_targs[0] , direction=__snake_case , buff=0.0 ) else: cpu_target.next_to(cpu_targs[i - 1] , direction=__snake_case , buff=0.0 ) self.add(__snake_case ) cpu_targs.append(__snake_case ) UpperCAmelCase : int = [mem.copy() for i in range(6 )] UpperCAmelCase : int = VGroup(*__snake_case ).arrange(__snake_case , buff=0 ) UpperCAmelCase : Any = Text('''Loaded Checkpoint''' , font_size=24 ) UpperCAmelCase : Union[str, Any] = Group(__snake_case , __snake_case ).arrange(__snake_case , aligned_edge=__snake_case , buff=0.4 ) checkpoint.move_to([3, 0.5, 0] ) UpperCAmelCase : Optional[int] = Square(side_length=2.2 ) key.move_to([-5, 2, 0] ) UpperCAmelCase : str = MarkupText( F"""<b>Key:</b>\n\n<span fgcolor='{YELLOW}'>●</span> Empty Model""" , font_size=18 , ) key_text.move_to([-5, 2.4, 0] ) self.add(__snake_case , __snake_case ) UpperCAmelCase : Tuple = MarkupText( F"""<span fgcolor='{BLUE}'>●</span> Checkpoint""" , font_size=18 , ) blue_text.next_to(__snake_case , DOWN * 2.4 , aligned_edge=key_text.get_left() ) UpperCAmelCase : List[Any] = MarkupText( F"""Next, a <i><span fgcolor=\"{BLUE}\">second</span></i> model is loaded into memory,\nwith the weights of a <span fgcolor=\"{BLUE}\">single shard</span>.""" , font_size=24 , ) step_a.move_to([2, 2, 0] ) self.play(Write(__snake_case ) , Write(__snake_case ) ) self.play(Write(__snake_case , run_time=1 ) , Create(__snake_case , run_time=1 ) ) UpperCAmelCase : Tuple = [] UpperCAmelCase : int = [] for i, rect in enumerate(__snake_case ): UpperCAmelCase : Any = fill.copy().set_fill(__snake_case , opacity=0.7 ) target.move_to(__snake_case ) first_animations.append(GrowFromCenter(__snake_case , run_time=1 ) ) UpperCAmelCase : List[str] = target.copy() cpu_target.generate_target() if i < 5: cpu_target.target.move_to(cpu_left_col_base[i + 1] ) else: cpu_target.target.move_to(cpu_right_col_base[i - 5] ) second_animations.append(MoveToTarget(__snake_case , run_time=1.5 ) ) self.play(*__snake_case ) self.play(*__snake_case ) self.wait()
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'''simple docstring''' from __future__ import annotations from fractions import Fraction def _UpperCamelCase ( __A , __A ) -> bool: '''simple docstring''' return ( num != den and num % 10 == den // 10 and (num // 10) / (den % 10) == num / den ) def _UpperCamelCase ( __A ) -> list[str]: '''simple docstring''' UpperCamelCase__ = [] UpperCamelCase__ = 11 UpperCamelCase__ = int("1" + "0" * digit_len ) for num in range(__A , __A ): while den <= 99: if (num != den) and (num % 10 == den // 10) and (den % 10 != 0): if is_digit_cancelling(__A , __A ): solutions.append(F'''{num}/{den}''' ) den += 1 num += 1 UpperCamelCase__ = 10 return solutions def _UpperCamelCase ( __A = 2 ) -> int: '''simple docstring''' UpperCamelCase__ = 1.0 for fraction in fraction_list(__A ): UpperCamelCase__ = Fraction(__A ) result *= frac.denominator / frac.numerator return int(__A ) if __name__ == "__main__": print(solution())
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'''simple docstring''' import argparse import gc import json import os import shutil import warnings import torch from transformers import LlamaConfig, LlamaForCausalLM, LlamaTokenizer try: from transformers import LlamaTokenizerFast except ImportError as e: warnings.warn(e) warnings.warn( "The converted tokenizer will be the `slow` tokenizer. To use the fast, update your `tokenizers` library and re-run the tokenizer conversion" ) UpperCamelCase__: str = None UpperCamelCase__: int = { "7B": 11008, "13B": 13824, "30B": 17920, "65B": 22016, "70B": 28672, } UpperCamelCase__: List[Any] = { "7B": 1, "7Bf": 1, "13B": 2, "13Bf": 2, "30B": 4, "65B": 8, "70B": 8, "70Bf": 8, } def snake_case_ ( _lowerCAmelCase : Tuple , _lowerCAmelCase : Tuple=1 , _lowerCAmelCase : List[Any]=256 ) -> Optional[Any]: return multiple_of * ((int(ffn_dim_multiplier * int(8 * n / 3 ) ) + multiple_of - 1) // multiple_of) def snake_case_ ( _lowerCAmelCase : List[str] ) -> str: with open(_lowerCAmelCase , '''r''' ) as f: return json.load(_lowerCAmelCase ) def snake_case_ ( _lowerCAmelCase : Union[str, Any] , _lowerCAmelCase : Any ) -> Optional[Any]: with open(_lowerCAmelCase , '''w''' ) as f: json.dump(_lowerCAmelCase , _lowerCAmelCase ) def snake_case_ ( _lowerCAmelCase : Dict , _lowerCAmelCase : Tuple , _lowerCAmelCase : Any , _lowerCAmelCase : Any=True ) -> List[Any]: os.makedirs(_lowerCAmelCase , exist_ok=_lowerCAmelCase ) UpperCAmelCase : int = os.path.join(_lowerCAmelCase , '''tmp''' ) os.makedirs(_lowerCAmelCase , exist_ok=_lowerCAmelCase ) UpperCAmelCase : List[str] = read_json(os.path.join(_lowerCAmelCase , '''params.json''' ) ) UpperCAmelCase : str = NUM_SHARDS[model_size] UpperCAmelCase : Any = params['''n_layers'''] UpperCAmelCase : str = params['''n_heads'''] UpperCAmelCase : Any = n_heads // num_shards UpperCAmelCase : List[str] = params['''dim'''] UpperCAmelCase : Optional[Any] = dim // n_heads UpperCAmelCase : str = 1_0_0_0_0.0 UpperCAmelCase : Optional[int] = 1.0 / (base ** (torch.arange(0 , _lowerCAmelCase , 2 ).float() / dims_per_head)) if "n_kv_heads" in params: UpperCAmelCase : Tuple = params['''n_kv_heads'''] # for GQA / MQA UpperCAmelCase : Optional[int] = n_heads_per_shard // num_key_value_heads UpperCAmelCase : Optional[Any] = dim // num_key_value_heads else: # compatibility with other checkpoints UpperCAmelCase : List[str] = n_heads UpperCAmelCase : Optional[int] = n_heads_per_shard UpperCAmelCase : List[str] = dim # permute for sliced rotary def permute(_lowerCAmelCase : Tuple , _lowerCAmelCase : Optional[Any]=n_heads , _lowerCAmelCase : int=dim , _lowerCAmelCase : Dict=dim ): return w.view(_lowerCAmelCase , dima // n_heads // 2 , 2 , _lowerCAmelCase ).transpose(1 , 2 ).reshape(_lowerCAmelCase , _lowerCAmelCase ) print(f"""Fetching all parameters from the checkpoint at {input_base_path}.""" ) # Load weights if model_size == "7B": # Not sharded # (The sharded implementation would also work, but this is simpler.) UpperCAmelCase : int = torch.load(os.path.join(_lowerCAmelCase , '''consolidated.00.pth''' ) , map_location='''cpu''' ) else: # Sharded UpperCAmelCase : Optional[Any] = [ torch.load(os.path.join(_lowerCAmelCase , f"""consolidated.{i:02d}.pth""" ) , map_location='''cpu''' ) for i in range(_lowerCAmelCase ) ] UpperCAmelCase : Any = 0 UpperCAmelCase : str = {'''weight_map''': {}} for layer_i in range(_lowerCAmelCase ): UpperCAmelCase : Optional[Any] = f"""pytorch_model-{layer_i + 1}-of-{n_layers + 1}.bin""" if model_size == "7B": # Unsharded UpperCAmelCase : Optional[int] = { f"""model.layers.{layer_i}.self_attn.q_proj.weight""": permute( loaded[f"""layers.{layer_i}.attention.wq.weight"""] ), f"""model.layers.{layer_i}.self_attn.k_proj.weight""": permute( loaded[f"""layers.{layer_i}.attention.wk.weight"""] ), f"""model.layers.{layer_i}.self_attn.v_proj.weight""": loaded[f"""layers.{layer_i}.attention.wv.weight"""], f"""model.layers.{layer_i}.self_attn.o_proj.weight""": loaded[f"""layers.{layer_i}.attention.wo.weight"""], f"""model.layers.{layer_i}.mlp.gate_proj.weight""": loaded[f"""layers.{layer_i}.feed_forward.w1.weight"""], f"""model.layers.{layer_i}.mlp.down_proj.weight""": loaded[f"""layers.{layer_i}.feed_forward.w2.weight"""], f"""model.layers.{layer_i}.mlp.up_proj.weight""": loaded[f"""layers.{layer_i}.feed_forward.w3.weight"""], f"""model.layers.{layer_i}.input_layernorm.weight""": loaded[f"""layers.{layer_i}.attention_norm.weight"""], f"""model.layers.{layer_i}.post_attention_layernorm.weight""": loaded[f"""layers.{layer_i}.ffn_norm.weight"""], } else: # Sharded # Note that attention.w{q,k,v,o}, feed_fordward.w[1,2,3], attention_norm.weight and ffn_norm.weight share # the same storage object, saving attention_norm and ffn_norm will save other weights too, which is # redundant as other weights will be stitched from multiple shards. To avoid that, they are cloned. UpperCAmelCase : List[str] = { f"""model.layers.{layer_i}.input_layernorm.weight""": loaded[0][ f"""layers.{layer_i}.attention_norm.weight""" ].clone(), f"""model.layers.{layer_i}.post_attention_layernorm.weight""": loaded[0][ f"""layers.{layer_i}.ffn_norm.weight""" ].clone(), } UpperCAmelCase : Union[str, Any] = permute( torch.cat( [ loaded[i][f"""layers.{layer_i}.attention.wq.weight"""].view(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ) for i in range(_lowerCAmelCase ) ] , dim=0 , ).reshape(_lowerCAmelCase , _lowerCAmelCase ) ) UpperCAmelCase : Optional[Any] = permute( torch.cat( [ loaded[i][f"""layers.{layer_i}.attention.wk.weight"""].view( _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ) for i in range(_lowerCAmelCase ) ] , dim=0 , ).reshape(_lowerCAmelCase , _lowerCAmelCase ) , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , ) UpperCAmelCase : str = torch.cat( [ loaded[i][f"""layers.{layer_i}.attention.wv.weight"""].view( _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ) for i in range(_lowerCAmelCase ) ] , dim=0 , ).reshape(_lowerCAmelCase , _lowerCAmelCase ) UpperCAmelCase : Optional[int] = torch.cat( [loaded[i][f"""layers.{layer_i}.attention.wo.weight"""] for i in range(_lowerCAmelCase )] , dim=1 ) UpperCAmelCase : Any = torch.cat( [loaded[i][f"""layers.{layer_i}.feed_forward.w1.weight"""] for i in range(_lowerCAmelCase )] , dim=0 ) UpperCAmelCase : str = torch.cat( [loaded[i][f"""layers.{layer_i}.feed_forward.w2.weight"""] for i in range(_lowerCAmelCase )] , dim=1 ) UpperCAmelCase : Tuple = torch.cat( [loaded[i][f"""layers.{layer_i}.feed_forward.w3.weight"""] for i in range(_lowerCAmelCase )] , dim=0 ) UpperCAmelCase : Any = inv_freq for k, v in state_dict.items(): UpperCAmelCase : List[Any] = filename param_count += v.numel() torch.save(_lowerCAmelCase , os.path.join(_lowerCAmelCase , _lowerCAmelCase ) ) UpperCAmelCase : Optional[int] = f"""pytorch_model-{n_layers + 1}-of-{n_layers + 1}.bin""" if model_size == "7B": # Unsharded UpperCAmelCase : str = { '''model.embed_tokens.weight''': loaded['''tok_embeddings.weight'''], '''model.norm.weight''': loaded['''norm.weight'''], '''lm_head.weight''': loaded['''output.weight'''], } else: UpperCAmelCase : Any = { '''model.norm.weight''': loaded[0]['''norm.weight'''], '''model.embed_tokens.weight''': torch.cat( [loaded[i]['''tok_embeddings.weight'''] for i in range(_lowerCAmelCase )] , dim=1 ), '''lm_head.weight''': torch.cat([loaded[i]['''output.weight'''] for i in range(_lowerCAmelCase )] , dim=0 ), } for k, v in state_dict.items(): UpperCAmelCase : Optional[int] = filename param_count += v.numel() torch.save(_lowerCAmelCase , os.path.join(_lowerCAmelCase , _lowerCAmelCase ) ) # Write configs UpperCAmelCase : Union[str, Any] = {'''total_size''': param_count * 2} write_json(_lowerCAmelCase , os.path.join(_lowerCAmelCase , '''pytorch_model.bin.index.json''' ) ) UpperCAmelCase : int = params['''ffn_dim_multiplier'''] if '''ffn_dim_multiplier''' in params else 1 UpperCAmelCase : Tuple = params['''multiple_of'''] if '''multiple_of''' in params else 256 UpperCAmelCase : Any = LlamaConfig( hidden_size=_lowerCAmelCase , intermediate_size=compute_intermediate_size(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ) , num_attention_heads=params['''n_heads'''] , num_hidden_layers=params['''n_layers'''] , rms_norm_eps=params['''norm_eps'''] , num_key_value_heads=_lowerCAmelCase , ) config.save_pretrained(_lowerCAmelCase ) # Make space so we can load the model properly now. del state_dict del loaded gc.collect() print('''Loading the checkpoint in a Llama model.''' ) UpperCAmelCase : Optional[int] = LlamaForCausalLM.from_pretrained(_lowerCAmelCase , torch_dtype=torch.floataa , low_cpu_mem_usage=_lowerCAmelCase ) # Avoid saving this as part of the config. del model.config._name_or_path print('''Saving in the Transformers format.''' ) model.save_pretrained(_lowerCAmelCase , safe_serialization=_lowerCAmelCase ) shutil.rmtree(_lowerCAmelCase ) def snake_case_ ( _lowerCAmelCase : int , _lowerCAmelCase : Union[str, Any] ) -> List[str]: # Initialize the tokenizer based on the `spm` model UpperCAmelCase : Dict = LlamaTokenizer if LlamaTokenizerFast is None else LlamaTokenizerFast print(f"""Saving a {tokenizer_class.__name__} to {tokenizer_path}.""" ) UpperCAmelCase : List[Any] = tokenizer_class(_lowerCAmelCase ) tokenizer.save_pretrained(_lowerCAmelCase ) def snake_case_ ( ) -> List[Any]: UpperCAmelCase : int = argparse.ArgumentParser() parser.add_argument( '''--input_dir''' , help='''Location of LLaMA weights, which contains tokenizer.model and model folders''' , ) parser.add_argument( '''--model_size''' , choices=['''7B''', '''7Bf''', '''13B''', '''13Bf''', '''30B''', '''65B''', '''70B''', '''70Bf''', '''tokenizer_only'''] , ) parser.add_argument( '''--output_dir''' , help='''Location to write HF model and tokenizer''' , ) parser.add_argument('''--safe_serialization''' , type=_lowerCAmelCase , help='''Whether or not to save using `safetensors`.''' ) UpperCAmelCase : List[Any] = parser.parse_args() if args.model_size != "tokenizer_only": write_model( model_path=args.output_dir , input_base_path=os.path.join(args.input_dir , args.model_size ) , model_size=args.model_size , safe_serialization=args.safe_serialization , ) UpperCAmelCase : Optional[int] = os.path.join(args.input_dir , '''tokenizer.model''' ) write_tokenizer(args.output_dir , _lowerCAmelCase ) if __name__ == "__main__": main()
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"""simple docstring""" def _A ( lowercase ): """simple docstring""" if n_term == "": return [] a =[] for temp in range(int(lowercase ) ): series.append(f'''1/{temp + 1}''' if series else '''1''' ) return series if __name__ == "__main__": lowerCamelCase_ : Union[str, Any] = input("""Enter the last number (nth term) of the Harmonic Series""") print("""Formula of Harmonic Series => 1+1/2+1/3 ..... 1/n""") print(harmonic_series(nth_term))
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'''simple docstring''' from math import cos, sin, sqrt, tau from audio_filters.iir_filter import IIRFilter def snake_case_ ( _lowerCAmelCase : int , _lowerCAmelCase : int , _lowerCAmelCase : float = 1 / sqrt(2 ) ) -> IIRFilter: UpperCAmelCase : Optional[int] = tau * frequency / samplerate UpperCAmelCase : List[Any] = sin(_lowerCAmelCase ) UpperCAmelCase : Optional[Any] = cos(_lowerCAmelCase ) UpperCAmelCase : int = _sin / (2 * q_factor) UpperCAmelCase : Any = (1 - _cos) / 2 UpperCAmelCase : List[Any] = 1 - _cos UpperCAmelCase : Union[str, Any] = 1 + alpha UpperCAmelCase : Any = -2 * _cos UpperCAmelCase : Dict = 1 - alpha UpperCAmelCase : Union[str, Any] = IIRFilter(2 ) filt.set_coefficients([aa, aa, aa] , [ba, ba, ba] ) return filt def snake_case_ ( _lowerCAmelCase : int , _lowerCAmelCase : int , _lowerCAmelCase : float = 1 / sqrt(2 ) ) -> IIRFilter: UpperCAmelCase : Any = tau * frequency / samplerate UpperCAmelCase : Tuple = sin(_lowerCAmelCase ) UpperCAmelCase : Tuple = cos(_lowerCAmelCase ) UpperCAmelCase : Dict = _sin / (2 * q_factor) UpperCAmelCase : int = (1 + _cos) / 2 UpperCAmelCase : List[Any] = -1 - _cos UpperCAmelCase : Tuple = 1 + alpha UpperCAmelCase : List[str] = -2 * _cos UpperCAmelCase : Optional[Any] = 1 - alpha UpperCAmelCase : Tuple = IIRFilter(2 ) filt.set_coefficients([aa, aa, aa] , [ba, ba, ba] ) return filt def snake_case_ ( _lowerCAmelCase : int , _lowerCAmelCase : int , _lowerCAmelCase : float = 1 / sqrt(2 ) ) -> IIRFilter: UpperCAmelCase : Optional[int] = tau * frequency / samplerate UpperCAmelCase : Optional[int] = sin(_lowerCAmelCase ) UpperCAmelCase : Tuple = cos(_lowerCAmelCase ) UpperCAmelCase : Optional[int] = _sin / (2 * q_factor) UpperCAmelCase : Union[str, Any] = _sin / 2 UpperCAmelCase : Any = 0 UpperCAmelCase : int = -ba UpperCAmelCase : Optional[Any] = 1 + alpha UpperCAmelCase : List[Any] = -2 * _cos UpperCAmelCase : Optional[Any] = 1 - alpha UpperCAmelCase : int = IIRFilter(2 ) filt.set_coefficients([aa, aa, aa] , [ba, ba, ba] ) return filt def snake_case_ ( _lowerCAmelCase : int , _lowerCAmelCase : int , _lowerCAmelCase : float = 1 / sqrt(2 ) ) -> IIRFilter: UpperCAmelCase : List[str] = tau * frequency / samplerate UpperCAmelCase : Union[str, Any] = sin(_lowerCAmelCase ) UpperCAmelCase : str = cos(_lowerCAmelCase ) UpperCAmelCase : Optional[Any] = _sin / (2 * q_factor) UpperCAmelCase : List[str] = 1 - alpha UpperCAmelCase : Any = -2 * _cos UpperCAmelCase : Optional[int] = 1 + alpha UpperCAmelCase : Union[str, Any] = IIRFilter(2 ) filt.set_coefficients([ba, ba, ba] , [ba, ba, ba] ) return filt def snake_case_ ( _lowerCAmelCase : int , _lowerCAmelCase : int , _lowerCAmelCase : float , _lowerCAmelCase : float = 1 / sqrt(2 ) , ) -> IIRFilter: UpperCAmelCase : Optional[Any] = tau * frequency / samplerate UpperCAmelCase : Union[str, Any] = sin(_lowerCAmelCase ) UpperCAmelCase : Optional[int] = cos(_lowerCAmelCase ) UpperCAmelCase : Dict = _sin / (2 * q_factor) UpperCAmelCase : str = 10 ** (gain_db / 40) UpperCAmelCase : int = 1 + alpha * big_a UpperCAmelCase : Union[str, Any] = -2 * _cos UpperCAmelCase : Optional[Any] = 1 - alpha * big_a UpperCAmelCase : Union[str, Any] = 1 + alpha / big_a UpperCAmelCase : Tuple = -2 * _cos UpperCAmelCase : Any = 1 - alpha / big_a UpperCAmelCase : Optional[Any] = IIRFilter(2 ) filt.set_coefficients([aa, aa, aa] , [ba, ba, ba] ) return filt def snake_case_ ( _lowerCAmelCase : int , _lowerCAmelCase : int , _lowerCAmelCase : float , _lowerCAmelCase : float = 1 / sqrt(2 ) , ) -> IIRFilter: UpperCAmelCase : Any = tau * frequency / samplerate UpperCAmelCase : Optional[int] = sin(_lowerCAmelCase ) UpperCAmelCase : Union[str, Any] = cos(_lowerCAmelCase ) UpperCAmelCase : str = _sin / (2 * q_factor) UpperCAmelCase : List[str] = 10 ** (gain_db / 40) UpperCAmelCase : Optional[int] = (big_a + 1) - (big_a - 1) * _cos UpperCAmelCase : int = (big_a + 1) + (big_a - 1) * _cos UpperCAmelCase : int = (big_a - 1) - (big_a + 1) * _cos UpperCAmelCase : Optional[int] = (big_a - 1) + (big_a + 1) * _cos UpperCAmelCase : str = 2 * sqrt(_lowerCAmelCase ) * alpha UpperCAmelCase : Dict = big_a * (pmc + aaa) UpperCAmelCase : Any = 2 * big_a * mpc UpperCAmelCase : Union[str, Any] = big_a * (pmc - aaa) UpperCAmelCase : Optional[int] = ppmc + aaa UpperCAmelCase : Optional[Any] = -2 * pmpc UpperCAmelCase : Optional[Any] = ppmc - aaa UpperCAmelCase : int = IIRFilter(2 ) filt.set_coefficients([aa, aa, aa] , [ba, ba, ba] ) return filt def snake_case_ ( _lowerCAmelCase : int , _lowerCAmelCase : int , _lowerCAmelCase : float , _lowerCAmelCase : float = 1 / sqrt(2 ) , ) -> IIRFilter: UpperCAmelCase : int = tau * frequency / samplerate UpperCAmelCase : Union[str, Any] = sin(_lowerCAmelCase ) UpperCAmelCase : Union[str, Any] = cos(_lowerCAmelCase ) UpperCAmelCase : Any = _sin / (2 * q_factor) UpperCAmelCase : int = 10 ** (gain_db / 40) UpperCAmelCase : List[str] = (big_a + 1) - (big_a - 1) * _cos UpperCAmelCase : Union[str, Any] = (big_a + 1) + (big_a - 1) * _cos UpperCAmelCase : Optional[Any] = (big_a - 1) - (big_a + 1) * _cos UpperCAmelCase : Union[str, Any] = (big_a - 1) + (big_a + 1) * _cos UpperCAmelCase : List[str] = 2 * sqrt(_lowerCAmelCase ) * alpha UpperCAmelCase : Any = big_a * (ppmc + aaa) UpperCAmelCase : str = -2 * big_a * pmpc UpperCAmelCase : List[Any] = big_a * (ppmc - aaa) UpperCAmelCase : Optional[Any] = pmc + aaa UpperCAmelCase : Any = 2 * mpc UpperCAmelCase : str = pmc - aaa UpperCAmelCase : Union[str, Any] = IIRFilter(2 ) filt.set_coefficients([aa, aa, aa] , [ba, ba, ba] ) return filt
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import os import time import warnings from dataclasses import dataclass, field from enum import Enum from typing import List, Optional, Union import torch from filelock import FileLock from torch.utils.data import Dataset from ...tokenization_utils_base import PreTrainedTokenizerBase from ...utils import logging from ..processors.glue import glue_convert_examples_to_features, glue_output_modes, glue_processors from ..processors.utils import InputFeatures A__ = logging.get_logger(__name__) @dataclass class __lowerCAmelCase : __lowerCamelCase = field(metadata={'''help''': '''The name of the task to train on: ''' + ''', '''.join(glue_processors.keys() )} ) __lowerCamelCase = field( metadata={'''help''': '''The input data dir. Should contain the .tsv files (or other data files) for the task.'''} ) __lowerCamelCase = field( default=128 , metadata={ '''help''': ( '''The maximum total input sequence length after tokenization. Sequences longer ''' '''than this will be truncated, sequences shorter will be padded.''' ) } , ) __lowerCamelCase = field( default=lowerCamelCase__ , metadata={'''help''': '''Overwrite the cached training and evaluation sets'''} ) def snake_case ( self ): """simple docstring""" _lowerCAmelCase = self.task_name.lower() class __lowerCAmelCase ( lowerCamelCase__ ): __lowerCamelCase = '''train''' __lowerCamelCase = '''dev''' __lowerCamelCase = '''test''' class __lowerCAmelCase ( lowerCamelCase__ ): __lowerCamelCase = 42 __lowerCamelCase = 42 __lowerCamelCase = 42 def __init__( self , _snake_case , _snake_case , _snake_case = None , _snake_case = Split.train , _snake_case = None , ): """simple docstring""" warnings.warn( """This dataset will be removed from the library soon, preprocessing should be handled with the 🤗 Datasets """ """library. You can have a look at this example script for pointers: """ """https://github.com/huggingface/transformers/blob/main/examples/pytorch/text-classification/run_glue.py""" , _snake_case , ) _lowerCAmelCase = args _lowerCAmelCase = glue_processors[args.task_name]() _lowerCAmelCase = glue_output_modes[args.task_name] if isinstance(_snake_case , _snake_case ): try: _lowerCAmelCase = Split[mode] except KeyError: raise KeyError("""mode is not a valid split name""" ) # Load data features from cache or dataset file _lowerCAmelCase = os.path.join( cache_dir if cache_dir is not None else args.data_dir , F'cached_{mode.value}_{tokenizer.__class__.__name__}_{args.max_seq_length}_{args.task_name}' , ) _lowerCAmelCase = self.processor.get_labels() if args.task_name in ["mnli", "mnli-mm"] and tokenizer.__class__.__name__ in ( "RobertaTokenizer", "RobertaTokenizerFast", "XLMRobertaTokenizer", "BartTokenizer", "BartTokenizerFast", ): # HACK(label indices are swapped in RoBERTa pretrained model) _lowerCAmelCase , _lowerCAmelCase = label_list[2], label_list[1] _lowerCAmelCase = label_list # Make sure only the first process in distributed training processes the dataset, # and the others will use the cache. _lowerCAmelCase = cached_features_file + """.lock""" with FileLock(_snake_case ): if os.path.exists(_snake_case ) and not args.overwrite_cache: _lowerCAmelCase = time.time() _lowerCAmelCase = torch.load(_snake_case ) logger.info( F'Loading features from cached file {cached_features_file} [took %.3f s]' , time.time() - start ) else: logger.info(F'Creating features from dataset file at {args.data_dir}' ) if mode == Split.dev: _lowerCAmelCase = self.processor.get_dev_examples(args.data_dir ) elif mode == Split.test: _lowerCAmelCase = self.processor.get_test_examples(args.data_dir ) else: _lowerCAmelCase = self.processor.get_train_examples(args.data_dir ) if limit_length is not None: _lowerCAmelCase = examples[:limit_length] _lowerCAmelCase = glue_convert_examples_to_features( _snake_case , _snake_case , max_length=args.max_seq_length , label_list=_snake_case , output_mode=self.output_mode , ) _lowerCAmelCase = time.time() torch.save(self.features , _snake_case ) # ^ This seems to take a lot of time so I want to investigate why and how we can improve. logger.info( F'Saving features into cached file {cached_features_file} [took {time.time() - start:.3f} s]' ) def __len__( self ): """simple docstring""" return len(self.features ) def __getitem__( self , _snake_case ): """simple docstring""" return self.features[i] def snake_case ( self ): """simple docstring""" return self.label_list
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'''simple docstring''' from __future__ import annotations def snake_case_ ( _lowerCAmelCase : str , _lowerCAmelCase : str ) -> bool: UpperCAmelCase : str = get_failure_array(_lowerCAmelCase ) # 2) Step through text searching for pattern UpperCAmelCase , UpperCAmelCase : Optional[Any] = 0, 0 # index into text, pattern while i < len(_lowerCAmelCase ): if pattern[j] == text[i]: if j == (len(_lowerCAmelCase ) - 1): return True j += 1 # if this is a prefix in our pattern # just go back far enough to continue elif j > 0: UpperCAmelCase : Optional[Any] = failure[j - 1] continue i += 1 return False def snake_case_ ( _lowerCAmelCase : str ) -> list[int]: UpperCAmelCase : Optional[Any] = [0] UpperCAmelCase : str = 0 UpperCAmelCase : List[str] = 1 while j < len(_lowerCAmelCase ): if pattern[i] == pattern[j]: i += 1 elif i > 0: UpperCAmelCase : Union[str, Any] = failure[i - 1] continue j += 1 failure.append(_lowerCAmelCase ) return failure if __name__ == "__main__": # Test 1) UpperCamelCase__: str = "abc1abc12" UpperCamelCase__: str = "alskfjaldsabc1abc1abc12k23adsfabcabc" UpperCamelCase__: Any = "alskfjaldsk23adsfabcabc" assert kmp(pattern, texta) and not kmp(pattern, texta) # Test 2) UpperCamelCase__: Tuple = "ABABX" UpperCamelCase__: Union[str, Any] = "ABABZABABYABABX" assert kmp(pattern, text) # Test 3) UpperCamelCase__: Any = "AAAB" UpperCamelCase__: str = "ABAAAAAB" assert kmp(pattern, text) # Test 4) UpperCamelCase__: int = "abcdabcy" UpperCamelCase__: Any = "abcxabcdabxabcdabcdabcy" assert kmp(pattern, text) # Test 5) UpperCamelCase__: List[str] = "aabaabaaa" assert get_failure_array(pattern) == [0, 1, 0, 1, 2, 3, 4, 5, 2]
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'''simple docstring''' def A__ ( UpperCAmelCase_ ): if upper_limit < 0: raise ValueError('Limit for the Catalan sequence must be ≥ 0' ) _UpperCamelCase : Tuple = [0] * (upper_limit + 1) # Base case: C(0) = C(1) = 1 _UpperCamelCase : Optional[Any] = 1 if upper_limit > 0: _UpperCamelCase : Optional[Any] = 1 # Recurrence relation: C(i) = sum(C(j).C(i-j-1)), from j = 0 to i for i in range(2 , upper_limit + 1 ): for j in range(UpperCAmelCase_ ): catalan_list[i] += catalan_list[j] * catalan_list[i - j - 1] return catalan_list if __name__ == "__main__": print('\n********* Catalan Numbers Using Dynamic Programming ************\n') print('\n*** Enter -1 at any time to quit ***') print('\nEnter the upper limit (≥ 0) for the Catalan number sequence: ', end='') try: while True: snake_case_ : Optional[Any] = int(input().strip()) if N < 0: print('\n********* Goodbye!! ************') break else: print(F"""The Catalan numbers from 0 through {N} are:""") print(catalan_numbers(N)) print('Try another upper limit for the sequence: ', end='') except (NameError, ValueError): print('\n********* Invalid input, goodbye! ************\n') import doctest doctest.testmod()
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'''simple docstring''' import argparse import json import os import fairseq import torch from fairseq.data import Dictionary from transformers import ( WavaVecaConfig, WavaVecaCTCTokenizer, WavaVecaFeatureExtractor, WavaVecaForCTC, WavaVecaForPreTraining, WavaVecaProcessor, logging, ) from transformers.models.wavaveca.modeling_wavaveca import WavaVecaForSequenceClassification logging.set_verbosity_info() UpperCamelCase__: int = logging.get_logger(__name__) UpperCamelCase__: Dict = { "post_extract_proj": "feature_projection.projection", "encoder.pos_conv.0": "encoder.pos_conv_embed.conv", "self_attn.k_proj": "encoder.layers.*.attention.k_proj", "self_attn.v_proj": "encoder.layers.*.attention.v_proj", "self_attn.q_proj": "encoder.layers.*.attention.q_proj", "self_attn.out_proj": "encoder.layers.*.attention.out_proj", "self_attn_layer_norm": "encoder.layers.*.layer_norm", "fc1": "encoder.layers.*.feed_forward.intermediate_dense", "fc2": "encoder.layers.*.feed_forward.output_dense", "final_layer_norm": "encoder.layers.*.final_layer_norm", "encoder.layer_norm": "encoder.layer_norm", "adapter_layer": "encoder.layers.*.adapter_layer", "w2v_model.layer_norm": "feature_projection.layer_norm", "quantizer.weight_proj": "quantizer.weight_proj", "quantizer.vars": "quantizer.codevectors", "project_q": "project_q", "final_proj": "project_hid", "w2v_encoder.proj": "lm_head", "mask_emb": "masked_spec_embed", "pooling_layer.linear": "projector", "pooling_layer.projection": "classifier", } UpperCamelCase__: Optional[Any] = [ "lm_head", "quantizer.weight_proj", "quantizer.codevectors", "project_q", "project_hid", "projector", "classifier", ] def snake_case_ ( _lowerCAmelCase : str ) -> Union[str, Any]: UpperCAmelCase : Optional[int] = {} with open(_lowerCAmelCase , '''r''' ) as file: for line_number, line in enumerate(_lowerCAmelCase ): UpperCAmelCase : List[str] = line.strip() if line: UpperCAmelCase : str = line.split() UpperCAmelCase : Union[str, Any] = line_number UpperCAmelCase : List[Any] = words[0] UpperCAmelCase : Union[str, Any] = value return result def snake_case_ ( _lowerCAmelCase : int , _lowerCAmelCase : Optional[Any] , _lowerCAmelCase : Optional[Any] , _lowerCAmelCase : Tuple , _lowerCAmelCase : str ) -> int: for attribute in key.split('''.''' ): UpperCAmelCase : Dict = getattr(_lowerCAmelCase , _lowerCAmelCase ) UpperCAmelCase : Dict = None for param_key in PARAM_MAPPING.keys(): if full_name.endswith(_lowerCAmelCase ): UpperCAmelCase : Any = PARAM_MAPPING[full_name.split('''.''' )[-1]] UpperCAmelCase : Dict = '''param''' if weight_type is not None and weight_type != "param": UpperCAmelCase : Optional[int] = getattr(_lowerCAmelCase , _lowerCAmelCase ).shape elif weight_type is not None and weight_type == "param": UpperCAmelCase : List[Any] = hf_pointer for attribute in hf_param_name.split('''.''' ): UpperCAmelCase : Optional[Any] = getattr(_lowerCAmelCase , _lowerCAmelCase ) UpperCAmelCase : int = shape_pointer.shape # let's reduce dimension UpperCAmelCase : Union[str, Any] = value[0] else: UpperCAmelCase : List[Any] = hf_pointer.shape if hf_shape != value.shape: raise ValueError( f"""Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be""" f""" {value.shape} for {full_name}""" ) if weight_type == "weight": UpperCAmelCase : int = value elif weight_type == "weight_g": UpperCAmelCase : str = value elif weight_type == "weight_v": UpperCAmelCase : Dict = value elif weight_type == "bias": UpperCAmelCase : str = value elif weight_type == "param": for attribute in hf_param_name.split('''.''' ): UpperCAmelCase : int = getattr(_lowerCAmelCase , _lowerCAmelCase ) UpperCAmelCase : Optional[int] = value else: UpperCAmelCase : Tuple = value logger.info(f"""{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.""" ) def snake_case_ ( _lowerCAmelCase : Optional[int] , _lowerCAmelCase : Any , _lowerCAmelCase : List[Any] , _lowerCAmelCase : Dict , _lowerCAmelCase : List[Any] ) -> List[Any]: UpperCAmelCase : List[str] = None for param_key in PARAM_MAPPING.keys(): if full_name.endswith(_lowerCAmelCase ): UpperCAmelCase : List[str] = PARAM_MAPPING[full_name.split('''.''' )[-1]] UpperCAmelCase : Any = '''param''' if weight_type is not None and weight_type != "param": UpperCAmelCase : Optional[int] = '''.'''.join([key, weight_type] ) elif weight_type is not None and weight_type == "param": UpperCAmelCase : Optional[int] = '''.'''.join([key, hf_param_name] ) else: UpperCAmelCase : List[Any] = key UpperCAmelCase : Tuple = value if '''lm_head''' in full_key else value[0] UpperCamelCase__: Tuple = { "W_a": "linear_1.weight", "W_b": "linear_2.weight", "b_a": "linear_1.bias", "b_b": "linear_2.bias", "ln_W": "norm.weight", "ln_b": "norm.bias", } def snake_case_ ( _lowerCAmelCase : str , _lowerCAmelCase : List[str] , _lowerCAmelCase : Any=None , _lowerCAmelCase : Optional[Any]=None ) -> int: UpperCAmelCase : List[Any] = False for key, mapped_key in MAPPING.items(): UpperCAmelCase : int = '''wav2vec2.''' + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key if key in name or key.split('''w2v_model.''' )[-1] == name.split('''.''' )[0]: UpperCAmelCase : Optional[Any] = True if "*" in mapped_key: UpperCAmelCase : Tuple = name.split(_lowerCAmelCase )[0].split('''.''' )[-2] UpperCAmelCase : List[Any] = mapped_key.replace('''*''' , _lowerCAmelCase ) if "weight_g" in name: UpperCAmelCase : str = '''weight_g''' elif "weight_v" in name: UpperCAmelCase : int = '''weight_v''' elif "bias" in name: UpperCAmelCase : int = '''bias''' elif "weight" in name: # TODO: don't match quantizer.weight_proj UpperCAmelCase : List[str] = '''weight''' else: UpperCAmelCase : Dict = None if hf_dict is not None: rename_dict(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ) else: set_recursively(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ) return is_used return is_used def snake_case_ ( _lowerCAmelCase : Union[str, Any] , _lowerCAmelCase : Optional[Any] , _lowerCAmelCase : Any ) -> Any: UpperCAmelCase : Dict = [] UpperCAmelCase : Dict = fairseq_model.state_dict() UpperCAmelCase : Union[str, Any] = hf_model.wavaveca.feature_extractor for name, value in fairseq_dict.items(): UpperCAmelCase : Dict = False if "conv_layers" in name: load_conv_layer( _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , hf_model.config.feat_extract_norm == '''group''' , ) UpperCAmelCase : Any = True else: UpperCAmelCase : Optional[Any] = load_wavaveca_layer(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ) if not is_used: unused_weights.append(_lowerCAmelCase ) logger.warning(f"""Unused weights: {unused_weights}""" ) def snake_case_ ( _lowerCAmelCase : Optional[int] , _lowerCAmelCase : Tuple , _lowerCAmelCase : Any , _lowerCAmelCase : Optional[int] , _lowerCAmelCase : Optional[int] ) -> Union[str, Any]: UpperCAmelCase : Any = full_name.split('''conv_layers.''' )[-1] UpperCAmelCase : Optional[int] = name.split('''.''' ) UpperCAmelCase : Tuple = int(items[0] ) UpperCAmelCase : Tuple = int(items[1] ) if type_id == 0: if "bias" in name: if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape: raise ValueError( f"""{full_name} has size {value.shape}, but""" f""" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.""" ) UpperCAmelCase : Tuple = value logger.info(f"""Feat extract conv layer {layer_id} was initialized from {full_name}.""" ) elif "weight" in name: if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape: raise ValueError( f"""{full_name} has size {value.shape}, but""" f""" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.""" ) UpperCAmelCase : Union[str, Any] = value logger.info(f"""Feat extract conv layer {layer_id} was initialized from {full_name}.""" ) elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm): if "bias" in name: if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape: raise ValueError( f"""{full_name} has size {value.shape}, but""" f""" {feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape} was found.""" ) UpperCAmelCase : Union[str, Any] = value logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.""" ) elif "weight" in name: if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape: raise ValueError( f"""{full_name} has size {value.shape}, but""" f""" {feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape} was found.""" ) UpperCAmelCase : List[str] = value logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.""" ) else: unused_weights.append(_lowerCAmelCase ) @torch.no_grad() def snake_case_ ( _lowerCAmelCase : Union[str, Any] , _lowerCAmelCase : List[Any] , _lowerCAmelCase : Dict=None , _lowerCAmelCase : List[Any]=None , _lowerCAmelCase : int=True , _lowerCAmelCase : Optional[int]=False ) -> Dict: if config_path is not None: UpperCAmelCase : List[str] = WavaVecaConfig.from_pretrained(_lowerCAmelCase ) else: UpperCAmelCase : List[Any] = WavaVecaConfig() if is_seq_class: UpperCAmelCase : Optional[Any] = read_txt_into_dict(_lowerCAmelCase ) UpperCAmelCase : Optional[int] = idalabel UpperCAmelCase : Optional[Any] = WavaVecaForSequenceClassification(_lowerCAmelCase ) UpperCAmelCase : Dict = WavaVecaFeatureExtractor( feature_size=1 , sampling_rate=16000 , padding_value=0 , do_normalize=_lowerCAmelCase , return_attention_mask=_lowerCAmelCase , ) feature_extractor.save_pretrained(_lowerCAmelCase ) elif is_finetuned: if dict_path: UpperCAmelCase : Dict = Dictionary.load(_lowerCAmelCase ) # important change bos & pad token id since CTC symbol is <pad> and # not <s> as in fairseq UpperCAmelCase : Any = target_dict.pad_index UpperCAmelCase : Tuple = target_dict.bos_index UpperCAmelCase : Optional[int] = target_dict.eos_index UpperCAmelCase : Union[str, Any] = len(target_dict.symbols ) UpperCAmelCase : Dict = os.path.join(_lowerCAmelCase , '''vocab.json''' ) if not os.path.isdir(_lowerCAmelCase ): logger.error('''--pytorch_dump_folder_path ({}) should be a directory'''.format(_lowerCAmelCase ) ) return os.makedirs(_lowerCAmelCase , exist_ok=_lowerCAmelCase ) UpperCAmelCase : List[Any] = target_dict.indices # fairseq has the <pad> and <s> switched UpperCAmelCase : List[str] = 0 UpperCAmelCase : List[str] = 1 with open(_lowerCAmelCase , '''w''' , encoding='''utf-8''' ) as vocab_handle: json.dump(_lowerCAmelCase , _lowerCAmelCase ) UpperCAmelCase : Optional[int] = WavaVecaCTCTokenizer( _lowerCAmelCase , unk_token=target_dict.unk_word , pad_token=target_dict.pad_word , bos_token=target_dict.bos_word , eos_token=target_dict.eos_word , word_delimiter_token='''|''' , do_lower_case=_lowerCAmelCase , ) UpperCAmelCase : int = True if config.feat_extract_norm == '''layer''' else False UpperCAmelCase : int = WavaVecaFeatureExtractor( feature_size=1 , sampling_rate=16000 , padding_value=0 , do_normalize=_lowerCAmelCase , return_attention_mask=_lowerCAmelCase , ) UpperCAmelCase : str = WavaVecaProcessor(feature_extractor=_lowerCAmelCase , tokenizer=_lowerCAmelCase ) processor.save_pretrained(_lowerCAmelCase ) UpperCAmelCase : Union[str, Any] = WavaVecaForCTC(_lowerCAmelCase ) else: UpperCAmelCase : Dict = WavaVecaForPreTraining(_lowerCAmelCase ) if is_finetuned or is_seq_class: UpperCAmelCase , UpperCAmelCase , UpperCAmelCase : List[str] = fairseq.checkpoint_utils.load_model_ensemble_and_task( [checkpoint_path] , arg_overrides={'''data''': '''/'''.join(dict_path.split('''/''' )[:-1] )} ) else: UpperCAmelCase : Optional[Any] = argparse.Namespace(task='''audio_pretraining''' ) UpperCAmelCase : List[Any] = fairseq.tasks.setup_task(_lowerCAmelCase ) UpperCAmelCase , UpperCAmelCase , UpperCAmelCase : int = fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path] , task=_lowerCAmelCase ) UpperCAmelCase : Optional[int] = model[0].eval() recursively_load_weights(_lowerCAmelCase , _lowerCAmelCase , not is_finetuned ) hf_wavavec.save_pretrained(_lowerCAmelCase ) if __name__ == "__main__": UpperCamelCase__: Dict = argparse.ArgumentParser() parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.") parser.add_argument("--checkpoint_path", default=None, type=str, help="Path to fairseq checkpoint") parser.add_argument("--dict_path", default=None, type=str, help="Path to dict of fine-tuned model") parser.add_argument("--config_path", default=None, type=str, help="Path to hf config.json of model to convert") parser.add_argument( "--not_finetuned", action="store_true", help="Whether the model to convert is a fine-tuned model or not" ) parser.add_argument( "--is_seq_class", action="store_true", help="Whether the model to convert is a fine-tuned sequence classification model or not", ) UpperCamelCase__: Any = parser.parse_args() UpperCamelCase__: int = not args.not_finetuned and not args.is_seq_class convert_wavaveca_checkpoint( args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, is_finetuned, args.is_seq_class, )
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"""simple docstring""" import argparse import os import sys from unittest.mock import patch import pytorch_lightning as pl import timeout_decorator import torch from distillation import SummarizationDistiller, distill_main from finetune import SummarizationModule, main from transformers import MarianMTModel from transformers.file_utils import cached_path from transformers.testing_utils import TestCasePlus, require_torch_gpu, slow from utils import load_json __UpperCAmelCase = 'sshleifer/mar_enro_6_3_student' class _SCREAMING_SNAKE_CASE ( A__ ): def __lowerCAmelCase ( self ) -> Optional[Any]: super().setUp() lowerCAmelCase_ :int = cached_path( """https://cdn-datasets.huggingface.co/translation/wmt_en_ro-tr40k-va0.5k-te0.5k.tar.gz""" , extract_compressed_file=__A , ) lowerCAmelCase_ :str = f"""{data_cached}/wmt_en_ro-tr40k-va0.5k-te0.5k""" @slow @require_torch_gpu def __lowerCAmelCase ( self ) -> Any: MarianMTModel.from_pretrained(__A ) @slow @require_torch_gpu def __lowerCAmelCase ( self ) -> Optional[Any]: lowerCAmelCase_ :str = { """$MAX_LEN""": 64, """$BS""": 64, """$GAS""": 1, """$ENRO_DIR""": self.data_dir, """facebook/mbart-large-cc25""": MARIAN_MODEL, # "val_check_interval=0.25": "val_check_interval=1.0", """--learning_rate=3e-5""": """--learning_rate 3e-4""", """--num_train_epochs 6""": """--num_train_epochs 1""", } # Clean up bash script lowerCAmelCase_ :int = (self.test_file_dir / """train_mbart_cc25_enro.sh""").open().read().split("""finetune.py""" )[1].strip() lowerCAmelCase_ :Tuple = bash_script.replace("""\\\n""" , """""" ).strip().replace("""\"$@\"""" , """""" ) for k, v in env_vars_to_replace.items(): lowerCAmelCase_ :List[Any] = bash_script.replace(__A , str(__A ) ) lowerCAmelCase_ :Any = self.get_auto_remove_tmp_dir() # bash_script = bash_script.replace("--fp16 ", "") lowerCAmelCase_ :Union[str, Any] = f""" --output_dir {output_dir} --tokenizer_name Helsinki-NLP/opus-mt-en-ro --sortish_sampler --do_predict --gpus 1 --freeze_encoder --n_train 40000 --n_val 500 --n_test 500 --fp16_opt_level O1 --num_sanity_val_steps 0 --eval_beams 2 """.split() # XXX: args.gpus > 1 : handle multi_gpu in the future lowerCAmelCase_ :Any = ["""finetune.py"""] + bash_script.split() + args with patch.object(__A , """argv""" , __A ): lowerCAmelCase_ :Optional[int] = argparse.ArgumentParser() lowerCAmelCase_ :str = pl.Trainer.add_argparse_args(__A ) lowerCAmelCase_ :Any = SummarizationModule.add_model_specific_args(__A , os.getcwd() ) lowerCAmelCase_ :List[Any] = parser.parse_args() lowerCAmelCase_ :Any = main(__A ) # Check metrics lowerCAmelCase_ :Tuple = load_json(model.metrics_save_path ) lowerCAmelCase_ :Union[str, Any] = metrics["""val"""][0] lowerCAmelCase_ :Any = metrics["""val"""][-1] self.assertEqual(len(metrics["""val"""] ) , (args.max_epochs / args.val_check_interval) ) assert isinstance(last_step_stats[f"""val_avg_{model.val_metric}"""] , __A ) self.assertGreater(last_step_stats["""val_avg_gen_time"""] , 0.0_1 ) # model hanging on generate. Maybe bad config was saved. (XXX: old comment/assert?) self.assertLessEqual(last_step_stats["""val_avg_gen_time"""] , 1.0 ) # test learning requirements: # 1. BLEU improves over the course of training by more than 2 pts self.assertGreater(last_step_stats["""val_avg_bleu"""] - first_step_stats["""val_avg_bleu"""] , 2 ) # 2. BLEU finishes above 17 self.assertGreater(last_step_stats["""val_avg_bleu"""] , 17 ) # 3. test BLEU and val BLEU within ~1.1 pt. self.assertLess(abs(metrics["""val"""][-1]["""val_avg_bleu"""] - metrics["""test"""][-1]["""test_avg_bleu"""] ) , 1.1 ) # check lightning ckpt can be loaded and has a reasonable statedict lowerCAmelCase_ :Optional[Any] = os.listdir(__A ) lowerCAmelCase_ :Any = [x for x in contents if x.endswith(""".ckpt""" )][0] lowerCAmelCase_ :int = os.path.join(args.output_dir , __A ) lowerCAmelCase_ :str = torch.load(__A , map_location="""cpu""" ) lowerCAmelCase_ :Union[str, Any] = """model.model.decoder.layers.0.encoder_attn_layer_norm.weight""" assert expected_key in ckpt["state_dict"] assert ckpt["state_dict"]["model.model.decoder.layers.0.encoder_attn_layer_norm.weight"].dtype == torch.floataa # TODO: turn on args.do_predict when PL bug fixed. if args.do_predict: lowerCAmelCase_ :int = {os.path.basename(__A ) for p in contents} assert "test_generations.txt" in contents assert "test_results.txt" in contents # assert len(metrics["val"]) == desired_n_evals assert len(metrics["""test"""] ) == 1 class _SCREAMING_SNAKE_CASE ( A__ ): @timeout_decorator.timeout(600 ) @slow @require_torch_gpu def __lowerCAmelCase ( self ) -> int: lowerCAmelCase_ :Dict = f"""{self.test_file_dir_str}/test_data/wmt_en_ro""" lowerCAmelCase_ :Any = { """--fp16_opt_level=O1""": """""", """$MAX_LEN""": 128, """$BS""": 16, """$GAS""": 1, """$ENRO_DIR""": data_dir, """$m""": """sshleifer/student_marian_en_ro_6_1""", """val_check_interval=0.25""": """val_check_interval=1.0""", } # Clean up bash script lowerCAmelCase_ :Dict = ( (self.test_file_dir / """distil_marian_no_teacher.sh""").open().read().split("""distillation.py""" )[1].strip() ) lowerCAmelCase_ :str = bash_script.replace("""\\\n""" , """""" ).strip().replace("""\"$@\"""" , """""" ) lowerCAmelCase_ :Optional[Any] = bash_script.replace("""--fp16 """ , """ """ ) for k, v in env_vars_to_replace.items(): lowerCAmelCase_ :str = bash_script.replace(__A , str(__A ) ) lowerCAmelCase_ :Dict = self.get_auto_remove_tmp_dir() lowerCAmelCase_ :List[Any] = bash_script.replace("""--fp16""" , """""" ) lowerCAmelCase_ :Dict = 6 lowerCAmelCase_ :Any = ( ["""distillation.py"""] + bash_script.split() + [ f"""--output_dir={output_dir}""", """--gpus=1""", """--learning_rate=1e-3""", f"""--num_train_epochs={epochs}""", """--warmup_steps=10""", """--val_check_interval=1.0""", """--do_predict""", ] ) with patch.object(__A , """argv""" , __A ): lowerCAmelCase_ :Dict = argparse.ArgumentParser() lowerCAmelCase_ :int = pl.Trainer.add_argparse_args(__A ) lowerCAmelCase_ :int = SummarizationDistiller.add_model_specific_args(__A , os.getcwd() ) lowerCAmelCase_ :Any = parser.parse_args() # assert args.gpus == gpus THIS BREAKS for multi_gpu lowerCAmelCase_ :Optional[Any] = distill_main(__A ) # Check metrics lowerCAmelCase_ :Union[str, Any] = load_json(model.metrics_save_path ) lowerCAmelCase_ :List[Any] = metrics["""val"""][0] lowerCAmelCase_ :str = metrics["""val"""][-1] assert len(metrics["""val"""] ) >= (args.max_epochs / args.val_check_interval) # +1 accounts for val_sanity_check assert last_step_stats["val_avg_gen_time"] >= 0.0_1 assert first_step_stats["val_avg_bleu"] < last_step_stats["val_avg_bleu"] # model learned nothing assert 1.0 >= last_step_stats["val_avg_gen_time"] # model hanging on generate. Maybe bad config was saved. assert isinstance(last_step_stats[f"""val_avg_{model.val_metric}"""] , __A ) # check lightning ckpt can be loaded and has a reasonable statedict lowerCAmelCase_ :int = os.listdir(__A ) lowerCAmelCase_ :str = [x for x in contents if x.endswith(""".ckpt""" )][0] lowerCAmelCase_ :List[str] = os.path.join(args.output_dir , __A ) lowerCAmelCase_ :Optional[Any] = torch.load(__A , map_location="""cpu""" ) lowerCAmelCase_ :Tuple = """model.model.decoder.layers.0.encoder_attn_layer_norm.weight""" assert expected_key in ckpt["state_dict"] assert ckpt["state_dict"]["model.model.decoder.layers.0.encoder_attn_layer_norm.weight"].dtype == torch.floataa # TODO: turn on args.do_predict when PL bug fixed. if args.do_predict: lowerCAmelCase_ :Tuple = {os.path.basename(__A ) for p in contents} assert "test_generations.txt" in contents assert "test_results.txt" in contents # assert len(metrics["val"]) == desired_n_evals assert len(metrics["""test"""] ) == 1
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'''simple docstring''' import os import tempfile import unittest import numpy as np from diffusers.utils import is_flax_available from diffusers.utils.testing_utils import require_flax, slow if is_flax_available(): import jax import jax.numpy as jnp from flax.jax_utils import replicate from flax.training.common_utils import shard from diffusers import FlaxDDIMScheduler, FlaxDiffusionPipeline, FlaxStableDiffusionPipeline @require_flax class SCREAMING_SNAKE_CASE( unittest.TestCase ): """simple docstring""" def A ( self : str ) -> int: with tempfile.TemporaryDirectory() as tmpdirname: # pipeline has Flax weights UpperCAmelCase : Optional[Any] = FlaxDiffusionPipeline.from_pretrained( '''hf-internal-testing/tiny-stable-diffusion-pipe''' , safety_checker=__snake_case , cache_dir=__snake_case ) UpperCAmelCase : str = [t[-1] for t in os.walk(os.path.join(__snake_case , os.listdir(__snake_case )[0] , '''snapshots''' ) )] UpperCAmelCase : str = [item for sublist in all_root_files for item in sublist] # None of the downloaded files should be a PyTorch file even if we have some here: # https://huggingface.co/hf-internal-testing/tiny-stable-diffusion-pipe/blob/main/unet/diffusion_pytorch_model.bin assert not any(f.endswith('''.bin''' ) for f in files ) @slow @require_flax class SCREAMING_SNAKE_CASE( unittest.TestCase ): """simple docstring""" def A ( self : List[str] ) -> Dict: UpperCAmelCase , UpperCAmelCase : str = FlaxStableDiffusionPipeline.from_pretrained( '''hf-internal-testing/tiny-stable-diffusion-pipe''' , safety_checker=__snake_case ) UpperCAmelCase : List[Any] = ( '''A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of''' ''' field, close up, split lighting, cinematic''' ) UpperCAmelCase : List[str] = jax.random.PRNGKey(0 ) UpperCAmelCase : Optional[Any] = 4 UpperCAmelCase : Optional[Any] = jax.device_count() UpperCAmelCase : Tuple = num_samples * [prompt] UpperCAmelCase : int = pipeline.prepare_inputs(__snake_case ) # shard inputs and rng UpperCAmelCase : Tuple = replicate(__snake_case ) UpperCAmelCase : Any = jax.random.split(__snake_case , __snake_case ) UpperCAmelCase : Optional[Any] = shard(__snake_case ) UpperCAmelCase : Optional[int] = pipeline(__snake_case , __snake_case , __snake_case , __snake_case , jit=__snake_case ).images assert images.shape == (num_samples, 1, 64, 64, 3) if jax.device_count() == 8: assert np.abs(np.abs(images[0, 0, :2, :2, -2:] , dtype=np.floataa ).sum() - 4.1_51_47_45 ) < 1E-3 assert np.abs(np.abs(__snake_case , dtype=np.floataa ).sum() - 4_99_47.8_75 ) < 5E-1 UpperCAmelCase : Union[str, Any] = pipeline.numpy_to_pil(np.asarray(images.reshape((num_samples,) + images.shape[-3:] ) ) ) assert len(__snake_case ) == num_samples def A ( self : List[Any] ) -> List[str]: UpperCAmelCase , UpperCAmelCase : Union[str, Any] = FlaxStableDiffusionPipeline.from_pretrained( '''CompVis/stable-diffusion-v1-4''' , revision='''flax''' , safety_checker=__snake_case ) UpperCAmelCase : Dict = ( '''A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of''' ''' field, close up, split lighting, cinematic''' ) UpperCAmelCase : Optional[Any] = jax.random.PRNGKey(0 ) UpperCAmelCase : Any = 50 UpperCAmelCase : Union[str, Any] = jax.device_count() UpperCAmelCase : int = num_samples * [prompt] UpperCAmelCase : Union[str, Any] = pipeline.prepare_inputs(__snake_case ) # shard inputs and rng UpperCAmelCase : Dict = replicate(__snake_case ) UpperCAmelCase : int = jax.random.split(__snake_case , __snake_case ) UpperCAmelCase : Tuple = shard(__snake_case ) UpperCAmelCase : Tuple = pipeline(__snake_case , __snake_case , __snake_case , __snake_case , jit=__snake_case ).images assert images.shape == (num_samples, 1, 512, 512, 3) if jax.device_count() == 8: assert np.abs((np.abs(images[0, 0, :2, :2, -2:] , dtype=np.floataa ).sum() - 0.05_65_24_01) ) < 1E-3 assert np.abs((np.abs(__snake_case , dtype=np.floataa ).sum() - 2_38_38_08.2) ) < 5E-1 def A ( self : int ) -> Dict: UpperCAmelCase , UpperCAmelCase : List[str] = FlaxStableDiffusionPipeline.from_pretrained( '''CompVis/stable-diffusion-v1-4''' , revision='''bf16''' , dtype=jnp.bfloataa , safety_checker=__snake_case ) UpperCAmelCase : Dict = ( '''A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of''' ''' field, close up, split lighting, cinematic''' ) UpperCAmelCase : Union[str, Any] = jax.random.PRNGKey(0 ) UpperCAmelCase : List[str] = 50 UpperCAmelCase : Union[str, Any] = jax.device_count() UpperCAmelCase : List[Any] = num_samples * [prompt] UpperCAmelCase : int = pipeline.prepare_inputs(__snake_case ) # shard inputs and rng UpperCAmelCase : Tuple = replicate(__snake_case ) UpperCAmelCase : List[Any] = jax.random.split(__snake_case , __snake_case ) UpperCAmelCase : Optional[int] = shard(__snake_case ) UpperCAmelCase : Optional[Any] = pipeline(__snake_case , __snake_case , __snake_case , __snake_case , jit=__snake_case ).images assert images.shape == (num_samples, 1, 512, 512, 3) if jax.device_count() == 8: assert np.abs((np.abs(images[0, 0, :2, :2, -2:] , dtype=np.floataa ).sum() - 0.04_00_39_06) ) < 1E-3 assert np.abs((np.abs(__snake_case , dtype=np.floataa ).sum() - 2_37_35_16.75) ) < 5E-1 def A ( self : int ) -> Any: UpperCAmelCase , UpperCAmelCase : Dict = FlaxStableDiffusionPipeline.from_pretrained( '''CompVis/stable-diffusion-v1-4''' , revision='''bf16''' , dtype=jnp.bfloataa ) UpperCAmelCase : List[str] = ( '''A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of''' ''' field, close up, split lighting, cinematic''' ) UpperCAmelCase : List[str] = jax.random.PRNGKey(0 ) UpperCAmelCase : Union[str, Any] = 50 UpperCAmelCase : Optional[int] = jax.device_count() UpperCAmelCase : List[str] = num_samples * [prompt] UpperCAmelCase : Dict = pipeline.prepare_inputs(__snake_case ) # shard inputs and rng UpperCAmelCase : Tuple = replicate(__snake_case ) UpperCAmelCase : Any = jax.random.split(__snake_case , __snake_case ) UpperCAmelCase : str = shard(__snake_case ) UpperCAmelCase : Optional[int] = pipeline(__snake_case , __snake_case , __snake_case , __snake_case , jit=__snake_case ).images assert images.shape == (num_samples, 1, 512, 512, 3) if jax.device_count() == 8: assert np.abs((np.abs(images[0, 0, :2, :2, -2:] , dtype=np.floataa ).sum() - 0.04_00_39_06) ) < 1E-3 assert np.abs((np.abs(__snake_case , dtype=np.floataa ).sum() - 2_37_35_16.75) ) < 5E-1 def A ( self : Tuple ) -> Optional[Any]: UpperCAmelCase : int = FlaxDDIMScheduler( beta_start=0.0_00_85 , beta_end=0.0_12 , beta_schedule='''scaled_linear''' , set_alpha_to_one=__snake_case , steps_offset=1 , ) UpperCAmelCase , UpperCAmelCase : Tuple = FlaxStableDiffusionPipeline.from_pretrained( '''CompVis/stable-diffusion-v1-4''' , revision='''bf16''' , dtype=jnp.bfloataa , scheduler=__snake_case , safety_checker=__snake_case , ) UpperCAmelCase : Tuple = scheduler.create_state() UpperCAmelCase : Dict = scheduler_state UpperCAmelCase : str = ( '''A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of''' ''' field, close up, split lighting, cinematic''' ) UpperCAmelCase : int = jax.random.PRNGKey(0 ) UpperCAmelCase : Union[str, Any] = 50 UpperCAmelCase : Optional[Any] = jax.device_count() UpperCAmelCase : Any = num_samples * [prompt] UpperCAmelCase : Dict = pipeline.prepare_inputs(__snake_case ) # shard inputs and rng UpperCAmelCase : str = replicate(__snake_case ) UpperCAmelCase : List[str] = jax.random.split(__snake_case , __snake_case ) UpperCAmelCase : Optional[int] = shard(__snake_case ) UpperCAmelCase : Dict = pipeline(__snake_case , __snake_case , __snake_case , __snake_case , jit=__snake_case ).images assert images.shape == (num_samples, 1, 512, 512, 3) if jax.device_count() == 8: assert np.abs((np.abs(images[0, 0, :2, :2, -2:] , dtype=np.floataa ).sum() - 0.0_45_04_39_45) ) < 1E-3 assert np.abs((np.abs(__snake_case , dtype=np.floataa ).sum() - 2_34_76_93.5) ) < 5E-1 def A ( self : Any ) -> Tuple: UpperCAmelCase : List[Any] = ( '''A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of''' ''' field, close up, split lighting, cinematic''' ) UpperCAmelCase : Union[str, Any] = jax.device_count() UpperCAmelCase : List[Any] = num_samples * [prompt] UpperCAmelCase : str = jax.random.split(jax.random.PRNGKey(0 ) , __snake_case ) UpperCAmelCase , UpperCAmelCase : Tuple = FlaxStableDiffusionPipeline.from_pretrained( '''CompVis/stable-diffusion-v1-4''' , revision='''bf16''' , dtype=jnp.bfloataa , safety_checker=__snake_case , ) UpperCAmelCase : Dict = replicate(__snake_case ) UpperCAmelCase : Optional[Any] = pipeline.prepare_inputs(__snake_case ) UpperCAmelCase : List[str] = shard(__snake_case ) UpperCAmelCase : Any = pipeline(__snake_case , __snake_case , __snake_case , jit=__snake_case ).images assert images.shape == (num_samples, 1, 512, 512, 3) UpperCAmelCase : Optional[int] = images[2, 0, 256, 10:17, 1] # With memory efficient attention UpperCAmelCase , UpperCAmelCase : Any = FlaxStableDiffusionPipeline.from_pretrained( '''CompVis/stable-diffusion-v1-4''' , revision='''bf16''' , dtype=jnp.bfloataa , safety_checker=__snake_case , use_memory_efficient_attention=__snake_case , ) UpperCAmelCase : int = replicate(__snake_case ) UpperCAmelCase : int = pipeline.prepare_inputs(__snake_case ) UpperCAmelCase : List[Any] = shard(__snake_case ) UpperCAmelCase : Optional[Any] = pipeline(__snake_case , __snake_case , __snake_case , jit=__snake_case ).images assert images_eff.shape == (num_samples, 1, 512, 512, 3) UpperCAmelCase : int = images[2, 0, 256, 10:17, 1] # I checked the results visually and they are very similar. However, I saw that the max diff is `1` and the `sum` # over the 8 images is exactly `256`, which is very suspicious. Testing a random slice for now. assert abs(slice_eff - slice ).max() < 1E-2
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'''simple docstring''' import re def UpperCamelCase_( snake_case : str ): '''simple docstring''' snake_case_ = re.compile(r"^(\+91[\-\s]?)?[0]?(91)?[789]\d{9}$" ) if match := re.search(snake_case , snake_case ): return match.string == phone return False if __name__ == "__main__": print(indian_phone_validator("+918827897895"))
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'''simple docstring''' import random from .binary_exp_mod import bin_exp_mod def snake_case_ ( _lowerCAmelCase : Tuple , _lowerCAmelCase : Optional[Any]=1000 ) -> int: if n < 2: return False if n % 2 == 0: return n == 2 # this means n is odd UpperCAmelCase : str = n - 1 UpperCAmelCase : List[Any] = 0 while d % 2 == 0: d /= 2 exp += 1 # n - 1=d*(2**exp) UpperCAmelCase : List[str] = 0 while count < prec: UpperCAmelCase : int = random.randint(2 , n - 1 ) UpperCAmelCase : List[str] = bin_exp_mod(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ) if b != 1: UpperCAmelCase : int = True for _ in range(_lowerCAmelCase ): if b == n - 1: UpperCAmelCase : Dict = False break UpperCAmelCase : str = b * b b %= n if flag: return False count += 1 return True if __name__ == "__main__": UpperCamelCase__: Optional[int] = abs(int(input("Enter bound : ").strip())) print("Here's the list of primes:") print(", ".join(str(i) for i in range(n + 1) if is_prime_big(i)))
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"""simple docstring""" from typing import Dict import numpy as np from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging from .base import PIPELINE_INIT_ARGS, GenericTensor, Pipeline, PipelineException if is_tf_available(): import tensorflow as tf from ..tf_utils import stable_softmax if is_torch_available(): import torch lowerCamelCase__ = logging.get_logger(__name__) @add_end_docstrings( _lowerCamelCase , R'\n top_k (`int`, defaults to 5):\n The number of predictions to return.\n targets (`str` or `List[str]`, *optional*):\n When passed, the model will limit the scores to the passed targets instead of looking up in the whole\n vocab. If the provided targets are not in the model vocab, they will be tokenized and the first resulting\n token will be used (with a warning, and that might be slower).\n\n ' , ) class A__ ( _lowerCamelCase): def __lowerCamelCase ( self , _SCREAMING_SNAKE_CASE ): if self.framework == "tf": __lowerCAmelCase : List[Any] = tf.where(input_ids == self.tokenizer.mask_token_id ).numpy() elif self.framework == "pt": __lowerCAmelCase : Tuple = torch.nonzero(input_ids == self.tokenizer.mask_token_id , as_tuple=_SCREAMING_SNAKE_CASE ) else: raise ValueError('Unsupported framework' ) return masked_index def __lowerCamelCase ( self , _SCREAMING_SNAKE_CASE ): __lowerCAmelCase : Union[str, Any] = self.get_masked_index(_SCREAMING_SNAKE_CASE ) __lowerCAmelCase : Dict = np.prod(masked_index.shape ) if numel < 1: raise PipelineException( 'fill-mask' , self.model.base_model_prefix , f"No mask_token ({self.tokenizer.mask_token}) found on the input" , ) def __lowerCamelCase ( self , _SCREAMING_SNAKE_CASE ): if isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ): for model_input in model_inputs: self._ensure_exactly_one_mask_token(model_input['input_ids'][0] ) else: for input_ids in model_inputs["input_ids"]: self._ensure_exactly_one_mask_token(_SCREAMING_SNAKE_CASE ) def __lowerCamelCase ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=None , **_SCREAMING_SNAKE_CASE ): if return_tensors is None: __lowerCAmelCase : Dict = self.framework __lowerCAmelCase : List[Any] = self.tokenizer(_SCREAMING_SNAKE_CASE , return_tensors=_SCREAMING_SNAKE_CASE ) self.ensure_exactly_one_mask_token(_SCREAMING_SNAKE_CASE ) return model_inputs def __lowerCamelCase ( self , _SCREAMING_SNAKE_CASE ): __lowerCAmelCase : Union[str, Any] = self.model(**_SCREAMING_SNAKE_CASE ) __lowerCAmelCase : Optional[Any] = model_inputs['input_ids'] return model_outputs def __lowerCamelCase ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=5 , _SCREAMING_SNAKE_CASE=None ): # Cap top_k if there are targets if target_ids is not None and target_ids.shape[0] < top_k: __lowerCAmelCase : Dict = target_ids.shape[0] __lowerCAmelCase : Tuple = model_outputs['input_ids'][0] __lowerCAmelCase : Union[str, Any] = model_outputs['logits'] if self.framework == "tf": __lowerCAmelCase : List[str] = tf.where(input_ids == self.tokenizer.mask_token_id ).numpy()[:, 0] __lowerCAmelCase : Optional[int] = outputs.numpy() __lowerCAmelCase : Dict = outputs[0, masked_index, :] __lowerCAmelCase : str = stable_softmax(_SCREAMING_SNAKE_CASE , axis=-1 ) if target_ids is not None: __lowerCAmelCase : Optional[Any] = tf.gather_nd(tf.squeeze(_SCREAMING_SNAKE_CASE , 0 ) , target_ids.reshape(-1 , 1 ) ) __lowerCAmelCase : Dict = tf.expand_dims(_SCREAMING_SNAKE_CASE , 0 ) __lowerCAmelCase : Tuple = tf.math.top_k(_SCREAMING_SNAKE_CASE , k=_SCREAMING_SNAKE_CASE ) __lowerCAmelCase , __lowerCAmelCase : Optional[int] = topk.values.numpy(), topk.indices.numpy() else: __lowerCAmelCase : List[Any] = torch.nonzero(input_ids == self.tokenizer.mask_token_id , as_tuple=_SCREAMING_SNAKE_CASE ).squeeze(-1 ) # Fill mask pipeline supports only one ${mask_token} per sample __lowerCAmelCase : List[str] = outputs[0, masked_index, :] __lowerCAmelCase : Tuple = logits.softmax(dim=-1 ) if target_ids is not None: __lowerCAmelCase : Dict = probs[..., target_ids] __lowerCAmelCase , __lowerCAmelCase : Any = probs.topk(_SCREAMING_SNAKE_CASE ) __lowerCAmelCase : Tuple = [] __lowerCAmelCase : int = values.shape[0] == 1 for i, (_values, _predictions) in enumerate(zip(values.tolist() , predictions.tolist() ) ): __lowerCAmelCase : Union[str, Any] = [] for v, p in zip(_values , _predictions ): # Copy is important since we're going to modify this array in place __lowerCAmelCase : Optional[Any] = input_ids.numpy().copy() if target_ids is not None: __lowerCAmelCase : int = target_ids[p].tolist() __lowerCAmelCase : List[Any] = p # Filter padding out: __lowerCAmelCase : Optional[Any] = tokens[np.where(tokens != self.tokenizer.pad_token_id )] # Originally we skip special tokens to give readable output. # For multi masks though, the other [MASK] would be removed otherwise # making the output look odd, so we add them back __lowerCAmelCase : int = self.tokenizer.decode(_SCREAMING_SNAKE_CASE , skip_special_tokens=_SCREAMING_SNAKE_CASE ) __lowerCAmelCase : Optional[int] = {'score': v, 'token': p, 'token_str': self.tokenizer.decode([p] ), 'sequence': sequence} row.append(_SCREAMING_SNAKE_CASE ) result.append(_SCREAMING_SNAKE_CASE ) if single_mask: return result[0] return result def __lowerCamelCase ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=None ): if isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ): __lowerCAmelCase : Union[str, Any] = [targets] try: __lowerCAmelCase : Dict = self.tokenizer.get_vocab() except Exception: __lowerCAmelCase : str = {} __lowerCAmelCase : Any = [] for target in targets: __lowerCAmelCase : Dict = vocab.get(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) if id_ is None: __lowerCAmelCase : List[Any] = self.tokenizer( _SCREAMING_SNAKE_CASE , add_special_tokens=_SCREAMING_SNAKE_CASE , return_attention_mask=_SCREAMING_SNAKE_CASE , return_token_type_ids=_SCREAMING_SNAKE_CASE , max_length=1 , truncation=_SCREAMING_SNAKE_CASE , )['input_ids'] if len(_SCREAMING_SNAKE_CASE ) == 0: logger.warning( f"The specified target token `{target}` does not exist in the model vocabulary. " 'We cannot replace it with anything meaningful, ignoring it' ) continue __lowerCAmelCase : Tuple = input_ids[0] # XXX: If users encounter this pass # it becomes pretty slow, so let's make sure # The warning enables them to fix the input to # get faster performance. logger.warning( f"The specified target token `{target}` does not exist in the model vocabulary. " f"Replacing with `{self.tokenizer.convert_ids_to_tokens(id_ )}`." ) target_ids.append(id_ ) __lowerCAmelCase : List[str] = list(set(_SCREAMING_SNAKE_CASE ) ) if len(_SCREAMING_SNAKE_CASE ) == 0: raise ValueError('At least one target must be provided when passed.' ) __lowerCAmelCase : List[str] = np.array(_SCREAMING_SNAKE_CASE ) return target_ids def __lowerCamelCase ( self , _SCREAMING_SNAKE_CASE=None , _SCREAMING_SNAKE_CASE=None ): __lowerCAmelCase : List[Any] = {} if targets is not None: __lowerCAmelCase : Optional[int] = self.get_target_ids(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) __lowerCAmelCase : Union[str, Any] = target_ids if top_k is not None: __lowerCAmelCase : Tuple = top_k if self.tokenizer.mask_token_id is None: raise PipelineException( 'fill-mask' , self.model.base_model_prefix , 'The tokenizer does not define a `mask_token`.' ) return {}, {}, postprocess_params def __call__( self , _SCREAMING_SNAKE_CASE , *_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE ): __lowerCAmelCase : Tuple = super().__call__(_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE ) if isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) and len(_SCREAMING_SNAKE_CASE ) == 1: return outputs[0] return outputs
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'''simple docstring''' import argparse import json from pathlib import Path import requests import torch from huggingface_hub import cached_download, hf_hub_url from PIL import Image from transformers import DPTConfig, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTImageProcessor from transformers.utils import logging logging.set_verbosity_info() UpperCamelCase__: Optional[int] = logging.get_logger(__name__) def snake_case_ ( _lowerCAmelCase : Optional[int] ) -> Optional[int]: UpperCAmelCase : Tuple = DPTConfig(embedding_type='''hybrid''' ) if "large" in checkpoint_url: UpperCAmelCase : Tuple = 1024 UpperCAmelCase : List[Any] = 4096 UpperCAmelCase : str = 24 UpperCAmelCase : List[Any] = 16 UpperCAmelCase : str = [5, 11, 17, 23] UpperCAmelCase : List[Any] = [256, 512, 1024, 1024] UpperCAmelCase : Tuple = (1, 384, 384) if "nyu" or "midas" in checkpoint_url: UpperCAmelCase : Optional[Any] = 768 UpperCAmelCase : Tuple = [1, 1, 1, 0.5] UpperCAmelCase : int = [256, 512, 768, 768] UpperCAmelCase : Any = 150 UpperCAmelCase : Tuple = 16 UpperCAmelCase : Any = (1, 384, 384) UpperCAmelCase : Optional[Any] = False UpperCAmelCase : Tuple = '''project''' if "ade" in checkpoint_url: UpperCAmelCase : Any = True UpperCAmelCase : str = 768 UpperCAmelCase : Optional[int] = [1, 1, 1, 0.5] UpperCAmelCase : List[Any] = 150 UpperCAmelCase : List[Any] = 16 UpperCAmelCase : str = '''huggingface/label-files''' UpperCAmelCase : Tuple = '''ade20k-id2label.json''' UpperCAmelCase : Any = json.load(open(cached_download(hf_hub_url(_lowerCAmelCase , _lowerCAmelCase , repo_type='''dataset''' ) ) , '''r''' ) ) UpperCAmelCase : Optional[Any] = {int(_lowerCAmelCase ): v for k, v in idalabel.items()} UpperCAmelCase : List[Any] = idalabel UpperCAmelCase : Optional[int] = {v: k for k, v in idalabel.items()} UpperCAmelCase : Union[str, Any] = [1, 150, 480, 480] return config, expected_shape def snake_case_ ( _lowerCAmelCase : Union[str, Any] ) -> int: UpperCAmelCase : List[str] = ['''pretrained.model.head.weight''', '''pretrained.model.head.bias'''] for k in ignore_keys: state_dict.pop(_lowerCAmelCase , _lowerCAmelCase ) def snake_case_ ( _lowerCAmelCase : Tuple ) -> Any: if ( "pretrained.model" in name and "cls_token" not in name and "pos_embed" not in name and "patch_embed" not in name ): UpperCAmelCase : Tuple = name.replace('''pretrained.model''' , '''dpt.encoder''' ) if "pretrained.model" in name: UpperCAmelCase : Union[str, Any] = name.replace('''pretrained.model''' , '''dpt.embeddings''' ) if "patch_embed" in name: UpperCAmelCase : int = name.replace('''patch_embed''' , '''''' ) if "pos_embed" in name: UpperCAmelCase : Tuple = name.replace('''pos_embed''' , '''position_embeddings''' ) if "attn.proj" in name: UpperCAmelCase : Any = name.replace('''attn.proj''' , '''attention.output.dense''' ) if "proj" in name and "project" not in name: UpperCAmelCase : str = name.replace('''proj''' , '''projection''' ) if "blocks" in name: UpperCAmelCase : Any = name.replace('''blocks''' , '''layer''' ) if "mlp.fc1" in name: UpperCAmelCase : Optional[int] = name.replace('''mlp.fc1''' , '''intermediate.dense''' ) if "mlp.fc2" in name: UpperCAmelCase : Optional[Any] = name.replace('''mlp.fc2''' , '''output.dense''' ) if "norm1" in name and "backbone" not in name: UpperCAmelCase : Dict = name.replace('''norm1''' , '''layernorm_before''' ) if "norm2" in name and "backbone" not in name: UpperCAmelCase : Tuple = name.replace('''norm2''' , '''layernorm_after''' ) if "scratch.output_conv" in name: UpperCAmelCase : Tuple = name.replace('''scratch.output_conv''' , '''head''' ) if "scratch" in name: UpperCAmelCase : str = name.replace('''scratch''' , '''neck''' ) if "layer1_rn" in name: UpperCAmelCase : Dict = name.replace('''layer1_rn''' , '''convs.0''' ) if "layer2_rn" in name: UpperCAmelCase : int = name.replace('''layer2_rn''' , '''convs.1''' ) if "layer3_rn" in name: UpperCAmelCase : Tuple = name.replace('''layer3_rn''' , '''convs.2''' ) if "layer4_rn" in name: UpperCAmelCase : int = name.replace('''layer4_rn''' , '''convs.3''' ) if "refinenet" in name: UpperCAmelCase : List[str] = int(name[len('''neck.refinenet''' ) : len('''neck.refinenet''' ) + 1] ) # tricky here: we need to map 4 to 0, 3 to 1, 2 to 2 and 1 to 3 UpperCAmelCase : str = name.replace(f"""refinenet{layer_idx}""" , f"""fusion_stage.layers.{abs(layer_idx-4 )}""" ) if "out_conv" in name: UpperCAmelCase : List[str] = name.replace('''out_conv''' , '''projection''' ) if "resConfUnit1" in name: UpperCAmelCase : Union[str, Any] = name.replace('''resConfUnit1''' , '''residual_layer1''' ) if "resConfUnit2" in name: UpperCAmelCase : Any = name.replace('''resConfUnit2''' , '''residual_layer2''' ) if "conv1" in name: UpperCAmelCase : Optional[int] = name.replace('''conv1''' , '''convolution1''' ) if "conv2" in name: UpperCAmelCase : Tuple = name.replace('''conv2''' , '''convolution2''' ) # readout blocks if "pretrained.act_postprocess1.0.project.0" in name: UpperCAmelCase : Dict = name.replace('''pretrained.act_postprocess1.0.project.0''' , '''neck.reassemble_stage.readout_projects.0.0''' ) if "pretrained.act_postprocess2.0.project.0" in name: UpperCAmelCase : int = name.replace('''pretrained.act_postprocess2.0.project.0''' , '''neck.reassemble_stage.readout_projects.1.0''' ) if "pretrained.act_postprocess3.0.project.0" in name: UpperCAmelCase : Any = name.replace('''pretrained.act_postprocess3.0.project.0''' , '''neck.reassemble_stage.readout_projects.2.0''' ) if "pretrained.act_postprocess4.0.project.0" in name: UpperCAmelCase : Optional[Any] = name.replace('''pretrained.act_postprocess4.0.project.0''' , '''neck.reassemble_stage.readout_projects.3.0''' ) # resize blocks if "pretrained.act_postprocess1.3" in name: UpperCAmelCase : List[Any] = name.replace('''pretrained.act_postprocess1.3''' , '''neck.reassemble_stage.layers.0.projection''' ) if "pretrained.act_postprocess1.4" in name: UpperCAmelCase : Any = name.replace('''pretrained.act_postprocess1.4''' , '''neck.reassemble_stage.layers.0.resize''' ) if "pretrained.act_postprocess2.3" in name: UpperCAmelCase : Optional[int] = name.replace('''pretrained.act_postprocess2.3''' , '''neck.reassemble_stage.layers.1.projection''' ) if "pretrained.act_postprocess2.4" in name: UpperCAmelCase : str = name.replace('''pretrained.act_postprocess2.4''' , '''neck.reassemble_stage.layers.1.resize''' ) if "pretrained.act_postprocess3.3" in name: UpperCAmelCase : List[str] = name.replace('''pretrained.act_postprocess3.3''' , '''neck.reassemble_stage.layers.2.projection''' ) if "pretrained.act_postprocess4.3" in name: UpperCAmelCase : Tuple = name.replace('''pretrained.act_postprocess4.3''' , '''neck.reassemble_stage.layers.3.projection''' ) if "pretrained.act_postprocess4.4" in name: UpperCAmelCase : int = name.replace('''pretrained.act_postprocess4.4''' , '''neck.reassemble_stage.layers.3.resize''' ) if "pretrained" in name: UpperCAmelCase : Optional[int] = name.replace('''pretrained''' , '''dpt''' ) if "bn" in name: UpperCAmelCase : Dict = name.replace('''bn''' , '''batch_norm''' ) if "head" in name: UpperCAmelCase : Any = name.replace('''head''' , '''head.head''' ) if "encoder.norm" in name: UpperCAmelCase : Optional[int] = name.replace('''encoder.norm''' , '''layernorm''' ) if "auxlayer" in name: UpperCAmelCase : Union[str, Any] = name.replace('''auxlayer''' , '''auxiliary_head.head''' ) if "backbone" in name: UpperCAmelCase : List[Any] = name.replace('''backbone''' , '''backbone.bit.encoder''' ) if ".." in name: UpperCAmelCase : Optional[int] = name.replace('''..''' , '''.''' ) if "stem.conv" in name: UpperCAmelCase : Optional[Any] = name.replace('''stem.conv''' , '''bit.embedder.convolution''' ) if "blocks" in name: UpperCAmelCase : Optional[int] = name.replace('''blocks''' , '''layers''' ) if "convolution" in name and "backbone" in name: UpperCAmelCase : List[Any] = name.replace('''convolution''' , '''conv''' ) if "layer" in name and "backbone" in name: UpperCAmelCase : List[str] = name.replace('''layer''' , '''layers''' ) if "backbone.bit.encoder.bit" in name: UpperCAmelCase : List[Any] = name.replace('''backbone.bit.encoder.bit''' , '''backbone.bit''' ) if "embedder.conv" in name: UpperCAmelCase : List[Any] = name.replace('''embedder.conv''' , '''embedder.convolution''' ) if "backbone.bit.encoder.stem.norm" in name: UpperCAmelCase : Tuple = name.replace('''backbone.bit.encoder.stem.norm''' , '''backbone.bit.embedder.norm''' ) return name def snake_case_ ( _lowerCAmelCase : List[str] , _lowerCAmelCase : List[Any] ) -> Optional[Any]: for i in range(config.num_hidden_layers ): # read in weights + bias of input projection layer (in timm, this is a single matrix + bias) UpperCAmelCase : Optional[int] = state_dict.pop(f"""dpt.encoder.layer.{i}.attn.qkv.weight""" ) UpperCAmelCase : Tuple = state_dict.pop(f"""dpt.encoder.layer.{i}.attn.qkv.bias""" ) # next, add query, keys and values (in that order) to the state dict UpperCAmelCase : Tuple = in_proj_weight[: config.hidden_size, :] UpperCAmelCase : int = in_proj_bias[: config.hidden_size] UpperCAmelCase : List[str] = in_proj_weight[ config.hidden_size : config.hidden_size * 2, : ] UpperCAmelCase : List[str] = in_proj_bias[ config.hidden_size : config.hidden_size * 2 ] UpperCAmelCase : str = in_proj_weight[ -config.hidden_size :, : ] UpperCAmelCase : Union[str, Any] = in_proj_bias[-config.hidden_size :] def snake_case_ ( ) -> List[str]: UpperCAmelCase : Optional[int] = '''http://images.cocodataset.org/val2017/000000039769.jpg''' UpperCAmelCase : Optional[int] = Image.open(requests.get(_lowerCAmelCase , stream=_lowerCAmelCase ).raw ) return im @torch.no_grad() def snake_case_ ( _lowerCAmelCase : Optional[Any] , _lowerCAmelCase : Union[str, Any] , _lowerCAmelCase : str , _lowerCAmelCase : Union[str, Any] , _lowerCAmelCase : List[str] ) -> Any: UpperCAmelCase , UpperCAmelCase : int = get_dpt_config(_lowerCAmelCase ) # load original state_dict from URL # state_dict = torch.hub.load_state_dict_from_url(checkpoint_url, map_location="cpu") UpperCAmelCase : List[Any] = torch.load(_lowerCAmelCase , map_location='''cpu''' ) # remove certain keys remove_ignore_keys_(_lowerCAmelCase ) # rename keys for key in state_dict.copy().keys(): UpperCAmelCase : Any = state_dict.pop(_lowerCAmelCase ) UpperCAmelCase : List[Any] = val # read in qkv matrices read_in_q_k_v(_lowerCAmelCase , _lowerCAmelCase ) # load HuggingFace model UpperCAmelCase : Optional[Any] = DPTForSemanticSegmentation(_lowerCAmelCase ) if '''ade''' in checkpoint_url else DPTForDepthEstimation(_lowerCAmelCase ) model.load_state_dict(_lowerCAmelCase ) model.eval() # Check outputs on an image UpperCAmelCase : int = 480 if '''ade''' in checkpoint_url else 384 UpperCAmelCase : List[Any] = DPTImageProcessor(size=_lowerCAmelCase ) UpperCAmelCase : Dict = prepare_img() UpperCAmelCase : Optional[int] = image_processor(_lowerCAmelCase , return_tensors='''pt''' ) # forward pass UpperCAmelCase : Any = model(**_lowerCAmelCase ).logits if '''ade''' in checkpoint_url else model(**_lowerCAmelCase ).predicted_depth if show_prediction: UpperCAmelCase : Dict = ( torch.nn.functional.interpolate( outputs.unsqueeze(1 ) , size=(image.size[1], image.size[0]) , mode='''bicubic''' , align_corners=_lowerCAmelCase , ) .squeeze() .cpu() .numpy() ) Image.fromarray((prediction / prediction.max()) * 255 ).show() if pytorch_dump_folder_path is not None: Path(_lowerCAmelCase ).mkdir(exist_ok=_lowerCAmelCase ) print(f"""Saving model to {pytorch_dump_folder_path}""" ) model.save_pretrained(_lowerCAmelCase ) print(f"""Saving image processor to {pytorch_dump_folder_path}""" ) image_processor.save_pretrained(_lowerCAmelCase ) if push_to_hub: model.push_to_hub('''ybelkada/dpt-hybrid-midas''' ) image_processor.push_to_hub('''ybelkada/dpt-hybrid-midas''' ) if __name__ == "__main__": UpperCamelCase__: Dict = argparse.ArgumentParser() # Required parameters parser.add_argument( "--checkpoint_url", default="https://github.com/intel-isl/DPT/releases/download/1_0/dpt_large-midas-2f21e586.pt", type=str, help="URL of the original DPT checkpoint you'd like to convert.", ) parser.add_argument( "--pytorch_dump_folder_path", default=None, type=str, required=False, help="Path to the output PyTorch model directory.", ) parser.add_argument( "--push_to_hub", action="store_true", ) parser.add_argument( "--model_name", default="dpt-large", type=str, help="Name of the model, in case you're pushing to the hub.", ) parser.add_argument( "--show_prediction", action="store_true", ) UpperCamelCase__: Tuple = parser.parse_args() convert_dpt_checkpoint( args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name, args.show_prediction )
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import argparse import collections import json import os import re import string import sys import numpy as np UpperCamelCase = re.compile(R'''\b(a|an|the)\b''', re.UNICODE) UpperCamelCase = None def lowercase_ ( ): lowercase__ : List[Any] = argparse.ArgumentParser("Official evaluation script for SQuAD version 2.0.") parser.add_argument("data_file" , metavar="data.json" , help="Input data JSON file.") parser.add_argument("pred_file" , metavar="pred.json" , help="Model predictions.") parser.add_argument( "--out-file" , "-o" , metavar="eval.json" , help="Write accuracy metrics to file (default is stdout).") parser.add_argument( "--na-prob-file" , "-n" , metavar="na_prob.json" , help="Model estimates of probability of no answer.") parser.add_argument( "--na-prob-thresh" , "-t" , type=_lowerCamelCase , default=1.0 , help="Predict \"\" if no-answer probability exceeds this (default = 1.0)." , ) parser.add_argument( "--out-image-dir" , "-p" , metavar="out_images" , default=_lowerCamelCase , help="Save precision-recall curves to directory.") parser.add_argument("--verbose" , "-v" , action="store_true") if len(sys.argv) == 1: parser.print_help() sys.exit(1) return parser.parse_args() def lowercase_ ( _lowerCamelCase : List[Any]): lowercase__ : Union[str, Any] = {} for article in dataset: for p in article["paragraphs"]: for qa in p["qas"]: lowercase__ : Optional[int] = bool(qa["answers"]["text"]) return qid_to_has_ans def lowercase_ ( _lowerCamelCase : Union[str, Any]): def remove_articles(_lowerCamelCase : List[str]): return ARTICLES_REGEX.sub(" " , _lowerCamelCase) def white_space_fix(_lowerCamelCase : int): return " ".join(text.split()) def remove_punc(_lowerCamelCase : Tuple): lowercase__ : Any = set(string.punctuation) return "".join(ch for ch in text if ch not in exclude) def lower(_lowerCamelCase : Any): return text.lower() return white_space_fix(remove_articles(remove_punc(lower(_lowerCamelCase)))) def lowercase_ ( _lowerCamelCase : str): if not s: return [] return normalize_answer(_lowerCamelCase).split() def lowercase_ ( _lowerCamelCase : Optional[int] , _lowerCamelCase : Any): return int(normalize_answer(_lowerCamelCase) == normalize_answer(_lowerCamelCase)) def lowercase_ ( _lowerCamelCase : Union[str, Any] , _lowerCamelCase : Optional[Any]): lowercase__ : str = get_tokens(_lowerCamelCase) lowercase__ : int = get_tokens(_lowerCamelCase) lowercase__ : List[Any] = collections.Counter(_lowerCamelCase) & collections.Counter(_lowerCamelCase) lowercase__ : List[Any] = sum(common.values()) if len(_lowerCamelCase) == 0 or len(_lowerCamelCase) == 0: # If either is no-answer, then F1 is 1 if they agree, 0 otherwise return int(gold_toks == pred_toks) if num_same == 0: return 0 lowercase__ : Union[str, Any] = 1.0 * num_same / len(_lowerCamelCase) lowercase__ : Optional[Any] = 1.0 * num_same / len(_lowerCamelCase) lowercase__ : Dict = (2 * precision * recall) / (precision + recall) return fa def lowercase_ ( _lowerCamelCase : Tuple , _lowerCamelCase : Any): lowercase__ : Union[str, Any] = {} lowercase__ : List[str] = {} for article in dataset: for p in article["paragraphs"]: for qa in p["qas"]: lowercase__ : List[Any] = qa["id"] lowercase__ : List[Any] = [t for t in qa["answers"]["text"] if normalize_answer(_lowerCamelCase)] if not gold_answers: # For unanswerable questions, only correct answer is empty string lowercase__ : List[str] = [""] if qid not in preds: print(f'''Missing prediction for {qid}''') continue lowercase__ : Any = preds[qid] # Take max over all gold answers lowercase__ : Tuple = max(compute_exact(_lowerCamelCase , _lowerCamelCase) for a in gold_answers) lowercase__ : Any = max(compute_fa(_lowerCamelCase , _lowerCamelCase) for a in gold_answers) return exact_scores, fa_scores def lowercase_ ( _lowerCamelCase : Union[str, Any] , _lowerCamelCase : Dict , _lowerCamelCase : Any , _lowerCamelCase : Any): lowercase__ : Union[str, Any] = {} for qid, s in scores.items(): lowercase__ : Optional[int] = na_probs[qid] > na_prob_thresh if pred_na: lowercase__ : Dict = float(not qid_to_has_ans[qid]) else: lowercase__ : List[Any] = s return new_scores def lowercase_ ( _lowerCamelCase : Dict , _lowerCamelCase : Union[str, Any] , _lowerCamelCase : Union[str, Any]=None): if not qid_list: lowercase__ : List[str] = len(_lowerCamelCase) return collections.OrderedDict( [ ("exact", 100.0 * sum(exact_scores.values()) / total), ("f1", 100.0 * sum(fa_scores.values()) / total), ("total", total), ]) else: lowercase__ : Union[str, Any] = len(_lowerCamelCase) return collections.OrderedDict( [ ("exact", 100.0 * sum(exact_scores[k] for k in qid_list) / total), ("f1", 100.0 * sum(fa_scores[k] for k in qid_list) / total), ("total", total), ]) def lowercase_ ( _lowerCamelCase : Dict , _lowerCamelCase : Any , _lowerCamelCase : Any): for k in new_eval: lowercase__ : Union[str, Any] = new_eval[k] def lowercase_ ( _lowerCamelCase : Optional[Any] , _lowerCamelCase : Optional[int] , _lowerCamelCase : Tuple , _lowerCamelCase : List[Any]): plt.step(_lowerCamelCase , _lowerCamelCase , color="b" , alpha=0.2 , where="post") plt.fill_between(_lowerCamelCase , _lowerCamelCase , step="post" , alpha=0.2 , color="b") plt.xlabel("Recall") plt.ylabel("Precision") plt.xlim([0.0, 1.05]) plt.ylim([0.0, 1.05]) plt.title(_lowerCamelCase) plt.savefig(_lowerCamelCase) plt.clf() def lowercase_ ( _lowerCamelCase : Any , _lowerCamelCase : List[Any] , _lowerCamelCase : int , _lowerCamelCase : List[Any] , _lowerCamelCase : Dict=None , _lowerCamelCase : Optional[int]=None): lowercase__ : Optional[Any] = sorted(_lowerCamelCase , key=lambda _lowerCamelCase: na_probs[k]) lowercase__ : Optional[int] = 0.0 lowercase__ : str = 1.0 lowercase__ : List[str] = 0.0 lowercase__ : List[str] = [1.0] lowercase__ : Union[str, Any] = [0.0] lowercase__ : Dict = 0.0 for i, qid in enumerate(_lowerCamelCase): if qid_to_has_ans[qid]: true_pos += scores[qid] lowercase__ : str = true_pos / float(i + 1) lowercase__ : List[str] = true_pos / float(_lowerCamelCase) if i == len(_lowerCamelCase) - 1 or na_probs[qid] != na_probs[qid_list[i + 1]]: # i.e., if we can put a threshold after this point avg_prec += cur_p * (cur_r - recalls[-1]) precisions.append(_lowerCamelCase) recalls.append(_lowerCamelCase) if out_image: plot_pr_curve(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase) return {"ap": 100.0 * avg_prec} def lowercase_ ( _lowerCamelCase : str , _lowerCamelCase : Optional[Any] , _lowerCamelCase : int , _lowerCamelCase : Tuple , _lowerCamelCase : Union[str, Any] , _lowerCamelCase : Optional[Any]): if out_image_dir and not os.path.exists(_lowerCamelCase): os.makedirs(_lowerCamelCase) lowercase__ : Union[str, Any] = sum(1 for v in qid_to_has_ans.values() if v) if num_true_pos == 0: return lowercase__ : Tuple = make_precision_recall_eval( _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , out_image=os.path.join(_lowerCamelCase , "pr_exact.png") , title="Precision-Recall curve for Exact Match score" , ) lowercase__ : Union[str, Any] = make_precision_recall_eval( _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , out_image=os.path.join(_lowerCamelCase , "pr_f1.png") , title="Precision-Recall curve for F1 score" , ) lowercase__ : Union[str, Any] = {k: float(_lowerCamelCase) for k, v in qid_to_has_ans.items()} lowercase__ : Optional[int] = make_precision_recall_eval( _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , out_image=os.path.join(_lowerCamelCase , "pr_oracle.png") , title="Oracle Precision-Recall curve (binary task of HasAns vs. NoAns)" , ) merge_eval(_lowerCamelCase , _lowerCamelCase , "pr_exact") merge_eval(_lowerCamelCase , _lowerCamelCase , "pr_f1") merge_eval(_lowerCamelCase , _lowerCamelCase , "pr_oracle") def lowercase_ ( _lowerCamelCase : Tuple , _lowerCamelCase : List[Any] , _lowerCamelCase : Optional[Any] , _lowerCamelCase : List[Any]): if not qid_list: return lowercase__ : List[str] = [na_probs[k] for k in qid_list] lowercase__ : Tuple = np.ones_like(_lowerCamelCase) / float(len(_lowerCamelCase)) plt.hist(_lowerCamelCase , weights=_lowerCamelCase , bins=20 , range=(0.0, 1.0)) plt.xlabel("Model probability of no-answer") plt.ylabel("Proportion of dataset") plt.title(f'''Histogram of no-answer probability: {name}''') plt.savefig(os.path.join(_lowerCamelCase , f'''na_prob_hist_{name}.png''')) plt.clf() def lowercase_ ( _lowerCamelCase : List[str] , _lowerCamelCase : Any , _lowerCamelCase : Any , _lowerCamelCase : Optional[int]): lowercase__ : Dict = sum(1 for k in qid_to_has_ans if not qid_to_has_ans[k]) lowercase__ : Union[str, Any] = num_no_ans lowercase__ : Optional[int] = cur_score lowercase__ : List[str] = 0.0 lowercase__ : List[Any] = sorted(_lowerCamelCase , key=lambda _lowerCamelCase: na_probs[k]) for i, qid in enumerate(_lowerCamelCase): if qid not in scores: continue if qid_to_has_ans[qid]: lowercase__ : List[Any] = scores[qid] else: if preds[qid]: lowercase__ : str = -1 else: lowercase__ : List[Any] = 0 cur_score += diff if cur_score > best_score: lowercase__ : Any = cur_score lowercase__ : Dict = na_probs[qid] return 100.0 * best_score / len(_lowerCamelCase), best_thresh def lowercase_ ( _lowerCamelCase : Optional[int] , _lowerCamelCase : str , _lowerCamelCase : List[str] , _lowerCamelCase : int , _lowerCamelCase : Optional[Any] , _lowerCamelCase : Optional[Any]): lowercase__ , lowercase__ : int = find_best_thresh(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase) lowercase__ , lowercase__ : List[str] = find_best_thresh(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase) lowercase__ : Optional[Any] = best_exact lowercase__ : int = exact_thresh lowercase__ : Tuple = best_fa lowercase__ : Optional[Any] = fa_thresh def lowercase_ ( ): with open(OPTS.data_file) as f: lowercase__ : List[Any] = json.load(_lowerCamelCase) lowercase__ : Union[str, Any] = dataset_json["data"] with open(OPTS.pred_file) as f: lowercase__ : Optional[int] = json.load(_lowerCamelCase) if OPTS.na_prob_file: with open(OPTS.na_prob_file) as f: lowercase__ : Union[str, Any] = json.load(_lowerCamelCase) else: lowercase__ : List[str] = {k: 0.0 for k in preds} lowercase__ : Tuple = make_qid_to_has_ans(_lowerCamelCase) # maps qid to True/False lowercase__ : int = [k for k, v in qid_to_has_ans.items() if v] lowercase__ : str = [k for k, v in qid_to_has_ans.items() if not v] lowercase__ , lowercase__ : Any = get_raw_scores(_lowerCamelCase , _lowerCamelCase) lowercase__ : int = apply_no_ans_threshold(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase , OPTS.na_prob_thresh) lowercase__ : int = apply_no_ans_threshold(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase , OPTS.na_prob_thresh) lowercase__ : Dict = make_eval_dict(_lowerCamelCase , _lowerCamelCase) if has_ans_qids: lowercase__ : Dict = make_eval_dict(_lowerCamelCase , _lowerCamelCase , qid_list=_lowerCamelCase) merge_eval(_lowerCamelCase , _lowerCamelCase , "HasAns") if no_ans_qids: lowercase__ : str = make_eval_dict(_lowerCamelCase , _lowerCamelCase , qid_list=_lowerCamelCase) merge_eval(_lowerCamelCase , _lowerCamelCase , "NoAns") if OPTS.na_prob_file: find_all_best_thresh(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase) if OPTS.na_prob_file and OPTS.out_image_dir: run_precision_recall_analysis(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , OPTS.out_image_dir) histogram_na_prob(_lowerCamelCase , _lowerCamelCase , OPTS.out_image_dir , "hasAns") histogram_na_prob(_lowerCamelCase , _lowerCamelCase , OPTS.out_image_dir , "noAns") if OPTS.out_file: with open(OPTS.out_file , "w") as f: json.dump(_lowerCamelCase , _lowerCamelCase) else: print(json.dumps(_lowerCamelCase , indent=2)) if __name__ == "__main__": UpperCamelCase = parse_args() if OPTS.out_image_dir: import matplotlib matplotlib.use('''Agg''') import matplotlib.pyplot as plt main()
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'''simple docstring''' # coding=utf-8 # Copyright 2020 The HuggingFace Inc. team. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # this script dumps information about the environment import os import sys import transformers UpperCamelCase__: Optional[int] = "3" print("Python version:", sys.version) print("transformers version:", transformers.__version__) try: import torch print("Torch version:", torch.__version__) print("Cuda available:", torch.cuda.is_available()) print("Cuda version:", torch.version.cuda) print("CuDNN version:", torch.backends.cudnn.version()) print("Number of GPUs available:", torch.cuda.device_count()) print("NCCL version:", torch.cuda.nccl.version()) except ImportError: print("Torch version:", None) try: import deepspeed print("DeepSpeed version:", deepspeed.__version__) except ImportError: print("DeepSpeed version:", None) try: import tensorflow as tf print("TensorFlow version:", tf.__version__) print("TF GPUs available:", bool(tf.config.list_physical_devices("GPU"))) print("Number of TF GPUs available:", len(tf.config.list_physical_devices("GPU"))) except ImportError: print("TensorFlow version:", None)
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import unittest import numpy as np def a__ ( A_, A_, A_, A_ = None, ): '''simple docstring''' __magic_name__ = np.shape(A_ ) __magic_name__ = np.shape(A_ ) __magic_name__ = np.shape(A_ ) if shape_a[0] != shape_b[0]: __magic_name__ = ( """Expected the same number of rows for A and B. """ f'''Instead found A of size {shape_a} and B of size {shape_b}''' ) raise ValueError(A_ ) if shape_b[1] != shape_c[1]: __magic_name__ = ( """Expected the same number of columns for B and C. """ f'''Instead found B of size {shape_b} and C of size {shape_c}''' ) raise ValueError(A_ ) __magic_name__ = pseudo_inv if a_inv is None: try: __magic_name__ = np.linalg.inv(A_ ) except np.linalg.LinAlgError: raise ValueError( """Input matrix A is not invertible. Cannot compute Schur complement.""" ) return mat_c - mat_b.T @ a_inv @ mat_b class UpperCAmelCase_ ( unittest.TestCase ): '''simple docstring''' def _lowercase ( self : int ) -> None: """simple docstring""" __magic_name__ = np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]] ) __magic_name__ = np.array([[0, 3], [3, 0], [2, 3]] ) __magic_name__ = np.array([[2, 1], [6, 3]] ) __magic_name__ = schur_complement(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ) __magic_name__ = np.block([[a, b], [b.T, c]] ) __magic_name__ = np.linalg.det(UpperCamelCase__ ) __magic_name__ = np.linalg.det(UpperCamelCase__ ) __magic_name__ = np.linalg.det(UpperCamelCase__ ) self.assertAlmostEqual(UpperCamelCase__ , det_a * det_s ) def _lowercase ( self : List[Any] ) -> None: """simple docstring""" __magic_name__ = np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]] ) __magic_name__ = np.array([[0, 3], [3, 0], [2, 3]] ) __magic_name__ = np.array([[2, 1], [6, 3]] ) with self.assertRaises(UpperCamelCase__ ): schur_complement(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ) def _lowercase ( self : int ) -> None: """simple docstring""" __magic_name__ = np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]] ) __magic_name__ = np.array([[0, 3], [3, 0], [2, 3]] ) __magic_name__ = np.array([[2, 1, 3], [6, 3, 5]] ) with self.assertRaises(UpperCamelCase__ ): schur_complement(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ) if __name__ == "__main__": import doctest doctest.testmod() unittest.main()
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'''simple docstring''' from __future__ import annotations def snake_case_ ( _lowerCAmelCase : list[int | float] , _lowerCAmelCase : int , _lowerCAmelCase : int ) -> int | float: if len(_lowerCAmelCase ) == 0: raise ValueError('''find_max() arg is an empty sequence''' ) if ( left >= len(_lowerCAmelCase ) or left < -len(_lowerCAmelCase ) or right >= len(_lowerCAmelCase ) or right < -len(_lowerCAmelCase ) ): raise IndexError('''list index out of range''' ) if left == right: return nums[left] UpperCAmelCase : List[Any] = (left + right) >> 1 # the middle UpperCAmelCase : Optional[Any] = find_max(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ) # find max in range[left, mid] UpperCAmelCase : Dict = find_max(_lowerCAmelCase , mid + 1 , _lowerCAmelCase ) # find max in range[mid + 1, right] return left_max if left_max >= right_max else right_max if __name__ == "__main__": import doctest doctest.testmod(verbose=True)
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'''simple docstring''' def __lowerCamelCase ( lowerCAmelCase_ = 4000000 ) -> int: _a : Optional[Any] = [0, 1] _a : str = 0 while fib[i] <= n: fib.append(fib[i] + fib[i + 1] ) if fib[i + 2] > n: break i += 1 _a : List[Any] = 0 for j in range(len(lowerCAmelCase_ ) - 1 ): if fib[j] % 2 == 0: total += fib[j] return total if __name__ == "__main__": print(f"""{solution() = }""")
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'''simple docstring''' from typing import List, Optional, Tuple, Union import torch from ...models import UNetaDModel from ...schedulers import ScoreSdeVeScheduler from ...utils import randn_tensor from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput class SCREAMING_SNAKE_CASE( A__ ): """simple docstring""" lowerCamelCase__ = 42 lowerCamelCase__ = 42 def __init__( self : Union[str, Any] , __snake_case : UNetaDModel , __snake_case : ScoreSdeVeScheduler ) -> int: super().__init__() self.register_modules(unet=__snake_case , scheduler=__snake_case ) @torch.no_grad() def __call__( self : Optional[int] , __snake_case : int = 1 , __snake_case : int = 2000 , __snake_case : Optional[Union[torch.Generator, List[torch.Generator]]] = None , __snake_case : Optional[str] = "pil" , __snake_case : bool = True , **__snake_case : Optional[int] , ) -> Union[ImagePipelineOutput, Tuple]: UpperCAmelCase : str = self.unet.config.sample_size UpperCAmelCase : Union[str, Any] = (batch_size, 3, img_size, img_size) UpperCAmelCase : int = self.unet UpperCAmelCase : Any = randn_tensor(__snake_case , generator=__snake_case ) * self.scheduler.init_noise_sigma UpperCAmelCase : List[Any] = sample.to(self.device ) self.scheduler.set_timesteps(__snake_case ) self.scheduler.set_sigmas(__snake_case ) for i, t in enumerate(self.progress_bar(self.scheduler.timesteps ) ): UpperCAmelCase : Any = self.scheduler.sigmas[i] * torch.ones(shape[0] , device=self.device ) # correction step for _ in range(self.scheduler.config.correct_steps ): UpperCAmelCase : Union[str, Any] = self.unet(__snake_case , __snake_case ).sample UpperCAmelCase : Optional[Any] = self.scheduler.step_correct(__snake_case , __snake_case , generator=__snake_case ).prev_sample # prediction step UpperCAmelCase : Optional[Any] = model(__snake_case , __snake_case ).sample UpperCAmelCase : List[str] = self.scheduler.step_pred(__snake_case , __snake_case , __snake_case , generator=__snake_case ) UpperCAmelCase , UpperCAmelCase : Optional[Any] = output.prev_sample, output.prev_sample_mean UpperCAmelCase : int = sample_mean.clamp(0 , 1 ) UpperCAmelCase : Union[str, Any] = sample.cpu().permute(0 , 2 , 3 , 1 ).numpy() if output_type == "pil": UpperCAmelCase : Optional[Any] = self.numpy_to_pil(__snake_case ) if not return_dict: return (sample,) return ImagePipelineOutput(images=__snake_case )
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from typing import List, Optional, Union import numpy as np from ...feature_extraction_sequence_utils import SequenceFeatureExtractor from ...feature_extraction_utils import BatchFeature from ...utils import PaddingStrategy, TensorType, logging __A = logging.get_logger(__name__) class __lowerCAmelCase ( __magic_name__ ): """simple docstring""" snake_case_ = ['''input_values''', '''padding_mask'''] def __init__( self , lowerCamelCase__ = 1 , lowerCamelCase__ = 24_000 , lowerCamelCase__ = 0.0 , lowerCamelCase__ = None , lowerCamelCase__ = None , **lowerCamelCase__ , ) -> Union[str, Any]: '''simple docstring''' super().__init__(feature_size=lowerCamelCase__ , sampling_rate=lowerCamelCase__ , padding_value=lowerCamelCase__ , **lowerCamelCase__ ) __lowerCamelCase = chunk_length_s __lowerCamelCase = overlap @property def lowercase_ ( self ) -> Optional[int]: '''simple docstring''' if self.chunk_length_s is None: return None else: return int(self.chunk_length_s * self.sampling_rate ) @property def lowercase_ ( self ) -> Optional[int]: '''simple docstring''' if self.chunk_length_s is None or self.overlap is None: return None else: return max(1 , int((1.0 - self.overlap) * self.chunk_length ) ) def __call__( self , lowerCamelCase__ , lowerCamelCase__ = None , lowerCamelCase__ = False , lowerCamelCase__ = None , lowerCamelCase__ = None , lowerCamelCase__ = None , ) -> BatchFeature: '''simple docstring''' if sampling_rate is not None: if sampling_rate != self.sampling_rate: raise ValueError( f"""The model corresponding to this feature extractor: {self} was trained using a sampling rate of""" f""" {self.sampling_rate}. Please make sure that the provided audio input was sampled with""" f""" {self.sampling_rate} and not {sampling_rate}.""" ) else: logger.warning( 'It is strongly recommended to pass the `sampling_rate` argument to this function. ' 'Failing to do so can result in silent errors that might be hard to debug.' ) if padding and truncation: raise ValueError('Both padding and truncation were set. Make sure you only set one.' ) elif padding is None: # by default let's pad the inputs __lowerCamelCase = True __lowerCamelCase = bool( isinstance(lowerCamelCase__ , (list, tuple) ) and (isinstance(raw_audio[0] , (np.ndarray, tuple, list) )) ) if is_batched: __lowerCamelCase = [np.asarray(lowerCamelCase__ , dtype=np.floataa ).T for audio in raw_audio] elif not is_batched and not isinstance(lowerCamelCase__ , np.ndarray ): __lowerCamelCase = np.asarray(lowerCamelCase__ , dtype=np.floataa ) elif isinstance(lowerCamelCase__ , np.ndarray ) and raw_audio.dtype is np.dtype(np.floataa ): __lowerCamelCase = raw_audio.astype(np.floataa ) # always return batch if not is_batched: __lowerCamelCase = [np.asarray(lowerCamelCase__ ).T] # verify inputs are valid for idx, example in enumerate(lowerCamelCase__ ): if example.ndim > 2: raise ValueError(f"""Expected input shape (channels, length) but got shape {example.shape}""" ) if self.feature_size == 1 and example.ndim != 1: raise ValueError(f"""Expected mono audio but example has {example.shape[-1]} channels""" ) if self.feature_size == 2 and example.shape[-1] != 2: raise ValueError(f"""Expected stereo audio but example has {example.shape[-1]} channels""" ) __lowerCamelCase = None __lowerCamelCase = BatchFeature({'input_values': raw_audio} ) if self.chunk_stride is not None and self.chunk_length is not None and max_length is None: if truncation: __lowerCamelCase = min(array.shape[0] for array in raw_audio ) __lowerCamelCase = int(np.floor(max_length / self.chunk_stride ) ) __lowerCamelCase = (nb_step - 1) * self.chunk_stride + self.chunk_length elif padding: __lowerCamelCase = max(array.shape[0] for array in raw_audio ) __lowerCamelCase = int(np.ceil(max_length / self.chunk_stride ) ) __lowerCamelCase = (nb_step - 1) * self.chunk_stride + self.chunk_length __lowerCamelCase = 'max_length' else: __lowerCamelCase = input_values # normal padding on batch if padded_inputs is None: __lowerCamelCase = self.pad( lowerCamelCase__ , max_length=lowerCamelCase__ , truncation=lowerCamelCase__ , padding=lowerCamelCase__ , return_attention_mask=lowerCamelCase__ , ) if padding: __lowerCamelCase = padded_inputs.pop('attention_mask' ) __lowerCamelCase = [] for example in padded_inputs.pop('input_values' ): if self.feature_size == 1: __lowerCamelCase = example[..., None] input_values.append(example.T ) __lowerCamelCase = input_values if return_tensors is not None: __lowerCamelCase = padded_inputs.convert_to_tensors(lowerCamelCase__ ) return padded_inputs
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'''simple docstring''' import warnings from contextlib import contextmanager from ....processing_utils import ProcessorMixin class SCREAMING_SNAKE_CASE( A__ ): """simple docstring""" lowerCamelCase__ = """MCTCTFeatureExtractor""" lowerCamelCase__ = """AutoTokenizer""" def __init__( self : Dict , __snake_case : Optional[int] , __snake_case : List[str] ) -> str: super().__init__(__snake_case , __snake_case ) UpperCAmelCase : List[Any] = self.feature_extractor UpperCAmelCase : Union[str, Any] = False def __call__( self : Any , *__snake_case : List[str] , **__snake_case : Any ) -> List[Any]: # For backward compatibility if self._in_target_context_manager: return self.current_processor(*__snake_case , **__snake_case ) if "raw_speech" in kwargs: warnings.warn('''Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead.''' ) UpperCAmelCase : int = kwargs.pop('''raw_speech''' ) else: UpperCAmelCase : Union[str, Any] = kwargs.pop('''audio''' , __snake_case ) UpperCAmelCase : Optional[Any] = kwargs.pop('''sampling_rate''' , __snake_case ) UpperCAmelCase : Dict = kwargs.pop('''text''' , __snake_case ) if len(__snake_case ) > 0: UpperCAmelCase : Any = args[0] UpperCAmelCase : Optional[int] = args[1:] if audio is None and text is None: raise ValueError('''You need to specify either an `audio` or `text` input to process.''' ) if audio is not None: UpperCAmelCase : List[str] = self.feature_extractor(__snake_case , *__snake_case , sampling_rate=__snake_case , **__snake_case ) if text is not None: UpperCAmelCase : int = self.tokenizer(__snake_case , **__snake_case ) if text is None: return inputs elif audio is None: return encodings else: UpperCAmelCase : str = encodings['''input_ids'''] return inputs def A ( self : List[Any] , *__snake_case : List[Any] , **__snake_case : List[Any] ) -> str: return self.tokenizer.batch_decode(*__snake_case , **__snake_case ) def A ( self : List[Any] , *__snake_case : int , **__snake_case : Optional[int] ) -> Any: # For backward compatibility if self._in_target_context_manager: return self.current_processor.pad(*__snake_case , **__snake_case ) UpperCAmelCase : List[Any] = kwargs.pop('''input_features''' , __snake_case ) UpperCAmelCase : Optional[Any] = kwargs.pop('''labels''' , __snake_case ) if len(__snake_case ) > 0: UpperCAmelCase : List[str] = args[0] UpperCAmelCase : List[Any] = args[1:] if input_features is not None: UpperCAmelCase : Tuple = self.feature_extractor.pad(__snake_case , *__snake_case , **__snake_case ) if labels is not None: UpperCAmelCase : Optional[int] = self.tokenizer.pad(__snake_case , **__snake_case ) if labels is None: return input_features elif input_features is None: return labels else: UpperCAmelCase : List[str] = labels['''input_ids'''] return input_features def A ( self : Union[str, Any] , *__snake_case : Optional[Any] , **__snake_case : Optional[int] ) -> Optional[Any]: return self.tokenizer.decode(*__snake_case , **__snake_case ) @contextmanager def A ( self : Any ) -> Optional[int]: warnings.warn( '''`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your ''' '''labels by using the argument `text` of the regular `__call__` method (either in the same call as ''' '''your audio inputs, or in a separate call.''' ) UpperCAmelCase : Dict = True UpperCAmelCase : List[Any] = self.tokenizer yield UpperCAmelCase : Tuple = self.feature_extractor UpperCAmelCase : List[Any] = False
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"""simple docstring""" from ...configuration_utils import PretrainedConfig from ...utils import logging UpperCAmelCase_ : Optional[int] = logging.get_logger(__name__) UpperCAmelCase_ : int = { """uw-madison/mra-base-512-4""": """https://huggingface.co/uw-madison/mra-base-512-4/resolve/main/config.json""", } class lowerCAmelCase__ ( UpperCAmelCase__ ): '''simple docstring''' __UpperCamelCase = "mra" def __init__( self : int , lowercase_ : Union[str, Any]=50265 , lowercase_ : Tuple=768 , lowercase_ : Optional[Any]=12 , lowercase_ : Tuple=12 , lowercase_ : Optional[int]=3072 , lowercase_ : Optional[Any]="gelu" , lowercase_ : Union[str, Any]=0.1 , lowercase_ : Optional[Any]=0.1 , lowercase_ : Optional[Any]=512 , lowercase_ : Union[str, Any]=1 , lowercase_ : Optional[int]=0.02 , lowercase_ : Optional[Any]=1e-5 , lowercase_ : int="absolute" , lowercase_ : Tuple=4 , lowercase_ : Any="full" , lowercase_ : int=0 , lowercase_ : List[Any]=0 , lowercase_ : List[Any]=1 , lowercase_ : Optional[int]=0 , lowercase_ : Optional[Any]=2 , **lowercase_ : Tuple , ): '''simple docstring''' super().__init__(pad_token_id=lowercase_ , bos_token_id=lowercase_ , eos_token_id=lowercase_ , **lowercase_) SCREAMING_SNAKE_CASE_ : Tuple = vocab_size SCREAMING_SNAKE_CASE_ : Union[str, Any] = max_position_embeddings SCREAMING_SNAKE_CASE_ : Tuple = hidden_size SCREAMING_SNAKE_CASE_ : Union[str, Any] = num_hidden_layers SCREAMING_SNAKE_CASE_ : str = num_attention_heads SCREAMING_SNAKE_CASE_ : Any = intermediate_size SCREAMING_SNAKE_CASE_ : List[str] = hidden_act SCREAMING_SNAKE_CASE_ : List[Any] = hidden_dropout_prob SCREAMING_SNAKE_CASE_ : Union[str, Any] = attention_probs_dropout_prob SCREAMING_SNAKE_CASE_ : List[str] = initializer_range SCREAMING_SNAKE_CASE_ : List[str] = type_vocab_size SCREAMING_SNAKE_CASE_ : str = layer_norm_eps SCREAMING_SNAKE_CASE_ : str = position_embedding_type SCREAMING_SNAKE_CASE_ : List[Any] = block_per_row SCREAMING_SNAKE_CASE_ : Any = approx_mode SCREAMING_SNAKE_CASE_ : List[Any] = initial_prior_first_n_blocks SCREAMING_SNAKE_CASE_ : Union[str, Any] = initial_prior_diagonal_n_blocks
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'''simple docstring''' from math import isclose, sqrt def snake_case_ ( _lowerCAmelCase : float , _lowerCAmelCase : float , _lowerCAmelCase : float ) -> tuple[float, float, float]: UpperCAmelCase : Optional[int] = point_y / 4 / point_x UpperCAmelCase : str = 2 * normal_gradient / (1 + normal_gradient * normal_gradient) UpperCAmelCase : Any = (1 - normal_gradient * normal_gradient) / ( 1 + normal_gradient * normal_gradient ) UpperCAmelCase : Union[str, Any] = (sa - ca * incoming_gradient) / (ca + sa * incoming_gradient) # to find the next point, solve the simultaeneous equations: # y^2 + 4x^2 = 100 # y - b = m * (x - a) # ==> A x^2 + B x + C = 0 UpperCAmelCase : Union[str, Any] = outgoing_gradient**2 + 4 UpperCAmelCase : Dict = 2 * outgoing_gradient * (point_y - outgoing_gradient * point_x) UpperCAmelCase : List[Any] = (point_y - outgoing_gradient * point_x) ** 2 - 100 UpperCAmelCase : List[str] = ( -linear_term - sqrt(linear_term**2 - 4 * quadratic_term * constant_term ) ) / (2 * quadratic_term) UpperCAmelCase : Optional[int] = ( -linear_term + sqrt(linear_term**2 - 4 * quadratic_term * constant_term ) ) / (2 * quadratic_term) # two solutions, one of which is our input point UpperCAmelCase : Optional[Any] = x_minus if isclose(_lowerCAmelCase , _lowerCAmelCase ) else x_plus UpperCAmelCase : Union[str, Any] = point_y + outgoing_gradient * (next_x - point_x) return next_x, next_y, outgoing_gradient def snake_case_ ( _lowerCAmelCase : float = 1.4 , _lowerCAmelCase : float = -9.6 ) -> int: UpperCAmelCase : int = 0 UpperCAmelCase : float = first_x_coord UpperCAmelCase : float = first_y_coord UpperCAmelCase : float = (1_0.1 - point_y) / (0.0 - point_x) while not (-0.0_1 <= point_x <= 0.0_1 and point_y > 0): UpperCAmelCase , UpperCAmelCase , UpperCAmelCase : str = next_point(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ) num_reflections += 1 return num_reflections if __name__ == "__main__": print(F"{solution() = }")
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import os from math import logaa def _a ( SCREAMING_SNAKE_CASE_ : str = "base_exp.txt" ): __lowerCAmelCase = 0 __lowerCAmelCase = 0 for i, line in enumerate(open(os.path.join(os.path.dirname(SCREAMING_SNAKE_CASE_ ) , SCREAMING_SNAKE_CASE_ ) ) ): __lowerCAmelCase , __lowerCAmelCase = list(map(SCREAMING_SNAKE_CASE_ , line.split("," ) ) ) if x * logaa(SCREAMING_SNAKE_CASE_ ) > largest: __lowerCAmelCase = x * logaa(SCREAMING_SNAKE_CASE_ ) __lowerCAmelCase = i + 1 return result if __name__ == "__main__": print(solution())
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'''simple docstring''' from typing import TYPE_CHECKING from ...utils import ( OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_tokenizers_available, is_torch_available, ) UpperCamelCase__: str = { "configuration_lxmert": ["LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "LxmertConfig"], "tokenization_lxmert": ["LxmertTokenizer"], } try: if not is_tokenizers_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: UpperCamelCase__: int = ["LxmertTokenizerFast"] try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: UpperCamelCase__: Union[str, Any] = [ "LxmertEncoder", "LxmertForPreTraining", "LxmertForQuestionAnswering", "LxmertModel", "LxmertPreTrainedModel", "LxmertVisualFeatureEncoder", "LxmertXLayer", ] try: if not is_tf_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: UpperCamelCase__: int = [ "TF_LXMERT_PRETRAINED_MODEL_ARCHIVE_LIST", "TFLxmertForPreTraining", "TFLxmertMainLayer", "TFLxmertModel", "TFLxmertPreTrainedModel", "TFLxmertVisualFeatureEncoder", ] if TYPE_CHECKING: from .configuration_lxmert import LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP, LxmertConfig from .tokenization_lxmert import LxmertTokenizer try: if not is_tokenizers_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .tokenization_lxmert_fast import LxmertTokenizerFast try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_lxmert import ( LxmertEncoder, LxmertForPreTraining, LxmertForQuestionAnswering, LxmertModel, LxmertPreTrainedModel, LxmertVisualFeatureEncoder, LxmertXLayer, ) try: if not is_tf_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_tf_lxmert import ( TF_LXMERT_PRETRAINED_MODEL_ARCHIVE_LIST, TFLxmertForPreTraining, TFLxmertMainLayer, TFLxmertModel, TFLxmertPreTrainedModel, TFLxmertVisualFeatureEncoder, ) else: import sys UpperCamelCase__: Tuple = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
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'''simple docstring''' from typing import TYPE_CHECKING from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available _lowercase : Union[str, Any] = { "configuration_pix2struct": [ "PIX2STRUCT_PRETRAINED_CONFIG_ARCHIVE_MAP", "Pix2StructConfig", "Pix2StructTextConfig", "Pix2StructVisionConfig", ], "processing_pix2struct": ["Pix2StructProcessor"], } try: if not is_vision_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: _lowercase : Dict = ["Pix2StructImageProcessor"] try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: _lowercase : List[str] = [ "PIX2STRUCT_PRETRAINED_MODEL_ARCHIVE_LIST", "Pix2StructPreTrainedModel", "Pix2StructForConditionalGeneration", "Pix2StructVisionModel", "Pix2StructTextModel", ] if TYPE_CHECKING: from .configuration_pixastruct import ( PIX2STRUCT_PRETRAINED_CONFIG_ARCHIVE_MAP, PixaStructConfig, PixaStructTextConfig, PixaStructVisionConfig, ) from .processing_pixastruct import PixaStructProcessor try: if not is_vision_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .image_processing_pixastruct import PixaStructImageProcessor try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_pixastruct import ( PIX2STRUCT_PRETRAINED_MODEL_ARCHIVE_LIST, PixaStructForConditionalGeneration, PixaStructPreTrainedModel, PixaStructTextModel, PixaStructVisionModel, ) else: import sys _lowercase : Optional[int] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
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'''simple docstring''' import gc import unittest import numpy as np import torch from transformers import CLIPTextConfig, CLIPTextModel, XLMRobertaTokenizer from diffusers import AltDiffusionPipeline, AutoencoderKL, DDIMScheduler, PNDMScheduler, UNetaDConditionModel from diffusers.pipelines.alt_diffusion.modeling_roberta_series import ( RobertaSeriesConfig, RobertaSeriesModelWithTransformation, ) from diffusers.utils import slow, torch_device from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin enable_full_determinism() class SCREAMING_SNAKE_CASE( A__ , A__ , A__ , unittest.TestCase ): """simple docstring""" lowerCamelCase__ = AltDiffusionPipeline lowerCamelCase__ = TEXT_TO_IMAGE_PARAMS lowerCamelCase__ = TEXT_TO_IMAGE_BATCH_PARAMS lowerCamelCase__ = TEXT_TO_IMAGE_IMAGE_PARAMS lowerCamelCase__ = TEXT_TO_IMAGE_IMAGE_PARAMS def A ( self : Dict ) -> int: torch.manual_seed(0 ) UpperCAmelCase : str = UNetaDConditionModel( block_out_channels=(32, 64) , layers_per_block=2 , sample_size=32 , in_channels=4 , out_channels=4 , down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''') , up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''') , cross_attention_dim=32 , ) UpperCAmelCase : Dict = DDIMScheduler( beta_start=0.0_00_85 , beta_end=0.0_12 , beta_schedule='''scaled_linear''' , clip_sample=__snake_case , set_alpha_to_one=__snake_case , ) torch.manual_seed(0 ) UpperCAmelCase : Union[str, Any] = AutoencoderKL( block_out_channels=[32, 64] , in_channels=3 , out_channels=3 , down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D'''] , up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D'''] , latent_channels=4 , ) # TODO: address the non-deterministic text encoder (fails for save-load tests) # torch.manual_seed(0) # text_encoder_config = RobertaSeriesConfig( # hidden_size=32, # project_dim=32, # intermediate_size=37, # layer_norm_eps=1e-05, # num_attention_heads=4, # num_hidden_layers=5, # vocab_size=5002, # ) # text_encoder = RobertaSeriesModelWithTransformation(text_encoder_config) torch.manual_seed(0 ) UpperCAmelCase : Dict = CLIPTextConfig( bos_token_id=0 , eos_token_id=2 , hidden_size=32 , projection_dim=32 , intermediate_size=37 , layer_norm_eps=1E-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=5002 , ) UpperCAmelCase : List[Any] = CLIPTextModel(__snake_case ) UpperCAmelCase : Optional[Any] = XLMRobertaTokenizer.from_pretrained('''hf-internal-testing/tiny-xlm-roberta''' ) UpperCAmelCase : Optional[int] = 77 UpperCAmelCase : Optional[int] = { '''unet''': unet, '''scheduler''': scheduler, '''vae''': vae, '''text_encoder''': text_encoder, '''tokenizer''': tokenizer, '''safety_checker''': None, '''feature_extractor''': None, } return components def A ( self : Optional[Any] , __snake_case : Dict , __snake_case : List[str]=0 ) -> Union[str, Any]: if str(__snake_case ).startswith('''mps''' ): UpperCAmelCase : str = torch.manual_seed(__snake_case ) else: UpperCAmelCase : Tuple = torch.Generator(device=__snake_case ).manual_seed(__snake_case ) UpperCAmelCase : Dict = { '''prompt''': '''A painting of a squirrel eating a burger''', '''generator''': generator, '''num_inference_steps''': 2, '''guidance_scale''': 6.0, '''output_type''': '''numpy''', } return inputs def A ( self : Union[str, Any] ) -> List[str]: super().test_attention_slicing_forward_pass(expected_max_diff=3E-3 ) def A ( self : Tuple ) -> List[str]: super().test_inference_batch_single_identical(expected_max_diff=3E-3 ) def A ( self : Tuple ) -> Optional[int]: UpperCAmelCase : List[str] = '''cpu''' # ensure determinism for the device-dependent torch.Generator UpperCAmelCase : Any = self.get_dummy_components() torch.manual_seed(0 ) UpperCAmelCase : Optional[int] = RobertaSeriesConfig( hidden_size=32 , project_dim=32 , intermediate_size=37 , layer_norm_eps=1E-05 , num_attention_heads=4 , num_hidden_layers=5 , vocab_size=5002 , ) # TODO: remove after fixing the non-deterministic text encoder UpperCAmelCase : List[str] = RobertaSeriesModelWithTransformation(__snake_case ) UpperCAmelCase : str = text_encoder UpperCAmelCase : Optional[int] = AltDiffusionPipeline(**__snake_case ) UpperCAmelCase : str = alt_pipe.to(__snake_case ) alt_pipe.set_progress_bar_config(disable=__snake_case ) UpperCAmelCase : Optional[int] = self.get_dummy_inputs(__snake_case ) UpperCAmelCase : Optional[int] = '''A photo of an astronaut''' UpperCAmelCase : List[Any] = alt_pipe(**__snake_case ) UpperCAmelCase : Optional[Any] = output.images UpperCAmelCase : str = image[0, -3:, -3:, -1] assert image.shape == (1, 64, 64, 3) UpperCAmelCase : List[str] = np.array( [0.5_74_81_62, 0.60_44_71_45, 0.48_82_12_17, 0.50_10_06_36, 0.5_43_11_85, 0.45_76_36_83, 0.49_65_76_96, 0.48_13_27_33, 0.47_57_30_93] ) assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2 def A ( self : int ) -> Union[str, Any]: UpperCAmelCase : Optional[int] = '''cpu''' # ensure determinism for the device-dependent torch.Generator UpperCAmelCase : int = self.get_dummy_components() UpperCAmelCase : int = PNDMScheduler(skip_prk_steps=__snake_case ) torch.manual_seed(0 ) UpperCAmelCase : int = RobertaSeriesConfig( hidden_size=32 , project_dim=32 , intermediate_size=37 , layer_norm_eps=1E-05 , num_attention_heads=4 , num_hidden_layers=5 , vocab_size=5002 , ) # TODO: remove after fixing the non-deterministic text encoder UpperCAmelCase : Union[str, Any] = RobertaSeriesModelWithTransformation(__snake_case ) UpperCAmelCase : Union[str, Any] = text_encoder UpperCAmelCase : Optional[int] = AltDiffusionPipeline(**__snake_case ) UpperCAmelCase : Dict = alt_pipe.to(__snake_case ) alt_pipe.set_progress_bar_config(disable=__snake_case ) UpperCAmelCase : int = self.get_dummy_inputs(__snake_case ) UpperCAmelCase : Optional[int] = alt_pipe(**__snake_case ) UpperCAmelCase : Optional[int] = output.images UpperCAmelCase : int = image[0, -3:, -3:, -1] assert image.shape == (1, 64, 64, 3) UpperCAmelCase : Optional[int] = np.array( [0.51_60_50_93, 0.5_70_72_41, 0.47_36_55_07, 0.50_57_88_86, 0.5_63_38_77, 0.4_64_25_03, 0.5_18_20_81, 0.48_76_34_84, 0.49_08_42_37] ) assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2 @slow @require_torch_gpu class SCREAMING_SNAKE_CASE( unittest.TestCase ): """simple docstring""" def A ( self : str ) -> Any: # clean up the VRAM after each test super().tearDown() gc.collect() torch.cuda.empty_cache() def A ( self : List[Any] ) -> Any: # make sure here that pndm scheduler skips prk UpperCAmelCase : List[Any] = AltDiffusionPipeline.from_pretrained('''BAAI/AltDiffusion''' , safety_checker=__snake_case ) UpperCAmelCase : Tuple = alt_pipe.to(__snake_case ) alt_pipe.set_progress_bar_config(disable=__snake_case ) UpperCAmelCase : List[Any] = '''A painting of a squirrel eating a burger''' UpperCAmelCase : Any = torch.manual_seed(0 ) UpperCAmelCase : Optional[int] = alt_pipe([prompt] , generator=__snake_case , guidance_scale=6.0 , num_inference_steps=20 , output_type='''np''' ) UpperCAmelCase : Dict = output.images UpperCAmelCase : Optional[Any] = image[0, -3:, -3:, -1] assert image.shape == (1, 512, 512, 3) UpperCAmelCase : List[str] = np.array([0.10_10, 0.08_00, 0.07_94, 0.08_85, 0.08_43, 0.07_62, 0.07_69, 0.07_29, 0.05_86] ) assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2 def A ( self : Tuple ) -> int: UpperCAmelCase : int = DDIMScheduler.from_pretrained('''BAAI/AltDiffusion''' , subfolder='''scheduler''' ) UpperCAmelCase : Tuple = AltDiffusionPipeline.from_pretrained('''BAAI/AltDiffusion''' , scheduler=__snake_case , safety_checker=__snake_case ) UpperCAmelCase : Dict = alt_pipe.to(__snake_case ) alt_pipe.set_progress_bar_config(disable=__snake_case ) UpperCAmelCase : Tuple = '''A painting of a squirrel eating a burger''' UpperCAmelCase : Optional[int] = torch.manual_seed(0 ) UpperCAmelCase : List[Any] = alt_pipe([prompt] , generator=__snake_case , num_inference_steps=2 , output_type='''numpy''' ) UpperCAmelCase : Dict = output.images UpperCAmelCase : Optional[Any] = image[0, -3:, -3:, -1] assert image.shape == (1, 512, 512, 3) UpperCAmelCase : Union[str, Any] = np.array([0.40_19, 0.40_52, 0.38_10, 0.41_19, 0.39_16, 0.39_82, 0.46_51, 0.41_95, 0.53_23] ) assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
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import sys snake_case : int = ( '''73167176531330624919225119674426574742355349194934''' '''96983520312774506326239578318016984801869478851843''' '''85861560789112949495459501737958331952853208805511''' '''12540698747158523863050715693290963295227443043557''' '''66896648950445244523161731856403098711121722383113''' '''62229893423380308135336276614282806444486645238749''' '''30358907296290491560440772390713810515859307960866''' '''70172427121883998797908792274921901699720888093776''' '''65727333001053367881220235421809751254540594752243''' '''52584907711670556013604839586446706324415722155397''' '''53697817977846174064955149290862569321978468622482''' '''83972241375657056057490261407972968652414535100474''' '''82166370484403199890008895243450658541227588666881''' '''16427171479924442928230863465674813919123162824586''' '''17866458359124566529476545682848912883142607690042''' '''24219022671055626321111109370544217506941658960408''' '''07198403850962455444362981230987879927244284909188''' '''84580156166097919133875499200524063689912560717606''' '''05886116467109405077541002256983155200055935729725''' '''71636269561882670428252483600823257530420752963450''' ) def __lowerCamelCase ( UpperCAmelCase_ : str = N ): """simple docstring""" a :Optional[Any] = -sys.maxsize - 1 for i in range(len(UpperCAmelCase_ ) - 12 ): a :Dict = 1 for j in range(13 ): product *= int(n[i + j] ) if product > largest_product: a :str = product return largest_product if __name__ == "__main__": print(F"""{solution() = }""")
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'''simple docstring''' import io import json import fsspec import pytest from datasets import Dataset, DatasetDict, Features, NamedSplit, Value from datasets.io.json import JsonDatasetReader, JsonDatasetWriter from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases def snake_case_ ( _lowerCAmelCase : List[str] , _lowerCAmelCase : Optional[Any] ) -> Optional[int]: assert isinstance(_lowerCAmelCase , _lowerCAmelCase ) assert dataset.num_rows == 4 assert dataset.num_columns == 3 assert dataset.column_names == ["col_1", "col_2", "col_3"] for feature, expected_dtype in expected_features.items(): assert dataset.features[feature].dtype == expected_dtype @pytest.mark.parametrize('''keep_in_memory''' , [False, True] ) def snake_case_ ( _lowerCAmelCase : int , _lowerCAmelCase : Dict , _lowerCAmelCase : Optional[int] ) -> Dict: UpperCAmelCase : Dict = tmp_path / '''cache''' UpperCAmelCase : List[Any] = {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''} with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase(): UpperCAmelCase : str = JsonDatasetReader(_lowerCAmelCase , cache_dir=_lowerCAmelCase , keep_in_memory=_lowerCAmelCase ).read() _check_json_dataset(_lowerCAmelCase , _lowerCAmelCase ) @pytest.mark.parametrize( '''features''' , [ None, {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}, {'''col_1''': '''string''', '''col_2''': '''string''', '''col_3''': '''string'''}, {'''col_1''': '''int32''', '''col_2''': '''int32''', '''col_3''': '''int32'''}, {'''col_1''': '''float32''', '''col_2''': '''float32''', '''col_3''': '''float32'''}, ] , ) def snake_case_ ( _lowerCAmelCase : Optional[int] , _lowerCAmelCase : Dict , _lowerCAmelCase : int ) -> Optional[int]: UpperCAmelCase : Any = tmp_path / '''cache''' UpperCAmelCase : Tuple = {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''} UpperCAmelCase : Any = features.copy() if features else default_expected_features UpperCAmelCase : List[Any] = ( Features({feature: Value(_lowerCAmelCase ) for feature, dtype in features.items()} ) if features is not None else None ) UpperCAmelCase : Dict = JsonDatasetReader(_lowerCAmelCase , features=_lowerCAmelCase , cache_dir=_lowerCAmelCase ).read() _check_json_dataset(_lowerCAmelCase , _lowerCAmelCase ) @pytest.mark.parametrize( '''features''' , [ None, {'''col_3''': '''float64''', '''col_1''': '''string''', '''col_2''': '''int64'''}, ] , ) def snake_case_ ( _lowerCAmelCase : Optional[Any] , _lowerCAmelCase : List[Any] , _lowerCAmelCase : Tuple ) -> Tuple: UpperCAmelCase : Optional[Any] = tmp_path / '''cache''' UpperCAmelCase : Optional[int] = {'''col_3''': '''float64''', '''col_1''': '''string''', '''col_2''': '''int64'''} UpperCAmelCase : int = features.copy() if features else default_expected_features UpperCAmelCase : Any = ( Features({feature: Value(_lowerCAmelCase ) for feature, dtype in features.items()} ) if features is not None else None ) UpperCAmelCase : Tuple = JsonDatasetReader(_lowerCAmelCase , features=_lowerCAmelCase , cache_dir=_lowerCAmelCase ).read() assert isinstance(_lowerCAmelCase , _lowerCAmelCase ) assert dataset.num_rows == 2 assert dataset.num_columns == 3 assert dataset.column_names == ["col_3", "col_1", "col_2"] for feature, expected_dtype in expected_features.items(): assert dataset.features[feature].dtype == expected_dtype def snake_case_ ( _lowerCAmelCase : List[str] , _lowerCAmelCase : Dict ) -> Union[str, Any]: # jsonl_312_path features are {"col_3": "float64", "col_1": "string", "col_2": "int64"} UpperCAmelCase : Tuple = {'''col_2''': '''int64''', '''col_3''': '''float64''', '''col_1''': '''string'''} UpperCAmelCase : List[str] = features.copy() UpperCAmelCase : Union[str, Any] = ( Features({feature: Value(_lowerCAmelCase ) for feature, dtype in features.items()} ) if features is not None else None ) UpperCAmelCase : Tuple = tmp_path / '''cache''' UpperCAmelCase : List[str] = JsonDatasetReader(_lowerCAmelCase , features=_lowerCAmelCase , cache_dir=_lowerCAmelCase ).read() assert isinstance(_lowerCAmelCase , _lowerCAmelCase ) assert dataset.num_rows == 2 assert dataset.num_columns == 3 assert dataset.column_names == ["col_2", "col_3", "col_1"] for feature, expected_dtype in expected_features.items(): assert dataset.features[feature].dtype == expected_dtype @pytest.mark.parametrize('''split''' , [None, NamedSplit('''train''' ), '''train''', '''test'''] ) def snake_case_ ( _lowerCAmelCase : Union[str, Any] , _lowerCAmelCase : Tuple , _lowerCAmelCase : List[str] ) -> Optional[Any]: UpperCAmelCase : Any = tmp_path / '''cache''' UpperCAmelCase : List[Any] = {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''} UpperCAmelCase : List[Any] = JsonDatasetReader(_lowerCAmelCase , cache_dir=_lowerCAmelCase , split=_lowerCAmelCase ).read() _check_json_dataset(_lowerCAmelCase , _lowerCAmelCase ) assert dataset.split == split if split else "train" @pytest.mark.parametrize('''path_type''' , [str, list] ) def snake_case_ ( _lowerCAmelCase : List[Any] , _lowerCAmelCase : str , _lowerCAmelCase : Any ) -> Dict: if issubclass(_lowerCAmelCase , _lowerCAmelCase ): UpperCAmelCase : str = jsonl_path elif issubclass(_lowerCAmelCase , _lowerCAmelCase ): UpperCAmelCase : Dict = [jsonl_path] UpperCAmelCase : int = tmp_path / '''cache''' UpperCAmelCase : Dict = {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''} UpperCAmelCase : Optional[int] = JsonDatasetReader(_lowerCAmelCase , cache_dir=_lowerCAmelCase ).read() _check_json_dataset(_lowerCAmelCase , _lowerCAmelCase ) def snake_case_ ( _lowerCAmelCase : List[str] , _lowerCAmelCase : Dict , _lowerCAmelCase : Dict=("train",) ) -> Union[str, Any]: assert isinstance(_lowerCAmelCase , _lowerCAmelCase ) for split in splits: UpperCAmelCase : List[str] = dataset_dict[split] assert dataset.num_rows == 4 assert dataset.num_columns == 3 assert dataset.column_names == ["col_1", "col_2", "col_3"] for feature, expected_dtype in expected_features.items(): assert dataset.features[feature].dtype == expected_dtype @pytest.mark.parametrize('''keep_in_memory''' , [False, True] ) def snake_case_ ( _lowerCAmelCase : int , _lowerCAmelCase : Optional[Any] , _lowerCAmelCase : str ) -> Any: UpperCAmelCase : Optional[Any] = tmp_path / '''cache''' UpperCAmelCase : List[str] = {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''} with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase(): UpperCAmelCase : Optional[int] = JsonDatasetReader({'''train''': jsonl_path} , cache_dir=_lowerCAmelCase , keep_in_memory=_lowerCAmelCase ).read() _check_json_datasetdict(_lowerCAmelCase , _lowerCAmelCase ) @pytest.mark.parametrize( '''features''' , [ None, {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}, {'''col_1''': '''string''', '''col_2''': '''string''', '''col_3''': '''string'''}, {'''col_1''': '''int32''', '''col_2''': '''int32''', '''col_3''': '''int32'''}, {'''col_1''': '''float32''', '''col_2''': '''float32''', '''col_3''': '''float32'''}, ] , ) def snake_case_ ( _lowerCAmelCase : Optional[Any] , _lowerCAmelCase : Optional[int] , _lowerCAmelCase : Union[str, Any] ) -> int: UpperCAmelCase : Dict = tmp_path / '''cache''' UpperCAmelCase : Dict = {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''} UpperCAmelCase : Optional[int] = features.copy() if features else default_expected_features UpperCAmelCase : Union[str, Any] = ( Features({feature: Value(_lowerCAmelCase ) for feature, dtype in features.items()} ) if features is not None else None ) UpperCAmelCase : Tuple = JsonDatasetReader({'''train''': jsonl_path} , features=_lowerCAmelCase , cache_dir=_lowerCAmelCase ).read() _check_json_datasetdict(_lowerCAmelCase , _lowerCAmelCase ) @pytest.mark.parametrize('''split''' , [None, NamedSplit('''train''' ), '''train''', '''test'''] ) def snake_case_ ( _lowerCAmelCase : Any , _lowerCAmelCase : Optional[int] , _lowerCAmelCase : Dict ) -> Union[str, Any]: if split: UpperCAmelCase : Optional[int] = {split: jsonl_path} else: UpperCAmelCase : Any = '''train''' UpperCAmelCase : Any = {'''train''': jsonl_path, '''test''': jsonl_path} UpperCAmelCase : Tuple = tmp_path / '''cache''' UpperCAmelCase : int = {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''} UpperCAmelCase : Optional[Any] = JsonDatasetReader(_lowerCAmelCase , cache_dir=_lowerCAmelCase ).read() _check_json_datasetdict(_lowerCAmelCase , _lowerCAmelCase , splits=list(path.keys() ) ) assert all(dataset[split].split == split for split in path.keys() ) def snake_case_ ( _lowerCAmelCase : List[str] ) -> str: return json.load(_lowerCAmelCase ) def snake_case_ ( _lowerCAmelCase : Dict ) -> str: return [json.loads(_lowerCAmelCase ) for line in buffer] class SCREAMING_SNAKE_CASE: """simple docstring""" @pytest.mark.parametrize('''lines, load_json_function''' , [(True, load_json_lines), (False, load_json)] ) def A ( self : Union[str, Any] , __snake_case : Tuple , __snake_case : Optional[Any] , __snake_case : Optional[int] ) -> Dict: with io.BytesIO() as buffer: JsonDatasetWriter(__snake_case , __snake_case , lines=__snake_case ).write() buffer.seek(0 ) UpperCAmelCase : Union[str, Any] = load_json_function(__snake_case ) assert isinstance(__snake_case , __snake_case ) assert isinstance(exported_content[0] , __snake_case ) assert len(__snake_case ) == 10 @pytest.mark.parametrize( '''orient, container, keys, len_at''' , [ ('''records''', list, {'''tokens''', '''labels''', '''answers''', '''id'''}, None), ('''split''', dict, {'''columns''', '''data'''}, '''data'''), ('''index''', dict, set('''0123456789''' ), None), ('''columns''', dict, {'''tokens''', '''labels''', '''answers''', '''id'''}, '''tokens'''), ('''values''', list, None, None), ('''table''', dict, {'''schema''', '''data'''}, '''data'''), ] , ) def A ( self : Optional[int] , __snake_case : Optional[Any] , __snake_case : str , __snake_case : Optional[Any] , __snake_case : List[str] , __snake_case : Optional[Any] ) -> List[Any]: with io.BytesIO() as buffer: JsonDatasetWriter(__snake_case , __snake_case , lines=__snake_case , orient=__snake_case ).write() buffer.seek(0 ) UpperCAmelCase : Union[str, Any] = load_json(__snake_case ) assert isinstance(__snake_case , __snake_case ) if keys: if container is dict: assert exported_content.keys() == keys else: assert exported_content[0].keys() == keys else: assert not hasattr(__snake_case , '''keys''' ) and not hasattr(exported_content[0] , '''keys''' ) if len_at: assert len(exported_content[len_at] ) == 10 else: assert len(__snake_case ) == 10 @pytest.mark.parametrize('''lines, load_json_function''' , [(True, load_json_lines), (False, load_json)] ) def A ( self : str , __snake_case : str , __snake_case : str , __snake_case : int ) -> Any: with io.BytesIO() as buffer: JsonDatasetWriter(__snake_case , __snake_case , lines=__snake_case , num_proc=2 ).write() buffer.seek(0 ) UpperCAmelCase : Any = load_json_function(__snake_case ) assert isinstance(__snake_case , __snake_case ) assert isinstance(exported_content[0] , __snake_case ) assert len(__snake_case ) == 10 @pytest.mark.parametrize( '''orient, container, keys, len_at''' , [ ('''records''', list, {'''tokens''', '''labels''', '''answers''', '''id'''}, None), ('''split''', dict, {'''columns''', '''data'''}, '''data'''), ('''index''', dict, set('''0123456789''' ), None), ('''columns''', dict, {'''tokens''', '''labels''', '''answers''', '''id'''}, '''tokens'''), ('''values''', list, None, None), ('''table''', dict, {'''schema''', '''data'''}, '''data'''), ] , ) def A ( self : Any , __snake_case : int , __snake_case : Union[str, Any] , __snake_case : Optional[int] , __snake_case : Optional[Any] , __snake_case : List[str] ) -> Any: with io.BytesIO() as buffer: JsonDatasetWriter(__snake_case , __snake_case , lines=__snake_case , orient=__snake_case , num_proc=2 ).write() buffer.seek(0 ) UpperCAmelCase : List[str] = load_json(__snake_case ) assert isinstance(__snake_case , __snake_case ) if keys: if container is dict: assert exported_content.keys() == keys else: assert exported_content[0].keys() == keys else: assert not hasattr(__snake_case , '''keys''' ) and not hasattr(exported_content[0] , '''keys''' ) if len_at: assert len(exported_content[len_at] ) == 10 else: assert len(__snake_case ) == 10 def A ( self : List[Any] , __snake_case : str ) -> Dict: with pytest.raises(__snake_case ): with io.BytesIO() as buffer: JsonDatasetWriter(__snake_case , __snake_case , num_proc=0 ) @pytest.mark.parametrize('''compression, extension''' , [('''gzip''', '''gz'''), ('''bz2''', '''bz2'''), ('''xz''', '''xz''')] ) def A ( self : Optional[int] , __snake_case : Any , __snake_case : str , __snake_case : Union[str, Any] , __snake_case : Union[str, Any] , __snake_case : Dict ) -> Union[str, Any]: UpperCAmelCase : List[str] = tmp_path_factory.mktemp('''data''' ) / F"""test.json.{extension}""" UpperCAmelCase : List[Any] = str(shared_datadir / F"""test_file.json.{extension}""" ) JsonDatasetWriter(__snake_case , __snake_case , compression=__snake_case ).write() with fsspec.open(__snake_case , '''rb''' , compression='''infer''' ) as f: UpperCAmelCase : str = f.read() with fsspec.open(__snake_case , '''rb''' , compression='''infer''' ) as f: UpperCAmelCase : Optional[int] = f.read() assert exported_content == original_content
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import shutil import tempfile import unittest import numpy as np import pytest from transformers.testing_utils import require_vision from transformers.utils import is_vision_available if is_vision_available(): from PIL import Image from transformers import AutoProcessor, BertTokenizer, BlipImageProcessor, BlipProcessor, PreTrainedTokenizerFast @require_vision class __lowerCAmelCase ( unittest.TestCase): def _lowercase ( self ) -> Optional[Any]: '''simple docstring''' a__ : List[Any] =tempfile.mkdtemp() a__ : List[Any] =BlipImageProcessor() a__ : int =BertTokenizer.from_pretrained("hf-internal-testing/tiny-random-BertModel" ) a__ : Optional[Any] =BlipProcessor(lowerCAmelCase__ , lowerCAmelCase__ ) processor.save_pretrained(self.tmpdirname ) def _lowercase ( self , **lowerCAmelCase__ ) -> List[Any]: '''simple docstring''' return AutoProcessor.from_pretrained(self.tmpdirname , **lowerCAmelCase__ ).tokenizer def _lowercase ( self , **lowerCAmelCase__ ) -> List[Any]: '''simple docstring''' return AutoProcessor.from_pretrained(self.tmpdirname , **lowerCAmelCase__ ).image_processor def _lowercase ( self ) -> Any: '''simple docstring''' shutil.rmtree(self.tmpdirname ) def _lowercase ( self ) -> Optional[Any]: '''simple docstring''' a__ : Union[str, Any] =[np.random.randint(2_5_5 , size=(3, 3_0, 4_0_0) , dtype=np.uinta )] a__ : Union[str, Any] =[Image.fromarray(np.moveaxis(lowerCAmelCase__ , 0 , -1 ) ) for x in image_inputs] return image_inputs def _lowercase ( self ) -> Union[str, Any]: '''simple docstring''' a__ : List[str] =BlipProcessor(tokenizer=self.get_tokenizer() , image_processor=self.get_image_processor() ) processor.save_pretrained(self.tmpdirname ) a__ : List[str] =self.get_tokenizer(bos_token="(BOS)" , eos_token="(EOS)" ) a__ : Union[str, Any] =self.get_image_processor(do_normalize=lowerCAmelCase__ , padding_value=1.0 ) a__ : Optional[Any] =BlipProcessor.from_pretrained( self.tmpdirname , bos_token="(BOS)" , eos_token="(EOS)" , do_normalize=lowerCAmelCase__ , padding_value=1.0 ) self.assertEqual(processor.tokenizer.get_vocab() , tokenizer_add_kwargs.get_vocab() ) self.assertIsInstance(processor.tokenizer , lowerCAmelCase__ ) self.assertEqual(processor.image_processor.to_json_string() , image_processor_add_kwargs.to_json_string() ) self.assertIsInstance(processor.image_processor , lowerCAmelCase__ ) def _lowercase ( self ) -> Union[str, Any]: '''simple docstring''' a__ : str =self.get_image_processor() a__ : int =self.get_tokenizer() a__ : Optional[Any] =BlipProcessor(tokenizer=lowerCAmelCase__ , image_processor=lowerCAmelCase__ ) a__ : Dict =self.prepare_image_inputs() a__ : List[str] =image_processor(lowerCAmelCase__ , return_tensors="np" ) a__ : Union[str, Any] =processor(images=lowerCAmelCase__ , return_tensors="np" ) for key in input_feat_extract.keys(): self.assertAlmostEqual(input_feat_extract[key].sum() , input_processor[key].sum() , delta=1E-2 ) def _lowercase ( self ) -> Optional[Any]: '''simple docstring''' a__ : int =self.get_image_processor() a__ : int =self.get_tokenizer() a__ : Dict =BlipProcessor(tokenizer=lowerCAmelCase__ , image_processor=lowerCAmelCase__ ) a__ : Dict ="lower newer" a__ : Any =processor(text=lowerCAmelCase__ ) a__ : Optional[int] =tokenizer(lowerCAmelCase__ , return_token_type_ids=lowerCAmelCase__ ) for key in encoded_tok.keys(): self.assertListEqual(encoded_tok[key] , encoded_processor[key] ) def _lowercase ( self ) -> List[Any]: '''simple docstring''' a__ : List[Any] =self.get_image_processor() a__ : Any =self.get_tokenizer() a__ : Dict =BlipProcessor(tokenizer=lowerCAmelCase__ , image_processor=lowerCAmelCase__ ) a__ : List[str] ="lower newer" a__ : List[str] =self.prepare_image_inputs() a__ : Union[str, Any] =processor(text=lowerCAmelCase__ , images=lowerCAmelCase__ ) self.assertListEqual(list(inputs.keys() ) , ["pixel_values", "input_ids", "attention_mask"] ) # test if it raises when no input is passed with pytest.raises(lowerCAmelCase__ ): processor() def _lowercase ( self ) -> int: '''simple docstring''' a__ : Optional[int] =self.get_image_processor() a__ : Tuple =self.get_tokenizer() a__ : Optional[Any] =BlipProcessor(tokenizer=lowerCAmelCase__ , image_processor=lowerCAmelCase__ ) a__ : Optional[Any] =[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]] a__ : List[str] =processor.batch_decode(lowerCAmelCase__ ) a__ : Dict =tokenizer.batch_decode(lowerCAmelCase__ ) self.assertListEqual(lowerCAmelCase__ , lowerCAmelCase__ ) def _lowercase ( self ) -> Optional[int]: '''simple docstring''' a__ : int =self.get_image_processor() a__ : str =self.get_tokenizer() a__ : Union[str, Any] =BlipProcessor(tokenizer=lowerCAmelCase__ , image_processor=lowerCAmelCase__ ) a__ : Optional[Any] ="lower newer" a__ : int =self.prepare_image_inputs() a__ : Optional[int] =processor(text=lowerCAmelCase__ , images=lowerCAmelCase__ ) # For now the processor supports only ['pixel_values', 'input_ids', 'attention_mask'] self.assertListEqual(list(inputs.keys() ) , ["pixel_values", "input_ids", "attention_mask"] )
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'''simple docstring''' import os from shutil import copyfile from typing import Any, Dict, List, Optional, Tuple import sentencepiece as spm from ...tokenization_utils import AddedToken, PreTrainedTokenizer from ...utils import logging UpperCamelCase__: Tuple = logging.get_logger(__name__) UpperCamelCase__: Optional[int] = {"vocab_file": "sentencepiece.bpe.model"} UpperCamelCase__: Optional[int] = { "vocab_file": { "moussaKam/mbarthez": "https://huggingface.co/moussaKam/mbarthez/resolve/main/sentencepiece.bpe.model", "moussaKam/barthez": "https://huggingface.co/moussaKam/barthez/resolve/main/sentencepiece.bpe.model", "moussaKam/barthez-orangesum-title": ( "https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/sentencepiece.bpe.model" ), }, } UpperCamelCase__: Dict = { "moussaKam/mbarthez": 1024, "moussaKam/barthez": 1024, "moussaKam/barthez-orangesum-title": 1024, } UpperCamelCase__: Tuple = "▁" class SCREAMING_SNAKE_CASE( A__ ): """simple docstring""" lowerCamelCase__ = VOCAB_FILES_NAMES lowerCamelCase__ = PRETRAINED_VOCAB_FILES_MAP lowerCamelCase__ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES lowerCamelCase__ = ["""input_ids""", """attention_mask"""] def __init__( self : List[Any] , __snake_case : List[Any] , __snake_case : Tuple="<s>" , __snake_case : List[Any]="</s>" , __snake_case : int="</s>" , __snake_case : Any="<s>" , __snake_case : Optional[int]="<unk>" , __snake_case : Union[str, Any]="<pad>" , __snake_case : Union[str, Any]="<mask>" , __snake_case : Optional[Dict[str, Any]] = None , **__snake_case : Dict , ) -> None: # Mask token behave like a normal word, i.e. include the space before it UpperCAmelCase : int = AddedToken(__snake_case , lstrip=__snake_case , rstrip=__snake_case ) if isinstance(__snake_case , __snake_case ) else mask_token UpperCAmelCase : Union[str, Any] = {} if sp_model_kwargs is None else sp_model_kwargs super().__init__( bos_token=__snake_case , eos_token=__snake_case , unk_token=__snake_case , sep_token=__snake_case , cls_token=__snake_case , pad_token=__snake_case , mask_token=__snake_case , sp_model_kwargs=self.sp_model_kwargs , **__snake_case , ) UpperCAmelCase : Optional[int] = vocab_file UpperCAmelCase : int = spm.SentencePieceProcessor(**self.sp_model_kwargs ) self.sp_model.Load(str(__snake_case ) ) UpperCAmelCase : int = {'''<s>''': 0, '''<pad>''': 1, '''</s>''': 2, '''<unk>''': 3} UpperCAmelCase : Optional[Any] = len(self.sp_model ) - 1 UpperCAmelCase : Optional[int] = {v: k for k, v in self.fairseq_tokens_to_ids.items()} def A ( self : Tuple , __snake_case : List[int] , __snake_case : Optional[List[int]] = None ) -> List[int]: if token_ids_a is None: return [self.cls_token_id] + token_ids_a + [self.sep_token_id] UpperCAmelCase : Union[str, Any] = [self.cls_token_id] UpperCAmelCase : Optional[Any] = [self.sep_token_id] return cls + token_ids_a + sep + sep + token_ids_a + sep def A ( self : int , __snake_case : List[int] , __snake_case : Optional[List[int]] = None , __snake_case : bool = False ) -> List[int]: if already_has_special_tokens: return super().get_special_tokens_mask( token_ids_a=__snake_case , token_ids_a=__snake_case , already_has_special_tokens=__snake_case ) if token_ids_a is None: return [1] + ([0] * len(__snake_case )) + [1] return [1] + ([0] * len(__snake_case )) + [1, 1] + ([0] * len(__snake_case )) + [1] def A ( self : Optional[int] , __snake_case : List[int] , __snake_case : Optional[List[int]] = None ) -> List[int]: UpperCAmelCase : Tuple = [self.sep_token_id] UpperCAmelCase : Tuple = [self.cls_token_id] if token_ids_a is None: return len(cls + token_ids_a + sep ) * [0] return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0] @property def A ( self : Dict ) -> Optional[int]: return len(self.sp_model ) def A ( self : List[str] ) -> Dict: UpperCAmelCase : Optional[Any] = {self.convert_ids_to_tokens(__snake_case ): i for i in range(self.vocab_size )} vocab.update(self.added_tokens_encoder ) return vocab def A ( self : Optional[Any] , __snake_case : str ) -> List[str]: return self.sp_model.encode(__snake_case , out_type=__snake_case ) def A ( self : int , __snake_case : int ) -> int: if token in self.fairseq_tokens_to_ids: return self.fairseq_tokens_to_ids[token] UpperCAmelCase : Optional[Any] = self.sp_model.PieceToId(__snake_case ) return spm_id if spm_id else self.unk_token_id def A ( self : int , __snake_case : Any ) -> List[Any]: if index in self.fairseq_ids_to_tokens: return self.fairseq_ids_to_tokens[index] return self.sp_model.IdToPiece(__snake_case ) def A ( self : List[Any] , __snake_case : Union[str, Any] ) -> List[str]: UpperCAmelCase : Optional[Any] = [] UpperCAmelCase : int = '''''' UpperCAmelCase : Union[str, Any] = False for token in tokens: # make sure that special tokens are not decoded using sentencepiece model if token in self.all_special_tokens: if not prev_is_special: out_string += " " out_string += self.sp_model.decode(__snake_case ) + token UpperCAmelCase : str = True UpperCAmelCase : List[str] = [] else: current_sub_tokens.append(__snake_case ) UpperCAmelCase : Optional[int] = False out_string += self.sp_model.decode(__snake_case ) return out_string.strip() def __getstate__( self : Union[str, Any] ) -> Union[str, Any]: UpperCAmelCase : Optional[Any] = self.__dict__.copy() UpperCAmelCase : Any = None return state def __setstate__( self : Optional[int] , __snake_case : Union[str, Any] ) -> List[Any]: UpperCAmelCase : Dict = d # for backward compatibility if not hasattr(self , '''sp_model_kwargs''' ): UpperCAmelCase : Optional[Any] = {} UpperCAmelCase : Any = spm.SentencePieceProcessor(**self.sp_model_kwargs ) self.sp_model.Load(self.vocab_file ) def A ( self : Optional[int] , __snake_case : str , __snake_case : Optional[str] = None ) -> Tuple[str]: if not os.path.isdir(__snake_case ): logger.error(F"""Vocabulary path ({save_directory}) should be a directory""" ) return UpperCAmelCase : Union[str, Any] = os.path.join( __snake_case , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] ) if os.path.abspath(self.vocab_file ) != os.path.abspath(__snake_case ) and os.path.isfile(self.vocab_file ): copyfile(self.vocab_file , __snake_case ) elif not os.path.isfile(self.vocab_file ): with open(__snake_case , '''wb''' ) as fi: UpperCAmelCase : Any = self.sp_model.serialized_model_proto() fi.write(__snake_case ) return (out_vocab_file,)
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"""simple docstring""" from typing import TYPE_CHECKING from ...utils import ( OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available, is_vision_available, ) lowercase__ = { """configuration_blip""": [ """BLIP_PRETRAINED_CONFIG_ARCHIVE_MAP""", """BlipConfig""", """BlipTextConfig""", """BlipVisionConfig""", ], """processing_blip""": ["""BlipProcessor"""], } try: if not is_vision_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: lowercase__ = ["""BlipImageProcessor"""] try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: lowercase__ = [ """BLIP_PRETRAINED_MODEL_ARCHIVE_LIST""", """BlipModel""", """BlipPreTrainedModel""", """BlipForConditionalGeneration""", """BlipForQuestionAnswering""", """BlipVisionModel""", """BlipTextModel""", """BlipForImageTextRetrieval""", ] try: if not is_tf_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: lowercase__ = [ """TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST""", """TFBlipModel""", """TFBlipPreTrainedModel""", """TFBlipForConditionalGeneration""", """TFBlipForQuestionAnswering""", """TFBlipVisionModel""", """TFBlipTextModel""", """TFBlipForImageTextRetrieval""", ] if TYPE_CHECKING: from .configuration_blip import BLIP_PRETRAINED_CONFIG_ARCHIVE_MAP, BlipConfig, BlipTextConfig, BlipVisionConfig from .processing_blip import BlipProcessor try: if not is_vision_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .image_processing_blip import BlipImageProcessor try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_blip import ( BLIP_PRETRAINED_MODEL_ARCHIVE_LIST, BlipForConditionalGeneration, BlipForImageTextRetrieval, BlipForQuestionAnswering, BlipModel, BlipPreTrainedModel, BlipTextModel, BlipVisionModel, ) try: if not is_tf_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_tf_blip import ( TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST, TFBlipForConditionalGeneration, TFBlipForImageTextRetrieval, TFBlipForQuestionAnswering, TFBlipModel, TFBlipPreTrainedModel, TFBlipTextModel, TFBlipVisionModel, ) else: import sys lowercase__ = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
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'''simple docstring''' import tempfile import unittest import numpy as np from huggingface_hub import HfFolder, delete_repo from requests.exceptions import HTTPError from transformers import BertConfig, is_flax_available from transformers.testing_utils import TOKEN, USER, is_staging_test, require_flax if is_flax_available(): import os from flax.core.frozen_dict import unfreeze from flax.traverse_util import flatten_dict from transformers import FlaxBertModel UpperCamelCase__: Tuple = "0.12" # assumed parallelism: 8 @require_flax @is_staging_test class SCREAMING_SNAKE_CASE( unittest.TestCase ): """simple docstring""" @classmethod def A ( cls : Union[str, Any] ) -> int: UpperCAmelCase : Optional[Any] = TOKEN HfFolder.save_token(__snake_case ) @classmethod def A ( cls : List[str] ) -> Tuple: try: delete_repo(token=cls._token , repo_id='''test-model-flax''' ) except HTTPError: pass try: delete_repo(token=cls._token , repo_id='''valid_org/test-model-flax-org''' ) except HTTPError: pass def A ( self : int ) -> Tuple: UpperCAmelCase : List[Any] = BertConfig( vocab_size=99 , hidden_size=32 , num_hidden_layers=5 , num_attention_heads=4 , intermediate_size=37 ) UpperCAmelCase : Dict = FlaxBertModel(__snake_case ) model.push_to_hub('''test-model-flax''' , use_auth_token=self._token ) UpperCAmelCase : Tuple = FlaxBertModel.from_pretrained(F"""{USER}/test-model-flax""" ) UpperCAmelCase : List[Any] = flatten_dict(unfreeze(model.params ) ) UpperCAmelCase : Tuple = flatten_dict(unfreeze(new_model.params ) ) for key in base_params.keys(): UpperCAmelCase : Union[str, Any] = (base_params[key] - new_params[key]).sum().item() self.assertLessEqual(__snake_case , 1E-3 , msg=F"""{key} not identical""" ) # Reset repo delete_repo(token=self._token , repo_id='''test-model-flax''' ) # Push to hub via save_pretrained with tempfile.TemporaryDirectory() as tmp_dir: model.save_pretrained(__snake_case , repo_id='''test-model-flax''' , push_to_hub=__snake_case , use_auth_token=self._token ) UpperCAmelCase : str = FlaxBertModel.from_pretrained(F"""{USER}/test-model-flax""" ) UpperCAmelCase : Any = flatten_dict(unfreeze(model.params ) ) UpperCAmelCase : str = flatten_dict(unfreeze(new_model.params ) ) for key in base_params.keys(): UpperCAmelCase : Optional[Any] = (base_params[key] - new_params[key]).sum().item() self.assertLessEqual(__snake_case , 1E-3 , msg=F"""{key} not identical""" ) def A ( self : Optional[Any] ) -> Union[str, Any]: UpperCAmelCase : Dict = BertConfig( vocab_size=99 , hidden_size=32 , num_hidden_layers=5 , num_attention_heads=4 , intermediate_size=37 ) UpperCAmelCase : Optional[Any] = FlaxBertModel(__snake_case ) model.push_to_hub('''valid_org/test-model-flax-org''' , use_auth_token=self._token ) UpperCAmelCase : Union[str, Any] = FlaxBertModel.from_pretrained('''valid_org/test-model-flax-org''' ) UpperCAmelCase : List[Any] = flatten_dict(unfreeze(model.params ) ) UpperCAmelCase : int = flatten_dict(unfreeze(new_model.params ) ) for key in base_params.keys(): UpperCAmelCase : Any = (base_params[key] - new_params[key]).sum().item() self.assertLessEqual(__snake_case , 1E-3 , msg=F"""{key} not identical""" ) # Reset repo delete_repo(token=self._token , repo_id='''valid_org/test-model-flax-org''' ) # Push to hub via save_pretrained with tempfile.TemporaryDirectory() as tmp_dir: model.save_pretrained( __snake_case , repo_id='''valid_org/test-model-flax-org''' , push_to_hub=__snake_case , use_auth_token=self._token ) UpperCAmelCase : str = FlaxBertModel.from_pretrained('''valid_org/test-model-flax-org''' ) UpperCAmelCase : Any = flatten_dict(unfreeze(model.params ) ) UpperCAmelCase : Optional[Any] = flatten_dict(unfreeze(new_model.params ) ) for key in base_params.keys(): UpperCAmelCase : int = (base_params[key] - new_params[key]).sum().item() self.assertLessEqual(__snake_case , 1E-3 , msg=F"""{key} not identical""" ) def snake_case_ ( _lowerCAmelCase : Optional[Any] , _lowerCAmelCase : Any ) -> Union[str, Any]: UpperCAmelCase : str = True UpperCAmelCase : int = flatten_dict(modela.params ) UpperCAmelCase : Dict = flatten_dict(modela.params ) for key in flat_params_a.keys(): if np.sum(np.abs(flat_params_a[key] - flat_params_a[key] ) ) > 1e-4: UpperCAmelCase : Dict = False return models_are_equal @require_flax class SCREAMING_SNAKE_CASE( unittest.TestCase ): """simple docstring""" def A ( self : Tuple ) -> Union[str, Any]: UpperCAmelCase : List[Any] = BertConfig.from_pretrained('''hf-internal-testing/tiny-bert-flax-only''' ) UpperCAmelCase : Dict = FlaxBertModel(__snake_case ) UpperCAmelCase : int = '''bert''' with tempfile.TemporaryDirectory() as tmp_dir: model.save_pretrained(os.path.join(__snake_case , __snake_case ) ) with self.assertRaises(__snake_case ): UpperCAmelCase : Tuple = FlaxBertModel.from_pretrained(__snake_case ) UpperCAmelCase : str = FlaxBertModel.from_pretrained(__snake_case , subfolder=__snake_case ) self.assertTrue(check_models_equal(__snake_case , __snake_case ) ) def A ( self : List[str] ) -> Dict: UpperCAmelCase : Dict = BertConfig.from_pretrained('''hf-internal-testing/tiny-bert-flax-only''' ) UpperCAmelCase : Dict = FlaxBertModel(__snake_case ) UpperCAmelCase : Optional[int] = '''bert''' with tempfile.TemporaryDirectory() as tmp_dir: model.save_pretrained(os.path.join(__snake_case , __snake_case ) , max_shard_size='''10KB''' ) with self.assertRaises(__snake_case ): UpperCAmelCase : Any = FlaxBertModel.from_pretrained(__snake_case ) UpperCAmelCase : Union[str, Any] = FlaxBertModel.from_pretrained(__snake_case , subfolder=__snake_case ) self.assertTrue(check_models_equal(__snake_case , __snake_case ) ) def A ( self : Optional[int] ) -> str: UpperCAmelCase : Dict = '''bert''' UpperCAmelCase : int = '''hf-internal-testing/tiny-random-bert-subfolder''' with self.assertRaises(__snake_case ): UpperCAmelCase : Optional[Any] = FlaxBertModel.from_pretrained(__snake_case ) UpperCAmelCase : Tuple = FlaxBertModel.from_pretrained(__snake_case , subfolder=__snake_case ) self.assertIsNotNone(__snake_case ) def A ( self : Dict ) -> List[Any]: UpperCAmelCase : Optional[int] = '''bert''' UpperCAmelCase : int = '''hf-internal-testing/tiny-random-bert-sharded-subfolder''' with self.assertRaises(__snake_case ): UpperCAmelCase : Dict = FlaxBertModel.from_pretrained(__snake_case ) UpperCAmelCase : Union[str, Any] = FlaxBertModel.from_pretrained(__snake_case , subfolder=__snake_case ) self.assertIsNotNone(__snake_case )
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0
'''simple docstring''' from ..utils import ( OptionalDependencyNotAvailable, is_flax_available, is_scipy_available, is_torch_available, is_torchsde_available, ) try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: from ..utils.dummy_pt_objects import * # noqa F403 else: from .scheduling_consistency_models import CMStochasticIterativeScheduler from .scheduling_ddim import DDIMScheduler from .scheduling_ddim_inverse import DDIMInverseScheduler from .scheduling_ddim_parallel import DDIMParallelScheduler from .scheduling_ddpm import DDPMScheduler from .scheduling_ddpm_parallel import DDPMParallelScheduler from .scheduling_deis_multistep import DEISMultistepScheduler from .scheduling_dpmsolver_multistep import DPMSolverMultistepScheduler from .scheduling_dpmsolver_multistep_inverse import DPMSolverMultistepInverseScheduler from .scheduling_dpmsolver_singlestep import DPMSolverSinglestepScheduler from .scheduling_euler_ancestral_discrete import EulerAncestralDiscreteScheduler from .scheduling_euler_discrete import EulerDiscreteScheduler from .scheduling_heun_discrete import HeunDiscreteScheduler from .scheduling_ipndm import IPNDMScheduler from .scheduling_k_dpm_2_ancestral_discrete import KDPMaAncestralDiscreteScheduler from .scheduling_k_dpm_2_discrete import KDPMaDiscreteScheduler from .scheduling_karras_ve import KarrasVeScheduler from .scheduling_pndm import PNDMScheduler from .scheduling_repaint import RePaintScheduler from .scheduling_sde_ve import ScoreSdeVeScheduler from .scheduling_sde_vp import ScoreSdeVpScheduler from .scheduling_unclip import UnCLIPScheduler from .scheduling_unipc_multistep import UniPCMultistepScheduler from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin from .scheduling_vq_diffusion import VQDiffusionScheduler try: if not is_flax_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: from ..utils.dummy_flax_objects import * # noqa F403 else: from .scheduling_ddim_flax import FlaxDDIMScheduler from .scheduling_ddpm_flax import FlaxDDPMScheduler from .scheduling_dpmsolver_multistep_flax import FlaxDPMSolverMultistepScheduler from .scheduling_karras_ve_flax import FlaxKarrasVeScheduler from .scheduling_lms_discrete_flax import FlaxLMSDiscreteScheduler from .scheduling_pndm_flax import FlaxPNDMScheduler from .scheduling_sde_ve_flax import FlaxScoreSdeVeScheduler from .scheduling_utils_flax import ( FlaxKarrasDiffusionSchedulers, FlaxSchedulerMixin, FlaxSchedulerOutput, broadcast_to_shape_from_left, ) try: if not (is_torch_available() and is_scipy_available()): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: from ..utils.dummy_torch_and_scipy_objects import * # noqa F403 else: from .scheduling_lms_discrete import LMSDiscreteScheduler try: if not (is_torch_available() and is_torchsde_available()): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: from ..utils.dummy_torch_and_torchsde_objects import * # noqa F403 else: from .scheduling_dpmsolver_sde import DPMSolverSDEScheduler
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'''simple docstring''' import unittest from transformers import MraConfig, is_torch_available from transformers.testing_utils import require_torch, slow, torch_device from ...test_configuration_common import ConfigTester from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask if is_torch_available(): import torch from transformers import ( MraForMaskedLM, MraForMultipleChoice, MraForQuestionAnswering, MraForSequenceClassification, MraForTokenClassification, MraModel, ) from transformers.models.mra.modeling_mra import MRA_PRETRAINED_MODEL_ARCHIVE_LIST class SCREAMING_SNAKE_CASE: """simple docstring""" def __init__( self : Optional[int] , __snake_case : str , __snake_case : Union[str, Any]=2 , __snake_case : Optional[int]=8 , __snake_case : Any=True , __snake_case : Union[str, Any]=True , __snake_case : Dict=True , __snake_case : int=True , __snake_case : List[Any]=99 , __snake_case : str=16 , __snake_case : Tuple=5 , __snake_case : Tuple=2 , __snake_case : str=36 , __snake_case : Dict="gelu" , __snake_case : str=0.0 , __snake_case : Optional[int]=0.0 , __snake_case : Optional[int]=512 , __snake_case : Optional[Any]=16 , __snake_case : int=2 , __snake_case : int=0.02 , __snake_case : str=3 , __snake_case : Dict=4 , __snake_case : str=None , ) -> Optional[int]: UpperCAmelCase : Optional[int] = parent UpperCAmelCase : Tuple = batch_size UpperCAmelCase : List[str] = seq_length UpperCAmelCase : List[Any] = is_training UpperCAmelCase : int = use_input_mask UpperCAmelCase : Any = use_token_type_ids UpperCAmelCase : str = use_labels UpperCAmelCase : Union[str, Any] = vocab_size UpperCAmelCase : List[str] = hidden_size UpperCAmelCase : Optional[Any] = num_hidden_layers UpperCAmelCase : Union[str, Any] = num_attention_heads UpperCAmelCase : Optional[Any] = intermediate_size UpperCAmelCase : Union[str, Any] = hidden_act UpperCAmelCase : int = hidden_dropout_prob UpperCAmelCase : Optional[int] = attention_probs_dropout_prob UpperCAmelCase : Union[str, Any] = max_position_embeddings UpperCAmelCase : str = type_vocab_size UpperCAmelCase : List[str] = type_sequence_label_size UpperCAmelCase : Tuple = initializer_range UpperCAmelCase : Optional[Any] = num_labels UpperCAmelCase : Optional[int] = num_choices UpperCAmelCase : Any = scope def A ( self : Tuple ) -> Union[str, Any]: UpperCAmelCase : Optional[int] = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size ) UpperCAmelCase : Optional[int] = None if self.use_input_mask: UpperCAmelCase : Dict = random_attention_mask([self.batch_size, self.seq_length] ) UpperCAmelCase : Dict = None if self.use_token_type_ids: UpperCAmelCase : Union[str, Any] = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size ) UpperCAmelCase : str = None UpperCAmelCase : Tuple = None UpperCAmelCase : int = None if self.use_labels: UpperCAmelCase : str = ids_tensor([self.batch_size] , self.type_sequence_label_size ) UpperCAmelCase : Dict = ids_tensor([self.batch_size, self.seq_length] , self.num_labels ) UpperCAmelCase : str = ids_tensor([self.batch_size] , self.num_choices ) UpperCAmelCase : List[str] = self.get_config() return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels def A ( self : int ) -> Tuple: return MraConfig( vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , is_decoder=__snake_case , initializer_range=self.initializer_range , ) def A ( self : Optional[Any] ) -> Any: UpperCAmelCase : Optional[Any] = self.get_config() UpperCAmelCase : int = 300 return config def A ( self : Optional[Any] ) -> Any: ( ( UpperCAmelCase ) , ( UpperCAmelCase ) , ( UpperCAmelCase ) , ( UpperCAmelCase ) , ( UpperCAmelCase ) , ( UpperCAmelCase ) , ( UpperCAmelCase ) , ) : Tuple = self.prepare_config_and_inputs() UpperCAmelCase : Dict = True UpperCAmelCase : Tuple = floats_tensor([self.batch_size, self.seq_length, self.hidden_size] ) UpperCAmelCase : str = ids_tensor([self.batch_size, self.seq_length] , vocab_size=2 ) return ( config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels, encoder_hidden_states, encoder_attention_mask, ) def A ( self : Union[str, Any] , __snake_case : Union[str, Any] , __snake_case : int , __snake_case : Optional[int] , __snake_case : int , __snake_case : Dict , __snake_case : Tuple , __snake_case : Optional[Any] ) -> List[str]: UpperCAmelCase : int = MraModel(config=__snake_case ) model.to(__snake_case ) model.eval() UpperCAmelCase : Tuple = model(__snake_case , attention_mask=__snake_case , token_type_ids=__snake_case ) UpperCAmelCase : Optional[int] = model(__snake_case , token_type_ids=__snake_case ) UpperCAmelCase : Dict = model(__snake_case ) self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) ) def A ( self : Tuple , __snake_case : Optional[Any] , __snake_case : List[str] , __snake_case : List[str] , __snake_case : int , __snake_case : Union[str, Any] , __snake_case : Optional[Any] , __snake_case : Any , __snake_case : List[Any] , __snake_case : Optional[Any] , ) -> Tuple: UpperCAmelCase : str = True UpperCAmelCase : Tuple = MraModel(__snake_case ) model.to(__snake_case ) model.eval() UpperCAmelCase : Optional[int] = model( __snake_case , attention_mask=__snake_case , token_type_ids=__snake_case , encoder_hidden_states=__snake_case , encoder_attention_mask=__snake_case , ) UpperCAmelCase : Optional[Any] = model( __snake_case , attention_mask=__snake_case , token_type_ids=__snake_case , encoder_hidden_states=__snake_case , ) UpperCAmelCase : str = model(__snake_case , attention_mask=__snake_case , token_type_ids=__snake_case ) self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) ) def A ( self : Tuple , __snake_case : str , __snake_case : Optional[Any] , __snake_case : List[str] , __snake_case : Tuple , __snake_case : Optional[Any] , __snake_case : List[str] , __snake_case : int ) -> Any: UpperCAmelCase : Dict = MraForMaskedLM(config=__snake_case ) model.to(__snake_case ) model.eval() UpperCAmelCase : Optional[int] = model(__snake_case , attention_mask=__snake_case , token_type_ids=__snake_case , labels=__snake_case ) self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) ) def A ( self : Tuple , __snake_case : Tuple , __snake_case : Dict , __snake_case : Dict , __snake_case : Any , __snake_case : int , __snake_case : Optional[Any] , __snake_case : Tuple ) -> Optional[int]: UpperCAmelCase : List[str] = MraForQuestionAnswering(config=__snake_case ) model.to(__snake_case ) model.eval() UpperCAmelCase : List[Any] = model( __snake_case , attention_mask=__snake_case , token_type_ids=__snake_case , start_positions=__snake_case , end_positions=__snake_case , ) self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) ) self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) ) def A ( self : str , __snake_case : Optional[int] , __snake_case : List[str] , __snake_case : str , __snake_case : int , __snake_case : Optional[Any] , __snake_case : Union[str, Any] , __snake_case : List[Any] ) -> int: UpperCAmelCase : int = self.num_labels UpperCAmelCase : Union[str, Any] = MraForSequenceClassification(__snake_case ) model.to(__snake_case ) model.eval() UpperCAmelCase : List[str] = model(__snake_case , attention_mask=__snake_case , token_type_ids=__snake_case , labels=__snake_case ) self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) ) def A ( self : str , __snake_case : Dict , __snake_case : Optional[Any] , __snake_case : Dict , __snake_case : Union[str, Any] , __snake_case : Union[str, Any] , __snake_case : Union[str, Any] , __snake_case : Dict ) -> int: UpperCAmelCase : Tuple = self.num_labels UpperCAmelCase : List[str] = MraForTokenClassification(config=__snake_case ) model.to(__snake_case ) model.eval() UpperCAmelCase : str = model(__snake_case , attention_mask=__snake_case , token_type_ids=__snake_case , labels=__snake_case ) self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) ) def A ( self : str , __snake_case : int , __snake_case : Any , __snake_case : Tuple , __snake_case : Optional[int] , __snake_case : List[str] , __snake_case : str , __snake_case : Union[str, Any] ) -> Optional[Any]: UpperCAmelCase : Tuple = self.num_choices UpperCAmelCase : int = MraForMultipleChoice(config=__snake_case ) model.to(__snake_case ) model.eval() UpperCAmelCase : str = input_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous() UpperCAmelCase : List[Any] = token_type_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous() UpperCAmelCase : List[str] = input_mask.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous() UpperCAmelCase : List[str] = model( __snake_case , attention_mask=__snake_case , token_type_ids=__snake_case , labels=__snake_case , ) self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) ) def A ( self : str ) -> Dict: UpperCAmelCase : Any = self.prepare_config_and_inputs() ( ( UpperCAmelCase ) , ( UpperCAmelCase ) , ( UpperCAmelCase ) , ( UpperCAmelCase ) , ( UpperCAmelCase ) , ( UpperCAmelCase ) , ( UpperCAmelCase ) , ) : List[str] = config_and_inputs UpperCAmelCase : Any = {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask} return config, inputs_dict @require_torch class SCREAMING_SNAKE_CASE( A__ , unittest.TestCase ): """simple docstring""" lowerCamelCase__ = ( ( MraModel, MraForMaskedLM, MraForMultipleChoice, MraForQuestionAnswering, MraForSequenceClassification, MraForTokenClassification, ) if is_torch_available() else () ) lowerCamelCase__ = False lowerCamelCase__ = False lowerCamelCase__ = False lowerCamelCase__ = False lowerCamelCase__ = () def A ( self : int ) -> Union[str, Any]: UpperCAmelCase : List[str] = MraModelTester(self ) UpperCAmelCase : Optional[int] = ConfigTester(self , config_class=__snake_case , hidden_size=37 ) def A ( self : Optional[Any] ) -> str: self.config_tester.run_common_tests() def A ( self : Tuple ) -> Optional[Any]: UpperCAmelCase : Optional[int] = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_model(*__snake_case ) def A ( self : List[Any] ) -> Optional[Any]: UpperCAmelCase : Tuple = self.model_tester.prepare_config_and_inputs() for type in ["absolute", "relative_key", "relative_key_query"]: UpperCAmelCase : List[Any] = type self.model_tester.create_and_check_model(*__snake_case ) def A ( self : Tuple ) -> Dict: UpperCAmelCase : List[Any] = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_masked_lm(*__snake_case ) def A ( self : Tuple ) -> List[str]: UpperCAmelCase : Tuple = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_multiple_choice(*__snake_case ) def A ( self : int ) -> Dict: UpperCAmelCase : Optional[int] = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_question_answering(*__snake_case ) def A ( self : Dict ) -> Optional[int]: UpperCAmelCase : Any = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_sequence_classification(*__snake_case ) def A ( self : Any ) -> Optional[int]: UpperCAmelCase : List[str] = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_token_classification(*__snake_case ) @slow def A ( self : Dict ) -> Any: for model_name in MRA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: UpperCAmelCase : str = MraModel.from_pretrained(__snake_case ) self.assertIsNotNone(__snake_case ) @unittest.skip(reason='''MRA does not output attentions''' ) def A ( self : str ) -> Optional[Any]: return @require_torch class SCREAMING_SNAKE_CASE( unittest.TestCase ): """simple docstring""" @slow def A ( self : Tuple ) -> List[Any]: UpperCAmelCase : int = MraModel.from_pretrained('''uw-madison/mra-base-512-4''' ) UpperCAmelCase : Optional[Any] = torch.arange(256 ).unsqueeze(0 ) with torch.no_grad(): UpperCAmelCase : List[Any] = model(__snake_case )[0] UpperCAmelCase : Optional[Any] = torch.Size((1, 256, 768) ) self.assertEqual(output.shape , __snake_case ) UpperCAmelCase : Any = torch.tensor( [[[-0.01_40, 0.08_30, -0.03_81], [0.15_46, 0.14_02, 0.02_20], [0.11_62, 0.08_51, 0.01_65]]] ) self.assertTrue(torch.allclose(output[:, :3, :3] , __snake_case , atol=1E-4 ) ) @slow def A ( self : Optional[Any] ) -> Any: UpperCAmelCase : Optional[int] = MraForMaskedLM.from_pretrained('''uw-madison/mra-base-512-4''' ) UpperCAmelCase : Dict = torch.arange(256 ).unsqueeze(0 ) with torch.no_grad(): UpperCAmelCase : List[Any] = model(__snake_case )[0] UpperCAmelCase : int = 50265 UpperCAmelCase : int = torch.Size((1, 256, vocab_size) ) self.assertEqual(output.shape , __snake_case ) UpperCAmelCase : Union[str, Any] = torch.tensor( [[[9.25_95, -3.60_38, 11.88_19], [9.38_69, -3.26_93, 11.09_56], [11.85_24, -3.49_38, 13.12_10]]] ) self.assertTrue(torch.allclose(output[:, :3, :3] , __snake_case , atol=1E-4 ) ) @slow def A ( self : str ) -> List[Any]: UpperCAmelCase : List[Any] = MraForMaskedLM.from_pretrained('''uw-madison/mra-base-4096-8-d3''' ) UpperCAmelCase : List[Any] = torch.arange(4096 ).unsqueeze(0 ) with torch.no_grad(): UpperCAmelCase : Tuple = model(__snake_case )[0] UpperCAmelCase : Optional[int] = 50265 UpperCAmelCase : Tuple = torch.Size((1, 4096, vocab_size) ) self.assertEqual(output.shape , __snake_case ) UpperCAmelCase : Optional[int] = torch.tensor( [[[5.47_89, -2.35_64, 7.50_64], [7.90_67, -1.33_69, 9.96_68], [9.07_12, -1.81_06, 7.03_80]]] ) self.assertTrue(torch.allclose(output[:, :3, :3] , __snake_case , atol=1E-4 ) )
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"""simple docstring""" from itertools import product def a_ ( lowerCamelCase , lowerCamelCase ): UpperCAmelCase__ = sides_number UpperCAmelCase__ = max_face_number * dice_number UpperCAmelCase__ = [0] * (max_total + 1) UpperCAmelCase__ = 1 UpperCAmelCase__ = range(lowerCamelCase , max_face_number + 1 ) for dice_numbers in product(lowerCamelCase , repeat=lowerCamelCase ): UpperCAmelCase__ = sum(lowerCamelCase ) totals_frequencies[total] += 1 return totals_frequencies def a_ ( ): UpperCAmelCase__ = total_frequency_distribution( sides_number=4 , dice_number=9 ) UpperCAmelCase__ = total_frequency_distribution( sides_number=6 , dice_number=6 ) UpperCAmelCase__ = 0 UpperCAmelCase__ = 9 UpperCAmelCase__ = 4 * 9 UpperCAmelCase__ = 6 for peter_total in range(lowerCamelCase , max_peter_total + 1 ): peter_wins_count += peter_totals_frequencies[peter_total] * sum( colin_totals_frequencies[min_colin_total:peter_total] ) UpperCAmelCase__ = (4**9) * (6**6) UpperCAmelCase__ = peter_wins_count / total_games_number UpperCAmelCase__ = round(lowerCamelCase , ndigits=7 ) return rounded_peter_win_probability if __name__ == "__main__": print(F"""{solution() = }""")
98
'''simple docstring''' import unittest import numpy as np from transformers.testing_utils import require_flax, require_tf, require_torch from transformers.utils import ( expand_dims, flatten_dict, is_flax_available, is_tf_available, is_torch_available, reshape, squeeze, transpose, ) if is_flax_available(): import jax.numpy as jnp if is_tf_available(): import tensorflow as tf if is_torch_available(): import torch class SCREAMING_SNAKE_CASE( unittest.TestCase ): """simple docstring""" def A ( self : Any ) -> str: UpperCAmelCase : Any = { '''task_specific_params''': { '''summarization''': {'''length_penalty''': 1.0, '''max_length''': 128, '''min_length''': 12, '''num_beams''': 4}, '''summarization_cnn''': {'''length_penalty''': 2.0, '''max_length''': 142, '''min_length''': 56, '''num_beams''': 4}, '''summarization_xsum''': {'''length_penalty''': 1.0, '''max_length''': 62, '''min_length''': 11, '''num_beams''': 6}, } } UpperCAmelCase : int = { '''task_specific_params.summarization.length_penalty''': 1.0, '''task_specific_params.summarization.max_length''': 128, '''task_specific_params.summarization.min_length''': 12, '''task_specific_params.summarization.num_beams''': 4, '''task_specific_params.summarization_cnn.length_penalty''': 2.0, '''task_specific_params.summarization_cnn.max_length''': 142, '''task_specific_params.summarization_cnn.min_length''': 56, '''task_specific_params.summarization_cnn.num_beams''': 4, '''task_specific_params.summarization_xsum.length_penalty''': 1.0, '''task_specific_params.summarization_xsum.max_length''': 62, '''task_specific_params.summarization_xsum.min_length''': 11, '''task_specific_params.summarization_xsum.num_beams''': 6, } self.assertEqual(flatten_dict(__snake_case ) , __snake_case ) def A ( self : int ) -> str: UpperCAmelCase : Any = np.random.randn(3 , 4 ) self.assertTrue(np.allclose(transpose(__snake_case ) , x.transpose() ) ) UpperCAmelCase : str = np.random.randn(3 , 4 , 5 ) self.assertTrue(np.allclose(transpose(__snake_case , axes=(1, 2, 0) ) , x.transpose((1, 2, 0) ) ) ) @require_torch def A ( self : str ) -> Union[str, Any]: UpperCAmelCase : Any = np.random.randn(3 , 4 ) UpperCAmelCase : List[Any] = torch.tensor(__snake_case ) self.assertTrue(np.allclose(transpose(__snake_case ) , transpose(__snake_case ).numpy() ) ) UpperCAmelCase : Tuple = np.random.randn(3 , 4 , 5 ) UpperCAmelCase : Any = torch.tensor(__snake_case ) self.assertTrue(np.allclose(transpose(__snake_case , axes=(1, 2, 0) ) , transpose(__snake_case , axes=(1, 2, 0) ).numpy() ) ) @require_tf def A ( self : List[str] ) -> Optional[Any]: UpperCAmelCase : int = np.random.randn(3 , 4 ) UpperCAmelCase : Optional[int] = tf.constant(__snake_case ) self.assertTrue(np.allclose(transpose(__snake_case ) , transpose(__snake_case ).numpy() ) ) UpperCAmelCase : Union[str, Any] = np.random.randn(3 , 4 , 5 ) UpperCAmelCase : str = tf.constant(__snake_case ) self.assertTrue(np.allclose(transpose(__snake_case , axes=(1, 2, 0) ) , transpose(__snake_case , axes=(1, 2, 0) ).numpy() ) ) @require_flax def A ( self : Tuple ) -> Any: UpperCAmelCase : List[Any] = np.random.randn(3 , 4 ) UpperCAmelCase : List[str] = jnp.array(__snake_case ) self.assertTrue(np.allclose(transpose(__snake_case ) , np.asarray(transpose(__snake_case ) ) ) ) UpperCAmelCase : Dict = np.random.randn(3 , 4 , 5 ) UpperCAmelCase : int = jnp.array(__snake_case ) self.assertTrue(np.allclose(transpose(__snake_case , axes=(1, 2, 0) ) , np.asarray(transpose(__snake_case , axes=(1, 2, 0) ) ) ) ) def A ( self : Optional[Any] ) -> Any: UpperCAmelCase : Union[str, Any] = np.random.randn(3 , 4 ) self.assertTrue(np.allclose(reshape(__snake_case , (4, 3) ) , np.reshape(__snake_case , (4, 3) ) ) ) UpperCAmelCase : Union[str, Any] = np.random.randn(3 , 4 , 5 ) self.assertTrue(np.allclose(reshape(__snake_case , (12, 5) ) , np.reshape(__snake_case , (12, 5) ) ) ) @require_torch def A ( self : Union[str, Any] ) -> int: UpperCAmelCase : Dict = np.random.randn(3 , 4 ) UpperCAmelCase : Optional[Any] = torch.tensor(__snake_case ) self.assertTrue(np.allclose(reshape(__snake_case , (4, 3) ) , reshape(__snake_case , (4, 3) ).numpy() ) ) UpperCAmelCase : List[str] = np.random.randn(3 , 4 , 5 ) UpperCAmelCase : List[Any] = torch.tensor(__snake_case ) self.assertTrue(np.allclose(reshape(__snake_case , (12, 5) ) , reshape(__snake_case , (12, 5) ).numpy() ) ) @require_tf def A ( self : int ) -> List[str]: UpperCAmelCase : List[Any] = np.random.randn(3 , 4 ) UpperCAmelCase : List[str] = tf.constant(__snake_case ) self.assertTrue(np.allclose(reshape(__snake_case , (4, 3) ) , reshape(__snake_case , (4, 3) ).numpy() ) ) UpperCAmelCase : List[Any] = np.random.randn(3 , 4 , 5 ) UpperCAmelCase : Optional[Any] = tf.constant(__snake_case ) self.assertTrue(np.allclose(reshape(__snake_case , (12, 5) ) , reshape(__snake_case , (12, 5) ).numpy() ) ) @require_flax def A ( self : Any ) -> Dict: UpperCAmelCase : Tuple = np.random.randn(3 , 4 ) UpperCAmelCase : Union[str, Any] = jnp.array(__snake_case ) self.assertTrue(np.allclose(reshape(__snake_case , (4, 3) ) , np.asarray(reshape(__snake_case , (4, 3) ) ) ) ) UpperCAmelCase : Any = np.random.randn(3 , 4 , 5 ) UpperCAmelCase : Optional[Any] = jnp.array(__snake_case ) self.assertTrue(np.allclose(reshape(__snake_case , (12, 5) ) , np.asarray(reshape(__snake_case , (12, 5) ) ) ) ) def A ( self : List[Any] ) -> List[Any]: UpperCAmelCase : Union[str, Any] = np.random.randn(1 , 3 , 4 ) self.assertTrue(np.allclose(squeeze(__snake_case ) , np.squeeze(__snake_case ) ) ) UpperCAmelCase : str = np.random.randn(1 , 4 , 1 , 5 ) self.assertTrue(np.allclose(squeeze(__snake_case , axis=2 ) , np.squeeze(__snake_case , axis=2 ) ) ) @require_torch def A ( self : Optional[int] ) -> Union[str, Any]: UpperCAmelCase : Optional[int] = np.random.randn(1 , 3 , 4 ) UpperCAmelCase : List[str] = torch.tensor(__snake_case ) self.assertTrue(np.allclose(squeeze(__snake_case ) , squeeze(__snake_case ).numpy() ) ) UpperCAmelCase : Any = np.random.randn(1 , 4 , 1 , 5 ) UpperCAmelCase : str = torch.tensor(__snake_case ) self.assertTrue(np.allclose(squeeze(__snake_case , axis=2 ) , squeeze(__snake_case , axis=2 ).numpy() ) ) @require_tf def A ( self : Optional[Any] ) -> Dict: UpperCAmelCase : int = np.random.randn(1 , 3 , 4 ) UpperCAmelCase : Optional[int] = tf.constant(__snake_case ) self.assertTrue(np.allclose(squeeze(__snake_case ) , squeeze(__snake_case ).numpy() ) ) UpperCAmelCase : List[str] = np.random.randn(1 , 4 , 1 , 5 ) UpperCAmelCase : Optional[int] = tf.constant(__snake_case ) self.assertTrue(np.allclose(squeeze(__snake_case , axis=2 ) , squeeze(__snake_case , axis=2 ).numpy() ) ) @require_flax def A ( self : List[Any] ) -> Dict: UpperCAmelCase : Optional[int] = np.random.randn(1 , 3 , 4 ) UpperCAmelCase : int = jnp.array(__snake_case ) self.assertTrue(np.allclose(squeeze(__snake_case ) , np.asarray(squeeze(__snake_case ) ) ) ) UpperCAmelCase : str = np.random.randn(1 , 4 , 1 , 5 ) UpperCAmelCase : int = jnp.array(__snake_case ) self.assertTrue(np.allclose(squeeze(__snake_case , axis=2 ) , np.asarray(squeeze(__snake_case , axis=2 ) ) ) ) def A ( self : Optional[Any] ) -> int: UpperCAmelCase : Optional[Any] = np.random.randn(3 , 4 ) self.assertTrue(np.allclose(expand_dims(__snake_case , axis=1 ) , np.expand_dims(__snake_case , axis=1 ) ) ) @require_torch def A ( self : List[str] ) -> Tuple: UpperCAmelCase : Tuple = np.random.randn(3 , 4 ) UpperCAmelCase : Tuple = torch.tensor(__snake_case ) self.assertTrue(np.allclose(expand_dims(__snake_case , axis=1 ) , expand_dims(__snake_case , axis=1 ).numpy() ) ) @require_tf def A ( self : List[str] ) -> Union[str, Any]: UpperCAmelCase : Union[str, Any] = np.random.randn(3 , 4 ) UpperCAmelCase : Any = tf.constant(__snake_case ) self.assertTrue(np.allclose(expand_dims(__snake_case , axis=1 ) , expand_dims(__snake_case , axis=1 ).numpy() ) ) @require_flax def A ( self : Any ) -> List[Any]: UpperCAmelCase : List[str] = np.random.randn(3 , 4 ) UpperCAmelCase : str = jnp.array(__snake_case ) self.assertTrue(np.allclose(expand_dims(__snake_case , axis=1 ) , np.asarray(expand_dims(__snake_case , axis=1 ) ) ) )
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0
import gc import importlib.metadata import tempfile import unittest from packaging import version from transformers import ( AutoModel, AutoModelForCausalLM, AutoModelForSeqaSeqLM, AutoModelForSequenceClassification, AutoTokenizer, BitsAndBytesConfig, pipeline, ) from transformers.testing_utils import ( is_torch_available, require_accelerate, require_bitsandbytes, require_torch, require_torch_gpu, require_torch_multi_gpu, slow, ) def A_ ( A__ ) -> Any: if model.config.model_type == "gpt2": return model.transformer.h[0].mlp.c_fc return model.transformer.h[0].mlp.dense_ah_to_h if is_torch_available(): import torch import torch.nn as nn class A__ ( nn.Module ): """simple docstring""" def __init__( self , lowercase , lowercase) -> Optional[int]: '''simple docstring''' super().__init__() a__ : int = module a__ : Dict = nn.Sequential( nn.Linear(module.in_features , lowercase , bias=lowercase) , nn.Linear(lowercase , module.out_features , bias=lowercase) , ) a__ : Dict = (2.0 / (5 * min(module.in_features , module.out_features))) ** 0.5 nn.init.normal_(self.adapter[0].weight , std=lowercase) nn.init.zeros_(self.adapter[1].weight) self.adapter.to(module.weight.device) def __lowercase ( self , lowercase , *lowercase , **lowercase) -> Any: '''simple docstring''' return self.module(lowercase , *lowercase , **lowercase) + self.adapter(lowercase) @require_bitsandbytes @require_accelerate @require_torch @require_torch_gpu @slow class A__ ( unittest.TestCase ): """simple docstring""" __A : Tuple = '''bigscience/bloom-1b7''' # Constant values __A : str = 2.109_6595_5269_2574 __A : List[Any] = '''Hello my name is''' __A : List[str] = set() EXPECTED_OUTPUTS.add('''Hello my name is John and I am a professional photographer. I''' ) EXPECTED_OUTPUTS.add('''Hello my name is John.\nI am a friend of your father.\n''' ) EXPECTED_OUTPUTS.add('''Hello my name is John Doe, I am a student at the University''' ) __A : List[Any] = 1_0 def __lowercase ( self) -> str: '''simple docstring''' a__ : Union[str, Any] = AutoTokenizer.from_pretrained(self.model_name) class A__ ( __UpperCAmelCase ): """simple docstring""" def __lowercase ( self) -> int: '''simple docstring''' super().setUp() # Models and tokenizer a__ : str = AutoModelForCausalLM.from_pretrained( self.model_name , torch_dtype=torch.floataa , device_map='auto') a__ : Union[str, Any] = AutoModelForCausalLM.from_pretrained(self.model_name , load_in_abit=lowercase , device_map='auto') def __lowercase ( self) -> Any: '''simple docstring''' del self.model_fpaa del self.model_abit gc.collect() torch.cuda.empty_cache() def __lowercase ( self) -> Optional[int]: '''simple docstring''' a__ : Dict = self.model_abit.config self.assertTrue(hasattr(lowercase , 'quantization_config')) a__ : str = config.to_dict() a__ : Dict = config.to_diff_dict() a__ : Dict = config.to_json_string() def __lowercase ( self) -> Optional[Any]: '''simple docstring''' from bitsandbytes.nn import Paramsabit a__ : Optional[Any] = self.model_fpaa.get_memory_footprint() a__ : Any = self.model_abit.get_memory_footprint() self.assertAlmostEqual(mem_fpaa / mem_abit , self.EXPECTED_RELATIVE_DIFFERENCE) a__ : Optional[int] = get_some_linear_layer(self.model_abit) self.assertTrue(linear.weight.__class__ == Paramsabit) def __lowercase ( self) -> str: '''simple docstring''' from transformers import TaPreTrainedModel self.model_fpaa.get_memory_footprint() self.model_abit.get_memory_footprint() for name, module in self.model_abit.named_modules(): if isinstance(lowercase , torch.nn.Linear): if name not in ["lm_head"] + TaPreTrainedModel._keep_in_fpaa_modules: # 4-bit parameters are packed in uint8 variables self.assertTrue(module.weight.dtype == torch.uinta) def __lowercase ( self) -> Optional[int]: '''simple docstring''' a__ : List[str] = self.tokenizer(self.input_text , return_tensors='pt') a__ : List[str] = self.model_abit.generate(input_ids=encoded_input['input_ids'].to(0) , max_new_tokens=10) self.assertIn(self.tokenizer.decode(output_sequences[0] , skip_special_tokens=lowercase) , self.EXPECTED_OUTPUTS) def __lowercase ( self) -> int: '''simple docstring''' a__ : Dict = BitsAndBytesConfig() a__ : Union[str, Any] = True a__ : Optional[Any] = AutoModelForCausalLM.from_pretrained( self.model_name , quantization_config=lowercase , device_map='auto') a__ : Any = self.tokenizer(self.input_text , return_tensors='pt') a__ : List[str] = model_abit_from_config.generate( input_ids=encoded_input['input_ids'].to(0) , max_new_tokens=10) self.assertIn(self.tokenizer.decode(output_sequences[0] , skip_special_tokens=lowercase) , self.EXPECTED_OUTPUTS) def __lowercase ( self) -> Any: '''simple docstring''' with self.assertRaises(lowercase), tempfile.TemporaryDirectory() as tmpdirname: self.model_abit.save_pretrained(lowercase) def __lowercase ( self) -> str: '''simple docstring''' a__ : Any = BitsAndBytesConfig() with self.assertRaises(lowercase): a__ : Any = AutoModelForCausalLM.from_pretrained( self.model_name , quantization_config=lowercase , load_in_abit=lowercase , device_map='auto' , bnb_abit_quant_type='nf4' , ) def __lowercase ( self) -> Optional[Any]: '''simple docstring''' with self.assertRaises(lowercase): # Tries with `str` self.model_abit.to('cpu') with self.assertRaises(lowercase): # Tries with a `dtype`` self.model_abit.to(torch.floataa) with self.assertRaises(lowercase): # Tries with a `device` self.model_abit.to(torch.device('cuda:0')) with self.assertRaises(lowercase): # Tries with a `device` self.model_abit.float() with self.assertRaises(lowercase): # Tries with a `device` self.model_abit.half() # Test if we did not break anything a__ : Optional[int] = self.tokenizer(self.input_text , return_tensors='pt') a__ : Optional[Any] = self.model_fpaa.to(torch.floataa) a__ : int = self.model_fpaa.generate(input_ids=encoded_input['input_ids'].to(0) , max_new_tokens=10) # Check this does not throw an error a__ : Tuple = self.model_fpaa.to('cpu') # Check this does not throw an error a__ : List[str] = self.model_fpaa.half() # Check this does not throw an error a__ : Any = self.model_fpaa.float() def __lowercase ( self) -> Optional[Any]: '''simple docstring''' a__ : List[Any] = AutoModelForSeqaSeqLM.from_pretrained('t5-small' , load_in_abit=lowercase , device_map='auto') self.assertTrue(model.decoder.block[0].layer[2].DenseReluDense.wo.weight.dtype == torch.floataa) @require_bitsandbytes @require_accelerate @require_torch @require_torch_gpu @slow class A__ ( unittest.TestCase ): """simple docstring""" @classmethod def __lowercase ( cls) -> List[str]: '''simple docstring''' a__ : Union[str, Any] = 't5-small' a__ : str = 'google/flan-t5-small' # flan-t5 uses dense-act instead of dense-relu-dense a__ : Dict = AutoTokenizer.from_pretrained(cls.model_name) a__ : List[str] = 'Translate in German: Hello, my dog is cute' def __lowercase ( self) -> List[Any]: '''simple docstring''' gc.collect() torch.cuda.empty_cache() def __lowercase ( self) -> List[Any]: '''simple docstring''' from transformers import TaForConditionalGeneration a__ : Tuple = TaForConditionalGeneration._keep_in_fpaa_modules a__ : Any = None # test with `t5-small` a__ : Tuple = TaForConditionalGeneration.from_pretrained(self.model_name , load_in_abit=lowercase , device_map='auto') a__ : Any = self.tokenizer(self.input_text , return_tensors='pt').to(0) a__ : Optional[Any] = model.generate(**lowercase) # test with `flan-t5-small` a__ : Dict = TaForConditionalGeneration.from_pretrained( self.dense_act_model_name , load_in_abit=lowercase , device_map='auto') a__ : Optional[Any] = self.tokenizer(self.input_text , return_tensors='pt').to(0) a__ : Union[str, Any] = model.generate(**lowercase) a__ : Dict = modules def __lowercase ( self) -> List[str]: '''simple docstring''' import bitsandbytes as bnb from transformers import TaForConditionalGeneration # test with `t5-small` a__ : Tuple = TaForConditionalGeneration.from_pretrained(self.model_name , load_in_abit=lowercase , device_map='auto') # there was a bug with decoders - this test checks that it is fixed self.assertTrue(isinstance(model.decoder.block[0].layer[0].SelfAttention.q , bnb.nn.Linearabit)) a__ : Optional[int] = self.tokenizer(self.input_text , return_tensors='pt').to(0) a__ : Union[str, Any] = model.generate(**lowercase) # test with `flan-t5-small` a__ : int = TaForConditionalGeneration.from_pretrained( self.dense_act_model_name , load_in_abit=lowercase , device_map='auto') a__ : str = self.tokenizer(self.input_text , return_tensors='pt').to(0) a__ : Any = model.generate(**lowercase) class A__ ( __UpperCAmelCase ): """simple docstring""" def __lowercase ( self) -> Tuple: '''simple docstring''' super().setUp() # model_name a__ : List[Any] = 'bigscience/bloom-560m' a__ : str = 't5-small' # Different types of model a__ : Optional[int] = AutoModel.from_pretrained(self.model_name , load_in_abit=lowercase , device_map='auto') # Sequence classification model a__ : Optional[int] = AutoModelForSequenceClassification.from_pretrained( self.model_name , load_in_abit=lowercase , device_map='auto') # CausalLM model a__ : List[Any] = AutoModelForCausalLM.from_pretrained(self.model_name , load_in_abit=lowercase , device_map='auto') # Seq2seq model a__ : List[Any] = AutoModelForSeqaSeqLM.from_pretrained( self.seq_to_seq_name , load_in_abit=lowercase , device_map='auto') def __lowercase ( self) -> List[Any]: '''simple docstring''' del self.base_model del self.sequence_model del self.model_abit del self.seq_to_seq_model gc.collect() torch.cuda.empty_cache() def __lowercase ( self) -> List[str]: '''simple docstring''' from bitsandbytes.nn import Paramsabit self.assertTrue(self.base_model.h[-1].mlp.dense_ah_to_h.weight.__class__ == Paramsabit) # Other heads should be nn.Parameter self.assertTrue(self.model_abit.lm_head.weight.__class__ == torch.nn.Parameter) self.assertTrue(self.sequence_model.score.weight.__class__ == torch.nn.Parameter) self.assertTrue(self.seq_to_seq_model.lm_head.weight.__class__ == torch.nn.Parameter) class A__ ( __UpperCAmelCase ): """simple docstring""" def __lowercase ( self) -> Union[str, Any]: '''simple docstring''' super().setUp() def __lowercase ( self) -> Optional[int]: '''simple docstring''' del self.pipe gc.collect() torch.cuda.empty_cache() def __lowercase ( self) -> str: '''simple docstring''' a__ : Optional[Any] = pipeline( 'text-generation' , model=self.model_name , model_kwargs={'device_map': 'auto', 'load_in_4bit': True, 'torch_dtype': torch.floataa} , max_new_tokens=self.MAX_NEW_TOKENS , ) # Real second forward pass a__ : int = self.pipe(self.input_text) self.assertIn(pipeline_output[0]['generated_text'] , self.EXPECTED_OUTPUTS) @require_torch_multi_gpu class A__ ( __UpperCAmelCase ): """simple docstring""" def __lowercase ( self) -> Union[str, Any]: '''simple docstring''' super().setUp() def __lowercase ( self) -> int: '''simple docstring''' a__ : Optional[Any] = AutoModelForCausalLM.from_pretrained( self.model_name , load_in_abit=lowercase , device_map='balanced') # Check correct device map self.assertEqual(set(model_parallel.hf_device_map.values()) , {0, 1}) # Check that inference pass works on the model a__ : Tuple = self.tokenizer(self.input_text , return_tensors='pt') # Second real batch a__ : Optional[Any] = model_parallel.generate(input_ids=encoded_input['input_ids'].to(0) , max_new_tokens=10) self.assertIn(self.tokenizer.decode(output_parallel[0] , skip_special_tokens=lowercase) , self.EXPECTED_OUTPUTS) class A__ ( __UpperCAmelCase ): """simple docstring""" def __lowercase ( self) -> Optional[Any]: '''simple docstring''' a__ : Optional[int] = 'facebook/opt-350m' super().setUp() def __lowercase ( self) -> List[Any]: '''simple docstring''' if version.parse(importlib.metadata.version('bitsandbytes')) < version.parse('0.37.0'): return # Step 1: freeze all parameters a__ : Optional[int] = AutoModelForCausalLM.from_pretrained(self.model_name , load_in_abit=lowercase) self.assertEqual(set(model.hf_device_map.values()) , {torch.cuda.current_device()}) for param in model.parameters(): a__ : Any = False # freeze the model - train adapters later if param.ndim == 1: # cast the small parameters (e.g. layernorm) to fp32 for stability a__ : List[Any] = param.data.to(torch.floataa) # Step 2: add adapters for _, module in model.named_modules(): if "OPTAttention" in repr(type(lowercase)): a__ : Optional[int] = LoRALayer(module.q_proj , rank=16) a__ : Optional[Any] = LoRALayer(module.k_proj , rank=16) a__ : Optional[Any] = LoRALayer(module.v_proj , rank=16) # Step 3: dummy batch a__ : Optional[int] = self.tokenizer('Test batch ' , return_tensors='pt').to(0) # Step 4: Check if the gradient is not None with torch.cuda.amp.autocast(): a__ : int = model.forward(**lowercase) out.logits.norm().backward() for module in model.modules(): if isinstance(lowercase , lowercase): self.assertTrue(module.adapter[1].weight.grad is not None) self.assertTrue(module.adapter[1].weight.grad.norm().item() > 0) elif isinstance(lowercase , nn.Embedding): self.assertTrue(module.weight.grad is None) class A__ ( __UpperCAmelCase ): """simple docstring""" __A : Tuple = '''gpt2-xl''' __A : Dict = 3.3191_8548_5415_2187
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'''simple docstring''' import argparse import os import re import packaging.version UpperCamelCase__: Union[str, Any] = "examples/" UpperCamelCase__: Optional[Any] = { "examples": (re.compile(r"^check_min_version\(\"[^\"]+\"\)\s*$", re.MULTILINE), "check_min_version(\"VERSION\")\n"), "init": (re.compile(r"^__version__\s+=\s+\"([^\"]+)\"\s*$", re.MULTILINE), "__version__ = \"VERSION\"\n"), "setup": (re.compile(r"^(\s*)version\s*=\s*\"[^\"]+\",", re.MULTILINE), r"\1version=\"VERSION\","), "doc": (re.compile(r"^(\s*)release\s*=\s*\"[^\"]+\"$", re.MULTILINE), "release = \"VERSION\"\n"), } UpperCamelCase__: Optional[int] = { "init": "src/diffusers/__init__.py", "setup": "setup.py", } UpperCamelCase__: List[Any] = "README.md" def snake_case_ ( _lowerCAmelCase : Optional[Any] , _lowerCAmelCase : List[Any] , _lowerCAmelCase : int ) -> Optional[int]: with open(_lowerCAmelCase , '''r''' , encoding='''utf-8''' , newline='''\n''' ) as f: UpperCAmelCase : Optional[int] = f.read() UpperCAmelCase , UpperCAmelCase : List[Any] = REPLACE_PATTERNS[pattern] UpperCAmelCase : List[Any] = replace.replace('''VERSION''' , _lowerCAmelCase ) UpperCAmelCase : Optional[Any] = re_pattern.sub(_lowerCAmelCase , _lowerCAmelCase ) with open(_lowerCAmelCase , '''w''' , encoding='''utf-8''' , newline='''\n''' ) as f: f.write(_lowerCAmelCase ) def snake_case_ ( _lowerCAmelCase : Any ) -> Optional[int]: for folder, directories, fnames in os.walk(_lowerCAmelCase ): # Removing some of the folders with non-actively maintained examples from the walk if "research_projects" in directories: directories.remove('''research_projects''' ) if "legacy" in directories: directories.remove('''legacy''' ) for fname in fnames: if fname.endswith('''.py''' ): update_version_in_file(os.path.join(_lowerCAmelCase , _lowerCAmelCase ) , _lowerCAmelCase , pattern='''examples''' ) def snake_case_ ( _lowerCAmelCase : Any , _lowerCAmelCase : str=False ) -> List[str]: for pattern, fname in REPLACE_FILES.items(): update_version_in_file(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ) if not patch: update_version_in_examples(_lowerCAmelCase ) def snake_case_ ( ) -> Optional[Any]: UpperCAmelCase : Optional[int] = '''🤗 Transformers currently provides the following architectures''' UpperCAmelCase : Optional[int] = '''1. Want to contribute a new model?''' with open(_lowerCAmelCase , '''r''' , encoding='''utf-8''' , newline='''\n''' ) as f: UpperCAmelCase : Optional[Any] = f.readlines() # Find the start of the list. UpperCAmelCase : List[Any] = 0 while not lines[start_index].startswith(_start_prompt ): start_index += 1 start_index += 1 UpperCAmelCase : Optional[Any] = start_index # Update the lines in the model list. while not lines[index].startswith(_end_prompt ): if lines[index].startswith('''1.''' ): UpperCAmelCase : Optional[int] = lines[index].replace( '''https://huggingface.co/docs/diffusers/main/model_doc''' , '''https://huggingface.co/docs/diffusers/model_doc''' , ) index += 1 with open(_lowerCAmelCase , '''w''' , encoding='''utf-8''' , newline='''\n''' ) as f: f.writelines(_lowerCAmelCase ) def snake_case_ ( ) -> Optional[Any]: with open(REPLACE_FILES['''init'''] , '''r''' ) as f: UpperCAmelCase : Union[str, Any] = f.read() UpperCAmelCase : int = REPLACE_PATTERNS['''init'''][0].search(_lowerCAmelCase ).groups()[0] return packaging.version.parse(_lowerCAmelCase ) def snake_case_ ( _lowerCAmelCase : List[str]=False ) -> Any: UpperCAmelCase : Optional[Any] = get_version() if patch and default_version.is_devrelease: raise ValueError('''Can\'t create a patch version from the dev branch, checkout a released version!''' ) if default_version.is_devrelease: UpperCAmelCase : Optional[int] = default_version.base_version elif patch: UpperCAmelCase : Union[str, Any] = f"""{default_version.major}.{default_version.minor}.{default_version.micro + 1}""" else: UpperCAmelCase : Union[str, Any] = f"""{default_version.major}.{default_version.minor + 1}.0""" # Now let's ask nicely if that's the right one. UpperCAmelCase : Dict = input(f"""Which version are you releasing? [{default_version}]""" ) if len(_lowerCAmelCase ) == 0: UpperCAmelCase : Tuple = default_version print(f"""Updating version to {version}.""" ) global_version_update(_lowerCAmelCase , patch=_lowerCAmelCase ) def snake_case_ ( ) -> Any: UpperCAmelCase : List[Any] = get_version() UpperCAmelCase : List[str] = f"""{current_version.major}.{current_version.minor + 1}.0.dev0""" UpperCAmelCase : List[Any] = current_version.base_version # Check with the user we got that right. UpperCAmelCase : Optional[int] = input(f"""Which version are we developing now? [{dev_version}]""" ) if len(_lowerCAmelCase ) == 0: UpperCAmelCase : Dict = dev_version print(f"""Updating version to {version}.""" ) global_version_update(_lowerCAmelCase ) # print("Cleaning main README, don't forget to run `make fix-copies`.") # clean_main_ref_in_model_list() if __name__ == "__main__": UpperCamelCase__: Union[str, Any] = argparse.ArgumentParser() parser.add_argument("--post_release", action="store_true", help="Whether this is pre or post release.") parser.add_argument("--patch", action="store_true", help="Whether or not this is a patch release.") UpperCamelCase__: Optional[Any] = parser.parse_args() if not args.post_release: pre_release_work(patch=args.patch) elif args.patch: print("Nothing to do after a patch :-)") else: post_release_work()
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"""simple docstring""" import argparse import os import torch from diffusers import ( CMStochasticIterativeScheduler, ConsistencyModelPipeline, UNetaDModel, ) __magic_name__ = { "sample_size": 32, "in_channels": 3, "out_channels": 3, "layers_per_block": 2, "num_class_embeds": 1000, "block_out_channels": [32, 64], "attention_head_dim": 8, "down_block_types": [ "ResnetDownsampleBlock2D", "AttnDownBlock2D", ], "up_block_types": [ "AttnUpBlock2D", "ResnetUpsampleBlock2D", ], "resnet_time_scale_shift": "scale_shift", "upsample_type": "resnet", "downsample_type": "resnet", } __magic_name__ = { "sample_size": 64, "in_channels": 3, "out_channels": 3, "layers_per_block": 3, "num_class_embeds": 1000, "block_out_channels": [192, 192 * 2, 192 * 3, 192 * 4], "attention_head_dim": 64, "down_block_types": [ "ResnetDownsampleBlock2D", "AttnDownBlock2D", "AttnDownBlock2D", "AttnDownBlock2D", ], "up_block_types": [ "AttnUpBlock2D", "AttnUpBlock2D", "AttnUpBlock2D", "ResnetUpsampleBlock2D", ], "resnet_time_scale_shift": "scale_shift", "upsample_type": "resnet", "downsample_type": "resnet", } __magic_name__ = { "sample_size": 256, "in_channels": 3, "out_channels": 3, "layers_per_block": 2, "num_class_embeds": None, "block_out_channels": [256, 256, 256 * 2, 256 * 2, 256 * 4, 256 * 4], "attention_head_dim": 64, "down_block_types": [ "ResnetDownsampleBlock2D", "ResnetDownsampleBlock2D", "ResnetDownsampleBlock2D", "AttnDownBlock2D", "AttnDownBlock2D", "AttnDownBlock2D", ], "up_block_types": [ "AttnUpBlock2D", "AttnUpBlock2D", "AttnUpBlock2D", "ResnetUpsampleBlock2D", "ResnetUpsampleBlock2D", "ResnetUpsampleBlock2D", ], "resnet_time_scale_shift": "default", "upsample_type": "resnet", "downsample_type": "resnet", } __magic_name__ = { "num_train_timesteps": 40, "sigma_min": 0.002, "sigma_max": 80.0, } __magic_name__ = { "num_train_timesteps": 201, "sigma_min": 0.002, "sigma_max": 80.0, } __magic_name__ = { "num_train_timesteps": 151, "sigma_min": 0.002, "sigma_max": 80.0, } def _lowerCAmelCase ( UpperCamelCase_ ): if isinstance(UpperCamelCase_ , UpperCamelCase_ ): return v if v.lower() in ("yes", "true", "t", "y", "1"): return True elif v.lower() in ("no", "false", "f", "n", "0"): return False else: raise argparse.ArgumentTypeError("""boolean value expected""" ) def _lowerCAmelCase ( UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_=False ): __SCREAMING_SNAKE_CASE = checkpoint[f"{old_prefix}.in_layers.0.weight"] __SCREAMING_SNAKE_CASE = checkpoint[f"{old_prefix}.in_layers.0.bias"] __SCREAMING_SNAKE_CASE = checkpoint[f"{old_prefix}.in_layers.2.weight"] __SCREAMING_SNAKE_CASE = checkpoint[f"{old_prefix}.in_layers.2.bias"] __SCREAMING_SNAKE_CASE = checkpoint[f"{old_prefix}.emb_layers.1.weight"] __SCREAMING_SNAKE_CASE = checkpoint[f"{old_prefix}.emb_layers.1.bias"] __SCREAMING_SNAKE_CASE = checkpoint[f"{old_prefix}.out_layers.0.weight"] __SCREAMING_SNAKE_CASE = checkpoint[f"{old_prefix}.out_layers.0.bias"] __SCREAMING_SNAKE_CASE = checkpoint[f"{old_prefix}.out_layers.3.weight"] __SCREAMING_SNAKE_CASE = checkpoint[f"{old_prefix}.out_layers.3.bias"] if has_skip: __SCREAMING_SNAKE_CASE = checkpoint[f"{old_prefix}.skip_connection.weight"] __SCREAMING_SNAKE_CASE = checkpoint[f"{old_prefix}.skip_connection.bias"] return new_checkpoint def _lowerCAmelCase ( UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_=None ): __SCREAMING_SNAKE_CASE ,__SCREAMING_SNAKE_CASE ,__SCREAMING_SNAKE_CASE = checkpoint[f"{old_prefix}.qkv.weight"].chunk(3 , dim=0 ) __SCREAMING_SNAKE_CASE ,__SCREAMING_SNAKE_CASE ,__SCREAMING_SNAKE_CASE = checkpoint[f"{old_prefix}.qkv.bias"].chunk(3 , dim=0 ) __SCREAMING_SNAKE_CASE = checkpoint[f"{old_prefix}.norm.weight"] __SCREAMING_SNAKE_CASE = checkpoint[f"{old_prefix}.norm.bias"] __SCREAMING_SNAKE_CASE = weight_q.squeeze(-1 ).squeeze(-1 ) __SCREAMING_SNAKE_CASE = bias_q.squeeze(-1 ).squeeze(-1 ) __SCREAMING_SNAKE_CASE = weight_k.squeeze(-1 ).squeeze(-1 ) __SCREAMING_SNAKE_CASE = bias_k.squeeze(-1 ).squeeze(-1 ) __SCREAMING_SNAKE_CASE = weight_v.squeeze(-1 ).squeeze(-1 ) __SCREAMING_SNAKE_CASE = bias_v.squeeze(-1 ).squeeze(-1 ) __SCREAMING_SNAKE_CASE = ( checkpoint[f"{old_prefix}.proj_out.weight"].squeeze(-1 ).squeeze(-1 ) ) __SCREAMING_SNAKE_CASE = checkpoint[f"{old_prefix}.proj_out.bias"].squeeze(-1 ).squeeze(-1 ) return new_checkpoint def _lowerCAmelCase ( UpperCamelCase_ , UpperCamelCase_ ): __SCREAMING_SNAKE_CASE = torch.load(UpperCamelCase_ , map_location="""cpu""" ) __SCREAMING_SNAKE_CASE = {} __SCREAMING_SNAKE_CASE = checkpoint["""time_embed.0.weight"""] __SCREAMING_SNAKE_CASE = checkpoint["""time_embed.0.bias"""] __SCREAMING_SNAKE_CASE = checkpoint["""time_embed.2.weight"""] __SCREAMING_SNAKE_CASE = checkpoint["""time_embed.2.bias"""] if unet_config["num_class_embeds"] is not None: __SCREAMING_SNAKE_CASE = checkpoint["""label_emb.weight"""] __SCREAMING_SNAKE_CASE = checkpoint["""input_blocks.0.0.weight"""] __SCREAMING_SNAKE_CASE = checkpoint["""input_blocks.0.0.bias"""] __SCREAMING_SNAKE_CASE = unet_config["""down_block_types"""] __SCREAMING_SNAKE_CASE = unet_config["""layers_per_block"""] __SCREAMING_SNAKE_CASE = unet_config["""attention_head_dim"""] __SCREAMING_SNAKE_CASE = unet_config["""block_out_channels"""] __SCREAMING_SNAKE_CASE = 1 __SCREAMING_SNAKE_CASE = channels_list[0] for i, layer_type in enumerate(UpperCamelCase_ ): __SCREAMING_SNAKE_CASE = channels_list[i] __SCREAMING_SNAKE_CASE = current_channels != prev_channels if layer_type == "ResnetDownsampleBlock2D": for j in range(UpperCamelCase_ ): __SCREAMING_SNAKE_CASE = f"down_blocks.{i}.resnets.{j}" __SCREAMING_SNAKE_CASE = f"input_blocks.{current_layer}.0" __SCREAMING_SNAKE_CASE = True if j == 0 and downsample_block_has_skip else False __SCREAMING_SNAKE_CASE = convert_resnet(UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , has_skip=UpperCamelCase_ ) current_layer += 1 elif layer_type == "AttnDownBlock2D": for j in range(UpperCamelCase_ ): __SCREAMING_SNAKE_CASE = f"down_blocks.{i}.resnets.{j}" __SCREAMING_SNAKE_CASE = f"input_blocks.{current_layer}.0" __SCREAMING_SNAKE_CASE = True if j == 0 and downsample_block_has_skip else False __SCREAMING_SNAKE_CASE = convert_resnet(UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , has_skip=UpperCamelCase_ ) __SCREAMING_SNAKE_CASE = f"down_blocks.{i}.attentions.{j}" __SCREAMING_SNAKE_CASE = f"input_blocks.{current_layer}.1" __SCREAMING_SNAKE_CASE = convert_attention( UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ ) current_layer += 1 if i != len(UpperCamelCase_ ) - 1: __SCREAMING_SNAKE_CASE = f"down_blocks.{i}.downsamplers.0" __SCREAMING_SNAKE_CASE = f"input_blocks.{current_layer}.0" __SCREAMING_SNAKE_CASE = convert_resnet(UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ ) current_layer += 1 __SCREAMING_SNAKE_CASE = current_channels # hardcoded the mid-block for now __SCREAMING_SNAKE_CASE = """mid_block.resnets.0""" __SCREAMING_SNAKE_CASE = """middle_block.0""" __SCREAMING_SNAKE_CASE = convert_resnet(UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ ) __SCREAMING_SNAKE_CASE = """mid_block.attentions.0""" __SCREAMING_SNAKE_CASE = """middle_block.1""" __SCREAMING_SNAKE_CASE = convert_attention(UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ ) __SCREAMING_SNAKE_CASE = """mid_block.resnets.1""" __SCREAMING_SNAKE_CASE = """middle_block.2""" __SCREAMING_SNAKE_CASE = convert_resnet(UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ ) __SCREAMING_SNAKE_CASE = 0 __SCREAMING_SNAKE_CASE = unet_config["""up_block_types"""] for i, layer_type in enumerate(UpperCamelCase_ ): if layer_type == "ResnetUpsampleBlock2D": for j in range(layers_per_block + 1 ): __SCREAMING_SNAKE_CASE = f"up_blocks.{i}.resnets.{j}" __SCREAMING_SNAKE_CASE = f"output_blocks.{current_layer}.0" __SCREAMING_SNAKE_CASE = convert_resnet(UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , has_skip=UpperCamelCase_ ) current_layer += 1 if i != len(UpperCamelCase_ ) - 1: __SCREAMING_SNAKE_CASE = f"up_blocks.{i}.upsamplers.0" __SCREAMING_SNAKE_CASE = f"output_blocks.{current_layer-1}.1" __SCREAMING_SNAKE_CASE = convert_resnet(UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ ) elif layer_type == "AttnUpBlock2D": for j in range(layers_per_block + 1 ): __SCREAMING_SNAKE_CASE = f"up_blocks.{i}.resnets.{j}" __SCREAMING_SNAKE_CASE = f"output_blocks.{current_layer}.0" __SCREAMING_SNAKE_CASE = convert_resnet(UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , has_skip=UpperCamelCase_ ) __SCREAMING_SNAKE_CASE = f"up_blocks.{i}.attentions.{j}" __SCREAMING_SNAKE_CASE = f"output_blocks.{current_layer}.1" __SCREAMING_SNAKE_CASE = convert_attention( UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ ) current_layer += 1 if i != len(UpperCamelCase_ ) - 1: __SCREAMING_SNAKE_CASE = f"up_blocks.{i}.upsamplers.0" __SCREAMING_SNAKE_CASE = f"output_blocks.{current_layer-1}.2" __SCREAMING_SNAKE_CASE = convert_resnet(UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ ) __SCREAMING_SNAKE_CASE = checkpoint["""out.0.weight"""] __SCREAMING_SNAKE_CASE = checkpoint["""out.0.bias"""] __SCREAMING_SNAKE_CASE = checkpoint["""out.2.weight"""] __SCREAMING_SNAKE_CASE = checkpoint["""out.2.bias"""] return new_checkpoint if __name__ == "__main__": __magic_name__ = argparse.ArgumentParser() parser.add_argument("--unet_path", default=None, type=str, required=True, help="Path to the unet.pt to convert.") parser.add_argument( "--dump_path", default=None, type=str, required=True, help="Path to output the converted UNet model." ) parser.add_argument("--class_cond", default=True, type=str, help="Whether the model is class-conditional.") __magic_name__ = parser.parse_args() __magic_name__ = strabool(args.class_cond) __magic_name__ = os.path.basename(args.unet_path) print(F"""Checkpoint: {ckpt_name}""") # Get U-Net config if "imagenet64" in ckpt_name: __magic_name__ = IMAGENET_64_UNET_CONFIG elif "256" in ckpt_name and (("bedroom" in ckpt_name) or ("cat" in ckpt_name)): __magic_name__ = LSUN_256_UNET_CONFIG elif "test" in ckpt_name: __magic_name__ = TEST_UNET_CONFIG else: raise ValueError(F"""Checkpoint type {ckpt_name} is not currently supported.""") if not args.class_cond: __magic_name__ = None __magic_name__ = con_pt_to_diffuser(args.unet_path, unet_config) __magic_name__ = UNetaDModel(**unet_config) image_unet.load_state_dict(converted_unet_ckpt) # Get scheduler config if "cd" in ckpt_name or "test" in ckpt_name: __magic_name__ = CD_SCHEDULER_CONFIG elif "ct" in ckpt_name and "imagenet64" in ckpt_name: __magic_name__ = CT_IMAGENET_64_SCHEDULER_CONFIG elif "ct" in ckpt_name and "256" in ckpt_name and (("bedroom" in ckpt_name) or ("cat" in ckpt_name)): __magic_name__ = CT_LSUN_256_SCHEDULER_CONFIG else: raise ValueError(F"""Checkpoint type {ckpt_name} is not currently supported.""") __magic_name__ = CMStochasticIterativeScheduler(**scheduler_config) __magic_name__ = ConsistencyModelPipeline(unet=image_unet, scheduler=cm_scheduler) consistency_model.save_pretrained(args.dump_path)
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'''simple docstring''' from __future__ import annotations import matplotlib.pyplot as plt # type: ignore import numpy # initial triangle of Koch snowflake UpperCamelCase__: Tuple = numpy.array([0, 0]) UpperCamelCase__: Union[str, Any] = numpy.array([0.5, 0.8660254]) UpperCamelCase__: Dict = numpy.array([1, 0]) UpperCamelCase__: int = [VECTOR_1, VECTOR_2, VECTOR_3, VECTOR_1] def snake_case_ ( _lowerCAmelCase : list[numpy.ndarray] , _lowerCAmelCase : int ) -> list[numpy.ndarray]: UpperCAmelCase : Union[str, Any] = initial_vectors for _ in range(_lowerCAmelCase ): UpperCAmelCase : Union[str, Any] = iteration_step(_lowerCAmelCase ) return vectors def snake_case_ ( _lowerCAmelCase : list[numpy.ndarray] ) -> list[numpy.ndarray]: UpperCAmelCase : Tuple = [] for i, start_vector in enumerate(vectors[:-1] ): UpperCAmelCase : List[str] = vectors[i + 1] new_vectors.append(_lowerCAmelCase ) UpperCAmelCase : Optional[Any] = end_vector - start_vector new_vectors.append(start_vector + difference_vector / 3 ) new_vectors.append( start_vector + difference_vector / 3 + rotate(difference_vector / 3 , 60 ) ) new_vectors.append(start_vector + difference_vector * 2 / 3 ) new_vectors.append(vectors[-1] ) return new_vectors def snake_case_ ( _lowerCAmelCase : numpy.ndarray , _lowerCAmelCase : float ) -> numpy.ndarray: UpperCAmelCase : List[str] = numpy.radians(_lowerCAmelCase ) UpperCAmelCase , UpperCAmelCase : Tuple = numpy.cos(_lowerCAmelCase ), numpy.sin(_lowerCAmelCase ) UpperCAmelCase : Union[str, Any] = numpy.array(((c, -s), (s, c)) ) return numpy.dot(_lowerCAmelCase , _lowerCAmelCase ) def snake_case_ ( _lowerCAmelCase : list[numpy.ndarray] ) -> None: UpperCAmelCase : List[Any] = plt.gca() axes.set_aspect('''equal''' ) # matplotlib.pyplot.plot takes a list of all x-coordinates and a list of all # y-coordinates as inputs, which are constructed from the vector-list using # zip() UpperCAmelCase , UpperCAmelCase : str = zip(*_lowerCAmelCase ) plt.plot(_lowerCAmelCase , _lowerCAmelCase ) plt.show() if __name__ == "__main__": import doctest doctest.testmod() UpperCamelCase__: List[Any] = iterate(INITIAL_VECTORS, 5) plot(processed_vectors)
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from __future__ import annotations import math import random from typing import Any class lowercase : def __init__( self): lowercase = [] lowercase = 0 lowercase = 0 def A__ ( self): return self.head == self.tail def A__ ( self ,A__): self.data.append(A__) lowercase = self.tail + 1 def A__ ( self): lowercase = self.data[self.head] lowercase = self.head + 1 return ret def A__ ( self): return self.tail - self.head def A__ ( self): print(self.data) print('''**************''') print(self.data[self.head : self.tail]) class lowercase : def __init__( self ,A__): lowercase = data lowercase = None lowercase = None lowercase = 1 def A__ ( self): return self.data def A__ ( self): return self.left def A__ ( self): return self.right def A__ ( self): return self.height def A__ ( self ,A__): lowercase = data def A__ ( self ,A__): lowercase = node def A__ ( self ,A__): lowercase = node def A__ ( self ,A__): lowercase = height def UpperCamelCase ( lowerCAmelCase__ ): '''simple docstring''' if node is None: return 0 return node.get_height() def UpperCamelCase ( lowerCAmelCase__ , lowerCAmelCase__ ): '''simple docstring''' if a > b: return a return b def UpperCamelCase ( lowerCAmelCase__ ): '''simple docstring''' print('''left rotation node:''' , node.get_data() ) lowercase = node.get_left() assert ret is not None node.set_left(ret.get_right() ) ret.set_right(lowerCAmelCase__ ) lowercase = my_max(get_height(node.get_right() ) , get_height(node.get_left() ) ) + 1 node.set_height(lowerCAmelCase__ ) lowercase = my_max(get_height(ret.get_right() ) , get_height(ret.get_left() ) ) + 1 ret.set_height(lowerCAmelCase__ ) return ret def UpperCamelCase ( lowerCAmelCase__ ): '''simple docstring''' print('''right rotation node:''' , node.get_data() ) lowercase = node.get_right() assert ret is not None node.set_right(ret.get_left() ) ret.set_left(lowerCAmelCase__ ) lowercase = my_max(get_height(node.get_right() ) , get_height(node.get_left() ) ) + 1 node.set_height(lowerCAmelCase__ ) lowercase = my_max(get_height(ret.get_right() ) , get_height(ret.get_left() ) ) + 1 ret.set_height(lowerCAmelCase__ ) return ret def UpperCamelCase ( lowerCAmelCase__ ): '''simple docstring''' lowercase = node.get_left() assert left_child is not None node.set_left(left_rotation(lowerCAmelCase__ ) ) return right_rotation(lowerCAmelCase__ ) def UpperCamelCase ( lowerCAmelCase__ ): '''simple docstring''' lowercase = node.get_right() assert right_child is not None node.set_right(right_rotation(lowerCAmelCase__ ) ) return left_rotation(lowerCAmelCase__ ) def UpperCamelCase ( lowerCAmelCase__ , lowerCAmelCase__ ): '''simple docstring''' if node is None: return MyNode(lowerCAmelCase__ ) if data < node.get_data(): node.set_left(insert_node(node.get_left() , lowerCAmelCase__ ) ) if ( get_height(node.get_left() ) - get_height(node.get_right() ) == 2 ): # an unbalance detected lowercase = node.get_left() assert left_child is not None if ( data < left_child.get_data() ): # new node is the left child of the left child lowercase = right_rotation(lowerCAmelCase__ ) else: lowercase = lr_rotation(lowerCAmelCase__ ) else: node.set_right(insert_node(node.get_right() , lowerCAmelCase__ ) ) if get_height(node.get_right() ) - get_height(node.get_left() ) == 2: lowercase = node.get_right() assert right_child is not None if data < right_child.get_data(): lowercase = rl_rotation(lowerCAmelCase__ ) else: lowercase = left_rotation(lowerCAmelCase__ ) lowercase = my_max(get_height(node.get_right() ) , get_height(node.get_left() ) ) + 1 node.set_height(lowerCAmelCase__ ) return node def UpperCamelCase ( lowerCAmelCase__ ): '''simple docstring''' while True: lowercase = root.get_right() if right_child is None: break lowercase = right_child return root.get_data() def UpperCamelCase ( lowerCAmelCase__ ): '''simple docstring''' while True: lowercase = root.get_left() if left_child is None: break lowercase = left_child return root.get_data() def UpperCamelCase ( lowerCAmelCase__ , lowerCAmelCase__ ): '''simple docstring''' lowercase = root.get_left() lowercase = root.get_right() if root.get_data() == data: if left_child is not None and right_child is not None: lowercase = get_left_most(lowerCAmelCase__ ) root.set_data(lowerCAmelCase__ ) root.set_right(del_node(lowerCAmelCase__ , lowerCAmelCase__ ) ) elif left_child is not None: lowercase = left_child elif right_child is not None: lowercase = right_child else: return None elif root.get_data() > data: if left_child is None: print('''No such data''' ) return root else: root.set_left(del_node(lowerCAmelCase__ , lowerCAmelCase__ ) ) else: # root.get_data() < data if right_child is None: return root else: root.set_right(del_node(lowerCAmelCase__ , lowerCAmelCase__ ) ) if get_height(lowerCAmelCase__ ) - get_height(lowerCAmelCase__ ) == 2: assert right_child is not None if get_height(right_child.get_right() ) > get_height(right_child.get_left() ): lowercase = left_rotation(lowerCAmelCase__ ) else: lowercase = rl_rotation(lowerCAmelCase__ ) elif get_height(lowerCAmelCase__ ) - get_height(lowerCAmelCase__ ) == -2: assert left_child is not None if get_height(left_child.get_left() ) > get_height(left_child.get_right() ): lowercase = right_rotation(lowerCAmelCase__ ) else: lowercase = lr_rotation(lowerCAmelCase__ ) lowercase = my_max(get_height(root.get_right() ) , get_height(root.get_left() ) ) + 1 root.set_height(lowerCAmelCase__ ) return root class lowercase : def __init__( self): lowercase = None def A__ ( self): return get_height(self.root) def A__ ( self ,A__): print('''insert:''' + str(A__)) lowercase = insert_node(self.root ,A__) def A__ ( self ,A__): print('''delete:''' + str(A__)) if self.root is None: print('''Tree is empty!''') return lowercase = del_node(self.root ,A__) def __str__( self ,): # a level traversale, gives a more intuitive look on the tree lowercase = '''''' lowercase = MyQueue() q.push(self.root) lowercase = self.get_height() if layer == 0: return output lowercase = 0 while not q.is_empty(): lowercase = q.pop() lowercase = ''' ''' * int(math.pow(2 ,layer - 1)) output += space if node is None: output += "*" q.push(A__) q.push(A__) else: output += str(node.get_data()) q.push(node.get_left()) q.push(node.get_right()) output += space lowercase = cnt + 1 for i in range(1_0_0): if cnt == math.pow(2 ,A__) - 1: lowercase = layer - 1 if layer == 0: output += "\n*************************************" return output output += "\n" break output += "\n*************************************" return output def UpperCamelCase ( ): '''simple docstring''' import doctest doctest.testmod() if __name__ == "__main__": _test() lowercase__ :Union[str, Any] = AVLtree() lowercase__ :List[str] = list(range(10)) random.shuffle(lst) for i in lst: t.insert(i) print(str(t)) random.shuffle(lst) for i in lst: t.del_node(i) print(str(t))
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'''simple docstring''' from manim import * class SCREAMING_SNAKE_CASE( A__ ): """simple docstring""" def A ( self : Union[str, Any] ) -> List[str]: UpperCAmelCase : Optional[Any] = Rectangle(height=0.5 , width=0.5 ) UpperCAmelCase : str = Rectangle(height=0.46 , width=0.46 ).set_stroke(width=0 ) UpperCAmelCase : Tuple = [mem.copy() for i in range(6 )] UpperCAmelCase : Optional[int] = [mem.copy() for i in range(6 )] UpperCAmelCase : Dict = VGroup(*__snake_case ).arrange(__snake_case , buff=0 ) UpperCAmelCase : Any = VGroup(*__snake_case ).arrange(__snake_case , buff=0 ) UpperCAmelCase : Union[str, Any] = VGroup(__snake_case , __snake_case ).arrange(__snake_case , buff=0 ) UpperCAmelCase : Optional[Any] = Text('''CPU''' , font_size=24 ) UpperCAmelCase : Union[str, Any] = Group(__snake_case , __snake_case ).arrange(__snake_case , buff=0.5 , aligned_edge=__snake_case ) cpu.move_to([-2.5, -0.5, 0] ) self.add(__snake_case ) UpperCAmelCase : Optional[Any] = [mem.copy() for i in range(4 )] UpperCAmelCase : Union[str, Any] = VGroup(*__snake_case ).arrange(__snake_case , buff=0 ) UpperCAmelCase : List[str] = Text('''GPU''' , font_size=24 ) UpperCAmelCase : Dict = Group(__snake_case , __snake_case ).arrange(__snake_case , buff=0.5 , aligned_edge=__snake_case ) gpu.move_to([-1, -1, 0] ) self.add(__snake_case ) UpperCAmelCase : int = [mem.copy() for i in range(6 )] UpperCAmelCase : Union[str, Any] = VGroup(*__snake_case ).arrange(__snake_case , buff=0 ) UpperCAmelCase : List[str] = Text('''Model''' , font_size=24 ) UpperCAmelCase : Tuple = Group(__snake_case , __snake_case ).arrange(__snake_case , buff=0.5 , aligned_edge=__snake_case ) model.move_to([3, -1.0, 0] ) self.add(__snake_case ) UpperCAmelCase : Any = [] for i, rect in enumerate(__snake_case ): rect.set_stroke(__snake_case ) # target = fill.copy().set_fill(YELLOW, opacity=0.7) # target.move_to(rect) # self.add(target) UpperCAmelCase : Dict = Rectangle(height=0.46 / 4 , width=0.46 / 3 ).set_stroke(width=0.0 ).set_fill(__snake_case , opacity=0.7 ) if i == 0: cpu_target.next_to(cpu_left_col_base[0].get_corner(DOWN + LEFT ) , buff=0.02 , direction=__snake_case ) cpu_target.set_x(cpu_target.get_x() + 0.1 ) elif i == 3: cpu_target.next_to(cpu_targs[0] , direction=__snake_case , buff=0.0 ) else: cpu_target.next_to(cpu_targs[i - 1] , direction=__snake_case , buff=0.0 ) self.add(__snake_case ) cpu_targs.append(__snake_case ) UpperCAmelCase : int = [mem.copy() for i in range(6 )] UpperCAmelCase : int = VGroup(*__snake_case ).arrange(__snake_case , buff=0 ) UpperCAmelCase : Any = Text('''Loaded Checkpoint''' , font_size=24 ) UpperCAmelCase : Union[str, Any] = Group(__snake_case , __snake_case ).arrange(__snake_case , aligned_edge=__snake_case , buff=0.4 ) checkpoint.move_to([3, 0.5, 0] ) UpperCAmelCase : Optional[int] = Square(side_length=2.2 ) key.move_to([-5, 2, 0] ) UpperCAmelCase : str = MarkupText( F"""<b>Key:</b>\n\n<span fgcolor='{YELLOW}'>●</span> Empty Model""" , font_size=18 , ) key_text.move_to([-5, 2.4, 0] ) self.add(__snake_case , __snake_case ) UpperCAmelCase : Tuple = MarkupText( F"""<span fgcolor='{BLUE}'>●</span> Checkpoint""" , font_size=18 , ) blue_text.next_to(__snake_case , DOWN * 2.4 , aligned_edge=key_text.get_left() ) UpperCAmelCase : List[Any] = MarkupText( F"""Next, a <i><span fgcolor=\"{BLUE}\">second</span></i> model is loaded into memory,\nwith the weights of a <span fgcolor=\"{BLUE}\">single shard</span>.""" , font_size=24 , ) step_a.move_to([2, 2, 0] ) self.play(Write(__snake_case ) , Write(__snake_case ) ) self.play(Write(__snake_case , run_time=1 ) , Create(__snake_case , run_time=1 ) ) UpperCAmelCase : Tuple = [] UpperCAmelCase : int = [] for i, rect in enumerate(__snake_case ): UpperCAmelCase : Any = fill.copy().set_fill(__snake_case , opacity=0.7 ) target.move_to(__snake_case ) first_animations.append(GrowFromCenter(__snake_case , run_time=1 ) ) UpperCAmelCase : List[str] = target.copy() cpu_target.generate_target() if i < 5: cpu_target.target.move_to(cpu_left_col_base[i + 1] ) else: cpu_target.target.move_to(cpu_right_col_base[i - 5] ) second_animations.append(MoveToTarget(__snake_case , run_time=1.5 ) ) self.play(*__snake_case ) self.play(*__snake_case ) self.wait()
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"""simple docstring""" from collections import OrderedDict from typing import TYPE_CHECKING, Any, Mapping, Optional from ...configuration_utils import PretrainedConfig from ...onnx import OnnxConfig from ...utils import logging if TYPE_CHECKING: from ... import FeatureExtractionMixin, TensorType SCREAMING_SNAKE_CASE : Tuple = logging.get_logger(__name__) SCREAMING_SNAKE_CASE : Any = { """openai/imagegpt-small""": """""", """openai/imagegpt-medium""": """""", """openai/imagegpt-large""": """""", } class _UpperCAmelCase ( __snake_case ): '''simple docstring''' lowerCamelCase__ ='imagegpt' lowerCamelCase__ =['past_key_values'] lowerCamelCase__ ={ 'hidden_size': 'n_embd', 'max_position_embeddings': 'n_positions', 'num_attention_heads': 'n_head', 'num_hidden_layers': 'n_layer', } def __init__(self , a_=5_12 + 1 , a_=32 * 32 , a_=5_12 , a_=24 , a_=8 , a_=None , a_="quick_gelu" , a_=0.1 , a_=0.1 , a_=0.1 , a_=1E-5 , a_=0.02 , a_=True , a_=True , a_=False , a_=False , a_=False , **a_ , ): '''simple docstring''' __snake_case : Dict = vocab_size __snake_case : int = n_positions __snake_case : int = n_embd __snake_case : List[Any] = n_layer __snake_case : Any = n_head __snake_case : List[Any] = n_inner __snake_case : Tuple = activation_function __snake_case : Optional[Any] = resid_pdrop __snake_case : str = embd_pdrop __snake_case : List[str] = attn_pdrop __snake_case : Dict = layer_norm_epsilon __snake_case : List[Any] = initializer_range __snake_case : Any = scale_attn_weights __snake_case : List[str] = use_cache __snake_case : int = scale_attn_by_inverse_layer_idx __snake_case : Optional[int] = reorder_and_upcast_attn __snake_case : Dict = tie_word_embeddings super().__init__(tie_word_embeddings=a_ , **a_ ) class _UpperCAmelCase ( __snake_case ): '''simple docstring''' @property def SCREAMING_SNAKE_CASE (self ): '''simple docstring''' return OrderedDict( [ ('''input_ids''', {0: '''batch''', 1: '''sequence'''}), ] ) def SCREAMING_SNAKE_CASE (self , a_ , a_ = 1 , a_ = -1 , a_ = False , a_ = None , a_ = 3 , a_ = 32 , a_ = 32 , ): '''simple docstring''' __snake_case : List[Any] = self._generate_dummy_images(a_ , a_ , a_ , a_ ) __snake_case : List[Any] = dict(preprocessor(images=a_ , return_tensors=a_ ) ) return inputs
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'''simple docstring''' import argparse import gc import json import os import shutil import warnings import torch from transformers import LlamaConfig, LlamaForCausalLM, LlamaTokenizer try: from transformers import LlamaTokenizerFast except ImportError as e: warnings.warn(e) warnings.warn( "The converted tokenizer will be the `slow` tokenizer. To use the fast, update your `tokenizers` library and re-run the tokenizer conversion" ) UpperCamelCase__: str = None UpperCamelCase__: int = { "7B": 11008, "13B": 13824, "30B": 17920, "65B": 22016, "70B": 28672, } UpperCamelCase__: List[Any] = { "7B": 1, "7Bf": 1, "13B": 2, "13Bf": 2, "30B": 4, "65B": 8, "70B": 8, "70Bf": 8, } def snake_case_ ( _lowerCAmelCase : Tuple , _lowerCAmelCase : Tuple=1 , _lowerCAmelCase : List[Any]=256 ) -> Optional[Any]: return multiple_of * ((int(ffn_dim_multiplier * int(8 * n / 3 ) ) + multiple_of - 1) // multiple_of) def snake_case_ ( _lowerCAmelCase : List[str] ) -> str: with open(_lowerCAmelCase , '''r''' ) as f: return json.load(_lowerCAmelCase ) def snake_case_ ( _lowerCAmelCase : Union[str, Any] , _lowerCAmelCase : Any ) -> Optional[Any]: with open(_lowerCAmelCase , '''w''' ) as f: json.dump(_lowerCAmelCase , _lowerCAmelCase ) def snake_case_ ( _lowerCAmelCase : Dict , _lowerCAmelCase : Tuple , _lowerCAmelCase : Any , _lowerCAmelCase : Any=True ) -> List[Any]: os.makedirs(_lowerCAmelCase , exist_ok=_lowerCAmelCase ) UpperCAmelCase : int = os.path.join(_lowerCAmelCase , '''tmp''' ) os.makedirs(_lowerCAmelCase , exist_ok=_lowerCAmelCase ) UpperCAmelCase : List[str] = read_json(os.path.join(_lowerCAmelCase , '''params.json''' ) ) UpperCAmelCase : str = NUM_SHARDS[model_size] UpperCAmelCase : Any = params['''n_layers'''] UpperCAmelCase : str = params['''n_heads'''] UpperCAmelCase : Any = n_heads // num_shards UpperCAmelCase : List[str] = params['''dim'''] UpperCAmelCase : Optional[Any] = dim // n_heads UpperCAmelCase : str = 1_0_0_0_0.0 UpperCAmelCase : Optional[int] = 1.0 / (base ** (torch.arange(0 , _lowerCAmelCase , 2 ).float() / dims_per_head)) if "n_kv_heads" in params: UpperCAmelCase : Tuple = params['''n_kv_heads'''] # for GQA / MQA UpperCAmelCase : Optional[int] = n_heads_per_shard // num_key_value_heads UpperCAmelCase : Optional[Any] = dim // num_key_value_heads else: # compatibility with other checkpoints UpperCAmelCase : List[str] = n_heads UpperCAmelCase : Optional[int] = n_heads_per_shard UpperCAmelCase : List[str] = dim # permute for sliced rotary def permute(_lowerCAmelCase : Tuple , _lowerCAmelCase : Optional[Any]=n_heads , _lowerCAmelCase : int=dim , _lowerCAmelCase : Dict=dim ): return w.view(_lowerCAmelCase , dima // n_heads // 2 , 2 , _lowerCAmelCase ).transpose(1 , 2 ).reshape(_lowerCAmelCase , _lowerCAmelCase ) print(f"""Fetching all parameters from the checkpoint at {input_base_path}.""" ) # Load weights if model_size == "7B": # Not sharded # (The sharded implementation would also work, but this is simpler.) UpperCAmelCase : int = torch.load(os.path.join(_lowerCAmelCase , '''consolidated.00.pth''' ) , map_location='''cpu''' ) else: # Sharded UpperCAmelCase : Optional[Any] = [ torch.load(os.path.join(_lowerCAmelCase , f"""consolidated.{i:02d}.pth""" ) , map_location='''cpu''' ) for i in range(_lowerCAmelCase ) ] UpperCAmelCase : Any = 0 UpperCAmelCase : str = {'''weight_map''': {}} for layer_i in range(_lowerCAmelCase ): UpperCAmelCase : Optional[Any] = f"""pytorch_model-{layer_i + 1}-of-{n_layers + 1}.bin""" if model_size == "7B": # Unsharded UpperCAmelCase : Optional[int] = { f"""model.layers.{layer_i}.self_attn.q_proj.weight""": permute( loaded[f"""layers.{layer_i}.attention.wq.weight"""] ), f"""model.layers.{layer_i}.self_attn.k_proj.weight""": permute( loaded[f"""layers.{layer_i}.attention.wk.weight"""] ), f"""model.layers.{layer_i}.self_attn.v_proj.weight""": loaded[f"""layers.{layer_i}.attention.wv.weight"""], f"""model.layers.{layer_i}.self_attn.o_proj.weight""": loaded[f"""layers.{layer_i}.attention.wo.weight"""], f"""model.layers.{layer_i}.mlp.gate_proj.weight""": loaded[f"""layers.{layer_i}.feed_forward.w1.weight"""], f"""model.layers.{layer_i}.mlp.down_proj.weight""": loaded[f"""layers.{layer_i}.feed_forward.w2.weight"""], f"""model.layers.{layer_i}.mlp.up_proj.weight""": loaded[f"""layers.{layer_i}.feed_forward.w3.weight"""], f"""model.layers.{layer_i}.input_layernorm.weight""": loaded[f"""layers.{layer_i}.attention_norm.weight"""], f"""model.layers.{layer_i}.post_attention_layernorm.weight""": loaded[f"""layers.{layer_i}.ffn_norm.weight"""], } else: # Sharded # Note that attention.w{q,k,v,o}, feed_fordward.w[1,2,3], attention_norm.weight and ffn_norm.weight share # the same storage object, saving attention_norm and ffn_norm will save other weights too, which is # redundant as other weights will be stitched from multiple shards. To avoid that, they are cloned. UpperCAmelCase : List[str] = { f"""model.layers.{layer_i}.input_layernorm.weight""": loaded[0][ f"""layers.{layer_i}.attention_norm.weight""" ].clone(), f"""model.layers.{layer_i}.post_attention_layernorm.weight""": loaded[0][ f"""layers.{layer_i}.ffn_norm.weight""" ].clone(), } UpperCAmelCase : Union[str, Any] = permute( torch.cat( [ loaded[i][f"""layers.{layer_i}.attention.wq.weight"""].view(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ) for i in range(_lowerCAmelCase ) ] , dim=0 , ).reshape(_lowerCAmelCase , _lowerCAmelCase ) ) UpperCAmelCase : Optional[Any] = permute( torch.cat( [ loaded[i][f"""layers.{layer_i}.attention.wk.weight"""].view( _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ) for i in range(_lowerCAmelCase ) ] , dim=0 , ).reshape(_lowerCAmelCase , _lowerCAmelCase ) , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , ) UpperCAmelCase : str = torch.cat( [ loaded[i][f"""layers.{layer_i}.attention.wv.weight"""].view( _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ) for i in range(_lowerCAmelCase ) ] , dim=0 , ).reshape(_lowerCAmelCase , _lowerCAmelCase ) UpperCAmelCase : Optional[int] = torch.cat( [loaded[i][f"""layers.{layer_i}.attention.wo.weight"""] for i in range(_lowerCAmelCase )] , dim=1 ) UpperCAmelCase : Any = torch.cat( [loaded[i][f"""layers.{layer_i}.feed_forward.w1.weight"""] for i in range(_lowerCAmelCase )] , dim=0 ) UpperCAmelCase : str = torch.cat( [loaded[i][f"""layers.{layer_i}.feed_forward.w2.weight"""] for i in range(_lowerCAmelCase )] , dim=1 ) UpperCAmelCase : Tuple = torch.cat( [loaded[i][f"""layers.{layer_i}.feed_forward.w3.weight"""] for i in range(_lowerCAmelCase )] , dim=0 ) UpperCAmelCase : Any = inv_freq for k, v in state_dict.items(): UpperCAmelCase : List[Any] = filename param_count += v.numel() torch.save(_lowerCAmelCase , os.path.join(_lowerCAmelCase , _lowerCAmelCase ) ) UpperCAmelCase : Optional[int] = f"""pytorch_model-{n_layers + 1}-of-{n_layers + 1}.bin""" if model_size == "7B": # Unsharded UpperCAmelCase : str = { '''model.embed_tokens.weight''': loaded['''tok_embeddings.weight'''], '''model.norm.weight''': loaded['''norm.weight'''], '''lm_head.weight''': loaded['''output.weight'''], } else: UpperCAmelCase : Any = { '''model.norm.weight''': loaded[0]['''norm.weight'''], '''model.embed_tokens.weight''': torch.cat( [loaded[i]['''tok_embeddings.weight'''] for i in range(_lowerCAmelCase )] , dim=1 ), '''lm_head.weight''': torch.cat([loaded[i]['''output.weight'''] for i in range(_lowerCAmelCase )] , dim=0 ), } for k, v in state_dict.items(): UpperCAmelCase : Optional[int] = filename param_count += v.numel() torch.save(_lowerCAmelCase , os.path.join(_lowerCAmelCase , _lowerCAmelCase ) ) # Write configs UpperCAmelCase : Union[str, Any] = {'''total_size''': param_count * 2} write_json(_lowerCAmelCase , os.path.join(_lowerCAmelCase , '''pytorch_model.bin.index.json''' ) ) UpperCAmelCase : int = params['''ffn_dim_multiplier'''] if '''ffn_dim_multiplier''' in params else 1 UpperCAmelCase : Tuple = params['''multiple_of'''] if '''multiple_of''' in params else 256 UpperCAmelCase : Any = LlamaConfig( hidden_size=_lowerCAmelCase , intermediate_size=compute_intermediate_size(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ) , num_attention_heads=params['''n_heads'''] , num_hidden_layers=params['''n_layers'''] , rms_norm_eps=params['''norm_eps'''] , num_key_value_heads=_lowerCAmelCase , ) config.save_pretrained(_lowerCAmelCase ) # Make space so we can load the model properly now. del state_dict del loaded gc.collect() print('''Loading the checkpoint in a Llama model.''' ) UpperCAmelCase : Optional[int] = LlamaForCausalLM.from_pretrained(_lowerCAmelCase , torch_dtype=torch.floataa , low_cpu_mem_usage=_lowerCAmelCase ) # Avoid saving this as part of the config. del model.config._name_or_path print('''Saving in the Transformers format.''' ) model.save_pretrained(_lowerCAmelCase , safe_serialization=_lowerCAmelCase ) shutil.rmtree(_lowerCAmelCase ) def snake_case_ ( _lowerCAmelCase : int , _lowerCAmelCase : Union[str, Any] ) -> List[str]: # Initialize the tokenizer based on the `spm` model UpperCAmelCase : Dict = LlamaTokenizer if LlamaTokenizerFast is None else LlamaTokenizerFast print(f"""Saving a {tokenizer_class.__name__} to {tokenizer_path}.""" ) UpperCAmelCase : List[Any] = tokenizer_class(_lowerCAmelCase ) tokenizer.save_pretrained(_lowerCAmelCase ) def snake_case_ ( ) -> List[Any]: UpperCAmelCase : int = argparse.ArgumentParser() parser.add_argument( '''--input_dir''' , help='''Location of LLaMA weights, which contains tokenizer.model and model folders''' , ) parser.add_argument( '''--model_size''' , choices=['''7B''', '''7Bf''', '''13B''', '''13Bf''', '''30B''', '''65B''', '''70B''', '''70Bf''', '''tokenizer_only'''] , ) parser.add_argument( '''--output_dir''' , help='''Location to write HF model and tokenizer''' , ) parser.add_argument('''--safe_serialization''' , type=_lowerCAmelCase , help='''Whether or not to save using `safetensors`.''' ) UpperCAmelCase : List[Any] = parser.parse_args() if args.model_size != "tokenizer_only": write_model( model_path=args.output_dir , input_base_path=os.path.join(args.input_dir , args.model_size ) , model_size=args.model_size , safe_serialization=args.safe_serialization , ) UpperCAmelCase : Optional[int] = os.path.join(args.input_dir , '''tokenizer.model''' ) write_tokenizer(args.output_dir , _lowerCAmelCase ) if __name__ == "__main__": main()
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def UpperCamelCase( __UpperCamelCase : List[Any] ): # if the collection is empty, returns empty if collection == []: return [] # get some information about the collection lowerCAmelCase_ : List[Any] = len(__UpperCamelCase ) lowerCAmelCase_ : Any = max(__UpperCamelCase ) lowerCAmelCase_ : str = min(__UpperCamelCase ) # create the counting array lowerCAmelCase_ : Union[str, Any] = coll_max + 1 - coll_min lowerCAmelCase_ : List[str] = [0] * counting_arr_length # count how much a number appears in the collection for number in collection: counting_arr[number - coll_min] += 1 # sum each position with it's predecessors. now, counting_arr[i] tells # us how many elements <= i has in the collection for i in range(1 ,__UpperCamelCase ): lowerCAmelCase_ : Optional[Any] = counting_arr[i] + counting_arr[i - 1] # create the output collection lowerCAmelCase_ : List[str] = [0] * coll_len # place the elements in the output, respecting the original order (stable # sort) from end to begin, updating counting_arr for i in reversed(range(0 ,__UpperCamelCase ) ): lowerCAmelCase_ : Dict = collection[i] counting_arr[collection[i] - coll_min] -= 1 return ordered def UpperCamelCase( __UpperCamelCase : Dict ): return "".join([chr(__UpperCamelCase ) for i in counting_sort([ord(__UpperCamelCase ) for c in string] )] ) if __name__ == "__main__": # Test string sort assert counting_sort_string('''thisisthestring''') == "eghhiiinrsssttt" A__ : Union[str, Any] = input('''Enter numbers separated by a comma:\n''').strip() A__ : Dict = [int(item) for item in user_input.split(''',''')] print(counting_sort(unsorted))
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'''simple docstring''' from math import cos, sin, sqrt, tau from audio_filters.iir_filter import IIRFilter def snake_case_ ( _lowerCAmelCase : int , _lowerCAmelCase : int , _lowerCAmelCase : float = 1 / sqrt(2 ) ) -> IIRFilter: UpperCAmelCase : Optional[int] = tau * frequency / samplerate UpperCAmelCase : List[Any] = sin(_lowerCAmelCase ) UpperCAmelCase : Optional[Any] = cos(_lowerCAmelCase ) UpperCAmelCase : int = _sin / (2 * q_factor) UpperCAmelCase : Any = (1 - _cos) / 2 UpperCAmelCase : List[Any] = 1 - _cos UpperCAmelCase : Union[str, Any] = 1 + alpha UpperCAmelCase : Any = -2 * _cos UpperCAmelCase : Dict = 1 - alpha UpperCAmelCase : Union[str, Any] = IIRFilter(2 ) filt.set_coefficients([aa, aa, aa] , [ba, ba, ba] ) return filt def snake_case_ ( _lowerCAmelCase : int , _lowerCAmelCase : int , _lowerCAmelCase : float = 1 / sqrt(2 ) ) -> IIRFilter: UpperCAmelCase : Any = tau * frequency / samplerate UpperCAmelCase : Tuple = sin(_lowerCAmelCase ) UpperCAmelCase : Tuple = cos(_lowerCAmelCase ) UpperCAmelCase : Dict = _sin / (2 * q_factor) UpperCAmelCase : int = (1 + _cos) / 2 UpperCAmelCase : List[Any] = -1 - _cos UpperCAmelCase : Tuple = 1 + alpha UpperCAmelCase : List[str] = -2 * _cos UpperCAmelCase : Optional[Any] = 1 - alpha UpperCAmelCase : Tuple = IIRFilter(2 ) filt.set_coefficients([aa, aa, aa] , [ba, ba, ba] ) return filt def snake_case_ ( _lowerCAmelCase : int , _lowerCAmelCase : int , _lowerCAmelCase : float = 1 / sqrt(2 ) ) -> IIRFilter: UpperCAmelCase : Optional[int] = tau * frequency / samplerate UpperCAmelCase : Optional[int] = sin(_lowerCAmelCase ) UpperCAmelCase : Tuple = cos(_lowerCAmelCase ) UpperCAmelCase : Optional[int] = _sin / (2 * q_factor) UpperCAmelCase : Union[str, Any] = _sin / 2 UpperCAmelCase : Any = 0 UpperCAmelCase : int = -ba UpperCAmelCase : Optional[Any] = 1 + alpha UpperCAmelCase : List[Any] = -2 * _cos UpperCAmelCase : Optional[Any] = 1 - alpha UpperCAmelCase : int = IIRFilter(2 ) filt.set_coefficients([aa, aa, aa] , [ba, ba, ba] ) return filt def snake_case_ ( _lowerCAmelCase : int , _lowerCAmelCase : int , _lowerCAmelCase : float = 1 / sqrt(2 ) ) -> IIRFilter: UpperCAmelCase : List[str] = tau * frequency / samplerate UpperCAmelCase : Union[str, Any] = sin(_lowerCAmelCase ) UpperCAmelCase : str = cos(_lowerCAmelCase ) UpperCAmelCase : Optional[Any] = _sin / (2 * q_factor) UpperCAmelCase : List[str] = 1 - alpha UpperCAmelCase : Any = -2 * _cos UpperCAmelCase : Optional[int] = 1 + alpha UpperCAmelCase : Union[str, Any] = IIRFilter(2 ) filt.set_coefficients([ba, ba, ba] , [ba, ba, ba] ) return filt def snake_case_ ( _lowerCAmelCase : int , _lowerCAmelCase : int , _lowerCAmelCase : float , _lowerCAmelCase : float = 1 / sqrt(2 ) , ) -> IIRFilter: UpperCAmelCase : Optional[Any] = tau * frequency / samplerate UpperCAmelCase : Union[str, Any] = sin(_lowerCAmelCase ) UpperCAmelCase : Optional[int] = cos(_lowerCAmelCase ) UpperCAmelCase : Dict = _sin / (2 * q_factor) UpperCAmelCase : str = 10 ** (gain_db / 40) UpperCAmelCase : int = 1 + alpha * big_a UpperCAmelCase : Union[str, Any] = -2 * _cos UpperCAmelCase : Optional[Any] = 1 - alpha * big_a UpperCAmelCase : Union[str, Any] = 1 + alpha / big_a UpperCAmelCase : Tuple = -2 * _cos UpperCAmelCase : Any = 1 - alpha / big_a UpperCAmelCase : Optional[Any] = IIRFilter(2 ) filt.set_coefficients([aa, aa, aa] , [ba, ba, ba] ) return filt def snake_case_ ( _lowerCAmelCase : int , _lowerCAmelCase : int , _lowerCAmelCase : float , _lowerCAmelCase : float = 1 / sqrt(2 ) , ) -> IIRFilter: UpperCAmelCase : Any = tau * frequency / samplerate UpperCAmelCase : Optional[int] = sin(_lowerCAmelCase ) UpperCAmelCase : Union[str, Any] = cos(_lowerCAmelCase ) UpperCAmelCase : str = _sin / (2 * q_factor) UpperCAmelCase : List[str] = 10 ** (gain_db / 40) UpperCAmelCase : Optional[int] = (big_a + 1) - (big_a - 1) * _cos UpperCAmelCase : int = (big_a + 1) + (big_a - 1) * _cos UpperCAmelCase : int = (big_a - 1) - (big_a + 1) * _cos UpperCAmelCase : Optional[int] = (big_a - 1) + (big_a + 1) * _cos UpperCAmelCase : str = 2 * sqrt(_lowerCAmelCase ) * alpha UpperCAmelCase : Dict = big_a * (pmc + aaa) UpperCAmelCase : Any = 2 * big_a * mpc UpperCAmelCase : Union[str, Any] = big_a * (pmc - aaa) UpperCAmelCase : Optional[int] = ppmc + aaa UpperCAmelCase : Optional[Any] = -2 * pmpc UpperCAmelCase : Optional[Any] = ppmc - aaa UpperCAmelCase : int = IIRFilter(2 ) filt.set_coefficients([aa, aa, aa] , [ba, ba, ba] ) return filt def snake_case_ ( _lowerCAmelCase : int , _lowerCAmelCase : int , _lowerCAmelCase : float , _lowerCAmelCase : float = 1 / sqrt(2 ) , ) -> IIRFilter: UpperCAmelCase : int = tau * frequency / samplerate UpperCAmelCase : Union[str, Any] = sin(_lowerCAmelCase ) UpperCAmelCase : Union[str, Any] = cos(_lowerCAmelCase ) UpperCAmelCase : Any = _sin / (2 * q_factor) UpperCAmelCase : int = 10 ** (gain_db / 40) UpperCAmelCase : List[str] = (big_a + 1) - (big_a - 1) * _cos UpperCAmelCase : Union[str, Any] = (big_a + 1) + (big_a - 1) * _cos UpperCAmelCase : Optional[Any] = (big_a - 1) - (big_a + 1) * _cos UpperCAmelCase : Union[str, Any] = (big_a - 1) + (big_a + 1) * _cos UpperCAmelCase : List[str] = 2 * sqrt(_lowerCAmelCase ) * alpha UpperCAmelCase : Any = big_a * (ppmc + aaa) UpperCAmelCase : str = -2 * big_a * pmpc UpperCAmelCase : List[Any] = big_a * (ppmc - aaa) UpperCAmelCase : Optional[Any] = pmc + aaa UpperCAmelCase : Any = 2 * mpc UpperCAmelCase : str = pmc - aaa UpperCAmelCase : Union[str, Any] = IIRFilter(2 ) filt.set_coefficients([aa, aa, aa] , [ba, ba, ba] ) return filt
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'''simple docstring''' from typing import List, Optional, Union from ...configuration_utils import PretrainedConfig from ...utils import logging lowerCAmelCase__ = logging.get_logger(__name__) lowerCAmelCase__ = { '''huggingface/informer-tourism-monthly''': ( '''https://huggingface.co/huggingface/informer-tourism-monthly/resolve/main/config.json''' ), # See all Informer models at https://huggingface.co/models?filter=informer } class lowercase_ (lowerCamelCase__ ): """simple docstring""" SCREAMING_SNAKE_CASE : Any = 'informer' SCREAMING_SNAKE_CASE : Optional[Any] = { 'hidden_size': 'd_model', 'num_attention_heads': 'encoder_attention_heads', 'num_hidden_layers': 'encoder_layers', } def __init__( self : Tuple ,lowercase__ : Optional[int] = None ,lowercase__ : Optional[int] = None ,lowercase__ : str = "student_t" ,lowercase__ : str = "nll" ,lowercase__ : int = 1 ,lowercase__ : List[int] = None ,lowercase__ : Optional[Union[str, bool]] = "mean" ,lowercase__ : int = 0 ,lowercase__ : int = 0 ,lowercase__ : int = 0 ,lowercase__ : int = 0 ,lowercase__ : Optional[List[int]] = None ,lowercase__ : Optional[List[int]] = None ,lowercase__ : int = 6_4 ,lowercase__ : int = 3_2 ,lowercase__ : int = 3_2 ,lowercase__ : int = 2 ,lowercase__ : int = 2 ,lowercase__ : int = 2 ,lowercase__ : int = 2 ,lowercase__ : bool = True ,lowercase__ : str = "gelu" ,lowercase__ : float = 0.0_5 ,lowercase__ : float = 0.1 ,lowercase__ : float = 0.1 ,lowercase__ : float = 0.1 ,lowercase__ : float = 0.1 ,lowercase__ : int = 1_0_0 ,lowercase__ : float = 0.0_2 ,lowercase__ : Any=True ,lowercase__ : str = "prob" ,lowercase__ : int = 5 ,lowercase__ : bool = True ,**lowercase__ : List[str] ,): # time series specific configuration __lowercase = prediction_length __lowercase = context_length or prediction_length __lowercase = distribution_output __lowercase = loss __lowercase = input_size __lowercase = num_time_features __lowercase = lags_sequence if lags_sequence is not None else [1, 2, 3, 4, 5, 6, 7] __lowercase = scaling __lowercase = num_dynamic_real_features __lowercase = num_static_real_features __lowercase = num_static_categorical_features # set cardinality if cardinality and num_static_categorical_features > 0: if len(lowercase__ ) != num_static_categorical_features: raise ValueError( '''The cardinality should be a list of the same length as `num_static_categorical_features`''' ) __lowercase = cardinality else: __lowercase = [0] # set embedding_dimension if embedding_dimension and num_static_categorical_features > 0: if len(lowercase__ ) != num_static_categorical_features: raise ValueError( '''The embedding dimension should be a list of the same length as `num_static_categorical_features`''' ) __lowercase = embedding_dimension else: __lowercase = [min(5_0 ,(cat + 1) // 2 ) for cat in self.cardinality] __lowercase = num_parallel_samples # Transformer architecture configuration __lowercase = input_size * len(self.lags_sequence ) + self._number_of_features __lowercase = d_model __lowercase = encoder_attention_heads __lowercase = decoder_attention_heads __lowercase = encoder_ffn_dim __lowercase = decoder_ffn_dim __lowercase = encoder_layers __lowercase = decoder_layers __lowercase = dropout __lowercase = attention_dropout __lowercase = activation_dropout __lowercase = encoder_layerdrop __lowercase = decoder_layerdrop __lowercase = activation_function __lowercase = init_std __lowercase = use_cache # Informer __lowercase = attention_type __lowercase = sampling_factor __lowercase = distil super().__init__(is_encoder_decoder=lowercase__ ,**lowercase__ ) @property def SCREAMING_SNAKE_CASE ( self : List[Any] ): return ( sum(self.embedding_dimension ) + self.num_dynamic_real_features + self.num_time_features + self.num_static_real_features + self.input_size * 2 # the log1p(abs(loc)) and log(scale) features )
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'''simple docstring''' from __future__ import annotations def snake_case_ ( _lowerCAmelCase : str , _lowerCAmelCase : str ) -> bool: UpperCAmelCase : str = get_failure_array(_lowerCAmelCase ) # 2) Step through text searching for pattern UpperCAmelCase , UpperCAmelCase : Optional[Any] = 0, 0 # index into text, pattern while i < len(_lowerCAmelCase ): if pattern[j] == text[i]: if j == (len(_lowerCAmelCase ) - 1): return True j += 1 # if this is a prefix in our pattern # just go back far enough to continue elif j > 0: UpperCAmelCase : Optional[Any] = failure[j - 1] continue i += 1 return False def snake_case_ ( _lowerCAmelCase : str ) -> list[int]: UpperCAmelCase : Optional[Any] = [0] UpperCAmelCase : str = 0 UpperCAmelCase : List[str] = 1 while j < len(_lowerCAmelCase ): if pattern[i] == pattern[j]: i += 1 elif i > 0: UpperCAmelCase : Union[str, Any] = failure[i - 1] continue j += 1 failure.append(_lowerCAmelCase ) return failure if __name__ == "__main__": # Test 1) UpperCamelCase__: str = "abc1abc12" UpperCamelCase__: str = "alskfjaldsabc1abc1abc12k23adsfabcabc" UpperCamelCase__: Any = "alskfjaldsk23adsfabcabc" assert kmp(pattern, texta) and not kmp(pattern, texta) # Test 2) UpperCamelCase__: Tuple = "ABABX" UpperCamelCase__: Union[str, Any] = "ABABZABABYABABX" assert kmp(pattern, text) # Test 3) UpperCamelCase__: Any = "AAAB" UpperCamelCase__: str = "ABAAAAAB" assert kmp(pattern, text) # Test 4) UpperCamelCase__: int = "abcdabcy" UpperCamelCase__: Any = "abcxabcdabxabcdabcdabcy" assert kmp(pattern, text) # Test 5) UpperCamelCase__: List[str] = "aabaabaaa" assert get_failure_array(pattern) == [0, 1, 0, 1, 2, 3, 4, 5, 2]
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"""simple docstring""" from math import factorial def _SCREAMING_SNAKE_CASE ( _lowercase : int = 100 ) ->int: '''simple docstring''' return sum(map(_lowercase , str(factorial(_lowercase ) ) ) ) if __name__ == "__main__": print(solution(int(input('''Enter the Number: ''').strip())))
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'''simple docstring''' import argparse import json import os import fairseq import torch from fairseq.data import Dictionary from transformers import ( WavaVecaConfig, WavaVecaCTCTokenizer, WavaVecaFeatureExtractor, WavaVecaForCTC, WavaVecaForPreTraining, WavaVecaProcessor, logging, ) from transformers.models.wavaveca.modeling_wavaveca import WavaVecaForSequenceClassification logging.set_verbosity_info() UpperCamelCase__: int = logging.get_logger(__name__) UpperCamelCase__: Dict = { "post_extract_proj": "feature_projection.projection", "encoder.pos_conv.0": "encoder.pos_conv_embed.conv", "self_attn.k_proj": "encoder.layers.*.attention.k_proj", "self_attn.v_proj": "encoder.layers.*.attention.v_proj", "self_attn.q_proj": "encoder.layers.*.attention.q_proj", "self_attn.out_proj": "encoder.layers.*.attention.out_proj", "self_attn_layer_norm": "encoder.layers.*.layer_norm", "fc1": "encoder.layers.*.feed_forward.intermediate_dense", "fc2": "encoder.layers.*.feed_forward.output_dense", "final_layer_norm": "encoder.layers.*.final_layer_norm", "encoder.layer_norm": "encoder.layer_norm", "adapter_layer": "encoder.layers.*.adapter_layer", "w2v_model.layer_norm": "feature_projection.layer_norm", "quantizer.weight_proj": "quantizer.weight_proj", "quantizer.vars": "quantizer.codevectors", "project_q": "project_q", "final_proj": "project_hid", "w2v_encoder.proj": "lm_head", "mask_emb": "masked_spec_embed", "pooling_layer.linear": "projector", "pooling_layer.projection": "classifier", } UpperCamelCase__: Optional[Any] = [ "lm_head", "quantizer.weight_proj", "quantizer.codevectors", "project_q", "project_hid", "projector", "classifier", ] def snake_case_ ( _lowerCAmelCase : str ) -> Union[str, Any]: UpperCAmelCase : Optional[int] = {} with open(_lowerCAmelCase , '''r''' ) as file: for line_number, line in enumerate(_lowerCAmelCase ): UpperCAmelCase : List[str] = line.strip() if line: UpperCAmelCase : str = line.split() UpperCAmelCase : Union[str, Any] = line_number UpperCAmelCase : List[Any] = words[0] UpperCAmelCase : Union[str, Any] = value return result def snake_case_ ( _lowerCAmelCase : int , _lowerCAmelCase : Optional[Any] , _lowerCAmelCase : Optional[Any] , _lowerCAmelCase : Tuple , _lowerCAmelCase : str ) -> int: for attribute in key.split('''.''' ): UpperCAmelCase : Dict = getattr(_lowerCAmelCase , _lowerCAmelCase ) UpperCAmelCase : Dict = None for param_key in PARAM_MAPPING.keys(): if full_name.endswith(_lowerCAmelCase ): UpperCAmelCase : Any = PARAM_MAPPING[full_name.split('''.''' )[-1]] UpperCAmelCase : Dict = '''param''' if weight_type is not None and weight_type != "param": UpperCAmelCase : Optional[int] = getattr(_lowerCAmelCase , _lowerCAmelCase ).shape elif weight_type is not None and weight_type == "param": UpperCAmelCase : List[Any] = hf_pointer for attribute in hf_param_name.split('''.''' ): UpperCAmelCase : Optional[Any] = getattr(_lowerCAmelCase , _lowerCAmelCase ) UpperCAmelCase : int = shape_pointer.shape # let's reduce dimension UpperCAmelCase : Union[str, Any] = value[0] else: UpperCAmelCase : List[Any] = hf_pointer.shape if hf_shape != value.shape: raise ValueError( f"""Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be""" f""" {value.shape} for {full_name}""" ) if weight_type == "weight": UpperCAmelCase : int = value elif weight_type == "weight_g": UpperCAmelCase : str = value elif weight_type == "weight_v": UpperCAmelCase : Dict = value elif weight_type == "bias": UpperCAmelCase : str = value elif weight_type == "param": for attribute in hf_param_name.split('''.''' ): UpperCAmelCase : int = getattr(_lowerCAmelCase , _lowerCAmelCase ) UpperCAmelCase : Optional[int] = value else: UpperCAmelCase : Tuple = value logger.info(f"""{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.""" ) def snake_case_ ( _lowerCAmelCase : Optional[int] , _lowerCAmelCase : Any , _lowerCAmelCase : List[Any] , _lowerCAmelCase : Dict , _lowerCAmelCase : List[Any] ) -> List[Any]: UpperCAmelCase : List[str] = None for param_key in PARAM_MAPPING.keys(): if full_name.endswith(_lowerCAmelCase ): UpperCAmelCase : List[str] = PARAM_MAPPING[full_name.split('''.''' )[-1]] UpperCAmelCase : Any = '''param''' if weight_type is not None and weight_type != "param": UpperCAmelCase : Optional[int] = '''.'''.join([key, weight_type] ) elif weight_type is not None and weight_type == "param": UpperCAmelCase : Optional[int] = '''.'''.join([key, hf_param_name] ) else: UpperCAmelCase : List[Any] = key UpperCAmelCase : Tuple = value if '''lm_head''' in full_key else value[0] UpperCamelCase__: Tuple = { "W_a": "linear_1.weight", "W_b": "linear_2.weight", "b_a": "linear_1.bias", "b_b": "linear_2.bias", "ln_W": "norm.weight", "ln_b": "norm.bias", } def snake_case_ ( _lowerCAmelCase : str , _lowerCAmelCase : List[str] , _lowerCAmelCase : Any=None , _lowerCAmelCase : Optional[Any]=None ) -> int: UpperCAmelCase : List[Any] = False for key, mapped_key in MAPPING.items(): UpperCAmelCase : int = '''wav2vec2.''' + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key if key in name or key.split('''w2v_model.''' )[-1] == name.split('''.''' )[0]: UpperCAmelCase : Optional[Any] = True if "*" in mapped_key: UpperCAmelCase : Tuple = name.split(_lowerCAmelCase )[0].split('''.''' )[-2] UpperCAmelCase : List[Any] = mapped_key.replace('''*''' , _lowerCAmelCase ) if "weight_g" in name: UpperCAmelCase : str = '''weight_g''' elif "weight_v" in name: UpperCAmelCase : int = '''weight_v''' elif "bias" in name: UpperCAmelCase : int = '''bias''' elif "weight" in name: # TODO: don't match quantizer.weight_proj UpperCAmelCase : List[str] = '''weight''' else: UpperCAmelCase : Dict = None if hf_dict is not None: rename_dict(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ) else: set_recursively(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ) return is_used return is_used def snake_case_ ( _lowerCAmelCase : Union[str, Any] , _lowerCAmelCase : Optional[Any] , _lowerCAmelCase : Any ) -> Any: UpperCAmelCase : Dict = [] UpperCAmelCase : Dict = fairseq_model.state_dict() UpperCAmelCase : Union[str, Any] = hf_model.wavaveca.feature_extractor for name, value in fairseq_dict.items(): UpperCAmelCase : Dict = False if "conv_layers" in name: load_conv_layer( _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , hf_model.config.feat_extract_norm == '''group''' , ) UpperCAmelCase : Any = True else: UpperCAmelCase : Optional[Any] = load_wavaveca_layer(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ) if not is_used: unused_weights.append(_lowerCAmelCase ) logger.warning(f"""Unused weights: {unused_weights}""" ) def snake_case_ ( _lowerCAmelCase : Optional[int] , _lowerCAmelCase : Tuple , _lowerCAmelCase : Any , _lowerCAmelCase : Optional[int] , _lowerCAmelCase : Optional[int] ) -> Union[str, Any]: UpperCAmelCase : Any = full_name.split('''conv_layers.''' )[-1] UpperCAmelCase : Optional[int] = name.split('''.''' ) UpperCAmelCase : Tuple = int(items[0] ) UpperCAmelCase : Tuple = int(items[1] ) if type_id == 0: if "bias" in name: if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape: raise ValueError( f"""{full_name} has size {value.shape}, but""" f""" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.""" ) UpperCAmelCase : Tuple = value logger.info(f"""Feat extract conv layer {layer_id} was initialized from {full_name}.""" ) elif "weight" in name: if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape: raise ValueError( f"""{full_name} has size {value.shape}, but""" f""" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.""" ) UpperCAmelCase : Union[str, Any] = value logger.info(f"""Feat extract conv layer {layer_id} was initialized from {full_name}.""" ) elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm): if "bias" in name: if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape: raise ValueError( f"""{full_name} has size {value.shape}, but""" f""" {feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape} was found.""" ) UpperCAmelCase : Union[str, Any] = value logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.""" ) elif "weight" in name: if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape: raise ValueError( f"""{full_name} has size {value.shape}, but""" f""" {feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape} was found.""" ) UpperCAmelCase : List[str] = value logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.""" ) else: unused_weights.append(_lowerCAmelCase ) @torch.no_grad() def snake_case_ ( _lowerCAmelCase : Union[str, Any] , _lowerCAmelCase : List[Any] , _lowerCAmelCase : Dict=None , _lowerCAmelCase : List[Any]=None , _lowerCAmelCase : int=True , _lowerCAmelCase : Optional[int]=False ) -> Dict: if config_path is not None: UpperCAmelCase : List[str] = WavaVecaConfig.from_pretrained(_lowerCAmelCase ) else: UpperCAmelCase : List[Any] = WavaVecaConfig() if is_seq_class: UpperCAmelCase : Optional[Any] = read_txt_into_dict(_lowerCAmelCase ) UpperCAmelCase : Optional[int] = idalabel UpperCAmelCase : Optional[Any] = WavaVecaForSequenceClassification(_lowerCAmelCase ) UpperCAmelCase : Dict = WavaVecaFeatureExtractor( feature_size=1 , sampling_rate=16000 , padding_value=0 , do_normalize=_lowerCAmelCase , return_attention_mask=_lowerCAmelCase , ) feature_extractor.save_pretrained(_lowerCAmelCase ) elif is_finetuned: if dict_path: UpperCAmelCase : Dict = Dictionary.load(_lowerCAmelCase ) # important change bos & pad token id since CTC symbol is <pad> and # not <s> as in fairseq UpperCAmelCase : Any = target_dict.pad_index UpperCAmelCase : Tuple = target_dict.bos_index UpperCAmelCase : Optional[int] = target_dict.eos_index UpperCAmelCase : Union[str, Any] = len(target_dict.symbols ) UpperCAmelCase : Dict = os.path.join(_lowerCAmelCase , '''vocab.json''' ) if not os.path.isdir(_lowerCAmelCase ): logger.error('''--pytorch_dump_folder_path ({}) should be a directory'''.format(_lowerCAmelCase ) ) return os.makedirs(_lowerCAmelCase , exist_ok=_lowerCAmelCase ) UpperCAmelCase : List[Any] = target_dict.indices # fairseq has the <pad> and <s> switched UpperCAmelCase : List[str] = 0 UpperCAmelCase : List[str] = 1 with open(_lowerCAmelCase , '''w''' , encoding='''utf-8''' ) as vocab_handle: json.dump(_lowerCAmelCase , _lowerCAmelCase ) UpperCAmelCase : Optional[int] = WavaVecaCTCTokenizer( _lowerCAmelCase , unk_token=target_dict.unk_word , pad_token=target_dict.pad_word , bos_token=target_dict.bos_word , eos_token=target_dict.eos_word , word_delimiter_token='''|''' , do_lower_case=_lowerCAmelCase , ) UpperCAmelCase : int = True if config.feat_extract_norm == '''layer''' else False UpperCAmelCase : int = WavaVecaFeatureExtractor( feature_size=1 , sampling_rate=16000 , padding_value=0 , do_normalize=_lowerCAmelCase , return_attention_mask=_lowerCAmelCase , ) UpperCAmelCase : str = WavaVecaProcessor(feature_extractor=_lowerCAmelCase , tokenizer=_lowerCAmelCase ) processor.save_pretrained(_lowerCAmelCase ) UpperCAmelCase : Union[str, Any] = WavaVecaForCTC(_lowerCAmelCase ) else: UpperCAmelCase : Dict = WavaVecaForPreTraining(_lowerCAmelCase ) if is_finetuned or is_seq_class: UpperCAmelCase , UpperCAmelCase , UpperCAmelCase : List[str] = fairseq.checkpoint_utils.load_model_ensemble_and_task( [checkpoint_path] , arg_overrides={'''data''': '''/'''.join(dict_path.split('''/''' )[:-1] )} ) else: UpperCAmelCase : Optional[Any] = argparse.Namespace(task='''audio_pretraining''' ) UpperCAmelCase : List[Any] = fairseq.tasks.setup_task(_lowerCAmelCase ) UpperCAmelCase , UpperCAmelCase , UpperCAmelCase : int = fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path] , task=_lowerCAmelCase ) UpperCAmelCase : Optional[int] = model[0].eval() recursively_load_weights(_lowerCAmelCase , _lowerCAmelCase , not is_finetuned ) hf_wavavec.save_pretrained(_lowerCAmelCase ) if __name__ == "__main__": UpperCamelCase__: Dict = argparse.ArgumentParser() parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.") parser.add_argument("--checkpoint_path", default=None, type=str, help="Path to fairseq checkpoint") parser.add_argument("--dict_path", default=None, type=str, help="Path to dict of fine-tuned model") parser.add_argument("--config_path", default=None, type=str, help="Path to hf config.json of model to convert") parser.add_argument( "--not_finetuned", action="store_true", help="Whether the model to convert is a fine-tuned model or not" ) parser.add_argument( "--is_seq_class", action="store_true", help="Whether the model to convert is a fine-tuned sequence classification model or not", ) UpperCamelCase__: Any = parser.parse_args() UpperCamelCase__: int = not args.not_finetuned and not args.is_seq_class convert_wavaveca_checkpoint( args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, is_finetuned, args.is_seq_class, )
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"""simple docstring""" import collections import json import os import re from typing import TYPE_CHECKING, List, Optional, Tuple import numpy as np from ...tokenization_utils_fast import PreTrainedTokenizer from ...utils import logging if TYPE_CHECKING: from transformers.pipelines.conversational import Conversation __UpperCamelCase : Any = logging.get_logger(__name__) __UpperCamelCase : Union[str, Any] = {'''vocab_file''': '''vocab.txt''', '''emoji_file''': '''emoji.json'''} __UpperCamelCase : List[Any] = { '''vocab_file''': { '''abeja/gpt-neox-japanese-2.7b''': '''https://huggingface.co/abeja/gpt-neox-japanese-2.7b/resolve/main/vocab.txt''', }, '''emoji_file''': { '''abeja/gpt-neox-japanese-2.7b''': '''https://huggingface.co/abeja/gpt-neox-japanese-2.7b/resolve/main/emoji.json''', }, } __UpperCamelCase : Optional[Any] = { '''abeja/gpt-neox-japanese-2.7b''': 2_0_4_8, } def __SCREAMING_SNAKE_CASE ( A_ , A_ ): with open(A_ , '''r''' , encoding='''utf-8''' ) as f: lowerCAmelCase__ : Dict = json.loads(f.read() ) lowerCAmelCase__ : Union[str, Any] = collections.OrderedDict() lowerCAmelCase__ : Optional[int] = collections.OrderedDict() lowerCAmelCase__ : Optional[Any] = collections.OrderedDict() with open(A_ , '''r''' , encoding='''utf-8''' ) as f: lowerCAmelCase__ : str = f.readlines() lowerCAmelCase__ : Optional[int] = [[t.rstrip('''\n''' )] if (t == ''',''' or ''',''' not in t) else t.rstrip('''\n''' ).split(''',''' ) for t in token] for idx, b in enumerate(A_ ): lowerCAmelCase__ : int = b lowerCAmelCase__ : Dict = idx for wd in b: lowerCAmelCase__ : List[str] = idx return vocab, raw_vocab, ids_to_tokens, emoji class SCREAMING_SNAKE_CASE ( a_ ): """simple docstring""" lowercase__ = VOCAB_FILES_NAMES lowercase__ = PRETRAINED_VOCAB_FILES_MAP lowercase__ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES lowercase__ = ["input_ids", "attention_mask"] def __init__( self : List[Any] ,lowercase_ : Dict ,lowercase_ : Dict ,lowercase_ : int="<|endoftext|>" ,lowercase_ : Dict="<|endoftext|>" ,lowercase_ : str="<|startoftext|>" ,lowercase_ : str="<|endoftext|>" ,lowercase_ : List[Any]=False ,**lowercase_ : Optional[Any] ,): super().__init__( unk_token=lowercase_ ,pad_token=lowercase_ ,bos_token=lowercase_ ,eos_token=lowercase_ ,do_clean_text=lowercase_ ,**lowercase_ ,) if not os.path.isfile(lowercase_ ): raise ValueError( F'Can\'t find a vocabulary file at path \'{vocab_file}\'. To load the vocabulary from a Google pretrained' ''' model use `tokenizer = GPTNeoXJapaneseokenizer.from_pretrained(PRETRAINED_MODEL_NAME)`''' ) if not os.path.isfile(lowercase_ ): raise ValueError( F'Can\'t find a emoji file at path \'{emoji_file}\'. To load the emoji information from a Google' ''' pretrained model use `tokenizer = GPTNeoXJapaneseokenizer.from_pretrained(PRETRAINED_MODEL_NAME)`''' ) lowerCAmelCase__ : Tuple = do_clean_text lowerCAmelCase__ ,lowerCAmelCase__ ,lowerCAmelCase__ ,lowerCAmelCase__ : Any = load_vocab_and_emoji(lowercase_ ,lowercase_ ) lowerCAmelCase__ : Tuple = SubWordJapaneseTokenizer( vocab=self.vocab ,ids_to_tokens=self.ids_to_tokens ,emoji=self.emoji ) @property def __lowerCAmelCase ( self : int ): # self.vocab contains support for character fluctuation unique to Japanese, and has a large number of vocab return len(self.raw_vocab ) def __lowerCAmelCase ( self : Union[str, Any] ): return dict(self.raw_vocab ,**self.added_tokens_encoder ) def __lowerCAmelCase ( self : Any ,lowercase_ : List[Any] ): return self.subword_tokenizer.tokenize(lowercase_ ,clean=self.do_clean_text ) def __lowerCAmelCase ( self : List[str] ,lowercase_ : List[str] ): return self.vocab.get(lowercase_ ,self.vocab.get(self.unk_token ) ) def __lowerCAmelCase ( self : int ,lowercase_ : str ): return self.subword_tokenizer.convert_id_to_token(lowercase_ ) def __lowerCAmelCase ( self : int ,lowercase_ : List[Any] ): lowerCAmelCase__ : Union[str, Any] = ''''''.join(lowercase_ ).strip() return out_string def __lowerCAmelCase ( self : Union[str, Any] ,lowercase_ : "Conversation" ): lowerCAmelCase__ : int = [] for is_user, text in conversation.iter_texts(): input_ids.extend(self.encode(lowercase_ ,add_special_tokens=lowercase_ ) + [self.eos_token_id] ) if len(lowercase_ ) > self.model_max_length: lowerCAmelCase__ : Dict = input_ids[-self.model_max_length :] return input_ids def __lowerCAmelCase ( self : List[str] ,lowercase_ : str ,lowercase_ : Optional[str] = None ): lowerCAmelCase__ : Tuple = 0 if os.path.isdir(lowercase_ ): lowerCAmelCase__ : Union[str, Any] = os.path.join( lowercase_ ,(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] ) lowerCAmelCase__ : List[Any] = os.path.join( lowercase_ ,(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''emoji_file'''] ) else: lowerCAmelCase__ : str = ( (filename_prefix + '''-''' if filename_prefix else '''''') + save_directory + VOCAB_FILES_NAMES['''vocab_file'''] ) lowerCAmelCase__ : Any = ( (filename_prefix + '''-''' if filename_prefix else '''''') + save_directory + VOCAB_FILES_NAMES['''emoji_file'''] ) with open(lowercase_ ,'''w''' ,encoding='''utf-8''' ) as writer: for token_index, token in self.ids_to_tokens.items(): if index != token_index: logger.warning( F'Saving vocabulary to {vocab_file}: vocabulary indices are not consecutive.' ''' Please check that the vocabulary is not corrupted!''' ) lowerCAmelCase__ : Dict = token_index writer.write(''','''.join(lowercase_ ) + '''\n''' ) index += 1 with open(lowercase_ ,'''w''' ,encoding='''utf-8''' ) as writer: json.dump(self.emoji ,lowercase_ ) return vocab_file, emoji_file class SCREAMING_SNAKE_CASE ( a_ ): """simple docstring""" def __init__( self : str ,lowercase_ : Optional[Any] ,lowercase_ : Dict ,lowercase_ : Any ): lowerCAmelCase__ : Optional[int] = vocab # same as swe lowerCAmelCase__ : Optional[int] = ids_to_tokens # same as bpe lowerCAmelCase__ : Optional[Any] = emoji lowerCAmelCase__ : Any = np.max([len(lowercase_ ) for w in self.vocab.keys()] ) lowerCAmelCase__ : int = re.compile(R'''(https?|ftp)(:\/\/[-_\.!~*\'()a-zA-Z0-9;\/?:\@&=\+$,%#]+)''' ) lowerCAmelCase__ : str = re.compile(R'''[A-Za-z0-9\._+]*@[\-_0-9A-Za-z]+(\.[A-Za-z]+)*''' ) lowerCAmelCase__ : List[str] = re.compile(R'''[\(]{0,1}[0-9]{2,4}[\)\-\(]{0,1}[0-9]{2,4}[\)\-]{0,1}[0-9]{3,4}''' ) lowerCAmelCase__ : Union[str, Any] = re.compile( R'''([12]\d{3}[/\-年])*(0?[1-9]|1[0-2])[/\-月]((0?[1-9]|[12][0-9]|3[01])日?)*(\d{1,2}|:|\d{1,2}時|\d{1,2}分|\(日\)|\(月\)|\(火\)|\(水\)|\(木\)|\(金\)|\(土\)|㈰|㈪|㈫|㈬|㈭|㈮|㈯)*''' ) lowerCAmelCase__ : List[Any] = re.compile( R'''(明治|大正|昭和|平成|令和|㍾|㍽|㍼|㍻|\u32ff)\d{1,2}年(0?[1-9]|1[0-2])月(0?[1-9]|[12][0-9]|3[01])日(\d{1,2}|:|\d{1,2}時|\d{1,2}分|\(日\)|\(月\)|\(火\)|\(水\)|\(木\)|\(金\)|\(土\)|㈰|㈪|㈫|㈬|㈭|㈮|㈯)*''' ) lowerCAmelCase__ : Optional[int] = re.compile( R'''((0|[1-9]\d*|[1-9]\d{0,2}(,\d{3})+)*億)*((0|[1-9]\d*|[1-9]\d{0,2}(,\d{3})+)*万)*((0|[1-9]\d*|[1-9]\d{0,2}(,\d{3})+)*千)*(0|[1-9]\d*|[1-9]\d{0,2}(,\d{3})+)*(千円|万円|千万円|円|千ドル|万ドル|千万ドル|ドル|千ユーロ|万ユーロ|千万ユーロ|ユーロ)+(\(税込\)|\(税抜\)|\+tax)*''' ) lowerCAmelCase__ : List[Any] = '''─━│┃┄┅┆┇┈┉┊┋┌┍┎┏┐┑┒┓└┕┖┗┘┙┚┛├┝┞┟┠┡┢┣┤┥┦┧┨┩┪┫┬┭┮┯┰┱┲┳┴┵┶┷┸┹┺┻┼┽┾┿╀╁╂╃╄╅╆╇╈╉╊╋╌╍╎╏═║╒╓╔╕╖╗╘╙╚╛╜╝╞╟╠╡╢╣╤╥╦╧╨╩╪╫╬╭╮╯╰╱╲╳╴╵╶╷╸╹╺╻╼╽╾╿''' lowerCAmelCase__ : List[Any] = '''▀▁▂▃▄▅▆▇█▉▊▋▌▍▎▏▐░▒▓▔▕▖▗▘▙▚▛▜▝▞▟''' lowerCAmelCase__ : List[str] = str.maketrans({k: '''<BLOCK>''' for k in keisen + blocks} ) def __len__( self : int ): return len(self.ids_to_tokens ) def __lowerCAmelCase ( self : Optional[int] ,lowercase_ : Dict ): lowerCAmelCase__ : Tuple = self.content_repattera.sub('''<URL>''' ,lowercase_ ) lowerCAmelCase__ : List[str] = self.content_repattera.sub('''<EMAIL>''' ,lowercase_ ) lowerCAmelCase__ : str = self.content_repattera.sub('''<TEL>''' ,lowercase_ ) lowerCAmelCase__ : Dict = self.content_repattera.sub('''<DATE>''' ,lowercase_ ) lowerCAmelCase__ : Optional[Any] = self.content_repattera.sub('''<DATE>''' ,lowercase_ ) lowerCAmelCase__ : Union[str, Any] = self.content_repattera.sub('''<PRICE>''' ,lowercase_ ) lowerCAmelCase__ : Optional[Any] = content.translate(self.content_transa ) while "<BLOCK><BLOCK>" in content: lowerCAmelCase__ : str = content.replace('''<BLOCK><BLOCK>''' ,'''<BLOCK>''' ) return content def __lowerCAmelCase ( self : List[str] ,lowercase_ : Union[str, Any] ,lowercase_ : List[str]=False ): lowerCAmelCase__ : str = text.replace(''' ''' ,'''<SP>''' ) lowerCAmelCase__ : Tuple = text.replace(''' ''' ,'''<SP>''' ) lowerCAmelCase__ : Optional[int] = text.replace('''\r\n''' ,'''<BR>''' ) lowerCAmelCase__ : str = text.replace('''\n''' ,'''<BR>''' ) lowerCAmelCase__ : Tuple = text.replace('''\r''' ,'''<BR>''' ) lowerCAmelCase__ : Union[str, Any] = text.replace('''\t''' ,'''<TAB>''' ) lowerCAmelCase__ : str = text.replace('''—''' ,'''ー''' ) lowerCAmelCase__ : Union[str, Any] = text.replace('''−''' ,'''ー''' ) for k, v in self.emoji["emoji"].items(): if k in text: lowerCAmelCase__ : Tuple = text.replace(lowercase_ ,lowercase_ ) if clean: lowerCAmelCase__ : str = self.clean_text(lowercase_ ) def check_simbol(lowercase_ : Optional[int] ): lowerCAmelCase__ : List[Any] = x.encode() if len(lowercase_ ) == 1 and len(lowercase_ ) == 2: lowerCAmelCase__ : Union[str, Any] = (int(e[0] ) << 8) + int(e[1] ) if ( (c >= 0xC2_A1 and c <= 0xC2_BF) or (c >= 0xC7_80 and c <= 0xC7_83) or (c >= 0xCA_B9 and c <= 0xCB_BF) or (c >= 0xCC_80 and c <= 0xCD_A2) ): return True return False def checkuae(lowercase_ : Union[str, Any] ): lowerCAmelCase__ : List[str] = x.encode() if len(lowercase_ ) == 1 and len(lowercase_ ) == 3: lowerCAmelCase__ : int = (int(e[0] ) << 1_6) + (int(e[1] ) << 8) + int(e[2] ) if c >= 0xE2_80_80 and c <= 0xE2_B0_7F: return True return False lowerCAmelCase__ : Tuple = 0 lowerCAmelCase__ : Optional[Any] = [] while pos < len(lowercase_ ): lowerCAmelCase__ : Optional[int] = min(len(lowercase_ ) ,pos + self.maxlen + 1 ) if text[pos] == '''<''' else pos + 3 lowerCAmelCase__ : int = [] # (token_id, token, pos) for e in range(lowercase_ ,lowercase_ ,-1 ): lowerCAmelCase__ : int = text[pos:e] if wd in self.vocab: if wd[0] == "<" and len(lowercase_ ) > 2: lowerCAmelCase__ : List[str] = [(self.vocab[wd], wd, e)] break else: candidates.append((self.vocab[wd], wd, e) ) if len(lowercase_ ) > 0: # the smallest token_id is adopted lowerCAmelCase__ ,lowerCAmelCase__ ,lowerCAmelCase__ : Optional[Any] = sorted(lowercase_ ,key=lambda lowercase_ : x[0] )[0] result.append(lowercase_ ) lowerCAmelCase__ : Tuple = e else: lowerCAmelCase__ : Any = pos + 1 lowerCAmelCase__ : Any = text[pos:end] if check_simbol(lowercase_ ): result.append('''<KIGOU>''' ) elif checkuae(lowercase_ ): result.append('''<U2000U2BFF>''' ) else: for i in wd.encode('''utf-8''' ): result.append('''<|byte%d|>''' % i ) lowerCAmelCase__ : Dict = end return result def __lowerCAmelCase ( self : Union[str, Any] ,lowercase_ : Union[str, Any] ,lowercase_ : Dict="\n" ): lowerCAmelCase__ : Dict = [] lowerCAmelCase__ : str = [] lowerCAmelCase__ : Any = self.ids_to_tokens[index][0] if word[:6] == "<|byte" and word[-2:] == "|>": byte_tokens.append(int(word[6:-2] ) ) else: if len(lowercase_ ) > 0: words.append(bytearray(lowercase_ ).decode('''utf-8''' ,errors='''replace''' ) ) lowerCAmelCase__ : Optional[int] = [] if word[:7] == "<|emoji" and word[-2:] == "|>": words.append(self.emoji['''emoji_inv'''][word] ) elif word == "<SP>": words.append(''' ''' ) elif word == "<BR>": words.append(lowercase_ ) elif word == "<TAB>": words.append('''\t''' ) elif word == "<BLOCK>": words.append('''▀''' ) elif word == "<KIGOU>": words.append('''ǀ''' ) elif word == "<U2000U2BFF>": words.append('''‖''' ) else: words.append(lowercase_ ) if len(lowercase_ ) > 0: words.append(bytearray(lowercase_ ).decode('''utf-8''' ,errors='''replace''' ) ) lowerCAmelCase__ : Any = ''''''.join(lowercase_ ) return text
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'''simple docstring''' import os import tempfile import unittest import numpy as np from diffusers.utils import is_flax_available from diffusers.utils.testing_utils import require_flax, slow if is_flax_available(): import jax import jax.numpy as jnp from flax.jax_utils import replicate from flax.training.common_utils import shard from diffusers import FlaxDDIMScheduler, FlaxDiffusionPipeline, FlaxStableDiffusionPipeline @require_flax class SCREAMING_SNAKE_CASE( unittest.TestCase ): """simple docstring""" def A ( self : str ) -> int: with tempfile.TemporaryDirectory() as tmpdirname: # pipeline has Flax weights UpperCAmelCase : Optional[Any] = FlaxDiffusionPipeline.from_pretrained( '''hf-internal-testing/tiny-stable-diffusion-pipe''' , safety_checker=__snake_case , cache_dir=__snake_case ) UpperCAmelCase : str = [t[-1] for t in os.walk(os.path.join(__snake_case , os.listdir(__snake_case )[0] , '''snapshots''' ) )] UpperCAmelCase : str = [item for sublist in all_root_files for item in sublist] # None of the downloaded files should be a PyTorch file even if we have some here: # https://huggingface.co/hf-internal-testing/tiny-stable-diffusion-pipe/blob/main/unet/diffusion_pytorch_model.bin assert not any(f.endswith('''.bin''' ) for f in files ) @slow @require_flax class SCREAMING_SNAKE_CASE( unittest.TestCase ): """simple docstring""" def A ( self : List[str] ) -> Dict: UpperCAmelCase , UpperCAmelCase : str = FlaxStableDiffusionPipeline.from_pretrained( '''hf-internal-testing/tiny-stable-diffusion-pipe''' , safety_checker=__snake_case ) UpperCAmelCase : List[Any] = ( '''A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of''' ''' field, close up, split lighting, cinematic''' ) UpperCAmelCase : List[str] = jax.random.PRNGKey(0 ) UpperCAmelCase : Optional[Any] = 4 UpperCAmelCase : Optional[Any] = jax.device_count() UpperCAmelCase : Tuple = num_samples * [prompt] UpperCAmelCase : int = pipeline.prepare_inputs(__snake_case ) # shard inputs and rng UpperCAmelCase : Tuple = replicate(__snake_case ) UpperCAmelCase : Any = jax.random.split(__snake_case , __snake_case ) UpperCAmelCase : Optional[Any] = shard(__snake_case ) UpperCAmelCase : Optional[int] = pipeline(__snake_case , __snake_case , __snake_case , __snake_case , jit=__snake_case ).images assert images.shape == (num_samples, 1, 64, 64, 3) if jax.device_count() == 8: assert np.abs(np.abs(images[0, 0, :2, :2, -2:] , dtype=np.floataa ).sum() - 4.1_51_47_45 ) < 1E-3 assert np.abs(np.abs(__snake_case , dtype=np.floataa ).sum() - 4_99_47.8_75 ) < 5E-1 UpperCAmelCase : Union[str, Any] = pipeline.numpy_to_pil(np.asarray(images.reshape((num_samples,) + images.shape[-3:] ) ) ) assert len(__snake_case ) == num_samples def A ( self : List[Any] ) -> List[str]: UpperCAmelCase , UpperCAmelCase : Union[str, Any] = FlaxStableDiffusionPipeline.from_pretrained( '''CompVis/stable-diffusion-v1-4''' , revision='''flax''' , safety_checker=__snake_case ) UpperCAmelCase : Dict = ( '''A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of''' ''' field, close up, split lighting, cinematic''' ) UpperCAmelCase : Optional[Any] = jax.random.PRNGKey(0 ) UpperCAmelCase : Any = 50 UpperCAmelCase : Union[str, Any] = jax.device_count() UpperCAmelCase : int = num_samples * [prompt] UpperCAmelCase : Union[str, Any] = pipeline.prepare_inputs(__snake_case ) # shard inputs and rng UpperCAmelCase : Dict = replicate(__snake_case ) UpperCAmelCase : int = jax.random.split(__snake_case , __snake_case ) UpperCAmelCase : Tuple = shard(__snake_case ) UpperCAmelCase : Tuple = pipeline(__snake_case , __snake_case , __snake_case , __snake_case , jit=__snake_case ).images assert images.shape == (num_samples, 1, 512, 512, 3) if jax.device_count() == 8: assert np.abs((np.abs(images[0, 0, :2, :2, -2:] , dtype=np.floataa ).sum() - 0.05_65_24_01) ) < 1E-3 assert np.abs((np.abs(__snake_case , dtype=np.floataa ).sum() - 2_38_38_08.2) ) < 5E-1 def A ( self : int ) -> Dict: UpperCAmelCase , UpperCAmelCase : List[str] = FlaxStableDiffusionPipeline.from_pretrained( '''CompVis/stable-diffusion-v1-4''' , revision='''bf16''' , dtype=jnp.bfloataa , safety_checker=__snake_case ) UpperCAmelCase : Dict = ( '''A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of''' ''' field, close up, split lighting, cinematic''' ) UpperCAmelCase : Union[str, Any] = jax.random.PRNGKey(0 ) UpperCAmelCase : List[str] = 50 UpperCAmelCase : Union[str, Any] = jax.device_count() UpperCAmelCase : List[Any] = num_samples * [prompt] UpperCAmelCase : int = pipeline.prepare_inputs(__snake_case ) # shard inputs and rng UpperCAmelCase : Tuple = replicate(__snake_case ) UpperCAmelCase : List[Any] = jax.random.split(__snake_case , __snake_case ) UpperCAmelCase : Optional[int] = shard(__snake_case ) UpperCAmelCase : Optional[Any] = pipeline(__snake_case , __snake_case , __snake_case , __snake_case , jit=__snake_case ).images assert images.shape == (num_samples, 1, 512, 512, 3) if jax.device_count() == 8: assert np.abs((np.abs(images[0, 0, :2, :2, -2:] , dtype=np.floataa ).sum() - 0.04_00_39_06) ) < 1E-3 assert np.abs((np.abs(__snake_case , dtype=np.floataa ).sum() - 2_37_35_16.75) ) < 5E-1 def A ( self : int ) -> Any: UpperCAmelCase , UpperCAmelCase : Dict = FlaxStableDiffusionPipeline.from_pretrained( '''CompVis/stable-diffusion-v1-4''' , revision='''bf16''' , dtype=jnp.bfloataa ) UpperCAmelCase : List[str] = ( '''A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of''' ''' field, close up, split lighting, cinematic''' ) UpperCAmelCase : List[str] = jax.random.PRNGKey(0 ) UpperCAmelCase : Union[str, Any] = 50 UpperCAmelCase : Optional[int] = jax.device_count() UpperCAmelCase : List[str] = num_samples * [prompt] UpperCAmelCase : Dict = pipeline.prepare_inputs(__snake_case ) # shard inputs and rng UpperCAmelCase : Tuple = replicate(__snake_case ) UpperCAmelCase : Any = jax.random.split(__snake_case , __snake_case ) UpperCAmelCase : str = shard(__snake_case ) UpperCAmelCase : Optional[int] = pipeline(__snake_case , __snake_case , __snake_case , __snake_case , jit=__snake_case ).images assert images.shape == (num_samples, 1, 512, 512, 3) if jax.device_count() == 8: assert np.abs((np.abs(images[0, 0, :2, :2, -2:] , dtype=np.floataa ).sum() - 0.04_00_39_06) ) < 1E-3 assert np.abs((np.abs(__snake_case , dtype=np.floataa ).sum() - 2_37_35_16.75) ) < 5E-1 def A ( self : Tuple ) -> Optional[Any]: UpperCAmelCase : int = FlaxDDIMScheduler( beta_start=0.0_00_85 , beta_end=0.0_12 , beta_schedule='''scaled_linear''' , set_alpha_to_one=__snake_case , steps_offset=1 , ) UpperCAmelCase , UpperCAmelCase : Tuple = FlaxStableDiffusionPipeline.from_pretrained( '''CompVis/stable-diffusion-v1-4''' , revision='''bf16''' , dtype=jnp.bfloataa , scheduler=__snake_case , safety_checker=__snake_case , ) UpperCAmelCase : Tuple = scheduler.create_state() UpperCAmelCase : Dict = scheduler_state UpperCAmelCase : str = ( '''A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of''' ''' field, close up, split lighting, cinematic''' ) UpperCAmelCase : int = jax.random.PRNGKey(0 ) UpperCAmelCase : Union[str, Any] = 50 UpperCAmelCase : Optional[Any] = jax.device_count() UpperCAmelCase : Any = num_samples * [prompt] UpperCAmelCase : Dict = pipeline.prepare_inputs(__snake_case ) # shard inputs and rng UpperCAmelCase : str = replicate(__snake_case ) UpperCAmelCase : List[str] = jax.random.split(__snake_case , __snake_case ) UpperCAmelCase : Optional[int] = shard(__snake_case ) UpperCAmelCase : Dict = pipeline(__snake_case , __snake_case , __snake_case , __snake_case , jit=__snake_case ).images assert images.shape == (num_samples, 1, 512, 512, 3) if jax.device_count() == 8: assert np.abs((np.abs(images[0, 0, :2, :2, -2:] , dtype=np.floataa ).sum() - 0.0_45_04_39_45) ) < 1E-3 assert np.abs((np.abs(__snake_case , dtype=np.floataa ).sum() - 2_34_76_93.5) ) < 5E-1 def A ( self : Any ) -> Tuple: UpperCAmelCase : List[Any] = ( '''A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of''' ''' field, close up, split lighting, cinematic''' ) UpperCAmelCase : Union[str, Any] = jax.device_count() UpperCAmelCase : List[Any] = num_samples * [prompt] UpperCAmelCase : str = jax.random.split(jax.random.PRNGKey(0 ) , __snake_case ) UpperCAmelCase , UpperCAmelCase : Tuple = FlaxStableDiffusionPipeline.from_pretrained( '''CompVis/stable-diffusion-v1-4''' , revision='''bf16''' , dtype=jnp.bfloataa , safety_checker=__snake_case , ) UpperCAmelCase : Dict = replicate(__snake_case ) UpperCAmelCase : Optional[Any] = pipeline.prepare_inputs(__snake_case ) UpperCAmelCase : List[str] = shard(__snake_case ) UpperCAmelCase : Any = pipeline(__snake_case , __snake_case , __snake_case , jit=__snake_case ).images assert images.shape == (num_samples, 1, 512, 512, 3) UpperCAmelCase : Optional[int] = images[2, 0, 256, 10:17, 1] # With memory efficient attention UpperCAmelCase , UpperCAmelCase : Any = FlaxStableDiffusionPipeline.from_pretrained( '''CompVis/stable-diffusion-v1-4''' , revision='''bf16''' , dtype=jnp.bfloataa , safety_checker=__snake_case , use_memory_efficient_attention=__snake_case , ) UpperCAmelCase : int = replicate(__snake_case ) UpperCAmelCase : int = pipeline.prepare_inputs(__snake_case ) UpperCAmelCase : List[Any] = shard(__snake_case ) UpperCAmelCase : Optional[Any] = pipeline(__snake_case , __snake_case , __snake_case , jit=__snake_case ).images assert images_eff.shape == (num_samples, 1, 512, 512, 3) UpperCAmelCase : int = images[2, 0, 256, 10:17, 1] # I checked the results visually and they are very similar. However, I saw that the max diff is `1` and the `sum` # over the 8 images is exactly `256`, which is very suspicious. Testing a random slice for now. assert abs(slice_eff - slice ).max() < 1E-2
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import unittest from transformers.testing_utils import require_bsa from transformers.utils import is_bsa_available from ...test_feature_extraction_common import FeatureExtractionSavingTestMixin if is_bsa_available(): from transformers import MarkupLMFeatureExtractor class snake_case__ (unittest.TestCase ): """simple docstring""" def __init__( self : str , __lowerCamelCase : Optional[int] ) -> List[str]: a = parent def __UpperCAmelCase ( self : Optional[int] ) -> List[str]: return {} def __magic_name__ ( ): '''simple docstring''' a = "<HTML>\n\n <HEAD>\n <TITLE>sample document</TITLE>\n </HEAD>\n\n <BODY BGCOLOR=\"FFFFFF\">\n <HR>\n <a href=\"http://google.com\">Goog</a>\n <H1>This is one header</H1>\n <H2>This is a another Header</H2>\n <P>Travel from\n <P>\n <B>SFO to JFK</B>\n <BR>\n <B><I>on May 2, 2015 at 2:00 pm. For details go to confirm.com </I></B>\n <HR>\n <div style=\"color:#0000FF\">\n <h3>Traveler <b> name </b> is\n <p> John Doe </p>\n </div>" a = "\n <!DOCTYPE html>\n <html>\n <body>\n\n <h1>My First Heading</h1>\n <p>My first paragraph.</p>\n\n </body>\n </html>\n " return [html_string_a, html_string_a] @require_bsa class snake_case__ (_UpperCamelCase , unittest.TestCase ): """simple docstring""" SCREAMING_SNAKE_CASE_ : Dict = MarkupLMFeatureExtractor if is_bsa_available() else None def __UpperCAmelCase ( self : int ) -> Union[str, Any]: a = MarkupLMFeatureExtractionTester(self ) @property def __UpperCAmelCase ( self : Optional[int] ) -> Optional[Any]: return self.feature_extract_tester.prepare_feat_extract_dict() def __UpperCAmelCase ( self : int ) -> Tuple: # Initialize feature_extractor a = self.feature_extraction_class() # Test not batched input a = get_html_strings()[0] a = feature_extractor(__lowerCamelCase ) # fmt: off a = [["sample document", "Goog", "This is one header", "This is a another Header", "Travel from", "SFO to JFK", "on May 2, 2015 at 2:00 pm. For details go to confirm.com", "Traveler", "name", "is", "John Doe"]] a = [["/html/head/title", "/html/body/a", "/html/body/h1", "/html/body/h2", "/html/body/p", "/html/body/p/p/b[1]", "/html/body/p/p/b[2]/i", "/html/body/p/p/div/h3", "/html/body/p/p/div/h3/b", "/html/body/p/p/div/h3", "/html/body/p/p/div/h3/p"]] # fmt: on self.assertEqual(encoding.nodes , __lowerCamelCase ) self.assertEqual(encoding.xpaths , __lowerCamelCase ) # Test batched a = get_html_strings() a = feature_extractor(__lowerCamelCase ) # fmt: off a = expected_nodes + [["My First Heading", "My first paragraph."]] a = expected_xpaths + [["/html/body/h1", "/html/body/p"]] self.assertEqual(len(encoding.nodes ) , 2 ) self.assertEqual(len(encoding.xpaths ) , 2 ) self.assertEqual(encoding.nodes , __lowerCamelCase ) self.assertEqual(encoding.xpaths , __lowerCamelCase )
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'''simple docstring''' import random from .binary_exp_mod import bin_exp_mod def snake_case_ ( _lowerCAmelCase : Tuple , _lowerCAmelCase : Optional[Any]=1000 ) -> int: if n < 2: return False if n % 2 == 0: return n == 2 # this means n is odd UpperCAmelCase : str = n - 1 UpperCAmelCase : List[Any] = 0 while d % 2 == 0: d /= 2 exp += 1 # n - 1=d*(2**exp) UpperCAmelCase : List[str] = 0 while count < prec: UpperCAmelCase : int = random.randint(2 , n - 1 ) UpperCAmelCase : List[str] = bin_exp_mod(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ) if b != 1: UpperCAmelCase : int = True for _ in range(_lowerCAmelCase ): if b == n - 1: UpperCAmelCase : Dict = False break UpperCAmelCase : str = b * b b %= n if flag: return False count += 1 return True if __name__ == "__main__": UpperCamelCase__: Optional[int] = abs(int(input("Enter bound : ").strip())) print("Here's the list of primes:") print(", ".join(str(i) for i in range(n + 1) if is_prime_big(i)))
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"""simple docstring""" from typing import TYPE_CHECKING from ...utils import _LazyModule lowerCAmelCase__ = {'''tokenization_bertweet''': ['''BertweetTokenizer''']} if TYPE_CHECKING: from .tokenization_bertweet import BertweetTokenizer else: import sys lowerCAmelCase__ = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
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'''simple docstring''' import argparse import json from pathlib import Path import requests import torch from huggingface_hub import cached_download, hf_hub_url from PIL import Image from transformers import DPTConfig, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTImageProcessor from transformers.utils import logging logging.set_verbosity_info() UpperCamelCase__: Optional[int] = logging.get_logger(__name__) def snake_case_ ( _lowerCAmelCase : Optional[int] ) -> Optional[int]: UpperCAmelCase : Tuple = DPTConfig(embedding_type='''hybrid''' ) if "large" in checkpoint_url: UpperCAmelCase : Tuple = 1024 UpperCAmelCase : List[Any] = 4096 UpperCAmelCase : str = 24 UpperCAmelCase : List[Any] = 16 UpperCAmelCase : str = [5, 11, 17, 23] UpperCAmelCase : List[Any] = [256, 512, 1024, 1024] UpperCAmelCase : Tuple = (1, 384, 384) if "nyu" or "midas" in checkpoint_url: UpperCAmelCase : Optional[Any] = 768 UpperCAmelCase : Tuple = [1, 1, 1, 0.5] UpperCAmelCase : int = [256, 512, 768, 768] UpperCAmelCase : Any = 150 UpperCAmelCase : Tuple = 16 UpperCAmelCase : Any = (1, 384, 384) UpperCAmelCase : Optional[Any] = False UpperCAmelCase : Tuple = '''project''' if "ade" in checkpoint_url: UpperCAmelCase : Any = True UpperCAmelCase : str = 768 UpperCAmelCase : Optional[int] = [1, 1, 1, 0.5] UpperCAmelCase : List[Any] = 150 UpperCAmelCase : List[Any] = 16 UpperCAmelCase : str = '''huggingface/label-files''' UpperCAmelCase : Tuple = '''ade20k-id2label.json''' UpperCAmelCase : Any = json.load(open(cached_download(hf_hub_url(_lowerCAmelCase , _lowerCAmelCase , repo_type='''dataset''' ) ) , '''r''' ) ) UpperCAmelCase : Optional[Any] = {int(_lowerCAmelCase ): v for k, v in idalabel.items()} UpperCAmelCase : List[Any] = idalabel UpperCAmelCase : Optional[int] = {v: k for k, v in idalabel.items()} UpperCAmelCase : Union[str, Any] = [1, 150, 480, 480] return config, expected_shape def snake_case_ ( _lowerCAmelCase : Union[str, Any] ) -> int: UpperCAmelCase : List[str] = ['''pretrained.model.head.weight''', '''pretrained.model.head.bias'''] for k in ignore_keys: state_dict.pop(_lowerCAmelCase , _lowerCAmelCase ) def snake_case_ ( _lowerCAmelCase : Tuple ) -> Any: if ( "pretrained.model" in name and "cls_token" not in name and "pos_embed" not in name and "patch_embed" not in name ): UpperCAmelCase : Tuple = name.replace('''pretrained.model''' , '''dpt.encoder''' ) if "pretrained.model" in name: UpperCAmelCase : Union[str, Any] = name.replace('''pretrained.model''' , '''dpt.embeddings''' ) if "patch_embed" in name: UpperCAmelCase : int = name.replace('''patch_embed''' , '''''' ) if "pos_embed" in name: UpperCAmelCase : Tuple = name.replace('''pos_embed''' , '''position_embeddings''' ) if "attn.proj" in name: UpperCAmelCase : Any = name.replace('''attn.proj''' , '''attention.output.dense''' ) if "proj" in name and "project" not in name: UpperCAmelCase : str = name.replace('''proj''' , '''projection''' ) if "blocks" in name: UpperCAmelCase : Any = name.replace('''blocks''' , '''layer''' ) if "mlp.fc1" in name: UpperCAmelCase : Optional[int] = name.replace('''mlp.fc1''' , '''intermediate.dense''' ) if "mlp.fc2" in name: UpperCAmelCase : Optional[Any] = name.replace('''mlp.fc2''' , '''output.dense''' ) if "norm1" in name and "backbone" not in name: UpperCAmelCase : Dict = name.replace('''norm1''' , '''layernorm_before''' ) if "norm2" in name and "backbone" not in name: UpperCAmelCase : Tuple = name.replace('''norm2''' , '''layernorm_after''' ) if "scratch.output_conv" in name: UpperCAmelCase : Tuple = name.replace('''scratch.output_conv''' , '''head''' ) if "scratch" in name: UpperCAmelCase : str = name.replace('''scratch''' , '''neck''' ) if "layer1_rn" in name: UpperCAmelCase : Dict = name.replace('''layer1_rn''' , '''convs.0''' ) if "layer2_rn" in name: UpperCAmelCase : int = name.replace('''layer2_rn''' , '''convs.1''' ) if "layer3_rn" in name: UpperCAmelCase : Tuple = name.replace('''layer3_rn''' , '''convs.2''' ) if "layer4_rn" in name: UpperCAmelCase : int = name.replace('''layer4_rn''' , '''convs.3''' ) if "refinenet" in name: UpperCAmelCase : List[str] = int(name[len('''neck.refinenet''' ) : len('''neck.refinenet''' ) + 1] ) # tricky here: we need to map 4 to 0, 3 to 1, 2 to 2 and 1 to 3 UpperCAmelCase : str = name.replace(f"""refinenet{layer_idx}""" , f"""fusion_stage.layers.{abs(layer_idx-4 )}""" ) if "out_conv" in name: UpperCAmelCase : List[str] = name.replace('''out_conv''' , '''projection''' ) if "resConfUnit1" in name: UpperCAmelCase : Union[str, Any] = name.replace('''resConfUnit1''' , '''residual_layer1''' ) if "resConfUnit2" in name: UpperCAmelCase : Any = name.replace('''resConfUnit2''' , '''residual_layer2''' ) if "conv1" in name: UpperCAmelCase : Optional[int] = name.replace('''conv1''' , '''convolution1''' ) if "conv2" in name: UpperCAmelCase : Tuple = name.replace('''conv2''' , '''convolution2''' ) # readout blocks if "pretrained.act_postprocess1.0.project.0" in name: UpperCAmelCase : Dict = name.replace('''pretrained.act_postprocess1.0.project.0''' , '''neck.reassemble_stage.readout_projects.0.0''' ) if "pretrained.act_postprocess2.0.project.0" in name: UpperCAmelCase : int = name.replace('''pretrained.act_postprocess2.0.project.0''' , '''neck.reassemble_stage.readout_projects.1.0''' ) if "pretrained.act_postprocess3.0.project.0" in name: UpperCAmelCase : Any = name.replace('''pretrained.act_postprocess3.0.project.0''' , '''neck.reassemble_stage.readout_projects.2.0''' ) if "pretrained.act_postprocess4.0.project.0" in name: UpperCAmelCase : Optional[Any] = name.replace('''pretrained.act_postprocess4.0.project.0''' , '''neck.reassemble_stage.readout_projects.3.0''' ) # resize blocks if "pretrained.act_postprocess1.3" in name: UpperCAmelCase : List[Any] = name.replace('''pretrained.act_postprocess1.3''' , '''neck.reassemble_stage.layers.0.projection''' ) if "pretrained.act_postprocess1.4" in name: UpperCAmelCase : Any = name.replace('''pretrained.act_postprocess1.4''' , '''neck.reassemble_stage.layers.0.resize''' ) if "pretrained.act_postprocess2.3" in name: UpperCAmelCase : Optional[int] = name.replace('''pretrained.act_postprocess2.3''' , '''neck.reassemble_stage.layers.1.projection''' ) if "pretrained.act_postprocess2.4" in name: UpperCAmelCase : str = name.replace('''pretrained.act_postprocess2.4''' , '''neck.reassemble_stage.layers.1.resize''' ) if "pretrained.act_postprocess3.3" in name: UpperCAmelCase : List[str] = name.replace('''pretrained.act_postprocess3.3''' , '''neck.reassemble_stage.layers.2.projection''' ) if "pretrained.act_postprocess4.3" in name: UpperCAmelCase : Tuple = name.replace('''pretrained.act_postprocess4.3''' , '''neck.reassemble_stage.layers.3.projection''' ) if "pretrained.act_postprocess4.4" in name: UpperCAmelCase : int = name.replace('''pretrained.act_postprocess4.4''' , '''neck.reassemble_stage.layers.3.resize''' ) if "pretrained" in name: UpperCAmelCase : Optional[int] = name.replace('''pretrained''' , '''dpt''' ) if "bn" in name: UpperCAmelCase : Dict = name.replace('''bn''' , '''batch_norm''' ) if "head" in name: UpperCAmelCase : Any = name.replace('''head''' , '''head.head''' ) if "encoder.norm" in name: UpperCAmelCase : Optional[int] = name.replace('''encoder.norm''' , '''layernorm''' ) if "auxlayer" in name: UpperCAmelCase : Union[str, Any] = name.replace('''auxlayer''' , '''auxiliary_head.head''' ) if "backbone" in name: UpperCAmelCase : List[Any] = name.replace('''backbone''' , '''backbone.bit.encoder''' ) if ".." in name: UpperCAmelCase : Optional[int] = name.replace('''..''' , '''.''' ) if "stem.conv" in name: UpperCAmelCase : Optional[Any] = name.replace('''stem.conv''' , '''bit.embedder.convolution''' ) if "blocks" in name: UpperCAmelCase : Optional[int] = name.replace('''blocks''' , '''layers''' ) if "convolution" in name and "backbone" in name: UpperCAmelCase : List[Any] = name.replace('''convolution''' , '''conv''' ) if "layer" in name and "backbone" in name: UpperCAmelCase : List[str] = name.replace('''layer''' , '''layers''' ) if "backbone.bit.encoder.bit" in name: UpperCAmelCase : List[Any] = name.replace('''backbone.bit.encoder.bit''' , '''backbone.bit''' ) if "embedder.conv" in name: UpperCAmelCase : List[Any] = name.replace('''embedder.conv''' , '''embedder.convolution''' ) if "backbone.bit.encoder.stem.norm" in name: UpperCAmelCase : Tuple = name.replace('''backbone.bit.encoder.stem.norm''' , '''backbone.bit.embedder.norm''' ) return name def snake_case_ ( _lowerCAmelCase : List[str] , _lowerCAmelCase : List[Any] ) -> Optional[Any]: for i in range(config.num_hidden_layers ): # read in weights + bias of input projection layer (in timm, this is a single matrix + bias) UpperCAmelCase : Optional[int] = state_dict.pop(f"""dpt.encoder.layer.{i}.attn.qkv.weight""" ) UpperCAmelCase : Tuple = state_dict.pop(f"""dpt.encoder.layer.{i}.attn.qkv.bias""" ) # next, add query, keys and values (in that order) to the state dict UpperCAmelCase : Tuple = in_proj_weight[: config.hidden_size, :] UpperCAmelCase : int = in_proj_bias[: config.hidden_size] UpperCAmelCase : List[str] = in_proj_weight[ config.hidden_size : config.hidden_size * 2, : ] UpperCAmelCase : List[str] = in_proj_bias[ config.hidden_size : config.hidden_size * 2 ] UpperCAmelCase : str = in_proj_weight[ -config.hidden_size :, : ] UpperCAmelCase : Union[str, Any] = in_proj_bias[-config.hidden_size :] def snake_case_ ( ) -> List[str]: UpperCAmelCase : Optional[int] = '''http://images.cocodataset.org/val2017/000000039769.jpg''' UpperCAmelCase : Optional[int] = Image.open(requests.get(_lowerCAmelCase , stream=_lowerCAmelCase ).raw ) return im @torch.no_grad() def snake_case_ ( _lowerCAmelCase : Optional[Any] , _lowerCAmelCase : Union[str, Any] , _lowerCAmelCase : str , _lowerCAmelCase : Union[str, Any] , _lowerCAmelCase : List[str] ) -> Any: UpperCAmelCase , UpperCAmelCase : int = get_dpt_config(_lowerCAmelCase ) # load original state_dict from URL # state_dict = torch.hub.load_state_dict_from_url(checkpoint_url, map_location="cpu") UpperCAmelCase : List[Any] = torch.load(_lowerCAmelCase , map_location='''cpu''' ) # remove certain keys remove_ignore_keys_(_lowerCAmelCase ) # rename keys for key in state_dict.copy().keys(): UpperCAmelCase : Any = state_dict.pop(_lowerCAmelCase ) UpperCAmelCase : List[Any] = val # read in qkv matrices read_in_q_k_v(_lowerCAmelCase , _lowerCAmelCase ) # load HuggingFace model UpperCAmelCase : Optional[Any] = DPTForSemanticSegmentation(_lowerCAmelCase ) if '''ade''' in checkpoint_url else DPTForDepthEstimation(_lowerCAmelCase ) model.load_state_dict(_lowerCAmelCase ) model.eval() # Check outputs on an image UpperCAmelCase : int = 480 if '''ade''' in checkpoint_url else 384 UpperCAmelCase : List[Any] = DPTImageProcessor(size=_lowerCAmelCase ) UpperCAmelCase : Dict = prepare_img() UpperCAmelCase : Optional[int] = image_processor(_lowerCAmelCase , return_tensors='''pt''' ) # forward pass UpperCAmelCase : Any = model(**_lowerCAmelCase ).logits if '''ade''' in checkpoint_url else model(**_lowerCAmelCase ).predicted_depth if show_prediction: UpperCAmelCase : Dict = ( torch.nn.functional.interpolate( outputs.unsqueeze(1 ) , size=(image.size[1], image.size[0]) , mode='''bicubic''' , align_corners=_lowerCAmelCase , ) .squeeze() .cpu() .numpy() ) Image.fromarray((prediction / prediction.max()) * 255 ).show() if pytorch_dump_folder_path is not None: Path(_lowerCAmelCase ).mkdir(exist_ok=_lowerCAmelCase ) print(f"""Saving model to {pytorch_dump_folder_path}""" ) model.save_pretrained(_lowerCAmelCase ) print(f"""Saving image processor to {pytorch_dump_folder_path}""" ) image_processor.save_pretrained(_lowerCAmelCase ) if push_to_hub: model.push_to_hub('''ybelkada/dpt-hybrid-midas''' ) image_processor.push_to_hub('''ybelkada/dpt-hybrid-midas''' ) if __name__ == "__main__": UpperCamelCase__: Dict = argparse.ArgumentParser() # Required parameters parser.add_argument( "--checkpoint_url", default="https://github.com/intel-isl/DPT/releases/download/1_0/dpt_large-midas-2f21e586.pt", type=str, help="URL of the original DPT checkpoint you'd like to convert.", ) parser.add_argument( "--pytorch_dump_folder_path", default=None, type=str, required=False, help="Path to the output PyTorch model directory.", ) parser.add_argument( "--push_to_hub", action="store_true", ) parser.add_argument( "--model_name", default="dpt-large", type=str, help="Name of the model, in case you're pushing to the hub.", ) parser.add_argument( "--show_prediction", action="store_true", ) UpperCamelCase__: Tuple = parser.parse_args() convert_dpt_checkpoint( args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name, args.show_prediction )
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"""simple docstring""" from collections import OrderedDict from typing import Mapping from ...configuration_utils import PretrainedConfig from ...onnx import OnnxConfig from ...utils import logging A: str = logging.get_logger(__name__) A: List[str] = { "kssteven/ibert-roberta-base": "https://huggingface.co/kssteven/ibert-roberta-base/resolve/main/config.json", "kssteven/ibert-roberta-large": "https://huggingface.co/kssteven/ibert-roberta-large/resolve/main/config.json", "kssteven/ibert-roberta-large-mnli": ( "https://huggingface.co/kssteven/ibert-roberta-large-mnli/resolve/main/config.json" ), } class SCREAMING_SNAKE_CASE__ ( UpperCAmelCase__ ): __lowerCAmelCase : Dict = 'ibert' def __init__( self , _SCREAMING_SNAKE_CASE=30522 , _SCREAMING_SNAKE_CASE=768 , _SCREAMING_SNAKE_CASE=12 , _SCREAMING_SNAKE_CASE=12 , _SCREAMING_SNAKE_CASE=3072 , _SCREAMING_SNAKE_CASE="gelu" , _SCREAMING_SNAKE_CASE=0.1 , _SCREAMING_SNAKE_CASE=0.1 , _SCREAMING_SNAKE_CASE=512 , _SCREAMING_SNAKE_CASE=2 , _SCREAMING_SNAKE_CASE=0.02 , _SCREAMING_SNAKE_CASE=1E-12 , _SCREAMING_SNAKE_CASE=1 , _SCREAMING_SNAKE_CASE=0 , _SCREAMING_SNAKE_CASE=2 , _SCREAMING_SNAKE_CASE="absolute" , _SCREAMING_SNAKE_CASE=False , _SCREAMING_SNAKE_CASE="none" , **_SCREAMING_SNAKE_CASE , ) -> List[str]: '''simple docstring''' super().__init__(pad_token_id=_SCREAMING_SNAKE_CASE , bos_token_id=_SCREAMING_SNAKE_CASE , eos_token_id=_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE ) UpperCAmelCase : Tuple = vocab_size UpperCAmelCase : Union[str, Any] = hidden_size UpperCAmelCase : Optional[int] = num_hidden_layers UpperCAmelCase : Union[str, Any] = num_attention_heads UpperCAmelCase : Dict = hidden_act UpperCAmelCase : List[str] = intermediate_size UpperCAmelCase : Any = hidden_dropout_prob UpperCAmelCase : Dict = attention_probs_dropout_prob UpperCAmelCase : Union[str, Any] = max_position_embeddings UpperCAmelCase : Union[str, Any] = type_vocab_size UpperCAmelCase : List[str] = initializer_range UpperCAmelCase : int = layer_norm_eps UpperCAmelCase : Any = position_embedding_type UpperCAmelCase : Optional[int] = quant_mode UpperCAmelCase : Tuple = force_dequant class SCREAMING_SNAKE_CASE__ ( UpperCAmelCase__ ): @property def SCREAMING_SNAKE_CASE ( self ) -> Mapping[str, Mapping[int, str]]: '''simple docstring''' if self.task == "multiple-choice": UpperCAmelCase : Any = {0: """batch""", 1: """choice""", 2: """sequence"""} else: UpperCAmelCase : Optional[int] = {0: """batch""", 1: """sequence"""} return OrderedDict( [ ("""input_ids""", dynamic_axis), ("""attention_mask""", dynamic_axis), ] )
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'''simple docstring''' # coding=utf-8 # Copyright 2020 The HuggingFace Inc. team. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # this script dumps information about the environment import os import sys import transformers UpperCamelCase__: Optional[int] = "3" print("Python version:", sys.version) print("transformers version:", transformers.__version__) try: import torch print("Torch version:", torch.__version__) print("Cuda available:", torch.cuda.is_available()) print("Cuda version:", torch.version.cuda) print("CuDNN version:", torch.backends.cudnn.version()) print("Number of GPUs available:", torch.cuda.device_count()) print("NCCL version:", torch.cuda.nccl.version()) except ImportError: print("Torch version:", None) try: import deepspeed print("DeepSpeed version:", deepspeed.__version__) except ImportError: print("DeepSpeed version:", None) try: import tensorflow as tf print("TensorFlow version:", tf.__version__) print("TF GPUs available:", bool(tf.config.list_physical_devices("GPU"))) print("Number of TF GPUs available:", len(tf.config.list_physical_devices("GPU"))) except ImportError: print("TensorFlow version:", None)
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import functools from typing import Any def _a ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ): """simple docstring""" if not isinstance(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) or len(SCREAMING_SNAKE_CASE ) == 0: raise ValueError('''the string should be not empty string''' ) if not isinstance(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) or not all( isinstance(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) and len(SCREAMING_SNAKE_CASE ) > 0 for item in words ): raise ValueError('''the words should be a list of non-empty strings''' ) # Build trie lowercase__ = {} lowercase__ = '''WORD_KEEPER''' for word in words: lowercase__ = trie for c in word: if c not in trie_node: lowercase__ = {} lowercase__ = trie_node[c] lowercase__ = True lowercase__ = len(SCREAMING_SNAKE_CASE ) # Dynamic programming method @functools.cache def is_breakable(SCREAMING_SNAKE_CASE ) -> bool: if index == len_string: return True lowercase__ = trie for i in range(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ): lowercase__ = trie_node.get(string[i] , SCREAMING_SNAKE_CASE ) if trie_node is None: return False if trie_node.get(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) and is_breakable(i + 1 ): return True return False return is_breakable(0 ) if __name__ == "__main__": import doctest doctest.testmod()
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'''simple docstring''' from __future__ import annotations def snake_case_ ( _lowerCAmelCase : list[int | float] , _lowerCAmelCase : int , _lowerCAmelCase : int ) -> int | float: if len(_lowerCAmelCase ) == 0: raise ValueError('''find_max() arg is an empty sequence''' ) if ( left >= len(_lowerCAmelCase ) or left < -len(_lowerCAmelCase ) or right >= len(_lowerCAmelCase ) or right < -len(_lowerCAmelCase ) ): raise IndexError('''list index out of range''' ) if left == right: return nums[left] UpperCAmelCase : List[Any] = (left + right) >> 1 # the middle UpperCAmelCase : Optional[Any] = find_max(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ) # find max in range[left, mid] UpperCAmelCase : Dict = find_max(_lowerCAmelCase , mid + 1 , _lowerCAmelCase ) # find max in range[mid + 1, right] return left_max if left_max >= right_max else right_max if __name__ == "__main__": import doctest doctest.testmod(verbose=True)
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import unittest from transformers.models.xlm_prophetnet.tokenization_xlm_prophetnet import SPIECE_UNDERLINE, XLMProphetNetTokenizer from transformers.testing_utils import get_tests_dir, require_sentencepiece, slow from transformers.utils import cached_property from ...test_tokenization_common import TokenizerTesterMixin _A = get_tests_dir('''fixtures/test_sentencepiece.model''') @require_sentencepiece class lowercase_ ( A__ , unittest.TestCase ): A__ : Any = XLMProphetNetTokenizer A__ : Any = False A__ : List[Any] = True def lowerCamelCase_ ( self ): """simple docstring""" super().setUp() # We have a SentencePiece fixture for testing UpperCamelCase_ = XLMProphetNetTokenizer(__snake_case , keep_accents=__snake_case ) tokenizer.save_pretrained(self.tmpdirname ) def lowerCamelCase_ ( self ): """simple docstring""" UpperCamelCase_ = '''[PAD]''' UpperCamelCase_ = 0 self.assertEqual(self.get_tokenizer()._convert_token_to_id(__snake_case ) , __snake_case ) self.assertEqual(self.get_tokenizer()._convert_id_to_token(__snake_case ) , __snake_case ) def lowerCamelCase_ ( self ): """simple docstring""" UpperCamelCase_ = list(self.get_tokenizer().get_vocab().keys() ) self.assertEqual(vocab_keys[0] , """[PAD]""" ) self.assertEqual(vocab_keys[1] , """[CLS]""" ) self.assertEqual(vocab_keys[-1] , """j""" ) self.assertEqual(len(__snake_case ) , 1_0_1_2 ) def lowerCamelCase_ ( self ): """simple docstring""" self.assertEqual(self.get_tokenizer().vocab_size , 1_0_1_2 ) def lowerCamelCase_ ( self ): """simple docstring""" UpperCamelCase_ = XLMProphetNetTokenizer(__snake_case , keep_accents=__snake_case ) UpperCamelCase_ = tokenizer.tokenize("""This is a test""" ) self.assertListEqual(__snake_case , ["""▁This""", """▁is""", """▁a""", """▁t""", """est"""] ) self.assertListEqual( tokenizer.convert_tokens_to_ids(__snake_case ) , [value + tokenizer.fairseq_offset for value in [2_8_5, 4_6, 1_0, 1_7_0, 3_8_2]] , ) UpperCamelCase_ = tokenizer.tokenize("""I was born in 92000, and this is falsé.""" ) self.assertListEqual( __snake_case , [ SPIECE_UNDERLINE + """I""", SPIECE_UNDERLINE + """was""", SPIECE_UNDERLINE + """b""", """or""", """n""", SPIECE_UNDERLINE + """in""", SPIECE_UNDERLINE + """""", """9""", """2""", """0""", """0""", """0""", """,""", SPIECE_UNDERLINE + """and""", SPIECE_UNDERLINE + """this""", SPIECE_UNDERLINE + """is""", SPIECE_UNDERLINE + """f""", """al""", """s""", """é""", """.""", ] , ) UpperCamelCase_ = tokenizer.convert_tokens_to_ids(__snake_case ) self.assertListEqual( __snake_case , [ value + tokenizer.fairseq_offset for value in [8, 2_1, 8_4, 5_5, 2_4, 1_9, 7, -9, 6_0_2, 3_4_7, 3_4_7, 3_4_7, 3, 1_2, 6_6, 4_6, 7_2, 8_0, 6, -9, 4] ] , ) UpperCamelCase_ = tokenizer.convert_ids_to_tokens(__snake_case ) self.assertListEqual( __snake_case , [ SPIECE_UNDERLINE + """I""", SPIECE_UNDERLINE + """was""", SPIECE_UNDERLINE + """b""", """or""", """n""", SPIECE_UNDERLINE + """in""", SPIECE_UNDERLINE + """""", """[UNK]""", """2""", """0""", """0""", """0""", """,""", SPIECE_UNDERLINE + """and""", SPIECE_UNDERLINE + """this""", SPIECE_UNDERLINE + """is""", SPIECE_UNDERLINE + """f""", """al""", """s""", """[UNK]""", """.""", ] , ) @cached_property def lowerCamelCase_ ( self ): """simple docstring""" return XLMProphetNetTokenizer.from_pretrained("""microsoft/xprophetnet-large-wiki100-cased""" ) @slow def lowerCamelCase_ ( self ): """simple docstring""" UpperCamelCase_ = '''Hello World!''' UpperCamelCase_ = [3_5_3_8_9, 6_6_7_2, 4_9, 2] self.assertListEqual(__snake_case , self.big_tokenizer.encode(__snake_case ) ) @slow def lowerCamelCase_ ( self ): """simple docstring""" UpperCamelCase_ = {'''input_ids''': [[1_1_0_7_3, 8_2_7_8_3, 1_8, 2_6, 8_2_7_8_3, 5_4_9, 5_1_5_4_0, 2_4_8, 1_7_2_0_9, 1_3_0_1, 2_1_7, 2_0, 2_1_5_1_8_6, 1_3_2_5, 1_4_7, 1_7_2_0_9, 1_3_0_1, 2_1_7, 2_0, 5_6_3_7_0, 5_3, 1_2_2_0_2_0, 2_0, 1_6_4_7_7, 2_7, 8_7_3_5_5, 4_5_4_8, 2_0, 4_7_2_8, 7_8_3_9_2, 1_7, 1_5_9_9_6_9, 1_8, 2_6, 2_4_4_9_1, 6_2_9, 1_5, 5_3_8, 2_2_7_0_4, 5_4_3_9, 1_5, 2_7_8_8, 2_4_4_9_1, 9_8_8_5, 1_5, 4_3_5_3_4, 6_0_5, 1_5, 8_1_4, 1_8_4_0_3, 3_3_2_0_0, 2_9, 1_5, 4_3_5_3_4, 2_4_4_5_8, 1_2_4_1_0, 1_1_1, 2_4_9_6_6, 8_3_6_6_9, 9_6_3_7, 1_4_4_0_6_8, 2_6, 8_5_0, 2_2_3_4_6, 2_7, 1_4_7, 2_4_9_6_6, 8_3_6_6_9, 8_3_4_9_0, 2_6, 3_9_1_1_3, 7_3_5, 2_7, 6_8_9, 6_5_6, 2_8_0_0, 1_3_3_9, 4_6_0_0, 5_3, 1_2_2_0_2_0, 1_1_5_7_8_5, 3_4, 8_1_6, 1_3_3_9, 4_6_8_8_7, 1_8, 1_4_7, 5_3_9_0_5, 1_9_5_1, 4_2_2_3_8, 4_1_1_7_0, 1_7_7_3_2, 8_3_4, 4_3_6, 1_5, 2_7_5_2_3, 9_8_7_3_3, 2_1_7, 1_4_7, 5_5_4_2, 4_9_8_1, 9_3_0, 1_7_3_4_7, 1_6, 2], [2_0_0_9_1, 6_2_9, 9_4, 8_2_7_8_6, 5_8, 4_9_0, 2_0, 1_5_2_8, 8_4, 5_3_9_0_5, 3_4_4, 8_0_5_9_2, 1_1_0_1_2_8, 1_8_8_2_2, 5_2_6_7, 1_3_0_6, 6_2, 1_5_2_5_3_7, 3_0_8, 7_9_9_7, 4_0_1, 1_2_4_4_2_7, 5_4_9, 3_5_4_4_2, 2_2_5, 1_0_9, 1_5_0_5_5, 2_5_7_4_8, 1_4_7, 7_1_1_9, 4_3_7_1_2, 3_4, 7_6_7, 1_3_5_3_6_6, 1_8, 1_6, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [5_9_2, 6_3_7_8_4, 1_1_9_4_6_6, 1_7, 1_4_7_8_0_8, 8_8_2_1_4, 1_8, 6_5_6, 8_1, 3_2, 3_2_9_6, 1_0_2_8_0, 1_6, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], '''attention_mask''': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]} # noqa: E501 # fmt: on self.tokenizer_integration_test_util( expected_encoding=__snake_case , model_name="""microsoft/xprophetnet-large-wiki100-cased""" , revision="""1acad1643ddd54a44df6a1b797ada8373685d90e""" , )
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'''simple docstring''' from typing import List, Optional, Tuple, Union import torch from ...models import UNetaDModel from ...schedulers import ScoreSdeVeScheduler from ...utils import randn_tensor from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput class SCREAMING_SNAKE_CASE( A__ ): """simple docstring""" lowerCamelCase__ = 42 lowerCamelCase__ = 42 def __init__( self : Union[str, Any] , __snake_case : UNetaDModel , __snake_case : ScoreSdeVeScheduler ) -> int: super().__init__() self.register_modules(unet=__snake_case , scheduler=__snake_case ) @torch.no_grad() def __call__( self : Optional[int] , __snake_case : int = 1 , __snake_case : int = 2000 , __snake_case : Optional[Union[torch.Generator, List[torch.Generator]]] = None , __snake_case : Optional[str] = "pil" , __snake_case : bool = True , **__snake_case : Optional[int] , ) -> Union[ImagePipelineOutput, Tuple]: UpperCAmelCase : str = self.unet.config.sample_size UpperCAmelCase : Union[str, Any] = (batch_size, 3, img_size, img_size) UpperCAmelCase : int = self.unet UpperCAmelCase : Any = randn_tensor(__snake_case , generator=__snake_case ) * self.scheduler.init_noise_sigma UpperCAmelCase : List[Any] = sample.to(self.device ) self.scheduler.set_timesteps(__snake_case ) self.scheduler.set_sigmas(__snake_case ) for i, t in enumerate(self.progress_bar(self.scheduler.timesteps ) ): UpperCAmelCase : Any = self.scheduler.sigmas[i] * torch.ones(shape[0] , device=self.device ) # correction step for _ in range(self.scheduler.config.correct_steps ): UpperCAmelCase : Union[str, Any] = self.unet(__snake_case , __snake_case ).sample UpperCAmelCase : Optional[Any] = self.scheduler.step_correct(__snake_case , __snake_case , generator=__snake_case ).prev_sample # prediction step UpperCAmelCase : Optional[Any] = model(__snake_case , __snake_case ).sample UpperCAmelCase : List[str] = self.scheduler.step_pred(__snake_case , __snake_case , __snake_case , generator=__snake_case ) UpperCAmelCase , UpperCAmelCase : Optional[Any] = output.prev_sample, output.prev_sample_mean UpperCAmelCase : int = sample_mean.clamp(0 , 1 ) UpperCAmelCase : Union[str, Any] = sample.cpu().permute(0 , 2 , 3 , 1 ).numpy() if output_type == "pil": UpperCAmelCase : Optional[Any] = self.numpy_to_pil(__snake_case ) if not return_dict: return (sample,) return ImagePipelineOutput(images=__snake_case )
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from __future__ import annotations from collections.abc import Callable from typing import Generic, TypeVar _lowerCamelCase =TypeVar("T") _lowerCamelCase =TypeVar("U") class a_ ( Generic[T, U] ): """simple docstring""" def __init__( self : Optional[int] ,snake_case : T | None ,snake_case : U | None ): SCREAMING_SNAKE_CASE =key SCREAMING_SNAKE_CASE =val SCREAMING_SNAKE_CASE =None SCREAMING_SNAKE_CASE =None def __repr__( self : int ): return ( f'Node: key: {self.key}, val: {self.val}, ' f'has next: {bool(self.next )}, has prev: {bool(self.prev )}' ) class a_ ( Generic[T, U] ): """simple docstring""" def __init__( self : Any ): SCREAMING_SNAKE_CASE =DoubleLinkedListNode(__snake_case ,__snake_case ) SCREAMING_SNAKE_CASE =DoubleLinkedListNode(__snake_case ,__snake_case ) SCREAMING_SNAKE_CASE =self.rear, self.head def __repr__( self : List[str] ): SCREAMING_SNAKE_CASE =['''DoubleLinkedList'''] SCREAMING_SNAKE_CASE =self.head while node.next is not None: rep.append(str(__snake_case ) ) SCREAMING_SNAKE_CASE =node.next rep.append(str(self.rear ) ) return ",\n ".join(__snake_case ) def _lowerCAmelCase ( self : List[str] ,snake_case : DoubleLinkedListNode[T, U] ): SCREAMING_SNAKE_CASE =self.rear.prev # All nodes other than self.head are guaranteed to have non-None previous assert previous is not None SCREAMING_SNAKE_CASE =node SCREAMING_SNAKE_CASE =previous SCREAMING_SNAKE_CASE =node SCREAMING_SNAKE_CASE =self.rear def _lowerCAmelCase ( self : Dict ,snake_case : DoubleLinkedListNode[T, U] ): if node.prev is None or node.next is None: return None SCREAMING_SNAKE_CASE =node.next SCREAMING_SNAKE_CASE =node.prev SCREAMING_SNAKE_CASE =None SCREAMING_SNAKE_CASE =None return node class a_ ( Generic[T, U] ): """simple docstring""" __UpperCAmelCase = {} def __init__( self : Dict ,snake_case : int ): SCREAMING_SNAKE_CASE =DoubleLinkedList() SCREAMING_SNAKE_CASE =capacity SCREAMING_SNAKE_CASE =0 SCREAMING_SNAKE_CASE =0 SCREAMING_SNAKE_CASE =0 SCREAMING_SNAKE_CASE ={} def __repr__( self : Optional[int] ): return ( f'CacheInfo(hits={self.hits}, misses={self.miss}, ' f'capacity={self.capacity}, current size={self.num_keys})' ) def __contains__( self : str ,snake_case : T ): return key in self.cache def _lowerCAmelCase ( self : Optional[Any] ,snake_case : T ): # Note: pythonic interface would throw KeyError rather than return None if key in self.cache: self.hits += 1 SCREAMING_SNAKE_CASE =self.cache[key] SCREAMING_SNAKE_CASE =self.list.remove(self.cache[key] ) assert node == value_node # node is guaranteed not None because it is in self.cache assert node is not None self.list.add(__snake_case ) return node.val self.miss += 1 return None def _lowerCAmelCase ( self : str ,snake_case : T ,snake_case : U ): if key not in self.cache: if self.num_keys >= self.capacity: # delete first node (oldest) when over capacity SCREAMING_SNAKE_CASE =self.list.head.next # guaranteed to have a non-None first node when num_keys > 0 # explain to type checker via assertions assert first_node is not None assert first_node.key is not None assert ( self.list.remove(__snake_case ) is not None ) # node guaranteed to be in list assert node.key is not None del self.cache[first_node.key] self.num_keys -= 1 SCREAMING_SNAKE_CASE =DoubleLinkedListNode(__snake_case ,__snake_case ) self.list.add(self.cache[key] ) self.num_keys += 1 else: # bump node to the end of the list, update value SCREAMING_SNAKE_CASE =self.list.remove(self.cache[key] ) assert node is not None # node guaranteed to be in list SCREAMING_SNAKE_CASE =value self.list.add(__snake_case ) @classmethod def _lowerCAmelCase ( cls : List[str] ,snake_case : int = 128 ): def cache_decorator_inner(snake_case : Callable[[T], U] ) -> Callable[..., U]: def cache_decorator_wrapper(*snake_case : T ) -> U: if func not in cls.decorator_function_to_instance_map: SCREAMING_SNAKE_CASE =LRUCache(__snake_case ) SCREAMING_SNAKE_CASE =cls.decorator_function_to_instance_map[func].get(args[0] ) if result is None: SCREAMING_SNAKE_CASE =func(*__snake_case ) cls.decorator_function_to_instance_map[func].put(args[0] ,__snake_case ) return result def cache_info() -> LRUCache[T, U]: return cls.decorator_function_to_instance_map[func] setattr(__snake_case ,'cache_info' ,__snake_case ) # noqa: B010 return cache_decorator_wrapper return cache_decorator_inner if __name__ == "__main__": import doctest doctest.testmod()
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'''simple docstring''' import warnings from contextlib import contextmanager from ....processing_utils import ProcessorMixin class SCREAMING_SNAKE_CASE( A__ ): """simple docstring""" lowerCamelCase__ = """MCTCTFeatureExtractor""" lowerCamelCase__ = """AutoTokenizer""" def __init__( self : Dict , __snake_case : Optional[int] , __snake_case : List[str] ) -> str: super().__init__(__snake_case , __snake_case ) UpperCAmelCase : List[Any] = self.feature_extractor UpperCAmelCase : Union[str, Any] = False def __call__( self : Any , *__snake_case : List[str] , **__snake_case : Any ) -> List[Any]: # For backward compatibility if self._in_target_context_manager: return self.current_processor(*__snake_case , **__snake_case ) if "raw_speech" in kwargs: warnings.warn('''Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead.''' ) UpperCAmelCase : int = kwargs.pop('''raw_speech''' ) else: UpperCAmelCase : Union[str, Any] = kwargs.pop('''audio''' , __snake_case ) UpperCAmelCase : Optional[Any] = kwargs.pop('''sampling_rate''' , __snake_case ) UpperCAmelCase : Dict = kwargs.pop('''text''' , __snake_case ) if len(__snake_case ) > 0: UpperCAmelCase : Any = args[0] UpperCAmelCase : Optional[int] = args[1:] if audio is None and text is None: raise ValueError('''You need to specify either an `audio` or `text` input to process.''' ) if audio is not None: UpperCAmelCase : List[str] = self.feature_extractor(__snake_case , *__snake_case , sampling_rate=__snake_case , **__snake_case ) if text is not None: UpperCAmelCase : int = self.tokenizer(__snake_case , **__snake_case ) if text is None: return inputs elif audio is None: return encodings else: UpperCAmelCase : str = encodings['''input_ids'''] return inputs def A ( self : List[Any] , *__snake_case : List[Any] , **__snake_case : List[Any] ) -> str: return self.tokenizer.batch_decode(*__snake_case , **__snake_case ) def A ( self : List[Any] , *__snake_case : int , **__snake_case : Optional[int] ) -> Any: # For backward compatibility if self._in_target_context_manager: return self.current_processor.pad(*__snake_case , **__snake_case ) UpperCAmelCase : List[Any] = kwargs.pop('''input_features''' , __snake_case ) UpperCAmelCase : Optional[Any] = kwargs.pop('''labels''' , __snake_case ) if len(__snake_case ) > 0: UpperCAmelCase : List[str] = args[0] UpperCAmelCase : List[Any] = args[1:] if input_features is not None: UpperCAmelCase : Tuple = self.feature_extractor.pad(__snake_case , *__snake_case , **__snake_case ) if labels is not None: UpperCAmelCase : Optional[int] = self.tokenizer.pad(__snake_case , **__snake_case ) if labels is None: return input_features elif input_features is None: return labels else: UpperCAmelCase : List[str] = labels['''input_ids'''] return input_features def A ( self : Union[str, Any] , *__snake_case : Optional[Any] , **__snake_case : Optional[int] ) -> Optional[Any]: return self.tokenizer.decode(*__snake_case , **__snake_case ) @contextmanager def A ( self : Any ) -> Optional[int]: warnings.warn( '''`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your ''' '''labels by using the argument `text` of the regular `__call__` method (either in the same call as ''' '''your audio inputs, or in a separate call.''' ) UpperCAmelCase : Dict = True UpperCAmelCase : List[Any] = self.tokenizer yield UpperCAmelCase : Tuple = self.feature_extractor UpperCAmelCase : List[Any] = False
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import os from typing import Dict, List, Tuple, TypeVar, Union _lowerCAmelCase : List[str] = TypeVar("T") _lowerCAmelCase : Any = Union[List[T], Tuple[T, ...]] _lowerCAmelCase : Dict = Union[T, List[T], Dict[str, T]] _lowerCAmelCase : Optional[Any] = Union[str, bytes, os.PathLike]
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'''simple docstring''' from math import isclose, sqrt def snake_case_ ( _lowerCAmelCase : float , _lowerCAmelCase : float , _lowerCAmelCase : float ) -> tuple[float, float, float]: UpperCAmelCase : Optional[int] = point_y / 4 / point_x UpperCAmelCase : str = 2 * normal_gradient / (1 + normal_gradient * normal_gradient) UpperCAmelCase : Any = (1 - normal_gradient * normal_gradient) / ( 1 + normal_gradient * normal_gradient ) UpperCAmelCase : Union[str, Any] = (sa - ca * incoming_gradient) / (ca + sa * incoming_gradient) # to find the next point, solve the simultaeneous equations: # y^2 + 4x^2 = 100 # y - b = m * (x - a) # ==> A x^2 + B x + C = 0 UpperCAmelCase : Union[str, Any] = outgoing_gradient**2 + 4 UpperCAmelCase : Dict = 2 * outgoing_gradient * (point_y - outgoing_gradient * point_x) UpperCAmelCase : List[Any] = (point_y - outgoing_gradient * point_x) ** 2 - 100 UpperCAmelCase : List[str] = ( -linear_term - sqrt(linear_term**2 - 4 * quadratic_term * constant_term ) ) / (2 * quadratic_term) UpperCAmelCase : Optional[int] = ( -linear_term + sqrt(linear_term**2 - 4 * quadratic_term * constant_term ) ) / (2 * quadratic_term) # two solutions, one of which is our input point UpperCAmelCase : Optional[Any] = x_minus if isclose(_lowerCAmelCase , _lowerCAmelCase ) else x_plus UpperCAmelCase : Union[str, Any] = point_y + outgoing_gradient * (next_x - point_x) return next_x, next_y, outgoing_gradient def snake_case_ ( _lowerCAmelCase : float = 1.4 , _lowerCAmelCase : float = -9.6 ) -> int: UpperCAmelCase : int = 0 UpperCAmelCase : float = first_x_coord UpperCAmelCase : float = first_y_coord UpperCAmelCase : float = (1_0.1 - point_y) / (0.0 - point_x) while not (-0.0_1 <= point_x <= 0.0_1 and point_y > 0): UpperCAmelCase , UpperCAmelCase , UpperCAmelCase : str = next_point(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ) num_reflections += 1 return num_reflections if __name__ == "__main__": print(F"{solution() = }")
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'''simple docstring''' def UpperCamelCase_ ( snake_case_ : int ) -> list: '''simple docstring''' if bit_count < 0: raise ValueError("""The given input must be positive""" ) # get the generated string sequence __lowerCAmelCase = gray_code_sequence_string(_lowerCAmelCase ) # # convert them to integers for i in range(len(_lowerCAmelCase ) ): __lowerCAmelCase = int(sequence[i] , 2 ) return sequence def UpperCamelCase_ ( snake_case_ : int ) -> list: '''simple docstring''' if bit_count == 0: return ["0"] if bit_count == 1: return ["0", "1"] __lowerCAmelCase = 1 << bit_count # defines the length of the sequence # 1<< n is equivalent to 2^n # recursive answer will generate answer for n-1 bits __lowerCAmelCase = gray_code_sequence_string(bit_count - 1 ) __lowerCAmelCase = [] # append 0 to first half of the smaller sequence generated for i in range(seq_len // 2 ): __lowerCAmelCase = '''0''' + smaller_sequence[i] sequence.append(_lowerCAmelCase ) # append 1 to second half ... start from the end of the list for i in reversed(range(seq_len // 2 ) ): __lowerCAmelCase = '''1''' + smaller_sequence[i] sequence.append(_lowerCAmelCase ) return sequence if __name__ == "__main__": import doctest doctest.testmod()
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'''simple docstring''' from typing import TYPE_CHECKING from ...utils import ( OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_tokenizers_available, is_torch_available, ) UpperCamelCase__: str = { "configuration_lxmert": ["LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "LxmertConfig"], "tokenization_lxmert": ["LxmertTokenizer"], } try: if not is_tokenizers_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: UpperCamelCase__: int = ["LxmertTokenizerFast"] try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: UpperCamelCase__: Union[str, Any] = [ "LxmertEncoder", "LxmertForPreTraining", "LxmertForQuestionAnswering", "LxmertModel", "LxmertPreTrainedModel", "LxmertVisualFeatureEncoder", "LxmertXLayer", ] try: if not is_tf_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: UpperCamelCase__: int = [ "TF_LXMERT_PRETRAINED_MODEL_ARCHIVE_LIST", "TFLxmertForPreTraining", "TFLxmertMainLayer", "TFLxmertModel", "TFLxmertPreTrainedModel", "TFLxmertVisualFeatureEncoder", ] if TYPE_CHECKING: from .configuration_lxmert import LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP, LxmertConfig from .tokenization_lxmert import LxmertTokenizer try: if not is_tokenizers_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .tokenization_lxmert_fast import LxmertTokenizerFast try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_lxmert import ( LxmertEncoder, LxmertForPreTraining, LxmertForQuestionAnswering, LxmertModel, LxmertPreTrainedModel, LxmertVisualFeatureEncoder, LxmertXLayer, ) try: if not is_tf_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_tf_lxmert import ( TF_LXMERT_PRETRAINED_MODEL_ARCHIVE_LIST, TFLxmertForPreTraining, TFLxmertMainLayer, TFLxmertModel, TFLxmertPreTrainedModel, TFLxmertVisualFeatureEncoder, ) else: import sys UpperCamelCase__: Tuple = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
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0
import argparse from pathlib import Path from typing import Dict, OrderedDict, Tuple import torch from audiocraft.models import MusicGen from transformers import ( AutoFeatureExtractor, AutoTokenizer, EncodecModel, MusicgenDecoderConfig, MusicgenForConditionalGeneration, MusicgenProcessor, TaEncoderModel, ) from transformers.models.musicgen.modeling_musicgen import MusicgenForCausalLM from transformers.utils import logging logging.set_verbosity_info() _a = logging.get_logger(__name__) _a = ["model.decoder.embed_positions.weights"] def lowerCAmelCase__(__snake_case ) -> Tuple: '''simple docstring''' if "emb" in name: lowerCamelCase__ = name.replace('''emb''' ,'''model.decoder.embed_tokens''' ) if "transformer" in name: lowerCamelCase__ = name.replace('''transformer''' ,'''model.decoder''' ) if "cross_attention" in name: lowerCamelCase__ = name.replace('''cross_attention''' ,'''encoder_attn''' ) if "linear1" in name: lowerCamelCase__ = name.replace('''linear1''' ,'''fc1''' ) if "linear2" in name: lowerCamelCase__ = name.replace('''linear2''' ,'''fc2''' ) if "norm1" in name: lowerCamelCase__ = name.replace('''norm1''' ,'''self_attn_layer_norm''' ) if "norm_cross" in name: lowerCamelCase__ = name.replace('''norm_cross''' ,'''encoder_attn_layer_norm''' ) if "norm2" in name: lowerCamelCase__ = name.replace('''norm2''' ,'''final_layer_norm''' ) if "out_norm" in name: lowerCamelCase__ = name.replace('''out_norm''' ,'''model.decoder.layer_norm''' ) if "linears" in name: lowerCamelCase__ = name.replace('''linears''' ,'''lm_heads''' ) if "condition_provider.conditioners.description.output_proj" in name: lowerCamelCase__ = name.replace('''condition_provider.conditioners.description.output_proj''' ,'''enc_to_dec_proj''' ) return name def lowerCAmelCase__(__snake_case ,__snake_case ) -> Tuple[Dict, Dict]: '''simple docstring''' lowerCamelCase__ = list(state_dict.keys() ) lowerCamelCase__ = {} for key in keys: lowerCamelCase__ = state_dict.pop(_lowerCAmelCase ) lowerCamelCase__ = rename_keys(_lowerCAmelCase ) if "in_proj_weight" in key: # split fused qkv proj lowerCamelCase__ = val[:hidden_size, :] lowerCamelCase__ = val[hidden_size : 2 * hidden_size, :] lowerCamelCase__ = val[-hidden_size:, :] elif "enc_to_dec_proj" in key: lowerCamelCase__ = val else: lowerCamelCase__ = val return state_dict, enc_dec_proj_state_dict def lowerCAmelCase__(__snake_case ) -> MusicgenDecoderConfig: '''simple docstring''' if checkpoint == "small": # default config values lowerCamelCase__ = 1024 lowerCamelCase__ = 24 lowerCamelCase__ = 16 elif checkpoint == "medium": lowerCamelCase__ = 1536 lowerCamelCase__ = 48 lowerCamelCase__ = 24 elif checkpoint == "large": lowerCamelCase__ = 2048 lowerCamelCase__ = 48 lowerCamelCase__ = 32 else: raise ValueError(F'Checkpoint should be one of `[\'small\', \'medium\', \'large\']`, got {checkpoint}.' ) lowerCamelCase__ = MusicgenDecoderConfig( hidden_size=_lowerCAmelCase ,ffn_dim=hidden_size * 4 ,num_hidden_layers=_lowerCAmelCase ,num_attention_heads=_lowerCAmelCase ,) return config @torch.no_grad() def lowerCAmelCase__(__snake_case ,__snake_case=None ,__snake_case=None ,__snake_case="cpu" ) -> str: '''simple docstring''' lowerCamelCase__ = MusicGen.get_pretrained(_lowerCAmelCase ,device=_lowerCAmelCase ) lowerCamelCase__ = decoder_config_from_checkpoint(_lowerCAmelCase ) lowerCamelCase__ = fairseq_model.lm.state_dict() lowerCamelCase__ = rename_state_dict( _lowerCAmelCase ,hidden_size=decoder_config.hidden_size ) lowerCamelCase__ = TaEncoderModel.from_pretrained('''t5-base''' ) lowerCamelCase__ = EncodecModel.from_pretrained('''facebook/encodec_32khz''' ) lowerCamelCase__ = MusicgenForCausalLM(_lowerCAmelCase ).eval() # load all decoder weights - expect that we'll be missing embeddings and enc-dec projection lowerCamelCase__ = decoder.load_state_dict(_lowerCAmelCase ,strict=_lowerCAmelCase ) for key in missing_keys.copy(): if key.startswith(('''text_encoder''', '''audio_encoder''') ) or key in EXPECTED_MISSING_KEYS: missing_keys.remove(_lowerCAmelCase ) if len(_lowerCAmelCase ) > 0: raise ValueError(F'Missing key(s) in state_dict: {missing_keys}' ) if len(_lowerCAmelCase ) > 0: raise ValueError(F'Unexpected key(s) in state_dict: {unexpected_keys}' ) # init the composite model lowerCamelCase__ = MusicgenForConditionalGeneration(text_encoder=_lowerCAmelCase ,audio_encoder=_lowerCAmelCase ,decoder=_lowerCAmelCase ) # load the pre-trained enc-dec projection (from the decoder state dict) model.enc_to_dec_proj.load_state_dict(_lowerCAmelCase ) # check we can do a forward pass lowerCamelCase__ = torch.arange(0 ,8 ,dtype=torch.long ).reshape(2 ,-1 ) lowerCamelCase__ = input_ids.reshape(2 * 4 ,-1 ) with torch.no_grad(): lowerCamelCase__ = model(input_ids=_lowerCAmelCase ,decoder_input_ids=_lowerCAmelCase ).logits if logits.shape != (8, 1, 2048): raise ValueError('''Incorrect shape for logits''' ) # now construct the processor lowerCamelCase__ = AutoTokenizer.from_pretrained('''t5-base''' ) lowerCamelCase__ = AutoFeatureExtractor.from_pretrained('''facebook/encodec_32khz''' ,padding_side='''left''' ) lowerCamelCase__ = MusicgenProcessor(feature_extractor=_lowerCAmelCase ,tokenizer=_lowerCAmelCase ) # set the appropriate bos/pad token ids lowerCamelCase__ = 2048 lowerCamelCase__ = 2048 # set other default generation config params lowerCamelCase__ = int(30 * audio_encoder.config.frame_rate ) lowerCamelCase__ = True lowerCamelCase__ = 3.0 if pytorch_dump_folder is not None: Path(_lowerCAmelCase ).mkdir(exist_ok=_lowerCAmelCase ) logger.info(F'Saving model {checkpoint} to {pytorch_dump_folder}' ) model.save_pretrained(_lowerCAmelCase ) processor.save_pretrained(_lowerCAmelCase ) if repo_id: logger.info(F'Pushing model {checkpoint} to {repo_id}' ) model.push_to_hub(_lowerCAmelCase ) processor.push_to_hub(_lowerCAmelCase ) if __name__ == "__main__": _a = argparse.ArgumentParser() # Required parameters parser.add_argument( "--checkpoint", default="small", type=str, help="Checkpoint size of the MusicGen model you'd like to convert. Can be one of: `['small', 'medium', 'large']`.", ) parser.add_argument( "--pytorch_dump_folder", required=True, default=None, type=str, help="Path to the output PyTorch model directory.", ) parser.add_argument( "--push_to_hub", default=None, type=str, help="Where to upload the converted model on the 🤗 hub." ) parser.add_argument( "--device", default="cpu", type=str, help="Torch device to run the conversion, either cpu or cuda." ) _a = parser.parse_args() convert_musicgen_checkpoint(args.checkpoint, args.pytorch_dump_folder, args.push_to_hub)
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'''simple docstring''' import gc import unittest import numpy as np import torch from transformers import CLIPTextConfig, CLIPTextModel, XLMRobertaTokenizer from diffusers import AltDiffusionPipeline, AutoencoderKL, DDIMScheduler, PNDMScheduler, UNetaDConditionModel from diffusers.pipelines.alt_diffusion.modeling_roberta_series import ( RobertaSeriesConfig, RobertaSeriesModelWithTransformation, ) from diffusers.utils import slow, torch_device from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin enable_full_determinism() class SCREAMING_SNAKE_CASE( A__ , A__ , A__ , unittest.TestCase ): """simple docstring""" lowerCamelCase__ = AltDiffusionPipeline lowerCamelCase__ = TEXT_TO_IMAGE_PARAMS lowerCamelCase__ = TEXT_TO_IMAGE_BATCH_PARAMS lowerCamelCase__ = TEXT_TO_IMAGE_IMAGE_PARAMS lowerCamelCase__ = TEXT_TO_IMAGE_IMAGE_PARAMS def A ( self : Dict ) -> int: torch.manual_seed(0 ) UpperCAmelCase : str = UNetaDConditionModel( block_out_channels=(32, 64) , layers_per_block=2 , sample_size=32 , in_channels=4 , out_channels=4 , down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''') , up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''') , cross_attention_dim=32 , ) UpperCAmelCase : Dict = DDIMScheduler( beta_start=0.0_00_85 , beta_end=0.0_12 , beta_schedule='''scaled_linear''' , clip_sample=__snake_case , set_alpha_to_one=__snake_case , ) torch.manual_seed(0 ) UpperCAmelCase : Union[str, Any] = AutoencoderKL( block_out_channels=[32, 64] , in_channels=3 , out_channels=3 , down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D'''] , up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D'''] , latent_channels=4 , ) # TODO: address the non-deterministic text encoder (fails for save-load tests) # torch.manual_seed(0) # text_encoder_config = RobertaSeriesConfig( # hidden_size=32, # project_dim=32, # intermediate_size=37, # layer_norm_eps=1e-05, # num_attention_heads=4, # num_hidden_layers=5, # vocab_size=5002, # ) # text_encoder = RobertaSeriesModelWithTransformation(text_encoder_config) torch.manual_seed(0 ) UpperCAmelCase : Dict = CLIPTextConfig( bos_token_id=0 , eos_token_id=2 , hidden_size=32 , projection_dim=32 , intermediate_size=37 , layer_norm_eps=1E-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=5002 , ) UpperCAmelCase : List[Any] = CLIPTextModel(__snake_case ) UpperCAmelCase : Optional[Any] = XLMRobertaTokenizer.from_pretrained('''hf-internal-testing/tiny-xlm-roberta''' ) UpperCAmelCase : Optional[int] = 77 UpperCAmelCase : Optional[int] = { '''unet''': unet, '''scheduler''': scheduler, '''vae''': vae, '''text_encoder''': text_encoder, '''tokenizer''': tokenizer, '''safety_checker''': None, '''feature_extractor''': None, } return components def A ( self : Optional[Any] , __snake_case : Dict , __snake_case : List[str]=0 ) -> Union[str, Any]: if str(__snake_case ).startswith('''mps''' ): UpperCAmelCase : str = torch.manual_seed(__snake_case ) else: UpperCAmelCase : Tuple = torch.Generator(device=__snake_case ).manual_seed(__snake_case ) UpperCAmelCase : Dict = { '''prompt''': '''A painting of a squirrel eating a burger''', '''generator''': generator, '''num_inference_steps''': 2, '''guidance_scale''': 6.0, '''output_type''': '''numpy''', } return inputs def A ( self : Union[str, Any] ) -> List[str]: super().test_attention_slicing_forward_pass(expected_max_diff=3E-3 ) def A ( self : Tuple ) -> List[str]: super().test_inference_batch_single_identical(expected_max_diff=3E-3 ) def A ( self : Tuple ) -> Optional[int]: UpperCAmelCase : List[str] = '''cpu''' # ensure determinism for the device-dependent torch.Generator UpperCAmelCase : Any = self.get_dummy_components() torch.manual_seed(0 ) UpperCAmelCase : Optional[int] = RobertaSeriesConfig( hidden_size=32 , project_dim=32 , intermediate_size=37 , layer_norm_eps=1E-05 , num_attention_heads=4 , num_hidden_layers=5 , vocab_size=5002 , ) # TODO: remove after fixing the non-deterministic text encoder UpperCAmelCase : List[str] = RobertaSeriesModelWithTransformation(__snake_case ) UpperCAmelCase : str = text_encoder UpperCAmelCase : Optional[int] = AltDiffusionPipeline(**__snake_case ) UpperCAmelCase : str = alt_pipe.to(__snake_case ) alt_pipe.set_progress_bar_config(disable=__snake_case ) UpperCAmelCase : Optional[int] = self.get_dummy_inputs(__snake_case ) UpperCAmelCase : Optional[int] = '''A photo of an astronaut''' UpperCAmelCase : List[Any] = alt_pipe(**__snake_case ) UpperCAmelCase : Optional[Any] = output.images UpperCAmelCase : str = image[0, -3:, -3:, -1] assert image.shape == (1, 64, 64, 3) UpperCAmelCase : List[str] = np.array( [0.5_74_81_62, 0.60_44_71_45, 0.48_82_12_17, 0.50_10_06_36, 0.5_43_11_85, 0.45_76_36_83, 0.49_65_76_96, 0.48_13_27_33, 0.47_57_30_93] ) assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2 def A ( self : int ) -> Union[str, Any]: UpperCAmelCase : Optional[int] = '''cpu''' # ensure determinism for the device-dependent torch.Generator UpperCAmelCase : int = self.get_dummy_components() UpperCAmelCase : int = PNDMScheduler(skip_prk_steps=__snake_case ) torch.manual_seed(0 ) UpperCAmelCase : int = RobertaSeriesConfig( hidden_size=32 , project_dim=32 , intermediate_size=37 , layer_norm_eps=1E-05 , num_attention_heads=4 , num_hidden_layers=5 , vocab_size=5002 , ) # TODO: remove after fixing the non-deterministic text encoder UpperCAmelCase : Union[str, Any] = RobertaSeriesModelWithTransformation(__snake_case ) UpperCAmelCase : Union[str, Any] = text_encoder UpperCAmelCase : Optional[int] = AltDiffusionPipeline(**__snake_case ) UpperCAmelCase : Dict = alt_pipe.to(__snake_case ) alt_pipe.set_progress_bar_config(disable=__snake_case ) UpperCAmelCase : int = self.get_dummy_inputs(__snake_case ) UpperCAmelCase : Optional[int] = alt_pipe(**__snake_case ) UpperCAmelCase : Optional[int] = output.images UpperCAmelCase : int = image[0, -3:, -3:, -1] assert image.shape == (1, 64, 64, 3) UpperCAmelCase : Optional[int] = np.array( [0.51_60_50_93, 0.5_70_72_41, 0.47_36_55_07, 0.50_57_88_86, 0.5_63_38_77, 0.4_64_25_03, 0.5_18_20_81, 0.48_76_34_84, 0.49_08_42_37] ) assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2 @slow @require_torch_gpu class SCREAMING_SNAKE_CASE( unittest.TestCase ): """simple docstring""" def A ( self : str ) -> Any: # clean up the VRAM after each test super().tearDown() gc.collect() torch.cuda.empty_cache() def A ( self : List[Any] ) -> Any: # make sure here that pndm scheduler skips prk UpperCAmelCase : List[Any] = AltDiffusionPipeline.from_pretrained('''BAAI/AltDiffusion''' , safety_checker=__snake_case ) UpperCAmelCase : Tuple = alt_pipe.to(__snake_case ) alt_pipe.set_progress_bar_config(disable=__snake_case ) UpperCAmelCase : List[Any] = '''A painting of a squirrel eating a burger''' UpperCAmelCase : Any = torch.manual_seed(0 ) UpperCAmelCase : Optional[int] = alt_pipe([prompt] , generator=__snake_case , guidance_scale=6.0 , num_inference_steps=20 , output_type='''np''' ) UpperCAmelCase : Dict = output.images UpperCAmelCase : Optional[Any] = image[0, -3:, -3:, -1] assert image.shape == (1, 512, 512, 3) UpperCAmelCase : List[str] = np.array([0.10_10, 0.08_00, 0.07_94, 0.08_85, 0.08_43, 0.07_62, 0.07_69, 0.07_29, 0.05_86] ) assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2 def A ( self : Tuple ) -> int: UpperCAmelCase : int = DDIMScheduler.from_pretrained('''BAAI/AltDiffusion''' , subfolder='''scheduler''' ) UpperCAmelCase : Tuple = AltDiffusionPipeline.from_pretrained('''BAAI/AltDiffusion''' , scheduler=__snake_case , safety_checker=__snake_case ) UpperCAmelCase : Dict = alt_pipe.to(__snake_case ) alt_pipe.set_progress_bar_config(disable=__snake_case ) UpperCAmelCase : Tuple = '''A painting of a squirrel eating a burger''' UpperCAmelCase : Optional[int] = torch.manual_seed(0 ) UpperCAmelCase : List[Any] = alt_pipe([prompt] , generator=__snake_case , num_inference_steps=2 , output_type='''numpy''' ) UpperCAmelCase : Dict = output.images UpperCAmelCase : Optional[Any] = image[0, -3:, -3:, -1] assert image.shape == (1, 512, 512, 3) UpperCAmelCase : Union[str, Any] = np.array([0.40_19, 0.40_52, 0.38_10, 0.41_19, 0.39_16, 0.39_82, 0.46_51, 0.41_95, 0.53_23] ) assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
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"""simple docstring""" import argparse import json import os import pickle import shutil import numpy as np import torch from distiller import Distiller from lm_seqs_dataset import LmSeqsDataset from transformers import ( BertConfig, BertForMaskedLM, BertTokenizer, DistilBertConfig, DistilBertForMaskedLM, DistilBertTokenizer, GPTaConfig, GPTaLMHeadModel, GPTaTokenizer, RobertaConfig, RobertaForMaskedLM, RobertaTokenizer, ) from utils import git_log, init_gpu_params, logger, set_seed UpperCAmelCase__ = { "distilbert": (DistilBertConfig, DistilBertForMaskedLM, DistilBertTokenizer), "roberta": (RobertaConfig, RobertaForMaskedLM, RobertaTokenizer), "bert": (BertConfig, BertForMaskedLM, BertTokenizer), "gpt2": (GPTaConfig, GPTaLMHeadModel, GPTaTokenizer), } def _UpperCAmelCase ( __lowerCamelCase : Optional[Any] ) -> Tuple: assert (args.mlm and args.alpha_mlm > 0.0) or (not args.mlm and args.alpha_mlm == 0.0) assert (args.alpha_mlm > 0.0 and args.alpha_clm == 0.0) or (args.alpha_mlm == 0.0 and args.alpha_clm > 0.0) if args.mlm: assert os.path.isfile(args.token_counts ) assert (args.student_type in ["roberta", "distilbert"]) and (args.teacher_type in ["roberta", "bert"]) else: assert (args.student_type in ["gpt2"]) and (args.teacher_type in ["gpt2"]) assert args.teacher_type == args.student_type or ( args.student_type == "distilbert" and args.teacher_type == "bert" ) assert os.path.isfile(args.student_config ) if args.student_pretrained_weights is not None: assert os.path.isfile(args.student_pretrained_weights ) if args.freeze_token_type_embds: assert args.student_type in ["roberta"] assert args.alpha_ce >= 0.0 assert args.alpha_mlm >= 0.0 assert args.alpha_clm >= 0.0 assert args.alpha_mse >= 0.0 assert args.alpha_cos >= 0.0 assert args.alpha_ce + args.alpha_mlm + args.alpha_clm + args.alpha_mse + args.alpha_cos > 0.0 def _UpperCAmelCase ( __lowerCamelCase : Dict , __lowerCamelCase : Optional[Any] ) -> str: if args.student_type == "roberta": _snake_case = False elif args.student_type == "gpt2": _snake_case = False def _UpperCAmelCase ( __lowerCamelCase : Optional[int] , __lowerCamelCase : Union[str, Any] ) -> str: if args.student_type == "roberta": _snake_case = False def _UpperCAmelCase ( ) -> Union[str, Any]: _snake_case = argparse.ArgumentParser(description='''Training''' ) parser.add_argument('''--force''' , action='''store_true''' , help='''Overwrite dump_path if it already exists.''' ) parser.add_argument( '''--dump_path''' , type=_lowerCAmelCase , required=_lowerCAmelCase , help='''The output directory (log, checkpoints, parameters, etc.)''' ) parser.add_argument( '''--data_file''' , type=_lowerCAmelCase , required=_lowerCAmelCase , help='''The binarized file (tokenized + tokens_to_ids) and grouped by sequence.''' , ) parser.add_argument( '''--student_type''' , type=_lowerCAmelCase , choices=['''distilbert''', '''roberta''', '''gpt2'''] , required=_lowerCAmelCase , help='''The student type (DistilBERT, RoBERTa).''' , ) parser.add_argument('''--student_config''' , type=_lowerCAmelCase , required=_lowerCAmelCase , help='''Path to the student configuration.''' ) parser.add_argument( '''--student_pretrained_weights''' , default=_lowerCAmelCase , type=_lowerCAmelCase , help='''Load student initialization checkpoint.''' ) parser.add_argument( '''--teacher_type''' , choices=['''bert''', '''roberta''', '''gpt2'''] , required=_lowerCAmelCase , help='''Teacher type (BERT, RoBERTa).''' ) parser.add_argument('''--teacher_name''' , type=_lowerCAmelCase , required=_lowerCAmelCase , help='''The teacher model.''' ) parser.add_argument('''--temperature''' , default=2.0 , type=_lowerCAmelCase , help='''Temperature for the softmax temperature.''' ) parser.add_argument( '''--alpha_ce''' , default=0.5 , type=_lowerCAmelCase , help='''Linear weight for the distillation loss. Must be >=0.''' ) parser.add_argument( '''--alpha_mlm''' , default=0.0 , type=_lowerCAmelCase , help='''Linear weight for the MLM loss. Must be >=0. Should be used in conjunction with `mlm` flag.''' , ) parser.add_argument('''--alpha_clm''' , default=0.5 , type=_lowerCAmelCase , help='''Linear weight for the CLM loss. Must be >=0.''' ) parser.add_argument('''--alpha_mse''' , default=0.0 , type=_lowerCAmelCase , help='''Linear weight of the MSE loss. Must be >=0.''' ) parser.add_argument( '''--alpha_cos''' , default=0.0 , type=_lowerCAmelCase , help='''Linear weight of the cosine embedding loss. Must be >=0.''' ) parser.add_argument( '''--mlm''' , action='''store_true''' , help='''The LM step: MLM or CLM. If `mlm` is True, the MLM is used over CLM.''' ) parser.add_argument( '''--mlm_mask_prop''' , default=0.15 , type=_lowerCAmelCase , help='''Proportion of tokens for which we need to make a prediction.''' , ) parser.add_argument('''--word_mask''' , default=0.8 , type=_lowerCAmelCase , help='''Proportion of tokens to mask out.''' ) parser.add_argument('''--word_keep''' , default=0.1 , type=_lowerCAmelCase , help='''Proportion of tokens to keep.''' ) parser.add_argument('''--word_rand''' , default=0.1 , type=_lowerCAmelCase , help='''Proportion of tokens to randomly replace.''' ) parser.add_argument( '''--mlm_smoothing''' , default=0.7 , type=_lowerCAmelCase , help='''Smoothing parameter to emphasize more rare tokens (see XLM, similar to word2vec).''' , ) parser.add_argument('''--token_counts''' , type=_lowerCAmelCase , help='''The token counts in the data_file for MLM.''' ) parser.add_argument( '''--restrict_ce_to_mask''' , action='''store_true''' , help='''If true, compute the distillation loss only the [MLM] prediction distribution.''' , ) parser.add_argument( '''--freeze_pos_embs''' , action='''store_true''' , help='''Freeze positional embeddings during distillation. For student_type in [\'roberta\', \'gpt2\'] only.''' , ) parser.add_argument( '''--freeze_token_type_embds''' , action='''store_true''' , help='''Freeze token type embeddings during distillation if existent. For student_type in [\'roberta\'] only.''' , ) parser.add_argument('''--n_epoch''' , type=_lowerCAmelCase , default=3 , help='''Number of pass on the whole dataset.''' ) parser.add_argument('''--batch_size''' , type=_lowerCAmelCase , default=5 , help='''Batch size (for each process).''' ) parser.add_argument( '''--group_by_size''' , action='''store_false''' , help='''If true, group sequences that have similar length into the same batch. Default is true.''' , ) parser.add_argument( '''--gradient_accumulation_steps''' , type=_lowerCAmelCase , default=50 , help='''Gradient accumulation for larger training batches.''' , ) parser.add_argument('''--warmup_prop''' , default=0.05 , type=_lowerCAmelCase , help='''Linear warmup proportion.''' ) parser.add_argument('''--weight_decay''' , default=0.0 , type=_lowerCAmelCase , help='''Weight decay if we apply some.''' ) parser.add_argument('''--learning_rate''' , default=5E-4 , type=_lowerCAmelCase , help='''The initial learning rate for Adam.''' ) parser.add_argument('''--adam_epsilon''' , default=1E-6 , type=_lowerCAmelCase , help='''Epsilon for Adam optimizer.''' ) parser.add_argument('''--max_grad_norm''' , default=5.0 , type=_lowerCAmelCase , help='''Max gradient norm.''' ) parser.add_argument('''--initializer_range''' , default=0.02 , type=_lowerCAmelCase , help='''Random initialization range.''' ) parser.add_argument( '''--fp16''' , action='''store_true''' , help='''Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit''' , ) parser.add_argument( '''--fp16_opt_level''' , type=_lowerCAmelCase , default='''O1''' , help=( '''For fp16: Apex AMP optimization level selected in [\'O0\', \'O1\', \'O2\', and \'O3\'].''' '''See details at https://nvidia.github.io/apex/amp.html''' ) , ) parser.add_argument('''--n_gpu''' , type=_lowerCAmelCase , default=1 , help='''Number of GPUs in the node.''' ) parser.add_argument('''--local_rank''' , type=_lowerCAmelCase , default=-1 , help='''Distributed training - Local rank''' ) parser.add_argument('''--seed''' , type=_lowerCAmelCase , default=56 , help='''Random seed''' ) parser.add_argument('''--log_interval''' , type=_lowerCAmelCase , default=5_00 , help='''Tensorboard logging interval.''' ) parser.add_argument('''--checkpoint_interval''' , type=_lowerCAmelCase , default=40_00 , help='''Checkpoint interval.''' ) _snake_case = parser.parse_args() sanity_checks(_lowerCAmelCase ) # ARGS # init_gpu_params(_lowerCAmelCase ) set_seed(_lowerCAmelCase ) if args.is_master: if os.path.exists(args.dump_path ): if not args.force: raise ValueError( f'''Serialization dir {args.dump_path} already exists, but you have not precised wheter to overwrite''' ''' itUse `--force` if you want to overwrite it''' ) else: shutil.rmtree(args.dump_path ) if not os.path.exists(args.dump_path ): os.makedirs(args.dump_path ) logger.info(f'''Experiment will be dumped and logged in {args.dump_path}''' ) # SAVE PARAMS # logger.info(f'''Param: {args}''' ) with open(os.path.join(args.dump_path , '''parameters.json''' ) , '''w''' ) as f: json.dump(vars(_lowerCAmelCase ) , _lowerCAmelCase , indent=4 ) git_log(args.dump_path ) _snake_case = MODEL_CLASSES[args.student_type] _snake_case = MODEL_CLASSES[args.teacher_type] # TOKENIZER # _snake_case = teacher_tokenizer_class.from_pretrained(args.teacher_name ) _snake_case = {} for tok_name, tok_symbol in tokenizer.special_tokens_map.items(): _snake_case = tokenizer.all_special_tokens.index(_lowerCAmelCase ) _snake_case = tokenizer.all_special_ids[idx] logger.info(f'''Special tokens {special_tok_ids}''' ) _snake_case = special_tok_ids _snake_case = tokenizer.max_model_input_sizes[args.teacher_name] # DATA LOADER # logger.info(f'''Loading data from {args.data_file}''' ) with open(args.data_file , '''rb''' ) as fp: _snake_case = pickle.load(_lowerCAmelCase ) if args.mlm: logger.info(f'''Loading token counts from {args.token_counts} (already pre-computed)''' ) with open(args.token_counts , '''rb''' ) as fp: _snake_case = pickle.load(_lowerCAmelCase ) _snake_case = np.maximum(_lowerCAmelCase , 1 ) ** -args.mlm_smoothing for idx in special_tok_ids.values(): _snake_case = 0.0 # do not predict special tokens _snake_case = torch.from_numpy(_lowerCAmelCase ) else: _snake_case = None _snake_case = LmSeqsDataset(params=_lowerCAmelCase , data=_lowerCAmelCase ) logger.info('''Data loader created.''' ) # STUDENT # logger.info(f'''Loading student config from {args.student_config}''' ) _snake_case = student_config_class.from_pretrained(args.student_config ) _snake_case = True if args.student_pretrained_weights is not None: logger.info(f'''Loading pretrained weights from {args.student_pretrained_weights}''' ) _snake_case = student_model_class.from_pretrained(args.student_pretrained_weights , config=_lowerCAmelCase ) else: _snake_case = student_model_class(_lowerCAmelCase ) if args.n_gpu > 0: student.to(f'''cuda:{args.local_rank}''' ) logger.info('''Student loaded.''' ) # TEACHER # _snake_case = teacher_model_class.from_pretrained(args.teacher_name , output_hidden_states=_lowerCAmelCase ) if args.n_gpu > 0: teacher.to(f'''cuda:{args.local_rank}''' ) logger.info(f'''Teacher loaded from {args.teacher_name}.''' ) # FREEZING # if args.freeze_pos_embs: freeze_pos_embeddings(_lowerCAmelCase , _lowerCAmelCase ) if args.freeze_token_type_embds: freeze_token_type_embeddings(_lowerCAmelCase , _lowerCAmelCase ) # SANITY CHECKS # assert student.config.vocab_size == teacher.config.vocab_size assert student.config.hidden_size == teacher.config.hidden_size assert student.config.max_position_embeddings == teacher.config.max_position_embeddings if args.mlm: assert token_probs.size(0 ) == stu_architecture_config.vocab_size # DISTILLER # torch.cuda.empty_cache() _snake_case = Distiller( params=_lowerCAmelCase , dataset=_lowerCAmelCase , token_probs=_lowerCAmelCase , student=_lowerCAmelCase , teacher=_lowerCAmelCase ) distiller.train() logger.info('''Let\'s go get some drinks.''' ) if __name__ == "__main__": main()
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'''simple docstring''' import io import json import fsspec import pytest from datasets import Dataset, DatasetDict, Features, NamedSplit, Value from datasets.io.json import JsonDatasetReader, JsonDatasetWriter from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases def snake_case_ ( _lowerCAmelCase : List[str] , _lowerCAmelCase : Optional[Any] ) -> Optional[int]: assert isinstance(_lowerCAmelCase , _lowerCAmelCase ) assert dataset.num_rows == 4 assert dataset.num_columns == 3 assert dataset.column_names == ["col_1", "col_2", "col_3"] for feature, expected_dtype in expected_features.items(): assert dataset.features[feature].dtype == expected_dtype @pytest.mark.parametrize('''keep_in_memory''' , [False, True] ) def snake_case_ ( _lowerCAmelCase : int , _lowerCAmelCase : Dict , _lowerCAmelCase : Optional[int] ) -> Dict: UpperCAmelCase : Dict = tmp_path / '''cache''' UpperCAmelCase : List[Any] = {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''} with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase(): UpperCAmelCase : str = JsonDatasetReader(_lowerCAmelCase , cache_dir=_lowerCAmelCase , keep_in_memory=_lowerCAmelCase ).read() _check_json_dataset(_lowerCAmelCase , _lowerCAmelCase ) @pytest.mark.parametrize( '''features''' , [ None, {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}, {'''col_1''': '''string''', '''col_2''': '''string''', '''col_3''': '''string'''}, {'''col_1''': '''int32''', '''col_2''': '''int32''', '''col_3''': '''int32'''}, {'''col_1''': '''float32''', '''col_2''': '''float32''', '''col_3''': '''float32'''}, ] , ) def snake_case_ ( _lowerCAmelCase : Optional[int] , _lowerCAmelCase : Dict , _lowerCAmelCase : int ) -> Optional[int]: UpperCAmelCase : Any = tmp_path / '''cache''' UpperCAmelCase : Tuple = {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''} UpperCAmelCase : Any = features.copy() if features else default_expected_features UpperCAmelCase : List[Any] = ( Features({feature: Value(_lowerCAmelCase ) for feature, dtype in features.items()} ) if features is not None else None ) UpperCAmelCase : Dict = JsonDatasetReader(_lowerCAmelCase , features=_lowerCAmelCase , cache_dir=_lowerCAmelCase ).read() _check_json_dataset(_lowerCAmelCase , _lowerCAmelCase ) @pytest.mark.parametrize( '''features''' , [ None, {'''col_3''': '''float64''', '''col_1''': '''string''', '''col_2''': '''int64'''}, ] , ) def snake_case_ ( _lowerCAmelCase : Optional[Any] , _lowerCAmelCase : List[Any] , _lowerCAmelCase : Tuple ) -> Tuple: UpperCAmelCase : Optional[Any] = tmp_path / '''cache''' UpperCAmelCase : Optional[int] = {'''col_3''': '''float64''', '''col_1''': '''string''', '''col_2''': '''int64'''} UpperCAmelCase : int = features.copy() if features else default_expected_features UpperCAmelCase : Any = ( Features({feature: Value(_lowerCAmelCase ) for feature, dtype in features.items()} ) if features is not None else None ) UpperCAmelCase : Tuple = JsonDatasetReader(_lowerCAmelCase , features=_lowerCAmelCase , cache_dir=_lowerCAmelCase ).read() assert isinstance(_lowerCAmelCase , _lowerCAmelCase ) assert dataset.num_rows == 2 assert dataset.num_columns == 3 assert dataset.column_names == ["col_3", "col_1", "col_2"] for feature, expected_dtype in expected_features.items(): assert dataset.features[feature].dtype == expected_dtype def snake_case_ ( _lowerCAmelCase : List[str] , _lowerCAmelCase : Dict ) -> Union[str, Any]: # jsonl_312_path features are {"col_3": "float64", "col_1": "string", "col_2": "int64"} UpperCAmelCase : Tuple = {'''col_2''': '''int64''', '''col_3''': '''float64''', '''col_1''': '''string'''} UpperCAmelCase : List[str] = features.copy() UpperCAmelCase : Union[str, Any] = ( Features({feature: Value(_lowerCAmelCase ) for feature, dtype in features.items()} ) if features is not None else None ) UpperCAmelCase : Tuple = tmp_path / '''cache''' UpperCAmelCase : List[str] = JsonDatasetReader(_lowerCAmelCase , features=_lowerCAmelCase , cache_dir=_lowerCAmelCase ).read() assert isinstance(_lowerCAmelCase , _lowerCAmelCase ) assert dataset.num_rows == 2 assert dataset.num_columns == 3 assert dataset.column_names == ["col_2", "col_3", "col_1"] for feature, expected_dtype in expected_features.items(): assert dataset.features[feature].dtype == expected_dtype @pytest.mark.parametrize('''split''' , [None, NamedSplit('''train''' ), '''train''', '''test'''] ) def snake_case_ ( _lowerCAmelCase : Union[str, Any] , _lowerCAmelCase : Tuple , _lowerCAmelCase : List[str] ) -> Optional[Any]: UpperCAmelCase : Any = tmp_path / '''cache''' UpperCAmelCase : List[Any] = {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''} UpperCAmelCase : List[Any] = JsonDatasetReader(_lowerCAmelCase , cache_dir=_lowerCAmelCase , split=_lowerCAmelCase ).read() _check_json_dataset(_lowerCAmelCase , _lowerCAmelCase ) assert dataset.split == split if split else "train" @pytest.mark.parametrize('''path_type''' , [str, list] ) def snake_case_ ( _lowerCAmelCase : List[Any] , _lowerCAmelCase : str , _lowerCAmelCase : Any ) -> Dict: if issubclass(_lowerCAmelCase , _lowerCAmelCase ): UpperCAmelCase : str = jsonl_path elif issubclass(_lowerCAmelCase , _lowerCAmelCase ): UpperCAmelCase : Dict = [jsonl_path] UpperCAmelCase : int = tmp_path / '''cache''' UpperCAmelCase : Dict = {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''} UpperCAmelCase : Optional[int] = JsonDatasetReader(_lowerCAmelCase , cache_dir=_lowerCAmelCase ).read() _check_json_dataset(_lowerCAmelCase , _lowerCAmelCase ) def snake_case_ ( _lowerCAmelCase : List[str] , _lowerCAmelCase : Dict , _lowerCAmelCase : Dict=("train",) ) -> Union[str, Any]: assert isinstance(_lowerCAmelCase , _lowerCAmelCase ) for split in splits: UpperCAmelCase : List[str] = dataset_dict[split] assert dataset.num_rows == 4 assert dataset.num_columns == 3 assert dataset.column_names == ["col_1", "col_2", "col_3"] for feature, expected_dtype in expected_features.items(): assert dataset.features[feature].dtype == expected_dtype @pytest.mark.parametrize('''keep_in_memory''' , [False, True] ) def snake_case_ ( _lowerCAmelCase : int , _lowerCAmelCase : Optional[Any] , _lowerCAmelCase : str ) -> Any: UpperCAmelCase : Optional[Any] = tmp_path / '''cache''' UpperCAmelCase : List[str] = {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''} with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase(): UpperCAmelCase : Optional[int] = JsonDatasetReader({'''train''': jsonl_path} , cache_dir=_lowerCAmelCase , keep_in_memory=_lowerCAmelCase ).read() _check_json_datasetdict(_lowerCAmelCase , _lowerCAmelCase ) @pytest.mark.parametrize( '''features''' , [ None, {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}, {'''col_1''': '''string''', '''col_2''': '''string''', '''col_3''': '''string'''}, {'''col_1''': '''int32''', '''col_2''': '''int32''', '''col_3''': '''int32'''}, {'''col_1''': '''float32''', '''col_2''': '''float32''', '''col_3''': '''float32'''}, ] , ) def snake_case_ ( _lowerCAmelCase : Optional[Any] , _lowerCAmelCase : Optional[int] , _lowerCAmelCase : Union[str, Any] ) -> int: UpperCAmelCase : Dict = tmp_path / '''cache''' UpperCAmelCase : Dict = {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''} UpperCAmelCase : Optional[int] = features.copy() if features else default_expected_features UpperCAmelCase : Union[str, Any] = ( Features({feature: Value(_lowerCAmelCase ) for feature, dtype in features.items()} ) if features is not None else None ) UpperCAmelCase : Tuple = JsonDatasetReader({'''train''': jsonl_path} , features=_lowerCAmelCase , cache_dir=_lowerCAmelCase ).read() _check_json_datasetdict(_lowerCAmelCase , _lowerCAmelCase ) @pytest.mark.parametrize('''split''' , [None, NamedSplit('''train''' ), '''train''', '''test'''] ) def snake_case_ ( _lowerCAmelCase : Any , _lowerCAmelCase : Optional[int] , _lowerCAmelCase : Dict ) -> Union[str, Any]: if split: UpperCAmelCase : Optional[int] = {split: jsonl_path} else: UpperCAmelCase : Any = '''train''' UpperCAmelCase : Any = {'''train''': jsonl_path, '''test''': jsonl_path} UpperCAmelCase : Tuple = tmp_path / '''cache''' UpperCAmelCase : int = {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''} UpperCAmelCase : Optional[Any] = JsonDatasetReader(_lowerCAmelCase , cache_dir=_lowerCAmelCase ).read() _check_json_datasetdict(_lowerCAmelCase , _lowerCAmelCase , splits=list(path.keys() ) ) assert all(dataset[split].split == split for split in path.keys() ) def snake_case_ ( _lowerCAmelCase : List[str] ) -> str: return json.load(_lowerCAmelCase ) def snake_case_ ( _lowerCAmelCase : Dict ) -> str: return [json.loads(_lowerCAmelCase ) for line in buffer] class SCREAMING_SNAKE_CASE: """simple docstring""" @pytest.mark.parametrize('''lines, load_json_function''' , [(True, load_json_lines), (False, load_json)] ) def A ( self : Union[str, Any] , __snake_case : Tuple , __snake_case : Optional[Any] , __snake_case : Optional[int] ) -> Dict: with io.BytesIO() as buffer: JsonDatasetWriter(__snake_case , __snake_case , lines=__snake_case ).write() buffer.seek(0 ) UpperCAmelCase : Union[str, Any] = load_json_function(__snake_case ) assert isinstance(__snake_case , __snake_case ) assert isinstance(exported_content[0] , __snake_case ) assert len(__snake_case ) == 10 @pytest.mark.parametrize( '''orient, container, keys, len_at''' , [ ('''records''', list, {'''tokens''', '''labels''', '''answers''', '''id'''}, None), ('''split''', dict, {'''columns''', '''data'''}, '''data'''), ('''index''', dict, set('''0123456789''' ), None), ('''columns''', dict, {'''tokens''', '''labels''', '''answers''', '''id'''}, '''tokens'''), ('''values''', list, None, None), ('''table''', dict, {'''schema''', '''data'''}, '''data'''), ] , ) def A ( self : Optional[int] , __snake_case : Optional[Any] , __snake_case : str , __snake_case : Optional[Any] , __snake_case : List[str] , __snake_case : Optional[Any] ) -> List[Any]: with io.BytesIO() as buffer: JsonDatasetWriter(__snake_case , __snake_case , lines=__snake_case , orient=__snake_case ).write() buffer.seek(0 ) UpperCAmelCase : Union[str, Any] = load_json(__snake_case ) assert isinstance(__snake_case , __snake_case ) if keys: if container is dict: assert exported_content.keys() == keys else: assert exported_content[0].keys() == keys else: assert not hasattr(__snake_case , '''keys''' ) and not hasattr(exported_content[0] , '''keys''' ) if len_at: assert len(exported_content[len_at] ) == 10 else: assert len(__snake_case ) == 10 @pytest.mark.parametrize('''lines, load_json_function''' , [(True, load_json_lines), (False, load_json)] ) def A ( self : str , __snake_case : str , __snake_case : str , __snake_case : int ) -> Any: with io.BytesIO() as buffer: JsonDatasetWriter(__snake_case , __snake_case , lines=__snake_case , num_proc=2 ).write() buffer.seek(0 ) UpperCAmelCase : Any = load_json_function(__snake_case ) assert isinstance(__snake_case , __snake_case ) assert isinstance(exported_content[0] , __snake_case ) assert len(__snake_case ) == 10 @pytest.mark.parametrize( '''orient, container, keys, len_at''' , [ ('''records''', list, {'''tokens''', '''labels''', '''answers''', '''id'''}, None), ('''split''', dict, {'''columns''', '''data'''}, '''data'''), ('''index''', dict, set('''0123456789''' ), None), ('''columns''', dict, {'''tokens''', '''labels''', '''answers''', '''id'''}, '''tokens'''), ('''values''', list, None, None), ('''table''', dict, {'''schema''', '''data'''}, '''data'''), ] , ) def A ( self : Any , __snake_case : int , __snake_case : Union[str, Any] , __snake_case : Optional[int] , __snake_case : Optional[Any] , __snake_case : List[str] ) -> Any: with io.BytesIO() as buffer: JsonDatasetWriter(__snake_case , __snake_case , lines=__snake_case , orient=__snake_case , num_proc=2 ).write() buffer.seek(0 ) UpperCAmelCase : List[str] = load_json(__snake_case ) assert isinstance(__snake_case , __snake_case ) if keys: if container is dict: assert exported_content.keys() == keys else: assert exported_content[0].keys() == keys else: assert not hasattr(__snake_case , '''keys''' ) and not hasattr(exported_content[0] , '''keys''' ) if len_at: assert len(exported_content[len_at] ) == 10 else: assert len(__snake_case ) == 10 def A ( self : List[Any] , __snake_case : str ) -> Dict: with pytest.raises(__snake_case ): with io.BytesIO() as buffer: JsonDatasetWriter(__snake_case , __snake_case , num_proc=0 ) @pytest.mark.parametrize('''compression, extension''' , [('''gzip''', '''gz'''), ('''bz2''', '''bz2'''), ('''xz''', '''xz''')] ) def A ( self : Optional[int] , __snake_case : Any , __snake_case : str , __snake_case : Union[str, Any] , __snake_case : Union[str, Any] , __snake_case : Dict ) -> Union[str, Any]: UpperCAmelCase : List[str] = tmp_path_factory.mktemp('''data''' ) / F"""test.json.{extension}""" UpperCAmelCase : List[Any] = str(shared_datadir / F"""test_file.json.{extension}""" ) JsonDatasetWriter(__snake_case , __snake_case , compression=__snake_case ).write() with fsspec.open(__snake_case , '''rb''' , compression='''infer''' ) as f: UpperCAmelCase : str = f.read() with fsspec.open(__snake_case , '''rb''' , compression='''infer''' ) as f: UpperCAmelCase : Optional[int] = f.read() assert exported_content == original_content
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0
import argparse import json import os import fairseq import torch from fairseq.data import Dictionary from transformers import ( WavaVecaConfig, WavaVecaCTCTokenizer, WavaVecaFeatureExtractor, WavaVecaForCTC, WavaVecaForPreTraining, WavaVecaProcessor, logging, ) from transformers.models.wavaveca.modeling_wavaveca import WavaVecaForSequenceClassification logging.set_verbosity_info() __lowerCamelCase : int = logging.get_logger(__name__) __lowerCamelCase : Dict = { "post_extract_proj": "feature_projection.projection", "encoder.pos_conv.0": "encoder.pos_conv_embed.conv", "self_attn.k_proj": "encoder.layers.*.attention.k_proj", "self_attn.v_proj": "encoder.layers.*.attention.v_proj", "self_attn.q_proj": "encoder.layers.*.attention.q_proj", "self_attn.out_proj": "encoder.layers.*.attention.out_proj", "self_attn_layer_norm": "encoder.layers.*.layer_norm", "fc1": "encoder.layers.*.feed_forward.intermediate_dense", "fc2": "encoder.layers.*.feed_forward.output_dense", "final_layer_norm": "encoder.layers.*.final_layer_norm", "encoder.layer_norm": "encoder.layer_norm", "adapter_layer": "encoder.layers.*.adapter_layer", "w2v_model.layer_norm": "feature_projection.layer_norm", "quantizer.weight_proj": "quantizer.weight_proj", "quantizer.vars": "quantizer.codevectors", "project_q": "project_q", "final_proj": "project_hid", "w2v_encoder.proj": "lm_head", "mask_emb": "masked_spec_embed", "pooling_layer.linear": "projector", "pooling_layer.projection": "classifier", } __lowerCamelCase : Optional[Any] = [ "lm_head", "quantizer.weight_proj", "quantizer.codevectors", "project_q", "project_hid", "projector", "classifier", ] def __SCREAMING_SNAKE_CASE ( __UpperCamelCase : str ) -> Union[str, Any]: """simple docstring""" SCREAMING_SNAKE_CASE__ = {} with open(_lowerCAmelCase , """r""" ) as file: for line_number, line in enumerate(_lowerCAmelCase ): SCREAMING_SNAKE_CASE__ = line.strip() if line: SCREAMING_SNAKE_CASE__ = line.split() SCREAMING_SNAKE_CASE__ = line_number SCREAMING_SNAKE_CASE__ = words[0] SCREAMING_SNAKE_CASE__ = value return result def __SCREAMING_SNAKE_CASE ( __UpperCamelCase : int , __UpperCamelCase : Optional[Any] , __UpperCamelCase : Optional[Any] , __UpperCamelCase : Tuple , __UpperCamelCase : str ) -> int: """simple docstring""" for attribute in key.split(""".""" ): SCREAMING_SNAKE_CASE__ = getattr(_lowerCAmelCase , _lowerCAmelCase ) SCREAMING_SNAKE_CASE__ = None for param_key in PARAM_MAPPING.keys(): if full_name.endswith(_lowerCAmelCase ): SCREAMING_SNAKE_CASE__ = PARAM_MAPPING[full_name.split(""".""" )[-1]] SCREAMING_SNAKE_CASE__ = '''param''' if weight_type is not None and weight_type != "param": SCREAMING_SNAKE_CASE__ = getattr(_lowerCAmelCase , _lowerCAmelCase ).shape elif weight_type is not None and weight_type == "param": SCREAMING_SNAKE_CASE__ = hf_pointer for attribute in hf_param_name.split(""".""" ): SCREAMING_SNAKE_CASE__ = getattr(_lowerCAmelCase , _lowerCAmelCase ) SCREAMING_SNAKE_CASE__ = shape_pointer.shape # let's reduce dimension SCREAMING_SNAKE_CASE__ = value[0] else: SCREAMING_SNAKE_CASE__ = hf_pointer.shape if hf_shape != value.shape: raise ValueError( f"""Shape of hf {key + '.' + weight_type if weight_type is not None else ''} is {hf_shape}, but should be""" f""" {value.shape} for {full_name}""" ) if weight_type == "weight": SCREAMING_SNAKE_CASE__ = value elif weight_type == "weight_g": SCREAMING_SNAKE_CASE__ = value elif weight_type == "weight_v": SCREAMING_SNAKE_CASE__ = value elif weight_type == "bias": SCREAMING_SNAKE_CASE__ = value elif weight_type == "param": for attribute in hf_param_name.split(""".""" ): SCREAMING_SNAKE_CASE__ = getattr(_lowerCAmelCase , _lowerCAmelCase ) SCREAMING_SNAKE_CASE__ = value else: SCREAMING_SNAKE_CASE__ = value logger.info(f"""{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}.""" ) def __SCREAMING_SNAKE_CASE ( __UpperCamelCase : Optional[int] , __UpperCamelCase : Any , __UpperCamelCase : List[Any] , __UpperCamelCase : Dict , __UpperCamelCase : List[Any] ) -> List[Any]: """simple docstring""" SCREAMING_SNAKE_CASE__ = None for param_key in PARAM_MAPPING.keys(): if full_name.endswith(_lowerCAmelCase ): SCREAMING_SNAKE_CASE__ = PARAM_MAPPING[full_name.split(""".""" )[-1]] SCREAMING_SNAKE_CASE__ = '''param''' if weight_type is not None and weight_type != "param": SCREAMING_SNAKE_CASE__ = '''.'''.join([key, weight_type] ) elif weight_type is not None and weight_type == "param": SCREAMING_SNAKE_CASE__ = '''.'''.join([key, hf_param_name] ) else: SCREAMING_SNAKE_CASE__ = key SCREAMING_SNAKE_CASE__ = value if '''lm_head''' in full_key else value[0] __lowerCamelCase : Tuple = { "W_a": "linear_1.weight", "W_b": "linear_2.weight", "b_a": "linear_1.bias", "b_b": "linear_2.bias", "ln_W": "norm.weight", "ln_b": "norm.bias", } def __SCREAMING_SNAKE_CASE ( __UpperCamelCase : str , __UpperCamelCase : List[str] , __UpperCamelCase : Any=None , __UpperCamelCase : Optional[Any]=None ) -> int: """simple docstring""" SCREAMING_SNAKE_CASE__ = False for key, mapped_key in MAPPING.items(): SCREAMING_SNAKE_CASE__ = '''wav2vec2.''' + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key if key in name or key.split("""w2v_model.""" )[-1] == name.split(""".""" )[0]: SCREAMING_SNAKE_CASE__ = True if "*" in mapped_key: SCREAMING_SNAKE_CASE__ = name.split(_lowerCAmelCase )[0].split(""".""" )[-2] SCREAMING_SNAKE_CASE__ = mapped_key.replace("""*""" , _lowerCAmelCase ) if "weight_g" in name: SCREAMING_SNAKE_CASE__ = '''weight_g''' elif "weight_v" in name: SCREAMING_SNAKE_CASE__ = '''weight_v''' elif "bias" in name: SCREAMING_SNAKE_CASE__ = '''bias''' elif "weight" in name: # TODO: don't match quantizer.weight_proj SCREAMING_SNAKE_CASE__ = '''weight''' else: SCREAMING_SNAKE_CASE__ = None if hf_dict is not None: rename_dict(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ) else: set_recursively(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ) return is_used return is_used def __SCREAMING_SNAKE_CASE ( __UpperCamelCase : Union[str, Any] , __UpperCamelCase : Optional[Any] , __UpperCamelCase : Any ) -> Any: """simple docstring""" SCREAMING_SNAKE_CASE__ = [] SCREAMING_SNAKE_CASE__ = fairseq_model.state_dict() SCREAMING_SNAKE_CASE__ = hf_model.wavaveca.feature_extractor for name, value in fairseq_dict.items(): SCREAMING_SNAKE_CASE__ = False if "conv_layers" in name: load_conv_layer( _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , hf_model.config.feat_extract_norm == """group""" , ) SCREAMING_SNAKE_CASE__ = True else: SCREAMING_SNAKE_CASE__ = load_wavaveca_layer(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ) if not is_used: unused_weights.append(_lowerCAmelCase ) logger.warning(f"""Unused weights: {unused_weights}""" ) def __SCREAMING_SNAKE_CASE ( __UpperCamelCase : Optional[int] , __UpperCamelCase : Tuple , __UpperCamelCase : Any , __UpperCamelCase : Optional[int] , __UpperCamelCase : Optional[int] ) -> Union[str, Any]: """simple docstring""" SCREAMING_SNAKE_CASE__ = full_name.split("""conv_layers.""" )[-1] SCREAMING_SNAKE_CASE__ = name.split(""".""" ) SCREAMING_SNAKE_CASE__ = int(items[0] ) SCREAMING_SNAKE_CASE__ = int(items[1] ) if type_id == 0: if "bias" in name: if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape: raise ValueError( f"""{full_name} has size {value.shape}, but""" f""" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.""" ) SCREAMING_SNAKE_CASE__ = value logger.info(f"""Feat extract conv layer {layer_id} was initialized from {full_name}.""" ) elif "weight" in name: if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape: raise ValueError( f"""{full_name} has size {value.shape}, but""" f""" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.""" ) SCREAMING_SNAKE_CASE__ = value logger.info(f"""Feat extract conv layer {layer_id} was initialized from {full_name}.""" ) elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm): if "bias" in name: if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape: raise ValueError( f"""{full_name} has size {value.shape}, but""" f""" {feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape} was found.""" ) SCREAMING_SNAKE_CASE__ = value logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.""" ) elif "weight" in name: if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape: raise ValueError( f"""{full_name} has size {value.shape}, but""" f""" {feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape} was found.""" ) SCREAMING_SNAKE_CASE__ = value logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.""" ) else: unused_weights.append(_lowerCAmelCase ) @torch.no_grad() def __SCREAMING_SNAKE_CASE ( __UpperCamelCase : Union[str, Any] , __UpperCamelCase : List[Any] , __UpperCamelCase : Dict=None , __UpperCamelCase : List[Any]=None , __UpperCamelCase : int=True , __UpperCamelCase : Optional[int]=False ) -> Dict: """simple docstring""" if config_path is not None: SCREAMING_SNAKE_CASE__ = WavaVecaConfig.from_pretrained(_lowerCAmelCase ) else: SCREAMING_SNAKE_CASE__ = WavaVecaConfig() if is_seq_class: SCREAMING_SNAKE_CASE__ = read_txt_into_dict(_lowerCAmelCase ) SCREAMING_SNAKE_CASE__ = idalabel SCREAMING_SNAKE_CASE__ = WavaVecaForSequenceClassification(_lowerCAmelCase ) SCREAMING_SNAKE_CASE__ = WavaVecaFeatureExtractor( feature_size=1 , sampling_rate=1_60_00 , padding_value=0 , do_normalize=_lowerCAmelCase , return_attention_mask=_lowerCAmelCase , ) feature_extractor.save_pretrained(_lowerCAmelCase ) elif is_finetuned: if dict_path: SCREAMING_SNAKE_CASE__ = Dictionary.load(_lowerCAmelCase ) # important change bos & pad token id since CTC symbol is <pad> and # not <s> as in fairseq SCREAMING_SNAKE_CASE__ = target_dict.pad_index SCREAMING_SNAKE_CASE__ = target_dict.bos_index SCREAMING_SNAKE_CASE__ = target_dict.eos_index SCREAMING_SNAKE_CASE__ = len(target_dict.symbols ) SCREAMING_SNAKE_CASE__ = os.path.join(_lowerCAmelCase , """vocab.json""" ) if not os.path.isdir(_lowerCAmelCase ): logger.error("""--pytorch_dump_folder_path ({}) should be a directory""".format(_lowerCAmelCase ) ) return os.makedirs(_lowerCAmelCase , exist_ok=_lowerCAmelCase ) SCREAMING_SNAKE_CASE__ = target_dict.indices # fairseq has the <pad> and <s> switched SCREAMING_SNAKE_CASE__ = 0 SCREAMING_SNAKE_CASE__ = 1 with open(_lowerCAmelCase , """w""" , encoding="""utf-8""" ) as vocab_handle: json.dump(_lowerCAmelCase , _lowerCAmelCase ) SCREAMING_SNAKE_CASE__ = WavaVecaCTCTokenizer( _lowerCAmelCase , unk_token=target_dict.unk_word , pad_token=target_dict.pad_word , bos_token=target_dict.bos_word , eos_token=target_dict.eos_word , word_delimiter_token="""|""" , do_lower_case=_lowerCAmelCase , ) SCREAMING_SNAKE_CASE__ = True if config.feat_extract_norm == '''layer''' else False SCREAMING_SNAKE_CASE__ = WavaVecaFeatureExtractor( feature_size=1 , sampling_rate=1_60_00 , padding_value=0 , do_normalize=_lowerCAmelCase , return_attention_mask=_lowerCAmelCase , ) SCREAMING_SNAKE_CASE__ = WavaVecaProcessor(feature_extractor=_lowerCAmelCase , tokenizer=_lowerCAmelCase ) processor.save_pretrained(_lowerCAmelCase ) SCREAMING_SNAKE_CASE__ = WavaVecaForCTC(_lowerCAmelCase ) else: SCREAMING_SNAKE_CASE__ = WavaVecaForPreTraining(_lowerCAmelCase ) if is_finetuned or is_seq_class: SCREAMING_SNAKE_CASE__ = fairseq.checkpoint_utils.load_model_ensemble_and_task( [checkpoint_path] , arg_overrides={"""data""": """/""".join(dict_path.split("""/""" )[:-1] )} ) else: SCREAMING_SNAKE_CASE__ = argparse.Namespace(task="""audio_pretraining""" ) SCREAMING_SNAKE_CASE__ = fairseq.tasks.setup_task(_lowerCAmelCase ) SCREAMING_SNAKE_CASE__ = fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path] , task=_lowerCAmelCase ) SCREAMING_SNAKE_CASE__ = model[0].eval() recursively_load_weights(_lowerCAmelCase , _lowerCAmelCase , not is_finetuned ) hf_wavavec.save_pretrained(_lowerCAmelCase ) if __name__ == "__main__": __lowerCamelCase : Dict = argparse.ArgumentParser() parser.add_argument('''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''') parser.add_argument('''--checkpoint_path''', default=None, type=str, help='''Path to fairseq checkpoint''') parser.add_argument('''--dict_path''', default=None, type=str, help='''Path to dict of fine-tuned model''') parser.add_argument('''--config_path''', default=None, type=str, help='''Path to hf config.json of model to convert''') parser.add_argument( '''--not_finetuned''', action='''store_true''', help='''Whether the model to convert is a fine-tuned model or not''' ) parser.add_argument( '''--is_seq_class''', action='''store_true''', help='''Whether the model to convert is a fine-tuned sequence classification model or not''', ) __lowerCamelCase : Any = parser.parse_args() __lowerCamelCase : int = not args.not_finetuned and not args.is_seq_class convert_wavaveca_checkpoint( args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, is_finetuned, args.is_seq_class, )
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'''simple docstring''' import os from shutil import copyfile from typing import Any, Dict, List, Optional, Tuple import sentencepiece as spm from ...tokenization_utils import AddedToken, PreTrainedTokenizer from ...utils import logging UpperCamelCase__: Tuple = logging.get_logger(__name__) UpperCamelCase__: Optional[int] = {"vocab_file": "sentencepiece.bpe.model"} UpperCamelCase__: Optional[int] = { "vocab_file": { "moussaKam/mbarthez": "https://huggingface.co/moussaKam/mbarthez/resolve/main/sentencepiece.bpe.model", "moussaKam/barthez": "https://huggingface.co/moussaKam/barthez/resolve/main/sentencepiece.bpe.model", "moussaKam/barthez-orangesum-title": ( "https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/sentencepiece.bpe.model" ), }, } UpperCamelCase__: Dict = { "moussaKam/mbarthez": 1024, "moussaKam/barthez": 1024, "moussaKam/barthez-orangesum-title": 1024, } UpperCamelCase__: Tuple = "▁" class SCREAMING_SNAKE_CASE( A__ ): """simple docstring""" lowerCamelCase__ = VOCAB_FILES_NAMES lowerCamelCase__ = PRETRAINED_VOCAB_FILES_MAP lowerCamelCase__ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES lowerCamelCase__ = ["""input_ids""", """attention_mask"""] def __init__( self : List[Any] , __snake_case : List[Any] , __snake_case : Tuple="<s>" , __snake_case : List[Any]="</s>" , __snake_case : int="</s>" , __snake_case : Any="<s>" , __snake_case : Optional[int]="<unk>" , __snake_case : Union[str, Any]="<pad>" , __snake_case : Union[str, Any]="<mask>" , __snake_case : Optional[Dict[str, Any]] = None , **__snake_case : Dict , ) -> None: # Mask token behave like a normal word, i.e. include the space before it UpperCAmelCase : int = AddedToken(__snake_case , lstrip=__snake_case , rstrip=__snake_case ) if isinstance(__snake_case , __snake_case ) else mask_token UpperCAmelCase : Union[str, Any] = {} if sp_model_kwargs is None else sp_model_kwargs super().__init__( bos_token=__snake_case , eos_token=__snake_case , unk_token=__snake_case , sep_token=__snake_case , cls_token=__snake_case , pad_token=__snake_case , mask_token=__snake_case , sp_model_kwargs=self.sp_model_kwargs , **__snake_case , ) UpperCAmelCase : Optional[int] = vocab_file UpperCAmelCase : int = spm.SentencePieceProcessor(**self.sp_model_kwargs ) self.sp_model.Load(str(__snake_case ) ) UpperCAmelCase : int = {'''<s>''': 0, '''<pad>''': 1, '''</s>''': 2, '''<unk>''': 3} UpperCAmelCase : Optional[Any] = len(self.sp_model ) - 1 UpperCAmelCase : Optional[int] = {v: k for k, v in self.fairseq_tokens_to_ids.items()} def A ( self : Tuple , __snake_case : List[int] , __snake_case : Optional[List[int]] = None ) -> List[int]: if token_ids_a is None: return [self.cls_token_id] + token_ids_a + [self.sep_token_id] UpperCAmelCase : Union[str, Any] = [self.cls_token_id] UpperCAmelCase : Optional[Any] = [self.sep_token_id] return cls + token_ids_a + sep + sep + token_ids_a + sep def A ( self : int , __snake_case : List[int] , __snake_case : Optional[List[int]] = None , __snake_case : bool = False ) -> List[int]: if already_has_special_tokens: return super().get_special_tokens_mask( token_ids_a=__snake_case , token_ids_a=__snake_case , already_has_special_tokens=__snake_case ) if token_ids_a is None: return [1] + ([0] * len(__snake_case )) + [1] return [1] + ([0] * len(__snake_case )) + [1, 1] + ([0] * len(__snake_case )) + [1] def A ( self : Optional[int] , __snake_case : List[int] , __snake_case : Optional[List[int]] = None ) -> List[int]: UpperCAmelCase : Tuple = [self.sep_token_id] UpperCAmelCase : Tuple = [self.cls_token_id] if token_ids_a is None: return len(cls + token_ids_a + sep ) * [0] return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0] @property def A ( self : Dict ) -> Optional[int]: return len(self.sp_model ) def A ( self : List[str] ) -> Dict: UpperCAmelCase : Optional[Any] = {self.convert_ids_to_tokens(__snake_case ): i for i in range(self.vocab_size )} vocab.update(self.added_tokens_encoder ) return vocab def A ( self : Optional[Any] , __snake_case : str ) -> List[str]: return self.sp_model.encode(__snake_case , out_type=__snake_case ) def A ( self : int , __snake_case : int ) -> int: if token in self.fairseq_tokens_to_ids: return self.fairseq_tokens_to_ids[token] UpperCAmelCase : Optional[Any] = self.sp_model.PieceToId(__snake_case ) return spm_id if spm_id else self.unk_token_id def A ( self : int , __snake_case : Any ) -> List[Any]: if index in self.fairseq_ids_to_tokens: return self.fairseq_ids_to_tokens[index] return self.sp_model.IdToPiece(__snake_case ) def A ( self : List[Any] , __snake_case : Union[str, Any] ) -> List[str]: UpperCAmelCase : Optional[Any] = [] UpperCAmelCase : int = '''''' UpperCAmelCase : Union[str, Any] = False for token in tokens: # make sure that special tokens are not decoded using sentencepiece model if token in self.all_special_tokens: if not prev_is_special: out_string += " " out_string += self.sp_model.decode(__snake_case ) + token UpperCAmelCase : str = True UpperCAmelCase : List[str] = [] else: current_sub_tokens.append(__snake_case ) UpperCAmelCase : Optional[int] = False out_string += self.sp_model.decode(__snake_case ) return out_string.strip() def __getstate__( self : Union[str, Any] ) -> Union[str, Any]: UpperCAmelCase : Optional[Any] = self.__dict__.copy() UpperCAmelCase : Any = None return state def __setstate__( self : Optional[int] , __snake_case : Union[str, Any] ) -> List[Any]: UpperCAmelCase : Dict = d # for backward compatibility if not hasattr(self , '''sp_model_kwargs''' ): UpperCAmelCase : Optional[Any] = {} UpperCAmelCase : Any = spm.SentencePieceProcessor(**self.sp_model_kwargs ) self.sp_model.Load(self.vocab_file ) def A ( self : Optional[int] , __snake_case : str , __snake_case : Optional[str] = None ) -> Tuple[str]: if not os.path.isdir(__snake_case ): logger.error(F"""Vocabulary path ({save_directory}) should be a directory""" ) return UpperCAmelCase : Union[str, Any] = os.path.join( __snake_case , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] ) if os.path.abspath(self.vocab_file ) != os.path.abspath(__snake_case ) and os.path.isfile(self.vocab_file ): copyfile(self.vocab_file , __snake_case ) elif not os.path.isfile(self.vocab_file ): with open(__snake_case , '''wb''' ) as fi: UpperCAmelCase : Any = self.sp_model.serialized_model_proto() fi.write(__snake_case ) return (out_vocab_file,)
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0
def __snake_case ( _UpperCAmelCase ): assert isinstance(_lowerCAmelCase , _lowerCAmelCase ), f'The input value of [n={number}] is not an integer' if number == 1: return 2 elif number < 1: __a = f'The input value of [n={number}] has to be > 0' raise ValueError(_lowerCAmelCase ) else: __a = sylvester(number - 1 ) __a = num - 1 __a = num return lower * upper + 1 if __name__ == "__main__": print(f'The 8th number in Sylvester\'s sequence: {sylvester(8)}')
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'''simple docstring''' import tempfile import unittest import numpy as np from huggingface_hub import HfFolder, delete_repo from requests.exceptions import HTTPError from transformers import BertConfig, is_flax_available from transformers.testing_utils import TOKEN, USER, is_staging_test, require_flax if is_flax_available(): import os from flax.core.frozen_dict import unfreeze from flax.traverse_util import flatten_dict from transformers import FlaxBertModel UpperCamelCase__: Tuple = "0.12" # assumed parallelism: 8 @require_flax @is_staging_test class SCREAMING_SNAKE_CASE( unittest.TestCase ): """simple docstring""" @classmethod def A ( cls : Union[str, Any] ) -> int: UpperCAmelCase : Optional[Any] = TOKEN HfFolder.save_token(__snake_case ) @classmethod def A ( cls : List[str] ) -> Tuple: try: delete_repo(token=cls._token , repo_id='''test-model-flax''' ) except HTTPError: pass try: delete_repo(token=cls._token , repo_id='''valid_org/test-model-flax-org''' ) except HTTPError: pass def A ( self : int ) -> Tuple: UpperCAmelCase : List[Any] = BertConfig( vocab_size=99 , hidden_size=32 , num_hidden_layers=5 , num_attention_heads=4 , intermediate_size=37 ) UpperCAmelCase : Dict = FlaxBertModel(__snake_case ) model.push_to_hub('''test-model-flax''' , use_auth_token=self._token ) UpperCAmelCase : Tuple = FlaxBertModel.from_pretrained(F"""{USER}/test-model-flax""" ) UpperCAmelCase : List[Any] = flatten_dict(unfreeze(model.params ) ) UpperCAmelCase : Tuple = flatten_dict(unfreeze(new_model.params ) ) for key in base_params.keys(): UpperCAmelCase : Union[str, Any] = (base_params[key] - new_params[key]).sum().item() self.assertLessEqual(__snake_case , 1E-3 , msg=F"""{key} not identical""" ) # Reset repo delete_repo(token=self._token , repo_id='''test-model-flax''' ) # Push to hub via save_pretrained with tempfile.TemporaryDirectory() as tmp_dir: model.save_pretrained(__snake_case , repo_id='''test-model-flax''' , push_to_hub=__snake_case , use_auth_token=self._token ) UpperCAmelCase : str = FlaxBertModel.from_pretrained(F"""{USER}/test-model-flax""" ) UpperCAmelCase : Any = flatten_dict(unfreeze(model.params ) ) UpperCAmelCase : str = flatten_dict(unfreeze(new_model.params ) ) for key in base_params.keys(): UpperCAmelCase : Optional[Any] = (base_params[key] - new_params[key]).sum().item() self.assertLessEqual(__snake_case , 1E-3 , msg=F"""{key} not identical""" ) def A ( self : Optional[Any] ) -> Union[str, Any]: UpperCAmelCase : Dict = BertConfig( vocab_size=99 , hidden_size=32 , num_hidden_layers=5 , num_attention_heads=4 , intermediate_size=37 ) UpperCAmelCase : Optional[Any] = FlaxBertModel(__snake_case ) model.push_to_hub('''valid_org/test-model-flax-org''' , use_auth_token=self._token ) UpperCAmelCase : Union[str, Any] = FlaxBertModel.from_pretrained('''valid_org/test-model-flax-org''' ) UpperCAmelCase : List[Any] = flatten_dict(unfreeze(model.params ) ) UpperCAmelCase : int = flatten_dict(unfreeze(new_model.params ) ) for key in base_params.keys(): UpperCAmelCase : Any = (base_params[key] - new_params[key]).sum().item() self.assertLessEqual(__snake_case , 1E-3 , msg=F"""{key} not identical""" ) # Reset repo delete_repo(token=self._token , repo_id='''valid_org/test-model-flax-org''' ) # Push to hub via save_pretrained with tempfile.TemporaryDirectory() as tmp_dir: model.save_pretrained( __snake_case , repo_id='''valid_org/test-model-flax-org''' , push_to_hub=__snake_case , use_auth_token=self._token ) UpperCAmelCase : str = FlaxBertModel.from_pretrained('''valid_org/test-model-flax-org''' ) UpperCAmelCase : Any = flatten_dict(unfreeze(model.params ) ) UpperCAmelCase : Optional[Any] = flatten_dict(unfreeze(new_model.params ) ) for key in base_params.keys(): UpperCAmelCase : int = (base_params[key] - new_params[key]).sum().item() self.assertLessEqual(__snake_case , 1E-3 , msg=F"""{key} not identical""" ) def snake_case_ ( _lowerCAmelCase : Optional[Any] , _lowerCAmelCase : Any ) -> Union[str, Any]: UpperCAmelCase : str = True UpperCAmelCase : int = flatten_dict(modela.params ) UpperCAmelCase : Dict = flatten_dict(modela.params ) for key in flat_params_a.keys(): if np.sum(np.abs(flat_params_a[key] - flat_params_a[key] ) ) > 1e-4: UpperCAmelCase : Dict = False return models_are_equal @require_flax class SCREAMING_SNAKE_CASE( unittest.TestCase ): """simple docstring""" def A ( self : Tuple ) -> Union[str, Any]: UpperCAmelCase : List[Any] = BertConfig.from_pretrained('''hf-internal-testing/tiny-bert-flax-only''' ) UpperCAmelCase : Dict = FlaxBertModel(__snake_case ) UpperCAmelCase : int = '''bert''' with tempfile.TemporaryDirectory() as tmp_dir: model.save_pretrained(os.path.join(__snake_case , __snake_case ) ) with self.assertRaises(__snake_case ): UpperCAmelCase : Tuple = FlaxBertModel.from_pretrained(__snake_case ) UpperCAmelCase : str = FlaxBertModel.from_pretrained(__snake_case , subfolder=__snake_case ) self.assertTrue(check_models_equal(__snake_case , __snake_case ) ) def A ( self : List[str] ) -> Dict: UpperCAmelCase : Dict = BertConfig.from_pretrained('''hf-internal-testing/tiny-bert-flax-only''' ) UpperCAmelCase : Dict = FlaxBertModel(__snake_case ) UpperCAmelCase : Optional[int] = '''bert''' with tempfile.TemporaryDirectory() as tmp_dir: model.save_pretrained(os.path.join(__snake_case , __snake_case ) , max_shard_size='''10KB''' ) with self.assertRaises(__snake_case ): UpperCAmelCase : Any = FlaxBertModel.from_pretrained(__snake_case ) UpperCAmelCase : Union[str, Any] = FlaxBertModel.from_pretrained(__snake_case , subfolder=__snake_case ) self.assertTrue(check_models_equal(__snake_case , __snake_case ) ) def A ( self : Optional[int] ) -> str: UpperCAmelCase : Dict = '''bert''' UpperCAmelCase : int = '''hf-internal-testing/tiny-random-bert-subfolder''' with self.assertRaises(__snake_case ): UpperCAmelCase : Optional[Any] = FlaxBertModel.from_pretrained(__snake_case ) UpperCAmelCase : Tuple = FlaxBertModel.from_pretrained(__snake_case , subfolder=__snake_case ) self.assertIsNotNone(__snake_case ) def A ( self : Dict ) -> List[Any]: UpperCAmelCase : Optional[int] = '''bert''' UpperCAmelCase : int = '''hf-internal-testing/tiny-random-bert-sharded-subfolder''' with self.assertRaises(__snake_case ): UpperCAmelCase : Dict = FlaxBertModel.from_pretrained(__snake_case ) UpperCAmelCase : Union[str, Any] = FlaxBertModel.from_pretrained(__snake_case , subfolder=__snake_case ) self.assertIsNotNone(__snake_case )
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0
import math from dataclasses import dataclass from typing import Optional, Tuple, Union import numpy as np import torch from ..configuration_utils import ConfigMixin, register_to_config from ..utils import BaseOutput, randn_tensor from .scheduling_utils import SchedulerMixin @dataclass # Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->UnCLIP class lowercase ( A__ ): lowercase_ : Tuple =42 lowercase_ : Optional[int] =None def UpperCamelCase ( lowerCAmelCase__ , lowerCAmelCase__=0.9_99 , lowerCAmelCase__="cosine" , ): '''simple docstring''' if alpha_transform_type == "cosine": def alpha_bar_fn(lowerCAmelCase__ ): return math.cos((t + 0.0_08) / 1.0_08 * math.pi / 2 ) ** 2 elif alpha_transform_type == "exp": def alpha_bar_fn(lowerCAmelCase__ ): return math.exp(t * -12.0 ) else: raise ValueError(f'Unsupported alpha_tranform_type: {alpha_transform_type}' ) lowercase = [] for i in range(_lowerCAmelCase ): lowercase = i / num_diffusion_timesteps lowercase = (i + 1) / num_diffusion_timesteps betas.append(min(1 - alpha_bar_fn(_lowerCAmelCase ) / alpha_bar_fn(_lowerCAmelCase ) , _lowerCAmelCase ) ) return torch.tensor(_lowerCAmelCase , dtype=torch.floataa ) class lowercase ( A__ , A__ ): @register_to_config def __init__( self ,A__ = 1_0_0_0 ,A__ = "fixed_small_log" ,A__ = True ,A__ = 1.0 ,A__ = "epsilon" ,A__ = "squaredcos_cap_v2" ,): if beta_schedule != "squaredcos_cap_v2": raise ValueError('''UnCLIPScheduler only supports `beta_schedule`: \'squaredcos_cap_v2\'''') lowercase = betas_for_alpha_bar(__snake_case) lowercase = 1.0 - self.betas lowercase = torch.cumprod(self.alphas ,dim=0) lowercase = torch.tensor(1.0) # standard deviation of the initial noise distribution lowercase = 1.0 # setable values lowercase = None lowercase = torch.from_numpy(np.arange(0 ,__snake_case)[::-1].copy()) lowercase = variance_type def A__ ( self ,A__ ,A__ = None): return sample def A__ ( self ,A__ ,A__ = None): lowercase = num_inference_steps lowercase = (self.config.num_train_timesteps - 1) / (self.num_inference_steps - 1) lowercase = (np.arange(0 ,__snake_case) * step_ratio).round()[::-1].copy().astype(np.intaa) lowercase = torch.from_numpy(__snake_case).to(__snake_case) def A__ ( self ,A__ ,A__=None ,A__=None ,A__=None): if prev_timestep is None: lowercase = t - 1 lowercase = self.alphas_cumprod[t] lowercase = self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one lowercase = 1 - alpha_prod_t lowercase = 1 - alpha_prod_t_prev if prev_timestep == t - 1: lowercase = self.betas[t] else: lowercase = 1 - alpha_prod_t / alpha_prod_t_prev # For t > 0, compute predicted variance βt (see formula (6) and (7) from https://arxiv.org/pdf/2006.11239.pdf) # and sample from it to get previous sample # x_{t-1} ~ N(pred_prev_sample, variance) == add variance to pred_sample lowercase = beta_prod_t_prev / beta_prod_t * beta if variance_type is None: lowercase = self.config.variance_type # hacks - were probably added for training stability if variance_type == "fixed_small_log": lowercase = torch.log(torch.clamp(__snake_case ,min=1E-20)) lowercase = torch.exp(0.5 * variance) elif variance_type == "learned_range": # NOTE difference with DDPM scheduler lowercase = variance.log() lowercase = beta.log() lowercase = (predicted_variance + 1) / 2 lowercase = frac * max_log + (1 - frac) * min_log return variance def A__ ( self ,A__ ,A__ ,A__ ,A__ = None ,A__=None ,A__ = True ,): lowercase = timestep if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type == "learned_range": lowercase = torch.split(__snake_case ,sample.shape[1] ,dim=1) else: lowercase = None # 1. compute alphas, betas if prev_timestep is None: lowercase = t - 1 lowercase = self.alphas_cumprod[t] lowercase = self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one lowercase = 1 - alpha_prod_t lowercase = 1 - alpha_prod_t_prev if prev_timestep == t - 1: lowercase = self.betas[t] lowercase = self.alphas[t] else: lowercase = 1 - alpha_prod_t / alpha_prod_t_prev lowercase = 1 - beta # 2. compute predicted original sample from predicted noise also called # "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf if self.config.prediction_type == "epsilon": lowercase = (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5 elif self.config.prediction_type == "sample": lowercase = model_output else: raise ValueError( f'prediction_type given as {self.config.prediction_type} must be one of `epsilon` or `sample`' ''' for the UnCLIPScheduler.''') # 3. Clip "predicted x_0" if self.config.clip_sample: lowercase = torch.clamp( __snake_case ,-self.config.clip_sample_range ,self.config.clip_sample_range) # 4. Compute coefficients for pred_original_sample x_0 and current sample x_t # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf lowercase = (alpha_prod_t_prev ** 0.5 * beta) / beta_prod_t lowercase = alpha ** 0.5 * beta_prod_t_prev / beta_prod_t # 5. Compute predicted previous sample µ_t # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf lowercase = pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample # 6. Add noise lowercase = 0 if t > 0: lowercase = randn_tensor( model_output.shape ,dtype=model_output.dtype ,generator=__snake_case ,device=model_output.device) lowercase = self._get_variance( __snake_case ,predicted_variance=__snake_case ,prev_timestep=__snake_case ,) if self.variance_type == "fixed_small_log": lowercase = variance elif self.variance_type == "learned_range": lowercase = (0.5 * variance).exp() else: raise ValueError( f'variance_type given as {self.variance_type} must be one of `fixed_small_log` or `learned_range`' ''' for the UnCLIPScheduler.''') lowercase = variance * variance_noise lowercase = pred_prev_sample + variance if not return_dict: return (pred_prev_sample,) return UnCLIPSchedulerOutput(prev_sample=__snake_case ,pred_original_sample=__snake_case) def A__ ( self ,A__ ,A__ ,A__ ,): # Make sure alphas_cumprod and timestep have same device and dtype as original_samples lowercase = self.alphas_cumprod.to(device=original_samples.device ,dtype=original_samples.dtype) lowercase = timesteps.to(original_samples.device) lowercase = alphas_cumprod[timesteps] ** 0.5 lowercase = sqrt_alpha_prod.flatten() while len(sqrt_alpha_prod.shape) < len(original_samples.shape): lowercase = sqrt_alpha_prod.unsqueeze(-1) lowercase = (1 - alphas_cumprod[timesteps]) ** 0.5 lowercase = sqrt_one_minus_alpha_prod.flatten() while len(sqrt_one_minus_alpha_prod.shape) < len(original_samples.shape): lowercase = sqrt_one_minus_alpha_prod.unsqueeze(-1) lowercase = sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise return noisy_samples
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'''simple docstring''' import unittest from transformers import MraConfig, is_torch_available from transformers.testing_utils import require_torch, slow, torch_device from ...test_configuration_common import ConfigTester from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask if is_torch_available(): import torch from transformers import ( MraForMaskedLM, MraForMultipleChoice, MraForQuestionAnswering, MraForSequenceClassification, MraForTokenClassification, MraModel, ) from transformers.models.mra.modeling_mra import MRA_PRETRAINED_MODEL_ARCHIVE_LIST class SCREAMING_SNAKE_CASE: """simple docstring""" def __init__( self : Optional[int] , __snake_case : str , __snake_case : Union[str, Any]=2 , __snake_case : Optional[int]=8 , __snake_case : Any=True , __snake_case : Union[str, Any]=True , __snake_case : Dict=True , __snake_case : int=True , __snake_case : List[Any]=99 , __snake_case : str=16 , __snake_case : Tuple=5 , __snake_case : Tuple=2 , __snake_case : str=36 , __snake_case : Dict="gelu" , __snake_case : str=0.0 , __snake_case : Optional[int]=0.0 , __snake_case : Optional[int]=512 , __snake_case : Optional[Any]=16 , __snake_case : int=2 , __snake_case : int=0.02 , __snake_case : str=3 , __snake_case : Dict=4 , __snake_case : str=None , ) -> Optional[int]: UpperCAmelCase : Optional[int] = parent UpperCAmelCase : Tuple = batch_size UpperCAmelCase : List[str] = seq_length UpperCAmelCase : List[Any] = is_training UpperCAmelCase : int = use_input_mask UpperCAmelCase : Any = use_token_type_ids UpperCAmelCase : str = use_labels UpperCAmelCase : Union[str, Any] = vocab_size UpperCAmelCase : List[str] = hidden_size UpperCAmelCase : Optional[Any] = num_hidden_layers UpperCAmelCase : Union[str, Any] = num_attention_heads UpperCAmelCase : Optional[Any] = intermediate_size UpperCAmelCase : Union[str, Any] = hidden_act UpperCAmelCase : int = hidden_dropout_prob UpperCAmelCase : Optional[int] = attention_probs_dropout_prob UpperCAmelCase : Union[str, Any] = max_position_embeddings UpperCAmelCase : str = type_vocab_size UpperCAmelCase : List[str] = type_sequence_label_size UpperCAmelCase : Tuple = initializer_range UpperCAmelCase : Optional[Any] = num_labels UpperCAmelCase : Optional[int] = num_choices UpperCAmelCase : Any = scope def A ( self : Tuple ) -> Union[str, Any]: UpperCAmelCase : Optional[int] = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size ) UpperCAmelCase : Optional[int] = None if self.use_input_mask: UpperCAmelCase : Dict = random_attention_mask([self.batch_size, self.seq_length] ) UpperCAmelCase : Dict = None if self.use_token_type_ids: UpperCAmelCase : Union[str, Any] = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size ) UpperCAmelCase : str = None UpperCAmelCase : Tuple = None UpperCAmelCase : int = None if self.use_labels: UpperCAmelCase : str = ids_tensor([self.batch_size] , self.type_sequence_label_size ) UpperCAmelCase : Dict = ids_tensor([self.batch_size, self.seq_length] , self.num_labels ) UpperCAmelCase : str = ids_tensor([self.batch_size] , self.num_choices ) UpperCAmelCase : List[str] = self.get_config() return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels def A ( self : int ) -> Tuple: return MraConfig( vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , is_decoder=__snake_case , initializer_range=self.initializer_range , ) def A ( self : Optional[Any] ) -> Any: UpperCAmelCase : Optional[Any] = self.get_config() UpperCAmelCase : int = 300 return config def A ( self : Optional[Any] ) -> Any: ( ( UpperCAmelCase ) , ( UpperCAmelCase ) , ( UpperCAmelCase ) , ( UpperCAmelCase ) , ( UpperCAmelCase ) , ( UpperCAmelCase ) , ( UpperCAmelCase ) , ) : Tuple = self.prepare_config_and_inputs() UpperCAmelCase : Dict = True UpperCAmelCase : Tuple = floats_tensor([self.batch_size, self.seq_length, self.hidden_size] ) UpperCAmelCase : str = ids_tensor([self.batch_size, self.seq_length] , vocab_size=2 ) return ( config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels, encoder_hidden_states, encoder_attention_mask, ) def A ( self : Union[str, Any] , __snake_case : Union[str, Any] , __snake_case : int , __snake_case : Optional[int] , __snake_case : int , __snake_case : Dict , __snake_case : Tuple , __snake_case : Optional[Any] ) -> List[str]: UpperCAmelCase : int = MraModel(config=__snake_case ) model.to(__snake_case ) model.eval() UpperCAmelCase : Tuple = model(__snake_case , attention_mask=__snake_case , token_type_ids=__snake_case ) UpperCAmelCase : Optional[int] = model(__snake_case , token_type_ids=__snake_case ) UpperCAmelCase : Dict = model(__snake_case ) self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) ) def A ( self : Tuple , __snake_case : Optional[Any] , __snake_case : List[str] , __snake_case : List[str] , __snake_case : int , __snake_case : Union[str, Any] , __snake_case : Optional[Any] , __snake_case : Any , __snake_case : List[Any] , __snake_case : Optional[Any] , ) -> Tuple: UpperCAmelCase : str = True UpperCAmelCase : Tuple = MraModel(__snake_case ) model.to(__snake_case ) model.eval() UpperCAmelCase : Optional[int] = model( __snake_case , attention_mask=__snake_case , token_type_ids=__snake_case , encoder_hidden_states=__snake_case , encoder_attention_mask=__snake_case , ) UpperCAmelCase : Optional[Any] = model( __snake_case , attention_mask=__snake_case , token_type_ids=__snake_case , encoder_hidden_states=__snake_case , ) UpperCAmelCase : str = model(__snake_case , attention_mask=__snake_case , token_type_ids=__snake_case ) self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) ) def A ( self : Tuple , __snake_case : str , __snake_case : Optional[Any] , __snake_case : List[str] , __snake_case : Tuple , __snake_case : Optional[Any] , __snake_case : List[str] , __snake_case : int ) -> Any: UpperCAmelCase : Dict = MraForMaskedLM(config=__snake_case ) model.to(__snake_case ) model.eval() UpperCAmelCase : Optional[int] = model(__snake_case , attention_mask=__snake_case , token_type_ids=__snake_case , labels=__snake_case ) self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) ) def A ( self : Tuple , __snake_case : Tuple , __snake_case : Dict , __snake_case : Dict , __snake_case : Any , __snake_case : int , __snake_case : Optional[Any] , __snake_case : Tuple ) -> Optional[int]: UpperCAmelCase : List[str] = MraForQuestionAnswering(config=__snake_case ) model.to(__snake_case ) model.eval() UpperCAmelCase : List[Any] = model( __snake_case , attention_mask=__snake_case , token_type_ids=__snake_case , start_positions=__snake_case , end_positions=__snake_case , ) self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) ) self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) ) def A ( self : str , __snake_case : Optional[int] , __snake_case : List[str] , __snake_case : str , __snake_case : int , __snake_case : Optional[Any] , __snake_case : Union[str, Any] , __snake_case : List[Any] ) -> int: UpperCAmelCase : int = self.num_labels UpperCAmelCase : Union[str, Any] = MraForSequenceClassification(__snake_case ) model.to(__snake_case ) model.eval() UpperCAmelCase : List[str] = model(__snake_case , attention_mask=__snake_case , token_type_ids=__snake_case , labels=__snake_case ) self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) ) def A ( self : str , __snake_case : Dict , __snake_case : Optional[Any] , __snake_case : Dict , __snake_case : Union[str, Any] , __snake_case : Union[str, Any] , __snake_case : Union[str, Any] , __snake_case : Dict ) -> int: UpperCAmelCase : Tuple = self.num_labels UpperCAmelCase : List[str] = MraForTokenClassification(config=__snake_case ) model.to(__snake_case ) model.eval() UpperCAmelCase : str = model(__snake_case , attention_mask=__snake_case , token_type_ids=__snake_case , labels=__snake_case ) self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) ) def A ( self : str , __snake_case : int , __snake_case : Any , __snake_case : Tuple , __snake_case : Optional[int] , __snake_case : List[str] , __snake_case : str , __snake_case : Union[str, Any] ) -> Optional[Any]: UpperCAmelCase : Tuple = self.num_choices UpperCAmelCase : int = MraForMultipleChoice(config=__snake_case ) model.to(__snake_case ) model.eval() UpperCAmelCase : str = input_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous() UpperCAmelCase : List[Any] = token_type_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous() UpperCAmelCase : List[str] = input_mask.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous() UpperCAmelCase : List[str] = model( __snake_case , attention_mask=__snake_case , token_type_ids=__snake_case , labels=__snake_case , ) self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) ) def A ( self : str ) -> Dict: UpperCAmelCase : Any = self.prepare_config_and_inputs() ( ( UpperCAmelCase ) , ( UpperCAmelCase ) , ( UpperCAmelCase ) , ( UpperCAmelCase ) , ( UpperCAmelCase ) , ( UpperCAmelCase ) , ( UpperCAmelCase ) , ) : List[str] = config_and_inputs UpperCAmelCase : Any = {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask} return config, inputs_dict @require_torch class SCREAMING_SNAKE_CASE( A__ , unittest.TestCase ): """simple docstring""" lowerCamelCase__ = ( ( MraModel, MraForMaskedLM, MraForMultipleChoice, MraForQuestionAnswering, MraForSequenceClassification, MraForTokenClassification, ) if is_torch_available() else () ) lowerCamelCase__ = False lowerCamelCase__ = False lowerCamelCase__ = False lowerCamelCase__ = False lowerCamelCase__ = () def A ( self : int ) -> Union[str, Any]: UpperCAmelCase : List[str] = MraModelTester(self ) UpperCAmelCase : Optional[int] = ConfigTester(self , config_class=__snake_case , hidden_size=37 ) def A ( self : Optional[Any] ) -> str: self.config_tester.run_common_tests() def A ( self : Tuple ) -> Optional[Any]: UpperCAmelCase : Optional[int] = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_model(*__snake_case ) def A ( self : List[Any] ) -> Optional[Any]: UpperCAmelCase : Tuple = self.model_tester.prepare_config_and_inputs() for type in ["absolute", "relative_key", "relative_key_query"]: UpperCAmelCase : List[Any] = type self.model_tester.create_and_check_model(*__snake_case ) def A ( self : Tuple ) -> Dict: UpperCAmelCase : List[Any] = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_masked_lm(*__snake_case ) def A ( self : Tuple ) -> List[str]: UpperCAmelCase : Tuple = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_multiple_choice(*__snake_case ) def A ( self : int ) -> Dict: UpperCAmelCase : Optional[int] = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_question_answering(*__snake_case ) def A ( self : Dict ) -> Optional[int]: UpperCAmelCase : Any = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_sequence_classification(*__snake_case ) def A ( self : Any ) -> Optional[int]: UpperCAmelCase : List[str] = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_token_classification(*__snake_case ) @slow def A ( self : Dict ) -> Any: for model_name in MRA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: UpperCAmelCase : str = MraModel.from_pretrained(__snake_case ) self.assertIsNotNone(__snake_case ) @unittest.skip(reason='''MRA does not output attentions''' ) def A ( self : str ) -> Optional[Any]: return @require_torch class SCREAMING_SNAKE_CASE( unittest.TestCase ): """simple docstring""" @slow def A ( self : Tuple ) -> List[Any]: UpperCAmelCase : int = MraModel.from_pretrained('''uw-madison/mra-base-512-4''' ) UpperCAmelCase : Optional[Any] = torch.arange(256 ).unsqueeze(0 ) with torch.no_grad(): UpperCAmelCase : List[Any] = model(__snake_case )[0] UpperCAmelCase : Optional[Any] = torch.Size((1, 256, 768) ) self.assertEqual(output.shape , __snake_case ) UpperCAmelCase : Any = torch.tensor( [[[-0.01_40, 0.08_30, -0.03_81], [0.15_46, 0.14_02, 0.02_20], [0.11_62, 0.08_51, 0.01_65]]] ) self.assertTrue(torch.allclose(output[:, :3, :3] , __snake_case , atol=1E-4 ) ) @slow def A ( self : Optional[Any] ) -> Any: UpperCAmelCase : Optional[int] = MraForMaskedLM.from_pretrained('''uw-madison/mra-base-512-4''' ) UpperCAmelCase : Dict = torch.arange(256 ).unsqueeze(0 ) with torch.no_grad(): UpperCAmelCase : List[Any] = model(__snake_case )[0] UpperCAmelCase : int = 50265 UpperCAmelCase : int = torch.Size((1, 256, vocab_size) ) self.assertEqual(output.shape , __snake_case ) UpperCAmelCase : Union[str, Any] = torch.tensor( [[[9.25_95, -3.60_38, 11.88_19], [9.38_69, -3.26_93, 11.09_56], [11.85_24, -3.49_38, 13.12_10]]] ) self.assertTrue(torch.allclose(output[:, :3, :3] , __snake_case , atol=1E-4 ) ) @slow def A ( self : str ) -> List[Any]: UpperCAmelCase : List[Any] = MraForMaskedLM.from_pretrained('''uw-madison/mra-base-4096-8-d3''' ) UpperCAmelCase : List[Any] = torch.arange(4096 ).unsqueeze(0 ) with torch.no_grad(): UpperCAmelCase : Tuple = model(__snake_case )[0] UpperCAmelCase : Optional[int] = 50265 UpperCAmelCase : Tuple = torch.Size((1, 4096, vocab_size) ) self.assertEqual(output.shape , __snake_case ) UpperCAmelCase : Optional[int] = torch.tensor( [[[5.47_89, -2.35_64, 7.50_64], [7.90_67, -1.33_69, 9.96_68], [9.07_12, -1.81_06, 7.03_80]]] ) self.assertTrue(torch.allclose(output[:, :3, :3] , __snake_case , atol=1E-4 ) )
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from __future__ import annotations from PIL import Image # Define glider example _SCREAMING_SNAKE_CASE = [ [0, 1, 0, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0], [1, 1, 1, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], ] # Define blinker example _SCREAMING_SNAKE_CASE = [[0, 1, 0], [0, 1, 0], [0, 1, 0]] def lowercase( UpperCamelCase_ ) -> list[list[int]]: '''simple docstring''' UpperCamelCase = [] for i in range(len(_lowerCAmelCase ) ): UpperCamelCase = [] for j in range(len(cells[i] ) ): # Get the number of live neighbours UpperCamelCase = 0 if i > 0 and j > 0: neighbour_count += cells[i - 1][j - 1] if i > 0: neighbour_count += cells[i - 1][j] if i > 0 and j < len(cells[i] ) - 1: neighbour_count += cells[i - 1][j + 1] if j > 0: neighbour_count += cells[i][j - 1] if j < len(cells[i] ) - 1: neighbour_count += cells[i][j + 1] if i < len(_lowerCAmelCase ) - 1 and j > 0: neighbour_count += cells[i + 1][j - 1] if i < len(_lowerCAmelCase ) - 1: neighbour_count += cells[i + 1][j] if i < len(_lowerCAmelCase ) - 1 and j < len(cells[i] ) - 1: neighbour_count += cells[i + 1][j + 1] # Rules of the game of life (excerpt from Wikipedia): # 1. Any live cell with two or three live neighbours survives. # 2. Any dead cell with three live neighbours becomes a live cell. # 3. All other live cells die in the next generation. # Similarly, all other dead cells stay dead. UpperCamelCase = cells[i][j] == 1 if ( (alive and 2 <= neighbour_count <= 3) or not alive and neighbour_count == 3 ): next_generation_row.append(1 ) else: next_generation_row.append(0 ) next_generation.append(_lowerCAmelCase ) return next_generation def lowercase( UpperCamelCase_ , UpperCamelCase_ ) -> list[Image.Image]: '''simple docstring''' UpperCamelCase = [] for _ in range(_lowerCAmelCase ): # Create output image UpperCamelCase = Image.new("""RGB""" , (len(cells[0] ), len(_lowerCAmelCase )) ) UpperCamelCase = img.load() # Save cells to image for x in range(len(_lowerCAmelCase ) ): for y in range(len(cells[0] ) ): UpperCamelCase = 255 - cells[y][x] * 255 UpperCamelCase = (colour, colour, colour) # Save image images.append(_lowerCAmelCase ) UpperCamelCase = new_generation(_lowerCAmelCase ) return images if __name__ == "__main__": _SCREAMING_SNAKE_CASE = generate_images(GLIDER, 1_6) images[0].save("""out.gif""", save_all=True, append_images=images[1:])
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'''simple docstring''' import unittest import numpy as np from transformers.testing_utils import require_flax, require_tf, require_torch from transformers.utils import ( expand_dims, flatten_dict, is_flax_available, is_tf_available, is_torch_available, reshape, squeeze, transpose, ) if is_flax_available(): import jax.numpy as jnp if is_tf_available(): import tensorflow as tf if is_torch_available(): import torch class SCREAMING_SNAKE_CASE( unittest.TestCase ): """simple docstring""" def A ( self : Any ) -> str: UpperCAmelCase : Any = { '''task_specific_params''': { '''summarization''': {'''length_penalty''': 1.0, '''max_length''': 128, '''min_length''': 12, '''num_beams''': 4}, '''summarization_cnn''': {'''length_penalty''': 2.0, '''max_length''': 142, '''min_length''': 56, '''num_beams''': 4}, '''summarization_xsum''': {'''length_penalty''': 1.0, '''max_length''': 62, '''min_length''': 11, '''num_beams''': 6}, } } UpperCAmelCase : int = { '''task_specific_params.summarization.length_penalty''': 1.0, '''task_specific_params.summarization.max_length''': 128, '''task_specific_params.summarization.min_length''': 12, '''task_specific_params.summarization.num_beams''': 4, '''task_specific_params.summarization_cnn.length_penalty''': 2.0, '''task_specific_params.summarization_cnn.max_length''': 142, '''task_specific_params.summarization_cnn.min_length''': 56, '''task_specific_params.summarization_cnn.num_beams''': 4, '''task_specific_params.summarization_xsum.length_penalty''': 1.0, '''task_specific_params.summarization_xsum.max_length''': 62, '''task_specific_params.summarization_xsum.min_length''': 11, '''task_specific_params.summarization_xsum.num_beams''': 6, } self.assertEqual(flatten_dict(__snake_case ) , __snake_case ) def A ( self : int ) -> str: UpperCAmelCase : Any = np.random.randn(3 , 4 ) self.assertTrue(np.allclose(transpose(__snake_case ) , x.transpose() ) ) UpperCAmelCase : str = np.random.randn(3 , 4 , 5 ) self.assertTrue(np.allclose(transpose(__snake_case , axes=(1, 2, 0) ) , x.transpose((1, 2, 0) ) ) ) @require_torch def A ( self : str ) -> Union[str, Any]: UpperCAmelCase : Any = np.random.randn(3 , 4 ) UpperCAmelCase : List[Any] = torch.tensor(__snake_case ) self.assertTrue(np.allclose(transpose(__snake_case ) , transpose(__snake_case ).numpy() ) ) UpperCAmelCase : Tuple = np.random.randn(3 , 4 , 5 ) UpperCAmelCase : Any = torch.tensor(__snake_case ) self.assertTrue(np.allclose(transpose(__snake_case , axes=(1, 2, 0) ) , transpose(__snake_case , axes=(1, 2, 0) ).numpy() ) ) @require_tf def A ( self : List[str] ) -> Optional[Any]: UpperCAmelCase : int = np.random.randn(3 , 4 ) UpperCAmelCase : Optional[int] = tf.constant(__snake_case ) self.assertTrue(np.allclose(transpose(__snake_case ) , transpose(__snake_case ).numpy() ) ) UpperCAmelCase : Union[str, Any] = np.random.randn(3 , 4 , 5 ) UpperCAmelCase : str = tf.constant(__snake_case ) self.assertTrue(np.allclose(transpose(__snake_case , axes=(1, 2, 0) ) , transpose(__snake_case , axes=(1, 2, 0) ).numpy() ) ) @require_flax def A ( self : Tuple ) -> Any: UpperCAmelCase : List[Any] = np.random.randn(3 , 4 ) UpperCAmelCase : List[str] = jnp.array(__snake_case ) self.assertTrue(np.allclose(transpose(__snake_case ) , np.asarray(transpose(__snake_case ) ) ) ) UpperCAmelCase : Dict = np.random.randn(3 , 4 , 5 ) UpperCAmelCase : int = jnp.array(__snake_case ) self.assertTrue(np.allclose(transpose(__snake_case , axes=(1, 2, 0) ) , np.asarray(transpose(__snake_case , axes=(1, 2, 0) ) ) ) ) def A ( self : Optional[Any] ) -> Any: UpperCAmelCase : Union[str, Any] = np.random.randn(3 , 4 ) self.assertTrue(np.allclose(reshape(__snake_case , (4, 3) ) , np.reshape(__snake_case , (4, 3) ) ) ) UpperCAmelCase : Union[str, Any] = np.random.randn(3 , 4 , 5 ) self.assertTrue(np.allclose(reshape(__snake_case , (12, 5) ) , np.reshape(__snake_case , (12, 5) ) ) ) @require_torch def A ( self : Union[str, Any] ) -> int: UpperCAmelCase : Dict = np.random.randn(3 , 4 ) UpperCAmelCase : Optional[Any] = torch.tensor(__snake_case ) self.assertTrue(np.allclose(reshape(__snake_case , (4, 3) ) , reshape(__snake_case , (4, 3) ).numpy() ) ) UpperCAmelCase : List[str] = np.random.randn(3 , 4 , 5 ) UpperCAmelCase : List[Any] = torch.tensor(__snake_case ) self.assertTrue(np.allclose(reshape(__snake_case , (12, 5) ) , reshape(__snake_case , (12, 5) ).numpy() ) ) @require_tf def A ( self : int ) -> List[str]: UpperCAmelCase : List[Any] = np.random.randn(3 , 4 ) UpperCAmelCase : List[str] = tf.constant(__snake_case ) self.assertTrue(np.allclose(reshape(__snake_case , (4, 3) ) , reshape(__snake_case , (4, 3) ).numpy() ) ) UpperCAmelCase : List[Any] = np.random.randn(3 , 4 , 5 ) UpperCAmelCase : Optional[Any] = tf.constant(__snake_case ) self.assertTrue(np.allclose(reshape(__snake_case , (12, 5) ) , reshape(__snake_case , (12, 5) ).numpy() ) ) @require_flax def A ( self : Any ) -> Dict: UpperCAmelCase : Tuple = np.random.randn(3 , 4 ) UpperCAmelCase : Union[str, Any] = jnp.array(__snake_case ) self.assertTrue(np.allclose(reshape(__snake_case , (4, 3) ) , np.asarray(reshape(__snake_case , (4, 3) ) ) ) ) UpperCAmelCase : Any = np.random.randn(3 , 4 , 5 ) UpperCAmelCase : Optional[Any] = jnp.array(__snake_case ) self.assertTrue(np.allclose(reshape(__snake_case , (12, 5) ) , np.asarray(reshape(__snake_case , (12, 5) ) ) ) ) def A ( self : List[Any] ) -> List[Any]: UpperCAmelCase : Union[str, Any] = np.random.randn(1 , 3 , 4 ) self.assertTrue(np.allclose(squeeze(__snake_case ) , np.squeeze(__snake_case ) ) ) UpperCAmelCase : str = np.random.randn(1 , 4 , 1 , 5 ) self.assertTrue(np.allclose(squeeze(__snake_case , axis=2 ) , np.squeeze(__snake_case , axis=2 ) ) ) @require_torch def A ( self : Optional[int] ) -> Union[str, Any]: UpperCAmelCase : Optional[int] = np.random.randn(1 , 3 , 4 ) UpperCAmelCase : List[str] = torch.tensor(__snake_case ) self.assertTrue(np.allclose(squeeze(__snake_case ) , squeeze(__snake_case ).numpy() ) ) UpperCAmelCase : Any = np.random.randn(1 , 4 , 1 , 5 ) UpperCAmelCase : str = torch.tensor(__snake_case ) self.assertTrue(np.allclose(squeeze(__snake_case , axis=2 ) , squeeze(__snake_case , axis=2 ).numpy() ) ) @require_tf def A ( self : Optional[Any] ) -> Dict: UpperCAmelCase : int = np.random.randn(1 , 3 , 4 ) UpperCAmelCase : Optional[int] = tf.constant(__snake_case ) self.assertTrue(np.allclose(squeeze(__snake_case ) , squeeze(__snake_case ).numpy() ) ) UpperCAmelCase : List[str] = np.random.randn(1 , 4 , 1 , 5 ) UpperCAmelCase : Optional[int] = tf.constant(__snake_case ) self.assertTrue(np.allclose(squeeze(__snake_case , axis=2 ) , squeeze(__snake_case , axis=2 ).numpy() ) ) @require_flax def A ( self : List[Any] ) -> Dict: UpperCAmelCase : Optional[int] = np.random.randn(1 , 3 , 4 ) UpperCAmelCase : int = jnp.array(__snake_case ) self.assertTrue(np.allclose(squeeze(__snake_case ) , np.asarray(squeeze(__snake_case ) ) ) ) UpperCAmelCase : str = np.random.randn(1 , 4 , 1 , 5 ) UpperCAmelCase : int = jnp.array(__snake_case ) self.assertTrue(np.allclose(squeeze(__snake_case , axis=2 ) , np.asarray(squeeze(__snake_case , axis=2 ) ) ) ) def A ( self : Optional[Any] ) -> int: UpperCAmelCase : Optional[Any] = np.random.randn(3 , 4 ) self.assertTrue(np.allclose(expand_dims(__snake_case , axis=1 ) , np.expand_dims(__snake_case , axis=1 ) ) ) @require_torch def A ( self : List[str] ) -> Tuple: UpperCAmelCase : Tuple = np.random.randn(3 , 4 ) UpperCAmelCase : Tuple = torch.tensor(__snake_case ) self.assertTrue(np.allclose(expand_dims(__snake_case , axis=1 ) , expand_dims(__snake_case , axis=1 ).numpy() ) ) @require_tf def A ( self : List[str] ) -> Union[str, Any]: UpperCAmelCase : Union[str, Any] = np.random.randn(3 , 4 ) UpperCAmelCase : Any = tf.constant(__snake_case ) self.assertTrue(np.allclose(expand_dims(__snake_case , axis=1 ) , expand_dims(__snake_case , axis=1 ).numpy() ) ) @require_flax def A ( self : Any ) -> List[Any]: UpperCAmelCase : List[str] = np.random.randn(3 , 4 ) UpperCAmelCase : str = jnp.array(__snake_case ) self.assertTrue(np.allclose(expand_dims(__snake_case , axis=1 ) , np.asarray(expand_dims(__snake_case , axis=1 ) ) ) )
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'''simple docstring''' import argparse import numpy as np import torch from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging logging.set_verbosity_info() __a = logging.get_logger("transformers.models.speecht5") def __snake_case( _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ) -> List[Any]: hf_model.apply_weight_norm() snake_case__ : List[Any] = checkpoint['''input_conv.weight_g'''] snake_case__ : Optional[int] = checkpoint['''input_conv.weight_v'''] snake_case__ : Any = checkpoint['''input_conv.bias'''] for i in range(len(config.upsample_rates ) ): snake_case__ : str = checkpoint[f"upsamples.{i}.1.weight_g"] snake_case__ : Optional[int] = checkpoint[f"upsamples.{i}.1.weight_v"] snake_case__ : str = checkpoint[f"upsamples.{i}.1.bias"] for i in range(len(config.upsample_rates ) * len(config.resblock_kernel_sizes ) ): for j in range(len(config.resblock_dilation_sizes ) ): snake_case__ : str = checkpoint[f"blocks.{i}.convs1.{j}.1.weight_g"] snake_case__ : List[str] = checkpoint[f"blocks.{i}.convs1.{j}.1.weight_v"] snake_case__ : Optional[int] = checkpoint[f"blocks.{i}.convs1.{j}.1.bias"] snake_case__ : List[Any] = checkpoint[f"blocks.{i}.convs2.{j}.1.weight_g"] snake_case__ : Union[str, Any] = checkpoint[f"blocks.{i}.convs2.{j}.1.weight_v"] snake_case__ : str = checkpoint[f"blocks.{i}.convs2.{j}.1.bias"] snake_case__ : Tuple = checkpoint['''output_conv.1.weight_g'''] snake_case__ : Union[str, Any] = checkpoint['''output_conv.1.weight_v'''] snake_case__ : Dict = checkpoint['''output_conv.1.bias'''] hf_model.remove_weight_norm() @torch.no_grad() def __snake_case( _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase=None , _lowerCAmelCase=None , ) -> List[Any]: if config_path is not None: snake_case__ : Optional[Any] = SpeechTaHifiGanConfig.from_pretrained(_lowerCAmelCase ) else: snake_case__ : Optional[Any] = SpeechTaHifiGanConfig() snake_case__ : Dict = SpeechTaHifiGan(_lowerCAmelCase ) snake_case__ : List[str] = torch.load(_lowerCAmelCase ) load_weights(orig_checkpoint["""model"""]["""generator"""] , _lowerCAmelCase , _lowerCAmelCase ) snake_case__ : Any = np.load(_lowerCAmelCase ) snake_case__ : Dict = stats[0].reshape(-1 ) snake_case__ : Tuple = stats[1].reshape(-1 ) snake_case__ : Tuple = torch.from_numpy(_lowerCAmelCase ).float() snake_case__ : List[str] = torch.from_numpy(_lowerCAmelCase ).float() model.save_pretrained(_lowerCAmelCase ) if repo_id: print("""Pushing to the hub...""" ) model.push_to_hub(_lowerCAmelCase ) if __name__ == "__main__": __a = argparse.ArgumentParser() parser.add_argument("--checkpoint_path", required=True, default=None, type=str, help="Path to original checkpoint") parser.add_argument("--stats_path", required=True, default=None, type=str, help="Path to stats.npy file") parser.add_argument("--config_path", default=None, type=str, help="Path to hf config.json of model to convert") parser.add_argument( "--pytorch_dump_folder_path", required=True, default=None, type=str, help="Path to the output PyTorch model." ) parser.add_argument( "--push_to_hub", default=None, type=str, help="Where to upload the converted model on the 🤗 hub." ) __a = parser.parse_args() convert_hifigan_checkpoint( args.checkpoint_path, args.stats_path, args.pytorch_dump_folder_path, args.config_path, args.push_to_hub, )
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'''simple docstring''' import argparse import os import re import packaging.version UpperCamelCase__: Union[str, Any] = "examples/" UpperCamelCase__: Optional[Any] = { "examples": (re.compile(r"^check_min_version\(\"[^\"]+\"\)\s*$", re.MULTILINE), "check_min_version(\"VERSION\")\n"), "init": (re.compile(r"^__version__\s+=\s+\"([^\"]+)\"\s*$", re.MULTILINE), "__version__ = \"VERSION\"\n"), "setup": (re.compile(r"^(\s*)version\s*=\s*\"[^\"]+\",", re.MULTILINE), r"\1version=\"VERSION\","), "doc": (re.compile(r"^(\s*)release\s*=\s*\"[^\"]+\"$", re.MULTILINE), "release = \"VERSION\"\n"), } UpperCamelCase__: Optional[int] = { "init": "src/diffusers/__init__.py", "setup": "setup.py", } UpperCamelCase__: List[Any] = "README.md" def snake_case_ ( _lowerCAmelCase : Optional[Any] , _lowerCAmelCase : List[Any] , _lowerCAmelCase : int ) -> Optional[int]: with open(_lowerCAmelCase , '''r''' , encoding='''utf-8''' , newline='''\n''' ) as f: UpperCAmelCase : Optional[int] = f.read() UpperCAmelCase , UpperCAmelCase : List[Any] = REPLACE_PATTERNS[pattern] UpperCAmelCase : List[Any] = replace.replace('''VERSION''' , _lowerCAmelCase ) UpperCAmelCase : Optional[Any] = re_pattern.sub(_lowerCAmelCase , _lowerCAmelCase ) with open(_lowerCAmelCase , '''w''' , encoding='''utf-8''' , newline='''\n''' ) as f: f.write(_lowerCAmelCase ) def snake_case_ ( _lowerCAmelCase : Any ) -> Optional[int]: for folder, directories, fnames in os.walk(_lowerCAmelCase ): # Removing some of the folders with non-actively maintained examples from the walk if "research_projects" in directories: directories.remove('''research_projects''' ) if "legacy" in directories: directories.remove('''legacy''' ) for fname in fnames: if fname.endswith('''.py''' ): update_version_in_file(os.path.join(_lowerCAmelCase , _lowerCAmelCase ) , _lowerCAmelCase , pattern='''examples''' ) def snake_case_ ( _lowerCAmelCase : Any , _lowerCAmelCase : str=False ) -> List[str]: for pattern, fname in REPLACE_FILES.items(): update_version_in_file(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ) if not patch: update_version_in_examples(_lowerCAmelCase ) def snake_case_ ( ) -> Optional[Any]: UpperCAmelCase : Optional[int] = '''🤗 Transformers currently provides the following architectures''' UpperCAmelCase : Optional[int] = '''1. Want to contribute a new model?''' with open(_lowerCAmelCase , '''r''' , encoding='''utf-8''' , newline='''\n''' ) as f: UpperCAmelCase : Optional[Any] = f.readlines() # Find the start of the list. UpperCAmelCase : List[Any] = 0 while not lines[start_index].startswith(_start_prompt ): start_index += 1 start_index += 1 UpperCAmelCase : Optional[Any] = start_index # Update the lines in the model list. while not lines[index].startswith(_end_prompt ): if lines[index].startswith('''1.''' ): UpperCAmelCase : Optional[int] = lines[index].replace( '''https://huggingface.co/docs/diffusers/main/model_doc''' , '''https://huggingface.co/docs/diffusers/model_doc''' , ) index += 1 with open(_lowerCAmelCase , '''w''' , encoding='''utf-8''' , newline='''\n''' ) as f: f.writelines(_lowerCAmelCase ) def snake_case_ ( ) -> Optional[Any]: with open(REPLACE_FILES['''init'''] , '''r''' ) as f: UpperCAmelCase : Union[str, Any] = f.read() UpperCAmelCase : int = REPLACE_PATTERNS['''init'''][0].search(_lowerCAmelCase ).groups()[0] return packaging.version.parse(_lowerCAmelCase ) def snake_case_ ( _lowerCAmelCase : List[str]=False ) -> Any: UpperCAmelCase : Optional[Any] = get_version() if patch and default_version.is_devrelease: raise ValueError('''Can\'t create a patch version from the dev branch, checkout a released version!''' ) if default_version.is_devrelease: UpperCAmelCase : Optional[int] = default_version.base_version elif patch: UpperCAmelCase : Union[str, Any] = f"""{default_version.major}.{default_version.minor}.{default_version.micro + 1}""" else: UpperCAmelCase : Union[str, Any] = f"""{default_version.major}.{default_version.minor + 1}.0""" # Now let's ask nicely if that's the right one. UpperCAmelCase : Dict = input(f"""Which version are you releasing? [{default_version}]""" ) if len(_lowerCAmelCase ) == 0: UpperCAmelCase : Tuple = default_version print(f"""Updating version to {version}.""" ) global_version_update(_lowerCAmelCase , patch=_lowerCAmelCase ) def snake_case_ ( ) -> Any: UpperCAmelCase : List[Any] = get_version() UpperCAmelCase : List[str] = f"""{current_version.major}.{current_version.minor + 1}.0.dev0""" UpperCAmelCase : List[Any] = current_version.base_version # Check with the user we got that right. UpperCAmelCase : Optional[int] = input(f"""Which version are we developing now? [{dev_version}]""" ) if len(_lowerCAmelCase ) == 0: UpperCAmelCase : Dict = dev_version print(f"""Updating version to {version}.""" ) global_version_update(_lowerCAmelCase ) # print("Cleaning main README, don't forget to run `make fix-copies`.") # clean_main_ref_in_model_list() if __name__ == "__main__": UpperCamelCase__: Union[str, Any] = argparse.ArgumentParser() parser.add_argument("--post_release", action="store_true", help="Whether this is pre or post release.") parser.add_argument("--patch", action="store_true", help="Whether or not this is a patch release.") UpperCamelCase__: Optional[Any] = parser.parse_args() if not args.post_release: pre_release_work(patch=args.patch) elif args.patch: print("Nothing to do after a patch :-)") else: post_release_work()
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import numpy as np from cva import COLOR_BGR2GRAY, CV_8UC3, cvtColor, filteraD, imread, imshow, waitKey def lowerCamelCase__ ( a__ : int , a__ : int , a__ : int , a__ : int , a__ : int , a__ : int ) -> np.ndarray: # prepare kernel # the kernel size have to be odd if (ksize % 2) == 0: UpperCamelCase_ = ksize + 1 UpperCamelCase_ = np.zeros((ksize, ksize) , dtype=np.floataa ) # each value for y in range(_lowerCAmelCase ): for x in range(_lowerCAmelCase ): # distance from center UpperCamelCase_ = x - ksize // 2 UpperCamelCase_ = y - ksize // 2 # degree to radiant UpperCamelCase_ = theta / 180 * np.pi UpperCamelCase_ = np.cos(_theta ) UpperCamelCase_ = np.sin(_theta ) # get kernel x UpperCamelCase_ = cos_theta * px + sin_theta * py # get kernel y UpperCamelCase_ = -sin_theta * px + cos_theta * py # fill kernel UpperCamelCase_ = np.exp( -(_x**2 + gamma**2 * _y**2) / (2 * sigma**2) ) * np.cos(2 * np.pi * _x / lambd + psi ) return gabor if __name__ == "__main__": import doctest doctest.testmod() # read original image _A = imread('''../image_data/lena.jpg''') # turn image in gray scale value _A = cvtColor(img, COLOR_BGR2GRAY) # Apply multiple Kernel to detect edges _A = np.zeros(gray.shape[:2]) for theta in [0, 30, 60, 90, 120, 150]: _A = gabor_filter_kernel(10, 8, theta, 10, 0, 0) out += filteraD(gray, CV_8UC3, kernel_aa) _A = out / out.max() * 255 _A = out.astype(np.uinta) imshow('''Original''', gray) imshow('''Gabor filter with 20x20 mask and 6 directions''', out) waitKey(0)
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'''simple docstring''' from __future__ import annotations import matplotlib.pyplot as plt # type: ignore import numpy # initial triangle of Koch snowflake UpperCamelCase__: Tuple = numpy.array([0, 0]) UpperCamelCase__: Union[str, Any] = numpy.array([0.5, 0.8660254]) UpperCamelCase__: Dict = numpy.array([1, 0]) UpperCamelCase__: int = [VECTOR_1, VECTOR_2, VECTOR_3, VECTOR_1] def snake_case_ ( _lowerCAmelCase : list[numpy.ndarray] , _lowerCAmelCase : int ) -> list[numpy.ndarray]: UpperCAmelCase : Union[str, Any] = initial_vectors for _ in range(_lowerCAmelCase ): UpperCAmelCase : Union[str, Any] = iteration_step(_lowerCAmelCase ) return vectors def snake_case_ ( _lowerCAmelCase : list[numpy.ndarray] ) -> list[numpy.ndarray]: UpperCAmelCase : Tuple = [] for i, start_vector in enumerate(vectors[:-1] ): UpperCAmelCase : List[str] = vectors[i + 1] new_vectors.append(_lowerCAmelCase ) UpperCAmelCase : Optional[Any] = end_vector - start_vector new_vectors.append(start_vector + difference_vector / 3 ) new_vectors.append( start_vector + difference_vector / 3 + rotate(difference_vector / 3 , 60 ) ) new_vectors.append(start_vector + difference_vector * 2 / 3 ) new_vectors.append(vectors[-1] ) return new_vectors def snake_case_ ( _lowerCAmelCase : numpy.ndarray , _lowerCAmelCase : float ) -> numpy.ndarray: UpperCAmelCase : List[str] = numpy.radians(_lowerCAmelCase ) UpperCAmelCase , UpperCAmelCase : Tuple = numpy.cos(_lowerCAmelCase ), numpy.sin(_lowerCAmelCase ) UpperCAmelCase : Union[str, Any] = numpy.array(((c, -s), (s, c)) ) return numpy.dot(_lowerCAmelCase , _lowerCAmelCase ) def snake_case_ ( _lowerCAmelCase : list[numpy.ndarray] ) -> None: UpperCAmelCase : List[Any] = plt.gca() axes.set_aspect('''equal''' ) # matplotlib.pyplot.plot takes a list of all x-coordinates and a list of all # y-coordinates as inputs, which are constructed from the vector-list using # zip() UpperCAmelCase , UpperCAmelCase : str = zip(*_lowerCAmelCase ) plt.plot(_lowerCAmelCase , _lowerCAmelCase ) plt.show() if __name__ == "__main__": import doctest doctest.testmod() UpperCamelCase__: List[Any] = iterate(INITIAL_VECTORS, 5) plot(processed_vectors)
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from __future__ import annotations import inspect import unittest import numpy as np from transformers import ResNetConfig from transformers.testing_utils import require_tf, require_vision, slow from transformers.utils import cached_property, is_tf_available, is_vision_available from ...test_configuration_common import ConfigTester from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor from ...test_pipeline_mixin import PipelineTesterMixin if is_tf_available(): import tensorflow as tf from transformers import TFResNetForImageClassification, TFResNetModel from transformers.models.resnet.modeling_tf_resnet import TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST if is_vision_available(): from PIL import Image from transformers import AutoImageProcessor class a_ : """simple docstring""" def __init__( self : List[Any] ,snake_case : Any ,snake_case : List[str]=3 ,snake_case : str=32 ,snake_case : Tuple=3 ,snake_case : Dict=10 ,snake_case : List[Any]=[10, 20, 30, 40] ,snake_case : List[Any]=[1, 1, 2, 1] ,snake_case : Any=True ,snake_case : Dict=True ,snake_case : Any="relu" ,snake_case : Union[str, Any]=3 ,snake_case : List[str]=None ,): SCREAMING_SNAKE_CASE =parent SCREAMING_SNAKE_CASE =batch_size SCREAMING_SNAKE_CASE =image_size SCREAMING_SNAKE_CASE =num_channels SCREAMING_SNAKE_CASE =embeddings_size SCREAMING_SNAKE_CASE =hidden_sizes SCREAMING_SNAKE_CASE =depths SCREAMING_SNAKE_CASE =is_training SCREAMING_SNAKE_CASE =use_labels SCREAMING_SNAKE_CASE =hidden_act SCREAMING_SNAKE_CASE =num_labels SCREAMING_SNAKE_CASE =scope SCREAMING_SNAKE_CASE =len(__snake_case ) def _lowerCAmelCase ( self : Union[str, Any] ): SCREAMING_SNAKE_CASE =floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] ) SCREAMING_SNAKE_CASE =None if self.use_labels: SCREAMING_SNAKE_CASE =ids_tensor([self.batch_size] ,self.num_labels ) SCREAMING_SNAKE_CASE =self.get_config() return config, pixel_values, labels def _lowerCAmelCase ( self : int ): return ResNetConfig( num_channels=self.num_channels ,embeddings_size=self.embeddings_size ,hidden_sizes=self.hidden_sizes ,depths=self.depths ,hidden_act=self.hidden_act ,num_labels=self.num_labels ,image_size=self.image_size ,) def _lowerCAmelCase ( self : Optional[int] ,snake_case : Union[str, Any] ,snake_case : List[Any] ,snake_case : List[Any] ): SCREAMING_SNAKE_CASE =TFResNetModel(config=__snake_case ) SCREAMING_SNAKE_CASE =model(__snake_case ) # expected last hidden states: B, C, H // 32, W // 32 self.parent.assertEqual( result.last_hidden_state.shape ,(self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32) ,) def _lowerCAmelCase ( self : List[str] ,snake_case : Optional[int] ,snake_case : Union[str, Any] ,snake_case : str ): SCREAMING_SNAKE_CASE =self.num_labels SCREAMING_SNAKE_CASE =TFResNetForImageClassification(__snake_case ) SCREAMING_SNAKE_CASE =model(__snake_case ,labels=__snake_case ) self.parent.assertEqual(result.logits.shape ,(self.batch_size, self.num_labels) ) def _lowerCAmelCase ( self : str ): SCREAMING_SNAKE_CASE =self.prepare_config_and_inputs() SCREAMING_SNAKE_CASE =config_and_inputs SCREAMING_SNAKE_CASE ={'''pixel_values''': pixel_values} return config, inputs_dict @require_tf class a_ ( A__ , A__ , unittest.TestCase ): """simple docstring""" __UpperCAmelCase = (TFResNetModel, TFResNetForImageClassification) if is_tf_available() else () __UpperCAmelCase = ( {'feature-extraction': TFResNetModel, 'image-classification': TFResNetForImageClassification} if is_tf_available() else {} ) __UpperCAmelCase = False __UpperCAmelCase = False __UpperCAmelCase = False __UpperCAmelCase = False __UpperCAmelCase = False def _lowerCAmelCase ( self : Any ): SCREAMING_SNAKE_CASE =TFResNetModelTester(self ) SCREAMING_SNAKE_CASE =ConfigTester(self ,config_class=__snake_case ,has_text_modality=__snake_case ) def _lowerCAmelCase ( self : List[str] ): self.create_and_test_config_common_properties() self.config_tester.create_and_test_config_to_json_string() self.config_tester.create_and_test_config_to_json_file() self.config_tester.create_and_test_config_from_and_save_pretrained() self.config_tester.create_and_test_config_with_num_labels() self.config_tester.check_config_can_be_init_without_params() self.config_tester.check_config_arguments_init() def _lowerCAmelCase ( self : Dict ): return @unittest.skip(reason='ResNet does not use inputs_embeds' ) def _lowerCAmelCase ( self : str ): pass @unittest.skip(reason='ResNet does not support input and output embeddings' ) def _lowerCAmelCase ( self : Dict ): pass def _lowerCAmelCase ( self : Optional[int] ): SCREAMING_SNAKE_CASE =self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: SCREAMING_SNAKE_CASE =model_class(__snake_case ) SCREAMING_SNAKE_CASE =inspect.signature(model.call ) # signature.parameters is an OrderedDict => so arg_names order is deterministic SCREAMING_SNAKE_CASE =[*signature.parameters.keys()] SCREAMING_SNAKE_CASE =['''pixel_values'''] self.assertListEqual(arg_names[:1] ,__snake_case ) def _lowerCAmelCase ( self : Any ): SCREAMING_SNAKE_CASE =self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_model(*__snake_case ) def _lowerCAmelCase ( self : Dict ): def check_hidden_states_output(snake_case : int ,snake_case : List[Any] ,snake_case : Optional[int] ): SCREAMING_SNAKE_CASE =model_class(__snake_case ) SCREAMING_SNAKE_CASE =model(**self._prepare_for_class(__snake_case ,__snake_case ) ) SCREAMING_SNAKE_CASE =outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states SCREAMING_SNAKE_CASE =self.model_tester.num_stages self.assertEqual(len(__snake_case ) ,expected_num_stages + 1 ) # ResNet's feature maps are of shape (batch_size, num_channels, height, width) self.assertListEqual( list(hidden_states[0].shape[-2:] ) ,[self.model_tester.image_size // 4, self.model_tester.image_size // 4] ,) SCREAMING_SNAKE_CASE =self.model_tester.prepare_config_and_inputs_for_common() SCREAMING_SNAKE_CASE =['''basic''', '''bottleneck'''] for model_class in self.all_model_classes: for layer_type in layers_type: SCREAMING_SNAKE_CASE =layer_type SCREAMING_SNAKE_CASE =True check_hidden_states_output(__snake_case ,__snake_case ,__snake_case ) # check that output_hidden_states also work using config del inputs_dict["output_hidden_states"] SCREAMING_SNAKE_CASE =True check_hidden_states_output(__snake_case ,__snake_case ,__snake_case ) def _lowerCAmelCase ( self : Tuple ): SCREAMING_SNAKE_CASE =self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_image_classification(*__snake_case ) @slow def _lowerCAmelCase ( self : str ): for model_name in TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: SCREAMING_SNAKE_CASE =TFResNetModel.from_pretrained(__snake_case ) self.assertIsNotNone(__snake_case ) def snake_case__ ( ): """simple docstring""" SCREAMING_SNAKE_CASE =Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' ) return image @require_tf @require_vision class a_ ( unittest.TestCase ): """simple docstring""" @cached_property def _lowerCAmelCase ( self : str ): return ( AutoImageProcessor.from_pretrained(TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0] ) if is_vision_available() else None ) @slow def _lowerCAmelCase ( self : Dict ): SCREAMING_SNAKE_CASE =TFResNetForImageClassification.from_pretrained(TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0] ) SCREAMING_SNAKE_CASE =self.default_image_processor SCREAMING_SNAKE_CASE =prepare_img() SCREAMING_SNAKE_CASE =image_processor(images=__snake_case ,return_tensors='tf' ) # forward pass SCREAMING_SNAKE_CASE =model(**__snake_case ) # verify the logits SCREAMING_SNAKE_CASE =tf.TensorShape((1, 1000) ) self.assertEqual(outputs.logits.shape ,__snake_case ) SCREAMING_SNAKE_CASE =tf.constant([-11.1_069, -9.7_877, -8.3_777] ) self.assertTrue(np.allclose(outputs.logits[0, :3].numpy() ,__snake_case ,atol=1e-4 ) )
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'''simple docstring''' from manim import * class SCREAMING_SNAKE_CASE( A__ ): """simple docstring""" def A ( self : Union[str, Any] ) -> List[str]: UpperCAmelCase : Optional[Any] = Rectangle(height=0.5 , width=0.5 ) UpperCAmelCase : str = Rectangle(height=0.46 , width=0.46 ).set_stroke(width=0 ) UpperCAmelCase : Tuple = [mem.copy() for i in range(6 )] UpperCAmelCase : Optional[int] = [mem.copy() for i in range(6 )] UpperCAmelCase : Dict = VGroup(*__snake_case ).arrange(__snake_case , buff=0 ) UpperCAmelCase : Any = VGroup(*__snake_case ).arrange(__snake_case , buff=0 ) UpperCAmelCase : Union[str, Any] = VGroup(__snake_case , __snake_case ).arrange(__snake_case , buff=0 ) UpperCAmelCase : Optional[Any] = Text('''CPU''' , font_size=24 ) UpperCAmelCase : Union[str, Any] = Group(__snake_case , __snake_case ).arrange(__snake_case , buff=0.5 , aligned_edge=__snake_case ) cpu.move_to([-2.5, -0.5, 0] ) self.add(__snake_case ) UpperCAmelCase : Optional[Any] = [mem.copy() for i in range(4 )] UpperCAmelCase : Union[str, Any] = VGroup(*__snake_case ).arrange(__snake_case , buff=0 ) UpperCAmelCase : List[str] = Text('''GPU''' , font_size=24 ) UpperCAmelCase : Dict = Group(__snake_case , __snake_case ).arrange(__snake_case , buff=0.5 , aligned_edge=__snake_case ) gpu.move_to([-1, -1, 0] ) self.add(__snake_case ) UpperCAmelCase : int = [mem.copy() for i in range(6 )] UpperCAmelCase : Union[str, Any] = VGroup(*__snake_case ).arrange(__snake_case , buff=0 ) UpperCAmelCase : List[str] = Text('''Model''' , font_size=24 ) UpperCAmelCase : Tuple = Group(__snake_case , __snake_case ).arrange(__snake_case , buff=0.5 , aligned_edge=__snake_case ) model.move_to([3, -1.0, 0] ) self.add(__snake_case ) UpperCAmelCase : Any = [] for i, rect in enumerate(__snake_case ): rect.set_stroke(__snake_case ) # target = fill.copy().set_fill(YELLOW, opacity=0.7) # target.move_to(rect) # self.add(target) UpperCAmelCase : Dict = Rectangle(height=0.46 / 4 , width=0.46 / 3 ).set_stroke(width=0.0 ).set_fill(__snake_case , opacity=0.7 ) if i == 0: cpu_target.next_to(cpu_left_col_base[0].get_corner(DOWN + LEFT ) , buff=0.02 , direction=__snake_case ) cpu_target.set_x(cpu_target.get_x() + 0.1 ) elif i == 3: cpu_target.next_to(cpu_targs[0] , direction=__snake_case , buff=0.0 ) else: cpu_target.next_to(cpu_targs[i - 1] , direction=__snake_case , buff=0.0 ) self.add(__snake_case ) cpu_targs.append(__snake_case ) UpperCAmelCase : int = [mem.copy() for i in range(6 )] UpperCAmelCase : int = VGroup(*__snake_case ).arrange(__snake_case , buff=0 ) UpperCAmelCase : Any = Text('''Loaded Checkpoint''' , font_size=24 ) UpperCAmelCase : Union[str, Any] = Group(__snake_case , __snake_case ).arrange(__snake_case , aligned_edge=__snake_case , buff=0.4 ) checkpoint.move_to([3, 0.5, 0] ) UpperCAmelCase : Optional[int] = Square(side_length=2.2 ) key.move_to([-5, 2, 0] ) UpperCAmelCase : str = MarkupText( F"""<b>Key:</b>\n\n<span fgcolor='{YELLOW}'>●</span> Empty Model""" , font_size=18 , ) key_text.move_to([-5, 2.4, 0] ) self.add(__snake_case , __snake_case ) UpperCAmelCase : Tuple = MarkupText( F"""<span fgcolor='{BLUE}'>●</span> Checkpoint""" , font_size=18 , ) blue_text.next_to(__snake_case , DOWN * 2.4 , aligned_edge=key_text.get_left() ) UpperCAmelCase : List[Any] = MarkupText( F"""Next, a <i><span fgcolor=\"{BLUE}\">second</span></i> model is loaded into memory,\nwith the weights of a <span fgcolor=\"{BLUE}\">single shard</span>.""" , font_size=24 , ) step_a.move_to([2, 2, 0] ) self.play(Write(__snake_case ) , Write(__snake_case ) ) self.play(Write(__snake_case , run_time=1 ) , Create(__snake_case , run_time=1 ) ) UpperCAmelCase : Tuple = [] UpperCAmelCase : int = [] for i, rect in enumerate(__snake_case ): UpperCAmelCase : Any = fill.copy().set_fill(__snake_case , opacity=0.7 ) target.move_to(__snake_case ) first_animations.append(GrowFromCenter(__snake_case , run_time=1 ) ) UpperCAmelCase : List[str] = target.copy() cpu_target.generate_target() if i < 5: cpu_target.target.move_to(cpu_left_col_base[i + 1] ) else: cpu_target.target.move_to(cpu_right_col_base[i - 5] ) second_animations.append(MoveToTarget(__snake_case , run_time=1.5 ) ) self.play(*__snake_case ) self.play(*__snake_case ) self.wait()
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from math import cos, sin, sqrt, tau from audio_filters.iir_filter import IIRFilter def UpperCamelCase_( _snake_case : int , _snake_case : int , _snake_case : float = 1 / sqrt(2 ) ): """simple docstring""" __a =tau * frequency / samplerate __a =sin(_lowerCAmelCase ) __a =cos(_lowerCAmelCase ) __a =_sin / (2 * q_factor) __a =(1 - _cos) / 2 __a =1 - _cos __a =1 + alpha __a =-2 * _cos __a =1 - alpha __a =IIRFilter(2 ) filt.set_coefficients([aa, aa, aa] , [ba, ba, ba] ) return filt def UpperCamelCase_( _snake_case : int , _snake_case : int , _snake_case : float = 1 / sqrt(2 ) ): """simple docstring""" __a =tau * frequency / samplerate __a =sin(_lowerCAmelCase ) __a =cos(_lowerCAmelCase ) __a =_sin / (2 * q_factor) __a =(1 + _cos) / 2 __a =-1 - _cos __a =1 + alpha __a =-2 * _cos __a =1 - alpha __a =IIRFilter(2 ) filt.set_coefficients([aa, aa, aa] , [ba, ba, ba] ) return filt def UpperCamelCase_( _snake_case : int , _snake_case : int , _snake_case : float = 1 / sqrt(2 ) ): """simple docstring""" __a =tau * frequency / samplerate __a =sin(_lowerCAmelCase ) __a =cos(_lowerCAmelCase ) __a =_sin / (2 * q_factor) __a =_sin / 2 __a =0 __a =-ba __a =1 + alpha __a =-2 * _cos __a =1 - alpha __a =IIRFilter(2 ) filt.set_coefficients([aa, aa, aa] , [ba, ba, ba] ) return filt def UpperCamelCase_( _snake_case : int , _snake_case : int , _snake_case : float = 1 / sqrt(2 ) ): """simple docstring""" __a =tau * frequency / samplerate __a =sin(_lowerCAmelCase ) __a =cos(_lowerCAmelCase ) __a =_sin / (2 * q_factor) __a =1 - alpha __a =-2 * _cos __a =1 + alpha __a =IIRFilter(2 ) filt.set_coefficients([ba, ba, ba] , [ba, ba, ba] ) return filt def UpperCamelCase_( _snake_case : int , _snake_case : int , _snake_case : float , _snake_case : float = 1 / sqrt(2 ) , ): """simple docstring""" __a =tau * frequency / samplerate __a =sin(_lowerCAmelCase ) __a =cos(_lowerCAmelCase ) __a =_sin / (2 * q_factor) __a =10 ** (gain_db / 40) __a =1 + alpha * big_a __a =-2 * _cos __a =1 - alpha * big_a __a =1 + alpha / big_a __a =-2 * _cos __a =1 - alpha / big_a __a =IIRFilter(2 ) filt.set_coefficients([aa, aa, aa] , [ba, ba, ba] ) return filt def UpperCamelCase_( _snake_case : int , _snake_case : int , _snake_case : float , _snake_case : float = 1 / sqrt(2 ) , ): """simple docstring""" __a =tau * frequency / samplerate __a =sin(_lowerCAmelCase ) __a =cos(_lowerCAmelCase ) __a =_sin / (2 * q_factor) __a =10 ** (gain_db / 40) __a =(big_a + 1) - (big_a - 1) * _cos __a =(big_a + 1) + (big_a - 1) * _cos __a =(big_a - 1) - (big_a + 1) * _cos __a =(big_a - 1) + (big_a + 1) * _cos __a =2 * sqrt(_lowerCAmelCase ) * alpha __a =big_a * (pmc + aaa) __a =2 * big_a * mpc __a =big_a * (pmc - aaa) __a =ppmc + aaa __a =-2 * pmpc __a =ppmc - aaa __a =IIRFilter(2 ) filt.set_coefficients([aa, aa, aa] , [ba, ba, ba] ) return filt def UpperCamelCase_( _snake_case : int , _snake_case : int , _snake_case : float , _snake_case : float = 1 / sqrt(2 ) , ): """simple docstring""" __a =tau * frequency / samplerate __a =sin(_lowerCAmelCase ) __a =cos(_lowerCAmelCase ) __a =_sin / (2 * q_factor) __a =10 ** (gain_db / 40) __a =(big_a + 1) - (big_a - 1) * _cos __a =(big_a + 1) + (big_a - 1) * _cos __a =(big_a - 1) - (big_a + 1) * _cos __a =(big_a - 1) + (big_a + 1) * _cos __a =2 * sqrt(_lowerCAmelCase ) * alpha __a =big_a * (ppmc + aaa) __a =-2 * big_a * pmpc __a =big_a * (ppmc - aaa) __a =pmc + aaa __a =2 * mpc __a =pmc - aaa __a =IIRFilter(2 ) filt.set_coefficients([aa, aa, aa] , [ba, ba, ba] ) return filt
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'''simple docstring''' import argparse import gc import json import os import shutil import warnings import torch from transformers import LlamaConfig, LlamaForCausalLM, LlamaTokenizer try: from transformers import LlamaTokenizerFast except ImportError as e: warnings.warn(e) warnings.warn( "The converted tokenizer will be the `slow` tokenizer. To use the fast, update your `tokenizers` library and re-run the tokenizer conversion" ) UpperCamelCase__: str = None UpperCamelCase__: int = { "7B": 11008, "13B": 13824, "30B": 17920, "65B": 22016, "70B": 28672, } UpperCamelCase__: List[Any] = { "7B": 1, "7Bf": 1, "13B": 2, "13Bf": 2, "30B": 4, "65B": 8, "70B": 8, "70Bf": 8, } def snake_case_ ( _lowerCAmelCase : Tuple , _lowerCAmelCase : Tuple=1 , _lowerCAmelCase : List[Any]=256 ) -> Optional[Any]: return multiple_of * ((int(ffn_dim_multiplier * int(8 * n / 3 ) ) + multiple_of - 1) // multiple_of) def snake_case_ ( _lowerCAmelCase : List[str] ) -> str: with open(_lowerCAmelCase , '''r''' ) as f: return json.load(_lowerCAmelCase ) def snake_case_ ( _lowerCAmelCase : Union[str, Any] , _lowerCAmelCase : Any ) -> Optional[Any]: with open(_lowerCAmelCase , '''w''' ) as f: json.dump(_lowerCAmelCase , _lowerCAmelCase ) def snake_case_ ( _lowerCAmelCase : Dict , _lowerCAmelCase : Tuple , _lowerCAmelCase : Any , _lowerCAmelCase : Any=True ) -> List[Any]: os.makedirs(_lowerCAmelCase , exist_ok=_lowerCAmelCase ) UpperCAmelCase : int = os.path.join(_lowerCAmelCase , '''tmp''' ) os.makedirs(_lowerCAmelCase , exist_ok=_lowerCAmelCase ) UpperCAmelCase : List[str] = read_json(os.path.join(_lowerCAmelCase , '''params.json''' ) ) UpperCAmelCase : str = NUM_SHARDS[model_size] UpperCAmelCase : Any = params['''n_layers'''] UpperCAmelCase : str = params['''n_heads'''] UpperCAmelCase : Any = n_heads // num_shards UpperCAmelCase : List[str] = params['''dim'''] UpperCAmelCase : Optional[Any] = dim // n_heads UpperCAmelCase : str = 1_0_0_0_0.0 UpperCAmelCase : Optional[int] = 1.0 / (base ** (torch.arange(0 , _lowerCAmelCase , 2 ).float() / dims_per_head)) if "n_kv_heads" in params: UpperCAmelCase : Tuple = params['''n_kv_heads'''] # for GQA / MQA UpperCAmelCase : Optional[int] = n_heads_per_shard // num_key_value_heads UpperCAmelCase : Optional[Any] = dim // num_key_value_heads else: # compatibility with other checkpoints UpperCAmelCase : List[str] = n_heads UpperCAmelCase : Optional[int] = n_heads_per_shard UpperCAmelCase : List[str] = dim # permute for sliced rotary def permute(_lowerCAmelCase : Tuple , _lowerCAmelCase : Optional[Any]=n_heads , _lowerCAmelCase : int=dim , _lowerCAmelCase : Dict=dim ): return w.view(_lowerCAmelCase , dima // n_heads // 2 , 2 , _lowerCAmelCase ).transpose(1 , 2 ).reshape(_lowerCAmelCase , _lowerCAmelCase ) print(f"""Fetching all parameters from the checkpoint at {input_base_path}.""" ) # Load weights if model_size == "7B": # Not sharded # (The sharded implementation would also work, but this is simpler.) UpperCAmelCase : int = torch.load(os.path.join(_lowerCAmelCase , '''consolidated.00.pth''' ) , map_location='''cpu''' ) else: # Sharded UpperCAmelCase : Optional[Any] = [ torch.load(os.path.join(_lowerCAmelCase , f"""consolidated.{i:02d}.pth""" ) , map_location='''cpu''' ) for i in range(_lowerCAmelCase ) ] UpperCAmelCase : Any = 0 UpperCAmelCase : str = {'''weight_map''': {}} for layer_i in range(_lowerCAmelCase ): UpperCAmelCase : Optional[Any] = f"""pytorch_model-{layer_i + 1}-of-{n_layers + 1}.bin""" if model_size == "7B": # Unsharded UpperCAmelCase : Optional[int] = { f"""model.layers.{layer_i}.self_attn.q_proj.weight""": permute( loaded[f"""layers.{layer_i}.attention.wq.weight"""] ), f"""model.layers.{layer_i}.self_attn.k_proj.weight""": permute( loaded[f"""layers.{layer_i}.attention.wk.weight"""] ), f"""model.layers.{layer_i}.self_attn.v_proj.weight""": loaded[f"""layers.{layer_i}.attention.wv.weight"""], f"""model.layers.{layer_i}.self_attn.o_proj.weight""": loaded[f"""layers.{layer_i}.attention.wo.weight"""], f"""model.layers.{layer_i}.mlp.gate_proj.weight""": loaded[f"""layers.{layer_i}.feed_forward.w1.weight"""], f"""model.layers.{layer_i}.mlp.down_proj.weight""": loaded[f"""layers.{layer_i}.feed_forward.w2.weight"""], f"""model.layers.{layer_i}.mlp.up_proj.weight""": loaded[f"""layers.{layer_i}.feed_forward.w3.weight"""], f"""model.layers.{layer_i}.input_layernorm.weight""": loaded[f"""layers.{layer_i}.attention_norm.weight"""], f"""model.layers.{layer_i}.post_attention_layernorm.weight""": loaded[f"""layers.{layer_i}.ffn_norm.weight"""], } else: # Sharded # Note that attention.w{q,k,v,o}, feed_fordward.w[1,2,3], attention_norm.weight and ffn_norm.weight share # the same storage object, saving attention_norm and ffn_norm will save other weights too, which is # redundant as other weights will be stitched from multiple shards. To avoid that, they are cloned. UpperCAmelCase : List[str] = { f"""model.layers.{layer_i}.input_layernorm.weight""": loaded[0][ f"""layers.{layer_i}.attention_norm.weight""" ].clone(), f"""model.layers.{layer_i}.post_attention_layernorm.weight""": loaded[0][ f"""layers.{layer_i}.ffn_norm.weight""" ].clone(), } UpperCAmelCase : Union[str, Any] = permute( torch.cat( [ loaded[i][f"""layers.{layer_i}.attention.wq.weight"""].view(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ) for i in range(_lowerCAmelCase ) ] , dim=0 , ).reshape(_lowerCAmelCase , _lowerCAmelCase ) ) UpperCAmelCase : Optional[Any] = permute( torch.cat( [ loaded[i][f"""layers.{layer_i}.attention.wk.weight"""].view( _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ) for i in range(_lowerCAmelCase ) ] , dim=0 , ).reshape(_lowerCAmelCase , _lowerCAmelCase ) , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , ) UpperCAmelCase : str = torch.cat( [ loaded[i][f"""layers.{layer_i}.attention.wv.weight"""].view( _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ) for i in range(_lowerCAmelCase ) ] , dim=0 , ).reshape(_lowerCAmelCase , _lowerCAmelCase ) UpperCAmelCase : Optional[int] = torch.cat( [loaded[i][f"""layers.{layer_i}.attention.wo.weight"""] for i in range(_lowerCAmelCase )] , dim=1 ) UpperCAmelCase : Any = torch.cat( [loaded[i][f"""layers.{layer_i}.feed_forward.w1.weight"""] for i in range(_lowerCAmelCase )] , dim=0 ) UpperCAmelCase : str = torch.cat( [loaded[i][f"""layers.{layer_i}.feed_forward.w2.weight"""] for i in range(_lowerCAmelCase )] , dim=1 ) UpperCAmelCase : Tuple = torch.cat( [loaded[i][f"""layers.{layer_i}.feed_forward.w3.weight"""] for i in range(_lowerCAmelCase )] , dim=0 ) UpperCAmelCase : Any = inv_freq for k, v in state_dict.items(): UpperCAmelCase : List[Any] = filename param_count += v.numel() torch.save(_lowerCAmelCase , os.path.join(_lowerCAmelCase , _lowerCAmelCase ) ) UpperCAmelCase : Optional[int] = f"""pytorch_model-{n_layers + 1}-of-{n_layers + 1}.bin""" if model_size == "7B": # Unsharded UpperCAmelCase : str = { '''model.embed_tokens.weight''': loaded['''tok_embeddings.weight'''], '''model.norm.weight''': loaded['''norm.weight'''], '''lm_head.weight''': loaded['''output.weight'''], } else: UpperCAmelCase : Any = { '''model.norm.weight''': loaded[0]['''norm.weight'''], '''model.embed_tokens.weight''': torch.cat( [loaded[i]['''tok_embeddings.weight'''] for i in range(_lowerCAmelCase )] , dim=1 ), '''lm_head.weight''': torch.cat([loaded[i]['''output.weight'''] for i in range(_lowerCAmelCase )] , dim=0 ), } for k, v in state_dict.items(): UpperCAmelCase : Optional[int] = filename param_count += v.numel() torch.save(_lowerCAmelCase , os.path.join(_lowerCAmelCase , _lowerCAmelCase ) ) # Write configs UpperCAmelCase : Union[str, Any] = {'''total_size''': param_count * 2} write_json(_lowerCAmelCase , os.path.join(_lowerCAmelCase , '''pytorch_model.bin.index.json''' ) ) UpperCAmelCase : int = params['''ffn_dim_multiplier'''] if '''ffn_dim_multiplier''' in params else 1 UpperCAmelCase : Tuple = params['''multiple_of'''] if '''multiple_of''' in params else 256 UpperCAmelCase : Any = LlamaConfig( hidden_size=_lowerCAmelCase , intermediate_size=compute_intermediate_size(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ) , num_attention_heads=params['''n_heads'''] , num_hidden_layers=params['''n_layers'''] , rms_norm_eps=params['''norm_eps'''] , num_key_value_heads=_lowerCAmelCase , ) config.save_pretrained(_lowerCAmelCase ) # Make space so we can load the model properly now. del state_dict del loaded gc.collect() print('''Loading the checkpoint in a Llama model.''' ) UpperCAmelCase : Optional[int] = LlamaForCausalLM.from_pretrained(_lowerCAmelCase , torch_dtype=torch.floataa , low_cpu_mem_usage=_lowerCAmelCase ) # Avoid saving this as part of the config. del model.config._name_or_path print('''Saving in the Transformers format.''' ) model.save_pretrained(_lowerCAmelCase , safe_serialization=_lowerCAmelCase ) shutil.rmtree(_lowerCAmelCase ) def snake_case_ ( _lowerCAmelCase : int , _lowerCAmelCase : Union[str, Any] ) -> List[str]: # Initialize the tokenizer based on the `spm` model UpperCAmelCase : Dict = LlamaTokenizer if LlamaTokenizerFast is None else LlamaTokenizerFast print(f"""Saving a {tokenizer_class.__name__} to {tokenizer_path}.""" ) UpperCAmelCase : List[Any] = tokenizer_class(_lowerCAmelCase ) tokenizer.save_pretrained(_lowerCAmelCase ) def snake_case_ ( ) -> List[Any]: UpperCAmelCase : int = argparse.ArgumentParser() parser.add_argument( '''--input_dir''' , help='''Location of LLaMA weights, which contains tokenizer.model and model folders''' , ) parser.add_argument( '''--model_size''' , choices=['''7B''', '''7Bf''', '''13B''', '''13Bf''', '''30B''', '''65B''', '''70B''', '''70Bf''', '''tokenizer_only'''] , ) parser.add_argument( '''--output_dir''' , help='''Location to write HF model and tokenizer''' , ) parser.add_argument('''--safe_serialization''' , type=_lowerCAmelCase , help='''Whether or not to save using `safetensors`.''' ) UpperCAmelCase : List[Any] = parser.parse_args() if args.model_size != "tokenizer_only": write_model( model_path=args.output_dir , input_base_path=os.path.join(args.input_dir , args.model_size ) , model_size=args.model_size , safe_serialization=args.safe_serialization , ) UpperCAmelCase : Optional[int] = os.path.join(args.input_dir , '''tokenizer.model''' ) write_tokenizer(args.output_dir , _lowerCAmelCase ) if __name__ == "__main__": main()
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'''simple docstring''' from typing import Dict, Optional, Union import numpy as np from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict from ...image_transforms import flip_channel_order, resize, to_channel_dimension_format, to_pil_image from ...image_utils import ( ChannelDimension, ImageInput, PILImageResampling, make_list_of_images, to_numpy_array, valid_images, ) from ...utils import TensorType, is_pytesseract_available, is_vision_available, logging, requires_backends if is_vision_available(): import PIL # soft dependency if is_pytesseract_available(): import pytesseract _A : str = logging.get_logger(__name__) def UpperCamelCase_ ( snake_case_ : List[Any] , snake_case_ : Tuple , snake_case_ : List[Any] ) -> List[str]: '''simple docstring''' return [ int(10_00 * (box[0] / width) ), int(10_00 * (box[1] / height) ), int(10_00 * (box[2] / width) ), int(10_00 * (box[3] / height) ), ] def UpperCamelCase_ ( snake_case_ : np.ndarray , snake_case_ : Optional[str] , snake_case_ : Optional[str] = None ) -> Optional[Any]: '''simple docstring''' __lowerCAmelCase = tesseract_config if tesseract_config is not None else '''''' # apply OCR __lowerCAmelCase = to_pil_image(_lowerCAmelCase ) __lowerCAmelCase = pil_image.size __lowerCAmelCase = pytesseract.image_to_data(_lowerCAmelCase , lang=_lowerCAmelCase , output_type="""dict""" , config=_lowerCAmelCase ) __lowerCAmelCase = data['''text'''], data['''left'''], data['''top'''], data['''width'''], data['''height'''] # filter empty words and corresponding coordinates __lowerCAmelCase = [idx for idx, word in enumerate(_lowerCAmelCase ) if not word.strip()] __lowerCAmelCase = [word for idx, word in enumerate(_lowerCAmelCase ) if idx not in irrelevant_indices] __lowerCAmelCase = [coord for idx, coord in enumerate(_lowerCAmelCase ) if idx not in irrelevant_indices] __lowerCAmelCase = [coord for idx, coord in enumerate(_lowerCAmelCase ) if idx not in irrelevant_indices] __lowerCAmelCase = [coord for idx, coord in enumerate(_lowerCAmelCase ) if idx not in irrelevant_indices] __lowerCAmelCase = [coord for idx, coord in enumerate(_lowerCAmelCase ) if idx not in irrelevant_indices] # turn coordinates into (left, top, left+width, top+height) format __lowerCAmelCase = [] for x, y, w, h in zip(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ): __lowerCAmelCase = [x, y, x + w, y + h] actual_boxes.append(_lowerCAmelCase ) # finally, normalize the bounding boxes __lowerCAmelCase = [] for box in actual_boxes: normalized_boxes.append(normalize_box(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ) ) assert len(_lowerCAmelCase ) == len(_lowerCAmelCase ), "Not as many words as there are bounding boxes" return words, normalized_boxes class _lowercase ( A__ ): '''simple docstring''' _SCREAMING_SNAKE_CASE : Tuple = ["""pixel_values"""] def __init__( self : Optional[int] , SCREAMING_SNAKE_CASE__ : bool = True , SCREAMING_SNAKE_CASE__ : Dict[str, int] = None , SCREAMING_SNAKE_CASE__ : PILImageResampling = PILImageResampling.BILINEAR , SCREAMING_SNAKE_CASE__ : bool = True , SCREAMING_SNAKE_CASE__ : Optional[str] = None , SCREAMING_SNAKE_CASE__ : Optional[str] = "" , **SCREAMING_SNAKE_CASE__ : str , ) -> None: super().__init__(**__snake_case ) __lowerCAmelCase = size if size is not None else {'''height''': 2_24, '''width''': 2_24} __lowerCAmelCase = get_size_dict(__snake_case ) __lowerCAmelCase = do_resize __lowerCAmelCase = size __lowerCAmelCase = resample __lowerCAmelCase = apply_ocr __lowerCAmelCase = ocr_lang __lowerCAmelCase = tesseract_config def a ( self : List[str] , SCREAMING_SNAKE_CASE__ : np.ndarray , SCREAMING_SNAKE_CASE__ : Dict[str, int] , SCREAMING_SNAKE_CASE__ : PILImageResampling = PILImageResampling.BILINEAR , SCREAMING_SNAKE_CASE__ : Optional[Union[str, ChannelDimension]] = None , **SCREAMING_SNAKE_CASE__ : List[str] , ) -> np.ndarray: __lowerCAmelCase = get_size_dict(__snake_case ) if "height" not in size or "width" not in size: raise ValueError(f"""The size dictionary must contain the keys 'height' and 'width'. Got {size.keys()}""" ) __lowerCAmelCase = (size['''height'''], size['''width''']) return resize(__snake_case , size=__snake_case , resample=__snake_case , data_format=__snake_case , **__snake_case ) def a ( self : Tuple , SCREAMING_SNAKE_CASE__ : ImageInput , SCREAMING_SNAKE_CASE__ : bool = None , SCREAMING_SNAKE_CASE__ : Dict[str, int] = None , SCREAMING_SNAKE_CASE__ : PILImageResampling = None , SCREAMING_SNAKE_CASE__ : bool = None , SCREAMING_SNAKE_CASE__ : Optional[str] = None , SCREAMING_SNAKE_CASE__ : Optional[str] = None , SCREAMING_SNAKE_CASE__ : Optional[Union[str, TensorType]] = None , SCREAMING_SNAKE_CASE__ : ChannelDimension = ChannelDimension.FIRST , **SCREAMING_SNAKE_CASE__ : List[Any] , ) -> PIL.Image.Image: __lowerCAmelCase = do_resize if do_resize is not None else self.do_resize __lowerCAmelCase = size if size is not None else self.size __lowerCAmelCase = get_size_dict(__snake_case ) __lowerCAmelCase = resample if resample is not None else self.resample __lowerCAmelCase = apply_ocr if apply_ocr is not None else self.apply_ocr __lowerCAmelCase = ocr_lang if ocr_lang is not None else self.ocr_lang __lowerCAmelCase = tesseract_config if tesseract_config is not None else self.tesseract_config __lowerCAmelCase = make_list_of_images(__snake_case ) if not valid_images(__snake_case ): raise ValueError( """Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """ """torch.Tensor, tf.Tensor or jax.ndarray.""" ) if do_resize and size is None: raise ValueError("""Size must be specified if do_resize is True.""" ) # All transformations expect numpy arrays. __lowerCAmelCase = [to_numpy_array(__snake_case ) for image in images] if apply_ocr: requires_backends(self , """pytesseract""" ) __lowerCAmelCase = [] __lowerCAmelCase = [] for image in images: __lowerCAmelCase = apply_tesseract(__snake_case , __snake_case , __snake_case ) words_batch.append(__snake_case ) boxes_batch.append(__snake_case ) if do_resize: __lowerCAmelCase = [self.resize(image=__snake_case , size=__snake_case , resample=__snake_case ) for image in images] # flip color channels from RGB to BGR (as Detectron2 requires this) __lowerCAmelCase = [flip_channel_order(__snake_case ) for image in images] __lowerCAmelCase = [to_channel_dimension_format(__snake_case , __snake_case ) for image in images] __lowerCAmelCase = BatchFeature(data={"""pixel_values""": images} , tensor_type=__snake_case ) if apply_ocr: __lowerCAmelCase = words_batch __lowerCAmelCase = boxes_batch return data
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'''simple docstring''' from math import cos, sin, sqrt, tau from audio_filters.iir_filter import IIRFilter def snake_case_ ( _lowerCAmelCase : int , _lowerCAmelCase : int , _lowerCAmelCase : float = 1 / sqrt(2 ) ) -> IIRFilter: UpperCAmelCase : Optional[int] = tau * frequency / samplerate UpperCAmelCase : List[Any] = sin(_lowerCAmelCase ) UpperCAmelCase : Optional[Any] = cos(_lowerCAmelCase ) UpperCAmelCase : int = _sin / (2 * q_factor) UpperCAmelCase : Any = (1 - _cos) / 2 UpperCAmelCase : List[Any] = 1 - _cos UpperCAmelCase : Union[str, Any] = 1 + alpha UpperCAmelCase : Any = -2 * _cos UpperCAmelCase : Dict = 1 - alpha UpperCAmelCase : Union[str, Any] = IIRFilter(2 ) filt.set_coefficients([aa, aa, aa] , [ba, ba, ba] ) return filt def snake_case_ ( _lowerCAmelCase : int , _lowerCAmelCase : int , _lowerCAmelCase : float = 1 / sqrt(2 ) ) -> IIRFilter: UpperCAmelCase : Any = tau * frequency / samplerate UpperCAmelCase : Tuple = sin(_lowerCAmelCase ) UpperCAmelCase : Tuple = cos(_lowerCAmelCase ) UpperCAmelCase : Dict = _sin / (2 * q_factor) UpperCAmelCase : int = (1 + _cos) / 2 UpperCAmelCase : List[Any] = -1 - _cos UpperCAmelCase : Tuple = 1 + alpha UpperCAmelCase : List[str] = -2 * _cos UpperCAmelCase : Optional[Any] = 1 - alpha UpperCAmelCase : Tuple = IIRFilter(2 ) filt.set_coefficients([aa, aa, aa] , [ba, ba, ba] ) return filt def snake_case_ ( _lowerCAmelCase : int , _lowerCAmelCase : int , _lowerCAmelCase : float = 1 / sqrt(2 ) ) -> IIRFilter: UpperCAmelCase : Optional[int] = tau * frequency / samplerate UpperCAmelCase : Optional[int] = sin(_lowerCAmelCase ) UpperCAmelCase : Tuple = cos(_lowerCAmelCase ) UpperCAmelCase : Optional[int] = _sin / (2 * q_factor) UpperCAmelCase : Union[str, Any] = _sin / 2 UpperCAmelCase : Any = 0 UpperCAmelCase : int = -ba UpperCAmelCase : Optional[Any] = 1 + alpha UpperCAmelCase : List[Any] = -2 * _cos UpperCAmelCase : Optional[Any] = 1 - alpha UpperCAmelCase : int = IIRFilter(2 ) filt.set_coefficients([aa, aa, aa] , [ba, ba, ba] ) return filt def snake_case_ ( _lowerCAmelCase : int , _lowerCAmelCase : int , _lowerCAmelCase : float = 1 / sqrt(2 ) ) -> IIRFilter: UpperCAmelCase : List[str] = tau * frequency / samplerate UpperCAmelCase : Union[str, Any] = sin(_lowerCAmelCase ) UpperCAmelCase : str = cos(_lowerCAmelCase ) UpperCAmelCase : Optional[Any] = _sin / (2 * q_factor) UpperCAmelCase : List[str] = 1 - alpha UpperCAmelCase : Any = -2 * _cos UpperCAmelCase : Optional[int] = 1 + alpha UpperCAmelCase : Union[str, Any] = IIRFilter(2 ) filt.set_coefficients([ba, ba, ba] , [ba, ba, ba] ) return filt def snake_case_ ( _lowerCAmelCase : int , _lowerCAmelCase : int , _lowerCAmelCase : float , _lowerCAmelCase : float = 1 / sqrt(2 ) , ) -> IIRFilter: UpperCAmelCase : Optional[Any] = tau * frequency / samplerate UpperCAmelCase : Union[str, Any] = sin(_lowerCAmelCase ) UpperCAmelCase : Optional[int] = cos(_lowerCAmelCase ) UpperCAmelCase : Dict = _sin / (2 * q_factor) UpperCAmelCase : str = 10 ** (gain_db / 40) UpperCAmelCase : int = 1 + alpha * big_a UpperCAmelCase : Union[str, Any] = -2 * _cos UpperCAmelCase : Optional[Any] = 1 - alpha * big_a UpperCAmelCase : Union[str, Any] = 1 + alpha / big_a UpperCAmelCase : Tuple = -2 * _cos UpperCAmelCase : Any = 1 - alpha / big_a UpperCAmelCase : Optional[Any] = IIRFilter(2 ) filt.set_coefficients([aa, aa, aa] , [ba, ba, ba] ) return filt def snake_case_ ( _lowerCAmelCase : int , _lowerCAmelCase : int , _lowerCAmelCase : float , _lowerCAmelCase : float = 1 / sqrt(2 ) , ) -> IIRFilter: UpperCAmelCase : Any = tau * frequency / samplerate UpperCAmelCase : Optional[int] = sin(_lowerCAmelCase ) UpperCAmelCase : Union[str, Any] = cos(_lowerCAmelCase ) UpperCAmelCase : str = _sin / (2 * q_factor) UpperCAmelCase : List[str] = 10 ** (gain_db / 40) UpperCAmelCase : Optional[int] = (big_a + 1) - (big_a - 1) * _cos UpperCAmelCase : int = (big_a + 1) + (big_a - 1) * _cos UpperCAmelCase : int = (big_a - 1) - (big_a + 1) * _cos UpperCAmelCase : Optional[int] = (big_a - 1) + (big_a + 1) * _cos UpperCAmelCase : str = 2 * sqrt(_lowerCAmelCase ) * alpha UpperCAmelCase : Dict = big_a * (pmc + aaa) UpperCAmelCase : Any = 2 * big_a * mpc UpperCAmelCase : Union[str, Any] = big_a * (pmc - aaa) UpperCAmelCase : Optional[int] = ppmc + aaa UpperCAmelCase : Optional[Any] = -2 * pmpc UpperCAmelCase : Optional[Any] = ppmc - aaa UpperCAmelCase : int = IIRFilter(2 ) filt.set_coefficients([aa, aa, aa] , [ba, ba, ba] ) return filt def snake_case_ ( _lowerCAmelCase : int , _lowerCAmelCase : int , _lowerCAmelCase : float , _lowerCAmelCase : float = 1 / sqrt(2 ) , ) -> IIRFilter: UpperCAmelCase : int = tau * frequency / samplerate UpperCAmelCase : Union[str, Any] = sin(_lowerCAmelCase ) UpperCAmelCase : Union[str, Any] = cos(_lowerCAmelCase ) UpperCAmelCase : Any = _sin / (2 * q_factor) UpperCAmelCase : int = 10 ** (gain_db / 40) UpperCAmelCase : List[str] = (big_a + 1) - (big_a - 1) * _cos UpperCAmelCase : Union[str, Any] = (big_a + 1) + (big_a - 1) * _cos UpperCAmelCase : Optional[Any] = (big_a - 1) - (big_a + 1) * _cos UpperCAmelCase : Union[str, Any] = (big_a - 1) + (big_a + 1) * _cos UpperCAmelCase : List[str] = 2 * sqrt(_lowerCAmelCase ) * alpha UpperCAmelCase : Any = big_a * (ppmc + aaa) UpperCAmelCase : str = -2 * big_a * pmpc UpperCAmelCase : List[Any] = big_a * (ppmc - aaa) UpperCAmelCase : Optional[Any] = pmc + aaa UpperCAmelCase : Any = 2 * mpc UpperCAmelCase : str = pmc - aaa UpperCAmelCase : Union[str, Any] = IIRFilter(2 ) filt.set_coefficients([aa, aa, aa] , [ba, ba, ba] ) return filt
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import random import unittest import torch from diffusers import IFImgaImgSuperResolutionPipeline from diffusers.utils import floats_tensor from diffusers.utils.import_utils import is_xformers_available from diffusers.utils.testing_utils import skip_mps, torch_device from ..pipeline_params import TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS from ..test_pipelines_common import PipelineTesterMixin from . import IFPipelineTesterMixin @skip_mps class __A ( A__ , A__ , unittest.TestCase ): '''simple docstring''' lowerCAmelCase_ = IFImgaImgSuperResolutionPipeline lowerCAmelCase_ = TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"""width""", """height"""} lowerCAmelCase_ = TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({"""original_image"""} ) lowerCAmelCase_ = PipelineTesterMixin.required_optional_params - {"""latents"""} def __lowerCamelCase ( self ): '''simple docstring''' return self._get_superresolution_dummy_components() def __lowerCamelCase ( self , __lowerCAmelCase , __lowerCAmelCase=0 ): '''simple docstring''' if str(__snake_case ).startswith('''mps''' ): lowerCamelCase__ = torch.manual_seed(__snake_case ) else: lowerCamelCase__ = torch.Generator(device=__snake_case ).manual_seed(__snake_case ) lowerCamelCase__ = floats_tensor((1, 3, 3_2, 3_2) , rng=random.Random(__snake_case ) ).to(__snake_case ) lowerCamelCase__ = floats_tensor((1, 3, 1_6, 1_6) , rng=random.Random(__snake_case ) ).to(__snake_case ) lowerCamelCase__ = { '''prompt''': '''A painting of a squirrel eating a burger''', '''image''': image, '''original_image''': original_image, '''generator''': generator, '''num_inference_steps''': 2, '''output_type''': '''numpy''', } return inputs @unittest.skipIf( torch_device != '''cuda''' or not is_xformers_available() , reason='''XFormers attention is only available with CUDA and `xformers` installed''' , ) def __lowerCamelCase ( self ): '''simple docstring''' self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3 ) def __lowerCamelCase ( self ): '''simple docstring''' self._test_save_load_optional_components() @unittest.skipIf(torch_device != '''cuda''' , reason='''float16 requires CUDA''' ) def __lowerCamelCase ( self ): '''simple docstring''' super().test_save_load_floataa(expected_max_diff=1E-1 ) def __lowerCamelCase ( self ): '''simple docstring''' self._test_attention_slicing_forward_pass(expected_max_diff=1E-2 ) def __lowerCamelCase ( self ): '''simple docstring''' self._test_save_load_local() def __lowerCamelCase ( self ): '''simple docstring''' self._test_inference_batch_single_identical( expected_max_diff=1E-2 , )
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'''simple docstring''' from __future__ import annotations def snake_case_ ( _lowerCAmelCase : str , _lowerCAmelCase : str ) -> bool: UpperCAmelCase : str = get_failure_array(_lowerCAmelCase ) # 2) Step through text searching for pattern UpperCAmelCase , UpperCAmelCase : Optional[Any] = 0, 0 # index into text, pattern while i < len(_lowerCAmelCase ): if pattern[j] == text[i]: if j == (len(_lowerCAmelCase ) - 1): return True j += 1 # if this is a prefix in our pattern # just go back far enough to continue elif j > 0: UpperCAmelCase : Optional[Any] = failure[j - 1] continue i += 1 return False def snake_case_ ( _lowerCAmelCase : str ) -> list[int]: UpperCAmelCase : Optional[Any] = [0] UpperCAmelCase : str = 0 UpperCAmelCase : List[str] = 1 while j < len(_lowerCAmelCase ): if pattern[i] == pattern[j]: i += 1 elif i > 0: UpperCAmelCase : Union[str, Any] = failure[i - 1] continue j += 1 failure.append(_lowerCAmelCase ) return failure if __name__ == "__main__": # Test 1) UpperCamelCase__: str = "abc1abc12" UpperCamelCase__: str = "alskfjaldsabc1abc1abc12k23adsfabcabc" UpperCamelCase__: Any = "alskfjaldsk23adsfabcabc" assert kmp(pattern, texta) and not kmp(pattern, texta) # Test 2) UpperCamelCase__: Tuple = "ABABX" UpperCamelCase__: Union[str, Any] = "ABABZABABYABABX" assert kmp(pattern, text) # Test 3) UpperCamelCase__: Any = "AAAB" UpperCamelCase__: str = "ABAAAAAB" assert kmp(pattern, text) # Test 4) UpperCamelCase__: int = "abcdabcy" UpperCamelCase__: Any = "abcxabcdabxabcdabcdabcy" assert kmp(pattern, text) # Test 5) UpperCamelCase__: List[str] = "aabaabaaa" assert get_failure_array(pattern) == [0, 1, 0, 1, 2, 3, 4, 5, 2]
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"""simple docstring""" from collections import OrderedDict from typing import Mapping from packaging import version from ...configuration_utils import PretrainedConfig from ...onnx import OnnxConfig from ...utils import logging UpperCAmelCase__ = logging.get_logger(__name__) UpperCAmelCase__ = { "google/vit-base-patch16-224": "https://huggingface.co/vit-base-patch16-224/resolve/main/config.json", # See all ViT models at https://huggingface.co/models?filter=vit } class lowerCAmelCase__ ( A__ ): __a = """vit""" def __init__( self : Dict , _lowerCamelCase : int=768 , _lowerCamelCase : Optional[int]=12 , _lowerCamelCase : Any=12 , _lowerCamelCase : Optional[Any]=3072 , _lowerCamelCase : Any="gelu" , _lowerCamelCase : str=0.0 , _lowerCamelCase : str=0.0 , _lowerCamelCase : Dict=0.0_2 , _lowerCamelCase : Optional[int]=1e-12 , _lowerCamelCase : List[str]=224 , _lowerCamelCase : Tuple=16 , _lowerCamelCase : Dict=3 , _lowerCamelCase : List[str]=True , _lowerCamelCase : Optional[int]=16 , **_lowerCamelCase : Dict , ): super().__init__(**__snake_case ) _snake_case = hidden_size _snake_case = num_hidden_layers _snake_case = num_attention_heads _snake_case = intermediate_size _snake_case = hidden_act _snake_case = hidden_dropout_prob _snake_case = attention_probs_dropout_prob _snake_case = initializer_range _snake_case = layer_norm_eps _snake_case = image_size _snake_case = patch_size _snake_case = num_channels _snake_case = qkv_bias _snake_case = encoder_stride class lowerCAmelCase__ ( A__ ): __a = version.parse("""1.11""" ) @property def lowercase ( self : List[Any] ): return OrderedDict( [ ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}), ] ) @property def lowercase ( self : Union[str, Any] ): return 1e-4
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'''simple docstring''' import argparse import json import os import fairseq import torch from fairseq.data import Dictionary from transformers import ( WavaVecaConfig, WavaVecaCTCTokenizer, WavaVecaFeatureExtractor, WavaVecaForCTC, WavaVecaForPreTraining, WavaVecaProcessor, logging, ) from transformers.models.wavaveca.modeling_wavaveca import WavaVecaForSequenceClassification logging.set_verbosity_info() UpperCamelCase__: int = logging.get_logger(__name__) UpperCamelCase__: Dict = { "post_extract_proj": "feature_projection.projection", "encoder.pos_conv.0": "encoder.pos_conv_embed.conv", "self_attn.k_proj": "encoder.layers.*.attention.k_proj", "self_attn.v_proj": "encoder.layers.*.attention.v_proj", "self_attn.q_proj": "encoder.layers.*.attention.q_proj", "self_attn.out_proj": "encoder.layers.*.attention.out_proj", "self_attn_layer_norm": "encoder.layers.*.layer_norm", "fc1": "encoder.layers.*.feed_forward.intermediate_dense", "fc2": "encoder.layers.*.feed_forward.output_dense", "final_layer_norm": "encoder.layers.*.final_layer_norm", "encoder.layer_norm": "encoder.layer_norm", "adapter_layer": "encoder.layers.*.adapter_layer", "w2v_model.layer_norm": "feature_projection.layer_norm", "quantizer.weight_proj": "quantizer.weight_proj", "quantizer.vars": "quantizer.codevectors", "project_q": "project_q", "final_proj": "project_hid", "w2v_encoder.proj": "lm_head", "mask_emb": "masked_spec_embed", "pooling_layer.linear": "projector", "pooling_layer.projection": "classifier", } UpperCamelCase__: Optional[Any] = [ "lm_head", "quantizer.weight_proj", "quantizer.codevectors", "project_q", "project_hid", "projector", "classifier", ] def snake_case_ ( _lowerCAmelCase : str ) -> Union[str, Any]: UpperCAmelCase : Optional[int] = {} with open(_lowerCAmelCase , '''r''' ) as file: for line_number, line in enumerate(_lowerCAmelCase ): UpperCAmelCase : List[str] = line.strip() if line: UpperCAmelCase : str = line.split() UpperCAmelCase : Union[str, Any] = line_number UpperCAmelCase : List[Any] = words[0] UpperCAmelCase : Union[str, Any] = value return result def snake_case_ ( _lowerCAmelCase : int , _lowerCAmelCase : Optional[Any] , _lowerCAmelCase : Optional[Any] , _lowerCAmelCase : Tuple , _lowerCAmelCase : str ) -> int: for attribute in key.split('''.''' ): UpperCAmelCase : Dict = getattr(_lowerCAmelCase , _lowerCAmelCase ) UpperCAmelCase : Dict = None for param_key in PARAM_MAPPING.keys(): if full_name.endswith(_lowerCAmelCase ): UpperCAmelCase : Any = PARAM_MAPPING[full_name.split('''.''' )[-1]] UpperCAmelCase : Dict = '''param''' if weight_type is not None and weight_type != "param": UpperCAmelCase : Optional[int] = getattr(_lowerCAmelCase , _lowerCAmelCase ).shape elif weight_type is not None and weight_type == "param": UpperCAmelCase : List[Any] = hf_pointer for attribute in hf_param_name.split('''.''' ): UpperCAmelCase : Optional[Any] = getattr(_lowerCAmelCase , _lowerCAmelCase ) UpperCAmelCase : int = shape_pointer.shape # let's reduce dimension UpperCAmelCase : Union[str, Any] = value[0] else: UpperCAmelCase : List[Any] = hf_pointer.shape if hf_shape != value.shape: raise ValueError( f"""Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be""" f""" {value.shape} for {full_name}""" ) if weight_type == "weight": UpperCAmelCase : int = value elif weight_type == "weight_g": UpperCAmelCase : str = value elif weight_type == "weight_v": UpperCAmelCase : Dict = value elif weight_type == "bias": UpperCAmelCase : str = value elif weight_type == "param": for attribute in hf_param_name.split('''.''' ): UpperCAmelCase : int = getattr(_lowerCAmelCase , _lowerCAmelCase ) UpperCAmelCase : Optional[int] = value else: UpperCAmelCase : Tuple = value logger.info(f"""{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.""" ) def snake_case_ ( _lowerCAmelCase : Optional[int] , _lowerCAmelCase : Any , _lowerCAmelCase : List[Any] , _lowerCAmelCase : Dict , _lowerCAmelCase : List[Any] ) -> List[Any]: UpperCAmelCase : List[str] = None for param_key in PARAM_MAPPING.keys(): if full_name.endswith(_lowerCAmelCase ): UpperCAmelCase : List[str] = PARAM_MAPPING[full_name.split('''.''' )[-1]] UpperCAmelCase : Any = '''param''' if weight_type is not None and weight_type != "param": UpperCAmelCase : Optional[int] = '''.'''.join([key, weight_type] ) elif weight_type is not None and weight_type == "param": UpperCAmelCase : Optional[int] = '''.'''.join([key, hf_param_name] ) else: UpperCAmelCase : List[Any] = key UpperCAmelCase : Tuple = value if '''lm_head''' in full_key else value[0] UpperCamelCase__: Tuple = { "W_a": "linear_1.weight", "W_b": "linear_2.weight", "b_a": "linear_1.bias", "b_b": "linear_2.bias", "ln_W": "norm.weight", "ln_b": "norm.bias", } def snake_case_ ( _lowerCAmelCase : str , _lowerCAmelCase : List[str] , _lowerCAmelCase : Any=None , _lowerCAmelCase : Optional[Any]=None ) -> int: UpperCAmelCase : List[Any] = False for key, mapped_key in MAPPING.items(): UpperCAmelCase : int = '''wav2vec2.''' + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key if key in name or key.split('''w2v_model.''' )[-1] == name.split('''.''' )[0]: UpperCAmelCase : Optional[Any] = True if "*" in mapped_key: UpperCAmelCase : Tuple = name.split(_lowerCAmelCase )[0].split('''.''' )[-2] UpperCAmelCase : List[Any] = mapped_key.replace('''*''' , _lowerCAmelCase ) if "weight_g" in name: UpperCAmelCase : str = '''weight_g''' elif "weight_v" in name: UpperCAmelCase : int = '''weight_v''' elif "bias" in name: UpperCAmelCase : int = '''bias''' elif "weight" in name: # TODO: don't match quantizer.weight_proj UpperCAmelCase : List[str] = '''weight''' else: UpperCAmelCase : Dict = None if hf_dict is not None: rename_dict(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ) else: set_recursively(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ) return is_used return is_used def snake_case_ ( _lowerCAmelCase : Union[str, Any] , _lowerCAmelCase : Optional[Any] , _lowerCAmelCase : Any ) -> Any: UpperCAmelCase : Dict = [] UpperCAmelCase : Dict = fairseq_model.state_dict() UpperCAmelCase : Union[str, Any] = hf_model.wavaveca.feature_extractor for name, value in fairseq_dict.items(): UpperCAmelCase : Dict = False if "conv_layers" in name: load_conv_layer( _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , hf_model.config.feat_extract_norm == '''group''' , ) UpperCAmelCase : Any = True else: UpperCAmelCase : Optional[Any] = load_wavaveca_layer(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ) if not is_used: unused_weights.append(_lowerCAmelCase ) logger.warning(f"""Unused weights: {unused_weights}""" ) def snake_case_ ( _lowerCAmelCase : Optional[int] , _lowerCAmelCase : Tuple , _lowerCAmelCase : Any , _lowerCAmelCase : Optional[int] , _lowerCAmelCase : Optional[int] ) -> Union[str, Any]: UpperCAmelCase : Any = full_name.split('''conv_layers.''' )[-1] UpperCAmelCase : Optional[int] = name.split('''.''' ) UpperCAmelCase : Tuple = int(items[0] ) UpperCAmelCase : Tuple = int(items[1] ) if type_id == 0: if "bias" in name: if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape: raise ValueError( f"""{full_name} has size {value.shape}, but""" f""" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.""" ) UpperCAmelCase : Tuple = value logger.info(f"""Feat extract conv layer {layer_id} was initialized from {full_name}.""" ) elif "weight" in name: if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape: raise ValueError( f"""{full_name} has size {value.shape}, but""" f""" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.""" ) UpperCAmelCase : Union[str, Any] = value logger.info(f"""Feat extract conv layer {layer_id} was initialized from {full_name}.""" ) elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm): if "bias" in name: if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape: raise ValueError( f"""{full_name} has size {value.shape}, but""" f""" {feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape} was found.""" ) UpperCAmelCase : Union[str, Any] = value logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.""" ) elif "weight" in name: if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape: raise ValueError( f"""{full_name} has size {value.shape}, but""" f""" {feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape} was found.""" ) UpperCAmelCase : List[str] = value logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.""" ) else: unused_weights.append(_lowerCAmelCase ) @torch.no_grad() def snake_case_ ( _lowerCAmelCase : Union[str, Any] , _lowerCAmelCase : List[Any] , _lowerCAmelCase : Dict=None , _lowerCAmelCase : List[Any]=None , _lowerCAmelCase : int=True , _lowerCAmelCase : Optional[int]=False ) -> Dict: if config_path is not None: UpperCAmelCase : List[str] = WavaVecaConfig.from_pretrained(_lowerCAmelCase ) else: UpperCAmelCase : List[Any] = WavaVecaConfig() if is_seq_class: UpperCAmelCase : Optional[Any] = read_txt_into_dict(_lowerCAmelCase ) UpperCAmelCase : Optional[int] = idalabel UpperCAmelCase : Optional[Any] = WavaVecaForSequenceClassification(_lowerCAmelCase ) UpperCAmelCase : Dict = WavaVecaFeatureExtractor( feature_size=1 , sampling_rate=16000 , padding_value=0 , do_normalize=_lowerCAmelCase , return_attention_mask=_lowerCAmelCase , ) feature_extractor.save_pretrained(_lowerCAmelCase ) elif is_finetuned: if dict_path: UpperCAmelCase : Dict = Dictionary.load(_lowerCAmelCase ) # important change bos & pad token id since CTC symbol is <pad> and # not <s> as in fairseq UpperCAmelCase : Any = target_dict.pad_index UpperCAmelCase : Tuple = target_dict.bos_index UpperCAmelCase : Optional[int] = target_dict.eos_index UpperCAmelCase : Union[str, Any] = len(target_dict.symbols ) UpperCAmelCase : Dict = os.path.join(_lowerCAmelCase , '''vocab.json''' ) if not os.path.isdir(_lowerCAmelCase ): logger.error('''--pytorch_dump_folder_path ({}) should be a directory'''.format(_lowerCAmelCase ) ) return os.makedirs(_lowerCAmelCase , exist_ok=_lowerCAmelCase ) UpperCAmelCase : List[Any] = target_dict.indices # fairseq has the <pad> and <s> switched UpperCAmelCase : List[str] = 0 UpperCAmelCase : List[str] = 1 with open(_lowerCAmelCase , '''w''' , encoding='''utf-8''' ) as vocab_handle: json.dump(_lowerCAmelCase , _lowerCAmelCase ) UpperCAmelCase : Optional[int] = WavaVecaCTCTokenizer( _lowerCAmelCase , unk_token=target_dict.unk_word , pad_token=target_dict.pad_word , bos_token=target_dict.bos_word , eos_token=target_dict.eos_word , word_delimiter_token='''|''' , do_lower_case=_lowerCAmelCase , ) UpperCAmelCase : int = True if config.feat_extract_norm == '''layer''' else False UpperCAmelCase : int = WavaVecaFeatureExtractor( feature_size=1 , sampling_rate=16000 , padding_value=0 , do_normalize=_lowerCAmelCase , return_attention_mask=_lowerCAmelCase , ) UpperCAmelCase : str = WavaVecaProcessor(feature_extractor=_lowerCAmelCase , tokenizer=_lowerCAmelCase ) processor.save_pretrained(_lowerCAmelCase ) UpperCAmelCase : Union[str, Any] = WavaVecaForCTC(_lowerCAmelCase ) else: UpperCAmelCase : Dict = WavaVecaForPreTraining(_lowerCAmelCase ) if is_finetuned or is_seq_class: UpperCAmelCase , UpperCAmelCase , UpperCAmelCase : List[str] = fairseq.checkpoint_utils.load_model_ensemble_and_task( [checkpoint_path] , arg_overrides={'''data''': '''/'''.join(dict_path.split('''/''' )[:-1] )} ) else: UpperCAmelCase : Optional[Any] = argparse.Namespace(task='''audio_pretraining''' ) UpperCAmelCase : List[Any] = fairseq.tasks.setup_task(_lowerCAmelCase ) UpperCAmelCase , UpperCAmelCase , UpperCAmelCase : int = fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path] , task=_lowerCAmelCase ) UpperCAmelCase : Optional[int] = model[0].eval() recursively_load_weights(_lowerCAmelCase , _lowerCAmelCase , not is_finetuned ) hf_wavavec.save_pretrained(_lowerCAmelCase ) if __name__ == "__main__": UpperCamelCase__: Dict = argparse.ArgumentParser() parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.") parser.add_argument("--checkpoint_path", default=None, type=str, help="Path to fairseq checkpoint") parser.add_argument("--dict_path", default=None, type=str, help="Path to dict of fine-tuned model") parser.add_argument("--config_path", default=None, type=str, help="Path to hf config.json of model to convert") parser.add_argument( "--not_finetuned", action="store_true", help="Whether the model to convert is a fine-tuned model or not" ) parser.add_argument( "--is_seq_class", action="store_true", help="Whether the model to convert is a fine-tuned sequence classification model or not", ) UpperCamelCase__: Any = parser.parse_args() UpperCamelCase__: int = not args.not_finetuned and not args.is_seq_class convert_wavaveca_checkpoint( args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, is_finetuned, args.is_seq_class, )
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import warnings from typing import List from unittest.mock import Mock import torch from torch.utils.data import DataLoader, IterableDataset, TensorDataset from accelerate.accelerator import Accelerator from accelerate.utils.dataclasses import DistributedType class __snake_case ( A__ ): def __init__( self : Union[str, Any] , _lowercase : Optional[int] ): """simple docstring""" SCREAMING_SNAKE_CASE__ = data def __iter__( self : Union[str, Any] ): """simple docstring""" for element in self.data: yield element def __SCREAMING_SNAKE_CASE ( __UpperCamelCase : str=True ) -> List[str]: """simple docstring""" SCREAMING_SNAKE_CASE__ = Accelerator(even_batches=_lowerCAmelCase ) assert accelerator.num_processes == 2, "this script expects that two GPUs are available" return accelerator def __SCREAMING_SNAKE_CASE ( __UpperCamelCase : Accelerator , __UpperCamelCase : int , __UpperCamelCase : int , __UpperCamelCase : bool = False ) -> List[Any]: """simple docstring""" if iterable: SCREAMING_SNAKE_CASE__ = DummyIterableDataset(torch.as_tensor(range(_lowerCAmelCase ) ) ) else: SCREAMING_SNAKE_CASE__ = TensorDataset(torch.as_tensor(range(_lowerCAmelCase ) ) ) SCREAMING_SNAKE_CASE__ = DataLoader(_lowerCAmelCase , batch_size=_lowerCAmelCase ) SCREAMING_SNAKE_CASE__ = accelerator.prepare(_lowerCAmelCase ) return dl def __SCREAMING_SNAKE_CASE ( __UpperCamelCase : Accelerator , __UpperCamelCase : int , __UpperCamelCase : int , __UpperCamelCase : List[int] , __UpperCamelCase : List[int] , ) -> Optional[int]: """simple docstring""" SCREAMING_SNAKE_CASE__ = create_dataloader(accelerator=_lowerCAmelCase , dataset_size=_lowerCAmelCase , batch_size=_lowerCAmelCase ) SCREAMING_SNAKE_CASE__ = [len(batch[0] ) for batch in dl] if accelerator.process_index == 0: assert batch_sizes == process_0_expected_batch_sizes elif accelerator.process_index == 1: assert batch_sizes == process_1_expected_batch_sizes def __SCREAMING_SNAKE_CASE ( ) -> Union[str, Any]: """simple docstring""" SCREAMING_SNAKE_CASE__ = create_accelerator() # without padding, we would expect a different number of batches verify_dataloader_batch_sizes( _lowerCAmelCase , dataset_size=3 , batch_size=1 , process_0_expected_batch_sizes=[1, 1] , process_1_expected_batch_sizes=[1, 1] , ) # without padding, we would expect the same number of batches, but different sizes verify_dataloader_batch_sizes( _lowerCAmelCase , dataset_size=7 , batch_size=2 , process_0_expected_batch_sizes=[2, 2] , process_1_expected_batch_sizes=[2, 2] , ) def __SCREAMING_SNAKE_CASE ( ) -> Dict: """simple docstring""" SCREAMING_SNAKE_CASE__ = create_accelerator(even_batches=_lowerCAmelCase ) verify_dataloader_batch_sizes( _lowerCAmelCase , dataset_size=3 , batch_size=1 , process_0_expected_batch_sizes=[1, 1] , process_1_expected_batch_sizes=[1] , ) verify_dataloader_batch_sizes( _lowerCAmelCase , dataset_size=7 , batch_size=2 , process_0_expected_batch_sizes=[2, 2] , process_1_expected_batch_sizes=[2, 1] , ) def __SCREAMING_SNAKE_CASE ( ) -> Tuple: """simple docstring""" SCREAMING_SNAKE_CASE__ = create_accelerator(even_batches=_lowerCAmelCase ) SCREAMING_SNAKE_CASE__ = torch.nn.Linear(1 , 1 ) SCREAMING_SNAKE_CASE__ = accelerator.prepare(_lowerCAmelCase ) SCREAMING_SNAKE_CASE__ = create_dataloader(_lowerCAmelCase , dataset_size=3 , batch_size=1 ) SCREAMING_SNAKE_CASE__ = [] with accelerator.join_uneven_inputs([ddp_model] ): for batch_idx, batch in enumerate(_lowerCAmelCase ): SCREAMING_SNAKE_CASE__ = ddp_model(batch[0].float() ) SCREAMING_SNAKE_CASE__ = output.sum() loss.backward() batch_idxs.append(_lowerCAmelCase ) accelerator.wait_for_everyone() if accelerator.process_index == 0: assert batch_idxs == [0, 1] elif accelerator.process_index == 1: assert batch_idxs == [0] def __SCREAMING_SNAKE_CASE ( __UpperCamelCase : Optional[int] ) -> List[str]: """simple docstring""" with warnings.catch_warnings(record=_lowerCAmelCase ) as w: with accelerator.join_uneven_inputs([Mock()] ): pass assert issubclass(w[-1].category , _lowerCAmelCase ) assert "only supported for multi-GPU" in str(w[-1].message ) def __SCREAMING_SNAKE_CASE ( ) -> Dict: """simple docstring""" SCREAMING_SNAKE_CASE__ = True SCREAMING_SNAKE_CASE__ = False SCREAMING_SNAKE_CASE__ = create_accelerator(even_batches=_lowerCAmelCase ) SCREAMING_SNAKE_CASE__ = torch.nn.Linear(1 , 1 ) SCREAMING_SNAKE_CASE__ = accelerator.prepare(_lowerCAmelCase ) SCREAMING_SNAKE_CASE__ = create_dataloader(_lowerCAmelCase , dataset_size=3 , batch_size=1 ) SCREAMING_SNAKE_CASE__ = create_dataloader(_lowerCAmelCase , dataset_size=3 , batch_size=1 ) with accelerator.join_uneven_inputs([ddp_model] , even_batches=_lowerCAmelCase ): SCREAMING_SNAKE_CASE__ = train_dl.batch_sampler.even_batches SCREAMING_SNAKE_CASE__ = valid_dl.batch_sampler.even_batches assert train_dl_overridden_value == overridden_even_batches assert valid_dl_overridden_value == overridden_even_batches assert train_dl.batch_sampler.even_batches == default_even_batches assert valid_dl.batch_sampler.even_batches == default_even_batches def __SCREAMING_SNAKE_CASE ( ) -> Dict: """simple docstring""" SCREAMING_SNAKE_CASE__ = True SCREAMING_SNAKE_CASE__ = False SCREAMING_SNAKE_CASE__ = create_accelerator(even_batches=_lowerCAmelCase ) SCREAMING_SNAKE_CASE__ = torch.nn.Linear(1 , 1 ) SCREAMING_SNAKE_CASE__ = accelerator.prepare(_lowerCAmelCase ) create_dataloader(_lowerCAmelCase , dataset_size=3 , batch_size=1 , iterable=_lowerCAmelCase ) SCREAMING_SNAKE_CASE__ = create_dataloader(_lowerCAmelCase , dataset_size=3 , batch_size=1 ) with warnings.catch_warnings(): warnings.filterwarnings("""ignore""" ) try: with accelerator.join_uneven_inputs([ddp_model] , even_batches=_lowerCAmelCase ): SCREAMING_SNAKE_CASE__ = batch_dl.batch_sampler.even_batches except AttributeError: # ensure attribute error is not raised when processing iterable dl raise AssertionError assert batch_dl_overridden_value == overridden_even_batches assert batch_dl.batch_sampler.even_batches == default_even_batches def __SCREAMING_SNAKE_CASE ( ) -> Dict: """simple docstring""" SCREAMING_SNAKE_CASE__ = create_accelerator() SCREAMING_SNAKE_CASE__ = torch.nn.Linear(1 , 1 ) SCREAMING_SNAKE_CASE__ = accelerator.prepare(_lowerCAmelCase ) create_dataloader(_lowerCAmelCase , dataset_size=3 , batch_size=1 , iterable=_lowerCAmelCase ) with warnings.catch_warnings(record=_lowerCAmelCase ) as w: with accelerator.join_uneven_inputs([ddp_model] , even_batches=_lowerCAmelCase ): pass assert issubclass(w[-1].category , _lowerCAmelCase ) assert "only supported for map-style datasets" in str(w[-1].message ) def __SCREAMING_SNAKE_CASE ( ) -> Optional[int]: """simple docstring""" SCREAMING_SNAKE_CASE__ = create_accelerator() accelerator.print("""Test that even_batches variable ensures uniform batches across processes""" ) test_default_ensures_even_batch_sizes() accelerator.print("""Run tests with even_batches disabled""" ) test_can_disable_even_batches() accelerator.print("""Test joining uneven inputs""" ) test_can_join_uneven_inputs() accelerator.print("""Test overriding even_batches when joining uneven inputs""" ) test_join_can_override_even_batches() accelerator.print("""Test overriding even_batches for mixed dataloader types""" ) test_join_can_override_for_mixed_type_dataloaders() accelerator.print("""Test overriding even_batches raises a warning for iterable dataloaders""" ) test_join_raises_warning_for_iterable_when_overriding_even_batches() accelerator.print("""Test join with non DDP distributed raises warning""" ) SCREAMING_SNAKE_CASE__ = accelerator.state.distributed_type SCREAMING_SNAKE_CASE__ = DistributedType.FSDP test_join_raises_warning_for_non_ddp_distributed(_lowerCAmelCase ) SCREAMING_SNAKE_CASE__ = original_state if __name__ == "__main__": main()
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'''simple docstring''' import os import tempfile import unittest import numpy as np from diffusers.utils import is_flax_available from diffusers.utils.testing_utils import require_flax, slow if is_flax_available(): import jax import jax.numpy as jnp from flax.jax_utils import replicate from flax.training.common_utils import shard from diffusers import FlaxDDIMScheduler, FlaxDiffusionPipeline, FlaxStableDiffusionPipeline @require_flax class SCREAMING_SNAKE_CASE( unittest.TestCase ): """simple docstring""" def A ( self : str ) -> int: with tempfile.TemporaryDirectory() as tmpdirname: # pipeline has Flax weights UpperCAmelCase : Optional[Any] = FlaxDiffusionPipeline.from_pretrained( '''hf-internal-testing/tiny-stable-diffusion-pipe''' , safety_checker=__snake_case , cache_dir=__snake_case ) UpperCAmelCase : str = [t[-1] for t in os.walk(os.path.join(__snake_case , os.listdir(__snake_case )[0] , '''snapshots''' ) )] UpperCAmelCase : str = [item for sublist in all_root_files for item in sublist] # None of the downloaded files should be a PyTorch file even if we have some here: # https://huggingface.co/hf-internal-testing/tiny-stable-diffusion-pipe/blob/main/unet/diffusion_pytorch_model.bin assert not any(f.endswith('''.bin''' ) for f in files ) @slow @require_flax class SCREAMING_SNAKE_CASE( unittest.TestCase ): """simple docstring""" def A ( self : List[str] ) -> Dict: UpperCAmelCase , UpperCAmelCase : str = FlaxStableDiffusionPipeline.from_pretrained( '''hf-internal-testing/tiny-stable-diffusion-pipe''' , safety_checker=__snake_case ) UpperCAmelCase : List[Any] = ( '''A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of''' ''' field, close up, split lighting, cinematic''' ) UpperCAmelCase : List[str] = jax.random.PRNGKey(0 ) UpperCAmelCase : Optional[Any] = 4 UpperCAmelCase : Optional[Any] = jax.device_count() UpperCAmelCase : Tuple = num_samples * [prompt] UpperCAmelCase : int = pipeline.prepare_inputs(__snake_case ) # shard inputs and rng UpperCAmelCase : Tuple = replicate(__snake_case ) UpperCAmelCase : Any = jax.random.split(__snake_case , __snake_case ) UpperCAmelCase : Optional[Any] = shard(__snake_case ) UpperCAmelCase : Optional[int] = pipeline(__snake_case , __snake_case , __snake_case , __snake_case , jit=__snake_case ).images assert images.shape == (num_samples, 1, 64, 64, 3) if jax.device_count() == 8: assert np.abs(np.abs(images[0, 0, :2, :2, -2:] , dtype=np.floataa ).sum() - 4.1_51_47_45 ) < 1E-3 assert np.abs(np.abs(__snake_case , dtype=np.floataa ).sum() - 4_99_47.8_75 ) < 5E-1 UpperCAmelCase : Union[str, Any] = pipeline.numpy_to_pil(np.asarray(images.reshape((num_samples,) + images.shape[-3:] ) ) ) assert len(__snake_case ) == num_samples def A ( self : List[Any] ) -> List[str]: UpperCAmelCase , UpperCAmelCase : Union[str, Any] = FlaxStableDiffusionPipeline.from_pretrained( '''CompVis/stable-diffusion-v1-4''' , revision='''flax''' , safety_checker=__snake_case ) UpperCAmelCase : Dict = ( '''A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of''' ''' field, close up, split lighting, cinematic''' ) UpperCAmelCase : Optional[Any] = jax.random.PRNGKey(0 ) UpperCAmelCase : Any = 50 UpperCAmelCase : Union[str, Any] = jax.device_count() UpperCAmelCase : int = num_samples * [prompt] UpperCAmelCase : Union[str, Any] = pipeline.prepare_inputs(__snake_case ) # shard inputs and rng UpperCAmelCase : Dict = replicate(__snake_case ) UpperCAmelCase : int = jax.random.split(__snake_case , __snake_case ) UpperCAmelCase : Tuple = shard(__snake_case ) UpperCAmelCase : Tuple = pipeline(__snake_case , __snake_case , __snake_case , __snake_case , jit=__snake_case ).images assert images.shape == (num_samples, 1, 512, 512, 3) if jax.device_count() == 8: assert np.abs((np.abs(images[0, 0, :2, :2, -2:] , dtype=np.floataa ).sum() - 0.05_65_24_01) ) < 1E-3 assert np.abs((np.abs(__snake_case , dtype=np.floataa ).sum() - 2_38_38_08.2) ) < 5E-1 def A ( self : int ) -> Dict: UpperCAmelCase , UpperCAmelCase : List[str] = FlaxStableDiffusionPipeline.from_pretrained( '''CompVis/stable-diffusion-v1-4''' , revision='''bf16''' , dtype=jnp.bfloataa , safety_checker=__snake_case ) UpperCAmelCase : Dict = ( '''A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of''' ''' field, close up, split lighting, cinematic''' ) UpperCAmelCase : Union[str, Any] = jax.random.PRNGKey(0 ) UpperCAmelCase : List[str] = 50 UpperCAmelCase : Union[str, Any] = jax.device_count() UpperCAmelCase : List[Any] = num_samples * [prompt] UpperCAmelCase : int = pipeline.prepare_inputs(__snake_case ) # shard inputs and rng UpperCAmelCase : Tuple = replicate(__snake_case ) UpperCAmelCase : List[Any] = jax.random.split(__snake_case , __snake_case ) UpperCAmelCase : Optional[int] = shard(__snake_case ) UpperCAmelCase : Optional[Any] = pipeline(__snake_case , __snake_case , __snake_case , __snake_case , jit=__snake_case ).images assert images.shape == (num_samples, 1, 512, 512, 3) if jax.device_count() == 8: assert np.abs((np.abs(images[0, 0, :2, :2, -2:] , dtype=np.floataa ).sum() - 0.04_00_39_06) ) < 1E-3 assert np.abs((np.abs(__snake_case , dtype=np.floataa ).sum() - 2_37_35_16.75) ) < 5E-1 def A ( self : int ) -> Any: UpperCAmelCase , UpperCAmelCase : Dict = FlaxStableDiffusionPipeline.from_pretrained( '''CompVis/stable-diffusion-v1-4''' , revision='''bf16''' , dtype=jnp.bfloataa ) UpperCAmelCase : List[str] = ( '''A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of''' ''' field, close up, split lighting, cinematic''' ) UpperCAmelCase : List[str] = jax.random.PRNGKey(0 ) UpperCAmelCase : Union[str, Any] = 50 UpperCAmelCase : Optional[int] = jax.device_count() UpperCAmelCase : List[str] = num_samples * [prompt] UpperCAmelCase : Dict = pipeline.prepare_inputs(__snake_case ) # shard inputs and rng UpperCAmelCase : Tuple = replicate(__snake_case ) UpperCAmelCase : Any = jax.random.split(__snake_case , __snake_case ) UpperCAmelCase : str = shard(__snake_case ) UpperCAmelCase : Optional[int] = pipeline(__snake_case , __snake_case , __snake_case , __snake_case , jit=__snake_case ).images assert images.shape == (num_samples, 1, 512, 512, 3) if jax.device_count() == 8: assert np.abs((np.abs(images[0, 0, :2, :2, -2:] , dtype=np.floataa ).sum() - 0.04_00_39_06) ) < 1E-3 assert np.abs((np.abs(__snake_case , dtype=np.floataa ).sum() - 2_37_35_16.75) ) < 5E-1 def A ( self : Tuple ) -> Optional[Any]: UpperCAmelCase : int = FlaxDDIMScheduler( beta_start=0.0_00_85 , beta_end=0.0_12 , beta_schedule='''scaled_linear''' , set_alpha_to_one=__snake_case , steps_offset=1 , ) UpperCAmelCase , UpperCAmelCase : Tuple = FlaxStableDiffusionPipeline.from_pretrained( '''CompVis/stable-diffusion-v1-4''' , revision='''bf16''' , dtype=jnp.bfloataa , scheduler=__snake_case , safety_checker=__snake_case , ) UpperCAmelCase : Tuple = scheduler.create_state() UpperCAmelCase : Dict = scheduler_state UpperCAmelCase : str = ( '''A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of''' ''' field, close up, split lighting, cinematic''' ) UpperCAmelCase : int = jax.random.PRNGKey(0 ) UpperCAmelCase : Union[str, Any] = 50 UpperCAmelCase : Optional[Any] = jax.device_count() UpperCAmelCase : Any = num_samples * [prompt] UpperCAmelCase : Dict = pipeline.prepare_inputs(__snake_case ) # shard inputs and rng UpperCAmelCase : str = replicate(__snake_case ) UpperCAmelCase : List[str] = jax.random.split(__snake_case , __snake_case ) UpperCAmelCase : Optional[int] = shard(__snake_case ) UpperCAmelCase : Dict = pipeline(__snake_case , __snake_case , __snake_case , __snake_case , jit=__snake_case ).images assert images.shape == (num_samples, 1, 512, 512, 3) if jax.device_count() == 8: assert np.abs((np.abs(images[0, 0, :2, :2, -2:] , dtype=np.floataa ).sum() - 0.0_45_04_39_45) ) < 1E-3 assert np.abs((np.abs(__snake_case , dtype=np.floataa ).sum() - 2_34_76_93.5) ) < 5E-1 def A ( self : Any ) -> Tuple: UpperCAmelCase : List[Any] = ( '''A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of''' ''' field, close up, split lighting, cinematic''' ) UpperCAmelCase : Union[str, Any] = jax.device_count() UpperCAmelCase : List[Any] = num_samples * [prompt] UpperCAmelCase : str = jax.random.split(jax.random.PRNGKey(0 ) , __snake_case ) UpperCAmelCase , UpperCAmelCase : Tuple = FlaxStableDiffusionPipeline.from_pretrained( '''CompVis/stable-diffusion-v1-4''' , revision='''bf16''' , dtype=jnp.bfloataa , safety_checker=__snake_case , ) UpperCAmelCase : Dict = replicate(__snake_case ) UpperCAmelCase : Optional[Any] = pipeline.prepare_inputs(__snake_case ) UpperCAmelCase : List[str] = shard(__snake_case ) UpperCAmelCase : Any = pipeline(__snake_case , __snake_case , __snake_case , jit=__snake_case ).images assert images.shape == (num_samples, 1, 512, 512, 3) UpperCAmelCase : Optional[int] = images[2, 0, 256, 10:17, 1] # With memory efficient attention UpperCAmelCase , UpperCAmelCase : Any = FlaxStableDiffusionPipeline.from_pretrained( '''CompVis/stable-diffusion-v1-4''' , revision='''bf16''' , dtype=jnp.bfloataa , safety_checker=__snake_case , use_memory_efficient_attention=__snake_case , ) UpperCAmelCase : int = replicate(__snake_case ) UpperCAmelCase : int = pipeline.prepare_inputs(__snake_case ) UpperCAmelCase : List[Any] = shard(__snake_case ) UpperCAmelCase : Optional[Any] = pipeline(__snake_case , __snake_case , __snake_case , jit=__snake_case ).images assert images_eff.shape == (num_samples, 1, 512, 512, 3) UpperCAmelCase : int = images[2, 0, 256, 10:17, 1] # I checked the results visually and they are very similar. However, I saw that the max diff is `1` and the `sum` # over the 8 images is exactly `256`, which is very suspicious. Testing a random slice for now. assert abs(slice_eff - slice ).max() < 1E-2
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from collections import OrderedDict from typing import TYPE_CHECKING, Any, Mapping, Optional, Union from ...configuration_utils import PretrainedConfig from ...onnx import OnnxConfig, OnnxSeqaSeqConfigWithPast from ...utils import logging if TYPE_CHECKING: from ...feature_extraction_utils import FeatureExtractionMixin from ...tokenization_utils_base import PreTrainedTokenizerBase from ...utils import TensorType __snake_case :Any = logging.get_logger(__name__) __snake_case :Union[str, Any] = { "openai/whisper-base": "https://huggingface.co/openai/whisper-base/resolve/main/config.json", } # fmt: off __snake_case :Optional[Any] = [ 1, 2, 7, 8, 9, 10, 14, 25, 26, 27, 28, 29, 31, 58, 59, 60, 61, 62, 63, 90, 91, 92, 93, 357, 366, 438, 532, 685, 705, 796, 930, 1058, 1220, 1267, 1279, 1303, 1343, 1377, 1391, 1635, 1782, 1875, 2162, 2361, 2488, 3467, 4008, 4211, 4600, 4808, 5299, 5855, 6329, 7203, 9609, 9959, 1_0563, 1_0786, 1_1420, 1_1709, 1_1907, 1_3163, 1_3697, 1_3700, 1_4808, 1_5306, 1_6410, 1_6791, 1_7992, 1_9203, 1_9510, 2_0724, 2_2305, 2_2935, 2_7007, 3_0109, 3_0420, 3_3409, 3_4949, 4_0283, 4_0493, 4_0549, 4_7282, 4_9146, 5_0257, 5_0359, 5_0360, 5_0361 ] __snake_case :List[str] = [ 1, 2, 7, 8, 9, 10, 14, 25, 26, 27, 28, 29, 31, 58, 59, 60, 61, 62, 63, 90, 91, 92, 93, 359, 503, 522, 542, 873, 893, 902, 918, 922, 931, 1350, 1853, 1982, 2460, 2627, 3246, 3253, 3268, 3536, 3846, 3961, 4183, 4667, 6585, 6647, 7273, 9061, 9383, 1_0428, 1_0929, 1_1938, 1_2033, 1_2331, 1_2562, 1_3793, 1_4157, 1_4635, 1_5265, 1_5618, 1_6553, 1_6604, 1_8362, 1_8956, 2_0075, 2_1675, 2_2520, 2_6130, 2_6161, 2_6435, 2_8279, 2_9464, 3_1650, 3_2302, 3_2470, 3_6865, 4_2863, 4_7425, 4_9870, 5_0254, 5_0258, 5_0360, 5_0361, 5_0362 ] class _A ( A__ ): UpperCamelCase__ : Tuple = '''whisper''' UpperCamelCase__ : Optional[int] = ['''past_key_values'''] UpperCamelCase__ : List[str] = {'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''} def __init__( self : Dict , __SCREAMING_SNAKE_CASE : Tuple=51_865 , __SCREAMING_SNAKE_CASE : Union[str, Any]=80 , __SCREAMING_SNAKE_CASE : str=6 , __SCREAMING_SNAKE_CASE : int=4 , __SCREAMING_SNAKE_CASE : Optional[Any]=6 , __SCREAMING_SNAKE_CASE : Tuple=4 , __SCREAMING_SNAKE_CASE : Optional[Any]=1_536 , __SCREAMING_SNAKE_CASE : Tuple=1_536 , __SCREAMING_SNAKE_CASE : Union[str, Any]=0.0 , __SCREAMING_SNAKE_CASE : List[str]=0.0 , __SCREAMING_SNAKE_CASE : Optional[int]=50_257 , __SCREAMING_SNAKE_CASE : Dict=True , __SCREAMING_SNAKE_CASE : int=True , __SCREAMING_SNAKE_CASE : Optional[int]="gelu" , __SCREAMING_SNAKE_CASE : Tuple=256 , __SCREAMING_SNAKE_CASE : Any=0.0 , __SCREAMING_SNAKE_CASE : List[Any]=0.0 , __SCREAMING_SNAKE_CASE : str=0.0 , __SCREAMING_SNAKE_CASE : str=0.02 , __SCREAMING_SNAKE_CASE : List[str]=False , __SCREAMING_SNAKE_CASE : Any=1_500 , __SCREAMING_SNAKE_CASE : List[Any]=448 , __SCREAMING_SNAKE_CASE : Any=50_256 , __SCREAMING_SNAKE_CASE : List[Any]=50_256 , __SCREAMING_SNAKE_CASE : Tuple=50_256 , __SCREAMING_SNAKE_CASE : Optional[Any]=None , __SCREAMING_SNAKE_CASE : str=[220, 50_256] , __SCREAMING_SNAKE_CASE : Tuple=False , __SCREAMING_SNAKE_CASE : Dict=256 , __SCREAMING_SNAKE_CASE : Tuple=False , __SCREAMING_SNAKE_CASE : Tuple=0.05 , __SCREAMING_SNAKE_CASE : int=10 , __SCREAMING_SNAKE_CASE : str=2 , __SCREAMING_SNAKE_CASE : Optional[Any]=0.0 , __SCREAMING_SNAKE_CASE : str=10 , __SCREAMING_SNAKE_CASE : Optional[int]=0 , __SCREAMING_SNAKE_CASE : Optional[int]=7 , **__SCREAMING_SNAKE_CASE : Optional[int] , ): '''simple docstring''' __a = vocab_size __a = num_mel_bins __a = d_model __a = encoder_layers __a = encoder_attention_heads __a = decoder_layers __a = decoder_attention_heads __a = decoder_ffn_dim __a = encoder_ffn_dim __a = dropout __a = attention_dropout __a = activation_dropout __a = activation_function __a = init_std __a = encoder_layerdrop __a = decoder_layerdrop __a = use_cache __a = encoder_layers __a = scale_embedding # scale factor will be sqrt(d_model) if True __a = max_source_positions __a = max_target_positions # Audio Classification-specific parameters. Feel free to ignore for other classes. __a = classifier_proj_size __a = use_weighted_layer_sum # fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779 __a = apply_spec_augment __a = mask_time_prob __a = mask_time_length __a = mask_time_min_masks __a = mask_feature_prob __a = mask_feature_length __a = mask_feature_min_masks __a = median_filter_width super().__init__( pad_token_id=__snake_case , bos_token_id=__snake_case , eos_token_id=__snake_case , is_encoder_decoder=__snake_case , decoder_start_token_id=__snake_case , suppress_tokens=__snake_case , begin_suppress_tokens=__snake_case , **__snake_case , ) class _A ( A__ ): @property def _lowerCamelCase ( self : Optional[int]): '''simple docstring''' __a = OrderedDict( [ ('''input_features''', {0: '''batch''', 1: '''feature_size''', 2: '''encoder_sequence'''}), ]) if self.use_past: __a = {0: '''batch'''} else: __a = {0: '''batch''', 1: '''decoder_sequence'''} if self.use_past: self.fill_with_past_key_values_(__snake_case , direction='''inputs''') return common_inputs def _lowerCamelCase ( self : List[str] , __SCREAMING_SNAKE_CASE : Union["PreTrainedTokenizerBase", "FeatureExtractionMixin"] , __SCREAMING_SNAKE_CASE : int = -1 , __SCREAMING_SNAKE_CASE : int = -1 , __SCREAMING_SNAKE_CASE : bool = False , __SCREAMING_SNAKE_CASE : Optional["TensorType"] = None , __SCREAMING_SNAKE_CASE : int = 22_050 , __SCREAMING_SNAKE_CASE : float = 5.0 , __SCREAMING_SNAKE_CASE : int = 220 , ): '''simple docstring''' __a = OrderedDict() __a = OnnxConfig.generate_dummy_inputs( self , preprocessor=preprocessor.feature_extractor , batch_size=__snake_case , framework=__snake_case , sampling_rate=__snake_case , time_duration=__snake_case , frequency=__snake_case , ) __a = encoder_inputs['''input_features'''].shape[2] __a = encoder_sequence_length // 2 if self.use_past else seq_length __a = super().generate_dummy_inputs( preprocessor.tokenizer , __snake_case , __snake_case , __snake_case , __snake_case) __a = encoder_inputs.pop('''input_features''') __a = decoder_inputs.pop('''decoder_input_ids''') if "past_key_values" in decoder_inputs: __a = decoder_inputs.pop('''past_key_values''') return dummy_inputs @property def _lowerCamelCase ( self : List[Any]): '''simple docstring''' return 1E-3
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'''simple docstring''' import random from .binary_exp_mod import bin_exp_mod def snake_case_ ( _lowerCAmelCase : Tuple , _lowerCAmelCase : Optional[Any]=1000 ) -> int: if n < 2: return False if n % 2 == 0: return n == 2 # this means n is odd UpperCAmelCase : str = n - 1 UpperCAmelCase : List[Any] = 0 while d % 2 == 0: d /= 2 exp += 1 # n - 1=d*(2**exp) UpperCAmelCase : List[str] = 0 while count < prec: UpperCAmelCase : int = random.randint(2 , n - 1 ) UpperCAmelCase : List[str] = bin_exp_mod(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ) if b != 1: UpperCAmelCase : int = True for _ in range(_lowerCAmelCase ): if b == n - 1: UpperCAmelCase : Dict = False break UpperCAmelCase : str = b * b b %= n if flag: return False count += 1 return True if __name__ == "__main__": UpperCamelCase__: Optional[int] = abs(int(input("Enter bound : ").strip())) print("Here's the list of primes:") print(", ".join(str(i) for i in range(n + 1) if is_prime_big(i)))
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from typing import TYPE_CHECKING from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available lowercase__ :Optional[Any] = { "configuration_clap": [ "CLAP_PRETRAINED_MODEL_ARCHIVE_LIST", "ClapAudioConfig", "ClapConfig", "ClapTextConfig", ], "processing_clap": ["ClapProcessor"], } try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: lowercase__ :Dict = [ "CLAP_PRETRAINED_MODEL_ARCHIVE_LIST", "ClapModel", "ClapPreTrainedModel", "ClapTextModel", "ClapTextModelWithProjection", "ClapAudioModel", "ClapAudioModelWithProjection", ] lowercase__ :Optional[Any] = ["ClapFeatureExtractor"] if TYPE_CHECKING: from .configuration_clap import ( CLAP_PRETRAINED_MODEL_ARCHIVE_LIST, ClapAudioConfig, ClapConfig, ClapTextConfig, ) from .processing_clap import ClapProcessor try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .feature_extraction_clap import ClapFeatureExtractor from .modeling_clap import ( CLAP_PRETRAINED_MODEL_ARCHIVE_LIST, ClapAudioModel, ClapAudioModelWithProjection, ClapModel, ClapPreTrainedModel, ClapTextModel, ClapTextModelWithProjection, ) else: import sys lowercase__ :Any = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
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'''simple docstring''' import argparse import json from pathlib import Path import requests import torch from huggingface_hub import cached_download, hf_hub_url from PIL import Image from transformers import DPTConfig, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTImageProcessor from transformers.utils import logging logging.set_verbosity_info() UpperCamelCase__: Optional[int] = logging.get_logger(__name__) def snake_case_ ( _lowerCAmelCase : Optional[int] ) -> Optional[int]: UpperCAmelCase : Tuple = DPTConfig(embedding_type='''hybrid''' ) if "large" in checkpoint_url: UpperCAmelCase : Tuple = 1024 UpperCAmelCase : List[Any] = 4096 UpperCAmelCase : str = 24 UpperCAmelCase : List[Any] = 16 UpperCAmelCase : str = [5, 11, 17, 23] UpperCAmelCase : List[Any] = [256, 512, 1024, 1024] UpperCAmelCase : Tuple = (1, 384, 384) if "nyu" or "midas" in checkpoint_url: UpperCAmelCase : Optional[Any] = 768 UpperCAmelCase : Tuple = [1, 1, 1, 0.5] UpperCAmelCase : int = [256, 512, 768, 768] UpperCAmelCase : Any = 150 UpperCAmelCase : Tuple = 16 UpperCAmelCase : Any = (1, 384, 384) UpperCAmelCase : Optional[Any] = False UpperCAmelCase : Tuple = '''project''' if "ade" in checkpoint_url: UpperCAmelCase : Any = True UpperCAmelCase : str = 768 UpperCAmelCase : Optional[int] = [1, 1, 1, 0.5] UpperCAmelCase : List[Any] = 150 UpperCAmelCase : List[Any] = 16 UpperCAmelCase : str = '''huggingface/label-files''' UpperCAmelCase : Tuple = '''ade20k-id2label.json''' UpperCAmelCase : Any = json.load(open(cached_download(hf_hub_url(_lowerCAmelCase , _lowerCAmelCase , repo_type='''dataset''' ) ) , '''r''' ) ) UpperCAmelCase : Optional[Any] = {int(_lowerCAmelCase ): v for k, v in idalabel.items()} UpperCAmelCase : List[Any] = idalabel UpperCAmelCase : Optional[int] = {v: k for k, v in idalabel.items()} UpperCAmelCase : Union[str, Any] = [1, 150, 480, 480] return config, expected_shape def snake_case_ ( _lowerCAmelCase : Union[str, Any] ) -> int: UpperCAmelCase : List[str] = ['''pretrained.model.head.weight''', '''pretrained.model.head.bias'''] for k in ignore_keys: state_dict.pop(_lowerCAmelCase , _lowerCAmelCase ) def snake_case_ ( _lowerCAmelCase : Tuple ) -> Any: if ( "pretrained.model" in name and "cls_token" not in name and "pos_embed" not in name and "patch_embed" not in name ): UpperCAmelCase : Tuple = name.replace('''pretrained.model''' , '''dpt.encoder''' ) if "pretrained.model" in name: UpperCAmelCase : Union[str, Any] = name.replace('''pretrained.model''' , '''dpt.embeddings''' ) if "patch_embed" in name: UpperCAmelCase : int = name.replace('''patch_embed''' , '''''' ) if "pos_embed" in name: UpperCAmelCase : Tuple = name.replace('''pos_embed''' , '''position_embeddings''' ) if "attn.proj" in name: UpperCAmelCase : Any = name.replace('''attn.proj''' , '''attention.output.dense''' ) if "proj" in name and "project" not in name: UpperCAmelCase : str = name.replace('''proj''' , '''projection''' ) if "blocks" in name: UpperCAmelCase : Any = name.replace('''blocks''' , '''layer''' ) if "mlp.fc1" in name: UpperCAmelCase : Optional[int] = name.replace('''mlp.fc1''' , '''intermediate.dense''' ) if "mlp.fc2" in name: UpperCAmelCase : Optional[Any] = name.replace('''mlp.fc2''' , '''output.dense''' ) if "norm1" in name and "backbone" not in name: UpperCAmelCase : Dict = name.replace('''norm1''' , '''layernorm_before''' ) if "norm2" in name and "backbone" not in name: UpperCAmelCase : Tuple = name.replace('''norm2''' , '''layernorm_after''' ) if "scratch.output_conv" in name: UpperCAmelCase : Tuple = name.replace('''scratch.output_conv''' , '''head''' ) if "scratch" in name: UpperCAmelCase : str = name.replace('''scratch''' , '''neck''' ) if "layer1_rn" in name: UpperCAmelCase : Dict = name.replace('''layer1_rn''' , '''convs.0''' ) if "layer2_rn" in name: UpperCAmelCase : int = name.replace('''layer2_rn''' , '''convs.1''' ) if "layer3_rn" in name: UpperCAmelCase : Tuple = name.replace('''layer3_rn''' , '''convs.2''' ) if "layer4_rn" in name: UpperCAmelCase : int = name.replace('''layer4_rn''' , '''convs.3''' ) if "refinenet" in name: UpperCAmelCase : List[str] = int(name[len('''neck.refinenet''' ) : len('''neck.refinenet''' ) + 1] ) # tricky here: we need to map 4 to 0, 3 to 1, 2 to 2 and 1 to 3 UpperCAmelCase : str = name.replace(f"""refinenet{layer_idx}""" , f"""fusion_stage.layers.{abs(layer_idx-4 )}""" ) if "out_conv" in name: UpperCAmelCase : List[str] = name.replace('''out_conv''' , '''projection''' ) if "resConfUnit1" in name: UpperCAmelCase : Union[str, Any] = name.replace('''resConfUnit1''' , '''residual_layer1''' ) if "resConfUnit2" in name: UpperCAmelCase : Any = name.replace('''resConfUnit2''' , '''residual_layer2''' ) if "conv1" in name: UpperCAmelCase : Optional[int] = name.replace('''conv1''' , '''convolution1''' ) if "conv2" in name: UpperCAmelCase : Tuple = name.replace('''conv2''' , '''convolution2''' ) # readout blocks if "pretrained.act_postprocess1.0.project.0" in name: UpperCAmelCase : Dict = name.replace('''pretrained.act_postprocess1.0.project.0''' , '''neck.reassemble_stage.readout_projects.0.0''' ) if "pretrained.act_postprocess2.0.project.0" in name: UpperCAmelCase : int = name.replace('''pretrained.act_postprocess2.0.project.0''' , '''neck.reassemble_stage.readout_projects.1.0''' ) if "pretrained.act_postprocess3.0.project.0" in name: UpperCAmelCase : Any = name.replace('''pretrained.act_postprocess3.0.project.0''' , '''neck.reassemble_stage.readout_projects.2.0''' ) if "pretrained.act_postprocess4.0.project.0" in name: UpperCAmelCase : Optional[Any] = name.replace('''pretrained.act_postprocess4.0.project.0''' , '''neck.reassemble_stage.readout_projects.3.0''' ) # resize blocks if "pretrained.act_postprocess1.3" in name: UpperCAmelCase : List[Any] = name.replace('''pretrained.act_postprocess1.3''' , '''neck.reassemble_stage.layers.0.projection''' ) if "pretrained.act_postprocess1.4" in name: UpperCAmelCase : Any = name.replace('''pretrained.act_postprocess1.4''' , '''neck.reassemble_stage.layers.0.resize''' ) if "pretrained.act_postprocess2.3" in name: UpperCAmelCase : Optional[int] = name.replace('''pretrained.act_postprocess2.3''' , '''neck.reassemble_stage.layers.1.projection''' ) if "pretrained.act_postprocess2.4" in name: UpperCAmelCase : str = name.replace('''pretrained.act_postprocess2.4''' , '''neck.reassemble_stage.layers.1.resize''' ) if "pretrained.act_postprocess3.3" in name: UpperCAmelCase : List[str] = name.replace('''pretrained.act_postprocess3.3''' , '''neck.reassemble_stage.layers.2.projection''' ) if "pretrained.act_postprocess4.3" in name: UpperCAmelCase : Tuple = name.replace('''pretrained.act_postprocess4.3''' , '''neck.reassemble_stage.layers.3.projection''' ) if "pretrained.act_postprocess4.4" in name: UpperCAmelCase : int = name.replace('''pretrained.act_postprocess4.4''' , '''neck.reassemble_stage.layers.3.resize''' ) if "pretrained" in name: UpperCAmelCase : Optional[int] = name.replace('''pretrained''' , '''dpt''' ) if "bn" in name: UpperCAmelCase : Dict = name.replace('''bn''' , '''batch_norm''' ) if "head" in name: UpperCAmelCase : Any = name.replace('''head''' , '''head.head''' ) if "encoder.norm" in name: UpperCAmelCase : Optional[int] = name.replace('''encoder.norm''' , '''layernorm''' ) if "auxlayer" in name: UpperCAmelCase : Union[str, Any] = name.replace('''auxlayer''' , '''auxiliary_head.head''' ) if "backbone" in name: UpperCAmelCase : List[Any] = name.replace('''backbone''' , '''backbone.bit.encoder''' ) if ".." in name: UpperCAmelCase : Optional[int] = name.replace('''..''' , '''.''' ) if "stem.conv" in name: UpperCAmelCase : Optional[Any] = name.replace('''stem.conv''' , '''bit.embedder.convolution''' ) if "blocks" in name: UpperCAmelCase : Optional[int] = name.replace('''blocks''' , '''layers''' ) if "convolution" in name and "backbone" in name: UpperCAmelCase : List[Any] = name.replace('''convolution''' , '''conv''' ) if "layer" in name and "backbone" in name: UpperCAmelCase : List[str] = name.replace('''layer''' , '''layers''' ) if "backbone.bit.encoder.bit" in name: UpperCAmelCase : List[Any] = name.replace('''backbone.bit.encoder.bit''' , '''backbone.bit''' ) if "embedder.conv" in name: UpperCAmelCase : List[Any] = name.replace('''embedder.conv''' , '''embedder.convolution''' ) if "backbone.bit.encoder.stem.norm" in name: UpperCAmelCase : Tuple = name.replace('''backbone.bit.encoder.stem.norm''' , '''backbone.bit.embedder.norm''' ) return name def snake_case_ ( _lowerCAmelCase : List[str] , _lowerCAmelCase : List[Any] ) -> Optional[Any]: for i in range(config.num_hidden_layers ): # read in weights + bias of input projection layer (in timm, this is a single matrix + bias) UpperCAmelCase : Optional[int] = state_dict.pop(f"""dpt.encoder.layer.{i}.attn.qkv.weight""" ) UpperCAmelCase : Tuple = state_dict.pop(f"""dpt.encoder.layer.{i}.attn.qkv.bias""" ) # next, add query, keys and values (in that order) to the state dict UpperCAmelCase : Tuple = in_proj_weight[: config.hidden_size, :] UpperCAmelCase : int = in_proj_bias[: config.hidden_size] UpperCAmelCase : List[str] = in_proj_weight[ config.hidden_size : config.hidden_size * 2, : ] UpperCAmelCase : List[str] = in_proj_bias[ config.hidden_size : config.hidden_size * 2 ] UpperCAmelCase : str = in_proj_weight[ -config.hidden_size :, : ] UpperCAmelCase : Union[str, Any] = in_proj_bias[-config.hidden_size :] def snake_case_ ( ) -> List[str]: UpperCAmelCase : Optional[int] = '''http://images.cocodataset.org/val2017/000000039769.jpg''' UpperCAmelCase : Optional[int] = Image.open(requests.get(_lowerCAmelCase , stream=_lowerCAmelCase ).raw ) return im @torch.no_grad() def snake_case_ ( _lowerCAmelCase : Optional[Any] , _lowerCAmelCase : Union[str, Any] , _lowerCAmelCase : str , _lowerCAmelCase : Union[str, Any] , _lowerCAmelCase : List[str] ) -> Any: UpperCAmelCase , UpperCAmelCase : int = get_dpt_config(_lowerCAmelCase ) # load original state_dict from URL # state_dict = torch.hub.load_state_dict_from_url(checkpoint_url, map_location="cpu") UpperCAmelCase : List[Any] = torch.load(_lowerCAmelCase , map_location='''cpu''' ) # remove certain keys remove_ignore_keys_(_lowerCAmelCase ) # rename keys for key in state_dict.copy().keys(): UpperCAmelCase : Any = state_dict.pop(_lowerCAmelCase ) UpperCAmelCase : List[Any] = val # read in qkv matrices read_in_q_k_v(_lowerCAmelCase , _lowerCAmelCase ) # load HuggingFace model UpperCAmelCase : Optional[Any] = DPTForSemanticSegmentation(_lowerCAmelCase ) if '''ade''' in checkpoint_url else DPTForDepthEstimation(_lowerCAmelCase ) model.load_state_dict(_lowerCAmelCase ) model.eval() # Check outputs on an image UpperCAmelCase : int = 480 if '''ade''' in checkpoint_url else 384 UpperCAmelCase : List[Any] = DPTImageProcessor(size=_lowerCAmelCase ) UpperCAmelCase : Dict = prepare_img() UpperCAmelCase : Optional[int] = image_processor(_lowerCAmelCase , return_tensors='''pt''' ) # forward pass UpperCAmelCase : Any = model(**_lowerCAmelCase ).logits if '''ade''' in checkpoint_url else model(**_lowerCAmelCase ).predicted_depth if show_prediction: UpperCAmelCase : Dict = ( torch.nn.functional.interpolate( outputs.unsqueeze(1 ) , size=(image.size[1], image.size[0]) , mode='''bicubic''' , align_corners=_lowerCAmelCase , ) .squeeze() .cpu() .numpy() ) Image.fromarray((prediction / prediction.max()) * 255 ).show() if pytorch_dump_folder_path is not None: Path(_lowerCAmelCase ).mkdir(exist_ok=_lowerCAmelCase ) print(f"""Saving model to {pytorch_dump_folder_path}""" ) model.save_pretrained(_lowerCAmelCase ) print(f"""Saving image processor to {pytorch_dump_folder_path}""" ) image_processor.save_pretrained(_lowerCAmelCase ) if push_to_hub: model.push_to_hub('''ybelkada/dpt-hybrid-midas''' ) image_processor.push_to_hub('''ybelkada/dpt-hybrid-midas''' ) if __name__ == "__main__": UpperCamelCase__: Dict = argparse.ArgumentParser() # Required parameters parser.add_argument( "--checkpoint_url", default="https://github.com/intel-isl/DPT/releases/download/1_0/dpt_large-midas-2f21e586.pt", type=str, help="URL of the original DPT checkpoint you'd like to convert.", ) parser.add_argument( "--pytorch_dump_folder_path", default=None, type=str, required=False, help="Path to the output PyTorch model directory.", ) parser.add_argument( "--push_to_hub", action="store_true", ) parser.add_argument( "--model_name", default="dpt-large", type=str, help="Name of the model, in case you're pushing to the hub.", ) parser.add_argument( "--show_prediction", action="store_true", ) UpperCamelCase__: Tuple = parser.parse_args() convert_dpt_checkpoint( args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name, args.show_prediction )
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import os import unittest from transformers import MobileBertTokenizer, MobileBertTokenizerFast from transformers.models.bert.tokenization_bert import ( VOCAB_FILES_NAMES, BasicTokenizer, WordpieceTokenizer, _is_control, _is_punctuation, _is_whitespace, ) from transformers.testing_utils import require_tokenizers, slow from ...test_tokenization_common import TokenizerTesterMixin, filter_non_english @require_tokenizers class SCREAMING_SNAKE_CASE_ ( A__ , unittest.TestCase ): __lowerCAmelCase = MobileBertTokenizer __lowerCAmelCase = MobileBertTokenizerFast __lowerCAmelCase = True __lowerCAmelCase = True __lowerCAmelCase = filter_non_english __lowerCAmelCase = """google/mobilebert-uncased""" def lowerCamelCase_ ( self : Any ): """simple docstring""" super().setUp() UpperCamelCase = [ '''[UNK]''', '''[CLS]''', '''[SEP]''', '''[PAD]''', '''[MASK]''', '''want''', '''##want''', '''##ed''', '''wa''', '''un''', '''runn''', '''##ing''', ''',''', '''low''', '''lowest''', ] UpperCamelCase = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["""vocab_file"""] ) with open(self.vocab_file , """w""" , encoding="""utf-8""" ) as vocab_writer: vocab_writer.write("""""".join([x + """\n""" for x in vocab_tokens] ) ) UpperCamelCase = [ (tokenizer_def[0], self.pre_trained_model_path, tokenizer_def[2]) # else the 'google/' prefix is stripped for tokenizer_def in self.tokenizers_list ] def lowerCamelCase_ ( self : Optional[Any] , lowerCamelCase_ : Union[str, Any] ): """simple docstring""" UpperCamelCase = '''UNwant\u00E9d,running''' UpperCamelCase = '''unwanted, running''' return input_text, output_text def lowerCamelCase_ ( self : Optional[Any] ): """simple docstring""" UpperCamelCase = self.tokenizer_class(self.vocab_file ) UpperCamelCase = tokenizer.tokenize("""UNwant\u00E9d,running""" ) self.assertListEqual(__snake_case , ["""un""", """##want""", """##ed""", """,""", """runn""", """##ing"""] ) self.assertListEqual(tokenizer.convert_tokens_to_ids(__snake_case ) , [9, 6, 7, 12, 10, 11] ) def lowerCamelCase_ ( self : Any ): """simple docstring""" if not self.test_rust_tokenizer: return UpperCamelCase = self.get_tokenizer() UpperCamelCase = self.get_rust_tokenizer() UpperCamelCase = '''UNwant\u00E9d,running''' UpperCamelCase = tokenizer.tokenize(__snake_case ) UpperCamelCase = rust_tokenizer.tokenize(__snake_case ) self.assertListEqual(__snake_case , __snake_case ) UpperCamelCase = tokenizer.encode(__snake_case , add_special_tokens=__snake_case ) UpperCamelCase = rust_tokenizer.encode(__snake_case , add_special_tokens=__snake_case ) self.assertListEqual(__snake_case , __snake_case ) UpperCamelCase = self.get_rust_tokenizer() UpperCamelCase = tokenizer.encode(__snake_case ) UpperCamelCase = rust_tokenizer.encode(__snake_case ) self.assertListEqual(__snake_case , __snake_case ) # With lower casing UpperCamelCase = self.get_tokenizer(do_lower_case=__snake_case ) UpperCamelCase = self.get_rust_tokenizer(do_lower_case=__snake_case ) UpperCamelCase = '''UNwant\u00E9d,running''' UpperCamelCase = tokenizer.tokenize(__snake_case ) UpperCamelCase = rust_tokenizer.tokenize(__snake_case ) self.assertListEqual(__snake_case , __snake_case ) UpperCamelCase = tokenizer.encode(__snake_case , add_special_tokens=__snake_case ) UpperCamelCase = rust_tokenizer.encode(__snake_case , add_special_tokens=__snake_case ) self.assertListEqual(__snake_case , __snake_case ) UpperCamelCase = self.get_rust_tokenizer() UpperCamelCase = tokenizer.encode(__snake_case ) UpperCamelCase = rust_tokenizer.encode(__snake_case ) self.assertListEqual(__snake_case , __snake_case ) def lowerCamelCase_ ( self : List[str] ): """simple docstring""" UpperCamelCase = BasicTokenizer() self.assertListEqual(tokenizer.tokenize("""ah\u535A\u63A8zz""" ) , ["""ah""", """\u535A""", """\u63A8""", """zz"""] ) def lowerCamelCase_ ( self : Optional[int] ): """simple docstring""" UpperCamelCase = BasicTokenizer(do_lower_case=__snake_case ) self.assertListEqual( tokenizer.tokenize(""" \tHeLLo!how \n Are yoU? """ ) , ["""hello""", """!""", """how""", """are""", """you""", """?"""] ) self.assertListEqual(tokenizer.tokenize("""H\u00E9llo""" ) , ["""hello"""] ) def lowerCamelCase_ ( self : Optional[int] ): """simple docstring""" UpperCamelCase = BasicTokenizer(do_lower_case=__snake_case , strip_accents=__snake_case ) self.assertListEqual( tokenizer.tokenize(""" \tHäLLo!how \n Are yoU? """ ) , ["""hällo""", """!""", """how""", """are""", """you""", """?"""] ) self.assertListEqual(tokenizer.tokenize("""H\u00E9llo""" ) , ["""h\u00E9llo"""] ) def lowerCamelCase_ ( self : Union[str, Any] ): """simple docstring""" UpperCamelCase = BasicTokenizer(do_lower_case=__snake_case , strip_accents=__snake_case ) self.assertListEqual( tokenizer.tokenize(""" \tHäLLo!how \n Are yoU? """ ) , ["""hallo""", """!""", """how""", """are""", """you""", """?"""] ) self.assertListEqual(tokenizer.tokenize("""H\u00E9llo""" ) , ["""hello"""] ) def lowerCamelCase_ ( self : str ): """simple docstring""" UpperCamelCase = BasicTokenizer(do_lower_case=__snake_case ) self.assertListEqual( tokenizer.tokenize(""" \tHäLLo!how \n Are yoU? """ ) , ["""hallo""", """!""", """how""", """are""", """you""", """?"""] ) self.assertListEqual(tokenizer.tokenize("""H\u00E9llo""" ) , ["""hello"""] ) def lowerCamelCase_ ( self : Union[str, Any] ): """simple docstring""" UpperCamelCase = BasicTokenizer(do_lower_case=__snake_case ) self.assertListEqual( tokenizer.tokenize(""" \tHeLLo!how \n Are yoU? """ ) , ["""HeLLo""", """!""", """how""", """Are""", """yoU""", """?"""] ) def lowerCamelCase_ ( self : Tuple ): """simple docstring""" UpperCamelCase = BasicTokenizer(do_lower_case=__snake_case , strip_accents=__snake_case ) self.assertListEqual( tokenizer.tokenize(""" \tHäLLo!how \n Are yoU? """ ) , ["""HäLLo""", """!""", """how""", """Are""", """yoU""", """?"""] ) def lowerCamelCase_ ( self : Optional[int] ): """simple docstring""" UpperCamelCase = BasicTokenizer(do_lower_case=__snake_case , strip_accents=__snake_case ) self.assertListEqual( tokenizer.tokenize(""" \tHäLLo!how \n Are yoU? """ ) , ["""HaLLo""", """!""", """how""", """Are""", """yoU""", """?"""] ) def lowerCamelCase_ ( self : Optional[Any] ): """simple docstring""" UpperCamelCase = BasicTokenizer(do_lower_case=__snake_case , never_split=["""[UNK]"""] ) self.assertListEqual( tokenizer.tokenize(""" \tHeLLo!how \n Are yoU? [UNK]""" ) , ["""HeLLo""", """!""", """how""", """Are""", """yoU""", """?""", """[UNK]"""] ) def lowerCamelCase_ ( self : List[Any] ): """simple docstring""" UpperCamelCase = ['''[UNK]''', '''[CLS]''', '''[SEP]''', '''want''', '''##want''', '''##ed''', '''wa''', '''un''', '''runn''', '''##ing'''] UpperCamelCase = {} for i, token in enumerate(__snake_case ): UpperCamelCase = i UpperCamelCase = WordpieceTokenizer(vocab=__snake_case , unk_token="""[UNK]""" ) self.assertListEqual(tokenizer.tokenize("""""" ) , [] ) self.assertListEqual(tokenizer.tokenize("""unwanted running""" ) , ["""un""", """##want""", """##ed""", """runn""", """##ing"""] ) self.assertListEqual(tokenizer.tokenize("""unwantedX running""" ) , ["""[UNK]""", """runn""", """##ing"""] ) def lowerCamelCase_ ( self : Union[str, Any] ): """simple docstring""" self.assertTrue(_is_whitespace(""" """ ) ) self.assertTrue(_is_whitespace("""\t""" ) ) self.assertTrue(_is_whitespace("""\r""" ) ) self.assertTrue(_is_whitespace("""\n""" ) ) self.assertTrue(_is_whitespace("""\u00A0""" ) ) self.assertFalse(_is_whitespace("""A""" ) ) self.assertFalse(_is_whitespace("""-""" ) ) def lowerCamelCase_ ( self : Union[str, Any] ): """simple docstring""" self.assertTrue(_is_control("""\u0005""" ) ) self.assertFalse(_is_control("""A""" ) ) self.assertFalse(_is_control(""" """ ) ) self.assertFalse(_is_control("""\t""" ) ) self.assertFalse(_is_control("""\r""" ) ) def lowerCamelCase_ ( self : Optional[int] ): """simple docstring""" self.assertTrue(_is_punctuation("""-""" ) ) self.assertTrue(_is_punctuation("""$""" ) ) self.assertTrue(_is_punctuation("""`""" ) ) self.assertTrue(_is_punctuation(""".""" ) ) self.assertFalse(_is_punctuation("""A""" ) ) self.assertFalse(_is_punctuation(""" """ ) ) def lowerCamelCase_ ( self : Union[str, Any] ): """simple docstring""" UpperCamelCase = self.get_tokenizer() UpperCamelCase = self.get_rust_tokenizer() # Example taken from the issue https://github.com/huggingface/tokenizers/issues/340 self.assertListEqual([tokenizer.tokenize(__snake_case ) for t in ["""Test""", """\xad""", """test"""]] , [["""[UNK]"""], [], ["""[UNK]"""]] ) self.assertListEqual( [rust_tokenizer.tokenize(__snake_case ) for t in ["""Test""", """\xad""", """test"""]] , [["""[UNK]"""], [], ["""[UNK]"""]] ) @slow def lowerCamelCase_ ( self : Union[str, Any] ): """simple docstring""" UpperCamelCase = self.tokenizer_class.from_pretrained("""google/mobilebert-uncased""" ) UpperCamelCase = tokenizer.encode("""sequence builders""" , add_special_tokens=__snake_case ) UpperCamelCase = tokenizer.encode("""multi-sequence build""" , add_special_tokens=__snake_case ) UpperCamelCase = tokenizer.build_inputs_with_special_tokens(__snake_case ) UpperCamelCase = tokenizer.build_inputs_with_special_tokens(__snake_case , __snake_case ) assert encoded_sentence == [101] + text + [102] assert encoded_pair == [101] + text + [102] + text_a + [102] def lowerCamelCase_ ( self : Optional[Any] ): """simple docstring""" for tokenizer, pretrained_name, kwargs in self.tokenizers_list: with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})""" ): UpperCamelCase = self.rust_tokenizer_class.from_pretrained(__snake_case , **__snake_case ) UpperCamelCase = f"""A, naïve {tokenizer_r.mask_token} AllenNLP sentence.""" UpperCamelCase = tokenizer_r.encode_plus( __snake_case , return_attention_mask=__snake_case , return_token_type_ids=__snake_case , return_offsets_mapping=__snake_case , add_special_tokens=__snake_case , ) UpperCamelCase = tokenizer_r.do_lower_case if hasattr(__snake_case , """do_lower_case""" ) else False UpperCamelCase = ( [ ((0, 0), tokenizer_r.cls_token), ((0, 1), '''A'''), ((1, 2), ''','''), ((3, 5), '''na'''), ((5, 6), '''##ï'''), ((6, 8), '''##ve'''), ((9, 15), tokenizer_r.mask_token), ((16, 21), '''Allen'''), ((21, 23), '''##NL'''), ((23, 24), '''##P'''), ((25, 33), '''sentence'''), ((33, 34), '''.'''), ((0, 0), tokenizer_r.sep_token), ] if not do_lower_case else [ ((0, 0), tokenizer_r.cls_token), ((0, 1), '''a'''), ((1, 2), ''','''), ((3, 8), '''naive'''), ((9, 15), tokenizer_r.mask_token), ((16, 21), '''allen'''), ((21, 23), '''##nl'''), ((23, 24), '''##p'''), ((25, 33), '''sentence'''), ((33, 34), '''.'''), ((0, 0), tokenizer_r.sep_token), ] ) self.assertEqual( [e[1] for e in expected_results] , tokenizer_r.convert_ids_to_tokens(tokens["""input_ids"""] ) ) self.assertEqual([e[0] for e in expected_results] , tokens["""offset_mapping"""] ) def lowerCamelCase_ ( self : Optional[int] ): """simple docstring""" UpperCamelCase = ['''的''', '''人''', '''有'''] UpperCamelCase = ''''''.join(__snake_case ) for tokenizer, pretrained_name, kwargs in self.tokenizers_list: with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})""" ): UpperCamelCase = True UpperCamelCase = self.tokenizer_class.from_pretrained(__snake_case , **__snake_case ) UpperCamelCase = self.rust_tokenizer_class.from_pretrained(__snake_case , **__snake_case ) UpperCamelCase = tokenizer_p.encode(__snake_case , add_special_tokens=__snake_case ) UpperCamelCase = tokenizer_r.encode(__snake_case , add_special_tokens=__snake_case ) UpperCamelCase = tokenizer_r.convert_ids_to_tokens(__snake_case ) UpperCamelCase = tokenizer_p.convert_ids_to_tokens(__snake_case ) # it is expected that each Chinese character is not preceded by "##" self.assertListEqual(__snake_case , __snake_case ) self.assertListEqual(__snake_case , __snake_case ) UpperCamelCase = False UpperCamelCase = self.rust_tokenizer_class.from_pretrained(__snake_case , **__snake_case ) UpperCamelCase = self.tokenizer_class.from_pretrained(__snake_case , **__snake_case ) UpperCamelCase = tokenizer_r.encode(__snake_case , add_special_tokens=__snake_case ) UpperCamelCase = tokenizer_p.encode(__snake_case , add_special_tokens=__snake_case ) UpperCamelCase = tokenizer_r.convert_ids_to_tokens(__snake_case ) UpperCamelCase = tokenizer_p.convert_ids_to_tokens(__snake_case ) # it is expected that only the first Chinese character is not preceded by "##". UpperCamelCase = [ f"""##{token}""" if idx != 0 else token for idx, token in enumerate(__snake_case ) ] self.assertListEqual(__snake_case , __snake_case ) self.assertListEqual(__snake_case , __snake_case )
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'''simple docstring''' # coding=utf-8 # Copyright 2020 The HuggingFace Inc. team. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # this script dumps information about the environment import os import sys import transformers UpperCamelCase__: Optional[int] = "3" print("Python version:", sys.version) print("transformers version:", transformers.__version__) try: import torch print("Torch version:", torch.__version__) print("Cuda available:", torch.cuda.is_available()) print("Cuda version:", torch.version.cuda) print("CuDNN version:", torch.backends.cudnn.version()) print("Number of GPUs available:", torch.cuda.device_count()) print("NCCL version:", torch.cuda.nccl.version()) except ImportError: print("Torch version:", None) try: import deepspeed print("DeepSpeed version:", deepspeed.__version__) except ImportError: print("DeepSpeed version:", None) try: import tensorflow as tf print("TensorFlow version:", tf.__version__) print("TF GPUs available:", bool(tf.config.list_physical_devices("GPU"))) print("Number of TF GPUs available:", len(tf.config.list_physical_devices("GPU"))) except ImportError: print("TensorFlow version:", None)
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'''simple docstring''' from math import factorial __a = {str(digit): factorial(digit) for digit in range(10)} def __snake_case( _lowerCAmelCase ) -> int: if not isinstance(_lowerCAmelCase , _lowerCAmelCase ): raise TypeError("""Parameter number must be int""" ) if number < 0: raise ValueError("""Parameter number must be greater than or equal to 0""" ) # Converts number in string to iterate on its digits and adds its factorial. return sum(DIGIT_FACTORIAL[digit] for digit in str(_lowerCAmelCase ) ) def __snake_case( _lowerCAmelCase = 60 , _lowerCAmelCase = 1_000_000 ) -> int: if not isinstance(_lowerCAmelCase , _lowerCAmelCase ) or not isinstance(_lowerCAmelCase , _lowerCAmelCase ): raise TypeError("""Parameters chain_length and number_limit must be int""" ) if chain_length <= 0 or number_limit <= 0: raise ValueError( """Parameters chain_length and number_limit must be greater than 0""" ) # the counter for the chains with the exact desired length snake_case__ : List[str] = 0 # the cached sizes of the previous chains snake_case__ : dict[int, int] = {} for start_chain_element in range(1 , _lowerCAmelCase ): # The temporary set will contain the elements of the chain snake_case__ : List[Any] = set() snake_case__ : List[Any] = 0 # Stop computing the chain when you find a cached size, a repeating item or the # length is greater then the desired one. snake_case__ : Any = start_chain_element while ( chain_element not in chain_sets_lengths and chain_element not in chain_set and chain_set_length <= chain_length ): chain_set.add(_lowerCAmelCase ) chain_set_length += 1 snake_case__ : int = digit_factorial_sum(_lowerCAmelCase ) if chain_element in chain_sets_lengths: chain_set_length += chain_sets_lengths[chain_element] snake_case__ : int = chain_set_length # If chain contains the exact amount of elements increase the counter if chain_set_length == chain_length: chains_counter += 1 return chains_counter if __name__ == "__main__": import doctest doctest.testmod() print(F"{solution()}")
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'''simple docstring''' from __future__ import annotations def snake_case_ ( _lowerCAmelCase : list[int | float] , _lowerCAmelCase : int , _lowerCAmelCase : int ) -> int | float: if len(_lowerCAmelCase ) == 0: raise ValueError('''find_max() arg is an empty sequence''' ) if ( left >= len(_lowerCAmelCase ) or left < -len(_lowerCAmelCase ) or right >= len(_lowerCAmelCase ) or right < -len(_lowerCAmelCase ) ): raise IndexError('''list index out of range''' ) if left == right: return nums[left] UpperCAmelCase : List[Any] = (left + right) >> 1 # the middle UpperCAmelCase : Optional[Any] = find_max(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ) # find max in range[left, mid] UpperCAmelCase : Dict = find_max(_lowerCAmelCase , mid + 1 , _lowerCAmelCase ) # find max in range[mid + 1, right] return left_max if left_max >= right_max else right_max if __name__ == "__main__": import doctest doctest.testmod(verbose=True)
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from typing import Dict, List, Optional, Union import numpy as np from .feature_extraction_utils import BatchFeature, FeatureExtractionMixin from .utils import PaddingStrategy, TensorType, is_tf_tensor, is_torch_tensor, logging, to_numpy _A = logging.get_logger(__name__) class lowercase_ ( A__ ): def __init__( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , **__UpperCamelCase ): """simple docstring""" UpperCamelCase_ = feature_size UpperCamelCase_ = sampling_rate UpperCamelCase_ = padding_value UpperCamelCase_ = kwargs.pop("""padding_side""" , """right""" ) UpperCamelCase_ = kwargs.pop("""return_attention_mask""" , __snake_case ) super().__init__(**__snake_case ) def lowerCamelCase_ ( self , __UpperCamelCase , __UpperCamelCase = True , __UpperCamelCase = None , __UpperCamelCase = False , __UpperCamelCase = None , __UpperCamelCase = None , __UpperCamelCase = None , ): """simple docstring""" if isinstance(__snake_case , (list, tuple) ) and isinstance(processed_features[0] , (dict, BatchFeature) ): UpperCamelCase_ = { key: [example[key] for example in processed_features] for key in processed_features[0].keys() } # The model's main input name, usually `input_values`, has be passed for padding if self.model_input_names[0] not in processed_features: raise ValueError( """You should supply an instance of `transformers.BatchFeature` or list of `transformers.BatchFeature`""" f''' to this method that includes {self.model_input_names[0]}, but you provided''' f''' {list(processed_features.keys() )}''' ) UpperCamelCase_ = processed_features[self.model_input_names[0]] UpperCamelCase_ = ( return_attention_mask if return_attention_mask is not None else self.return_attention_mask ) if len(__snake_case ) == 0: if return_attention_mask: UpperCamelCase_ = [] return processed_features # If we have PyTorch/TF tensors or lists as inputs, we cast them as Numpy arrays # and rebuild them afterwards if no return_tensors is specified # Note that we lose the specific device the tensor may be on for PyTorch UpperCamelCase_ = required_input[0] if isinstance(__snake_case , (list, tuple) ): # first_element might be an empty list/tuple in some edge cases so we grab the first non empty element. UpperCamelCase_ = 0 while len(required_input[index] ) == 0: index += 1 if index < len(__snake_case ): UpperCamelCase_ = required_input[index][0] if return_tensors is None: if is_tf_tensor(__snake_case ): UpperCamelCase_ = '''tf''' elif is_torch_tensor(__snake_case ): UpperCamelCase_ = '''pt''' elif isinstance(__snake_case , (int, float, list, tuple, np.ndarray) ): UpperCamelCase_ = '''np''' else: raise ValueError( f'''type of {first_element} unknown: {type(__snake_case )}. ''' """Should be one of a python, numpy, pytorch or tensorflow object.""" ) for key, value in processed_features.items(): if isinstance(value[0] , (int, float) ): UpperCamelCase_ = to_numpy(__snake_case ) else: UpperCamelCase_ = [to_numpy(__snake_case ) for v in value] # Convert padding_strategy in PaddingStrategy UpperCamelCase_ = self._get_padding_strategies(padding=__snake_case , max_length=__snake_case ) UpperCamelCase_ = processed_features[self.model_input_names[0]] UpperCamelCase_ = len(__snake_case ) if not all(len(__snake_case ) == batch_size for v in processed_features.values() ): raise ValueError("""Some items in the output dictionary have a different batch size than others.""" ) UpperCamelCase_ = [] for i in range(__snake_case ): UpperCamelCase_ = {k: v[i] for k, v in processed_features.items()} # truncation UpperCamelCase_ = self._truncate( __snake_case , max_length=__snake_case , pad_to_multiple_of=__snake_case , truncation=__snake_case , ) truncated_inputs.append(__snake_case ) if padding_strategy == PaddingStrategy.LONGEST: # make sure that `max_length` cannot be longer than the longest truncated length UpperCamelCase_ = max(len(input_slice[self.model_input_names[0]] ) for input_slice in truncated_inputs ) UpperCamelCase_ = PaddingStrategy.MAX_LENGTH UpperCamelCase_ = {} for i in range(__snake_case ): # padding UpperCamelCase_ = self._pad( truncated_inputs[i] , max_length=__snake_case , padding_strategy=__snake_case , pad_to_multiple_of=__snake_case , return_attention_mask=__snake_case , ) for key, value in outputs.items(): if key not in batch_outputs: UpperCamelCase_ = [] if value.dtype is np.dtype(np.floataa ): UpperCamelCase_ = value.astype(np.floataa ) batch_outputs[key].append(__snake_case ) return BatchFeature(__snake_case , tensor_type=__snake_case ) def lowerCamelCase_ ( self , __UpperCamelCase , __UpperCamelCase = None , __UpperCamelCase = PaddingStrategy.DO_NOT_PAD , __UpperCamelCase = None , __UpperCamelCase = None , ): """simple docstring""" UpperCamelCase_ = processed_features[self.model_input_names[0]] if padding_strategy == PaddingStrategy.LONGEST: UpperCamelCase_ = len(__snake_case ) if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0): UpperCamelCase_ = ((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of UpperCamelCase_ = padding_strategy != PaddingStrategy.DO_NOT_PAD and len(__snake_case ) < max_length if return_attention_mask and "attention_mask" not in processed_features: UpperCamelCase_ = np.ones(len(__snake_case ) , dtype=np.intaa ) if needs_to_be_padded: UpperCamelCase_ = max_length - len(__snake_case ) if self.padding_side == "right": if return_attention_mask: UpperCamelCase_ = np.pad( processed_features["""attention_mask"""] , (0, difference) ) UpperCamelCase_ = ((0, difference), (0, 0)) if self.feature_size > 1 else (0, difference) UpperCamelCase_ = np.pad( __snake_case , __snake_case , """constant""" , constant_values=self.padding_value ) elif self.padding_side == "left": if return_attention_mask: UpperCamelCase_ = np.pad( processed_features["""attention_mask"""] , (difference, 0) ) UpperCamelCase_ = ((difference, 0), (0, 0)) if self.feature_size > 1 else (difference, 0) UpperCamelCase_ = np.pad( __snake_case , __snake_case , """constant""" , constant_values=self.padding_value ) else: raise ValueError("""Invalid padding strategy:""" + str(self.padding_side ) ) return processed_features def lowerCamelCase_ ( self , __UpperCamelCase , __UpperCamelCase = None , __UpperCamelCase = None , __UpperCamelCase = None , ): """simple docstring""" if not truncation: return processed_features elif truncation and max_length is None: raise ValueError("""When setting ``truncation=True``, make sure that ``max_length`` is defined.""" ) UpperCamelCase_ = processed_features[self.model_input_names[0]] # find `max_length` that fits `pad_to_multiple_of` if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0): UpperCamelCase_ = ((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of UpperCamelCase_ = len(__snake_case ) > max_length if needs_to_be_truncated: UpperCamelCase_ = processed_features[self.model_input_names[0]][:max_length] if "attention_mask" in processed_features: UpperCamelCase_ = processed_features['''attention_mask'''][:max_length] return processed_features def lowerCamelCase_ ( self , __UpperCamelCase=False , __UpperCamelCase=None ): """simple docstring""" if padding is not False: if padding is True: UpperCamelCase_ = PaddingStrategy.LONGEST # Default to pad to the longest sequence in the batch elif not isinstance(__snake_case , __snake_case ): UpperCamelCase_ = PaddingStrategy(__snake_case ) elif isinstance(__snake_case , __snake_case ): UpperCamelCase_ = padding else: UpperCamelCase_ = PaddingStrategy.DO_NOT_PAD # Set max length if needed if max_length is None: if padding_strategy == PaddingStrategy.MAX_LENGTH: raise ValueError( f'''When setting ``padding={PaddingStrategy.MAX_LENGTH}``, make sure that max_length is defined''' ) # Test if we have a padding value if padding_strategy != PaddingStrategy.DO_NOT_PAD and (self.padding_value is None): raise ValueError( """Asking to pad but the feature_extractor does not have a padding value. Please select a value to use""" """ as `padding_value`. For example: `feature_extractor.padding_value = 0.0`.""" ) return padding_strategy
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'''simple docstring''' from typing import List, Optional, Tuple, Union import torch from ...models import UNetaDModel from ...schedulers import ScoreSdeVeScheduler from ...utils import randn_tensor from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput class SCREAMING_SNAKE_CASE( A__ ): """simple docstring""" lowerCamelCase__ = 42 lowerCamelCase__ = 42 def __init__( self : Union[str, Any] , __snake_case : UNetaDModel , __snake_case : ScoreSdeVeScheduler ) -> int: super().__init__() self.register_modules(unet=__snake_case , scheduler=__snake_case ) @torch.no_grad() def __call__( self : Optional[int] , __snake_case : int = 1 , __snake_case : int = 2000 , __snake_case : Optional[Union[torch.Generator, List[torch.Generator]]] = None , __snake_case : Optional[str] = "pil" , __snake_case : bool = True , **__snake_case : Optional[int] , ) -> Union[ImagePipelineOutput, Tuple]: UpperCAmelCase : str = self.unet.config.sample_size UpperCAmelCase : Union[str, Any] = (batch_size, 3, img_size, img_size) UpperCAmelCase : int = self.unet UpperCAmelCase : Any = randn_tensor(__snake_case , generator=__snake_case ) * self.scheduler.init_noise_sigma UpperCAmelCase : List[Any] = sample.to(self.device ) self.scheduler.set_timesteps(__snake_case ) self.scheduler.set_sigmas(__snake_case ) for i, t in enumerate(self.progress_bar(self.scheduler.timesteps ) ): UpperCAmelCase : Any = self.scheduler.sigmas[i] * torch.ones(shape[0] , device=self.device ) # correction step for _ in range(self.scheduler.config.correct_steps ): UpperCAmelCase : Union[str, Any] = self.unet(__snake_case , __snake_case ).sample UpperCAmelCase : Optional[Any] = self.scheduler.step_correct(__snake_case , __snake_case , generator=__snake_case ).prev_sample # prediction step UpperCAmelCase : Optional[Any] = model(__snake_case , __snake_case ).sample UpperCAmelCase : List[str] = self.scheduler.step_pred(__snake_case , __snake_case , __snake_case , generator=__snake_case ) UpperCAmelCase , UpperCAmelCase : Optional[Any] = output.prev_sample, output.prev_sample_mean UpperCAmelCase : int = sample_mean.clamp(0 , 1 ) UpperCAmelCase : Union[str, Any] = sample.cpu().permute(0 , 2 , 3 , 1 ).numpy() if output_type == "pil": UpperCAmelCase : Optional[Any] = self.numpy_to_pil(__snake_case ) if not return_dict: return (sample,) return ImagePipelineOutput(images=__snake_case )
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from ...configuration_utils import PretrainedConfig from ...utils import logging _lowerCamelCase =logging.get_logger(__name__) _lowerCamelCase ={ "unc-nlp/lxmert-base-uncased": "https://huggingface.co/unc-nlp/lxmert-base-uncased/resolve/main/config.json", } class a_ ( A__ ): """simple docstring""" __UpperCAmelCase = 'lxmert' __UpperCAmelCase = {} def __init__( self : Tuple ,snake_case : int=30522 ,snake_case : Union[str, Any]=768 ,snake_case : List[str]=12 ,snake_case : Any=9500 ,snake_case : int=1600 ,snake_case : Any=400 ,snake_case : Dict=3072 ,snake_case : int="gelu" ,snake_case : List[Any]=0.1 ,snake_case : Any=0.1 ,snake_case : Optional[Any]=512 ,snake_case : str=2 ,snake_case : Optional[Any]=0.02 ,snake_case : Optional[Any]=1e-12 ,snake_case : Dict=9 ,snake_case : Any=5 ,snake_case : int=5 ,snake_case : Tuple=2048 ,snake_case : Union[str, Any]=4 ,snake_case : Optional[Any]=6.67 ,snake_case : Optional[int]=True ,snake_case : str=True ,snake_case : List[Any]=True ,snake_case : List[Any]=True ,snake_case : int=True ,snake_case : Dict=True ,snake_case : int=True ,**snake_case : int ,): SCREAMING_SNAKE_CASE =vocab_size SCREAMING_SNAKE_CASE =hidden_size SCREAMING_SNAKE_CASE =num_attention_heads SCREAMING_SNAKE_CASE =hidden_act SCREAMING_SNAKE_CASE =intermediate_size SCREAMING_SNAKE_CASE =hidden_dropout_prob SCREAMING_SNAKE_CASE =attention_probs_dropout_prob SCREAMING_SNAKE_CASE =max_position_embeddings SCREAMING_SNAKE_CASE =type_vocab_size SCREAMING_SNAKE_CASE =initializer_range SCREAMING_SNAKE_CASE =layer_norm_eps SCREAMING_SNAKE_CASE =num_qa_labels SCREAMING_SNAKE_CASE =num_object_labels SCREAMING_SNAKE_CASE =num_attr_labels SCREAMING_SNAKE_CASE =l_layers SCREAMING_SNAKE_CASE =x_layers SCREAMING_SNAKE_CASE =r_layers SCREAMING_SNAKE_CASE =visual_feat_dim SCREAMING_SNAKE_CASE =visual_pos_dim SCREAMING_SNAKE_CASE =visual_loss_normalizer SCREAMING_SNAKE_CASE =task_matched SCREAMING_SNAKE_CASE =task_mask_lm SCREAMING_SNAKE_CASE =task_obj_predict SCREAMING_SNAKE_CASE =task_qa SCREAMING_SNAKE_CASE =visual_obj_loss SCREAMING_SNAKE_CASE =visual_attr_loss SCREAMING_SNAKE_CASE =visual_feat_loss SCREAMING_SNAKE_CASE ={'''vision''': r_layers, '''cross_encoder''': x_layers, '''language''': l_layers} super().__init__(**__snake_case )
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'''simple docstring''' import warnings from contextlib import contextmanager from ....processing_utils import ProcessorMixin class SCREAMING_SNAKE_CASE( A__ ): """simple docstring""" lowerCamelCase__ = """MCTCTFeatureExtractor""" lowerCamelCase__ = """AutoTokenizer""" def __init__( self : Dict , __snake_case : Optional[int] , __snake_case : List[str] ) -> str: super().__init__(__snake_case , __snake_case ) UpperCAmelCase : List[Any] = self.feature_extractor UpperCAmelCase : Union[str, Any] = False def __call__( self : Any , *__snake_case : List[str] , **__snake_case : Any ) -> List[Any]: # For backward compatibility if self._in_target_context_manager: return self.current_processor(*__snake_case , **__snake_case ) if "raw_speech" in kwargs: warnings.warn('''Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead.''' ) UpperCAmelCase : int = kwargs.pop('''raw_speech''' ) else: UpperCAmelCase : Union[str, Any] = kwargs.pop('''audio''' , __snake_case ) UpperCAmelCase : Optional[Any] = kwargs.pop('''sampling_rate''' , __snake_case ) UpperCAmelCase : Dict = kwargs.pop('''text''' , __snake_case ) if len(__snake_case ) > 0: UpperCAmelCase : Any = args[0] UpperCAmelCase : Optional[int] = args[1:] if audio is None and text is None: raise ValueError('''You need to specify either an `audio` or `text` input to process.''' ) if audio is not None: UpperCAmelCase : List[str] = self.feature_extractor(__snake_case , *__snake_case , sampling_rate=__snake_case , **__snake_case ) if text is not None: UpperCAmelCase : int = self.tokenizer(__snake_case , **__snake_case ) if text is None: return inputs elif audio is None: return encodings else: UpperCAmelCase : str = encodings['''input_ids'''] return inputs def A ( self : List[Any] , *__snake_case : List[Any] , **__snake_case : List[Any] ) -> str: return self.tokenizer.batch_decode(*__snake_case , **__snake_case ) def A ( self : List[Any] , *__snake_case : int , **__snake_case : Optional[int] ) -> Any: # For backward compatibility if self._in_target_context_manager: return self.current_processor.pad(*__snake_case , **__snake_case ) UpperCAmelCase : List[Any] = kwargs.pop('''input_features''' , __snake_case ) UpperCAmelCase : Optional[Any] = kwargs.pop('''labels''' , __snake_case ) if len(__snake_case ) > 0: UpperCAmelCase : List[str] = args[0] UpperCAmelCase : List[Any] = args[1:] if input_features is not None: UpperCAmelCase : Tuple = self.feature_extractor.pad(__snake_case , *__snake_case , **__snake_case ) if labels is not None: UpperCAmelCase : Optional[int] = self.tokenizer.pad(__snake_case , **__snake_case ) if labels is None: return input_features elif input_features is None: return labels else: UpperCAmelCase : List[str] = labels['''input_ids'''] return input_features def A ( self : Union[str, Any] , *__snake_case : Optional[Any] , **__snake_case : Optional[int] ) -> Optional[Any]: return self.tokenizer.decode(*__snake_case , **__snake_case ) @contextmanager def A ( self : Any ) -> Optional[int]: warnings.warn( '''`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your ''' '''labels by using the argument `text` of the regular `__call__` method (either in the same call as ''' '''your audio inputs, or in a separate call.''' ) UpperCAmelCase : Dict = True UpperCAmelCase : List[Any] = self.tokenizer yield UpperCAmelCase : Tuple = self.feature_extractor UpperCAmelCase : List[Any] = False
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import os from shutil import copyfile from typing import List, Optional, Tuple import sentencepiece as spm from ...tokenization_utils import PreTrainedTokenizer from ...utils import logging _lowerCAmelCase : Optional[int] = logging.get_logger(__name__) _lowerCAmelCase : List[str] = {"vocab_file": "sentencepiece.model"} _lowerCAmelCase : Tuple = { "vocab_file": { "google/rembert": "https://huggingface.co/google/rembert/resolve/main/sentencepiece.model", }, } _lowerCAmelCase : Optional[int] = { "google/rembert": 256, } class __magic_name__ ( A__ ): SCREAMING_SNAKE_CASE = VOCAB_FILES_NAMES SCREAMING_SNAKE_CASE = PRETRAINED_VOCAB_FILES_MAP SCREAMING_SNAKE_CASE = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES def __init__( self , __snake_case , __snake_case=False , __snake_case=True , __snake_case=True , __snake_case="[CLS]" , __snake_case="[SEP]" , __snake_case="[UNK]" , __snake_case="[SEP]" , __snake_case="[PAD]" , __snake_case="[CLS]" , __snake_case="[MASK]" , **__snake_case , ) -> int: '''simple docstring''' super().__init__( do_lower_case=__snake_case , remove_space=__snake_case , keep_accents=__snake_case , bos_token=__snake_case , eos_token=__snake_case , unk_token=__snake_case , sep_token=__snake_case , pad_token=__snake_case , cls_token=__snake_case , mask_token=__snake_case , **__snake_case , ) __a =do_lower_case __a =remove_space __a =keep_accents __a =vocab_file __a =spm.SentencePieceProcessor() self.sp_model.Load(__snake_case ) @property def __magic_name__ ( self ) -> Optional[int]: '''simple docstring''' return len(self.sp_model ) def __magic_name__ ( self ) -> Optional[Any]: '''simple docstring''' __a ={self.convert_ids_to_tokens(__snake_case ): i for i in range(self.vocab_size )} vocab.update(self.added_tokens_encoder ) return vocab def __getstate__( self ) -> Optional[Any]: '''simple docstring''' __a =self.__dict__.copy() __a =None return state def __setstate__( self , __snake_case ) -> Union[str, Any]: '''simple docstring''' __a =d __a =spm.SentencePieceProcessor() self.sp_model.Load(self.vocab_file ) def __magic_name__ ( self , __snake_case , __snake_case=False ) -> Union[str, Any]: '''simple docstring''' __a =self.sp_model.EncodeAsPieces(__snake_case ) return pieces def __magic_name__ ( self , __snake_case ) -> int: '''simple docstring''' return self.sp_model.PieceToId(__snake_case ) def __magic_name__ ( self , __snake_case ) -> List[Any]: '''simple docstring''' return self.sp_model.IdToPiece(__snake_case ) def __magic_name__ ( self , __snake_case ) -> List[Any]: '''simple docstring''' __a =self.sp_model.decode_pieces(__snake_case ) return out_string def __magic_name__ ( self , __snake_case , __snake_case = None ) -> List[int]: '''simple docstring''' __a =[self.sep_token_id] __a =[self.cls_token_id] if token_ids_a is None: return cls + token_ids_a + sep return cls + token_ids_a + sep + token_ids_a + sep def __magic_name__ ( self , __snake_case , __snake_case = None , __snake_case = False ) -> List[int]: '''simple docstring''' if already_has_special_tokens: if token_ids_a is not None: raise ValueError( 'You should not supply a second sequence if the provided sequence of ' 'ids is already formatted with special tokens for the model.' ) return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a] if token_ids_a is not None: return [1] + ([0] * len(__snake_case )) + [1] + ([0] * len(__snake_case )) + [1] return [1] + ([0] * len(__snake_case )) + [1] def __magic_name__ ( self , __snake_case , __snake_case = None ) -> List[int]: '''simple docstring''' __a =[self.sep_token_id] __a =[self.cls_token_id] if token_ids_a is None: return len(cls + token_ids_a + sep ) * [0] return len(cls + token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1] def __magic_name__ ( self , __snake_case , __snake_case = None ) -> Tuple[str]: '''simple docstring''' if not os.path.isdir(__snake_case ): logger.error('Vocabulary path ({}) should be a directory'.format(__snake_case ) ) return __a =os.path.join( __snake_case , (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'] ) if os.path.abspath(self.vocab_file ) != os.path.abspath(__snake_case ): copyfile(self.vocab_file , __snake_case ) return (out_vocab_file,)
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'''simple docstring''' from math import isclose, sqrt def snake_case_ ( _lowerCAmelCase : float , _lowerCAmelCase : float , _lowerCAmelCase : float ) -> tuple[float, float, float]: UpperCAmelCase : Optional[int] = point_y / 4 / point_x UpperCAmelCase : str = 2 * normal_gradient / (1 + normal_gradient * normal_gradient) UpperCAmelCase : Any = (1 - normal_gradient * normal_gradient) / ( 1 + normal_gradient * normal_gradient ) UpperCAmelCase : Union[str, Any] = (sa - ca * incoming_gradient) / (ca + sa * incoming_gradient) # to find the next point, solve the simultaeneous equations: # y^2 + 4x^2 = 100 # y - b = m * (x - a) # ==> A x^2 + B x + C = 0 UpperCAmelCase : Union[str, Any] = outgoing_gradient**2 + 4 UpperCAmelCase : Dict = 2 * outgoing_gradient * (point_y - outgoing_gradient * point_x) UpperCAmelCase : List[Any] = (point_y - outgoing_gradient * point_x) ** 2 - 100 UpperCAmelCase : List[str] = ( -linear_term - sqrt(linear_term**2 - 4 * quadratic_term * constant_term ) ) / (2 * quadratic_term) UpperCAmelCase : Optional[int] = ( -linear_term + sqrt(linear_term**2 - 4 * quadratic_term * constant_term ) ) / (2 * quadratic_term) # two solutions, one of which is our input point UpperCAmelCase : Optional[Any] = x_minus if isclose(_lowerCAmelCase , _lowerCAmelCase ) else x_plus UpperCAmelCase : Union[str, Any] = point_y + outgoing_gradient * (next_x - point_x) return next_x, next_y, outgoing_gradient def snake_case_ ( _lowerCAmelCase : float = 1.4 , _lowerCAmelCase : float = -9.6 ) -> int: UpperCAmelCase : int = 0 UpperCAmelCase : float = first_x_coord UpperCAmelCase : float = first_y_coord UpperCAmelCase : float = (1_0.1 - point_y) / (0.0 - point_x) while not (-0.0_1 <= point_x <= 0.0_1 and point_y > 0): UpperCAmelCase , UpperCAmelCase , UpperCAmelCase : str = next_point(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ) num_reflections += 1 return num_reflections if __name__ == "__main__": print(F"{solution() = }")
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'''simple docstring''' import os from datetime import datetime as dt from github import Github _A : Union[str, Any] = [ "good first issue", "feature request", "wip", ] def UpperCamelCase_ ( ) -> int: '''simple docstring''' __lowerCAmelCase = Github(os.environ["""GITHUB_TOKEN"""] ) __lowerCAmelCase = g.get_repo("""huggingface/accelerate""" ) __lowerCAmelCase = repo.get_issues(state="""open""" ) for issue in open_issues: __lowerCAmelCase = sorted([comment for comment in issue.get_comments()] , key=lambda snake_case_ : i.created_at , reverse=_lowerCAmelCase ) __lowerCAmelCase = comments[0] if len(_lowerCAmelCase ) > 0 else None __lowerCAmelCase = dt.utcnow() __lowerCAmelCase = (current_time - issue.updated_at).days __lowerCAmelCase = (current_time - issue.created_at).days if ( last_comment is not None and last_comment.user.login == "github-actions[bot]" and days_since_updated > 7 and days_since_creation >= 30 and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels() ) ): # Close issue since it has been 7 days of inactivity since bot mention. issue.edit(state="""closed""" ) elif ( days_since_updated > 23 and days_since_creation >= 30 and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels() ) ): # Add stale comment issue.create_comment( """This issue has been automatically marked as stale because it has not had """ """recent activity. If you think this still needs to be addressed """ """please comment on this thread.\n\nPlease note that issues that do not follow the """ """[contributing guidelines](https://github.com/huggingface/accelerate/blob/main/CONTRIBUTING.md) """ """are likely to be ignored.""" ) if __name__ == "__main__": main()
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'''simple docstring''' from typing import TYPE_CHECKING from ...utils import ( OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_tokenizers_available, is_torch_available, ) UpperCamelCase__: str = { "configuration_lxmert": ["LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "LxmertConfig"], "tokenization_lxmert": ["LxmertTokenizer"], } try: if not is_tokenizers_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: UpperCamelCase__: int = ["LxmertTokenizerFast"] try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: UpperCamelCase__: Union[str, Any] = [ "LxmertEncoder", "LxmertForPreTraining", "LxmertForQuestionAnswering", "LxmertModel", "LxmertPreTrainedModel", "LxmertVisualFeatureEncoder", "LxmertXLayer", ] try: if not is_tf_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: UpperCamelCase__: int = [ "TF_LXMERT_PRETRAINED_MODEL_ARCHIVE_LIST", "TFLxmertForPreTraining", "TFLxmertMainLayer", "TFLxmertModel", "TFLxmertPreTrainedModel", "TFLxmertVisualFeatureEncoder", ] if TYPE_CHECKING: from .configuration_lxmert import LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP, LxmertConfig from .tokenization_lxmert import LxmertTokenizer try: if not is_tokenizers_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .tokenization_lxmert_fast import LxmertTokenizerFast try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_lxmert import ( LxmertEncoder, LxmertForPreTraining, LxmertForQuestionAnswering, LxmertModel, LxmertPreTrainedModel, LxmertVisualFeatureEncoder, LxmertXLayer, ) try: if not is_tf_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_tf_lxmert import ( TF_LXMERT_PRETRAINED_MODEL_ARCHIVE_LIST, TFLxmertForPreTraining, TFLxmertMainLayer, TFLxmertModel, TFLxmertPreTrainedModel, TFLxmertVisualFeatureEncoder, ) else: import sys UpperCamelCase__: Tuple = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
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import unittest from diffusers.models.unet_ad_blocks import * # noqa F403 from diffusers.utils import torch_device from .test_unet_blocks_common import UNetBlockTesterMixin class __A ( A__ , unittest.TestCase ): '''simple docstring''' lowerCAmelCase_ = DownBlockaD # noqa F405 lowerCAmelCase_ = """down""" def __lowerCamelCase ( self ): '''simple docstring''' lowerCamelCase__ = [-0.0232, -0.9869, 0.8054, -0.0637, -0.1688, -1.4264, 0.4470, -1.3394, 0.0904] super().test_output(__snake_case ) class __A ( A__ , unittest.TestCase ): '''simple docstring''' lowerCAmelCase_ = ResnetDownsampleBlockaD # noqa F405 lowerCAmelCase_ = """down""" def __lowerCamelCase ( self ): '''simple docstring''' lowerCamelCase__ = [0.0710, 0.2410, -0.7320, -1.0757, -1.1343, 0.3540, -0.0133, -0.2576, 0.0948] super().test_output(__snake_case ) class __A ( A__ , unittest.TestCase ): '''simple docstring''' lowerCAmelCase_ = AttnDownBlockaD # noqa F405 lowerCAmelCase_ = """down""" def __lowerCamelCase ( self ): '''simple docstring''' lowerCamelCase__ = [0.0636, 0.8964, -0.6234, -1.0131, 0.0844, 0.4935, 0.3437, 0.0911, -0.2957] super().test_output(__snake_case ) class __A ( A__ , unittest.TestCase ): '''simple docstring''' lowerCAmelCase_ = CrossAttnDownBlockaD # noqa F405 lowerCAmelCase_ = """down""" def __lowerCamelCase ( self ): '''simple docstring''' lowerCamelCase__ = super().prepare_init_args_and_inputs_for_common() lowerCamelCase__ = 3_2 return init_dict, inputs_dict def __lowerCamelCase ( self ): '''simple docstring''' lowerCamelCase__ = [0.2238, -0.7396, -0.2255, -0.3829, 0.1925, 1.1665, 0.0603, -0.7295, 0.1983] super().test_output(__snake_case ) class __A ( A__ , unittest.TestCase ): '''simple docstring''' lowerCAmelCase_ = SimpleCrossAttnDownBlockaD # noqa F405 lowerCAmelCase_ = """down""" @property def __lowerCamelCase ( self ): '''simple docstring''' return super().get_dummy_input(include_encoder_hidden_states=__snake_case ) def __lowerCamelCase ( self ): '''simple docstring''' lowerCamelCase__ = super().prepare_init_args_and_inputs_for_common() lowerCamelCase__ = 3_2 return init_dict, inputs_dict @unittest.skipIf(torch_device == '''mps''' , '''MPS result is not consistent''' ) def __lowerCamelCase ( self ): '''simple docstring''' lowerCamelCase__ = [0.7921, -0.0992, -0.1962, -0.7695, -0.4242, 0.7804, 0.4737, 0.2765, 0.3338] super().test_output(__snake_case ) class __A ( A__ , unittest.TestCase ): '''simple docstring''' lowerCAmelCase_ = SkipDownBlockaD # noqa F405 lowerCAmelCase_ = """down""" @property def __lowerCamelCase ( self ): '''simple docstring''' return super().get_dummy_input(include_skip_sample=__snake_case ) def __lowerCamelCase ( self ): '''simple docstring''' lowerCamelCase__ = [-0.0845, -0.2087, -0.2465, 0.0971, 0.1900, -0.0484, 0.2664, 0.4179, 0.5069] super().test_output(__snake_case ) class __A ( A__ , unittest.TestCase ): '''simple docstring''' lowerCAmelCase_ = AttnSkipDownBlockaD # noqa F405 lowerCAmelCase_ = """down""" @property def __lowerCamelCase ( self ): '''simple docstring''' return super().get_dummy_input(include_skip_sample=__snake_case ) def __lowerCamelCase ( self ): '''simple docstring''' lowerCamelCase__ = [0.5539, 0.1609, 0.4924, 0.0537, -0.1995, 0.4050, 0.0979, -0.2721, -0.0642] super().test_output(__snake_case ) class __A ( A__ , unittest.TestCase ): '''simple docstring''' lowerCAmelCase_ = DownEncoderBlockaD # noqa F405 lowerCAmelCase_ = """down""" @property def __lowerCamelCase ( self ): '''simple docstring''' return super().get_dummy_input(include_temb=__snake_case ) def __lowerCamelCase ( self ): '''simple docstring''' lowerCamelCase__ = { '''in_channels''': 3_2, '''out_channels''': 3_2, } lowerCamelCase__ = self.dummy_input return init_dict, inputs_dict def __lowerCamelCase ( self ): '''simple docstring''' lowerCamelCase__ = [1.1102, 0.5302, 0.4872, -0.0023, -0.8042, 0.0483, -0.3489, -0.5632, 0.7626] super().test_output(__snake_case ) class __A ( A__ , unittest.TestCase ): '''simple docstring''' lowerCAmelCase_ = AttnDownEncoderBlockaD # noqa F405 lowerCAmelCase_ = """down""" @property def __lowerCamelCase ( self ): '''simple docstring''' return super().get_dummy_input(include_temb=__snake_case ) def __lowerCamelCase ( self ): '''simple docstring''' lowerCamelCase__ = { '''in_channels''': 3_2, '''out_channels''': 3_2, } lowerCamelCase__ = self.dummy_input return init_dict, inputs_dict def __lowerCamelCase ( self ): '''simple docstring''' lowerCamelCase__ = [0.8966, -0.1486, 0.8568, 0.8141, -0.9046, -0.1342, -0.0972, -0.7417, 0.1538] super().test_output(__snake_case ) class __A ( A__ , unittest.TestCase ): '''simple docstring''' lowerCAmelCase_ = UNetMidBlockaD # noqa F405 lowerCAmelCase_ = """mid""" def __lowerCamelCase ( self ): '''simple docstring''' lowerCamelCase__ = { '''in_channels''': 3_2, '''temb_channels''': 1_2_8, } lowerCamelCase__ = self.dummy_input return init_dict, inputs_dict def __lowerCamelCase ( self ): '''simple docstring''' lowerCamelCase__ = [-0.1062, 1.7248, 0.3494, 1.4569, -0.0910, -1.2421, -0.9984, 0.6736, 1.0028] super().test_output(__snake_case ) class __A ( A__ , unittest.TestCase ): '''simple docstring''' lowerCAmelCase_ = UNetMidBlockaDCrossAttn # noqa F405 lowerCAmelCase_ = """mid""" def __lowerCamelCase ( self ): '''simple docstring''' lowerCamelCase__ = super().prepare_init_args_and_inputs_for_common() lowerCamelCase__ = 3_2 return init_dict, inputs_dict def __lowerCamelCase ( self ): '''simple docstring''' lowerCamelCase__ = [0.0187, 2.4220, 0.4484, 1.1203, -0.6121, -1.5122, -0.8270, 0.7851, 1.8335] super().test_output(__snake_case ) class __A ( A__ , unittest.TestCase ): '''simple docstring''' lowerCAmelCase_ = UNetMidBlockaDSimpleCrossAttn # noqa F405 lowerCAmelCase_ = """mid""" @property def __lowerCamelCase ( self ): '''simple docstring''' return super().get_dummy_input(include_encoder_hidden_states=__snake_case ) def __lowerCamelCase ( self ): '''simple docstring''' lowerCamelCase__ = super().prepare_init_args_and_inputs_for_common() lowerCamelCase__ = 3_2 return init_dict, inputs_dict def __lowerCamelCase ( self ): '''simple docstring''' lowerCamelCase__ = [0.7143, 1.9974, 0.5448, 1.3977, 0.1282, -1.1237, -1.4238, 0.5530, 0.8880] super().test_output(__snake_case ) class __A ( A__ , unittest.TestCase ): '''simple docstring''' lowerCAmelCase_ = UpBlockaD # noqa F405 lowerCAmelCase_ = """up""" @property def __lowerCamelCase ( self ): '''simple docstring''' return super().get_dummy_input(include_res_hidden_states_tuple=__snake_case ) def __lowerCamelCase ( self ): '''simple docstring''' lowerCamelCase__ = [-0.2041, -0.4165, -0.3022, 0.0041, -0.6628, -0.7053, 0.1928, -0.0325, 0.0523] super().test_output(__snake_case ) class __A ( A__ , unittest.TestCase ): '''simple docstring''' lowerCAmelCase_ = ResnetUpsampleBlockaD # noqa F405 lowerCAmelCase_ = """up""" @property def __lowerCamelCase ( self ): '''simple docstring''' return super().get_dummy_input(include_res_hidden_states_tuple=__snake_case ) def __lowerCamelCase ( self ): '''simple docstring''' lowerCamelCase__ = [0.2287, 0.3549, -0.1346, 0.4797, -0.1715, -0.9649, 0.7305, -0.5864, -0.6244] super().test_output(__snake_case ) class __A ( A__ , unittest.TestCase ): '''simple docstring''' lowerCAmelCase_ = CrossAttnUpBlockaD # noqa F405 lowerCAmelCase_ = """up""" @property def __lowerCamelCase ( self ): '''simple docstring''' return super().get_dummy_input(include_res_hidden_states_tuple=__snake_case ) def __lowerCamelCase ( self ): '''simple docstring''' lowerCamelCase__ = super().prepare_init_args_and_inputs_for_common() lowerCamelCase__ = 3_2 return init_dict, inputs_dict def __lowerCamelCase ( self ): '''simple docstring''' lowerCamelCase__ = [-0.1403, -0.3515, -0.0420, -0.1425, 0.3167, 0.5094, -0.2181, 0.5931, 0.5582] super().test_output(__snake_case ) class __A ( A__ , unittest.TestCase ): '''simple docstring''' lowerCAmelCase_ = SimpleCrossAttnUpBlockaD # noqa F405 lowerCAmelCase_ = """up""" @property def __lowerCamelCase ( self ): '''simple docstring''' return super().get_dummy_input(include_res_hidden_states_tuple=__snake_case , include_encoder_hidden_states=__snake_case ) def __lowerCamelCase ( self ): '''simple docstring''' lowerCamelCase__ = super().prepare_init_args_and_inputs_for_common() lowerCamelCase__ = 3_2 return init_dict, inputs_dict def __lowerCamelCase ( self ): '''simple docstring''' lowerCamelCase__ = [0.2645, 0.1480, 0.0909, 0.8044, -0.9758, -0.9083, 0.0994, -1.1453, -0.7402] super().test_output(__snake_case ) class __A ( A__ , unittest.TestCase ): '''simple docstring''' lowerCAmelCase_ = AttnUpBlockaD # noqa F405 lowerCAmelCase_ = """up""" @property def __lowerCamelCase ( self ): '''simple docstring''' return super().get_dummy_input(include_res_hidden_states_tuple=__snake_case ) @unittest.skipIf(torch_device == '''mps''' , '''MPS result is not consistent''' ) def __lowerCamelCase ( self ): '''simple docstring''' lowerCamelCase__ = [0.0979, 0.1326, 0.0021, 0.0659, 0.2249, 0.0059, 0.1132, 0.5952, 0.1033] super().test_output(__snake_case ) class __A ( A__ , unittest.TestCase ): '''simple docstring''' lowerCAmelCase_ = SkipUpBlockaD # noqa F405 lowerCAmelCase_ = """up""" @property def __lowerCamelCase ( self ): '''simple docstring''' return super().get_dummy_input(include_res_hidden_states_tuple=__snake_case ) def __lowerCamelCase ( self ): '''simple docstring''' lowerCamelCase__ = [-0.0893, -0.1234, -0.1506, -0.0332, 0.0123, -0.0211, 0.0566, 0.0143, 0.0362] super().test_output(__snake_case ) class __A ( A__ , unittest.TestCase ): '''simple docstring''' lowerCAmelCase_ = AttnSkipUpBlockaD # noqa F405 lowerCAmelCase_ = """up""" @property def __lowerCamelCase ( self ): '''simple docstring''' return super().get_dummy_input(include_res_hidden_states_tuple=__snake_case ) def __lowerCamelCase ( self ): '''simple docstring''' lowerCamelCase__ = [0.0361, 0.0617, 0.2787, -0.0350, 0.0342, 0.3421, -0.0843, 0.0913, 0.3015] super().test_output(__snake_case ) class __A ( A__ , unittest.TestCase ): '''simple docstring''' lowerCAmelCase_ = UpDecoderBlockaD # noqa F405 lowerCAmelCase_ = """up""" @property def __lowerCamelCase ( self ): '''simple docstring''' return super().get_dummy_input(include_temb=__snake_case ) def __lowerCamelCase ( self ): '''simple docstring''' lowerCamelCase__ = {'''in_channels''': 3_2, '''out_channels''': 3_2} lowerCamelCase__ = self.dummy_input return init_dict, inputs_dict def __lowerCamelCase ( self ): '''simple docstring''' lowerCamelCase__ = [0.4404, 0.1998, -0.9886, -0.3320, -0.3128, -0.7034, -0.6955, -0.2338, -0.3137] super().test_output(__snake_case ) class __A ( A__ , unittest.TestCase ): '''simple docstring''' lowerCAmelCase_ = AttnUpDecoderBlockaD # noqa F405 lowerCAmelCase_ = """up""" @property def __lowerCamelCase ( self ): '''simple docstring''' return super().get_dummy_input(include_temb=__snake_case ) def __lowerCamelCase ( self ): '''simple docstring''' lowerCamelCase__ = {'''in_channels''': 3_2, '''out_channels''': 3_2} lowerCamelCase__ = self.dummy_input return init_dict, inputs_dict def __lowerCamelCase ( self ): '''simple docstring''' lowerCamelCase__ = [0.6738, 0.4491, 0.1055, 1.0710, 0.7316, 0.3339, 0.3352, 0.1023, 0.3568] super().test_output(__snake_case )
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'''simple docstring''' import gc import unittest import numpy as np import torch from transformers import CLIPTextConfig, CLIPTextModel, XLMRobertaTokenizer from diffusers import AltDiffusionPipeline, AutoencoderKL, DDIMScheduler, PNDMScheduler, UNetaDConditionModel from diffusers.pipelines.alt_diffusion.modeling_roberta_series import ( RobertaSeriesConfig, RobertaSeriesModelWithTransformation, ) from diffusers.utils import slow, torch_device from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin enable_full_determinism() class SCREAMING_SNAKE_CASE( A__ , A__ , A__ , unittest.TestCase ): """simple docstring""" lowerCamelCase__ = AltDiffusionPipeline lowerCamelCase__ = TEXT_TO_IMAGE_PARAMS lowerCamelCase__ = TEXT_TO_IMAGE_BATCH_PARAMS lowerCamelCase__ = TEXT_TO_IMAGE_IMAGE_PARAMS lowerCamelCase__ = TEXT_TO_IMAGE_IMAGE_PARAMS def A ( self : Dict ) -> int: torch.manual_seed(0 ) UpperCAmelCase : str = UNetaDConditionModel( block_out_channels=(32, 64) , layers_per_block=2 , sample_size=32 , in_channels=4 , out_channels=4 , down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''') , up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''') , cross_attention_dim=32 , ) UpperCAmelCase : Dict = DDIMScheduler( beta_start=0.0_00_85 , beta_end=0.0_12 , beta_schedule='''scaled_linear''' , clip_sample=__snake_case , set_alpha_to_one=__snake_case , ) torch.manual_seed(0 ) UpperCAmelCase : Union[str, Any] = AutoencoderKL( block_out_channels=[32, 64] , in_channels=3 , out_channels=3 , down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D'''] , up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D'''] , latent_channels=4 , ) # TODO: address the non-deterministic text encoder (fails for save-load tests) # torch.manual_seed(0) # text_encoder_config = RobertaSeriesConfig( # hidden_size=32, # project_dim=32, # intermediate_size=37, # layer_norm_eps=1e-05, # num_attention_heads=4, # num_hidden_layers=5, # vocab_size=5002, # ) # text_encoder = RobertaSeriesModelWithTransformation(text_encoder_config) torch.manual_seed(0 ) UpperCAmelCase : Dict = CLIPTextConfig( bos_token_id=0 , eos_token_id=2 , hidden_size=32 , projection_dim=32 , intermediate_size=37 , layer_norm_eps=1E-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=5002 , ) UpperCAmelCase : List[Any] = CLIPTextModel(__snake_case ) UpperCAmelCase : Optional[Any] = XLMRobertaTokenizer.from_pretrained('''hf-internal-testing/tiny-xlm-roberta''' ) UpperCAmelCase : Optional[int] = 77 UpperCAmelCase : Optional[int] = { '''unet''': unet, '''scheduler''': scheduler, '''vae''': vae, '''text_encoder''': text_encoder, '''tokenizer''': tokenizer, '''safety_checker''': None, '''feature_extractor''': None, } return components def A ( self : Optional[Any] , __snake_case : Dict , __snake_case : List[str]=0 ) -> Union[str, Any]: if str(__snake_case ).startswith('''mps''' ): UpperCAmelCase : str = torch.manual_seed(__snake_case ) else: UpperCAmelCase : Tuple = torch.Generator(device=__snake_case ).manual_seed(__snake_case ) UpperCAmelCase : Dict = { '''prompt''': '''A painting of a squirrel eating a burger''', '''generator''': generator, '''num_inference_steps''': 2, '''guidance_scale''': 6.0, '''output_type''': '''numpy''', } return inputs def A ( self : Union[str, Any] ) -> List[str]: super().test_attention_slicing_forward_pass(expected_max_diff=3E-3 ) def A ( self : Tuple ) -> List[str]: super().test_inference_batch_single_identical(expected_max_diff=3E-3 ) def A ( self : Tuple ) -> Optional[int]: UpperCAmelCase : List[str] = '''cpu''' # ensure determinism for the device-dependent torch.Generator UpperCAmelCase : Any = self.get_dummy_components() torch.manual_seed(0 ) UpperCAmelCase : Optional[int] = RobertaSeriesConfig( hidden_size=32 , project_dim=32 , intermediate_size=37 , layer_norm_eps=1E-05 , num_attention_heads=4 , num_hidden_layers=5 , vocab_size=5002 , ) # TODO: remove after fixing the non-deterministic text encoder UpperCAmelCase : List[str] = RobertaSeriesModelWithTransformation(__snake_case ) UpperCAmelCase : str = text_encoder UpperCAmelCase : Optional[int] = AltDiffusionPipeline(**__snake_case ) UpperCAmelCase : str = alt_pipe.to(__snake_case ) alt_pipe.set_progress_bar_config(disable=__snake_case ) UpperCAmelCase : Optional[int] = self.get_dummy_inputs(__snake_case ) UpperCAmelCase : Optional[int] = '''A photo of an astronaut''' UpperCAmelCase : List[Any] = alt_pipe(**__snake_case ) UpperCAmelCase : Optional[Any] = output.images UpperCAmelCase : str = image[0, -3:, -3:, -1] assert image.shape == (1, 64, 64, 3) UpperCAmelCase : List[str] = np.array( [0.5_74_81_62, 0.60_44_71_45, 0.48_82_12_17, 0.50_10_06_36, 0.5_43_11_85, 0.45_76_36_83, 0.49_65_76_96, 0.48_13_27_33, 0.47_57_30_93] ) assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2 def A ( self : int ) -> Union[str, Any]: UpperCAmelCase : Optional[int] = '''cpu''' # ensure determinism for the device-dependent torch.Generator UpperCAmelCase : int = self.get_dummy_components() UpperCAmelCase : int = PNDMScheduler(skip_prk_steps=__snake_case ) torch.manual_seed(0 ) UpperCAmelCase : int = RobertaSeriesConfig( hidden_size=32 , project_dim=32 , intermediate_size=37 , layer_norm_eps=1E-05 , num_attention_heads=4 , num_hidden_layers=5 , vocab_size=5002 , ) # TODO: remove after fixing the non-deterministic text encoder UpperCAmelCase : Union[str, Any] = RobertaSeriesModelWithTransformation(__snake_case ) UpperCAmelCase : Union[str, Any] = text_encoder UpperCAmelCase : Optional[int] = AltDiffusionPipeline(**__snake_case ) UpperCAmelCase : Dict = alt_pipe.to(__snake_case ) alt_pipe.set_progress_bar_config(disable=__snake_case ) UpperCAmelCase : int = self.get_dummy_inputs(__snake_case ) UpperCAmelCase : Optional[int] = alt_pipe(**__snake_case ) UpperCAmelCase : Optional[int] = output.images UpperCAmelCase : int = image[0, -3:, -3:, -1] assert image.shape == (1, 64, 64, 3) UpperCAmelCase : Optional[int] = np.array( [0.51_60_50_93, 0.5_70_72_41, 0.47_36_55_07, 0.50_57_88_86, 0.5_63_38_77, 0.4_64_25_03, 0.5_18_20_81, 0.48_76_34_84, 0.49_08_42_37] ) assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2 @slow @require_torch_gpu class SCREAMING_SNAKE_CASE( unittest.TestCase ): """simple docstring""" def A ( self : str ) -> Any: # clean up the VRAM after each test super().tearDown() gc.collect() torch.cuda.empty_cache() def A ( self : List[Any] ) -> Any: # make sure here that pndm scheduler skips prk UpperCAmelCase : List[Any] = AltDiffusionPipeline.from_pretrained('''BAAI/AltDiffusion''' , safety_checker=__snake_case ) UpperCAmelCase : Tuple = alt_pipe.to(__snake_case ) alt_pipe.set_progress_bar_config(disable=__snake_case ) UpperCAmelCase : List[Any] = '''A painting of a squirrel eating a burger''' UpperCAmelCase : Any = torch.manual_seed(0 ) UpperCAmelCase : Optional[int] = alt_pipe([prompt] , generator=__snake_case , guidance_scale=6.0 , num_inference_steps=20 , output_type='''np''' ) UpperCAmelCase : Dict = output.images UpperCAmelCase : Optional[Any] = image[0, -3:, -3:, -1] assert image.shape == (1, 512, 512, 3) UpperCAmelCase : List[str] = np.array([0.10_10, 0.08_00, 0.07_94, 0.08_85, 0.08_43, 0.07_62, 0.07_69, 0.07_29, 0.05_86] ) assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2 def A ( self : Tuple ) -> int: UpperCAmelCase : int = DDIMScheduler.from_pretrained('''BAAI/AltDiffusion''' , subfolder='''scheduler''' ) UpperCAmelCase : Tuple = AltDiffusionPipeline.from_pretrained('''BAAI/AltDiffusion''' , scheduler=__snake_case , safety_checker=__snake_case ) UpperCAmelCase : Dict = alt_pipe.to(__snake_case ) alt_pipe.set_progress_bar_config(disable=__snake_case ) UpperCAmelCase : Tuple = '''A painting of a squirrel eating a burger''' UpperCAmelCase : Optional[int] = torch.manual_seed(0 ) UpperCAmelCase : List[Any] = alt_pipe([prompt] , generator=__snake_case , num_inference_steps=2 , output_type='''numpy''' ) UpperCAmelCase : Dict = output.images UpperCAmelCase : Optional[Any] = image[0, -3:, -3:, -1] assert image.shape == (1, 512, 512, 3) UpperCAmelCase : Union[str, Any] = np.array([0.40_19, 0.40_52, 0.38_10, 0.41_19, 0.39_16, 0.39_82, 0.46_51, 0.41_95, 0.53_23] ) assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
23
0
"""simple docstring""" import os from pickle import UnpicklingError from typing import Dict, Tuple import jax import jax.numpy as jnp import numpy as np from flax.serialization import from_bytes from flax.traverse_util import flatten_dict, unflatten_dict import transformers from .utils import logging UpperCAmelCase__ = logging.get_logger(__name__) def _UpperCAmelCase ( __lowerCamelCase : Dict , __lowerCamelCase : List[str] , __lowerCamelCase : List[str] , __lowerCamelCase : List[str]=False ) -> List[str]: try: import torch # noqa: F401 except ImportError: logger.error( '''Loading a PyTorch model in Flax, requires both PyTorch and Flax to be installed. Please see''' ''' https://pytorch.org/ and https://flax.readthedocs.io/en/latest/installation.html for installation''' ''' instructions.''' ) raise if not is_sharded: _snake_case = os.path.abspath(_lowerCAmelCase ) logger.info(f'''Loading PyTorch weights from {pt_path}''' ) _snake_case = torch.load(_lowerCAmelCase , map_location='''cpu''' ) logger.info(f'''PyTorch checkpoint contains {sum(t.numel() for t in pt_state_dict.values() ):,} parameters.''' ) _snake_case = convert_pytorch_state_dict_to_flax(_lowerCAmelCase , _lowerCAmelCase ) else: # model is sharded and pytorch_checkpoint_path already contains the list of .pt shard files _snake_case = convert_pytorch_sharded_state_dict_to_flax(_lowerCAmelCase , _lowerCAmelCase ) return flax_state_dict def _UpperCAmelCase ( __lowerCamelCase : Tuple[str] , __lowerCamelCase : np.ndarray , __lowerCamelCase : Dict[str, jnp.ndarray] , __lowerCamelCase : str , ) -> (Tuple[str], np.ndarray): def is_key_or_prefix_key_in_dict(__lowerCamelCase : Tuple[str] ) -> bool: return len(set(_lowerCAmelCase ) & {key, (model_prefix,) + key} ) > 0 # layer norm _snake_case = pt_tuple_key[:-1] + ('''scale''',) if pt_tuple_key[-1] in ["weight", "gamma"] and is_key_or_prefix_key_in_dict(_lowerCAmelCase ): return renamed_pt_tuple_key, pt_tensor # batch norm layer mean _snake_case = pt_tuple_key[:-1] + ('''mean''',) if pt_tuple_key[-1] == "running_mean" and not is_key_or_prefix_key_in_dict(_lowerCAmelCase ): return renamed_pt_tuple_key, pt_tensor # batch norm layer var _snake_case = pt_tuple_key[:-1] + ('''var''',) if pt_tuple_key[-1] == "running_var" and not is_key_or_prefix_key_in_dict(_lowerCAmelCase ): return renamed_pt_tuple_key, pt_tensor # embedding _snake_case = pt_tuple_key[:-1] + ('''embedding''',) if pt_tuple_key[-1] == "weight" and is_key_or_prefix_key_in_dict(_lowerCAmelCase ): return renamed_pt_tuple_key, pt_tensor # conv layer _snake_case = pt_tuple_key[:-1] + ('''kernel''',) if pt_tuple_key[-1] == "weight" and pt_tensor.ndim == 4 and not is_key_or_prefix_key_in_dict(_lowerCAmelCase ): _snake_case = pt_tensor.transpose(2 , 3 , 1 , 0 ) return renamed_pt_tuple_key, pt_tensor # linear layer _snake_case = pt_tuple_key[:-1] + ('''kernel''',) if pt_tuple_key[-1] == "weight" and not is_key_or_prefix_key_in_dict(_lowerCAmelCase ): _snake_case = pt_tensor.T return renamed_pt_tuple_key, pt_tensor # old PyTorch layer norm weight _snake_case = pt_tuple_key[:-1] + ('''weight''',) if pt_tuple_key[-1] == "gamma": return renamed_pt_tuple_key, pt_tensor # old PyTorch layer norm bias _snake_case = pt_tuple_key[:-1] + ('''bias''',) if pt_tuple_key[-1] == "beta": return renamed_pt_tuple_key, pt_tensor # New `weight_norm` from https://github.com/huggingface/transformers/pull/24030 _snake_case = None if pt_tuple_key[-3::2] == ("parametrizations", "original0"): _snake_case = pt_tuple_key[-2] + '''_g''' elif pt_tuple_key[-3::2] == ("parametrizations", "original1"): _snake_case = pt_tuple_key[-2] + '''_v''' if name is not None: _snake_case = pt_tuple_key[:-3] + (name,) return renamed_pt_tuple_key, pt_tensor return pt_tuple_key, pt_tensor def _UpperCAmelCase ( __lowerCamelCase : Optional[Any] , __lowerCamelCase : Optional[Any] ) -> Optional[Any]: # convert pytorch tensor to numpy _snake_case = {k: v.numpy() for k, v in pt_state_dict.items()} _snake_case = flax_model.base_model_prefix # use params dict if the model contains batch norm layers if "params" in flax_model.params: _snake_case = flax_model.params['''params'''] else: _snake_case = flax_model.params _snake_case = flatten_dict(_lowerCAmelCase ) # add batch_stats keys,values to dict if "batch_stats" in flax_model.params: _snake_case = flatten_dict(flax_model.params['''batch_stats'''] ) random_flax_state_dict.update(_lowerCAmelCase ) _snake_case = {} _snake_case = (model_prefix not in flax_model_params) and ( model_prefix in {k.split('''.''' )[0] for k in pt_state_dict.keys()} ) _snake_case = (model_prefix in flax_model_params) and ( model_prefix not in {k.split('''.''' )[0] for k in pt_state_dict.keys()} ) # Need to change some parameters name to match Flax names for pt_key, pt_tensor in pt_state_dict.items(): _snake_case = tuple(pt_key.split('''.''' ) ) # remove base model prefix if necessary _snake_case = pt_tuple_key[0] == model_prefix if load_model_with_head_into_base_model and has_base_model_prefix: _snake_case = pt_tuple_key[1:] # Correctly rename weight parameters _snake_case = rename_key_and_reshape_tensor( _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ) # add model prefix if necessary _snake_case = (model_prefix,) + flax_key in random_flax_state_dict if load_base_model_into_model_with_head and require_base_model_prefix: _snake_case = (model_prefix,) + flax_key if flax_key in random_flax_state_dict: if flax_tensor.shape != random_flax_state_dict[flax_key].shape: raise ValueError( f'''PyTorch checkpoint seems to be incorrect. Weight {pt_key} was expected to be of shape ''' f'''{random_flax_state_dict[flax_key].shape}, but is {flax_tensor.shape}.''' ) # add batch stats if the model contains batchnorm layers if "batch_stats" in flax_model.params: if "mean" in flax_key[-1] or "var" in flax_key[-1]: _snake_case = jnp.asarray(_lowerCAmelCase ) continue # remove num_batches_tracked key if "num_batches_tracked" in flax_key[-1]: flax_state_dict.pop(_lowerCAmelCase , _lowerCAmelCase ) continue # also add unexpected weight so that warning is thrown _snake_case = jnp.asarray(_lowerCAmelCase ) else: # also add unexpected weight so that warning is thrown _snake_case = jnp.asarray(_lowerCAmelCase ) return unflatten_dict(_lowerCAmelCase ) def _UpperCAmelCase ( __lowerCamelCase : Optional[int] , __lowerCamelCase : Any ) -> str: import torch # Load the index _snake_case = {} for shard_file in shard_filenames: # load using msgpack utils _snake_case = torch.load(_lowerCAmelCase ) _snake_case = {k: v.numpy() for k, v in pt_state_dict.items()} _snake_case = flax_model.base_model_prefix # use params dict if the model contains batch norm layers and then add batch_stats keys,values to dict if "batch_stats" in flax_model.params: _snake_case = flax_model.params['''params'''] _snake_case = flatten_dict(_lowerCAmelCase ) random_flax_state_dict.update(flatten_dict(flax_model.params['''batch_stats'''] ) ) else: _snake_case = flax_model.params _snake_case = flatten_dict(_lowerCAmelCase ) _snake_case = (model_prefix not in flax_model_params) and ( model_prefix in {k.split('''.''' )[0] for k in pt_state_dict.keys()} ) _snake_case = (model_prefix in flax_model_params) and ( model_prefix not in {k.split('''.''' )[0] for k in pt_state_dict.keys()} ) # Need to change some parameters name to match Flax names for pt_key, pt_tensor in pt_state_dict.items(): _snake_case = tuple(pt_key.split('''.''' ) ) # remove base model prefix if necessary _snake_case = pt_tuple_key[0] == model_prefix if load_model_with_head_into_base_model and has_base_model_prefix: _snake_case = pt_tuple_key[1:] # Correctly rename weight parameters _snake_case = rename_key_and_reshape_tensor( _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ) # add model prefix if necessary _snake_case = (model_prefix,) + flax_key in random_flax_state_dict if load_base_model_into_model_with_head and require_base_model_prefix: _snake_case = (model_prefix,) + flax_key if flax_key in random_flax_state_dict: if flax_tensor.shape != random_flax_state_dict[flax_key].shape: raise ValueError( f'''PyTorch checkpoint seems to be incorrect. Weight {pt_key} was expected to be of shape ''' f'''{random_flax_state_dict[flax_key].shape}, but is {flax_tensor.shape}.''' ) # add batch stats if the model contains batchnorm layers if "batch_stats" in flax_model.params: if "mean" in flax_key[-1]: _snake_case = jnp.asarray(_lowerCAmelCase ) continue if "var" in flax_key[-1]: _snake_case = jnp.asarray(_lowerCAmelCase ) continue # remove num_batches_tracked key if "num_batches_tracked" in flax_key[-1]: flax_state_dict.pop(_lowerCAmelCase , _lowerCAmelCase ) continue # also add unexpected weight so that warning is thrown _snake_case = jnp.asarray(_lowerCAmelCase ) else: # also add unexpected weight so that warning is thrown _snake_case = jnp.asarray(_lowerCAmelCase ) return unflatten_dict(_lowerCAmelCase ) def _UpperCAmelCase ( __lowerCamelCase : Dict , __lowerCamelCase : List[Any] ) -> Tuple: _snake_case = os.path.abspath(_lowerCAmelCase ) logger.info(f'''Loading Flax weights from {flax_checkpoint_path}''' ) # import correct flax class _snake_case = getattr(_lowerCAmelCase , '''Flax''' + model.__class__.__name__ ) # load flax weight dict with open(_lowerCAmelCase , '''rb''' ) as state_f: try: _snake_case = from_bytes(_lowerCAmelCase , state_f.read() ) except UnpicklingError: raise EnvironmentError(f'''Unable to convert {flax_checkpoint_path} to Flax deserializable object. ''' ) return load_flax_weights_in_pytorch_model(_lowerCAmelCase , _lowerCAmelCase ) def _UpperCAmelCase ( __lowerCamelCase : Optional[int] , __lowerCamelCase : str ) -> Any: try: import torch # noqa: F401 except ImportError: logger.error( '''Loading a Flax weights in PyTorch, requires both PyTorch and Flax to be installed. Please see''' ''' https://pytorch.org/ and https://flax.readthedocs.io/en/latest/installation.html for installation''' ''' instructions.''' ) raise # check if we have bf16 weights _snake_case = flatten_dict(jax.tree_util.tree_map(lambda __lowerCamelCase : x.dtype == jnp.bfloataa , _lowerCAmelCase ) ).values() if any(_lowerCAmelCase ): # convert all weights to fp32 if the are bf16 since torch.from_numpy can-not handle bf16 # and bf16 is not fully supported in PT yet. logger.warning( '''Found ``bfloat16`` weights in Flax model. Casting all ``bfloat16`` weights to ``float32`` ''' '''before loading those in PyTorch model.''' ) _snake_case = jax.tree_util.tree_map( lambda __lowerCamelCase : params.astype(np.floataa ) if params.dtype == jnp.bfloataa else params , _lowerCAmelCase ) _snake_case = flatten_dict(_lowerCAmelCase ) _snake_case = pt_model.state_dict() _snake_case = (pt_model.base_model_prefix in flax_state) and ( pt_model.base_model_prefix not in {k.split('''.''' )[0] for k in pt_model_dict.keys()} ) _snake_case = (pt_model.base_model_prefix not in flax_state) and ( pt_model.base_model_prefix in {k.split('''.''' )[0] for k in pt_model_dict.keys()} ) # keep track of unexpected & missing keys _snake_case = [] _snake_case = set(pt_model_dict.keys() ) for flax_key_tuple, flax_tensor in flax_state_dict.items(): _snake_case = flax_key_tuple[0] == pt_model.base_model_prefix _snake_case = '''.'''.join((pt_model.base_model_prefix,) + flax_key_tuple ) in pt_model_dict # adapt flax_key to prepare for loading from/to base model only if load_model_with_head_into_base_model and has_base_model_prefix: _snake_case = flax_key_tuple[1:] elif load_base_model_into_model_with_head and require_base_model_prefix: _snake_case = (pt_model.base_model_prefix,) + flax_key_tuple # rename flax weights to PyTorch format if flax_key_tuple[-1] == "kernel" and flax_tensor.ndim == 4 and ".".join(_lowerCAmelCase ) not in pt_model_dict: # conv layer _snake_case = flax_key_tuple[:-1] + ('''weight''',) _snake_case = jnp.transpose(_lowerCAmelCase , (3, 2, 0, 1) ) elif flax_key_tuple[-1] == "kernel" and ".".join(_lowerCAmelCase ) not in pt_model_dict: # linear layer _snake_case = flax_key_tuple[:-1] + ('''weight''',) _snake_case = flax_tensor.T elif flax_key_tuple[-1] in ["scale", "embedding"]: _snake_case = flax_key_tuple[:-1] + ('''weight''',) # adding batch stats from flax batch norm to pt elif "mean" in flax_key_tuple[-1]: _snake_case = flax_key_tuple[:-1] + ('''running_mean''',) elif "var" in flax_key_tuple[-1]: _snake_case = flax_key_tuple[:-1] + ('''running_var''',) if "batch_stats" in flax_state: _snake_case = '''.'''.join(flax_key_tuple[1:] ) # Remove the params/batch_stats header else: _snake_case = '''.'''.join(_lowerCAmelCase ) # We also need to look at `pt_model_dict` and see if there are keys requiring further transformation. _snake_case = {} # New `weight_norm` from https://github.com/huggingface/transformers/pull/24030 for key in pt_model_dict: _snake_case = key.split('''.''' ) _snake_case = None if key_components[-3::2] == ["parametrizations", "original0"]: _snake_case = key_components[-2] + '''_g''' elif key_components[-3::2] == ["parametrizations", "original1"]: _snake_case = key_components[-2] + '''_v''' if name is not None: _snake_case = key_components[:-3] + [name] _snake_case = '''.'''.join(_lowerCAmelCase ) _snake_case = key if flax_key in special_pt_names: _snake_case = special_pt_names[flax_key] if flax_key in pt_model_dict: if flax_tensor.shape != pt_model_dict[flax_key].shape: raise ValueError( f'''Flax checkpoint seems to be incorrect. Weight {flax_key_tuple} was expected ''' f'''to be of shape {pt_model_dict[flax_key].shape}, but is {flax_tensor.shape}.''' ) else: # add weight to pytorch dict _snake_case = np.asarray(_lowerCAmelCase ) if not isinstance(_lowerCAmelCase , np.ndarray ) else flax_tensor _snake_case = torch.from_numpy(_lowerCAmelCase ) # remove from missing keys missing_keys.remove(_lowerCAmelCase ) else: # weight is not expected by PyTorch model unexpected_keys.append(_lowerCAmelCase ) pt_model.load_state_dict(_lowerCAmelCase ) # re-transform missing_keys to list _snake_case = list(_lowerCAmelCase ) if len(_lowerCAmelCase ) > 0: logger.warning( '''Some weights of the Flax model were not used when initializing the PyTorch model''' f''' {pt_model.__class__.__name__}: {unexpected_keys}\n- This IS expected if you are initializing''' f''' {pt_model.__class__.__name__} from a Flax model trained on another task or with another architecture''' ''' (e.g. initializing a BertForSequenceClassification model from a FlaxBertForPreTraining model).\n- This''' f''' IS NOT expected if you are initializing {pt_model.__class__.__name__} from a Flax model that you expect''' ''' to be exactly identical (e.g. initializing a BertForSequenceClassification model from a''' ''' FlaxBertForSequenceClassification model).''' ) else: logger.warning(f'''All Flax model weights were used when initializing {pt_model.__class__.__name__}.\n''' ) if len(_lowerCAmelCase ) > 0: logger.warning( f'''Some weights of {pt_model.__class__.__name__} were not initialized from the Flax model and are newly''' f''' initialized: {missing_keys}\nYou should probably TRAIN this model on a down-stream task to be able to''' ''' use it for predictions and inference.''' ) else: logger.warning( f'''All the weights of {pt_model.__class__.__name__} were initialized from the Flax model.\n''' '''If your task is similar to the task the model of the checkpoint was trained on, ''' f'''you can already use {pt_model.__class__.__name__} for predictions without further training.''' ) return pt_model
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'''simple docstring''' import io import json import fsspec import pytest from datasets import Dataset, DatasetDict, Features, NamedSplit, Value from datasets.io.json import JsonDatasetReader, JsonDatasetWriter from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases def snake_case_ ( _lowerCAmelCase : List[str] , _lowerCAmelCase : Optional[Any] ) -> Optional[int]: assert isinstance(_lowerCAmelCase , _lowerCAmelCase ) assert dataset.num_rows == 4 assert dataset.num_columns == 3 assert dataset.column_names == ["col_1", "col_2", "col_3"] for feature, expected_dtype in expected_features.items(): assert dataset.features[feature].dtype == expected_dtype @pytest.mark.parametrize('''keep_in_memory''' , [False, True] ) def snake_case_ ( _lowerCAmelCase : int , _lowerCAmelCase : Dict , _lowerCAmelCase : Optional[int] ) -> Dict: UpperCAmelCase : Dict = tmp_path / '''cache''' UpperCAmelCase : List[Any] = {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''} with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase(): UpperCAmelCase : str = JsonDatasetReader(_lowerCAmelCase , cache_dir=_lowerCAmelCase , keep_in_memory=_lowerCAmelCase ).read() _check_json_dataset(_lowerCAmelCase , _lowerCAmelCase ) @pytest.mark.parametrize( '''features''' , [ None, {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}, {'''col_1''': '''string''', '''col_2''': '''string''', '''col_3''': '''string'''}, {'''col_1''': '''int32''', '''col_2''': '''int32''', '''col_3''': '''int32'''}, {'''col_1''': '''float32''', '''col_2''': '''float32''', '''col_3''': '''float32'''}, ] , ) def snake_case_ ( _lowerCAmelCase : Optional[int] , _lowerCAmelCase : Dict , _lowerCAmelCase : int ) -> Optional[int]: UpperCAmelCase : Any = tmp_path / '''cache''' UpperCAmelCase : Tuple = {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''} UpperCAmelCase : Any = features.copy() if features else default_expected_features UpperCAmelCase : List[Any] = ( Features({feature: Value(_lowerCAmelCase ) for feature, dtype in features.items()} ) if features is not None else None ) UpperCAmelCase : Dict = JsonDatasetReader(_lowerCAmelCase , features=_lowerCAmelCase , cache_dir=_lowerCAmelCase ).read() _check_json_dataset(_lowerCAmelCase , _lowerCAmelCase ) @pytest.mark.parametrize( '''features''' , [ None, {'''col_3''': '''float64''', '''col_1''': '''string''', '''col_2''': '''int64'''}, ] , ) def snake_case_ ( _lowerCAmelCase : Optional[Any] , _lowerCAmelCase : List[Any] , _lowerCAmelCase : Tuple ) -> Tuple: UpperCAmelCase : Optional[Any] = tmp_path / '''cache''' UpperCAmelCase : Optional[int] = {'''col_3''': '''float64''', '''col_1''': '''string''', '''col_2''': '''int64'''} UpperCAmelCase : int = features.copy() if features else default_expected_features UpperCAmelCase : Any = ( Features({feature: Value(_lowerCAmelCase ) for feature, dtype in features.items()} ) if features is not None else None ) UpperCAmelCase : Tuple = JsonDatasetReader(_lowerCAmelCase , features=_lowerCAmelCase , cache_dir=_lowerCAmelCase ).read() assert isinstance(_lowerCAmelCase , _lowerCAmelCase ) assert dataset.num_rows == 2 assert dataset.num_columns == 3 assert dataset.column_names == ["col_3", "col_1", "col_2"] for feature, expected_dtype in expected_features.items(): assert dataset.features[feature].dtype == expected_dtype def snake_case_ ( _lowerCAmelCase : List[str] , _lowerCAmelCase : Dict ) -> Union[str, Any]: # jsonl_312_path features are {"col_3": "float64", "col_1": "string", "col_2": "int64"} UpperCAmelCase : Tuple = {'''col_2''': '''int64''', '''col_3''': '''float64''', '''col_1''': '''string'''} UpperCAmelCase : List[str] = features.copy() UpperCAmelCase : Union[str, Any] = ( Features({feature: Value(_lowerCAmelCase ) for feature, dtype in features.items()} ) if features is not None else None ) UpperCAmelCase : Tuple = tmp_path / '''cache''' UpperCAmelCase : List[str] = JsonDatasetReader(_lowerCAmelCase , features=_lowerCAmelCase , cache_dir=_lowerCAmelCase ).read() assert isinstance(_lowerCAmelCase , _lowerCAmelCase ) assert dataset.num_rows == 2 assert dataset.num_columns == 3 assert dataset.column_names == ["col_2", "col_3", "col_1"] for feature, expected_dtype in expected_features.items(): assert dataset.features[feature].dtype == expected_dtype @pytest.mark.parametrize('''split''' , [None, NamedSplit('''train''' ), '''train''', '''test'''] ) def snake_case_ ( _lowerCAmelCase : Union[str, Any] , _lowerCAmelCase : Tuple , _lowerCAmelCase : List[str] ) -> Optional[Any]: UpperCAmelCase : Any = tmp_path / '''cache''' UpperCAmelCase : List[Any] = {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''} UpperCAmelCase : List[Any] = JsonDatasetReader(_lowerCAmelCase , cache_dir=_lowerCAmelCase , split=_lowerCAmelCase ).read() _check_json_dataset(_lowerCAmelCase , _lowerCAmelCase ) assert dataset.split == split if split else "train" @pytest.mark.parametrize('''path_type''' , [str, list] ) def snake_case_ ( _lowerCAmelCase : List[Any] , _lowerCAmelCase : str , _lowerCAmelCase : Any ) -> Dict: if issubclass(_lowerCAmelCase , _lowerCAmelCase ): UpperCAmelCase : str = jsonl_path elif issubclass(_lowerCAmelCase , _lowerCAmelCase ): UpperCAmelCase : Dict = [jsonl_path] UpperCAmelCase : int = tmp_path / '''cache''' UpperCAmelCase : Dict = {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''} UpperCAmelCase : Optional[int] = JsonDatasetReader(_lowerCAmelCase , cache_dir=_lowerCAmelCase ).read() _check_json_dataset(_lowerCAmelCase , _lowerCAmelCase ) def snake_case_ ( _lowerCAmelCase : List[str] , _lowerCAmelCase : Dict , _lowerCAmelCase : Dict=("train",) ) -> Union[str, Any]: assert isinstance(_lowerCAmelCase , _lowerCAmelCase ) for split in splits: UpperCAmelCase : List[str] = dataset_dict[split] assert dataset.num_rows == 4 assert dataset.num_columns == 3 assert dataset.column_names == ["col_1", "col_2", "col_3"] for feature, expected_dtype in expected_features.items(): assert dataset.features[feature].dtype == expected_dtype @pytest.mark.parametrize('''keep_in_memory''' , [False, True] ) def snake_case_ ( _lowerCAmelCase : int , _lowerCAmelCase : Optional[Any] , _lowerCAmelCase : str ) -> Any: UpperCAmelCase : Optional[Any] = tmp_path / '''cache''' UpperCAmelCase : List[str] = {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''} with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase(): UpperCAmelCase : Optional[int] = JsonDatasetReader({'''train''': jsonl_path} , cache_dir=_lowerCAmelCase , keep_in_memory=_lowerCAmelCase ).read() _check_json_datasetdict(_lowerCAmelCase , _lowerCAmelCase ) @pytest.mark.parametrize( '''features''' , [ None, {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}, {'''col_1''': '''string''', '''col_2''': '''string''', '''col_3''': '''string'''}, {'''col_1''': '''int32''', '''col_2''': '''int32''', '''col_3''': '''int32'''}, {'''col_1''': '''float32''', '''col_2''': '''float32''', '''col_3''': '''float32'''}, ] , ) def snake_case_ ( _lowerCAmelCase : Optional[Any] , _lowerCAmelCase : Optional[int] , _lowerCAmelCase : Union[str, Any] ) -> int: UpperCAmelCase : Dict = tmp_path / '''cache''' UpperCAmelCase : Dict = {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''} UpperCAmelCase : Optional[int] = features.copy() if features else default_expected_features UpperCAmelCase : Union[str, Any] = ( Features({feature: Value(_lowerCAmelCase ) for feature, dtype in features.items()} ) if features is not None else None ) UpperCAmelCase : Tuple = JsonDatasetReader({'''train''': jsonl_path} , features=_lowerCAmelCase , cache_dir=_lowerCAmelCase ).read() _check_json_datasetdict(_lowerCAmelCase , _lowerCAmelCase ) @pytest.mark.parametrize('''split''' , [None, NamedSplit('''train''' ), '''train''', '''test'''] ) def snake_case_ ( _lowerCAmelCase : Any , _lowerCAmelCase : Optional[int] , _lowerCAmelCase : Dict ) -> Union[str, Any]: if split: UpperCAmelCase : Optional[int] = {split: jsonl_path} else: UpperCAmelCase : Any = '''train''' UpperCAmelCase : Any = {'''train''': jsonl_path, '''test''': jsonl_path} UpperCAmelCase : Tuple = tmp_path / '''cache''' UpperCAmelCase : int = {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''} UpperCAmelCase : Optional[Any] = JsonDatasetReader(_lowerCAmelCase , cache_dir=_lowerCAmelCase ).read() _check_json_datasetdict(_lowerCAmelCase , _lowerCAmelCase , splits=list(path.keys() ) ) assert all(dataset[split].split == split for split in path.keys() ) def snake_case_ ( _lowerCAmelCase : List[str] ) -> str: return json.load(_lowerCAmelCase ) def snake_case_ ( _lowerCAmelCase : Dict ) -> str: return [json.loads(_lowerCAmelCase ) for line in buffer] class SCREAMING_SNAKE_CASE: """simple docstring""" @pytest.mark.parametrize('''lines, load_json_function''' , [(True, load_json_lines), (False, load_json)] ) def A ( self : Union[str, Any] , __snake_case : Tuple , __snake_case : Optional[Any] , __snake_case : Optional[int] ) -> Dict: with io.BytesIO() as buffer: JsonDatasetWriter(__snake_case , __snake_case , lines=__snake_case ).write() buffer.seek(0 ) UpperCAmelCase : Union[str, Any] = load_json_function(__snake_case ) assert isinstance(__snake_case , __snake_case ) assert isinstance(exported_content[0] , __snake_case ) assert len(__snake_case ) == 10 @pytest.mark.parametrize( '''orient, container, keys, len_at''' , [ ('''records''', list, {'''tokens''', '''labels''', '''answers''', '''id'''}, None), ('''split''', dict, {'''columns''', '''data'''}, '''data'''), ('''index''', dict, set('''0123456789''' ), None), ('''columns''', dict, {'''tokens''', '''labels''', '''answers''', '''id'''}, '''tokens'''), ('''values''', list, None, None), ('''table''', dict, {'''schema''', '''data'''}, '''data'''), ] , ) def A ( self : Optional[int] , __snake_case : Optional[Any] , __snake_case : str , __snake_case : Optional[Any] , __snake_case : List[str] , __snake_case : Optional[Any] ) -> List[Any]: with io.BytesIO() as buffer: JsonDatasetWriter(__snake_case , __snake_case , lines=__snake_case , orient=__snake_case ).write() buffer.seek(0 ) UpperCAmelCase : Union[str, Any] = load_json(__snake_case ) assert isinstance(__snake_case , __snake_case ) if keys: if container is dict: assert exported_content.keys() == keys else: assert exported_content[0].keys() == keys else: assert not hasattr(__snake_case , '''keys''' ) and not hasattr(exported_content[0] , '''keys''' ) if len_at: assert len(exported_content[len_at] ) == 10 else: assert len(__snake_case ) == 10 @pytest.mark.parametrize('''lines, load_json_function''' , [(True, load_json_lines), (False, load_json)] ) def A ( self : str , __snake_case : str , __snake_case : str , __snake_case : int ) -> Any: with io.BytesIO() as buffer: JsonDatasetWriter(__snake_case , __snake_case , lines=__snake_case , num_proc=2 ).write() buffer.seek(0 ) UpperCAmelCase : Any = load_json_function(__snake_case ) assert isinstance(__snake_case , __snake_case ) assert isinstance(exported_content[0] , __snake_case ) assert len(__snake_case ) == 10 @pytest.mark.parametrize( '''orient, container, keys, len_at''' , [ ('''records''', list, {'''tokens''', '''labels''', '''answers''', '''id'''}, None), ('''split''', dict, {'''columns''', '''data'''}, '''data'''), ('''index''', dict, set('''0123456789''' ), None), ('''columns''', dict, {'''tokens''', '''labels''', '''answers''', '''id'''}, '''tokens'''), ('''values''', list, None, None), ('''table''', dict, {'''schema''', '''data'''}, '''data'''), ] , ) def A ( self : Any , __snake_case : int , __snake_case : Union[str, Any] , __snake_case : Optional[int] , __snake_case : Optional[Any] , __snake_case : List[str] ) -> Any: with io.BytesIO() as buffer: JsonDatasetWriter(__snake_case , __snake_case , lines=__snake_case , orient=__snake_case , num_proc=2 ).write() buffer.seek(0 ) UpperCAmelCase : List[str] = load_json(__snake_case ) assert isinstance(__snake_case , __snake_case ) if keys: if container is dict: assert exported_content.keys() == keys else: assert exported_content[0].keys() == keys else: assert not hasattr(__snake_case , '''keys''' ) and not hasattr(exported_content[0] , '''keys''' ) if len_at: assert len(exported_content[len_at] ) == 10 else: assert len(__snake_case ) == 10 def A ( self : List[Any] , __snake_case : str ) -> Dict: with pytest.raises(__snake_case ): with io.BytesIO() as buffer: JsonDatasetWriter(__snake_case , __snake_case , num_proc=0 ) @pytest.mark.parametrize('''compression, extension''' , [('''gzip''', '''gz'''), ('''bz2''', '''bz2'''), ('''xz''', '''xz''')] ) def A ( self : Optional[int] , __snake_case : Any , __snake_case : str , __snake_case : Union[str, Any] , __snake_case : Union[str, Any] , __snake_case : Dict ) -> Union[str, Any]: UpperCAmelCase : List[str] = tmp_path_factory.mktemp('''data''' ) / F"""test.json.{extension}""" UpperCAmelCase : List[Any] = str(shared_datadir / F"""test_file.json.{extension}""" ) JsonDatasetWriter(__snake_case , __snake_case , compression=__snake_case ).write() with fsspec.open(__snake_case , '''rb''' , compression='''infer''' ) as f: UpperCAmelCase : str = f.read() with fsspec.open(__snake_case , '''rb''' , compression='''infer''' ) as f: UpperCAmelCase : Optional[int] = f.read() assert exported_content == original_content
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import argparse import logging import sys from unittest.mock import patch import run_glue_deebert from transformers.testing_utils import TestCasePlus, get_gpu_count, require_torch_non_multi_gpu, slow logging.basicConfig(level=logging.DEBUG) __lowerCamelCase : Dict = logging.getLogger() def __SCREAMING_SNAKE_CASE ( ) -> Dict: """simple docstring""" SCREAMING_SNAKE_CASE__ = argparse.ArgumentParser() parser.add_argument("""-f""" ) SCREAMING_SNAKE_CASE__ = parser.parse_args() return args.f class __snake_case ( A__ ): def __a ( self : Optional[int] ): """simple docstring""" SCREAMING_SNAKE_CASE__ = logging.StreamHandler(sys.stdout ) logger.addHandler(__snake_case ) def __a ( self : str , _lowercase : Optional[int] ): """simple docstring""" SCREAMING_SNAKE_CASE__ = get_gpu_count() if n_gpu > 1: pass # XXX: doesn't quite work with n_gpu > 1 https://github.com/huggingface/transformers/issues/10560 # script = f"{self.examples_dir_str}/research_projects/deebert/run_glue_deebert.py" # distributed_args = f"-m torch.distributed.launch --nproc_per_node={n_gpu} {script}".split() # cmd = [sys.executable] + distributed_args + args # execute_subprocess_async(cmd, env=self.get_env()) # XXX: test the results - need to save them first into .json file else: args.insert(0 , """run_glue_deebert.py""" ) with patch.object(__snake_case , """argv""" , __snake_case ): SCREAMING_SNAKE_CASE__ = run_glue_deebert.main() for value in result.values(): self.assertGreaterEqual(__snake_case , 0.6_66 ) @slow @require_torch_non_multi_gpu def __a ( self : Tuple ): """simple docstring""" SCREAMING_SNAKE_CASE__ = ''' --model_type roberta --model_name_or_path roberta-base --task_name MRPC --do_train --do_eval --do_lower_case --data_dir ./tests/fixtures/tests_samples/MRPC/ --max_seq_length 128 --per_gpu_eval_batch_size=1 --per_gpu_train_batch_size=8 --learning_rate 2e-4 --num_train_epochs 3 --overwrite_output_dir --seed 42 --output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage --plot_data_dir ./examples/deebert/results/ --save_steps 0 --overwrite_cache --eval_after_first_stage '''.split() self.run_and_check(__snake_case ) SCREAMING_SNAKE_CASE__ = ''' --model_type roberta --model_name_or_path ./examples/deebert/saved_models/roberta-base/MRPC/two_stage --task_name MRPC --do_eval --do_lower_case --data_dir ./tests/fixtures/tests_samples/MRPC/ --output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage --plot_data_dir ./examples/deebert/results/ --max_seq_length 128 --eval_each_highway --eval_highway --overwrite_cache --per_gpu_eval_batch_size=1 '''.split() self.run_and_check(__snake_case ) SCREAMING_SNAKE_CASE__ = ''' --model_type roberta --model_name_or_path ./examples/deebert/saved_models/roberta-base/MRPC/two_stage --task_name MRPC --do_eval --do_lower_case --data_dir ./tests/fixtures/tests_samples/MRPC/ --output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage --plot_data_dir ./examples/deebert/results/ --max_seq_length 128 --early_exit_entropy 0.1 --eval_highway --overwrite_cache --per_gpu_eval_batch_size=1 '''.split() self.run_and_check(__snake_case )
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'''simple docstring''' import os from shutil import copyfile from typing import Any, Dict, List, Optional, Tuple import sentencepiece as spm from ...tokenization_utils import AddedToken, PreTrainedTokenizer from ...utils import logging UpperCamelCase__: Tuple = logging.get_logger(__name__) UpperCamelCase__: Optional[int] = {"vocab_file": "sentencepiece.bpe.model"} UpperCamelCase__: Optional[int] = { "vocab_file": { "moussaKam/mbarthez": "https://huggingface.co/moussaKam/mbarthez/resolve/main/sentencepiece.bpe.model", "moussaKam/barthez": "https://huggingface.co/moussaKam/barthez/resolve/main/sentencepiece.bpe.model", "moussaKam/barthez-orangesum-title": ( "https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/sentencepiece.bpe.model" ), }, } UpperCamelCase__: Dict = { "moussaKam/mbarthez": 1024, "moussaKam/barthez": 1024, "moussaKam/barthez-orangesum-title": 1024, } UpperCamelCase__: Tuple = "▁" class SCREAMING_SNAKE_CASE( A__ ): """simple docstring""" lowerCamelCase__ = VOCAB_FILES_NAMES lowerCamelCase__ = PRETRAINED_VOCAB_FILES_MAP lowerCamelCase__ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES lowerCamelCase__ = ["""input_ids""", """attention_mask"""] def __init__( self : List[Any] , __snake_case : List[Any] , __snake_case : Tuple="<s>" , __snake_case : List[Any]="</s>" , __snake_case : int="</s>" , __snake_case : Any="<s>" , __snake_case : Optional[int]="<unk>" , __snake_case : Union[str, Any]="<pad>" , __snake_case : Union[str, Any]="<mask>" , __snake_case : Optional[Dict[str, Any]] = None , **__snake_case : Dict , ) -> None: # Mask token behave like a normal word, i.e. include the space before it UpperCAmelCase : int = AddedToken(__snake_case , lstrip=__snake_case , rstrip=__snake_case ) if isinstance(__snake_case , __snake_case ) else mask_token UpperCAmelCase : Union[str, Any] = {} if sp_model_kwargs is None else sp_model_kwargs super().__init__( bos_token=__snake_case , eos_token=__snake_case , unk_token=__snake_case , sep_token=__snake_case , cls_token=__snake_case , pad_token=__snake_case , mask_token=__snake_case , sp_model_kwargs=self.sp_model_kwargs , **__snake_case , ) UpperCAmelCase : Optional[int] = vocab_file UpperCAmelCase : int = spm.SentencePieceProcessor(**self.sp_model_kwargs ) self.sp_model.Load(str(__snake_case ) ) UpperCAmelCase : int = {'''<s>''': 0, '''<pad>''': 1, '''</s>''': 2, '''<unk>''': 3} UpperCAmelCase : Optional[Any] = len(self.sp_model ) - 1 UpperCAmelCase : Optional[int] = {v: k for k, v in self.fairseq_tokens_to_ids.items()} def A ( self : Tuple , __snake_case : List[int] , __snake_case : Optional[List[int]] = None ) -> List[int]: if token_ids_a is None: return [self.cls_token_id] + token_ids_a + [self.sep_token_id] UpperCAmelCase : Union[str, Any] = [self.cls_token_id] UpperCAmelCase : Optional[Any] = [self.sep_token_id] return cls + token_ids_a + sep + sep + token_ids_a + sep def A ( self : int , __snake_case : List[int] , __snake_case : Optional[List[int]] = None , __snake_case : bool = False ) -> List[int]: if already_has_special_tokens: return super().get_special_tokens_mask( token_ids_a=__snake_case , token_ids_a=__snake_case , already_has_special_tokens=__snake_case ) if token_ids_a is None: return [1] + ([0] * len(__snake_case )) + [1] return [1] + ([0] * len(__snake_case )) + [1, 1] + ([0] * len(__snake_case )) + [1] def A ( self : Optional[int] , __snake_case : List[int] , __snake_case : Optional[List[int]] = None ) -> List[int]: UpperCAmelCase : Tuple = [self.sep_token_id] UpperCAmelCase : Tuple = [self.cls_token_id] if token_ids_a is None: return len(cls + token_ids_a + sep ) * [0] return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0] @property def A ( self : Dict ) -> Optional[int]: return len(self.sp_model ) def A ( self : List[str] ) -> Dict: UpperCAmelCase : Optional[Any] = {self.convert_ids_to_tokens(__snake_case ): i for i in range(self.vocab_size )} vocab.update(self.added_tokens_encoder ) return vocab def A ( self : Optional[Any] , __snake_case : str ) -> List[str]: return self.sp_model.encode(__snake_case , out_type=__snake_case ) def A ( self : int , __snake_case : int ) -> int: if token in self.fairseq_tokens_to_ids: return self.fairseq_tokens_to_ids[token] UpperCAmelCase : Optional[Any] = self.sp_model.PieceToId(__snake_case ) return spm_id if spm_id else self.unk_token_id def A ( self : int , __snake_case : Any ) -> List[Any]: if index in self.fairseq_ids_to_tokens: return self.fairseq_ids_to_tokens[index] return self.sp_model.IdToPiece(__snake_case ) def A ( self : List[Any] , __snake_case : Union[str, Any] ) -> List[str]: UpperCAmelCase : Optional[Any] = [] UpperCAmelCase : int = '''''' UpperCAmelCase : Union[str, Any] = False for token in tokens: # make sure that special tokens are not decoded using sentencepiece model if token in self.all_special_tokens: if not prev_is_special: out_string += " " out_string += self.sp_model.decode(__snake_case ) + token UpperCAmelCase : str = True UpperCAmelCase : List[str] = [] else: current_sub_tokens.append(__snake_case ) UpperCAmelCase : Optional[int] = False out_string += self.sp_model.decode(__snake_case ) return out_string.strip() def __getstate__( self : Union[str, Any] ) -> Union[str, Any]: UpperCAmelCase : Optional[Any] = self.__dict__.copy() UpperCAmelCase : Any = None return state def __setstate__( self : Optional[int] , __snake_case : Union[str, Any] ) -> List[Any]: UpperCAmelCase : Dict = d # for backward compatibility if not hasattr(self , '''sp_model_kwargs''' ): UpperCAmelCase : Optional[Any] = {} UpperCAmelCase : Any = spm.SentencePieceProcessor(**self.sp_model_kwargs ) self.sp_model.Load(self.vocab_file ) def A ( self : Optional[int] , __snake_case : str , __snake_case : Optional[str] = None ) -> Tuple[str]: if not os.path.isdir(__snake_case ): logger.error(F"""Vocabulary path ({save_directory}) should be a directory""" ) return UpperCAmelCase : Union[str, Any] = os.path.join( __snake_case , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] ) if os.path.abspath(self.vocab_file ) != os.path.abspath(__snake_case ) and os.path.isfile(self.vocab_file ): copyfile(self.vocab_file , __snake_case ) elif not os.path.isfile(self.vocab_file ): with open(__snake_case , '''wb''' ) as fi: UpperCAmelCase : Any = self.sp_model.serialized_model_proto() fi.write(__snake_case ) return (out_vocab_file,)
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def __snake_case ( _UpperCAmelCase ): if a < 0: raise ValueError('''Input value must be a positive integer''' ) elif isinstance(_lowerCAmelCase , _lowerCAmelCase ): raise TypeError('''Input value must be a \'int\' type''' ) return bin(_lowerCAmelCase ).count('''1''' ) if __name__ == "__main__": import doctest doctest.testmod()
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'''simple docstring''' import tempfile import unittest import numpy as np from huggingface_hub import HfFolder, delete_repo from requests.exceptions import HTTPError from transformers import BertConfig, is_flax_available from transformers.testing_utils import TOKEN, USER, is_staging_test, require_flax if is_flax_available(): import os from flax.core.frozen_dict import unfreeze from flax.traverse_util import flatten_dict from transformers import FlaxBertModel UpperCamelCase__: Tuple = "0.12" # assumed parallelism: 8 @require_flax @is_staging_test class SCREAMING_SNAKE_CASE( unittest.TestCase ): """simple docstring""" @classmethod def A ( cls : Union[str, Any] ) -> int: UpperCAmelCase : Optional[Any] = TOKEN HfFolder.save_token(__snake_case ) @classmethod def A ( cls : List[str] ) -> Tuple: try: delete_repo(token=cls._token , repo_id='''test-model-flax''' ) except HTTPError: pass try: delete_repo(token=cls._token , repo_id='''valid_org/test-model-flax-org''' ) except HTTPError: pass def A ( self : int ) -> Tuple: UpperCAmelCase : List[Any] = BertConfig( vocab_size=99 , hidden_size=32 , num_hidden_layers=5 , num_attention_heads=4 , intermediate_size=37 ) UpperCAmelCase : Dict = FlaxBertModel(__snake_case ) model.push_to_hub('''test-model-flax''' , use_auth_token=self._token ) UpperCAmelCase : Tuple = FlaxBertModel.from_pretrained(F"""{USER}/test-model-flax""" ) UpperCAmelCase : List[Any] = flatten_dict(unfreeze(model.params ) ) UpperCAmelCase : Tuple = flatten_dict(unfreeze(new_model.params ) ) for key in base_params.keys(): UpperCAmelCase : Union[str, Any] = (base_params[key] - new_params[key]).sum().item() self.assertLessEqual(__snake_case , 1E-3 , msg=F"""{key} not identical""" ) # Reset repo delete_repo(token=self._token , repo_id='''test-model-flax''' ) # Push to hub via save_pretrained with tempfile.TemporaryDirectory() as tmp_dir: model.save_pretrained(__snake_case , repo_id='''test-model-flax''' , push_to_hub=__snake_case , use_auth_token=self._token ) UpperCAmelCase : str = FlaxBertModel.from_pretrained(F"""{USER}/test-model-flax""" ) UpperCAmelCase : Any = flatten_dict(unfreeze(model.params ) ) UpperCAmelCase : str = flatten_dict(unfreeze(new_model.params ) ) for key in base_params.keys(): UpperCAmelCase : Optional[Any] = (base_params[key] - new_params[key]).sum().item() self.assertLessEqual(__snake_case , 1E-3 , msg=F"""{key} not identical""" ) def A ( self : Optional[Any] ) -> Union[str, Any]: UpperCAmelCase : Dict = BertConfig( vocab_size=99 , hidden_size=32 , num_hidden_layers=5 , num_attention_heads=4 , intermediate_size=37 ) UpperCAmelCase : Optional[Any] = FlaxBertModel(__snake_case ) model.push_to_hub('''valid_org/test-model-flax-org''' , use_auth_token=self._token ) UpperCAmelCase : Union[str, Any] = FlaxBertModel.from_pretrained('''valid_org/test-model-flax-org''' ) UpperCAmelCase : List[Any] = flatten_dict(unfreeze(model.params ) ) UpperCAmelCase : int = flatten_dict(unfreeze(new_model.params ) ) for key in base_params.keys(): UpperCAmelCase : Any = (base_params[key] - new_params[key]).sum().item() self.assertLessEqual(__snake_case , 1E-3 , msg=F"""{key} not identical""" ) # Reset repo delete_repo(token=self._token , repo_id='''valid_org/test-model-flax-org''' ) # Push to hub via save_pretrained with tempfile.TemporaryDirectory() as tmp_dir: model.save_pretrained( __snake_case , repo_id='''valid_org/test-model-flax-org''' , push_to_hub=__snake_case , use_auth_token=self._token ) UpperCAmelCase : str = FlaxBertModel.from_pretrained('''valid_org/test-model-flax-org''' ) UpperCAmelCase : Any = flatten_dict(unfreeze(model.params ) ) UpperCAmelCase : Optional[Any] = flatten_dict(unfreeze(new_model.params ) ) for key in base_params.keys(): UpperCAmelCase : int = (base_params[key] - new_params[key]).sum().item() self.assertLessEqual(__snake_case , 1E-3 , msg=F"""{key} not identical""" ) def snake_case_ ( _lowerCAmelCase : Optional[Any] , _lowerCAmelCase : Any ) -> Union[str, Any]: UpperCAmelCase : str = True UpperCAmelCase : int = flatten_dict(modela.params ) UpperCAmelCase : Dict = flatten_dict(modela.params ) for key in flat_params_a.keys(): if np.sum(np.abs(flat_params_a[key] - flat_params_a[key] ) ) > 1e-4: UpperCAmelCase : Dict = False return models_are_equal @require_flax class SCREAMING_SNAKE_CASE( unittest.TestCase ): """simple docstring""" def A ( self : Tuple ) -> Union[str, Any]: UpperCAmelCase : List[Any] = BertConfig.from_pretrained('''hf-internal-testing/tiny-bert-flax-only''' ) UpperCAmelCase : Dict = FlaxBertModel(__snake_case ) UpperCAmelCase : int = '''bert''' with tempfile.TemporaryDirectory() as tmp_dir: model.save_pretrained(os.path.join(__snake_case , __snake_case ) ) with self.assertRaises(__snake_case ): UpperCAmelCase : Tuple = FlaxBertModel.from_pretrained(__snake_case ) UpperCAmelCase : str = FlaxBertModel.from_pretrained(__snake_case , subfolder=__snake_case ) self.assertTrue(check_models_equal(__snake_case , __snake_case ) ) def A ( self : List[str] ) -> Dict: UpperCAmelCase : Dict = BertConfig.from_pretrained('''hf-internal-testing/tiny-bert-flax-only''' ) UpperCAmelCase : Dict = FlaxBertModel(__snake_case ) UpperCAmelCase : Optional[int] = '''bert''' with tempfile.TemporaryDirectory() as tmp_dir: model.save_pretrained(os.path.join(__snake_case , __snake_case ) , max_shard_size='''10KB''' ) with self.assertRaises(__snake_case ): UpperCAmelCase : Any = FlaxBertModel.from_pretrained(__snake_case ) UpperCAmelCase : Union[str, Any] = FlaxBertModel.from_pretrained(__snake_case , subfolder=__snake_case ) self.assertTrue(check_models_equal(__snake_case , __snake_case ) ) def A ( self : Optional[int] ) -> str: UpperCAmelCase : Dict = '''bert''' UpperCAmelCase : int = '''hf-internal-testing/tiny-random-bert-subfolder''' with self.assertRaises(__snake_case ): UpperCAmelCase : Optional[Any] = FlaxBertModel.from_pretrained(__snake_case ) UpperCAmelCase : Tuple = FlaxBertModel.from_pretrained(__snake_case , subfolder=__snake_case ) self.assertIsNotNone(__snake_case ) def A ( self : Dict ) -> List[Any]: UpperCAmelCase : Optional[int] = '''bert''' UpperCAmelCase : int = '''hf-internal-testing/tiny-random-bert-sharded-subfolder''' with self.assertRaises(__snake_case ): UpperCAmelCase : Dict = FlaxBertModel.from_pretrained(__snake_case ) UpperCAmelCase : Union[str, Any] = FlaxBertModel.from_pretrained(__snake_case , subfolder=__snake_case ) self.assertIsNotNone(__snake_case )
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import unittest from parameterized import parameterized from transformers import OpenLlamaConfig, is_torch_available, set_seed from transformers.testing_utils import require_torch, torch_device from ...generation.test_utils import GenerationTesterMixin from ...test_configuration_common import ConfigTester from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask from ...test_pipeline_mixin import PipelineTesterMixin if is_torch_available(): import torch from transformers import OpenLlamaForCausalLM, OpenLlamaForSequenceClassification, OpenLlamaModel class lowercase : def __init__( self ,A__ ,A__=1_3 ,A__=7 ,A__=True ,A__=True ,A__=False ,A__=True ,A__=9_9 ,A__=3_2 ,A__=5 ,A__=4 ,A__=3_7 ,A__="gelu" ,A__=0.1 ,A__=0.1 ,A__=5_1_2 ,A__=1_6 ,A__=2 ,A__=0.02 ,A__=3 ,A__=4 ,A__=None ,): lowercase = parent lowercase = batch_size lowercase = seq_length lowercase = is_training lowercase = use_input_mask lowercase = use_token_type_ids lowercase = use_labels lowercase = vocab_size lowercase = hidden_size lowercase = num_hidden_layers lowercase = num_attention_heads lowercase = intermediate_size lowercase = hidden_act lowercase = hidden_dropout_prob lowercase = attention_probs_dropout_prob lowercase = max_position_embeddings lowercase = type_vocab_size lowercase = type_sequence_label_size lowercase = initializer_range lowercase = num_labels lowercase = num_choices lowercase = scope def A__ ( self): lowercase = ids_tensor([self.batch_size, self.seq_length] ,self.vocab_size) lowercase = None if self.use_input_mask: lowercase = random_attention_mask([self.batch_size, self.seq_length]) lowercase = None if self.use_token_type_ids: lowercase = ids_tensor([self.batch_size, self.seq_length] ,self.type_vocab_size) lowercase = None lowercase = None lowercase = None if self.use_labels: lowercase = ids_tensor([self.batch_size] ,self.type_sequence_label_size) lowercase = ids_tensor([self.batch_size, self.seq_length] ,self.num_labels) lowercase = ids_tensor([self.batch_size] ,self.num_choices) lowercase = self.get_config() return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels def A__ ( self): return OpenLlamaConfig( vocab_size=self.vocab_size ,hidden_size=self.hidden_size ,num_hidden_layers=self.num_hidden_layers ,num_attention_heads=self.num_attention_heads ,intermediate_size=self.intermediate_size ,hidden_act=self.hidden_act ,hidden_dropout_prob=self.hidden_dropout_prob ,attention_probs_dropout_prob=self.attention_probs_dropout_prob ,max_position_embeddings=self.max_position_embeddings ,type_vocab_size=self.type_vocab_size ,is_decoder=__snake_case ,initializer_range=self.initializer_range ,use_stable_embedding=__snake_case ,) def A__ ( self ,A__ ,A__ ,A__ ,A__ ,A__ ,A__ ,A__): lowercase = OpenLlamaModel(config=__snake_case) model.to(__snake_case) model.eval() lowercase = model(__snake_case ,attention_mask=__snake_case) lowercase = model(__snake_case) self.parent.assertEqual(result.last_hidden_state.shape ,(self.batch_size, self.seq_length, self.hidden_size)) def A__ ( self ,A__ ,A__ ,A__ ,A__ ,A__ ,A__ ,A__ ,A__ ,A__ ,): lowercase = True lowercase = OpenLlamaModel(__snake_case) model.to(__snake_case) model.eval() lowercase = model( __snake_case ,attention_mask=__snake_case ,encoder_hidden_states=__snake_case ,encoder_attention_mask=__snake_case ,) lowercase = model( __snake_case ,attention_mask=__snake_case ,encoder_hidden_states=__snake_case ,) lowercase = model(__snake_case ,attention_mask=__snake_case) self.parent.assertEqual(result.last_hidden_state.shape ,(self.batch_size, self.seq_length, self.hidden_size)) def A__ ( self ,A__ ,A__ ,A__ ,A__ ,A__ ,A__ ,A__ ,A__ ,A__ ,): lowercase = OpenLlamaForCausalLM(config=__snake_case) model.to(__snake_case) model.eval() lowercase = model(__snake_case ,attention_mask=__snake_case ,labels=__snake_case) self.parent.assertEqual(result.logits.shape ,(self.batch_size, self.seq_length, self.vocab_size)) def A__ ( self ,A__ ,A__ ,A__ ,A__ ,A__ ,A__ ,A__ ,A__ ,A__ ,): lowercase = True lowercase = True lowercase = OpenLlamaForCausalLM(config=__snake_case) model.to(__snake_case) model.eval() # first forward pass lowercase = model( __snake_case ,attention_mask=__snake_case ,encoder_hidden_states=__snake_case ,encoder_attention_mask=__snake_case ,use_cache=__snake_case ,) lowercase = outputs.past_key_values # create hypothetical multiple next token and extent to next_input_ids lowercase = ids_tensor((self.batch_size, 3) ,config.vocab_size) lowercase = ids_tensor((self.batch_size, 3) ,vocab_size=2) # append to next input_ids and lowercase = torch.cat([input_ids, next_tokens] ,dim=-1) lowercase = torch.cat([input_mask, next_mask] ,dim=-1) lowercase = model( __snake_case ,attention_mask=__snake_case ,encoder_hidden_states=__snake_case ,encoder_attention_mask=__snake_case ,output_hidden_states=__snake_case ,)['''hidden_states'''][0] lowercase = model( __snake_case ,attention_mask=__snake_case ,encoder_hidden_states=__snake_case ,encoder_attention_mask=__snake_case ,past_key_values=__snake_case ,output_hidden_states=__snake_case ,)['''hidden_states'''][0] # select random slice lowercase = ids_tensor((1,) ,output_from_past.shape[-1]).item() lowercase = output_from_no_past[:, -3:, random_slice_idx].detach() lowercase = output_from_past[:, :, random_slice_idx].detach() self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1]) # test that outputs are equal for slice self.parent.assertTrue(torch.allclose(__snake_case ,__snake_case ,atol=1E-3)) def A__ ( self): lowercase = self.prepare_config_and_inputs() ( lowercase ) = config_and_inputs lowercase = {'''input_ids''': input_ids, '''attention_mask''': input_mask} return config, inputs_dict @require_torch class lowercase ( A__ , A__ , A__ , unittest.TestCase ): lowercase_ : List[Any] =( (OpenLlamaModel, OpenLlamaForCausalLM, OpenLlamaForSequenceClassification) if is_torch_available() else () ) lowercase_ : str =(OpenLlamaForCausalLM,) if is_torch_available() else () lowercase_ : str =( { '''feature-extraction''': OpenLlamaModel, '''text-classification''': OpenLlamaForSequenceClassification, '''text-generation''': OpenLlamaForCausalLM, '''zero-shot''': OpenLlamaForSequenceClassification, } if is_torch_available() else {} ) lowercase_ : Tuple =False lowercase_ : Dict =False def A__ ( self): lowercase = OpenLlamaModelTester(self) lowercase = ConfigTester(self ,config_class=__snake_case ,hidden_size=3_7) def A__ ( self): self.config_tester.run_common_tests() def A__ ( self): lowercase = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_model(*__snake_case) def A__ ( self): lowercase = self.model_tester.prepare_config_and_inputs() for type in ["absolute", "relative_key", "relative_key_query"]: lowercase = type self.model_tester.create_and_check_model(*__snake_case) def A__ ( self): lowercase = self.model_tester.prepare_config_and_inputs_for_common() lowercase = 3 lowercase = input_dict['''input_ids'''] lowercase = input_ids.ne(1).to(__snake_case) lowercase = ids_tensor([self.model_tester.batch_size] ,self.model_tester.type_sequence_label_size) lowercase = OpenLlamaForSequenceClassification(__snake_case) model.to(__snake_case) model.eval() lowercase = model(__snake_case ,attention_mask=__snake_case ,labels=__snake_case) self.assertEqual(result.logits.shape ,(self.model_tester.batch_size, self.model_tester.num_labels)) def A__ ( self): lowercase = self.model_tester.prepare_config_and_inputs_for_common() lowercase = 3 lowercase = '''single_label_classification''' lowercase = input_dict['''input_ids'''] lowercase = input_ids.ne(1).to(__snake_case) lowercase = ids_tensor([self.model_tester.batch_size] ,self.model_tester.type_sequence_label_size) lowercase = OpenLlamaForSequenceClassification(__snake_case) model.to(__snake_case) model.eval() lowercase = model(__snake_case ,attention_mask=__snake_case ,labels=__snake_case) self.assertEqual(result.logits.shape ,(self.model_tester.batch_size, self.model_tester.num_labels)) def A__ ( self): lowercase = self.model_tester.prepare_config_and_inputs_for_common() lowercase = 3 lowercase = '''multi_label_classification''' lowercase = input_dict['''input_ids'''] lowercase = input_ids.ne(1).to(__snake_case) lowercase = ids_tensor( [self.model_tester.batch_size, config.num_labels] ,self.model_tester.type_sequence_label_size).to(torch.float) lowercase = OpenLlamaForSequenceClassification(__snake_case) model.to(__snake_case) model.eval() lowercase = model(__snake_case ,attention_mask=__snake_case ,labels=__snake_case) self.assertEqual(result.logits.shape ,(self.model_tester.batch_size, self.model_tester.num_labels)) @unittest.skip('''Open-Llama buffers include complex numbers, which breaks this test''') def A__ ( self): pass @parameterized.expand([('''linear''',), ('''dynamic''',)]) def A__ ( self ,A__): lowercase = self.model_tester.prepare_config_and_inputs_for_common() lowercase = ids_tensor([1, 1_0] ,config.vocab_size) lowercase = ids_tensor([1, int(config.max_position_embeddings * 1.5)] ,config.vocab_size) set_seed(4_2) # Fixed seed at init time so the two models get the same random weights lowercase = OpenLlamaModel(__snake_case) original_model.to(__snake_case) original_model.eval() lowercase = original_model(__snake_case).last_hidden_state lowercase = original_model(__snake_case).last_hidden_state set_seed(4_2) # Fixed seed at init time so the two models get the same random weights lowercase = {'''type''': scaling_type, '''factor''': 10.0} lowercase = OpenLlamaModel(__snake_case) scaled_model.to(__snake_case) scaled_model.eval() lowercase = scaled_model(__snake_case).last_hidden_state lowercase = scaled_model(__snake_case).last_hidden_state # Dynamic scaling does not change the RoPE embeddings until it receives an input longer than the original # maximum sequence length, so the outputs for the short input should match. if scaling_type == "dynamic": self.assertTrue(torch.allclose(__snake_case ,__snake_case ,atol=1E-5)) else: self.assertFalse(torch.allclose(__snake_case ,__snake_case ,atol=1E-5)) # The output should be different for long inputs self.assertFalse(torch.allclose(__snake_case ,__snake_case ,atol=1E-5))
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'''simple docstring''' import unittest from transformers import MraConfig, is_torch_available from transformers.testing_utils import require_torch, slow, torch_device from ...test_configuration_common import ConfigTester from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask if is_torch_available(): import torch from transformers import ( MraForMaskedLM, MraForMultipleChoice, MraForQuestionAnswering, MraForSequenceClassification, MraForTokenClassification, MraModel, ) from transformers.models.mra.modeling_mra import MRA_PRETRAINED_MODEL_ARCHIVE_LIST class SCREAMING_SNAKE_CASE: """simple docstring""" def __init__( self : Optional[int] , __snake_case : str , __snake_case : Union[str, Any]=2 , __snake_case : Optional[int]=8 , __snake_case : Any=True , __snake_case : Union[str, Any]=True , __snake_case : Dict=True , __snake_case : int=True , __snake_case : List[Any]=99 , __snake_case : str=16 , __snake_case : Tuple=5 , __snake_case : Tuple=2 , __snake_case : str=36 , __snake_case : Dict="gelu" , __snake_case : str=0.0 , __snake_case : Optional[int]=0.0 , __snake_case : Optional[int]=512 , __snake_case : Optional[Any]=16 , __snake_case : int=2 , __snake_case : int=0.02 , __snake_case : str=3 , __snake_case : Dict=4 , __snake_case : str=None , ) -> Optional[int]: UpperCAmelCase : Optional[int] = parent UpperCAmelCase : Tuple = batch_size UpperCAmelCase : List[str] = seq_length UpperCAmelCase : List[Any] = is_training UpperCAmelCase : int = use_input_mask UpperCAmelCase : Any = use_token_type_ids UpperCAmelCase : str = use_labels UpperCAmelCase : Union[str, Any] = vocab_size UpperCAmelCase : List[str] = hidden_size UpperCAmelCase : Optional[Any] = num_hidden_layers UpperCAmelCase : Union[str, Any] = num_attention_heads UpperCAmelCase : Optional[Any] = intermediate_size UpperCAmelCase : Union[str, Any] = hidden_act UpperCAmelCase : int = hidden_dropout_prob UpperCAmelCase : Optional[int] = attention_probs_dropout_prob UpperCAmelCase : Union[str, Any] = max_position_embeddings UpperCAmelCase : str = type_vocab_size UpperCAmelCase : List[str] = type_sequence_label_size UpperCAmelCase : Tuple = initializer_range UpperCAmelCase : Optional[Any] = num_labels UpperCAmelCase : Optional[int] = num_choices UpperCAmelCase : Any = scope def A ( self : Tuple ) -> Union[str, Any]: UpperCAmelCase : Optional[int] = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size ) UpperCAmelCase : Optional[int] = None if self.use_input_mask: UpperCAmelCase : Dict = random_attention_mask([self.batch_size, self.seq_length] ) UpperCAmelCase : Dict = None if self.use_token_type_ids: UpperCAmelCase : Union[str, Any] = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size ) UpperCAmelCase : str = None UpperCAmelCase : Tuple = None UpperCAmelCase : int = None if self.use_labels: UpperCAmelCase : str = ids_tensor([self.batch_size] , self.type_sequence_label_size ) UpperCAmelCase : Dict = ids_tensor([self.batch_size, self.seq_length] , self.num_labels ) UpperCAmelCase : str = ids_tensor([self.batch_size] , self.num_choices ) UpperCAmelCase : List[str] = self.get_config() return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels def A ( self : int ) -> Tuple: return MraConfig( vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , is_decoder=__snake_case , initializer_range=self.initializer_range , ) def A ( self : Optional[Any] ) -> Any: UpperCAmelCase : Optional[Any] = self.get_config() UpperCAmelCase : int = 300 return config def A ( self : Optional[Any] ) -> Any: ( ( UpperCAmelCase ) , ( UpperCAmelCase ) , ( UpperCAmelCase ) , ( UpperCAmelCase ) , ( UpperCAmelCase ) , ( UpperCAmelCase ) , ( UpperCAmelCase ) , ) : Tuple = self.prepare_config_and_inputs() UpperCAmelCase : Dict = True UpperCAmelCase : Tuple = floats_tensor([self.batch_size, self.seq_length, self.hidden_size] ) UpperCAmelCase : str = ids_tensor([self.batch_size, self.seq_length] , vocab_size=2 ) return ( config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels, encoder_hidden_states, encoder_attention_mask, ) def A ( self : Union[str, Any] , __snake_case : Union[str, Any] , __snake_case : int , __snake_case : Optional[int] , __snake_case : int , __snake_case : Dict , __snake_case : Tuple , __snake_case : Optional[Any] ) -> List[str]: UpperCAmelCase : int = MraModel(config=__snake_case ) model.to(__snake_case ) model.eval() UpperCAmelCase : Tuple = model(__snake_case , attention_mask=__snake_case , token_type_ids=__snake_case ) UpperCAmelCase : Optional[int] = model(__snake_case , token_type_ids=__snake_case ) UpperCAmelCase : Dict = model(__snake_case ) self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) ) def A ( self : Tuple , __snake_case : Optional[Any] , __snake_case : List[str] , __snake_case : List[str] , __snake_case : int , __snake_case : Union[str, Any] , __snake_case : Optional[Any] , __snake_case : Any , __snake_case : List[Any] , __snake_case : Optional[Any] , ) -> Tuple: UpperCAmelCase : str = True UpperCAmelCase : Tuple = MraModel(__snake_case ) model.to(__snake_case ) model.eval() UpperCAmelCase : Optional[int] = model( __snake_case , attention_mask=__snake_case , token_type_ids=__snake_case , encoder_hidden_states=__snake_case , encoder_attention_mask=__snake_case , ) UpperCAmelCase : Optional[Any] = model( __snake_case , attention_mask=__snake_case , token_type_ids=__snake_case , encoder_hidden_states=__snake_case , ) UpperCAmelCase : str = model(__snake_case , attention_mask=__snake_case , token_type_ids=__snake_case ) self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) ) def A ( self : Tuple , __snake_case : str , __snake_case : Optional[Any] , __snake_case : List[str] , __snake_case : Tuple , __snake_case : Optional[Any] , __snake_case : List[str] , __snake_case : int ) -> Any: UpperCAmelCase : Dict = MraForMaskedLM(config=__snake_case ) model.to(__snake_case ) model.eval() UpperCAmelCase : Optional[int] = model(__snake_case , attention_mask=__snake_case , token_type_ids=__snake_case , labels=__snake_case ) self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) ) def A ( self : Tuple , __snake_case : Tuple , __snake_case : Dict , __snake_case : Dict , __snake_case : Any , __snake_case : int , __snake_case : Optional[Any] , __snake_case : Tuple ) -> Optional[int]: UpperCAmelCase : List[str] = MraForQuestionAnswering(config=__snake_case ) model.to(__snake_case ) model.eval() UpperCAmelCase : List[Any] = model( __snake_case , attention_mask=__snake_case , token_type_ids=__snake_case , start_positions=__snake_case , end_positions=__snake_case , ) self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) ) self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) ) def A ( self : str , __snake_case : Optional[int] , __snake_case : List[str] , __snake_case : str , __snake_case : int , __snake_case : Optional[Any] , __snake_case : Union[str, Any] , __snake_case : List[Any] ) -> int: UpperCAmelCase : int = self.num_labels UpperCAmelCase : Union[str, Any] = MraForSequenceClassification(__snake_case ) model.to(__snake_case ) model.eval() UpperCAmelCase : List[str] = model(__snake_case , attention_mask=__snake_case , token_type_ids=__snake_case , labels=__snake_case ) self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) ) def A ( self : str , __snake_case : Dict , __snake_case : Optional[Any] , __snake_case : Dict , __snake_case : Union[str, Any] , __snake_case : Union[str, Any] , __snake_case : Union[str, Any] , __snake_case : Dict ) -> int: UpperCAmelCase : Tuple = self.num_labels UpperCAmelCase : List[str] = MraForTokenClassification(config=__snake_case ) model.to(__snake_case ) model.eval() UpperCAmelCase : str = model(__snake_case , attention_mask=__snake_case , token_type_ids=__snake_case , labels=__snake_case ) self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) ) def A ( self : str , __snake_case : int , __snake_case : Any , __snake_case : Tuple , __snake_case : Optional[int] , __snake_case : List[str] , __snake_case : str , __snake_case : Union[str, Any] ) -> Optional[Any]: UpperCAmelCase : Tuple = self.num_choices UpperCAmelCase : int = MraForMultipleChoice(config=__snake_case ) model.to(__snake_case ) model.eval() UpperCAmelCase : str = input_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous() UpperCAmelCase : List[Any] = token_type_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous() UpperCAmelCase : List[str] = input_mask.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous() UpperCAmelCase : List[str] = model( __snake_case , attention_mask=__snake_case , token_type_ids=__snake_case , labels=__snake_case , ) self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) ) def A ( self : str ) -> Dict: UpperCAmelCase : Any = self.prepare_config_and_inputs() ( ( UpperCAmelCase ) , ( UpperCAmelCase ) , ( UpperCAmelCase ) , ( UpperCAmelCase ) , ( UpperCAmelCase ) , ( UpperCAmelCase ) , ( UpperCAmelCase ) , ) : List[str] = config_and_inputs UpperCAmelCase : Any = {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask} return config, inputs_dict @require_torch class SCREAMING_SNAKE_CASE( A__ , unittest.TestCase ): """simple docstring""" lowerCamelCase__ = ( ( MraModel, MraForMaskedLM, MraForMultipleChoice, MraForQuestionAnswering, MraForSequenceClassification, MraForTokenClassification, ) if is_torch_available() else () ) lowerCamelCase__ = False lowerCamelCase__ = False lowerCamelCase__ = False lowerCamelCase__ = False lowerCamelCase__ = () def A ( self : int ) -> Union[str, Any]: UpperCAmelCase : List[str] = MraModelTester(self ) UpperCAmelCase : Optional[int] = ConfigTester(self , config_class=__snake_case , hidden_size=37 ) def A ( self : Optional[Any] ) -> str: self.config_tester.run_common_tests() def A ( self : Tuple ) -> Optional[Any]: UpperCAmelCase : Optional[int] = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_model(*__snake_case ) def A ( self : List[Any] ) -> Optional[Any]: UpperCAmelCase : Tuple = self.model_tester.prepare_config_and_inputs() for type in ["absolute", "relative_key", "relative_key_query"]: UpperCAmelCase : List[Any] = type self.model_tester.create_and_check_model(*__snake_case ) def A ( self : Tuple ) -> Dict: UpperCAmelCase : List[Any] = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_masked_lm(*__snake_case ) def A ( self : Tuple ) -> List[str]: UpperCAmelCase : Tuple = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_multiple_choice(*__snake_case ) def A ( self : int ) -> Dict: UpperCAmelCase : Optional[int] = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_question_answering(*__snake_case ) def A ( self : Dict ) -> Optional[int]: UpperCAmelCase : Any = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_sequence_classification(*__snake_case ) def A ( self : Any ) -> Optional[int]: UpperCAmelCase : List[str] = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_token_classification(*__snake_case ) @slow def A ( self : Dict ) -> Any: for model_name in MRA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: UpperCAmelCase : str = MraModel.from_pretrained(__snake_case ) self.assertIsNotNone(__snake_case ) @unittest.skip(reason='''MRA does not output attentions''' ) def A ( self : str ) -> Optional[Any]: return @require_torch class SCREAMING_SNAKE_CASE( unittest.TestCase ): """simple docstring""" @slow def A ( self : Tuple ) -> List[Any]: UpperCAmelCase : int = MraModel.from_pretrained('''uw-madison/mra-base-512-4''' ) UpperCAmelCase : Optional[Any] = torch.arange(256 ).unsqueeze(0 ) with torch.no_grad(): UpperCAmelCase : List[Any] = model(__snake_case )[0] UpperCAmelCase : Optional[Any] = torch.Size((1, 256, 768) ) self.assertEqual(output.shape , __snake_case ) UpperCAmelCase : Any = torch.tensor( [[[-0.01_40, 0.08_30, -0.03_81], [0.15_46, 0.14_02, 0.02_20], [0.11_62, 0.08_51, 0.01_65]]] ) self.assertTrue(torch.allclose(output[:, :3, :3] , __snake_case , atol=1E-4 ) ) @slow def A ( self : Optional[Any] ) -> Any: UpperCAmelCase : Optional[int] = MraForMaskedLM.from_pretrained('''uw-madison/mra-base-512-4''' ) UpperCAmelCase : Dict = torch.arange(256 ).unsqueeze(0 ) with torch.no_grad(): UpperCAmelCase : List[Any] = model(__snake_case )[0] UpperCAmelCase : int = 50265 UpperCAmelCase : int = torch.Size((1, 256, vocab_size) ) self.assertEqual(output.shape , __snake_case ) UpperCAmelCase : Union[str, Any] = torch.tensor( [[[9.25_95, -3.60_38, 11.88_19], [9.38_69, -3.26_93, 11.09_56], [11.85_24, -3.49_38, 13.12_10]]] ) self.assertTrue(torch.allclose(output[:, :3, :3] , __snake_case , atol=1E-4 ) ) @slow def A ( self : str ) -> List[Any]: UpperCAmelCase : List[Any] = MraForMaskedLM.from_pretrained('''uw-madison/mra-base-4096-8-d3''' ) UpperCAmelCase : List[Any] = torch.arange(4096 ).unsqueeze(0 ) with torch.no_grad(): UpperCAmelCase : Tuple = model(__snake_case )[0] UpperCAmelCase : Optional[int] = 50265 UpperCAmelCase : Tuple = torch.Size((1, 4096, vocab_size) ) self.assertEqual(output.shape , __snake_case ) UpperCAmelCase : Optional[int] = torch.tensor( [[[5.47_89, -2.35_64, 7.50_64], [7.90_67, -1.33_69, 9.96_68], [9.07_12, -1.81_06, 7.03_80]]] ) self.assertTrue(torch.allclose(output[:, :3, :3] , __snake_case , atol=1E-4 ) )
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from typing import Optional from urllib.parse import quote import huggingface_hub as hfh from packaging import version def lowercase( UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ = None ) -> str: '''simple docstring''' if version.parse(hfh.__version__ ).release < version.parse("""0.11.0""" ).release: # old versions of hfh don't url-encode the file path UpperCamelCase = quote(_lowerCAmelCase ) return hfh.hf_hub_url(_lowerCAmelCase , _lowerCAmelCase , repo_type="""dataset""" , revision=_lowerCAmelCase )
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'''simple docstring''' import unittest import numpy as np from transformers.testing_utils import require_flax, require_tf, require_torch from transformers.utils import ( expand_dims, flatten_dict, is_flax_available, is_tf_available, is_torch_available, reshape, squeeze, transpose, ) if is_flax_available(): import jax.numpy as jnp if is_tf_available(): import tensorflow as tf if is_torch_available(): import torch class SCREAMING_SNAKE_CASE( unittest.TestCase ): """simple docstring""" def A ( self : Any ) -> str: UpperCAmelCase : Any = { '''task_specific_params''': { '''summarization''': {'''length_penalty''': 1.0, '''max_length''': 128, '''min_length''': 12, '''num_beams''': 4}, '''summarization_cnn''': {'''length_penalty''': 2.0, '''max_length''': 142, '''min_length''': 56, '''num_beams''': 4}, '''summarization_xsum''': {'''length_penalty''': 1.0, '''max_length''': 62, '''min_length''': 11, '''num_beams''': 6}, } } UpperCAmelCase : int = { '''task_specific_params.summarization.length_penalty''': 1.0, '''task_specific_params.summarization.max_length''': 128, '''task_specific_params.summarization.min_length''': 12, '''task_specific_params.summarization.num_beams''': 4, '''task_specific_params.summarization_cnn.length_penalty''': 2.0, '''task_specific_params.summarization_cnn.max_length''': 142, '''task_specific_params.summarization_cnn.min_length''': 56, '''task_specific_params.summarization_cnn.num_beams''': 4, '''task_specific_params.summarization_xsum.length_penalty''': 1.0, '''task_specific_params.summarization_xsum.max_length''': 62, '''task_specific_params.summarization_xsum.min_length''': 11, '''task_specific_params.summarization_xsum.num_beams''': 6, } self.assertEqual(flatten_dict(__snake_case ) , __snake_case ) def A ( self : int ) -> str: UpperCAmelCase : Any = np.random.randn(3 , 4 ) self.assertTrue(np.allclose(transpose(__snake_case ) , x.transpose() ) ) UpperCAmelCase : str = np.random.randn(3 , 4 , 5 ) self.assertTrue(np.allclose(transpose(__snake_case , axes=(1, 2, 0) ) , x.transpose((1, 2, 0) ) ) ) @require_torch def A ( self : str ) -> Union[str, Any]: UpperCAmelCase : Any = np.random.randn(3 , 4 ) UpperCAmelCase : List[Any] = torch.tensor(__snake_case ) self.assertTrue(np.allclose(transpose(__snake_case ) , transpose(__snake_case ).numpy() ) ) UpperCAmelCase : Tuple = np.random.randn(3 , 4 , 5 ) UpperCAmelCase : Any = torch.tensor(__snake_case ) self.assertTrue(np.allclose(transpose(__snake_case , axes=(1, 2, 0) ) , transpose(__snake_case , axes=(1, 2, 0) ).numpy() ) ) @require_tf def A ( self : List[str] ) -> Optional[Any]: UpperCAmelCase : int = np.random.randn(3 , 4 ) UpperCAmelCase : Optional[int] = tf.constant(__snake_case ) self.assertTrue(np.allclose(transpose(__snake_case ) , transpose(__snake_case ).numpy() ) ) UpperCAmelCase : Union[str, Any] = np.random.randn(3 , 4 , 5 ) UpperCAmelCase : str = tf.constant(__snake_case ) self.assertTrue(np.allclose(transpose(__snake_case , axes=(1, 2, 0) ) , transpose(__snake_case , axes=(1, 2, 0) ).numpy() ) ) @require_flax def A ( self : Tuple ) -> Any: UpperCAmelCase : List[Any] = np.random.randn(3 , 4 ) UpperCAmelCase : List[str] = jnp.array(__snake_case ) self.assertTrue(np.allclose(transpose(__snake_case ) , np.asarray(transpose(__snake_case ) ) ) ) UpperCAmelCase : Dict = np.random.randn(3 , 4 , 5 ) UpperCAmelCase : int = jnp.array(__snake_case ) self.assertTrue(np.allclose(transpose(__snake_case , axes=(1, 2, 0) ) , np.asarray(transpose(__snake_case , axes=(1, 2, 0) ) ) ) ) def A ( self : Optional[Any] ) -> Any: UpperCAmelCase : Union[str, Any] = np.random.randn(3 , 4 ) self.assertTrue(np.allclose(reshape(__snake_case , (4, 3) ) , np.reshape(__snake_case , (4, 3) ) ) ) UpperCAmelCase : Union[str, Any] = np.random.randn(3 , 4 , 5 ) self.assertTrue(np.allclose(reshape(__snake_case , (12, 5) ) , np.reshape(__snake_case , (12, 5) ) ) ) @require_torch def A ( self : Union[str, Any] ) -> int: UpperCAmelCase : Dict = np.random.randn(3 , 4 ) UpperCAmelCase : Optional[Any] = torch.tensor(__snake_case ) self.assertTrue(np.allclose(reshape(__snake_case , (4, 3) ) , reshape(__snake_case , (4, 3) ).numpy() ) ) UpperCAmelCase : List[str] = np.random.randn(3 , 4 , 5 ) UpperCAmelCase : List[Any] = torch.tensor(__snake_case ) self.assertTrue(np.allclose(reshape(__snake_case , (12, 5) ) , reshape(__snake_case , (12, 5) ).numpy() ) ) @require_tf def A ( self : int ) -> List[str]: UpperCAmelCase : List[Any] = np.random.randn(3 , 4 ) UpperCAmelCase : List[str] = tf.constant(__snake_case ) self.assertTrue(np.allclose(reshape(__snake_case , (4, 3) ) , reshape(__snake_case , (4, 3) ).numpy() ) ) UpperCAmelCase : List[Any] = np.random.randn(3 , 4 , 5 ) UpperCAmelCase : Optional[Any] = tf.constant(__snake_case ) self.assertTrue(np.allclose(reshape(__snake_case , (12, 5) ) , reshape(__snake_case , (12, 5) ).numpy() ) ) @require_flax def A ( self : Any ) -> Dict: UpperCAmelCase : Tuple = np.random.randn(3 , 4 ) UpperCAmelCase : Union[str, Any] = jnp.array(__snake_case ) self.assertTrue(np.allclose(reshape(__snake_case , (4, 3) ) , np.asarray(reshape(__snake_case , (4, 3) ) ) ) ) UpperCAmelCase : Any = np.random.randn(3 , 4 , 5 ) UpperCAmelCase : Optional[Any] = jnp.array(__snake_case ) self.assertTrue(np.allclose(reshape(__snake_case , (12, 5) ) , np.asarray(reshape(__snake_case , (12, 5) ) ) ) ) def A ( self : List[Any] ) -> List[Any]: UpperCAmelCase : Union[str, Any] = np.random.randn(1 , 3 , 4 ) self.assertTrue(np.allclose(squeeze(__snake_case ) , np.squeeze(__snake_case ) ) ) UpperCAmelCase : str = np.random.randn(1 , 4 , 1 , 5 ) self.assertTrue(np.allclose(squeeze(__snake_case , axis=2 ) , np.squeeze(__snake_case , axis=2 ) ) ) @require_torch def A ( self : Optional[int] ) -> Union[str, Any]: UpperCAmelCase : Optional[int] = np.random.randn(1 , 3 , 4 ) UpperCAmelCase : List[str] = torch.tensor(__snake_case ) self.assertTrue(np.allclose(squeeze(__snake_case ) , squeeze(__snake_case ).numpy() ) ) UpperCAmelCase : Any = np.random.randn(1 , 4 , 1 , 5 ) UpperCAmelCase : str = torch.tensor(__snake_case ) self.assertTrue(np.allclose(squeeze(__snake_case , axis=2 ) , squeeze(__snake_case , axis=2 ).numpy() ) ) @require_tf def A ( self : Optional[Any] ) -> Dict: UpperCAmelCase : int = np.random.randn(1 , 3 , 4 ) UpperCAmelCase : Optional[int] = tf.constant(__snake_case ) self.assertTrue(np.allclose(squeeze(__snake_case ) , squeeze(__snake_case ).numpy() ) ) UpperCAmelCase : List[str] = np.random.randn(1 , 4 , 1 , 5 ) UpperCAmelCase : Optional[int] = tf.constant(__snake_case ) self.assertTrue(np.allclose(squeeze(__snake_case , axis=2 ) , squeeze(__snake_case , axis=2 ).numpy() ) ) @require_flax def A ( self : List[Any] ) -> Dict: UpperCAmelCase : Optional[int] = np.random.randn(1 , 3 , 4 ) UpperCAmelCase : int = jnp.array(__snake_case ) self.assertTrue(np.allclose(squeeze(__snake_case ) , np.asarray(squeeze(__snake_case ) ) ) ) UpperCAmelCase : str = np.random.randn(1 , 4 , 1 , 5 ) UpperCAmelCase : int = jnp.array(__snake_case ) self.assertTrue(np.allclose(squeeze(__snake_case , axis=2 ) , np.asarray(squeeze(__snake_case , axis=2 ) ) ) ) def A ( self : Optional[Any] ) -> int: UpperCAmelCase : Optional[Any] = np.random.randn(3 , 4 ) self.assertTrue(np.allclose(expand_dims(__snake_case , axis=1 ) , np.expand_dims(__snake_case , axis=1 ) ) ) @require_torch def A ( self : List[str] ) -> Tuple: UpperCAmelCase : Tuple = np.random.randn(3 , 4 ) UpperCAmelCase : Tuple = torch.tensor(__snake_case ) self.assertTrue(np.allclose(expand_dims(__snake_case , axis=1 ) , expand_dims(__snake_case , axis=1 ).numpy() ) ) @require_tf def A ( self : List[str] ) -> Union[str, Any]: UpperCAmelCase : Union[str, Any] = np.random.randn(3 , 4 ) UpperCAmelCase : Any = tf.constant(__snake_case ) self.assertTrue(np.allclose(expand_dims(__snake_case , axis=1 ) , expand_dims(__snake_case , axis=1 ).numpy() ) ) @require_flax def A ( self : Any ) -> List[Any]: UpperCAmelCase : List[str] = np.random.randn(3 , 4 ) UpperCAmelCase : str = jnp.array(__snake_case ) self.assertTrue(np.allclose(expand_dims(__snake_case , axis=1 ) , np.asarray(expand_dims(__snake_case , axis=1 ) ) ) )
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'''simple docstring''' import tempfile import unittest from transformers import AutoModelForSeqaSeqLM, AutoTokenizer from transformers.testing_utils import ( is_torch_available, require_optimum, require_torch, slow, ) if is_torch_available(): import torch @require_torch @require_optimum @slow class UpperCAmelCase_ ( unittest.TestCase ): """simple docstring""" def lowerCamelCase ( self : int ): snake_case__ : List[Any] = '''hf-internal-testing/tiny-random-t5''' snake_case__ : str = AutoTokenizer.from_pretrained(__snake_case ) snake_case__ : Any = AutoModelForSeqaSeqLM.from_pretrained(__snake_case ) snake_case__ : Tuple = tokenizer("""This is me""" , return_tensors="""pt""" ) snake_case__ : Optional[Any] = model.to_bettertransformer() self.assertTrue(any("""BetterTransformer""" in mod.__class__.__name__ for _, mod in model.named_modules() ) ) snake_case__ : str = model.generate(**__snake_case ) snake_case__ : List[Any] = model.reverse_bettertransformer() self.assertFalse(any("""BetterTransformer""" in mod.__class__.__name__ for _, mod in model.named_modules() ) ) with tempfile.TemporaryDirectory() as tmpdirname: model.save_pretrained(__snake_case ) snake_case__ : Tuple = AutoModelForSeqaSeqLM.from_pretrained(__snake_case ) self.assertFalse( any("""BetterTransformer""" in mod.__class__.__name__ for _, mod in model_reloaded.named_modules() ) ) snake_case__ : Optional[int] = model_reloaded.generate(**__snake_case ) self.assertTrue(torch.allclose(__snake_case , __snake_case ) ) def lowerCamelCase ( self : Optional[Any] ): snake_case__ : Tuple = '''hf-internal-testing/tiny-random-t5''' snake_case__ : Any = AutoModelForSeqaSeqLM.from_pretrained(__snake_case ) snake_case__ : List[str] = model.to_bettertransformer() with tempfile.TemporaryDirectory() as tmpdirname: with self.assertRaises(__snake_case ): model.save_pretrained(__snake_case ) snake_case__ : List[str] = model.reverse_bettertransformer() model.save_pretrained(__snake_case )
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'''simple docstring''' import argparse import os import re import packaging.version UpperCamelCase__: Union[str, Any] = "examples/" UpperCamelCase__: Optional[Any] = { "examples": (re.compile(r"^check_min_version\(\"[^\"]+\"\)\s*$", re.MULTILINE), "check_min_version(\"VERSION\")\n"), "init": (re.compile(r"^__version__\s+=\s+\"([^\"]+)\"\s*$", re.MULTILINE), "__version__ = \"VERSION\"\n"), "setup": (re.compile(r"^(\s*)version\s*=\s*\"[^\"]+\",", re.MULTILINE), r"\1version=\"VERSION\","), "doc": (re.compile(r"^(\s*)release\s*=\s*\"[^\"]+\"$", re.MULTILINE), "release = \"VERSION\"\n"), } UpperCamelCase__: Optional[int] = { "init": "src/diffusers/__init__.py", "setup": "setup.py", } UpperCamelCase__: List[Any] = "README.md" def snake_case_ ( _lowerCAmelCase : Optional[Any] , _lowerCAmelCase : List[Any] , _lowerCAmelCase : int ) -> Optional[int]: with open(_lowerCAmelCase , '''r''' , encoding='''utf-8''' , newline='''\n''' ) as f: UpperCAmelCase : Optional[int] = f.read() UpperCAmelCase , UpperCAmelCase : List[Any] = REPLACE_PATTERNS[pattern] UpperCAmelCase : List[Any] = replace.replace('''VERSION''' , _lowerCAmelCase ) UpperCAmelCase : Optional[Any] = re_pattern.sub(_lowerCAmelCase , _lowerCAmelCase ) with open(_lowerCAmelCase , '''w''' , encoding='''utf-8''' , newline='''\n''' ) as f: f.write(_lowerCAmelCase ) def snake_case_ ( _lowerCAmelCase : Any ) -> Optional[int]: for folder, directories, fnames in os.walk(_lowerCAmelCase ): # Removing some of the folders with non-actively maintained examples from the walk if "research_projects" in directories: directories.remove('''research_projects''' ) if "legacy" in directories: directories.remove('''legacy''' ) for fname in fnames: if fname.endswith('''.py''' ): update_version_in_file(os.path.join(_lowerCAmelCase , _lowerCAmelCase ) , _lowerCAmelCase , pattern='''examples''' ) def snake_case_ ( _lowerCAmelCase : Any , _lowerCAmelCase : str=False ) -> List[str]: for pattern, fname in REPLACE_FILES.items(): update_version_in_file(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ) if not patch: update_version_in_examples(_lowerCAmelCase ) def snake_case_ ( ) -> Optional[Any]: UpperCAmelCase : Optional[int] = '''🤗 Transformers currently provides the following architectures''' UpperCAmelCase : Optional[int] = '''1. Want to contribute a new model?''' with open(_lowerCAmelCase , '''r''' , encoding='''utf-8''' , newline='''\n''' ) as f: UpperCAmelCase : Optional[Any] = f.readlines() # Find the start of the list. UpperCAmelCase : List[Any] = 0 while not lines[start_index].startswith(_start_prompt ): start_index += 1 start_index += 1 UpperCAmelCase : Optional[Any] = start_index # Update the lines in the model list. while not lines[index].startswith(_end_prompt ): if lines[index].startswith('''1.''' ): UpperCAmelCase : Optional[int] = lines[index].replace( '''https://huggingface.co/docs/diffusers/main/model_doc''' , '''https://huggingface.co/docs/diffusers/model_doc''' , ) index += 1 with open(_lowerCAmelCase , '''w''' , encoding='''utf-8''' , newline='''\n''' ) as f: f.writelines(_lowerCAmelCase ) def snake_case_ ( ) -> Optional[Any]: with open(REPLACE_FILES['''init'''] , '''r''' ) as f: UpperCAmelCase : Union[str, Any] = f.read() UpperCAmelCase : int = REPLACE_PATTERNS['''init'''][0].search(_lowerCAmelCase ).groups()[0] return packaging.version.parse(_lowerCAmelCase ) def snake_case_ ( _lowerCAmelCase : List[str]=False ) -> Any: UpperCAmelCase : Optional[Any] = get_version() if patch and default_version.is_devrelease: raise ValueError('''Can\'t create a patch version from the dev branch, checkout a released version!''' ) if default_version.is_devrelease: UpperCAmelCase : Optional[int] = default_version.base_version elif patch: UpperCAmelCase : Union[str, Any] = f"""{default_version.major}.{default_version.minor}.{default_version.micro + 1}""" else: UpperCAmelCase : Union[str, Any] = f"""{default_version.major}.{default_version.minor + 1}.0""" # Now let's ask nicely if that's the right one. UpperCAmelCase : Dict = input(f"""Which version are you releasing? [{default_version}]""" ) if len(_lowerCAmelCase ) == 0: UpperCAmelCase : Tuple = default_version print(f"""Updating version to {version}.""" ) global_version_update(_lowerCAmelCase , patch=_lowerCAmelCase ) def snake_case_ ( ) -> Any: UpperCAmelCase : List[Any] = get_version() UpperCAmelCase : List[str] = f"""{current_version.major}.{current_version.minor + 1}.0.dev0""" UpperCAmelCase : List[Any] = current_version.base_version # Check with the user we got that right. UpperCAmelCase : Optional[int] = input(f"""Which version are we developing now? [{dev_version}]""" ) if len(_lowerCAmelCase ) == 0: UpperCAmelCase : Dict = dev_version print(f"""Updating version to {version}.""" ) global_version_update(_lowerCAmelCase ) # print("Cleaning main README, don't forget to run `make fix-copies`.") # clean_main_ref_in_model_list() if __name__ == "__main__": UpperCamelCase__: Union[str, Any] = argparse.ArgumentParser() parser.add_argument("--post_release", action="store_true", help="Whether this is pre or post release.") parser.add_argument("--patch", action="store_true", help="Whether or not this is a patch release.") UpperCamelCase__: Optional[Any] = parser.parse_args() if not args.post_release: pre_release_work(patch=args.patch) elif args.patch: print("Nothing to do after a patch :-)") else: post_release_work()
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from __future__ import annotations from numpy import array, cos, cross, floataa, radians, sin from numpy.typing import NDArray def lowerCamelCase__ ( a__ : float , a__ : float , a__ : bool = False ) -> list[float]: if radian_mode: return [magnitude * cos(_lowerCAmelCase ), magnitude * sin(_lowerCAmelCase )] return [magnitude * cos(radians(_lowerCAmelCase ) ), magnitude * sin(radians(_lowerCAmelCase ) )] def lowerCamelCase__ ( a__ : NDArray[floataa] , a__ : NDArray[floataa] , a__ : float = 10**-1 ) -> bool: UpperCamelCase_ = cross(_lowerCAmelCase , _lowerCAmelCase ) UpperCamelCase_ = sum(_lowerCAmelCase ) return abs(_lowerCAmelCase ) < eps if __name__ == "__main__": # Test to check if it works _A = array( [ polar_force(7_1_8.4, 180 - 30), polar_force(8_7_9.5_4, 45), polar_force(100, -90), ] ) _A = array([[0, 0], [0, 0], [0, 0]]) assert in_static_equilibrium(forces, location) # Problem 1 in image_data/2D_problems.jpg _A = array( [ polar_force(30 * 9.8_1, 15), polar_force(215, 180 - 45), polar_force(264, 90 - 30), ] ) _A = array([[0, 0], [0, 0], [0, 0]]) assert in_static_equilibrium(forces, location) # Problem in image_data/2D_problems_1.jpg _A = array([[0, -2_000], [0, -1_200], [0, 15_600], [0, -12_400]]) _A = array([[0, 0], [6, 0], [10, 0], [12, 0]]) assert in_static_equilibrium(forces, location) import doctest doctest.testmod()
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'''simple docstring''' from __future__ import annotations import matplotlib.pyplot as plt # type: ignore import numpy # initial triangle of Koch snowflake UpperCamelCase__: Tuple = numpy.array([0, 0]) UpperCamelCase__: Union[str, Any] = numpy.array([0.5, 0.8660254]) UpperCamelCase__: Dict = numpy.array([1, 0]) UpperCamelCase__: int = [VECTOR_1, VECTOR_2, VECTOR_3, VECTOR_1] def snake_case_ ( _lowerCAmelCase : list[numpy.ndarray] , _lowerCAmelCase : int ) -> list[numpy.ndarray]: UpperCAmelCase : Union[str, Any] = initial_vectors for _ in range(_lowerCAmelCase ): UpperCAmelCase : Union[str, Any] = iteration_step(_lowerCAmelCase ) return vectors def snake_case_ ( _lowerCAmelCase : list[numpy.ndarray] ) -> list[numpy.ndarray]: UpperCAmelCase : Tuple = [] for i, start_vector in enumerate(vectors[:-1] ): UpperCAmelCase : List[str] = vectors[i + 1] new_vectors.append(_lowerCAmelCase ) UpperCAmelCase : Optional[Any] = end_vector - start_vector new_vectors.append(start_vector + difference_vector / 3 ) new_vectors.append( start_vector + difference_vector / 3 + rotate(difference_vector / 3 , 60 ) ) new_vectors.append(start_vector + difference_vector * 2 / 3 ) new_vectors.append(vectors[-1] ) return new_vectors def snake_case_ ( _lowerCAmelCase : numpy.ndarray , _lowerCAmelCase : float ) -> numpy.ndarray: UpperCAmelCase : List[str] = numpy.radians(_lowerCAmelCase ) UpperCAmelCase , UpperCAmelCase : Tuple = numpy.cos(_lowerCAmelCase ), numpy.sin(_lowerCAmelCase ) UpperCAmelCase : Union[str, Any] = numpy.array(((c, -s), (s, c)) ) return numpy.dot(_lowerCAmelCase , _lowerCAmelCase ) def snake_case_ ( _lowerCAmelCase : list[numpy.ndarray] ) -> None: UpperCAmelCase : List[Any] = plt.gca() axes.set_aspect('''equal''' ) # matplotlib.pyplot.plot takes a list of all x-coordinates and a list of all # y-coordinates as inputs, which are constructed from the vector-list using # zip() UpperCAmelCase , UpperCAmelCase : str = zip(*_lowerCAmelCase ) plt.plot(_lowerCAmelCase , _lowerCAmelCase ) plt.show() if __name__ == "__main__": import doctest doctest.testmod() UpperCamelCase__: List[Any] = iterate(INITIAL_VECTORS, 5) plot(processed_vectors)
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from ...configuration_utils import PretrainedConfig _lowerCamelCase ={ "google/tapas-base-finetuned-sqa": ( "https://huggingface.co/google/tapas-base-finetuned-sqa/resolve/main/config.json" ), "google/tapas-base-finetuned-wtq": ( "https://huggingface.co/google/tapas-base-finetuned-wtq/resolve/main/config.json" ), "google/tapas-base-finetuned-wikisql-supervised": ( "https://huggingface.co/google/tapas-base-finetuned-wikisql-supervised/resolve/main/config.json" ), "google/tapas-base-finetuned-tabfact": ( "https://huggingface.co/google/tapas-base-finetuned-tabfact/resolve/main/config.json" ), } class a_ ( A__ ): """simple docstring""" __UpperCAmelCase = 'tapas' def __init__( self : Dict ,snake_case : Tuple=30522 ,snake_case : Dict=768 ,snake_case : Any=12 ,snake_case : List[str]=12 ,snake_case : Optional[Any]=3072 ,snake_case : Any="gelu" ,snake_case : List[Any]=0.1 ,snake_case : Any=0.1 ,snake_case : Union[str, Any]=1024 ,snake_case : List[str]=[3, 256, 256, 2, 256, 256, 10] ,snake_case : Any=0.02 ,snake_case : Optional[Any]=1e-12 ,snake_case : Optional[int]=0 ,snake_case : Tuple=10.0 ,snake_case : Any=0 ,snake_case : Optional[int]=1.0 ,snake_case : Tuple=None ,snake_case : Optional[Any]=1.0 ,snake_case : Any=False ,snake_case : Tuple=None ,snake_case : int=1.0 ,snake_case : Union[str, Any]=1.0 ,snake_case : Dict=False ,snake_case : List[Any]=False ,snake_case : Any="ratio" ,snake_case : Union[str, Any]=None ,snake_case : Union[str, Any]=None ,snake_case : int=64 ,snake_case : List[str]=32 ,snake_case : Optional[int]=False ,snake_case : Optional[int]=True ,snake_case : str=False ,snake_case : List[Any]=False ,snake_case : Any=True ,snake_case : int=False ,snake_case : Dict=None ,snake_case : Union[str, Any]=None ,**snake_case : Optional[int] ,): super().__init__(pad_token_id=__snake_case ,**__snake_case ) # BERT hyperparameters (with updated max_position_embeddings and type_vocab_sizes) SCREAMING_SNAKE_CASE =vocab_size SCREAMING_SNAKE_CASE =hidden_size SCREAMING_SNAKE_CASE =num_hidden_layers SCREAMING_SNAKE_CASE =num_attention_heads SCREAMING_SNAKE_CASE =hidden_act SCREAMING_SNAKE_CASE =intermediate_size SCREAMING_SNAKE_CASE =hidden_dropout_prob SCREAMING_SNAKE_CASE =attention_probs_dropout_prob SCREAMING_SNAKE_CASE =max_position_embeddings SCREAMING_SNAKE_CASE =type_vocab_sizes SCREAMING_SNAKE_CASE =initializer_range SCREAMING_SNAKE_CASE =layer_norm_eps # Fine-tuning task hyperparameters SCREAMING_SNAKE_CASE =positive_label_weight SCREAMING_SNAKE_CASE =num_aggregation_labels SCREAMING_SNAKE_CASE =aggregation_loss_weight SCREAMING_SNAKE_CASE =use_answer_as_supervision SCREAMING_SNAKE_CASE =answer_loss_importance SCREAMING_SNAKE_CASE =use_normalized_answer_loss SCREAMING_SNAKE_CASE =huber_loss_delta SCREAMING_SNAKE_CASE =temperature SCREAMING_SNAKE_CASE =aggregation_temperature SCREAMING_SNAKE_CASE =use_gumbel_for_cells SCREAMING_SNAKE_CASE =use_gumbel_for_aggregation SCREAMING_SNAKE_CASE =average_approximation_function SCREAMING_SNAKE_CASE =cell_selection_preference SCREAMING_SNAKE_CASE =answer_loss_cutoff SCREAMING_SNAKE_CASE =max_num_rows SCREAMING_SNAKE_CASE =max_num_columns SCREAMING_SNAKE_CASE =average_logits_per_cell SCREAMING_SNAKE_CASE =select_one_column SCREAMING_SNAKE_CASE =allow_empty_column_selection SCREAMING_SNAKE_CASE =init_cell_selection_weights_to_zero SCREAMING_SNAKE_CASE =reset_position_index_per_cell SCREAMING_SNAKE_CASE =disable_per_token_loss # Aggregation hyperparameters SCREAMING_SNAKE_CASE =aggregation_labels SCREAMING_SNAKE_CASE =no_aggregation_label_index if isinstance(self.aggregation_labels ,__snake_case ): SCREAMING_SNAKE_CASE ={int(__snake_case ): v for k, v in aggregation_labels.items()}
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'''simple docstring''' from manim import * class SCREAMING_SNAKE_CASE( A__ ): """simple docstring""" def A ( self : Union[str, Any] ) -> List[str]: UpperCAmelCase : Optional[Any] = Rectangle(height=0.5 , width=0.5 ) UpperCAmelCase : str = Rectangle(height=0.46 , width=0.46 ).set_stroke(width=0 ) UpperCAmelCase : Tuple = [mem.copy() for i in range(6 )] UpperCAmelCase : Optional[int] = [mem.copy() for i in range(6 )] UpperCAmelCase : Dict = VGroup(*__snake_case ).arrange(__snake_case , buff=0 ) UpperCAmelCase : Any = VGroup(*__snake_case ).arrange(__snake_case , buff=0 ) UpperCAmelCase : Union[str, Any] = VGroup(__snake_case , __snake_case ).arrange(__snake_case , buff=0 ) UpperCAmelCase : Optional[Any] = Text('''CPU''' , font_size=24 ) UpperCAmelCase : Union[str, Any] = Group(__snake_case , __snake_case ).arrange(__snake_case , buff=0.5 , aligned_edge=__snake_case ) cpu.move_to([-2.5, -0.5, 0] ) self.add(__snake_case ) UpperCAmelCase : Optional[Any] = [mem.copy() for i in range(4 )] UpperCAmelCase : Union[str, Any] = VGroup(*__snake_case ).arrange(__snake_case , buff=0 ) UpperCAmelCase : List[str] = Text('''GPU''' , font_size=24 ) UpperCAmelCase : Dict = Group(__snake_case , __snake_case ).arrange(__snake_case , buff=0.5 , aligned_edge=__snake_case ) gpu.move_to([-1, -1, 0] ) self.add(__snake_case ) UpperCAmelCase : int = [mem.copy() for i in range(6 )] UpperCAmelCase : Union[str, Any] = VGroup(*__snake_case ).arrange(__snake_case , buff=0 ) UpperCAmelCase : List[str] = Text('''Model''' , font_size=24 ) UpperCAmelCase : Tuple = Group(__snake_case , __snake_case ).arrange(__snake_case , buff=0.5 , aligned_edge=__snake_case ) model.move_to([3, -1.0, 0] ) self.add(__snake_case ) UpperCAmelCase : Any = [] for i, rect in enumerate(__snake_case ): rect.set_stroke(__snake_case ) # target = fill.copy().set_fill(YELLOW, opacity=0.7) # target.move_to(rect) # self.add(target) UpperCAmelCase : Dict = Rectangle(height=0.46 / 4 , width=0.46 / 3 ).set_stroke(width=0.0 ).set_fill(__snake_case , opacity=0.7 ) if i == 0: cpu_target.next_to(cpu_left_col_base[0].get_corner(DOWN + LEFT ) , buff=0.02 , direction=__snake_case ) cpu_target.set_x(cpu_target.get_x() + 0.1 ) elif i == 3: cpu_target.next_to(cpu_targs[0] , direction=__snake_case , buff=0.0 ) else: cpu_target.next_to(cpu_targs[i - 1] , direction=__snake_case , buff=0.0 ) self.add(__snake_case ) cpu_targs.append(__snake_case ) UpperCAmelCase : int = [mem.copy() for i in range(6 )] UpperCAmelCase : int = VGroup(*__snake_case ).arrange(__snake_case , buff=0 ) UpperCAmelCase : Any = Text('''Loaded Checkpoint''' , font_size=24 ) UpperCAmelCase : Union[str, Any] = Group(__snake_case , __snake_case ).arrange(__snake_case , aligned_edge=__snake_case , buff=0.4 ) checkpoint.move_to([3, 0.5, 0] ) UpperCAmelCase : Optional[int] = Square(side_length=2.2 ) key.move_to([-5, 2, 0] ) UpperCAmelCase : str = MarkupText( F"""<b>Key:</b>\n\n<span fgcolor='{YELLOW}'>●</span> Empty Model""" , font_size=18 , ) key_text.move_to([-5, 2.4, 0] ) self.add(__snake_case , __snake_case ) UpperCAmelCase : Tuple = MarkupText( F"""<span fgcolor='{BLUE}'>●</span> Checkpoint""" , font_size=18 , ) blue_text.next_to(__snake_case , DOWN * 2.4 , aligned_edge=key_text.get_left() ) UpperCAmelCase : List[Any] = MarkupText( F"""Next, a <i><span fgcolor=\"{BLUE}\">second</span></i> model is loaded into memory,\nwith the weights of a <span fgcolor=\"{BLUE}\">single shard</span>.""" , font_size=24 , ) step_a.move_to([2, 2, 0] ) self.play(Write(__snake_case ) , Write(__snake_case ) ) self.play(Write(__snake_case , run_time=1 ) , Create(__snake_case , run_time=1 ) ) UpperCAmelCase : Tuple = [] UpperCAmelCase : int = [] for i, rect in enumerate(__snake_case ): UpperCAmelCase : Any = fill.copy().set_fill(__snake_case , opacity=0.7 ) target.move_to(__snake_case ) first_animations.append(GrowFromCenter(__snake_case , run_time=1 ) ) UpperCAmelCase : List[str] = target.copy() cpu_target.generate_target() if i < 5: cpu_target.target.move_to(cpu_left_col_base[i + 1] ) else: cpu_target.target.move_to(cpu_right_col_base[i - 5] ) second_animations.append(MoveToTarget(__snake_case , run_time=1.5 ) ) self.play(*__snake_case ) self.play(*__snake_case ) self.wait()
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import json import os from pathlib import Path from shutil import copyfile from typing import Any, Dict, List, Optional, Tuple, Union import sentencepiece from ...tokenization_utils import BatchEncoding, PreTrainedTokenizer from ...utils import logging _lowerCAmelCase : Tuple = logging.get_logger(__name__) _lowerCAmelCase : Optional[Any] = "▁" _lowerCAmelCase : Tuple = { "vocab_file": "vocab.json", "spm_file": "sentencepiece.bpe.model", "tokenizer_config_file": "tokenizer_config.json", } _lowerCAmelCase : Any = { "vocab_file": { "facebook/m2m100_418M": "https://huggingface.co/facebook/m2m100_418M/resolve/main/vocab.json", "facebook/m2m100_1.2B": "https://huggingface.co/facebook/m2m100_1.2B/resolve/main/vocab.json", }, "spm_file": { "facebook/m2m100_418M": "https://huggingface.co/facebook/m2m100_418M/resolve/main/sentencepiece.bpe.model", "facebook/m2m100_1.2B": "https://huggingface.co/facebook/m2m100_1.2B/resolve/main/sentencepiece.bpe.model", }, "tokenizer_config_file": { "facebook/m2m100_418M": "https://huggingface.co/facebook/m2m100_418M/resolve/main/tokenizer_config.json", "facebook/m2m100_1.2B": "https://huggingface.co/facebook/m2m100_1.2B/resolve/main/tokenizer_config.json", }, } _lowerCAmelCase : Dict = { "facebook/m2m100_418M": 1_024, } # fmt: off _lowerCAmelCase : str = { "m2m100": ["af", "am", "ar", "ast", "az", "ba", "be", "bg", "bn", "br", "bs", "ca", "ceb", "cs", "cy", "da", "de", "el", "en", "es", "et", "fa", "ff", "fi", "fr", "fy", "ga", "gd", "gl", "gu", "ha", "he", "hi", "hr", "ht", "hu", "hy", "id", "ig", "ilo", "is", "it", "ja", "jv", "ka", "kk", "km", "kn", "ko", "lb", "lg", "ln", "lo", "lt", "lv", "mg", "mk", "ml", "mn", "mr", "ms", "my", "ne", "nl", "no", "ns", "oc", "or", "pa", "pl", "ps", "pt", "ro", "ru", "sd", "si", "sk", "sl", "so", "sq", "sr", "ss", "su", "sv", "sw", "ta", "th", "tl", "tn", "tr", "uk", "ur", "uz", "vi", "wo", "xh", "yi", "yo", "zh", "zu"], "wmt21": ["en", "ha", "is", "ja", "cs", "ru", "zh", "de"] } class __magic_name__ ( A__ ): SCREAMING_SNAKE_CASE = VOCAB_FILES_NAMES SCREAMING_SNAKE_CASE = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES SCREAMING_SNAKE_CASE = PRETRAINED_VOCAB_FILES_MAP SCREAMING_SNAKE_CASE = ['input_ids', 'attention_mask'] SCREAMING_SNAKE_CASE = [] SCREAMING_SNAKE_CASE = [] def __init__( self , __snake_case , __snake_case , __snake_case=None , __snake_case=None , __snake_case="<s>" , __snake_case="</s>" , __snake_case="</s>" , __snake_case="<pad>" , __snake_case="<unk>" , __snake_case="m2m100" , __snake_case = None , __snake_case=8 , **__snake_case , ) -> None: '''simple docstring''' __a ={} if sp_model_kwargs is None else sp_model_kwargs __a =language_codes __a =FAIRSEQ_LANGUAGE_CODES[language_codes] __a ={lang_code: f'__{lang_code}__' for lang_code in fairseq_language_code} __a =kwargs.get('additional_special_tokens' , [] ) kwargs["additional_special_tokens"] += [ self.get_lang_token(__snake_case ) for lang_code in fairseq_language_code if self.get_lang_token(__snake_case ) not in kwargs["additional_special_tokens"] ] super().__init__( src_lang=__snake_case , tgt_lang=__snake_case , bos_token=__snake_case , eos_token=__snake_case , sep_token=__snake_case , unk_token=__snake_case , pad_token=__snake_case , language_codes=__snake_case , sp_model_kwargs=self.sp_model_kwargs , num_madeup_words=__snake_case , **__snake_case , ) __a =vocab_file __a =load_json(__snake_case ) __a ={v: k for k, v in self.encoder.items()} __a =spm_file __a =load_spm(__snake_case , self.sp_model_kwargs ) __a =len(self.encoder ) __a ={ self.get_lang_token(__snake_case ): self.encoder_size + i for i, lang_code in enumerate(__snake_case ) } __a ={lang_code: self.encoder_size + i for i, lang_code in enumerate(__snake_case )} __a ={v: k for k, v in self.lang_token_to_id.items()} __a =src_lang if src_lang is not None else '''en''' __a =tgt_lang __a =self.get_lang_id(self._src_lang ) self.set_src_lang_special_tokens(self._src_lang ) __a =num_madeup_words @property def __magic_name__ ( self ) -> int: '''simple docstring''' return len(self.encoder ) + len(self.lang_token_to_id ) @property def __magic_name__ ( self ) -> str: '''simple docstring''' return self._src_lang @src_lang.setter def __magic_name__ ( self , __snake_case ) -> None: '''simple docstring''' __a =new_src_lang self.set_src_lang_special_tokens(self._src_lang ) def __magic_name__ ( self , __snake_case ) -> List[str]: '''simple docstring''' return self.sp_model.encode(__snake_case , out_type=__snake_case ) def __magic_name__ ( self , __snake_case ) -> Tuple: '''simple docstring''' if token in self.lang_token_to_id: return self.lang_token_to_id[token] return self.encoder.get(__snake_case , self.encoder[self.unk_token] ) def __magic_name__ ( self , __snake_case ) -> str: '''simple docstring''' if index in self.id_to_lang_token: return self.id_to_lang_token[index] return self.decoder.get(__snake_case , self.unk_token ) def __magic_name__ ( self , __snake_case ) -> Tuple: '''simple docstring''' __a =[] __a ='''''' for token in tokens: # make sure that special tokens are not decoded using sentencepiece model if token in self.all_special_tokens: out_string += self.sp_model.decode(__snake_case ) + token __a =[] else: current_sub_tokens.append(__snake_case ) out_string += self.sp_model.decode(__snake_case ) return out_string.strip() def __magic_name__ ( self , __snake_case , __snake_case = None , __snake_case = False ) -> List[int]: '''simple docstring''' if already_has_special_tokens: return super().get_special_tokens_mask( token_ids_a=__snake_case , token_ids_a=__snake_case , already_has_special_tokens=__snake_case ) __a =[1] * len(self.prefix_tokens ) __a =[1] * len(self.suffix_tokens ) if token_ids_a is None: return prefix_ones + ([0] * len(__snake_case )) + suffix_ones return prefix_ones + ([0] * len(__snake_case )) + ([0] * len(__snake_case )) + suffix_ones def __magic_name__ ( self , __snake_case , __snake_case = None ) -> List[int]: '''simple docstring''' if token_ids_a is None: return self.prefix_tokens + token_ids_a + self.suffix_tokens # We don't expect to process pairs, but leave the pair logic for API consistency return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens def __magic_name__ ( self ) -> Dict: '''simple docstring''' __a ={self.convert_ids_to_tokens(__snake_case ): i for i in range(self.vocab_size )} vocab.update(self.added_tokens_encoder ) return vocab def __getstate__( self ) -> Dict: '''simple docstring''' __a =self.__dict__.copy() __a =None return state def __setstate__( self , __snake_case ) -> None: '''simple docstring''' __a =d # for backward compatibility if not hasattr(self , 'sp_model_kwargs' ): __a ={} __a =load_spm(self.spm_file , self.sp_model_kwargs ) def __magic_name__ ( self , __snake_case , __snake_case = None ) -> Tuple[str]: '''simple docstring''' __a =Path(__snake_case ) if not save_dir.is_dir(): raise OSError(f'{save_directory} should be a directory' ) __a =save_dir / ( (filename_prefix + '''-''' if filename_prefix else '''''') + self.vocab_files_names['''vocab_file'''] ) __a =save_dir / ( (filename_prefix + '''-''' if filename_prefix else '''''') + self.vocab_files_names['''spm_file'''] ) save_json(self.encoder , __snake_case ) if os.path.abspath(self.spm_file ) != os.path.abspath(__snake_case ) and os.path.isfile(self.spm_file ): copyfile(self.spm_file , __snake_case ) elif not os.path.isfile(self.spm_file ): with open(__snake_case , 'wb' ) as fi: __a =self.sp_model.serialized_model_proto() fi.write(__snake_case ) return (str(__snake_case ), str(__snake_case )) def __magic_name__ ( self , __snake_case , __snake_case = "en" , __snake_case = None , __snake_case = "ro" , **__snake_case , ) -> BatchEncoding: '''simple docstring''' __a =src_lang __a =tgt_lang self.set_src_lang_special_tokens(self.src_lang ) return super().prepare_seqaseq_batch(__snake_case , __snake_case , **__snake_case ) def __magic_name__ ( self , __snake_case , __snake_case , __snake_case , **__snake_case ) -> Optional[int]: '''simple docstring''' if src_lang is None or tgt_lang is None: raise ValueError('Translation requires a `src_lang` and a `tgt_lang` for this model' ) __a =src_lang __a =self(__snake_case , add_special_tokens=__snake_case , **__snake_case ) __a =self.get_lang_id(__snake_case ) __a =tgt_lang_id return inputs def __magic_name__ ( self ) -> int: '''simple docstring''' self.set_src_lang_special_tokens(self.src_lang ) def __magic_name__ ( self ) -> int: '''simple docstring''' self.set_tgt_lang_special_tokens(self.tgt_lang ) def __magic_name__ ( self , __snake_case ) -> None: '''simple docstring''' __a =self.get_lang_token(__snake_case ) __a =self.lang_token_to_id[lang_token] __a =[self.cur_lang_id] __a =[self.eos_token_id] def __magic_name__ ( self , __snake_case ) -> None: '''simple docstring''' __a =self.get_lang_token(__snake_case ) __a =self.lang_token_to_id[lang_token] __a =[self.cur_lang_id] __a =[self.eos_token_id] def __magic_name__ ( self , __snake_case ) -> str: '''simple docstring''' return self.lang_code_to_token[lang] def __magic_name__ ( self , __snake_case ) -> int: '''simple docstring''' __a =self.get_lang_token(__snake_case ) return self.lang_token_to_id[lang_token] def UpperCamelCase_( _snake_case : str , _snake_case : Dict[str, Any] ): """simple docstring""" __a =sentencepiece.SentencePieceProcessor(**_lowerCAmelCase ) spm.Load(str(_lowerCAmelCase ) ) return spm def UpperCamelCase_( _snake_case : str ): """simple docstring""" with open(_lowerCAmelCase , 'r' ) as f: return json.load(_lowerCAmelCase ) def UpperCamelCase_( _snake_case : List[str] , _snake_case : str ): """simple docstring""" with open(_lowerCAmelCase , 'w' ) as f: json.dump(_lowerCAmelCase , _lowerCAmelCase , indent=2 )
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'''simple docstring''' import argparse import gc import json import os import shutil import warnings import torch from transformers import LlamaConfig, LlamaForCausalLM, LlamaTokenizer try: from transformers import LlamaTokenizerFast except ImportError as e: warnings.warn(e) warnings.warn( "The converted tokenizer will be the `slow` tokenizer. To use the fast, update your `tokenizers` library and re-run the tokenizer conversion" ) UpperCamelCase__: str = None UpperCamelCase__: int = { "7B": 11008, "13B": 13824, "30B": 17920, "65B": 22016, "70B": 28672, } UpperCamelCase__: List[Any] = { "7B": 1, "7Bf": 1, "13B": 2, "13Bf": 2, "30B": 4, "65B": 8, "70B": 8, "70Bf": 8, } def snake_case_ ( _lowerCAmelCase : Tuple , _lowerCAmelCase : Tuple=1 , _lowerCAmelCase : List[Any]=256 ) -> Optional[Any]: return multiple_of * ((int(ffn_dim_multiplier * int(8 * n / 3 ) ) + multiple_of - 1) // multiple_of) def snake_case_ ( _lowerCAmelCase : List[str] ) -> str: with open(_lowerCAmelCase , '''r''' ) as f: return json.load(_lowerCAmelCase ) def snake_case_ ( _lowerCAmelCase : Union[str, Any] , _lowerCAmelCase : Any ) -> Optional[Any]: with open(_lowerCAmelCase , '''w''' ) as f: json.dump(_lowerCAmelCase , _lowerCAmelCase ) def snake_case_ ( _lowerCAmelCase : Dict , _lowerCAmelCase : Tuple , _lowerCAmelCase : Any , _lowerCAmelCase : Any=True ) -> List[Any]: os.makedirs(_lowerCAmelCase , exist_ok=_lowerCAmelCase ) UpperCAmelCase : int = os.path.join(_lowerCAmelCase , '''tmp''' ) os.makedirs(_lowerCAmelCase , exist_ok=_lowerCAmelCase ) UpperCAmelCase : List[str] = read_json(os.path.join(_lowerCAmelCase , '''params.json''' ) ) UpperCAmelCase : str = NUM_SHARDS[model_size] UpperCAmelCase : Any = params['''n_layers'''] UpperCAmelCase : str = params['''n_heads'''] UpperCAmelCase : Any = n_heads // num_shards UpperCAmelCase : List[str] = params['''dim'''] UpperCAmelCase : Optional[Any] = dim // n_heads UpperCAmelCase : str = 1_0_0_0_0.0 UpperCAmelCase : Optional[int] = 1.0 / (base ** (torch.arange(0 , _lowerCAmelCase , 2 ).float() / dims_per_head)) if "n_kv_heads" in params: UpperCAmelCase : Tuple = params['''n_kv_heads'''] # for GQA / MQA UpperCAmelCase : Optional[int] = n_heads_per_shard // num_key_value_heads UpperCAmelCase : Optional[Any] = dim // num_key_value_heads else: # compatibility with other checkpoints UpperCAmelCase : List[str] = n_heads UpperCAmelCase : Optional[int] = n_heads_per_shard UpperCAmelCase : List[str] = dim # permute for sliced rotary def permute(_lowerCAmelCase : Tuple , _lowerCAmelCase : Optional[Any]=n_heads , _lowerCAmelCase : int=dim , _lowerCAmelCase : Dict=dim ): return w.view(_lowerCAmelCase , dima // n_heads // 2 , 2 , _lowerCAmelCase ).transpose(1 , 2 ).reshape(_lowerCAmelCase , _lowerCAmelCase ) print(f"""Fetching all parameters from the checkpoint at {input_base_path}.""" ) # Load weights if model_size == "7B": # Not sharded # (The sharded implementation would also work, but this is simpler.) UpperCAmelCase : int = torch.load(os.path.join(_lowerCAmelCase , '''consolidated.00.pth''' ) , map_location='''cpu''' ) else: # Sharded UpperCAmelCase : Optional[Any] = [ torch.load(os.path.join(_lowerCAmelCase , f"""consolidated.{i:02d}.pth""" ) , map_location='''cpu''' ) for i in range(_lowerCAmelCase ) ] UpperCAmelCase : Any = 0 UpperCAmelCase : str = {'''weight_map''': {}} for layer_i in range(_lowerCAmelCase ): UpperCAmelCase : Optional[Any] = f"""pytorch_model-{layer_i + 1}-of-{n_layers + 1}.bin""" if model_size == "7B": # Unsharded UpperCAmelCase : Optional[int] = { f"""model.layers.{layer_i}.self_attn.q_proj.weight""": permute( loaded[f"""layers.{layer_i}.attention.wq.weight"""] ), f"""model.layers.{layer_i}.self_attn.k_proj.weight""": permute( loaded[f"""layers.{layer_i}.attention.wk.weight"""] ), f"""model.layers.{layer_i}.self_attn.v_proj.weight""": loaded[f"""layers.{layer_i}.attention.wv.weight"""], f"""model.layers.{layer_i}.self_attn.o_proj.weight""": loaded[f"""layers.{layer_i}.attention.wo.weight"""], f"""model.layers.{layer_i}.mlp.gate_proj.weight""": loaded[f"""layers.{layer_i}.feed_forward.w1.weight"""], f"""model.layers.{layer_i}.mlp.down_proj.weight""": loaded[f"""layers.{layer_i}.feed_forward.w2.weight"""], f"""model.layers.{layer_i}.mlp.up_proj.weight""": loaded[f"""layers.{layer_i}.feed_forward.w3.weight"""], f"""model.layers.{layer_i}.input_layernorm.weight""": loaded[f"""layers.{layer_i}.attention_norm.weight"""], f"""model.layers.{layer_i}.post_attention_layernorm.weight""": loaded[f"""layers.{layer_i}.ffn_norm.weight"""], } else: # Sharded # Note that attention.w{q,k,v,o}, feed_fordward.w[1,2,3], attention_norm.weight and ffn_norm.weight share # the same storage object, saving attention_norm and ffn_norm will save other weights too, which is # redundant as other weights will be stitched from multiple shards. To avoid that, they are cloned. UpperCAmelCase : List[str] = { f"""model.layers.{layer_i}.input_layernorm.weight""": loaded[0][ f"""layers.{layer_i}.attention_norm.weight""" ].clone(), f"""model.layers.{layer_i}.post_attention_layernorm.weight""": loaded[0][ f"""layers.{layer_i}.ffn_norm.weight""" ].clone(), } UpperCAmelCase : Union[str, Any] = permute( torch.cat( [ loaded[i][f"""layers.{layer_i}.attention.wq.weight"""].view(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ) for i in range(_lowerCAmelCase ) ] , dim=0 , ).reshape(_lowerCAmelCase , _lowerCAmelCase ) ) UpperCAmelCase : Optional[Any] = permute( torch.cat( [ loaded[i][f"""layers.{layer_i}.attention.wk.weight"""].view( _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ) for i in range(_lowerCAmelCase ) ] , dim=0 , ).reshape(_lowerCAmelCase , _lowerCAmelCase ) , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , ) UpperCAmelCase : str = torch.cat( [ loaded[i][f"""layers.{layer_i}.attention.wv.weight"""].view( _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ) for i in range(_lowerCAmelCase ) ] , dim=0 , ).reshape(_lowerCAmelCase , _lowerCAmelCase ) UpperCAmelCase : Optional[int] = torch.cat( [loaded[i][f"""layers.{layer_i}.attention.wo.weight"""] for i in range(_lowerCAmelCase )] , dim=1 ) UpperCAmelCase : Any = torch.cat( [loaded[i][f"""layers.{layer_i}.feed_forward.w1.weight"""] for i in range(_lowerCAmelCase )] , dim=0 ) UpperCAmelCase : str = torch.cat( [loaded[i][f"""layers.{layer_i}.feed_forward.w2.weight"""] for i in range(_lowerCAmelCase )] , dim=1 ) UpperCAmelCase : Tuple = torch.cat( [loaded[i][f"""layers.{layer_i}.feed_forward.w3.weight"""] for i in range(_lowerCAmelCase )] , dim=0 ) UpperCAmelCase : Any = inv_freq for k, v in state_dict.items(): UpperCAmelCase : List[Any] = filename param_count += v.numel() torch.save(_lowerCAmelCase , os.path.join(_lowerCAmelCase , _lowerCAmelCase ) ) UpperCAmelCase : Optional[int] = f"""pytorch_model-{n_layers + 1}-of-{n_layers + 1}.bin""" if model_size == "7B": # Unsharded UpperCAmelCase : str = { '''model.embed_tokens.weight''': loaded['''tok_embeddings.weight'''], '''model.norm.weight''': loaded['''norm.weight'''], '''lm_head.weight''': loaded['''output.weight'''], } else: UpperCAmelCase : Any = { '''model.norm.weight''': loaded[0]['''norm.weight'''], '''model.embed_tokens.weight''': torch.cat( [loaded[i]['''tok_embeddings.weight'''] for i in range(_lowerCAmelCase )] , dim=1 ), '''lm_head.weight''': torch.cat([loaded[i]['''output.weight'''] for i in range(_lowerCAmelCase )] , dim=0 ), } for k, v in state_dict.items(): UpperCAmelCase : Optional[int] = filename param_count += v.numel() torch.save(_lowerCAmelCase , os.path.join(_lowerCAmelCase , _lowerCAmelCase ) ) # Write configs UpperCAmelCase : Union[str, Any] = {'''total_size''': param_count * 2} write_json(_lowerCAmelCase , os.path.join(_lowerCAmelCase , '''pytorch_model.bin.index.json''' ) ) UpperCAmelCase : int = params['''ffn_dim_multiplier'''] if '''ffn_dim_multiplier''' in params else 1 UpperCAmelCase : Tuple = params['''multiple_of'''] if '''multiple_of''' in params else 256 UpperCAmelCase : Any = LlamaConfig( hidden_size=_lowerCAmelCase , intermediate_size=compute_intermediate_size(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ) , num_attention_heads=params['''n_heads'''] , num_hidden_layers=params['''n_layers'''] , rms_norm_eps=params['''norm_eps'''] , num_key_value_heads=_lowerCAmelCase , ) config.save_pretrained(_lowerCAmelCase ) # Make space so we can load the model properly now. del state_dict del loaded gc.collect() print('''Loading the checkpoint in a Llama model.''' ) UpperCAmelCase : Optional[int] = LlamaForCausalLM.from_pretrained(_lowerCAmelCase , torch_dtype=torch.floataa , low_cpu_mem_usage=_lowerCAmelCase ) # Avoid saving this as part of the config. del model.config._name_or_path print('''Saving in the Transformers format.''' ) model.save_pretrained(_lowerCAmelCase , safe_serialization=_lowerCAmelCase ) shutil.rmtree(_lowerCAmelCase ) def snake_case_ ( _lowerCAmelCase : int , _lowerCAmelCase : Union[str, Any] ) -> List[str]: # Initialize the tokenizer based on the `spm` model UpperCAmelCase : Dict = LlamaTokenizer if LlamaTokenizerFast is None else LlamaTokenizerFast print(f"""Saving a {tokenizer_class.__name__} to {tokenizer_path}.""" ) UpperCAmelCase : List[Any] = tokenizer_class(_lowerCAmelCase ) tokenizer.save_pretrained(_lowerCAmelCase ) def snake_case_ ( ) -> List[Any]: UpperCAmelCase : int = argparse.ArgumentParser() parser.add_argument( '''--input_dir''' , help='''Location of LLaMA weights, which contains tokenizer.model and model folders''' , ) parser.add_argument( '''--model_size''' , choices=['''7B''', '''7Bf''', '''13B''', '''13Bf''', '''30B''', '''65B''', '''70B''', '''70Bf''', '''tokenizer_only'''] , ) parser.add_argument( '''--output_dir''' , help='''Location to write HF model and tokenizer''' , ) parser.add_argument('''--safe_serialization''' , type=_lowerCAmelCase , help='''Whether or not to save using `safetensors`.''' ) UpperCAmelCase : List[Any] = parser.parse_args() if args.model_size != "tokenizer_only": write_model( model_path=args.output_dir , input_base_path=os.path.join(args.input_dir , args.model_size ) , model_size=args.model_size , safe_serialization=args.safe_serialization , ) UpperCAmelCase : Optional[int] = os.path.join(args.input_dir , '''tokenizer.model''' ) write_tokenizer(args.output_dir , _lowerCAmelCase ) if __name__ == "__main__": main()
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'''simple docstring''' import warnings from ...utils import logging from .image_processing_segformer import SegformerImageProcessor _A : int = logging.get_logger(__name__) class _lowercase ( A__ ): '''simple docstring''' def __init__( self : Optional[Any] , *SCREAMING_SNAKE_CASE__ : Optional[int] , **SCREAMING_SNAKE_CASE__ : Optional[Any] ) -> None: warnings.warn( """The class SegformerFeatureExtractor is deprecated and will be removed in version 5 of Transformers.""" """ Please use SegformerImageProcessor instead.""" , __snake_case , ) super().__init__(*__snake_case , **__snake_case )
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'''simple docstring''' from math import cos, sin, sqrt, tau from audio_filters.iir_filter import IIRFilter def snake_case_ ( _lowerCAmelCase : int , _lowerCAmelCase : int , _lowerCAmelCase : float = 1 / sqrt(2 ) ) -> IIRFilter: UpperCAmelCase : Optional[int] = tau * frequency / samplerate UpperCAmelCase : List[Any] = sin(_lowerCAmelCase ) UpperCAmelCase : Optional[Any] = cos(_lowerCAmelCase ) UpperCAmelCase : int = _sin / (2 * q_factor) UpperCAmelCase : Any = (1 - _cos) / 2 UpperCAmelCase : List[Any] = 1 - _cos UpperCAmelCase : Union[str, Any] = 1 + alpha UpperCAmelCase : Any = -2 * _cos UpperCAmelCase : Dict = 1 - alpha UpperCAmelCase : Union[str, Any] = IIRFilter(2 ) filt.set_coefficients([aa, aa, aa] , [ba, ba, ba] ) return filt def snake_case_ ( _lowerCAmelCase : int , _lowerCAmelCase : int , _lowerCAmelCase : float = 1 / sqrt(2 ) ) -> IIRFilter: UpperCAmelCase : Any = tau * frequency / samplerate UpperCAmelCase : Tuple = sin(_lowerCAmelCase ) UpperCAmelCase : Tuple = cos(_lowerCAmelCase ) UpperCAmelCase : Dict = _sin / (2 * q_factor) UpperCAmelCase : int = (1 + _cos) / 2 UpperCAmelCase : List[Any] = -1 - _cos UpperCAmelCase : Tuple = 1 + alpha UpperCAmelCase : List[str] = -2 * _cos UpperCAmelCase : Optional[Any] = 1 - alpha UpperCAmelCase : Tuple = IIRFilter(2 ) filt.set_coefficients([aa, aa, aa] , [ba, ba, ba] ) return filt def snake_case_ ( _lowerCAmelCase : int , _lowerCAmelCase : int , _lowerCAmelCase : float = 1 / sqrt(2 ) ) -> IIRFilter: UpperCAmelCase : Optional[int] = tau * frequency / samplerate UpperCAmelCase : Optional[int] = sin(_lowerCAmelCase ) UpperCAmelCase : Tuple = cos(_lowerCAmelCase ) UpperCAmelCase : Optional[int] = _sin / (2 * q_factor) UpperCAmelCase : Union[str, Any] = _sin / 2 UpperCAmelCase : Any = 0 UpperCAmelCase : int = -ba UpperCAmelCase : Optional[Any] = 1 + alpha UpperCAmelCase : List[Any] = -2 * _cos UpperCAmelCase : Optional[Any] = 1 - alpha UpperCAmelCase : int = IIRFilter(2 ) filt.set_coefficients([aa, aa, aa] , [ba, ba, ba] ) return filt def snake_case_ ( _lowerCAmelCase : int , _lowerCAmelCase : int , _lowerCAmelCase : float = 1 / sqrt(2 ) ) -> IIRFilter: UpperCAmelCase : List[str] = tau * frequency / samplerate UpperCAmelCase : Union[str, Any] = sin(_lowerCAmelCase ) UpperCAmelCase : str = cos(_lowerCAmelCase ) UpperCAmelCase : Optional[Any] = _sin / (2 * q_factor) UpperCAmelCase : List[str] = 1 - alpha UpperCAmelCase : Any = -2 * _cos UpperCAmelCase : Optional[int] = 1 + alpha UpperCAmelCase : Union[str, Any] = IIRFilter(2 ) filt.set_coefficients([ba, ba, ba] , [ba, ba, ba] ) return filt def snake_case_ ( _lowerCAmelCase : int , _lowerCAmelCase : int , _lowerCAmelCase : float , _lowerCAmelCase : float = 1 / sqrt(2 ) , ) -> IIRFilter: UpperCAmelCase : Optional[Any] = tau * frequency / samplerate UpperCAmelCase : Union[str, Any] = sin(_lowerCAmelCase ) UpperCAmelCase : Optional[int] = cos(_lowerCAmelCase ) UpperCAmelCase : Dict = _sin / (2 * q_factor) UpperCAmelCase : str = 10 ** (gain_db / 40) UpperCAmelCase : int = 1 + alpha * big_a UpperCAmelCase : Union[str, Any] = -2 * _cos UpperCAmelCase : Optional[Any] = 1 - alpha * big_a UpperCAmelCase : Union[str, Any] = 1 + alpha / big_a UpperCAmelCase : Tuple = -2 * _cos UpperCAmelCase : Any = 1 - alpha / big_a UpperCAmelCase : Optional[Any] = IIRFilter(2 ) filt.set_coefficients([aa, aa, aa] , [ba, ba, ba] ) return filt def snake_case_ ( _lowerCAmelCase : int , _lowerCAmelCase : int , _lowerCAmelCase : float , _lowerCAmelCase : float = 1 / sqrt(2 ) , ) -> IIRFilter: UpperCAmelCase : Any = tau * frequency / samplerate UpperCAmelCase : Optional[int] = sin(_lowerCAmelCase ) UpperCAmelCase : Union[str, Any] = cos(_lowerCAmelCase ) UpperCAmelCase : str = _sin / (2 * q_factor) UpperCAmelCase : List[str] = 10 ** (gain_db / 40) UpperCAmelCase : Optional[int] = (big_a + 1) - (big_a - 1) * _cos UpperCAmelCase : int = (big_a + 1) + (big_a - 1) * _cos UpperCAmelCase : int = (big_a - 1) - (big_a + 1) * _cos UpperCAmelCase : Optional[int] = (big_a - 1) + (big_a + 1) * _cos UpperCAmelCase : str = 2 * sqrt(_lowerCAmelCase ) * alpha UpperCAmelCase : Dict = big_a * (pmc + aaa) UpperCAmelCase : Any = 2 * big_a * mpc UpperCAmelCase : Union[str, Any] = big_a * (pmc - aaa) UpperCAmelCase : Optional[int] = ppmc + aaa UpperCAmelCase : Optional[Any] = -2 * pmpc UpperCAmelCase : Optional[Any] = ppmc - aaa UpperCAmelCase : int = IIRFilter(2 ) filt.set_coefficients([aa, aa, aa] , [ba, ba, ba] ) return filt def snake_case_ ( _lowerCAmelCase : int , _lowerCAmelCase : int , _lowerCAmelCase : float , _lowerCAmelCase : float = 1 / sqrt(2 ) , ) -> IIRFilter: UpperCAmelCase : int = tau * frequency / samplerate UpperCAmelCase : Union[str, Any] = sin(_lowerCAmelCase ) UpperCAmelCase : Union[str, Any] = cos(_lowerCAmelCase ) UpperCAmelCase : Any = _sin / (2 * q_factor) UpperCAmelCase : int = 10 ** (gain_db / 40) UpperCAmelCase : List[str] = (big_a + 1) - (big_a - 1) * _cos UpperCAmelCase : Union[str, Any] = (big_a + 1) + (big_a - 1) * _cos UpperCAmelCase : Optional[Any] = (big_a - 1) - (big_a + 1) * _cos UpperCAmelCase : Union[str, Any] = (big_a - 1) + (big_a + 1) * _cos UpperCAmelCase : List[str] = 2 * sqrt(_lowerCAmelCase ) * alpha UpperCAmelCase : Any = big_a * (ppmc + aaa) UpperCAmelCase : str = -2 * big_a * pmpc UpperCAmelCase : List[Any] = big_a * (ppmc - aaa) UpperCAmelCase : Optional[Any] = pmc + aaa UpperCAmelCase : Any = 2 * mpc UpperCAmelCase : str = pmc - aaa UpperCAmelCase : Union[str, Any] = IIRFilter(2 ) filt.set_coefficients([aa, aa, aa] , [ba, ba, ba] ) return filt
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from manim import * class __A ( A__ ): '''simple docstring''' def __lowerCamelCase ( self ): '''simple docstring''' lowerCamelCase__ = Rectangle(height=0.5 , width=0.5 ) lowerCamelCase__ = Rectangle(height=0.46 , width=0.46 ).set_stroke(width=0 ) lowerCamelCase__ = [mem.copy() for i in range(6 )] lowerCamelCase__ = [mem.copy() for i in range(6 )] lowerCamelCase__ = VGroup(*__snake_case ).arrange(__snake_case , buff=0 ) lowerCamelCase__ = VGroup(*__snake_case ).arrange(__snake_case , buff=0 ) lowerCamelCase__ = VGroup(__snake_case , __snake_case ).arrange(__snake_case , buff=0 ) lowerCamelCase__ = Text('''CPU''' , font_size=2_4 ) lowerCamelCase__ = Group(__snake_case , __snake_case ).arrange(__snake_case , buff=0.5 , aligned_edge=__snake_case ) cpu.move_to([-2.5, -0.5, 0] ) self.add(__snake_case ) lowerCamelCase__ = [mem.copy() for i in range(4 )] lowerCamelCase__ = VGroup(*__snake_case ).arrange(__snake_case , buff=0 ) lowerCamelCase__ = Text('''GPU''' , font_size=2_4 ) lowerCamelCase__ = Group(__snake_case , __snake_case ).arrange(__snake_case , buff=0.5 , aligned_edge=__snake_case ) gpu.move_to([-1, -1, 0] ) self.add(__snake_case ) lowerCamelCase__ = [mem.copy() for i in range(6 )] lowerCamelCase__ = VGroup(*__snake_case ).arrange(__snake_case , buff=0 ) lowerCamelCase__ = Text('''Model''' , font_size=2_4 ) lowerCamelCase__ = Group(__snake_case , __snake_case ).arrange(__snake_case , buff=0.5 , aligned_edge=__snake_case ) model.move_to([3, -1.0, 0] ) self.add(__snake_case ) lowerCamelCase__ = [] for i, rect in enumerate(__snake_case ): rect.set_stroke(__snake_case ) # target = fill.copy().set_fill(YELLOW, opacity=0.7) # target.move_to(rect) # self.add(target) lowerCamelCase__ = Rectangle(height=0.46 / 4 , width=0.46 / 3 ).set_stroke(width=0.0 ).set_fill(__snake_case , opacity=0.7 ) if i == 0: cpu_target.next_to(cpu_left_col_base[0].get_corner(DOWN + LEFT ) , buff=0.02 , direction=__snake_case ) cpu_target.set_x(cpu_target.get_x() + 0.1 ) elif i == 3: cpu_target.next_to(cpu_targs[0] , direction=__snake_case , buff=0.0 ) else: cpu_target.next_to(cpu_targs[i - 1] , direction=__snake_case , buff=0.0 ) self.add(__snake_case ) cpu_targs.append(__snake_case ) lowerCamelCase__ = [mem.copy() for i in range(6 )] lowerCamelCase__ = VGroup(*__snake_case ).arrange(__snake_case , buff=0 ) lowerCamelCase__ = Text('''Loaded Checkpoint''' , font_size=2_4 ) lowerCamelCase__ = Group(__snake_case , __snake_case ).arrange(__snake_case , aligned_edge=__snake_case , buff=0.4 ) checkpoint.move_to([3, 0.5, 0] ) lowerCamelCase__ = Square(side_length=2.2 ) key.move_to([-5, 2, 0] ) lowerCamelCase__ = MarkupText( F'<b>Key:</b>\n\n<span fgcolor=\'{YELLOW}\'>●</span> Empty Model' , font_size=1_8 , ) key_text.move_to([-5, 2.4, 0] ) self.add(__snake_case , __snake_case ) lowerCamelCase__ = MarkupText( F'<span fgcolor=\'{BLUE}\'>●</span> Checkpoint' , font_size=1_8 , ) blue_text.next_to(__snake_case , DOWN * 2.4 , aligned_edge=key_text.get_left() ) lowerCamelCase__ = MarkupText( F'Next, a <i><span fgcolor=\"{BLUE}\">second</span></i> model is loaded into memory,\nwith the weights of a <span fgcolor=\"{BLUE}\">single shard</span>.' , font_size=2_4 , ) step_a.move_to([2, 2, 0] ) self.play(Write(__snake_case ) , Write(__snake_case ) ) self.play(Write(__snake_case , run_time=1 ) , Create(__snake_case , run_time=1 ) ) lowerCamelCase__ = [] lowerCamelCase__ = [] for i, rect in enumerate(__snake_case ): lowerCamelCase__ = fill.copy().set_fill(__snake_case , opacity=0.7 ) target.move_to(__snake_case ) first_animations.append(GrowFromCenter(__snake_case , run_time=1 ) ) lowerCamelCase__ = target.copy() cpu_target.generate_target() if i < 5: cpu_target.target.move_to(cpu_left_col_base[i + 1] ) else: cpu_target.target.move_to(cpu_right_col_base[i - 5] ) second_animations.append(MoveToTarget(__snake_case , run_time=1.5 ) ) self.play(*__snake_case ) self.play(*__snake_case ) self.wait()
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'''simple docstring''' from __future__ import annotations def snake_case_ ( _lowerCAmelCase : str , _lowerCAmelCase : str ) -> bool: UpperCAmelCase : str = get_failure_array(_lowerCAmelCase ) # 2) Step through text searching for pattern UpperCAmelCase , UpperCAmelCase : Optional[Any] = 0, 0 # index into text, pattern while i < len(_lowerCAmelCase ): if pattern[j] == text[i]: if j == (len(_lowerCAmelCase ) - 1): return True j += 1 # if this is a prefix in our pattern # just go back far enough to continue elif j > 0: UpperCAmelCase : Optional[Any] = failure[j - 1] continue i += 1 return False def snake_case_ ( _lowerCAmelCase : str ) -> list[int]: UpperCAmelCase : Optional[Any] = [0] UpperCAmelCase : str = 0 UpperCAmelCase : List[str] = 1 while j < len(_lowerCAmelCase ): if pattern[i] == pattern[j]: i += 1 elif i > 0: UpperCAmelCase : Union[str, Any] = failure[i - 1] continue j += 1 failure.append(_lowerCAmelCase ) return failure if __name__ == "__main__": # Test 1) UpperCamelCase__: str = "abc1abc12" UpperCamelCase__: str = "alskfjaldsabc1abc1abc12k23adsfabcabc" UpperCamelCase__: Any = "alskfjaldsk23adsfabcabc" assert kmp(pattern, texta) and not kmp(pattern, texta) # Test 2) UpperCamelCase__: Tuple = "ABABX" UpperCamelCase__: Union[str, Any] = "ABABZABABYABABX" assert kmp(pattern, text) # Test 3) UpperCamelCase__: Any = "AAAB" UpperCamelCase__: str = "ABAAAAAB" assert kmp(pattern, text) # Test 4) UpperCamelCase__: int = "abcdabcy" UpperCamelCase__: Any = "abcxabcdabxabcdabcdabcy" assert kmp(pattern, text) # Test 5) UpperCamelCase__: List[str] = "aabaabaaa" assert get_failure_array(pattern) == [0, 1, 0, 1, 2, 3, 4, 5, 2]
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"""simple docstring""" import timeit import numpy as np import datasets from datasets.arrow_writer import ArrowWriter from datasets.features.features import _ArrayXD def _UpperCAmelCase ( __lowerCamelCase : List[str] ) -> str: def wrapper(*__lowerCamelCase : str , **__lowerCamelCase : Tuple ): _snake_case = timeit.default_timer() _snake_case = func(*_lowerCAmelCase , **_lowerCAmelCase ) _snake_case = timeit.default_timer() - starttime return delta _snake_case = func.__name__ return wrapper def _UpperCAmelCase ( __lowerCamelCase : dict , __lowerCamelCase : Any=1_00 , __lowerCamelCase : Optional[int]=None ) -> Optional[int]: _snake_case = [] _snake_case = seq_shapes or {} for i in range(_lowerCAmelCase ): _snake_case = {} for col_id, (k, v) in enumerate(features.items() ): if isinstance(_lowerCAmelCase , _ArrayXD ): _snake_case = np.random.rand(*v.shape ).astype(v.dtype ) elif isinstance(_lowerCAmelCase , datasets.Value ): if v.dtype == "string": _snake_case = '''The small grey turtle was surprisingly fast when challenged.''' else: _snake_case = np.random.randint(10 , size=1 ).astype(v.dtype ).item() elif isinstance(_lowerCAmelCase , datasets.Sequence ): while isinstance(_lowerCAmelCase , datasets.Sequence ): _snake_case = v.feature _snake_case = seq_shapes[k] _snake_case = np.random.rand(*_lowerCAmelCase ).astype(v.dtype ) _snake_case = data dummy_data.append((i, example) ) return dummy_data def _UpperCAmelCase ( __lowerCamelCase : Optional[Any] , __lowerCamelCase : Union[str, Any] , __lowerCamelCase : List[Any]=1_00 , __lowerCamelCase : Dict=None ) -> Union[str, Any]: _snake_case = generate_examples(_lowerCAmelCase , num_examples=_lowerCAmelCase , seq_shapes=_lowerCAmelCase ) with ArrowWriter(features=_lowerCAmelCase , path=_lowerCAmelCase ) as writer: for key, record in dummy_data: _snake_case = features.encode_example(_lowerCAmelCase ) writer.write(_lowerCAmelCase ) _snake_case = writer.finalize() if not num_final_examples == num_examples: raise ValueError( f'''Error writing the dataset, wrote {num_final_examples} examples but should have written {num_examples}.''' ) _snake_case = datasets.Dataset.from_file(filename=_lowerCAmelCase , info=datasets.DatasetInfo(features=_lowerCAmelCase ) ) return dataset
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'''simple docstring''' import argparse import json import os import fairseq import torch from fairseq.data import Dictionary from transformers import ( WavaVecaConfig, WavaVecaCTCTokenizer, WavaVecaFeatureExtractor, WavaVecaForCTC, WavaVecaForPreTraining, WavaVecaProcessor, logging, ) from transformers.models.wavaveca.modeling_wavaveca import WavaVecaForSequenceClassification logging.set_verbosity_info() UpperCamelCase__: int = logging.get_logger(__name__) UpperCamelCase__: Dict = { "post_extract_proj": "feature_projection.projection", "encoder.pos_conv.0": "encoder.pos_conv_embed.conv", "self_attn.k_proj": "encoder.layers.*.attention.k_proj", "self_attn.v_proj": "encoder.layers.*.attention.v_proj", "self_attn.q_proj": "encoder.layers.*.attention.q_proj", "self_attn.out_proj": "encoder.layers.*.attention.out_proj", "self_attn_layer_norm": "encoder.layers.*.layer_norm", "fc1": "encoder.layers.*.feed_forward.intermediate_dense", "fc2": "encoder.layers.*.feed_forward.output_dense", "final_layer_norm": "encoder.layers.*.final_layer_norm", "encoder.layer_norm": "encoder.layer_norm", "adapter_layer": "encoder.layers.*.adapter_layer", "w2v_model.layer_norm": "feature_projection.layer_norm", "quantizer.weight_proj": "quantizer.weight_proj", "quantizer.vars": "quantizer.codevectors", "project_q": "project_q", "final_proj": "project_hid", "w2v_encoder.proj": "lm_head", "mask_emb": "masked_spec_embed", "pooling_layer.linear": "projector", "pooling_layer.projection": "classifier", } UpperCamelCase__: Optional[Any] = [ "lm_head", "quantizer.weight_proj", "quantizer.codevectors", "project_q", "project_hid", "projector", "classifier", ] def snake_case_ ( _lowerCAmelCase : str ) -> Union[str, Any]: UpperCAmelCase : Optional[int] = {} with open(_lowerCAmelCase , '''r''' ) as file: for line_number, line in enumerate(_lowerCAmelCase ): UpperCAmelCase : List[str] = line.strip() if line: UpperCAmelCase : str = line.split() UpperCAmelCase : Union[str, Any] = line_number UpperCAmelCase : List[Any] = words[0] UpperCAmelCase : Union[str, Any] = value return result def snake_case_ ( _lowerCAmelCase : int , _lowerCAmelCase : Optional[Any] , _lowerCAmelCase : Optional[Any] , _lowerCAmelCase : Tuple , _lowerCAmelCase : str ) -> int: for attribute in key.split('''.''' ): UpperCAmelCase : Dict = getattr(_lowerCAmelCase , _lowerCAmelCase ) UpperCAmelCase : Dict = None for param_key in PARAM_MAPPING.keys(): if full_name.endswith(_lowerCAmelCase ): UpperCAmelCase : Any = PARAM_MAPPING[full_name.split('''.''' )[-1]] UpperCAmelCase : Dict = '''param''' if weight_type is not None and weight_type != "param": UpperCAmelCase : Optional[int] = getattr(_lowerCAmelCase , _lowerCAmelCase ).shape elif weight_type is not None and weight_type == "param": UpperCAmelCase : List[Any] = hf_pointer for attribute in hf_param_name.split('''.''' ): UpperCAmelCase : Optional[Any] = getattr(_lowerCAmelCase , _lowerCAmelCase ) UpperCAmelCase : int = shape_pointer.shape # let's reduce dimension UpperCAmelCase : Union[str, Any] = value[0] else: UpperCAmelCase : List[Any] = hf_pointer.shape if hf_shape != value.shape: raise ValueError( f"""Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be""" f""" {value.shape} for {full_name}""" ) if weight_type == "weight": UpperCAmelCase : int = value elif weight_type == "weight_g": UpperCAmelCase : str = value elif weight_type == "weight_v": UpperCAmelCase : Dict = value elif weight_type == "bias": UpperCAmelCase : str = value elif weight_type == "param": for attribute in hf_param_name.split('''.''' ): UpperCAmelCase : int = getattr(_lowerCAmelCase , _lowerCAmelCase ) UpperCAmelCase : Optional[int] = value else: UpperCAmelCase : Tuple = value logger.info(f"""{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.""" ) def snake_case_ ( _lowerCAmelCase : Optional[int] , _lowerCAmelCase : Any , _lowerCAmelCase : List[Any] , _lowerCAmelCase : Dict , _lowerCAmelCase : List[Any] ) -> List[Any]: UpperCAmelCase : List[str] = None for param_key in PARAM_MAPPING.keys(): if full_name.endswith(_lowerCAmelCase ): UpperCAmelCase : List[str] = PARAM_MAPPING[full_name.split('''.''' )[-1]] UpperCAmelCase : Any = '''param''' if weight_type is not None and weight_type != "param": UpperCAmelCase : Optional[int] = '''.'''.join([key, weight_type] ) elif weight_type is not None and weight_type == "param": UpperCAmelCase : Optional[int] = '''.'''.join([key, hf_param_name] ) else: UpperCAmelCase : List[Any] = key UpperCAmelCase : Tuple = value if '''lm_head''' in full_key else value[0] UpperCamelCase__: Tuple = { "W_a": "linear_1.weight", "W_b": "linear_2.weight", "b_a": "linear_1.bias", "b_b": "linear_2.bias", "ln_W": "norm.weight", "ln_b": "norm.bias", } def snake_case_ ( _lowerCAmelCase : str , _lowerCAmelCase : List[str] , _lowerCAmelCase : Any=None , _lowerCAmelCase : Optional[Any]=None ) -> int: UpperCAmelCase : List[Any] = False for key, mapped_key in MAPPING.items(): UpperCAmelCase : int = '''wav2vec2.''' + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key if key in name or key.split('''w2v_model.''' )[-1] == name.split('''.''' )[0]: UpperCAmelCase : Optional[Any] = True if "*" in mapped_key: UpperCAmelCase : Tuple = name.split(_lowerCAmelCase )[0].split('''.''' )[-2] UpperCAmelCase : List[Any] = mapped_key.replace('''*''' , _lowerCAmelCase ) if "weight_g" in name: UpperCAmelCase : str = '''weight_g''' elif "weight_v" in name: UpperCAmelCase : int = '''weight_v''' elif "bias" in name: UpperCAmelCase : int = '''bias''' elif "weight" in name: # TODO: don't match quantizer.weight_proj UpperCAmelCase : List[str] = '''weight''' else: UpperCAmelCase : Dict = None if hf_dict is not None: rename_dict(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ) else: set_recursively(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ) return is_used return is_used def snake_case_ ( _lowerCAmelCase : Union[str, Any] , _lowerCAmelCase : Optional[Any] , _lowerCAmelCase : Any ) -> Any: UpperCAmelCase : Dict = [] UpperCAmelCase : Dict = fairseq_model.state_dict() UpperCAmelCase : Union[str, Any] = hf_model.wavaveca.feature_extractor for name, value in fairseq_dict.items(): UpperCAmelCase : Dict = False if "conv_layers" in name: load_conv_layer( _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , hf_model.config.feat_extract_norm == '''group''' , ) UpperCAmelCase : Any = True else: UpperCAmelCase : Optional[Any] = load_wavaveca_layer(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ) if not is_used: unused_weights.append(_lowerCAmelCase ) logger.warning(f"""Unused weights: {unused_weights}""" ) def snake_case_ ( _lowerCAmelCase : Optional[int] , _lowerCAmelCase : Tuple , _lowerCAmelCase : Any , _lowerCAmelCase : Optional[int] , _lowerCAmelCase : Optional[int] ) -> Union[str, Any]: UpperCAmelCase : Any = full_name.split('''conv_layers.''' )[-1] UpperCAmelCase : Optional[int] = name.split('''.''' ) UpperCAmelCase : Tuple = int(items[0] ) UpperCAmelCase : Tuple = int(items[1] ) if type_id == 0: if "bias" in name: if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape: raise ValueError( f"""{full_name} has size {value.shape}, but""" f""" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.""" ) UpperCAmelCase : Tuple = value logger.info(f"""Feat extract conv layer {layer_id} was initialized from {full_name}.""" ) elif "weight" in name: if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape: raise ValueError( f"""{full_name} has size {value.shape}, but""" f""" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.""" ) UpperCAmelCase : Union[str, Any] = value logger.info(f"""Feat extract conv layer {layer_id} was initialized from {full_name}.""" ) elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm): if "bias" in name: if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape: raise ValueError( f"""{full_name} has size {value.shape}, but""" f""" {feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape} was found.""" ) UpperCAmelCase : Union[str, Any] = value logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.""" ) elif "weight" in name: if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape: raise ValueError( f"""{full_name} has size {value.shape}, but""" f""" {feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape} was found.""" ) UpperCAmelCase : List[str] = value logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.""" ) else: unused_weights.append(_lowerCAmelCase ) @torch.no_grad() def snake_case_ ( _lowerCAmelCase : Union[str, Any] , _lowerCAmelCase : List[Any] , _lowerCAmelCase : Dict=None , _lowerCAmelCase : List[Any]=None , _lowerCAmelCase : int=True , _lowerCAmelCase : Optional[int]=False ) -> Dict: if config_path is not None: UpperCAmelCase : List[str] = WavaVecaConfig.from_pretrained(_lowerCAmelCase ) else: UpperCAmelCase : List[Any] = WavaVecaConfig() if is_seq_class: UpperCAmelCase : Optional[Any] = read_txt_into_dict(_lowerCAmelCase ) UpperCAmelCase : Optional[int] = idalabel UpperCAmelCase : Optional[Any] = WavaVecaForSequenceClassification(_lowerCAmelCase ) UpperCAmelCase : Dict = WavaVecaFeatureExtractor( feature_size=1 , sampling_rate=16000 , padding_value=0 , do_normalize=_lowerCAmelCase , return_attention_mask=_lowerCAmelCase , ) feature_extractor.save_pretrained(_lowerCAmelCase ) elif is_finetuned: if dict_path: UpperCAmelCase : Dict = Dictionary.load(_lowerCAmelCase ) # important change bos & pad token id since CTC symbol is <pad> and # not <s> as in fairseq UpperCAmelCase : Any = target_dict.pad_index UpperCAmelCase : Tuple = target_dict.bos_index UpperCAmelCase : Optional[int] = target_dict.eos_index UpperCAmelCase : Union[str, Any] = len(target_dict.symbols ) UpperCAmelCase : Dict = os.path.join(_lowerCAmelCase , '''vocab.json''' ) if not os.path.isdir(_lowerCAmelCase ): logger.error('''--pytorch_dump_folder_path ({}) should be a directory'''.format(_lowerCAmelCase ) ) return os.makedirs(_lowerCAmelCase , exist_ok=_lowerCAmelCase ) UpperCAmelCase : List[Any] = target_dict.indices # fairseq has the <pad> and <s> switched UpperCAmelCase : List[str] = 0 UpperCAmelCase : List[str] = 1 with open(_lowerCAmelCase , '''w''' , encoding='''utf-8''' ) as vocab_handle: json.dump(_lowerCAmelCase , _lowerCAmelCase ) UpperCAmelCase : Optional[int] = WavaVecaCTCTokenizer( _lowerCAmelCase , unk_token=target_dict.unk_word , pad_token=target_dict.pad_word , bos_token=target_dict.bos_word , eos_token=target_dict.eos_word , word_delimiter_token='''|''' , do_lower_case=_lowerCAmelCase , ) UpperCAmelCase : int = True if config.feat_extract_norm == '''layer''' else False UpperCAmelCase : int = WavaVecaFeatureExtractor( feature_size=1 , sampling_rate=16000 , padding_value=0 , do_normalize=_lowerCAmelCase , return_attention_mask=_lowerCAmelCase , ) UpperCAmelCase : str = WavaVecaProcessor(feature_extractor=_lowerCAmelCase , tokenizer=_lowerCAmelCase ) processor.save_pretrained(_lowerCAmelCase ) UpperCAmelCase : Union[str, Any] = WavaVecaForCTC(_lowerCAmelCase ) else: UpperCAmelCase : Dict = WavaVecaForPreTraining(_lowerCAmelCase ) if is_finetuned or is_seq_class: UpperCAmelCase , UpperCAmelCase , UpperCAmelCase : List[str] = fairseq.checkpoint_utils.load_model_ensemble_and_task( [checkpoint_path] , arg_overrides={'''data''': '''/'''.join(dict_path.split('''/''' )[:-1] )} ) else: UpperCAmelCase : Optional[Any] = argparse.Namespace(task='''audio_pretraining''' ) UpperCAmelCase : List[Any] = fairseq.tasks.setup_task(_lowerCAmelCase ) UpperCAmelCase , UpperCAmelCase , UpperCAmelCase : int = fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path] , task=_lowerCAmelCase ) UpperCAmelCase : Optional[int] = model[0].eval() recursively_load_weights(_lowerCAmelCase , _lowerCAmelCase , not is_finetuned ) hf_wavavec.save_pretrained(_lowerCAmelCase ) if __name__ == "__main__": UpperCamelCase__: Dict = argparse.ArgumentParser() parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.") parser.add_argument("--checkpoint_path", default=None, type=str, help="Path to fairseq checkpoint") parser.add_argument("--dict_path", default=None, type=str, help="Path to dict of fine-tuned model") parser.add_argument("--config_path", default=None, type=str, help="Path to hf config.json of model to convert") parser.add_argument( "--not_finetuned", action="store_true", help="Whether the model to convert is a fine-tuned model or not" ) parser.add_argument( "--is_seq_class", action="store_true", help="Whether the model to convert is a fine-tuned sequence classification model or not", ) UpperCamelCase__: Any = parser.parse_args() UpperCamelCase__: int = not args.not_finetuned and not args.is_seq_class convert_wavaveca_checkpoint( args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, is_finetuned, args.is_seq_class, )
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from __future__ import annotations from typing import Any class __snake_case : def __init__( self : Tuple , _lowercase : int ): """simple docstring""" SCREAMING_SNAKE_CASE__ = num_of_nodes SCREAMING_SNAKE_CASE__ = [] SCREAMING_SNAKE_CASE__ = {} def __a ( self : List[str] , _lowercase : int , _lowercase : int , _lowercase : int ): """simple docstring""" self.m_edges.append([u_node, v_node, weight] ) def __a ( self : Union[str, Any] , _lowercase : int ): """simple docstring""" if self.m_component[u_node] == u_node: return u_node return self.find_component(self.m_component[u_node] ) def __a ( self : Any , _lowercase : int ): """simple docstring""" if self.m_component[u_node] != u_node: for k in self.m_component: SCREAMING_SNAKE_CASE__ = self.find_component(__snake_case ) def __a ( self : Dict , _lowercase : list[int] , _lowercase : int , _lowercase : int ): """simple docstring""" if component_size[u_node] <= component_size[v_node]: SCREAMING_SNAKE_CASE__ = v_node component_size[v_node] += component_size[u_node] self.set_component(__snake_case ) elif component_size[u_node] >= component_size[v_node]: SCREAMING_SNAKE_CASE__ = self.find_component(__snake_case ) component_size[u_node] += component_size[v_node] self.set_component(__snake_case ) def __a ( self : Optional[int] ): """simple docstring""" SCREAMING_SNAKE_CASE__ = [] SCREAMING_SNAKE_CASE__ = 0 SCREAMING_SNAKE_CASE__ = [-1] * self.m_num_of_nodes # A list of components (initialized to all of the nodes) for node in range(self.m_num_of_nodes ): self.m_component.update({node: node} ) component_size.append(1 ) SCREAMING_SNAKE_CASE__ = self.m_num_of_nodes while num_of_components > 1: for edge in self.m_edges: SCREAMING_SNAKE_CASE__ = edge SCREAMING_SNAKE_CASE__ = self.m_component[u] SCREAMING_SNAKE_CASE__ = self.m_component[v] if u_component != v_component: for component in (u_component, v_component): if ( minimum_weight_edge[component] == -1 or minimum_weight_edge[component][2] > w ): SCREAMING_SNAKE_CASE__ = [u, v, w] for edge in minimum_weight_edge: if isinstance(__snake_case , __snake_case ): SCREAMING_SNAKE_CASE__ = edge SCREAMING_SNAKE_CASE__ = self.m_component[u] SCREAMING_SNAKE_CASE__ = self.m_component[v] if u_component != v_component: mst_weight += w self.union(__snake_case , __snake_case , __snake_case ) print(f"""Added edge [{u} - {v}]\nAdded weight: {w}\n""" ) num_of_components -= 1 SCREAMING_SNAKE_CASE__ = [-1] * self.m_num_of_nodes print(f"""The total weight of the minimal spanning tree is: {mst_weight}""" ) def __SCREAMING_SNAKE_CASE ( ) -> None: """simple docstring""" pass if __name__ == "__main__": import doctest doctest.testmod()
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'''simple docstring''' import os import tempfile import unittest import numpy as np from diffusers.utils import is_flax_available from diffusers.utils.testing_utils import require_flax, slow if is_flax_available(): import jax import jax.numpy as jnp from flax.jax_utils import replicate from flax.training.common_utils import shard from diffusers import FlaxDDIMScheduler, FlaxDiffusionPipeline, FlaxStableDiffusionPipeline @require_flax class SCREAMING_SNAKE_CASE( unittest.TestCase ): """simple docstring""" def A ( self : str ) -> int: with tempfile.TemporaryDirectory() as tmpdirname: # pipeline has Flax weights UpperCAmelCase : Optional[Any] = FlaxDiffusionPipeline.from_pretrained( '''hf-internal-testing/tiny-stable-diffusion-pipe''' , safety_checker=__snake_case , cache_dir=__snake_case ) UpperCAmelCase : str = [t[-1] for t in os.walk(os.path.join(__snake_case , os.listdir(__snake_case )[0] , '''snapshots''' ) )] UpperCAmelCase : str = [item for sublist in all_root_files for item in sublist] # None of the downloaded files should be a PyTorch file even if we have some here: # https://huggingface.co/hf-internal-testing/tiny-stable-diffusion-pipe/blob/main/unet/diffusion_pytorch_model.bin assert not any(f.endswith('''.bin''' ) for f in files ) @slow @require_flax class SCREAMING_SNAKE_CASE( unittest.TestCase ): """simple docstring""" def A ( self : List[str] ) -> Dict: UpperCAmelCase , UpperCAmelCase : str = FlaxStableDiffusionPipeline.from_pretrained( '''hf-internal-testing/tiny-stable-diffusion-pipe''' , safety_checker=__snake_case ) UpperCAmelCase : List[Any] = ( '''A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of''' ''' field, close up, split lighting, cinematic''' ) UpperCAmelCase : List[str] = jax.random.PRNGKey(0 ) UpperCAmelCase : Optional[Any] = 4 UpperCAmelCase : Optional[Any] = jax.device_count() UpperCAmelCase : Tuple = num_samples * [prompt] UpperCAmelCase : int = pipeline.prepare_inputs(__snake_case ) # shard inputs and rng UpperCAmelCase : Tuple = replicate(__snake_case ) UpperCAmelCase : Any = jax.random.split(__snake_case , __snake_case ) UpperCAmelCase : Optional[Any] = shard(__snake_case ) UpperCAmelCase : Optional[int] = pipeline(__snake_case , __snake_case , __snake_case , __snake_case , jit=__snake_case ).images assert images.shape == (num_samples, 1, 64, 64, 3) if jax.device_count() == 8: assert np.abs(np.abs(images[0, 0, :2, :2, -2:] , dtype=np.floataa ).sum() - 4.1_51_47_45 ) < 1E-3 assert np.abs(np.abs(__snake_case , dtype=np.floataa ).sum() - 4_99_47.8_75 ) < 5E-1 UpperCAmelCase : Union[str, Any] = pipeline.numpy_to_pil(np.asarray(images.reshape((num_samples,) + images.shape[-3:] ) ) ) assert len(__snake_case ) == num_samples def A ( self : List[Any] ) -> List[str]: UpperCAmelCase , UpperCAmelCase : Union[str, Any] = FlaxStableDiffusionPipeline.from_pretrained( '''CompVis/stable-diffusion-v1-4''' , revision='''flax''' , safety_checker=__snake_case ) UpperCAmelCase : Dict = ( '''A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of''' ''' field, close up, split lighting, cinematic''' ) UpperCAmelCase : Optional[Any] = jax.random.PRNGKey(0 ) UpperCAmelCase : Any = 50 UpperCAmelCase : Union[str, Any] = jax.device_count() UpperCAmelCase : int = num_samples * [prompt] UpperCAmelCase : Union[str, Any] = pipeline.prepare_inputs(__snake_case ) # shard inputs and rng UpperCAmelCase : Dict = replicate(__snake_case ) UpperCAmelCase : int = jax.random.split(__snake_case , __snake_case ) UpperCAmelCase : Tuple = shard(__snake_case ) UpperCAmelCase : Tuple = pipeline(__snake_case , __snake_case , __snake_case , __snake_case , jit=__snake_case ).images assert images.shape == (num_samples, 1, 512, 512, 3) if jax.device_count() == 8: assert np.abs((np.abs(images[0, 0, :2, :2, -2:] , dtype=np.floataa ).sum() - 0.05_65_24_01) ) < 1E-3 assert np.abs((np.abs(__snake_case , dtype=np.floataa ).sum() - 2_38_38_08.2) ) < 5E-1 def A ( self : int ) -> Dict: UpperCAmelCase , UpperCAmelCase : List[str] = FlaxStableDiffusionPipeline.from_pretrained( '''CompVis/stable-diffusion-v1-4''' , revision='''bf16''' , dtype=jnp.bfloataa , safety_checker=__snake_case ) UpperCAmelCase : Dict = ( '''A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of''' ''' field, close up, split lighting, cinematic''' ) UpperCAmelCase : Union[str, Any] = jax.random.PRNGKey(0 ) UpperCAmelCase : List[str] = 50 UpperCAmelCase : Union[str, Any] = jax.device_count() UpperCAmelCase : List[Any] = num_samples * [prompt] UpperCAmelCase : int = pipeline.prepare_inputs(__snake_case ) # shard inputs and rng UpperCAmelCase : Tuple = replicate(__snake_case ) UpperCAmelCase : List[Any] = jax.random.split(__snake_case , __snake_case ) UpperCAmelCase : Optional[int] = shard(__snake_case ) UpperCAmelCase : Optional[Any] = pipeline(__snake_case , __snake_case , __snake_case , __snake_case , jit=__snake_case ).images assert images.shape == (num_samples, 1, 512, 512, 3) if jax.device_count() == 8: assert np.abs((np.abs(images[0, 0, :2, :2, -2:] , dtype=np.floataa ).sum() - 0.04_00_39_06) ) < 1E-3 assert np.abs((np.abs(__snake_case , dtype=np.floataa ).sum() - 2_37_35_16.75) ) < 5E-1 def A ( self : int ) -> Any: UpperCAmelCase , UpperCAmelCase : Dict = FlaxStableDiffusionPipeline.from_pretrained( '''CompVis/stable-diffusion-v1-4''' , revision='''bf16''' , dtype=jnp.bfloataa ) UpperCAmelCase : List[str] = ( '''A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of''' ''' field, close up, split lighting, cinematic''' ) UpperCAmelCase : List[str] = jax.random.PRNGKey(0 ) UpperCAmelCase : Union[str, Any] = 50 UpperCAmelCase : Optional[int] = jax.device_count() UpperCAmelCase : List[str] = num_samples * [prompt] UpperCAmelCase : Dict = pipeline.prepare_inputs(__snake_case ) # shard inputs and rng UpperCAmelCase : Tuple = replicate(__snake_case ) UpperCAmelCase : Any = jax.random.split(__snake_case , __snake_case ) UpperCAmelCase : str = shard(__snake_case ) UpperCAmelCase : Optional[int] = pipeline(__snake_case , __snake_case , __snake_case , __snake_case , jit=__snake_case ).images assert images.shape == (num_samples, 1, 512, 512, 3) if jax.device_count() == 8: assert np.abs((np.abs(images[0, 0, :2, :2, -2:] , dtype=np.floataa ).sum() - 0.04_00_39_06) ) < 1E-3 assert np.abs((np.abs(__snake_case , dtype=np.floataa ).sum() - 2_37_35_16.75) ) < 5E-1 def A ( self : Tuple ) -> Optional[Any]: UpperCAmelCase : int = FlaxDDIMScheduler( beta_start=0.0_00_85 , beta_end=0.0_12 , beta_schedule='''scaled_linear''' , set_alpha_to_one=__snake_case , steps_offset=1 , ) UpperCAmelCase , UpperCAmelCase : Tuple = FlaxStableDiffusionPipeline.from_pretrained( '''CompVis/stable-diffusion-v1-4''' , revision='''bf16''' , dtype=jnp.bfloataa , scheduler=__snake_case , safety_checker=__snake_case , ) UpperCAmelCase : Tuple = scheduler.create_state() UpperCAmelCase : Dict = scheduler_state UpperCAmelCase : str = ( '''A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of''' ''' field, close up, split lighting, cinematic''' ) UpperCAmelCase : int = jax.random.PRNGKey(0 ) UpperCAmelCase : Union[str, Any] = 50 UpperCAmelCase : Optional[Any] = jax.device_count() UpperCAmelCase : Any = num_samples * [prompt] UpperCAmelCase : Dict = pipeline.prepare_inputs(__snake_case ) # shard inputs and rng UpperCAmelCase : str = replicate(__snake_case ) UpperCAmelCase : List[str] = jax.random.split(__snake_case , __snake_case ) UpperCAmelCase : Optional[int] = shard(__snake_case ) UpperCAmelCase : Dict = pipeline(__snake_case , __snake_case , __snake_case , __snake_case , jit=__snake_case ).images assert images.shape == (num_samples, 1, 512, 512, 3) if jax.device_count() == 8: assert np.abs((np.abs(images[0, 0, :2, :2, -2:] , dtype=np.floataa ).sum() - 0.0_45_04_39_45) ) < 1E-3 assert np.abs((np.abs(__snake_case , dtype=np.floataa ).sum() - 2_34_76_93.5) ) < 5E-1 def A ( self : Any ) -> Tuple: UpperCAmelCase : List[Any] = ( '''A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of''' ''' field, close up, split lighting, cinematic''' ) UpperCAmelCase : Union[str, Any] = jax.device_count() UpperCAmelCase : List[Any] = num_samples * [prompt] UpperCAmelCase : str = jax.random.split(jax.random.PRNGKey(0 ) , __snake_case ) UpperCAmelCase , UpperCAmelCase : Tuple = FlaxStableDiffusionPipeline.from_pretrained( '''CompVis/stable-diffusion-v1-4''' , revision='''bf16''' , dtype=jnp.bfloataa , safety_checker=__snake_case , ) UpperCAmelCase : Dict = replicate(__snake_case ) UpperCAmelCase : Optional[Any] = pipeline.prepare_inputs(__snake_case ) UpperCAmelCase : List[str] = shard(__snake_case ) UpperCAmelCase : Any = pipeline(__snake_case , __snake_case , __snake_case , jit=__snake_case ).images assert images.shape == (num_samples, 1, 512, 512, 3) UpperCAmelCase : Optional[int] = images[2, 0, 256, 10:17, 1] # With memory efficient attention UpperCAmelCase , UpperCAmelCase : Any = FlaxStableDiffusionPipeline.from_pretrained( '''CompVis/stable-diffusion-v1-4''' , revision='''bf16''' , dtype=jnp.bfloataa , safety_checker=__snake_case , use_memory_efficient_attention=__snake_case , ) UpperCAmelCase : int = replicate(__snake_case ) UpperCAmelCase : int = pipeline.prepare_inputs(__snake_case ) UpperCAmelCase : List[Any] = shard(__snake_case ) UpperCAmelCase : Optional[Any] = pipeline(__snake_case , __snake_case , __snake_case , jit=__snake_case ).images assert images_eff.shape == (num_samples, 1, 512, 512, 3) UpperCAmelCase : int = images[2, 0, 256, 10:17, 1] # I checked the results visually and they are very similar. However, I saw that the max diff is `1` and the `sum` # over the 8 images is exactly `256`, which is very suspicious. Testing a random slice for now. assert abs(slice_eff - slice ).max() < 1E-2
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from math import ceil def __snake_case ( _UpperCAmelCase , _UpperCAmelCase ): __a = list(range(0 , _lowerCAmelCase ) ) __a = [item for sublist in list(device_map.values() ) for item in sublist] # Duplicate check __a = [] for i in device_map_blocks: if device_map_blocks.count(_lowerCAmelCase ) > 1 and i not in duplicate_blocks: duplicate_blocks.append(_lowerCAmelCase ) # Missing blocks __a = [i for i in blocks if i not in device_map_blocks] __a = [i for i in device_map_blocks if i not in blocks] if len(_lowerCAmelCase ) != 0: raise ValueError( '''Duplicate attention blocks specified in device_map. Attention blocks must be specified to one device.''' ''' These attention blocks were specified more than once: ''' + str(_lowerCAmelCase ) ) if len(_lowerCAmelCase ) != 0: raise ValueError( '''There are attention blocks for this model that are not specified in the device_map. Add these attention ''' '''blocks to a device on the device_map: ''' + str(_lowerCAmelCase ) ) if len(_lowerCAmelCase ) != 0: raise ValueError( '''The device_map contains more attention blocks than this model has. Remove these from the device_map:''' + str(_lowerCAmelCase ) ) def __snake_case ( _UpperCAmelCase , _UpperCAmelCase ): __a = list(range(_lowerCAmelCase ) ) __a = int(ceil(n_layers / len(_lowerCAmelCase ) ) ) __a = [layers[i : i + n_blocks] for i in range(0 , _lowerCAmelCase , _lowerCAmelCase )] return dict(zip(_lowerCAmelCase , _lowerCAmelCase ) )
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'''simple docstring''' import random from .binary_exp_mod import bin_exp_mod def snake_case_ ( _lowerCAmelCase : Tuple , _lowerCAmelCase : Optional[Any]=1000 ) -> int: if n < 2: return False if n % 2 == 0: return n == 2 # this means n is odd UpperCAmelCase : str = n - 1 UpperCAmelCase : List[Any] = 0 while d % 2 == 0: d /= 2 exp += 1 # n - 1=d*(2**exp) UpperCAmelCase : List[str] = 0 while count < prec: UpperCAmelCase : int = random.randint(2 , n - 1 ) UpperCAmelCase : List[str] = bin_exp_mod(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ) if b != 1: UpperCAmelCase : int = True for _ in range(_lowerCAmelCase ): if b == n - 1: UpperCAmelCase : Dict = False break UpperCAmelCase : str = b * b b %= n if flag: return False count += 1 return True if __name__ == "__main__": UpperCamelCase__: Optional[int] = abs(int(input("Enter bound : ").strip())) print("Here's the list of primes:") print(", ".join(str(i) for i in range(n + 1) if is_prime_big(i)))
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from dataclasses import dataclass, field from typing import TYPE_CHECKING, Any, ClassVar, Dict, List, Optional, Union import pyarrow as pa if TYPE_CHECKING: from .features import FeatureType @dataclass class lowercase : lowercase_ : str =42 lowercase_ : Tuple =None # Automatically constructed lowercase_ : Dict ='''dict''' lowercase_ : Any =None lowercase_ : Optional[int] =field(default='''Translation''' , init=A__ , repr=A__ ) def __call__( self): return pa.struct({lang: pa.string() for lang in sorted(self.languages)}) def A__ ( self): from .features import Value return {k: Value('''string''') for k in sorted(self.languages)} @dataclass class lowercase : lowercase_ : List[Any] =None lowercase_ : Union[str, Any] =None lowercase_ : Optional[int] =None # Automatically constructed lowercase_ : Optional[int] ='''dict''' lowercase_ : Union[str, Any] =None lowercase_ : List[str] =field(default='''TranslationVariableLanguages''' , init=A__ , repr=A__ ) def A__ ( self): lowercase = sorted(set(self.languages)) if self.languages else None lowercase = len(self.languages) if self.languages else None def __call__( self): return pa.struct({'''language''': pa.list_(pa.string()), '''translation''': pa.list_(pa.string())}) def A__ ( self ,A__): lowercase = set(self.languages) if self.languages and set(__snake_case) - lang_set: raise ValueError( f'Some languages in example ({", ".join(sorted(set(__snake_case) - lang_set))}) are not in valid set ({", ".join(__snake_case)}).') # Convert dictionary into tuples, splitting out cases where there are # multiple translations for a single language. lowercase = [] for lang, text in translation_dict.items(): if isinstance(__snake_case ,__snake_case): translation_tuples.append((lang, text)) else: translation_tuples.extend([(lang, el) for el in text]) # Ensure translations are in ascending order by language code. lowercase = zip(*sorted(__snake_case)) return {"language": languages, "translation": translations} def A__ ( self): from .features import Sequence, Value return { "language": Sequence(Value('''string''')), "translation": Sequence(Value('''string''')), }
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'''simple docstring''' import argparse import json from pathlib import Path import requests import torch from huggingface_hub import cached_download, hf_hub_url from PIL import Image from transformers import DPTConfig, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTImageProcessor from transformers.utils import logging logging.set_verbosity_info() UpperCamelCase__: Optional[int] = logging.get_logger(__name__) def snake_case_ ( _lowerCAmelCase : Optional[int] ) -> Optional[int]: UpperCAmelCase : Tuple = DPTConfig(embedding_type='''hybrid''' ) if "large" in checkpoint_url: UpperCAmelCase : Tuple = 1024 UpperCAmelCase : List[Any] = 4096 UpperCAmelCase : str = 24 UpperCAmelCase : List[Any] = 16 UpperCAmelCase : str = [5, 11, 17, 23] UpperCAmelCase : List[Any] = [256, 512, 1024, 1024] UpperCAmelCase : Tuple = (1, 384, 384) if "nyu" or "midas" in checkpoint_url: UpperCAmelCase : Optional[Any] = 768 UpperCAmelCase : Tuple = [1, 1, 1, 0.5] UpperCAmelCase : int = [256, 512, 768, 768] UpperCAmelCase : Any = 150 UpperCAmelCase : Tuple = 16 UpperCAmelCase : Any = (1, 384, 384) UpperCAmelCase : Optional[Any] = False UpperCAmelCase : Tuple = '''project''' if "ade" in checkpoint_url: UpperCAmelCase : Any = True UpperCAmelCase : str = 768 UpperCAmelCase : Optional[int] = [1, 1, 1, 0.5] UpperCAmelCase : List[Any] = 150 UpperCAmelCase : List[Any] = 16 UpperCAmelCase : str = '''huggingface/label-files''' UpperCAmelCase : Tuple = '''ade20k-id2label.json''' UpperCAmelCase : Any = json.load(open(cached_download(hf_hub_url(_lowerCAmelCase , _lowerCAmelCase , repo_type='''dataset''' ) ) , '''r''' ) ) UpperCAmelCase : Optional[Any] = {int(_lowerCAmelCase ): v for k, v in idalabel.items()} UpperCAmelCase : List[Any] = idalabel UpperCAmelCase : Optional[int] = {v: k for k, v in idalabel.items()} UpperCAmelCase : Union[str, Any] = [1, 150, 480, 480] return config, expected_shape def snake_case_ ( _lowerCAmelCase : Union[str, Any] ) -> int: UpperCAmelCase : List[str] = ['''pretrained.model.head.weight''', '''pretrained.model.head.bias'''] for k in ignore_keys: state_dict.pop(_lowerCAmelCase , _lowerCAmelCase ) def snake_case_ ( _lowerCAmelCase : Tuple ) -> Any: if ( "pretrained.model" in name and "cls_token" not in name and "pos_embed" not in name and "patch_embed" not in name ): UpperCAmelCase : Tuple = name.replace('''pretrained.model''' , '''dpt.encoder''' ) if "pretrained.model" in name: UpperCAmelCase : Union[str, Any] = name.replace('''pretrained.model''' , '''dpt.embeddings''' ) if "patch_embed" in name: UpperCAmelCase : int = name.replace('''patch_embed''' , '''''' ) if "pos_embed" in name: UpperCAmelCase : Tuple = name.replace('''pos_embed''' , '''position_embeddings''' ) if "attn.proj" in name: UpperCAmelCase : Any = name.replace('''attn.proj''' , '''attention.output.dense''' ) if "proj" in name and "project" not in name: UpperCAmelCase : str = name.replace('''proj''' , '''projection''' ) if "blocks" in name: UpperCAmelCase : Any = name.replace('''blocks''' , '''layer''' ) if "mlp.fc1" in name: UpperCAmelCase : Optional[int] = name.replace('''mlp.fc1''' , '''intermediate.dense''' ) if "mlp.fc2" in name: UpperCAmelCase : Optional[Any] = name.replace('''mlp.fc2''' , '''output.dense''' ) if "norm1" in name and "backbone" not in name: UpperCAmelCase : Dict = name.replace('''norm1''' , '''layernorm_before''' ) if "norm2" in name and "backbone" not in name: UpperCAmelCase : Tuple = name.replace('''norm2''' , '''layernorm_after''' ) if "scratch.output_conv" in name: UpperCAmelCase : Tuple = name.replace('''scratch.output_conv''' , '''head''' ) if "scratch" in name: UpperCAmelCase : str = name.replace('''scratch''' , '''neck''' ) if "layer1_rn" in name: UpperCAmelCase : Dict = name.replace('''layer1_rn''' , '''convs.0''' ) if "layer2_rn" in name: UpperCAmelCase : int = name.replace('''layer2_rn''' , '''convs.1''' ) if "layer3_rn" in name: UpperCAmelCase : Tuple = name.replace('''layer3_rn''' , '''convs.2''' ) if "layer4_rn" in name: UpperCAmelCase : int = name.replace('''layer4_rn''' , '''convs.3''' ) if "refinenet" in name: UpperCAmelCase : List[str] = int(name[len('''neck.refinenet''' ) : len('''neck.refinenet''' ) + 1] ) # tricky here: we need to map 4 to 0, 3 to 1, 2 to 2 and 1 to 3 UpperCAmelCase : str = name.replace(f"""refinenet{layer_idx}""" , f"""fusion_stage.layers.{abs(layer_idx-4 )}""" ) if "out_conv" in name: UpperCAmelCase : List[str] = name.replace('''out_conv''' , '''projection''' ) if "resConfUnit1" in name: UpperCAmelCase : Union[str, Any] = name.replace('''resConfUnit1''' , '''residual_layer1''' ) if "resConfUnit2" in name: UpperCAmelCase : Any = name.replace('''resConfUnit2''' , '''residual_layer2''' ) if "conv1" in name: UpperCAmelCase : Optional[int] = name.replace('''conv1''' , '''convolution1''' ) if "conv2" in name: UpperCAmelCase : Tuple = name.replace('''conv2''' , '''convolution2''' ) # readout blocks if "pretrained.act_postprocess1.0.project.0" in name: UpperCAmelCase : Dict = name.replace('''pretrained.act_postprocess1.0.project.0''' , '''neck.reassemble_stage.readout_projects.0.0''' ) if "pretrained.act_postprocess2.0.project.0" in name: UpperCAmelCase : int = name.replace('''pretrained.act_postprocess2.0.project.0''' , '''neck.reassemble_stage.readout_projects.1.0''' ) if "pretrained.act_postprocess3.0.project.0" in name: UpperCAmelCase : Any = name.replace('''pretrained.act_postprocess3.0.project.0''' , '''neck.reassemble_stage.readout_projects.2.0''' ) if "pretrained.act_postprocess4.0.project.0" in name: UpperCAmelCase : Optional[Any] = name.replace('''pretrained.act_postprocess4.0.project.0''' , '''neck.reassemble_stage.readout_projects.3.0''' ) # resize blocks if "pretrained.act_postprocess1.3" in name: UpperCAmelCase : List[Any] = name.replace('''pretrained.act_postprocess1.3''' , '''neck.reassemble_stage.layers.0.projection''' ) if "pretrained.act_postprocess1.4" in name: UpperCAmelCase : Any = name.replace('''pretrained.act_postprocess1.4''' , '''neck.reassemble_stage.layers.0.resize''' ) if "pretrained.act_postprocess2.3" in name: UpperCAmelCase : Optional[int] = name.replace('''pretrained.act_postprocess2.3''' , '''neck.reassemble_stage.layers.1.projection''' ) if "pretrained.act_postprocess2.4" in name: UpperCAmelCase : str = name.replace('''pretrained.act_postprocess2.4''' , '''neck.reassemble_stage.layers.1.resize''' ) if "pretrained.act_postprocess3.3" in name: UpperCAmelCase : List[str] = name.replace('''pretrained.act_postprocess3.3''' , '''neck.reassemble_stage.layers.2.projection''' ) if "pretrained.act_postprocess4.3" in name: UpperCAmelCase : Tuple = name.replace('''pretrained.act_postprocess4.3''' , '''neck.reassemble_stage.layers.3.projection''' ) if "pretrained.act_postprocess4.4" in name: UpperCAmelCase : int = name.replace('''pretrained.act_postprocess4.4''' , '''neck.reassemble_stage.layers.3.resize''' ) if "pretrained" in name: UpperCAmelCase : Optional[int] = name.replace('''pretrained''' , '''dpt''' ) if "bn" in name: UpperCAmelCase : Dict = name.replace('''bn''' , '''batch_norm''' ) if "head" in name: UpperCAmelCase : Any = name.replace('''head''' , '''head.head''' ) if "encoder.norm" in name: UpperCAmelCase : Optional[int] = name.replace('''encoder.norm''' , '''layernorm''' ) if "auxlayer" in name: UpperCAmelCase : Union[str, Any] = name.replace('''auxlayer''' , '''auxiliary_head.head''' ) if "backbone" in name: UpperCAmelCase : List[Any] = name.replace('''backbone''' , '''backbone.bit.encoder''' ) if ".." in name: UpperCAmelCase : Optional[int] = name.replace('''..''' , '''.''' ) if "stem.conv" in name: UpperCAmelCase : Optional[Any] = name.replace('''stem.conv''' , '''bit.embedder.convolution''' ) if "blocks" in name: UpperCAmelCase : Optional[int] = name.replace('''blocks''' , '''layers''' ) if "convolution" in name and "backbone" in name: UpperCAmelCase : List[Any] = name.replace('''convolution''' , '''conv''' ) if "layer" in name and "backbone" in name: UpperCAmelCase : List[str] = name.replace('''layer''' , '''layers''' ) if "backbone.bit.encoder.bit" in name: UpperCAmelCase : List[Any] = name.replace('''backbone.bit.encoder.bit''' , '''backbone.bit''' ) if "embedder.conv" in name: UpperCAmelCase : List[Any] = name.replace('''embedder.conv''' , '''embedder.convolution''' ) if "backbone.bit.encoder.stem.norm" in name: UpperCAmelCase : Tuple = name.replace('''backbone.bit.encoder.stem.norm''' , '''backbone.bit.embedder.norm''' ) return name def snake_case_ ( _lowerCAmelCase : List[str] , _lowerCAmelCase : List[Any] ) -> Optional[Any]: for i in range(config.num_hidden_layers ): # read in weights + bias of input projection layer (in timm, this is a single matrix + bias) UpperCAmelCase : Optional[int] = state_dict.pop(f"""dpt.encoder.layer.{i}.attn.qkv.weight""" ) UpperCAmelCase : Tuple = state_dict.pop(f"""dpt.encoder.layer.{i}.attn.qkv.bias""" ) # next, add query, keys and values (in that order) to the state dict UpperCAmelCase : Tuple = in_proj_weight[: config.hidden_size, :] UpperCAmelCase : int = in_proj_bias[: config.hidden_size] UpperCAmelCase : List[str] = in_proj_weight[ config.hidden_size : config.hidden_size * 2, : ] UpperCAmelCase : List[str] = in_proj_bias[ config.hidden_size : config.hidden_size * 2 ] UpperCAmelCase : str = in_proj_weight[ -config.hidden_size :, : ] UpperCAmelCase : Union[str, Any] = in_proj_bias[-config.hidden_size :] def snake_case_ ( ) -> List[str]: UpperCAmelCase : Optional[int] = '''http://images.cocodataset.org/val2017/000000039769.jpg''' UpperCAmelCase : Optional[int] = Image.open(requests.get(_lowerCAmelCase , stream=_lowerCAmelCase ).raw ) return im @torch.no_grad() def snake_case_ ( _lowerCAmelCase : Optional[Any] , _lowerCAmelCase : Union[str, Any] , _lowerCAmelCase : str , _lowerCAmelCase : Union[str, Any] , _lowerCAmelCase : List[str] ) -> Any: UpperCAmelCase , UpperCAmelCase : int = get_dpt_config(_lowerCAmelCase ) # load original state_dict from URL # state_dict = torch.hub.load_state_dict_from_url(checkpoint_url, map_location="cpu") UpperCAmelCase : List[Any] = torch.load(_lowerCAmelCase , map_location='''cpu''' ) # remove certain keys remove_ignore_keys_(_lowerCAmelCase ) # rename keys for key in state_dict.copy().keys(): UpperCAmelCase : Any = state_dict.pop(_lowerCAmelCase ) UpperCAmelCase : List[Any] = val # read in qkv matrices read_in_q_k_v(_lowerCAmelCase , _lowerCAmelCase ) # load HuggingFace model UpperCAmelCase : Optional[Any] = DPTForSemanticSegmentation(_lowerCAmelCase ) if '''ade''' in checkpoint_url else DPTForDepthEstimation(_lowerCAmelCase ) model.load_state_dict(_lowerCAmelCase ) model.eval() # Check outputs on an image UpperCAmelCase : int = 480 if '''ade''' in checkpoint_url else 384 UpperCAmelCase : List[Any] = DPTImageProcessor(size=_lowerCAmelCase ) UpperCAmelCase : Dict = prepare_img() UpperCAmelCase : Optional[int] = image_processor(_lowerCAmelCase , return_tensors='''pt''' ) # forward pass UpperCAmelCase : Any = model(**_lowerCAmelCase ).logits if '''ade''' in checkpoint_url else model(**_lowerCAmelCase ).predicted_depth if show_prediction: UpperCAmelCase : Dict = ( torch.nn.functional.interpolate( outputs.unsqueeze(1 ) , size=(image.size[1], image.size[0]) , mode='''bicubic''' , align_corners=_lowerCAmelCase , ) .squeeze() .cpu() .numpy() ) Image.fromarray((prediction / prediction.max()) * 255 ).show() if pytorch_dump_folder_path is not None: Path(_lowerCAmelCase ).mkdir(exist_ok=_lowerCAmelCase ) print(f"""Saving model to {pytorch_dump_folder_path}""" ) model.save_pretrained(_lowerCAmelCase ) print(f"""Saving image processor to {pytorch_dump_folder_path}""" ) image_processor.save_pretrained(_lowerCAmelCase ) if push_to_hub: model.push_to_hub('''ybelkada/dpt-hybrid-midas''' ) image_processor.push_to_hub('''ybelkada/dpt-hybrid-midas''' ) if __name__ == "__main__": UpperCamelCase__: Dict = argparse.ArgumentParser() # Required parameters parser.add_argument( "--checkpoint_url", default="https://github.com/intel-isl/DPT/releases/download/1_0/dpt_large-midas-2f21e586.pt", type=str, help="URL of the original DPT checkpoint you'd like to convert.", ) parser.add_argument( "--pytorch_dump_folder_path", default=None, type=str, required=False, help="Path to the output PyTorch model directory.", ) parser.add_argument( "--push_to_hub", action="store_true", ) parser.add_argument( "--model_name", default="dpt-large", type=str, help="Name of the model, in case you're pushing to the hub.", ) parser.add_argument( "--show_prediction", action="store_true", ) UpperCamelCase__: Tuple = parser.parse_args() convert_dpt_checkpoint( args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name, args.show_prediction )
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import cmath import math def lowercase( UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ ) -> complex: '''simple docstring''' UpperCamelCase = math.radians(_lowerCAmelCase ) UpperCamelCase = math.radians(_lowerCAmelCase ) # Convert voltage and current to rectangular form UpperCamelCase = cmath.rect(_lowerCAmelCase , _lowerCAmelCase ) UpperCamelCase = cmath.rect(_lowerCAmelCase , _lowerCAmelCase ) # Calculate apparent power return voltage_rect * current_rect if __name__ == "__main__": import doctest doctest.testmod()
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'''simple docstring''' # coding=utf-8 # Copyright 2020 The HuggingFace Inc. team. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # this script dumps information about the environment import os import sys import transformers UpperCamelCase__: Optional[int] = "3" print("Python version:", sys.version) print("transformers version:", transformers.__version__) try: import torch print("Torch version:", torch.__version__) print("Cuda available:", torch.cuda.is_available()) print("Cuda version:", torch.version.cuda) print("CuDNN version:", torch.backends.cudnn.version()) print("Number of GPUs available:", torch.cuda.device_count()) print("NCCL version:", torch.cuda.nccl.version()) except ImportError: print("Torch version:", None) try: import deepspeed print("DeepSpeed version:", deepspeed.__version__) except ImportError: print("DeepSpeed version:", None) try: import tensorflow as tf print("TensorFlow version:", tf.__version__) print("TF GPUs available:", bool(tf.config.list_physical_devices("GPU"))) print("Number of TF GPUs available:", len(tf.config.list_physical_devices("GPU"))) except ImportError: print("TensorFlow version:", None)
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'''simple docstring''' import argparse from tax import checkpoints from transformers import AutoConfig, FlaxAutoModelForSeqaSeqLM def __snake_case( _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ) -> List[Any]: snake_case__ : List[str] = AutoConfig.from_pretrained(_lowerCAmelCase ) snake_case__ : Tuple = FlaxAutoModelForSeqaSeqLM.from_config(config=_lowerCAmelCase ) snake_case__ : Union[str, Any] = checkpoints.load_tax_checkpoint(_lowerCAmelCase ) snake_case__ : Dict = '''wi_0''' in tax_model['''target''']['''encoder''']['''layers_0''']['''mlp'''] if config.model_type == "t5": snake_case__ : str = '''SelfAttention''' if config.model_type == "longt5" and config.encoder_attention_type == "local": snake_case__ : List[Any] = '''LocalSelfAttention''' elif config.model_type == "longt5" and config.encoder_attention_type == "transient-global": snake_case__ : Optional[Any] = '''TransientGlobalSelfAttention''' else: raise ValueError( """Given config is expected to have `model_type=\'t5\'`, or `model_type=\'longt5` with `encoder_attention_type`""" """ attribute with a value from [\'local\', \'transient-global].""" ) # Encoder for layer_index in range(config.num_layers ): snake_case__ : Dict = f"layers_{str(_lowerCAmelCase )}" # Self-Attention snake_case__ : Any = tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''key''']['''kernel'''] snake_case__ : Optional[Any] = tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''out''']['''kernel'''] snake_case__ : List[str] = tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''query''']['''kernel'''] snake_case__ : Dict = tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''value''']['''kernel'''] # Global input layer norm if config.model_type == "longt5" and config.encoder_attention_type == "transient-global": snake_case__ : List[Any] = tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''T5LayerNorm_0''']['''scale'''] # Layer Normalization snake_case__ : Optional[int] = tax_model['''target''']['''encoder'''][layer_name]['''pre_attention_layer_norm''']['''scale'''] if split_mlp_wi: snake_case__ : Optional[Any] = tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi_0''']['''kernel'''] snake_case__ : int = tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi_1''']['''kernel'''] else: snake_case__ : List[Any] = tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi''']['''kernel'''] snake_case__ : List[str] = tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wo''']['''kernel'''] # Layer Normalization snake_case__ : Tuple = tax_model['''target''']['''encoder'''][layer_name]['''pre_mlp_layer_norm''']['''scale'''] # Assigning snake_case__ : List[Any] = flax_model.params['''encoder''']['''block'''][str(_lowerCAmelCase )]['''layer'''] snake_case__ : Union[str, Any] = tax_attention_key snake_case__ : int = tax_attention_out snake_case__ : Union[str, Any] = tax_attention_query snake_case__ : Any = tax_attention_value snake_case__ : str = tax_attention_layer_norm # Global input layer norm if config.model_type == "longt5" and config.encoder_attention_type == "transient-global": snake_case__ : List[Any] = tax_global_layer_norm if split_mlp_wi: snake_case__ : str = tax_mlp_wi_a snake_case__ : int = tax_mlp_wi_a else: snake_case__ : List[str] = tax_mlp_wi snake_case__ : Any = tax_mlp_wo snake_case__ : int = tax_mlp_layer_norm snake_case__ : List[Any] = flax_model_encoder_layer_block # Only for layer 0: snake_case__ : Tuple = tax_model['''target''']['''encoder''']['''relpos_bias''']['''rel_embedding'''].T snake_case__ : Any = tax_encoder_rel_embedding # Side/global relative position_bias + layer norm if config.model_type == "longt5" and config.encoder_attention_type == "transient-global": snake_case__ : Tuple = tax_model['''target''']['''encoder''']['''side_relpos_bias''']['''rel_embedding'''].T snake_case__ : int = tax_encoder_global_rel_embedding # Assigning snake_case__ : int = tax_model['''target''']['''encoder''']['''encoder_norm''']['''scale'''] snake_case__ : List[Any] = tax_encoder_norm # Decoder for layer_index in range(config.num_layers ): snake_case__ : str = f"layers_{str(_lowerCAmelCase )}" # Self-Attention snake_case__ : List[Any] = tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''key''']['''kernel'''] snake_case__ : int = tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''out''']['''kernel'''] snake_case__ : Dict = tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''query''']['''kernel'''] snake_case__ : Dict = tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''value''']['''kernel'''] # Layer Normalization snake_case__ : Dict = tax_model['''target''']['''decoder'''][layer_name]['''pre_self_attention_layer_norm'''][ '''scale''' ] # Encoder-Decoder-Attention snake_case__ : Optional[int] = tax_model['''target''']['''decoder'''][layer_name]['''encoder_decoder_attention'''] snake_case__ : List[Any] = tax_enc_dec_attention_module['''key''']['''kernel'''] snake_case__ : Any = tax_enc_dec_attention_module['''out''']['''kernel'''] snake_case__ : Optional[int] = tax_enc_dec_attention_module['''query''']['''kernel'''] snake_case__ : str = tax_enc_dec_attention_module['''value''']['''kernel'''] # Layer Normalization snake_case__ : Dict = tax_model['''target''']['''decoder'''][layer_name]['''pre_cross_attention_layer_norm''']['''scale'''] # MLP if split_mlp_wi: snake_case__ : Dict = tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi_0''']['''kernel'''] snake_case__ : Optional[int] = tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi_1''']['''kernel'''] else: snake_case__ : Tuple = tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi''']['''kernel'''] snake_case__ : List[str] = tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wo''']['''kernel'''] # Layer Normalization snake_case__ : List[Any] = tax_model['''target''']['''decoder'''][layer_name]['''pre_mlp_layer_norm''']['''scale'''] # Assigning snake_case__ : List[Any] = flax_model.params['''decoder''']['''block'''][str(_lowerCAmelCase )]['''layer'''] snake_case__ : Optional[int] = tax_attention_key snake_case__ : Optional[int] = tax_attention_out snake_case__ : Union[str, Any] = tax_attention_query snake_case__ : List[str] = tax_attention_value snake_case__ : List[str] = tax_pre_attention_layer_norm snake_case__ : List[str] = tax_enc_dec_attention_key snake_case__ : int = tax_enc_dec_attention_out snake_case__ : Dict = tax_enc_dec_attention_query snake_case__ : List[str] = tax_enc_dec_attention_value snake_case__ : str = tax_cross_layer_norm if split_mlp_wi: snake_case__ : Tuple = tax_mlp_wi_a snake_case__ : int = tax_mlp_wi_a else: snake_case__ : List[str] = tax_mlp_wi snake_case__ : Union[str, Any] = tax_mlp_wo snake_case__ : str = txa_mlp_layer_norm snake_case__ : str = flax_model_decoder_layer_block # Decoder Normalization snake_case__ : List[Any] = tax_model['''target''']['''decoder''']['''decoder_norm''']['''scale'''] snake_case__ : Any = txa_decoder_norm # Only for layer 0: snake_case__ : str = tax_model['''target''']['''decoder''']['''relpos_bias''']['''rel_embedding'''].T snake_case__ : Optional[int] = tax_decoder_rel_embedding # Token Embeddings snake_case__ : Union[str, Any] = tax_model['''target''']['''token_embedder''']['''embedding'''] snake_case__ : str = txa_token_embeddings # LM Head (only in v1.1 and LongT5 checkpoints) if "logits_dense" in tax_model["target"]["decoder"]: snake_case__ : Optional[Any] = tax_model['''target''']['''decoder''']['''logits_dense''']['''kernel'''] flax_model.save_pretrained(_lowerCAmelCase ) print("""T5X Model was sucessfully converted!""" ) if __name__ == "__main__": __a = argparse.ArgumentParser() # Required parameters parser.add_argument( "--t5x_checkpoint_path", default=None, type=str, required=True, help="Path the T5X checkpoint." ) parser.add_argument("--config_name", default=None, type=str, required=True, help="Config name of LongT5/T5 model.") parser.add_argument( "--flax_dump_folder_path", default=None, type=str, required=True, help="Path to the output FLAX model." ) __a = parser.parse_args() convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path)
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'''simple docstring''' from __future__ import annotations def snake_case_ ( _lowerCAmelCase : list[int | float] , _lowerCAmelCase : int , _lowerCAmelCase : int ) -> int | float: if len(_lowerCAmelCase ) == 0: raise ValueError('''find_max() arg is an empty sequence''' ) if ( left >= len(_lowerCAmelCase ) or left < -len(_lowerCAmelCase ) or right >= len(_lowerCAmelCase ) or right < -len(_lowerCAmelCase ) ): raise IndexError('''list index out of range''' ) if left == right: return nums[left] UpperCAmelCase : List[Any] = (left + right) >> 1 # the middle UpperCAmelCase : Optional[Any] = find_max(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ) # find max in range[left, mid] UpperCAmelCase : Dict = find_max(_lowerCAmelCase , mid + 1 , _lowerCAmelCase ) # find max in range[mid + 1, right] return left_max if left_max >= right_max else right_max if __name__ == "__main__": import doctest doctest.testmod(verbose=True)
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import unittest from transformers import MraConfig, is_torch_available from transformers.testing_utils import require_torch, slow, torch_device from ...test_configuration_common import ConfigTester from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask if is_torch_available(): import torch from transformers import ( MraForMaskedLM, MraForMultipleChoice, MraForQuestionAnswering, MraForSequenceClassification, MraForTokenClassification, MraModel, ) from transformers.models.mra.modeling_mra import MRA_PRETRAINED_MODEL_ARCHIVE_LIST class lowercase_ : def __init__( self , __UpperCamelCase , __UpperCamelCase=2 , __UpperCamelCase=8 , __UpperCamelCase=True , __UpperCamelCase=True , __UpperCamelCase=True , __UpperCamelCase=True , __UpperCamelCase=9_9 , __UpperCamelCase=1_6 , __UpperCamelCase=5 , __UpperCamelCase=2 , __UpperCamelCase=3_6 , __UpperCamelCase="gelu" , __UpperCamelCase=0.0 , __UpperCamelCase=0.0 , __UpperCamelCase=5_1_2 , __UpperCamelCase=1_6 , __UpperCamelCase=2 , __UpperCamelCase=0.02 , __UpperCamelCase=3 , __UpperCamelCase=4 , __UpperCamelCase=None , ): """simple docstring""" UpperCamelCase_ = parent UpperCamelCase_ = batch_size UpperCamelCase_ = seq_length UpperCamelCase_ = is_training UpperCamelCase_ = use_input_mask UpperCamelCase_ = use_token_type_ids UpperCamelCase_ = use_labels UpperCamelCase_ = vocab_size UpperCamelCase_ = hidden_size UpperCamelCase_ = num_hidden_layers UpperCamelCase_ = num_attention_heads UpperCamelCase_ = intermediate_size UpperCamelCase_ = hidden_act UpperCamelCase_ = hidden_dropout_prob UpperCamelCase_ = attention_probs_dropout_prob UpperCamelCase_ = max_position_embeddings UpperCamelCase_ = type_vocab_size UpperCamelCase_ = type_sequence_label_size UpperCamelCase_ = initializer_range UpperCamelCase_ = num_labels UpperCamelCase_ = num_choices UpperCamelCase_ = scope def lowerCamelCase_ ( self ): """simple docstring""" UpperCamelCase_ = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size ) UpperCamelCase_ = None if self.use_input_mask: UpperCamelCase_ = random_attention_mask([self.batch_size, self.seq_length] ) UpperCamelCase_ = None if self.use_token_type_ids: UpperCamelCase_ = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size ) UpperCamelCase_ = None UpperCamelCase_ = None UpperCamelCase_ = None if self.use_labels: UpperCamelCase_ = ids_tensor([self.batch_size] , self.type_sequence_label_size ) UpperCamelCase_ = ids_tensor([self.batch_size, self.seq_length] , self.num_labels ) UpperCamelCase_ = ids_tensor([self.batch_size] , self.num_choices ) UpperCamelCase_ = self.get_config() return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels def lowerCamelCase_ ( self ): """simple docstring""" return MraConfig( vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , is_decoder=__snake_case , initializer_range=self.initializer_range , ) def lowerCamelCase_ ( self ): """simple docstring""" UpperCamelCase_ = self.get_config() UpperCamelCase_ = 3_0_0 return config def lowerCamelCase_ ( self ): """simple docstring""" ( UpperCamelCase_ ) = self.prepare_config_and_inputs() UpperCamelCase_ = True UpperCamelCase_ = floats_tensor([self.batch_size, self.seq_length, self.hidden_size] ) UpperCamelCase_ = ids_tensor([self.batch_size, self.seq_length] , vocab_size=2 ) return ( config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels, encoder_hidden_states, encoder_attention_mask, ) def lowerCamelCase_ ( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ): """simple docstring""" UpperCamelCase_ = MraModel(config=__snake_case ) model.to(__snake_case ) model.eval() UpperCamelCase_ = model(__snake_case , attention_mask=__snake_case , token_type_ids=__snake_case ) UpperCamelCase_ = model(__snake_case , token_type_ids=__snake_case ) UpperCamelCase_ = model(__snake_case ) self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) ) def lowerCamelCase_ ( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , ): """simple docstring""" UpperCamelCase_ = True UpperCamelCase_ = MraModel(__snake_case ) model.to(__snake_case ) model.eval() UpperCamelCase_ = model( __snake_case , attention_mask=__snake_case , token_type_ids=__snake_case , encoder_hidden_states=__snake_case , encoder_attention_mask=__snake_case , ) UpperCamelCase_ = model( __snake_case , attention_mask=__snake_case , token_type_ids=__snake_case , encoder_hidden_states=__snake_case , ) UpperCamelCase_ = model(__snake_case , attention_mask=__snake_case , token_type_ids=__snake_case ) self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) ) def lowerCamelCase_ ( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ): """simple docstring""" UpperCamelCase_ = MraForMaskedLM(config=__snake_case ) model.to(__snake_case ) model.eval() UpperCamelCase_ = model(__snake_case , attention_mask=__snake_case , token_type_ids=__snake_case , labels=__snake_case ) self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) ) def lowerCamelCase_ ( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ): """simple docstring""" UpperCamelCase_ = MraForQuestionAnswering(config=__snake_case ) model.to(__snake_case ) model.eval() UpperCamelCase_ = model( __snake_case , attention_mask=__snake_case , token_type_ids=__snake_case , start_positions=__snake_case , end_positions=__snake_case , ) self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) ) self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) ) def lowerCamelCase_ ( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ): """simple docstring""" UpperCamelCase_ = self.num_labels UpperCamelCase_ = MraForSequenceClassification(__snake_case ) model.to(__snake_case ) model.eval() UpperCamelCase_ = model(__snake_case , attention_mask=__snake_case , token_type_ids=__snake_case , labels=__snake_case ) self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) ) def lowerCamelCase_ ( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ): """simple docstring""" UpperCamelCase_ = self.num_labels UpperCamelCase_ = MraForTokenClassification(config=__snake_case ) model.to(__snake_case ) model.eval() UpperCamelCase_ = model(__snake_case , attention_mask=__snake_case , token_type_ids=__snake_case , labels=__snake_case ) self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) ) def lowerCamelCase_ ( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ): """simple docstring""" UpperCamelCase_ = self.num_choices UpperCamelCase_ = MraForMultipleChoice(config=__snake_case ) model.to(__snake_case ) model.eval() UpperCamelCase_ = input_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous() UpperCamelCase_ = token_type_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous() UpperCamelCase_ = input_mask.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous() UpperCamelCase_ = model( __snake_case , attention_mask=__snake_case , token_type_ids=__snake_case , labels=__snake_case , ) self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) ) def lowerCamelCase_ ( self ): """simple docstring""" UpperCamelCase_ = self.prepare_config_and_inputs() ( UpperCamelCase_ ) = config_and_inputs UpperCamelCase_ = {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask} return config, inputs_dict @require_torch class lowercase_ ( A__ , unittest.TestCase ): A__ : List[str] = ( ( MraModel, MraForMaskedLM, MraForMultipleChoice, MraForQuestionAnswering, MraForSequenceClassification, MraForTokenClassification, ) if is_torch_available() else () ) A__ : List[str] = False A__ : Union[str, Any] = False A__ : Dict = False A__ : Optional[int] = False A__ : Optional[Any] = () def lowerCamelCase_ ( self ): """simple docstring""" UpperCamelCase_ = MraModelTester(self ) UpperCamelCase_ = ConfigTester(self , config_class=__snake_case , hidden_size=3_7 ) def lowerCamelCase_ ( self ): """simple docstring""" self.config_tester.run_common_tests() def lowerCamelCase_ ( self ): """simple docstring""" UpperCamelCase_ = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_model(*__snake_case ) def lowerCamelCase_ ( self ): """simple docstring""" UpperCamelCase_ = self.model_tester.prepare_config_and_inputs() for type in ["absolute", "relative_key", "relative_key_query"]: UpperCamelCase_ = type self.model_tester.create_and_check_model(*__snake_case ) def lowerCamelCase_ ( self ): """simple docstring""" UpperCamelCase_ = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_masked_lm(*__snake_case ) def lowerCamelCase_ ( self ): """simple docstring""" UpperCamelCase_ = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_multiple_choice(*__snake_case ) def lowerCamelCase_ ( self ): """simple docstring""" UpperCamelCase_ = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_question_answering(*__snake_case ) def lowerCamelCase_ ( self ): """simple docstring""" UpperCamelCase_ = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_sequence_classification(*__snake_case ) def lowerCamelCase_ ( self ): """simple docstring""" UpperCamelCase_ = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_token_classification(*__snake_case ) @slow def lowerCamelCase_ ( self ): """simple docstring""" for model_name in MRA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: UpperCamelCase_ = MraModel.from_pretrained(__snake_case ) self.assertIsNotNone(__snake_case ) @unittest.skip(reason="""MRA does not output attentions""" ) def lowerCamelCase_ ( self ): """simple docstring""" return @require_torch class lowercase_ ( unittest.TestCase ): @slow def lowerCamelCase_ ( self ): """simple docstring""" UpperCamelCase_ = MraModel.from_pretrained("""uw-madison/mra-base-512-4""" ) UpperCamelCase_ = torch.arange(2_5_6 ).unsqueeze(0 ) with torch.no_grad(): UpperCamelCase_ = model(__snake_case )[0] UpperCamelCase_ = torch.Size((1, 2_5_6, 7_6_8) ) self.assertEqual(output.shape , __snake_case ) UpperCamelCase_ = torch.tensor( [[[-0.0_140, 0.0_830, -0.0_381], [0.1_546, 0.1_402, 0.0_220], [0.1_162, 0.0_851, 0.0_165]]] ) self.assertTrue(torch.allclose(output[:, :3, :3] , __snake_case , atol=1e-4 ) ) @slow def lowerCamelCase_ ( self ): """simple docstring""" UpperCamelCase_ = MraForMaskedLM.from_pretrained("""uw-madison/mra-base-512-4""" ) UpperCamelCase_ = torch.arange(2_5_6 ).unsqueeze(0 ) with torch.no_grad(): UpperCamelCase_ = model(__snake_case )[0] UpperCamelCase_ = 5_0_2_6_5 UpperCamelCase_ = torch.Size((1, 2_5_6, vocab_size) ) self.assertEqual(output.shape , __snake_case ) UpperCamelCase_ = torch.tensor( [[[9.2_595, -3.6_038, 11.8_819], [9.3_869, -3.2_693, 11.0_956], [11.8_524, -3.4_938, 13.1_210]]] ) self.assertTrue(torch.allclose(output[:, :3, :3] , __snake_case , atol=1e-4 ) ) @slow def lowerCamelCase_ ( self ): """simple docstring""" UpperCamelCase_ = MraForMaskedLM.from_pretrained("""uw-madison/mra-base-4096-8-d3""" ) UpperCamelCase_ = torch.arange(4_0_9_6 ).unsqueeze(0 ) with torch.no_grad(): UpperCamelCase_ = model(__snake_case )[0] UpperCamelCase_ = 5_0_2_6_5 UpperCamelCase_ = torch.Size((1, 4_0_9_6, vocab_size) ) self.assertEqual(output.shape , __snake_case ) UpperCamelCase_ = torch.tensor( [[[5.4_789, -2.3_564, 7.5_064], [7.9_067, -1.3_369, 9.9_668], [9.0_712, -1.8_106, 7.0_380]]] ) self.assertTrue(torch.allclose(output[:, :3, :3] , __snake_case , atol=1e-4 ) )
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'''simple docstring''' from typing import List, Optional, Tuple, Union import torch from ...models import UNetaDModel from ...schedulers import ScoreSdeVeScheduler from ...utils import randn_tensor from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput class SCREAMING_SNAKE_CASE( A__ ): """simple docstring""" lowerCamelCase__ = 42 lowerCamelCase__ = 42 def __init__( self : Union[str, Any] , __snake_case : UNetaDModel , __snake_case : ScoreSdeVeScheduler ) -> int: super().__init__() self.register_modules(unet=__snake_case , scheduler=__snake_case ) @torch.no_grad() def __call__( self : Optional[int] , __snake_case : int = 1 , __snake_case : int = 2000 , __snake_case : Optional[Union[torch.Generator, List[torch.Generator]]] = None , __snake_case : Optional[str] = "pil" , __snake_case : bool = True , **__snake_case : Optional[int] , ) -> Union[ImagePipelineOutput, Tuple]: UpperCAmelCase : str = self.unet.config.sample_size UpperCAmelCase : Union[str, Any] = (batch_size, 3, img_size, img_size) UpperCAmelCase : int = self.unet UpperCAmelCase : Any = randn_tensor(__snake_case , generator=__snake_case ) * self.scheduler.init_noise_sigma UpperCAmelCase : List[Any] = sample.to(self.device ) self.scheduler.set_timesteps(__snake_case ) self.scheduler.set_sigmas(__snake_case ) for i, t in enumerate(self.progress_bar(self.scheduler.timesteps ) ): UpperCAmelCase : Any = self.scheduler.sigmas[i] * torch.ones(shape[0] , device=self.device ) # correction step for _ in range(self.scheduler.config.correct_steps ): UpperCAmelCase : Union[str, Any] = self.unet(__snake_case , __snake_case ).sample UpperCAmelCase : Optional[Any] = self.scheduler.step_correct(__snake_case , __snake_case , generator=__snake_case ).prev_sample # prediction step UpperCAmelCase : Optional[Any] = model(__snake_case , __snake_case ).sample UpperCAmelCase : List[str] = self.scheduler.step_pred(__snake_case , __snake_case , __snake_case , generator=__snake_case ) UpperCAmelCase , UpperCAmelCase : Optional[Any] = output.prev_sample, output.prev_sample_mean UpperCAmelCase : int = sample_mean.clamp(0 , 1 ) UpperCAmelCase : Union[str, Any] = sample.cpu().permute(0 , 2 , 3 , 1 ).numpy() if output_type == "pil": UpperCAmelCase : Optional[Any] = self.numpy_to_pil(__snake_case ) if not return_dict: return (sample,) return ImagePipelineOutput(images=__snake_case )
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from __future__ import annotations import unittest from transformers import BlenderbotConfig, BlenderbotTokenizer, is_tf_available from transformers.testing_utils import require_tf, require_tokenizers, slow from transformers.utils import cached_property from ...test_configuration_common import ConfigTester from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor from ...test_pipeline_mixin import PipelineTesterMixin if is_tf_available(): import tensorflow as tf from transformers import TFAutoModelForSeqaSeqLM, TFBlenderbotForConditionalGeneration, TFBlenderbotModel @require_tf class a_ : """simple docstring""" __UpperCAmelCase = BlenderbotConfig __UpperCAmelCase = {} __UpperCAmelCase = 'gelu' def __init__( self : Any ,snake_case : str ,snake_case : int=13 ,snake_case : Union[str, Any]=7 ,snake_case : Optional[Any]=True ,snake_case : Optional[int]=False ,snake_case : Optional[int]=99 ,snake_case : str=32 ,snake_case : Dict=2 ,snake_case : int=4 ,snake_case : List[Any]=37 ,snake_case : Union[str, Any]=0.1 ,snake_case : Optional[Any]=0.1 ,snake_case : Optional[int]=20 ,snake_case : int=2 ,snake_case : Optional[Any]=1 ,snake_case : List[str]=0 ,): SCREAMING_SNAKE_CASE =parent SCREAMING_SNAKE_CASE =batch_size SCREAMING_SNAKE_CASE =seq_length SCREAMING_SNAKE_CASE =is_training SCREAMING_SNAKE_CASE =use_labels SCREAMING_SNAKE_CASE =vocab_size SCREAMING_SNAKE_CASE =hidden_size SCREAMING_SNAKE_CASE =num_hidden_layers SCREAMING_SNAKE_CASE =num_attention_heads SCREAMING_SNAKE_CASE =intermediate_size SCREAMING_SNAKE_CASE =hidden_dropout_prob SCREAMING_SNAKE_CASE =attention_probs_dropout_prob SCREAMING_SNAKE_CASE =max_position_embeddings SCREAMING_SNAKE_CASE =eos_token_id SCREAMING_SNAKE_CASE =pad_token_id SCREAMING_SNAKE_CASE =bos_token_id def _lowerCAmelCase ( self : Tuple ): SCREAMING_SNAKE_CASE =ids_tensor([self.batch_size, self.seq_length - 1] ,self.vocab_size ) SCREAMING_SNAKE_CASE =tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size ) ,1 ) SCREAMING_SNAKE_CASE =tf.concat([input_ids, eos_tensor] ,axis=1 ) SCREAMING_SNAKE_CASE =ids_tensor([self.batch_size, self.seq_length] ,self.vocab_size ) SCREAMING_SNAKE_CASE =self.config_cls( vocab_size=self.vocab_size ,d_model=self.hidden_size ,encoder_layers=self.num_hidden_layers ,decoder_layers=self.num_hidden_layers ,encoder_attention_heads=self.num_attention_heads ,decoder_attention_heads=self.num_attention_heads ,encoder_ffn_dim=self.intermediate_size ,decoder_ffn_dim=self.intermediate_size ,dropout=self.hidden_dropout_prob ,attention_dropout=self.attention_probs_dropout_prob ,max_position_embeddings=self.max_position_embeddings ,eos_token_ids=[2] ,bos_token_id=self.bos_token_id ,pad_token_id=self.pad_token_id ,decoder_start_token_id=self.pad_token_id ,**self.config_updates ,) SCREAMING_SNAKE_CASE =prepare_blenderbot_inputs_dict(__snake_case ,__snake_case ,__snake_case ) return config, inputs_dict def _lowerCAmelCase ( self : List[str] ,snake_case : int ,snake_case : Optional[int] ): SCREAMING_SNAKE_CASE =TFBlenderbotModel(config=__snake_case ).get_decoder() SCREAMING_SNAKE_CASE =inputs_dict['''input_ids'''] SCREAMING_SNAKE_CASE =input_ids[:1, :] SCREAMING_SNAKE_CASE =inputs_dict['''attention_mask'''][:1, :] SCREAMING_SNAKE_CASE =inputs_dict['''head_mask'''] SCREAMING_SNAKE_CASE =1 # first forward pass SCREAMING_SNAKE_CASE =model(__snake_case ,attention_mask=__snake_case ,head_mask=__snake_case ,use_cache=__snake_case ) SCREAMING_SNAKE_CASE =outputs.to_tuple() # create hypothetical next token and extent to next_input_ids SCREAMING_SNAKE_CASE =ids_tensor((self.batch_size, 3) ,config.vocab_size ) SCREAMING_SNAKE_CASE =tf.cast(ids_tensor((self.batch_size, 3) ,2 ) ,tf.inta ) # append to next input_ids and SCREAMING_SNAKE_CASE =tf.concat([input_ids, next_tokens] ,axis=-1 ) SCREAMING_SNAKE_CASE =tf.concat([attention_mask, next_attn_mask] ,axis=-1 ) SCREAMING_SNAKE_CASE =model(__snake_case ,attention_mask=__snake_case )[0] SCREAMING_SNAKE_CASE =model(__snake_case ,attention_mask=__snake_case ,past_key_values=__snake_case )[0] self.parent.assertEqual(next_tokens.shape[1] ,output_from_past.shape[1] ) # select random slice SCREAMING_SNAKE_CASE =int(ids_tensor((1,) ,output_from_past.shape[-1] ) ) SCREAMING_SNAKE_CASE =output_from_no_past[:, -3:, random_slice_idx] SCREAMING_SNAKE_CASE =output_from_past[:, :, random_slice_idx] # test that outputs are equal for slice tf.debugging.assert_near(__snake_case ,__snake_case ,rtol=1e-3 ) def snake_case__ ( lowerCAmelCase_, lowerCAmelCase_, lowerCAmelCase_, lowerCAmelCase_=None, lowerCAmelCase_=None, lowerCAmelCase_=None, lowerCAmelCase_=None, lowerCAmelCase_=None, ): """simple docstring""" if attention_mask is None: SCREAMING_SNAKE_CASE =tf.cast(tf.math.not_equal(_lowerCAmelCase, config.pad_token_id ), tf.inta ) if decoder_attention_mask is None: SCREAMING_SNAKE_CASE =tf.concat( [ tf.ones(decoder_input_ids[:, :1].shape, dtype=tf.inta ), tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:], config.pad_token_id ), tf.inta ), ], axis=-1, ) if head_mask is None: SCREAMING_SNAKE_CASE =tf.ones((config.encoder_layers, config.encoder_attention_heads) ) if decoder_head_mask is None: SCREAMING_SNAKE_CASE =tf.ones((config.decoder_layers, config.decoder_attention_heads) ) if cross_attn_head_mask is None: SCREAMING_SNAKE_CASE =tf.ones((config.decoder_layers, config.decoder_attention_heads) ) return { "input_ids": input_ids, "decoder_input_ids": decoder_input_ids, "attention_mask": attention_mask, "decoder_attention_mask": decoder_attention_mask, "head_mask": head_mask, "decoder_head_mask": decoder_head_mask, "cross_attn_head_mask": cross_attn_head_mask, } @require_tf class a_ ( A__ , A__ , unittest.TestCase ): """simple docstring""" __UpperCAmelCase = (TFBlenderbotForConditionalGeneration, TFBlenderbotModel) if is_tf_available() else () __UpperCAmelCase = (TFBlenderbotForConditionalGeneration,) if is_tf_available() else () __UpperCAmelCase = ( { 'conversational': TFBlenderbotForConditionalGeneration, 'feature-extraction': TFBlenderbotModel, 'summarization': TFBlenderbotForConditionalGeneration, 'text2text-generation': TFBlenderbotForConditionalGeneration, 'translation': TFBlenderbotForConditionalGeneration, } if is_tf_available() else {} ) __UpperCAmelCase = True __UpperCAmelCase = False __UpperCAmelCase = False def _lowerCAmelCase ( self : str ): SCREAMING_SNAKE_CASE =TFBlenderbotModelTester(self ) SCREAMING_SNAKE_CASE =ConfigTester(self ,config_class=__snake_case ) def _lowerCAmelCase ( self : int ): self.config_tester.run_common_tests() def _lowerCAmelCase ( self : Any ): SCREAMING_SNAKE_CASE =self.model_tester.prepare_config_and_inputs_for_common() self.model_tester.check_decoder_model_past_large_inputs(*__snake_case ) @require_tokenizers @require_tf class a_ ( unittest.TestCase ): """simple docstring""" __UpperCAmelCase = ['My friends are cool but they eat too many carbs.'] __UpperCAmelCase = 'facebook/blenderbot-400M-distill' @cached_property def _lowerCAmelCase ( self : Union[str, Any] ): return BlenderbotTokenizer.from_pretrained(self.model_name ) @cached_property def _lowerCAmelCase ( self : Union[str, Any] ): SCREAMING_SNAKE_CASE =TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name ) return model @slow def _lowerCAmelCase ( self : Dict ): SCREAMING_SNAKE_CASE =self.tokenizer(self.src_text ,return_tensors='tf' ) SCREAMING_SNAKE_CASE =self.model.generate( model_inputs.input_ids ,) SCREAMING_SNAKE_CASE =self.tokenizer.batch_decode(generated_ids.numpy() ,skip_special_tokens=__snake_case )[0] assert ( generated_words == " That's unfortunate. Are they trying to lose weight or are they just trying to be healthier?" )
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'''simple docstring''' import warnings from contextlib import contextmanager from ....processing_utils import ProcessorMixin class SCREAMING_SNAKE_CASE( A__ ): """simple docstring""" lowerCamelCase__ = """MCTCTFeatureExtractor""" lowerCamelCase__ = """AutoTokenizer""" def __init__( self : Dict , __snake_case : Optional[int] , __snake_case : List[str] ) -> str: super().__init__(__snake_case , __snake_case ) UpperCAmelCase : List[Any] = self.feature_extractor UpperCAmelCase : Union[str, Any] = False def __call__( self : Any , *__snake_case : List[str] , **__snake_case : Any ) -> List[Any]: # For backward compatibility if self._in_target_context_manager: return self.current_processor(*__snake_case , **__snake_case ) if "raw_speech" in kwargs: warnings.warn('''Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead.''' ) UpperCAmelCase : int = kwargs.pop('''raw_speech''' ) else: UpperCAmelCase : Union[str, Any] = kwargs.pop('''audio''' , __snake_case ) UpperCAmelCase : Optional[Any] = kwargs.pop('''sampling_rate''' , __snake_case ) UpperCAmelCase : Dict = kwargs.pop('''text''' , __snake_case ) if len(__snake_case ) > 0: UpperCAmelCase : Any = args[0] UpperCAmelCase : Optional[int] = args[1:] if audio is None and text is None: raise ValueError('''You need to specify either an `audio` or `text` input to process.''' ) if audio is not None: UpperCAmelCase : List[str] = self.feature_extractor(__snake_case , *__snake_case , sampling_rate=__snake_case , **__snake_case ) if text is not None: UpperCAmelCase : int = self.tokenizer(__snake_case , **__snake_case ) if text is None: return inputs elif audio is None: return encodings else: UpperCAmelCase : str = encodings['''input_ids'''] return inputs def A ( self : List[Any] , *__snake_case : List[Any] , **__snake_case : List[Any] ) -> str: return self.tokenizer.batch_decode(*__snake_case , **__snake_case ) def A ( self : List[Any] , *__snake_case : int , **__snake_case : Optional[int] ) -> Any: # For backward compatibility if self._in_target_context_manager: return self.current_processor.pad(*__snake_case , **__snake_case ) UpperCAmelCase : List[Any] = kwargs.pop('''input_features''' , __snake_case ) UpperCAmelCase : Optional[Any] = kwargs.pop('''labels''' , __snake_case ) if len(__snake_case ) > 0: UpperCAmelCase : List[str] = args[0] UpperCAmelCase : List[Any] = args[1:] if input_features is not None: UpperCAmelCase : Tuple = self.feature_extractor.pad(__snake_case , *__snake_case , **__snake_case ) if labels is not None: UpperCAmelCase : Optional[int] = self.tokenizer.pad(__snake_case , **__snake_case ) if labels is None: return input_features elif input_features is None: return labels else: UpperCAmelCase : List[str] = labels['''input_ids'''] return input_features def A ( self : Union[str, Any] , *__snake_case : Optional[Any] , **__snake_case : Optional[int] ) -> Optional[Any]: return self.tokenizer.decode(*__snake_case , **__snake_case ) @contextmanager def A ( self : Any ) -> Optional[int]: warnings.warn( '''`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your ''' '''labels by using the argument `text` of the regular `__call__` method (either in the same call as ''' '''your audio inputs, or in a separate call.''' ) UpperCAmelCase : Dict = True UpperCAmelCase : List[Any] = self.tokenizer yield UpperCAmelCase : Tuple = self.feature_extractor UpperCAmelCase : List[Any] = False
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from math import isclose, sqrt def UpperCamelCase_( _snake_case : float , _snake_case : float , _snake_case : float ): """simple docstring""" __a =point_y / 4 / point_x __a =2 * normal_gradient / (1 + normal_gradient * normal_gradient) __a =(1 - normal_gradient * normal_gradient) / ( 1 + normal_gradient * normal_gradient ) __a =(sa - ca * incoming_gradient) / (ca + sa * incoming_gradient) # to find the next point, solve the simultaeneous equations: # y^2 + 4x^2 = 100 # y - b = m * (x - a) # ==> A x^2 + B x + C = 0 __a =outgoing_gradient**2 + 4 __a =2 * outgoing_gradient * (point_y - outgoing_gradient * point_x) __a =(point_y - outgoing_gradient * point_x) ** 2 - 100 __a =( -linear_term - sqrt(linear_term**2 - 4 * quadratic_term * constant_term ) ) / (2 * quadratic_term) __a =( -linear_term + sqrt(linear_term**2 - 4 * quadratic_term * constant_term ) ) / (2 * quadratic_term) # two solutions, one of which is our input point __a =x_minus if isclose(_lowerCAmelCase , _lowerCAmelCase ) else x_plus __a =point_y + outgoing_gradient * (next_x - point_x) return next_x, next_y, outgoing_gradient def UpperCamelCase_( _snake_case : float = 1.4 , _snake_case : float = -9.6 ): """simple docstring""" __a =0 __a =first_x_coord __a =first_y_coord __a =(10.1 - point_y) / (0.0 - point_x) while not (-0.01 <= point_x <= 0.01 and point_y > 0): __a =next_point(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ) num_reflections += 1 return num_reflections if __name__ == "__main__": print(f'''{solution() = }''')
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'''simple docstring''' from math import isclose, sqrt def snake_case_ ( _lowerCAmelCase : float , _lowerCAmelCase : float , _lowerCAmelCase : float ) -> tuple[float, float, float]: UpperCAmelCase : Optional[int] = point_y / 4 / point_x UpperCAmelCase : str = 2 * normal_gradient / (1 + normal_gradient * normal_gradient) UpperCAmelCase : Any = (1 - normal_gradient * normal_gradient) / ( 1 + normal_gradient * normal_gradient ) UpperCAmelCase : Union[str, Any] = (sa - ca * incoming_gradient) / (ca + sa * incoming_gradient) # to find the next point, solve the simultaeneous equations: # y^2 + 4x^2 = 100 # y - b = m * (x - a) # ==> A x^2 + B x + C = 0 UpperCAmelCase : Union[str, Any] = outgoing_gradient**2 + 4 UpperCAmelCase : Dict = 2 * outgoing_gradient * (point_y - outgoing_gradient * point_x) UpperCAmelCase : List[Any] = (point_y - outgoing_gradient * point_x) ** 2 - 100 UpperCAmelCase : List[str] = ( -linear_term - sqrt(linear_term**2 - 4 * quadratic_term * constant_term ) ) / (2 * quadratic_term) UpperCAmelCase : Optional[int] = ( -linear_term + sqrt(linear_term**2 - 4 * quadratic_term * constant_term ) ) / (2 * quadratic_term) # two solutions, one of which is our input point UpperCAmelCase : Optional[Any] = x_minus if isclose(_lowerCAmelCase , _lowerCAmelCase ) else x_plus UpperCAmelCase : Union[str, Any] = point_y + outgoing_gradient * (next_x - point_x) return next_x, next_y, outgoing_gradient def snake_case_ ( _lowerCAmelCase : float = 1.4 , _lowerCAmelCase : float = -9.6 ) -> int: UpperCAmelCase : int = 0 UpperCAmelCase : float = first_x_coord UpperCAmelCase : float = first_y_coord UpperCAmelCase : float = (1_0.1 - point_y) / (0.0 - point_x) while not (-0.0_1 <= point_x <= 0.0_1 and point_y > 0): UpperCAmelCase , UpperCAmelCase , UpperCAmelCase : str = next_point(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ) num_reflections += 1 return num_reflections if __name__ == "__main__": print(F"{solution() = }")
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'''simple docstring''' from __future__ import annotations import typing from collections import Counter def UpperCamelCase_ ( snake_case_ : int ) -> typing.Counter[int]: '''simple docstring''' __lowerCAmelCase = Counter() for base in range(1 , max_perimeter + 1 ): for perpendicular in range(_lowerCAmelCase , max_perimeter + 1 ): __lowerCAmelCase = (base * base + perpendicular * perpendicular) ** 0.5 if hypotenuse == int(_lowerCAmelCase ): __lowerCAmelCase = int(base + perpendicular + hypotenuse ) if perimeter > max_perimeter: continue triplets[perimeter] += 1 return triplets def UpperCamelCase_ ( snake_case_ : int = 10_00 ) -> int: '''simple docstring''' __lowerCAmelCase = pythagorean_triple(_lowerCAmelCase ) return triplets.most_common(1 )[0][0] if __name__ == "__main__": print(f'Perimeter {solution()} has maximum solutions')
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'''simple docstring''' from typing import TYPE_CHECKING from ...utils import ( OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_tokenizers_available, is_torch_available, ) UpperCamelCase__: str = { "configuration_lxmert": ["LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "LxmertConfig"], "tokenization_lxmert": ["LxmertTokenizer"], } try: if not is_tokenizers_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: UpperCamelCase__: int = ["LxmertTokenizerFast"] try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: UpperCamelCase__: Union[str, Any] = [ "LxmertEncoder", "LxmertForPreTraining", "LxmertForQuestionAnswering", "LxmertModel", "LxmertPreTrainedModel", "LxmertVisualFeatureEncoder", "LxmertXLayer", ] try: if not is_tf_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: UpperCamelCase__: int = [ "TF_LXMERT_PRETRAINED_MODEL_ARCHIVE_LIST", "TFLxmertForPreTraining", "TFLxmertMainLayer", "TFLxmertModel", "TFLxmertPreTrainedModel", "TFLxmertVisualFeatureEncoder", ] if TYPE_CHECKING: from .configuration_lxmert import LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP, LxmertConfig from .tokenization_lxmert import LxmertTokenizer try: if not is_tokenizers_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .tokenization_lxmert_fast import LxmertTokenizerFast try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_lxmert import ( LxmertEncoder, LxmertForPreTraining, LxmertForQuestionAnswering, LxmertModel, LxmertPreTrainedModel, LxmertVisualFeatureEncoder, LxmertXLayer, ) try: if not is_tf_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_tf_lxmert import ( TF_LXMERT_PRETRAINED_MODEL_ARCHIVE_LIST, TFLxmertForPreTraining, TFLxmertMainLayer, TFLxmertModel, TFLxmertPreTrainedModel, TFLxmertVisualFeatureEncoder, ) else: import sys UpperCamelCase__: Tuple = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
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import os from shutil import copyfile from typing import Any, Dict, List, Optional, Tuple import sentencepiece as spm from ...tokenization_utils import AddedToken, PreTrainedTokenizer from ...utils import logging _a = logging.get_logger(__name__) _a = {"vocab_file": "sentencepiece.bpe.model"} _a = { "vocab_file": { "moussaKam/mbarthez": "https://huggingface.co/moussaKam/mbarthez/resolve/main/sentencepiece.bpe.model", "moussaKam/barthez": "https://huggingface.co/moussaKam/barthez/resolve/main/sentencepiece.bpe.model", "moussaKam/barthez-orangesum-title": ( "https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/sentencepiece.bpe.model" ), }, } _a = { "moussaKam/mbarthez": 1_024, "moussaKam/barthez": 1_024, "moussaKam/barthez-orangesum-title": 1_024, } _a = "▁" class __A ( A__ ): '''simple docstring''' lowerCAmelCase_ = VOCAB_FILES_NAMES lowerCAmelCase_ = PRETRAINED_VOCAB_FILES_MAP lowerCAmelCase_ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES lowerCAmelCase_ = ["""input_ids""", """attention_mask"""] def __init__( self , __lowerCAmelCase , __lowerCAmelCase="<s>" , __lowerCAmelCase="</s>" , __lowerCAmelCase="</s>" , __lowerCAmelCase="<s>" , __lowerCAmelCase="<unk>" , __lowerCAmelCase="<pad>" , __lowerCAmelCase="<mask>" , __lowerCAmelCase = None , **__lowerCAmelCase , ): '''simple docstring''' lowerCamelCase__ = AddedToken(__snake_case , lstrip=__snake_case , rstrip=__snake_case ) if isinstance(__snake_case , __snake_case ) else mask_token lowerCamelCase__ = {} if sp_model_kwargs is None else sp_model_kwargs super().__init__( bos_token=__snake_case , eos_token=__snake_case , unk_token=__snake_case , sep_token=__snake_case , cls_token=__snake_case , pad_token=__snake_case , mask_token=__snake_case , sp_model_kwargs=self.sp_model_kwargs , **__snake_case , ) lowerCamelCase__ = vocab_file lowerCamelCase__ = spm.SentencePieceProcessor(**self.sp_model_kwargs ) self.sp_model.Load(str(__snake_case ) ) lowerCamelCase__ = {'''<s>''': 0, '''<pad>''': 1, '''</s>''': 2, '''<unk>''': 3} lowerCamelCase__ = len(self.sp_model ) - 1 lowerCamelCase__ = {v: k for k, v in self.fairseq_tokens_to_ids.items()} def __lowerCamelCase ( self , __lowerCAmelCase , __lowerCAmelCase = None ): '''simple docstring''' if token_ids_a is None: return [self.cls_token_id] + token_ids_a + [self.sep_token_id] lowerCamelCase__ = [self.cls_token_id] lowerCamelCase__ = [self.sep_token_id] return cls + token_ids_a + sep + sep + token_ids_a + sep def __lowerCamelCase ( self , __lowerCAmelCase , __lowerCAmelCase = None , __lowerCAmelCase = False ): '''simple docstring''' if already_has_special_tokens: return super().get_special_tokens_mask( token_ids_a=__snake_case , token_ids_a=__snake_case , already_has_special_tokens=__snake_case ) if token_ids_a is None: return [1] + ([0] * len(__snake_case )) + [1] return [1] + ([0] * len(__snake_case )) + [1, 1] + ([0] * len(__snake_case )) + [1] def __lowerCamelCase ( self , __lowerCAmelCase , __lowerCAmelCase = None ): '''simple docstring''' lowerCamelCase__ = [self.sep_token_id] lowerCamelCase__ = [self.cls_token_id] if token_ids_a is None: return len(cls + token_ids_a + sep ) * [0] return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0] @property def __lowerCamelCase ( self ): '''simple docstring''' return len(self.sp_model ) def __lowerCamelCase ( self ): '''simple docstring''' lowerCamelCase__ = {self.convert_ids_to_tokens(__snake_case ): i for i in range(self.vocab_size )} vocab.update(self.added_tokens_encoder ) return vocab def __lowerCamelCase ( self , __lowerCAmelCase ): '''simple docstring''' return self.sp_model.encode(__snake_case , out_type=__snake_case ) def __lowerCamelCase ( self , __lowerCAmelCase ): '''simple docstring''' if token in self.fairseq_tokens_to_ids: return self.fairseq_tokens_to_ids[token] lowerCamelCase__ = self.sp_model.PieceToId(__snake_case ) return spm_id if spm_id else self.unk_token_id def __lowerCamelCase ( self , __lowerCAmelCase ): '''simple docstring''' if index in self.fairseq_ids_to_tokens: return self.fairseq_ids_to_tokens[index] return self.sp_model.IdToPiece(__snake_case ) def __lowerCamelCase ( self , __lowerCAmelCase ): '''simple docstring''' lowerCamelCase__ = [] lowerCamelCase__ = '''''' lowerCamelCase__ = False for token in tokens: # make sure that special tokens are not decoded using sentencepiece model if token in self.all_special_tokens: if not prev_is_special: out_string += " " out_string += self.sp_model.decode(__snake_case ) + token lowerCamelCase__ = True lowerCamelCase__ = [] else: current_sub_tokens.append(__snake_case ) lowerCamelCase__ = False out_string += self.sp_model.decode(__snake_case ) return out_string.strip() def __getstate__( self ): '''simple docstring''' lowerCamelCase__ = self.__dict__.copy() lowerCamelCase__ = None return state def __setstate__( self , __lowerCAmelCase ): '''simple docstring''' lowerCamelCase__ = d # for backward compatibility if not hasattr(self , '''sp_model_kwargs''' ): lowerCamelCase__ = {} lowerCamelCase__ = spm.SentencePieceProcessor(**self.sp_model_kwargs ) self.sp_model.Load(self.vocab_file ) def __lowerCamelCase ( self , __lowerCAmelCase , __lowerCAmelCase = None ): '''simple docstring''' if not os.path.isdir(__snake_case ): logger.error(F'Vocabulary path ({save_directory}) should be a directory' ) return lowerCamelCase__ = os.path.join( __snake_case , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] ) if os.path.abspath(self.vocab_file ) != os.path.abspath(__snake_case ) and os.path.isfile(self.vocab_file ): copyfile(self.vocab_file , __snake_case ) elif not os.path.isfile(self.vocab_file ): with open(__snake_case , '''wb''' ) as fi: lowerCamelCase__ = self.sp_model.serialized_model_proto() fi.write(__snake_case ) return (out_vocab_file,)
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'''simple docstring''' import gc import unittest import numpy as np import torch from transformers import CLIPTextConfig, CLIPTextModel, XLMRobertaTokenizer from diffusers import AltDiffusionPipeline, AutoencoderKL, DDIMScheduler, PNDMScheduler, UNetaDConditionModel from diffusers.pipelines.alt_diffusion.modeling_roberta_series import ( RobertaSeriesConfig, RobertaSeriesModelWithTransformation, ) from diffusers.utils import slow, torch_device from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin enable_full_determinism() class SCREAMING_SNAKE_CASE( A__ , A__ , A__ , unittest.TestCase ): """simple docstring""" lowerCamelCase__ = AltDiffusionPipeline lowerCamelCase__ = TEXT_TO_IMAGE_PARAMS lowerCamelCase__ = TEXT_TO_IMAGE_BATCH_PARAMS lowerCamelCase__ = TEXT_TO_IMAGE_IMAGE_PARAMS lowerCamelCase__ = TEXT_TO_IMAGE_IMAGE_PARAMS def A ( self : Dict ) -> int: torch.manual_seed(0 ) UpperCAmelCase : str = UNetaDConditionModel( block_out_channels=(32, 64) , layers_per_block=2 , sample_size=32 , in_channels=4 , out_channels=4 , down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''') , up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''') , cross_attention_dim=32 , ) UpperCAmelCase : Dict = DDIMScheduler( beta_start=0.0_00_85 , beta_end=0.0_12 , beta_schedule='''scaled_linear''' , clip_sample=__snake_case , set_alpha_to_one=__snake_case , ) torch.manual_seed(0 ) UpperCAmelCase : Union[str, Any] = AutoencoderKL( block_out_channels=[32, 64] , in_channels=3 , out_channels=3 , down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D'''] , up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D'''] , latent_channels=4 , ) # TODO: address the non-deterministic text encoder (fails for save-load tests) # torch.manual_seed(0) # text_encoder_config = RobertaSeriesConfig( # hidden_size=32, # project_dim=32, # intermediate_size=37, # layer_norm_eps=1e-05, # num_attention_heads=4, # num_hidden_layers=5, # vocab_size=5002, # ) # text_encoder = RobertaSeriesModelWithTransformation(text_encoder_config) torch.manual_seed(0 ) UpperCAmelCase : Dict = CLIPTextConfig( bos_token_id=0 , eos_token_id=2 , hidden_size=32 , projection_dim=32 , intermediate_size=37 , layer_norm_eps=1E-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=5002 , ) UpperCAmelCase : List[Any] = CLIPTextModel(__snake_case ) UpperCAmelCase : Optional[Any] = XLMRobertaTokenizer.from_pretrained('''hf-internal-testing/tiny-xlm-roberta''' ) UpperCAmelCase : Optional[int] = 77 UpperCAmelCase : Optional[int] = { '''unet''': unet, '''scheduler''': scheduler, '''vae''': vae, '''text_encoder''': text_encoder, '''tokenizer''': tokenizer, '''safety_checker''': None, '''feature_extractor''': None, } return components def A ( self : Optional[Any] , __snake_case : Dict , __snake_case : List[str]=0 ) -> Union[str, Any]: if str(__snake_case ).startswith('''mps''' ): UpperCAmelCase : str = torch.manual_seed(__snake_case ) else: UpperCAmelCase : Tuple = torch.Generator(device=__snake_case ).manual_seed(__snake_case ) UpperCAmelCase : Dict = { '''prompt''': '''A painting of a squirrel eating a burger''', '''generator''': generator, '''num_inference_steps''': 2, '''guidance_scale''': 6.0, '''output_type''': '''numpy''', } return inputs def A ( self : Union[str, Any] ) -> List[str]: super().test_attention_slicing_forward_pass(expected_max_diff=3E-3 ) def A ( self : Tuple ) -> List[str]: super().test_inference_batch_single_identical(expected_max_diff=3E-3 ) def A ( self : Tuple ) -> Optional[int]: UpperCAmelCase : List[str] = '''cpu''' # ensure determinism for the device-dependent torch.Generator UpperCAmelCase : Any = self.get_dummy_components() torch.manual_seed(0 ) UpperCAmelCase : Optional[int] = RobertaSeriesConfig( hidden_size=32 , project_dim=32 , intermediate_size=37 , layer_norm_eps=1E-05 , num_attention_heads=4 , num_hidden_layers=5 , vocab_size=5002 , ) # TODO: remove after fixing the non-deterministic text encoder UpperCAmelCase : List[str] = RobertaSeriesModelWithTransformation(__snake_case ) UpperCAmelCase : str = text_encoder UpperCAmelCase : Optional[int] = AltDiffusionPipeline(**__snake_case ) UpperCAmelCase : str = alt_pipe.to(__snake_case ) alt_pipe.set_progress_bar_config(disable=__snake_case ) UpperCAmelCase : Optional[int] = self.get_dummy_inputs(__snake_case ) UpperCAmelCase : Optional[int] = '''A photo of an astronaut''' UpperCAmelCase : List[Any] = alt_pipe(**__snake_case ) UpperCAmelCase : Optional[Any] = output.images UpperCAmelCase : str = image[0, -3:, -3:, -1] assert image.shape == (1, 64, 64, 3) UpperCAmelCase : List[str] = np.array( [0.5_74_81_62, 0.60_44_71_45, 0.48_82_12_17, 0.50_10_06_36, 0.5_43_11_85, 0.45_76_36_83, 0.49_65_76_96, 0.48_13_27_33, 0.47_57_30_93] ) assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2 def A ( self : int ) -> Union[str, Any]: UpperCAmelCase : Optional[int] = '''cpu''' # ensure determinism for the device-dependent torch.Generator UpperCAmelCase : int = self.get_dummy_components() UpperCAmelCase : int = PNDMScheduler(skip_prk_steps=__snake_case ) torch.manual_seed(0 ) UpperCAmelCase : int = RobertaSeriesConfig( hidden_size=32 , project_dim=32 , intermediate_size=37 , layer_norm_eps=1E-05 , num_attention_heads=4 , num_hidden_layers=5 , vocab_size=5002 , ) # TODO: remove after fixing the non-deterministic text encoder UpperCAmelCase : Union[str, Any] = RobertaSeriesModelWithTransformation(__snake_case ) UpperCAmelCase : Union[str, Any] = text_encoder UpperCAmelCase : Optional[int] = AltDiffusionPipeline(**__snake_case ) UpperCAmelCase : Dict = alt_pipe.to(__snake_case ) alt_pipe.set_progress_bar_config(disable=__snake_case ) UpperCAmelCase : int = self.get_dummy_inputs(__snake_case ) UpperCAmelCase : Optional[int] = alt_pipe(**__snake_case ) UpperCAmelCase : Optional[int] = output.images UpperCAmelCase : int = image[0, -3:, -3:, -1] assert image.shape == (1, 64, 64, 3) UpperCAmelCase : Optional[int] = np.array( [0.51_60_50_93, 0.5_70_72_41, 0.47_36_55_07, 0.50_57_88_86, 0.5_63_38_77, 0.4_64_25_03, 0.5_18_20_81, 0.48_76_34_84, 0.49_08_42_37] ) assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2 @slow @require_torch_gpu class SCREAMING_SNAKE_CASE( unittest.TestCase ): """simple docstring""" def A ( self : str ) -> Any: # clean up the VRAM after each test super().tearDown() gc.collect() torch.cuda.empty_cache() def A ( self : List[Any] ) -> Any: # make sure here that pndm scheduler skips prk UpperCAmelCase : List[Any] = AltDiffusionPipeline.from_pretrained('''BAAI/AltDiffusion''' , safety_checker=__snake_case ) UpperCAmelCase : Tuple = alt_pipe.to(__snake_case ) alt_pipe.set_progress_bar_config(disable=__snake_case ) UpperCAmelCase : List[Any] = '''A painting of a squirrel eating a burger''' UpperCAmelCase : Any = torch.manual_seed(0 ) UpperCAmelCase : Optional[int] = alt_pipe([prompt] , generator=__snake_case , guidance_scale=6.0 , num_inference_steps=20 , output_type='''np''' ) UpperCAmelCase : Dict = output.images UpperCAmelCase : Optional[Any] = image[0, -3:, -3:, -1] assert image.shape == (1, 512, 512, 3) UpperCAmelCase : List[str] = np.array([0.10_10, 0.08_00, 0.07_94, 0.08_85, 0.08_43, 0.07_62, 0.07_69, 0.07_29, 0.05_86] ) assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2 def A ( self : Tuple ) -> int: UpperCAmelCase : int = DDIMScheduler.from_pretrained('''BAAI/AltDiffusion''' , subfolder='''scheduler''' ) UpperCAmelCase : Tuple = AltDiffusionPipeline.from_pretrained('''BAAI/AltDiffusion''' , scheduler=__snake_case , safety_checker=__snake_case ) UpperCAmelCase : Dict = alt_pipe.to(__snake_case ) alt_pipe.set_progress_bar_config(disable=__snake_case ) UpperCAmelCase : Tuple = '''A painting of a squirrel eating a burger''' UpperCAmelCase : Optional[int] = torch.manual_seed(0 ) UpperCAmelCase : List[Any] = alt_pipe([prompt] , generator=__snake_case , num_inference_steps=2 , output_type='''numpy''' ) UpperCAmelCase : Dict = output.images UpperCAmelCase : Optional[Any] = image[0, -3:, -3:, -1] assert image.shape == (1, 512, 512, 3) UpperCAmelCase : Union[str, Any] = np.array([0.40_19, 0.40_52, 0.38_10, 0.41_19, 0.39_16, 0.39_82, 0.46_51, 0.41_95, 0.53_23] ) assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
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"""simple docstring""" from __future__ import annotations from cmath import sqrt def _UpperCAmelCase ( __lowerCamelCase : int , __lowerCamelCase : int , __lowerCamelCase : int ) -> tuple[complex, complex]: if a == 0: raise ValueError('''Coefficient \'a\' must not be zero.''' ) _snake_case = b * b - 4 * a * c _snake_case = (-b + sqrt(_lowerCAmelCase )) / (2 * a) _snake_case = (-b - sqrt(_lowerCAmelCase )) / (2 * a) return ( root_a.real if not root_a.imag else root_a, root_a.real if not root_a.imag else root_a, ) def _UpperCAmelCase ( ) -> Union[str, Any]: _snake_case = quadratic_roots(a=5 , b=6 , c=1 ) print(f'''The solutions are: {solutiona} and {solutiona}''' ) if __name__ == "__main__": main()
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'''simple docstring''' import io import json import fsspec import pytest from datasets import Dataset, DatasetDict, Features, NamedSplit, Value from datasets.io.json import JsonDatasetReader, JsonDatasetWriter from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases def snake_case_ ( _lowerCAmelCase : List[str] , _lowerCAmelCase : Optional[Any] ) -> Optional[int]: assert isinstance(_lowerCAmelCase , _lowerCAmelCase ) assert dataset.num_rows == 4 assert dataset.num_columns == 3 assert dataset.column_names == ["col_1", "col_2", "col_3"] for feature, expected_dtype in expected_features.items(): assert dataset.features[feature].dtype == expected_dtype @pytest.mark.parametrize('''keep_in_memory''' , [False, True] ) def snake_case_ ( _lowerCAmelCase : int , _lowerCAmelCase : Dict , _lowerCAmelCase : Optional[int] ) -> Dict: UpperCAmelCase : Dict = tmp_path / '''cache''' UpperCAmelCase : List[Any] = {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''} with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase(): UpperCAmelCase : str = JsonDatasetReader(_lowerCAmelCase , cache_dir=_lowerCAmelCase , keep_in_memory=_lowerCAmelCase ).read() _check_json_dataset(_lowerCAmelCase , _lowerCAmelCase ) @pytest.mark.parametrize( '''features''' , [ None, {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}, {'''col_1''': '''string''', '''col_2''': '''string''', '''col_3''': '''string'''}, {'''col_1''': '''int32''', '''col_2''': '''int32''', '''col_3''': '''int32'''}, {'''col_1''': '''float32''', '''col_2''': '''float32''', '''col_3''': '''float32'''}, ] , ) def snake_case_ ( _lowerCAmelCase : Optional[int] , _lowerCAmelCase : Dict , _lowerCAmelCase : int ) -> Optional[int]: UpperCAmelCase : Any = tmp_path / '''cache''' UpperCAmelCase : Tuple = {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''} UpperCAmelCase : Any = features.copy() if features else default_expected_features UpperCAmelCase : List[Any] = ( Features({feature: Value(_lowerCAmelCase ) for feature, dtype in features.items()} ) if features is not None else None ) UpperCAmelCase : Dict = JsonDatasetReader(_lowerCAmelCase , features=_lowerCAmelCase , cache_dir=_lowerCAmelCase ).read() _check_json_dataset(_lowerCAmelCase , _lowerCAmelCase ) @pytest.mark.parametrize( '''features''' , [ None, {'''col_3''': '''float64''', '''col_1''': '''string''', '''col_2''': '''int64'''}, ] , ) def snake_case_ ( _lowerCAmelCase : Optional[Any] , _lowerCAmelCase : List[Any] , _lowerCAmelCase : Tuple ) -> Tuple: UpperCAmelCase : Optional[Any] = tmp_path / '''cache''' UpperCAmelCase : Optional[int] = {'''col_3''': '''float64''', '''col_1''': '''string''', '''col_2''': '''int64'''} UpperCAmelCase : int = features.copy() if features else default_expected_features UpperCAmelCase : Any = ( Features({feature: Value(_lowerCAmelCase ) for feature, dtype in features.items()} ) if features is not None else None ) UpperCAmelCase : Tuple = JsonDatasetReader(_lowerCAmelCase , features=_lowerCAmelCase , cache_dir=_lowerCAmelCase ).read() assert isinstance(_lowerCAmelCase , _lowerCAmelCase ) assert dataset.num_rows == 2 assert dataset.num_columns == 3 assert dataset.column_names == ["col_3", "col_1", "col_2"] for feature, expected_dtype in expected_features.items(): assert dataset.features[feature].dtype == expected_dtype def snake_case_ ( _lowerCAmelCase : List[str] , _lowerCAmelCase : Dict ) -> Union[str, Any]: # jsonl_312_path features are {"col_3": "float64", "col_1": "string", "col_2": "int64"} UpperCAmelCase : Tuple = {'''col_2''': '''int64''', '''col_3''': '''float64''', '''col_1''': '''string'''} UpperCAmelCase : List[str] = features.copy() UpperCAmelCase : Union[str, Any] = ( Features({feature: Value(_lowerCAmelCase ) for feature, dtype in features.items()} ) if features is not None else None ) UpperCAmelCase : Tuple = tmp_path / '''cache''' UpperCAmelCase : List[str] = JsonDatasetReader(_lowerCAmelCase , features=_lowerCAmelCase , cache_dir=_lowerCAmelCase ).read() assert isinstance(_lowerCAmelCase , _lowerCAmelCase ) assert dataset.num_rows == 2 assert dataset.num_columns == 3 assert dataset.column_names == ["col_2", "col_3", "col_1"] for feature, expected_dtype in expected_features.items(): assert dataset.features[feature].dtype == expected_dtype @pytest.mark.parametrize('''split''' , [None, NamedSplit('''train''' ), '''train''', '''test'''] ) def snake_case_ ( _lowerCAmelCase : Union[str, Any] , _lowerCAmelCase : Tuple , _lowerCAmelCase : List[str] ) -> Optional[Any]: UpperCAmelCase : Any = tmp_path / '''cache''' UpperCAmelCase : List[Any] = {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''} UpperCAmelCase : List[Any] = JsonDatasetReader(_lowerCAmelCase , cache_dir=_lowerCAmelCase , split=_lowerCAmelCase ).read() _check_json_dataset(_lowerCAmelCase , _lowerCAmelCase ) assert dataset.split == split if split else "train" @pytest.mark.parametrize('''path_type''' , [str, list] ) def snake_case_ ( _lowerCAmelCase : List[Any] , _lowerCAmelCase : str , _lowerCAmelCase : Any ) -> Dict: if issubclass(_lowerCAmelCase , _lowerCAmelCase ): UpperCAmelCase : str = jsonl_path elif issubclass(_lowerCAmelCase , _lowerCAmelCase ): UpperCAmelCase : Dict = [jsonl_path] UpperCAmelCase : int = tmp_path / '''cache''' UpperCAmelCase : Dict = {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''} UpperCAmelCase : Optional[int] = JsonDatasetReader(_lowerCAmelCase , cache_dir=_lowerCAmelCase ).read() _check_json_dataset(_lowerCAmelCase , _lowerCAmelCase ) def snake_case_ ( _lowerCAmelCase : List[str] , _lowerCAmelCase : Dict , _lowerCAmelCase : Dict=("train",) ) -> Union[str, Any]: assert isinstance(_lowerCAmelCase , _lowerCAmelCase ) for split in splits: UpperCAmelCase : List[str] = dataset_dict[split] assert dataset.num_rows == 4 assert dataset.num_columns == 3 assert dataset.column_names == ["col_1", "col_2", "col_3"] for feature, expected_dtype in expected_features.items(): assert dataset.features[feature].dtype == expected_dtype @pytest.mark.parametrize('''keep_in_memory''' , [False, True] ) def snake_case_ ( _lowerCAmelCase : int , _lowerCAmelCase : Optional[Any] , _lowerCAmelCase : str ) -> Any: UpperCAmelCase : Optional[Any] = tmp_path / '''cache''' UpperCAmelCase : List[str] = {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''} with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase(): UpperCAmelCase : Optional[int] = JsonDatasetReader({'''train''': jsonl_path} , cache_dir=_lowerCAmelCase , keep_in_memory=_lowerCAmelCase ).read() _check_json_datasetdict(_lowerCAmelCase , _lowerCAmelCase ) @pytest.mark.parametrize( '''features''' , [ None, {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}, {'''col_1''': '''string''', '''col_2''': '''string''', '''col_3''': '''string'''}, {'''col_1''': '''int32''', '''col_2''': '''int32''', '''col_3''': '''int32'''}, {'''col_1''': '''float32''', '''col_2''': '''float32''', '''col_3''': '''float32'''}, ] , ) def snake_case_ ( _lowerCAmelCase : Optional[Any] , _lowerCAmelCase : Optional[int] , _lowerCAmelCase : Union[str, Any] ) -> int: UpperCAmelCase : Dict = tmp_path / '''cache''' UpperCAmelCase : Dict = {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''} UpperCAmelCase : Optional[int] = features.copy() if features else default_expected_features UpperCAmelCase : Union[str, Any] = ( Features({feature: Value(_lowerCAmelCase ) for feature, dtype in features.items()} ) if features is not None else None ) UpperCAmelCase : Tuple = JsonDatasetReader({'''train''': jsonl_path} , features=_lowerCAmelCase , cache_dir=_lowerCAmelCase ).read() _check_json_datasetdict(_lowerCAmelCase , _lowerCAmelCase ) @pytest.mark.parametrize('''split''' , [None, NamedSplit('''train''' ), '''train''', '''test'''] ) def snake_case_ ( _lowerCAmelCase : Any , _lowerCAmelCase : Optional[int] , _lowerCAmelCase : Dict ) -> Union[str, Any]: if split: UpperCAmelCase : Optional[int] = {split: jsonl_path} else: UpperCAmelCase : Any = '''train''' UpperCAmelCase : Any = {'''train''': jsonl_path, '''test''': jsonl_path} UpperCAmelCase : Tuple = tmp_path / '''cache''' UpperCAmelCase : int = {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''} UpperCAmelCase : Optional[Any] = JsonDatasetReader(_lowerCAmelCase , cache_dir=_lowerCAmelCase ).read() _check_json_datasetdict(_lowerCAmelCase , _lowerCAmelCase , splits=list(path.keys() ) ) assert all(dataset[split].split == split for split in path.keys() ) def snake_case_ ( _lowerCAmelCase : List[str] ) -> str: return json.load(_lowerCAmelCase ) def snake_case_ ( _lowerCAmelCase : Dict ) -> str: return [json.loads(_lowerCAmelCase ) for line in buffer] class SCREAMING_SNAKE_CASE: """simple docstring""" @pytest.mark.parametrize('''lines, load_json_function''' , [(True, load_json_lines), (False, load_json)] ) def A ( self : Union[str, Any] , __snake_case : Tuple , __snake_case : Optional[Any] , __snake_case : Optional[int] ) -> Dict: with io.BytesIO() as buffer: JsonDatasetWriter(__snake_case , __snake_case , lines=__snake_case ).write() buffer.seek(0 ) UpperCAmelCase : Union[str, Any] = load_json_function(__snake_case ) assert isinstance(__snake_case , __snake_case ) assert isinstance(exported_content[0] , __snake_case ) assert len(__snake_case ) == 10 @pytest.mark.parametrize( '''orient, container, keys, len_at''' , [ ('''records''', list, {'''tokens''', '''labels''', '''answers''', '''id'''}, None), ('''split''', dict, {'''columns''', '''data'''}, '''data'''), ('''index''', dict, set('''0123456789''' ), None), ('''columns''', dict, {'''tokens''', '''labels''', '''answers''', '''id'''}, '''tokens'''), ('''values''', list, None, None), ('''table''', dict, {'''schema''', '''data'''}, '''data'''), ] , ) def A ( self : Optional[int] , __snake_case : Optional[Any] , __snake_case : str , __snake_case : Optional[Any] , __snake_case : List[str] , __snake_case : Optional[Any] ) -> List[Any]: with io.BytesIO() as buffer: JsonDatasetWriter(__snake_case , __snake_case , lines=__snake_case , orient=__snake_case ).write() buffer.seek(0 ) UpperCAmelCase : Union[str, Any] = load_json(__snake_case ) assert isinstance(__snake_case , __snake_case ) if keys: if container is dict: assert exported_content.keys() == keys else: assert exported_content[0].keys() == keys else: assert not hasattr(__snake_case , '''keys''' ) and not hasattr(exported_content[0] , '''keys''' ) if len_at: assert len(exported_content[len_at] ) == 10 else: assert len(__snake_case ) == 10 @pytest.mark.parametrize('''lines, load_json_function''' , [(True, load_json_lines), (False, load_json)] ) def A ( self : str , __snake_case : str , __snake_case : str , __snake_case : int ) -> Any: with io.BytesIO() as buffer: JsonDatasetWriter(__snake_case , __snake_case , lines=__snake_case , num_proc=2 ).write() buffer.seek(0 ) UpperCAmelCase : Any = load_json_function(__snake_case ) assert isinstance(__snake_case , __snake_case ) assert isinstance(exported_content[0] , __snake_case ) assert len(__snake_case ) == 10 @pytest.mark.parametrize( '''orient, container, keys, len_at''' , [ ('''records''', list, {'''tokens''', '''labels''', '''answers''', '''id'''}, None), ('''split''', dict, {'''columns''', '''data'''}, '''data'''), ('''index''', dict, set('''0123456789''' ), None), ('''columns''', dict, {'''tokens''', '''labels''', '''answers''', '''id'''}, '''tokens'''), ('''values''', list, None, None), ('''table''', dict, {'''schema''', '''data'''}, '''data'''), ] , ) def A ( self : Any , __snake_case : int , __snake_case : Union[str, Any] , __snake_case : Optional[int] , __snake_case : Optional[Any] , __snake_case : List[str] ) -> Any: with io.BytesIO() as buffer: JsonDatasetWriter(__snake_case , __snake_case , lines=__snake_case , orient=__snake_case , num_proc=2 ).write() buffer.seek(0 ) UpperCAmelCase : List[str] = load_json(__snake_case ) assert isinstance(__snake_case , __snake_case ) if keys: if container is dict: assert exported_content.keys() == keys else: assert exported_content[0].keys() == keys else: assert not hasattr(__snake_case , '''keys''' ) and not hasattr(exported_content[0] , '''keys''' ) if len_at: assert len(exported_content[len_at] ) == 10 else: assert len(__snake_case ) == 10 def A ( self : List[Any] , __snake_case : str ) -> Dict: with pytest.raises(__snake_case ): with io.BytesIO() as buffer: JsonDatasetWriter(__snake_case , __snake_case , num_proc=0 ) @pytest.mark.parametrize('''compression, extension''' , [('''gzip''', '''gz'''), ('''bz2''', '''bz2'''), ('''xz''', '''xz''')] ) def A ( self : Optional[int] , __snake_case : Any , __snake_case : str , __snake_case : Union[str, Any] , __snake_case : Union[str, Any] , __snake_case : Dict ) -> Union[str, Any]: UpperCAmelCase : List[str] = tmp_path_factory.mktemp('''data''' ) / F"""test.json.{extension}""" UpperCAmelCase : List[Any] = str(shared_datadir / F"""test_file.json.{extension}""" ) JsonDatasetWriter(__snake_case , __snake_case , compression=__snake_case ).write() with fsspec.open(__snake_case , '''rb''' , compression='''infer''' ) as f: UpperCAmelCase : str = f.read() with fsspec.open(__snake_case , '''rb''' , compression='''infer''' ) as f: UpperCAmelCase : Optional[int] = f.read() assert exported_content == original_content
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