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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" ) ) )
| 61 |
'''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,
)
| 23 | 0 |
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()
| 62 |
'''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
| 23 | 0 |
'''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,
)
| 63 |
'''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)))
| 23 | 0 |
"""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,
}, )
| 64 |
'''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
)
| 23 | 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 )
| 65 |
'''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)
| 23 | 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.""")
| 66 |
'''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)
| 23 | 0 |
'''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)
| 67 |
'''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 )
| 23 | 0 |
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"] )
| 68 |
'''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
| 23 | 0 |
"""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.""")
| 69 |
'''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() = }")
| 23 | 0 |
'''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__)
| 70 |
'''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__)
| 23 | 0 |
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
| 71 |
'''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"""
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))
| 72 |
'''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
| 23 | 0 |
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
| 73 |
'''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,)
| 23 | 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 | 74 |
'''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 )
| 23 | 0 |
'''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))
| 75 |
'''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 ) )
| 23 | 0 |
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 ) | 76 |
'''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 ) ) ) )
| 23 | 0 |
"""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()
| 77 |
'''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()
| 23 | 0 |
"""simple docstring"""
from .glue import GlueDataset, GlueDataTrainingArguments
from .language_modeling import (
LineByLineTextDataset,
LineByLineWithRefDataset,
LineByLineWithSOPTextDataset,
TextDataset,
TextDatasetForNextSentencePrediction,
)
from .squad import SquadDataset, SquadDataTrainingArguments
| 78 |
'''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)
| 23 | 0 |
'''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 )
| 79 |
'''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()
| 23 | 0 |
'''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())
| 80 |
'''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()
| 23 | 0 |
"""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)) | 81 |
'''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
| 23 | 0 |
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
| 82 |
'''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]
| 23 | 0 |
'''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()
| 83 |
'''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,
)
| 23 | 0 |
"""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
| 84 |
'''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
| 23 | 0 |
'''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"))
| 85 |
'''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)))
| 23 | 0 |
"""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 | 86 |
'''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
)
| 23 | 0 |
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()
| 87 |
'''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)
| 23 | 0 |
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()
| 88 |
'''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)
| 23 | 0 |
'''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() = }""")
| 89 |
'''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 )
| 23 | 0 |
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
| 90 |
'''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
| 23 | 0 |
"""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
| 91 |
'''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() = }")
| 23 | 0 |
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())
| 92 |
'''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__)
| 23 | 0 |
'''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__)
| 93 |
'''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 |
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() = }""")
| 94 |
'''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
| 23 | 0 |
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"] )
| 95 |
'''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,)
| 23 | 0 |
"""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__) | 96 |
'''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 )
| 23 | 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 | 97 |
'''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 ) )
| 23 | 0 |
"""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 ) ) ) )
| 23 | 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
| 99 |
'''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()
| 23 | 0 |
"""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)
| 100 |
'''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)
| 23 | 0 |
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))
| 101 |
'''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()
| 23 | 0 |
"""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
| 102 |
'''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()
| 23 | 0 |
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))
| 103 |
'''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
| 23 | 0 |
'''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
)
| 104 |
'''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]
| 23 | 0 |
"""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())))
| 105 |
'''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,
)
| 23 | 0 |
"""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
| 106 |
'''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
| 23 | 0 |
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 )
| 107 |
'''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)))
| 23 | 0 |
"""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__)
| 108 |
'''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
)
| 23 | 0 |
"""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),
] )
| 109 |
'''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)
| 23 | 0 |
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()
| 110 |
'''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)
| 23 | 0 |
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""" , )
| 122 |
'''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 )
| 23 | 0 |
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()
| 334 |
'''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
| 23 | 0 |
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]
| 218 |
'''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() = }")
| 23 | 0 |
'''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()
| 229 |
'''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__)
| 23 | 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)
| 209 |
'''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 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()
| 288 |
'''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
| 23 | 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,
)
| 219 |
'''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,)
| 23 | 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)}')
| 49 |
'''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 )
| 23 | 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
| 101 |
'''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 ) )
| 23 | 0 |
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:])
| 343 |
'''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 ) ) ) )
| 23 | 0 |
'''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,
)
| 35 |
'''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()
| 23 | 0 |
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)
| 122 |
'''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)
| 23 | 0 |
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 ) )
| 334 |
'''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()
| 23 | 0 |
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
| 218 |
'''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()
| 23 | 0 |
'''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
| 229 |
'''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
| 23 | 0 |
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 , )
| 209 |
'''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]
| 23 | 0 |
"""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
| 288 |
'''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,
)
| 23 | 0 |
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()
| 219 |
'''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
| 23 | 0 |
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
| 49 |
'''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)))
| 23 | 0 |
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__)
| 101 |
'''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
)
| 23 | 0 |
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 )
| 343 |
'''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)
| 23 | 0 |
'''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()}")
| 35 |
'''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)
| 23 | 0 |
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
| 122 |
'''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 )
| 23 | 0 |
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 )
| 334 |
'''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
| 23 | 0 |
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,)
| 218 |
'''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() = }")
| 23 | 0 |
'''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()
| 229 |
'''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__)
| 23 | 0 |
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 )
| 209 |
'''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
| 288 |
'''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
| 23 | 0 |
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 )
| 219 |
'''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,)
| 23 | 0 |
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()
| 49 |
'''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 )
| 23 | 0 |
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))
| 101 |
'''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 ) )
| 23 | 0 |
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 )
| 343 |
'''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 ) ) ) )
| 23 | 0 |
'''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 )
| 35 |
'''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()
| 23 | 0 |
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()
| 122 |
'''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)
| 23 | 0 |
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()}
| 334 |
'''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()
| 23 | 0 |
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 )
| 218 |
'''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()
| 23 | 0 |
'''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 )
| 229 |
'''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
| 23 | 0 |
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()
| 209 |
'''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]
| 23 | 0 |
"""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
| 288 |
'''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,
)
| 23 | 0 |
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()
| 219 |
'''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
| 23 | 0 |
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 ) )
| 49 |
'''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)))
| 23 | 0 |
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''')),
}
| 101 |
'''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
)
| 23 | 0 |
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()
| 343 |
'''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)
| 23 | 0 |
'''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)
| 35 |
'''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)
| 23 | 0 |
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 ) )
| 122 |
'''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 )
| 23 | 0 |
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?"
)
| 334 |
'''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
| 23 | 0 |
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() = }''')
| 218 |
'''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() = }")
| 23 | 0 |
'''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')
| 229 |
'''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__)
| 23 | 0 |
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,)
| 209 |
'''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"""
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()
| 288 |
'''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
| 23 | 0 |
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