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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 from ..auto import CONFIG_MAPPING UpperCamelCase = logging.get_logger(__name__) UpperCamelCase = { '''microsoft/table-transformer-detection''': ( '''https://huggingface.co/microsoft/table-transformer-detection/resolve/main/config.json''' ), } class snake_case_ ( __A ): __A : Optional[Any] = "table-transformer" __A : Tuple = ["past_key_values"] __A : Union[str, Any] = { "hidden_size": "d_model", "num_attention_heads": "encoder_attention_heads", } def __init__( self : Dict , lowercase_ : Union[str, Any]=True , lowercase_ : Any=None , lowercase_ : int=3 , lowercase_ : List[str]=1_00 , lowercase_ : List[Any]=6 , lowercase_ : List[str]=20_48 , lowercase_ : Any=8 , lowercase_ : Any=6 , lowercase_ : Optional[int]=20_48 , lowercase_ : Tuple=8 , lowercase_ : Tuple=0.0 , lowercase_ : Tuple=0.0 , lowercase_ : Any=True , lowercase_ : List[Any]="relu" , lowercase_ : List[Any]=2_56 , lowercase_ : List[str]=0.1 , lowercase_ : int=0.0 , lowercase_ : Any=0.0 , lowercase_ : List[str]=0.02 , lowercase_ : str=1.0 , lowercase_ : Dict=False , lowercase_ : Tuple="sine" , lowercase_ : str="resnet50" , lowercase_ : Optional[int]=True , lowercase_ : int=False , lowercase_ : Any=1 , lowercase_ : Any=5 , lowercase_ : Tuple=2 , lowercase_ : Union[str, Any]=1 , lowercase_ : Optional[Any]=1 , lowercase_ : Dict=5 , lowercase_ : int=2 , lowercase_ : Tuple=0.1 , **lowercase_ : str , ) -> List[Any]: if backbone_config is not None and use_timm_backbone: raise ValueError("You can't specify both `backbone_config` and `use_timm_backbone`." ) if not use_timm_backbone: if backbone_config is None: logger.info("`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone." ) lowercase__ : int = CONFIG_MAPPING["resnet"](out_features=["stage4"] ) elif isinstance(lowercase_ , lowercase_ ): lowercase__ : Any = backbone_config.get("model_type" ) lowercase__ : List[Any] = CONFIG_MAPPING[backbone_model_type] lowercase__ : Union[str, Any] = config_class.from_dict(lowercase_ ) # set timm attributes to None lowercase__ : str = None, None, None lowercase__ : Optional[int] = use_timm_backbone lowercase__ : List[str] = backbone_config lowercase__ : Optional[int] = num_channels lowercase__ : List[str] = num_queries lowercase__ : List[str] = d_model lowercase__ : Tuple = encoder_ffn_dim lowercase__ : Tuple = encoder_layers lowercase__ : Any = encoder_attention_heads lowercase__ : Optional[Any] = decoder_ffn_dim lowercase__ : Any = decoder_layers lowercase__ : Optional[int] = decoder_attention_heads lowercase__ : Union[str, Any] = dropout lowercase__ : int = attention_dropout lowercase__ : Optional[int] = activation_dropout lowercase__ : Tuple = activation_function lowercase__ : Optional[Any] = init_std lowercase__ : Dict = init_xavier_std lowercase__ : Any = encoder_layerdrop lowercase__ : Dict = decoder_layerdrop lowercase__ : Any = encoder_layers lowercase__ : Union[str, Any] = auxiliary_loss lowercase__ : List[Any] = position_embedding_type lowercase__ : Optional[Any] = backbone lowercase__ : str = use_pretrained_backbone lowercase__ : Dict = dilation # Hungarian matcher lowercase__ : str = class_cost lowercase__ : Optional[int] = bbox_cost lowercase__ : List[Any] = giou_cost # Loss coefficients lowercase__ : Optional[int] = mask_loss_coefficient lowercase__ : List[Any] = dice_loss_coefficient lowercase__ : Optional[int] = bbox_loss_coefficient lowercase__ : Any = giou_loss_coefficient lowercase__ : Union[str, Any] = eos_coefficient super().__init__(is_encoder_decoder=lowercase_ , **lowercase_ ) @property def __UpperCamelCase ( self : Any ) -> int: return self.encoder_attention_heads @property def __UpperCamelCase ( self : Any ) -> int: return self.d_model class snake_case_ ( __A ): __A : Optional[int] = version.parse("1.11" ) @property def __UpperCamelCase ( self : Optional[int] ) -> Mapping[str, Mapping[int, str]]: return OrderedDict( [ ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}), ("pixel_mask", {0: "batch"}), ] ) @property def __UpperCamelCase ( self : Dict ) -> float: return 1E-5 @property def __UpperCamelCase ( self : Optional[Any] ) -> int: return 12
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import gc import unittest import torch from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer from diffusers import ( AutoencoderKL, DDIMScheduler, DDPMScheduler, PriorTransformer, StableUnCLIPPipeline, UNetaDConditionModel, ) from diffusers.pipelines.stable_diffusion.stable_unclip_image_normalizer import StableUnCLIPImageNormalizer from diffusers.utils.testing_utils import enable_full_determinism, load_numpy, require_torch_gpu, slow, torch_device 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, assert_mean_pixel_difference, ) enable_full_determinism() class snake_case_ ( __A ,__A ,__A ,unittest.TestCase ): __A : int = StableUnCLIPPipeline __A : int = TEXT_TO_IMAGE_PARAMS __A : Any = TEXT_TO_IMAGE_BATCH_PARAMS __A : int = TEXT_TO_IMAGE_IMAGE_PARAMS __A : Dict = TEXT_TO_IMAGE_IMAGE_PARAMS # TODO(will) Expected attn_bias.stride(1) == 0 to be true, but got false __A : int = False def __UpperCamelCase ( self : Optional[int] ) -> List[str]: lowercase__ : str = 32 lowercase__ : Any = embedder_hidden_size # prior components torch.manual_seed(0 ) lowercase__ : Optional[Any] = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip" ) torch.manual_seed(0 ) lowercase__ : List[str] = CLIPTextModelWithProjection( CLIPTextConfig( bos_token_id=0 , eos_token_id=2 , hidden_size=lowercase_ , projection_dim=lowercase_ , intermediate_size=37 , layer_norm_eps=1E-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=10_00 , ) ) torch.manual_seed(0 ) lowercase__ : Any = PriorTransformer( num_attention_heads=2 , attention_head_dim=12 , embedding_dim=lowercase_ , num_layers=1 , ) torch.manual_seed(0 ) lowercase__ : Union[str, Any] = DDPMScheduler( variance_type="fixed_small_log" , prediction_type="sample" , num_train_timesteps=10_00 , clip_sample=lowercase_ , clip_sample_range=5.0 , beta_schedule="squaredcos_cap_v2" , ) # regular denoising components torch.manual_seed(0 ) lowercase__ : List[str] = StableUnCLIPImageNormalizer(embedding_dim=lowercase_ ) lowercase__ : Tuple = DDPMScheduler(beta_schedule="squaredcos_cap_v2" ) torch.manual_seed(0 ) lowercase__ : Optional[int] = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip" ) torch.manual_seed(0 ) lowercase__ : Tuple = CLIPTextModel( CLIPTextConfig( bos_token_id=0 , eos_token_id=2 , hidden_size=lowercase_ , 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=10_00 , ) ) torch.manual_seed(0 ) lowercase__ : str = UNetaDConditionModel( sample_size=32 , in_channels=4 , out_channels=4 , down_block_types=("CrossAttnDownBlock2D", "DownBlock2D") , up_block_types=("UpBlock2D", "CrossAttnUpBlock2D") , block_out_channels=(32, 64) , attention_head_dim=(2, 4) , class_embed_type="projection" , projection_class_embeddings_input_dim=embedder_projection_dim * 2 , cross_attention_dim=lowercase_ , layers_per_block=1 , upcast_attention=lowercase_ , use_linear_projection=lowercase_ , ) torch.manual_seed(0 ) lowercase__ : Any = DDIMScheduler( beta_schedule="scaled_linear" , beta_start=0.0_00_85 , beta_end=0.0_12 , prediction_type="v_prediction" , set_alpha_to_one=lowercase_ , steps_offset=1 , ) torch.manual_seed(0 ) lowercase__ : List[str] = AutoencoderKL() lowercase__ : List[Any] = { # prior components "prior_tokenizer": prior_tokenizer, "prior_text_encoder": prior_text_encoder, "prior": prior, "prior_scheduler": prior_scheduler, # image noising components "image_normalizer": image_normalizer, "image_noising_scheduler": image_noising_scheduler, # regular denoising components "tokenizer": tokenizer, "text_encoder": text_encoder, "unet": unet, "scheduler": scheduler, "vae": vae, } return components def __UpperCamelCase ( self : Any , lowercase_ : Tuple , lowercase_ : Dict=0 ) -> Any: if str(lowercase_ ).startswith("mps" ): lowercase__ : Any = torch.manual_seed(lowercase_ ) else: lowercase__ : Any = torch.Generator(device=lowercase_ ).manual_seed(lowercase_ ) lowercase__ : Optional[Any] = { "prompt": "A painting of a squirrel eating a burger", "generator": generator, "num_inference_steps": 2, "prior_num_inference_steps": 2, "output_type": "numpy", } return inputs def __UpperCamelCase ( self : Union[str, Any] ) -> List[str]: lowercase__ : Union[str, Any] = torch_device == "cpu" self._test_attention_slicing_forward_pass(test_max_difference=lowercase_ ) def __UpperCamelCase ( self : List[Any] ) -> List[str]: lowercase__ : str = torch_device in ["cpu", "mps"] self._test_inference_batch_single_identical(test_max_difference=lowercase_ ) @slow @require_torch_gpu class snake_case_ ( unittest.TestCase ): def __UpperCamelCase ( self : Tuple ) -> Any: # clean up the VRAM after each test super().tearDown() gc.collect() torch.cuda.empty_cache() def __UpperCamelCase ( self : int ) -> int: lowercase__ : Optional[int] = load_numpy( "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_l_anime_turtle_fp16.npy" ) lowercase__ : List[str] = StableUnCLIPPipeline.from_pretrained("fusing/stable-unclip-2-1-l" , torch_dtype=torch.floataa ) pipe.to(lowercase_ ) pipe.set_progress_bar_config(disable=lowercase_ ) # stable unclip will oom when integration tests are run on a V100, # so turn on memory savings pipe.enable_attention_slicing() pipe.enable_sequential_cpu_offload() lowercase__ : Optional[int] = torch.Generator(device="cpu" ).manual_seed(0 ) lowercase__ : Dict = pipe("anime turle" , generator=lowercase_ , output_type="np" ) lowercase__ : Optional[int] = output.images[0] assert image.shape == (7_68, 7_68, 3) assert_mean_pixel_difference(lowercase_ , lowercase_ ) def __UpperCamelCase ( self : Union[str, Any] ) -> Dict: torch.cuda.empty_cache() torch.cuda.reset_max_memory_allocated() torch.cuda.reset_peak_memory_stats() lowercase__ : Union[str, Any] = StableUnCLIPPipeline.from_pretrained("fusing/stable-unclip-2-1-l" , torch_dtype=torch.floataa ) lowercase__ : int = pipe.to(lowercase_ ) pipe.set_progress_bar_config(disable=lowercase_ ) pipe.enable_attention_slicing() pipe.enable_sequential_cpu_offload() lowercase__ : str = pipe( "anime turtle" , prior_num_inference_steps=2 , num_inference_steps=2 , output_type="np" , ) lowercase__ : Any = torch.cuda.max_memory_allocated() # make sure that less than 7 GB is allocated assert mem_bytes < 7 * 10**9
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import shutil import tempfile import unittest import numpy as np from transformers.testing_utils import ( is_pt_tf_cross_test, require_tf, require_torch, require_torchvision, require_vision, ) from transformers.utils import is_tf_available, is_torch_available, is_vision_available if is_vision_available(): from PIL import Image from transformers import AutoProcessor, SamImageProcessor, SamProcessor if is_torch_available(): import torch if is_tf_available(): import tensorflow as tf @require_vision @require_torchvision class snake_case_ ( unittest.TestCase ): def __UpperCamelCase ( self : List[Any] ) -> Any: lowercase__ : List[Any] = tempfile.mkdtemp() lowercase__ : Optional[Any] = SamImageProcessor() lowercase__ : Dict = SamProcessor(lowercase_ ) processor.save_pretrained(self.tmpdirname ) def __UpperCamelCase ( self : Dict , **lowercase_ : List[Any] ) -> Dict: return AutoProcessor.from_pretrained(self.tmpdirname , **lowercase_ ).image_processor def __UpperCamelCase ( self : Any ) -> Union[str, Any]: shutil.rmtree(self.tmpdirname ) def __UpperCamelCase ( self : Tuple ) -> Dict: lowercase__ : str = [np.random.randint(2_55 , size=(3, 30, 4_00) , dtype=np.uinta )] lowercase__ : List[str] = [Image.fromarray(np.moveaxis(lowercase_ , 0 , -1 ) ) for x in image_inputs] return image_inputs def __UpperCamelCase ( self : int ) -> Optional[Any]: lowercase__ : Union[str, Any] = SamProcessor(image_processor=self.get_image_processor() ) processor.save_pretrained(self.tmpdirname ) lowercase__ : List[str] = self.get_image_processor(do_normalize=lowercase_ , padding_value=1.0 ) lowercase__ : Tuple = SamProcessor.from_pretrained(self.tmpdirname , do_normalize=lowercase_ , padding_value=1.0 ) self.assertEqual(processor.image_processor.to_json_string() , image_processor_add_kwargs.to_json_string() ) self.assertIsInstance(processor.image_processor , lowercase_ ) def __UpperCamelCase ( self : List[str] ) -> List[Any]: lowercase__ : Optional[int] = self.get_image_processor() lowercase__ : Any = SamProcessor(image_processor=lowercase_ ) lowercase__ : int = self.prepare_image_inputs() lowercase__ : Tuple = image_processor(lowercase_ , return_tensors="np" ) lowercase__ : List[str] = processor(images=lowercase_ , return_tensors="np" ) input_feat_extract.pop("original_sizes" ) # pop original_sizes as it is popped in the processor input_feat_extract.pop("reshaped_input_sizes" ) # pop original_sizes as it is popped in the processor for key in input_feat_extract.keys(): self.assertAlmostEqual(input_feat_extract[key].sum() , input_processor[key].sum() , delta=1E-2 ) @require_torch def __UpperCamelCase ( self : Optional[int] ) -> List[Any]: lowercase__ : Union[str, Any] = self.get_image_processor() lowercase__ : Optional[int] = SamProcessor(image_processor=lowercase_ ) lowercase__ : Union[str, Any] = [torch.ones((1, 3, 5, 5) )] lowercase__ : Optional[int] = [[17_64, 26_46]] lowercase__ : str = [[6_83, 10_24]] lowercase__ : List[Any] = processor.post_process_masks(lowercase_ , lowercase_ , lowercase_ ) self.assertEqual(masks[0].shape , (1, 3, 17_64, 26_46) ) lowercase__ : Optional[Any] = processor.post_process_masks( lowercase_ , torch.tensor(lowercase_ ) , torch.tensor(lowercase_ ) ) self.assertEqual(masks[0].shape , (1, 3, 17_64, 26_46) ) # should also work with np lowercase__ : Any = [np.ones((1, 3, 5, 5) )] lowercase__ : Optional[int] = processor.post_process_masks(lowercase_ , np.array(lowercase_ ) , np.array(lowercase_ ) ) self.assertEqual(masks[0].shape , (1, 3, 17_64, 26_46) ) lowercase__ : Optional[Any] = [[1, 0], [0, 1]] with self.assertRaises(lowercase_ ): lowercase__ : Union[str, Any] = processor.post_process_masks(lowercase_ , np.array(lowercase_ ) , np.array(lowercase_ ) ) @require_vision @require_tf class snake_case_ ( unittest.TestCase ): def __UpperCamelCase ( self : Dict ) -> Dict: lowercase__ : int = tempfile.mkdtemp() lowercase__ : Dict = SamImageProcessor() lowercase__ : Optional[int] = SamProcessor(lowercase_ ) processor.save_pretrained(self.tmpdirname ) def __UpperCamelCase ( self : List[Any] , **lowercase_ : Optional[Any] ) -> str: return AutoProcessor.from_pretrained(self.tmpdirname , **lowercase_ ).image_processor def __UpperCamelCase ( self : str ) -> Dict: shutil.rmtree(self.tmpdirname ) def __UpperCamelCase ( self : int ) -> Any: lowercase__ : int = [np.random.randint(2_55 , size=(3, 30, 4_00) , dtype=np.uinta )] lowercase__ : int = [Image.fromarray(np.moveaxis(lowercase_ , 0 , -1 ) ) for x in image_inputs] return image_inputs def __UpperCamelCase ( self : Optional[Any] ) -> Dict: lowercase__ : Optional[Any] = SamProcessor(image_processor=self.get_image_processor() ) processor.save_pretrained(self.tmpdirname ) lowercase__ : Optional[int] = self.get_image_processor(do_normalize=lowercase_ , padding_value=1.0 ) lowercase__ : Union[str, Any] = SamProcessor.from_pretrained(self.tmpdirname , do_normalize=lowercase_ , padding_value=1.0 ) self.assertEqual(processor.image_processor.to_json_string() , image_processor_add_kwargs.to_json_string() ) self.assertIsInstance(processor.image_processor , lowercase_ ) def __UpperCamelCase ( self : Any ) -> List[Any]: lowercase__ : List[str] = self.get_image_processor() lowercase__ : Tuple = SamProcessor(image_processor=lowercase_ ) lowercase__ : int = self.prepare_image_inputs() lowercase__ : Optional[int] = image_processor(lowercase_ , return_tensors="np" ) lowercase__ : str = processor(images=lowercase_ , return_tensors="np" ) input_feat_extract.pop("original_sizes" ) # pop original_sizes as it is popped in the processor input_feat_extract.pop("reshaped_input_sizes" ) # pop reshaped_input_sizes as it is popped in the processor for key in input_feat_extract.keys(): self.assertAlmostEqual(input_feat_extract[key].sum() , input_processor[key].sum() , delta=1E-2 ) @require_tf def __UpperCamelCase ( self : int ) -> Dict: lowercase__ : List[str] = self.get_image_processor() lowercase__ : str = SamProcessor(image_processor=lowercase_ ) lowercase__ : Dict = [tf.ones((1, 3, 5, 5) )] lowercase__ : Optional[Any] = [[17_64, 26_46]] lowercase__ : Any = [[6_83, 10_24]] lowercase__ : List[Any] = processor.post_process_masks(lowercase_ , lowercase_ , lowercase_ , return_tensors="tf" ) self.assertEqual(masks[0].shape , (1, 3, 17_64, 26_46) ) lowercase__ : Any = processor.post_process_masks( lowercase_ , tf.convert_to_tensor(lowercase_ ) , tf.convert_to_tensor(lowercase_ ) , return_tensors="tf" , ) self.assertEqual(masks[0].shape , (1, 3, 17_64, 26_46) ) # should also work with np lowercase__ : Tuple = [np.ones((1, 3, 5, 5) )] lowercase__ : Union[str, Any] = processor.post_process_masks( lowercase_ , np.array(lowercase_ ) , np.array(lowercase_ ) , return_tensors="tf" ) self.assertEqual(masks[0].shape , (1, 3, 17_64, 26_46) ) lowercase__ : Tuple = [[1, 0], [0, 1]] with self.assertRaises(tf.errors.InvalidArgumentError ): lowercase__ : str = processor.post_process_masks( lowercase_ , np.array(lowercase_ ) , np.array(lowercase_ ) , return_tensors="tf" ) @require_vision @require_torchvision class snake_case_ ( unittest.TestCase ): def __UpperCamelCase ( self : List[Any] ) -> Optional[int]: lowercase__ : str = tempfile.mkdtemp() lowercase__ : Tuple = SamImageProcessor() lowercase__ : Dict = SamProcessor(lowercase_ ) processor.save_pretrained(self.tmpdirname ) def __UpperCamelCase ( self : List[Any] , **lowercase_ : Any ) -> int: return AutoProcessor.from_pretrained(self.tmpdirname , **lowercase_ ).image_processor def __UpperCamelCase ( self : Any ) -> Optional[int]: shutil.rmtree(self.tmpdirname ) def __UpperCamelCase ( self : List[Any] ) -> Optional[Any]: lowercase__ : Union[str, Any] = [np.random.randint(2_55 , size=(3, 30, 4_00) , dtype=np.uinta )] lowercase__ : Dict = [Image.fromarray(np.moveaxis(lowercase_ , 0 , -1 ) ) for x in image_inputs] return image_inputs @is_pt_tf_cross_test def __UpperCamelCase ( self : Union[str, Any] ) -> Union[str, Any]: lowercase__ : int = self.get_image_processor() lowercase__ : Optional[int] = SamProcessor(image_processor=lowercase_ ) lowercase__ : int = np.random.randint(0 , 2 , size=(1, 3, 5, 5) ).astype(np.floataa ) lowercase__ : List[str] = [tf.convert_to_tensor(lowercase_ )] lowercase__ : Dict = [torch.tensor(lowercase_ )] lowercase__ : int = [[17_64, 26_46]] lowercase__ : Dict = [[6_83, 10_24]] lowercase__ : str = processor.post_process_masks( lowercase_ , lowercase_ , lowercase_ , return_tensors="tf" ) lowercase__ : Tuple = processor.post_process_masks( lowercase_ , lowercase_ , lowercase_ , return_tensors="pt" ) self.assertTrue(np.all(tf_masks[0].numpy() == pt_masks[0].numpy() ) ) @is_pt_tf_cross_test def __UpperCamelCase ( self : Union[str, Any] ) -> Optional[Any]: lowercase__ : List[str] = self.get_image_processor() lowercase__ : Any = SamProcessor(image_processor=lowercase_ ) lowercase__ : Optional[int] = self.prepare_image_inputs() lowercase__ : Any = image_processor(lowercase_ , return_tensors="pt" )["pixel_values"].numpy() lowercase__ : str = processor(images=lowercase_ , return_tensors="pt" )["pixel_values"].numpy() lowercase__ : Optional[int] = image_processor(lowercase_ , return_tensors="tf" )["pixel_values"].numpy() lowercase__ : Any = processor(images=lowercase_ , return_tensors="tf" )["pixel_values"].numpy() self.assertTrue(np.allclose(lowercase_ , lowercase_ ) ) self.assertTrue(np.allclose(lowercase_ , lowercase_ ) ) self.assertTrue(np.allclose(lowercase_ , lowercase_ ) )
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import asyncio import os import shutil import subprocess import sys import tempfile import unittest from distutils.util import strtobool from functools import partial from pathlib import Path from typing import List, Union from unittest import mock import torch from ..state import AcceleratorState, PartialState from ..utils import ( gather, is_bnb_available, is_comet_ml_available, is_datasets_available, is_deepspeed_available, is_mps_available, is_safetensors_available, is_tensorboard_available, is_torch_version, is_tpu_available, is_transformers_available, is_wandb_available, is_xpu_available, ) def lowercase_ ( _lowerCamelCase : Union[str, Any] , _lowerCamelCase : int=False): try: lowercase__ : str = os.environ[key] except KeyError: # KEY isn't set, default to `default`. lowercase__ : Union[str, Any] = default else: # KEY is set, convert it to True or False. try: lowercase__ : Union[str, Any] = strtobool(_lowerCamelCase) except ValueError: # More values are supported, but let's keep the message simple. raise ValueError(f'''If set, {key} must be yes or no.''') return _value UpperCamelCase = parse_flag_from_env('''RUN_SLOW''', default=False) def lowercase_ ( _lowerCamelCase : int): return unittest.skip("Test was skipped")(_lowerCamelCase) def lowercase_ ( _lowerCamelCase : Tuple): return unittest.skipUnless(_run_slow_tests , "test is slow")(_lowerCamelCase) def lowercase_ ( _lowerCamelCase : Union[str, Any]): return unittest.skipUnless(not torch.cuda.is_available() , "test requires only a CPU")(_lowerCamelCase) def lowercase_ ( _lowerCamelCase : Dict): return unittest.skipUnless(torch.cuda.is_available() , "test requires a GPU")(_lowerCamelCase) def lowercase_ ( _lowerCamelCase : int): return unittest.skipUnless(is_xpu_available() , "test requires a XPU")(_lowerCamelCase) def lowercase_ ( _lowerCamelCase : List[str]): return unittest.skipUnless(is_mps_available() , "test requires a `mps` backend support in `torch`")(_lowerCamelCase) def lowercase_ ( _lowerCamelCase : List[str]): return unittest.skipUnless( is_transformers_available() and is_datasets_available() , "test requires the Hugging Face suite")(_lowerCamelCase) def lowercase_ ( _lowerCamelCase : Union[str, Any]): return unittest.skipUnless(is_bnb_available() , "test requires the bitsandbytes library")(_lowerCamelCase) def lowercase_ ( _lowerCamelCase : Union[str, Any]): return unittest.skipUnless(is_tpu_available() , "test requires TPU")(_lowerCamelCase) def lowercase_ ( _lowerCamelCase : List[Any]): return unittest.skipUnless(torch.cuda.device_count() == 1 , "test requires a GPU")(_lowerCamelCase) def lowercase_ ( _lowerCamelCase : Union[str, Any]): return unittest.skipUnless(torch.xpu.device_count() == 1 , "test requires a XPU")(_lowerCamelCase) def lowercase_ ( _lowerCamelCase : List[str]): return unittest.skipUnless(torch.cuda.device_count() > 1 , "test requires multiple GPUs")(_lowerCamelCase) def lowercase_ ( _lowerCamelCase : int): return unittest.skipUnless(torch.xpu.device_count() > 1 , "test requires multiple XPUs")(_lowerCamelCase) def lowercase_ ( _lowerCamelCase : List[str]): return unittest.skipUnless(is_safetensors_available() , "test requires safetensors")(_lowerCamelCase) def lowercase_ ( _lowerCamelCase : str): return unittest.skipUnless(is_deepspeed_available() , "test requires DeepSpeed")(_lowerCamelCase) def lowercase_ ( _lowerCamelCase : Any): return unittest.skipUnless(is_torch_version(">=" , "1.12.0") , "test requires torch version >= 1.12.0")(_lowerCamelCase) def lowercase_ ( _lowerCamelCase : List[Any]=None , _lowerCamelCase : Dict=None): if test_case is None: return partial(_lowerCamelCase , version=_lowerCamelCase) return unittest.skipUnless(is_torch_version(">=" , _lowerCamelCase) , f'''test requires torch version >= {version}''')(_lowerCamelCase) def lowercase_ ( _lowerCamelCase : List[Any]): return unittest.skipUnless(is_tensorboard_available() , "test requires Tensorboard")(_lowerCamelCase) def lowercase_ ( _lowerCamelCase : int): return unittest.skipUnless(is_wandb_available() , "test requires wandb")(_lowerCamelCase) def lowercase_ ( _lowerCamelCase : List[str]): return unittest.skipUnless(is_comet_ml_available() , "test requires comet_ml")(_lowerCamelCase) UpperCamelCase = ( any([is_wandb_available(), is_tensorboard_available()]) and not is_comet_ml_available() ) def lowercase_ ( _lowerCamelCase : Any): return unittest.skipUnless( _atleast_one_tracker_available , "test requires at least one tracker to be available and for `comet_ml` to not be installed" , )(_lowerCamelCase) class snake_case_ ( unittest.TestCase ): __A : int = True @classmethod def __UpperCamelCase ( cls : str ) -> str: lowercase__ : str = tempfile.mkdtemp() @classmethod def __UpperCamelCase ( cls : List[str] ) -> Optional[Any]: if os.path.exists(cls.tmpdir ): shutil.rmtree(cls.tmpdir ) def __UpperCamelCase ( self : str ) -> Optional[int]: if self.clear_on_setup: for path in Path(self.tmpdir ).glob("**/*" ): if path.is_file(): path.unlink() elif path.is_dir(): shutil.rmtree(lowercase_ ) class snake_case_ ( unittest.TestCase ): def __UpperCamelCase ( self : List[str] ) -> Union[str, Any]: super().tearDown() # Reset the state of the AcceleratorState singleton. AcceleratorState._reset_state() PartialState._reset_state() class snake_case_ ( unittest.TestCase ): def __UpperCamelCase ( self : List[Any] , lowercase_ : Union[mock.Mock, List[mock.Mock]] ) -> str: lowercase__ : Tuple = mocks if isinstance(lowercase_ , (tuple, list) ) else [mocks] for m in self.mocks: m.start() self.addCleanup(m.stop ) def lowercase_ ( _lowerCamelCase : int): lowercase__ : Tuple = AcceleratorState() lowercase__ : Optional[int] = tensor[None].clone().to(state.device) lowercase__ : Optional[int] = gather(_lowerCamelCase).cpu() lowercase__ : Optional[Any] = tensor[0].cpu() for i in range(tensors.shape[0]): if not torch.equal(tensors[i] , _lowerCamelCase): return False return True class snake_case_ : def __init__( self : str , lowercase_ : int , lowercase_ : Optional[Any] , lowercase_ : int ) -> Union[str, Any]: lowercase__ : int = returncode lowercase__ : Dict = stdout lowercase__ : List[Any] = stderr async def lowercase_ ( _lowerCamelCase : Optional[int] , _lowerCamelCase : str): while True: lowercase__ : int = await stream.readline() if line: callback(_lowerCamelCase) else: break async def lowercase_ ( _lowerCamelCase : List[Any] , _lowerCamelCase : Dict=None , _lowerCamelCase : Tuple=None , _lowerCamelCase : Optional[Any]=None , _lowerCamelCase : Tuple=False , _lowerCamelCase : str=False): if echo: print("\nRunning: " , " ".join(_lowerCamelCase)) lowercase__ : str = await asyncio.create_subprocess_exec( cmd[0] , *cmd[1:] , stdin=_lowerCamelCase , stdout=asyncio.subprocess.PIPE , stderr=asyncio.subprocess.PIPE , env=_lowerCamelCase , ) # note: there is a warning for a possible deadlock when using `wait` with huge amounts of data in the pipe # https://docs.python.org/3/library/asyncio-subprocess.html#asyncio.asyncio.subprocess.Process.wait # # If it starts hanging, will need to switch to the following code. The problem is that no data # will be seen until it's done and if it hangs for example there will be no debug info. # out, err = await p.communicate() # return _RunOutput(p.returncode, out, err) lowercase__ : Tuple = [] lowercase__ : List[Any] = [] def tee(_lowerCamelCase : str , _lowerCamelCase : str , _lowerCamelCase : int , _lowerCamelCase : Optional[int]=""): lowercase__ : Optional[int] = line.decode("utf-8").rstrip() sink.append(_lowerCamelCase) if not quiet: print(_lowerCamelCase , _lowerCamelCase , file=_lowerCamelCase) # XXX: the timeout doesn't seem to make any difference here await asyncio.wait( [ asyncio.create_task(_read_stream(p.stdout , lambda _lowerCamelCase: tee(_lowerCamelCase , _lowerCamelCase , sys.stdout , label="stdout:"))), asyncio.create_task(_read_stream(p.stderr , lambda _lowerCamelCase: tee(_lowerCamelCase , _lowerCamelCase , sys.stderr , label="stderr:"))), ] , timeout=_lowerCamelCase , ) return _RunOutput(await p.wait() , _lowerCamelCase , _lowerCamelCase) def lowercase_ ( _lowerCamelCase : int , _lowerCamelCase : Tuple=None , _lowerCamelCase : Optional[Any]=None , _lowerCamelCase : List[str]=180 , _lowerCamelCase : Dict=False , _lowerCamelCase : Dict=True): lowercase__ : Optional[Any] = asyncio.get_event_loop() lowercase__ : List[Any] = loop.run_until_complete( _stream_subprocess(_lowerCamelCase , env=_lowerCamelCase , stdin=_lowerCamelCase , timeout=_lowerCamelCase , quiet=_lowerCamelCase , echo=_lowerCamelCase)) lowercase__ : str = " ".join(_lowerCamelCase) if result.returncode > 0: lowercase__ : Dict = "\n".join(result.stderr) raise RuntimeError( f'''\'{cmd_str}\' failed with returncode {result.returncode}\n\n''' f'''The combined stderr from workers follows:\n{stderr}''') return result class snake_case_ ( __A ): pass def lowercase_ ( _lowerCamelCase : List[str] , _lowerCamelCase : Any=False): try: lowercase__ : Optional[int] = subprocess.check_output(_lowerCamelCase , stderr=subprocess.STDOUT) if return_stdout: if hasattr(_lowerCamelCase , "decode"): lowercase__ : Optional[Any] = output.decode("utf-8") return output except subprocess.CalledProcessError as e: raise SubprocessCallException( f'''Command `{" ".join(_lowerCamelCase)}` failed with the following error:\n\n{e.output.decode()}''') from e
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import itertools import random import unittest import numpy as np from transformers import WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST, WavaVecaConfig, WavaVecaFeatureExtractor from transformers.testing_utils import require_torch, slow from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin UpperCamelCase = random.Random() def lowercase_ ( _lowerCamelCase : Union[str, Any] , _lowerCamelCase : Union[str, Any]=1.0 , _lowerCamelCase : Dict=None , _lowerCamelCase : Optional[int]=None): if rng is None: lowercase__ : int = global_rng lowercase__ : List[Any] = [] for batch_idx in range(shape[0]): values.append([]) for _ in range(shape[1]): values[-1].append(rng.random() * scale) return values class snake_case_ ( unittest.TestCase ): def __init__( self : Union[str, Any] , lowercase_ : str , lowercase_ : Optional[int]=7 , lowercase_ : Union[str, Any]=4_00 , lowercase_ : Any=20_00 , lowercase_ : Any=1 , lowercase_ : Optional[int]=0.0 , lowercase_ : Union[str, Any]=1_60_00 , lowercase_ : Union[str, Any]=True , lowercase_ : List[Any]=True , ) -> Tuple: lowercase__ : str = parent lowercase__ : Any = batch_size lowercase__ : Union[str, Any] = min_seq_length lowercase__ : List[str] = max_seq_length lowercase__ : Union[str, Any] = (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1) lowercase__ : str = feature_size lowercase__ : Any = padding_value lowercase__ : Dict = sampling_rate lowercase__ : Union[str, Any] = return_attention_mask lowercase__ : str = do_normalize def __UpperCamelCase ( self : Optional[Any] ) -> Optional[Any]: return { "feature_size": self.feature_size, "padding_value": self.padding_value, "sampling_rate": self.sampling_rate, "return_attention_mask": self.return_attention_mask, "do_normalize": self.do_normalize, } def __UpperCamelCase ( self : str , lowercase_ : str=False , lowercase_ : Tuple=False ) -> Optional[Any]: def _flatten(lowercase_ : List[str] ): return list(itertools.chain(*lowercase_ ) ) if equal_length: lowercase__ : Union[str, Any] = floats_list((self.batch_size, self.max_seq_length) ) else: # make sure that inputs increase in size lowercase__ : str = [ _flatten(floats_list((x, self.feature_size) ) ) for x in range(self.min_seq_length , self.max_seq_length , self.seq_length_diff ) ] if numpify: lowercase__ : Optional[Any] = [np.asarray(lowercase_ ) for x in speech_inputs] return speech_inputs class snake_case_ ( __A ,unittest.TestCase ): __A : Optional[Any] = WavaVecaFeatureExtractor def __UpperCamelCase ( self : Optional[int] ) -> str: lowercase__ : Dict = WavaVecaFeatureExtractionTester(self ) def __UpperCamelCase ( self : Any , lowercase_ : Optional[Any] ) -> Optional[int]: self.assertTrue(np.all(np.mean(lowercase_ , axis=0 ) < 1E-3 ) ) self.assertTrue(np.all(np.abs(np.var(lowercase_ , axis=0 ) - 1 ) < 1E-3 ) ) def __UpperCamelCase ( self : int ) -> Dict: # Tests that all call wrap to encode_plus and batch_encode_plus lowercase__ : Optional[Any] = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() ) # create three inputs of length 800, 1000, and 1200 lowercase__ : Tuple = [floats_list((1, x) )[0] for x in range(8_00 , 14_00 , 2_00 )] lowercase__ : int = [np.asarray(lowercase_ ) for speech_input in speech_inputs] # Test not batched input lowercase__ : List[str] = feat_extract(speech_inputs[0] , return_tensors="np" ).input_values lowercase__ : Dict = feat_extract(np_speech_inputs[0] , return_tensors="np" ).input_values self.assertTrue(np.allclose(lowercase_ , lowercase_ , atol=1E-3 ) ) # Test batched lowercase__ : Union[str, Any] = feat_extract(lowercase_ , return_tensors="np" ).input_values lowercase__ : Dict = feat_extract(lowercase_ , return_tensors="np" ).input_values for enc_seq_a, enc_seq_a in zip(lowercase_ , lowercase_ ): self.assertTrue(np.allclose(lowercase_ , lowercase_ , atol=1E-3 ) ) # Test 2-D numpy arrays are batched. lowercase__ : Tuple = [floats_list((1, x) )[0] for x in (8_00, 8_00, 8_00)] lowercase__ : Union[str, Any] = np.asarray(lowercase_ ) lowercase__ : str = feat_extract(lowercase_ , return_tensors="np" ).input_values lowercase__ : Optional[Any] = feat_extract(lowercase_ , return_tensors="np" ).input_values for enc_seq_a, enc_seq_a in zip(lowercase_ , lowercase_ ): self.assertTrue(np.allclose(lowercase_ , lowercase_ , atol=1E-3 ) ) def __UpperCamelCase ( self : Any ) -> Optional[Any]: lowercase__ : Dict = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() ) lowercase__ : Union[str, Any] = [floats_list((1, x) )[0] for x in range(8_00 , 14_00 , 2_00 )] lowercase__ : Any = ["longest", "max_length", "do_not_pad"] lowercase__ : Tuple = [None, 16_00, None] for max_length, padding in zip(lowercase_ , lowercase_ ): lowercase__ : str = feat_extract(lowercase_ , padding=lowercase_ , max_length=lowercase_ , return_tensors="np" ) lowercase__ : List[Any] = processed.input_values self._check_zero_mean_unit_variance(input_values[0][:8_00] ) self.assertTrue(input_values[0][8_00:].sum() < 1E-6 ) self._check_zero_mean_unit_variance(input_values[1][:10_00] ) self.assertTrue(input_values[0][10_00:].sum() < 1E-6 ) self._check_zero_mean_unit_variance(input_values[2][:12_00] ) def __UpperCamelCase ( self : Tuple ) -> str: lowercase__ : str = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() ) lowercase__ : Optional[Any] = range(8_00 , 14_00 , 2_00 ) lowercase__ : Dict = [floats_list((1, x) )[0] for x in lengths] lowercase__ : Union[str, Any] = ["longest", "max_length", "do_not_pad"] lowercase__ : Optional[int] = [None, 16_00, None] for max_length, padding in zip(lowercase_ , lowercase_ ): lowercase__ : Any = feat_extract(lowercase_ , max_length=lowercase_ , padding=lowercase_ ) lowercase__ : str = processed.input_values self._check_zero_mean_unit_variance(input_values[0][:8_00] ) self._check_zero_mean_unit_variance(input_values[1][:10_00] ) self._check_zero_mean_unit_variance(input_values[2][:12_00] ) def __UpperCamelCase ( self : Optional[Any] ) -> List[Any]: lowercase__ : Union[str, Any] = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() ) lowercase__ : List[Any] = [floats_list((1, x) )[0] for x in range(8_00 , 14_00 , 2_00 )] lowercase__ : List[str] = feat_extract( lowercase_ , truncation=lowercase_ , max_length=10_00 , padding="max_length" , return_tensors="np" ) lowercase__ : Tuple = processed.input_values self._check_zero_mean_unit_variance(input_values[0, :8_00] ) self._check_zero_mean_unit_variance(input_values[1] ) self._check_zero_mean_unit_variance(input_values[2] ) def __UpperCamelCase ( self : List[Any] ) -> int: lowercase__ : Tuple = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() ) lowercase__ : int = [floats_list((1, x) )[0] for x in range(8_00 , 14_00 , 2_00 )] lowercase__ : Tuple = feat_extract( lowercase_ , truncation=lowercase_ , max_length=10_00 , padding="longest" , return_tensors="np" ) lowercase__ : Union[str, Any] = processed.input_values self._check_zero_mean_unit_variance(input_values[0, :8_00] ) self._check_zero_mean_unit_variance(input_values[1, :10_00] ) self._check_zero_mean_unit_variance(input_values[2] ) # make sure that if max_length < longest -> then pad to max_length self.assertTrue(input_values.shape == (3, 10_00) ) lowercase__ : List[Any] = [floats_list((1, x) )[0] for x in range(8_00 , 14_00 , 2_00 )] lowercase__ : str = feat_extract( lowercase_ , truncation=lowercase_ , max_length=20_00 , padding="longest" , return_tensors="np" ) lowercase__ : List[Any] = processed.input_values self._check_zero_mean_unit_variance(input_values[0, :8_00] ) self._check_zero_mean_unit_variance(input_values[1, :10_00] ) self._check_zero_mean_unit_variance(input_values[2] ) # make sure that if max_length > longest -> then pad to longest self.assertTrue(input_values.shape == (3, 12_00) ) @require_torch def __UpperCamelCase ( self : Any ) -> List[Any]: import torch lowercase__ : List[Any] = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() ) lowercase__ : Any = np.random.rand(1_00 ).astype(np.floataa ) lowercase__ : str = np_speech_inputs.tolist() for inputs in [py_speech_inputs, np_speech_inputs]: lowercase__ : Any = feature_extractor.pad([{"input_values": inputs}] , return_tensors="np" ) self.assertTrue(np_processed.input_values.dtype == np.floataa ) lowercase__ : int = feature_extractor.pad([{"input_values": inputs}] , return_tensors="pt" ) self.assertTrue(pt_processed.input_values.dtype == torch.floataa ) @slow @require_torch def __UpperCamelCase ( self : Union[str, Any] ) -> Optional[int]: # this test makes sure that models that are using # group norm don't have their feature extractor return the # attention_mask for model_id in WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST: lowercase__ : Optional[Any] = WavaVecaConfig.from_pretrained(lowercase_ ) lowercase__ : Dict = WavaVecaFeatureExtractor.from_pretrained(lowercase_ ) # only "layer" feature extraction norm should make use of # attention_mask self.assertEqual(feat_extract.return_attention_mask , config.feat_extract_norm == "layer" )
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from ..utils import DummyObject, requires_backends class snake_case_ ( metaclass=__A ): __A : List[Any] = ["flax"] def __init__( self : Optional[int] , *lowercase_ : Optional[int] , **lowercase_ : List[Any] ) -> Tuple: requires_backends(self , ["flax"] ) @classmethod def __UpperCamelCase ( cls : Tuple , *lowercase_ : int , **lowercase_ : List[str] ) -> List[str]: requires_backends(cls , ["flax"] ) @classmethod def __UpperCamelCase ( cls : Tuple , *lowercase_ : List[str] , **lowercase_ : Tuple ) -> Any: requires_backends(cls , ["flax"] ) class snake_case_ ( metaclass=__A ): __A : Dict = ["flax"] def __init__( self : int , *lowercase_ : Any , **lowercase_ : int ) -> Union[str, Any]: requires_backends(self , ["flax"] ) @classmethod def __UpperCamelCase ( cls : Dict , *lowercase_ : List[str] , **lowercase_ : List[str] ) -> Optional[Any]: requires_backends(cls , ["flax"] ) @classmethod def __UpperCamelCase ( cls : Tuple , *lowercase_ : List[Any] , **lowercase_ : Any ) -> Dict: requires_backends(cls , ["flax"] ) class snake_case_ ( metaclass=__A ): __A : Dict = ["flax"] def __init__( self : Dict , *lowercase_ : str , **lowercase_ : int ) -> Union[str, Any]: requires_backends(self , ["flax"] ) @classmethod def __UpperCamelCase ( cls : Any , *lowercase_ : Union[str, Any] , **lowercase_ : Tuple ) -> List[str]: requires_backends(cls , ["flax"] ) @classmethod def __UpperCamelCase ( cls : Any , *lowercase_ : Any , **lowercase_ : Optional[int] ) -> List[str]: requires_backends(cls , ["flax"] ) class snake_case_ ( metaclass=__A ): __A : int = ["flax"] def __init__( self : Dict , *lowercase_ : Dict , **lowercase_ : Any ) -> int: requires_backends(self , ["flax"] ) @classmethod def __UpperCamelCase ( cls : List[Any] , *lowercase_ : int , **lowercase_ : Dict ) -> Optional[int]: requires_backends(cls , ["flax"] ) @classmethod def __UpperCamelCase ( cls : Optional[int] , *lowercase_ : Optional[Any] , **lowercase_ : Any ) -> Tuple: requires_backends(cls , ["flax"] ) class snake_case_ ( metaclass=__A ): __A : List[Any] = ["flax"] def __init__( self : List[str] , *lowercase_ : str , **lowercase_ : Union[str, Any] ) -> Optional[Any]: requires_backends(self , ["flax"] ) @classmethod def __UpperCamelCase ( cls : Tuple , *lowercase_ : Optional[Any] , **lowercase_ : Optional[int] ) -> Optional[int]: requires_backends(cls , ["flax"] ) @classmethod def __UpperCamelCase ( cls : Optional[Any] , *lowercase_ : Tuple , **lowercase_ : Dict ) -> Dict: requires_backends(cls , ["flax"] ) class snake_case_ ( metaclass=__A ): __A : Dict = ["flax"] def __init__( self : int , *lowercase_ : List[str] , **lowercase_ : List[Any] ) -> Dict: requires_backends(self , ["flax"] ) @classmethod def __UpperCamelCase ( cls : Optional[int] , *lowercase_ : int , **lowercase_ : Optional[int] ) -> Dict: requires_backends(cls , ["flax"] ) @classmethod def __UpperCamelCase ( cls : List[Any] , *lowercase_ : Optional[Any] , **lowercase_ : List[str] ) -> int: requires_backends(cls , ["flax"] ) class snake_case_ ( metaclass=__A ): __A : Optional[Any] = ["flax"] def __init__( self : int , *lowercase_ : Union[str, Any] , **lowercase_ : Optional[Any] ) -> Union[str, Any]: requires_backends(self , ["flax"] ) @classmethod def __UpperCamelCase ( cls : Dict , *lowercase_ : Tuple , **lowercase_ : int ) -> List[Any]: requires_backends(cls , ["flax"] ) @classmethod def __UpperCamelCase ( cls : Union[str, Any] , *lowercase_ : List[Any] , **lowercase_ : List[str] ) -> Union[str, Any]: requires_backends(cls , ["flax"] ) class snake_case_ ( metaclass=__A ): __A : Dict = ["flax"] def __init__( self : Any , *lowercase_ : int , **lowercase_ : int ) -> Optional[int]: requires_backends(self , ["flax"] ) @classmethod def __UpperCamelCase ( cls : Optional[int] , *lowercase_ : Any , **lowercase_ : List[Any] ) -> Tuple: requires_backends(cls , ["flax"] ) @classmethod def __UpperCamelCase ( cls : Optional[Any] , *lowercase_ : Any , **lowercase_ : Union[str, Any] ) -> Optional[Any]: requires_backends(cls , ["flax"] ) class snake_case_ ( metaclass=__A ): __A : List[Any] = ["flax"] def __init__( self : Union[str, Any] , *lowercase_ : int , **lowercase_ : Optional[int] ) -> Union[str, Any]: requires_backends(self , ["flax"] ) @classmethod def __UpperCamelCase ( cls : Optional[int] , *lowercase_ : Any , **lowercase_ : Optional[Any] ) -> List[Any]: requires_backends(cls , ["flax"] ) @classmethod def __UpperCamelCase ( cls : Dict , *lowercase_ : List[str] , **lowercase_ : str ) -> Optional[Any]: requires_backends(cls , ["flax"] ) class snake_case_ ( metaclass=__A ): __A : List[Any] = ["flax"] def __init__( self : List[Any] , *lowercase_ : Union[str, Any] , **lowercase_ : Optional[Any] ) -> Dict: requires_backends(self , ["flax"] ) @classmethod def __UpperCamelCase ( cls : Optional[Any] , *lowercase_ : Any , **lowercase_ : int ) -> Union[str, Any]: requires_backends(cls , ["flax"] ) @classmethod def __UpperCamelCase ( cls : str , *lowercase_ : Optional[Any] , **lowercase_ : Optional[int] ) -> List[Any]: requires_backends(cls , ["flax"] ) class snake_case_ ( metaclass=__A ): __A : Optional[int] = ["flax"] def __init__( self : Any , *lowercase_ : str , **lowercase_ : Dict ) -> int: requires_backends(self , ["flax"] ) @classmethod def __UpperCamelCase ( cls : str , *lowercase_ : int , **lowercase_ : Optional[int] ) -> Tuple: requires_backends(cls , ["flax"] ) @classmethod def __UpperCamelCase ( cls : Tuple , *lowercase_ : List[Any] , **lowercase_ : Tuple ) -> Dict: requires_backends(cls , ["flax"] ) class snake_case_ ( metaclass=__A ): __A : int = ["flax"] def __init__( self : List[str] , *lowercase_ : int , **lowercase_ : Union[str, Any] ) -> Dict: requires_backends(self , ["flax"] ) @classmethod def __UpperCamelCase ( cls : List[Any] , *lowercase_ : int , **lowercase_ : Dict ) -> List[Any]: requires_backends(cls , ["flax"] ) @classmethod def __UpperCamelCase ( cls : Union[str, Any] , *lowercase_ : Dict , **lowercase_ : int ) -> Optional[Any]: requires_backends(cls , ["flax"] ) class snake_case_ ( metaclass=__A ): __A : List[str] = ["flax"] def __init__( self : Tuple , *lowercase_ : List[Any] , **lowercase_ : Tuple ) -> Tuple: requires_backends(self , ["flax"] ) @classmethod def __UpperCamelCase ( cls : Any , *lowercase_ : Union[str, Any] , **lowercase_ : Optional[int] ) -> Union[str, Any]: requires_backends(cls , ["flax"] ) @classmethod def __UpperCamelCase ( cls : List[str] , *lowercase_ : Union[str, Any] , **lowercase_ : Dict ) -> List[Any]: requires_backends(cls , ["flax"] )
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"""simple docstring""" from ...configuration_utils import PretrainedConfig UpperCamelCase = { '''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 snake_case_ ( __A ): __A : Any = "tapas" def __init__( self : List[str] , lowercase_ : Union[str, Any]=3_05_22 , lowercase_ : List[Any]=7_68 , lowercase_ : Optional[Any]=12 , lowercase_ : int=12 , lowercase_ : Dict=30_72 , lowercase_ : str="gelu" , lowercase_ : str=0.1 , lowercase_ : List[Any]=0.1 , lowercase_ : Union[str, Any]=10_24 , lowercase_ : Any=[3, 2_56, 2_56, 2, 2_56, 2_56, 10] , lowercase_ : List[str]=0.02 , lowercase_ : List[Any]=1E-12 , lowercase_ : str=0 , lowercase_ : Optional[int]=10.0 , lowercase_ : int=0 , lowercase_ : str=1.0 , lowercase_ : Optional[Any]=None , lowercase_ : List[Any]=1.0 , lowercase_ : List[Any]=False , lowercase_ : Optional[Any]=None , lowercase_ : Any=1.0 , lowercase_ : Optional[int]=1.0 , lowercase_ : Dict=False , lowercase_ : Any=False , lowercase_ : Dict="ratio" , lowercase_ : List[str]=None , lowercase_ : List[str]=None , lowercase_ : Dict=64 , lowercase_ : List[Any]=32 , lowercase_ : Optional[Any]=False , lowercase_ : str=True , lowercase_ : Optional[int]=False , lowercase_ : Optional[Any]=False , lowercase_ : Union[str, Any]=True , lowercase_ : Dict=False , lowercase_ : str=None , lowercase_ : Union[str, Any]=None , **lowercase_ : Any , ) -> Optional[int]: super().__init__(pad_token_id=lowercase_ , **lowercase_ ) # BERT hyperparameters (with updated max_position_embeddings and type_vocab_sizes) lowercase__ : Any = vocab_size lowercase__ : Dict = hidden_size lowercase__ : Optional[int] = num_hidden_layers lowercase__ : str = num_attention_heads lowercase__ : int = hidden_act lowercase__ : Optional[int] = intermediate_size lowercase__ : List[str] = hidden_dropout_prob lowercase__ : Tuple = attention_probs_dropout_prob lowercase__ : List[str] = max_position_embeddings lowercase__ : Union[str, Any] = type_vocab_sizes lowercase__ : str = initializer_range lowercase__ : Optional[Any] = layer_norm_eps # Fine-tuning task hyperparameters lowercase__ : List[str] = positive_label_weight lowercase__ : Union[str, Any] = num_aggregation_labels lowercase__ : Optional[Any] = aggregation_loss_weight lowercase__ : List[str] = use_answer_as_supervision lowercase__ : str = answer_loss_importance lowercase__ : List[str] = use_normalized_answer_loss lowercase__ : Tuple = huber_loss_delta lowercase__ : List[Any] = temperature lowercase__ : Optional[Any] = aggregation_temperature lowercase__ : List[Any] = use_gumbel_for_cells lowercase__ : Tuple = use_gumbel_for_aggregation lowercase__ : int = average_approximation_function lowercase__ : Optional[int] = cell_selection_preference lowercase__ : List[Any] = answer_loss_cutoff lowercase__ : str = max_num_rows lowercase__ : List[Any] = max_num_columns lowercase__ : List[Any] = average_logits_per_cell lowercase__ : List[str] = select_one_column lowercase__ : Any = allow_empty_column_selection lowercase__ : Union[str, Any] = init_cell_selection_weights_to_zero lowercase__ : Union[str, Any] = reset_position_index_per_cell lowercase__ : str = disable_per_token_loss # Aggregation hyperparameters lowercase__ : List[Any] = aggregation_labels lowercase__ : Union[str, Any] = no_aggregation_label_index if isinstance(self.aggregation_labels , lowercase_ ): lowercase__ : int = {int(lowercase_ ): v for k, v in aggregation_labels.items()}
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from ...configuration_utils import PretrainedConfig from ...utils import logging UpperCamelCase = logging.get_logger(__name__) UpperCamelCase = { '''facebook/vit-mae-base''': '''https://huggingface.co/facebook/vit-mae-base/resolve/main/config.json''', # See all ViT MAE models at https://huggingface.co/models?filter=vit-mae } class snake_case_ ( __A ): __A : List[str] = "vit_mae" def __init__( self : List[Any] , lowercase_ : List[Any]=7_68 , lowercase_ : Tuple=12 , lowercase_ : Tuple=12 , lowercase_ : Optional[Any]=30_72 , lowercase_ : str="gelu" , lowercase_ : Tuple=0.0 , lowercase_ : int=0.0 , lowercase_ : Dict=0.02 , lowercase_ : int=1E-12 , lowercase_ : Tuple=2_24 , lowercase_ : Any=16 , lowercase_ : Dict=3 , lowercase_ : List[Any]=True , lowercase_ : Dict=16 , lowercase_ : List[str]=5_12 , lowercase_ : Tuple=8 , lowercase_ : Any=20_48 , lowercase_ : int=0.75 , lowercase_ : Tuple=False , **lowercase_ : Optional[int] , ) -> Optional[Any]: super().__init__(**lowercase_ ) lowercase__ : List[str] = hidden_size lowercase__ : str = num_hidden_layers lowercase__ : Optional[int] = num_attention_heads lowercase__ : List[Any] = intermediate_size lowercase__ : str = hidden_act lowercase__ : List[str] = hidden_dropout_prob lowercase__ : Optional[Any] = attention_probs_dropout_prob lowercase__ : Any = initializer_range lowercase__ : Optional[Any] = layer_norm_eps lowercase__ : Optional[Any] = image_size lowercase__ : Optional[int] = patch_size lowercase__ : Any = num_channels lowercase__ : str = qkv_bias lowercase__ : Optional[Any] = decoder_num_attention_heads lowercase__ : Any = decoder_hidden_size lowercase__ : Any = decoder_num_hidden_layers lowercase__ : Union[str, Any] = decoder_intermediate_size lowercase__ : int = mask_ratio lowercase__ : Tuple = norm_pix_loss
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import argparse import datetime def lowercase_ ( _lowerCamelCase : str): lowercase__ : Optional[Any] = { "0": "Sunday", "1": "Monday", "2": "Tuesday", "3": "Wednesday", "4": "Thursday", "5": "Friday", "6": "Saturday", } lowercase__ : Any = {0: 1, 1: 2, 2: 3, 3: 4, 4: 5, 5: 6, 6: 0} # Validate if not 0 < len(_lowerCamelCase) < 11: raise ValueError("Must be 10 characters long") # Get month lowercase__ : int = int(date_input[0] + date_input[1]) # Validate if not 0 < m < 13: raise ValueError("Month must be between 1 - 12") lowercase__ : str = date_input[2] # Validate if sep_a not in ["-", "/"]: raise ValueError("Date separator must be '-' or '/'") # Get day lowercase__ : int = int(date_input[3] + date_input[4]) # Validate if not 0 < d < 32: raise ValueError("Date must be between 1 - 31") # Get second separator lowercase__ : str = date_input[5] # Validate if sep_a not in ["-", "/"]: raise ValueError("Date separator must be '-' or '/'") # Get year lowercase__ : int = int(date_input[6] + date_input[7] + date_input[8] + date_input[9]) # Arbitrary year range if not 45 < y < 8500: raise ValueError( "Year out of range. There has to be some sort of limit...right?") # Get datetime obj for validation lowercase__ : Union[str, Any] = datetime.date(int(_lowerCamelCase) , int(_lowerCamelCase) , int(_lowerCamelCase)) # Start math if m <= 2: lowercase__ : Optional[Any] = y - 1 lowercase__ : int = m + 12 # maths var lowercase__ : int = int(str(_lowerCamelCase)[:2]) lowercase__ : int = int(str(_lowerCamelCase)[2:]) lowercase__ : int = int(2.6 * m - 5.39) lowercase__ : int = int(c / 4) lowercase__ : int = int(k / 4) lowercase__ : int = int(d + k) lowercase__ : int = int(t + u + v + x) lowercase__ : int = int(z - (2 * c)) lowercase__ : int = round(w % 7) # End math # Validate math if f != convert_datetime_days[dt_ck.weekday()]: raise AssertionError("The date was evaluated incorrectly. Contact developer.") # Response lowercase__ : str = f'''Your date {date_input}, is a {days[str(_lowerCamelCase)]}!''' return response if __name__ == "__main__": import doctest doctest.testmod() UpperCamelCase : Any = argparse.ArgumentParser( description=( '''Find out what day of the week nearly any date is or was. Enter ''' '''date as a string in the mm-dd-yyyy or mm/dd/yyyy format''' ) ) parser.add_argument( '''date_input''', type=str, help='''Date as a string (mm-dd-yyyy or mm/dd/yyyy)''' ) UpperCamelCase : Optional[Any] = parser.parse_args() zeller(args.date_input)
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def lowercase_ ( _lowerCamelCase : int , _lowerCamelCase : int): while a != 0: lowercase__ , lowercase__ : Dict = b % a, a return b def lowercase_ ( _lowerCamelCase : int , _lowerCamelCase : int): if gcd(_lowerCamelCase , _lowerCamelCase) != 1: lowercase__ : Tuple = f'''mod inverse of {a!r} and {m!r} does not exist''' raise ValueError(_lowerCamelCase) lowercase__ , lowercase__ , lowercase__ : Optional[int] = 1, 0, a lowercase__ , lowercase__ , lowercase__ : Union[str, Any] = 0, 1, m while va != 0: lowercase__ : Tuple = ua // va lowercase__ , lowercase__ , lowercase__ , lowercase__ , lowercase__ , lowercase__ : Any = (ua - q * va), (ua - q * va), (ua - q * va), va, va, va return ua % m
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"""simple docstring""" UpperCamelCase = 8.314_462 # Unit - J mol-1 K-1 def lowercase_ ( _lowerCamelCase : float , _lowerCamelCase : float , _lowerCamelCase : float): if moles < 0 or kelvin < 0 or volume < 0: raise ValueError("Invalid inputs. Enter positive value.") return moles * kelvin * UNIVERSAL_GAS_CONSTANT / volume def lowercase_ ( _lowerCamelCase : float , _lowerCamelCase : float , _lowerCamelCase : float): if moles < 0 or kelvin < 0 or pressure < 0: raise ValueError("Invalid inputs. Enter positive value.") return moles * kelvin * UNIVERSAL_GAS_CONSTANT / pressure if __name__ == "__main__": from doctest import testmod testmod()
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import logging import os from dataclasses import dataclass, field from functools import partial from pathlib import Path from tempfile import TemporaryDirectory from typing import List, Optional import faiss import torch from datasets import Features, Sequence, Value, load_dataset from transformers import DPRContextEncoder, DPRContextEncoderTokenizerFast, HfArgumentParser UpperCamelCase = logging.getLogger(__name__) torch.set_grad_enabled(False) UpperCamelCase = '''cuda''' if torch.cuda.is_available() else '''cpu''' def lowercase_ ( _lowerCamelCase : str , _lowerCamelCase : Tuple=100 , _lowerCamelCase : Tuple=" "): lowercase__ : Union[str, Any] = text.split(_lowerCamelCase) return [character.join(text[i : i + n]).strip() for i in range(0 , len(_lowerCamelCase) , _lowerCamelCase)] def lowercase_ ( _lowerCamelCase : dict): lowercase__ , lowercase__ : List[str] = [], [] for title, text in zip(documents["title"] , documents["text"]): if text is not None: for passage in split_text(_lowerCamelCase): titles.append(title if title is not None else "") texts.append(_lowerCamelCase) return {"title": titles, "text": texts} def lowercase_ ( _lowerCamelCase : dict , _lowerCamelCase : DPRContextEncoder , _lowerCamelCase : DPRContextEncoderTokenizerFast): lowercase__ : Union[str, Any] = ctx_tokenizer( documents["title"] , documents["text"] , truncation=_lowerCamelCase , padding="longest" , return_tensors="pt")["input_ids"] lowercase__ : Any = ctx_encoder(input_ids.to(device=_lowerCamelCase) , return_dict=_lowerCamelCase).pooler_output return {"embeddings": embeddings.detach().cpu().numpy()} def lowercase_ ( _lowerCamelCase : "RagExampleArguments" , _lowerCamelCase : "ProcessingArguments" , _lowerCamelCase : "IndexHnswArguments" , ): ###################################### logger.info("Step 1 - Create the dataset") ###################################### # The dataset needed for RAG must have three columns: # - title (string): title of the document # - text (string): text of a passage of the document # - embeddings (array of dimension d): DPR representation of the passage # Let's say you have documents in tab-separated csv files with columns "title" and "text" assert os.path.isfile(rag_example_args.csv_path), "Please provide a valid path to a csv file" # You can load a Dataset object this way lowercase__ : str = load_dataset( "csv" , data_files=[rag_example_args.csv_path] , split="train" , delimiter="\t" , column_names=["title", "text"]) # More info about loading csv files in the documentation: https://huggingface.co/docs/datasets/loading_datasets.html?highlight=csv#csv-files # Then split the documents into passages of 100 words lowercase__ : List[Any] = dataset.map(_lowerCamelCase , batched=_lowerCamelCase , num_proc=processing_args.num_proc) # And compute the embeddings lowercase__ : Optional[Any] = DPRContextEncoder.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name).to(device=_lowerCamelCase) lowercase__ : Any = DPRContextEncoderTokenizerFast.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name) lowercase__ : List[Any] = Features( {"text": Value("string"), "title": Value("string"), "embeddings": Sequence(Value("float32"))}) # optional, save as float32 instead of float64 to save space lowercase__ : List[Any] = dataset.map( partial(_lowerCamelCase , ctx_encoder=_lowerCamelCase , ctx_tokenizer=_lowerCamelCase) , batched=_lowerCamelCase , batch_size=processing_args.batch_size , features=_lowerCamelCase , ) # And finally save your dataset lowercase__ : Optional[int] = os.path.join(rag_example_args.output_dir , "my_knowledge_dataset") dataset.save_to_disk(_lowerCamelCase) # from datasets import load_from_disk # dataset = load_from_disk(passages_path) # to reload the dataset ###################################### logger.info("Step 2 - Index the dataset") ###################################### # Let's use the Faiss implementation of HNSW for fast approximate nearest neighbor search lowercase__ : Tuple = faiss.IndexHNSWFlat(index_hnsw_args.d , index_hnsw_args.m , faiss.METRIC_INNER_PRODUCT) dataset.add_faiss_index("embeddings" , custom_index=_lowerCamelCase) # And save the index lowercase__ : Union[str, Any] = os.path.join(rag_example_args.output_dir , "my_knowledge_dataset_hnsw_index.faiss") dataset.get_index("embeddings").save(_lowerCamelCase) # dataset.load_faiss_index("embeddings", index_path) # to reload the index @dataclass class snake_case_ : __A : str = field( default=str(Path(__A ).parent / "test_run" / "dummy-kb" / "my_knowledge_dataset.csv" ) ,metadata={"help": "Path to a tab-separated csv file with columns 'title' and 'text'"} ,) __A : Optional[str] = field( default=__A ,metadata={"help": "Question that is passed as input to RAG. Default is 'What does Moses' rod turn into ?'."} ,) __A : str = field( default="facebook/rag-sequence-nq" ,metadata={"help": "The RAG model to use. Either 'facebook/rag-sequence-nq' or 'facebook/rag-token-nq'"} ,) __A : str = field( default="facebook/dpr-ctx_encoder-multiset-base" ,metadata={ "help": ( "The DPR context encoder model to use. Either 'facebook/dpr-ctx_encoder-single-nq-base' or" " 'facebook/dpr-ctx_encoder-multiset-base'" ) } ,) __A : Optional[str] = field( default=str(Path(__A ).parent / "test_run" / "dummy-kb" ) ,metadata={"help": "Path to a directory where the dataset passages and the index will be saved"} ,) @dataclass class snake_case_ : __A : Optional[int] = field( default=__A ,metadata={ "help": "The number of processes to use to split the documents into passages. Default is single process." } ,) __A : int = field( default=16 ,metadata={ "help": "The batch size to use when computing the passages embeddings using the DPR context encoder." } ,) @dataclass class snake_case_ : __A : int = field( default=768 ,metadata={"help": "The dimension of the embeddings to pass to the HNSW Faiss index."} ,) __A : int = field( default=128 ,metadata={ "help": ( "The number of bi-directional links created for every new element during the HNSW index construction." ) } ,) if __name__ == "__main__": logging.basicConfig(level=logging.WARNING) logger.setLevel(logging.INFO) UpperCamelCase = HfArgumentParser((RagExampleArguments, ProcessingArguments, IndexHnswArguments)) UpperCamelCase , UpperCamelCase , UpperCamelCase = parser.parse_args_into_dataclasses() with TemporaryDirectory() as tmp_dir: UpperCamelCase = rag_example_args.output_dir or tmp_dir main(rag_example_args, processing_args, index_hnsw_args)
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"""simple docstring""" class snake_case_ : def __init__( self : List[Any] , lowercase_ : str = "" , lowercase_ : bool = False ) -> None: # Mapping from the first character of the prefix of the node lowercase__ : dict[str, RadixNode] = {} # A node will be a leaf if the tree contains its word lowercase__ : Union[str, Any] = is_leaf lowercase__ : Union[str, Any] = prefix def __UpperCamelCase ( self : str , lowercase_ : str ) -> tuple[str, str, str]: lowercase__ : List[str] = 0 for q, w in zip(self.prefix , lowercase_ ): if q != w: break x += 1 return self.prefix[:x], self.prefix[x:], word[x:] def __UpperCamelCase ( self : List[Any] , lowercase_ : list[str] ) -> None: for word in words: self.insert(lowercase_ ) def __UpperCamelCase ( self : Union[str, Any] , lowercase_ : str ) -> None: # Case 1: If the word is the prefix of the node # Solution: We set the current node as leaf if self.prefix == word: lowercase__ : Dict = True # Case 2: The node has no edges that have a prefix to the word # Solution: We create an edge from the current node to a new one # containing the word elif word[0] not in self.nodes: lowercase__ : List[str] = RadixNode(prefix=lowercase_ , is_leaf=lowercase_ ) else: lowercase__ : Dict = self.nodes[word[0]] lowercase__ : Optional[Any] = incoming_node.match( lowercase_ ) # Case 3: The node prefix is equal to the matching # Solution: We insert remaining word on the next node if remaining_prefix == "": self.nodes[matching_string[0]].insert(lowercase_ ) # Case 4: The word is greater equal to the matching # Solution: Create a node in between both nodes, change # prefixes and add the new node for the remaining word else: lowercase__ : Optional[Any] = remaining_prefix lowercase__ : Dict = self.nodes[matching_string[0]] lowercase__ : Optional[Any] = RadixNode(lowercase_ , lowercase_ ) lowercase__ : Tuple = aux_node if remaining_word == "": lowercase__ : int = True else: self.nodes[matching_string[0]].insert(lowercase_ ) def __UpperCamelCase ( self : Any , lowercase_ : str ) -> bool: lowercase__ : Optional[int] = self.nodes.get(word[0] , lowercase_ ) if not incoming_node: return False else: lowercase__ : Optional[Any] = incoming_node.match( lowercase_ ) # If there is remaining prefix, the word can't be on the tree if remaining_prefix != "": return False # This applies when the word and the prefix are equal elif remaining_word == "": return incoming_node.is_leaf # We have word remaining so we check the next node else: return incoming_node.find(lowercase_ ) def __UpperCamelCase ( self : int , lowercase_ : str ) -> bool: lowercase__ : Dict = self.nodes.get(word[0] , lowercase_ ) if not incoming_node: return False else: lowercase__ : Union[str, Any] = incoming_node.match( lowercase_ ) # If there is remaining prefix, the word can't be on the tree if remaining_prefix != "": return False # We have word remaining so we check the next node elif remaining_word != "": return incoming_node.delete(lowercase_ ) else: # If it is not a leaf, we don't have to delete if not incoming_node.is_leaf: return False else: # We delete the nodes if no edges go from it if len(incoming_node.nodes ) == 0: del self.nodes[word[0]] # We merge the current node with its only child if len(self.nodes ) == 1 and not self.is_leaf: lowercase__ : Optional[int] = list(self.nodes.values() )[0] lowercase__ : Dict = merging_node.is_leaf self.prefix += merging_node.prefix lowercase__ : int = merging_node.nodes # If there is more than 1 edge, we just mark it as non-leaf elif len(incoming_node.nodes ) > 1: lowercase__ : Union[str, Any] = False # If there is 1 edge, we merge it with its child else: lowercase__ : Optional[int] = list(incoming_node.nodes.values() )[0] lowercase__ : Optional[Any] = merging_node.is_leaf incoming_node.prefix += merging_node.prefix lowercase__ : Any = merging_node.nodes return True def __UpperCamelCase ( self : Tuple , lowercase_ : int = 0 ) -> None: if self.prefix != "": print("-" * height , self.prefix , " (leaf)" if self.is_leaf else "" ) for value in self.nodes.values(): value.print_tree(height + 1 ) def lowercase_ ( ): lowercase__ : Optional[int] = "banana bananas bandana band apple all beast".split() lowercase__ : List[Any] = RadixNode() root.insert_many(_lowerCamelCase) assert all(root.find(_lowerCamelCase) for word in words) assert not root.find("bandanas") assert not root.find("apps") root.delete("all") assert not root.find("all") root.delete("banana") assert not root.find("banana") assert root.find("bananas") return True def lowercase_ ( ): assert test_trie() def lowercase_ ( ): lowercase__ : str = RadixNode() lowercase__ : List[Any] = "banana bananas bandanas bandana band apple all beast".split() root.insert_many(_lowerCamelCase) print("Words:" , _lowerCamelCase) print("Tree:") root.print_tree() if __name__ == "__main__": main()
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import argparse import datetime def lowercase_ ( _lowerCamelCase : str): lowercase__ : Optional[Any] = { "0": "Sunday", "1": "Monday", "2": "Tuesday", "3": "Wednesday", "4": "Thursday", "5": "Friday", "6": "Saturday", } lowercase__ : Any = {0: 1, 1: 2, 2: 3, 3: 4, 4: 5, 5: 6, 6: 0} # Validate if not 0 < len(_lowerCamelCase) < 11: raise ValueError("Must be 10 characters long") # Get month lowercase__ : int = int(date_input[0] + date_input[1]) # Validate if not 0 < m < 13: raise ValueError("Month must be between 1 - 12") lowercase__ : str = date_input[2] # Validate if sep_a not in ["-", "/"]: raise ValueError("Date separator must be '-' or '/'") # Get day lowercase__ : int = int(date_input[3] + date_input[4]) # Validate if not 0 < d < 32: raise ValueError("Date must be between 1 - 31") # Get second separator lowercase__ : str = date_input[5] # Validate if sep_a not in ["-", "/"]: raise ValueError("Date separator must be '-' or '/'") # Get year lowercase__ : int = int(date_input[6] + date_input[7] + date_input[8] + date_input[9]) # Arbitrary year range if not 45 < y < 8500: raise ValueError( "Year out of range. There has to be some sort of limit...right?") # Get datetime obj for validation lowercase__ : Union[str, Any] = datetime.date(int(_lowerCamelCase) , int(_lowerCamelCase) , int(_lowerCamelCase)) # Start math if m <= 2: lowercase__ : Optional[Any] = y - 1 lowercase__ : int = m + 12 # maths var lowercase__ : int = int(str(_lowerCamelCase)[:2]) lowercase__ : int = int(str(_lowerCamelCase)[2:]) lowercase__ : int = int(2.6 * m - 5.39) lowercase__ : int = int(c / 4) lowercase__ : int = int(k / 4) lowercase__ : int = int(d + k) lowercase__ : int = int(t + u + v + x) lowercase__ : int = int(z - (2 * c)) lowercase__ : int = round(w % 7) # End math # Validate math if f != convert_datetime_days[dt_ck.weekday()]: raise AssertionError("The date was evaluated incorrectly. Contact developer.") # Response lowercase__ : str = f'''Your date {date_input}, is a {days[str(_lowerCamelCase)]}!''' return response if __name__ == "__main__": import doctest doctest.testmod() UpperCamelCase = argparse.ArgumentParser( description=( '''Find out what day of the week nearly any date is or was. Enter ''' '''date as a string in the mm-dd-yyyy or mm/dd/yyyy format''' ) ) parser.add_argument( '''date_input''', type=str, help='''Date as a string (mm-dd-yyyy or mm/dd/yyyy)''' ) UpperCamelCase = parser.parse_args() zeller(args.date_input)
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"""simple docstring""" import argparse import torch from transformers import BlenderbotConfig, BlenderbotForConditionalGeneration from transformers.utils import logging logging.set_verbosity_info() UpperCamelCase = logging.get_logger(__name__) UpperCamelCase = [ ['''attention''', '''attn'''], ['''encoder_attention''', '''encoder_attn'''], ['''q_lin''', '''q_proj'''], ['''k_lin''', '''k_proj'''], ['''v_lin''', '''v_proj'''], ['''out_lin''', '''out_proj'''], ['''norm_embeddings''', '''layernorm_embedding'''], ['''position_embeddings''', '''embed_positions'''], ['''embeddings''', '''embed_tokens'''], ['''ffn.lin''', '''fc'''], ] def lowercase_ ( _lowerCamelCase : Union[str, Any]): if k == "embeddings.weight": return "shared.weight" for parlai_name, hf_name in PATTERNS: lowercase__ : List[str] = k.replace(_lowerCamelCase , _lowerCamelCase) if k.startswith("encoder"): lowercase__ : Tuple = k.replace(".attn" , ".self_attn") lowercase__ : Optional[Any] = k.replace("norm1" , "self_attn_layer_norm") lowercase__ : Optional[Any] = k.replace("norm2" , "final_layer_norm") elif k.startswith("decoder"): lowercase__ : Dict = k.replace("norm1" , "self_attn_layer_norm") lowercase__ : Tuple = k.replace("norm2" , "encoder_attn_layer_norm") lowercase__ : Any = k.replace("norm3" , "final_layer_norm") return k def lowercase_ ( _lowerCamelCase : Optional[Any]): lowercase__ : List[str] = [ "model.encoder.layernorm_embedding.weight", "model.encoder.layernorm_embedding.bias", "model.decoder.layernorm_embedding.weight", "model.decoder.layernorm_embedding.bias", ] for k in keys: lowercase__ : Optional[int] = sd.pop(_lowerCamelCase) lowercase__ : Optional[Any] = k.replace("layernorm_embedding" , "layer_norm") assert new_k not in sd lowercase__ : Optional[int] = v UpperCamelCase = ['''START'''] @torch.no_grad() def lowercase_ ( _lowerCamelCase : List[Any] , _lowerCamelCase : Tuple , _lowerCamelCase : Any): lowercase__ : Tuple = torch.load(_lowerCamelCase , map_location="cpu") lowercase__ : Dict = model["model"] lowercase__ : Optional[Any] = BlenderbotConfig.from_json_file(_lowerCamelCase) lowercase__ : Any = BlenderbotForConditionalGeneration(_lowerCamelCase) lowercase__ : Optional[Any] = m.model.state_dict().keys() lowercase__ : Optional[int] = [] lowercase__ : Any = {} for k, v in sd.items(): if k in IGNORE_KEYS: continue lowercase__ : Optional[Any] = rename_state_dict_key(_lowerCamelCase) if new_k not in valid_keys: failures.append([k, new_k]) else: lowercase__ : int = v if cfg.normalize_before: # Blenderbot-3B checkpoints. Rename layernorm_embedding -> layer_norm rename_layernorm_keys(_lowerCamelCase) m.model.load_state_dict(_lowerCamelCase , strict=_lowerCamelCase) m.half() m.save_pretrained(_lowerCamelCase) if __name__ == "__main__": UpperCamelCase = argparse.ArgumentParser() # Required parameters parser.add_argument('''--src_path''', type=str, help='''like blenderbot-model.bin''') parser.add_argument('''--save_dir''', default='''hf_blenderbot''', type=str, help='''Where to save converted model.''') parser.add_argument( '''--hf_config_json''', default='''blenderbot-3b-config.json''', type=str, help='''Path to config to use''' ) UpperCamelCase = parser.parse_args() convert_parlai_checkpoint(args.src_path, args.save_dir, args.hf_config_json)
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import os from argparse import ArgumentParser from typing import List import torch.utils.data from datasets import Dataset, IterableDataset from datasets.distributed import split_dataset_by_node UpperCamelCase = 4 UpperCamelCase = 3 class snake_case_ ( __A ): pass def lowercase_ ( _lowerCamelCase : List[str]): for shard in shards: for i in range(_lowerCamelCase): yield {"i": i, "shard": shard} def lowercase_ ( ): lowercase__ : List[str] = int(os.environ["RANK"]) lowercase__ : Union[str, Any] = int(os.environ["WORLD_SIZE"]) lowercase__ : Union[str, Any] = ArgumentParser() parser.add_argument("--streaming" , type=_lowerCamelCase) parser.add_argument("--local_rank" , type=_lowerCamelCase) parser.add_argument("--num_workers" , type=_lowerCamelCase , default=0) lowercase__ : int = parser.parse_args() lowercase__ : Union[str, Any] = args.streaming lowercase__ : List[Any] = args.num_workers lowercase__ : Dict = {"shards": [f'''shard_{shard_idx}''' for shard_idx in range(_lowerCamelCase)]} lowercase__ : int = IterableDataset.from_generator(_lowerCamelCase , gen_kwargs=_lowerCamelCase) if not streaming: lowercase__ : str = Dataset.from_list(list(_lowerCamelCase)) lowercase__ : List[str] = split_dataset_by_node(_lowerCamelCase , rank=_lowerCamelCase , world_size=_lowerCamelCase) lowercase__ : Any = torch.utils.data.DataLoader(_lowerCamelCase , num_workers=_lowerCamelCase) lowercase__ : Dict = NUM_SHARDS * NUM_ITEMS_PER_SHARD lowercase__ : Any = full_size // world_size expected_local_size += int(rank < (full_size % world_size)) lowercase__ : List[str] = sum(1 for _ in dataloader) if local_size != expected_local_size: raise FailedTestError(f'''local_size {local_size} != expected_local_size {expected_local_size}''') if __name__ == "__main__": main()
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UpperCamelCase = [sum(int(c, 10) ** 2 for c in i.__str__()) for i in range(10_0000)] def lowercase_ ( _lowerCamelCase : int): lowercase__ : Any = 0 while number: # Increased Speed Slightly by checking every 5 digits together. sum_of_digits_squared += DIGITS_SQUARED[number % 10_0000] number //= 10_0000 return sum_of_digits_squared # There are 2 Chains made, # One ends with 89 with the chain member 58 being the one which when declared first, # there will be the least number of iterations for all the members to be checked. # The other one ends with 1 and has only one element 1. # So 58 and 1 are chosen to be declared at the starting. # Changed dictionary to an array to quicken the solution UpperCamelCase = [None] * 1000_0000 UpperCamelCase = True UpperCamelCase = False def lowercase_ ( _lowerCamelCase : int): if CHAINS[number - 1] is not None: return CHAINS[number - 1] # type: ignore lowercase__ : List[str] = chain(next_number(_lowerCamelCase)) lowercase__ : Optional[int] = number_chain while number < 1000_0000: lowercase__ : Union[str, Any] = number_chain number *= 10 return number_chain def lowercase_ ( _lowerCamelCase : int = 1000_0000): for i in range(1 , _lowerCamelCase): if CHAINS[i] is None: chain(i + 1) return CHAINS[:number].count(_lowerCamelCase) if __name__ == "__main__": import doctest doctest.testmod() print(f"{solution() = }")
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import functools import operator from ...configuration_utils import PretrainedConfig from ...utils import logging UpperCamelCase = logging.get_logger(__name__) UpperCamelCase = { '''microsoft/unispeech-large-1500h-cv''': ( '''https://huggingface.co/microsoft/unispeech-large-1500h-cv/resolve/main/config.json''' ), # See all UniSpeech models at https://huggingface.co/models?filter=unispeech } class snake_case_ ( __A ): __A : List[str] = "unispeech" def __init__( self : List[Any] , lowercase_ : Optional[int]=32 , lowercase_ : Optional[int]=7_68 , lowercase_ : List[str]=12 , lowercase_ : Union[str, Any]=12 , lowercase_ : Union[str, Any]=30_72 , lowercase_ : List[Any]="gelu" , lowercase_ : int=0.1 , lowercase_ : Union[str, Any]=0.1 , lowercase_ : str=0.1 , lowercase_ : Union[str, Any]=0.0 , lowercase_ : List[str]=0.0 , lowercase_ : List[Any]=0.1 , lowercase_ : Any=0.1 , lowercase_ : Optional[Any]=0.02 , lowercase_ : int=1E-5 , lowercase_ : int="group" , lowercase_ : Tuple="gelu" , lowercase_ : Dict=(5_12, 5_12, 5_12, 5_12, 5_12, 5_12, 5_12) , lowercase_ : Union[str, Any]=(5, 2, 2, 2, 2, 2, 2) , lowercase_ : List[str]=(10, 3, 3, 3, 3, 2, 2) , lowercase_ : int=False , lowercase_ : List[Any]=1_28 , lowercase_ : Optional[Any]=16 , lowercase_ : Union[str, Any]=False , lowercase_ : Tuple=True , lowercase_ : Union[str, Any]=0.05 , lowercase_ : Optional[Any]=10 , lowercase_ : Any=2 , lowercase_ : int=0.0 , lowercase_ : Union[str, Any]=10 , lowercase_ : Optional[Any]=0 , lowercase_ : List[str]=3_20 , lowercase_ : Dict=2 , lowercase_ : Optional[int]=0.1 , lowercase_ : Tuple=1_00 , lowercase_ : Dict=2_56 , lowercase_ : Optional[Any]=2_56 , lowercase_ : Union[str, Any]=0.1 , lowercase_ : List[Any]="mean" , lowercase_ : Union[str, Any]=False , lowercase_ : Tuple=False , lowercase_ : Dict=2_56 , lowercase_ : Union[str, Any]=80 , lowercase_ : int=0 , lowercase_ : Union[str, Any]=1 , lowercase_ : Dict=2 , lowercase_ : Optional[int]=0.5 , **lowercase_ : Union[str, Any] , ) -> Any: super().__init__(**lowercase_ , pad_token_id=lowercase_ , bos_token_id=lowercase_ , eos_token_id=lowercase_ ) lowercase__ : List[str] = hidden_size lowercase__ : Any = feat_extract_norm lowercase__ : Optional[Any] = feat_extract_activation lowercase__ : Dict = list(lowercase_ ) lowercase__ : Union[str, Any] = list(lowercase_ ) lowercase__ : List[str] = list(lowercase_ ) lowercase__ : List[str] = conv_bias lowercase__ : Any = num_conv_pos_embeddings lowercase__ : Dict = num_conv_pos_embedding_groups lowercase__ : int = len(self.conv_dim ) lowercase__ : str = num_hidden_layers lowercase__ : Any = intermediate_size lowercase__ : Optional[int] = hidden_act lowercase__ : int = num_attention_heads lowercase__ : Union[str, Any] = hidden_dropout lowercase__ : Any = attention_dropout lowercase__ : Union[str, Any] = activation_dropout lowercase__ : Any = feat_proj_dropout lowercase__ : str = final_dropout lowercase__ : int = layerdrop lowercase__ : Optional[int] = layer_norm_eps lowercase__ : List[Any] = initializer_range lowercase__ : Any = num_ctc_classes lowercase__ : int = vocab_size lowercase__ : str = do_stable_layer_norm lowercase__ : Any = use_weighted_layer_sum lowercase__ : Dict = classifier_proj_size if ( (len(self.conv_stride ) != self.num_feat_extract_layers) or (len(self.conv_kernel ) != self.num_feat_extract_layers) or (len(self.conv_dim ) != self.num_feat_extract_layers) ): raise ValueError( "Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` ==" " `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) =" F''' {len(self.conv_dim )}`, `len(config.conv_stride) = {len(self.conv_stride )}`,''' F''' `len(config.conv_kernel) = {len(self.conv_kernel )}`.''' ) # fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779 lowercase__ : List[Any] = apply_spec_augment lowercase__ : Dict = mask_time_prob lowercase__ : Tuple = mask_time_length lowercase__ : str = mask_time_min_masks lowercase__ : List[Any] = mask_feature_prob lowercase__ : int = mask_feature_length lowercase__ : Optional[int] = mask_feature_min_masks # parameters for pretraining with codevector quantized representations lowercase__ : Optional[int] = num_codevectors_per_group lowercase__ : List[str] = num_codevector_groups lowercase__ : Dict = contrastive_logits_temperature lowercase__ : Tuple = feat_quantizer_dropout lowercase__ : Any = num_negatives lowercase__ : Dict = codevector_dim lowercase__ : Tuple = proj_codevector_dim lowercase__ : List[str] = diversity_loss_weight # ctc loss lowercase__ : Tuple = ctc_loss_reduction lowercase__ : Dict = ctc_zero_infinity # pretraining loss lowercase__ : Optional[Any] = replace_prob @property def __UpperCamelCase ( self : Dict ) -> Tuple: return functools.reduce(operator.mul , self.conv_stride , 1 )
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import copy from dataclasses import dataclass, field from typing import ClassVar, Dict from ..features import ClassLabel, Features, Value from .base import TaskTemplate @dataclass(frozen=__A ) class snake_case_ ( __A ): # `task` is not a ClassVar since we want it to be part of the `asdict` output for JSON serialization __A : str = field(default="text-classification" ,metadata={"include_in_asdict_even_if_is_default": True} ) __A : ClassVar[Features] = Features({"text": Value("string" )} ) __A : ClassVar[Features] = Features({"labels": ClassLabel} ) __A : str = "text" __A : str = "labels" def __UpperCamelCase ( self : Dict , lowercase_ : Optional[Any] ) -> int: if self.label_column not in features: raise ValueError(F'''Column {self.label_column} is not present in features.''' ) if not isinstance(features[self.label_column] , lowercase_ ): raise ValueError(F'''Column {self.label_column} is not a ClassLabel.''' ) lowercase__ : Optional[int] = copy.deepcopy(self ) lowercase__ : Tuple = self.label_schema.copy() lowercase__ : Union[str, Any] = features[self.label_column] lowercase__ : int = label_schema return task_template @property def __UpperCamelCase ( self : Optional[Any] ) -> Dict[str, str]: return { self.text_column: "text", self.label_column: "labels", }
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def lowercase_ ( _lowerCamelCase : list): for i in range(len(_lowerCamelCase) - 1 , 0 , -1): lowercase__ : int = False for j in range(_lowerCamelCase , 0 , -1): if unsorted[j] < unsorted[j - 1]: lowercase__ , lowercase__ : int = unsorted[j - 1], unsorted[j] lowercase__ : List[str] = True for j in range(_lowerCamelCase): if unsorted[j] > unsorted[j + 1]: lowercase__ , lowercase__ : Optional[int] = unsorted[j + 1], unsorted[j] lowercase__ : Dict = True if not swapped: break return unsorted if __name__ == "__main__": import doctest doctest.testmod() UpperCamelCase = input('''Enter numbers separated by a comma:\n''').strip() UpperCamelCase = [int(item) for item in user_input.split(''',''')] print(f"{cocktail_shaker_sort(unsorted) = }")
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from ...configuration_utils import PretrainedConfig class snake_case_ ( __A ): __A : Optional[Any] = "bert-generation" def __init__( self : Optional[int] , lowercase_ : Any=5_03_58 , lowercase_ : List[Any]=10_24 , lowercase_ : Optional[Any]=24 , lowercase_ : Union[str, Any]=16 , lowercase_ : Tuple=40_96 , lowercase_ : List[Any]="gelu" , lowercase_ : str=0.1 , lowercase_ : Dict=0.1 , lowercase_ : Tuple=5_12 , lowercase_ : Optional[int]=0.02 , lowercase_ : Dict=1E-12 , lowercase_ : Optional[Any]=0 , lowercase_ : str=2 , lowercase_ : int=1 , lowercase_ : Union[str, Any]="absolute" , lowercase_ : Union[str, Any]=True , **lowercase_ : Dict , ) -> Optional[int]: super().__init__(pad_token_id=lowercase_ , bos_token_id=lowercase_ , eos_token_id=lowercase_ , **lowercase_ ) lowercase__ : Tuple = vocab_size lowercase__ : Dict = hidden_size lowercase__ : Dict = num_hidden_layers lowercase__ : Union[str, Any] = num_attention_heads lowercase__ : int = hidden_act lowercase__ : int = intermediate_size lowercase__ : Any = hidden_dropout_prob lowercase__ : Optional[int] = attention_probs_dropout_prob lowercase__ : str = max_position_embeddings lowercase__ : Optional[int] = initializer_range lowercase__ : List[Any] = layer_norm_eps lowercase__ : Any = position_embedding_type lowercase__ : Optional[int] = use_cache
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import argparse import glob import logging import os from argparse import Namespace from importlib import import_module import numpy as np import torch from lightning_base import BaseTransformer, add_generic_args, generic_train from seqeval.metrics import accuracy_score, fa_score, precision_score, recall_score from torch.nn import CrossEntropyLoss from torch.utils.data import DataLoader, TensorDataset from utils_ner import TokenClassificationTask UpperCamelCase = logging.getLogger(__name__) class snake_case_ ( __A ): __A : int = "token-classification" def __init__( self : Tuple , lowercase_ : Dict ) -> List[str]: if type(lowercase_ ) == dict: lowercase__ : Dict = Namespace(**lowercase_ ) lowercase__ : str = import_module("tasks" ) try: lowercase__ : Tuple = getattr(lowercase_ , hparams.task_type ) lowercase__ : TokenClassificationTask = token_classification_task_clazz() except AttributeError: raise ValueError( F'''Task {hparams.task_type} needs to be defined as a TokenClassificationTask subclass in {module}. ''' F'''Available tasks classes are: {TokenClassificationTask.__subclasses__()}''' ) lowercase__ : Optional[Any] = self.token_classification_task.get_labels(hparams.labels ) lowercase__ : int = CrossEntropyLoss().ignore_index super().__init__(lowercase_ , len(self.labels ) , self.mode ) def __UpperCamelCase ( self : Union[str, Any] , **lowercase_ : List[str] ) -> Any: return self.model(**lowercase_ ) def __UpperCamelCase ( self : Optional[Any] , lowercase_ : str , lowercase_ : Optional[int] ) -> Tuple: lowercase__ : int = {"input_ids": batch[0], "attention_mask": batch[1], "labels": batch[3]} if self.config.model_type != "distilbert": lowercase__ : Tuple = ( batch[2] if self.config.model_type in ["bert", "xlnet"] else None ) # XLM and RoBERTa don"t use token_type_ids lowercase__ : Optional[int] = self(**lowercase_ ) lowercase__ : Union[str, Any] = outputs[0] # tensorboard_logs = {"loss": loss, "rate": self.lr_scheduler.get_last_lr()[-1]} return {"loss": loss} def __UpperCamelCase ( self : Tuple ) -> Union[str, Any]: lowercase__ : Tuple = self.hparams for mode in ["train", "dev", "test"]: lowercase__ : Any = self._feature_file(lowercase_ ) if os.path.exists(lowercase_ ) and not args.overwrite_cache: logger.info("Loading features from cached file %s" , lowercase_ ) lowercase__ : str = torch.load(lowercase_ ) else: logger.info("Creating features from dataset file at %s" , args.data_dir ) lowercase__ : Optional[Any] = self.token_classification_task.read_examples_from_file(args.data_dir , lowercase_ ) lowercase__ : Dict = self.token_classification_task.convert_examples_to_features( lowercase_ , self.labels , args.max_seq_length , self.tokenizer , cls_token_at_end=bool(self.config.model_type in ["xlnet"] ) , cls_token=self.tokenizer.cls_token , cls_token_segment_id=2 if self.config.model_type in ["xlnet"] else 0 , sep_token=self.tokenizer.sep_token , sep_token_extra=lowercase_ , pad_on_left=bool(self.config.model_type in ["xlnet"] ) , pad_token=self.tokenizer.pad_token_id , pad_token_segment_id=self.tokenizer.pad_token_type_id , pad_token_label_id=self.pad_token_label_id , ) logger.info("Saving features into cached file %s" , lowercase_ ) torch.save(lowercase_ , lowercase_ ) def __UpperCamelCase ( self : Optional[Any] , lowercase_ : int , lowercase_ : int , lowercase_ : bool = False ) -> DataLoader: lowercase__ : str = self._feature_file(lowercase_ ) logger.info("Loading features from cached file %s" , lowercase_ ) lowercase__ : str = torch.load(lowercase_ ) lowercase__ : List[str] = torch.tensor([f.input_ids for f in features] , dtype=torch.long ) lowercase__ : str = torch.tensor([f.attention_mask for f in features] , dtype=torch.long ) if features[0].token_type_ids is not None: lowercase__ : Dict = torch.tensor([f.token_type_ids for f in features] , dtype=torch.long ) else: lowercase__ : Dict = torch.tensor([0 for f in features] , dtype=torch.long ) # HACK(we will not use this anymore soon) lowercase__ : List[str] = torch.tensor([f.label_ids for f in features] , dtype=torch.long ) return DataLoader( TensorDataset(lowercase_ , lowercase_ , lowercase_ , lowercase_ ) , batch_size=lowercase_ ) def __UpperCamelCase ( self : str , lowercase_ : Dict , lowercase_ : Tuple ) -> str: """Compute validation""" "" lowercase__ : Union[str, Any] = {"input_ids": batch[0], "attention_mask": batch[1], "labels": batch[3]} if self.config.model_type != "distilbert": lowercase__ : int = ( batch[2] if self.config.model_type in ["bert", "xlnet"] else None ) # XLM and RoBERTa don"t use token_type_ids lowercase__ : List[Any] = self(**lowercase_ ) lowercase__ , lowercase__ : Any = outputs[:2] lowercase__ : Optional[Any] = logits.detach().cpu().numpy() lowercase__ : int = inputs["labels"].detach().cpu().numpy() return {"val_loss": tmp_eval_loss.detach().cpu(), "pred": preds, "target": out_label_ids} def __UpperCamelCase ( self : Optional[int] , lowercase_ : Any ) -> List[Any]: lowercase__ : int = torch.stack([x["val_loss"] for x in outputs] ).mean() lowercase__ : Any = np.concatenate([x["pred"] for x in outputs] , axis=0 ) lowercase__ : Dict = np.argmax(lowercase_ , axis=2 ) lowercase__ : int = np.concatenate([x["target"] for x in outputs] , axis=0 ) lowercase__ : Any = dict(enumerate(self.labels ) ) lowercase__ : List[Any] = [[] for _ in range(out_label_ids.shape[0] )] lowercase__ : Dict = [[] for _ in range(out_label_ids.shape[0] )] for i in range(out_label_ids.shape[0] ): for j in range(out_label_ids.shape[1] ): if out_label_ids[i, j] != self.pad_token_label_id: out_label_list[i].append(label_map[out_label_ids[i][j]] ) preds_list[i].append(label_map[preds[i][j]] ) lowercase__ : Any = { "val_loss": val_loss_mean, "accuracy_score": accuracy_score(lowercase_ , lowercase_ ), "precision": precision_score(lowercase_ , lowercase_ ), "recall": recall_score(lowercase_ , lowercase_ ), "f1": fa_score(lowercase_ , lowercase_ ), } lowercase__ : List[Any] = dict(results.items() ) lowercase__ : List[str] = results return ret, preds_list, out_label_list def __UpperCamelCase ( self : Any , lowercase_ : Dict ) -> Dict: # when stable lowercase__ , lowercase__ , lowercase__ : Dict = self._eval_end(lowercase_ ) lowercase__ : Any = ret["log"] return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs} def __UpperCamelCase ( self : str , lowercase_ : Tuple ) -> int: # updating to test_epoch_end instead of deprecated test_end lowercase__ , lowercase__ , lowercase__ : Dict = self._eval_end(lowercase_ ) # Converting to the dict required by pl # https://github.com/PyTorchLightning/pytorch-lightning/blob/master/\ # pytorch_lightning/trainer/logging.py#L139 lowercase__ : Optional[int] = ret["log"] # `val_loss` is the key returned by `self._eval_end()` but actually refers to `test_loss` return {"avg_test_loss": logs["val_loss"], "log": logs, "progress_bar": logs} @staticmethod def __UpperCamelCase ( lowercase_ : int , lowercase_ : Union[str, Any] ) -> Tuple: # Add NER specific options BaseTransformer.add_model_specific_args(lowercase_ , lowercase_ ) parser.add_argument( "--task_type" , default="NER" , type=lowercase_ , help="Task type to fine tune in training (e.g. NER, POS, etc)" ) parser.add_argument( "--max_seq_length" , default=1_28 , type=lowercase_ , help=( "The maximum total input sequence length after tokenization. Sequences longer " "than this will be truncated, sequences shorter will be padded." ) , ) parser.add_argument( "--labels" , default="" , type=lowercase_ , help="Path to a file containing all labels. If not specified, CoNLL-2003 labels are used." , ) parser.add_argument( "--gpus" , default=0 , type=lowercase_ , help="The number of GPUs allocated for this, it is by default 0 meaning none" , ) parser.add_argument( "--overwrite_cache" , action="store_true" , help="Overwrite the cached training and evaluation sets" ) return parser if __name__ == "__main__": UpperCamelCase = argparse.ArgumentParser() add_generic_args(parser, os.getcwd()) UpperCamelCase = NERTransformer.add_model_specific_args(parser, os.getcwd()) UpperCamelCase = parser.parse_args() UpperCamelCase = NERTransformer(args) UpperCamelCase = generic_train(model, args) if args.do_predict: # See https://github.com/huggingface/transformers/issues/3159 # pl use this default format to create a checkpoint: # https://github.com/PyTorchLightning/pytorch-lightning/blob/master\ # /pytorch_lightning/callbacks/model_checkpoint.py#L322 UpperCamelCase = sorted(glob.glob(os.path.join(args.output_dir, '''checkpoint-epoch=*.ckpt'''), recursive=True)) UpperCamelCase = model.load_from_checkpoint(checkpoints[-1]) trainer.test(model)
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import math import sys def lowercase_ ( _lowerCamelCase : int): if number != int(_lowerCamelCase): raise ValueError("the value of input must be a natural number") if number < 0: raise ValueError("the value of input must not be a negative number") if number == 0: return 1 lowercase__ : List[Any] = [-1] * (number + 1) lowercase__ : Any = 0 for i in range(1 , number + 1): lowercase__ : Optional[int] = sys.maxsize lowercase__ : Union[str, Any] = int(math.sqrt(_lowerCamelCase)) for j in range(1 , root + 1): lowercase__ : List[str] = 1 + answers[i - (j**2)] lowercase__ : List[Any] = min(_lowerCamelCase , _lowerCamelCase) lowercase__ : Tuple = answer return answers[number] if __name__ == "__main__": import doctest doctest.testmod()
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from typing import TYPE_CHECKING from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available UpperCamelCase = { '''configuration_mask2former''': [ '''MASK2FORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''Mask2FormerConfig''', ], } try: if not is_vision_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: UpperCamelCase = ['''Mask2FormerImageProcessor'''] try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: UpperCamelCase = [ '''MASK2FORMER_PRETRAINED_MODEL_ARCHIVE_LIST''', '''Mask2FormerForUniversalSegmentation''', '''Mask2FormerModel''', '''Mask2FormerPreTrainedModel''', ] if TYPE_CHECKING: from .configuration_maskaformer import MASK2FORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, MaskaFormerConfig try: if not is_vision_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .image_processing_maskaformer import MaskaFormerImageProcessor try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_maskaformer import ( MASK2FORMER_PRETRAINED_MODEL_ARCHIVE_LIST, MaskaFormerForUniversalSegmentation, MaskaFormerModel, MaskaFormerPreTrainedModel, ) else: import sys UpperCamelCase = _LazyModule(__name__, globals()['''__file__'''], _import_structure)
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from .configuration_bert_masked import MaskedBertConfig from .modeling_bert_masked import ( MaskedBertForMultipleChoice, MaskedBertForQuestionAnswering, MaskedBertForSequenceClassification, MaskedBertForTokenClassification, MaskedBertModel, ) from .modules import *
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# Logistic Regression from scratch # In[62]: # In[63]: # importing all the required libraries import numpy as np from matplotlib import pyplot as plt from sklearn import datasets def lowercase_ ( _lowerCamelCase : List[str]): return 1 / (1 + np.exp(-z)) def lowercase_ ( _lowerCamelCase : Dict , _lowerCamelCase : Tuple): return (-y * np.log(_lowerCamelCase) - (1 - y) * np.log(1 - h)).mean() def lowercase_ ( _lowerCamelCase : str , _lowerCamelCase : Union[str, Any] , _lowerCamelCase : Tuple): lowercase__ : Union[str, Any] = np.dot(_lowerCamelCase , _lowerCamelCase) return np.sum(y * scores - np.log(1 + np.exp(_lowerCamelCase))) def lowercase_ ( _lowerCamelCase : Tuple , _lowerCamelCase : Tuple , _lowerCamelCase : Optional[Any] , _lowerCamelCase : str=7_0000): lowercase__ : Optional[int] = np.zeros(x.shape[1]) for iterations in range(_lowerCamelCase): lowercase__ : Union[str, Any] = np.dot(_lowerCamelCase , _lowerCamelCase) lowercase__ : Tuple = sigmoid_function(_lowerCamelCase) lowercase__ : Dict = np.dot(x.T , h - y) / y.size lowercase__ : int = theta - alpha * gradient # updating the weights lowercase__ : List[str] = np.dot(_lowerCamelCase , _lowerCamelCase) lowercase__ : Union[str, Any] = sigmoid_function(_lowerCamelCase) lowercase__ : Optional[Any] = cost_function(_lowerCamelCase , _lowerCamelCase) if iterations % 100 == 0: print(f'''loss: {j} \t''') # printing the loss after every 100 iterations return theta # In[68]: if __name__ == "__main__": UpperCamelCase = datasets.load_iris() UpperCamelCase = iris.data[:, :2] UpperCamelCase = (iris.target != 0) * 1 UpperCamelCase = 0.1 UpperCamelCase = logistic_reg(alpha, x, y, max_iterations=7_0000) print('''theta: ''', theta) # printing the theta i.e our weights vector def lowercase_ ( _lowerCamelCase : List[Any]): return sigmoid_function( np.dot(_lowerCamelCase , _lowerCamelCase)) # predicting the value of probability from the logistic regression algorithm plt.figure(figsize=(10, 6)) plt.scatter(x[y == 0][:, 0], x[y == 0][:, 1], color='''b''', label='''0''') plt.scatter(x[y == 1][:, 0], x[y == 1][:, 1], color='''r''', label='''1''') ((UpperCamelCase) , (UpperCamelCase)) = (x[:, 0].min(), x[:, 0].max()) ((UpperCamelCase) , (UpperCamelCase)) = (x[:, 1].min(), x[:, 1].max()) ((UpperCamelCase) , (UpperCamelCase)) = np.meshgrid(np.linspace(xa_min, xa_max), np.linspace(xa_min, xa_max)) UpperCamelCase = np.c_[xxa.ravel(), xxa.ravel()] UpperCamelCase = predict_prob(grid).reshape(xxa.shape) plt.contour(xxa, xxa, probs, [0.5], linewidths=1, colors='''black''') plt.legend() plt.show()
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import tempfile import numpy as np import torch from transformers import AutoTokenizer, TaEncoderModel from diffusers import DDPMScheduler, UNetaDConditionModel from diffusers.models.attention_processor import AttnAddedKVProcessor from diffusers.pipelines.deepfloyd_if import IFWatermarker from diffusers.utils.testing_utils import torch_device from ..test_pipelines_common import to_np class snake_case_ : def __UpperCamelCase ( self : List[str] ) -> str: torch.manual_seed(0 ) lowercase__ : List[Any] = TaEncoderModel.from_pretrained("hf-internal-testing/tiny-random-t5" ) torch.manual_seed(0 ) lowercase__ : int = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-t5" ) torch.manual_seed(0 ) lowercase__ : List[str] = UNetaDConditionModel( sample_size=32 , layers_per_block=1 , block_out_channels=[32, 64] , down_block_types=[ "ResnetDownsampleBlock2D", "SimpleCrossAttnDownBlock2D", ] , mid_block_type="UNetMidBlock2DSimpleCrossAttn" , up_block_types=["SimpleCrossAttnUpBlock2D", "ResnetUpsampleBlock2D"] , in_channels=3 , out_channels=6 , cross_attention_dim=32 , encoder_hid_dim=32 , attention_head_dim=8 , addition_embed_type="text" , addition_embed_type_num_heads=2 , cross_attention_norm="group_norm" , resnet_time_scale_shift="scale_shift" , act_fn="gelu" , ) unet.set_attn_processor(AttnAddedKVProcessor() ) # For reproducibility tests torch.manual_seed(0 ) lowercase__ : Union[str, Any] = DDPMScheduler( num_train_timesteps=10_00 , beta_schedule="squaredcos_cap_v2" , beta_start=0.00_01 , beta_end=0.02 , thresholding=lowercase_ , dynamic_thresholding_ratio=0.95 , sample_max_value=1.0 , prediction_type="epsilon" , variance_type="learned_range" , ) torch.manual_seed(0 ) lowercase__ : str = IFWatermarker() return { "text_encoder": text_encoder, "tokenizer": tokenizer, "unet": unet, "scheduler": scheduler, "watermarker": watermarker, "safety_checker": None, "feature_extractor": None, } def __UpperCamelCase ( self : Dict ) -> List[Any]: torch.manual_seed(0 ) lowercase__ : Any = TaEncoderModel.from_pretrained("hf-internal-testing/tiny-random-t5" ) torch.manual_seed(0 ) lowercase__ : List[str] = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-t5" ) torch.manual_seed(0 ) lowercase__ : Optional[int] = UNetaDConditionModel( sample_size=32 , layers_per_block=[1, 2] , block_out_channels=[32, 64] , down_block_types=[ "ResnetDownsampleBlock2D", "SimpleCrossAttnDownBlock2D", ] , mid_block_type="UNetMidBlock2DSimpleCrossAttn" , up_block_types=["SimpleCrossAttnUpBlock2D", "ResnetUpsampleBlock2D"] , in_channels=6 , out_channels=6 , cross_attention_dim=32 , encoder_hid_dim=32 , attention_head_dim=8 , addition_embed_type="text" , addition_embed_type_num_heads=2 , cross_attention_norm="group_norm" , resnet_time_scale_shift="scale_shift" , act_fn="gelu" , class_embed_type="timestep" , mid_block_scale_factor=1.4_14 , time_embedding_act_fn="gelu" , time_embedding_dim=32 , ) unet.set_attn_processor(AttnAddedKVProcessor() ) # For reproducibility tests torch.manual_seed(0 ) lowercase__ : Tuple = DDPMScheduler( num_train_timesteps=10_00 , beta_schedule="squaredcos_cap_v2" , beta_start=0.00_01 , beta_end=0.02 , thresholding=lowercase_ , dynamic_thresholding_ratio=0.95 , sample_max_value=1.0 , prediction_type="epsilon" , variance_type="learned_range" , ) torch.manual_seed(0 ) lowercase__ : List[Any] = DDPMScheduler( num_train_timesteps=10_00 , beta_schedule="squaredcos_cap_v2" , beta_start=0.00_01 , beta_end=0.02 , ) torch.manual_seed(0 ) lowercase__ : List[str] = IFWatermarker() return { "text_encoder": text_encoder, "tokenizer": tokenizer, "unet": unet, "scheduler": scheduler, "image_noising_scheduler": image_noising_scheduler, "watermarker": watermarker, "safety_checker": None, "feature_extractor": None, } def __UpperCamelCase ( self : Any ) -> Optional[int]: lowercase__ : str = self.get_dummy_components() lowercase__ : Optional[int] = self.pipeline_class(**lowercase_ ) pipe.to(lowercase_ ) pipe.set_progress_bar_config(disable=lowercase_ ) lowercase__ : Dict = self.get_dummy_inputs(lowercase_ ) lowercase__ : Dict = inputs["prompt"] lowercase__ : List[str] = inputs["generator"] lowercase__ : Tuple = inputs["num_inference_steps"] lowercase__ : Union[str, Any] = inputs["output_type"] if "image" in inputs: lowercase__ : Optional[int] = inputs["image"] else: lowercase__ : List[Any] = None if "mask_image" in inputs: lowercase__ : int = inputs["mask_image"] else: lowercase__ : Union[str, Any] = None if "original_image" in inputs: lowercase__ : Tuple = inputs["original_image"] else: lowercase__ : Tuple = None lowercase__ : Optional[int] = pipe.encode_prompt(lowercase_ ) # inputs with prompt converted to embeddings lowercase__ : Optional[int] = { "prompt_embeds": prompt_embeds, "negative_prompt_embeds": negative_prompt_embeds, "generator": generator, "num_inference_steps": num_inference_steps, "output_type": output_type, } if image is not None: lowercase__ : List[str] = image if mask_image is not None: lowercase__ : List[Any] = mask_image if original_image is not None: lowercase__ : Tuple = original_image # set all optional components to None for optional_component in pipe._optional_components: setattr(lowercase_ , lowercase_ , lowercase_ ) lowercase__ : str = pipe(**lowercase_ )[0] with tempfile.TemporaryDirectory() as tmpdir: pipe.save_pretrained(lowercase_ ) lowercase__ : Dict = self.pipeline_class.from_pretrained(lowercase_ ) pipe_loaded.to(lowercase_ ) pipe_loaded.set_progress_bar_config(disable=lowercase_ ) pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor() ) # For reproducibility tests for optional_component in pipe._optional_components: self.assertTrue( getattr(lowercase_ , lowercase_ ) is None , F'''`{optional_component}` did not stay set to None after loading.''' , ) lowercase__ : Union[str, Any] = self.get_dummy_inputs(lowercase_ ) lowercase__ : str = inputs["generator"] lowercase__ : Optional[Any] = inputs["num_inference_steps"] lowercase__ : Any = inputs["output_type"] # inputs with prompt converted to embeddings lowercase__ : Optional[Any] = { "prompt_embeds": prompt_embeds, "negative_prompt_embeds": negative_prompt_embeds, "generator": generator, "num_inference_steps": num_inference_steps, "output_type": output_type, } if image is not None: lowercase__ : int = image if mask_image is not None: lowercase__ : Optional[int] = mask_image if original_image is not None: lowercase__ : Tuple = original_image lowercase__ : Tuple = pipe_loaded(**lowercase_ )[0] lowercase__ : Any = np.abs(to_np(lowercase_ ) - to_np(lowercase_ ) ).max() self.assertLess(lowercase_ , 1E-4 ) def __UpperCamelCase ( self : Optional[Any] ) -> int: lowercase__ : List[Any] = self.get_dummy_components() lowercase__ : Dict = self.pipeline_class(**lowercase_ ) pipe.to(lowercase_ ) pipe.set_progress_bar_config(disable=lowercase_ ) lowercase__ : Optional[Any] = self.get_dummy_inputs(lowercase_ ) lowercase__ : Optional[Any] = pipe(**lowercase_ )[0] with tempfile.TemporaryDirectory() as tmpdir: pipe.save_pretrained(lowercase_ ) lowercase__ : Optional[int] = self.pipeline_class.from_pretrained(lowercase_ ) pipe_loaded.to(lowercase_ ) pipe_loaded.set_progress_bar_config(disable=lowercase_ ) pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor() ) # For reproducibility tests lowercase__ : Dict = self.get_dummy_inputs(lowercase_ ) lowercase__ : Union[str, Any] = pipe_loaded(**lowercase_ )[0] lowercase__ : List[str] = np.abs(to_np(lowercase_ ) - to_np(lowercase_ ) ).max() self.assertLess(lowercase_ , 1E-4 )
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import copy from dataclasses import dataclass, field from typing import ClassVar, Dict from ..features import ClassLabel, Features, Value from .base import TaskTemplate @dataclass(frozen=__A ) class snake_case_ ( __A ): # `task` is not a ClassVar since we want it to be part of the `asdict` output for JSON serialization __A : str = field(default="text-classification" ,metadata={"include_in_asdict_even_if_is_default": True} ) __A : ClassVar[Features] = Features({"text": Value("string" )} ) __A : ClassVar[Features] = Features({"labels": ClassLabel} ) __A : str = "text" __A : str = "labels" def __UpperCamelCase ( self : Dict , lowercase_ : Optional[Any] ) -> int: if self.label_column not in features: raise ValueError(F'''Column {self.label_column} is not present in features.''' ) if not isinstance(features[self.label_column] , lowercase_ ): raise ValueError(F'''Column {self.label_column} is not a ClassLabel.''' ) lowercase__ : Optional[int] = copy.deepcopy(self ) lowercase__ : Tuple = self.label_schema.copy() lowercase__ : Union[str, Any] = features[self.label_column] lowercase__ : int = label_schema return task_template @property def __UpperCamelCase ( self : Optional[Any] ) -> Dict[str, str]: return { self.text_column: "text", self.label_column: "labels", }
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UpperCamelCase = ''' # Installazione di Transformers ! pip install transformers datasets # Per installare dalla fonte invece dell\'ultima versione rilasciata, commenta il comando sopra e # rimuovi la modalità commento al comando seguente. # ! pip install git+https://github.com/huggingface/transformers.git ''' UpperCamelCase = [{'''type''': '''code''', '''content''': INSTALL_CONTENT}] UpperCamelCase = { '''{processor_class}''': '''FakeProcessorClass''', '''{model_class}''': '''FakeModelClass''', '''{object_class}''': '''FakeObjectClass''', }
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def lowercase_ ( _lowerCamelCase : int = 10 , _lowerCamelCase : int = 1000 , _lowerCamelCase : bool = True): assert ( isinstance(_lowerCamelCase , _lowerCamelCase) and isinstance(_lowerCamelCase , _lowerCamelCase) and isinstance(_lowerCamelCase , _lowerCamelCase) ), "Invalid type of value(s) specified to function!" if min_val > max_val: raise ValueError("Invalid value for min_val or max_val (min_value < max_value)") return min_val if option else max_val def lowercase_ ( _lowerCamelCase : int , _lowerCamelCase : int): return int((number_a + number_a) / 2) def lowercase_ ( _lowerCamelCase : int , _lowerCamelCase : int , _lowerCamelCase : int): assert ( isinstance(_lowerCamelCase , _lowerCamelCase) and isinstance(_lowerCamelCase , _lowerCamelCase) and isinstance(_lowerCamelCase , _lowerCamelCase) ), 'argument values must be type of "int"' if lower > higher: raise ValueError("argument value for lower and higher must be(lower > higher)") if not lower < to_guess < higher: raise ValueError( "guess value must be within the range of lower and higher value") def answer(_lowerCamelCase : int) -> str: if number > to_guess: return "high" elif number < to_guess: return "low" else: return "same" print("started...") lowercase__ : Optional[int] = lower lowercase__ : List[Any] = higher lowercase__ : Dict = [] while True: lowercase__ : Any = get_avg(_lowerCamelCase , _lowerCamelCase) last_numbers.append(_lowerCamelCase) if answer(_lowerCamelCase) == "low": lowercase__ : List[str] = number elif answer(_lowerCamelCase) == "high": lowercase__ : Optional[int] = number else: break print(f'''guess the number : {last_numbers[-1]}''') print(f'''details : {last_numbers!s}''') def lowercase_ ( ): lowercase__ : Tuple = int(input("Enter lower value : ").strip()) lowercase__ : Optional[int] = int(input("Enter high value : ").strip()) lowercase__ : Optional[Any] = int(input("Enter value to guess : ").strip()) guess_the_number(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase) if __name__ == "__main__": main()
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from collections import OrderedDict from typing import List, Mapping from packaging import version from ...configuration_utils import PretrainedConfig from ...onnx import OnnxConfig from ...utils import logging UpperCamelCase : Optional[int] = logging.get_logger(__name__) UpperCamelCase : int = { '''google/efficientnet-b7''': '''https://huggingface.co/google/efficientnet-b7/resolve/main/config.json''', } class snake_case_ ( __A ): __A : List[Any] = "efficientnet" def __init__( self : Optional[Any] , lowercase_ : int = 3 , lowercase_ : int = 6_00 , lowercase_ : float = 2.0 , lowercase_ : float = 3.1 , lowercase_ : int = 8 , lowercase_ : List[int] = [3, 3, 5, 3, 5, 5, 3] , lowercase_ : List[int] = [32, 16, 24, 40, 80, 1_12, 1_92] , lowercase_ : List[int] = [16, 24, 40, 80, 1_12, 1_92, 3_20] , lowercase_ : List[int] = [] , lowercase_ : List[int] = [1, 2, 2, 2, 1, 2, 1] , lowercase_ : List[int] = [1, 2, 2, 3, 3, 4, 1] , lowercase_ : List[int] = [1, 6, 6, 6, 6, 6, 6] , lowercase_ : float = 0.25 , lowercase_ : str = "swish" , lowercase_ : int = 25_60 , lowercase_ : str = "mean" , lowercase_ : float = 0.02 , lowercase_ : float = 0.0_01 , lowercase_ : float = 0.99 , lowercase_ : float = 0.5 , lowercase_ : float = 0.2 , **lowercase_ : Optional[int] , ) -> List[Any]: super().__init__(**lowercase_ ) lowercase__ : Tuple = num_channels lowercase__ : Optional[Any] = image_size lowercase__ : List[Any] = width_coefficient lowercase__ : List[str] = depth_coefficient lowercase__ : Optional[Any] = depth_divisor lowercase__ : Dict = kernel_sizes lowercase__ : List[str] = in_channels lowercase__ : Dict = out_channels lowercase__ : str = depthwise_padding lowercase__ : int = strides lowercase__ : int = num_block_repeats lowercase__ : int = expand_ratios lowercase__ : Any = squeeze_expansion_ratio lowercase__ : List[Any] = hidden_act lowercase__ : int = hidden_dim lowercase__ : Optional[Any] = pooling_type lowercase__ : str = initializer_range lowercase__ : Optional[Any] = batch_norm_eps lowercase__ : Dict = batch_norm_momentum lowercase__ : List[str] = dropout_rate lowercase__ : Optional[int] = drop_connect_rate lowercase__ : Union[str, Any] = sum(lowercase_ ) * 4 class snake_case_ ( __A ): __A : Union[str, Any] = version.parse("1.11" ) @property def __UpperCamelCase ( self : Optional[Any] ) -> Mapping[str, Mapping[int, str]]: return OrderedDict( [ ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}), ] ) @property def __UpperCamelCase ( self : List[Any] ) -> float: return 1E-5
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import os import re import shutil import sys import tempfile import unittest import black UpperCamelCase = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__)))) sys.path.append(os.path.join(git_repo_path, '''utils''')) import check_copies # noqa: E402 # This is the reference code that will be used in the tests. # If DDPMSchedulerOutput is changed in scheduling_ddpm.py, this code needs to be manually updated. UpperCamelCase = ''' \""" Output class for the scheduler\'s step function output. Args: prev_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images): Computed sample (x_{t-1}) of previous timestep. `prev_sample` should be used as next model input in the denoising loop. pred_original_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images): The predicted denoised sample (x_{0}) based on the model output from the current timestep. `pred_original_sample` can be used to preview progress or for guidance. \""" prev_sample: torch.FloatTensor pred_original_sample: Optional[torch.FloatTensor] = None ''' class snake_case_ ( unittest.TestCase ): def __UpperCamelCase ( self : str ) -> List[str]: lowercase__ : str = tempfile.mkdtemp() os.makedirs(os.path.join(self.diffusers_dir , "schedulers/" ) ) lowercase__ : List[Any] = self.diffusers_dir shutil.copy( os.path.join(lowercase_ , "src/diffusers/schedulers/scheduling_ddpm.py" ) , os.path.join(self.diffusers_dir , "schedulers/scheduling_ddpm.py" ) , ) def __UpperCamelCase ( self : Optional[int] ) -> List[str]: lowercase__ : Dict = "src/diffusers" shutil.rmtree(self.diffusers_dir ) def __UpperCamelCase ( self : Tuple , lowercase_ : Optional[Any] , lowercase_ : Any , lowercase_ : str , lowercase_ : Tuple=None ) -> Tuple: lowercase__ : Optional[int] = comment + F'''\nclass {class_name}(nn.Module):\n''' + class_code if overwrite_result is not None: lowercase__ : Optional[int] = comment + F'''\nclass {class_name}(nn.Module):\n''' + overwrite_result lowercase__ : Optional[Any] = black.Mode(target_versions={black.TargetVersion.PYaa} , line_length=1_19 ) lowercase__ : List[str] = black.format_str(lowercase_ , mode=lowercase_ ) lowercase__ : Optional[int] = os.path.join(self.diffusers_dir , "new_code.py" ) with open(lowercase_ , "w" , newline="\n" ) as f: f.write(lowercase_ ) if overwrite_result is None: self.assertTrue(len(check_copies.is_copy_consistent(lowercase_ ) ) == 0 ) else: check_copies.is_copy_consistent(f.name , overwrite=lowercase_ ) with open(lowercase_ , "r" ) as f: self.assertTrue(f.read() , lowercase_ ) def __UpperCamelCase ( self : str ) -> Optional[int]: lowercase__ : Optional[Any] = check_copies.find_code_in_diffusers("schedulers.scheduling_ddpm.DDPMSchedulerOutput" ) self.assertEqual(lowercase_ , lowercase_ ) def __UpperCamelCase ( self : int ) -> str: # Base copy consistency self.check_copy_consistency( "# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput" , "DDPMSchedulerOutput" , REFERENCE_CODE + "\n" , ) # With no empty line at the end self.check_copy_consistency( "# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput" , "DDPMSchedulerOutput" , lowercase_ , ) # Copy consistency with rename self.check_copy_consistency( "# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->Test" , "TestSchedulerOutput" , re.sub("DDPM" , "Test" , lowercase_ ) , ) # Copy consistency with a really long name lowercase__ : Optional[int] = "TestClassWithAReallyLongNameBecauseSomePeopleLikeThatForSomeReason" self.check_copy_consistency( F'''# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->{long_class_name}''' , F'''{long_class_name}SchedulerOutput''' , re.sub("Bert" , lowercase_ , lowercase_ ) , ) # Copy consistency with overwrite self.check_copy_consistency( "# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->Test" , "TestSchedulerOutput" , lowercase_ , overwrite_result=re.sub("DDPM" , "Test" , lowercase_ ) , )
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import math from datetime import datetime, timedelta def lowercase_ ( _lowerCamelCase : int): lowercase__ : Dict = year % 19 lowercase__ : int = year % 4 lowercase__ : Dict = year % 7 lowercase__ : int = math.floor(year / 100) lowercase__ : Tuple = math.floor((13 + 8 * leap_day_inhibits) / 25) lowercase__ : str = leap_day_inhibits / 4 lowercase__ : int = ( 15 - lunar_orbit_correction + leap_day_inhibits - leap_day_reinstall_number ) % 30 lowercase__ : Dict = (4 + leap_day_inhibits - leap_day_reinstall_number) % 7 # days to be added to March 21 lowercase__ : Union[str, Any] = (19 * metonic_cycle + secular_moon_shift) % 30 # PHM -> Paschal Full Moon lowercase__ : Optional[Any] = ( 2 * julian_leap_year + 4 * non_leap_year + 6 * days_to_add + century_starting_point ) % 7 if days_to_add == 29 and days_from_phm_to_sunday == 6: return datetime(_lowerCamelCase , 4 , 19) elif days_to_add == 28 and days_from_phm_to_sunday == 6: return datetime(_lowerCamelCase , 4 , 18) else: return datetime(_lowerCamelCase , 3 , 22) + timedelta( days=int(days_to_add + days_from_phm_to_sunday)) if __name__ == "__main__": for year in (1994, 2000, 2010, 2021, 2023): UpperCamelCase = '''will be''' if year > datetime.now().year else '''was''' print(f"Easter in {year} {tense} {gauss_easter(year)}")
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from copy import deepcopy import torch import torch.nn.functional as F from torch.optim import AdamW from torch.optim.lr_scheduler import LambdaLR from torch.utils.data import DataLoader from accelerate.accelerator import Accelerator from accelerate.state import GradientState from accelerate.test_utils import RegressionDataset, RegressionModel from accelerate.utils import DistributedType, is_torch_version, set_seed def lowercase_ ( _lowerCamelCase : Optional[Any] , _lowerCamelCase : Tuple , _lowerCamelCase : Dict , _lowerCamelCase : Tuple): for param, grad_param in zip(model_a.parameters() , model_b.parameters()): if not param.requires_grad: continue if not did_step: # Grads should not be in sync assert ( torch.allclose(param.grad , grad_param.grad) is False ), f'''Gradients in sync when they should not be at iteration {iteration}:\nmodel_a grad ({param.grad}) == model_b grad ({grad_param.grad})''' else: # Grads should be in sync assert ( torch.allclose(param.grad , grad_param.grad) is True ), f'''Gradients not in sync when they should be at iteration {iteration}:\nmodel_a grad ({param.grad}) != model_b grad ({grad_param.grad})''' def lowercase_ ( _lowerCamelCase : int , _lowerCamelCase : List[str] , _lowerCamelCase : Optional[int] , _lowerCamelCase : int , _lowerCamelCase : Union[str, Any]=True): model.train() lowercase__ : Tuple = model(_lowerCamelCase) lowercase__ : Union[str, Any] = F.mse_loss(_lowerCamelCase , target.to(output.device)) if not do_backward: loss /= accelerator.gradient_accumulation_steps loss.backward() else: accelerator.backward(_lowerCamelCase) def lowercase_ ( _lowerCamelCase : List[Any] , _lowerCamelCase : str=False): set_seed(42) lowercase__ : Dict = RegressionModel() lowercase__ : int = deepcopy(_lowerCamelCase) lowercase__ : str = RegressionDataset(length=80) lowercase__ : List[Any] = DataLoader(_lowerCamelCase , batch_size=16) model.to(accelerator.device) if sched: lowercase__ : Union[str, Any] = AdamW(params=model.parameters() , lr=1E-3) lowercase__ : Union[str, Any] = AdamW(params=ddp_model.parameters() , lr=1E-3) lowercase__ : Optional[int] = LambdaLR(_lowerCamelCase , lr_lambda=lambda _lowerCamelCase: epoch**0.65) lowercase__ : Union[str, Any] = LambdaLR(_lowerCamelCase , lr_lambda=lambda _lowerCamelCase: epoch**0.65) # Make a copy of `model` if sched: lowercase__ , lowercase__ , lowercase__ , lowercase__ : Tuple = accelerator.prepare(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase) else: lowercase__ , lowercase__ : int = accelerator.prepare(_lowerCamelCase , _lowerCamelCase) if sched: return (model, opt, sched, dataloader, ddp_model, ddp_opt, ddp_sched) return model, ddp_model, dataloader def lowercase_ ( _lowerCamelCase : Tuple): # Test when on a single CPU or GPU that the context manager does nothing lowercase__ , lowercase__ , lowercase__ : List[Any] = get_training_setup(_lowerCamelCase) # Use a single batch lowercase__ , lowercase__ : int = next(iter(_lowerCamelCase)).values() for iteration in range(3): # Gather the distributed inputs and targs for the base model lowercase__ , lowercase__ : Optional[int] = accelerator.gather((ddp_input, ddp_target)) lowercase__ , lowercase__ : Union[str, Any] = input.to(accelerator.device), target.to(accelerator.device) # Perform our initial ground truth step in non "DDP" step_model(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase) # Do "gradient accumulation" (noop) if iteration % 2 == 0: # Accumulate grads locally with accelerator.no_sync(_lowerCamelCase): step_model(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase) else: # Sync grads step_model(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase) # Since `no_sync` is a noop, `ddp_model` and `model` grads should always be in sync check_model_parameters(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase) for param, ddp_param in zip(model.parameters() , ddp_model.parameters()): if not param.requires_grad: continue assert torch.allclose( param.grad , ddp_param.grad), f'''Gradients not in sync when they should be:\nModel grad ({param.grad}) != DDP grad ({ddp_param.grad})''' # Shuffle ddp_input on each iteration torch.manual_seed(1337 + iteration) lowercase__ : int = ddp_input[torch.randperm(len(_lowerCamelCase))] def lowercase_ ( _lowerCamelCase : Any): # Test on distributed setup that context manager behaves properly lowercase__ , lowercase__ , lowercase__ : str = get_training_setup(_lowerCamelCase) # Use a single batch lowercase__ , lowercase__ : Dict = next(iter(_lowerCamelCase)).values() for iteration in range(3): # Gather the distributed inputs and targs for the base model lowercase__ , lowercase__ : List[str] = accelerator.gather((ddp_input, ddp_target)) lowercase__ , lowercase__ : Any = input.to(accelerator.device), target.to(accelerator.device) # Perform our initial ground truth step in non "DDP" step_model(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase) # Do "gradient accumulation" (noop) if iteration % 2 == 0: # Accumulate grads locally with accelerator.no_sync(_lowerCamelCase): step_model(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase) else: # Sync grads step_model(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase) # DDP model and model should only be in sync when not (iteration % 2 == 0) for param, ddp_param in zip(model.parameters() , ddp_model.parameters()): if not param.requires_grad: continue if iteration % 2 == 0: # Grads should not be in sync assert ( torch.allclose(param.grad , ddp_param.grad) is False ), f'''Gradients in sync when they should not be:\nModel grad ({param.grad}) == DDP grad ({ddp_param.grad})''' else: # Grads should be in sync assert ( torch.allclose(param.grad , ddp_param.grad) is True ), f'''Gradients not in sync when they should be:\nModel grad ({param.grad}) != DDP grad ({ddp_param.grad})''' # Shuffle ddp_input on each iteration torch.manual_seed(1337 + iteration) lowercase__ : Tuple = ddp_input[torch.randperm(len(_lowerCamelCase))] def lowercase_ ( _lowerCamelCase : Optional[Any]=False , _lowerCamelCase : Union[str, Any]=False): lowercase__ : int = Accelerator( split_batches=_lowerCamelCase , dispatch_batches=_lowerCamelCase , gradient_accumulation_steps=2) # Test that context manager behaves properly lowercase__ , lowercase__ , lowercase__ : Optional[int] = get_training_setup(_lowerCamelCase) for iteration, batch in enumerate(_lowerCamelCase): lowercase__ , lowercase__ : str = batch.values() # Gather the distributed inputs and targs for the base model lowercase__ , lowercase__ : Optional[Any] = accelerator.gather((ddp_input, ddp_target)) lowercase__ , lowercase__ : Union[str, Any] = input.to(accelerator.device), target.to(accelerator.device) # Perform our initial ground truth step in non "DDP" step_model(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase) # Do "gradient accumulation" (noop) with accelerator.accumulate(_lowerCamelCase): step_model(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase) # DDP model and model should only be in sync when not (iteration % 2 == 0) for param, ddp_param in zip(model.parameters() , ddp_model.parameters()): if not param.requires_grad: continue if ((iteration + 1) % 2 == 0) or (iteration == len(_lowerCamelCase) - 1): # Grads should be in sync assert ( torch.allclose(param.grad , ddp_param.grad) is True ), f'''Gradients not in sync when they should be at iteration {iteration}:\nModel grad ({param.grad}) != DDP grad ({ddp_param.grad})''' else: # Grads should not be in sync assert ( torch.allclose(param.grad , ddp_param.grad) is False ), f'''Gradients in sync when they should not be at iteration {iteration}:\nModel grad ({param.grad}) == DDP grad ({ddp_param.grad})''' # Shuffle ddp_input on each iteration torch.manual_seed(1337 + iteration) lowercase__ : Dict = ddp_input[torch.randperm(len(_lowerCamelCase))] GradientState._reset_state() def lowercase_ ( _lowerCamelCase : List[str]=False , _lowerCamelCase : int=False): lowercase__ : Dict = Accelerator( split_batches=_lowerCamelCase , dispatch_batches=_lowerCamelCase , gradient_accumulation_steps=2) # Test that context manager behaves properly lowercase__ , lowercase__ , lowercase__ , lowercase__ , lowercase__ , lowercase__ , lowercase__ : str = get_training_setup(_lowerCamelCase , _lowerCamelCase) for iteration, batch in enumerate(_lowerCamelCase): lowercase__ , lowercase__ : Any = batch.values() # Gather the distributed inputs and targs for the base model lowercase__ , lowercase__ : Tuple = accelerator.gather((ddp_input, ddp_target)) lowercase__ , lowercase__ : List[str] = input.to(accelerator.device), target.to(accelerator.device) # Perform our initial ground truth step in non "DDP" model.train() ddp_model.train() step_model(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase) opt.step() if ((iteration + 1) % 2 == 0) or ((iteration + 1) == len(_lowerCamelCase)): if split_batches: sched.step() else: for _ in range(accelerator.num_processes): sched.step() opt.zero_grad() # Perform gradient accumulation under wrapper with accelerator.accumulate(_lowerCamelCase): step_model(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase) ddp_opt.step() ddp_sched.step() ddp_opt.zero_grad() # Learning rates should be the same assert ( opt.param_groups[0]["lr"] == ddp_opt.param_groups[0]["lr"] ), f'''Learning rates found in each optimizer did not align\nopt: {opt.param_groups[0]["lr"]}\nDDP opt: {ddp_opt.param_groups[0]["lr"]}\n''' lowercase__ : Tuple = (((iteration + 1) % 2) == 0) or ((iteration + 1) == len(_lowerCamelCase)) if accelerator.num_processes > 1: check_model_parameters(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase) # Shuffle ddp_input on each iteration torch.manual_seed(1337 + iteration) GradientState._reset_state() def lowercase_ ( ): lowercase__ : List[str] = Accelerator() lowercase__ : List[Any] = RegressionDataset(length=80) lowercase__ : Tuple = DataLoader(_lowerCamelCase , batch_size=16) lowercase__ : int = RegressionDataset(length=96) lowercase__ : List[str] = DataLoader(_lowerCamelCase , batch_size=16) lowercase__ , lowercase__ : Dict = accelerator.prepare(_lowerCamelCase , _lowerCamelCase) assert accelerator.gradient_state.active_dataloader is None for iteration, _ in enumerate(_lowerCamelCase): assert id(accelerator.gradient_state.active_dataloader) == id(_lowerCamelCase) if iteration < len(_lowerCamelCase) - 1: assert not accelerator.gradient_state.end_of_dataloader if iteration == 1: for batch_num, _ in enumerate(_lowerCamelCase): assert id(accelerator.gradient_state.active_dataloader) == id(_lowerCamelCase) if batch_num < len(_lowerCamelCase) - 1: assert not accelerator.gradient_state.end_of_dataloader else: assert accelerator.gradient_state.end_of_dataloader else: assert accelerator.gradient_state.end_of_dataloader assert accelerator.gradient_state.active_dataloader is None def lowercase_ ( ): lowercase__ : str = Accelerator() lowercase__ : Dict = accelerator.state if state.local_process_index == 0: print("**Test `accumulate` gradient accumulation with dataloader break**") test_dataloader_break() if state.distributed_type == DistributedType.NO: if state.local_process_index == 0: print("**Test NOOP `no_sync` context manager**") test_noop_sync(_lowerCamelCase) if state.distributed_type in (DistributedType.MULTI_GPU, DistributedType.MULTI_CPU): if state.local_process_index == 0: print("**Test Distributed `no_sync` context manager**") test_distributed_sync(_lowerCamelCase) if state.distributed_type == DistributedType.MULTI_GPU: for split_batch in [True, False]: for dispatch_batches in [True, False]: if state.local_process_index == 0: print( "**Test `accumulate` gradient accumulation, " , f'''`split_batches={split_batch}` and `dispatch_batches={dispatch_batches}`**''' , ) test_gradient_accumulation(_lowerCamelCase , _lowerCamelCase) # Currently will break on torch 2.0 +, need to investigate why if is_torch_version("<" , "2.0") or state.distributed_type == DistributedType.NO: if state.local_process_index == 0: print( "**Test `accumulate` gradient accumulation with optimizer and scheduler, " , "`split_batches=False`, `dispatch_batches=False`**" , ) test_gradient_accumulation_with_opt_and_scheduler() if state.distributed_type == DistributedType.MULTI_GPU: for split_batch in [True, False]: for dispatch_batches in [True, False]: if not split_batch and not dispatch_batches: continue if state.local_process_index == 0: print( "**Test `accumulate` gradient accumulation with optimizer and scheduler, " , f'''`split_batches={split_batch}` and `dispatch_batches={dispatch_batches}`**''' , ) test_gradient_accumulation_with_opt_and_scheduler(_lowerCamelCase , _lowerCamelCase) def lowercase_ ( _lowerCamelCase : Any): # For xla_spawn (TPUs) main() if __name__ == "__main__": main()
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import os from typing import List, Optional, Union from ...image_processing_utils import BatchFeature from ...image_utils import ImageInput from ...processing_utils import ProcessorMixin from ...tokenization_utils_base import PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy from ...utils import TensorType from ..auto import AutoTokenizer class snake_case_ ( __A ): __A : Optional[int] = ["image_processor", "tokenizer"] __A : int = "BlipImageProcessor" __A : Any = "AutoTokenizer" def __init__( self : Any , lowercase_ : List[str] , lowercase_ : Optional[int] , lowercase_ : Union[str, Any] ) -> Optional[int]: super().__init__(lowercase_ , lowercase_ ) # add QFormer tokenizer lowercase__ : Union[str, Any] = qformer_tokenizer def __call__( self : int , lowercase_ : ImageInput = None , lowercase_ : Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None , lowercase_ : bool = True , lowercase_ : Union[bool, str, PaddingStrategy] = False , lowercase_ : Union[bool, str, TruncationStrategy] = None , lowercase_ : Optional[int] = None , lowercase_ : int = 0 , lowercase_ : Optional[int] = None , lowercase_ : Optional[bool] = None , lowercase_ : bool = False , lowercase_ : bool = False , lowercase_ : bool = False , lowercase_ : bool = False , lowercase_ : bool = False , lowercase_ : bool = True , lowercase_ : Optional[Union[str, TensorType]] = None , **lowercase_ : List[str] , ) -> BatchFeature: if images is None and text is None: raise ValueError("You have to specify at least images or text." ) lowercase__ : Optional[int] = BatchFeature() if text is not None: lowercase__ : int = self.tokenizer( text=lowercase_ , add_special_tokens=lowercase_ , padding=lowercase_ , truncation=lowercase_ , max_length=lowercase_ , stride=lowercase_ , pad_to_multiple_of=lowercase_ , return_attention_mask=lowercase_ , return_overflowing_tokens=lowercase_ , return_special_tokens_mask=lowercase_ , return_offsets_mapping=lowercase_ , return_token_type_ids=lowercase_ , return_length=lowercase_ , verbose=lowercase_ , return_tensors=lowercase_ , **lowercase_ , ) encoding.update(lowercase_ ) lowercase__ : str = self.qformer_tokenizer( text=lowercase_ , add_special_tokens=lowercase_ , padding=lowercase_ , truncation=lowercase_ , max_length=lowercase_ , stride=lowercase_ , pad_to_multiple_of=lowercase_ , return_attention_mask=lowercase_ , return_overflowing_tokens=lowercase_ , return_special_tokens_mask=lowercase_ , return_offsets_mapping=lowercase_ , return_token_type_ids=lowercase_ , return_length=lowercase_ , verbose=lowercase_ , return_tensors=lowercase_ , **lowercase_ , ) lowercase__ : int = qformer_text_encoding.pop("input_ids" ) lowercase__ : str = qformer_text_encoding.pop("attention_mask" ) if images is not None: lowercase__ : Any = self.image_processor(lowercase_ , return_tensors=lowercase_ ) encoding.update(lowercase_ ) return encoding def __UpperCamelCase ( self : int , *lowercase_ : Tuple , **lowercase_ : Optional[int] ) -> Tuple: return self.tokenizer.batch_decode(*lowercase_ , **lowercase_ ) def __UpperCamelCase ( self : Tuple , *lowercase_ : Optional[Any] , **lowercase_ : Dict ) -> Optional[Any]: return self.tokenizer.decode(*lowercase_ , **lowercase_ ) @property # Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names def __UpperCamelCase ( self : List[Any] ) -> Optional[Any]: lowercase__ : Union[str, Any] = self.tokenizer.model_input_names lowercase__ : Optional[Any] = self.image_processor.model_input_names return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names ) ) def __UpperCamelCase ( self : Dict , lowercase_ : Any , **lowercase_ : Optional[Any] ) -> Optional[Any]: if os.path.isfile(lowercase_ ): raise ValueError(F'''Provided path ({save_directory}) should be a directory, not a file''' ) os.makedirs(lowercase_ , exist_ok=lowercase_ ) lowercase__ : Optional[Any] = os.path.join(lowercase_ , "qformer_tokenizer" ) self.qformer_tokenizer.save_pretrained(lowercase_ ) return super().save_pretrained(lowercase_ , **lowercase_ ) @classmethod def __UpperCamelCase ( cls : Any , lowercase_ : List[str] , **lowercase_ : Any ) -> Any: lowercase__ : Optional[int] = AutoTokenizer.from_pretrained(lowercase_ , subfolder="qformer_tokenizer" ) lowercase__ : Dict = cls._get_arguments_from_pretrained(lowercase_ , **lowercase_ ) args.append(lowercase_ ) return cls(*lowercase_ )
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import argparse from tax import checkpoints from transformers import AutoConfig, FlaxAutoModelForSeqaSeqLM def lowercase_ ( _lowerCamelCase : int , _lowerCamelCase : Any , _lowerCamelCase : str): lowercase__ : Optional[Any] = AutoConfig.from_pretrained(_lowerCamelCase) lowercase__ : List[str] = FlaxAutoModelForSeqaSeqLM.from_config(config=_lowerCamelCase) lowercase__ : List[str] = checkpoints.load_tax_checkpoint(_lowerCamelCase) lowercase__ : Dict = "wi_0" in tax_model["target"]["encoder"]["layers_0"]["mlp"] if config.model_type == "t5": lowercase__ : Any = "SelfAttention" if config.model_type == "longt5" and config.encoder_attention_type == "local": lowercase__ : int = "LocalSelfAttention" elif config.model_type == "longt5" and config.encoder_attention_type == "transient-global": lowercase__ : Dict = "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): lowercase__ : str = f'''layers_{str(_lowerCamelCase)}''' # Self-Attention lowercase__ : List[Any] = tax_model["target"]["encoder"][layer_name]["attention"]["key"]["kernel"] lowercase__ : Optional[Any] = tax_model["target"]["encoder"][layer_name]["attention"]["out"]["kernel"] lowercase__ : Tuple = tax_model["target"]["encoder"][layer_name]["attention"]["query"]["kernel"] lowercase__ : Any = 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": lowercase__ : Optional[Any] = tax_model["target"]["encoder"][layer_name]["attention"]["T5LayerNorm_0"]["scale"] # Layer Normalization lowercase__ : Optional[int] = tax_model["target"]["encoder"][layer_name]["pre_attention_layer_norm"]["scale"] if split_mlp_wi: lowercase__ : Tuple = tax_model["target"]["encoder"][layer_name]["mlp"]["wi_0"]["kernel"] lowercase__ : List[str] = tax_model["target"]["encoder"][layer_name]["mlp"]["wi_1"]["kernel"] else: lowercase__ : Optional[int] = tax_model["target"]["encoder"][layer_name]["mlp"]["wi"]["kernel"] lowercase__ : str = tax_model["target"]["encoder"][layer_name]["mlp"]["wo"]["kernel"] # Layer Normalization lowercase__ : int = tax_model["target"]["encoder"][layer_name]["pre_mlp_layer_norm"]["scale"] # Assigning lowercase__ : int = flax_model.params["encoder"]["block"][str(_lowerCamelCase)]["layer"] lowercase__ : Any = tax_attention_key lowercase__ : Any = tax_attention_out lowercase__ : Any = tax_attention_query lowercase__ : List[str] = tax_attention_value lowercase__ : List[str] = tax_attention_layer_norm # Global input layer norm if config.model_type == "longt5" and config.encoder_attention_type == "transient-global": lowercase__ : Any = tax_global_layer_norm if split_mlp_wi: lowercase__ : Tuple = tax_mlp_wi_a lowercase__ : str = tax_mlp_wi_a else: lowercase__ : List[Any] = tax_mlp_wi lowercase__ : str = tax_mlp_wo lowercase__ : int = tax_mlp_layer_norm lowercase__ : List[str] = flax_model_encoder_layer_block # Only for layer 0: lowercase__ : Dict = tax_model["target"]["encoder"]["relpos_bias"]["rel_embedding"].T lowercase__ : Optional[int] = tax_encoder_rel_embedding # Side/global relative position_bias + layer norm if config.model_type == "longt5" and config.encoder_attention_type == "transient-global": lowercase__ : Tuple = tax_model["target"]["encoder"]["side_relpos_bias"]["rel_embedding"].T lowercase__ : str = tax_encoder_global_rel_embedding # Assigning lowercase__ : Optional[int] = tax_model["target"]["encoder"]["encoder_norm"]["scale"] lowercase__ : Union[str, Any] = tax_encoder_norm # Decoder for layer_index in range(config.num_layers): lowercase__ : Dict = f'''layers_{str(_lowerCamelCase)}''' # Self-Attention lowercase__ : str = tax_model["target"]["decoder"][layer_name]["self_attention"]["key"]["kernel"] lowercase__ : Tuple = tax_model["target"]["decoder"][layer_name]["self_attention"]["out"]["kernel"] lowercase__ : List[Any] = tax_model["target"]["decoder"][layer_name]["self_attention"]["query"]["kernel"] lowercase__ : List[str] = tax_model["target"]["decoder"][layer_name]["self_attention"]["value"]["kernel"] # Layer Normalization lowercase__ : Union[str, Any] = tax_model["target"]["decoder"][layer_name]["pre_self_attention_layer_norm"][ "scale" ] # Encoder-Decoder-Attention lowercase__ : int = tax_model["target"]["decoder"][layer_name]["encoder_decoder_attention"] lowercase__ : Any = tax_enc_dec_attention_module["key"]["kernel"] lowercase__ : Union[str, Any] = tax_enc_dec_attention_module["out"]["kernel"] lowercase__ : Any = tax_enc_dec_attention_module["query"]["kernel"] lowercase__ : Tuple = tax_enc_dec_attention_module["value"]["kernel"] # Layer Normalization lowercase__ : Dict = tax_model["target"]["decoder"][layer_name]["pre_cross_attention_layer_norm"]["scale"] # MLP if split_mlp_wi: lowercase__ : Union[str, Any] = tax_model["target"]["decoder"][layer_name]["mlp"]["wi_0"]["kernel"] lowercase__ : Any = tax_model["target"]["decoder"][layer_name]["mlp"]["wi_1"]["kernel"] else: lowercase__ : List[Any] = tax_model["target"]["decoder"][layer_name]["mlp"]["wi"]["kernel"] lowercase__ : Optional[Any] = tax_model["target"]["decoder"][layer_name]["mlp"]["wo"]["kernel"] # Layer Normalization lowercase__ : Optional[int] = tax_model["target"]["decoder"][layer_name]["pre_mlp_layer_norm"]["scale"] # Assigning lowercase__ : Optional[Any] = flax_model.params["decoder"]["block"][str(_lowerCamelCase)]["layer"] lowercase__ : Any = tax_attention_key lowercase__ : List[Any] = tax_attention_out lowercase__ : Any = tax_attention_query lowercase__ : List[Any] = tax_attention_value lowercase__ : List[str] = tax_pre_attention_layer_norm lowercase__ : List[Any] = tax_enc_dec_attention_key lowercase__ : Optional[Any] = tax_enc_dec_attention_out lowercase__ : str = tax_enc_dec_attention_query lowercase__ : Union[str, Any] = tax_enc_dec_attention_value lowercase__ : Tuple = tax_cross_layer_norm if split_mlp_wi: lowercase__ : List[str] = tax_mlp_wi_a lowercase__ : List[Any] = tax_mlp_wi_a else: lowercase__ : Tuple = tax_mlp_wi lowercase__ : Any = tax_mlp_wo lowercase__ : Tuple = txa_mlp_layer_norm lowercase__ : int = flax_model_decoder_layer_block # Decoder Normalization lowercase__ : str = tax_model["target"]["decoder"]["decoder_norm"]["scale"] lowercase__ : List[Any] = txa_decoder_norm # Only for layer 0: lowercase__ : List[str] = tax_model["target"]["decoder"]["relpos_bias"]["rel_embedding"].T lowercase__ : str = tax_decoder_rel_embedding # Token Embeddings lowercase__ : Optional[Any] = tax_model["target"]["token_embedder"]["embedding"] lowercase__ : Optional[Any] = txa_token_embeddings # LM Head (only in v1.1 and LongT5 checkpoints) if "logits_dense" in tax_model["target"]["decoder"]: lowercase__ : Optional[int] = tax_model["target"]["decoder"]["logits_dense"]["kernel"] flax_model.save_pretrained(_lowerCamelCase) print("T5X Model was sucessfully converted!") if __name__ == "__main__": UpperCamelCase = 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.''' ) UpperCamelCase = parser.parse_args() convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path)
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from typing import Dict, List, Optional, Union import numpy as np from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict from ...image_transforms import rescale, resize, to_channel_dimension_format from ...image_utils import ( ChannelDimension, ImageInput, PILImageResampling, make_list_of_images, to_numpy_array, valid_images, ) from ...utils import TensorType, is_vision_available, logging if is_vision_available(): import PIL UpperCamelCase = logging.get_logger(__name__) def lowercase_ ( _lowerCamelCase : Union[str, Any] , _lowerCamelCase : int): lowercase__ : Any = b.T lowercase__ : Optional[int] = np.sum(np.square(_lowerCamelCase) , axis=1) lowercase__ : Dict = np.sum(np.square(_lowerCamelCase) , axis=0) lowercase__ : int = np.matmul(_lowerCamelCase , _lowerCamelCase) lowercase__ : Union[str, Any] = aa[:, None] - 2 * ab + ba[None, :] return d def lowercase_ ( _lowerCamelCase : List[str] , _lowerCamelCase : Dict): lowercase__ : Dict = x.reshape(-1 , 3) lowercase__ : Optional[int] = squared_euclidean_distance(_lowerCamelCase , _lowerCamelCase) return np.argmin(_lowerCamelCase , axis=1) class snake_case_ ( __A ): __A : Tuple = ["pixel_values"] def __init__( self : Optional[int] , lowercase_ : Optional[Union[List[List[int]], np.ndarray]] = None , lowercase_ : bool = True , lowercase_ : Dict[str, int] = None , lowercase_ : PILImageResampling = PILImageResampling.BILINEAR , lowercase_ : bool = True , lowercase_ : bool = True , **lowercase_ : Tuple , ) -> None: super().__init__(**lowercase_ ) lowercase__ : Dict = size if size is not None else {"height": 2_56, "width": 2_56} lowercase__ : Optional[int] = get_size_dict(lowercase_ ) lowercase__ : List[Any] = np.array(lowercase_ ) if clusters is not None else None lowercase__ : Tuple = do_resize lowercase__ : List[str] = size lowercase__ : List[str] = resample lowercase__ : Any = do_normalize lowercase__ : Tuple = do_color_quantize def __UpperCamelCase ( self : str , lowercase_ : np.ndarray , lowercase_ : Dict[str, int] , lowercase_ : PILImageResampling = PILImageResampling.BILINEAR , lowercase_ : Optional[Union[str, ChannelDimension]] = None , **lowercase_ : Optional[int] , ) -> np.ndarray: lowercase__ : Optional[Any] = get_size_dict(lowercase_ ) if "height" not in size or "width" not in size: raise ValueError(F'''Size dictionary must contain both height and width keys. Got {size.keys()}''' ) return resize( lowercase_ , size=(size["height"], size["width"]) , resample=lowercase_ , data_format=lowercase_ , **lowercase_ ) def __UpperCamelCase ( self : Any , lowercase_ : np.ndarray , lowercase_ : Optional[Union[str, ChannelDimension]] = None , ) -> np.ndarray: lowercase__ : Union[str, Any] = rescale(image=lowercase_ , scale=1 / 1_27.5 , data_format=lowercase_ ) lowercase__ : Tuple = image - 1 return image def __UpperCamelCase ( self : List[Any] , lowercase_ : ImageInput , lowercase_ : bool = None , lowercase_ : Dict[str, int] = None , lowercase_ : PILImageResampling = None , lowercase_ : bool = None , lowercase_ : Optional[bool] = None , lowercase_ : Optional[Union[List[List[int]], np.ndarray]] = None , lowercase_ : Optional[Union[str, TensorType]] = None , lowercase_ : Optional[Union[str, ChannelDimension]] = ChannelDimension.FIRST , **lowercase_ : int , ) -> PIL.Image.Image: lowercase__ : str = do_resize if do_resize is not None else self.do_resize lowercase__ : int = size if size is not None else self.size lowercase__ : Dict = get_size_dict(lowercase_ ) lowercase__ : Optional[int] = resample if resample is not None else self.resample lowercase__ : Union[str, Any] = do_normalize if do_normalize is not None else self.do_normalize lowercase__ : int = do_color_quantize if do_color_quantize is not None else self.do_color_quantize lowercase__ : Any = clusters if clusters is not None else self.clusters lowercase__ : Optional[Any] = np.array(lowercase_ ) lowercase__ : Union[str, Any] = make_list_of_images(lowercase_ ) if not valid_images(lowercase_ ): 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 or resample is None: raise ValueError("Size and resample must be specified if do_resize is True." ) if do_color_quantize and clusters is None: raise ValueError("Clusters must be specified if do_color_quantize is True." ) # All transformations expect numpy arrays. lowercase__ : int = [to_numpy_array(lowercase_ ) for image in images] if do_resize: lowercase__ : Any = [self.resize(image=lowercase_ , size=lowercase_ , resample=lowercase_ ) for image in images] if do_normalize: lowercase__ : List[Any] = [self.normalize(image=lowercase_ ) for image in images] if do_color_quantize: lowercase__ : Union[str, Any] = [to_channel_dimension_format(lowercase_ , ChannelDimension.LAST ) for image in images] # color quantize from (batch_size, height, width, 3) to (batch_size, height, width) lowercase__ : List[Any] = np.array(lowercase_ ) lowercase__ : Dict = color_quantize(lowercase_ , lowercase_ ).reshape(images.shape[:-1] ) # flatten to (batch_size, height*width) lowercase__ : Union[str, Any] = images.shape[0] lowercase__ : Tuple = images.reshape(lowercase_ , -1 ) # We need to convert back to a list of images to keep consistent behaviour across processors. lowercase__ : str = list(lowercase_ ) else: lowercase__ : Union[str, Any] = [to_channel_dimension_format(lowercase_ , lowercase_ ) for image in images] lowercase__ : Optional[Any] = {"input_ids": images} return BatchFeature(data=lowercase_ , tensor_type=lowercase_ )
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from ...configuration_utils import PretrainedConfig from ...utils import logging UpperCamelCase = logging.get_logger(__name__) UpperCamelCase = { '''RWKV/rwkv-4-169m-pile''': '''https://huggingface.co/RWKV/rwkv-4-169m-pile/resolve/main/config.json''', '''RWKV/rwkv-4-430m-pile''': '''https://huggingface.co/RWKV/rwkv-4-430m-pile/resolve/main/config.json''', '''RWKV/rwkv-4-1b5-pile''': '''https://huggingface.co/RWKV/rwkv-4-1b5-pile/resolve/main/config.json''', '''RWKV/rwkv-4-3b-pile''': '''https://huggingface.co/RWKV/rwkv-4-3b-pile/resolve/main/config.json''', '''RWKV/rwkv-4-7b-pile''': '''https://huggingface.co/RWKV/rwkv-4-7b-pile/resolve/main/config.json''', '''RWKV/rwkv-4-14b-pile''': '''https://huggingface.co/RWKV/rwkv-4-14b-pile/resolve/main/config.json''', '''RWKV/rwkv-raven-1b5''': '''https://huggingface.co/RWKV/rwkv-raven-1b5/resolve/main/config.json''', '''RWKV/rwkv-raven-3b''': '''https://huggingface.co/RWKV/rwkv-raven-3b/resolve/main/config.json''', '''RWKV/rwkv-raven-7b''': '''https://huggingface.co/RWKV/rwkv-raven-7b/resolve/main/config.json''', '''RWKV/rwkv-raven-14b''': '''https://huggingface.co/RWKV/rwkv-raven-14b/resolve/main/config.json''', } class snake_case_ ( __A ): __A : Optional[int] = "rwkv" __A : List[str] = {"max_position_embeddings": "context_length"} def __init__( self : Dict , lowercase_ : List[Any]=5_02_77 , lowercase_ : Union[str, Any]=10_24 , lowercase_ : Any=40_96 , lowercase_ : int=32 , lowercase_ : Dict=None , lowercase_ : str=None , lowercase_ : Any=1E-5 , lowercase_ : Optional[Any]=0 , lowercase_ : Any=0 , lowercase_ : List[str]=6 , lowercase_ : List[Any]=False , lowercase_ : int=True , **lowercase_ : List[str] , ) -> int: lowercase__ : List[str] = vocab_size lowercase__ : str = context_length lowercase__ : List[Any] = hidden_size lowercase__ : Optional[Any] = num_hidden_layers lowercase__ : Optional[Any] = attention_hidden_size if attention_hidden_size is not None else hidden_size lowercase__ : str = intermediate_size if intermediate_size is not None else 4 * hidden_size lowercase__ : List[Any] = layer_norm_epsilon lowercase__ : str = rescale_every lowercase__ : Optional[int] = use_cache lowercase__ : int = bos_token_id lowercase__ : Optional[Any] = eos_token_id super().__init__( tie_word_embeddings=lowercase_ , bos_token_id=lowercase_ , eos_token_id=lowercase_ , **lowercase_ )
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import itertools import random import unittest import numpy as np from transformers import is_speech_available from transformers.testing_utils import require_torch, require_torchaudio from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin if is_speech_available(): from transformers import SpeechaTextFeatureExtractor UpperCamelCase = random.Random() def lowercase_ ( _lowerCamelCase : Tuple , _lowerCamelCase : Dict=1.0 , _lowerCamelCase : Any=None , _lowerCamelCase : Dict=None): """simple docstring""" if rng is None: lowercase__ : str = global_rng lowercase__ : Optional[Any] = [] for batch_idx in range(shape[0]): values.append([]) for _ in range(shape[1]): values[-1].append(rng.random() * scale) return values @require_torch @require_torchaudio class snake_case_ ( unittest.TestCase ): def __init__( self : int , lowercase_ : int , lowercase_ : Any=7 , lowercase_ : Any=4_00 , lowercase_ : Any=20_00 , lowercase_ : str=24 , lowercase_ : Union[str, Any]=24 , lowercase_ : List[str]=0.0 , lowercase_ : Union[str, Any]=1_60_00 , lowercase_ : Tuple=True , lowercase_ : Union[str, Any]=True , ) -> Optional[int]: lowercase__ : Optional[Any] = parent lowercase__ : int = batch_size lowercase__ : str = min_seq_length lowercase__ : Union[str, Any] = max_seq_length lowercase__ : Union[str, Any] = (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1) lowercase__ : Optional[int] = feature_size lowercase__ : List[Any] = num_mel_bins lowercase__ : Optional[int] = padding_value lowercase__ : Union[str, Any] = sampling_rate lowercase__ : Any = return_attention_mask lowercase__ : List[str] = do_normalize def __UpperCamelCase ( self : Union[str, Any] ) -> Any: return { "feature_size": self.feature_size, "num_mel_bins": self.num_mel_bins, "padding_value": self.padding_value, "sampling_rate": self.sampling_rate, "return_attention_mask": self.return_attention_mask, "do_normalize": self.do_normalize, } def __UpperCamelCase ( self : List[Any] , lowercase_ : int=False , lowercase_ : Optional[int]=False ) -> str: def _flatten(lowercase_ : Any ): return list(itertools.chain(*lowercase_ ) ) if equal_length: lowercase__ : Optional[int] = [floats_list((self.max_seq_length, self.feature_size) ) for _ in range(self.batch_size )] else: # make sure that inputs increase in size lowercase__ : Dict = [ floats_list((x, self.feature_size) ) for x in range(self.min_seq_length , self.max_seq_length , self.seq_length_diff ) ] if numpify: lowercase__ : str = [np.asarray(lowercase_ ) for x in speech_inputs] return speech_inputs @require_torch @require_torchaudio class snake_case_ ( __A ,unittest.TestCase ): __A : Tuple = SpeechaTextFeatureExtractor if is_speech_available() else None def __UpperCamelCase ( self : Any ) -> int: lowercase__ : Dict = SpeechaTextFeatureExtractionTester(self ) def __UpperCamelCase ( self : Tuple , lowercase_ : Optional[int] ) -> Dict: self.assertTrue(np.all(np.mean(lowercase_ , axis=0 ) < 1E-3 ) ) self.assertTrue(np.all(np.abs(np.var(lowercase_ , axis=0 ) - 1 ) < 1E-3 ) ) def __UpperCamelCase ( self : str ) -> Union[str, Any]: # Tests that all call wrap to encode_plus and batch_encode_plus lowercase__ : List[Any] = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() ) # create three inputs of length 800, 1000, and 1200 lowercase__ : List[Any] = [floats_list((1, x) )[0] for x in range(8_00 , 14_00 , 2_00 )] lowercase__ : Optional[int] = [np.asarray(lowercase_ ) for speech_input in speech_inputs] # Test feature size lowercase__ : List[str] = feature_extractor(lowercase_ , padding=lowercase_ , return_tensors="np" ).input_features self.assertTrue(input_features.ndim == 3 ) self.assertTrue(input_features.shape[-1] == feature_extractor.feature_size ) # Test not batched input lowercase__ : List[Any] = feature_extractor(speech_inputs[0] , return_tensors="np" ).input_features lowercase__ : int = feature_extractor(np_speech_inputs[0] , return_tensors="np" ).input_features self.assertTrue(np.allclose(lowercase_ , lowercase_ , atol=1E-3 ) ) # Test batched lowercase__ : Dict = feature_extractor(lowercase_ , return_tensors="np" ).input_features lowercase__ : Dict = feature_extractor(lowercase_ , return_tensors="np" ).input_features for enc_seq_a, enc_seq_a in zip(lowercase_ , lowercase_ ): self.assertTrue(np.allclose(lowercase_ , lowercase_ , atol=1E-3 ) ) # Test 2-D numpy arrays are batched. lowercase__ : Any = [floats_list((1, x) )[0] for x in (8_00, 8_00, 8_00)] lowercase__ : Dict = np.asarray(lowercase_ ) lowercase__ : int = feature_extractor(lowercase_ , return_tensors="np" ).input_features lowercase__ : List[str] = feature_extractor(lowercase_ , return_tensors="np" ).input_features for enc_seq_a, enc_seq_a in zip(lowercase_ , lowercase_ ): self.assertTrue(np.allclose(lowercase_ , lowercase_ , atol=1E-3 ) ) def __UpperCamelCase ( self : Union[str, Any] ) -> Optional[Any]: lowercase__ : Any = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() ) lowercase__ : Optional[Any] = [floats_list((1, x) )[0] for x in range(8_00 , 14_00 , 2_00 )] lowercase__ : int = ["longest", "max_length", "do_not_pad"] lowercase__ : Union[str, Any] = [None, 16, None] for max_length, padding in zip(lowercase_ , lowercase_ ): lowercase__ : List[Any] = feature_extractor( lowercase_ , padding=lowercase_ , max_length=lowercase_ , return_attention_mask=lowercase_ ) lowercase__ : Union[str, Any] = inputs.input_features lowercase__ : Dict = inputs.attention_mask lowercase__ : List[Any] = [np.sum(lowercase_ ) for x in attention_mask] self._check_zero_mean_unit_variance(input_features[0][: fbank_feat_lengths[0]] ) self._check_zero_mean_unit_variance(input_features[1][: fbank_feat_lengths[1]] ) self._check_zero_mean_unit_variance(input_features[2][: fbank_feat_lengths[2]] ) def __UpperCamelCase ( self : Dict ) -> Union[str, Any]: lowercase__ : Optional[int] = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() ) lowercase__ : Optional[Any] = [floats_list((1, x) )[0] for x in range(8_00 , 14_00 , 2_00 )] lowercase__ : List[Any] = ["longest", "max_length", "do_not_pad"] lowercase__ : int = [None, 16, None] for max_length, padding in zip(lowercase_ , lowercase_ ): lowercase__ : Tuple = feature_extractor( lowercase_ , max_length=lowercase_ , padding=lowercase_ , return_tensors="np" , return_attention_mask=lowercase_ ) lowercase__ : List[Any] = inputs.input_features lowercase__ : Any = inputs.attention_mask lowercase__ : List[Any] = [np.sum(lowercase_ ) for x in attention_mask] self._check_zero_mean_unit_variance(input_features[0][: fbank_feat_lengths[0]] ) self.assertTrue(input_features[0][fbank_feat_lengths[0] :].sum() < 1E-6 ) self._check_zero_mean_unit_variance(input_features[1][: fbank_feat_lengths[1]] ) self.assertTrue(input_features[0][fbank_feat_lengths[1] :].sum() < 1E-6 ) self._check_zero_mean_unit_variance(input_features[2][: fbank_feat_lengths[2]] ) def __UpperCamelCase ( self : List[str] ) -> List[str]: lowercase__ : List[str] = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() ) lowercase__ : Union[str, Any] = [floats_list((1, x) )[0] for x in range(8_00 , 14_00 , 2_00 )] lowercase__ : str = feature_extractor( lowercase_ , padding="max_length" , max_length=4 , truncation=lowercase_ , return_tensors="np" , return_attention_mask=lowercase_ , ) lowercase__ : Tuple = inputs.input_features lowercase__ : Tuple = inputs.attention_mask lowercase__ : Union[str, Any] = np.sum(attention_mask == 1 , axis=1 ) self._check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]] ) self._check_zero_mean_unit_variance(input_features[1] ) self._check_zero_mean_unit_variance(input_features[2] ) def __UpperCamelCase ( self : List[Any] ) -> str: lowercase__ : Tuple = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() ) lowercase__ : Dict = [floats_list((1, x) )[0] for x in range(8_00 , 14_00 , 2_00 )] lowercase__ : Tuple = feature_extractor( lowercase_ , padding="longest" , max_length=4 , truncation=lowercase_ , return_tensors="np" , return_attention_mask=lowercase_ , ) lowercase__ : Dict = inputs.input_features lowercase__ : List[Any] = inputs.attention_mask lowercase__ : int = np.sum(attention_mask == 1 , axis=1 ) self._check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]] ) self._check_zero_mean_unit_variance(input_features[1, : fbank_feat_lengths[1]] ) self._check_zero_mean_unit_variance(input_features[2] ) # make sure that if max_length < longest -> then pad to max_length self.assertEqual(input_features.shape , (3, 4, 24) ) lowercase__ : Any = [floats_list((1, x) )[0] for x in range(8_00 , 14_00 , 2_00 )] lowercase__ : Optional[int] = feature_extractor( lowercase_ , padding="longest" , max_length=16 , truncation=lowercase_ , return_tensors="np" , return_attention_mask=lowercase_ , ) lowercase__ : List[Any] = inputs.input_features lowercase__ : Union[str, Any] = inputs.attention_mask lowercase__ : List[str] = np.sum(attention_mask == 1 , axis=1 ) self._check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]] ) self._check_zero_mean_unit_variance(input_features[1, : fbank_feat_lengths[1]] ) self._check_zero_mean_unit_variance(input_features[2] ) # make sure that if max_length < longest -> then pad to max_length self.assertEqual(input_features.shape , (3, 6, 24) ) def __UpperCamelCase ( self : Dict ) -> Optional[Any]: import torch lowercase__ : int = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() ) lowercase__ : str = np.random.rand(1_00 , 32 ).astype(np.floataa ) lowercase__ : List[str] = np_speech_inputs.tolist() for inputs in [py_speech_inputs, np_speech_inputs]: lowercase__ : List[Any] = feature_extractor.pad([{"input_features": inputs}] , return_tensors="np" ) self.assertTrue(np_processed.input_features.dtype == np.floataa ) lowercase__ : int = feature_extractor.pad([{"input_features": inputs}] , return_tensors="pt" ) self.assertTrue(pt_processed.input_features.dtype == torch.floataa ) def __UpperCamelCase ( self : Dict , lowercase_ : str ) -> List[str]: from datasets import load_dataset lowercase__ : List[str] = load_dataset("hf-internal-testing/librispeech_asr_dummy" , "clean" , split="validation" ) # automatic decoding with librispeech lowercase__ : Optional[Any] = ds.sort("id" ).select(range(lowercase_ ) )[:num_samples]["audio"] return [x["array"] for x in speech_samples] def __UpperCamelCase ( self : str ) -> str: # fmt: off lowercase__ : Optional[int] = np.array([ -1.57_45, -1.77_13, -1.70_20, -1.60_69, -1.22_50, -1.11_05, -0.90_72, -0.82_41, -1.23_10, -0.80_98, -0.33_20, -0.41_01, -0.79_85, -0.49_96, -0.82_13, -0.91_28, -1.04_20, -1.12_86, -1.04_40, -0.79_99, -0.84_05, -1.22_75, -1.54_43, -1.46_25, ] ) # fmt: on lowercase__ : List[str] = self._load_datasamples(1 ) lowercase__ : Any = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() ) lowercase__ : Optional[int] = feature_extractor(lowercase_ , return_tensors="pt" ).input_features self.assertEquals(input_features.shape , (1, 5_84, 24) ) self.assertTrue(np.allclose(input_features[0, 0, :30] , lowercase_ , atol=1E-4 ) )
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class snake_case_ : def __init__( self : int ) -> Optional[int]: lowercase__ : Optional[int] = 0 lowercase__ : List[str] = 0 lowercase__ : Any = {} def __UpperCamelCase ( self : Dict , lowercase_ : List[Any] ) -> Union[str, Any]: if vertex not in self.adjacency: lowercase__ : List[Any] = {} self.num_vertices += 1 def __UpperCamelCase ( self : int , lowercase_ : List[str] , lowercase_ : Any , lowercase_ : str ) -> Optional[Any]: self.add_vertex(lowercase_ ) self.add_vertex(lowercase_ ) if head == tail: return lowercase__ : int = weight lowercase__ : Any = weight def __UpperCamelCase ( self : Dict ) -> Optional[int]: lowercase__ : List[Any] = self.get_edges() for edge in edges: lowercase__ , lowercase__ , lowercase__ : int = edge edges.remove((tail, head, weight) ) for i in range(len(lowercase_ ) ): lowercase__ : Tuple = list(edges[i] ) edges.sort(key=lambda lowercase_ : e[2] ) for i in range(len(lowercase_ ) - 1 ): if edges[i][2] >= edges[i + 1][2]: lowercase__ : int = edges[i][2] + 1 for edge in edges: lowercase__ , lowercase__ , lowercase__ : Optional[int] = edge lowercase__ : Union[str, Any] = weight lowercase__ : Dict = weight def __str__( self : str ) -> Any: lowercase__ : str = "" for tail in self.adjacency: for head in self.adjacency[tail]: lowercase__ : Optional[Any] = self.adjacency[head][tail] string += F'''{head} -> {tail} == {weight}\n''' return string.rstrip("\n" ) def __UpperCamelCase ( self : Optional[Any] ) -> Optional[int]: lowercase__ : Any = [] for tail in self.adjacency: for head in self.adjacency[tail]: output.append((tail, head, self.adjacency[head][tail]) ) return output def __UpperCamelCase ( self : List[str] ) -> Dict: return self.adjacency.keys() @staticmethod def __UpperCamelCase ( lowercase_ : Dict=None , lowercase_ : Any=None ) -> Optional[int]: lowercase__ : Any = Graph() if vertices is None: lowercase__ : str = [] if edges is None: lowercase__ : List[Any] = [] for vertex in vertices: g.add_vertex(lowercase_ ) for edge in edges: g.add_edge(*lowercase_ ) return g class snake_case_ : def __init__( self : int ) -> List[str]: lowercase__ : Dict = {} lowercase__ : Tuple = {} def __len__( self : Union[str, Any] ) -> Union[str, Any]: return len(self.parent ) def __UpperCamelCase ( self : Tuple , lowercase_ : List[str] ) -> Tuple: if item in self.parent: return self.find(lowercase_ ) lowercase__ : Union[str, Any] = item lowercase__ : int = 0 return item def __UpperCamelCase ( self : Union[str, Any] , lowercase_ : List[str] ) -> Any: if item not in self.parent: return self.make_set(lowercase_ ) if item != self.parent[item]: lowercase__ : Union[str, Any] = self.find(self.parent[item] ) return self.parent[item] def __UpperCamelCase ( self : Dict , lowercase_ : Dict , lowercase_ : str ) -> Optional[Any]: lowercase__ : Dict = self.find(lowercase_ ) lowercase__ : Optional[int] = self.find(lowercase_ ) if roota == roota: return roota if self.rank[roota] > self.rank[roota]: lowercase__ : Dict = roota return roota if self.rank[roota] < self.rank[roota]: lowercase__ : int = roota return roota if self.rank[roota] == self.rank[roota]: self.rank[roota] += 1 lowercase__ : Tuple = roota return roota return None @staticmethod def __UpperCamelCase ( lowercase_ : Dict ) -> Optional[Any]: lowercase__ : List[Any] = graph.num_vertices lowercase__ : Optional[Any] = Graph.UnionFind() lowercase__ : int = [] while num_components > 1: lowercase__ : List[Any] = {} for vertex in graph.get_vertices(): lowercase__ : Any = -1 lowercase__ : List[str] = graph.get_edges() for edge in edges: lowercase__ , lowercase__ , lowercase__ : str = edge edges.remove((tail, head, weight) ) for edge in edges: lowercase__ , lowercase__ , lowercase__ : List[str] = edge lowercase__ : List[str] = union_find.find(lowercase_ ) lowercase__ : Union[str, Any] = union_find.find(lowercase_ ) if seta != seta: if cheap_edge[seta] == -1 or cheap_edge[seta][2] > weight: lowercase__ : int = [head, tail, weight] if cheap_edge[seta] == -1 or cheap_edge[seta][2] > weight: lowercase__ : Dict = [head, tail, weight] for vertex in cheap_edge: if cheap_edge[vertex] != -1: lowercase__ , lowercase__ , lowercase__ : List[Any] = cheap_edge[vertex] if union_find.find(lowercase_ ) != union_find.find(lowercase_ ): union_find.union(lowercase_ , lowercase_ ) mst_edges.append(cheap_edge[vertex] ) lowercase__ : Optional[Any] = num_components - 1 lowercase__ : List[Any] = Graph.build(edges=lowercase_ ) return mst
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import gc import unittest import numpy as np import torch from diffusers import StableDiffusionKDiffusionPipeline from diffusers.utils import slow, torch_device from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu enable_full_determinism() @slow @require_torch_gpu class snake_case_ ( unittest.TestCase ): def __UpperCamelCase ( self : str ) -> List[Any]: # clean up the VRAM after each test super().tearDown() gc.collect() torch.cuda.empty_cache() def __UpperCamelCase ( self : Optional[Any] ) -> Union[str, Any]: lowercase__ : Tuple = StableDiffusionKDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4" ) lowercase__ : Tuple = sd_pipe.to(lowercase_ ) sd_pipe.set_progress_bar_config(disable=lowercase_ ) sd_pipe.set_scheduler("sample_euler" ) lowercase__ : Dict = "A painting of a squirrel eating a burger" lowercase__ : Union[str, Any] = torch.manual_seed(0 ) lowercase__ : List[Any] = sd_pipe([prompt] , generator=lowercase_ , guidance_scale=9.0 , num_inference_steps=20 , output_type="np" ) lowercase__ : Dict = output.images lowercase__ : Any = image[0, -3:, -3:, -1] assert image.shape == (1, 5_12, 5_12, 3) lowercase__ : Any = np.array([0.04_47, 0.04_92, 0.04_68, 0.04_08, 0.03_83, 0.04_08, 0.03_54, 0.03_80, 0.03_39] ) assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2 def __UpperCamelCase ( self : str ) -> Dict: lowercase__ : Dict = StableDiffusionKDiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-2-1-base" ) lowercase__ : Tuple = sd_pipe.to(lowercase_ ) sd_pipe.set_progress_bar_config(disable=lowercase_ ) sd_pipe.set_scheduler("sample_euler" ) lowercase__ : Optional[Any] = "A painting of a squirrel eating a burger" lowercase__ : Optional[Any] = torch.manual_seed(0 ) lowercase__ : str = sd_pipe([prompt] , generator=lowercase_ , guidance_scale=9.0 , num_inference_steps=20 , output_type="np" ) lowercase__ : List[Any] = output.images lowercase__ : Union[str, Any] = image[0, -3:, -3:, -1] assert image.shape == (1, 5_12, 5_12, 3) lowercase__ : Union[str, Any] = np.array([0.12_37, 0.13_20, 0.14_38, 0.13_59, 0.13_90, 0.11_32, 0.12_77, 0.11_75, 0.11_12] ) assert np.abs(image_slice.flatten() - expected_slice ).max() < 5E-1 def __UpperCamelCase ( self : Dict ) -> Optional[int]: lowercase__ : List[str] = StableDiffusionKDiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-2-1-base" ) lowercase__ : int = sd_pipe.to(lowercase_ ) sd_pipe.set_progress_bar_config(disable=lowercase_ ) sd_pipe.set_scheduler("sample_dpmpp_2m" ) lowercase__ : Any = "A painting of a squirrel eating a burger" lowercase__ : Any = torch.manual_seed(0 ) lowercase__ : List[Any] = sd_pipe( [prompt] , generator=lowercase_ , guidance_scale=7.5 , num_inference_steps=15 , output_type="np" , use_karras_sigmas=lowercase_ , ) lowercase__ : Any = output.images lowercase__ : int = image[0, -3:, -3:, -1] assert image.shape == (1, 5_12, 5_12, 3) lowercase__ : List[str] = np.array( [0.11_38_16_89, 0.12_11_29_21, 0.1_38_94_57, 0.12_54_96_06, 0.1_24_49_64, 0.10_83_15_17, 0.11_56_28_66, 0.10_86_78_16, 0.10_49_90_48] ) assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
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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 = logging.get_logger(__name__) def lowercase_ ( _lowerCamelCase : str): lowercase__ : Optional[Any] = DPTConfig() if "large" in checkpoint_url: lowercase__ : str = 1024 lowercase__ : List[str] = 4096 lowercase__ : List[Any] = 24 lowercase__ : Dict = 16 lowercase__ : Union[str, Any] = [5, 11, 17, 23] lowercase__ : Any = [256, 512, 1024, 1024] lowercase__ : Optional[int] = (1, 384, 384) if "ade" in checkpoint_url: lowercase__ : Union[str, Any] = True lowercase__ : Tuple = 150 lowercase__ : Optional[int] = "huggingface/label-files" lowercase__ : str = "ade20k-id2label.json" lowercase__ : Union[str, Any] = json.load(open(cached_download(hf_hub_url(_lowerCamelCase , _lowerCamelCase , repo_type="dataset")) , "r")) lowercase__ : Union[str, Any] = {int(_lowerCamelCase): v for k, v in idalabel.items()} lowercase__ : Optional[Any] = idalabel lowercase__ : Union[str, Any] = {v: k for k, v in idalabel.items()} lowercase__ : Tuple = [1, 150, 480, 480] return config, expected_shape def lowercase_ ( _lowerCamelCase : List[Any]): lowercase__ : int = ["pretrained.model.head.weight", "pretrained.model.head.bias"] for k in ignore_keys: state_dict.pop(_lowerCamelCase , _lowerCamelCase) def lowercase_ ( _lowerCamelCase : Tuple): if ( "pretrained.model" in name and "cls_token" not in name and "pos_embed" not in name and "patch_embed" not in name ): lowercase__ : Dict = name.replace("pretrained.model" , "dpt.encoder") if "pretrained.model" in name: lowercase__ : List[str] = name.replace("pretrained.model" , "dpt.embeddings") if "patch_embed" in name: lowercase__ : Any = name.replace("patch_embed" , "patch_embeddings") if "pos_embed" in name: lowercase__ : Union[str, Any] = name.replace("pos_embed" , "position_embeddings") if "attn.proj" in name: lowercase__ : Optional[int] = name.replace("attn.proj" , "attention.output.dense") if "proj" in name and "project" not in name: lowercase__ : int = name.replace("proj" , "projection") if "blocks" in name: lowercase__ : List[str] = name.replace("blocks" , "layer") if "mlp.fc1" in name: lowercase__ : List[str] = name.replace("mlp.fc1" , "intermediate.dense") if "mlp.fc2" in name: lowercase__ : Optional[int] = name.replace("mlp.fc2" , "output.dense") if "norm1" in name: lowercase__ : List[str] = name.replace("norm1" , "layernorm_before") if "norm2" in name: lowercase__ : Dict = name.replace("norm2" , "layernorm_after") if "scratch.output_conv" in name: lowercase__ : Union[str, Any] = name.replace("scratch.output_conv" , "head") if "scratch" in name: lowercase__ : str = name.replace("scratch" , "neck") if "layer1_rn" in name: lowercase__ : int = name.replace("layer1_rn" , "convs.0") if "layer2_rn" in name: lowercase__ : int = name.replace("layer2_rn" , "convs.1") if "layer3_rn" in name: lowercase__ : Tuple = name.replace("layer3_rn" , "convs.2") if "layer4_rn" in name: lowercase__ : Union[str, Any] = name.replace("layer4_rn" , "convs.3") if "refinenet" in name: lowercase__ : Dict = 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 lowercase__ : str = name.replace(f'''refinenet{layer_idx}''' , f'''fusion_stage.layers.{abs(layer_idx-4)}''') if "out_conv" in name: lowercase__ : str = name.replace("out_conv" , "projection") if "resConfUnit1" in name: lowercase__ : int = name.replace("resConfUnit1" , "residual_layer1") if "resConfUnit2" in name: lowercase__ : Optional[Any] = name.replace("resConfUnit2" , "residual_layer2") if "conv1" in name: lowercase__ : List[Any] = name.replace("conv1" , "convolution1") if "conv2" in name: lowercase__ : Tuple = name.replace("conv2" , "convolution2") # readout blocks if "pretrained.act_postprocess1.0.project.0" in name: lowercase__ : int = name.replace("pretrained.act_postprocess1.0.project.0" , "neck.reassemble_stage.readout_projects.0.0") if "pretrained.act_postprocess2.0.project.0" in name: lowercase__ : Any = name.replace("pretrained.act_postprocess2.0.project.0" , "neck.reassemble_stage.readout_projects.1.0") if "pretrained.act_postprocess3.0.project.0" in name: lowercase__ : Optional[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: lowercase__ : List[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: lowercase__ : Union[str, Any] = name.replace("pretrained.act_postprocess1.3" , "neck.reassemble_stage.layers.0.projection") if "pretrained.act_postprocess1.4" in name: lowercase__ : Optional[Any] = name.replace("pretrained.act_postprocess1.4" , "neck.reassemble_stage.layers.0.resize") if "pretrained.act_postprocess2.3" in name: lowercase__ : int = name.replace("pretrained.act_postprocess2.3" , "neck.reassemble_stage.layers.1.projection") if "pretrained.act_postprocess2.4" in name: lowercase__ : str = name.replace("pretrained.act_postprocess2.4" , "neck.reassemble_stage.layers.1.resize") if "pretrained.act_postprocess3.3" in name: lowercase__ : Dict = name.replace("pretrained.act_postprocess3.3" , "neck.reassemble_stage.layers.2.projection") if "pretrained.act_postprocess4.3" in name: lowercase__ : Any = name.replace("pretrained.act_postprocess4.3" , "neck.reassemble_stage.layers.3.projection") if "pretrained.act_postprocess4.4" in name: lowercase__ : int = name.replace("pretrained.act_postprocess4.4" , "neck.reassemble_stage.layers.3.resize") if "pretrained" in name: lowercase__ : Any = name.replace("pretrained" , "dpt") if "bn" in name: lowercase__ : str = name.replace("bn" , "batch_norm") if "head" in name: lowercase__ : Optional[Any] = name.replace("head" , "head.head") if "encoder.norm" in name: lowercase__ : Tuple = name.replace("encoder.norm" , "layernorm") if "auxlayer" in name: lowercase__ : int = name.replace("auxlayer" , "auxiliary_head.head") return name def lowercase_ ( _lowerCamelCase : Union[str, Any] , _lowerCamelCase : str): for i in range(config.num_hidden_layers): # read in weights + bias of input projection layer (in timm, this is a single matrix + bias) lowercase__ : Union[str, Any] = state_dict.pop(f'''dpt.encoder.layer.{i}.attn.qkv.weight''') lowercase__ : Union[str, Any] = state_dict.pop(f'''dpt.encoder.layer.{i}.attn.qkv.bias''') # next, add query, keys and values (in that order) to the state dict lowercase__ : Optional[int] = in_proj_weight[: config.hidden_size, :] lowercase__ : Optional[int] = in_proj_bias[: config.hidden_size] lowercase__ : Optional[Any] = in_proj_weight[ config.hidden_size : config.hidden_size * 2, : ] lowercase__ : Optional[Any] = in_proj_bias[ config.hidden_size : config.hidden_size * 2 ] lowercase__ : List[Any] = in_proj_weight[ -config.hidden_size :, : ] lowercase__ : int = in_proj_bias[-config.hidden_size :] def lowercase_ ( ): lowercase__ : Any = "http://images.cocodataset.org/val2017/000000039769.jpg" lowercase__ : Optional[int] = Image.open(requests.get(_lowerCamelCase , stream=_lowerCamelCase).raw) return im @torch.no_grad() def lowercase_ ( _lowerCamelCase : Dict , _lowerCamelCase : List[str] , _lowerCamelCase : Optional[Any] , _lowerCamelCase : Dict): lowercase__ , lowercase__ : Optional[int] = get_dpt_config(_lowerCamelCase) # load original state_dict from URL lowercase__ : Tuple = torch.hub.load_state_dict_from_url(_lowerCamelCase , map_location="cpu") # remove certain keys remove_ignore_keys_(_lowerCamelCase) # rename keys for key in state_dict.copy().keys(): lowercase__ : List[str] = state_dict.pop(_lowerCamelCase) lowercase__ : List[Any] = val # read in qkv matrices read_in_q_k_v(_lowerCamelCase , _lowerCamelCase) # load HuggingFace model lowercase__ : Any = DPTForSemanticSegmentation(_lowerCamelCase) if "ade" in checkpoint_url else DPTForDepthEstimation(_lowerCamelCase) model.load_state_dict(_lowerCamelCase) model.eval() # Check outputs on an image lowercase__ : Optional[Any] = 480 if "ade" in checkpoint_url else 384 lowercase__ : Union[str, Any] = DPTImageProcessor(size=_lowerCamelCase) lowercase__ : List[str] = prepare_img() lowercase__ : Dict = image_processor(_lowerCamelCase , return_tensors="pt") # forward pass lowercase__ : Tuple = model(**_lowerCamelCase).logits if "ade" in checkpoint_url else model(**_lowerCamelCase).predicted_depth # Assert logits lowercase__ : Union[str, Any] = torch.tensor([[6.3199, 6.3629, 6.4148], [6.3850, 6.3615, 6.4166], [6.3519, 6.3176, 6.3575]]) if "ade" in checkpoint_url: lowercase__ : List[str] = torch.tensor([[4.0480, 4.2420, 4.4360], [4.3124, 4.5693, 4.8261], [4.5768, 4.8965, 5.2163]]) assert outputs.shape == torch.Size(_lowerCamelCase) assert ( torch.allclose(outputs[0, 0, :3, :3] , _lowerCamelCase , atol=1E-4) if "ade" in checkpoint_url else torch.allclose(outputs[0, :3, :3] , _lowerCamelCase) ) 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: print("Pushing model to hub...") model.push_to_hub( repo_path_or_name=Path(_lowerCamelCase , _lowerCamelCase) , organization="nielsr" , commit_message="Add model" , use_temp_dir=_lowerCamelCase , ) image_processor.push_to_hub( repo_path_or_name=Path(_lowerCamelCase , _lowerCamelCase) , organization="nielsr" , commit_message="Add image processor" , use_temp_dir=_lowerCamelCase , ) if __name__ == "__main__": UpperCamelCase = 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=True, 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.''', ) UpperCamelCase = parser.parse_args() convert_dpt_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)
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# Copyright 2021 The HuggingFace Team. All rights reserved. # # 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. import argparse import os from accelerate.test_utils import execute_subprocess_async def lowercase_ ( _lowerCamelCase : int=None) -> Any: if subparsers is not None: lowercase__ : Dict = subparsers.add_parser("test") else: lowercase__ : List[str] = argparse.ArgumentParser("Accelerate test command") parser.add_argument( "--config_file" , default=_lowerCamelCase , help=( "The path to use to store the config file. Will default to a file named default_config.yaml in the cache " "location, which is the content of the environment `HF_HOME` suffixed with 'accelerate', or if you don't have " "such an environment variable, your cache directory ('~/.cache' or the content of `XDG_CACHE_HOME`) suffixed " "with 'huggingface'." ) , ) if subparsers is not None: parser.set_defaults(func=_lowerCamelCase) return parser def lowercase_ ( _lowerCamelCase : List[Any]) -> Tuple: lowercase__ : List[str] = os.path.sep.join(__file__.split(os.path.sep)[:-2] + ["test_utils", "scripts", "test_script.py"]) if args.config_file is None: lowercase__ : Union[str, Any] = script_name else: lowercase__ : Union[str, Any] = f'''--config_file={args.config_file} {script_name}''' lowercase__ : List[str] = ["accelerate-launch"] + test_args.split() lowercase__ : Optional[Any] = execute_subprocess_async(_lowerCamelCase , env=os.environ.copy()) if result.returncode == 0: print("Test is a success! You are ready for your distributed training!") def lowercase_ ( ) -> Optional[Any]: lowercase__ : int = test_command_parser() lowercase__ : Optional[int] = parser.parse_args() test_command(_lowerCamelCase) if __name__ == "__main__": main()
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def lowercase_ ( _lowerCamelCase : int = 1 , _lowerCamelCase : int = 1000): lowercase__ : Union[str, Any] = 1 lowercase__ : int = 0 for divide_by_number in range(_lowerCamelCase , digit + 1): lowercase__ : list[int] = [] lowercase__ : Dict = numerator for _ in range(1 , digit + 1): if now_divide in has_been_divided: if longest_list_length < len(_lowerCamelCase): lowercase__ : Union[str, Any] = len(_lowerCamelCase) lowercase__ : Optional[int] = divide_by_number else: has_been_divided.append(_lowerCamelCase) lowercase__ : Optional[Any] = now_divide * 10 % divide_by_number return the_digit # Tests if __name__ == "__main__": import doctest doctest.testmod()
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from collections import UserDict from typing import List, Union from ..utils import ( add_end_docstrings, is_tf_available, is_torch_available, is_vision_available, logging, requires_backends, ) from .base import PIPELINE_INIT_ARGS, Pipeline if is_vision_available(): from PIL import Image from ..image_utils import load_image if is_torch_available(): from ..models.auto.modeling_auto import MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING if is_tf_available(): from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING from ..tf_utils import stable_softmax UpperCamelCase = logging.get_logger(__name__) @add_end_docstrings(__A ) class snake_case_ ( __A ): def __init__( self : List[Any] , **lowercase_ : Union[str, Any] ) -> Optional[int]: super().__init__(**lowercase_ ) requires_backends(self , "vision" ) self.check_model_type( TF_MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING if self.framework == "tf" else MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING ) def __call__( self : Dict , lowercase_ : Union[str, List[str], "Image", List["Image"]] , **lowercase_ : Any ) -> Optional[int]: return super().__call__(lowercase_ , **lowercase_ ) def __UpperCamelCase ( self : Dict , **lowercase_ : int ) -> Optional[int]: lowercase__ : List[Any] = {} if "candidate_labels" in kwargs: lowercase__ : Optional[Any] = kwargs["candidate_labels"] if "hypothesis_template" in kwargs: lowercase__ : Optional[Any] = kwargs["hypothesis_template"] return preprocess_params, {}, {} def __UpperCamelCase ( self : List[str] , lowercase_ : Optional[int] , lowercase_ : int=None , lowercase_ : Any="This is a photo of {}." ) -> List[str]: lowercase__ : Tuple = load_image(lowercase_ ) lowercase__ : Union[str, Any] = self.image_processor(images=[image] , return_tensors=self.framework ) lowercase__ : Union[str, Any] = candidate_labels lowercase__ : List[str] = [hypothesis_template.format(lowercase_ ) for x in candidate_labels] lowercase__ : List[Any] = self.tokenizer(lowercase_ , return_tensors=self.framework , padding=lowercase_ ) lowercase__ : Optional[int] = [text_inputs] return inputs def __UpperCamelCase ( self : Union[str, Any] , lowercase_ : str ) -> Tuple: lowercase__ : int = model_inputs.pop("candidate_labels" ) lowercase__ : Dict = model_inputs.pop("text_inputs" ) if isinstance(text_inputs[0] , lowercase_ ): lowercase__ : Any = text_inputs[0] else: # Batching case. lowercase__ : int = text_inputs[0][0] lowercase__ : Union[str, Any] = self.model(**lowercase_ , **lowercase_ ) lowercase__ : List[Any] = { "candidate_labels": candidate_labels, "logits": outputs.logits_per_image, } return model_outputs def __UpperCamelCase ( self : List[str] , lowercase_ : Union[str, Any] ) -> Tuple: lowercase__ : Optional[Any] = model_outputs.pop("candidate_labels" ) lowercase__ : Dict = model_outputs["logits"][0] if self.framework == "pt": lowercase__ : Optional[int] = logits.softmax(dim=-1 ).squeeze(-1 ) lowercase__ : Optional[Any] = probs.tolist() if not isinstance(lowercase_ , lowercase_ ): lowercase__ : List[str] = [scores] elif self.framework == "tf": lowercase__ : str = stable_softmax(lowercase_ , axis=-1 ) lowercase__ : Union[str, Any] = probs.numpy().tolist() else: raise ValueError(F'''Unsupported framework: {self.framework}''' ) lowercase__ : Union[str, Any] = [ {"score": score, "label": candidate_label} for score, candidate_label in sorted(zip(lowercase_ , lowercase_ ) , key=lambda lowercase_ : -x[0] ) ] return result
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import gc import unittest import torch from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer from diffusers import ( AutoencoderKL, DDIMScheduler, DDPMScheduler, PriorTransformer, StableUnCLIPPipeline, UNetaDConditionModel, ) from diffusers.pipelines.stable_diffusion.stable_unclip_image_normalizer import StableUnCLIPImageNormalizer from diffusers.utils.testing_utils import enable_full_determinism, load_numpy, require_torch_gpu, slow, torch_device 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, assert_mean_pixel_difference, ) enable_full_determinism() class snake_case_ ( __A ,__A ,__A ,unittest.TestCase ): __A : int = StableUnCLIPPipeline __A : int = TEXT_TO_IMAGE_PARAMS __A : Any = TEXT_TO_IMAGE_BATCH_PARAMS __A : int = TEXT_TO_IMAGE_IMAGE_PARAMS __A : Dict = TEXT_TO_IMAGE_IMAGE_PARAMS # TODO(will) Expected attn_bias.stride(1) == 0 to be true, but got false __A : int = False def __UpperCamelCase ( self : Optional[int] ) -> List[str]: lowercase__ : str = 32 lowercase__ : Any = embedder_hidden_size # prior components torch.manual_seed(0 ) lowercase__ : Optional[Any] = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip" ) torch.manual_seed(0 ) lowercase__ : List[str] = CLIPTextModelWithProjection( CLIPTextConfig( bos_token_id=0 , eos_token_id=2 , hidden_size=lowercase_ , projection_dim=lowercase_ , intermediate_size=37 , layer_norm_eps=1E-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=10_00 , ) ) torch.manual_seed(0 ) lowercase__ : Any = PriorTransformer( num_attention_heads=2 , attention_head_dim=12 , embedding_dim=lowercase_ , num_layers=1 , ) torch.manual_seed(0 ) lowercase__ : Union[str, Any] = DDPMScheduler( variance_type="fixed_small_log" , prediction_type="sample" , num_train_timesteps=10_00 , clip_sample=lowercase_ , clip_sample_range=5.0 , beta_schedule="squaredcos_cap_v2" , ) # regular denoising components torch.manual_seed(0 ) lowercase__ : List[str] = StableUnCLIPImageNormalizer(embedding_dim=lowercase_ ) lowercase__ : Tuple = DDPMScheduler(beta_schedule="squaredcos_cap_v2" ) torch.manual_seed(0 ) lowercase__ : Optional[int] = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip" ) torch.manual_seed(0 ) lowercase__ : Tuple = CLIPTextModel( CLIPTextConfig( bos_token_id=0 , eos_token_id=2 , hidden_size=lowercase_ , 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=10_00 , ) ) torch.manual_seed(0 ) lowercase__ : str = UNetaDConditionModel( sample_size=32 , in_channels=4 , out_channels=4 , down_block_types=("CrossAttnDownBlock2D", "DownBlock2D") , up_block_types=("UpBlock2D", "CrossAttnUpBlock2D") , block_out_channels=(32, 64) , attention_head_dim=(2, 4) , class_embed_type="projection" , projection_class_embeddings_input_dim=embedder_projection_dim * 2 , cross_attention_dim=lowercase_ , layers_per_block=1 , upcast_attention=lowercase_ , use_linear_projection=lowercase_ , ) torch.manual_seed(0 ) lowercase__ : Any = DDIMScheduler( beta_schedule="scaled_linear" , beta_start=0.0_00_85 , beta_end=0.0_12 , prediction_type="v_prediction" , set_alpha_to_one=lowercase_ , steps_offset=1 , ) torch.manual_seed(0 ) lowercase__ : List[str] = AutoencoderKL() lowercase__ : List[Any] = { # prior components "prior_tokenizer": prior_tokenizer, "prior_text_encoder": prior_text_encoder, "prior": prior, "prior_scheduler": prior_scheduler, # image noising components "image_normalizer": image_normalizer, "image_noising_scheduler": image_noising_scheduler, # regular denoising components "tokenizer": tokenizer, "text_encoder": text_encoder, "unet": unet, "scheduler": scheduler, "vae": vae, } return components def __UpperCamelCase ( self : Any , lowercase_ : Tuple , lowercase_ : Dict=0 ) -> Any: if str(lowercase_ ).startswith("mps" ): lowercase__ : Any = torch.manual_seed(lowercase_ ) else: lowercase__ : Any = torch.Generator(device=lowercase_ ).manual_seed(lowercase_ ) lowercase__ : Optional[Any] = { "prompt": "A painting of a squirrel eating a burger", "generator": generator, "num_inference_steps": 2, "prior_num_inference_steps": 2, "output_type": "numpy", } return inputs def __UpperCamelCase ( self : Union[str, Any] ) -> List[str]: lowercase__ : Union[str, Any] = torch_device == "cpu" self._test_attention_slicing_forward_pass(test_max_difference=lowercase_ ) def __UpperCamelCase ( self : List[Any] ) -> List[str]: lowercase__ : str = torch_device in ["cpu", "mps"] self._test_inference_batch_single_identical(test_max_difference=lowercase_ ) @slow @require_torch_gpu class snake_case_ ( unittest.TestCase ): def __UpperCamelCase ( self : Tuple ) -> Any: # clean up the VRAM after each test super().tearDown() gc.collect() torch.cuda.empty_cache() def __UpperCamelCase ( self : int ) -> int: lowercase__ : Optional[int] = load_numpy( "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_l_anime_turtle_fp16.npy" ) lowercase__ : List[str] = StableUnCLIPPipeline.from_pretrained("fusing/stable-unclip-2-1-l" , torch_dtype=torch.floataa ) pipe.to(lowercase_ ) pipe.set_progress_bar_config(disable=lowercase_ ) # stable unclip will oom when integration tests are run on a V100, # so turn on memory savings pipe.enable_attention_slicing() pipe.enable_sequential_cpu_offload() lowercase__ : Optional[int] = torch.Generator(device="cpu" ).manual_seed(0 ) lowercase__ : Dict = pipe("anime turle" , generator=lowercase_ , output_type="np" ) lowercase__ : Optional[int] = output.images[0] assert image.shape == (7_68, 7_68, 3) assert_mean_pixel_difference(lowercase_ , lowercase_ ) def __UpperCamelCase ( self : Union[str, Any] ) -> Dict: torch.cuda.empty_cache() torch.cuda.reset_max_memory_allocated() torch.cuda.reset_peak_memory_stats() lowercase__ : Union[str, Any] = StableUnCLIPPipeline.from_pretrained("fusing/stable-unclip-2-1-l" , torch_dtype=torch.floataa ) lowercase__ : int = pipe.to(lowercase_ ) pipe.set_progress_bar_config(disable=lowercase_ ) pipe.enable_attention_slicing() pipe.enable_sequential_cpu_offload() lowercase__ : str = pipe( "anime turtle" , prior_num_inference_steps=2 , num_inference_steps=2 , output_type="np" , ) lowercase__ : Any = torch.cuda.max_memory_allocated() # make sure that less than 7 GB is allocated assert mem_bytes < 7 * 10**9
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import torch from diffusers import DDIMParallelScheduler from .test_schedulers import SchedulerCommonTest class snake_case_ ( __A ): __A : Dict = (DDIMParallelScheduler,) __A : int = (("eta", 0.0), ("num_inference_steps", 50)) def __UpperCamelCase ( self : Optional[Any] , **lowercase_ : str ) -> Union[str, Any]: lowercase__ : Dict = { "num_train_timesteps": 10_00, "beta_start": 0.00_01, "beta_end": 0.02, "beta_schedule": "linear", "clip_sample": True, } config.update(**lowercase_ ) return config def __UpperCamelCase ( self : Union[str, Any] , **lowercase_ : List[Any] ) -> int: lowercase__ : Any = self.scheduler_classes[0] lowercase__ : Any = self.get_scheduler_config(**lowercase_ ) lowercase__ : Optional[int] = scheduler_class(**lowercase_ ) lowercase__ : Any = 10, 0.0 lowercase__ : List[str] = self.dummy_model() lowercase__ : Optional[int] = self.dummy_sample_deter scheduler.set_timesteps(lowercase_ ) for t in scheduler.timesteps: lowercase__ : List[Any] = model(lowercase_ , lowercase_ ) lowercase__ : str = scheduler.step(lowercase_ , lowercase_ , lowercase_ , lowercase_ ).prev_sample return sample def __UpperCamelCase ( self : str ) -> Dict: for timesteps in [1_00, 5_00, 10_00]: self.check_over_configs(num_train_timesteps=lowercase_ ) def __UpperCamelCase ( self : List[str] ) -> List[Any]: for steps_offset in [0, 1]: self.check_over_configs(steps_offset=lowercase_ ) lowercase__ : List[str] = self.scheduler_classes[0] lowercase__ : str = self.get_scheduler_config(steps_offset=1 ) lowercase__ : List[Any] = scheduler_class(**lowercase_ ) scheduler.set_timesteps(5 ) assert torch.equal(scheduler.timesteps , torch.LongTensor([8_01, 6_01, 4_01, 2_01, 1] ) ) def __UpperCamelCase ( self : List[str] ) -> Dict: for beta_start, beta_end in zip([0.00_01, 0.0_01, 0.01, 0.1] , [0.0_02, 0.02, 0.2, 2] ): self.check_over_configs(beta_start=lowercase_ , beta_end=lowercase_ ) def __UpperCamelCase ( self : str ) -> Tuple: for schedule in ["linear", "squaredcos_cap_v2"]: self.check_over_configs(beta_schedule=lowercase_ ) def __UpperCamelCase ( self : Union[str, Any] ) -> Optional[Any]: for prediction_type in ["epsilon", "v_prediction"]: self.check_over_configs(prediction_type=lowercase_ ) def __UpperCamelCase ( self : int ) -> int: for clip_sample in [True, False]: self.check_over_configs(clip_sample=lowercase_ ) def __UpperCamelCase ( self : List[str] ) -> List[Any]: for timestep_spacing in ["trailing", "leading"]: self.check_over_configs(timestep_spacing=lowercase_ ) def __UpperCamelCase ( self : List[str] ) -> List[str]: for rescale_betas_zero_snr in [True, False]: self.check_over_configs(rescale_betas_zero_snr=lowercase_ ) def __UpperCamelCase ( self : Union[str, Any] ) -> Union[str, Any]: self.check_over_configs(thresholding=lowercase_ ) for threshold in [0.5, 1.0, 2.0]: for prediction_type in ["epsilon", "v_prediction"]: self.check_over_configs( thresholding=lowercase_ , prediction_type=lowercase_ , sample_max_value=lowercase_ , ) def __UpperCamelCase ( self : Optional[Any] ) -> List[Any]: for t in [1, 10, 49]: self.check_over_forward(time_step=lowercase_ ) def __UpperCamelCase ( self : int ) -> List[Any]: for t, num_inference_steps in zip([1, 10, 50] , [10, 50, 5_00] ): self.check_over_forward(time_step=lowercase_ , num_inference_steps=lowercase_ ) def __UpperCamelCase ( self : str ) -> Optional[Any]: for t, eta in zip([1, 10, 49] , [0.0, 0.5, 1.0] ): self.check_over_forward(time_step=lowercase_ , eta=lowercase_ ) def __UpperCamelCase ( self : Union[str, Any] ) -> Optional[Any]: lowercase__ : Dict = self.scheduler_classes[0] lowercase__ : int = self.get_scheduler_config() lowercase__ : Optional[int] = scheduler_class(**lowercase_ ) assert torch.sum(torch.abs(scheduler._get_variance(0 , 0 ) - 0.0 ) ) < 1E-5 assert torch.sum(torch.abs(scheduler._get_variance(4_20 , 4_00 ) - 0.1_47_71 ) ) < 1E-5 assert torch.sum(torch.abs(scheduler._get_variance(9_80 , 9_60 ) - 0.3_24_60 ) ) < 1E-5 assert torch.sum(torch.abs(scheduler._get_variance(0 , 0 ) - 0.0 ) ) < 1E-5 assert torch.sum(torch.abs(scheduler._get_variance(4_87 , 4_86 ) - 0.0_09_79 ) ) < 1E-5 assert torch.sum(torch.abs(scheduler._get_variance(9_99 , 9_98 ) - 0.02 ) ) < 1E-5 def __UpperCamelCase ( self : Tuple ) -> Dict: lowercase__ : List[Any] = self.scheduler_classes[0] lowercase__ : Optional[int] = self.get_scheduler_config() lowercase__ : str = scheduler_class(**lowercase_ ) lowercase__ : Dict = 10, 0.0 scheduler.set_timesteps(lowercase_ ) lowercase__ : int = self.dummy_model() lowercase__ : Union[str, Any] = self.dummy_sample_deter lowercase__ : Optional[Any] = self.dummy_sample_deter + 0.1 lowercase__ : int = self.dummy_sample_deter - 0.1 lowercase__ : Any = samplea.shape[0] lowercase__ : Any = torch.stack([samplea, samplea, samplea] , dim=0 ) lowercase__ : List[Any] = torch.arange(lowercase_ )[0:3, None].repeat(1 , lowercase_ ) lowercase__ : Any = model(samples.flatten(0 , 1 ) , timesteps.flatten(0 , 1 ) ) lowercase__ : List[Any] = scheduler.batch_step_no_noise(lowercase_ , timesteps.flatten(0 , 1 ) , samples.flatten(0 , 1 ) , lowercase_ ) lowercase__ : Union[str, Any] = torch.sum(torch.abs(lowercase_ ) ) lowercase__ : List[str] = torch.mean(torch.abs(lowercase_ ) ) assert abs(result_sum.item() - 11_47.79_04 ) < 1E-2 assert abs(result_mean.item() - 0.49_82 ) < 1E-3 def __UpperCamelCase ( self : List[str] ) -> Dict: lowercase__ : Dict = self.full_loop() lowercase__ : Dict = torch.sum(torch.abs(lowercase_ ) ) lowercase__ : int = torch.mean(torch.abs(lowercase_ ) ) assert abs(result_sum.item() - 1_72.00_67 ) < 1E-2 assert abs(result_mean.item() - 0.22_39_67 ) < 1E-3 def __UpperCamelCase ( self : List[str] ) -> Tuple: lowercase__ : List[str] = self.full_loop(prediction_type="v_prediction" ) lowercase__ : List[str] = torch.sum(torch.abs(lowercase_ ) ) lowercase__ : str = torch.mean(torch.abs(lowercase_ ) ) assert abs(result_sum.item() - 52.53_02 ) < 1E-2 assert abs(result_mean.item() - 0.06_84 ) < 1E-3 def __UpperCamelCase ( self : Optional[int] ) -> Union[str, Any]: # We specify different beta, so that the first alpha is 0.99 lowercase__ : Optional[Any] = self.full_loop(set_alpha_to_one=lowercase_ , beta_start=0.01 ) lowercase__ : int = torch.sum(torch.abs(lowercase_ ) ) lowercase__ : Tuple = torch.mean(torch.abs(lowercase_ ) ) assert abs(result_sum.item() - 1_49.82_95 ) < 1E-2 assert abs(result_mean.item() - 0.19_51 ) < 1E-3 def __UpperCamelCase ( self : List[Any] ) -> List[Any]: # We specify different beta, so that the first alpha is 0.99 lowercase__ : List[Any] = self.full_loop(set_alpha_to_one=lowercase_ , beta_start=0.01 ) lowercase__ : str = torch.sum(torch.abs(lowercase_ ) ) lowercase__ : Tuple = torch.mean(torch.abs(lowercase_ ) ) assert abs(result_sum.item() - 1_49.07_84 ) < 1E-2 assert abs(result_mean.item() - 0.19_41 ) < 1E-3
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import asyncio import os import shutil import subprocess import sys import tempfile import unittest from distutils.util import strtobool from functools import partial from pathlib import Path from typing import List, Union from unittest import mock import torch from ..state import AcceleratorState, PartialState from ..utils import ( gather, is_bnb_available, is_comet_ml_available, is_datasets_available, is_deepspeed_available, is_mps_available, is_safetensors_available, is_tensorboard_available, is_torch_version, is_tpu_available, is_transformers_available, is_wandb_available, is_xpu_available, ) def lowercase_ ( _lowerCamelCase : Union[str, Any] , _lowerCamelCase : int=False): try: lowercase__ : str = os.environ[key] except KeyError: # KEY isn't set, default to `default`. lowercase__ : Union[str, Any] = default else: # KEY is set, convert it to True or False. try: lowercase__ : Union[str, Any] = strtobool(_lowerCamelCase) except ValueError: # More values are supported, but let's keep the message simple. raise ValueError(f'''If set, {key} must be yes or no.''') return _value UpperCamelCase = parse_flag_from_env('''RUN_SLOW''', default=False) def lowercase_ ( _lowerCamelCase : int): return unittest.skip("Test was skipped")(_lowerCamelCase) def lowercase_ ( _lowerCamelCase : Tuple): return unittest.skipUnless(_run_slow_tests , "test is slow")(_lowerCamelCase) def lowercase_ ( _lowerCamelCase : Union[str, Any]): return unittest.skipUnless(not torch.cuda.is_available() , "test requires only a CPU")(_lowerCamelCase) def lowercase_ ( _lowerCamelCase : Dict): return unittest.skipUnless(torch.cuda.is_available() , "test requires a GPU")(_lowerCamelCase) def lowercase_ ( _lowerCamelCase : int): return unittest.skipUnless(is_xpu_available() , "test requires a XPU")(_lowerCamelCase) def lowercase_ ( _lowerCamelCase : List[str]): return unittest.skipUnless(is_mps_available() , "test requires a `mps` backend support in `torch`")(_lowerCamelCase) def lowercase_ ( _lowerCamelCase : List[str]): return unittest.skipUnless( is_transformers_available() and is_datasets_available() , "test requires the Hugging Face suite")(_lowerCamelCase) def lowercase_ ( _lowerCamelCase : Union[str, Any]): return unittest.skipUnless(is_bnb_available() , "test requires the bitsandbytes library")(_lowerCamelCase) def lowercase_ ( _lowerCamelCase : Union[str, Any]): return unittest.skipUnless(is_tpu_available() , "test requires TPU")(_lowerCamelCase) def lowercase_ ( _lowerCamelCase : List[Any]): return unittest.skipUnless(torch.cuda.device_count() == 1 , "test requires a GPU")(_lowerCamelCase) def lowercase_ ( _lowerCamelCase : Union[str, Any]): return unittest.skipUnless(torch.xpu.device_count() == 1 , "test requires a XPU")(_lowerCamelCase) def lowercase_ ( _lowerCamelCase : List[str]): return unittest.skipUnless(torch.cuda.device_count() > 1 , "test requires multiple GPUs")(_lowerCamelCase) def lowercase_ ( _lowerCamelCase : int): return unittest.skipUnless(torch.xpu.device_count() > 1 , "test requires multiple XPUs")(_lowerCamelCase) def lowercase_ ( _lowerCamelCase : List[str]): return unittest.skipUnless(is_safetensors_available() , "test requires safetensors")(_lowerCamelCase) def lowercase_ ( _lowerCamelCase : str): return unittest.skipUnless(is_deepspeed_available() , "test requires DeepSpeed")(_lowerCamelCase) def lowercase_ ( _lowerCamelCase : Any): return unittest.skipUnless(is_torch_version(">=" , "1.12.0") , "test requires torch version >= 1.12.0")(_lowerCamelCase) def lowercase_ ( _lowerCamelCase : List[Any]=None , _lowerCamelCase : Dict=None): if test_case is None: return partial(_lowerCamelCase , version=_lowerCamelCase) return unittest.skipUnless(is_torch_version(">=" , _lowerCamelCase) , f'''test requires torch version >= {version}''')(_lowerCamelCase) def lowercase_ ( _lowerCamelCase : List[Any]): return unittest.skipUnless(is_tensorboard_available() , "test requires Tensorboard")(_lowerCamelCase) def lowercase_ ( _lowerCamelCase : int): return unittest.skipUnless(is_wandb_available() , "test requires wandb")(_lowerCamelCase) def lowercase_ ( _lowerCamelCase : List[str]): return unittest.skipUnless(is_comet_ml_available() , "test requires comet_ml")(_lowerCamelCase) UpperCamelCase = ( any([is_wandb_available(), is_tensorboard_available()]) and not is_comet_ml_available() ) def lowercase_ ( _lowerCamelCase : Any): return unittest.skipUnless( _atleast_one_tracker_available , "test requires at least one tracker to be available and for `comet_ml` to not be installed" , )(_lowerCamelCase) class snake_case_ ( unittest.TestCase ): __A : int = True @classmethod def __UpperCamelCase ( cls : str ) -> str: lowercase__ : str = tempfile.mkdtemp() @classmethod def __UpperCamelCase ( cls : List[str] ) -> Optional[Any]: if os.path.exists(cls.tmpdir ): shutil.rmtree(cls.tmpdir ) def __UpperCamelCase ( self : str ) -> Optional[int]: if self.clear_on_setup: for path in Path(self.tmpdir ).glob("**/*" ): if path.is_file(): path.unlink() elif path.is_dir(): shutil.rmtree(lowercase_ ) class snake_case_ ( unittest.TestCase ): def __UpperCamelCase ( self : List[str] ) -> Union[str, Any]: super().tearDown() # Reset the state of the AcceleratorState singleton. AcceleratorState._reset_state() PartialState._reset_state() class snake_case_ ( unittest.TestCase ): def __UpperCamelCase ( self : List[Any] , lowercase_ : Union[mock.Mock, List[mock.Mock]] ) -> str: lowercase__ : Tuple = mocks if isinstance(lowercase_ , (tuple, list) ) else [mocks] for m in self.mocks: m.start() self.addCleanup(m.stop ) def lowercase_ ( _lowerCamelCase : int): lowercase__ : Tuple = AcceleratorState() lowercase__ : Optional[int] = tensor[None].clone().to(state.device) lowercase__ : Optional[int] = gather(_lowerCamelCase).cpu() lowercase__ : Optional[Any] = tensor[0].cpu() for i in range(tensors.shape[0]): if not torch.equal(tensors[i] , _lowerCamelCase): return False return True class snake_case_ : def __init__( self : str , lowercase_ : int , lowercase_ : Optional[Any] , lowercase_ : int ) -> Union[str, Any]: lowercase__ : int = returncode lowercase__ : Dict = stdout lowercase__ : List[Any] = stderr async def lowercase_ ( _lowerCamelCase : Optional[int] , _lowerCamelCase : str): while True: lowercase__ : int = await stream.readline() if line: callback(_lowerCamelCase) else: break async def lowercase_ ( _lowerCamelCase : List[Any] , _lowerCamelCase : Dict=None , _lowerCamelCase : Tuple=None , _lowerCamelCase : Optional[Any]=None , _lowerCamelCase : Tuple=False , _lowerCamelCase : str=False): if echo: print("\nRunning: " , " ".join(_lowerCamelCase)) lowercase__ : str = await asyncio.create_subprocess_exec( cmd[0] , *cmd[1:] , stdin=_lowerCamelCase , stdout=asyncio.subprocess.PIPE , stderr=asyncio.subprocess.PIPE , env=_lowerCamelCase , ) # note: there is a warning for a possible deadlock when using `wait` with huge amounts of data in the pipe # https://docs.python.org/3/library/asyncio-subprocess.html#asyncio.asyncio.subprocess.Process.wait # # If it starts hanging, will need to switch to the following code. The problem is that no data # will be seen until it's done and if it hangs for example there will be no debug info. # out, err = await p.communicate() # return _RunOutput(p.returncode, out, err) lowercase__ : Tuple = [] lowercase__ : List[Any] = [] def tee(_lowerCamelCase : str , _lowerCamelCase : str , _lowerCamelCase : int , _lowerCamelCase : Optional[int]=""): lowercase__ : Optional[int] = line.decode("utf-8").rstrip() sink.append(_lowerCamelCase) if not quiet: print(_lowerCamelCase , _lowerCamelCase , file=_lowerCamelCase) # XXX: the timeout doesn't seem to make any difference here await asyncio.wait( [ asyncio.create_task(_read_stream(p.stdout , lambda _lowerCamelCase: tee(_lowerCamelCase , _lowerCamelCase , sys.stdout , label="stdout:"))), asyncio.create_task(_read_stream(p.stderr , lambda _lowerCamelCase: tee(_lowerCamelCase , _lowerCamelCase , sys.stderr , label="stderr:"))), ] , timeout=_lowerCamelCase , ) return _RunOutput(await p.wait() , _lowerCamelCase , _lowerCamelCase) def lowercase_ ( _lowerCamelCase : int , _lowerCamelCase : Tuple=None , _lowerCamelCase : Optional[Any]=None , _lowerCamelCase : List[str]=180 , _lowerCamelCase : Dict=False , _lowerCamelCase : Dict=True): lowercase__ : Optional[Any] = asyncio.get_event_loop() lowercase__ : List[Any] = loop.run_until_complete( _stream_subprocess(_lowerCamelCase , env=_lowerCamelCase , stdin=_lowerCamelCase , timeout=_lowerCamelCase , quiet=_lowerCamelCase , echo=_lowerCamelCase)) lowercase__ : str = " ".join(_lowerCamelCase) if result.returncode > 0: lowercase__ : Dict = "\n".join(result.stderr) raise RuntimeError( f'''\'{cmd_str}\' failed with returncode {result.returncode}\n\n''' f'''The combined stderr from workers follows:\n{stderr}''') return result class snake_case_ ( __A ): pass def lowercase_ ( _lowerCamelCase : List[str] , _lowerCamelCase : Any=False): try: lowercase__ : Optional[int] = subprocess.check_output(_lowerCamelCase , stderr=subprocess.STDOUT) if return_stdout: if hasattr(_lowerCamelCase , "decode"): lowercase__ : Optional[Any] = output.decode("utf-8") return output except subprocess.CalledProcessError as e: raise SubprocessCallException( f'''Command `{" ".join(_lowerCamelCase)}` failed with the following error:\n\n{e.output.decode()}''') from e
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import functools def lowercase_ ( _lowerCamelCase : list[int] , _lowerCamelCase : list[int]): # Validation if not isinstance(_lowerCamelCase , _lowerCamelCase) or not all(isinstance(_lowerCamelCase , _lowerCamelCase) for day in days): raise ValueError("The parameter days should be a list of integers") if len(_lowerCamelCase) != 3 or not all(isinstance(_lowerCamelCase , _lowerCamelCase) for cost in costs): raise ValueError("The parameter costs should be a list of three integers") if len(_lowerCamelCase) == 0: return 0 if min(_lowerCamelCase) <= 0: raise ValueError("All days elements should be greater than 0") if max(_lowerCamelCase) >= 366: raise ValueError("All days elements should be less than 366") lowercase__ : List[str] = set(_lowerCamelCase) @functools.cache def dynamic_programming(_lowerCamelCase : int) -> int: if index > 365: return 0 if index not in days_set: return dynamic_programming(index + 1) return min( costs[0] + dynamic_programming(index + 1) , costs[1] + dynamic_programming(index + 7) , costs[2] + dynamic_programming(index + 30) , ) return dynamic_programming(1) if __name__ == "__main__": import doctest doctest.testmod()
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from ..utils import DummyObject, requires_backends class snake_case_ ( metaclass=__A ): __A : List[Any] = ["flax"] def __init__( self : Optional[int] , *lowercase_ : Optional[int] , **lowercase_ : List[Any] ) -> Tuple: requires_backends(self , ["flax"] ) @classmethod def __UpperCamelCase ( cls : Tuple , *lowercase_ : int , **lowercase_ : List[str] ) -> List[str]: requires_backends(cls , ["flax"] ) @classmethod def __UpperCamelCase ( cls : Tuple , *lowercase_ : List[str] , **lowercase_ : Tuple ) -> Any: requires_backends(cls , ["flax"] ) class snake_case_ ( metaclass=__A ): __A : Dict = ["flax"] def __init__( self : int , *lowercase_ : Any , **lowercase_ : int ) -> Union[str, Any]: requires_backends(self , ["flax"] ) @classmethod def __UpperCamelCase ( cls : Dict , *lowercase_ : List[str] , **lowercase_ : List[str] ) -> Optional[Any]: requires_backends(cls , ["flax"] ) @classmethod def __UpperCamelCase ( cls : Tuple , *lowercase_ : List[Any] , **lowercase_ : Any ) -> Dict: requires_backends(cls , ["flax"] ) class snake_case_ ( metaclass=__A ): __A : Dict = ["flax"] def __init__( self : Dict , *lowercase_ : str , **lowercase_ : int ) -> Union[str, Any]: requires_backends(self , ["flax"] ) @classmethod def __UpperCamelCase ( cls : Any , *lowercase_ : Union[str, Any] , **lowercase_ : Tuple ) -> List[str]: requires_backends(cls , ["flax"] ) @classmethod def __UpperCamelCase ( cls : Any , *lowercase_ : Any , **lowercase_ : Optional[int] ) -> List[str]: requires_backends(cls , ["flax"] ) class snake_case_ ( metaclass=__A ): __A : int = ["flax"] def __init__( self : Dict , *lowercase_ : Dict , **lowercase_ : Any ) -> int: requires_backends(self , ["flax"] ) @classmethod def __UpperCamelCase ( cls : List[Any] , *lowercase_ : int , **lowercase_ : Dict ) -> Optional[int]: requires_backends(cls , ["flax"] ) @classmethod def __UpperCamelCase ( cls : Optional[int] , *lowercase_ : Optional[Any] , **lowercase_ : Any ) -> Tuple: requires_backends(cls , ["flax"] ) class snake_case_ ( metaclass=__A ): __A : List[Any] = ["flax"] def __init__( self : List[str] , *lowercase_ : str , **lowercase_ : Union[str, Any] ) -> Optional[Any]: requires_backends(self , ["flax"] ) @classmethod def __UpperCamelCase ( cls : Tuple , *lowercase_ : Optional[Any] , **lowercase_ : Optional[int] ) -> Optional[int]: requires_backends(cls , ["flax"] ) @classmethod def __UpperCamelCase ( cls : Optional[Any] , *lowercase_ : Tuple , **lowercase_ : Dict ) -> Dict: requires_backends(cls , ["flax"] ) class snake_case_ ( metaclass=__A ): __A : Dict = ["flax"] def __init__( self : int , *lowercase_ : List[str] , **lowercase_ : List[Any] ) -> Dict: requires_backends(self , ["flax"] ) @classmethod def __UpperCamelCase ( cls : Optional[int] , *lowercase_ : int , **lowercase_ : Optional[int] ) -> Dict: requires_backends(cls , ["flax"] ) @classmethod def __UpperCamelCase ( cls : List[Any] , *lowercase_ : Optional[Any] , **lowercase_ : List[str] ) -> int: requires_backends(cls , ["flax"] ) class snake_case_ ( metaclass=__A ): __A : Optional[Any] = ["flax"] def __init__( self : int , *lowercase_ : Union[str, Any] , **lowercase_ : Optional[Any] ) -> Union[str, Any]: requires_backends(self , ["flax"] ) @classmethod def __UpperCamelCase ( cls : Dict , *lowercase_ : Tuple , **lowercase_ : int ) -> List[Any]: requires_backends(cls , ["flax"] ) @classmethod def __UpperCamelCase ( cls : Union[str, Any] , *lowercase_ : List[Any] , **lowercase_ : List[str] ) -> Union[str, Any]: requires_backends(cls , ["flax"] ) class snake_case_ ( metaclass=__A ): __A : Dict = ["flax"] def __init__( self : Any , *lowercase_ : int , **lowercase_ : int ) -> Optional[int]: requires_backends(self , ["flax"] ) @classmethod def __UpperCamelCase ( cls : Optional[int] , *lowercase_ : Any , **lowercase_ : List[Any] ) -> Tuple: requires_backends(cls , ["flax"] ) @classmethod def __UpperCamelCase ( cls : Optional[Any] , *lowercase_ : Any , **lowercase_ : Union[str, Any] ) -> Optional[Any]: requires_backends(cls , ["flax"] ) class snake_case_ ( metaclass=__A ): __A : List[Any] = ["flax"] def __init__( self : Union[str, Any] , *lowercase_ : int , **lowercase_ : Optional[int] ) -> Union[str, Any]: requires_backends(self , ["flax"] ) @classmethod def __UpperCamelCase ( cls : Optional[int] , *lowercase_ : Any , **lowercase_ : Optional[Any] ) -> List[Any]: requires_backends(cls , ["flax"] ) @classmethod def __UpperCamelCase ( cls : Dict , *lowercase_ : List[str] , **lowercase_ : str ) -> Optional[Any]: requires_backends(cls , ["flax"] ) class snake_case_ ( metaclass=__A ): __A : List[Any] = ["flax"] def __init__( self : List[Any] , *lowercase_ : Union[str, Any] , **lowercase_ : Optional[Any] ) -> Dict: requires_backends(self , ["flax"] ) @classmethod def __UpperCamelCase ( cls : Optional[Any] , *lowercase_ : Any , **lowercase_ : int ) -> Union[str, Any]: requires_backends(cls , ["flax"] ) @classmethod def __UpperCamelCase ( cls : str , *lowercase_ : Optional[Any] , **lowercase_ : Optional[int] ) -> List[Any]: requires_backends(cls , ["flax"] ) class snake_case_ ( metaclass=__A ): __A : Optional[int] = ["flax"] def __init__( self : Any , *lowercase_ : str , **lowercase_ : Dict ) -> int: requires_backends(self , ["flax"] ) @classmethod def __UpperCamelCase ( cls : str , *lowercase_ : int , **lowercase_ : Optional[int] ) -> Tuple: requires_backends(cls , ["flax"] ) @classmethod def __UpperCamelCase ( cls : Tuple , *lowercase_ : List[Any] , **lowercase_ : Tuple ) -> Dict: requires_backends(cls , ["flax"] ) class snake_case_ ( metaclass=__A ): __A : int = ["flax"] def __init__( self : List[str] , *lowercase_ : int , **lowercase_ : Union[str, Any] ) -> Dict: requires_backends(self , ["flax"] ) @classmethod def __UpperCamelCase ( cls : List[Any] , *lowercase_ : int , **lowercase_ : Dict ) -> List[Any]: requires_backends(cls , ["flax"] ) @classmethod def __UpperCamelCase ( cls : Union[str, Any] , *lowercase_ : Dict , **lowercase_ : int ) -> Optional[Any]: requires_backends(cls , ["flax"] ) class snake_case_ ( metaclass=__A ): __A : List[str] = ["flax"] def __init__( self : Tuple , *lowercase_ : List[Any] , **lowercase_ : Tuple ) -> Tuple: requires_backends(self , ["flax"] ) @classmethod def __UpperCamelCase ( cls : Any , *lowercase_ : Union[str, Any] , **lowercase_ : Optional[int] ) -> Union[str, Any]: requires_backends(cls , ["flax"] ) @classmethod def __UpperCamelCase ( cls : List[str] , *lowercase_ : Union[str, Any] , **lowercase_ : Dict ) -> List[Any]: requires_backends(cls , ["flax"] )
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"""simple docstring""" import os import re import shutil import sys import tempfile import unittest import black UpperCamelCase = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__)))) sys.path.append(os.path.join(git_repo_path, '''utils''')) import check_copies # noqa: E402 # This is the reference code that will be used in the tests. # If DDPMSchedulerOutput is changed in scheduling_ddpm.py, this code needs to be manually updated. UpperCamelCase = ''' \""" Output class for the scheduler\'s step function output. Args: prev_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images): Computed sample (x_{t-1}) of previous timestep. `prev_sample` should be used as next model input in the denoising loop. pred_original_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images): The predicted denoised sample (x_{0}) based on the model output from the current timestep. `pred_original_sample` can be used to preview progress or for guidance. \""" prev_sample: torch.FloatTensor pred_original_sample: Optional[torch.FloatTensor] = None ''' class snake_case_ ( unittest.TestCase ): def __UpperCamelCase ( self : str ) -> List[str]: lowercase__ : str = tempfile.mkdtemp() os.makedirs(os.path.join(self.diffusers_dir , "schedulers/" ) ) lowercase__ : List[Any] = self.diffusers_dir shutil.copy( os.path.join(lowercase_ , "src/diffusers/schedulers/scheduling_ddpm.py" ) , os.path.join(self.diffusers_dir , "schedulers/scheduling_ddpm.py" ) , ) def __UpperCamelCase ( self : Optional[int] ) -> List[str]: lowercase__ : Dict = "src/diffusers" shutil.rmtree(self.diffusers_dir ) def __UpperCamelCase ( self : Tuple , lowercase_ : Optional[Any] , lowercase_ : Any , lowercase_ : str , lowercase_ : Tuple=None ) -> Tuple: lowercase__ : Optional[int] = comment + F'''\nclass {class_name}(nn.Module):\n''' + class_code if overwrite_result is not None: lowercase__ : Optional[int] = comment + F'''\nclass {class_name}(nn.Module):\n''' + overwrite_result lowercase__ : Optional[Any] = black.Mode(target_versions={black.TargetVersion.PYaa} , line_length=1_19 ) lowercase__ : List[str] = black.format_str(lowercase_ , mode=lowercase_ ) lowercase__ : Optional[int] = os.path.join(self.diffusers_dir , "new_code.py" ) with open(lowercase_ , "w" , newline="\n" ) as f: f.write(lowercase_ ) if overwrite_result is None: self.assertTrue(len(check_copies.is_copy_consistent(lowercase_ ) ) == 0 ) else: check_copies.is_copy_consistent(f.name , overwrite=lowercase_ ) with open(lowercase_ , "r" ) as f: self.assertTrue(f.read() , lowercase_ ) def __UpperCamelCase ( self : str ) -> Optional[int]: lowercase__ : Optional[Any] = check_copies.find_code_in_diffusers("schedulers.scheduling_ddpm.DDPMSchedulerOutput" ) self.assertEqual(lowercase_ , lowercase_ ) def __UpperCamelCase ( self : int ) -> str: # Base copy consistency self.check_copy_consistency( "# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput" , "DDPMSchedulerOutput" , REFERENCE_CODE + "\n" , ) # With no empty line at the end self.check_copy_consistency( "# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput" , "DDPMSchedulerOutput" , lowercase_ , ) # Copy consistency with rename self.check_copy_consistency( "# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->Test" , "TestSchedulerOutput" , re.sub("DDPM" , "Test" , lowercase_ ) , ) # Copy consistency with a really long name lowercase__ : Optional[int] = "TestClassWithAReallyLongNameBecauseSomePeopleLikeThatForSomeReason" self.check_copy_consistency( F'''# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->{long_class_name}''' , F'''{long_class_name}SchedulerOutput''' , re.sub("Bert" , lowercase_ , lowercase_ ) , ) # Copy consistency with overwrite self.check_copy_consistency( "# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->Test" , "TestSchedulerOutput" , lowercase_ , overwrite_result=re.sub("DDPM" , "Test" , lowercase_ ) , )
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from ...configuration_utils import PretrainedConfig from ...utils import logging UpperCamelCase = logging.get_logger(__name__) UpperCamelCase = { '''facebook/vit-mae-base''': '''https://huggingface.co/facebook/vit-mae-base/resolve/main/config.json''', # See all ViT MAE models at https://huggingface.co/models?filter=vit-mae } class snake_case_ ( __A ): __A : List[str] = "vit_mae" def __init__( self : List[Any] , lowercase_ : List[Any]=7_68 , lowercase_ : Tuple=12 , lowercase_ : Tuple=12 , lowercase_ : Optional[Any]=30_72 , lowercase_ : str="gelu" , lowercase_ : Tuple=0.0 , lowercase_ : int=0.0 , lowercase_ : Dict=0.02 , lowercase_ : int=1E-12 , lowercase_ : Tuple=2_24 , lowercase_ : Any=16 , lowercase_ : Dict=3 , lowercase_ : List[Any]=True , lowercase_ : Dict=16 , lowercase_ : List[str]=5_12 , lowercase_ : Tuple=8 , lowercase_ : Any=20_48 , lowercase_ : int=0.75 , lowercase_ : Tuple=False , **lowercase_ : Optional[int] , ) -> Optional[Any]: super().__init__(**lowercase_ ) lowercase__ : List[str] = hidden_size lowercase__ : str = num_hidden_layers lowercase__ : Optional[int] = num_attention_heads lowercase__ : List[Any] = intermediate_size lowercase__ : str = hidden_act lowercase__ : List[str] = hidden_dropout_prob lowercase__ : Optional[Any] = attention_probs_dropout_prob lowercase__ : Any = initializer_range lowercase__ : Optional[Any] = layer_norm_eps lowercase__ : Optional[Any] = image_size lowercase__ : Optional[int] = patch_size lowercase__ : Any = num_channels lowercase__ : str = qkv_bias lowercase__ : Optional[Any] = decoder_num_attention_heads lowercase__ : Any = decoder_hidden_size lowercase__ : Any = decoder_num_hidden_layers lowercase__ : Union[str, Any] = decoder_intermediate_size lowercase__ : int = mask_ratio lowercase__ : Tuple = norm_pix_loss
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from typing import TYPE_CHECKING from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available UpperCamelCase : Dict = { '''configuration_squeezebert''': [ '''SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''SqueezeBertConfig''', '''SqueezeBertOnnxConfig''', ], '''tokenization_squeezebert''': ['''SqueezeBertTokenizer'''], } try: if not is_tokenizers_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: UpperCamelCase : Union[str, Any] = ['''SqueezeBertTokenizerFast'''] try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: UpperCamelCase : Optional[int] = [ '''SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST''', '''SqueezeBertForMaskedLM''', '''SqueezeBertForMultipleChoice''', '''SqueezeBertForQuestionAnswering''', '''SqueezeBertForSequenceClassification''', '''SqueezeBertForTokenClassification''', '''SqueezeBertModel''', '''SqueezeBertModule''', '''SqueezeBertPreTrainedModel''', ] if TYPE_CHECKING: from .configuration_squeezebert import ( SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, SqueezeBertConfig, SqueezeBertOnnxConfig, ) from .tokenization_squeezebert import SqueezeBertTokenizer try: if not is_tokenizers_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .tokenization_squeezebert_fast import SqueezeBertTokenizerFast try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_squeezebert import ( SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST, SqueezeBertForMaskedLM, SqueezeBertForMultipleChoice, SqueezeBertForQuestionAnswering, SqueezeBertForSequenceClassification, SqueezeBertForTokenClassification, SqueezeBertModel, SqueezeBertModule, SqueezeBertPreTrainedModel, ) else: import sys UpperCamelCase : Union[str, Any] = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
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def lowercase_ ( _lowerCamelCase : int , _lowerCamelCase : int): while a != 0: lowercase__ , lowercase__ : Dict = b % a, a return b def lowercase_ ( _lowerCamelCase : int , _lowerCamelCase : int): if gcd(_lowerCamelCase , _lowerCamelCase) != 1: lowercase__ : Tuple = f'''mod inverse of {a!r} and {m!r} does not exist''' raise ValueError(_lowerCamelCase) lowercase__ , lowercase__ , lowercase__ : Optional[int] = 1, 0, a lowercase__ , lowercase__ , lowercase__ : Union[str, Any] = 0, 1, m while va != 0: lowercase__ : Tuple = ua // va lowercase__ , lowercase__ , lowercase__ , lowercase__ , lowercase__ , lowercase__ : Any = (ua - q * va), (ua - q * va), (ua - q * va), va, va, va return ua % m
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"""simple docstring""" from datetime import datetime import matplotlib.pyplot as plt import torch def lowercase_ ( _lowerCamelCase : Dict): for param in module.parameters(): lowercase__ : Optional[int] = False def lowercase_ ( ): lowercase__ : int = "cuda" if torch.cuda.is_available() else "cpu" if torch.backends.mps.is_available() and torch.backends.mps.is_built(): lowercase__ : int = "mps" if device == "mps": print( "WARNING: MPS currently doesn't seem to work, and messes up backpropagation without any visible torch" " errors. I recommend using CUDA on a colab notebook or CPU instead if you're facing inexplicable issues" " with generations.") return device def lowercase_ ( _lowerCamelCase : Optional[int]): lowercase__ : Union[str, Any] = plt.imshow(_lowerCamelCase) fig.axes.get_xaxis().set_visible(_lowerCamelCase) fig.axes.get_yaxis().set_visible(_lowerCamelCase) plt.show() def lowercase_ ( ): lowercase__ : Union[str, Any] = datetime.now() lowercase__ : List[str] = current_time.strftime("%H:%M:%S") return timestamp
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import logging import os from dataclasses import dataclass, field from functools import partial from pathlib import Path from tempfile import TemporaryDirectory from typing import List, Optional import faiss import torch from datasets import Features, Sequence, Value, load_dataset from transformers import DPRContextEncoder, DPRContextEncoderTokenizerFast, HfArgumentParser UpperCamelCase = logging.getLogger(__name__) torch.set_grad_enabled(False) UpperCamelCase = '''cuda''' if torch.cuda.is_available() else '''cpu''' def lowercase_ ( _lowerCamelCase : str , _lowerCamelCase : Tuple=100 , _lowerCamelCase : Tuple=" "): lowercase__ : Union[str, Any] = text.split(_lowerCamelCase) return [character.join(text[i : i + n]).strip() for i in range(0 , len(_lowerCamelCase) , _lowerCamelCase)] def lowercase_ ( _lowerCamelCase : dict): lowercase__ , lowercase__ : List[str] = [], [] for title, text in zip(documents["title"] , documents["text"]): if text is not None: for passage in split_text(_lowerCamelCase): titles.append(title if title is not None else "") texts.append(_lowerCamelCase) return {"title": titles, "text": texts} def lowercase_ ( _lowerCamelCase : dict , _lowerCamelCase : DPRContextEncoder , _lowerCamelCase : DPRContextEncoderTokenizerFast): lowercase__ : Union[str, Any] = ctx_tokenizer( documents["title"] , documents["text"] , truncation=_lowerCamelCase , padding="longest" , return_tensors="pt")["input_ids"] lowercase__ : Any = ctx_encoder(input_ids.to(device=_lowerCamelCase) , return_dict=_lowerCamelCase).pooler_output return {"embeddings": embeddings.detach().cpu().numpy()} def lowercase_ ( _lowerCamelCase : "RagExampleArguments" , _lowerCamelCase : "ProcessingArguments" , _lowerCamelCase : "IndexHnswArguments" , ): ###################################### logger.info("Step 1 - Create the dataset") ###################################### # The dataset needed for RAG must have three columns: # - title (string): title of the document # - text (string): text of a passage of the document # - embeddings (array of dimension d): DPR representation of the passage # Let's say you have documents in tab-separated csv files with columns "title" and "text" assert os.path.isfile(rag_example_args.csv_path), "Please provide a valid path to a csv file" # You can load a Dataset object this way lowercase__ : str = load_dataset( "csv" , data_files=[rag_example_args.csv_path] , split="train" , delimiter="\t" , column_names=["title", "text"]) # More info about loading csv files in the documentation: https://huggingface.co/docs/datasets/loading_datasets.html?highlight=csv#csv-files # Then split the documents into passages of 100 words lowercase__ : List[Any] = dataset.map(_lowerCamelCase , batched=_lowerCamelCase , num_proc=processing_args.num_proc) # And compute the embeddings lowercase__ : Optional[Any] = DPRContextEncoder.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name).to(device=_lowerCamelCase) lowercase__ : Any = DPRContextEncoderTokenizerFast.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name) lowercase__ : List[Any] = Features( {"text": Value("string"), "title": Value("string"), "embeddings": Sequence(Value("float32"))}) # optional, save as float32 instead of float64 to save space lowercase__ : List[Any] = dataset.map( partial(_lowerCamelCase , ctx_encoder=_lowerCamelCase , ctx_tokenizer=_lowerCamelCase) , batched=_lowerCamelCase , batch_size=processing_args.batch_size , features=_lowerCamelCase , ) # And finally save your dataset lowercase__ : Optional[int] = os.path.join(rag_example_args.output_dir , "my_knowledge_dataset") dataset.save_to_disk(_lowerCamelCase) # from datasets import load_from_disk # dataset = load_from_disk(passages_path) # to reload the dataset ###################################### logger.info("Step 2 - Index the dataset") ###################################### # Let's use the Faiss implementation of HNSW for fast approximate nearest neighbor search lowercase__ : Tuple = faiss.IndexHNSWFlat(index_hnsw_args.d , index_hnsw_args.m , faiss.METRIC_INNER_PRODUCT) dataset.add_faiss_index("embeddings" , custom_index=_lowerCamelCase) # And save the index lowercase__ : Union[str, Any] = os.path.join(rag_example_args.output_dir , "my_knowledge_dataset_hnsw_index.faiss") dataset.get_index("embeddings").save(_lowerCamelCase) # dataset.load_faiss_index("embeddings", index_path) # to reload the index @dataclass class snake_case_ : __A : str = field( default=str(Path(__A ).parent / "test_run" / "dummy-kb" / "my_knowledge_dataset.csv" ) ,metadata={"help": "Path to a tab-separated csv file with columns 'title' and 'text'"} ,) __A : Optional[str] = field( default=__A ,metadata={"help": "Question that is passed as input to RAG. Default is 'What does Moses' rod turn into ?'."} ,) __A : str = field( default="facebook/rag-sequence-nq" ,metadata={"help": "The RAG model to use. Either 'facebook/rag-sequence-nq' or 'facebook/rag-token-nq'"} ,) __A : str = field( default="facebook/dpr-ctx_encoder-multiset-base" ,metadata={ "help": ( "The DPR context encoder model to use. Either 'facebook/dpr-ctx_encoder-single-nq-base' or" " 'facebook/dpr-ctx_encoder-multiset-base'" ) } ,) __A : Optional[str] = field( default=str(Path(__A ).parent / "test_run" / "dummy-kb" ) ,metadata={"help": "Path to a directory where the dataset passages and the index will be saved"} ,) @dataclass class snake_case_ : __A : Optional[int] = field( default=__A ,metadata={ "help": "The number of processes to use to split the documents into passages. Default is single process." } ,) __A : int = field( default=16 ,metadata={ "help": "The batch size to use when computing the passages embeddings using the DPR context encoder." } ,) @dataclass class snake_case_ : __A : int = field( default=768 ,metadata={"help": "The dimension of the embeddings to pass to the HNSW Faiss index."} ,) __A : int = field( default=128 ,metadata={ "help": ( "The number of bi-directional links created for every new element during the HNSW index construction." ) } ,) if __name__ == "__main__": logging.basicConfig(level=logging.WARNING) logger.setLevel(logging.INFO) UpperCamelCase = HfArgumentParser((RagExampleArguments, ProcessingArguments, IndexHnswArguments)) UpperCamelCase , UpperCamelCase , UpperCamelCase = parser.parse_args_into_dataclasses() with TemporaryDirectory() as tmp_dir: UpperCamelCase = rag_example_args.output_dir or tmp_dir main(rag_example_args, processing_args, index_hnsw_args)
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"""simple docstring""" import math from enum import Enum from typing import Optional, Union from torch.optim import Optimizer from torch.optim.lr_scheduler import LambdaLR from .utils import logging UpperCamelCase = logging.get_logger(__name__) class snake_case_ ( __A ): __A : Any = "linear" __A : Any = "cosine" __A : Dict = "cosine_with_restarts" __A : Any = "polynomial" __A : Optional[Any] = "constant" __A : Any = "constant_with_warmup" __A : Any = "piecewise_constant" def lowercase_ ( _lowerCamelCase : Optimizer , _lowerCamelCase : int = -1): return LambdaLR(_lowerCamelCase , lambda _lowerCamelCase: 1 , last_epoch=_lowerCamelCase) def lowercase_ ( _lowerCamelCase : Optimizer , _lowerCamelCase : int , _lowerCamelCase : int = -1): def lr_lambda(_lowerCamelCase : int): if current_step < num_warmup_steps: return float(_lowerCamelCase) / float(max(1.0 , _lowerCamelCase)) return 1.0 return LambdaLR(_lowerCamelCase , _lowerCamelCase , last_epoch=_lowerCamelCase) def lowercase_ ( _lowerCamelCase : Optimizer , _lowerCamelCase : str , _lowerCamelCase : int = -1): lowercase__ : Dict = {} lowercase__ : Union[str, Any] = step_rules.split(",") for rule_str in rule_list[:-1]: lowercase__ : List[str] = rule_str.split(":") lowercase__ : int = int(_lowerCamelCase) lowercase__ : List[str] = float(_lowerCamelCase) lowercase__ : Dict = value lowercase__ : int = float(rule_list[-1]) def create_rules_function(_lowerCamelCase : Optional[int] , _lowerCamelCase : Any): def rule_func(_lowerCamelCase : int) -> float: lowercase__ : Optional[int] = sorted(rules_dict.keys()) for i, sorted_step in enumerate(_lowerCamelCase): if steps < sorted_step: return rules_dict[sorted_steps[i]] return last_lr_multiple return rule_func lowercase__ : Tuple = create_rules_function(_lowerCamelCase , _lowerCamelCase) return LambdaLR(_lowerCamelCase , _lowerCamelCase , last_epoch=_lowerCamelCase) def lowercase_ ( _lowerCamelCase : Any , _lowerCamelCase : Dict , _lowerCamelCase : List[Any] , _lowerCamelCase : List[Any]=-1): def lr_lambda(_lowerCamelCase : int): if current_step < num_warmup_steps: return float(_lowerCamelCase) / float(max(1 , _lowerCamelCase)) return max( 0.0 , float(num_training_steps - current_step) / float(max(1 , num_training_steps - num_warmup_steps))) return LambdaLR(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase) def lowercase_ ( _lowerCamelCase : Optimizer , _lowerCamelCase : int , _lowerCamelCase : int , _lowerCamelCase : float = 0.5 , _lowerCamelCase : int = -1): def lr_lambda(_lowerCamelCase : str): if current_step < num_warmup_steps: return float(_lowerCamelCase) / float(max(1 , _lowerCamelCase)) lowercase__ : Optional[Any] = float(current_step - num_warmup_steps) / float(max(1 , num_training_steps - num_warmup_steps)) return max(0.0 , 0.5 * (1.0 + math.cos(math.pi * float(_lowerCamelCase) * 2.0 * progress))) return LambdaLR(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase) def lowercase_ ( _lowerCamelCase : Optimizer , _lowerCamelCase : int , _lowerCamelCase : int , _lowerCamelCase : int = 1 , _lowerCamelCase : int = -1): def lr_lambda(_lowerCamelCase : List[Any]): if current_step < num_warmup_steps: return float(_lowerCamelCase) / float(max(1 , _lowerCamelCase)) lowercase__ : Tuple = float(current_step - num_warmup_steps) / float(max(1 , num_training_steps - num_warmup_steps)) if progress >= 1.0: return 0.0 return max(0.0 , 0.5 * (1.0 + math.cos(math.pi * ((float(_lowerCamelCase) * progress) % 1.0)))) return LambdaLR(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase) def lowercase_ ( _lowerCamelCase : Tuple , _lowerCamelCase : Optional[Any] , _lowerCamelCase : int , _lowerCamelCase : Optional[Any]=1E-7 , _lowerCamelCase : Union[str, Any]=1.0 , _lowerCamelCase : List[Any]=-1): lowercase__ : Any = optimizer.defaults["lr"] if not (lr_init > lr_end): raise ValueError(f'''lr_end ({lr_end}) must be be smaller than initial lr ({lr_init})''') def lr_lambda(_lowerCamelCase : int): if current_step < num_warmup_steps: return float(_lowerCamelCase) / float(max(1 , _lowerCamelCase)) elif current_step > num_training_steps: return lr_end / lr_init # as LambdaLR multiplies by lr_init else: lowercase__ : List[str] = lr_init - lr_end lowercase__ : Optional[Any] = num_training_steps - num_warmup_steps lowercase__ : Tuple = 1 - (current_step - num_warmup_steps) / decay_steps lowercase__ : List[str] = lr_range * pct_remaining**power + lr_end return decay / lr_init # as LambdaLR multiplies by lr_init return LambdaLR(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase) UpperCamelCase = { SchedulerType.LINEAR: get_linear_schedule_with_warmup, SchedulerType.COSINE: get_cosine_schedule_with_warmup, SchedulerType.COSINE_WITH_RESTARTS: get_cosine_with_hard_restarts_schedule_with_warmup, SchedulerType.POLYNOMIAL: get_polynomial_decay_schedule_with_warmup, SchedulerType.CONSTANT: get_constant_schedule, SchedulerType.CONSTANT_WITH_WARMUP: get_constant_schedule_with_warmup, SchedulerType.PIECEWISE_CONSTANT: get_piecewise_constant_schedule, } def lowercase_ ( _lowerCamelCase : Union[str, SchedulerType] , _lowerCamelCase : Optimizer , _lowerCamelCase : Optional[str] = None , _lowerCamelCase : Optional[int] = None , _lowerCamelCase : Optional[int] = None , _lowerCamelCase : int = 1 , _lowerCamelCase : float = 1.0 , _lowerCamelCase : int = -1 , ): lowercase__ : Optional[int] = SchedulerType(_lowerCamelCase) lowercase__ : Optional[Any] = TYPE_TO_SCHEDULER_FUNCTION[name] if name == SchedulerType.CONSTANT: return schedule_func(_lowerCamelCase , last_epoch=_lowerCamelCase) if name == SchedulerType.PIECEWISE_CONSTANT: return schedule_func(_lowerCamelCase , step_rules=_lowerCamelCase , last_epoch=_lowerCamelCase) # All other schedulers require `num_warmup_steps` if num_warmup_steps is None: raise ValueError(f'''{name} requires `num_warmup_steps`, please provide that argument.''') if name == SchedulerType.CONSTANT_WITH_WARMUP: return schedule_func(_lowerCamelCase , num_warmup_steps=_lowerCamelCase , last_epoch=_lowerCamelCase) # All other schedulers require `num_training_steps` if num_training_steps is None: raise ValueError(f'''{name} requires `num_training_steps`, please provide that argument.''') if name == SchedulerType.COSINE_WITH_RESTARTS: return schedule_func( _lowerCamelCase , num_warmup_steps=_lowerCamelCase , num_training_steps=_lowerCamelCase , num_cycles=_lowerCamelCase , last_epoch=_lowerCamelCase , ) if name == SchedulerType.POLYNOMIAL: return schedule_func( _lowerCamelCase , num_warmup_steps=_lowerCamelCase , num_training_steps=_lowerCamelCase , power=_lowerCamelCase , last_epoch=_lowerCamelCase , ) return schedule_func( _lowerCamelCase , num_warmup_steps=_lowerCamelCase , num_training_steps=_lowerCamelCase , last_epoch=_lowerCamelCase)
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import argparse import datetime def lowercase_ ( _lowerCamelCase : str): lowercase__ : Optional[Any] = { "0": "Sunday", "1": "Monday", "2": "Tuesday", "3": "Wednesday", "4": "Thursday", "5": "Friday", "6": "Saturday", } lowercase__ : Any = {0: 1, 1: 2, 2: 3, 3: 4, 4: 5, 5: 6, 6: 0} # Validate if not 0 < len(_lowerCamelCase) < 11: raise ValueError("Must be 10 characters long") # Get month lowercase__ : int = int(date_input[0] + date_input[1]) # Validate if not 0 < m < 13: raise ValueError("Month must be between 1 - 12") lowercase__ : str = date_input[2] # Validate if sep_a not in ["-", "/"]: raise ValueError("Date separator must be '-' or '/'") # Get day lowercase__ : int = int(date_input[3] + date_input[4]) # Validate if not 0 < d < 32: raise ValueError("Date must be between 1 - 31") # Get second separator lowercase__ : str = date_input[5] # Validate if sep_a not in ["-", "/"]: raise ValueError("Date separator must be '-' or '/'") # Get year lowercase__ : int = int(date_input[6] + date_input[7] + date_input[8] + date_input[9]) # Arbitrary year range if not 45 < y < 8500: raise ValueError( "Year out of range. There has to be some sort of limit...right?") # Get datetime obj for validation lowercase__ : Union[str, Any] = datetime.date(int(_lowerCamelCase) , int(_lowerCamelCase) , int(_lowerCamelCase)) # Start math if m <= 2: lowercase__ : Optional[Any] = y - 1 lowercase__ : int = m + 12 # maths var lowercase__ : int = int(str(_lowerCamelCase)[:2]) lowercase__ : int = int(str(_lowerCamelCase)[2:]) lowercase__ : int = int(2.6 * m - 5.39) lowercase__ : int = int(c / 4) lowercase__ : int = int(k / 4) lowercase__ : int = int(d + k) lowercase__ : int = int(t + u + v + x) lowercase__ : int = int(z - (2 * c)) lowercase__ : int = round(w % 7) # End math # Validate math if f != convert_datetime_days[dt_ck.weekday()]: raise AssertionError("The date was evaluated incorrectly. Contact developer.") # Response lowercase__ : str = f'''Your date {date_input}, is a {days[str(_lowerCamelCase)]}!''' return response if __name__ == "__main__": import doctest doctest.testmod() UpperCamelCase = argparse.ArgumentParser( description=( '''Find out what day of the week nearly any date is or was. Enter ''' '''date as a string in the mm-dd-yyyy or mm/dd/yyyy format''' ) ) parser.add_argument( '''date_input''', type=str, help='''Date as a string (mm-dd-yyyy or mm/dd/yyyy)''' ) UpperCamelCase = parser.parse_args() zeller(args.date_input)
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"""simple docstring""" def lowercase_ ( _lowerCamelCase : int): lowercase__ : Optional[Any] = n ** (1 / 3) return (val * val * val) == n if __name__ == "__main__": print(perfect_cube(27)) print(perfect_cube(4))
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import os from argparse import ArgumentParser from typing import List import torch.utils.data from datasets import Dataset, IterableDataset from datasets.distributed import split_dataset_by_node UpperCamelCase = 4 UpperCamelCase = 3 class snake_case_ ( __A ): pass def lowercase_ ( _lowerCamelCase : List[str]): for shard in shards: for i in range(_lowerCamelCase): yield {"i": i, "shard": shard} def lowercase_ ( ): lowercase__ : List[str] = int(os.environ["RANK"]) lowercase__ : Union[str, Any] = int(os.environ["WORLD_SIZE"]) lowercase__ : Union[str, Any] = ArgumentParser() parser.add_argument("--streaming" , type=_lowerCamelCase) parser.add_argument("--local_rank" , type=_lowerCamelCase) parser.add_argument("--num_workers" , type=_lowerCamelCase , default=0) lowercase__ : int = parser.parse_args() lowercase__ : Union[str, Any] = args.streaming lowercase__ : List[Any] = args.num_workers lowercase__ : Dict = {"shards": [f'''shard_{shard_idx}''' for shard_idx in range(_lowerCamelCase)]} lowercase__ : int = IterableDataset.from_generator(_lowerCamelCase , gen_kwargs=_lowerCamelCase) if not streaming: lowercase__ : str = Dataset.from_list(list(_lowerCamelCase)) lowercase__ : List[str] = split_dataset_by_node(_lowerCamelCase , rank=_lowerCamelCase , world_size=_lowerCamelCase) lowercase__ : Any = torch.utils.data.DataLoader(_lowerCamelCase , num_workers=_lowerCamelCase) lowercase__ : Dict = NUM_SHARDS * NUM_ITEMS_PER_SHARD lowercase__ : Any = full_size // world_size expected_local_size += int(rank < (full_size % world_size)) lowercase__ : List[str] = sum(1 for _ in dataloader) if local_size != expected_local_size: raise FailedTestError(f'''local_size {local_size} != expected_local_size {expected_local_size}''') if __name__ == "__main__": main()
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import os import zipfile import requests from get_ci_error_statistics import download_artifact, get_artifacts_links def lowercase_ ( _lowerCamelCase : Optional[Any] , _lowerCamelCase : List[Any]=7): lowercase__ : Any = None if token is not None: lowercase__ : List[str] = {"Accept": "application/vnd.github+json", "Authorization": f'''Bearer {token}'''} # The id of a workflow (not of a workflow run) lowercase__ : Tuple = "636036" lowercase__ : int = f'''https://api.github.com/repos/huggingface/transformers/actions/workflows/{workflow_id}/runs''' # On `main` branch + event being `schedule` + not returning PRs + only `num_runs` results url += f'''?branch=main&event=schedule&exclude_pull_requests=true&per_page={num_runs}''' lowercase__ : List[str] = requests.get(_lowerCamelCase , headers=_lowerCamelCase).json() return result["workflow_runs"] def lowercase_ ( _lowerCamelCase : Tuple): lowercase__ : Tuple = get_daily_ci_runs(_lowerCamelCase) lowercase__ : List[str] = None for workflow_run in workflow_runs: if workflow_run["status"] == "completed": lowercase__ : List[str] = workflow_run["id"] break return workflow_run_id def lowercase_ ( _lowerCamelCase : int , _lowerCamelCase : str , _lowerCamelCase : Tuple): lowercase__ : Tuple = get_last_daily_ci_runs(_lowerCamelCase) if workflow_run_id is not None: lowercase__ : Optional[Any] = get_artifacts_links(worflow_run_id=_lowerCamelCase , token=_lowerCamelCase) for artifact_name in artifact_names: if artifact_name in artifacts_links: lowercase__ : List[Any] = artifacts_links[artifact_name] download_artifact( artifact_name=_lowerCamelCase , artifact_url=_lowerCamelCase , output_dir=_lowerCamelCase , token=_lowerCamelCase) def lowercase_ ( _lowerCamelCase : int , _lowerCamelCase : List[str] , _lowerCamelCase : str): get_last_daily_ci_artifacts(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase) lowercase__ : Optional[int] = {} for artifact_name in artifact_names: lowercase__ : str = os.path.join(_lowerCamelCase , f'''{artifact_name}.zip''') if os.path.isfile(_lowerCamelCase): lowercase__ : Optional[int] = {} with zipfile.ZipFile(_lowerCamelCase) as z: for filename in z.namelist(): if not os.path.isdir(_lowerCamelCase): # read the file with z.open(_lowerCamelCase) as f: lowercase__ : Dict = f.read().decode("UTF-8") return results
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import functools import operator from ...configuration_utils import PretrainedConfig from ...utils import logging UpperCamelCase = logging.get_logger(__name__) UpperCamelCase = { '''microsoft/unispeech-large-1500h-cv''': ( '''https://huggingface.co/microsoft/unispeech-large-1500h-cv/resolve/main/config.json''' ), # See all UniSpeech models at https://huggingface.co/models?filter=unispeech } class snake_case_ ( __A ): __A : List[str] = "unispeech" def __init__( self : List[Any] , lowercase_ : Optional[int]=32 , lowercase_ : Optional[int]=7_68 , lowercase_ : List[str]=12 , lowercase_ : Union[str, Any]=12 , lowercase_ : Union[str, Any]=30_72 , lowercase_ : List[Any]="gelu" , lowercase_ : int=0.1 , lowercase_ : Union[str, Any]=0.1 , lowercase_ : str=0.1 , lowercase_ : Union[str, Any]=0.0 , lowercase_ : List[str]=0.0 , lowercase_ : List[Any]=0.1 , lowercase_ : Any=0.1 , lowercase_ : Optional[Any]=0.02 , lowercase_ : int=1E-5 , lowercase_ : int="group" , lowercase_ : Tuple="gelu" , lowercase_ : Dict=(5_12, 5_12, 5_12, 5_12, 5_12, 5_12, 5_12) , lowercase_ : Union[str, Any]=(5, 2, 2, 2, 2, 2, 2) , lowercase_ : List[str]=(10, 3, 3, 3, 3, 2, 2) , lowercase_ : int=False , lowercase_ : List[Any]=1_28 , lowercase_ : Optional[Any]=16 , lowercase_ : Union[str, Any]=False , lowercase_ : Tuple=True , lowercase_ : Union[str, Any]=0.05 , lowercase_ : Optional[Any]=10 , lowercase_ : Any=2 , lowercase_ : int=0.0 , lowercase_ : Union[str, Any]=10 , lowercase_ : Optional[Any]=0 , lowercase_ : List[str]=3_20 , lowercase_ : Dict=2 , lowercase_ : Optional[int]=0.1 , lowercase_ : Tuple=1_00 , lowercase_ : Dict=2_56 , lowercase_ : Optional[Any]=2_56 , lowercase_ : Union[str, Any]=0.1 , lowercase_ : List[Any]="mean" , lowercase_ : Union[str, Any]=False , lowercase_ : Tuple=False , lowercase_ : Dict=2_56 , lowercase_ : Union[str, Any]=80 , lowercase_ : int=0 , lowercase_ : Union[str, Any]=1 , lowercase_ : Dict=2 , lowercase_ : Optional[int]=0.5 , **lowercase_ : Union[str, Any] , ) -> Any: super().__init__(**lowercase_ , pad_token_id=lowercase_ , bos_token_id=lowercase_ , eos_token_id=lowercase_ ) lowercase__ : List[str] = hidden_size lowercase__ : Any = feat_extract_norm lowercase__ : Optional[Any] = feat_extract_activation lowercase__ : Dict = list(lowercase_ ) lowercase__ : Union[str, Any] = list(lowercase_ ) lowercase__ : List[str] = list(lowercase_ ) lowercase__ : List[str] = conv_bias lowercase__ : Any = num_conv_pos_embeddings lowercase__ : Dict = num_conv_pos_embedding_groups lowercase__ : int = len(self.conv_dim ) lowercase__ : str = num_hidden_layers lowercase__ : Any = intermediate_size lowercase__ : Optional[int] = hidden_act lowercase__ : int = num_attention_heads lowercase__ : Union[str, Any] = hidden_dropout lowercase__ : Any = attention_dropout lowercase__ : Union[str, Any] = activation_dropout lowercase__ : Any = feat_proj_dropout lowercase__ : str = final_dropout lowercase__ : int = layerdrop lowercase__ : Optional[int] = layer_norm_eps lowercase__ : List[Any] = initializer_range lowercase__ : Any = num_ctc_classes lowercase__ : int = vocab_size lowercase__ : str = do_stable_layer_norm lowercase__ : Any = use_weighted_layer_sum lowercase__ : Dict = classifier_proj_size if ( (len(self.conv_stride ) != self.num_feat_extract_layers) or (len(self.conv_kernel ) != self.num_feat_extract_layers) or (len(self.conv_dim ) != self.num_feat_extract_layers) ): raise ValueError( "Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` ==" " `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) =" F''' {len(self.conv_dim )}`, `len(config.conv_stride) = {len(self.conv_stride )}`,''' F''' `len(config.conv_kernel) = {len(self.conv_kernel )}`.''' ) # fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779 lowercase__ : List[Any] = apply_spec_augment lowercase__ : Dict = mask_time_prob lowercase__ : Tuple = mask_time_length lowercase__ : str = mask_time_min_masks lowercase__ : List[Any] = mask_feature_prob lowercase__ : int = mask_feature_length lowercase__ : Optional[int] = mask_feature_min_masks # parameters for pretraining with codevector quantized representations lowercase__ : Optional[int] = num_codevectors_per_group lowercase__ : List[str] = num_codevector_groups lowercase__ : Dict = contrastive_logits_temperature lowercase__ : Tuple = feat_quantizer_dropout lowercase__ : Any = num_negatives lowercase__ : Dict = codevector_dim lowercase__ : Tuple = proj_codevector_dim lowercase__ : List[str] = diversity_loss_weight # ctc loss lowercase__ : Tuple = ctc_loss_reduction lowercase__ : Dict = ctc_zero_infinity # pretraining loss lowercase__ : Optional[Any] = replace_prob @property def __UpperCamelCase ( self : Dict ) -> Tuple: return functools.reduce(operator.mul , self.conv_stride , 1 )
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from ...configuration_utils import PretrainedConfig from ...utils import logging UpperCamelCase = logging.get_logger(__name__) UpperCamelCase = { '''facebook/vit-mae-base''': '''https://huggingface.co/facebook/vit-mae-base/resolve/main/config.json''', # See all ViT MAE models at https://huggingface.co/models?filter=vit-mae } class snake_case_ ( __A ): __A : List[str] = "vit_mae" def __init__( self : List[Any] , lowercase_ : List[Any]=7_68 , lowercase_ : Tuple=12 , lowercase_ : Tuple=12 , lowercase_ : Optional[Any]=30_72 , lowercase_ : str="gelu" , lowercase_ : Tuple=0.0 , lowercase_ : int=0.0 , lowercase_ : Dict=0.02 , lowercase_ : int=1E-12 , lowercase_ : Tuple=2_24 , lowercase_ : Any=16 , lowercase_ : Dict=3 , lowercase_ : List[Any]=True , lowercase_ : Dict=16 , lowercase_ : List[str]=5_12 , lowercase_ : Tuple=8 , lowercase_ : Any=20_48 , lowercase_ : int=0.75 , lowercase_ : Tuple=False , **lowercase_ : Optional[int] , ) -> Optional[Any]: super().__init__(**lowercase_ ) lowercase__ : List[str] = hidden_size lowercase__ : str = num_hidden_layers lowercase__ : Optional[int] = num_attention_heads lowercase__ : List[Any] = intermediate_size lowercase__ : str = hidden_act lowercase__ : List[str] = hidden_dropout_prob lowercase__ : Optional[Any] = attention_probs_dropout_prob lowercase__ : Any = initializer_range lowercase__ : Optional[Any] = layer_norm_eps lowercase__ : Optional[Any] = image_size lowercase__ : Optional[int] = patch_size lowercase__ : Any = num_channels lowercase__ : str = qkv_bias lowercase__ : Optional[Any] = decoder_num_attention_heads lowercase__ : Any = decoder_hidden_size lowercase__ : Any = decoder_num_hidden_layers lowercase__ : Union[str, Any] = decoder_intermediate_size lowercase__ : int = mask_ratio lowercase__ : Tuple = norm_pix_loss
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def lowercase_ ( _lowerCamelCase : list): for i in range(len(_lowerCamelCase) - 1 , 0 , -1): lowercase__ : int = False for j in range(_lowerCamelCase , 0 , -1): if unsorted[j] < unsorted[j - 1]: lowercase__ , lowercase__ : int = unsorted[j - 1], unsorted[j] lowercase__ : List[str] = True for j in range(_lowerCamelCase): if unsorted[j] > unsorted[j + 1]: lowercase__ , lowercase__ : Optional[int] = unsorted[j + 1], unsorted[j] lowercase__ : Dict = True if not swapped: break return unsorted if __name__ == "__main__": import doctest doctest.testmod() UpperCamelCase = input('''Enter numbers separated by a comma:\n''').strip() UpperCamelCase = [int(item) for item in user_input.split(''',''')] print(f"{cocktail_shaker_sort(unsorted) = }")
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import warnings from typing import List import numpy as np from ...processing_utils import ProcessorMixin from ...tokenization_utils_base import BatchEncoding from ...utils import is_flax_available, is_tf_available, is_torch_available class snake_case_ ( __A ): __A : Any = ["image_processor", "tokenizer"] __A : str = "OwlViTImageProcessor" __A : Optional[Any] = ("CLIPTokenizer", "CLIPTokenizerFast") def __init__( self : int , lowercase_ : Union[str, Any]=None , lowercase_ : Tuple=None , **lowercase_ : Any ) -> str: lowercase__ : str = None if "feature_extractor" in kwargs: warnings.warn( "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`" " instead." , lowercase_ , ) lowercase__ : Any = kwargs.pop("feature_extractor" ) lowercase__ : List[Any] = image_processor if image_processor is not None else feature_extractor if image_processor is None: raise ValueError("You need to specify an `image_processor`." ) if tokenizer is None: raise ValueError("You need to specify a `tokenizer`." ) super().__init__(lowercase_ , lowercase_ ) def __call__( self : List[Any] , lowercase_ : List[Any]=None , lowercase_ : str=None , lowercase_ : Union[str, Any]=None , lowercase_ : int="max_length" , lowercase_ : List[str]="np" , **lowercase_ : Any ) -> List[Any]: if text is None and query_images is None and images is None: raise ValueError( "You have to specify at least one text or query image or image. All three cannot be none." ) if text is not None: if isinstance(lowercase_ , lowercase_ ) or (isinstance(lowercase_ , lowercase_ ) and not isinstance(text[0] , lowercase_ )): lowercase__ : Optional[int] = [self.tokenizer(lowercase_ , padding=lowercase_ , return_tensors=lowercase_ , **lowercase_ )] elif isinstance(lowercase_ , lowercase_ ) and isinstance(text[0] , lowercase_ ): lowercase__ : str = [] # Maximum number of queries across batch lowercase__ : int = max([len(lowercase_ ) for t in text] ) # Pad all batch samples to max number of text queries for t in text: if len(lowercase_ ) != max_num_queries: lowercase__ : Tuple = t + [" "] * (max_num_queries - len(lowercase_ )) lowercase__ : Optional[int] = self.tokenizer(lowercase_ , padding=lowercase_ , return_tensors=lowercase_ , **lowercase_ ) encodings.append(lowercase_ ) else: raise TypeError("Input text should be a string, a list of strings or a nested list of strings" ) if return_tensors == "np": lowercase__ : Dict = np.concatenate([encoding["input_ids"] for encoding in encodings] , axis=0 ) lowercase__ : int = np.concatenate([encoding["attention_mask"] for encoding in encodings] , axis=0 ) elif return_tensors == "jax" and is_flax_available(): import jax.numpy as jnp lowercase__ : Union[str, Any] = jnp.concatenate([encoding["input_ids"] for encoding in encodings] , axis=0 ) lowercase__ : str = jnp.concatenate([encoding["attention_mask"] for encoding in encodings] , axis=0 ) elif return_tensors == "pt" and is_torch_available(): import torch lowercase__ : List[Any] = torch.cat([encoding["input_ids"] for encoding in encodings] , dim=0 ) lowercase__ : Dict = torch.cat([encoding["attention_mask"] for encoding in encodings] , dim=0 ) elif return_tensors == "tf" and is_tf_available(): import tensorflow as tf lowercase__ : str = tf.stack([encoding["input_ids"] for encoding in encodings] , axis=0 ) lowercase__ : int = tf.stack([encoding["attention_mask"] for encoding in encodings] , axis=0 ) else: raise ValueError("Target return tensor type could not be returned" ) lowercase__ : int = BatchEncoding() lowercase__ : Optional[Any] = input_ids lowercase__ : Dict = attention_mask if query_images is not None: lowercase__ : Any = BatchEncoding() lowercase__ : Union[str, Any] = self.image_processor( lowercase_ , return_tensors=lowercase_ , **lowercase_ ).pixel_values lowercase__ : int = query_pixel_values if images is not None: lowercase__ : List[Any] = self.image_processor(lowercase_ , return_tensors=lowercase_ , **lowercase_ ) if text is not None and images is not None: lowercase__ : Optional[int] = image_features.pixel_values return encoding elif query_images is not None and images is not None: lowercase__ : int = image_features.pixel_values return encoding elif text is not None or query_images is not None: return encoding else: return BatchEncoding(data=dict(**lowercase_ ) , tensor_type=lowercase_ ) def __UpperCamelCase ( self : Optional[int] , *lowercase_ : Optional[Any] , **lowercase_ : int ) -> List[Any]: return self.image_processor.post_process(*lowercase_ , **lowercase_ ) def __UpperCamelCase ( self : str , *lowercase_ : str , **lowercase_ : Optional[Any] ) -> Optional[int]: return self.image_processor.post_process_object_detection(*lowercase_ , **lowercase_ ) def __UpperCamelCase ( self : Union[str, Any] , *lowercase_ : List[str] , **lowercase_ : List[Any] ) -> Any: return self.image_processor.post_process_image_guided_detection(*lowercase_ , **lowercase_ ) def __UpperCamelCase ( self : str , *lowercase_ : Dict , **lowercase_ : str ) -> List[str]: return self.tokenizer.batch_decode(*lowercase_ , **lowercase_ ) def __UpperCamelCase ( self : Any , *lowercase_ : str , **lowercase_ : List[Any] ) -> int: return self.tokenizer.decode(*lowercase_ , **lowercase_ ) @property def __UpperCamelCase ( self : Tuple ) -> Optional[int]: warnings.warn( "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead." , lowercase_ , ) return self.image_processor_class @property def __UpperCamelCase ( self : Any ) -> Optional[int]: warnings.warn( "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead." , lowercase_ , ) return self.image_processor
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import argparse import glob import logging import os from argparse import Namespace from importlib import import_module import numpy as np import torch from lightning_base import BaseTransformer, add_generic_args, generic_train from seqeval.metrics import accuracy_score, fa_score, precision_score, recall_score from torch.nn import CrossEntropyLoss from torch.utils.data import DataLoader, TensorDataset from utils_ner import TokenClassificationTask UpperCamelCase = logging.getLogger(__name__) class snake_case_ ( __A ): __A : int = "token-classification" def __init__( self : Tuple , lowercase_ : Dict ) -> List[str]: if type(lowercase_ ) == dict: lowercase__ : Dict = Namespace(**lowercase_ ) lowercase__ : str = import_module("tasks" ) try: lowercase__ : Tuple = getattr(lowercase_ , hparams.task_type ) lowercase__ : TokenClassificationTask = token_classification_task_clazz() except AttributeError: raise ValueError( F'''Task {hparams.task_type} needs to be defined as a TokenClassificationTask subclass in {module}. ''' F'''Available tasks classes are: {TokenClassificationTask.__subclasses__()}''' ) lowercase__ : Optional[Any] = self.token_classification_task.get_labels(hparams.labels ) lowercase__ : int = CrossEntropyLoss().ignore_index super().__init__(lowercase_ , len(self.labels ) , self.mode ) def __UpperCamelCase ( self : Union[str, Any] , **lowercase_ : List[str] ) -> Any: return self.model(**lowercase_ ) def __UpperCamelCase ( self : Optional[Any] , lowercase_ : str , lowercase_ : Optional[int] ) -> Tuple: lowercase__ : int = {"input_ids": batch[0], "attention_mask": batch[1], "labels": batch[3]} if self.config.model_type != "distilbert": lowercase__ : Tuple = ( batch[2] if self.config.model_type in ["bert", "xlnet"] else None ) # XLM and RoBERTa don"t use token_type_ids lowercase__ : Optional[int] = self(**lowercase_ ) lowercase__ : Union[str, Any] = outputs[0] # tensorboard_logs = {"loss": loss, "rate": self.lr_scheduler.get_last_lr()[-1]} return {"loss": loss} def __UpperCamelCase ( self : Tuple ) -> Union[str, Any]: lowercase__ : Tuple = self.hparams for mode in ["train", "dev", "test"]: lowercase__ : Any = self._feature_file(lowercase_ ) if os.path.exists(lowercase_ ) and not args.overwrite_cache: logger.info("Loading features from cached file %s" , lowercase_ ) lowercase__ : str = torch.load(lowercase_ ) else: logger.info("Creating features from dataset file at %s" , args.data_dir ) lowercase__ : Optional[Any] = self.token_classification_task.read_examples_from_file(args.data_dir , lowercase_ ) lowercase__ : Dict = self.token_classification_task.convert_examples_to_features( lowercase_ , self.labels , args.max_seq_length , self.tokenizer , cls_token_at_end=bool(self.config.model_type in ["xlnet"] ) , cls_token=self.tokenizer.cls_token , cls_token_segment_id=2 if self.config.model_type in ["xlnet"] else 0 , sep_token=self.tokenizer.sep_token , sep_token_extra=lowercase_ , pad_on_left=bool(self.config.model_type in ["xlnet"] ) , pad_token=self.tokenizer.pad_token_id , pad_token_segment_id=self.tokenizer.pad_token_type_id , pad_token_label_id=self.pad_token_label_id , ) logger.info("Saving features into cached file %s" , lowercase_ ) torch.save(lowercase_ , lowercase_ ) def __UpperCamelCase ( self : Optional[Any] , lowercase_ : int , lowercase_ : int , lowercase_ : bool = False ) -> DataLoader: lowercase__ : str = self._feature_file(lowercase_ ) logger.info("Loading features from cached file %s" , lowercase_ ) lowercase__ : str = torch.load(lowercase_ ) lowercase__ : List[str] = torch.tensor([f.input_ids for f in features] , dtype=torch.long ) lowercase__ : str = torch.tensor([f.attention_mask for f in features] , dtype=torch.long ) if features[0].token_type_ids is not None: lowercase__ : Dict = torch.tensor([f.token_type_ids for f in features] , dtype=torch.long ) else: lowercase__ : Dict = torch.tensor([0 for f in features] , dtype=torch.long ) # HACK(we will not use this anymore soon) lowercase__ : List[str] = torch.tensor([f.label_ids for f in features] , dtype=torch.long ) return DataLoader( TensorDataset(lowercase_ , lowercase_ , lowercase_ , lowercase_ ) , batch_size=lowercase_ ) def __UpperCamelCase ( self : str , lowercase_ : Dict , lowercase_ : Tuple ) -> str: """Compute validation""" "" lowercase__ : Union[str, Any] = {"input_ids": batch[0], "attention_mask": batch[1], "labels": batch[3]} if self.config.model_type != "distilbert": lowercase__ : int = ( batch[2] if self.config.model_type in ["bert", "xlnet"] else None ) # XLM and RoBERTa don"t use token_type_ids lowercase__ : List[Any] = self(**lowercase_ ) lowercase__ , lowercase__ : Any = outputs[:2] lowercase__ : Optional[Any] = logits.detach().cpu().numpy() lowercase__ : int = inputs["labels"].detach().cpu().numpy() return {"val_loss": tmp_eval_loss.detach().cpu(), "pred": preds, "target": out_label_ids} def __UpperCamelCase ( self : Optional[int] , lowercase_ : Any ) -> List[Any]: lowercase__ : int = torch.stack([x["val_loss"] for x in outputs] ).mean() lowercase__ : Any = np.concatenate([x["pred"] for x in outputs] , axis=0 ) lowercase__ : Dict = np.argmax(lowercase_ , axis=2 ) lowercase__ : int = np.concatenate([x["target"] for x in outputs] , axis=0 ) lowercase__ : Any = dict(enumerate(self.labels ) ) lowercase__ : List[Any] = [[] for _ in range(out_label_ids.shape[0] )] lowercase__ : Dict = [[] for _ in range(out_label_ids.shape[0] )] for i in range(out_label_ids.shape[0] ): for j in range(out_label_ids.shape[1] ): if out_label_ids[i, j] != self.pad_token_label_id: out_label_list[i].append(label_map[out_label_ids[i][j]] ) preds_list[i].append(label_map[preds[i][j]] ) lowercase__ : Any = { "val_loss": val_loss_mean, "accuracy_score": accuracy_score(lowercase_ , lowercase_ ), "precision": precision_score(lowercase_ , lowercase_ ), "recall": recall_score(lowercase_ , lowercase_ ), "f1": fa_score(lowercase_ , lowercase_ ), } lowercase__ : List[Any] = dict(results.items() ) lowercase__ : List[str] = results return ret, preds_list, out_label_list def __UpperCamelCase ( self : Any , lowercase_ : Dict ) -> Dict: # when stable lowercase__ , lowercase__ , lowercase__ : Dict = self._eval_end(lowercase_ ) lowercase__ : Any = ret["log"] return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs} def __UpperCamelCase ( self : str , lowercase_ : Tuple ) -> int: # updating to test_epoch_end instead of deprecated test_end lowercase__ , lowercase__ , lowercase__ : Dict = self._eval_end(lowercase_ ) # Converting to the dict required by pl # https://github.com/PyTorchLightning/pytorch-lightning/blob/master/\ # pytorch_lightning/trainer/logging.py#L139 lowercase__ : Optional[int] = ret["log"] # `val_loss` is the key returned by `self._eval_end()` but actually refers to `test_loss` return {"avg_test_loss": logs["val_loss"], "log": logs, "progress_bar": logs} @staticmethod def __UpperCamelCase ( lowercase_ : int , lowercase_ : Union[str, Any] ) -> Tuple: # Add NER specific options BaseTransformer.add_model_specific_args(lowercase_ , lowercase_ ) parser.add_argument( "--task_type" , default="NER" , type=lowercase_ , help="Task type to fine tune in training (e.g. NER, POS, etc)" ) parser.add_argument( "--max_seq_length" , default=1_28 , type=lowercase_ , help=( "The maximum total input sequence length after tokenization. Sequences longer " "than this will be truncated, sequences shorter will be padded." ) , ) parser.add_argument( "--labels" , default="" , type=lowercase_ , help="Path to a file containing all labels. If not specified, CoNLL-2003 labels are used." , ) parser.add_argument( "--gpus" , default=0 , type=lowercase_ , help="The number of GPUs allocated for this, it is by default 0 meaning none" , ) parser.add_argument( "--overwrite_cache" , action="store_true" , help="Overwrite the cached training and evaluation sets" ) return parser if __name__ == "__main__": UpperCamelCase = argparse.ArgumentParser() add_generic_args(parser, os.getcwd()) UpperCamelCase = NERTransformer.add_model_specific_args(parser, os.getcwd()) UpperCamelCase = parser.parse_args() UpperCamelCase = NERTransformer(args) UpperCamelCase = generic_train(model, args) if args.do_predict: # See https://github.com/huggingface/transformers/issues/3159 # pl use this default format to create a checkpoint: # https://github.com/PyTorchLightning/pytorch-lightning/blob/master\ # /pytorch_lightning/callbacks/model_checkpoint.py#L322 UpperCamelCase = sorted(glob.glob(os.path.join(args.output_dir, '''checkpoint-epoch=*.ckpt'''), recursive=True)) UpperCamelCase = model.load_from_checkpoint(checkpoints[-1]) trainer.test(model)
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from collections import OrderedDict from typing import Any, Mapping, Optional from ... import PreTrainedTokenizer from ...configuration_utils import PretrainedConfig from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast from ...onnx.utils import compute_effective_axis_dimension from ...utils import TensorType, is_torch_available, logging UpperCamelCase = logging.get_logger(__name__) UpperCamelCase = { '''Helsinki-NLP/opus-mt-en-de''': '''https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/config.json''', # See all Marian models at https://huggingface.co/models?filter=marian } class snake_case_ ( __A ): __A : List[Any] = "marian" __A : Dict = ["past_key_values"] __A : Dict = {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"} def __init__( self : int , lowercase_ : Any=5_81_01 , lowercase_ : Union[str, Any]=None , lowercase_ : Optional[Any]=10_24 , lowercase_ : Tuple=12 , lowercase_ : Tuple=40_96 , lowercase_ : List[Any]=16 , lowercase_ : str=12 , lowercase_ : int=40_96 , lowercase_ : Optional[int]=16 , lowercase_ : List[Any]=0.0 , lowercase_ : str=0.0 , lowercase_ : Tuple=True , lowercase_ : Optional[Any]=True , lowercase_ : Any="gelu" , lowercase_ : Dict=10_24 , lowercase_ : List[str]=0.1 , lowercase_ : List[str]=0.0 , lowercase_ : Dict=0.0 , lowercase_ : List[Any]=0.02 , lowercase_ : Tuple=5_81_00 , lowercase_ : Any=False , lowercase_ : List[Any]=5_81_00 , lowercase_ : Dict=0 , lowercase_ : Tuple=0 , lowercase_ : Union[str, Any]=True , **lowercase_ : List[Any] , ) -> Optional[int]: lowercase__ : Dict = vocab_size lowercase__ : int = decoder_vocab_size or vocab_size lowercase__ : str = max_position_embeddings lowercase__ : Optional[Any] = d_model lowercase__ : List[Any] = encoder_ffn_dim lowercase__ : Tuple = encoder_layers lowercase__ : Union[str, Any] = encoder_attention_heads lowercase__ : Optional[Any] = decoder_ffn_dim lowercase__ : Optional[int] = decoder_layers lowercase__ : Optional[int] = decoder_attention_heads lowercase__ : int = dropout lowercase__ : int = attention_dropout lowercase__ : Optional[int] = activation_dropout lowercase__ : List[Any] = activation_function lowercase__ : Any = init_std lowercase__ : int = encoder_layerdrop lowercase__ : Dict = decoder_layerdrop lowercase__ : Any = use_cache lowercase__ : int = encoder_layers lowercase__ : Any = scale_embedding # scale factor will be sqrt(d_model) if True lowercase__ : int = share_encoder_decoder_embeddings super().__init__( pad_token_id=lowercase_ , eos_token_id=lowercase_ , is_encoder_decoder=lowercase_ , decoder_start_token_id=lowercase_ , forced_eos_token_id=lowercase_ , **lowercase_ , ) class snake_case_ ( __A ): @property # Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.inputs def __UpperCamelCase ( self : Dict ) -> Mapping[str, Mapping[int, str]]: if self.task in ["default", "seq2seq-lm"]: lowercase__ : Dict = OrderedDict( [ ("input_ids", {0: "batch", 1: "encoder_sequence"}), ("attention_mask", {0: "batch", 1: "encoder_sequence"}), ] ) if self.use_past: lowercase__ : Tuple = {0: "batch"} lowercase__ : int = {0: "batch", 1: "past_decoder_sequence + sequence"} else: lowercase__ : str = {0: "batch", 1: "decoder_sequence"} lowercase__ : List[Any] = {0: "batch", 1: "decoder_sequence"} if self.use_past: self.fill_with_past_key_values_(lowercase_ , direction="inputs" ) elif self.task == "causal-lm": # TODO: figure this case out. lowercase__ : int = OrderedDict( [ ("input_ids", {0: "batch", 1: "encoder_sequence"}), ("attention_mask", {0: "batch", 1: "encoder_sequence"}), ] ) if self.use_past: lowercase__ : List[Any] = self.num_layers for i in range(lowercase_ ): lowercase__ : Union[str, Any] = {0: "batch", 2: "past_sequence + sequence"} lowercase__ : str = {0: "batch", 2: "past_sequence + sequence"} else: lowercase__ : Tuple = OrderedDict( [ ("input_ids", {0: "batch", 1: "encoder_sequence"}), ("attention_mask", {0: "batch", 1: "encoder_sequence"}), ("decoder_input_ids", {0: "batch", 1: "decoder_sequence"}), ("decoder_attention_mask", {0: "batch", 1: "decoder_sequence"}), ] ) return common_inputs @property # Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.outputs def __UpperCamelCase ( self : Optional[int] ) -> Mapping[str, Mapping[int, str]]: if self.task in ["default", "seq2seq-lm"]: lowercase__ : List[Any] = super().outputs else: lowercase__ : Union[str, Any] = super(lowercase_ , self ).outputs if self.use_past: lowercase__ : List[Any] = self.num_layers for i in range(lowercase_ ): lowercase__ : List[Any] = {0: "batch", 2: "past_sequence + sequence"} lowercase__ : Union[str, Any] = {0: "batch", 2: "past_sequence + sequence"} return common_outputs def __UpperCamelCase ( self : Any , lowercase_ : PreTrainedTokenizer , lowercase_ : int = -1 , lowercase_ : int = -1 , lowercase_ : bool = False , lowercase_ : Optional[TensorType] = None , ) -> Mapping[str, Any]: lowercase__ : List[str] = self._generate_dummy_inputs_for_encoder_and_decoder( lowercase_ , lowercase_ , lowercase_ , lowercase_ , lowercase_ ) # Generate decoder inputs lowercase__ : Tuple = seq_length if not self.use_past else 1 lowercase__ : Union[str, Any] = self._generate_dummy_inputs_for_encoder_and_decoder( lowercase_ , lowercase_ , lowercase_ , lowercase_ , lowercase_ ) lowercase__ : Dict = {F'''decoder_{name}''': tensor for name, tensor in decoder_inputs.items()} lowercase__ : int = dict(**lowercase_ , **lowercase_ ) if self.use_past: if not is_torch_available(): raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed." ) else: import torch lowercase__ : List[Any] = common_inputs["input_ids"].shape lowercase__ : Optional[Any] = common_inputs["decoder_input_ids"].shape[1] lowercase__ : Optional[int] = self.num_attention_heads lowercase__ : Any = ( batch, num_encoder_attention_heads, encoder_seq_length, self._config.hidden_size // num_encoder_attention_heads, ) lowercase__ : Union[str, Any] = decoder_seq_length + 3 lowercase__ : Tuple = ( batch, num_decoder_attention_heads, decoder_past_length, self._config.hidden_size // num_decoder_attention_heads, ) lowercase__ : Tuple = torch.cat( [common_inputs["decoder_attention_mask"], torch.ones(lowercase_ , lowercase_ )] , dim=1 ) lowercase__ : int = [] # If the number of encoder and decoder layers are present in the model configuration, both are considered lowercase__ : str = self.num_layers lowercase__ : Union[str, Any] = min(lowercase_ , lowercase_ ) lowercase__ : Tuple = max(lowercase_ , lowercase_ ) - min_num_layers lowercase__ : List[Any] = "encoder" if num_encoder_layers > num_decoder_layers else "decoder" for _ in range(lowercase_ ): common_inputs["past_key_values"].append( ( torch.zeros(lowercase_ ), torch.zeros(lowercase_ ), torch.zeros(lowercase_ ), torch.zeros(lowercase_ ), ) ) # TODO: test this. lowercase__ : List[str] = encoder_shape if remaining_side_name == "encoder" else decoder_shape for _ in range(lowercase_ , lowercase_ ): common_inputs["past_key_values"].append((torch.zeros(lowercase_ ), torch.zeros(lowercase_ )) ) return common_inputs def __UpperCamelCase ( self : Optional[Any] , lowercase_ : PreTrainedTokenizer , lowercase_ : int = -1 , lowercase_ : int = -1 , lowercase_ : bool = False , lowercase_ : Optional[TensorType] = None , ) -> Mapping[str, Any]: lowercase__ : List[Any] = self._generate_dummy_inputs_for_encoder_and_decoder( lowercase_ , lowercase_ , lowercase_ , lowercase_ , lowercase_ ) if self.use_past: if not is_torch_available(): raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed." ) else: import torch lowercase__ : Any = common_inputs["input_ids"].shape # Not using the same length for past_key_values lowercase__ : Optional[Any] = seqlen + 2 lowercase__ : Union[str, Any] = self.num_layers lowercase__ : Any = self.num_attention_heads lowercase__ : int = ( batch, num_encoder_attention_heads, past_key_values_length, self._config.hidden_size // num_encoder_attention_heads, ) lowercase__ : List[str] = common_inputs["attention_mask"].dtype lowercase__ : Dict = torch.cat( [common_inputs["attention_mask"], torch.ones(lowercase_ , lowercase_ , dtype=lowercase_ )] , dim=1 ) lowercase__ : Dict = [ (torch.zeros(lowercase_ ), torch.zeros(lowercase_ )) for _ in range(lowercase_ ) ] return common_inputs def __UpperCamelCase ( self : Tuple , lowercase_ : PreTrainedTokenizer , lowercase_ : int = -1 , lowercase_ : int = -1 , lowercase_ : bool = False , lowercase_ : Optional[TensorType] = None , ) -> Mapping[str, Any]: # Copied from OnnxConfig.generate_dummy_inputs # Did not use super(OnnxConfigWithPast, self).generate_dummy_inputs for code clarity. # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX lowercase__ : str = compute_effective_axis_dimension( lowercase_ , fixed_dimension=OnnxConfig.default_fixed_batch , num_token_to_add=0 ) # If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX lowercase__ : List[Any] = tokenizer.num_special_tokens_to_add(lowercase_ ) lowercase__ : Dict = compute_effective_axis_dimension( lowercase_ , fixed_dimension=OnnxConfig.default_fixed_sequence , num_token_to_add=lowercase_ ) # Generate dummy inputs according to compute batch and sequence lowercase__ : str = [" ".join([tokenizer.unk_token] ) * seq_length] * batch_size lowercase__ : Optional[Any] = dict(tokenizer(lowercase_ , return_tensors=lowercase_ ) ) return common_inputs def __UpperCamelCase ( self : Any , lowercase_ : PreTrainedTokenizer , lowercase_ : int = -1 , lowercase_ : int = -1 , lowercase_ : bool = False , lowercase_ : Optional[TensorType] = None , ) -> Mapping[str, Any]: if self.task in ["default", "seq2seq-lm"]: lowercase__ : Union[str, Any] = self._generate_dummy_inputs_for_default_and_seqaseq_lm( lowercase_ , batch_size=lowercase_ , seq_length=lowercase_ , is_pair=lowercase_ , framework=lowercase_ ) else: lowercase__ : Optional[Any] = self._generate_dummy_inputs_for_causal_lm( lowercase_ , batch_size=lowercase_ , seq_length=lowercase_ , is_pair=lowercase_ , framework=lowercase_ ) return common_inputs def __UpperCamelCase ( self : Any , lowercase_ : List[Any] , lowercase_ : List[str] , lowercase_ : List[str] , lowercase_ : Tuple ) -> Union[str, Any]: if self.task in ["default", "seq2seq-lm"]: lowercase__ : List[str] = super()._flatten_past_key_values_(lowercase_ , lowercase_ , lowercase_ , lowercase_ ) else: lowercase__ : Optional[Any] = super(lowercase_ , self )._flatten_past_key_values_( lowercase_ , lowercase_ , lowercase_ , lowercase_ ) @property def __UpperCamelCase ( self : str ) -> float: return 1E-4
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from typing import TYPE_CHECKING from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available UpperCamelCase = { '''configuration_mask2former''': [ '''MASK2FORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''Mask2FormerConfig''', ], } try: if not is_vision_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: UpperCamelCase = ['''Mask2FormerImageProcessor'''] try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: UpperCamelCase = [ '''MASK2FORMER_PRETRAINED_MODEL_ARCHIVE_LIST''', '''Mask2FormerForUniversalSegmentation''', '''Mask2FormerModel''', '''Mask2FormerPreTrainedModel''', ] if TYPE_CHECKING: from .configuration_maskaformer import MASK2FORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, MaskaFormerConfig try: if not is_vision_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .image_processing_maskaformer import MaskaFormerImageProcessor try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_maskaformer import ( MASK2FORMER_PRETRAINED_MODEL_ARCHIVE_LIST, MaskaFormerForUniversalSegmentation, MaskaFormerModel, MaskaFormerPreTrainedModel, ) else: import sys UpperCamelCase = _LazyModule(__name__, globals()['''__file__'''], _import_structure)
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def lowercase_ ( _lowerCamelCase : int , _lowerCamelCase : int): while a != 0: lowercase__ : Dict = b % a, a return b def lowercase_ ( _lowerCamelCase : int , _lowerCamelCase : int): if gcd(_lowerCamelCase , _lowerCamelCase) != 1: lowercase__ : Tuple = f'''mod inverse of {a!r} and {m!r} does not exist''' raise ValueError(_lowerCamelCase) lowercase__ : Optional[int] = 1, 0, a lowercase__ : Union[str, Any] = 0, 1, m while va != 0: lowercase__ : Tuple = ua // va lowercase__ : Any = (ua - q * va), (ua - q * va), (ua - q * va), va, va, va return ua % m
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# Logistic Regression from scratch # In[62]: # In[63]: # importing all the required libraries import numpy as np from matplotlib import pyplot as plt from sklearn import datasets def lowercase_ ( _lowerCamelCase : List[str]): return 1 / (1 + np.exp(-z)) def lowercase_ ( _lowerCamelCase : Dict , _lowerCamelCase : Tuple): return (-y * np.log(_lowerCamelCase) - (1 - y) * np.log(1 - h)).mean() def lowercase_ ( _lowerCamelCase : str , _lowerCamelCase : Union[str, Any] , _lowerCamelCase : Tuple): lowercase__ : Union[str, Any] = np.dot(_lowerCamelCase , _lowerCamelCase) return np.sum(y * scores - np.log(1 + np.exp(_lowerCamelCase))) def lowercase_ ( _lowerCamelCase : Tuple , _lowerCamelCase : Tuple , _lowerCamelCase : Optional[Any] , _lowerCamelCase : str=7_0000): lowercase__ : Optional[int] = np.zeros(x.shape[1]) for iterations in range(_lowerCamelCase): lowercase__ : Union[str, Any] = np.dot(_lowerCamelCase , _lowerCamelCase) lowercase__ : Tuple = sigmoid_function(_lowerCamelCase) lowercase__ : Dict = np.dot(x.T , h - y) / y.size lowercase__ : int = theta - alpha * gradient # updating the weights lowercase__ : List[str] = np.dot(_lowerCamelCase , _lowerCamelCase) lowercase__ : Union[str, Any] = sigmoid_function(_lowerCamelCase) lowercase__ : Optional[Any] = cost_function(_lowerCamelCase , _lowerCamelCase) if iterations % 100 == 0: print(f'''loss: {j} \t''') # printing the loss after every 100 iterations return theta # In[68]: if __name__ == "__main__": UpperCamelCase = datasets.load_iris() UpperCamelCase = iris.data[:, :2] UpperCamelCase = (iris.target != 0) * 1 UpperCamelCase = 0.1 UpperCamelCase = logistic_reg(alpha, x, y, max_iterations=7_0000) print('''theta: ''', theta) # printing the theta i.e our weights vector def lowercase_ ( _lowerCamelCase : List[Any]): return sigmoid_function( np.dot(_lowerCamelCase , _lowerCamelCase)) # predicting the value of probability from the logistic regression algorithm plt.figure(figsize=(10, 6)) plt.scatter(x[y == 0][:, 0], x[y == 0][:, 1], color='''b''', label='''0''') plt.scatter(x[y == 1][:, 0], x[y == 1][:, 1], color='''r''', label='''1''') ((UpperCamelCase) , (UpperCamelCase)) = (x[:, 0].min(), x[:, 0].max()) ((UpperCamelCase) , (UpperCamelCase)) = (x[:, 1].min(), x[:, 1].max()) ((UpperCamelCase) , (UpperCamelCase)) = np.meshgrid(np.linspace(xa_min, xa_max), np.linspace(xa_min, xa_max)) UpperCamelCase = np.c_[xxa.ravel(), xxa.ravel()] UpperCamelCase = predict_prob(grid).reshape(xxa.shape) plt.contour(xxa, xxa, probs, [0.5], linewidths=1, colors='''black''') plt.legend() plt.show()
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def lowercase_ ( _lowerCamelCase : int): lowercase__ : List[str] = 0 while num > 0: digit_sum += num % 10 num //= 10 return digit_sum def lowercase_ ( _lowerCamelCase : int = 100): lowercase__ : Any = 1 lowercase__ : str = 2 for i in range(2 , max_n + 1): lowercase__ : List[str] = pre_numerator lowercase__ : Optional[int] = 2 * i // 3 if i % 3 == 0 else 1 lowercase__ : Any = cur_numerator lowercase__ : List[str] = e_cont * pre_numerator + temp return sum_digits(_lowerCamelCase) if __name__ == "__main__": print(f"{solution() = }")
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import copy from dataclasses import dataclass, field from typing import ClassVar, Dict from ..features import ClassLabel, Features, Value from .base import TaskTemplate @dataclass(frozen=__A ) class snake_case_ ( __A ): # `task` is not a ClassVar since we want it to be part of the `asdict` output for JSON serialization __A : str = field(default="text-classification" ,metadata={"include_in_asdict_even_if_is_default": True} ) __A : ClassVar[Features] = Features({"text": Value("string" )} ) __A : ClassVar[Features] = Features({"labels": ClassLabel} ) __A : str = "text" __A : str = "labels" def __UpperCamelCase ( self : Dict , lowercase_ : Optional[Any] ) -> int: if self.label_column not in features: raise ValueError(F'''Column {self.label_column} is not present in features.''' ) if not isinstance(features[self.label_column] , lowercase_ ): raise ValueError(F'''Column {self.label_column} is not a ClassLabel.''' ) lowercase__ : Optional[int] = copy.deepcopy(self ) lowercase__ : Tuple = self.label_schema.copy() lowercase__ : Union[str, Any] = features[self.label_column] lowercase__ : int = label_schema return task_template @property def __UpperCamelCase ( self : Optional[Any] ) -> Dict[str, str]: return { self.text_column: "text", self.label_column: "labels", }
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from collections import OrderedDict from typing import Mapping from ...configuration_utils import PretrainedConfig from ...onnx import OnnxConfig from ...utils import logging UpperCamelCase = logging.get_logger(__name__) UpperCamelCase = { '''distilbert-base-uncased''': '''https://huggingface.co/distilbert-base-uncased/resolve/main/config.json''', '''distilbert-base-uncased-distilled-squad''': ( '''https://huggingface.co/distilbert-base-uncased-distilled-squad/resolve/main/config.json''' ), '''distilbert-base-cased''': '''https://huggingface.co/distilbert-base-cased/resolve/main/config.json''', '''distilbert-base-cased-distilled-squad''': ( '''https://huggingface.co/distilbert-base-cased-distilled-squad/resolve/main/config.json''' ), '''distilbert-base-german-cased''': '''https://huggingface.co/distilbert-base-german-cased/resolve/main/config.json''', '''distilbert-base-multilingual-cased''': ( '''https://huggingface.co/distilbert-base-multilingual-cased/resolve/main/config.json''' ), '''distilbert-base-uncased-finetuned-sst-2-english''': ( '''https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english/resolve/main/config.json''' ), } class snake_case_ ( __A ): __A : int = "distilbert" __A : int = { "hidden_size": "dim", "num_attention_heads": "n_heads", "num_hidden_layers": "n_layers", } def __init__( self : List[str] , lowercase_ : int=3_05_22 , lowercase_ : Any=5_12 , lowercase_ : Tuple=False , lowercase_ : Any=6 , lowercase_ : int=12 , lowercase_ : List[str]=7_68 , lowercase_ : Dict=4 * 7_68 , lowercase_ : List[str]=0.1 , lowercase_ : int=0.1 , lowercase_ : int="gelu" , lowercase_ : Any=0.02 , lowercase_ : Optional[Any]=0.1 , lowercase_ : str=0.2 , lowercase_ : List[Any]=0 , **lowercase_ : Any , ) -> List[str]: lowercase__ : Optional[Any] = vocab_size lowercase__ : Optional[int] = max_position_embeddings lowercase__ : Dict = sinusoidal_pos_embds lowercase__ : int = n_layers lowercase__ : Tuple = n_heads lowercase__ : Any = dim lowercase__ : List[Any] = hidden_dim lowercase__ : Optional[Any] = dropout lowercase__ : Optional[Any] = attention_dropout lowercase__ : str = activation lowercase__ : Dict = initializer_range lowercase__ : Tuple = qa_dropout lowercase__ : Tuple = seq_classif_dropout super().__init__(**lowercase_ , pad_token_id=lowercase_ ) class snake_case_ ( __A ): @property def __UpperCamelCase ( self : Optional[Any] ) -> Mapping[str, Mapping[int, str]]: if self.task == "multiple-choice": lowercase__ : int = {0: "batch", 1: "choice", 2: "sequence"} else: lowercase__ : Optional[int] = {0: "batch", 1: "sequence"} return OrderedDict( [ ("input_ids", dynamic_axis), ("attention_mask", dynamic_axis), ] )
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def lowercase_ ( _lowerCamelCase : int = 10 , _lowerCamelCase : int = 1000 , _lowerCamelCase : bool = True): assert ( isinstance(_lowerCamelCase , _lowerCamelCase) and isinstance(_lowerCamelCase , _lowerCamelCase) and isinstance(_lowerCamelCase , _lowerCamelCase) ), "Invalid type of value(s) specified to function!" if min_val > max_val: raise ValueError("Invalid value for min_val or max_val (min_value < max_value)") return min_val if option else max_val def lowercase_ ( _lowerCamelCase : int , _lowerCamelCase : int): return int((number_a + number_a) / 2) def lowercase_ ( _lowerCamelCase : int , _lowerCamelCase : int , _lowerCamelCase : int): assert ( isinstance(_lowerCamelCase , _lowerCamelCase) and isinstance(_lowerCamelCase , _lowerCamelCase) and isinstance(_lowerCamelCase , _lowerCamelCase) ), 'argument values must be type of "int"' if lower > higher: raise ValueError("argument value for lower and higher must be(lower > higher)") if not lower < to_guess < higher: raise ValueError( "guess value must be within the range of lower and higher value") def answer(_lowerCamelCase : int) -> str: if number > to_guess: return "high" elif number < to_guess: return "low" else: return "same" print("started...") lowercase__ : Optional[int] = lower lowercase__ : List[Any] = higher lowercase__ : Dict = [] while True: lowercase__ : Any = get_avg(_lowerCamelCase , _lowerCamelCase) last_numbers.append(_lowerCamelCase) if answer(_lowerCamelCase) == "low": lowercase__ : List[str] = number elif answer(_lowerCamelCase) == "high": lowercase__ : Optional[int] = number else: break print(f'''guess the number : {last_numbers[-1]}''') print(f'''details : {last_numbers!s}''') def lowercase_ ( ): lowercase__ : Tuple = int(input("Enter lower value : ").strip()) lowercase__ : Optional[int] = int(input("Enter high value : ").strip()) lowercase__ : Optional[Any] = int(input("Enter value to guess : ").strip()) guess_the_number(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase) if __name__ == "__main__": main()
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from typing import TYPE_CHECKING from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available UpperCamelCase : Tuple = { '''configuration_poolformer''': [ '''POOLFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''PoolFormerConfig''', '''PoolFormerOnnxConfig''', ] } try: if not is_vision_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: UpperCamelCase : Tuple = ['''PoolFormerFeatureExtractor'''] UpperCamelCase : List[Any] = ['''PoolFormerImageProcessor'''] try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: UpperCamelCase : List[Any] = [ '''POOLFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''', '''PoolFormerForImageClassification''', '''PoolFormerModel''', '''PoolFormerPreTrainedModel''', ] if TYPE_CHECKING: from .configuration_poolformer import ( POOLFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, PoolFormerConfig, PoolFormerOnnxConfig, ) try: if not is_vision_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .feature_extraction_poolformer import PoolFormerFeatureExtractor from .image_processing_poolformer import PoolFormerImageProcessor try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_poolformer import ( POOLFORMER_PRETRAINED_MODEL_ARCHIVE_LIST, PoolFormerForImageClassification, PoolFormerModel, PoolFormerPreTrainedModel, ) else: import sys UpperCamelCase : Any = _LazyModule(__name__, globals()['''__file__'''], _import_structure)
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import os import re import shutil import sys import tempfile import unittest import black UpperCamelCase = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__)))) sys.path.append(os.path.join(git_repo_path, '''utils''')) import check_copies # noqa: E402 # This is the reference code that will be used in the tests. # If DDPMSchedulerOutput is changed in scheduling_ddpm.py, this code needs to be manually updated. UpperCamelCase = ''' \""" Output class for the scheduler\'s step function output. Args: prev_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images): Computed sample (x_{t-1}) of previous timestep. `prev_sample` should be used as next model input in the denoising loop. pred_original_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images): The predicted denoised sample (x_{0}) based on the model output from the current timestep. `pred_original_sample` can be used to preview progress or for guidance. \""" prev_sample: torch.FloatTensor pred_original_sample: Optional[torch.FloatTensor] = None ''' class snake_case_ ( unittest.TestCase ): def __UpperCamelCase ( self : str ) -> List[str]: lowercase__ : str = tempfile.mkdtemp() os.makedirs(os.path.join(self.diffusers_dir , "schedulers/" ) ) lowercase__ : List[Any] = self.diffusers_dir shutil.copy( os.path.join(lowercase_ , "src/diffusers/schedulers/scheduling_ddpm.py" ) , os.path.join(self.diffusers_dir , "schedulers/scheduling_ddpm.py" ) , ) def __UpperCamelCase ( self : Optional[int] ) -> List[str]: lowercase__ : Dict = "src/diffusers" shutil.rmtree(self.diffusers_dir ) def __UpperCamelCase ( self : Tuple , lowercase_ : Optional[Any] , lowercase_ : Any , lowercase_ : str , lowercase_ : Tuple=None ) -> Tuple: lowercase__ : Optional[int] = comment + F'''\nclass {class_name}(nn.Module):\n''' + class_code if overwrite_result is not None: lowercase__ : Optional[int] = comment + F'''\nclass {class_name}(nn.Module):\n''' + overwrite_result lowercase__ : Optional[Any] = black.Mode(target_versions={black.TargetVersion.PYaa} , line_length=1_19 ) lowercase__ : List[str] = black.format_str(lowercase_ , mode=lowercase_ ) lowercase__ : Optional[int] = os.path.join(self.diffusers_dir , "new_code.py" ) with open(lowercase_ , "w" , newline="\n" ) as f: f.write(lowercase_ ) if overwrite_result is None: self.assertTrue(len(check_copies.is_copy_consistent(lowercase_ ) ) == 0 ) else: check_copies.is_copy_consistent(f.name , overwrite=lowercase_ ) with open(lowercase_ , "r" ) as f: self.assertTrue(f.read() , lowercase_ ) def __UpperCamelCase ( self : str ) -> Optional[int]: lowercase__ : Optional[Any] = check_copies.find_code_in_diffusers("schedulers.scheduling_ddpm.DDPMSchedulerOutput" ) self.assertEqual(lowercase_ , lowercase_ ) def __UpperCamelCase ( self : int ) -> str: # Base copy consistency self.check_copy_consistency( "# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput" , "DDPMSchedulerOutput" , REFERENCE_CODE + "\n" , ) # With no empty line at the end self.check_copy_consistency( "# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput" , "DDPMSchedulerOutput" , lowercase_ , ) # Copy consistency with rename self.check_copy_consistency( "# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->Test" , "TestSchedulerOutput" , re.sub("DDPM" , "Test" , lowercase_ ) , ) # Copy consistency with a really long name lowercase__ : Optional[int] = "TestClassWithAReallyLongNameBecauseSomePeopleLikeThatForSomeReason" self.check_copy_consistency( F'''# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->{long_class_name}''' , F'''{long_class_name}SchedulerOutput''' , re.sub("Bert" , lowercase_ , lowercase_ ) , ) # Copy consistency with overwrite self.check_copy_consistency( "# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->Test" , "TestSchedulerOutput" , lowercase_ , overwrite_result=re.sub("DDPM" , "Test" , lowercase_ ) , )
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def lowercase_ ( _lowerCamelCase : int = 1 , _lowerCamelCase : int = 1000): lowercase__ : Union[str, Any] = 1 lowercase__ : int = 0 for divide_by_number in range(_lowerCamelCase , digit + 1): lowercase__ : list[int] = [] lowercase__ : Dict = numerator for _ in range(1 , digit + 1): if now_divide in has_been_divided: if longest_list_length < len(_lowerCamelCase): lowercase__ : Union[str, Any] = len(_lowerCamelCase) lowercase__ : Optional[int] = divide_by_number else: has_been_divided.append(_lowerCamelCase) lowercase__ : Optional[Any] = now_divide * 10 % divide_by_number return the_digit # Tests if __name__ == "__main__": import doctest doctest.testmod()
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from copy import deepcopy import torch import torch.nn.functional as F from torch.optim import AdamW from torch.optim.lr_scheduler import LambdaLR from torch.utils.data import DataLoader from accelerate.accelerator import Accelerator from accelerate.state import GradientState from accelerate.test_utils import RegressionDataset, RegressionModel from accelerate.utils import DistributedType, is_torch_version, set_seed def lowercase_ ( _lowerCamelCase : Optional[Any] , _lowerCamelCase : Tuple , _lowerCamelCase : Dict , _lowerCamelCase : Tuple): for param, grad_param in zip(model_a.parameters() , model_b.parameters()): if not param.requires_grad: continue if not did_step: # Grads should not be in sync assert ( torch.allclose(param.grad , grad_param.grad) is False ), f'''Gradients in sync when they should not be at iteration {iteration}:\nmodel_a grad ({param.grad}) == model_b grad ({grad_param.grad})''' else: # Grads should be in sync assert ( torch.allclose(param.grad , grad_param.grad) is True ), f'''Gradients not in sync when they should be at iteration {iteration}:\nmodel_a grad ({param.grad}) != model_b grad ({grad_param.grad})''' def lowercase_ ( _lowerCamelCase : int , _lowerCamelCase : List[str] , _lowerCamelCase : Optional[int] , _lowerCamelCase : int , _lowerCamelCase : Union[str, Any]=True): model.train() lowercase__ : Tuple = model(_lowerCamelCase) lowercase__ : Union[str, Any] = F.mse_loss(_lowerCamelCase , target.to(output.device)) if not do_backward: loss /= accelerator.gradient_accumulation_steps loss.backward() else: accelerator.backward(_lowerCamelCase) def lowercase_ ( _lowerCamelCase : List[Any] , _lowerCamelCase : str=False): set_seed(42) lowercase__ : Dict = RegressionModel() lowercase__ : int = deepcopy(_lowerCamelCase) lowercase__ : str = RegressionDataset(length=80) lowercase__ : List[Any] = DataLoader(_lowerCamelCase , batch_size=16) model.to(accelerator.device) if sched: lowercase__ : Union[str, Any] = AdamW(params=model.parameters() , lr=1E-3) lowercase__ : Union[str, Any] = AdamW(params=ddp_model.parameters() , lr=1E-3) lowercase__ : Optional[int] = LambdaLR(_lowerCamelCase , lr_lambda=lambda _lowerCamelCase: epoch**0.65) lowercase__ : Union[str, Any] = LambdaLR(_lowerCamelCase , lr_lambda=lambda _lowerCamelCase: epoch**0.65) # Make a copy of `model` if sched: lowercase__ , lowercase__ , lowercase__ , lowercase__ : Tuple = accelerator.prepare(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase) else: lowercase__ , lowercase__ : int = accelerator.prepare(_lowerCamelCase , _lowerCamelCase) if sched: return (model, opt, sched, dataloader, ddp_model, ddp_opt, ddp_sched) return model, ddp_model, dataloader def lowercase_ ( _lowerCamelCase : Tuple): # Test when on a single CPU or GPU that the context manager does nothing lowercase__ , lowercase__ , lowercase__ : List[Any] = get_training_setup(_lowerCamelCase) # Use a single batch lowercase__ , lowercase__ : int = next(iter(_lowerCamelCase)).values() for iteration in range(3): # Gather the distributed inputs and targs for the base model lowercase__ , lowercase__ : Optional[int] = accelerator.gather((ddp_input, ddp_target)) lowercase__ , lowercase__ : Union[str, Any] = input.to(accelerator.device), target.to(accelerator.device) # Perform our initial ground truth step in non "DDP" step_model(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase) # Do "gradient accumulation" (noop) if iteration % 2 == 0: # Accumulate grads locally with accelerator.no_sync(_lowerCamelCase): step_model(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase) else: # Sync grads step_model(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase) # Since `no_sync` is a noop, `ddp_model` and `model` grads should always be in sync check_model_parameters(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase) for param, ddp_param in zip(model.parameters() , ddp_model.parameters()): if not param.requires_grad: continue assert torch.allclose( param.grad , ddp_param.grad), f'''Gradients not in sync when they should be:\nModel grad ({param.grad}) != DDP grad ({ddp_param.grad})''' # Shuffle ddp_input on each iteration torch.manual_seed(1337 + iteration) lowercase__ : int = ddp_input[torch.randperm(len(_lowerCamelCase))] def lowercase_ ( _lowerCamelCase : Any): # Test on distributed setup that context manager behaves properly lowercase__ , lowercase__ , lowercase__ : str = get_training_setup(_lowerCamelCase) # Use a single batch lowercase__ , lowercase__ : Dict = next(iter(_lowerCamelCase)).values() for iteration in range(3): # Gather the distributed inputs and targs for the base model lowercase__ , lowercase__ : List[str] = accelerator.gather((ddp_input, ddp_target)) lowercase__ , lowercase__ : Any = input.to(accelerator.device), target.to(accelerator.device) # Perform our initial ground truth step in non "DDP" step_model(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase) # Do "gradient accumulation" (noop) if iteration % 2 == 0: # Accumulate grads locally with accelerator.no_sync(_lowerCamelCase): step_model(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase) else: # Sync grads step_model(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase) # DDP model and model should only be in sync when not (iteration % 2 == 0) for param, ddp_param in zip(model.parameters() , ddp_model.parameters()): if not param.requires_grad: continue if iteration % 2 == 0: # Grads should not be in sync assert ( torch.allclose(param.grad , ddp_param.grad) is False ), f'''Gradients in sync when they should not be:\nModel grad ({param.grad}) == DDP grad ({ddp_param.grad})''' else: # Grads should be in sync assert ( torch.allclose(param.grad , ddp_param.grad) is True ), f'''Gradients not in sync when they should be:\nModel grad ({param.grad}) != DDP grad ({ddp_param.grad})''' # Shuffle ddp_input on each iteration torch.manual_seed(1337 + iteration) lowercase__ : Tuple = ddp_input[torch.randperm(len(_lowerCamelCase))] def lowercase_ ( _lowerCamelCase : Optional[Any]=False , _lowerCamelCase : Union[str, Any]=False): lowercase__ : int = Accelerator( split_batches=_lowerCamelCase , dispatch_batches=_lowerCamelCase , gradient_accumulation_steps=2) # Test that context manager behaves properly lowercase__ , lowercase__ , lowercase__ : Optional[int] = get_training_setup(_lowerCamelCase) for iteration, batch in enumerate(_lowerCamelCase): lowercase__ , lowercase__ : str = batch.values() # Gather the distributed inputs and targs for the base model lowercase__ , lowercase__ : Optional[Any] = accelerator.gather((ddp_input, ddp_target)) lowercase__ , lowercase__ : Union[str, Any] = input.to(accelerator.device), target.to(accelerator.device) # Perform our initial ground truth step in non "DDP" step_model(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase) # Do "gradient accumulation" (noop) with accelerator.accumulate(_lowerCamelCase): step_model(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase) # DDP model and model should only be in sync when not (iteration % 2 == 0) for param, ddp_param in zip(model.parameters() , ddp_model.parameters()): if not param.requires_grad: continue if ((iteration + 1) % 2 == 0) or (iteration == len(_lowerCamelCase) - 1): # Grads should be in sync assert ( torch.allclose(param.grad , ddp_param.grad) is True ), f'''Gradients not in sync when they should be at iteration {iteration}:\nModel grad ({param.grad}) != DDP grad ({ddp_param.grad})''' else: # Grads should not be in sync assert ( torch.allclose(param.grad , ddp_param.grad) is False ), f'''Gradients in sync when they should not be at iteration {iteration}:\nModel grad ({param.grad}) == DDP grad ({ddp_param.grad})''' # Shuffle ddp_input on each iteration torch.manual_seed(1337 + iteration) lowercase__ : Dict = ddp_input[torch.randperm(len(_lowerCamelCase))] GradientState._reset_state() def lowercase_ ( _lowerCamelCase : List[str]=False , _lowerCamelCase : int=False): lowercase__ : Dict = Accelerator( split_batches=_lowerCamelCase , dispatch_batches=_lowerCamelCase , gradient_accumulation_steps=2) # Test that context manager behaves properly lowercase__ , lowercase__ , lowercase__ , lowercase__ , lowercase__ , lowercase__ , lowercase__ : str = get_training_setup(_lowerCamelCase , _lowerCamelCase) for iteration, batch in enumerate(_lowerCamelCase): lowercase__ , lowercase__ : Any = batch.values() # Gather the distributed inputs and targs for the base model lowercase__ , lowercase__ : Tuple = accelerator.gather((ddp_input, ddp_target)) lowercase__ , lowercase__ : List[str] = input.to(accelerator.device), target.to(accelerator.device) # Perform our initial ground truth step in non "DDP" model.train() ddp_model.train() step_model(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase) opt.step() if ((iteration + 1) % 2 == 0) or ((iteration + 1) == len(_lowerCamelCase)): if split_batches: sched.step() else: for _ in range(accelerator.num_processes): sched.step() opt.zero_grad() # Perform gradient accumulation under wrapper with accelerator.accumulate(_lowerCamelCase): step_model(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase) ddp_opt.step() ddp_sched.step() ddp_opt.zero_grad() # Learning rates should be the same assert ( opt.param_groups[0]["lr"] == ddp_opt.param_groups[0]["lr"] ), f'''Learning rates found in each optimizer did not align\nopt: {opt.param_groups[0]["lr"]}\nDDP opt: {ddp_opt.param_groups[0]["lr"]}\n''' lowercase__ : Tuple = (((iteration + 1) % 2) == 0) or ((iteration + 1) == len(_lowerCamelCase)) if accelerator.num_processes > 1: check_model_parameters(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase) # Shuffle ddp_input on each iteration torch.manual_seed(1337 + iteration) GradientState._reset_state() def lowercase_ ( ): lowercase__ : List[str] = Accelerator() lowercase__ : List[Any] = RegressionDataset(length=80) lowercase__ : Tuple = DataLoader(_lowerCamelCase , batch_size=16) lowercase__ : int = RegressionDataset(length=96) lowercase__ : List[str] = DataLoader(_lowerCamelCase , batch_size=16) lowercase__ , lowercase__ : Dict = accelerator.prepare(_lowerCamelCase , _lowerCamelCase) assert accelerator.gradient_state.active_dataloader is None for iteration, _ in enumerate(_lowerCamelCase): assert id(accelerator.gradient_state.active_dataloader) == id(_lowerCamelCase) if iteration < len(_lowerCamelCase) - 1: assert not accelerator.gradient_state.end_of_dataloader if iteration == 1: for batch_num, _ in enumerate(_lowerCamelCase): assert id(accelerator.gradient_state.active_dataloader) == id(_lowerCamelCase) if batch_num < len(_lowerCamelCase) - 1: assert not accelerator.gradient_state.end_of_dataloader else: assert accelerator.gradient_state.end_of_dataloader else: assert accelerator.gradient_state.end_of_dataloader assert accelerator.gradient_state.active_dataloader is None def lowercase_ ( ): lowercase__ : str = Accelerator() lowercase__ : Dict = accelerator.state if state.local_process_index == 0: print("**Test `accumulate` gradient accumulation with dataloader break**") test_dataloader_break() if state.distributed_type == DistributedType.NO: if state.local_process_index == 0: print("**Test NOOP `no_sync` context manager**") test_noop_sync(_lowerCamelCase) if state.distributed_type in (DistributedType.MULTI_GPU, DistributedType.MULTI_CPU): if state.local_process_index == 0: print("**Test Distributed `no_sync` context manager**") test_distributed_sync(_lowerCamelCase) if state.distributed_type == DistributedType.MULTI_GPU: for split_batch in [True, False]: for dispatch_batches in [True, False]: if state.local_process_index == 0: print( "**Test `accumulate` gradient accumulation, " , f'''`split_batches={split_batch}` and `dispatch_batches={dispatch_batches}`**''' , ) test_gradient_accumulation(_lowerCamelCase , _lowerCamelCase) # Currently will break on torch 2.0 +, need to investigate why if is_torch_version("<" , "2.0") or state.distributed_type == DistributedType.NO: if state.local_process_index == 0: print( "**Test `accumulate` gradient accumulation with optimizer and scheduler, " , "`split_batches=False`, `dispatch_batches=False`**" , ) test_gradient_accumulation_with_opt_and_scheduler() if state.distributed_type == DistributedType.MULTI_GPU: for split_batch in [True, False]: for dispatch_batches in [True, False]: if not split_batch and not dispatch_batches: continue if state.local_process_index == 0: print( "**Test `accumulate` gradient accumulation with optimizer and scheduler, " , f'''`split_batches={split_batch}` and `dispatch_batches={dispatch_batches}`**''' , ) test_gradient_accumulation_with_opt_and_scheduler(_lowerCamelCase , _lowerCamelCase) def lowercase_ ( _lowerCamelCase : Any): # For xla_spawn (TPUs) main() if __name__ == "__main__": main()
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def lowercase_ ( ): return [list(range(1000 - i , -1000 - i , -1)) for i in range(1000)] UpperCamelCase = generate_large_matrix() UpperCamelCase = ( [[4, 3, 2, -1], [3, 2, 1, -1], [1, 1, -1, -2], [-1, -1, -2, -3]], [[3, 2], [1, 0]], [[7, 7, 6]], [[7, 7, 6], [-1, -2, -3]], grid, ) def lowercase_ ( _lowerCamelCase : list[list[int]]): assert all(row == sorted(_lowerCamelCase , reverse=_lowerCamelCase) for row in grid) assert all(list(_lowerCamelCase) == sorted(_lowerCamelCase , reverse=_lowerCamelCase) for col in zip(*_lowerCamelCase)) def lowercase_ ( _lowerCamelCase : list[int]): lowercase__ : Tuple = 0 lowercase__ : str = len(_lowerCamelCase) - 1 # Edge cases such as no values or all numbers are negative. if not array or array[0] < 0: return 0 while right + 1 > left: lowercase__ : int = (left + right) // 2 lowercase__ : Optional[int] = array[mid] # Num must be negative and the index must be greater than or equal to 0. if num < 0 and array[mid - 1] >= 0: return mid if num >= 0: lowercase__ : Optional[Any] = mid + 1 else: lowercase__ : Optional[Any] = mid - 1 # No negative numbers so return the last index of the array + 1 which is the length. return len(_lowerCamelCase) def lowercase_ ( _lowerCamelCase : list[list[int]]): lowercase__ : str = 0 lowercase__ : Optional[Any] = len(grid[0]) for i in range(len(_lowerCamelCase)): lowercase__ : List[Any] = find_negative_index(grid[i][:bound]) total += bound return (len(_lowerCamelCase) * len(grid[0])) - total def lowercase_ ( _lowerCamelCase : list[list[int]]): return len([number for row in grid for number in row if number < 0]) def lowercase_ ( _lowerCamelCase : list[list[int]]): lowercase__ : List[Any] = 0 for row in grid: for i, number in enumerate(_lowerCamelCase): if number < 0: total += len(_lowerCamelCase) - i break return total def lowercase_ ( ): from timeit import timeit print("Running benchmarks") lowercase__ : Optional[int] = ( "from __main__ import count_negatives_binary_search, " "count_negatives_brute_force, count_negatives_brute_force_with_break, grid" ) for func in ( "count_negatives_binary_search", # took 0.7727 seconds "count_negatives_brute_force_with_break", # took 4.6505 seconds "count_negatives_brute_force", # took 12.8160 seconds ): lowercase__ : Any = timeit(f'''{func}(grid=grid)''' , setup=_lowerCamelCase , number=500) print(f'''{func}() took {time:0.4f} seconds''') if __name__ == "__main__": import doctest doctest.testmod() benchmark()
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import argparse from tax import checkpoints from transformers import AutoConfig, FlaxAutoModelForSeqaSeqLM def lowercase_ ( _lowerCamelCase : int , _lowerCamelCase : Any , _lowerCamelCase : str): lowercase__ : Optional[Any] = AutoConfig.from_pretrained(_lowerCamelCase) lowercase__ : List[str] = FlaxAutoModelForSeqaSeqLM.from_config(config=_lowerCamelCase) lowercase__ : List[str] = checkpoints.load_tax_checkpoint(_lowerCamelCase) lowercase__ : Dict = "wi_0" in tax_model["target"]["encoder"]["layers_0"]["mlp"] if config.model_type == "t5": lowercase__ : Any = "SelfAttention" if config.model_type == "longt5" and config.encoder_attention_type == "local": lowercase__ : int = "LocalSelfAttention" elif config.model_type == "longt5" and config.encoder_attention_type == "transient-global": lowercase__ : Dict = "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): lowercase__ : str = f'''layers_{str(_lowerCamelCase)}''' # Self-Attention lowercase__ : List[Any] = tax_model["target"]["encoder"][layer_name]["attention"]["key"]["kernel"] lowercase__ : Optional[Any] = tax_model["target"]["encoder"][layer_name]["attention"]["out"]["kernel"] lowercase__ : Tuple = tax_model["target"]["encoder"][layer_name]["attention"]["query"]["kernel"] lowercase__ : Any = 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": lowercase__ : Optional[Any] = tax_model["target"]["encoder"][layer_name]["attention"]["T5LayerNorm_0"]["scale"] # Layer Normalization lowercase__ : Optional[int] = tax_model["target"]["encoder"][layer_name]["pre_attention_layer_norm"]["scale"] if split_mlp_wi: lowercase__ : Tuple = tax_model["target"]["encoder"][layer_name]["mlp"]["wi_0"]["kernel"] lowercase__ : List[str] = tax_model["target"]["encoder"][layer_name]["mlp"]["wi_1"]["kernel"] else: lowercase__ : Optional[int] = tax_model["target"]["encoder"][layer_name]["mlp"]["wi"]["kernel"] lowercase__ : str = tax_model["target"]["encoder"][layer_name]["mlp"]["wo"]["kernel"] # Layer Normalization lowercase__ : int = tax_model["target"]["encoder"][layer_name]["pre_mlp_layer_norm"]["scale"] # Assigning lowercase__ : int = flax_model.params["encoder"]["block"][str(_lowerCamelCase)]["layer"] lowercase__ : Any = tax_attention_key lowercase__ : Any = tax_attention_out lowercase__ : Any = tax_attention_query lowercase__ : List[str] = tax_attention_value lowercase__ : List[str] = tax_attention_layer_norm # Global input layer norm if config.model_type == "longt5" and config.encoder_attention_type == "transient-global": lowercase__ : Any = tax_global_layer_norm if split_mlp_wi: lowercase__ : Tuple = tax_mlp_wi_a lowercase__ : str = tax_mlp_wi_a else: lowercase__ : List[Any] = tax_mlp_wi lowercase__ : str = tax_mlp_wo lowercase__ : int = tax_mlp_layer_norm lowercase__ : List[str] = flax_model_encoder_layer_block # Only for layer 0: lowercase__ : Dict = tax_model["target"]["encoder"]["relpos_bias"]["rel_embedding"].T lowercase__ : Optional[int] = tax_encoder_rel_embedding # Side/global relative position_bias + layer norm if config.model_type == "longt5" and config.encoder_attention_type == "transient-global": lowercase__ : Tuple = tax_model["target"]["encoder"]["side_relpos_bias"]["rel_embedding"].T lowercase__ : str = tax_encoder_global_rel_embedding # Assigning lowercase__ : Optional[int] = tax_model["target"]["encoder"]["encoder_norm"]["scale"] lowercase__ : Union[str, Any] = tax_encoder_norm # Decoder for layer_index in range(config.num_layers): lowercase__ : Dict = f'''layers_{str(_lowerCamelCase)}''' # Self-Attention lowercase__ : str = tax_model["target"]["decoder"][layer_name]["self_attention"]["key"]["kernel"] lowercase__ : Tuple = tax_model["target"]["decoder"][layer_name]["self_attention"]["out"]["kernel"] lowercase__ : List[Any] = tax_model["target"]["decoder"][layer_name]["self_attention"]["query"]["kernel"] lowercase__ : List[str] = tax_model["target"]["decoder"][layer_name]["self_attention"]["value"]["kernel"] # Layer Normalization lowercase__ : Union[str, Any] = tax_model["target"]["decoder"][layer_name]["pre_self_attention_layer_norm"][ "scale" ] # Encoder-Decoder-Attention lowercase__ : int = tax_model["target"]["decoder"][layer_name]["encoder_decoder_attention"] lowercase__ : Any = tax_enc_dec_attention_module["key"]["kernel"] lowercase__ : Union[str, Any] = tax_enc_dec_attention_module["out"]["kernel"] lowercase__ : Any = tax_enc_dec_attention_module["query"]["kernel"] lowercase__ : Tuple = tax_enc_dec_attention_module["value"]["kernel"] # Layer Normalization lowercase__ : Dict = tax_model["target"]["decoder"][layer_name]["pre_cross_attention_layer_norm"]["scale"] # MLP if split_mlp_wi: lowercase__ : Union[str, Any] = tax_model["target"]["decoder"][layer_name]["mlp"]["wi_0"]["kernel"] lowercase__ : Any = tax_model["target"]["decoder"][layer_name]["mlp"]["wi_1"]["kernel"] else: lowercase__ : List[Any] = tax_model["target"]["decoder"][layer_name]["mlp"]["wi"]["kernel"] lowercase__ : Optional[Any] = tax_model["target"]["decoder"][layer_name]["mlp"]["wo"]["kernel"] # Layer Normalization lowercase__ : Optional[int] = tax_model["target"]["decoder"][layer_name]["pre_mlp_layer_norm"]["scale"] # Assigning lowercase__ : Optional[Any] = flax_model.params["decoder"]["block"][str(_lowerCamelCase)]["layer"] lowercase__ : Any = tax_attention_key lowercase__ : List[Any] = tax_attention_out lowercase__ : Any = tax_attention_query lowercase__ : List[Any] = tax_attention_value lowercase__ : List[str] = tax_pre_attention_layer_norm lowercase__ : List[Any] = tax_enc_dec_attention_key lowercase__ : Optional[Any] = tax_enc_dec_attention_out lowercase__ : str = tax_enc_dec_attention_query lowercase__ : Union[str, Any] = tax_enc_dec_attention_value lowercase__ : Tuple = tax_cross_layer_norm if split_mlp_wi: lowercase__ : List[str] = tax_mlp_wi_a lowercase__ : List[Any] = tax_mlp_wi_a else: lowercase__ : Tuple = tax_mlp_wi lowercase__ : Any = tax_mlp_wo lowercase__ : Tuple = txa_mlp_layer_norm lowercase__ : int = flax_model_decoder_layer_block # Decoder Normalization lowercase__ : str = tax_model["target"]["decoder"]["decoder_norm"]["scale"] lowercase__ : List[Any] = txa_decoder_norm # Only for layer 0: lowercase__ : List[str] = tax_model["target"]["decoder"]["relpos_bias"]["rel_embedding"].T lowercase__ : str = tax_decoder_rel_embedding # Token Embeddings lowercase__ : Optional[Any] = tax_model["target"]["token_embedder"]["embedding"] lowercase__ : Optional[Any] = txa_token_embeddings # LM Head (only in v1.1 and LongT5 checkpoints) if "logits_dense" in tax_model["target"]["decoder"]: lowercase__ : Optional[int] = tax_model["target"]["decoder"]["logits_dense"]["kernel"] flax_model.save_pretrained(_lowerCamelCase) print("T5X Model was sucessfully converted!") if __name__ == "__main__": UpperCamelCase = 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.''' ) UpperCamelCase = parser.parse_args() convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path)
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import datasets import faiss import numpy as np import streamlit as st import torch from elasticsearch import Elasticsearch from elia_utils import ( embed_questions_for_retrieval, make_qa_sas_model, qa_sas_generate, query_es_index, query_qa_dense_index, ) import transformers from transformers import AutoModel, AutoModelForSeqaSeqLM, AutoTokenizer UpperCamelCase = '''bart''' UpperCamelCase = True @st.cache(allow_output_mutation=_lowerCamelCase) def lowercase_ ( ): if LOAD_DENSE_INDEX: lowercase__ : int = AutoTokenizer.from_pretrained("yjernite/retribert-base-uncased") lowercase__ : List[str] = AutoModel.from_pretrained("yjernite/retribert-base-uncased").to("cuda:0") lowercase__ : Optional[int] = qar_model.eval() else: lowercase__ : Any = (None, None) if MODEL_TYPE == "bart": lowercase__ : str = AutoTokenizer.from_pretrained("yjernite/bart_eli5") lowercase__ : Tuple = AutoModelForSeqaSeqLM.from_pretrained("yjernite/bart_eli5").to("cuda:0") lowercase__ : Union[str, Any] = torch.load("seq2seq_models/eli5_bart_model_blm_2.pth") sas_model.load_state_dict(save_dict["model"]) lowercase__ : Union[str, Any] = sas_model.eval() else: lowercase__ : Union[str, Any] = make_qa_sas_model( model_name="t5-small" , from_file="seq2seq_models/eli5_t5_model_1024_4.pth" , device="cuda:0") return (qar_tokenizer, qar_model, sas_tokenizer, sas_model) @st.cache(allow_output_mutation=_lowerCamelCase) def lowercase_ ( ): if LOAD_DENSE_INDEX: lowercase__ : Any = faiss.StandardGpuResources() lowercase__ : int = datasets.load_dataset(path="wiki_snippets" , name="wiki40b_en_100_0")["train"] lowercase__ : int = np.memmap( "wiki40b_passages_reps_32_l-8_h-768_b-512-512.dat" , dtype="float32" , mode="r" , shape=(wikiaab_passages.num_rows, 128) , ) lowercase__ : List[Any] = faiss.IndexFlatIP(128) lowercase__ : str = faiss.index_cpu_to_gpu(_lowerCamelCase , 1 , _lowerCamelCase) wikiaab_gpu_index_flat.add(_lowerCamelCase) # TODO fix for larger GPU else: lowercase__ : str = (None, None) lowercase__ : int = Elasticsearch([{"host": "localhost", "port": "9200"}]) return (wikiaab_passages, wikiaab_gpu_index_flat, es_client) @st.cache(allow_output_mutation=_lowerCamelCase) def lowercase_ ( ): lowercase__ : Any = datasets.load_dataset("eli5" , name="LFQA_reddit") lowercase__ : Optional[int] = elia["train_eli5"] lowercase__ : Optional[int] = np.memmap( "eli5_questions_reps.dat" , dtype="float32" , mode="r" , shape=(elia_train.num_rows, 128)) lowercase__ : Optional[Any] = faiss.IndexFlatIP(128) eli5_train_q_index.add(_lowerCamelCase) return (elia_train, eli5_train_q_index) UpperCamelCase , UpperCamelCase , UpperCamelCase = load_indexes() UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase = load_models() UpperCamelCase , UpperCamelCase = load_train_data() def lowercase_ ( _lowerCamelCase : Any , _lowerCamelCase : List[Any]=10): lowercase__ : Dict = embed_questions_for_retrieval([question] , _lowerCamelCase , _lowerCamelCase) lowercase__ : Optional[Any] = eli5_train_q_index.search(_lowerCamelCase , _lowerCamelCase) lowercase__ : str = [elia_train[int(_lowerCamelCase)] for i in I[0]] return nn_examples def lowercase_ ( _lowerCamelCase : List[str] , _lowerCamelCase : List[Any]="wiki40b" , _lowerCamelCase : int="dense" , _lowerCamelCase : Optional[Any]=10): if source == "none": lowercase__ : str = (" <P> ".join(["" for _ in range(11)]).strip(), []) else: if method == "dense": lowercase__ : Tuple = query_qa_dense_index( _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase) else: lowercase__ : Dict = query_es_index( _lowerCamelCase , _lowerCamelCase , index_name="english_wiki40b_snippets_100w" , n_results=_lowerCamelCase , ) lowercase__ : Optional[Any] = [ (res["article_title"], res["section_title"].strip(), res["score"], res["passage_text"]) for res in hit_lst ] lowercase__ : int = "question: {} context: {}".format(_lowerCamelCase , _lowerCamelCase) return question_doc, support_list @st.cache( hash_funcs={ torch.Tensor: (lambda _lowerCamelCase: None), transformers.models.bart.tokenization_bart.BartTokenizer: (lambda _lowerCamelCase: None), }) def lowercase_ ( _lowerCamelCase : Optional[int] , _lowerCamelCase : List[Any] , _lowerCamelCase : str , _lowerCamelCase : Optional[int]=64 , _lowerCamelCase : Optional[int]=256 , _lowerCamelCase : Dict=False , _lowerCamelCase : Optional[int]=2 , _lowerCamelCase : List[str]=0.95 , _lowerCamelCase : int=0.8): with torch.no_grad(): lowercase__ : int = qa_sas_generate( _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , num_answers=1 , num_beams=_lowerCamelCase , min_len=_lowerCamelCase , max_len=_lowerCamelCase , do_sample=_lowerCamelCase , temp=_lowerCamelCase , top_p=_lowerCamelCase , top_k=_lowerCamelCase , max_input_length=1024 , device="cuda:0" , )[0] return (answer, support_list) st.title('''Long Form Question Answering with ELI5''') # Start sidebar UpperCamelCase = '''<img src=\'https://huggingface.co/front/assets/huggingface_logo.svg\'>''' UpperCamelCase = ''' <html> <head> <style> .img-container { padding-left: 90px; padding-right: 90px; padding-top: 50px; padding-bottom: 50px; background-color: #f0f3f9; } </style> </head> <body> <span class="img-container"> <!-- Inline parent element --> %s </span> </body> </html> ''' % ( header_html, ) st.sidebar.markdown( header_full, unsafe_allow_html=True, ) # Long Form QA with ELI5 and Wikipedia UpperCamelCase = ''' This demo presents a model trained to [provide long-form answers to open-domain questions](https://yjernite.github.io/lfqa.html). First, a document retriever fetches a set of relevant Wikipedia passages given the question from the [Wiki40b](https://research.google/pubs/pub49029/) dataset, a pre-processed fixed snapshot of Wikipedia. ''' st.sidebar.markdown(description, unsafe_allow_html=True) UpperCamelCase = [ '''Answer the question''', '''View the retrieved document only''', '''View the most similar ELI5 question and answer''', '''Show me everything, please!''', ] UpperCamelCase = st.sidebar.checkbox('''Demo options''') if demo_options: UpperCamelCase = st.sidebar.selectbox( '''''', action_list, index=3, ) UpperCamelCase = action_list.index(action_st) UpperCamelCase = st.sidebar.selectbox( '''''', ['''Show full text of passages''', '''Show passage section titles'''], index=0, ) UpperCamelCase = show_type == '''Show full text of passages''' else: UpperCamelCase = 3 UpperCamelCase = True UpperCamelCase = st.sidebar.checkbox('''Retrieval options''') if retrieval_options: UpperCamelCase = ''' ### Information retriever options The **sparse** retriever uses ElasticSearch, while the **dense** retriever uses max-inner-product search between a question and passage embedding trained using the [ELI5](https://arxiv.org/abs/1907.09190) questions-answer pairs. The answer is then generated by sequence to sequence model which takes the question and retrieved document as input. ''' st.sidebar.markdown(retriever_info) UpperCamelCase = st.sidebar.selectbox('''Which Wikipedia format should the model use?''', ['''wiki40b''', '''none''']) UpperCamelCase = st.sidebar.selectbox('''Which Wikipedia indexer should the model use?''', ['''dense''', '''sparse''', '''mixed''']) else: UpperCamelCase = '''wiki40b''' UpperCamelCase = '''dense''' UpperCamelCase = '''beam''' UpperCamelCase = 2 UpperCamelCase = 64 UpperCamelCase = 256 UpperCamelCase = None UpperCamelCase = None UpperCamelCase = st.sidebar.checkbox('''Generation options''') if generate_options: UpperCamelCase = ''' ### Answer generation options The sequence-to-sequence model was initialized with [BART](https://huggingface.co/facebook/bart-large) weights and fine-tuned on the ELI5 QA pairs and retrieved documents. You can use the model for greedy decoding with **beam** search, or **sample** from the decoder\'s output probabilities. ''' st.sidebar.markdown(generate_info) UpperCamelCase = st.sidebar.selectbox('''Would you like to use beam search or sample an answer?''', ['''beam''', '''sampled''']) UpperCamelCase = st.sidebar.slider( '''Minimum generation length''', min_value=8, max_value=256, value=64, step=8, format=None, key=None ) UpperCamelCase = st.sidebar.slider( '''Maximum generation length''', min_value=64, max_value=512, value=256, step=16, format=None, key=None ) if sampled == "beam": UpperCamelCase = st.sidebar.slider('''Beam size''', min_value=1, max_value=8, value=2, step=None, format=None, key=None) else: UpperCamelCase = st.sidebar.slider( '''Nucleus sampling p''', min_value=0.1, max_value=1.0, value=0.95, step=0.01, format=None, key=None ) UpperCamelCase = st.sidebar.slider( '''Temperature''', min_value=0.1, max_value=1.0, value=0.7, step=0.01, format=None, key=None ) UpperCamelCase = None # start main text UpperCamelCase = [ '''<MY QUESTION>''', '''How do people make chocolate?''', '''Why do we get a fever when we are sick?''', '''How can different animals perceive different colors?''', '''What is natural language processing?''', '''What\'s the best way to treat a sunburn?''', '''What exactly are vitamins ?''', '''How does nuclear energy provide electricity?''', '''What\'s the difference between viruses and bacteria?''', '''Why are flutes classified as woodwinds when most of them are made out of metal ?''', '''Why do people like drinking coffee even though it tastes so bad?''', '''What happens when wine ages? How does it make the wine taste better?''', '''If an animal is an herbivore, where does it get the protein that it needs to survive if it only eats grass?''', '''How can we set a date to the beginning or end of an artistic period? Doesn\'t the change happen gradually?''', '''How does New Zealand have so many large bird predators?''', ] UpperCamelCase = st.selectbox( '''What would you like to ask? ---- select <MY QUESTION> to enter a new query''', questions_list, index=1, ) if question_s == "<MY QUESTION>": UpperCamelCase = st.text_input('''Enter your question here:''', '''''') else: UpperCamelCase = question_s if st.button('''Show me!'''): if action in [0, 1, 3]: if index_type == "mixed": UpperCamelCase , UpperCamelCase = make_support(question, source=wiki_source, method='''dense''', n_results=10) UpperCamelCase , UpperCamelCase = make_support(question, source=wiki_source, method='''sparse''', n_results=10) UpperCamelCase = [] for res_d, res_s in zip(support_list_dense, support_list_sparse): if tuple(res_d) not in support_list: support_list += [tuple(res_d)] if tuple(res_s) not in support_list: support_list += [tuple(res_s)] UpperCamelCase = support_list[:10] UpperCamelCase = '''<P> ''' + ''' <P> '''.join([res[-1] for res in support_list]) else: UpperCamelCase , UpperCamelCase = make_support(question, source=wiki_source, method=index_type, n_results=10) if action in [0, 3]: UpperCamelCase , UpperCamelCase = answer_question( question_doc, sas_model, sas_tokenizer, min_len=min_len, max_len=int(max_len), sampling=(sampled == '''sampled'''), n_beams=n_beams, top_p=top_p, temp=temp, ) st.markdown('''### The model generated answer is:''') st.write(answer) if action in [0, 1, 3] and wiki_source != "none": st.markdown('''--- \n ### The model is drawing information from the following Wikipedia passages:''') for i, res in enumerate(support_list): UpperCamelCase = '''https://en.wikipedia.org/wiki/{}'''.format(res[0].replace(''' ''', '''_''')) UpperCamelCase = res[1].strip() if sec_titles == "": UpperCamelCase = '''[{}]({})'''.format(res[0], wiki_url) else: UpperCamelCase = sec_titles.split(''' & ''') UpperCamelCase = ''' & '''.join( ['''[{}]({}#{})'''.format(sec.strip(), wiki_url, sec.strip().replace(''' ''', '''_''')) for sec in sec_list] ) st.markdown( '''{0:02d} - **Article**: {1:<18} <br> _Section_: {2}'''.format(i + 1, res[0], sections), unsafe_allow_html=True, ) if show_passages: st.write( '''> <span style="font-family:arial; font-size:10pt;">''' + res[-1] + '''</span>''', unsafe_allow_html=True ) if action in [2, 3]: UpperCamelCase = find_nearest_training(question) UpperCamelCase = nn_train_list[0] st.markdown( '''--- \n ### The most similar question in the ELI5 training set was: \n\n {}'''.format(train_exple['''title''']) ) UpperCamelCase = [ '''{}. {}'''.format(i + 1, ''' \n'''.join([line.strip() for line in ans.split('''\n''') if line.strip() != ''''''])) for i, (ans, sc) in enumerate(zip(train_exple['''answers''']['''text'''], train_exple['''answers''']['''score'''])) if i == 0 or sc > 2 ] st.markdown('''##### Its answers were: \n\n {}'''.format('''\n'''.join(answers_st))) UpperCamelCase = ''' --- **Disclaimer** *The intent of this app is to provide some (hopefully entertaining) insights into the behavior of a current LFQA system. Evaluating biases of such a model and ensuring factual generations are still very much open research problems. Therefore, until some significant progress is achieved, we caution against using the generated answers for practical purposes.* ''' st.sidebar.markdown(disclaimer, unsafe_allow_html=True)
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from ...configuration_utils import PretrainedConfig from ...utils import logging UpperCamelCase = logging.get_logger(__name__) UpperCamelCase = { '''RWKV/rwkv-4-169m-pile''': '''https://huggingface.co/RWKV/rwkv-4-169m-pile/resolve/main/config.json''', '''RWKV/rwkv-4-430m-pile''': '''https://huggingface.co/RWKV/rwkv-4-430m-pile/resolve/main/config.json''', '''RWKV/rwkv-4-1b5-pile''': '''https://huggingface.co/RWKV/rwkv-4-1b5-pile/resolve/main/config.json''', '''RWKV/rwkv-4-3b-pile''': '''https://huggingface.co/RWKV/rwkv-4-3b-pile/resolve/main/config.json''', '''RWKV/rwkv-4-7b-pile''': '''https://huggingface.co/RWKV/rwkv-4-7b-pile/resolve/main/config.json''', '''RWKV/rwkv-4-14b-pile''': '''https://huggingface.co/RWKV/rwkv-4-14b-pile/resolve/main/config.json''', '''RWKV/rwkv-raven-1b5''': '''https://huggingface.co/RWKV/rwkv-raven-1b5/resolve/main/config.json''', '''RWKV/rwkv-raven-3b''': '''https://huggingface.co/RWKV/rwkv-raven-3b/resolve/main/config.json''', '''RWKV/rwkv-raven-7b''': '''https://huggingface.co/RWKV/rwkv-raven-7b/resolve/main/config.json''', '''RWKV/rwkv-raven-14b''': '''https://huggingface.co/RWKV/rwkv-raven-14b/resolve/main/config.json''', } class snake_case_ ( __A ): __A : Optional[int] = "rwkv" __A : List[str] = {"max_position_embeddings": "context_length"} def __init__( self : Dict , lowercase_ : List[Any]=5_02_77 , lowercase_ : Union[str, Any]=10_24 , lowercase_ : Any=40_96 , lowercase_ : int=32 , lowercase_ : Dict=None , lowercase_ : str=None , lowercase_ : Any=1E-5 , lowercase_ : Optional[Any]=0 , lowercase_ : Any=0 , lowercase_ : List[str]=6 , lowercase_ : List[Any]=False , lowercase_ : int=True , **lowercase_ : List[str] , ) -> int: lowercase__ : List[str] = vocab_size lowercase__ : str = context_length lowercase__ : List[Any] = hidden_size lowercase__ : Optional[Any] = num_hidden_layers lowercase__ : Optional[Any] = attention_hidden_size if attention_hidden_size is not None else hidden_size lowercase__ : str = intermediate_size if intermediate_size is not None else 4 * hidden_size lowercase__ : List[Any] = layer_norm_epsilon lowercase__ : str = rescale_every lowercase__ : Optional[int] = use_cache lowercase__ : int = bos_token_id lowercase__ : Optional[Any] = eos_token_id super().__init__( tie_word_embeddings=lowercase_ , bos_token_id=lowercase_ , eos_token_id=lowercase_ , **lowercase_ )
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import argparse import glob import importlib.util import os import re import black from doc_builder.style_doc import style_docstrings_in_code # All paths are set with the intent you should run this script from the root of the repo with the command # python utils/check_copies.py UpperCamelCase = '''src/diffusers''' UpperCamelCase = '''.''' # This is to make sure the diffusers module imported is the one in the repo. UpperCamelCase = importlib.util.spec_from_file_location( '''diffusers''', os.path.join(DIFFUSERS_PATH, '''__init__.py'''), submodule_search_locations=[DIFFUSERS_PATH], ) UpperCamelCase = spec.loader.load_module() def lowercase_ ( _lowerCamelCase : Tuple , _lowerCamelCase : Optional[Any]): """simple docstring""" return line.startswith(_lowerCamelCase) or len(_lowerCamelCase) <= 1 or re.search(R"^\s*\)(\s*->.*:|:)\s*$" , _lowerCamelCase) is not None def lowercase_ ( _lowerCamelCase : Union[str, Any]): """simple docstring""" lowercase__ : Tuple = object_name.split(".") lowercase__ : int = 0 # First let's find the module where our object lives. lowercase__ : List[Any] = parts[i] while i < len(_lowerCamelCase) and not os.path.isfile(os.path.join(_lowerCamelCase , f'''{module}.py''')): i += 1 if i < len(_lowerCamelCase): lowercase__ : Optional[int] = os.path.join(_lowerCamelCase , parts[i]) if i >= len(_lowerCamelCase): raise ValueError(f'''`object_name` should begin with the name of a module of diffusers but got {object_name}.''') with open(os.path.join(_lowerCamelCase , f'''{module}.py''') , "r" , encoding="utf-8" , newline="\n") as f: lowercase__ : str = f.readlines() # Now let's find the class / func in the code! lowercase__ : Dict = "" lowercase__ : Optional[int] = 0 for name in parts[i + 1 :]: while ( line_index < len(_lowerCamelCase) and re.search(Rf'''^{indent}(class|def)\s+{name}(\(|\:)''' , lines[line_index]) is None ): line_index += 1 indent += " " line_index += 1 if line_index >= len(_lowerCamelCase): raise ValueError(f''' {object_name} does not match any function or class in {module}.''') # We found the beginning of the class / func, now let's find the end (when the indent diminishes). lowercase__ : int = line_index while line_index < len(_lowerCamelCase) and _should_continue(lines[line_index] , _lowerCamelCase): line_index += 1 # Clean up empty lines at the end (if any). while len(lines[line_index - 1]) <= 1: line_index -= 1 lowercase__ : str = lines[start_index:line_index] return "".join(_lowerCamelCase) UpperCamelCase = re.compile(R'''^(\s*)#\s*Copied from\s+diffusers\.(\S+\.\S+)\s*($|\S.*$)''') UpperCamelCase = re.compile(R'''^\s*(\S+)->(\S+)(\s+.*|$)''') UpperCamelCase = re.compile(R'''<FILL\s+[^>]*>''') def lowercase_ ( _lowerCamelCase : Tuple): """simple docstring""" lowercase__ : List[str] = code.split("\n") lowercase__ : Any = 0 while idx < len(_lowerCamelCase) and len(lines[idx]) == 0: idx += 1 if idx < len(_lowerCamelCase): return re.search(R"^(\s*)\S" , lines[idx]).groups()[0] return "" def lowercase_ ( _lowerCamelCase : Tuple): """simple docstring""" lowercase__ : Union[str, Any] = len(get_indent(_lowerCamelCase)) > 0 if has_indent: lowercase__ : List[str] = f'''class Bla:\n{code}''' lowercase__ : Optional[Any] = black.Mode(target_versions={black.TargetVersion.PYaa} , line_length=119 , preview=_lowerCamelCase) lowercase__ : Tuple = black.format_str(_lowerCamelCase , mode=_lowerCamelCase) lowercase__ : Dict = style_docstrings_in_code(_lowerCamelCase) return result[len("class Bla:\n") :] if has_indent else result def lowercase_ ( _lowerCamelCase : List[str] , _lowerCamelCase : Union[str, Any]=False): """simple docstring""" with open(_lowerCamelCase , "r" , encoding="utf-8" , newline="\n") as f: lowercase__ : Optional[int] = f.readlines() lowercase__ : Optional[Any] = [] lowercase__ : int = 0 # Not a for loop cause `lines` is going to change (if `overwrite=True`). while line_index < len(_lowerCamelCase): lowercase__ : Dict = _re_copy_warning.search(lines[line_index]) if search is None: line_index += 1 continue # There is some copied code here, let's retrieve the original. lowercase__ : Any = search.groups() lowercase__ : Dict = find_code_in_diffusers(_lowerCamelCase) lowercase__ : Optional[Any] = get_indent(_lowerCamelCase) lowercase__ : List[Any] = line_index + 1 if indent == theoretical_indent else line_index + 2 lowercase__ : List[str] = theoretical_indent lowercase__ : Union[str, Any] = start_index # Loop to check the observed code, stop when indentation diminishes or if we see a End copy comment. lowercase__ : str = True while line_index < len(_lowerCamelCase) and should_continue: line_index += 1 if line_index >= len(_lowerCamelCase): break lowercase__ : Dict = lines[line_index] lowercase__ : List[str] = _should_continue(_lowerCamelCase , _lowerCamelCase) and re.search(f'''^{indent}# End copy''' , _lowerCamelCase) is None # Clean up empty lines at the end (if any). while len(lines[line_index - 1]) <= 1: line_index -= 1 lowercase__ : Any = lines[start_index:line_index] lowercase__ : List[Any] = "".join(_lowerCamelCase) # Remove any nested `Copied from` comments to avoid circular copies lowercase__ : List[Any] = [line for line in theoretical_code.split("\n") if _re_copy_warning.search(_lowerCamelCase) is None] lowercase__ : Optional[Any] = "\n".join(_lowerCamelCase) # Before comparing, use the `replace_pattern` on the original code. if len(_lowerCamelCase) > 0: lowercase__ : Dict = replace_pattern.replace("with" , "").split(",") lowercase__ : Any = [_re_replace_pattern.search(_lowerCamelCase) for p in patterns] for pattern in patterns: if pattern is None: continue lowercase__ : int = pattern.groups() lowercase__ : List[str] = re.sub(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase) if option.strip() == "all-casing": lowercase__ : Optional[Any] = re.sub(obja.lower() , obja.lower() , _lowerCamelCase) lowercase__ : int = re.sub(obja.upper() , obja.upper() , _lowerCamelCase) # Blackify after replacement. To be able to do that, we need the header (class or function definition) # from the previous line lowercase__ : Dict = blackify(lines[start_index - 1] + theoretical_code) lowercase__ : Tuple = theoretical_code[len(lines[start_index - 1]) :] # Test for a diff and act accordingly. if observed_code != theoretical_code: diffs.append([object_name, start_index]) if overwrite: lowercase__ : Optional[int] = lines[:start_index] + [theoretical_code] + lines[line_index:] lowercase__ : Optional[int] = start_index + 1 if overwrite and len(_lowerCamelCase) > 0: # Warn the user a file has been modified. print(f'''Detected changes, rewriting {filename}.''') with open(_lowerCamelCase , "w" , encoding="utf-8" , newline="\n") as f: f.writelines(_lowerCamelCase) return diffs def lowercase_ ( _lowerCamelCase : bool = False): """simple docstring""" lowercase__ : Optional[Any] = glob.glob(os.path.join(_lowerCamelCase , "**/*.py") , recursive=_lowerCamelCase) lowercase__ : str = [] for filename in all_files: lowercase__ : List[str] = is_copy_consistent(_lowerCamelCase , _lowerCamelCase) diffs += [f'''- {filename}: copy does not match {d[0]} at line {d[1]}''' for d in new_diffs] if not overwrite and len(_lowerCamelCase) > 0: lowercase__ : Tuple = "\n".join(_lowerCamelCase) raise Exception( "Found the following copy inconsistencies:\n" + diff + "\nRun `make fix-copies` or `python utils/check_copies.py --fix_and_overwrite` to fix them.") if __name__ == "__main__": UpperCamelCase = argparse.ArgumentParser() parser.add_argument('''--fix_and_overwrite''', action='''store_true''', help='''Whether to fix inconsistencies.''') UpperCamelCase = parser.parse_args() check_copies(args.fix_and_overwrite)
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class snake_case_ : def __init__( self : int ) -> Optional[int]: lowercase__ : Optional[int] = 0 lowercase__ : List[str] = 0 lowercase__ : Any = {} def __UpperCamelCase ( self : Dict , lowercase_ : List[Any] ) -> Union[str, Any]: if vertex not in self.adjacency: lowercase__ : List[Any] = {} self.num_vertices += 1 def __UpperCamelCase ( self : int , lowercase_ : List[str] , lowercase_ : Any , lowercase_ : str ) -> Optional[Any]: self.add_vertex(lowercase_ ) self.add_vertex(lowercase_ ) if head == tail: return lowercase__ : int = weight lowercase__ : Any = weight def __UpperCamelCase ( self : Dict ) -> Optional[int]: lowercase__ : List[Any] = self.get_edges() for edge in edges: lowercase__ , lowercase__ , lowercase__ : int = edge edges.remove((tail, head, weight) ) for i in range(len(lowercase_ ) ): lowercase__ : Tuple = list(edges[i] ) edges.sort(key=lambda lowercase_ : e[2] ) for i in range(len(lowercase_ ) - 1 ): if edges[i][2] >= edges[i + 1][2]: lowercase__ : int = edges[i][2] + 1 for edge in edges: lowercase__ , lowercase__ , lowercase__ : Optional[int] = edge lowercase__ : Union[str, Any] = weight lowercase__ : Dict = weight def __str__( self : str ) -> Any: lowercase__ : str = "" for tail in self.adjacency: for head in self.adjacency[tail]: lowercase__ : Optional[Any] = self.adjacency[head][tail] string += F'''{head} -> {tail} == {weight}\n''' return string.rstrip("\n" ) def __UpperCamelCase ( self : Optional[Any] ) -> Optional[int]: lowercase__ : Any = [] for tail in self.adjacency: for head in self.adjacency[tail]: output.append((tail, head, self.adjacency[head][tail]) ) return output def __UpperCamelCase ( self : List[str] ) -> Dict: return self.adjacency.keys() @staticmethod def __UpperCamelCase ( lowercase_ : Dict=None , lowercase_ : Any=None ) -> Optional[int]: lowercase__ : Any = Graph() if vertices is None: lowercase__ : str = [] if edges is None: lowercase__ : List[Any] = [] for vertex in vertices: g.add_vertex(lowercase_ ) for edge in edges: g.add_edge(*lowercase_ ) return g class snake_case_ : def __init__( self : int ) -> List[str]: lowercase__ : Dict = {} lowercase__ : Tuple = {} def __len__( self : Union[str, Any] ) -> Union[str, Any]: return len(self.parent ) def __UpperCamelCase ( self : Tuple , lowercase_ : List[str] ) -> Tuple: if item in self.parent: return self.find(lowercase_ ) lowercase__ : Union[str, Any] = item lowercase__ : int = 0 return item def __UpperCamelCase ( self : Union[str, Any] , lowercase_ : List[str] ) -> Any: if item not in self.parent: return self.make_set(lowercase_ ) if item != self.parent[item]: lowercase__ : Union[str, Any] = self.find(self.parent[item] ) return self.parent[item] def __UpperCamelCase ( self : Dict , lowercase_ : Dict , lowercase_ : str ) -> Optional[Any]: lowercase__ : Dict = self.find(lowercase_ ) lowercase__ : Optional[int] = self.find(lowercase_ ) if roota == roota: return roota if self.rank[roota] > self.rank[roota]: lowercase__ : Dict = roota return roota if self.rank[roota] < self.rank[roota]: lowercase__ : int = roota return roota if self.rank[roota] == self.rank[roota]: self.rank[roota] += 1 lowercase__ : Tuple = roota return roota return None @staticmethod def __UpperCamelCase ( lowercase_ : Dict ) -> Optional[Any]: lowercase__ : List[Any] = graph.num_vertices lowercase__ : Optional[Any] = Graph.UnionFind() lowercase__ : int = [] while num_components > 1: lowercase__ : List[Any] = {} for vertex in graph.get_vertices(): lowercase__ : Any = -1 lowercase__ : List[str] = graph.get_edges() for edge in edges: lowercase__ , lowercase__ , lowercase__ : str = edge edges.remove((tail, head, weight) ) for edge in edges: lowercase__ , lowercase__ , lowercase__ : List[str] = edge lowercase__ : List[str] = union_find.find(lowercase_ ) lowercase__ : Union[str, Any] = union_find.find(lowercase_ ) if seta != seta: if cheap_edge[seta] == -1 or cheap_edge[seta][2] > weight: lowercase__ : int = [head, tail, weight] if cheap_edge[seta] == -1 or cheap_edge[seta][2] > weight: lowercase__ : Dict = [head, tail, weight] for vertex in cheap_edge: if cheap_edge[vertex] != -1: lowercase__ , lowercase__ , lowercase__ : List[Any] = cheap_edge[vertex] if union_find.find(lowercase_ ) != union_find.find(lowercase_ ): union_find.union(lowercase_ , lowercase_ ) mst_edges.append(cheap_edge[vertex] ) lowercase__ : Optional[Any] = num_components - 1 lowercase__ : List[Any] = Graph.build(edges=lowercase_ ) return mst
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# Copyright 2021 The HuggingFace Team. All rights reserved. # # 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. 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_ : Dict ) -> Union[str, Any]: lowercase__ : Union[str, Any] = data def __iter__( self : Optional[Any] ) -> Tuple: for element in self.data: yield element def lowercase_ ( _lowerCamelCase : Any=True): lowercase__ : Union[str, Any] = Accelerator(even_batches=_lowerCamelCase) assert accelerator.num_processes == 2, "this script expects that two GPUs are available" return accelerator def lowercase_ ( _lowerCamelCase : Accelerator , _lowerCamelCase : int , _lowerCamelCase : int , _lowerCamelCase : bool = False): if iterable: lowercase__ : Tuple = DummyIterableDataset(torch.as_tensor(range(_lowerCamelCase))) else: lowercase__ : List[str] = TensorDataset(torch.as_tensor(range(_lowerCamelCase))) lowercase__ : List[Any] = DataLoader(_lowerCamelCase , batch_size=_lowerCamelCase) lowercase__ : str = accelerator.prepare(_lowerCamelCase) return dl def lowercase_ ( _lowerCamelCase : Accelerator , _lowerCamelCase : int , _lowerCamelCase : int , _lowerCamelCase : List[int] , _lowerCamelCase : List[int] , ): lowercase__ : Tuple = create_dataloader(accelerator=_lowerCamelCase , dataset_size=_lowerCamelCase , batch_size=_lowerCamelCase) lowercase__ : List[str] = [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 lowercase_ ( ): lowercase__ : Tuple = 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 lowercase_ ( ): lowercase__ : Dict = 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 lowercase_ ( ): lowercase__ : Union[str, Any] = create_accelerator(even_batches=_lowerCamelCase) lowercase__ : Optional[int] = torch.nn.Linear(1 , 1) lowercase__ : str = accelerator.prepare(_lowerCamelCase) lowercase__ : Optional[Any] = create_dataloader(_lowerCamelCase , dataset_size=3 , batch_size=1) lowercase__ : Tuple = [] with accelerator.join_uneven_inputs([ddp_model]): for batch_idx, batch in enumerate(_lowerCamelCase): lowercase__ : List[str] = ddp_model(batch[0].float()) lowercase__ : Union[str, Any] = 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 lowercase_ ( _lowerCamelCase : str): 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 lowercase_ ( ): lowercase__ : Union[str, Any] = True lowercase__ : str = False lowercase__ : Any = create_accelerator(even_batches=_lowerCamelCase) lowercase__ : Union[str, Any] = torch.nn.Linear(1 , 1) lowercase__ : List[Any] = accelerator.prepare(_lowerCamelCase) lowercase__ : Tuple = create_dataloader(_lowerCamelCase , dataset_size=3 , batch_size=1) lowercase__ : Dict = create_dataloader(_lowerCamelCase , dataset_size=3 , batch_size=1) with accelerator.join_uneven_inputs([ddp_model] , even_batches=_lowerCamelCase): lowercase__ : Dict = train_dl.batch_sampler.even_batches lowercase__ : Any = 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 lowercase_ ( ): lowercase__ : Optional[Any] = True lowercase__ : List[str] = False lowercase__ : Dict = create_accelerator(even_batches=_lowerCamelCase) lowercase__ : Any = torch.nn.Linear(1 , 1) lowercase__ : List[Any] = accelerator.prepare(_lowerCamelCase) create_dataloader(_lowerCamelCase , dataset_size=3 , batch_size=1 , iterable=_lowerCamelCase) lowercase__ : Optional[int] = 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): lowercase__ : Union[str, Any] = 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 lowercase_ ( ): lowercase__ : Optional[int] = create_accelerator() lowercase__ : Any = torch.nn.Linear(1 , 1) lowercase__ : Union[str, Any] = 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 lowercase_ ( ): lowercase__ : str = 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") lowercase__ : Optional[Any] = accelerator.state.distributed_type lowercase__ : Union[str, Any] = DistributedType.FSDP test_join_raises_warning_for_non_ddp_distributed(_lowerCamelCase) lowercase__ : Dict = original_state if __name__ == "__main__": main()
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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 = logging.get_logger(__name__) def lowercase_ ( _lowerCamelCase : str): lowercase__ : Optional[Any] = DPTConfig() if "large" in checkpoint_url: lowercase__ : str = 1024 lowercase__ : List[str] = 4096 lowercase__ : List[Any] = 24 lowercase__ : Dict = 16 lowercase__ : Union[str, Any] = [5, 11, 17, 23] lowercase__ : Any = [256, 512, 1024, 1024] lowercase__ : Optional[int] = (1, 384, 384) if "ade" in checkpoint_url: lowercase__ : Union[str, Any] = True lowercase__ : Tuple = 150 lowercase__ : Optional[int] = "huggingface/label-files" lowercase__ : str = "ade20k-id2label.json" lowercase__ : Union[str, Any] = json.load(open(cached_download(hf_hub_url(_lowerCamelCase , _lowerCamelCase , repo_type="dataset")) , "r")) lowercase__ : Union[str, Any] = {int(_lowerCamelCase): v for k, v in idalabel.items()} lowercase__ : Optional[Any] = idalabel lowercase__ : Union[str, Any] = {v: k for k, v in idalabel.items()} lowercase__ : Tuple = [1, 150, 480, 480] return config, expected_shape def lowercase_ ( _lowerCamelCase : List[Any]): lowercase__ : int = ["pretrained.model.head.weight", "pretrained.model.head.bias"] for k in ignore_keys: state_dict.pop(_lowerCamelCase , _lowerCamelCase) def lowercase_ ( _lowerCamelCase : Tuple): if ( "pretrained.model" in name and "cls_token" not in name and "pos_embed" not in name and "patch_embed" not in name ): lowercase__ : Dict = name.replace("pretrained.model" , "dpt.encoder") if "pretrained.model" in name: lowercase__ : List[str] = name.replace("pretrained.model" , "dpt.embeddings") if "patch_embed" in name: lowercase__ : Any = name.replace("patch_embed" , "patch_embeddings") if "pos_embed" in name: lowercase__ : Union[str, Any] = name.replace("pos_embed" , "position_embeddings") if "attn.proj" in name: lowercase__ : Optional[int] = name.replace("attn.proj" , "attention.output.dense") if "proj" in name and "project" not in name: lowercase__ : int = name.replace("proj" , "projection") if "blocks" in name: lowercase__ : List[str] = name.replace("blocks" , "layer") if "mlp.fc1" in name: lowercase__ : List[str] = name.replace("mlp.fc1" , "intermediate.dense") if "mlp.fc2" in name: lowercase__ : Optional[int] = name.replace("mlp.fc2" , "output.dense") if "norm1" in name: lowercase__ : List[str] = name.replace("norm1" , "layernorm_before") if "norm2" in name: lowercase__ : Dict = name.replace("norm2" , "layernorm_after") if "scratch.output_conv" in name: lowercase__ : Union[str, Any] = name.replace("scratch.output_conv" , "head") if "scratch" in name: lowercase__ : str = name.replace("scratch" , "neck") if "layer1_rn" in name: lowercase__ : int = name.replace("layer1_rn" , "convs.0") if "layer2_rn" in name: lowercase__ : int = name.replace("layer2_rn" , "convs.1") if "layer3_rn" in name: lowercase__ : Tuple = name.replace("layer3_rn" , "convs.2") if "layer4_rn" in name: lowercase__ : Union[str, Any] = name.replace("layer4_rn" , "convs.3") if "refinenet" in name: lowercase__ : Dict = 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 lowercase__ : str = name.replace(f'''refinenet{layer_idx}''' , f'''fusion_stage.layers.{abs(layer_idx-4)}''') if "out_conv" in name: lowercase__ : str = name.replace("out_conv" , "projection") if "resConfUnit1" in name: lowercase__ : int = name.replace("resConfUnit1" , "residual_layer1") if "resConfUnit2" in name: lowercase__ : Optional[Any] = name.replace("resConfUnit2" , "residual_layer2") if "conv1" in name: lowercase__ : List[Any] = name.replace("conv1" , "convolution1") if "conv2" in name: lowercase__ : Tuple = name.replace("conv2" , "convolution2") # readout blocks if "pretrained.act_postprocess1.0.project.0" in name: lowercase__ : int = name.replace("pretrained.act_postprocess1.0.project.0" , "neck.reassemble_stage.readout_projects.0.0") if "pretrained.act_postprocess2.0.project.0" in name: lowercase__ : Any = name.replace("pretrained.act_postprocess2.0.project.0" , "neck.reassemble_stage.readout_projects.1.0") if "pretrained.act_postprocess3.0.project.0" in name: lowercase__ : Optional[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: lowercase__ : List[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: lowercase__ : Union[str, Any] = name.replace("pretrained.act_postprocess1.3" , "neck.reassemble_stage.layers.0.projection") if "pretrained.act_postprocess1.4" in name: lowercase__ : Optional[Any] = name.replace("pretrained.act_postprocess1.4" , "neck.reassemble_stage.layers.0.resize") if "pretrained.act_postprocess2.3" in name: lowercase__ : int = name.replace("pretrained.act_postprocess2.3" , "neck.reassemble_stage.layers.1.projection") if "pretrained.act_postprocess2.4" in name: lowercase__ : str = name.replace("pretrained.act_postprocess2.4" , "neck.reassemble_stage.layers.1.resize") if "pretrained.act_postprocess3.3" in name: lowercase__ : Dict = name.replace("pretrained.act_postprocess3.3" , "neck.reassemble_stage.layers.2.projection") if "pretrained.act_postprocess4.3" in name: lowercase__ : Any = name.replace("pretrained.act_postprocess4.3" , "neck.reassemble_stage.layers.3.projection") if "pretrained.act_postprocess4.4" in name: lowercase__ : int = name.replace("pretrained.act_postprocess4.4" , "neck.reassemble_stage.layers.3.resize") if "pretrained" in name: lowercase__ : Any = name.replace("pretrained" , "dpt") if "bn" in name: lowercase__ : str = name.replace("bn" , "batch_norm") if "head" in name: lowercase__ : Optional[Any] = name.replace("head" , "head.head") if "encoder.norm" in name: lowercase__ : Tuple = name.replace("encoder.norm" , "layernorm") if "auxlayer" in name: lowercase__ : int = name.replace("auxlayer" , "auxiliary_head.head") return name def lowercase_ ( _lowerCamelCase : Union[str, Any] , _lowerCamelCase : str): for i in range(config.num_hidden_layers): # read in weights + bias of input projection layer (in timm, this is a single matrix + bias) lowercase__ : Union[str, Any] = state_dict.pop(f'''dpt.encoder.layer.{i}.attn.qkv.weight''') lowercase__ : Union[str, Any] = state_dict.pop(f'''dpt.encoder.layer.{i}.attn.qkv.bias''') # next, add query, keys and values (in that order) to the state dict lowercase__ : Optional[int] = in_proj_weight[: config.hidden_size, :] lowercase__ : Optional[int] = in_proj_bias[: config.hidden_size] lowercase__ : Optional[Any] = in_proj_weight[ config.hidden_size : config.hidden_size * 2, : ] lowercase__ : Optional[Any] = in_proj_bias[ config.hidden_size : config.hidden_size * 2 ] lowercase__ : List[Any] = in_proj_weight[ -config.hidden_size :, : ] lowercase__ : int = in_proj_bias[-config.hidden_size :] def lowercase_ ( ): lowercase__ : Any = "http://images.cocodataset.org/val2017/000000039769.jpg" lowercase__ : Optional[int] = Image.open(requests.get(_lowerCamelCase , stream=_lowerCamelCase).raw) return im @torch.no_grad() def lowercase_ ( _lowerCamelCase : Dict , _lowerCamelCase : List[str] , _lowerCamelCase : Optional[Any] , _lowerCamelCase : Dict): lowercase__ , lowercase__ : Optional[int] = get_dpt_config(_lowerCamelCase) # load original state_dict from URL lowercase__ : Tuple = torch.hub.load_state_dict_from_url(_lowerCamelCase , map_location="cpu") # remove certain keys remove_ignore_keys_(_lowerCamelCase) # rename keys for key in state_dict.copy().keys(): lowercase__ : List[str] = state_dict.pop(_lowerCamelCase) lowercase__ : List[Any] = val # read in qkv matrices read_in_q_k_v(_lowerCamelCase , _lowerCamelCase) # load HuggingFace model lowercase__ : Any = DPTForSemanticSegmentation(_lowerCamelCase) if "ade" in checkpoint_url else DPTForDepthEstimation(_lowerCamelCase) model.load_state_dict(_lowerCamelCase) model.eval() # Check outputs on an image lowercase__ : Optional[Any] = 480 if "ade" in checkpoint_url else 384 lowercase__ : Union[str, Any] = DPTImageProcessor(size=_lowerCamelCase) lowercase__ : List[str] = prepare_img() lowercase__ : Dict = image_processor(_lowerCamelCase , return_tensors="pt") # forward pass lowercase__ : Tuple = model(**_lowerCamelCase).logits if "ade" in checkpoint_url else model(**_lowerCamelCase).predicted_depth # Assert logits lowercase__ : Union[str, Any] = torch.tensor([[6.3199, 6.3629, 6.4148], [6.3850, 6.3615, 6.4166], [6.3519, 6.3176, 6.3575]]) if "ade" in checkpoint_url: lowercase__ : List[str] = torch.tensor([[4.0480, 4.2420, 4.4360], [4.3124, 4.5693, 4.8261], [4.5768, 4.8965, 5.2163]]) assert outputs.shape == torch.Size(_lowerCamelCase) assert ( torch.allclose(outputs[0, 0, :3, :3] , _lowerCamelCase , atol=1E-4) if "ade" in checkpoint_url else torch.allclose(outputs[0, :3, :3] , _lowerCamelCase) ) 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: print("Pushing model to hub...") model.push_to_hub( repo_path_or_name=Path(_lowerCamelCase , _lowerCamelCase) , organization="nielsr" , commit_message="Add model" , use_temp_dir=_lowerCamelCase , ) image_processor.push_to_hub( repo_path_or_name=Path(_lowerCamelCase , _lowerCamelCase) , organization="nielsr" , commit_message="Add image processor" , use_temp_dir=_lowerCamelCase , ) if __name__ == "__main__": UpperCamelCase = 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=True, 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.''', ) UpperCamelCase = parser.parse_args() convert_dpt_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)
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import re from pathlib import Path from unittest import TestCase import pytest @pytest.mark.integration class snake_case_ ( __A ): def __UpperCamelCase ( self : int , lowercase_ : str ) -> Optional[Any]: with open(lowercase_ , encoding="utf-8" ) as input_file: lowercase__ : List[str] = re.compile(R"(?!.*\b(?:encoding|rb|w|wb|w+|wb+|ab|ab+)\b)(?<=\s)(open)\((.*)\)" ) lowercase__ : List[str] = input_file.read() lowercase__ : Optional[Any] = regexp.search(lowercase_ ) return match def __UpperCamelCase ( self : Tuple , lowercase_ : str ) -> Dict: with open(lowercase_ , encoding="utf-8" ) as input_file: lowercase__ : Union[str, Any] = re.compile(R"#[^\r\n]*print\(|\"[^\r\n]*print\(|\"\"\".*?print\(.*?\"\"\"|(print\()" , re.DOTALL ) lowercase__ : Union[str, Any] = input_file.read() # use `re.finditer` to handle the case where the ignored groups would be matched first by `re.search` lowercase__ : Union[str, Any] = regexp.finditer(lowercase_ ) lowercase__ : Dict = [match for match in matches if match is not None and match.group(1 ) is not None] return matches[0] if matches else None def __UpperCamelCase ( self : Any ) -> List[str]: lowercase__ : List[str] = Path("./datasets" ) lowercase__ : Union[str, Any] = list(dataset_paths.absolute().glob("**/*.py" ) ) for dataset in dataset_files: if self._no_encoding_on_file_open(str(lowercase_ ) ): raise AssertionError(F'''open(...) must use utf-8 encoding in {dataset}''' ) def __UpperCamelCase ( self : List[str] ) -> str: lowercase__ : Dict = Path("./datasets" ) lowercase__ : Any = list(dataset_paths.absolute().glob("**/*.py" ) ) for dataset in dataset_files: if self._no_print_statements(str(lowercase_ ) ): raise AssertionError(F'''print statement found in {dataset}. Use datasets.logger/logging instead.''' )
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def lowercase_ ( _lowerCamelCase : int = 1 , _lowerCamelCase : int = 1000): lowercase__ : Union[str, Any] = 1 lowercase__ : int = 0 for divide_by_number in range(_lowerCamelCase , digit + 1): lowercase__ : list[int] = [] lowercase__ : Dict = numerator for _ in range(1 , digit + 1): if now_divide in has_been_divided: if longest_list_length < len(_lowerCamelCase): lowercase__ : Union[str, Any] = len(_lowerCamelCase) lowercase__ : Optional[int] = divide_by_number else: has_been_divided.append(_lowerCamelCase) lowercase__ : Optional[Any] = now_divide * 10 % divide_by_number return the_digit # Tests if __name__ == "__main__": import doctest doctest.testmod()
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def lowercase_ ( _lowerCamelCase : int = 100): lowercase__ : List[str] = n * (n + 1) * (2 * n + 1) / 6 lowercase__ : Optional[Any] = (n * (n + 1) / 2) ** 2 return int(square_of_sum - sum_of_squares) if __name__ == "__main__": print(f"{solution() = }")
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import gc import unittest import torch from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer from diffusers import ( AutoencoderKL, DDIMScheduler, DDPMScheduler, PriorTransformer, StableUnCLIPPipeline, UNetaDConditionModel, ) from diffusers.pipelines.stable_diffusion.stable_unclip_image_normalizer import StableUnCLIPImageNormalizer from diffusers.utils.testing_utils import enable_full_determinism, load_numpy, require_torch_gpu, slow, torch_device 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, assert_mean_pixel_difference, ) enable_full_determinism() class snake_case_ ( __A ,__A ,__A ,unittest.TestCase ): __A : int = StableUnCLIPPipeline __A : int = TEXT_TO_IMAGE_PARAMS __A : Any = TEXT_TO_IMAGE_BATCH_PARAMS __A : int = TEXT_TO_IMAGE_IMAGE_PARAMS __A : Dict = TEXT_TO_IMAGE_IMAGE_PARAMS # TODO(will) Expected attn_bias.stride(1) == 0 to be true, but got false __A : int = False def __UpperCamelCase ( self : Optional[int] ) -> List[str]: lowercase__ : str = 32 lowercase__ : Any = embedder_hidden_size # prior components torch.manual_seed(0 ) lowercase__ : Optional[Any] = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip" ) torch.manual_seed(0 ) lowercase__ : List[str] = CLIPTextModelWithProjection( CLIPTextConfig( bos_token_id=0 , eos_token_id=2 , hidden_size=lowercase_ , projection_dim=lowercase_ , intermediate_size=37 , layer_norm_eps=1E-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=10_00 , ) ) torch.manual_seed(0 ) lowercase__ : Any = PriorTransformer( num_attention_heads=2 , attention_head_dim=12 , embedding_dim=lowercase_ , num_layers=1 , ) torch.manual_seed(0 ) lowercase__ : Union[str, Any] = DDPMScheduler( variance_type="fixed_small_log" , prediction_type="sample" , num_train_timesteps=10_00 , clip_sample=lowercase_ , clip_sample_range=5.0 , beta_schedule="squaredcos_cap_v2" , ) # regular denoising components torch.manual_seed(0 ) lowercase__ : List[str] = StableUnCLIPImageNormalizer(embedding_dim=lowercase_ ) lowercase__ : Tuple = DDPMScheduler(beta_schedule="squaredcos_cap_v2" ) torch.manual_seed(0 ) lowercase__ : Optional[int] = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip" ) torch.manual_seed(0 ) lowercase__ : Tuple = CLIPTextModel( CLIPTextConfig( bos_token_id=0 , eos_token_id=2 , hidden_size=lowercase_ , 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=10_00 , ) ) torch.manual_seed(0 ) lowercase__ : str = UNetaDConditionModel( sample_size=32 , in_channels=4 , out_channels=4 , down_block_types=("CrossAttnDownBlock2D", "DownBlock2D") , up_block_types=("UpBlock2D", "CrossAttnUpBlock2D") , block_out_channels=(32, 64) , attention_head_dim=(2, 4) , class_embed_type="projection" , projection_class_embeddings_input_dim=embedder_projection_dim * 2 , cross_attention_dim=lowercase_ , layers_per_block=1 , upcast_attention=lowercase_ , use_linear_projection=lowercase_ , ) torch.manual_seed(0 ) lowercase__ : Any = DDIMScheduler( beta_schedule="scaled_linear" , beta_start=0.0_00_85 , beta_end=0.0_12 , prediction_type="v_prediction" , set_alpha_to_one=lowercase_ , steps_offset=1 , ) torch.manual_seed(0 ) lowercase__ : List[str] = AutoencoderKL() lowercase__ : List[Any] = { # prior components "prior_tokenizer": prior_tokenizer, "prior_text_encoder": prior_text_encoder, "prior": prior, "prior_scheduler": prior_scheduler, # image noising components "image_normalizer": image_normalizer, "image_noising_scheduler": image_noising_scheduler, # regular denoising components "tokenizer": tokenizer, "text_encoder": text_encoder, "unet": unet, "scheduler": scheduler, "vae": vae, } return components def __UpperCamelCase ( self : Any , lowercase_ : Tuple , lowercase_ : Dict=0 ) -> Any: if str(lowercase_ ).startswith("mps" ): lowercase__ : Any = torch.manual_seed(lowercase_ ) else: lowercase__ : Any = torch.Generator(device=lowercase_ ).manual_seed(lowercase_ ) lowercase__ : Optional[Any] = { "prompt": "A painting of a squirrel eating a burger", "generator": generator, "num_inference_steps": 2, "prior_num_inference_steps": 2, "output_type": "numpy", } return inputs def __UpperCamelCase ( self : Union[str, Any] ) -> List[str]: lowercase__ : Union[str, Any] = torch_device == "cpu" self._test_attention_slicing_forward_pass(test_max_difference=lowercase_ ) def __UpperCamelCase ( self : List[Any] ) -> List[str]: lowercase__ : str = torch_device in ["cpu", "mps"] self._test_inference_batch_single_identical(test_max_difference=lowercase_ ) @slow @require_torch_gpu class snake_case_ ( unittest.TestCase ): def __UpperCamelCase ( self : Tuple ) -> Any: # clean up the VRAM after each test super().tearDown() gc.collect() torch.cuda.empty_cache() def __UpperCamelCase ( self : int ) -> int: lowercase__ : Optional[int] = load_numpy( "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_l_anime_turtle_fp16.npy" ) lowercase__ : List[str] = StableUnCLIPPipeline.from_pretrained("fusing/stable-unclip-2-1-l" , torch_dtype=torch.floataa ) pipe.to(lowercase_ ) pipe.set_progress_bar_config(disable=lowercase_ ) # stable unclip will oom when integration tests are run on a V100, # so turn on memory savings pipe.enable_attention_slicing() pipe.enable_sequential_cpu_offload() lowercase__ : Optional[int] = torch.Generator(device="cpu" ).manual_seed(0 ) lowercase__ : Dict = pipe("anime turle" , generator=lowercase_ , output_type="np" ) lowercase__ : Optional[int] = output.images[0] assert image.shape == (7_68, 7_68, 3) assert_mean_pixel_difference(lowercase_ , lowercase_ ) def __UpperCamelCase ( self : Union[str, Any] ) -> Dict: torch.cuda.empty_cache() torch.cuda.reset_max_memory_allocated() torch.cuda.reset_peak_memory_stats() lowercase__ : Union[str, Any] = StableUnCLIPPipeline.from_pretrained("fusing/stable-unclip-2-1-l" , torch_dtype=torch.floataa ) lowercase__ : int = pipe.to(lowercase_ ) pipe.set_progress_bar_config(disable=lowercase_ ) pipe.enable_attention_slicing() pipe.enable_sequential_cpu_offload() lowercase__ : str = pipe( "anime turtle" , prior_num_inference_steps=2 , num_inference_steps=2 , output_type="np" , ) lowercase__ : Any = torch.cuda.max_memory_allocated() # make sure that less than 7 GB is allocated assert mem_bytes < 7 * 10**9
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import dataclasses import json import sys import types from argparse import ArgumentDefaultsHelpFormatter, ArgumentParser, ArgumentTypeError from copy import copy from enum import Enum from inspect import isclass from pathlib import Path from typing import Any, Callable, Dict, Iterable, List, Literal, NewType, Optional, Tuple, Union, get_type_hints import yaml UpperCamelCase = NewType('''DataClass''', Any) UpperCamelCase = NewType('''DataClassType''', Any) def lowercase_ ( _lowerCamelCase : Dict): if isinstance(_lowerCamelCase , _lowerCamelCase): 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 ArgumentTypeError( f'''Truthy value expected: got {v} but expected one of yes/no, true/false, t/f, y/n, 1/0 (case insensitive).''') def lowercase_ ( _lowerCamelCase : list): lowercase__ : Dict = {str(_lowerCamelCase): choice for choice in choices} return lambda _lowerCamelCase: str_to_choice.get(_lowerCamelCase , _lowerCamelCase) def lowercase_ ( *, _lowerCamelCase : Union[str, List[str]] = None , _lowerCamelCase : str = None , _lowerCamelCase : Any = dataclasses.MISSING , _lowerCamelCase : Callable[[], Any] = dataclasses.MISSING , _lowerCamelCase : dict = None , **_lowerCamelCase : int , ): if metadata is None: # Important, don't use as default param in function signature because dict is mutable and shared across function calls lowercase__ : List[Any] = {} if aliases is not None: lowercase__ : Optional[int] = aliases if help is not None: lowercase__ : Optional[int] = help return dataclasses.field(metadata=_lowerCamelCase , default=_lowerCamelCase , default_factory=_lowerCamelCase , **_lowerCamelCase) class snake_case_ ( __A ): __A : Iterable[DataClassType] def __init__( self : Dict , lowercase_ : Union[DataClassType, Iterable[DataClassType]] , **lowercase_ : Tuple ) -> Dict: # To make the default appear when using --help if "formatter_class" not in kwargs: lowercase__ : Optional[int] = ArgumentDefaultsHelpFormatter super().__init__(**lowercase_ ) if dataclasses.is_dataclass(lowercase_ ): lowercase__ : int = [dataclass_types] lowercase__ : List[Any] = list(lowercase_ ) for dtype in self.dataclass_types: self._add_dataclass_arguments(lowercase_ ) @staticmethod def __UpperCamelCase ( lowercase_ : ArgumentParser , lowercase_ : dataclasses.Field ) -> str: lowercase__ : List[Any] = F'''--{field.name}''' lowercase__ : Tuple = field.metadata.copy() # field.metadata is not used at all by Data Classes, # it is provided as a third-party extension mechanism. if isinstance(field.type , lowercase_ ): raise RuntimeError( "Unresolved type detected, which should have been done with the help of " "`typing.get_type_hints` method by default" ) lowercase__ : Optional[Any] = kwargs.pop("aliases" , [] ) if isinstance(lowercase_ , lowercase_ ): lowercase__ : str = [aliases] lowercase__ : Optional[int] = getattr(field.type , "__origin__" , field.type ) if origin_type is Union or (hasattr(lowercase_ , "UnionType" ) and isinstance(lowercase_ , types.UnionType )): if str not in field.type.__args__ and ( len(field.type.__args__ ) != 2 or type(lowercase_ ) not in field.type.__args__ ): raise ValueError( "Only `Union[X, NoneType]` (i.e., `Optional[X]`) is allowed for `Union` because" " the argument parser only supports one type per argument." F''' Problem encountered in field \'{field.name}\'.''' ) if type(lowercase_ ) not in field.type.__args__: # filter `str` in Union lowercase__ : int = field.type.__args__[0] if field.type.__args__[1] == str else field.type.__args__[1] lowercase__ : str = getattr(field.type , "__origin__" , field.type ) elif bool not in field.type.__args__: # filter `NoneType` in Union (except for `Union[bool, NoneType]`) lowercase__ : Optional[int] = ( field.type.__args__[0] if isinstance(lowercase_ , field.type.__args__[1] ) else field.type.__args__[1] ) lowercase__ : List[str] = getattr(field.type , "__origin__" , field.type ) # A variable to store kwargs for a boolean field, if needed # so that we can init a `no_*` complement argument (see below) lowercase__ : Dict = {} if origin_type is Literal or (isinstance(field.type , lowercase_ ) and issubclass(field.type , lowercase_ )): if origin_type is Literal: lowercase__ : List[Any] = field.type.__args__ else: lowercase__ : Any = [x.value for x in field.type] lowercase__ : Tuple = make_choice_type_function(kwargs["choices"] ) if field.default is not dataclasses.MISSING: lowercase__ : int = field.default else: lowercase__ : Optional[Any] = True elif field.type is bool or field.type == Optional[bool]: # Copy the currect kwargs to use to instantiate a `no_*` complement argument below. # We do not initialize it here because the `no_*` alternative must be instantiated after the real argument lowercase__ : Union[str, Any] = copy(lowercase_ ) # Hack because type=bool in argparse does not behave as we want. lowercase__ : Optional[int] = string_to_bool if field.type is bool or (field.default is not None and field.default is not dataclasses.MISSING): # Default value is False if we have no default when of type bool. lowercase__ : Optional[Any] = False if field.default is dataclasses.MISSING else field.default # This is the value that will get picked if we don't include --field_name in any way lowercase__ : str = default # This tells argparse we accept 0 or 1 value after --field_name lowercase__ : str = "?" # This is the value that will get picked if we do --field_name (without value) lowercase__ : List[str] = True elif isclass(lowercase_ ) and issubclass(lowercase_ , lowercase_ ): lowercase__ : str = field.type.__args__[0] lowercase__ : Tuple = "+" if field.default_factory is not dataclasses.MISSING: lowercase__ : Any = field.default_factory() elif field.default is dataclasses.MISSING: lowercase__ : int = True else: lowercase__ : List[str] = field.type if field.default is not dataclasses.MISSING: lowercase__ : Tuple = field.default elif field.default_factory is not dataclasses.MISSING: lowercase__ : Dict = field.default_factory() else: lowercase__ : List[str] = True parser.add_argument(lowercase_ , *lowercase_ , **lowercase_ ) # Add a complement `no_*` argument for a boolean field AFTER the initial field has already been added. # Order is important for arguments with the same destination! # We use a copy of earlier kwargs because the original kwargs have changed a lot before reaching down # here and we do not need those changes/additional keys. if field.default is True and (field.type is bool or field.type == Optional[bool]): lowercase__ : Union[str, Any] = False parser.add_argument(F'''--no_{field.name}''' , action="store_false" , dest=field.name , **lowercase_ ) def __UpperCamelCase ( self : Any , lowercase_ : DataClassType ) -> List[Any]: if hasattr(lowercase_ , "_argument_group_name" ): lowercase__ : List[str] = self.add_argument_group(dtype._argument_group_name ) else: lowercase__ : Optional[Any] = self try: lowercase__ : Dict[str, type] = get_type_hints(lowercase_ ) except NameError: raise RuntimeError( F'''Type resolution failed for {dtype}. Try declaring the class in global scope or ''' "removing line of `from __future__ import annotations` which opts in Postponed " "Evaluation of Annotations (PEP 563)" ) except TypeError as ex: # Remove this block when we drop Python 3.9 support if sys.version_info[:2] < (3, 10) and "unsupported operand type(s) for |" in str(lowercase_ ): lowercase__ : Optional[Any] = ".".join(map(lowercase_ , sys.version_info[:3] ) ) raise RuntimeError( F'''Type resolution failed for {dtype} on Python {python_version}. Try removing ''' "line of `from __future__ import annotations` which opts in union types as " "`X | Y` (PEP 604) via Postponed Evaluation of Annotations (PEP 563). To " "support Python versions that lower than 3.10, you need to use " "`typing.Union[X, Y]` instead of `X | Y` and `typing.Optional[X]` instead of " "`X | None`." ) from ex raise for field in dataclasses.fields(lowercase_ ): if not field.init: continue lowercase__ : Optional[int] = type_hints[field.name] self._parse_dataclass_field(lowercase_ , lowercase_ ) def __UpperCamelCase ( self : Dict , lowercase_ : int=None , lowercase_ : Optional[Any]=False , lowercase_ : List[Any]=True , lowercase_ : List[str]=None , lowercase_ : Any=None , ) -> Tuple[DataClass, ...]: if args_file_flag or args_filename or (look_for_args_file and len(sys.argv )): lowercase__ : List[Any] = [] if args_filename: args_files.append(Path(lowercase_ ) ) elif look_for_args_file and len(sys.argv ): args_files.append(Path(sys.argv[0] ).with_suffix(".args" ) ) # args files specified via command line flag should overwrite default args files so we add them last if args_file_flag: # Create special parser just to extract the args_file_flag values lowercase__ : str = ArgumentParser() args_file_parser.add_argument(lowercase_ , type=lowercase_ , action="append" ) # Use only remaining args for further parsing (remove the args_file_flag) lowercase__ : Union[str, Any] = args_file_parser.parse_known_args(args=lowercase_ ) lowercase__ : Union[str, Any] = vars(lowercase_ ).get(args_file_flag.lstrip("-" ) , lowercase_ ) if cmd_args_file_paths: args_files.extend([Path(lowercase_ ) for p in cmd_args_file_paths] ) lowercase__ : Any = [] for args_file in args_files: if args_file.exists(): file_args += args_file.read_text().split() # in case of duplicate arguments the last one has precedence # args specified via the command line should overwrite args from files, so we add them last lowercase__ : List[Any] = file_args + args if args is not None else file_args + sys.argv[1:] lowercase__ : List[str] = self.parse_known_args(args=lowercase_ ) lowercase__ : int = [] for dtype in self.dataclass_types: lowercase__ : int = {f.name for f in dataclasses.fields(lowercase_ ) if f.init} lowercase__ : Tuple = {k: v for k, v in vars(lowercase_ ).items() if k in keys} for k in keys: delattr(lowercase_ , lowercase_ ) lowercase__ : Tuple = dtype(**lowercase_ ) outputs.append(lowercase_ ) if len(namespace.__dict__ ) > 0: # additional namespace. outputs.append(lowercase_ ) if return_remaining_strings: return (*outputs, remaining_args) else: if remaining_args: raise ValueError(F'''Some specified arguments are not used by the HfArgumentParser: {remaining_args}''' ) return (*outputs,) def __UpperCamelCase ( self : Dict , lowercase_ : Dict[str, Any] , lowercase_ : bool = False ) -> Tuple[DataClass, ...]: lowercase__ : Optional[int] = set(args.keys() ) lowercase__ : List[str] = [] for dtype in self.dataclass_types: lowercase__ : int = {f.name for f in dataclasses.fields(lowercase_ ) if f.init} lowercase__ : Optional[int] = {k: v for k, v in args.items() if k in keys} unused_keys.difference_update(inputs.keys() ) lowercase__ : Optional[int] = dtype(**lowercase_ ) outputs.append(lowercase_ ) if not allow_extra_keys and unused_keys: raise ValueError(F'''Some keys are not used by the HfArgumentParser: {sorted(lowercase_ )}''' ) return tuple(lowercase_ ) def __UpperCamelCase ( self : List[str] , lowercase_ : str , lowercase_ : bool = False ) -> Tuple[DataClass, ...]: with open(Path(lowercase_ ) , encoding="utf-8" ) as open_json_file: lowercase__ : Tuple = json.loads(open_json_file.read() ) lowercase__ : Dict = self.parse_dict(lowercase_ , allow_extra_keys=lowercase_ ) return tuple(lowercase_ ) def __UpperCamelCase ( self : Optional[int] , lowercase_ : str , lowercase_ : bool = False ) -> Tuple[DataClass, ...]: lowercase__ : Tuple = self.parse_dict(yaml.safe_load(Path(lowercase_ ).read_text() ) , allow_extra_keys=lowercase_ ) return tuple(lowercase_ )
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import asyncio import os import shutil import subprocess import sys import tempfile import unittest from distutils.util import strtobool from functools import partial from pathlib import Path from typing import List, Union from unittest import mock import torch from ..state import AcceleratorState, PartialState from ..utils import ( gather, is_bnb_available, is_comet_ml_available, is_datasets_available, is_deepspeed_available, is_mps_available, is_safetensors_available, is_tensorboard_available, is_torch_version, is_tpu_available, is_transformers_available, is_wandb_available, is_xpu_available, ) def lowercase_ ( _lowerCamelCase : Union[str, Any] , _lowerCamelCase : int=False): try: lowercase__ : str = os.environ[key] except KeyError: # KEY isn't set, default to `default`. lowercase__ : Union[str, Any] = default else: # KEY is set, convert it to True or False. try: lowercase__ : Union[str, Any] = strtobool(_lowerCamelCase) except ValueError: # More values are supported, but let's keep the message simple. raise ValueError(f'''If set, {key} must be yes or no.''') return _value UpperCamelCase = parse_flag_from_env('''RUN_SLOW''', default=False) def lowercase_ ( _lowerCamelCase : int): return unittest.skip("Test was skipped")(_lowerCamelCase) def lowercase_ ( _lowerCamelCase : Tuple): return unittest.skipUnless(_run_slow_tests , "test is slow")(_lowerCamelCase) def lowercase_ ( _lowerCamelCase : Union[str, Any]): return unittest.skipUnless(not torch.cuda.is_available() , "test requires only a CPU")(_lowerCamelCase) def lowercase_ ( _lowerCamelCase : Dict): return unittest.skipUnless(torch.cuda.is_available() , "test requires a GPU")(_lowerCamelCase) def lowercase_ ( _lowerCamelCase : int): return unittest.skipUnless(is_xpu_available() , "test requires a XPU")(_lowerCamelCase) def lowercase_ ( _lowerCamelCase : List[str]): return unittest.skipUnless(is_mps_available() , "test requires a `mps` backend support in `torch`")(_lowerCamelCase) def lowercase_ ( _lowerCamelCase : List[str]): return unittest.skipUnless( is_transformers_available() and is_datasets_available() , "test requires the Hugging Face suite")(_lowerCamelCase) def lowercase_ ( _lowerCamelCase : Union[str, Any]): return unittest.skipUnless(is_bnb_available() , "test requires the bitsandbytes library")(_lowerCamelCase) def lowercase_ ( _lowerCamelCase : Union[str, Any]): return unittest.skipUnless(is_tpu_available() , "test requires TPU")(_lowerCamelCase) def lowercase_ ( _lowerCamelCase : List[Any]): return unittest.skipUnless(torch.cuda.device_count() == 1 , "test requires a GPU")(_lowerCamelCase) def lowercase_ ( _lowerCamelCase : Union[str, Any]): return unittest.skipUnless(torch.xpu.device_count() == 1 , "test requires a XPU")(_lowerCamelCase) def lowercase_ ( _lowerCamelCase : List[str]): return unittest.skipUnless(torch.cuda.device_count() > 1 , "test requires multiple GPUs")(_lowerCamelCase) def lowercase_ ( _lowerCamelCase : int): return unittest.skipUnless(torch.xpu.device_count() > 1 , "test requires multiple XPUs")(_lowerCamelCase) def lowercase_ ( _lowerCamelCase : List[str]): return unittest.skipUnless(is_safetensors_available() , "test requires safetensors")(_lowerCamelCase) def lowercase_ ( _lowerCamelCase : str): return unittest.skipUnless(is_deepspeed_available() , "test requires DeepSpeed")(_lowerCamelCase) def lowercase_ ( _lowerCamelCase : Any): return unittest.skipUnless(is_torch_version(">=" , "1.12.0") , "test requires torch version >= 1.12.0")(_lowerCamelCase) def lowercase_ ( _lowerCamelCase : List[Any]=None , _lowerCamelCase : Dict=None): if test_case is None: return partial(_lowerCamelCase , version=_lowerCamelCase) return unittest.skipUnless(is_torch_version(">=" , _lowerCamelCase) , f'''test requires torch version >= {version}''')(_lowerCamelCase) def lowercase_ ( _lowerCamelCase : List[Any]): return unittest.skipUnless(is_tensorboard_available() , "test requires Tensorboard")(_lowerCamelCase) def lowercase_ ( _lowerCamelCase : int): return unittest.skipUnless(is_wandb_available() , "test requires wandb")(_lowerCamelCase) def lowercase_ ( _lowerCamelCase : List[str]): return unittest.skipUnless(is_comet_ml_available() , "test requires comet_ml")(_lowerCamelCase) UpperCamelCase = ( any([is_wandb_available(), is_tensorboard_available()]) and not is_comet_ml_available() ) def lowercase_ ( _lowerCamelCase : Any): return unittest.skipUnless( _atleast_one_tracker_available , "test requires at least one tracker to be available and for `comet_ml` to not be installed" , )(_lowerCamelCase) class snake_case_ ( unittest.TestCase ): __A : int = True @classmethod def __UpperCamelCase ( cls : str ) -> str: lowercase__ : str = tempfile.mkdtemp() @classmethod def __UpperCamelCase ( cls : List[str] ) -> Optional[Any]: if os.path.exists(cls.tmpdir ): shutil.rmtree(cls.tmpdir ) def __UpperCamelCase ( self : str ) -> Optional[int]: if self.clear_on_setup: for path in Path(self.tmpdir ).glob("**/*" ): if path.is_file(): path.unlink() elif path.is_dir(): shutil.rmtree(lowercase_ ) class snake_case_ ( unittest.TestCase ): def __UpperCamelCase ( self : List[str] ) -> Union[str, Any]: super().tearDown() # Reset the state of the AcceleratorState singleton. AcceleratorState._reset_state() PartialState._reset_state() class snake_case_ ( unittest.TestCase ): def __UpperCamelCase ( self : List[Any] , lowercase_ : Union[mock.Mock, List[mock.Mock]] ) -> str: lowercase__ : Tuple = mocks if isinstance(lowercase_ , (tuple, list) ) else [mocks] for m in self.mocks: m.start() self.addCleanup(m.stop ) def lowercase_ ( _lowerCamelCase : int): lowercase__ : Tuple = AcceleratorState() lowercase__ : Optional[int] = tensor[None].clone().to(state.device) lowercase__ : Optional[int] = gather(_lowerCamelCase).cpu() lowercase__ : Optional[Any] = tensor[0].cpu() for i in range(tensors.shape[0]): if not torch.equal(tensors[i] , _lowerCamelCase): return False return True class snake_case_ : def __init__( self : str , lowercase_ : int , lowercase_ : Optional[Any] , lowercase_ : int ) -> Union[str, Any]: lowercase__ : int = returncode lowercase__ : Dict = stdout lowercase__ : List[Any] = stderr async def lowercase_ ( _lowerCamelCase : Optional[int] , _lowerCamelCase : str): while True: lowercase__ : int = await stream.readline() if line: callback(_lowerCamelCase) else: break async def lowercase_ ( _lowerCamelCase : List[Any] , _lowerCamelCase : Dict=None , _lowerCamelCase : Tuple=None , _lowerCamelCase : Optional[Any]=None , _lowerCamelCase : Tuple=False , _lowerCamelCase : str=False): if echo: print("\nRunning: " , " ".join(_lowerCamelCase)) lowercase__ : str = await asyncio.create_subprocess_exec( cmd[0] , *cmd[1:] , stdin=_lowerCamelCase , stdout=asyncio.subprocess.PIPE , stderr=asyncio.subprocess.PIPE , env=_lowerCamelCase , ) # note: there is a warning for a possible deadlock when using `wait` with huge amounts of data in the pipe # https://docs.python.org/3/library/asyncio-subprocess.html#asyncio.asyncio.subprocess.Process.wait # # If it starts hanging, will need to switch to the following code. The problem is that no data # will be seen until it's done and if it hangs for example there will be no debug info. # out, err = await p.communicate() # return _RunOutput(p.returncode, out, err) lowercase__ : Tuple = [] lowercase__ : List[Any] = [] def tee(_lowerCamelCase : str , _lowerCamelCase : str , _lowerCamelCase : int , _lowerCamelCase : Optional[int]=""): lowercase__ : Optional[int] = line.decode("utf-8").rstrip() sink.append(_lowerCamelCase) if not quiet: print(_lowerCamelCase , _lowerCamelCase , file=_lowerCamelCase) # XXX: the timeout doesn't seem to make any difference here await asyncio.wait( [ asyncio.create_task(_read_stream(p.stdout , lambda _lowerCamelCase: tee(_lowerCamelCase , _lowerCamelCase , sys.stdout , label="stdout:"))), asyncio.create_task(_read_stream(p.stderr , lambda _lowerCamelCase: tee(_lowerCamelCase , _lowerCamelCase , sys.stderr , label="stderr:"))), ] , timeout=_lowerCamelCase , ) return _RunOutput(await p.wait() , _lowerCamelCase , _lowerCamelCase) def lowercase_ ( _lowerCamelCase : int , _lowerCamelCase : Tuple=None , _lowerCamelCase : Optional[Any]=None , _lowerCamelCase : List[str]=180 , _lowerCamelCase : Dict=False , _lowerCamelCase : Dict=True): lowercase__ : Optional[Any] = asyncio.get_event_loop() lowercase__ : List[Any] = loop.run_until_complete( _stream_subprocess(_lowerCamelCase , env=_lowerCamelCase , stdin=_lowerCamelCase , timeout=_lowerCamelCase , quiet=_lowerCamelCase , echo=_lowerCamelCase)) lowercase__ : str = " ".join(_lowerCamelCase) if result.returncode > 0: lowercase__ : Dict = "\n".join(result.stderr) raise RuntimeError( f'''\'{cmd_str}\' failed with returncode {result.returncode}\n\n''' f'''The combined stderr from workers follows:\n{stderr}''') return result class snake_case_ ( __A ): pass def lowercase_ ( _lowerCamelCase : List[str] , _lowerCamelCase : Any=False): try: lowercase__ : Optional[int] = subprocess.check_output(_lowerCamelCase , stderr=subprocess.STDOUT) if return_stdout: if hasattr(_lowerCamelCase , "decode"): lowercase__ : Optional[Any] = output.decode("utf-8") return output except subprocess.CalledProcessError as e: raise SubprocessCallException( f'''Command `{" ".join(_lowerCamelCase)}` failed with the following error:\n\n{e.output.decode()}''') from e
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from typing import Dict, Optional import numpy as np import datasets UpperCamelCase = ''' IoU is the area of overlap between the predicted segmentation and the ground truth divided by the area of union between the predicted segmentation and the ground truth. For binary (two classes) or multi-class segmentation, the mean IoU of the image is calculated by taking the IoU of each class and averaging them. ''' UpperCamelCase = ''' Args: predictions (`List[ndarray]`): List of predicted segmentation maps, each of shape (height, width). Each segmentation map can be of a different size. references (`List[ndarray]`): List of ground truth segmentation maps, each of shape (height, width). Each segmentation map can be of a different size. num_labels (`int`): Number of classes (categories). ignore_index (`int`): Index that will be ignored during evaluation. nan_to_num (`int`, *optional*): If specified, NaN values will be replaced by the number defined by the user. label_map (`dict`, *optional*): If specified, dictionary mapping old label indices to new label indices. reduce_labels (`bool`, *optional*, defaults to `False`): Whether or not to reduce all label values of segmentation maps by 1. Usually used for datasets where 0 is used for background, and background itself is not included in all classes of a dataset (e.g. ADE20k). The background label will be replaced by 255. Returns: `Dict[str, float | ndarray]` comprising various elements: - *mean_iou* (`float`): Mean Intersection-over-Union (IoU averaged over all categories). - *mean_accuracy* (`float`): Mean accuracy (averaged over all categories). - *overall_accuracy* (`float`): Overall accuracy on all images. - *per_category_accuracy* (`ndarray` of shape `(num_labels,)`): Per category accuracy. - *per_category_iou* (`ndarray` of shape `(num_labels,)`): Per category IoU. Examples: >>> import numpy as np >>> mean_iou = datasets.load_metric("mean_iou") >>> # suppose one has 3 different segmentation maps predicted >>> predicted_1 = np.array([[1, 2], [3, 4], [5, 255]]) >>> actual_1 = np.array([[0, 3], [5, 4], [6, 255]]) >>> predicted_2 = np.array([[2, 7], [9, 2], [3, 6]]) >>> actual_2 = np.array([[1, 7], [9, 2], [3, 6]]) >>> predicted_3 = np.array([[2, 2, 3], [8, 2, 4], [3, 255, 2]]) >>> actual_3 = np.array([[1, 2, 2], [8, 2, 1], [3, 255, 1]]) >>> predicted = [predicted_1, predicted_2, predicted_3] >>> ground_truth = [actual_1, actual_2, actual_3] >>> results = mean_iou.compute(predictions=predicted, references=ground_truth, num_labels=10, ignore_index=255, reduce_labels=False) >>> print(results) # doctest: +NORMALIZE_WHITESPACE {\'mean_iou\': 0.47750000000000004, \'mean_accuracy\': 0.5916666666666666, \'overall_accuracy\': 0.5263157894736842, \'per_category_iou\': array([0. , 0. , 0.375, 0.4 , 0.5 , 0. , 0.5 , 1. , 1. , 1. ]), \'per_category_accuracy\': array([0. , 0. , 0.75 , 0.66666667, 1. , 0. , 0.5 , 1. , 1. , 1. ])} ''' UpperCamelCase = '''\ @software{MMSegmentation_Contributors_OpenMMLab_Semantic_Segmentation_2020, author = {{MMSegmentation Contributors}}, license = {Apache-2.0}, month = {7}, title = {{OpenMMLab Semantic Segmentation Toolbox and Benchmark}}, url = {https://github.com/open-mmlab/mmsegmentation}, year = {2020} }''' def lowercase_ ( _lowerCamelCase : Optional[int] , _lowerCamelCase : List[Any] , _lowerCamelCase : Optional[int] , _lowerCamelCase : bool , _lowerCamelCase : Optional[Dict[int, int]] = None , _lowerCamelCase : bool = False , ): if label_map is not None: for old_id, new_id in label_map.items(): lowercase__ : Union[str, Any] = new_id # turn into Numpy arrays lowercase__ : Union[str, Any] = np.array(_lowerCamelCase) lowercase__ : Optional[int] = np.array(_lowerCamelCase) if reduce_labels: lowercase__ : Tuple = 255 lowercase__ : Tuple = label - 1 lowercase__ : Optional[Any] = 255 lowercase__ : Union[str, Any] = label != ignore_index lowercase__ : str = np.not_equal(_lowerCamelCase , _lowerCamelCase) lowercase__ : Any = pred_label[mask] lowercase__ : Union[str, Any] = np.array(_lowerCamelCase)[mask] lowercase__ : Optional[int] = pred_label[pred_label == label] lowercase__ : Dict = np.histogram(_lowerCamelCase , bins=_lowerCamelCase , range=(0, num_labels - 1))[0] lowercase__ : Any = np.histogram(_lowerCamelCase , bins=_lowerCamelCase , range=(0, num_labels - 1))[0] lowercase__ : Union[str, Any] = np.histogram(_lowerCamelCase , bins=_lowerCamelCase , range=(0, num_labels - 1))[0] lowercase__ : Tuple = area_pred_label + area_label - area_intersect return area_intersect, area_union, area_pred_label, area_label def lowercase_ ( _lowerCamelCase : int , _lowerCamelCase : str , _lowerCamelCase : Optional[Any] , _lowerCamelCase : bool , _lowerCamelCase : Optional[Dict[int, int]] = None , _lowerCamelCase : bool = False , ): lowercase__ : Dict = np.zeros((num_labels,) , dtype=np.floataa) lowercase__ : Union[str, Any] = np.zeros((num_labels,) , dtype=np.floataa) lowercase__ : List[Any] = np.zeros((num_labels,) , dtype=np.floataa) lowercase__ : List[str] = np.zeros((num_labels,) , dtype=np.floataa) for result, gt_seg_map in zip(_lowerCamelCase , _lowerCamelCase): lowercase__ : Any = intersect_and_union( _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase) total_area_intersect += area_intersect total_area_union += area_union total_area_pred_label += area_pred_label total_area_label += area_label return total_area_intersect, total_area_union, total_area_pred_label, total_area_label def lowercase_ ( _lowerCamelCase : Tuple , _lowerCamelCase : Dict , _lowerCamelCase : int , _lowerCamelCase : bool , _lowerCamelCase : Optional[int] = None , _lowerCamelCase : Optional[Dict[int, int]] = None , _lowerCamelCase : bool = False , ): lowercase__ : Optional[int] = total_intersect_and_union( _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase) # compute metrics lowercase__ : Any = {} lowercase__ : List[Any] = total_area_intersect.sum() / total_area_label.sum() lowercase__ : str = total_area_intersect / total_area_union lowercase__ : List[Any] = total_area_intersect / total_area_label lowercase__ : List[Any] = np.nanmean(_lowerCamelCase) lowercase__ : Union[str, Any] = np.nanmean(_lowerCamelCase) lowercase__ : Tuple = all_acc lowercase__ : Optional[Any] = iou lowercase__ : Dict = acc if nan_to_num is not None: lowercase__ : Union[str, Any] = {metric: np.nan_to_num(_lowerCamelCase , nan=_lowerCamelCase) for metric, metric_value in metrics.items()} return metrics @datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION ,_KWARGS_DESCRIPTION ) class snake_case_ ( datasets.Metric ): def __UpperCamelCase ( self : List[Any] ) -> Tuple: return datasets.MetricInfo( description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features( # 1st Seq - height dim, 2nd - width dim { "predictions": datasets.Sequence(datasets.Sequence(datasets.Value("uint16" ) ) ), "references": datasets.Sequence(datasets.Sequence(datasets.Value("uint16" ) ) ), } ) , reference_urls=[ "https://github.com/open-mmlab/mmsegmentation/blob/71c201b1813267d78764f306a297ca717827c4bf/mmseg/core/evaluation/metrics.py" ] , ) def __UpperCamelCase ( self : str , lowercase_ : Union[str, Any] , lowercase_ : Optional[Any] , lowercase_ : int , lowercase_ : bool , lowercase_ : Optional[int] = None , lowercase_ : Optional[Dict[int, int]] = None , lowercase_ : bool = False , ) -> Optional[Any]: lowercase__ : Dict = mean_iou( results=lowercase_ , gt_seg_maps=lowercase_ , num_labels=lowercase_ , ignore_index=lowercase_ , nan_to_num=lowercase_ , label_map=lowercase_ , reduce_labels=lowercase_ , ) return iou_result
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from ..utils import DummyObject, requires_backends class snake_case_ ( metaclass=__A ): __A : List[Any] = ["flax"] def __init__( self : Optional[int] , *lowercase_ : Optional[int] , **lowercase_ : List[Any] ) -> Tuple: requires_backends(self , ["flax"] ) @classmethod def __UpperCamelCase ( cls : Tuple , *lowercase_ : int , **lowercase_ : List[str] ) -> List[str]: requires_backends(cls , ["flax"] ) @classmethod def __UpperCamelCase ( cls : Tuple , *lowercase_ : List[str] , **lowercase_ : Tuple ) -> Any: requires_backends(cls , ["flax"] ) class snake_case_ ( metaclass=__A ): __A : Dict = ["flax"] def __init__( self : int , *lowercase_ : Any , **lowercase_ : int ) -> Union[str, Any]: requires_backends(self , ["flax"] ) @classmethod def __UpperCamelCase ( cls : Dict , *lowercase_ : List[str] , **lowercase_ : List[str] ) -> Optional[Any]: requires_backends(cls , ["flax"] ) @classmethod def __UpperCamelCase ( cls : Tuple , *lowercase_ : List[Any] , **lowercase_ : Any ) -> Dict: requires_backends(cls , ["flax"] ) class snake_case_ ( metaclass=__A ): __A : Dict = ["flax"] def __init__( self : Dict , *lowercase_ : str , **lowercase_ : int ) -> Union[str, Any]: requires_backends(self , ["flax"] ) @classmethod def __UpperCamelCase ( cls : Any , *lowercase_ : Union[str, Any] , **lowercase_ : Tuple ) -> List[str]: requires_backends(cls , ["flax"] ) @classmethod def __UpperCamelCase ( cls : Any , *lowercase_ : Any , **lowercase_ : Optional[int] ) -> List[str]: requires_backends(cls , ["flax"] ) class snake_case_ ( metaclass=__A ): __A : int = ["flax"] def __init__( self : Dict , *lowercase_ : Dict , **lowercase_ : Any ) -> int: requires_backends(self , ["flax"] ) @classmethod def __UpperCamelCase ( cls : List[Any] , *lowercase_ : int , **lowercase_ : Dict ) -> Optional[int]: requires_backends(cls , ["flax"] ) @classmethod def __UpperCamelCase ( cls : Optional[int] , *lowercase_ : Optional[Any] , **lowercase_ : Any ) -> Tuple: requires_backends(cls , ["flax"] ) class snake_case_ ( metaclass=__A ): __A : List[Any] = ["flax"] def __init__( self : List[str] , *lowercase_ : str , **lowercase_ : Union[str, Any] ) -> Optional[Any]: requires_backends(self , ["flax"] ) @classmethod def __UpperCamelCase ( cls : Tuple , *lowercase_ : Optional[Any] , **lowercase_ : Optional[int] ) -> Optional[int]: requires_backends(cls , ["flax"] ) @classmethod def __UpperCamelCase ( cls : Optional[Any] , *lowercase_ : Tuple , **lowercase_ : Dict ) -> Dict: requires_backends(cls , ["flax"] ) class snake_case_ ( metaclass=__A ): __A : Dict = ["flax"] def __init__( self : int , *lowercase_ : List[str] , **lowercase_ : List[Any] ) -> Dict: requires_backends(self , ["flax"] ) @classmethod def __UpperCamelCase ( cls : Optional[int] , *lowercase_ : int , **lowercase_ : Optional[int] ) -> Dict: requires_backends(cls , ["flax"] ) @classmethod def __UpperCamelCase ( cls : List[Any] , *lowercase_ : Optional[Any] , **lowercase_ : List[str] ) -> int: requires_backends(cls , ["flax"] ) class snake_case_ ( metaclass=__A ): __A : Optional[Any] = ["flax"] def __init__( self : int , *lowercase_ : Union[str, Any] , **lowercase_ : Optional[Any] ) -> Union[str, Any]: requires_backends(self , ["flax"] ) @classmethod def __UpperCamelCase ( cls : Dict , *lowercase_ : Tuple , **lowercase_ : int ) -> List[Any]: requires_backends(cls , ["flax"] ) @classmethod def __UpperCamelCase ( cls : Union[str, Any] , *lowercase_ : List[Any] , **lowercase_ : List[str] ) -> Union[str, Any]: requires_backends(cls , ["flax"] ) class snake_case_ ( metaclass=__A ): __A : Dict = ["flax"] def __init__( self : Any , *lowercase_ : int , **lowercase_ : int ) -> Optional[int]: requires_backends(self , ["flax"] ) @classmethod def __UpperCamelCase ( cls : Optional[int] , *lowercase_ : Any , **lowercase_ : List[Any] ) -> Tuple: requires_backends(cls , ["flax"] ) @classmethod def __UpperCamelCase ( cls : Optional[Any] , *lowercase_ : Any , **lowercase_ : Union[str, Any] ) -> Optional[Any]: requires_backends(cls , ["flax"] ) class snake_case_ ( metaclass=__A ): __A : List[Any] = ["flax"] def __init__( self : Union[str, Any] , *lowercase_ : int , **lowercase_ : Optional[int] ) -> Union[str, Any]: requires_backends(self , ["flax"] ) @classmethod def __UpperCamelCase ( cls : Optional[int] , *lowercase_ : Any , **lowercase_ : Optional[Any] ) -> List[Any]: requires_backends(cls , ["flax"] ) @classmethod def __UpperCamelCase ( cls : Dict , *lowercase_ : List[str] , **lowercase_ : str ) -> Optional[Any]: requires_backends(cls , ["flax"] ) class snake_case_ ( metaclass=__A ): __A : List[Any] = ["flax"] def __init__( self : List[Any] , *lowercase_ : Union[str, Any] , **lowercase_ : Optional[Any] ) -> Dict: requires_backends(self , ["flax"] ) @classmethod def __UpperCamelCase ( cls : Optional[Any] , *lowercase_ : Any , **lowercase_ : int ) -> Union[str, Any]: requires_backends(cls , ["flax"] ) @classmethod def __UpperCamelCase ( cls : str , *lowercase_ : Optional[Any] , **lowercase_ : Optional[int] ) -> List[Any]: requires_backends(cls , ["flax"] ) class snake_case_ ( metaclass=__A ): __A : Optional[int] = ["flax"] def __init__( self : Any , *lowercase_ : str , **lowercase_ : Dict ) -> int: requires_backends(self , ["flax"] ) @classmethod def __UpperCamelCase ( cls : str , *lowercase_ : int , **lowercase_ : Optional[int] ) -> Tuple: requires_backends(cls , ["flax"] ) @classmethod def __UpperCamelCase ( cls : Tuple , *lowercase_ : List[Any] , **lowercase_ : Tuple ) -> Dict: requires_backends(cls , ["flax"] ) class snake_case_ ( metaclass=__A ): __A : int = ["flax"] def __init__( self : List[str] , *lowercase_ : int , **lowercase_ : Union[str, Any] ) -> Dict: requires_backends(self , ["flax"] ) @classmethod def __UpperCamelCase ( cls : List[Any] , *lowercase_ : int , **lowercase_ : Dict ) -> List[Any]: requires_backends(cls , ["flax"] ) @classmethod def __UpperCamelCase ( cls : Union[str, Any] , *lowercase_ : Dict , **lowercase_ : int ) -> Optional[Any]: requires_backends(cls , ["flax"] ) class snake_case_ ( metaclass=__A ): __A : List[str] = ["flax"] def __init__( self : Tuple , *lowercase_ : List[Any] , **lowercase_ : Tuple ) -> Tuple: requires_backends(self , ["flax"] ) @classmethod def __UpperCamelCase ( cls : Any , *lowercase_ : Union[str, Any] , **lowercase_ : Optional[int] ) -> Union[str, Any]: requires_backends(cls , ["flax"] ) @classmethod def __UpperCamelCase ( cls : List[str] , *lowercase_ : Union[str, Any] , **lowercase_ : Dict ) -> List[Any]: requires_backends(cls , ["flax"] )
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"""simple docstring""" import argparse import struct import unittest class snake_case_ : def __init__( self : Tuple , lowercase_ : bytes ) -> None: lowercase__ : Optional[int] = data # Initialize hash values lowercase__ : Optional[Any] = [ 0x6A_09_E6_67, 0xBB_67_AE_85, 0x3C_6E_F3_72, 0xA5_4F_F5_3A, 0x51_0E_52_7F, 0x9B_05_68_8C, 0x1F_83_D9_AB, 0x5B_E0_CD_19, ] # Initialize round constants lowercase__ : Optional[Any] = [ 0x42_8A_2F_98, 0x71_37_44_91, 0xB5_C0_FB_CF, 0xE9_B5_DB_A5, 0x39_56_C2_5B, 0x59_F1_11_F1, 0x92_3F_82_A4, 0xAB_1C_5E_D5, 0xD8_07_AA_98, 0x12_83_5B_01, 0x24_31_85_BE, 0x55_0C_7D_C3, 0x72_BE_5D_74, 0x80_DE_B1_FE, 0x9B_DC_06_A7, 0xC1_9B_F1_74, 0xE4_9B_69_C1, 0xEF_BE_47_86, 0x0F_C1_9D_C6, 0x24_0C_A1_CC, 0x2D_E9_2C_6F, 0x4A_74_84_AA, 0x5C_B0_A9_DC, 0x76_F9_88_DA, 0x98_3E_51_52, 0xA8_31_C6_6D, 0xB0_03_27_C8, 0xBF_59_7F_C7, 0xC6_E0_0B_F3, 0xD5_A7_91_47, 0x06_CA_63_51, 0x14_29_29_67, 0x27_B7_0A_85, 0x2E_1B_21_38, 0x4D_2C_6D_FC, 0x53_38_0D_13, 0x65_0A_73_54, 0x76_6A_0A_BB, 0x81_C2_C9_2E, 0x92_72_2C_85, 0xA2_BF_E8_A1, 0xA8_1A_66_4B, 0xC2_4B_8B_70, 0xC7_6C_51_A3, 0xD1_92_E8_19, 0xD6_99_06_24, 0xF4_0E_35_85, 0x10_6A_A0_70, 0x19_A4_C1_16, 0x1E_37_6C_08, 0x27_48_77_4C, 0x34_B0_BC_B5, 0x39_1C_0C_B3, 0x4E_D8_AA_4A, 0x5B_9C_CA_4F, 0x68_2E_6F_F3, 0x74_8F_82_EE, 0x78_A5_63_6F, 0x84_C8_78_14, 0x8C_C7_02_08, 0x90_BE_FF_FA, 0xA4_50_6C_EB, 0xBE_F9_A3_F7, 0xC6_71_78_F2, ] lowercase__ : Optional[int] = self.preprocessing(self.data ) self.final_hash() @staticmethod def __UpperCamelCase ( lowercase_ : bytes ) -> bytes: lowercase__ : List[Any] = b"\x80" + (b"\x00" * (63 - (len(lowercase_ ) + 8) % 64)) lowercase__ : int = struct.pack(">Q" , (len(lowercase_ ) * 8) ) return data + padding + big_endian_integer def __UpperCamelCase ( self : Optional[Any] ) -> None: # Convert into blocks of 64 bytes lowercase__ : Tuple = [ self.preprocessed_data[x : x + 64] for x in range(0 , len(self.preprocessed_data ) , 64 ) ] for block in self.blocks: # Convert the given block into a list of 4 byte integers lowercase__ : int = list(struct.unpack(">16L" , lowercase_ ) ) # add 48 0-ed integers words += [0] * 48 lowercase__ : Tuple = self.hashes for index in range(0 , 64 ): if index > 15: # modify the zero-ed indexes at the end of the array lowercase__ : Optional[int] = ( self.ror(words[index - 15] , 7 ) ^ self.ror(words[index - 15] , 18 ) ^ (words[index - 15] >> 3) ) lowercase__ : int = ( self.ror(words[index - 2] , 17 ) ^ self.ror(words[index - 2] , 19 ) ^ (words[index - 2] >> 10) ) lowercase__ : int = ( words[index - 16] + sa + words[index - 7] + sa ) % 0x1_00_00_00_00 # Compression lowercase__ : str = self.ror(lowercase_ , 6 ) ^ self.ror(lowercase_ , 11 ) ^ self.ror(lowercase_ , 25 ) lowercase__ : List[str] = (e & f) ^ ((~e & 0xFF_FF_FF_FF) & g) lowercase__ : Dict = ( h + sa + ch + self.round_constants[index] + words[index] ) % 0x1_00_00_00_00 lowercase__ : Union[str, Any] = self.ror(lowercase_ , 2 ) ^ self.ror(lowercase_ , 13 ) ^ self.ror(lowercase_ , 22 ) lowercase__ : List[Any] = (a & b) ^ (a & c) ^ (b & c) lowercase__ : Union[str, Any] = (sa + maj) % 0x1_00_00_00_00 lowercase__ : Optional[Any] = ( g, f, e, ((d + tempa) % 0x1_00_00_00_00), c, b, a, ((tempa + tempa) % 0x1_00_00_00_00), ) lowercase__ : int = [a, b, c, d, e, f, g, h] # Modify final values lowercase__ : Tuple = [ ((element + mutated_hash_values[index]) % 0x1_00_00_00_00) for index, element in enumerate(self.hashes ) ] lowercase__ : List[Any] = "".join([hex(lowercase_ )[2:].zfill(8 ) for value in self.hashes] ) def __UpperCamelCase ( self : List[Any] , lowercase_ : int , lowercase_ : int ) -> int: return 0xFF_FF_FF_FF & (value << (32 - rotations)) | (value >> rotations) class snake_case_ ( unittest.TestCase ): def __UpperCamelCase ( self : Optional[int] ) -> None: import hashlib lowercase__ : Optional[int] = bytes("Test String" , "utf-8" ) self.assertEqual(SHAaaa(lowercase_ ).hash , hashlib.shaaaa(lowercase_ ).hexdigest() ) def lowercase_ ( ): import doctest doctest.testmod() lowercase__ : Dict = argparse.ArgumentParser() parser.add_argument( "-s" , "--string" , dest="input_string" , default="Hello World!! Welcome to Cryptography" , help="Hash the string" , ) parser.add_argument( "-f" , "--file" , dest="input_file" , help="Hash contents of a file") lowercase__ : Union[str, Any] = parser.parse_args() lowercase__ : Optional[Any] = args.input_string # hash input should be a bytestring if args.input_file: with open(args.input_file , "rb") as f: lowercase__ : Optional[int] = f.read() else: lowercase__ : Union[str, Any] = bytes(_lowerCamelCase , "utf-8") print(SHAaaa(_lowerCamelCase).hash) if __name__ == "__main__": main()
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from ...configuration_utils import PretrainedConfig from ...utils import logging UpperCamelCase = logging.get_logger(__name__) UpperCamelCase = { '''facebook/vit-mae-base''': '''https://huggingface.co/facebook/vit-mae-base/resolve/main/config.json''', # See all ViT MAE models at https://huggingface.co/models?filter=vit-mae } class snake_case_ ( __A ): __A : List[str] = "vit_mae" def __init__( self : List[Any] , lowercase_ : List[Any]=7_68 , lowercase_ : Tuple=12 , lowercase_ : Tuple=12 , lowercase_ : Optional[Any]=30_72 , lowercase_ : str="gelu" , lowercase_ : Tuple=0.0 , lowercase_ : int=0.0 , lowercase_ : Dict=0.02 , lowercase_ : int=1E-12 , lowercase_ : Tuple=2_24 , lowercase_ : Any=16 , lowercase_ : Dict=3 , lowercase_ : List[Any]=True , lowercase_ : Dict=16 , lowercase_ : List[str]=5_12 , lowercase_ : Tuple=8 , lowercase_ : Any=20_48 , lowercase_ : int=0.75 , lowercase_ : Tuple=False , **lowercase_ : Optional[int] , ) -> Optional[Any]: super().__init__(**lowercase_ ) lowercase__ : List[str] = hidden_size lowercase__ : str = num_hidden_layers lowercase__ : Optional[int] = num_attention_heads lowercase__ : List[Any] = intermediate_size lowercase__ : str = hidden_act lowercase__ : List[str] = hidden_dropout_prob lowercase__ : Optional[Any] = attention_probs_dropout_prob lowercase__ : Any = initializer_range lowercase__ : Optional[Any] = layer_norm_eps lowercase__ : Optional[Any] = image_size lowercase__ : Optional[int] = patch_size lowercase__ : Any = num_channels lowercase__ : str = qkv_bias lowercase__ : Optional[Any] = decoder_num_attention_heads lowercase__ : Any = decoder_hidden_size lowercase__ : Any = decoder_num_hidden_layers lowercase__ : Union[str, Any] = decoder_intermediate_size lowercase__ : int = mask_ratio lowercase__ : Tuple = norm_pix_loss
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import logging import os from dataclasses import dataclass, field from functools import partial from pathlib import Path from tempfile import TemporaryDirectory from typing import List, Optional import faiss import torch from datasets import Features, Sequence, Value, load_dataset from transformers import DPRContextEncoder, DPRContextEncoderTokenizerFast, HfArgumentParser UpperCamelCase : List[str] = logging.getLogger(__name__) torch.set_grad_enabled(False) UpperCamelCase : Dict = '''cuda''' if torch.cuda.is_available() else '''cpu''' def lowercase_ ( _lowerCamelCase : str , _lowerCamelCase : Tuple=100 , _lowerCamelCase : Tuple=" "): lowercase__ : Union[str, Any] = text.split(_lowerCamelCase) return [character.join(text[i : i + n]).strip() for i in range(0 , len(_lowerCamelCase) , _lowerCamelCase)] def lowercase_ ( _lowerCamelCase : dict): lowercase__ : List[str] = [], [] for title, text in zip(documents["title"] , documents["text"]): if text is not None: for passage in split_text(_lowerCamelCase): titles.append(title if title is not None else "") texts.append(_lowerCamelCase) return {"title": titles, "text": texts} def lowercase_ ( _lowerCamelCase : dict , _lowerCamelCase : DPRContextEncoder , _lowerCamelCase : DPRContextEncoderTokenizerFast): lowercase__ : Union[str, Any] = ctx_tokenizer( documents["title"] , documents["text"] , truncation=_lowerCamelCase , padding="longest" , return_tensors="pt")["input_ids"] lowercase__ : Any = ctx_encoder(input_ids.to(device=_lowerCamelCase) , return_dict=_lowerCamelCase).pooler_output return {"embeddings": embeddings.detach().cpu().numpy()} def lowercase_ ( _lowerCamelCase : "RagExampleArguments" , _lowerCamelCase : "ProcessingArguments" , _lowerCamelCase : "IndexHnswArguments" , ): ###################################### logger.info("Step 1 - Create the dataset") ###################################### # The dataset needed for RAG must have three columns: # - title (string): title of the document # - text (string): text of a passage of the document # - embeddings (array of dimension d): DPR representation of the passage # Let's say you have documents in tab-separated csv files with columns "title" and "text" assert os.path.isfile(rag_example_args.csv_path), "Please provide a valid path to a csv file" # You can load a Dataset object this way lowercase__ : str = load_dataset( "csv" , data_files=[rag_example_args.csv_path] , split="train" , delimiter="\t" , column_names=["title", "text"]) # More info about loading csv files in the documentation: https://huggingface.co/docs/datasets/loading_datasets.html?highlight=csv#csv-files # Then split the documents into passages of 100 words lowercase__ : List[Any] = dataset.map(_lowerCamelCase , batched=_lowerCamelCase , num_proc=processing_args.num_proc) # And compute the embeddings lowercase__ : Optional[Any] = DPRContextEncoder.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name).to(device=_lowerCamelCase) lowercase__ : Any = DPRContextEncoderTokenizerFast.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name) lowercase__ : List[Any] = Features( {"text": Value("string"), "title": Value("string"), "embeddings": Sequence(Value("float32"))}) # optional, save as float32 instead of float64 to save space lowercase__ : List[Any] = dataset.map( partial(_lowerCamelCase , ctx_encoder=_lowerCamelCase , ctx_tokenizer=_lowerCamelCase) , batched=_lowerCamelCase , batch_size=processing_args.batch_size , features=_lowerCamelCase , ) # And finally save your dataset lowercase__ : Optional[int] = os.path.join(rag_example_args.output_dir , "my_knowledge_dataset") dataset.save_to_disk(_lowerCamelCase) # from datasets import load_from_disk # dataset = load_from_disk(passages_path) # to reload the dataset ###################################### logger.info("Step 2 - Index the dataset") ###################################### # Let's use the Faiss implementation of HNSW for fast approximate nearest neighbor search lowercase__ : Tuple = faiss.IndexHNSWFlat(index_hnsw_args.d , index_hnsw_args.m , faiss.METRIC_INNER_PRODUCT) dataset.add_faiss_index("embeddings" , custom_index=_lowerCamelCase) # And save the index lowercase__ : Union[str, Any] = os.path.join(rag_example_args.output_dir , "my_knowledge_dataset_hnsw_index.faiss") dataset.get_index("embeddings").save(_lowerCamelCase) # dataset.load_faiss_index("embeddings", index_path) # to reload the index @dataclass class snake_case_ : __A : str = field( default=str(Path(__A ).parent / "test_run" / "dummy-kb" / "my_knowledge_dataset.csv" ) ,metadata={"help": "Path to a tab-separated csv file with columns 'title' and 'text'"} ,) __A : Optional[str] = field( default=__A ,metadata={"help": "Question that is passed as input to RAG. Default is 'What does Moses' rod turn into ?'."} ,) __A : str = field( default="facebook/rag-sequence-nq" ,metadata={"help": "The RAG model to use. Either 'facebook/rag-sequence-nq' or 'facebook/rag-token-nq'"} ,) __A : str = field( default="facebook/dpr-ctx_encoder-multiset-base" ,metadata={ "help": ( "The DPR context encoder model to use. Either 'facebook/dpr-ctx_encoder-single-nq-base' or" " 'facebook/dpr-ctx_encoder-multiset-base'" ) } ,) __A : Optional[str] = field( default=str(Path(__A ).parent / "test_run" / "dummy-kb" ) ,metadata={"help": "Path to a directory where the dataset passages and the index will be saved"} ,) @dataclass class snake_case_ : __A : Optional[int] = field( default=__A ,metadata={ "help": "The number of processes to use to split the documents into passages. Default is single process." } ,) __A : int = field( default=16 ,metadata={ "help": "The batch size to use when computing the passages embeddings using the DPR context encoder." } ,) @dataclass class snake_case_ : __A : int = field( default=768 ,metadata={"help": "The dimension of the embeddings to pass to the HNSW Faiss index."} ,) __A : int = field( default=128 ,metadata={ "help": ( "The number of bi-directional links created for every new element during the HNSW index construction." ) } ,) if __name__ == "__main__": logging.basicConfig(level=logging.WARNING) logger.setLevel(logging.INFO) UpperCamelCase : List[str] = HfArgumentParser((RagExampleArguments, ProcessingArguments, IndexHnswArguments)) UpperCamelCase , UpperCamelCase , UpperCamelCase : int = parser.parse_args_into_dataclasses() with TemporaryDirectory() as tmp_dir: UpperCamelCase : Dict = rag_example_args.output_dir or tmp_dir main(rag_example_args, processing_args, index_hnsw_args)
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def lowercase_ ( _lowerCamelCase : int , _lowerCamelCase : int): while a != 0: lowercase__ , lowercase__ : Dict = b % a, a return b def lowercase_ ( _lowerCamelCase : int , _lowerCamelCase : int): if gcd(_lowerCamelCase , _lowerCamelCase) != 1: lowercase__ : Tuple = f'''mod inverse of {a!r} and {m!r} does not exist''' raise ValueError(_lowerCamelCase) lowercase__ , lowercase__ , lowercase__ : Optional[int] = 1, 0, a lowercase__ , lowercase__ , lowercase__ : Union[str, Any] = 0, 1, m while va != 0: lowercase__ : Tuple = ua // va lowercase__ , lowercase__ , lowercase__ , lowercase__ , lowercase__ , lowercase__ : Any = (ua - q * va), (ua - q * va), (ua - q * va), va, va, va return ua % m
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"""simple docstring""" import argparse import logging import pickle from collections import Counter logging.basicConfig( format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''', datefmt='''%m/%d/%Y %H:%M:%S''', level=logging.INFO ) UpperCamelCase = logging.getLogger(__name__) if __name__ == "__main__": UpperCamelCase = argparse.ArgumentParser( description='''Token Counts for smoothing the masking probabilities in MLM (cf XLM/word2vec)''' ) parser.add_argument( '''--data_file''', type=str, default='''data/dump.bert-base-uncased.pickle''', help='''The binarized dataset.''' ) parser.add_argument( '''--token_counts_dump''', type=str, default='''data/token_counts.bert-base-uncased.pickle''', help='''The dump file.''' ) parser.add_argument('''--vocab_size''', default=3_0522, type=int) UpperCamelCase = parser.parse_args() logger.info(f"Loading data from {args.data_file}") with open(args.data_file, '''rb''') as fp: UpperCamelCase = pickle.load(fp) logger.info('''Counting occurrences for MLM.''') UpperCamelCase = Counter() for tk_ids in data: counter.update(tk_ids) UpperCamelCase = [0] * args.vocab_size for k, v in counter.items(): UpperCamelCase = v logger.info(f"Dump to {args.token_counts_dump}") with open(args.token_counts_dump, '''wb''') as handle: pickle.dump(counts, handle, protocol=pickle.HIGHEST_PROTOCOL)
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import logging import os from dataclasses import dataclass, field from functools import partial from pathlib import Path from tempfile import TemporaryDirectory from typing import List, Optional import faiss import torch from datasets import Features, Sequence, Value, load_dataset from transformers import DPRContextEncoder, DPRContextEncoderTokenizerFast, HfArgumentParser UpperCamelCase = logging.getLogger(__name__) torch.set_grad_enabled(False) UpperCamelCase = '''cuda''' if torch.cuda.is_available() else '''cpu''' def lowercase_ ( _lowerCamelCase : str , _lowerCamelCase : Tuple=100 , _lowerCamelCase : Tuple=" "): lowercase__ : Union[str, Any] = text.split(_lowerCamelCase) return [character.join(text[i : i + n]).strip() for i in range(0 , len(_lowerCamelCase) , _lowerCamelCase)] def lowercase_ ( _lowerCamelCase : dict): lowercase__ , lowercase__ : List[str] = [], [] for title, text in zip(documents["title"] , documents["text"]): if text is not None: for passage in split_text(_lowerCamelCase): titles.append(title if title is not None else "") texts.append(_lowerCamelCase) return {"title": titles, "text": texts} def lowercase_ ( _lowerCamelCase : dict , _lowerCamelCase : DPRContextEncoder , _lowerCamelCase : DPRContextEncoderTokenizerFast): lowercase__ : Union[str, Any] = ctx_tokenizer( documents["title"] , documents["text"] , truncation=_lowerCamelCase , padding="longest" , return_tensors="pt")["input_ids"] lowercase__ : Any = ctx_encoder(input_ids.to(device=_lowerCamelCase) , return_dict=_lowerCamelCase).pooler_output return {"embeddings": embeddings.detach().cpu().numpy()} def lowercase_ ( _lowerCamelCase : "RagExampleArguments" , _lowerCamelCase : "ProcessingArguments" , _lowerCamelCase : "IndexHnswArguments" , ): ###################################### logger.info("Step 1 - Create the dataset") ###################################### # The dataset needed for RAG must have three columns: # - title (string): title of the document # - text (string): text of a passage of the document # - embeddings (array of dimension d): DPR representation of the passage # Let's say you have documents in tab-separated csv files with columns "title" and "text" assert os.path.isfile(rag_example_args.csv_path), "Please provide a valid path to a csv file" # You can load a Dataset object this way lowercase__ : str = load_dataset( "csv" , data_files=[rag_example_args.csv_path] , split="train" , delimiter="\t" , column_names=["title", "text"]) # More info about loading csv files in the documentation: https://huggingface.co/docs/datasets/loading_datasets.html?highlight=csv#csv-files # Then split the documents into passages of 100 words lowercase__ : List[Any] = dataset.map(_lowerCamelCase , batched=_lowerCamelCase , num_proc=processing_args.num_proc) # And compute the embeddings lowercase__ : Optional[Any] = DPRContextEncoder.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name).to(device=_lowerCamelCase) lowercase__ : Any = DPRContextEncoderTokenizerFast.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name) lowercase__ : List[Any] = Features( {"text": Value("string"), "title": Value("string"), "embeddings": Sequence(Value("float32"))}) # optional, save as float32 instead of float64 to save space lowercase__ : List[Any] = dataset.map( partial(_lowerCamelCase , ctx_encoder=_lowerCamelCase , ctx_tokenizer=_lowerCamelCase) , batched=_lowerCamelCase , batch_size=processing_args.batch_size , features=_lowerCamelCase , ) # And finally save your dataset lowercase__ : Optional[int] = os.path.join(rag_example_args.output_dir , "my_knowledge_dataset") dataset.save_to_disk(_lowerCamelCase) # from datasets import load_from_disk # dataset = load_from_disk(passages_path) # to reload the dataset ###################################### logger.info("Step 2 - Index the dataset") ###################################### # Let's use the Faiss implementation of HNSW for fast approximate nearest neighbor search lowercase__ : Tuple = faiss.IndexHNSWFlat(index_hnsw_args.d , index_hnsw_args.m , faiss.METRIC_INNER_PRODUCT) dataset.add_faiss_index("embeddings" , custom_index=_lowerCamelCase) # And save the index lowercase__ : Union[str, Any] = os.path.join(rag_example_args.output_dir , "my_knowledge_dataset_hnsw_index.faiss") dataset.get_index("embeddings").save(_lowerCamelCase) # dataset.load_faiss_index("embeddings", index_path) # to reload the index @dataclass class snake_case_ : __A : str = field( default=str(Path(__A ).parent / "test_run" / "dummy-kb" / "my_knowledge_dataset.csv" ) ,metadata={"help": "Path to a tab-separated csv file with columns 'title' and 'text'"} ,) __A : Optional[str] = field( default=__A ,metadata={"help": "Question that is passed as input to RAG. Default is 'What does Moses' rod turn into ?'."} ,) __A : str = field( default="facebook/rag-sequence-nq" ,metadata={"help": "The RAG model to use. Either 'facebook/rag-sequence-nq' or 'facebook/rag-token-nq'"} ,) __A : str = field( default="facebook/dpr-ctx_encoder-multiset-base" ,metadata={ "help": ( "The DPR context encoder model to use. Either 'facebook/dpr-ctx_encoder-single-nq-base' or" " 'facebook/dpr-ctx_encoder-multiset-base'" ) } ,) __A : Optional[str] = field( default=str(Path(__A ).parent / "test_run" / "dummy-kb" ) ,metadata={"help": "Path to a directory where the dataset passages and the index will be saved"} ,) @dataclass class snake_case_ : __A : Optional[int] = field( default=__A ,metadata={ "help": "The number of processes to use to split the documents into passages. Default is single process." } ,) __A : int = field( default=16 ,metadata={ "help": "The batch size to use when computing the passages embeddings using the DPR context encoder." } ,) @dataclass class snake_case_ : __A : int = field( default=768 ,metadata={"help": "The dimension of the embeddings to pass to the HNSW Faiss index."} ,) __A : int = field( default=128 ,metadata={ "help": ( "The number of bi-directional links created for every new element during the HNSW index construction." ) } ,) if __name__ == "__main__": logging.basicConfig(level=logging.WARNING) logger.setLevel(logging.INFO) UpperCamelCase = HfArgumentParser((RagExampleArguments, ProcessingArguments, IndexHnswArguments)) UpperCamelCase , UpperCamelCase , UpperCamelCase = parser.parse_args_into_dataclasses() with TemporaryDirectory() as tmp_dir: UpperCamelCase = rag_example_args.output_dir or tmp_dir main(rag_example_args, processing_args, index_hnsw_args)
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"""simple docstring""" import unittest from transformers import is_torch_available from transformers.testing_utils import require_torch if is_torch_available(): import torch from transformers.generation import DisjunctiveConstraint @require_torch class snake_case_ ( unittest.TestCase ): def __UpperCamelCase ( self : int ) -> Union[str, Any]: # For consistency across different places the DisjunctiveConstraint is called, # dc.token_ids is a list of integers. It is also initialized only by integers. lowercase__ : Dict = [[1, 2, 4], [1, 2, 3, 4]] lowercase__ : Optional[int] = DisjunctiveConstraint(lowercase_ ) self.assertTrue(isinstance(dc.token_ids , lowercase_ ) ) with self.assertRaises(lowercase_ ): DisjunctiveConstraint(torch.LongTensor([[1, 2, 4], [1, 2, 3]] ) ) with self.assertRaises(lowercase_ ): DisjunctiveConstraint([torch.LongTensor([1, 2, 4] ), torch.LongTensor([1, 2, 3, 4, 5] )] ) def __UpperCamelCase ( self : Dict ) -> int: # We can't have constraints that are complete subsets of another. This leads to a preverse # interpretation of "constraint fulfillment": does generating [1,2,3] fulfill the constraint? # It would mean that it generated [1,2] which fulfills it, but it's in the middle of potentially # fulfilling [1,2,3,4]. If we believe that [1,2,3] does fulfill the constraint, then the algorithm # will necessarily never reach [1,2,3,4], giving users a false sense of control (better to just not allow it). lowercase__ : Dict = [[1, 2], [1, 2, 3, 4]] with self.assertRaises(lowercase_ ): DisjunctiveConstraint(lowercase_ ) # fails here def __UpperCamelCase ( self : Tuple ) -> Any: lowercase__ : Tuple = [[1, 2, 3], [1, 2, 4]] lowercase__ : str = DisjunctiveConstraint(lowercase_ ) lowercase__ : Tuple = dc.update(1 ) lowercase__ : Any = stepped is True and completed is False and reset is False self.assertTrue(lowercase_ ) self.assertTrue(not dc.completed ) self.assertTrue(dc.current_seq == [1] ) lowercase__ : Tuple = dc.update(2 ) lowercase__ : Union[str, Any] = stepped is True and completed is False and reset is False self.assertTrue(lowercase_ ) self.assertTrue(not dc.completed ) self.assertTrue(dc.current_seq == [1, 2] ) lowercase__ : List[str] = dc.update(3 ) lowercase__ : Optional[Any] = stepped is True and completed is True and reset is False self.assertTrue(lowercase_ ) self.assertTrue(dc.completed ) # Completed! self.assertTrue(dc.current_seq == [1, 2, 3] ) def __UpperCamelCase ( self : List[str] ) -> Tuple: lowercase__ : List[Any] = [[1, 2, 3], [1, 2, 4, 5], [1, 2, 5]] lowercase__ : Dict = DisjunctiveConstraint(lowercase_ ) lowercase__ : Optional[int] = dc.update(1 ) self.assertTrue(not dc.completed ) self.assertTrue(dc.current_seq == [1] ) lowercase__ : Optional[int] = dc.update(2 ) self.assertTrue(not dc.completed ) self.assertTrue(dc.current_seq == [1, 2] ) lowercase__ : int = dc.update(4 ) self.assertTrue(not dc.completed ) self.assertTrue(dc.current_seq == [1, 2, 4] ) lowercase__ : int = dc.update(5 ) self.assertTrue(dc.completed ) # Completed! self.assertTrue(dc.current_seq == [1, 2, 4, 5] ) dc.reset() lowercase__ : Union[str, Any] = dc.update(1 ) self.assertTrue(not dc.completed ) self.assertTrue(dc.remaining() == 3 ) self.assertTrue(dc.current_seq == [1] ) lowercase__ : int = dc.update(2 ) self.assertTrue(not dc.completed ) self.assertTrue(dc.remaining() == 2 ) self.assertTrue(dc.current_seq == [1, 2] ) lowercase__ : Optional[int] = dc.update(5 ) self.assertTrue(dc.completed ) # Completed! self.assertTrue(dc.remaining() == 0 ) self.assertTrue(dc.current_seq == [1, 2, 5] )
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import argparse import datetime def lowercase_ ( _lowerCamelCase : str): lowercase__ : Optional[Any] = { "0": "Sunday", "1": "Monday", "2": "Tuesday", "3": "Wednesday", "4": "Thursday", "5": "Friday", "6": "Saturday", } lowercase__ : Any = {0: 1, 1: 2, 2: 3, 3: 4, 4: 5, 5: 6, 6: 0} # Validate if not 0 < len(_lowerCamelCase) < 11: raise ValueError("Must be 10 characters long") # Get month lowercase__ : int = int(date_input[0] + date_input[1]) # Validate if not 0 < m < 13: raise ValueError("Month must be between 1 - 12") lowercase__ : str = date_input[2] # Validate if sep_a not in ["-", "/"]: raise ValueError("Date separator must be '-' or '/'") # Get day lowercase__ : int = int(date_input[3] + date_input[4]) # Validate if not 0 < d < 32: raise ValueError("Date must be between 1 - 31") # Get second separator lowercase__ : str = date_input[5] # Validate if sep_a not in ["-", "/"]: raise ValueError("Date separator must be '-' or '/'") # Get year lowercase__ : int = int(date_input[6] + date_input[7] + date_input[8] + date_input[9]) # Arbitrary year range if not 45 < y < 8500: raise ValueError( "Year out of range. There has to be some sort of limit...right?") # Get datetime obj for validation lowercase__ : Union[str, Any] = datetime.date(int(_lowerCamelCase) , int(_lowerCamelCase) , int(_lowerCamelCase)) # Start math if m <= 2: lowercase__ : Optional[Any] = y - 1 lowercase__ : int = m + 12 # maths var lowercase__ : int = int(str(_lowerCamelCase)[:2]) lowercase__ : int = int(str(_lowerCamelCase)[2:]) lowercase__ : int = int(2.6 * m - 5.39) lowercase__ : int = int(c / 4) lowercase__ : int = int(k / 4) lowercase__ : int = int(d + k) lowercase__ : int = int(t + u + v + x) lowercase__ : int = int(z - (2 * c)) lowercase__ : int = round(w % 7) # End math # Validate math if f != convert_datetime_days[dt_ck.weekday()]: raise AssertionError("The date was evaluated incorrectly. Contact developer.") # Response lowercase__ : str = f'''Your date {date_input}, is a {days[str(_lowerCamelCase)]}!''' return response if __name__ == "__main__": import doctest doctest.testmod() UpperCamelCase = argparse.ArgumentParser( description=( '''Find out what day of the week nearly any date is or was. Enter ''' '''date as a string in the mm-dd-yyyy or mm/dd/yyyy format''' ) ) parser.add_argument( '''date_input''', type=str, help='''Date as a string (mm-dd-yyyy or mm/dd/yyyy)''' ) UpperCamelCase = parser.parse_args() zeller(args.date_input)
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'''simple docstring''' UpperCamelCase = tuple[float, float, float] UpperCamelCase = tuple[float, float, float] def SCREAMING_SNAKE_CASE( __lowercase , __lowercase ) -> Vectorad: A: Optional[Any] = end_pointa[0] - end_pointa[0] A: Any = end_pointa[1] - end_pointa[1] A: str = end_pointa[2] - end_pointa[2] return (x, y, z) def SCREAMING_SNAKE_CASE( __lowercase , __lowercase ) -> Vectorad: A: Union[str, Any] = ab[1] * ac[2] - ab[2] * ac[1] # *i A: Any = (ab[0] * ac[2] - ab[2] * ac[0]) * -1 # *j A: Tuple = ab[0] * ac[1] - ab[1] * ac[0] # *k return (x, y, z) def SCREAMING_SNAKE_CASE( __lowercase , __lowercase ) -> bool: return tuple(round(__lowercase , __lowercase ) for x in vector ) == (0, 0, 0) def SCREAMING_SNAKE_CASE( __lowercase , __lowercase , __lowercase , __lowercase = 1_0 ) -> bool: A: Any = create_vector(__lowercase , __lowercase ) A: Any = create_vector(__lowercase , __lowercase ) return is_zero_vector(get_ad_vectors_cross(__lowercase , __lowercase ) , __lowercase )
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'''simple docstring''' import requests UpperCamelCase = '''https://newsapi.org/v1/articles?source=bbc-news&sortBy=top&apiKey=''' def SCREAMING_SNAKE_CASE( __lowercase ) -> None: # fetching a list of articles in json format A: Tuple = requests.get(_NEWS_API + bbc_news_api_key ).json() # each article in the list is a dict for i, article in enumerate(bbc_news_page['''articles'''] , 1 ): print(F"""{i}.) {article['title']}""" ) if __name__ == "__main__": fetch_bbc_news(bbc_news_api_key='''<Your BBC News API key goes here>''')
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'''simple docstring''' from math import cos, sin, sqrt, tau from audio_filters.iir_filter import IIRFilter def SCREAMING_SNAKE_CASE( __lowercase , __lowercase , __lowercase = 1 / sqrt(2 ) ) -> IIRFilter: A: str = tau * frequency / samplerate A: Optional[Any] = sin(__lowercase ) A: Dict = cos(__lowercase ) A: Optional[Any] = _sin / (2 * q_factor) A: Union[str, Any] = (1 - _cos) / 2 A: Tuple = 1 - _cos A: str = 1 + alpha A: List[str] = -2 * _cos A: Tuple = 1 - alpha A: List[str] = IIRFilter(2 ) filt.set_coefficients([aa, aa, aa] , [ba, ba, ba] ) return filt def SCREAMING_SNAKE_CASE( __lowercase , __lowercase , __lowercase = 1 / sqrt(2 ) ) -> IIRFilter: A: Optional[Any] = tau * frequency / samplerate A: Tuple = sin(__lowercase ) A: Dict = cos(__lowercase ) A: Tuple = _sin / (2 * q_factor) A: Any = (1 + _cos) / 2 A: List[Any] = -1 - _cos A: int = 1 + alpha A: Optional[Any] = -2 * _cos A: Optional[int] = 1 - alpha A: Dict = IIRFilter(2 ) filt.set_coefficients([aa, aa, aa] , [ba, ba, ba] ) return filt def SCREAMING_SNAKE_CASE( __lowercase , __lowercase , __lowercase = 1 / sqrt(2 ) ) -> IIRFilter: A: List[Any] = tau * frequency / samplerate A: List[str] = sin(__lowercase ) A: List[Any] = cos(__lowercase ) A: Union[str, Any] = _sin / (2 * q_factor) A: int = _sin / 2 A: List[str] = 0 A: Optional[Any] = -ba A: Optional[Any] = 1 + alpha A: Any = -2 * _cos A: int = 1 - alpha A: Tuple = IIRFilter(2 ) filt.set_coefficients([aa, aa, aa] , [ba, ba, ba] ) return filt def SCREAMING_SNAKE_CASE( __lowercase , __lowercase , __lowercase = 1 / sqrt(2 ) ) -> IIRFilter: A: List[Any] = tau * frequency / samplerate A: Any = sin(__lowercase ) A: Optional[int] = cos(__lowercase ) A: Union[str, Any] = _sin / (2 * q_factor) A: Dict = 1 - alpha A: Dict = -2 * _cos A: str = 1 + alpha A: Any = IIRFilter(2 ) filt.set_coefficients([ba, ba, ba] , [ba, ba, ba] ) return filt def SCREAMING_SNAKE_CASE( __lowercase , __lowercase , __lowercase , __lowercase = 1 / sqrt(2 ) , ) -> IIRFilter: A: Optional[Any] = tau * frequency / samplerate A: Dict = sin(__lowercase ) A: str = cos(__lowercase ) A: Union[str, Any] = _sin / (2 * q_factor) A: Union[str, Any] = 1_0 ** (gain_db / 4_0) A: int = 1 + alpha * big_a A: Tuple = -2 * _cos A: Dict = 1 - alpha * big_a A: Any = 1 + alpha / big_a A: Tuple = -2 * _cos A: List[Any] = 1 - alpha / big_a A: int = IIRFilter(2 ) filt.set_coefficients([aa, aa, aa] , [ba, ba, ba] ) return filt def SCREAMING_SNAKE_CASE( __lowercase , __lowercase , __lowercase , __lowercase = 1 / sqrt(2 ) , ) -> IIRFilter: A: Union[str, Any] = tau * frequency / samplerate A: int = sin(__lowercase ) A: Dict = cos(__lowercase ) A: int = _sin / (2 * q_factor) A: int = 1_0 ** (gain_db / 4_0) A: Optional[int] = (big_a + 1) - (big_a - 1) * _cos A: List[Any] = (big_a + 1) + (big_a - 1) * _cos A: List[Any] = (big_a - 1) - (big_a + 1) * _cos A: List[Any] = (big_a - 1) + (big_a + 1) * _cos A: int = 2 * sqrt(__lowercase ) * alpha A: Tuple = big_a * (pmc + aaa) A: Any = 2 * big_a * mpc A: int = big_a * (pmc - aaa) A: int = ppmc + aaa A: List[Any] = -2 * pmpc A: List[Any] = ppmc - aaa A: str = IIRFilter(2 ) filt.set_coefficients([aa, aa, aa] , [ba, ba, ba] ) return filt def SCREAMING_SNAKE_CASE( __lowercase , __lowercase , __lowercase , __lowercase = 1 / sqrt(2 ) , ) -> IIRFilter: A: List[Any] = tau * frequency / samplerate A: Union[str, Any] = sin(__lowercase ) A: str = cos(__lowercase ) A: List[Any] = _sin / (2 * q_factor) A: Optional[Any] = 1_0 ** (gain_db / 4_0) A: Optional[Any] = (big_a + 1) - (big_a - 1) * _cos A: List[str] = (big_a + 1) + (big_a - 1) * _cos A: str = (big_a - 1) - (big_a + 1) * _cos A: int = (big_a - 1) + (big_a + 1) * _cos A: List[str] = 2 * sqrt(__lowercase ) * alpha A: Union[str, Any] = big_a * (ppmc + aaa) A: List[Any] = -2 * big_a * pmpc A: Any = big_a * (ppmc - aaa) A: List[Any] = pmc + aaa A: Tuple = 2 * mpc A: Tuple = pmc - aaa A: Optional[int] = IIRFilter(2 ) filt.set_coefficients([aa, aa, aa] , [ba, ba, ba] ) return filt
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'''simple docstring''' import os from shutil import copyfile from typing import List, Optional, Tuple from ...tokenization_utils import AddedToken from ...tokenization_utils_fast import PreTrainedTokenizerFast from ...utils import is_sentencepiece_available, logging if is_sentencepiece_available(): from .tokenization_camembert import CamembertTokenizer else: UpperCamelCase = None UpperCamelCase = logging.get_logger(__name__) UpperCamelCase = {'''vocab_file''': '''sentencepiece.bpe.model''', '''tokenizer_file''': '''tokenizer.json'''} UpperCamelCase = { '''vocab_file''': { '''camembert-base''': '''https://huggingface.co/camembert-base/resolve/main/sentencepiece.bpe.model''', }, '''tokenizer_file''': { '''camembert-base''': '''https://huggingface.co/camembert-base/resolve/main/tokenizer.json''', }, } UpperCamelCase = { '''camembert-base''': 512, } UpperCamelCase = '''▁''' class lowerCAmelCase_ ( UpperCAmelCase_ ): '''simple docstring''' UpperCamelCase_ : Tuple = VOCAB_FILES_NAMES UpperCamelCase_ : Optional[int] = PRETRAINED_VOCAB_FILES_MAP UpperCamelCase_ : Union[str, Any] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES UpperCamelCase_ : str = ["""input_ids""", """attention_mask"""] UpperCamelCase_ : int = CamembertTokenizer def __init__( self : Tuple , SCREAMING_SNAKE_CASE_ : int=None , SCREAMING_SNAKE_CASE_ : List[str]=None , SCREAMING_SNAKE_CASE_ : str="<s>" , SCREAMING_SNAKE_CASE_ : Optional[Any]="</s>" , SCREAMING_SNAKE_CASE_ : List[Any]="</s>" , SCREAMING_SNAKE_CASE_ : int="<s>" , SCREAMING_SNAKE_CASE_ : Union[str, Any]="<unk>" , SCREAMING_SNAKE_CASE_ : str="<pad>" , SCREAMING_SNAKE_CASE_ : List[str]="<mask>" , SCREAMING_SNAKE_CASE_ : Union[str, Any]=["<s>NOTUSED", "</s>NOTUSED"] , **SCREAMING_SNAKE_CASE_ : Any , ) -> Any: '''simple docstring''' A: Tuple = AddedToken(SCREAMING_SNAKE_CASE_ , lstrip=SCREAMING_SNAKE_CASE_ , rstrip=SCREAMING_SNAKE_CASE_ ) if isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) else mask_token super().__init__( SCREAMING_SNAKE_CASE_ , tokenizer_file=SCREAMING_SNAKE_CASE_ , bos_token=SCREAMING_SNAKE_CASE_ , eos_token=SCREAMING_SNAKE_CASE_ , sep_token=SCREAMING_SNAKE_CASE_ , cls_token=SCREAMING_SNAKE_CASE_ , unk_token=SCREAMING_SNAKE_CASE_ , pad_token=SCREAMING_SNAKE_CASE_ , mask_token=SCREAMING_SNAKE_CASE_ , additional_special_tokens=SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ , ) A: Any = vocab_file A: Any = False if not self.vocab_file else True def _snake_case ( self : int , SCREAMING_SNAKE_CASE_ : List[int] , SCREAMING_SNAKE_CASE_ : Optional[List[int]] = None ) -> List[int]: '''simple docstring''' if token_ids_a is None: return [self.cls_token_id] + token_ids_a + [self.sep_token_id] A: List[str] = [self.cls_token_id] A: List[str] = [self.sep_token_id] return cls + token_ids_a + sep + sep + token_ids_a + sep def _snake_case ( self : int , SCREAMING_SNAKE_CASE_ : List[int] , SCREAMING_SNAKE_CASE_ : Optional[List[int]] = None ) -> List[int]: '''simple docstring''' A: List[str] = [self.sep_token_id] A: Optional[int] = [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] def _snake_case ( self : int , SCREAMING_SNAKE_CASE_ : str , SCREAMING_SNAKE_CASE_ : Optional[str] = None ) -> Tuple[str]: '''simple docstring''' if not self.can_save_slow_tokenizer: raise ValueError( '''Your fast tokenizer does not have the necessary information to save the vocabulary for a slow ''' '''tokenizer.''' ) if not os.path.isdir(SCREAMING_SNAKE_CASE_ ): logger.error(f"""Vocabulary path ({save_directory}) should be a directory""" ) return A: Dict = os.path.join( SCREAMING_SNAKE_CASE_ , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] ) if os.path.abspath(self.vocab_file ) != os.path.abspath(SCREAMING_SNAKE_CASE_ ): copyfile(self.vocab_file , SCREAMING_SNAKE_CASE_ ) return (out_vocab_file,)
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'''simple docstring''' from pathlib import PurePosixPath from typing import Optional import fsspec from fsspec import AbstractFileSystem from huggingface_hub.hf_api import DatasetInfo from ..utils.file_utils import get_authentication_headers_for_url from ..utils.hub import hf_hub_url class lowerCAmelCase_ ( UpperCAmelCase_ ): '''simple docstring''' UpperCamelCase_ : Union[str, Any] = """""" UpperCamelCase_ : List[Any] = """hf-legacy""" # "hf://"" is reserved for hffs def __init__( self : Tuple , SCREAMING_SNAKE_CASE_ : Optional[DatasetInfo] = None , SCREAMING_SNAKE_CASE_ : Optional[str] = None , **SCREAMING_SNAKE_CASE_ : Tuple , ) -> int: '''simple docstring''' super().__init__(self , **SCREAMING_SNAKE_CASE_ ) A: Tuple = repo_info A: int = token A: Optional[Any] = None def _snake_case ( self : Optional[Any] ) -> Optional[int]: '''simple docstring''' if self.dir_cache is None: A: Dict = {} for hf_file in self.repo_info.siblings: # TODO(QL): add sizes A: Union[str, Any] = { '''name''': hf_file.rfilename, '''size''': None, '''type''': '''file''', } self.dir_cache.update( { str(SCREAMING_SNAKE_CASE_ ): {'''name''': str(SCREAMING_SNAKE_CASE_ ), '''size''': None, '''type''': '''directory'''} for d in list(PurePosixPath(hf_file.rfilename ).parents )[:-1] } ) def _snake_case ( self : Optional[int] , SCREAMING_SNAKE_CASE_ : str , SCREAMING_SNAKE_CASE_ : str = "rb" , **SCREAMING_SNAKE_CASE_ : int , ) -> Any: '''simple docstring''' if not isinstance(self.repo_info , SCREAMING_SNAKE_CASE_ ): raise NotImplementedError(f"""Open is only implemented for dataset repositories, but got {self.repo_info}""" ) A: Union[str, Any] = hf_hub_url(self.repo_info.id , SCREAMING_SNAKE_CASE_ , revision=self.repo_info.sha ) return fsspec.open( SCREAMING_SNAKE_CASE_ , mode=SCREAMING_SNAKE_CASE_ , headers=get_authentication_headers_for_url(SCREAMING_SNAKE_CASE_ , use_auth_token=self.token ) , client_kwargs={'''trust_env''': True} , ).open() def _snake_case ( self : str , SCREAMING_SNAKE_CASE_ : Optional[Any] , **SCREAMING_SNAKE_CASE_ : int ) -> Dict: '''simple docstring''' self._get_dirs() A: Optional[Any] = self._strip_protocol(SCREAMING_SNAKE_CASE_ ) if path in self.dir_cache: return self.dir_cache[path] else: raise FileNotFoundError(SCREAMING_SNAKE_CASE_ ) def _snake_case ( self : List[Any] , SCREAMING_SNAKE_CASE_ : Tuple , SCREAMING_SNAKE_CASE_ : Optional[int]=False , **SCREAMING_SNAKE_CASE_ : int ) -> List[str]: '''simple docstring''' self._get_dirs() A: int = PurePosixPath(path.strip('''/''' ) ) A: str = {} for p, f in self.dir_cache.items(): A: Dict = PurePosixPath(p.strip('''/''' ) ) A: Optional[Any] = p.parent if root == path: A: Dict = f A: Dict = list(paths.values() ) if detail: return out else: return sorted(f['''name'''] for f in out )
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'''simple docstring''' import os from distutils.util import strtobool def SCREAMING_SNAKE_CASE( __lowercase , __lowercase ) -> List[Any]: for e in env_keys: A: Dict = int(os.environ.get(__lowercase , -1 ) ) if val >= 0: return val return default def SCREAMING_SNAKE_CASE( __lowercase , __lowercase=False ) -> List[str]: A: str = os.environ.get(__lowercase , str(__lowercase ) ) return strtobool(__lowercase ) == 1 # As its name indicates `strtobool` actually returns an int... def SCREAMING_SNAKE_CASE( __lowercase , __lowercase="no" ) -> str: A: Optional[int] = os.environ.get(__lowercase , str(__lowercase ) ) return value
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'''simple docstring''' def SCREAMING_SNAKE_CASE( __lowercase ) -> list: A: List[Any] = [0] * len(__lowercase ) for i in range(1 , len(__lowercase ) ): # use last results for better performance - dynamic programming A: Union[str, Any] = prefix_result[i - 1] while j > 0 and input_string[i] != input_string[j]: A: int = prefix_result[j - 1] if input_string[i] == input_string[j]: j += 1 A: Optional[int] = j return prefix_result def SCREAMING_SNAKE_CASE( __lowercase ) -> int: return max(prefix_function(__lowercase ) ) if __name__ == "__main__": import doctest doctest.testmod()
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'''simple docstring''' import argparse import torch from transformers import ( EncodecConfig, EncodecFeatureExtractor, EncodecModel, logging, ) # checkpoints downloaded from: # https://dl.fbaipublicfiles.com/encodec/v0/encodec_24khz-d7cc33bc.th # https://huggingface.co/facebook/musicgen-small/resolve/main/compression_state_dict.bin # https://dl.fbaipublicfiles.com/encodec/v0/encodec_48khz-7e698e3e.th logging.set_verbosity_info() UpperCamelCase = logging.get_logger('''transformers.models.encodec''') UpperCamelCase = { '''quantizer.vq.layers.*._codebook.inited''': '''quantizer.layers.*.codebook.inited''', '''quantizer.vq.layers.*._codebook.cluster_size''': '''quantizer.layers.*.codebook.cluster_size''', '''quantizer.vq.layers.*._codebook.embed''': '''quantizer.layers.*.codebook.embed''', '''quantizer.vq.layers.*._codebook.embed_avg''': '''quantizer.layers.*.codebook.embed_avg''', } UpperCamelCase = { '''encoder.model.0.conv.conv''': '''encoder.layers.0.conv''', '''encoder.model.1.block.1.conv.conv''': '''encoder.layers.1.block.1.conv''', '''encoder.model.1.block.3.conv.conv''': '''encoder.layers.1.block.3.conv''', '''encoder.model.1.shortcut.conv.conv''': '''encoder.layers.1.shortcut.conv''', '''encoder.model.3.conv.conv''': '''encoder.layers.3.conv''', '''encoder.model.4.block.1.conv.conv''': '''encoder.layers.4.block.1.conv''', '''encoder.model.4.block.3.conv.conv''': '''encoder.layers.4.block.3.conv''', '''encoder.model.4.shortcut.conv.conv''': '''encoder.layers.4.shortcut.conv''', '''encoder.model.6.conv.conv''': '''encoder.layers.6.conv''', '''encoder.model.7.block.1.conv.conv''': '''encoder.layers.7.block.1.conv''', '''encoder.model.7.block.3.conv.conv''': '''encoder.layers.7.block.3.conv''', '''encoder.model.7.shortcut.conv.conv''': '''encoder.layers.7.shortcut.conv''', '''encoder.model.9.conv.conv''': '''encoder.layers.9.conv''', '''encoder.model.10.block.1.conv.conv''': '''encoder.layers.10.block.1.conv''', '''encoder.model.10.block.3.conv.conv''': '''encoder.layers.10.block.3.conv''', '''encoder.model.10.shortcut.conv.conv''': '''encoder.layers.10.shortcut.conv''', '''encoder.model.12.conv.conv''': '''encoder.layers.12.conv''', '''encoder.model.13.lstm''': '''encoder.layers.13.lstm''', '''encoder.model.15.conv.conv''': '''encoder.layers.15.conv''', } UpperCamelCase = { '''encoder.model.0.conv.norm''': '''encoder.layers.0.norm''', '''encoder.model.1.block.1.conv.norm''': '''encoder.layers.1.block.1.norm''', '''encoder.model.1.block.3.conv.norm''': '''encoder.layers.1.block.3.norm''', '''encoder.model.1.shortcut.conv.norm''': '''encoder.layers.1.shortcut.norm''', '''encoder.model.3.conv.norm''': '''encoder.layers.3.norm''', '''encoder.model.4.block.1.conv.norm''': '''encoder.layers.4.block.1.norm''', '''encoder.model.4.block.3.conv.norm''': '''encoder.layers.4.block.3.norm''', '''encoder.model.4.shortcut.conv.norm''': '''encoder.layers.4.shortcut.norm''', '''encoder.model.6.conv.norm''': '''encoder.layers.6.norm''', '''encoder.model.7.block.1.conv.norm''': '''encoder.layers.7.block.1.norm''', '''encoder.model.7.block.3.conv.norm''': '''encoder.layers.7.block.3.norm''', '''encoder.model.7.shortcut.conv.norm''': '''encoder.layers.7.shortcut.norm''', '''encoder.model.9.conv.norm''': '''encoder.layers.9.norm''', '''encoder.model.10.block.1.conv.norm''': '''encoder.layers.10.block.1.norm''', '''encoder.model.10.block.3.conv.norm''': '''encoder.layers.10.block.3.norm''', '''encoder.model.10.shortcut.conv.norm''': '''encoder.layers.10.shortcut.norm''', '''encoder.model.12.conv.norm''': '''encoder.layers.12.norm''', '''encoder.model.15.conv.norm''': '''encoder.layers.15.norm''', } UpperCamelCase = { '''decoder.model.0.conv.conv''': '''decoder.layers.0.conv''', '''decoder.model.1.lstm''': '''decoder.layers.1.lstm''', '''decoder.model.3.convtr.convtr''': '''decoder.layers.3.conv''', '''decoder.model.4.block.1.conv.conv''': '''decoder.layers.4.block.1.conv''', '''decoder.model.4.block.3.conv.conv''': '''decoder.layers.4.block.3.conv''', '''decoder.model.4.shortcut.conv.conv''': '''decoder.layers.4.shortcut.conv''', '''decoder.model.6.convtr.convtr''': '''decoder.layers.6.conv''', '''decoder.model.7.block.1.conv.conv''': '''decoder.layers.7.block.1.conv''', '''decoder.model.7.block.3.conv.conv''': '''decoder.layers.7.block.3.conv''', '''decoder.model.7.shortcut.conv.conv''': '''decoder.layers.7.shortcut.conv''', '''decoder.model.9.convtr.convtr''': '''decoder.layers.9.conv''', '''decoder.model.10.block.1.conv.conv''': '''decoder.layers.10.block.1.conv''', '''decoder.model.10.block.3.conv.conv''': '''decoder.layers.10.block.3.conv''', '''decoder.model.10.shortcut.conv.conv''': '''decoder.layers.10.shortcut.conv''', '''decoder.model.12.convtr.convtr''': '''decoder.layers.12.conv''', '''decoder.model.13.block.1.conv.conv''': '''decoder.layers.13.block.1.conv''', '''decoder.model.13.block.3.conv.conv''': '''decoder.layers.13.block.3.conv''', '''decoder.model.13.shortcut.conv.conv''': '''decoder.layers.13.shortcut.conv''', '''decoder.model.15.conv.conv''': '''decoder.layers.15.conv''', } UpperCamelCase = { '''decoder.model.0.conv.norm''': '''decoder.layers.0.norm''', '''decoder.model.3.convtr.norm''': '''decoder.layers.3.norm''', '''decoder.model.4.block.1.conv.norm''': '''decoder.layers.4.block.1.norm''', '''decoder.model.4.block.3.conv.norm''': '''decoder.layers.4.block.3.norm''', '''decoder.model.4.shortcut.conv.norm''': '''decoder.layers.4.shortcut.norm''', '''decoder.model.6.convtr.norm''': '''decoder.layers.6.norm''', '''decoder.model.7.block.1.conv.norm''': '''decoder.layers.7.block.1.norm''', '''decoder.model.7.block.3.conv.norm''': '''decoder.layers.7.block.3.norm''', '''decoder.model.7.shortcut.conv.norm''': '''decoder.layers.7.shortcut.norm''', '''decoder.model.9.convtr.norm''': '''decoder.layers.9.norm''', '''decoder.model.10.block.1.conv.norm''': '''decoder.layers.10.block.1.norm''', '''decoder.model.10.block.3.conv.norm''': '''decoder.layers.10.block.3.norm''', '''decoder.model.10.shortcut.conv.norm''': '''decoder.layers.10.shortcut.norm''', '''decoder.model.12.convtr.norm''': '''decoder.layers.12.norm''', '''decoder.model.13.block.1.conv.norm''': '''decoder.layers.13.block.1.norm''', '''decoder.model.13.block.3.conv.norm''': '''decoder.layers.13.block.3.norm''', '''decoder.model.13.shortcut.conv.norm''': '''decoder.layers.13.shortcut.norm''', '''decoder.model.15.conv.norm''': '''decoder.layers.15.norm''', } UpperCamelCase = { **MAPPING_QUANTIZER, **MAPPING_ENCODER, **MAPPING_DECODER, } UpperCamelCase = { **MAPPING_QUANTIZER, **MAPPING_ENCODER, **MAPPING_ENCODER_48K, **MAPPING_DECODER, **MAPPING_DECODER_48K, } UpperCamelCase = [] UpperCamelCase = [] def SCREAMING_SNAKE_CASE( __lowercase , __lowercase , __lowercase , __lowercase , __lowercase ) -> Dict: for attribute in key.split('''.''' ): A: Union[str, Any] = getattr(__lowercase , __lowercase ) if weight_type is not None: A: Tuple = getattr(__lowercase , __lowercase ).shape else: A: str = 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": A: Dict = value elif weight_type == "weight_g": A: Tuple = value elif weight_type == "weight_v": A: Any = value elif weight_type == "bias": A: str = value elif weight_type == "running_mean": A: List[Any] = value elif weight_type == "running_var": A: Dict = value elif weight_type == "num_batches_tracked": A: List[str] = value elif weight_type == "weight_ih_l0": A: Dict = value elif weight_type == "weight_hh_l0": A: Optional[int] = value elif weight_type == "bias_ih_l0": A: List[Any] = value elif weight_type == "bias_hh_l0": A: str = value elif weight_type == "weight_ih_l1": A: Optional[int] = value elif weight_type == "weight_hh_l1": A: int = value elif weight_type == "bias_ih_l1": A: Optional[Any] = value elif weight_type == "bias_hh_l1": A: str = value else: A: Optional[int] = value logger.info(F"""{key + ('.' + weight_type if weight_type is not None else '')} was initialized from {full_name}.""" ) def SCREAMING_SNAKE_CASE( __lowercase , __lowercase ) -> Optional[Any]: for key in ignore_keys: if key.endswith('''.*''' ): if name.startswith(key[:-1] ): return True elif ".*." in key: A , A: Any = key.split('''.*.''' ) if prefix in name and suffix in name: return True elif key in name: return True return False def SCREAMING_SNAKE_CASE( __lowercase , __lowercase , __lowercase ) -> Tuple: A: Any = [] if model_name == "encodec_24khz" or "encodec_32khz": A: List[str] = MAPPING_24K elif model_name == "encodec_48khz": A: List[Any] = MAPPING_48K else: raise ValueError(F"""Unsupported model: {model_name}""" ) for name, value in orig_dict.items(): if should_ignore(__lowercase , __lowercase ): logger.info(F"""{name} was ignored""" ) continue A: Optional[int] = False for key, mapped_key in MAPPING.items(): if "*" in key: A , A: Optional[int] = key.split('''.*.''' ) if prefix in name and suffix in name: A: str = suffix if key in name: # HACK otherwise .embed gets initialized with .embed_avg too if key.endswith('''embed''' ) and name.endswith('''embed_avg''' ): continue A: Optional[Any] = True if "*" in mapped_key: A: Any = name.split(__lowercase )[0].split('''.''' )[-2] A: Tuple = mapped_key.replace('''*''' , __lowercase ) if "weight_g" in name: A: str = '''weight_g''' elif "weight_v" in name: A: List[Any] = '''weight_v''' elif "weight_ih_l0" in name: A: Dict = '''weight_ih_l0''' elif "weight_hh_l0" in name: A: int = '''weight_hh_l0''' elif "bias_ih_l0" in name: A: Union[str, Any] = '''bias_ih_l0''' elif "bias_hh_l0" in name: A: Tuple = '''bias_hh_l0''' elif "weight_ih_l1" in name: A: int = '''weight_ih_l1''' elif "weight_hh_l1" in name: A: Optional[Any] = '''weight_hh_l1''' elif "bias_ih_l1" in name: A: Dict = '''bias_ih_l1''' elif "bias_hh_l1" in name: A: str = '''bias_hh_l1''' elif "bias" in name: A: Union[str, Any] = '''bias''' elif "weight" in name: A: Dict = '''weight''' elif "running_mean" in name: A: Tuple = '''running_mean''' elif "running_var" in name: A: Any = '''running_var''' elif "num_batches_tracked" in name: A: str = '''num_batches_tracked''' else: A: Tuple = None set_recursively(__lowercase , __lowercase , __lowercase , __lowercase , __lowercase ) continue if not is_used: unused_weights.append(__lowercase ) logger.warning(F"""Unused weights: {unused_weights}""" ) @torch.no_grad() def SCREAMING_SNAKE_CASE( __lowercase , __lowercase , __lowercase , __lowercase=None , __lowercase=None , ) -> Dict: if config_path is not None: A: Tuple = EncodecConfig.from_pretrained(__lowercase ) else: A: Union[str, Any] = EncodecConfig() if model_name == "encodec_24khz": pass # config is already correct elif model_name == "encodec_32khz": A: Union[str, Any] = [8, 5, 4, 4] A: Dict = [2.2] A: List[Any] = 6_4 A: Optional[Any] = 3_2_0_0_0 A: List[Any] = 2_0_4_8 A: Optional[Any] = False A: int = False A: Union[str, Any] = False elif model_name == "encodec_48khz": A: Optional[int] = [8, 5, 4, 2] A: List[Any] = [3.0, 6.0, 1_2.0, 2_4.0] A: List[Any] = 4_8_0_0_0 A: int = 2 A: List[Any] = False A: Any = '''time_group_norm''' A: Optional[Any] = True A: Any = 1.0 A: Any = 0.0_1 else: raise ValueError(F"""Unknown model name: {model_name}""" ) A: str = EncodecModel(__lowercase ) A: Optional[Any] = EncodecFeatureExtractor( feature_size=config.audio_channels , sampling_rate=config.sampling_rate , chunk_length_s=config.chunk_length_s , overlap=config.overlap , ) feature_extractor.save_pretrained(__lowercase ) A: Union[str, Any] = torch.load(__lowercase ) if "best_state" in original_checkpoint: # we might have a training state saved, in which case discard the yaml results and just retain the weights A: Optional[int] = original_checkpoint['''best_state'''] recursively_load_weights(__lowercase , __lowercase , __lowercase ) model.save_pretrained(__lowercase ) if repo_id: print('''Pushing to the hub...''' ) feature_extractor.push_to_hub(__lowercase ) model.push_to_hub(__lowercase ) if __name__ == "__main__": UpperCamelCase = argparse.ArgumentParser() parser.add_argument( '''--model''', default='''encodec_24khz''', type=str, help='''The model to convert. Should be one of \'encodec_24khz\', \'encodec_32khz\', \'encodec_48khz\'.''', ) parser.add_argument('''--checkpoint_path''', required=True, default=None, type=str, help='''Path to original checkpoint''') 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.''' ) UpperCamelCase = parser.parse_args() convert_checkpoint( args.model, args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.push_to_hub, )
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'''simple docstring''' from __future__ import annotations # This is the precision for this function which can be altered. # It is recommended for users to keep this number greater than or equal to 10. UpperCamelCase = 10 def SCREAMING_SNAKE_CASE( __lowercase , __lowercase , __lowercase , __lowercase ) -> int: for i in range(__lowercase , __lowercase ): if array[i] == target: return i return -1 def SCREAMING_SNAKE_CASE( __lowercase , __lowercase ) -> int: A: str = 0 A: List[Any] = len(__lowercase ) while left <= right: if right - left < precision: return lin_search(__lowercase , __lowercase , __lowercase , __lowercase ) A: Dict = (left + right) // 3 + 1 A: Dict = 2 * (left + right) // 3 + 1 if array[one_third] == target: return one_third elif array[two_third] == target: return two_third elif target < array[one_third]: A: Dict = one_third - 1 elif array[two_third] < target: A: List[str] = two_third + 1 else: A: Optional[Any] = one_third + 1 A: Optional[int] = two_third - 1 else: return -1 def SCREAMING_SNAKE_CASE( __lowercase , __lowercase , __lowercase , __lowercase ) -> int: if left < right: if right - left < precision: return lin_search(__lowercase , __lowercase , __lowercase , __lowercase ) A: Dict = (left + right) // 3 + 1 A: str = 2 * (left + right) // 3 + 1 if array[one_third] == target: return one_third elif array[two_third] == target: return two_third elif target < array[one_third]: return rec_ternary_search(__lowercase , one_third - 1 , __lowercase , __lowercase ) elif array[two_third] < target: return rec_ternary_search(two_third + 1 , __lowercase , __lowercase , __lowercase ) else: return rec_ternary_search(one_third + 1 , two_third - 1 , __lowercase , __lowercase ) else: return -1 if __name__ == "__main__": import doctest doctest.testmod() UpperCamelCase = input('''Enter numbers separated by comma:\n''').strip() UpperCamelCase = [int(item.strip()) for item in user_input.split(''',''')] assert collection == sorted(collection), f"List must be ordered.\n{collection}." UpperCamelCase = int(input('''Enter the number to be found in the list:\n''').strip()) UpperCamelCase = ite_ternary_search(collection, target) UpperCamelCase = rec_ternary_search(0, len(collection) - 1, collection, target) if resulta != -1: print(f'Iterative search: {target} found at positions: {resulta}') print(f'Recursive search: {target} found at positions: {resulta}') else: print('''Not found''')
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'''simple docstring''' from typing import TYPE_CHECKING from ...utils import ( OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_tf_available, is_torch_available, ) UpperCamelCase = { '''configuration_vision_encoder_decoder''': ['''VisionEncoderDecoderConfig''', '''VisionEncoderDecoderOnnxConfig'''] } try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: UpperCamelCase = ['''VisionEncoderDecoderModel'''] try: if not is_tf_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: UpperCamelCase = ['''TFVisionEncoderDecoderModel'''] try: if not is_flax_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: UpperCamelCase = ['''FlaxVisionEncoderDecoderModel'''] if TYPE_CHECKING: from .configuration_vision_encoder_decoder import VisionEncoderDecoderConfig, VisionEncoderDecoderOnnxConfig try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_vision_encoder_decoder import VisionEncoderDecoderModel try: if not is_tf_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_tf_vision_encoder_decoder import TFVisionEncoderDecoderModel try: if not is_flax_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_flax_vision_encoder_decoder import FlaxVisionEncoderDecoderModel else: import sys UpperCamelCase = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
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'''simple docstring''' from ...configuration_utils import PretrainedConfig from ...utils import logging UpperCamelCase = logging.get_logger(__name__) UpperCamelCase = { '''facebook/nllb-moe-54B''': '''https://huggingface.co/facebook/nllb-moe-54b/resolve/main/config.json''', } class lowerCAmelCase_ ( UpperCAmelCase_ ): '''simple docstring''' UpperCamelCase_ : Union[str, Any] = """nllb-moe""" UpperCamelCase_ : List[str] = ["""past_key_values"""] UpperCamelCase_ : int = {"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""} def __init__( self : List[str] , SCREAMING_SNAKE_CASE_ : Optional[Any]=12_81_12 , SCREAMING_SNAKE_CASE_ : Dict=10_24 , SCREAMING_SNAKE_CASE_ : List[Any]=12 , SCREAMING_SNAKE_CASE_ : Dict=40_96 , SCREAMING_SNAKE_CASE_ : int=16 , SCREAMING_SNAKE_CASE_ : int=12 , SCREAMING_SNAKE_CASE_ : List[str]=40_96 , SCREAMING_SNAKE_CASE_ : List[Any]=16 , SCREAMING_SNAKE_CASE_ : List[Any]=0.05 , SCREAMING_SNAKE_CASE_ : Optional[int]=0.05 , SCREAMING_SNAKE_CASE_ : List[str]=True , SCREAMING_SNAKE_CASE_ : Tuple=True , SCREAMING_SNAKE_CASE_ : List[Any]="relu" , SCREAMING_SNAKE_CASE_ : str=10_24 , SCREAMING_SNAKE_CASE_ : List[str]=0.1 , SCREAMING_SNAKE_CASE_ : Optional[int]=0.1 , SCREAMING_SNAKE_CASE_ : int=0.0 , SCREAMING_SNAKE_CASE_ : List[str]=0.02 , SCREAMING_SNAKE_CASE_ : Any=2 , SCREAMING_SNAKE_CASE_ : Tuple=True , SCREAMING_SNAKE_CASE_ : Optional[int]=False , SCREAMING_SNAKE_CASE_ : Dict="float32" , SCREAMING_SNAKE_CASE_ : Optional[int]=False , SCREAMING_SNAKE_CASE_ : List[Any]=1_28 , SCREAMING_SNAKE_CASE_ : Union[str, Any]=64 , SCREAMING_SNAKE_CASE_ : str=4 , SCREAMING_SNAKE_CASE_ : str=4 , SCREAMING_SNAKE_CASE_ : List[Any]=0.001 , SCREAMING_SNAKE_CASE_ : Optional[Any]=0.001 , SCREAMING_SNAKE_CASE_ : Optional[int]="all" , SCREAMING_SNAKE_CASE_ : Union[str, Any]=False , SCREAMING_SNAKE_CASE_ : Any=False , SCREAMING_SNAKE_CASE_ : List[Any]=1.0 , SCREAMING_SNAKE_CASE_ : Any=0.2 , SCREAMING_SNAKE_CASE_ : Optional[Any]=1 , SCREAMING_SNAKE_CASE_ : Optional[Any]=0 , SCREAMING_SNAKE_CASE_ : Union[str, Any]=2 , SCREAMING_SNAKE_CASE_ : Tuple=False , **SCREAMING_SNAKE_CASE_ : Tuple , ) -> Optional[Any]: '''simple docstring''' A: List[str] = vocab_size A: Tuple = max_position_embeddings A: str = d_model A: int = encoder_ffn_dim A: Optional[Any] = encoder_layers A: int = encoder_attention_heads A: List[str] = decoder_ffn_dim A: int = decoder_layers A: Any = decoder_attention_heads A: List[str] = dropout A: List[str] = attention_dropout A: Optional[int] = activation_dropout A: List[Any] = activation_function A: List[Any] = init_std A: Optional[int] = encoder_layerdrop A: Any = decoder_layerdrop A: List[str] = use_cache A: Dict = encoder_layers A: Union[str, Any] = scale_embedding # scale factor will be sqrt(d_model) if True A: Tuple = router_z_loss_coef A: Optional[int] = router_aux_loss_coef A: Tuple = decoder_sparse_step A: List[Any] = encoder_sparse_step A: Any = num_experts A: Union[str, Any] = expert_capacity A: List[Any] = router_bias if router_dtype not in ["float32", "float16", "bfloat16"]: raise ValueError(f"""`router_dtype` must be one of 'float32', 'float16' or 'bfloat16', got {router_dtype}""" ) A: List[str] = router_dtype A: Union[str, Any] = router_ignore_padding_tokens A: Tuple = batch_prioritized_routing A: Optional[Any] = second_expert_policy A: Any = normalize_router_prob_before_dropping A: Tuple = moe_eval_capacity_token_fraction A: Tuple = moe_token_dropout A: Tuple = output_router_logits super().__init__( pad_token_id=SCREAMING_SNAKE_CASE_ , bos_token_id=SCREAMING_SNAKE_CASE_ , eos_token_id=SCREAMING_SNAKE_CASE_ , is_encoder_decoder=SCREAMING_SNAKE_CASE_ , decoder_start_token_id=SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ , )
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'''simple docstring''' def SCREAMING_SNAKE_CASE( __lowercase ) -> list[list[float]]: A: list[list[float]] = [] for data in source_data: for i, el in enumerate(__lowercase ): if len(__lowercase ) < i + 1: data_lists.append([] ) data_lists[i].append(float(__lowercase ) ) return data_lists def SCREAMING_SNAKE_CASE( __lowercase , __lowercase ) -> list[list[float]]: A: list[list[float]] = [] for dlist, weight in zip(__lowercase , __lowercase ): A: List[str] = min(__lowercase ) A: Union[str, Any] = max(__lowercase ) A: list[float] = [] # for weight 0 score is 1 - actual score if weight == 0: for item in dlist: try: score.append(1 - ((item - mind) / (maxd - mind)) ) except ZeroDivisionError: score.append(1 ) elif weight == 1: for item in dlist: try: score.append((item - mind) / (maxd - mind) ) except ZeroDivisionError: score.append(0 ) # weight not 0 or 1 else: A: List[str] = F"""Invalid weight of {weight:f} provided""" raise ValueError(__lowercase ) score_lists.append(__lowercase ) return score_lists def SCREAMING_SNAKE_CASE( __lowercase ) -> list[float]: A: list[float] = [0 for i in range(len(score_lists[0] ) )] for slist in score_lists: for j, ele in enumerate(__lowercase ): A: str = final_scores[j] + ele return final_scores def SCREAMING_SNAKE_CASE( __lowercase , __lowercase ) -> list[list[float]]: A: Any = get_data(__lowercase ) A: str = calculate_each_score(__lowercase , __lowercase ) A: int = generate_final_scores(__lowercase ) # append scores to source data for i, ele in enumerate(__lowercase ): source_data[i].append(__lowercase ) return source_data
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'''simple docstring''' import argparse import os import sys from unittest.mock import patch import pytorch_lightning as pl import timeout_decorator import torch from distillation import SummarizationDistiller, distill_main from finetune import SummarizationModule, main from transformers import MarianMTModel from transformers.file_utils import cached_path from transformers.testing_utils import TestCasePlus, require_torch_gpu, slow from utils import load_json UpperCamelCase = '''sshleifer/mar_enro_6_3_student''' class lowerCAmelCase_ ( UpperCAmelCase_ ): '''simple docstring''' def _snake_case ( self : Dict ) -> Dict: '''simple docstring''' super().setUp() A: str = cached_path( '''https://cdn-datasets.huggingface.co/translation/wmt_en_ro-tr40k-va0.5k-te0.5k.tar.gz''' , extract_compressed_file=SCREAMING_SNAKE_CASE_ , ) A: Union[str, Any] = f"""{data_cached}/wmt_en_ro-tr40k-va0.5k-te0.5k""" @slow @require_torch_gpu def _snake_case ( self : List[Any] ) -> Any: '''simple docstring''' MarianMTModel.from_pretrained(SCREAMING_SNAKE_CASE_ ) @slow @require_torch_gpu def _snake_case ( self : int ) -> Dict: '''simple docstring''' A: Dict = { '''$MAX_LEN''': 64, '''$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 A: Any = (self.test_file_dir / '''train_mbart_cc25_enro.sh''').open().read().split('''finetune.py''' )[1].strip() A: Any = bash_script.replace('''\\\n''' , '''''' ).strip().replace('''"$@"''' , '''''' ) for k, v in env_vars_to_replace.items(): A: Optional[Any] = bash_script.replace(SCREAMING_SNAKE_CASE_ , str(SCREAMING_SNAKE_CASE_ ) ) A: Any = self.get_auto_remove_tmp_dir() # bash_script = bash_script.replace("--fp16 ", "") A: str = 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 A: int = ['''finetune.py'''] + bash_script.split() + args with patch.object(SCREAMING_SNAKE_CASE_ , '''argv''' , SCREAMING_SNAKE_CASE_ ): A: Optional[Any] = argparse.ArgumentParser() A: Optional[int] = pl.Trainer.add_argparse_args(SCREAMING_SNAKE_CASE_ ) A: List[Any] = SummarizationModule.add_model_specific_args(SCREAMING_SNAKE_CASE_ , os.getcwd() ) A: int = parser.parse_args() A: List[str] = main(SCREAMING_SNAKE_CASE_ ) # Check metrics A: List[Any] = load_json(model.metrics_save_path ) A: Union[str, Any] = metrics['''val'''][0] A: List[str] = 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}"""] , SCREAMING_SNAKE_CASE_ ) self.assertGreater(last_step_stats['''val_avg_gen_time'''] , 0.01 ) # 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 A: Optional[Any] = os.listdir(SCREAMING_SNAKE_CASE_ ) A: Union[str, Any] = [x for x in contents if x.endswith('''.ckpt''' )][0] A: Any = os.path.join(args.output_dir , SCREAMING_SNAKE_CASE_ ) A: List[Any] = torch.load(SCREAMING_SNAKE_CASE_ , map_location='''cpu''' ) A: int = '''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: A: List[str] = {os.path.basename(SCREAMING_SNAKE_CASE_ ) 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 lowerCAmelCase_ ( UpperCAmelCase_ ): '''simple docstring''' @timeout_decorator.timeout(6_00 ) @slow @require_torch_gpu def _snake_case ( self : Dict ) -> str: '''simple docstring''' A: List[str] = f"""{self.test_file_dir_str}/test_data/wmt_en_ro""" A: str = { '''--fp16_opt_level=O1''': '''''', '''$MAX_LEN''': 1_28, '''$BS''': 16, '''$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 A: List[Any] = ( (self.test_file_dir / '''distil_marian_no_teacher.sh''').open().read().split('''distillation.py''' )[1].strip() ) A: Optional[int] = bash_script.replace('''\\\n''' , '''''' ).strip().replace('''"$@"''' , '''''' ) A: str = bash_script.replace('''--fp16 ''' , ''' ''' ) for k, v in env_vars_to_replace.items(): A: Tuple = bash_script.replace(SCREAMING_SNAKE_CASE_ , str(SCREAMING_SNAKE_CASE_ ) ) A: List[Any] = self.get_auto_remove_tmp_dir() A: List[Any] = bash_script.replace('''--fp16''' , '''''' ) A: int = 6 A: int = ( ['''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(SCREAMING_SNAKE_CASE_ , '''argv''' , SCREAMING_SNAKE_CASE_ ): A: List[Any] = argparse.ArgumentParser() A: List[Any] = pl.Trainer.add_argparse_args(SCREAMING_SNAKE_CASE_ ) A: List[str] = SummarizationDistiller.add_model_specific_args(SCREAMING_SNAKE_CASE_ , os.getcwd() ) A: List[str] = parser.parse_args() # assert args.gpus == gpus THIS BREAKS for multi_gpu A: Optional[Any] = distill_main(SCREAMING_SNAKE_CASE_ ) # Check metrics A: List[str] = load_json(model.metrics_save_path ) A: Any = metrics['''val'''][0] A: Tuple = 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"] >= 0.01 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}"""] , SCREAMING_SNAKE_CASE_ ) # check lightning ckpt can be loaded and has a reasonable statedict A: Any = os.listdir(SCREAMING_SNAKE_CASE_ ) A: Optional[int] = [x for x in contents if x.endswith('''.ckpt''' )][0] A: Optional[Any] = os.path.join(args.output_dir , SCREAMING_SNAKE_CASE_ ) A: Tuple = torch.load(SCREAMING_SNAKE_CASE_ , map_location='''cpu''' ) A: List[str] = '''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: A: Optional[Any] = {os.path.basename(SCREAMING_SNAKE_CASE_ ) for p in contents} assert "test_generations.txt" in contents assert "test_results.txt" in contents # assert len(metrics["val"]) == desired_n_evals assert len(metrics['''test'''] ) == 1
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'''simple docstring''' import collections from typing import List, Optional, Union from ...tokenization_utils_base import BatchEncoding from ...utils import TensorType, add_end_docstrings, add_start_docstrings, logging from ..bert.tokenization_bert_fast import BertTokenizerFast from .tokenization_dpr import DPRContextEncoderTokenizer, DPRQuestionEncoderTokenizer, DPRReaderTokenizer UpperCamelCase = logging.get_logger(__name__) UpperCamelCase = {'''vocab_file''': '''vocab.txt''', '''tokenizer_file''': '''tokenizer.json'''} UpperCamelCase = { '''vocab_file''': { '''facebook/dpr-ctx_encoder-single-nq-base''': ( '''https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/vocab.txt''' ), '''facebook/dpr-ctx_encoder-multiset-base''': ( '''https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/vocab.txt''' ), }, '''tokenizer_file''': { '''facebook/dpr-ctx_encoder-single-nq-base''': ( '''https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/tokenizer.json''' ), '''facebook/dpr-ctx_encoder-multiset-base''': ( '''https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/tokenizer.json''' ), }, } UpperCamelCase = { '''vocab_file''': { '''facebook/dpr-question_encoder-single-nq-base''': ( '''https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/vocab.txt''' ), '''facebook/dpr-question_encoder-multiset-base''': ( '''https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/vocab.txt''' ), }, '''tokenizer_file''': { '''facebook/dpr-question_encoder-single-nq-base''': ( '''https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/tokenizer.json''' ), '''facebook/dpr-question_encoder-multiset-base''': ( '''https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/tokenizer.json''' ), }, } UpperCamelCase = { '''vocab_file''': { '''facebook/dpr-reader-single-nq-base''': ( '''https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/vocab.txt''' ), '''facebook/dpr-reader-multiset-base''': ( '''https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/vocab.txt''' ), }, '''tokenizer_file''': { '''facebook/dpr-reader-single-nq-base''': ( '''https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/tokenizer.json''' ), '''facebook/dpr-reader-multiset-base''': ( '''https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/tokenizer.json''' ), }, } UpperCamelCase = { '''facebook/dpr-ctx_encoder-single-nq-base''': 512, '''facebook/dpr-ctx_encoder-multiset-base''': 512, } UpperCamelCase = { '''facebook/dpr-question_encoder-single-nq-base''': 512, '''facebook/dpr-question_encoder-multiset-base''': 512, } UpperCamelCase = { '''facebook/dpr-reader-single-nq-base''': 512, '''facebook/dpr-reader-multiset-base''': 512, } UpperCamelCase = { '''facebook/dpr-ctx_encoder-single-nq-base''': {'''do_lower_case''': True}, '''facebook/dpr-ctx_encoder-multiset-base''': {'''do_lower_case''': True}, } UpperCamelCase = { '''facebook/dpr-question_encoder-single-nq-base''': {'''do_lower_case''': True}, '''facebook/dpr-question_encoder-multiset-base''': {'''do_lower_case''': True}, } UpperCamelCase = { '''facebook/dpr-reader-single-nq-base''': {'''do_lower_case''': True}, '''facebook/dpr-reader-multiset-base''': {'''do_lower_case''': True}, } class lowerCAmelCase_ ( UpperCAmelCase_ ): '''simple docstring''' UpperCamelCase_ : Union[str, Any] = VOCAB_FILES_NAMES UpperCamelCase_ : Union[str, Any] = CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP UpperCamelCase_ : Union[str, Any] = CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES UpperCamelCase_ : Optional[Any] = CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION UpperCamelCase_ : Any = DPRContextEncoderTokenizer class lowerCAmelCase_ ( UpperCAmelCase_ ): '''simple docstring''' UpperCamelCase_ : Dict = VOCAB_FILES_NAMES UpperCamelCase_ : List[str] = QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP UpperCamelCase_ : List[Any] = QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES UpperCamelCase_ : Tuple = QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION UpperCamelCase_ : Optional[int] = DPRQuestionEncoderTokenizer UpperCamelCase = collections.namedtuple( '''DPRSpanPrediction''', ['''span_score''', '''relevance_score''', '''doc_id''', '''start_index''', '''end_index''', '''text'''] ) UpperCamelCase = collections.namedtuple('''DPRReaderOutput''', ['''start_logits''', '''end_logits''', '''relevance_logits''']) UpperCamelCase = R''' Return a dictionary with the token ids of the input strings and other information to give to `.decode_best_spans`. It converts the strings of a question and different passages (title and text) in a sequence of IDs (integers), using the tokenizer and vocabulary. The resulting `input_ids` is a matrix of size `(n_passages, sequence_length)` with the format: [CLS] <question token ids> [SEP] <titles ids> [SEP] <texts ids> Args: questions (`str` or `List[str]`): The questions to be encoded. You can specify one question for many passages. In this case, the question will be duplicated like `[questions] * n_passages`. Otherwise you have to specify as many questions as in `titles` or `texts`. titles (`str` or `List[str]`): The passages titles to be encoded. This can be a string or a list of strings if there are several passages. texts (`str` or `List[str]`): The passages texts to be encoded. This can be a string or a list of strings if there are several passages. padding (`bool`, `str` or [`~utils.PaddingStrategy`], *optional*, defaults to `False`): Activates and controls padding. Accepts the following values: - `True` or `\'longest\'`: Pad to the longest sequence in the batch (or no padding if only a single sequence if provided). - `\'max_length\'`: Pad to a maximum length specified with the argument `max_length` or to the maximum acceptable input length for the model if that argument is not provided. - `False` or `\'do_not_pad\'` (default): No padding (i.e., can output a batch with sequences of different lengths). truncation (`bool`, `str` or [`~tokenization_utils_base.TruncationStrategy`], *optional*, defaults to `False`): Activates and controls truncation. Accepts the following values: - `True` or `\'longest_first\'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum acceptable input length for the model if that argument is not provided. This will truncate token by token, removing a token from the longest sequence in the pair if a pair of sequences (or a batch of pairs) is provided. - `\'only_first\'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum acceptable input length for the model if that argument is not provided. This will only truncate the first sequence of a pair if a pair of sequences (or a batch of pairs) is provided. - `\'only_second\'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum acceptable input length for the model if that argument is not provided. This will only truncate the second sequence of a pair if a pair of sequences (or a batch of pairs) is provided. - `False` or `\'do_not_truncate\'` (default): No truncation (i.e., can output batch with sequence lengths greater than the model maximum admissible input size). max_length (`int`, *optional*): Controls the maximum length to use by one of the truncation/padding parameters. If left unset or set to `None`, this will use the predefined model maximum length if a maximum length is required by one of the truncation/padding parameters. If the model has no specific maximum input length (like XLNet) truncation/padding to a maximum length will be deactivated. return_tensors (`str` or [`~utils.TensorType`], *optional*): If set, will return tensors instead of list of python integers. Acceptable values are: - `\'tf\'`: Return TensorFlow `tf.constant` objects. - `\'pt\'`: Return PyTorch `torch.Tensor` objects. - `\'np\'`: Return Numpy `np.ndarray` objects. return_attention_mask (`bool`, *optional*): Whether or not to return the attention mask. If not set, will return the attention mask according to the specific tokenizer\'s default, defined by the `return_outputs` attribute. [What are attention masks?](../glossary#attention-mask) Return: `Dict[str, List[List[int]]]`: A dictionary with the following keys: - `input_ids`: List of token ids to be fed to a model. - `attention_mask`: List of indices specifying which tokens should be attended to by the model. ''' @add_start_docstrings(UpperCAmelCase_ ) class lowerCAmelCase_ : '''simple docstring''' def __call__( self : Dict , SCREAMING_SNAKE_CASE_ : Union[str, Any] , SCREAMING_SNAKE_CASE_ : Optional[str] = None , SCREAMING_SNAKE_CASE_ : Optional[str] = None , SCREAMING_SNAKE_CASE_ : Union[bool, str] = False , SCREAMING_SNAKE_CASE_ : Union[bool, str] = False , SCREAMING_SNAKE_CASE_ : Optional[int] = None , SCREAMING_SNAKE_CASE_ : Optional[Union[str, TensorType]] = None , SCREAMING_SNAKE_CASE_ : Optional[bool] = None , **SCREAMING_SNAKE_CASE_ : Dict , ) -> BatchEncoding: '''simple docstring''' if titles is None and texts is None: return super().__call__( SCREAMING_SNAKE_CASE_ , padding=SCREAMING_SNAKE_CASE_ , truncation=SCREAMING_SNAKE_CASE_ , max_length=SCREAMING_SNAKE_CASE_ , return_tensors=SCREAMING_SNAKE_CASE_ , return_attention_mask=SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ , ) elif titles is None or texts is None: A: Union[str, Any] = titles if texts is None else texts return super().__call__( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , padding=SCREAMING_SNAKE_CASE_ , truncation=SCREAMING_SNAKE_CASE_ , max_length=SCREAMING_SNAKE_CASE_ , return_tensors=SCREAMING_SNAKE_CASE_ , return_attention_mask=SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ , ) A: Union[str, Any] = titles if not isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) else [titles] A: Optional[Any] = texts if not isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) else [texts] A: str = len(SCREAMING_SNAKE_CASE_ ) A: List[Any] = questions if not isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) else [questions] * n_passages assert len(SCREAMING_SNAKE_CASE_ ) == len( SCREAMING_SNAKE_CASE_ ), f"""There should be as many titles than texts but got {len(SCREAMING_SNAKE_CASE_ )} titles and {len(SCREAMING_SNAKE_CASE_ )} texts.""" A: Union[str, Any] = super().__call__(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , padding=SCREAMING_SNAKE_CASE_ , truncation=SCREAMING_SNAKE_CASE_ )['''input_ids'''] A: Dict = super().__call__(SCREAMING_SNAKE_CASE_ , add_special_tokens=SCREAMING_SNAKE_CASE_ , padding=SCREAMING_SNAKE_CASE_ , truncation=SCREAMING_SNAKE_CASE_ )['''input_ids'''] A: str = { '''input_ids''': [ (encoded_question_and_title + encoded_text)[:max_length] if max_length is not None and truncation else encoded_question_and_title + encoded_text for encoded_question_and_title, encoded_text in zip(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) ] } if return_attention_mask is not False: A: Union[str, Any] = [] for input_ids in encoded_inputs["input_ids"]: attention_mask.append([int(input_id != self.pad_token_id ) for input_id in input_ids] ) A: Optional[Any] = attention_mask return self.pad(SCREAMING_SNAKE_CASE_ , padding=SCREAMING_SNAKE_CASE_ , max_length=SCREAMING_SNAKE_CASE_ , return_tensors=SCREAMING_SNAKE_CASE_ ) def _snake_case ( self : int , SCREAMING_SNAKE_CASE_ : BatchEncoding , SCREAMING_SNAKE_CASE_ : DPRReaderOutput , SCREAMING_SNAKE_CASE_ : int = 16 , SCREAMING_SNAKE_CASE_ : int = 64 , SCREAMING_SNAKE_CASE_ : int = 4 , ) -> List[DPRSpanPrediction]: '''simple docstring''' A: Any = reader_input['''input_ids'''] A , A , A: str = reader_output[:3] A: str = len(SCREAMING_SNAKE_CASE_ ) A: Union[str, Any] = sorted(range(SCREAMING_SNAKE_CASE_ ) , reverse=SCREAMING_SNAKE_CASE_ , key=relevance_logits.__getitem__ ) A: List[DPRReaderOutput] = [] for doc_id in sorted_docs: A: List[str] = list(input_ids[doc_id] ) # assuming question & title information is at the beginning of the sequence A: Dict = sequence_ids.index(self.sep_token_id , 2 ) + 1 # second sep id if sequence_ids[-1] == self.pad_token_id: A: Union[str, Any] = sequence_ids.index(self.pad_token_id ) else: A: int = len(SCREAMING_SNAKE_CASE_ ) A: Dict = self._get_best_spans( start_logits=start_logits[doc_id][passage_offset:sequence_len] , end_logits=end_logits[doc_id][passage_offset:sequence_len] , max_answer_length=SCREAMING_SNAKE_CASE_ , top_spans=SCREAMING_SNAKE_CASE_ , ) for start_index, end_index in best_spans: start_index += passage_offset end_index += passage_offset nbest_spans_predictions.append( DPRSpanPrediction( span_score=start_logits[doc_id][start_index] + end_logits[doc_id][end_index] , relevance_score=relevance_logits[doc_id] , doc_id=SCREAMING_SNAKE_CASE_ , start_index=SCREAMING_SNAKE_CASE_ , end_index=SCREAMING_SNAKE_CASE_ , text=self.decode(sequence_ids[start_index : end_index + 1] ) , ) ) if len(SCREAMING_SNAKE_CASE_ ) >= num_spans: break return nbest_spans_predictions[:num_spans] def _snake_case ( self : Union[str, Any] , SCREAMING_SNAKE_CASE_ : List[int] , SCREAMING_SNAKE_CASE_ : List[int] , SCREAMING_SNAKE_CASE_ : int , SCREAMING_SNAKE_CASE_ : int , ) -> List[DPRSpanPrediction]: '''simple docstring''' A: Union[str, Any] = [] for start_index, start_score in enumerate(SCREAMING_SNAKE_CASE_ ): for answer_length, end_score in enumerate(end_logits[start_index : start_index + max_answer_length] ): scores.append(((start_index, start_index + answer_length), start_score + end_score) ) A: Any = sorted(SCREAMING_SNAKE_CASE_ , key=lambda SCREAMING_SNAKE_CASE_ : x[1] , reverse=SCREAMING_SNAKE_CASE_ ) A: Dict = [] for (start_index, end_index), score in scores: assert start_index <= end_index, f"""Wrong span indices: [{start_index}:{end_index}]""" A: int = end_index - start_index + 1 assert length <= max_answer_length, f"""Span is too long: {length} > {max_answer_length}""" if any( start_index <= prev_start_index <= prev_end_index <= end_index or prev_start_index <= start_index <= end_index <= prev_end_index for (prev_start_index, prev_end_index) in chosen_span_intervals ): continue chosen_span_intervals.append((start_index, end_index) ) if len(SCREAMING_SNAKE_CASE_ ) == top_spans: break return chosen_span_intervals @add_end_docstrings(UpperCAmelCase_ ) class lowerCAmelCase_ ( UpperCAmelCase_ , UpperCAmelCase_ ): '''simple docstring''' UpperCamelCase_ : Tuple = VOCAB_FILES_NAMES UpperCamelCase_ : List[Any] = READER_PRETRAINED_VOCAB_FILES_MAP UpperCamelCase_ : Union[str, Any] = READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES UpperCamelCase_ : Dict = READER_PRETRAINED_INIT_CONFIGURATION UpperCamelCase_ : Any = ["""input_ids""", """attention_mask"""] UpperCamelCase_ : Optional[Any] = DPRReaderTokenizer
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'''simple docstring''' from typing import TYPE_CHECKING from ...utils import ( OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available, is_tokenizers_available, is_torch_available, ) UpperCamelCase = { '''configuration_llama''': ['''LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''LlamaConfig'''], } try: if not is_sentencepiece_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: UpperCamelCase = ['''LlamaTokenizer'''] try: if not is_tokenizers_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: UpperCamelCase = ['''LlamaTokenizerFast'''] try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: UpperCamelCase = [ '''LlamaForCausalLM''', '''LlamaModel''', '''LlamaPreTrainedModel''', '''LlamaForSequenceClassification''', ] if TYPE_CHECKING: from .configuration_llama import LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP, LlamaConfig try: if not is_sentencepiece_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .tokenization_llama import LlamaTokenizer try: if not is_tokenizers_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .tokenization_llama_fast import LlamaTokenizerFast try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_llama import LlamaForCausalLM, LlamaForSequenceClassification, LlamaModel, LlamaPreTrainedModel else: import sys UpperCamelCase = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
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'''simple docstring''' from typing import TYPE_CHECKING from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available UpperCamelCase = {} try: if not is_sentencepiece_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: UpperCamelCase = ['''GPTSw3Tokenizer'''] if TYPE_CHECKING: try: if not is_sentencepiece_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .tokenization_gpt_swa import GPTSwaTokenizer else: import sys UpperCamelCase = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
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'''simple docstring''' import requests UpperCamelCase = '''YOUR API KEY''' def SCREAMING_SNAKE_CASE( __lowercase , __lowercase = giphy_api_key ) -> list: A: int = '''+'''.join(query.split() ) A: Union[str, Any] = F"""https://api.giphy.com/v1/gifs/search?q={formatted_query}&api_key={api_key}""" A: str = requests.get(__lowercase ).json()['''data'''] return [gif["url"] for gif in gifs] if __name__ == "__main__": print('''\n'''.join(get_gifs('''space ship''')))
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'''simple docstring''' from __future__ import annotations from typing import Any class lowerCAmelCase_ ( UpperCAmelCase_ ): '''simple docstring''' pass class lowerCAmelCase_ : '''simple docstring''' def __init__( self : List[Any] , SCREAMING_SNAKE_CASE_ : Any ) -> None: '''simple docstring''' A: Any = data A: Node | None = None def __iter__( self : Optional[int] ) -> List[str]: '''simple docstring''' A: List[str] = self A: Dict = [] while node: if node in visited: raise ContainsLoopError visited.append(SCREAMING_SNAKE_CASE_ ) yield node.data A: str = node.next_node @property def _snake_case ( self : List[str] ) -> bool: '''simple docstring''' try: list(self ) return False except ContainsLoopError: return True if __name__ == "__main__": UpperCamelCase = Node(1) UpperCamelCase = Node(2) UpperCamelCase = Node(3) UpperCamelCase = Node(4) print(root_node.has_loop) # False UpperCamelCase = root_node.next_node print(root_node.has_loop) # True UpperCamelCase = Node(5) UpperCamelCase = Node(6) UpperCamelCase = Node(5) UpperCamelCase = Node(6) print(root_node.has_loop) # False UpperCamelCase = Node(1) print(root_node.has_loop) # False
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'''simple docstring''' import unittest import numpy as np import torch from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer from diffusers import ( AutoencoderKL, DDIMScheduler, DPMSolverMultistepScheduler, TextToVideoSDPipeline, UNetaDConditionModel, ) from diffusers.utils import is_xformers_available, load_numpy, skip_mps, slow, torch_device from diffusers.utils.testing_utils import enable_full_determinism from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS from ..test_pipelines_common import PipelineTesterMixin enable_full_determinism() @skip_mps class lowerCAmelCase_ ( UpperCAmelCase_ , unittest.TestCase ): '''simple docstring''' UpperCamelCase_ : List[str] = TextToVideoSDPipeline UpperCamelCase_ : str = TEXT_TO_IMAGE_PARAMS UpperCamelCase_ : str = TEXT_TO_IMAGE_BATCH_PARAMS # No `output_type`. UpperCamelCase_ : Any = frozenset( [ """num_inference_steps""", """generator""", """latents""", """return_dict""", """callback""", """callback_steps""", ] ) def _snake_case ( self : Any ) -> Tuple: '''simple docstring''' torch.manual_seed(0 ) A: List[Any] = UNetaDConditionModel( block_out_channels=(32, 64, 64, 64) , layers_per_block=2 , sample_size=32 , in_channels=4 , out_channels=4 , down_block_types=('''CrossAttnDownBlock3D''', '''CrossAttnDownBlock3D''', '''CrossAttnDownBlock3D''', '''DownBlock3D''') , up_block_types=('''UpBlock3D''', '''CrossAttnUpBlock3D''', '''CrossAttnUpBlock3D''', '''CrossAttnUpBlock3D''') , cross_attention_dim=32 , attention_head_dim=4 , ) A: Optional[Any] = DDIMScheduler( beta_start=0.0_0085 , beta_end=0.012 , beta_schedule='''scaled_linear''' , clip_sample=SCREAMING_SNAKE_CASE_ , set_alpha_to_one=SCREAMING_SNAKE_CASE_ , ) torch.manual_seed(0 ) A: List[str] = 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 , sample_size=1_28 , ) torch.manual_seed(0 ) A: Optional[Any] = CLIPTextConfig( bos_token_id=0 , eos_token_id=2 , hidden_size=32 , intermediate_size=37 , layer_norm_eps=1E-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=10_00 , hidden_act='''gelu''' , projection_dim=5_12 , ) A: Dict = CLIPTextModel(SCREAMING_SNAKE_CASE_ ) A: List[str] = CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''' ) A: Optional[int] = { '''unet''': unet, '''scheduler''': scheduler, '''vae''': vae, '''text_encoder''': text_encoder, '''tokenizer''': tokenizer, } return components def _snake_case ( self : Tuple , SCREAMING_SNAKE_CASE_ : List[Any] , SCREAMING_SNAKE_CASE_ : int=0 ) -> Any: '''simple docstring''' if str(SCREAMING_SNAKE_CASE_ ).startswith('''mps''' ): A: Union[str, Any] = torch.manual_seed(SCREAMING_SNAKE_CASE_ ) else: A: Optional[int] = torch.Generator(device=SCREAMING_SNAKE_CASE_ ).manual_seed(SCREAMING_SNAKE_CASE_ ) A: Dict = { '''prompt''': '''A painting of a squirrel eating a burger''', '''generator''': generator, '''num_inference_steps''': 2, '''guidance_scale''': 6.0, '''output_type''': '''pt''', } return inputs def _snake_case ( self : Dict ) -> Tuple: '''simple docstring''' A: Dict = '''cpu''' # ensure determinism for the device-dependent torch.Generator A: Tuple = self.get_dummy_components() A: List[Any] = TextToVideoSDPipeline(**SCREAMING_SNAKE_CASE_ ) A: Optional[Any] = sd_pipe.to(SCREAMING_SNAKE_CASE_ ) sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_ ) A: Union[str, Any] = self.get_dummy_inputs(SCREAMING_SNAKE_CASE_ ) A: str = '''np''' A: Dict = sd_pipe(**SCREAMING_SNAKE_CASE_ ).frames A: int = frames[0][-3:, -3:, -1] assert frames[0].shape == (64, 64, 3) A: List[Any] = np.array([158.0, 160.0, 153.0, 125.0, 100.0, 121.0, 111.0, 93.0, 113.0] ) assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2 def _snake_case ( self : List[str] ) -> List[Any]: '''simple docstring''' self._test_attention_slicing_forward_pass(test_mean_pixel_difference=SCREAMING_SNAKE_CASE_ , expected_max_diff=3E-3 ) @unittest.skipIf( torch_device != '''cuda''' or not is_xformers_available() , reason='''XFormers attention is only available with CUDA and `xformers` installed''' , ) def _snake_case ( self : List[Any] ) -> Union[str, Any]: '''simple docstring''' self._test_xformers_attention_forwardGenerator_pass(test_mean_pixel_difference=SCREAMING_SNAKE_CASE_ , expected_max_diff=1E-2 ) @unittest.skip(reason='''Batching needs to be properly figured out first for this pipeline.''' ) def _snake_case ( self : Dict ) -> List[Any]: '''simple docstring''' pass @unittest.skip(reason='''Batching needs to be properly figured out first for this pipeline.''' ) def _snake_case ( self : Any ) -> int: '''simple docstring''' pass @unittest.skip(reason='''`num_images_per_prompt` argument is not supported for this pipeline.''' ) def _snake_case ( self : List[str] ) -> int: '''simple docstring''' pass def _snake_case ( self : Any ) -> str: '''simple docstring''' return super().test_progress_bar() @slow @skip_mps class lowerCAmelCase_ ( unittest.TestCase ): '''simple docstring''' def _snake_case ( self : Any ) -> Optional[Any]: '''simple docstring''' A: Tuple = load_numpy( '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/text_to_video/video.npy''' ) A: Dict = TextToVideoSDPipeline.from_pretrained('''damo-vilab/text-to-video-ms-1.7b''' ) A: Tuple = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config ) A: Dict = pipe.to('''cuda''' ) A: Union[str, Any] = '''Spiderman is surfing''' A: str = torch.Generator(device='''cpu''' ).manual_seed(0 ) A: Any = pipe(SCREAMING_SNAKE_CASE_ , generator=SCREAMING_SNAKE_CASE_ , num_inference_steps=25 , output_type='''pt''' ).frames A: Any = video_frames.cpu().numpy() assert np.abs(expected_video - video ).mean() < 5E-2 def _snake_case ( self : Tuple ) -> List[str]: '''simple docstring''' A: Any = load_numpy( '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/text_to_video/video_2step.npy''' ) A: Optional[int] = TextToVideoSDPipeline.from_pretrained('''damo-vilab/text-to-video-ms-1.7b''' ) A: Union[str, Any] = pipe.to('''cuda''' ) A: Optional[int] = '''Spiderman is surfing''' A: List[str] = torch.Generator(device='''cpu''' ).manual_seed(0 ) A: str = pipe(SCREAMING_SNAKE_CASE_ , generator=SCREAMING_SNAKE_CASE_ , num_inference_steps=2 , output_type='''pt''' ).frames A: Dict = video_frames.cpu().numpy() assert np.abs(expected_video - video ).mean() < 5E-2
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'''simple docstring''' from __future__ import annotations def SCREAMING_SNAKE_CASE( __lowercase = 4 ) -> list[list[int]]: A: Tuple = abs(__lowercase ) or 4 return [[1 + x + y * row_size for x in range(__lowercase )] for y in range(__lowercase )] def SCREAMING_SNAKE_CASE( __lowercase ) -> list[list[int]]: return reverse_row(transpose(__lowercase ) ) # OR.. transpose(reverse_column(matrix)) def SCREAMING_SNAKE_CASE( __lowercase ) -> list[list[int]]: return reverse_row(reverse_column(__lowercase ) ) # OR.. reverse_column(reverse_row(matrix)) def SCREAMING_SNAKE_CASE( __lowercase ) -> list[list[int]]: return reverse_column(transpose(__lowercase ) ) # OR.. transpose(reverse_row(matrix)) def SCREAMING_SNAKE_CASE( __lowercase ) -> list[list[int]]: A: Union[str, Any] = [list(__lowercase ) for x in zip(*__lowercase )] return matrix def SCREAMING_SNAKE_CASE( __lowercase ) -> list[list[int]]: A: Optional[int] = matrix[::-1] return matrix def SCREAMING_SNAKE_CASE( __lowercase ) -> list[list[int]]: A: Optional[Any] = [x[::-1] for x in matrix] return matrix def SCREAMING_SNAKE_CASE( __lowercase ) -> None: for i in matrix: print(*__lowercase ) if __name__ == "__main__": UpperCamelCase = make_matrix() print('''\norigin:\n''') print_matrix(matrix) print('''\nrotate 90 counterclockwise:\n''') print_matrix(rotate_aa(matrix)) UpperCamelCase = make_matrix() print('''\norigin:\n''') print_matrix(matrix) print('''\nrotate 180:\n''') print_matrix(rotate_aaa(matrix)) UpperCamelCase = make_matrix() print('''\norigin:\n''') print_matrix(matrix) print('''\nrotate 270 counterclockwise:\n''') print_matrix(rotate_aaa(matrix))
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'''simple docstring''' from typing import List, Optional, Union import numpy as np from ....audio_utils import mel_filter_bank, optimal_fft_length, spectrogram, window_function from ....feature_extraction_sequence_utils import SequenceFeatureExtractor from ....feature_extraction_utils import BatchFeature from ....file_utils import PaddingStrategy, TensorType from ....utils import logging UpperCamelCase = logging.get_logger(__name__) class lowerCAmelCase_ ( UpperCAmelCase_ ): '''simple docstring''' UpperCamelCase_ : Union[str, Any] = ["""input_features""", """attention_mask"""] def __init__( self : str , SCREAMING_SNAKE_CASE_ : Optional[int]=80 , SCREAMING_SNAKE_CASE_ : List[Any]=1_60_00 , SCREAMING_SNAKE_CASE_ : List[str]=0.0 , SCREAMING_SNAKE_CASE_ : Optional[Any]=10 , SCREAMING_SNAKE_CASE_ : Any=25 , SCREAMING_SNAKE_CASE_ : Dict="hamming_window" , SCREAMING_SNAKE_CASE_ : List[str]=3_2768.0 , SCREAMING_SNAKE_CASE_ : Optional[Any]=0.97 , SCREAMING_SNAKE_CASE_ : List[str]=1.0 , SCREAMING_SNAKE_CASE_ : int=True , SCREAMING_SNAKE_CASE_ : Optional[int]=True , SCREAMING_SNAKE_CASE_ : Optional[Any]=False , **SCREAMING_SNAKE_CASE_ : Any , ) -> List[Any]: '''simple docstring''' super().__init__(feature_size=SCREAMING_SNAKE_CASE_ , sampling_rate=SCREAMING_SNAKE_CASE_ , padding_value=SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ ) A: Optional[int] = feature_size A: List[str] = sampling_rate A: Tuple = padding_value A: int = hop_length A: List[str] = win_length A: List[Any] = frame_signal_scale A: Optional[Any] = preemphasis_coeff A: Union[str, Any] = mel_floor A: int = normalize_means A: str = normalize_vars A: int = win_function A: Union[str, Any] = return_attention_mask A: int = win_length * sampling_rate // 10_00 A: Union[str, Any] = hop_length * sampling_rate // 10_00 A: Optional[int] = optimal_fft_length(self.sample_size ) A: Union[str, Any] = (self.n_fft // 2) + 1 def _snake_case ( self : Dict , SCREAMING_SNAKE_CASE_ : np.array ) -> np.ndarray: '''simple docstring''' if self.win_function == "hamming_window": A: Dict = window_function(window_length=self.sample_size , name=self.win_function , periodic=SCREAMING_SNAKE_CASE_ ) else: A: Union[str, Any] = window_function(window_length=self.sample_size , name=self.win_function ) A: List[Any] = mel_filter_bank( num_frequency_bins=self.n_freqs , num_mel_filters=self.feature_size , min_frequency=0.0 , max_frequency=self.sampling_rate / 2.0 , sampling_rate=self.sampling_rate , ) A: str = spectrogram( one_waveform * self.frame_signal_scale , window=SCREAMING_SNAKE_CASE_ , frame_length=self.sample_size , hop_length=self.sample_stride , fft_length=self.n_fft , center=SCREAMING_SNAKE_CASE_ , preemphasis=self.preemphasis_coeff , mel_filters=SCREAMING_SNAKE_CASE_ , mel_floor=self.mel_floor , log_mel='''log''' , ) return msfc_features.T def _snake_case ( self : List[Any] , SCREAMING_SNAKE_CASE_ : Any , SCREAMING_SNAKE_CASE_ : Any , SCREAMING_SNAKE_CASE_ : Union[str, Any] ) -> int: '''simple docstring''' if self.normalize_means: A: Dict = x[:input_length].mean(axis=0 ) A: Tuple = np.subtract(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) if self.normalize_vars: A: List[Any] = x[:input_length].std(axis=0 ) A: List[Any] = np.divide(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) if input_length < x.shape[0]: A: int = padding_value # make sure array is in float32 A: str = x.astype(np.floataa ) return x def _snake_case ( self : List[str] , SCREAMING_SNAKE_CASE_ : List[np.ndarray] , SCREAMING_SNAKE_CASE_ : Optional[np.ndarray] = None ) -> List[np.ndarray]: '''simple docstring''' A: Any = attention_mask.sum(-1 ) if attention_mask is not None else [x.shape[0] for x in input_features] return [self._normalize_one(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , self.padding_value ) for x, n in zip(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )] def __call__( self : List[Any] , SCREAMING_SNAKE_CASE_ : Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]] , SCREAMING_SNAKE_CASE_ : Union[bool, str, PaddingStrategy] = False , SCREAMING_SNAKE_CASE_ : Optional[int] = None , SCREAMING_SNAKE_CASE_ : bool = False , SCREAMING_SNAKE_CASE_ : Optional[int] = None , SCREAMING_SNAKE_CASE_ : Optional[bool] = None , SCREAMING_SNAKE_CASE_ : Optional[Union[str, TensorType]] = None , SCREAMING_SNAKE_CASE_ : Optional[int] = None , **SCREAMING_SNAKE_CASE_ : str , ) -> 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 `raw_speech` 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.''' ) A: str = isinstance(SCREAMING_SNAKE_CASE_ , np.ndarray ) and len(raw_speech.shape ) > 1 if is_batched_numpy and len(raw_speech.shape ) > 2: raise ValueError(f"""Only mono-channel audio is supported for input to {self}""" ) A: Any = is_batched_numpy or ( isinstance(SCREAMING_SNAKE_CASE_ , (list, tuple) ) and (isinstance(raw_speech[0] , (np.ndarray, tuple, list) )) ) if is_batched: A: Union[str, Any] = [np.asarray(SCREAMING_SNAKE_CASE_ , dtype=np.floataa ) for speech in raw_speech] elif not is_batched and not isinstance(SCREAMING_SNAKE_CASE_ , np.ndarray ): A: Optional[int] = np.asarray(SCREAMING_SNAKE_CASE_ , dtype=np.floataa ) elif isinstance(SCREAMING_SNAKE_CASE_ , np.ndarray ) and raw_speech.dtype is np.dtype(np.floataa ): A: Union[str, Any] = raw_speech.astype(np.floataa ) # always return batch if not is_batched: A: Optional[int] = [raw_speech] # extract fbank features A: Union[str, Any] = [self._extract_mfsc_features(SCREAMING_SNAKE_CASE_ ) for one_waveform in raw_speech] # convert into correct format for padding A: List[str] = BatchFeature({'''input_features''': features} ) A: List[Any] = self.pad( SCREAMING_SNAKE_CASE_ , padding=SCREAMING_SNAKE_CASE_ , max_length=SCREAMING_SNAKE_CASE_ , truncation=SCREAMING_SNAKE_CASE_ , pad_to_multiple_of=SCREAMING_SNAKE_CASE_ , return_attention_mask=SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ , ) # make sure list is in array format A: List[Any] = padded_inputs.get('''input_features''' ) if isinstance(input_features[0] , SCREAMING_SNAKE_CASE_ ): A: Any = [np.asarray(SCREAMING_SNAKE_CASE_ , dtype=np.floataa ) for feature in input_features] A: List[str] = padded_inputs.get('''attention_mask''' ) if attention_mask is not None: A: Optional[int] = [np.asarray(SCREAMING_SNAKE_CASE_ , dtype=np.intaa ) for array in attention_mask] if self.normalize_means or self.normalize_vars: A: Any = ( np.array(SCREAMING_SNAKE_CASE_ , dtype=np.intaa ) if self._get_padding_strategies(SCREAMING_SNAKE_CASE_ , max_length=SCREAMING_SNAKE_CASE_ ) is not PaddingStrategy.DO_NOT_PAD and padding else None ) A: str = self.normalize( padded_inputs['''input_features'''] , attention_mask=SCREAMING_SNAKE_CASE_ ) if return_tensors is not None: A: str = padded_inputs.convert_to_tensors(SCREAMING_SNAKE_CASE_ ) return padded_inputs
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'''simple docstring''' from __future__ import annotations import numpy as np def SCREAMING_SNAKE_CASE( __lowercase ) -> Dict: return np.maximum(0 , __lowercase ) if __name__ == "__main__": print(np.array(relu([-1, 0, 5]))) # --> [0, 0, 5]
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'''simple docstring''' from typing import TYPE_CHECKING from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available UpperCamelCase = {} try: if not is_sentencepiece_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: UpperCamelCase = ['''GPTSw3Tokenizer'''] if TYPE_CHECKING: try: if not is_sentencepiece_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .tokenization_gpt_swa import GPTSwaTokenizer else: import sys UpperCamelCase = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
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'''simple docstring''' from typing import TYPE_CHECKING from ...utils import ( OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available, is_speech_available, is_tf_available, is_torch_available, ) UpperCamelCase = { '''configuration_speech_to_text''': ['''SPEECH_TO_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''Speech2TextConfig'''], '''processing_speech_to_text''': ['''Speech2TextProcessor'''], } try: if not is_sentencepiece_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: UpperCamelCase = ['''Speech2TextTokenizer'''] try: if not is_speech_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: UpperCamelCase = ['''Speech2TextFeatureExtractor'''] try: if not is_tf_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: UpperCamelCase = [ '''TF_SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST''', '''TFSpeech2TextForConditionalGeneration''', '''TFSpeech2TextModel''', '''TFSpeech2TextPreTrainedModel''', ] try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: UpperCamelCase = [ '''SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST''', '''Speech2TextForConditionalGeneration''', '''Speech2TextModel''', '''Speech2TextPreTrainedModel''', ] if TYPE_CHECKING: from .configuration_speech_to_text import SPEECH_TO_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP, SpeechaTextConfig from .processing_speech_to_text import SpeechaTextProcessor try: if not is_sentencepiece_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .tokenization_speech_to_text import SpeechaTextTokenizer try: if not is_speech_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .feature_extraction_speech_to_text import SpeechaTextFeatureExtractor try: if not is_tf_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_tf_speech_to_text import ( TF_SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST, TFSpeechaTextForConditionalGeneration, TFSpeechaTextModel, TFSpeechaTextPreTrainedModel, ) try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_speech_to_text import ( SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST, SpeechaTextForConditionalGeneration, SpeechaTextModel, SpeechaTextPreTrainedModel, ) else: import sys UpperCamelCase = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
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'''simple docstring''' import os from shutil import copyfile from typing import List, Optional, Tuple from tokenizers import processors from ...tokenization_utils import AddedToken, BatchEncoding from ...tokenization_utils_fast import PreTrainedTokenizerFast from ...utils import is_sentencepiece_available, logging if is_sentencepiece_available(): from .tokenization_mbart import MBartTokenizer else: UpperCamelCase = None UpperCamelCase = logging.get_logger(__name__) UpperCamelCase = {'''vocab_file''': '''sentencepiece.bpe.model''', '''tokenizer_file''': '''tokenizer.json'''} UpperCamelCase = { '''vocab_file''': { '''facebook/mbart-large-en-ro''': ( '''https://huggingface.co/facebook/mbart-large-en-ro/resolve/main/sentencepiece.bpe.model''' ), '''facebook/mbart-large-cc25''': ( '''https://huggingface.co/facebook/mbart-large-cc25/resolve/main/sentencepiece.bpe.model''' ), }, '''tokenizer_file''': { '''facebook/mbart-large-en-ro''': '''https://huggingface.co/facebook/mbart-large-en-ro/resolve/main/tokenizer.json''', '''facebook/mbart-large-cc25''': '''https://huggingface.co/facebook/mbart-large-cc25/resolve/main/tokenizer.json''', }, } UpperCamelCase = { '''facebook/mbart-large-en-ro''': 1024, '''facebook/mbart-large-cc25''': 1024, } # fmt: off UpperCamelCase = ['''ar_AR''', '''cs_CZ''', '''de_DE''', '''en_XX''', '''es_XX''', '''et_EE''', '''fi_FI''', '''fr_XX''', '''gu_IN''', '''hi_IN''', '''it_IT''', '''ja_XX''', '''kk_KZ''', '''ko_KR''', '''lt_LT''', '''lv_LV''', '''my_MM''', '''ne_NP''', '''nl_XX''', '''ro_RO''', '''ru_RU''', '''si_LK''', '''tr_TR''', '''vi_VN''', '''zh_CN'''] class lowerCAmelCase_ ( UpperCAmelCase_ ): '''simple docstring''' UpperCamelCase_ : int = VOCAB_FILES_NAMES UpperCamelCase_ : Any = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES UpperCamelCase_ : Optional[int] = PRETRAINED_VOCAB_FILES_MAP UpperCamelCase_ : Any = ["""input_ids""", """attention_mask"""] UpperCamelCase_ : List[Any] = MBartTokenizer UpperCamelCase_ : List[int] = [] UpperCamelCase_ : List[int] = [] def __init__( self : List[Any] , SCREAMING_SNAKE_CASE_ : int=None , SCREAMING_SNAKE_CASE_ : str=None , SCREAMING_SNAKE_CASE_ : str="<s>" , SCREAMING_SNAKE_CASE_ : int="</s>" , SCREAMING_SNAKE_CASE_ : Optional[Any]="</s>" , SCREAMING_SNAKE_CASE_ : Any="<s>" , SCREAMING_SNAKE_CASE_ : List[str]="<unk>" , SCREAMING_SNAKE_CASE_ : Optional[Any]="<pad>" , SCREAMING_SNAKE_CASE_ : Any="<mask>" , SCREAMING_SNAKE_CASE_ : Tuple=None , SCREAMING_SNAKE_CASE_ : str=None , SCREAMING_SNAKE_CASE_ : Dict=None , **SCREAMING_SNAKE_CASE_ : str , ) -> List[str]: '''simple docstring''' A: List[Any] = AddedToken(SCREAMING_SNAKE_CASE_ , lstrip=SCREAMING_SNAKE_CASE_ , rstrip=SCREAMING_SNAKE_CASE_ ) if isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) else mask_token super().__init__( vocab_file=SCREAMING_SNAKE_CASE_ , tokenizer_file=SCREAMING_SNAKE_CASE_ , bos_token=SCREAMING_SNAKE_CASE_ , eos_token=SCREAMING_SNAKE_CASE_ , sep_token=SCREAMING_SNAKE_CASE_ , cls_token=SCREAMING_SNAKE_CASE_ , unk_token=SCREAMING_SNAKE_CASE_ , pad_token=SCREAMING_SNAKE_CASE_ , mask_token=SCREAMING_SNAKE_CASE_ , src_lang=SCREAMING_SNAKE_CASE_ , tgt_lang=SCREAMING_SNAKE_CASE_ , additional_special_tokens=SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ , ) A: List[Any] = vocab_file A: int = False if not self.vocab_file else True A: Optional[Any] = FAIRSEQ_LANGUAGE_CODES.copy() if additional_special_tokens is not None: # Only add those special tokens if they are not already there. _additional_special_tokens.extend( [t for t in additional_special_tokens if t not in _additional_special_tokens] ) self.add_special_tokens({'''additional_special_tokens''': _additional_special_tokens} ) A: int = { lang_code: self.convert_tokens_to_ids(SCREAMING_SNAKE_CASE_ ) for lang_code in FAIRSEQ_LANGUAGE_CODES } A: Any = src_lang if src_lang is not None else '''en_XX''' A: List[str] = self.convert_tokens_to_ids(self._src_lang ) A: List[Any] = tgt_lang self.set_src_lang_special_tokens(self._src_lang ) @property def _snake_case ( self : Dict ) -> str: '''simple docstring''' return self._src_lang @src_lang.setter def _snake_case ( self : Union[str, Any] , SCREAMING_SNAKE_CASE_ : str ) -> None: '''simple docstring''' A: List[str] = new_src_lang self.set_src_lang_special_tokens(self._src_lang ) def _snake_case ( self : List[Any] , SCREAMING_SNAKE_CASE_ : List[int] , SCREAMING_SNAKE_CASE_ : Optional[List[int]] = 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 _snake_case ( self : Any , SCREAMING_SNAKE_CASE_ : List[int] , SCREAMING_SNAKE_CASE_ : Optional[List[int]] = None ) -> List[int]: '''simple docstring''' A: Tuple = [self.sep_token_id] A: Dict = [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] def _snake_case ( self : str , SCREAMING_SNAKE_CASE_ : List[str] , SCREAMING_SNAKE_CASE_ : str , SCREAMING_SNAKE_CASE_ : Optional[str] , SCREAMING_SNAKE_CASE_ : Optional[str] , **SCREAMING_SNAKE_CASE_ : List[str] ) -> Tuple: '''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: int = src_lang A: int = self(SCREAMING_SNAKE_CASE_ , add_special_tokens=SCREAMING_SNAKE_CASE_ , return_tensors=SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ ) A: Union[str, Any] = self.convert_tokens_to_ids(SCREAMING_SNAKE_CASE_ ) A: int = tgt_lang_id return inputs def _snake_case ( self : List[Any] , SCREAMING_SNAKE_CASE_ : List[str] , SCREAMING_SNAKE_CASE_ : str = "en_XX" , SCREAMING_SNAKE_CASE_ : Optional[List[str]] = None , SCREAMING_SNAKE_CASE_ : str = "ro_RO" , **SCREAMING_SNAKE_CASE_ : List[Any] , ) -> BatchEncoding: '''simple docstring''' A: Any = src_lang A: str = tgt_lang return super().prepare_seqaseq_batch(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ ) def _snake_case ( self : int ) -> Tuple: '''simple docstring''' return self.set_src_lang_special_tokens(self.src_lang ) def _snake_case ( self : Optional[int] ) -> Tuple: '''simple docstring''' return self.set_tgt_lang_special_tokens(self.tgt_lang ) def _snake_case ( self : str , SCREAMING_SNAKE_CASE_ : List[Any] ) -> None: '''simple docstring''' A: Tuple = self.convert_tokens_to_ids(SCREAMING_SNAKE_CASE_ ) A: str = [] A: Optional[int] = [self.eos_token_id, self.cur_lang_code] A: str = self.convert_ids_to_tokens(self.prefix_tokens ) A: str = self.convert_ids_to_tokens(self.suffix_tokens ) A: Any = processors.TemplateProcessing( single=prefix_tokens_str + ['''$A'''] + suffix_tokens_str , pair=prefix_tokens_str + ['''$A''', '''$B'''] + suffix_tokens_str , special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str , self.prefix_tokens + self.suffix_tokens ) ) , ) def _snake_case ( self : str , SCREAMING_SNAKE_CASE_ : str ) -> None: '''simple docstring''' A: Union[str, Any] = self.convert_tokens_to_ids(SCREAMING_SNAKE_CASE_ ) A: List[str] = [] A: Optional[Any] = [self.eos_token_id, self.cur_lang_code] A: List[str] = self.convert_ids_to_tokens(self.prefix_tokens ) A: str = self.convert_ids_to_tokens(self.suffix_tokens ) A: List[str] = processors.TemplateProcessing( single=prefix_tokens_str + ['''$A'''] + suffix_tokens_str , pair=prefix_tokens_str + ['''$A''', '''$B'''] + suffix_tokens_str , special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str , self.prefix_tokens + self.suffix_tokens ) ) , ) def _snake_case ( self : Any , SCREAMING_SNAKE_CASE_ : str , SCREAMING_SNAKE_CASE_ : Optional[str] = None ) -> Tuple[str]: '''simple docstring''' if not self.can_save_slow_tokenizer: raise ValueError( '''Your fast tokenizer does not have the necessary information to save the vocabulary for a slow ''' '''tokenizer.''' ) if not os.path.isdir(SCREAMING_SNAKE_CASE_ ): logger.error(f"""Vocabulary path ({save_directory}) should be a directory.""" ) return A: List[str] = os.path.join( SCREAMING_SNAKE_CASE_ , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] ) if os.path.abspath(self.vocab_file ) != os.path.abspath(SCREAMING_SNAKE_CASE_ ): copyfile(self.vocab_file , SCREAMING_SNAKE_CASE_ ) return (out_vocab_file,)
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'''simple docstring''' from ..utils import is_flax_available, is_torch_available if is_torch_available(): from .autoencoder_kl import AutoencoderKL from .controlnet import ControlNetModel from .dual_transformer_ad import DualTransformeraDModel from .modeling_utils import ModelMixin from .prior_transformer import PriorTransformer from .ta_film_transformer import TaFilmDecoder from .transformer_ad import TransformeraDModel from .unet_ad import UNetaDModel from .unet_ad import UNetaDModel from .unet_ad_condition import UNetaDConditionModel from .unet_ad_condition import UNetaDConditionModel from .vq_model import VQModel if is_flax_available(): from .controlnet_flax import FlaxControlNetModel from .unet_ad_condition_flax import FlaxUNetaDConditionModel from .vae_flax import FlaxAutoencoderKL
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'''simple docstring''' import random def SCREAMING_SNAKE_CASE( __lowercase , __lowercase , __lowercase ) -> Union[str, Any]: A: Union[str, Any] = a[left_index] A: List[Any] = left_index + 1 for j in range(left_index + 1 , __lowercase ): if a[j] < pivot: A , A: Tuple = a[i], a[j] i += 1 A , A: Any = a[i - 1], a[left_index] return i - 1 def SCREAMING_SNAKE_CASE( __lowercase , __lowercase , __lowercase ) -> Optional[int]: if left < right: A: List[Any] = random.randint(__lowercase , right - 1 ) A , A: Union[str, Any] = ( a[left], a[pivot], ) # switches the pivot with the left most bound A: List[Any] = partition(__lowercase , __lowercase , __lowercase ) quick_sort_random( __lowercase , __lowercase , __lowercase ) # recursive quicksort to the left of the pivot point quick_sort_random( __lowercase , pivot_index + 1 , __lowercase ) # recursive quicksort to the right of the pivot point def SCREAMING_SNAKE_CASE( ) -> Optional[int]: A: str = input('''Enter numbers separated by a comma:\n''' ).strip() A: Dict = [int(__lowercase ) for item in user_input.split(''',''' )] quick_sort_random(__lowercase , 0 , len(__lowercase ) ) print(__lowercase ) if __name__ == "__main__": main()
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'''simple docstring''' from dataclasses import dataclass from typing import Optional, Tuple, Union import torch import torch.nn as nn from ..configuration_utils import ConfigMixin, register_to_config from ..utils import BaseOutput from .embeddings import GaussianFourierProjection, TimestepEmbedding, Timesteps from .modeling_utils import ModelMixin from .unet_ad_blocks import get_down_block, get_mid_block, get_out_block, get_up_block @dataclass class lowerCAmelCase_ ( UpperCAmelCase_ ): '''simple docstring''' UpperCamelCase_ : torch.FloatTensor class lowerCAmelCase_ ( UpperCAmelCase_ , UpperCAmelCase_ ): '''simple docstring''' @register_to_config def __init__( self : str , SCREAMING_SNAKE_CASE_ : int = 6_55_36 , SCREAMING_SNAKE_CASE_ : Optional[int] = None , SCREAMING_SNAKE_CASE_ : int = 2 , SCREAMING_SNAKE_CASE_ : int = 2 , SCREAMING_SNAKE_CASE_ : int = 0 , SCREAMING_SNAKE_CASE_ : str = "fourier" , SCREAMING_SNAKE_CASE_ : bool = True , SCREAMING_SNAKE_CASE_ : bool = False , SCREAMING_SNAKE_CASE_ : float = 0.0 , SCREAMING_SNAKE_CASE_ : Tuple[str] = ("DownBlock1DNoSkip", "DownBlock1D", "AttnDownBlock1D") , SCREAMING_SNAKE_CASE_ : Tuple[str] = ("AttnUpBlock1D", "UpBlock1D", "UpBlock1DNoSkip") , SCREAMING_SNAKE_CASE_ : Tuple[str] = "UNetMidBlock1D" , SCREAMING_SNAKE_CASE_ : str = None , SCREAMING_SNAKE_CASE_ : Tuple[int] = (32, 32, 64) , SCREAMING_SNAKE_CASE_ : str = None , SCREAMING_SNAKE_CASE_ : int = 8 , SCREAMING_SNAKE_CASE_ : int = 1 , SCREAMING_SNAKE_CASE_ : bool = False , ) -> Tuple: '''simple docstring''' super().__init__() A: Optional[Any] = sample_size # time if time_embedding_type == "fourier": A: Tuple = GaussianFourierProjection( embedding_size=8 , set_W_to_weight=SCREAMING_SNAKE_CASE_ , log=SCREAMING_SNAKE_CASE_ , flip_sin_to_cos=SCREAMING_SNAKE_CASE_ ) A: List[str] = 2 * block_out_channels[0] elif time_embedding_type == "positional": A: str = Timesteps( block_out_channels[0] , flip_sin_to_cos=SCREAMING_SNAKE_CASE_ , downscale_freq_shift=SCREAMING_SNAKE_CASE_ ) A: Any = block_out_channels[0] if use_timestep_embedding: A: Optional[Any] = block_out_channels[0] * 4 A: List[Any] = TimestepEmbedding( in_channels=SCREAMING_SNAKE_CASE_ , time_embed_dim=SCREAMING_SNAKE_CASE_ , act_fn=SCREAMING_SNAKE_CASE_ , out_dim=block_out_channels[0] , ) A: Optional[Any] = nn.ModuleList([] ) A: str = None A: str = nn.ModuleList([] ) A: Tuple = None # down A: Any = in_channels for i, down_block_type in enumerate(SCREAMING_SNAKE_CASE_ ): A: Optional[int] = output_channel A: List[Any] = block_out_channels[i] if i == 0: input_channel += extra_in_channels A: List[Any] = i == len(SCREAMING_SNAKE_CASE_ ) - 1 A: Optional[int] = get_down_block( SCREAMING_SNAKE_CASE_ , num_layers=SCREAMING_SNAKE_CASE_ , in_channels=SCREAMING_SNAKE_CASE_ , out_channels=SCREAMING_SNAKE_CASE_ , temb_channels=block_out_channels[0] , add_downsample=not is_final_block or downsample_each_block , ) self.down_blocks.append(SCREAMING_SNAKE_CASE_ ) # mid A: Union[str, Any] = get_mid_block( SCREAMING_SNAKE_CASE_ , in_channels=block_out_channels[-1] , mid_channels=block_out_channels[-1] , out_channels=block_out_channels[-1] , embed_dim=block_out_channels[0] , num_layers=SCREAMING_SNAKE_CASE_ , add_downsample=SCREAMING_SNAKE_CASE_ , ) # up A: Optional[Any] = list(reversed(SCREAMING_SNAKE_CASE_ ) ) A: List[str] = reversed_block_out_channels[0] if out_block_type is None: A: int = out_channels else: A: Union[str, Any] = block_out_channels[0] for i, up_block_type in enumerate(SCREAMING_SNAKE_CASE_ ): A: List[Any] = output_channel A: int = ( reversed_block_out_channels[i + 1] if i < len(SCREAMING_SNAKE_CASE_ ) - 1 else final_upsample_channels ) A: Optional[int] = i == len(SCREAMING_SNAKE_CASE_ ) - 1 A: Optional[Any] = get_up_block( SCREAMING_SNAKE_CASE_ , num_layers=SCREAMING_SNAKE_CASE_ , in_channels=SCREAMING_SNAKE_CASE_ , out_channels=SCREAMING_SNAKE_CASE_ , temb_channels=block_out_channels[0] , add_upsample=not is_final_block , ) self.up_blocks.append(SCREAMING_SNAKE_CASE_ ) A: Any = output_channel # out A: List[str] = norm_num_groups if norm_num_groups is not None else min(block_out_channels[0] // 4 , 32 ) A: Optional[int] = get_out_block( out_block_type=SCREAMING_SNAKE_CASE_ , num_groups_out=SCREAMING_SNAKE_CASE_ , embed_dim=block_out_channels[0] , out_channels=SCREAMING_SNAKE_CASE_ , act_fn=SCREAMING_SNAKE_CASE_ , fc_dim=block_out_channels[-1] // 4 , ) def _snake_case ( self : List[Any] , SCREAMING_SNAKE_CASE_ : torch.FloatTensor , SCREAMING_SNAKE_CASE_ : Union[torch.Tensor, float, int] , SCREAMING_SNAKE_CASE_ : bool = True , ) -> Union[UNetaDOutput, Tuple]: '''simple docstring''' A: Any = timestep if not torch.is_tensor(SCREAMING_SNAKE_CASE_ ): A: Union[str, Any] = torch.tensor([timesteps] , dtype=torch.long , device=sample.device ) elif torch.is_tensor(SCREAMING_SNAKE_CASE_ ) and len(timesteps.shape ) == 0: A: List[str] = timesteps[None].to(sample.device ) A: int = self.time_proj(SCREAMING_SNAKE_CASE_ ) if self.config.use_timestep_embedding: A: List[Any] = self.time_mlp(SCREAMING_SNAKE_CASE_ ) else: A: str = timestep_embed[..., None] A: Union[str, Any] = timestep_embed.repeat([1, 1, sample.shape[2]] ).to(sample.dtype ) A: Tuple = timestep_embed.broadcast_to((sample.shape[:1] + timestep_embed.shape[1:]) ) # 2. down A: List[str] = () for downsample_block in self.down_blocks: A , A: Optional[int] = downsample_block(hidden_states=SCREAMING_SNAKE_CASE_ , temb=SCREAMING_SNAKE_CASE_ ) down_block_res_samples += res_samples # 3. mid if self.mid_block: A: Dict = self.mid_block(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) # 4. up for i, upsample_block in enumerate(self.up_blocks ): A: List[Any] = down_block_res_samples[-1:] A: List[str] = down_block_res_samples[:-1] A: Optional[int] = upsample_block(SCREAMING_SNAKE_CASE_ , res_hidden_states_tuple=SCREAMING_SNAKE_CASE_ , temb=SCREAMING_SNAKE_CASE_ ) # 5. post-process if self.out_block: A: Any = self.out_block(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) if not return_dict: return (sample,) return UNetaDOutput(sample=SCREAMING_SNAKE_CASE_ )
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'''simple docstring''' import argparse import json from dataclasses import dataclass, field from functools import partial from pathlib import Path from typing import List import timm import torch import torch.nn as nn from huggingface_hub import hf_hub_download from torch import Tensor from transformers import AutoImageProcessor, ResNetConfig, ResNetForImageClassification from transformers.utils import logging logging.set_verbosity_info() UpperCamelCase = logging.get_logger() @dataclass class lowerCAmelCase_ : '''simple docstring''' UpperCamelCase_ : nn.Module UpperCamelCase_ : List[nn.Module] = field(default_factory=UpperCAmelCase_ ) UpperCamelCase_ : list = field(default_factory=UpperCAmelCase_ ) def _snake_case ( self : str , SCREAMING_SNAKE_CASE_ : Union[str, Any] , SCREAMING_SNAKE_CASE_ : Tensor , SCREAMING_SNAKE_CASE_ : Tensor ) -> int: '''simple docstring''' A: List[str] = len(list(m.modules() ) ) == 1 or isinstance(SCREAMING_SNAKE_CASE_ , nn.Convad ) or isinstance(SCREAMING_SNAKE_CASE_ , nn.BatchNormad ) if has_not_submodules: self.traced.append(SCREAMING_SNAKE_CASE_ ) def __call__( self : List[Any] , SCREAMING_SNAKE_CASE_ : Tensor ) -> Dict: '''simple docstring''' for m in self.module.modules(): self.handles.append(m.register_forward_hook(self._forward_hook ) ) self.module(SCREAMING_SNAKE_CASE_ ) [x.remove() for x in self.handles] return self @property def _snake_case ( self : Optional[Any] ) -> Optional[int]: '''simple docstring''' return list(filter(lambda SCREAMING_SNAKE_CASE_ : len(list(x.state_dict().keys() ) ) > 0 , self.traced ) ) @dataclass class lowerCAmelCase_ : '''simple docstring''' UpperCamelCase_ : nn.Module UpperCamelCase_ : nn.Module UpperCamelCase_ : int = 0 UpperCamelCase_ : List = field(default_factory=UpperCAmelCase_ ) UpperCamelCase_ : List = field(default_factory=UpperCAmelCase_ ) def __call__( self : Any , SCREAMING_SNAKE_CASE_ : Tensor ) -> Optional[Any]: '''simple docstring''' A: Dict = Tracker(self.dest )(SCREAMING_SNAKE_CASE_ ).parametrized A: Tuple = Tracker(self.src )(SCREAMING_SNAKE_CASE_ ).parametrized A: str = list(filter(lambda SCREAMING_SNAKE_CASE_ : type(SCREAMING_SNAKE_CASE_ ) not in self.src_skip , SCREAMING_SNAKE_CASE_ ) ) A: str = list(filter(lambda SCREAMING_SNAKE_CASE_ : type(SCREAMING_SNAKE_CASE_ ) not in self.dest_skip , SCREAMING_SNAKE_CASE_ ) ) if len(SCREAMING_SNAKE_CASE_ ) != len(SCREAMING_SNAKE_CASE_ ): raise Exception( f"""Numbers of operations are different. Source module has {len(SCREAMING_SNAKE_CASE_ )} operations while""" f""" destination module has {len(SCREAMING_SNAKE_CASE_ )}.""" ) for dest_m, src_m in zip(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ): dest_m.load_state_dict(src_m.state_dict() ) if self.verbose == 1: print(f"""Transfered from={src_m} to={dest_m}""" ) def SCREAMING_SNAKE_CASE( __lowercase , __lowercase , __lowercase , __lowercase = True ) -> Any: print(F"""Converting {name}...""" ) with torch.no_grad(): A: Union[str, Any] = timm.create_model(__lowercase , pretrained=__lowercase ).eval() A: List[str] = ResNetForImageClassification(__lowercase ).eval() A: int = ModuleTransfer(src=__lowercase , dest=__lowercase ) A: List[str] = torch.randn((1, 3, 2_2_4, 2_2_4) ) module_transfer(__lowercase ) assert torch.allclose(from_model(__lowercase ) , our_model(__lowercase ).logits ), "The model logits don't match the original one." A: str = F"""resnet{'-'.join(name.split('resnet' ) )}""" print(__lowercase ) if push_to_hub: our_model.push_to_hub( repo_path_or_name=save_directory / checkpoint_name , commit_message='''Add model''' , use_temp_dir=__lowercase , ) # we can use the convnext one A: Any = AutoImageProcessor.from_pretrained('''facebook/convnext-base-224-22k-1k''' ) image_processor.push_to_hub( repo_path_or_name=save_directory / checkpoint_name , commit_message='''Add image processor''' , use_temp_dir=__lowercase , ) print(F"""Pushed {checkpoint_name}""" ) def SCREAMING_SNAKE_CASE( __lowercase , __lowercase = None , __lowercase = True ) -> List[Any]: A: Union[str, Any] = '''imagenet-1k-id2label.json''' A: Union[str, Any] = 1_0_0_0 A: Optional[int] = (1, num_labels) A: Dict = '''huggingface/label-files''' A: Any = num_labels A: Union[str, Any] = json.load(open(hf_hub_download(__lowercase , __lowercase , repo_type='''dataset''' ) , '''r''' ) ) A: Optional[int] = {int(__lowercase ): v for k, v in idalabel.items()} A: Optional[int] = idalabel A: List[str] = {v: k for k, v in idalabel.items()} A: str = partial(__lowercase , num_labels=__lowercase , idalabel=__lowercase , labelaid=__lowercase ) A: Optional[Any] = { '''resnet18''': ImageNetPreTrainedConfig( depths=[2, 2, 2, 2] , hidden_sizes=[6_4, 1_2_8, 2_5_6, 5_1_2] , layer_type='''basic''' ), '''resnet26''': ImageNetPreTrainedConfig( depths=[2, 2, 2, 2] , hidden_sizes=[2_5_6, 5_1_2, 1_0_2_4, 2_0_4_8] , layer_type='''bottleneck''' ), '''resnet34''': ImageNetPreTrainedConfig( depths=[3, 4, 6, 3] , hidden_sizes=[6_4, 1_2_8, 2_5_6, 5_1_2] , layer_type='''basic''' ), '''resnet50''': ImageNetPreTrainedConfig( depths=[3, 4, 6, 3] , hidden_sizes=[2_5_6, 5_1_2, 1_0_2_4, 2_0_4_8] , layer_type='''bottleneck''' ), '''resnet101''': ImageNetPreTrainedConfig( depths=[3, 4, 2_3, 3] , hidden_sizes=[2_5_6, 5_1_2, 1_0_2_4, 2_0_4_8] , layer_type='''bottleneck''' ), '''resnet152''': ImageNetPreTrainedConfig( depths=[3, 8, 3_6, 3] , hidden_sizes=[2_5_6, 5_1_2, 1_0_2_4, 2_0_4_8] , layer_type='''bottleneck''' ), } if model_name: convert_weight_and_push(__lowercase , names_to_config[model_name] , __lowercase , __lowercase ) else: for model_name, config in names_to_config.items(): convert_weight_and_push(__lowercase , __lowercase , __lowercase , __lowercase ) return config, expected_shape if __name__ == "__main__": UpperCamelCase = argparse.ArgumentParser() # Required parameters parser.add_argument( '''--model_name''', default=None, type=str, help=( '''The name of the model you wish to convert, it must be one of the supported resnet* architecture,''' ''' currently: resnet18,26,34,50,101,152. If `None`, all of them will the converted.''' ), ) parser.add_argument( '''--pytorch_dump_folder_path''', default=None, type=Path, required=True, help='''Path to the output PyTorch model directory.''', ) parser.add_argument( '''--push_to_hub''', default=True, type=bool, required=False, help='''If True, push model and image processor to the hub.''', ) UpperCamelCase = parser.parse_args() UpperCamelCase = args.pytorch_dump_folder_path pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True) convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)
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'''simple docstring''' import warnings from ...utils import logging from .image_processing_segformer import SegformerImageProcessor UpperCamelCase = logging.get_logger(__name__) class lowerCAmelCase_ ( UpperCAmelCase_ ): '''simple docstring''' def __init__( self : int , *SCREAMING_SNAKE_CASE_ : str , **SCREAMING_SNAKE_CASE_ : Dict ) -> None: '''simple docstring''' warnings.warn( '''The class SegformerFeatureExtractor is deprecated and will be removed in version 5 of Transformers.''' ''' Please use SegformerImageProcessor instead.''' , SCREAMING_SNAKE_CASE_ , ) super().__init__(*SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
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'''simple docstring''' UpperCamelCase = 8.31_44_62 # Unit - J mol-1 K-1 def SCREAMING_SNAKE_CASE( __lowercase , __lowercase , __lowercase ) -> float: if moles < 0 or kelvin < 0 or volume < 0: raise ValueError('''Invalid inputs. Enter positive value.''' ) return moles * kelvin * UNIVERSAL_GAS_CONSTANT / volume def SCREAMING_SNAKE_CASE( __lowercase , __lowercase , __lowercase ) -> float: if moles < 0 or kelvin < 0 or pressure < 0: raise ValueError('''Invalid inputs. Enter positive value.''' ) return moles * kelvin * UNIVERSAL_GAS_CONSTANT / pressure if __name__ == "__main__": from doctest import testmod testmod()
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'''simple docstring''' from collections import deque class lowerCAmelCase_ : '''simple docstring''' def __init__( self : Optional[Any] , SCREAMING_SNAKE_CASE_ : str , SCREAMING_SNAKE_CASE_ : int , SCREAMING_SNAKE_CASE_ : int ) -> None: '''simple docstring''' A: Union[str, Any] = process_name # process name A: List[str] = arrival_time # arrival time of the process # completion time of finished process or last interrupted time A: Dict = arrival_time A: Optional[Any] = burst_time # remaining burst time A: Any = 0 # total time of the process wait in ready queue A: Any = 0 # time from arrival time to completion time class lowerCAmelCase_ : '''simple docstring''' def __init__( self : int , SCREAMING_SNAKE_CASE_ : int , SCREAMING_SNAKE_CASE_ : list[int] , SCREAMING_SNAKE_CASE_ : deque[Process] , SCREAMING_SNAKE_CASE_ : int , ) -> None: '''simple docstring''' A: Dict = number_of_queues # time slice of queues that round robin algorithm applied A: int = time_slices # unfinished process is in this ready_queue A: Tuple = queue # current time A: int = current_time # finished process is in this sequence queue A: deque[Process] = deque() def _snake_case ( self : List[Any] ) -> list[str]: '''simple docstring''' A: str = [] for i in range(len(self.finish_queue ) ): sequence.append(self.finish_queue[i].process_name ) return sequence def _snake_case ( self : List[Any] , SCREAMING_SNAKE_CASE_ : list[Process] ) -> list[int]: '''simple docstring''' A: Optional[int] = [] for i in range(len(SCREAMING_SNAKE_CASE_ ) ): waiting_times.append(queue[i].waiting_time ) return waiting_times def _snake_case ( self : Any , SCREAMING_SNAKE_CASE_ : list[Process] ) -> list[int]: '''simple docstring''' A: Any = [] for i in range(len(SCREAMING_SNAKE_CASE_ ) ): turnaround_times.append(queue[i].turnaround_time ) return turnaround_times def _snake_case ( self : str , SCREAMING_SNAKE_CASE_ : list[Process] ) -> list[int]: '''simple docstring''' A: List[Any] = [] for i in range(len(SCREAMING_SNAKE_CASE_ ) ): completion_times.append(queue[i].stop_time ) return completion_times def _snake_case ( self : Tuple , SCREAMING_SNAKE_CASE_ : deque[Process] ) -> list[int]: '''simple docstring''' return [q.burst_time for q in queue] def _snake_case ( self : int , SCREAMING_SNAKE_CASE_ : Process ) -> int: '''simple docstring''' process.waiting_time += self.current_time - process.stop_time return process.waiting_time def _snake_case ( self : List[str] , SCREAMING_SNAKE_CASE_ : deque[Process] ) -> deque[Process]: '''simple docstring''' A: deque[Process] = deque() # sequence deque of finished process while len(SCREAMING_SNAKE_CASE_ ) != 0: A: Optional[Any] = ready_queue.popleft() # current process # if process's arrival time is later than current time, update current time if self.current_time < cp.arrival_time: self.current_time += cp.arrival_time # update waiting time of current process self.update_waiting_time(SCREAMING_SNAKE_CASE_ ) # update current time self.current_time += cp.burst_time # finish the process and set the process's burst-time 0 A: Any = 0 # set the process's turnaround time because it is finished A: int = self.current_time - cp.arrival_time # set the completion time A: List[str] = self.current_time # add the process to queue that has finished queue finished.append(SCREAMING_SNAKE_CASE_ ) self.finish_queue.extend(SCREAMING_SNAKE_CASE_ ) # add finished process to finish queue # FCFS will finish all remaining processes return finished def _snake_case ( self : List[Any] , SCREAMING_SNAKE_CASE_ : deque[Process] , SCREAMING_SNAKE_CASE_ : int ) -> tuple[deque[Process], deque[Process]]: '''simple docstring''' A: deque[Process] = deque() # sequence deque of terminated process # just for 1 cycle and unfinished processes will go back to queue for _ in range(len(SCREAMING_SNAKE_CASE_ ) ): A: Dict = ready_queue.popleft() # current process # if process's arrival time is later than current time, update current time if self.current_time < cp.arrival_time: self.current_time += cp.arrival_time # update waiting time of unfinished processes self.update_waiting_time(SCREAMING_SNAKE_CASE_ ) # if the burst time of process is bigger than time-slice if cp.burst_time > time_slice: # use CPU for only time-slice self.current_time += time_slice # update remaining burst time cp.burst_time -= time_slice # update end point time A: Optional[Any] = self.current_time # locate the process behind the queue because it is not finished ready_queue.append(SCREAMING_SNAKE_CASE_ ) else: # use CPU for remaining burst time self.current_time += cp.burst_time # set burst time 0 because the process is finished A: int = 0 # set the finish time A: Union[str, Any] = self.current_time # update the process' turnaround time because it is finished A: Tuple = self.current_time - cp.arrival_time # add the process to queue that has finished queue finished.append(SCREAMING_SNAKE_CASE_ ) self.finish_queue.extend(SCREAMING_SNAKE_CASE_ ) # add finished process to finish queue # return finished processes queue and remaining processes queue return finished, ready_queue def _snake_case ( self : Optional[Any] ) -> deque[Process]: '''simple docstring''' for i in range(self.number_of_queues - 1 ): A , A: Optional[Any] = self.round_robin( self.ready_queue , self.time_slices[i] ) # the last queue has first_come_first_served algorithm self.first_come_first_served(self.ready_queue ) return self.finish_queue if __name__ == "__main__": import doctest UpperCamelCase = Process('''P1''', 0, 53) UpperCamelCase = Process('''P2''', 0, 17) UpperCamelCase = Process('''P3''', 0, 68) UpperCamelCase = Process('''P4''', 0, 24) UpperCamelCase = 3 UpperCamelCase = [17, 25] UpperCamelCase = deque([Pa, Pa, Pa, Pa]) if len(time_slices) != number_of_queues - 1: raise SystemExit(0) doctest.testmod(extraglobs={'''queue''': deque([Pa, Pa, Pa, Pa])}) UpperCamelCase = Process('''P1''', 0, 53) UpperCamelCase = Process('''P2''', 0, 17) UpperCamelCase = Process('''P3''', 0, 68) UpperCamelCase = Process('''P4''', 0, 24) UpperCamelCase = 3 UpperCamelCase = [17, 25] UpperCamelCase = deque([Pa, Pa, Pa, Pa]) UpperCamelCase = MLFQ(number_of_queues, time_slices, queue, 0) UpperCamelCase = mlfq.multi_level_feedback_queue() # print total waiting times of processes(P1, P2, P3, P4) print( f'waiting time:\ \t\t\t{MLFQ.calculate_waiting_time(mlfq, [Pa, Pa, Pa, Pa])}' ) # print completion times of processes(P1, P2, P3, P4) print( f'completion time:\ \t\t{MLFQ.calculate_completion_time(mlfq, [Pa, Pa, Pa, Pa])}' ) # print total turnaround times of processes(P1, P2, P3, P4) print( f'turnaround time:\ \t\t{MLFQ.calculate_turnaround_time(mlfq, [Pa, Pa, Pa, Pa])}' ) # print sequence of finished processes print( f'sequence of finished processes:\ {mlfq.calculate_sequence_of_finish_queue()}' )
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'''simple docstring''' import math import os import sys def SCREAMING_SNAKE_CASE( __lowercase ) -> str: A: Optional[int] = '''''' try: with open(__lowercase , '''rb''' ) as binary_file: A: List[str] = binary_file.read() for dat in data: A: Any = F"""{dat:08b}""" result += curr_byte return result except OSError: print('''File not accessible''' ) sys.exit() def SCREAMING_SNAKE_CASE( __lowercase , __lowercase , __lowercase , __lowercase ) -> None: lexicon.pop(__lowercase ) A: Dict = last_match_id if math.loga(__lowercase ).is_integer(): for curr_key in lexicon: A: Tuple = '''0''' + lexicon[curr_key] A: str = bin(__lowercase )[2:] def SCREAMING_SNAKE_CASE( __lowercase ) -> str: A: Union[str, Any] = {'''0''': '''0''', '''1''': '''1'''} A , A: List[Any] = '''''', '''''' A: Tuple = len(__lowercase ) for i in range(len(__lowercase ) ): curr_string += data_bits[i] if curr_string not in lexicon: continue A: List[str] = lexicon[curr_string] result += last_match_id add_key_to_lexicon(__lowercase , __lowercase , __lowercase , __lowercase ) index += 1 A: List[str] = '''''' while curr_string != "" and curr_string not in lexicon: curr_string += "0" if curr_string != "": A: List[str] = lexicon[curr_string] result += last_match_id return result def SCREAMING_SNAKE_CASE( __lowercase , __lowercase ) -> str: A: str = os.path.getsize(__lowercase ) A: Optional[Any] = bin(__lowercase )[2:] A: List[str] = len(__lowercase ) return "0" * (length_length - 1) + file_length_binary + compressed def SCREAMING_SNAKE_CASE( __lowercase , __lowercase ) -> None: A: Tuple = 8 try: with open(__lowercase , '''wb''' ) as opened_file: A: Optional[Any] = [ to_write[i : i + byte_length] for i in range(0 , len(__lowercase ) , __lowercase ) ] if len(result_byte_array[-1] ) % byte_length == 0: result_byte_array.append('''10000000''' ) else: result_byte_array[-1] += "1" + "0" * ( byte_length - len(result_byte_array[-1] ) - 1 ) for elem in result_byte_array: opened_file.write(int(__lowercase , 2 ).to_bytes(1 , byteorder='''big''' ) ) except OSError: print('''File not accessible''' ) sys.exit() def SCREAMING_SNAKE_CASE( __lowercase , __lowercase ) -> None: A: Any = read_file_binary(__lowercase ) A: List[Any] = compress_data(__lowercase ) A: Any = add_file_length(__lowercase , __lowercase ) write_file_binary(__lowercase , __lowercase ) if __name__ == "__main__": compress(sys.argv[1], sys.argv[2])
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'''simple docstring''' import argparse import json from dataclasses import dataclass, field from functools import partial from pathlib import Path from typing import List import timm import torch import torch.nn as nn from huggingface_hub import hf_hub_download from torch import Tensor from transformers import AutoImageProcessor, ResNetConfig, ResNetForImageClassification from transformers.utils import logging logging.set_verbosity_info() UpperCamelCase = logging.get_logger() @dataclass class lowerCAmelCase_ : '''simple docstring''' UpperCamelCase_ : nn.Module UpperCamelCase_ : List[nn.Module] = field(default_factory=UpperCAmelCase_ ) UpperCamelCase_ : list = field(default_factory=UpperCAmelCase_ ) def _snake_case ( self : str , SCREAMING_SNAKE_CASE_ : Union[str, Any] , SCREAMING_SNAKE_CASE_ : Tensor , SCREAMING_SNAKE_CASE_ : Tensor ) -> int: '''simple docstring''' A: List[str] = len(list(m.modules() ) ) == 1 or isinstance(SCREAMING_SNAKE_CASE_ , nn.Convad ) or isinstance(SCREAMING_SNAKE_CASE_ , nn.BatchNormad ) if has_not_submodules: self.traced.append(SCREAMING_SNAKE_CASE_ ) def __call__( self : List[Any] , SCREAMING_SNAKE_CASE_ : Tensor ) -> Dict: '''simple docstring''' for m in self.module.modules(): self.handles.append(m.register_forward_hook(self._forward_hook ) ) self.module(SCREAMING_SNAKE_CASE_ ) [x.remove() for x in self.handles] return self @property def _snake_case ( self : Optional[Any] ) -> Optional[int]: '''simple docstring''' return list(filter(lambda SCREAMING_SNAKE_CASE_ : len(list(x.state_dict().keys() ) ) > 0 , self.traced ) ) @dataclass class lowerCAmelCase_ : '''simple docstring''' UpperCamelCase_ : nn.Module UpperCamelCase_ : nn.Module UpperCamelCase_ : int = 0 UpperCamelCase_ : List = field(default_factory=UpperCAmelCase_ ) UpperCamelCase_ : List = field(default_factory=UpperCAmelCase_ ) def __call__( self : Any , SCREAMING_SNAKE_CASE_ : Tensor ) -> Optional[Any]: '''simple docstring''' A: Dict = Tracker(self.dest )(SCREAMING_SNAKE_CASE_ ).parametrized A: Tuple = Tracker(self.src )(SCREAMING_SNAKE_CASE_ ).parametrized A: str = list(filter(lambda SCREAMING_SNAKE_CASE_ : type(SCREAMING_SNAKE_CASE_ ) not in self.src_skip , SCREAMING_SNAKE_CASE_ ) ) A: str = list(filter(lambda SCREAMING_SNAKE_CASE_ : type(SCREAMING_SNAKE_CASE_ ) not in self.dest_skip , SCREAMING_SNAKE_CASE_ ) ) if len(SCREAMING_SNAKE_CASE_ ) != len(SCREAMING_SNAKE_CASE_ ): raise Exception( f"""Numbers of operations are different. Source module has {len(SCREAMING_SNAKE_CASE_ )} operations while""" f""" destination module has {len(SCREAMING_SNAKE_CASE_ )}.""" ) for dest_m, src_m in zip(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ): dest_m.load_state_dict(src_m.state_dict() ) if self.verbose == 1: print(f"""Transfered from={src_m} to={dest_m}""" ) def SCREAMING_SNAKE_CASE( __lowercase , __lowercase , __lowercase , __lowercase = True ) -> Any: print(F"""Converting {name}...""" ) with torch.no_grad(): A: Union[str, Any] = timm.create_model(__lowercase , pretrained=__lowercase ).eval() A: List[str] = ResNetForImageClassification(__lowercase ).eval() A: int = ModuleTransfer(src=__lowercase , dest=__lowercase ) A: List[str] = torch.randn((1, 3, 2_2_4, 2_2_4) ) module_transfer(__lowercase ) assert torch.allclose(from_model(__lowercase ) , our_model(__lowercase ).logits ), "The model logits don't match the original one." A: str = F"""resnet{'-'.join(name.split('resnet' ) )}""" print(__lowercase ) if push_to_hub: our_model.push_to_hub( repo_path_or_name=save_directory / checkpoint_name , commit_message='''Add model''' , use_temp_dir=__lowercase , ) # we can use the convnext one A: Any = AutoImageProcessor.from_pretrained('''facebook/convnext-base-224-22k-1k''' ) image_processor.push_to_hub( repo_path_or_name=save_directory / checkpoint_name , commit_message='''Add image processor''' , use_temp_dir=__lowercase , ) print(F"""Pushed {checkpoint_name}""" ) def SCREAMING_SNAKE_CASE( __lowercase , __lowercase = None , __lowercase = True ) -> List[Any]: A: Union[str, Any] = '''imagenet-1k-id2label.json''' A: Union[str, Any] = 1_0_0_0 A: Optional[int] = (1, num_labels) A: Dict = '''huggingface/label-files''' A: Any = num_labels A: Union[str, Any] = json.load(open(hf_hub_download(__lowercase , __lowercase , repo_type='''dataset''' ) , '''r''' ) ) A: Optional[int] = {int(__lowercase ): v for k, v in idalabel.items()} A: Optional[int] = idalabel A: List[str] = {v: k for k, v in idalabel.items()} A: str = partial(__lowercase , num_labels=__lowercase , idalabel=__lowercase , labelaid=__lowercase ) A: Optional[Any] = { '''resnet18''': ImageNetPreTrainedConfig( depths=[2, 2, 2, 2] , hidden_sizes=[6_4, 1_2_8, 2_5_6, 5_1_2] , layer_type='''basic''' ), '''resnet26''': ImageNetPreTrainedConfig( depths=[2, 2, 2, 2] , hidden_sizes=[2_5_6, 5_1_2, 1_0_2_4, 2_0_4_8] , layer_type='''bottleneck''' ), '''resnet34''': ImageNetPreTrainedConfig( depths=[3, 4, 6, 3] , hidden_sizes=[6_4, 1_2_8, 2_5_6, 5_1_2] , layer_type='''basic''' ), '''resnet50''': ImageNetPreTrainedConfig( depths=[3, 4, 6, 3] , hidden_sizes=[2_5_6, 5_1_2, 1_0_2_4, 2_0_4_8] , layer_type='''bottleneck''' ), '''resnet101''': ImageNetPreTrainedConfig( depths=[3, 4, 2_3, 3] , hidden_sizes=[2_5_6, 5_1_2, 1_0_2_4, 2_0_4_8] , layer_type='''bottleneck''' ), '''resnet152''': ImageNetPreTrainedConfig( depths=[3, 8, 3_6, 3] , hidden_sizes=[2_5_6, 5_1_2, 1_0_2_4, 2_0_4_8] , layer_type='''bottleneck''' ), } if model_name: convert_weight_and_push(__lowercase , names_to_config[model_name] , __lowercase , __lowercase ) else: for model_name, config in names_to_config.items(): convert_weight_and_push(__lowercase , __lowercase , __lowercase , __lowercase ) return config, expected_shape if __name__ == "__main__": UpperCamelCase = argparse.ArgumentParser() # Required parameters parser.add_argument( '''--model_name''', default=None, type=str, help=( '''The name of the model you wish to convert, it must be one of the supported resnet* architecture,''' ''' currently: resnet18,26,34,50,101,152. If `None`, all of them will the converted.''' ), ) parser.add_argument( '''--pytorch_dump_folder_path''', default=None, type=Path, required=True, help='''Path to the output PyTorch model directory.''', ) parser.add_argument( '''--push_to_hub''', default=True, type=bool, required=False, help='''If True, push model and image processor to the hub.''', ) UpperCamelCase = parser.parse_args() UpperCamelCase = args.pytorch_dump_folder_path pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True) convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)
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'''simple docstring''' from __future__ import annotations def SCREAMING_SNAKE_CASE( __lowercase = 4 ) -> list[list[int]]: A: Tuple = abs(__lowercase ) or 4 return [[1 + x + y * row_size for x in range(__lowercase )] for y in range(__lowercase )] def SCREAMING_SNAKE_CASE( __lowercase ) -> list[list[int]]: return reverse_row(transpose(__lowercase ) ) # OR.. transpose(reverse_column(matrix)) def SCREAMING_SNAKE_CASE( __lowercase ) -> list[list[int]]: return reverse_row(reverse_column(__lowercase ) ) # OR.. reverse_column(reverse_row(matrix)) def SCREAMING_SNAKE_CASE( __lowercase ) -> list[list[int]]: return reverse_column(transpose(__lowercase ) ) # OR.. transpose(reverse_row(matrix)) def SCREAMING_SNAKE_CASE( __lowercase ) -> list[list[int]]: A: Union[str, Any] = [list(__lowercase ) for x in zip(*__lowercase )] return matrix def SCREAMING_SNAKE_CASE( __lowercase ) -> list[list[int]]: A: Optional[int] = matrix[::-1] return matrix def SCREAMING_SNAKE_CASE( __lowercase ) -> list[list[int]]: A: Optional[Any] = [x[::-1] for x in matrix] return matrix def SCREAMING_SNAKE_CASE( __lowercase ) -> None: for i in matrix: print(*__lowercase ) if __name__ == "__main__": UpperCamelCase = make_matrix() print('''\norigin:\n''') print_matrix(matrix) print('''\nrotate 90 counterclockwise:\n''') print_matrix(rotate_aa(matrix)) UpperCamelCase = make_matrix() print('''\norigin:\n''') print_matrix(matrix) print('''\nrotate 180:\n''') print_matrix(rotate_aaa(matrix)) UpperCamelCase = make_matrix() print('''\norigin:\n''') print_matrix(matrix) print('''\nrotate 270 counterclockwise:\n''') print_matrix(rotate_aaa(matrix))
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'''simple docstring''' from __future__ import annotations from collections.abc import Sequence from typing import Literal def SCREAMING_SNAKE_CASE( __lowercase , __lowercase ) -> str | Literal[False]: A: List[str] = list(__lowercase ) A: Optional[Any] = list(__lowercase ) A: int = 0 for i in range(len(__lowercase ) ): if lista[i] != lista[i]: count += 1 A: Optional[Any] = '''_''' if count > 1: return False else: return "".join(__lowercase ) def SCREAMING_SNAKE_CASE( __lowercase ) -> list[str]: A: Any = [] while True: A: Dict = ['''$'''] * len(__lowercase ) A: Union[str, Any] = [] for i in range(len(__lowercase ) ): for j in range(i + 1 , len(__lowercase ) ): A: Any = compare_string(binary[i] , binary[j] ) if k is False: A: Any = '''*''' A: List[Any] = '''*''' temp.append('''X''' ) for i in range(len(__lowercase ) ): if checka[i] == "$": pi.append(binary[i] ) if len(__lowercase ) == 0: return pi A: List[Any] = list(set(__lowercase ) ) def SCREAMING_SNAKE_CASE( __lowercase , __lowercase ) -> list[str]: A: Optional[int] = [] for minterm in minterms: A: Optional[int] = '''''' for _ in range(__lowercase ): A: List[Any] = str(minterm % 2 ) + string minterm //= 2 temp.append(__lowercase ) return temp def SCREAMING_SNAKE_CASE( __lowercase , __lowercase , __lowercase ) -> bool: A: Union[str, Any] = list(__lowercase ) A: Union[str, Any] = list(__lowercase ) A: Optional[int] = 0 for i in range(len(__lowercase ) ): if lista[i] != lista[i]: count_n += 1 return count_n == count def SCREAMING_SNAKE_CASE( __lowercase , __lowercase ) -> list[str]: A: List[Any] = [] A: Dict = [0] * len(__lowercase ) for i in range(len(chart[0] ) ): A: List[str] = 0 A: str = -1 for j in range(len(__lowercase ) ): if chart[j][i] == 1: count += 1 A: Any = j if count == 1: A: Any = 1 for i in range(len(__lowercase ) ): if select[i] == 1: for j in range(len(chart[0] ) ): if chart[i][j] == 1: for k in range(len(__lowercase ) ): A: Optional[int] = 0 temp.append(prime_implicants[i] ) while True: A: Dict = 0 A: Optional[int] = -1 A: Dict = 0 for i in range(len(__lowercase ) ): A: str = chart[i].count(1 ) if count_n > max_n: A: Tuple = count_n A: Optional[Any] = i if max_n == 0: return temp temp.append(prime_implicants[rem] ) for i in range(len(chart[0] ) ): if chart[rem][i] == 1: for j in range(len(__lowercase ) ): A: Any = 0 def SCREAMING_SNAKE_CASE( __lowercase , __lowercase ) -> list[list[int]]: A: str = [[0 for x in range(len(__lowercase ) )] for x in range(len(__lowercase ) )] for i in range(len(__lowercase ) ): A: Tuple = prime_implicants[i].count('''_''' ) for j in range(len(__lowercase ) ): if is_for_table(prime_implicants[i] , binary[j] , __lowercase ): A: Optional[Any] = 1 return chart def SCREAMING_SNAKE_CASE( ) -> None: A: int = int(input('''Enter the no. of variables\n''' ) ) A: Optional[int] = [ float(__lowercase ) for x in input( '''Enter the decimal representation of Minterms \'Spaces Separated\'\n''' ).split() ] A: List[str] = decimal_to_binary(__lowercase , __lowercase ) A: str = check(__lowercase ) print('''Prime Implicants are:''' ) print(__lowercase ) A: List[Any] = prime_implicant_chart(__lowercase , __lowercase ) A: Any = selection(__lowercase , __lowercase ) print('''Essential Prime Implicants are:''' ) print(__lowercase ) if __name__ == "__main__": import doctest doctest.testmod() main()
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'''simple docstring''' import argparse import json import os import pickle import shutil import numpy as np import torch from distiller import Distiller from lm_seqs_dataset import LmSeqsDataset from transformers import ( BertConfig, BertForMaskedLM, BertTokenizer, DistilBertConfig, DistilBertForMaskedLM, DistilBertTokenizer, GPTaConfig, GPTaLMHeadModel, GPTaTokenizer, RobertaConfig, RobertaForMaskedLM, RobertaTokenizer, ) from utils import git_log, init_gpu_params, logger, set_seed UpperCamelCase = { '''distilbert''': (DistilBertConfig, DistilBertForMaskedLM, DistilBertTokenizer), '''roberta''': (RobertaConfig, RobertaForMaskedLM, RobertaTokenizer), '''bert''': (BertConfig, BertForMaskedLM, BertTokenizer), '''gpt2''': (GPTaConfig, GPTaLMHeadModel, GPTaTokenizer), } def SCREAMING_SNAKE_CASE( __lowercase ) -> Any: 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 SCREAMING_SNAKE_CASE( __lowercase , __lowercase ) -> List[str]: if args.student_type == "roberta": A: Optional[int] = False elif args.student_type == "gpt2": A: List[Any] = False def SCREAMING_SNAKE_CASE( __lowercase , __lowercase ) -> List[Any]: if args.student_type == "roberta": A: Union[str, Any] = False def SCREAMING_SNAKE_CASE( ) -> Union[str, Any]: A: str = 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=__lowercase , required=__lowercase , help='''The output directory (log, checkpoints, parameters, etc.)''' ) parser.add_argument( '''--data_file''' , type=__lowercase , required=__lowercase , help='''The binarized file (tokenized + tokens_to_ids) and grouped by sequence.''' , ) parser.add_argument( '''--student_type''' , type=__lowercase , choices=['''distilbert''', '''roberta''', '''gpt2'''] , required=__lowercase , help='''The student type (DistilBERT, RoBERTa).''' , ) parser.add_argument('''--student_config''' , type=__lowercase , required=__lowercase , help='''Path to the student configuration.''' ) parser.add_argument( '''--student_pretrained_weights''' , default=__lowercase , type=__lowercase , help='''Load student initialization checkpoint.''' ) parser.add_argument( '''--teacher_type''' , choices=['''bert''', '''roberta''', '''gpt2'''] , required=__lowercase , help='''Teacher type (BERT, RoBERTa).''' ) parser.add_argument('''--teacher_name''' , type=__lowercase , required=__lowercase , help='''The teacher model.''' ) parser.add_argument('''--temperature''' , default=2.0 , type=__lowercase , help='''Temperature for the softmax temperature.''' ) parser.add_argument( '''--alpha_ce''' , default=0.5 , type=__lowercase , help='''Linear weight for the distillation loss. Must be >=0.''' ) parser.add_argument( '''--alpha_mlm''' , default=0.0 , type=__lowercase , 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=__lowercase , help='''Linear weight for the CLM loss. Must be >=0.''' ) parser.add_argument('''--alpha_mse''' , default=0.0 , type=__lowercase , help='''Linear weight of the MSE loss. Must be >=0.''' ) parser.add_argument( '''--alpha_cos''' , default=0.0 , type=__lowercase , 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=0.1_5 , type=__lowercase , help='''Proportion of tokens for which we need to make a prediction.''' , ) parser.add_argument('''--word_mask''' , default=0.8 , type=__lowercase , help='''Proportion of tokens to mask out.''' ) parser.add_argument('''--word_keep''' , default=0.1 , type=__lowercase , help='''Proportion of tokens to keep.''' ) parser.add_argument('''--word_rand''' , default=0.1 , type=__lowercase , help='''Proportion of tokens to randomly replace.''' ) parser.add_argument( '''--mlm_smoothing''' , default=0.7 , type=__lowercase , help='''Smoothing parameter to emphasize more rare tokens (see XLM, similar to word2vec).''' , ) parser.add_argument('''--token_counts''' , type=__lowercase , 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=__lowercase , default=3 , help='''Number of pass on the whole dataset.''' ) parser.add_argument('''--batch_size''' , type=__lowercase , 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=__lowercase , default=5_0 , help='''Gradient accumulation for larger training batches.''' , ) parser.add_argument('''--warmup_prop''' , default=0.0_5 , type=__lowercase , help='''Linear warmup proportion.''' ) parser.add_argument('''--weight_decay''' , default=0.0 , type=__lowercase , help='''Weight decay if we apply some.''' ) parser.add_argument('''--learning_rate''' , default=5E-4 , type=__lowercase , help='''The initial learning rate for Adam.''' ) parser.add_argument('''--adam_epsilon''' , default=1E-6 , type=__lowercase , help='''Epsilon for Adam optimizer.''' ) parser.add_argument('''--max_grad_norm''' , default=5.0 , type=__lowercase , help='''Max gradient norm.''' ) parser.add_argument('''--initializer_range''' , default=0.0_2 , type=__lowercase , 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=__lowercase , 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=__lowercase , default=1 , help='''Number of GPUs in the node.''' ) parser.add_argument('''--local_rank''' , type=__lowercase , default=-1 , help='''Distributed training - Local rank''' ) parser.add_argument('''--seed''' , type=__lowercase , default=5_6 , help='''Random seed''' ) parser.add_argument('''--log_interval''' , type=__lowercase , default=5_0_0 , help='''Tensorboard logging interval.''' ) parser.add_argument('''--checkpoint_interval''' , type=__lowercase , default=4_0_0_0 , help='''Checkpoint interval.''' ) A: List[str] = parser.parse_args() sanity_checks(__lowercase ) # ARGS # init_gpu_params(__lowercase ) set_seed(__lowercase ) 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(__lowercase ) , __lowercase , indent=4 ) git_log(args.dump_path ) A , A , A: Union[str, Any] = MODEL_CLASSES[args.student_type] A , A , A: int = MODEL_CLASSES[args.teacher_type] # TOKENIZER # A: Tuple = teacher_tokenizer_class.from_pretrained(args.teacher_name ) A: int = {} for tok_name, tok_symbol in tokenizer.special_tokens_map.items(): A: int = tokenizer.all_special_tokens.index(__lowercase ) A: Any = tokenizer.all_special_ids[idx] logger.info(F"""Special tokens {special_tok_ids}""" ) A: Tuple = special_tok_ids A: Optional[int] = 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: A: Any = pickle.load(__lowercase ) if args.mlm: logger.info(F"""Loading token counts from {args.token_counts} (already pre-computed)""" ) with open(args.token_counts , '''rb''' ) as fp: A: Optional[int] = pickle.load(__lowercase ) A: str = np.maximum(__lowercase , 1 ) ** -args.mlm_smoothing for idx in special_tok_ids.values(): A: Any = 0.0 # do not predict special tokens A: Optional[Any] = torch.from_numpy(__lowercase ) else: A: Tuple = None A: Optional[int] = LmSeqsDataset(params=__lowercase , data=__lowercase ) logger.info('''Data loader created.''' ) # STUDENT # logger.info(F"""Loading student config from {args.student_config}""" ) A: Tuple = student_config_class.from_pretrained(args.student_config ) A: Any = True if args.student_pretrained_weights is not None: logger.info(F"""Loading pretrained weights from {args.student_pretrained_weights}""" ) A: str = student_model_class.from_pretrained(args.student_pretrained_weights , config=__lowercase ) else: A: int = student_model_class(__lowercase ) if args.n_gpu > 0: student.to(F"""cuda:{args.local_rank}""" ) logger.info('''Student loaded.''' ) # TEACHER # A: List[Any] = teacher_model_class.from_pretrained(args.teacher_name , output_hidden_states=__lowercase ) 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(__lowercase , __lowercase ) if args.freeze_token_type_embds: freeze_token_type_embeddings(__lowercase , __lowercase ) # 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() A: str = Distiller( params=__lowercase , dataset=__lowercase , token_probs=__lowercase , student=__lowercase , teacher=__lowercase ) distiller.train() logger.info('''Let\'s go get some drinks.''' ) if __name__ == "__main__": main()
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'''simple docstring''' def SCREAMING_SNAKE_CASE( __lowercase ) -> Tuple: A: Tuple = len(__lowercase ) for i in range(length - 1 ): A: Dict = i for k in range(i + 1 , __lowercase ): if collection[k] < collection[least]: A: List[str] = k if least != i: A , A: Tuple = (collection[i], collection[least]) return collection if __name__ == "__main__": UpperCamelCase = input('''Enter numbers separated by a comma:\n''').strip() UpperCamelCase = [int(item) for item in user_input.split(''',''')] print(selection_sort(unsorted))
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'''simple docstring''' from __future__ import annotations def SCREAMING_SNAKE_CASE( __lowercase , __lowercase ) -> Any: # Checks if the entire collection has been sorted if len(__lowercase ) <= 1 or n <= 1: return insert_next(__lowercase , n - 1 ) rec_insertion_sort(__lowercase , n - 1 ) def SCREAMING_SNAKE_CASE( __lowercase , __lowercase ) -> Dict: # Checks order between adjacent elements if index >= len(__lowercase ) or collection[index - 1] <= collection[index]: return # Swaps adjacent elements since they are not in ascending order A , A: List[str] = ( collection[index], collection[index - 1], ) insert_next(__lowercase , index + 1 ) if __name__ == "__main__": UpperCamelCase = input('''Enter integers separated by spaces: ''') UpperCamelCase = [int(num) for num in numbers.split()] rec_insertion_sort(number_list, len(number_list)) print(number_list)
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'''simple docstring''' class lowerCAmelCase_ : '''simple docstring''' def __init__( self : Any , SCREAMING_SNAKE_CASE_ : str , SCREAMING_SNAKE_CASE_ : Optional[int] , SCREAMING_SNAKE_CASE_ : List[str] ) -> int: '''simple docstring''' A: Tuple = None A: Dict = None A: Optional[int] = graph self._normalize_graph(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) A: str = len(SCREAMING_SNAKE_CASE_ ) A: Optional[Any] = None def _snake_case ( self : Tuple , SCREAMING_SNAKE_CASE_ : Optional[int] , SCREAMING_SNAKE_CASE_ : Dict ) -> str: '''simple docstring''' if sources is int: A: Union[str, Any] = [sources] if sinks is int: A: Tuple = [sinks] if len(SCREAMING_SNAKE_CASE_ ) == 0 or len(SCREAMING_SNAKE_CASE_ ) == 0: return A: List[str] = sources[0] A: Optional[int] = sinks[0] # make fake vertex if there are more # than one source or sink if len(SCREAMING_SNAKE_CASE_ ) > 1 or len(SCREAMING_SNAKE_CASE_ ) > 1: A: Any = 0 for i in sources: max_input_flow += sum(self.graph[i] ) A: Dict = len(self.graph ) + 1 for room in self.graph: room.insert(0 , 0 ) self.graph.insert(0 , [0] * size ) for i in sources: A: Optional[Any] = max_input_flow A: Optional[Any] = 0 A: str = len(self.graph ) + 1 for room in self.graph: room.append(0 ) self.graph.append([0] * size ) for i in sinks: A: Optional[Any] = max_input_flow A: str = size - 1 def _snake_case ( self : List[Any] ) -> Union[str, Any]: '''simple docstring''' if self.maximum_flow_algorithm is None: raise Exception('''You need to set maximum flow algorithm before.''' ) if self.source_index is None or self.sink_index is None: return 0 self.maximum_flow_algorithm.execute() return self.maximum_flow_algorithm.getMaximumFlow() def _snake_case ( self : Tuple , SCREAMING_SNAKE_CASE_ : Union[str, Any] ) -> Tuple: '''simple docstring''' A: Optional[Any] = algorithm(self ) class lowerCAmelCase_ : '''simple docstring''' def __init__( self : int , SCREAMING_SNAKE_CASE_ : List[Any] ) -> Optional[Any]: '''simple docstring''' A: str = flow_network A: List[str] = flow_network.verticesCount A: Dict = flow_network.sourceIndex A: Any = flow_network.sinkIndex # it's just a reference, so you shouldn't change # it in your algorithms, use deep copy before doing that A: str = flow_network.graph A: str = False def _snake_case ( self : int ) -> Union[str, Any]: '''simple docstring''' if not self.executed: self._algorithm() A: str = True def _snake_case ( self : Tuple ) -> Optional[Any]: '''simple docstring''' pass class lowerCAmelCase_ ( UpperCAmelCase_ ): '''simple docstring''' def __init__( self : List[str] , SCREAMING_SNAKE_CASE_ : List[str] ) -> Optional[int]: '''simple docstring''' super().__init__(SCREAMING_SNAKE_CASE_ ) # use this to save your result A: Any = -1 def _snake_case ( self : List[str] ) -> Optional[Any]: '''simple docstring''' if not self.executed: raise Exception('''You should execute algorithm before using its result!''' ) return self.maximum_flow class lowerCAmelCase_ ( UpperCAmelCase_ ): '''simple docstring''' def __init__( self : int , SCREAMING_SNAKE_CASE_ : Any ) -> Optional[int]: '''simple docstring''' super().__init__(SCREAMING_SNAKE_CASE_ ) A: Optional[int] = [[0] * self.verticies_count for i in range(self.verticies_count )] A: Any = [0] * self.verticies_count A: Optional[Any] = [0] * self.verticies_count def _snake_case ( self : str ) -> Optional[Any]: '''simple docstring''' A: Any = self.verticies_count # push some substance to graph for nextvertex_index, bandwidth in enumerate(self.graph[self.source_index] ): self.preflow[self.source_index][nextvertex_index] += bandwidth self.preflow[nextvertex_index][self.source_index] -= bandwidth self.excesses[nextvertex_index] += bandwidth # Relabel-to-front selection rule A: str = [ i for i in range(self.verticies_count ) if i != self.source_index and i != self.sink_index ] # move through list A: Dict = 0 while i < len(SCREAMING_SNAKE_CASE_ ): A: Any = vertices_list[i] A: str = self.heights[vertex_index] self.process_vertex(SCREAMING_SNAKE_CASE_ ) if self.heights[vertex_index] > previous_height: # if it was relabeled, swap elements # and start from 0 index vertices_list.insert(0 , vertices_list.pop(SCREAMING_SNAKE_CASE_ ) ) A: Tuple = 0 else: i += 1 A: Tuple = sum(self.preflow[self.source_index] ) def _snake_case ( self : int , SCREAMING_SNAKE_CASE_ : List[str] ) -> str: '''simple docstring''' while self.excesses[vertex_index] > 0: for neighbour_index in range(self.verticies_count ): # if it's neighbour and current vertex is higher if ( self.graph[vertex_index][neighbour_index] - self.preflow[vertex_index][neighbour_index] > 0 and self.heights[vertex_index] > self.heights[neighbour_index] ): self.push(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) self.relabel(SCREAMING_SNAKE_CASE_ ) def _snake_case ( self : Union[str, Any] , SCREAMING_SNAKE_CASE_ : Union[str, Any] , SCREAMING_SNAKE_CASE_ : Optional[Any] ) -> int: '''simple docstring''' A: Optional[int] = min( self.excesses[from_index] , self.graph[from_index][to_index] - self.preflow[from_index][to_index] , ) self.preflow[from_index][to_index] += preflow_delta self.preflow[to_index][from_index] -= preflow_delta self.excesses[from_index] -= preflow_delta self.excesses[to_index] += preflow_delta def _snake_case ( self : Optional[int] , SCREAMING_SNAKE_CASE_ : str ) -> int: '''simple docstring''' A: Optional[Any] = None for to_index in range(self.verticies_count ): if ( self.graph[vertex_index][to_index] - self.preflow[vertex_index][to_index] > 0 ) and (min_height is None or self.heights[to_index] < min_height): A: List[Any] = self.heights[to_index] if min_height is not None: A: int = min_height + 1 if __name__ == "__main__": UpperCamelCase = [0] UpperCamelCase = [3] # graph = [ # [0, 0, 4, 6, 0, 0], # [0, 0, 5, 2, 0, 0], # [0, 0, 0, 0, 4, 4], # [0, 0, 0, 0, 6, 6], # [0, 0, 0, 0, 0, 0], # [0, 0, 0, 0, 0, 0], # ] UpperCamelCase = [[0, 7, 0, 0], [0, 0, 6, 0], [0, 0, 0, 8], [9, 0, 0, 0]] # prepare our network UpperCamelCase = FlowNetwork(graph, entrances, exits) # set algorithm flow_network.set_maximum_flow_algorithm(PushRelabelExecutor) # and calculate UpperCamelCase = flow_network.find_maximum_flow() print(f'maximum flow is {maximum_flow}')
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'''simple docstring''' from ...configuration_utils import PretrainedConfig from ...utils import logging UpperCamelCase = logging.get_logger(__name__) UpperCamelCase = { '''bigcode/gpt_bigcode-santacoder''': '''https://huggingface.co/bigcode/gpt_bigcode-santacoder/resolve/main/config.json''', } class lowerCAmelCase_ ( UpperCAmelCase_ ): '''simple docstring''' UpperCamelCase_ : List[str] = """gpt_bigcode""" UpperCamelCase_ : Tuple = ["""past_key_values"""] UpperCamelCase_ : Optional[Any] = { """hidden_size""": """n_embd""", """max_position_embeddings""": """n_positions""", """num_attention_heads""": """n_head""", """num_hidden_layers""": """n_layer""", } def __init__( self : str , SCREAMING_SNAKE_CASE_ : Optional[Any]=5_02_57 , SCREAMING_SNAKE_CASE_ : Optional[int]=10_24 , SCREAMING_SNAKE_CASE_ : Optional[Any]=7_68 , SCREAMING_SNAKE_CASE_ : Union[str, Any]=12 , SCREAMING_SNAKE_CASE_ : List[Any]=12 , SCREAMING_SNAKE_CASE_ : List[str]=None , SCREAMING_SNAKE_CASE_ : Union[str, Any]="gelu_pytorch_tanh" , SCREAMING_SNAKE_CASE_ : Dict=0.1 , SCREAMING_SNAKE_CASE_ : Optional[int]=0.1 , SCREAMING_SNAKE_CASE_ : str=0.1 , SCREAMING_SNAKE_CASE_ : Optional[int]=1E-5 , SCREAMING_SNAKE_CASE_ : Optional[Any]=0.02 , SCREAMING_SNAKE_CASE_ : Tuple=True , SCREAMING_SNAKE_CASE_ : List[Any]=True , SCREAMING_SNAKE_CASE_ : Optional[Any]=5_02_56 , SCREAMING_SNAKE_CASE_ : str=5_02_56 , SCREAMING_SNAKE_CASE_ : Any=True , SCREAMING_SNAKE_CASE_ : Tuple=True , SCREAMING_SNAKE_CASE_ : Optional[int]=True , **SCREAMING_SNAKE_CASE_ : List[str] , ) -> Optional[Any]: '''simple docstring''' A: str = vocab_size A: Dict = n_positions A: Any = n_embd A: Dict = n_layer A: Tuple = n_head A: List[str] = n_inner A: str = activation_function A: Optional[Any] = resid_pdrop A: int = embd_pdrop A: Optional[int] = attn_pdrop A: List[str] = layer_norm_epsilon A: Optional[Any] = initializer_range A: Union[str, Any] = scale_attn_weights A: Dict = use_cache A: int = attention_softmax_in_fpaa A: str = scale_attention_softmax_in_fpaa A: int = multi_query A: Optional[Any] = bos_token_id A: Any = eos_token_id super().__init__(bos_token_id=SCREAMING_SNAKE_CASE_ , eos_token_id=SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
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'''simple docstring''' from typing import List, Optional, Union import numpy as np import torch import torchaudio.compliance.kaldi as ta_kaldi from ...feature_extraction_sequence_utils import SequenceFeatureExtractor from ...feature_extraction_utils import BatchFeature from ...utils import PaddingStrategy, TensorType, logging UpperCamelCase = logging.get_logger(__name__) class lowerCAmelCase_ ( UpperCAmelCase_ ): '''simple docstring''' UpperCamelCase_ : Optional[Any] = ["""input_features""", """attention_mask"""] def __init__( self : List[Any] , SCREAMING_SNAKE_CASE_ : Tuple=80 , SCREAMING_SNAKE_CASE_ : Union[str, Any]=1_60_00 , SCREAMING_SNAKE_CASE_ : int=80 , SCREAMING_SNAKE_CASE_ : Union[str, Any]=0.0 , SCREAMING_SNAKE_CASE_ : Any=True , SCREAMING_SNAKE_CASE_ : Tuple=True , SCREAMING_SNAKE_CASE_ : Union[str, Any]=True , **SCREAMING_SNAKE_CASE_ : List[str] , ) -> List[Any]: '''simple docstring''' super().__init__(feature_size=SCREAMING_SNAKE_CASE_ , sampling_rate=SCREAMING_SNAKE_CASE_ , padding_value=SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ ) A: Union[str, Any] = num_mel_bins A: str = do_ceptral_normalize A: int = normalize_means A: List[Any] = normalize_vars A: Any = True def _snake_case ( self : Any , SCREAMING_SNAKE_CASE_ : np.ndarray , ) -> np.ndarray: '''simple docstring''' A: Optional[int] = waveform * (2**15) # Kaldi compliance: 16-bit signed integers A: Optional[int] = torch.from_numpy(SCREAMING_SNAKE_CASE_ ).unsqueeze(0 ) A: List[Any] = ta_kaldi.fbank(SCREAMING_SNAKE_CASE_ , num_mel_bins=self.num_mel_bins , sample_frequency=self.sampling_rate ) return features.numpy() @staticmethod def _snake_case ( SCREAMING_SNAKE_CASE_ : np.ndarray , SCREAMING_SNAKE_CASE_ : int , SCREAMING_SNAKE_CASE_ : Optional[bool] = True , SCREAMING_SNAKE_CASE_ : Optional[bool] = True , SCREAMING_SNAKE_CASE_ : float = 0.0 , ) -> np.ndarray: '''simple docstring''' if normalize_means: A: str = x[:input_length].mean(axis=0 ) A: Dict = np.subtract(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) if normalize_vars: A: Tuple = x[:input_length].std(axis=0 ) A: List[Any] = np.divide(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) if input_length < x.shape[0]: A: Optional[int] = padding_value # make sure array is in float32 A: Optional[Any] = x.astype(np.floataa ) return x def _snake_case ( self : Optional[Any] , SCREAMING_SNAKE_CASE_ : List[np.ndarray] , SCREAMING_SNAKE_CASE_ : Optional[np.ndarray] = None ) -> List[np.ndarray]: '''simple docstring''' A: int = attention_mask.sum(-1 ) if attention_mask is not None else [x.shape[0] for x in input_features] return [ self.utterance_cmvn(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , self.normalize_means , self.normalize_vars , self.padding_value ) for x, n in zip(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) ] def __call__( self : Tuple , SCREAMING_SNAKE_CASE_ : Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]] , SCREAMING_SNAKE_CASE_ : Union[bool, str, PaddingStrategy] = False , SCREAMING_SNAKE_CASE_ : Optional[int] = None , SCREAMING_SNAKE_CASE_ : bool = False , SCREAMING_SNAKE_CASE_ : Optional[int] = None , SCREAMING_SNAKE_CASE_ : Optional[Union[str, TensorType]] = None , SCREAMING_SNAKE_CASE_ : Optional[int] = None , SCREAMING_SNAKE_CASE_ : Optional[bool] = None , **SCREAMING_SNAKE_CASE_ : Dict , ) -> 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 `raw_speech` 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.''' ) A: Any = isinstance(SCREAMING_SNAKE_CASE_ , np.ndarray ) and len(raw_speech.shape ) > 1 if is_batched_numpy and len(raw_speech.shape ) > 2: raise ValueError(f"""Only mono-channel audio is supported for input to {self}""" ) A: Optional[Any] = is_batched_numpy or ( isinstance(SCREAMING_SNAKE_CASE_ , (list, tuple) ) and (isinstance(raw_speech[0] , (np.ndarray, tuple, list) )) ) if is_batched: A: Optional[int] = [np.asarray(SCREAMING_SNAKE_CASE_ , dtype=np.floataa ) for speech in raw_speech] elif not is_batched and not isinstance(SCREAMING_SNAKE_CASE_ , np.ndarray ): A: int = np.asarray(SCREAMING_SNAKE_CASE_ , dtype=np.floataa ) elif isinstance(SCREAMING_SNAKE_CASE_ , np.ndarray ) and raw_speech.dtype is np.dtype(np.floataa ): A: Any = raw_speech.astype(np.floataa ) # always return batch if not is_batched: A: Union[str, Any] = [raw_speech] # extract fbank features A: str = [self._extract_fbank_features(SCREAMING_SNAKE_CASE_ ) for waveform in raw_speech] # convert into correct format for padding A: int = BatchFeature({'''input_features''': features} ) A: int = self.pad( SCREAMING_SNAKE_CASE_ , padding=SCREAMING_SNAKE_CASE_ , max_length=SCREAMING_SNAKE_CASE_ , truncation=SCREAMING_SNAKE_CASE_ , pad_to_multiple_of=SCREAMING_SNAKE_CASE_ , return_attention_mask=SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ , ) # make sure list is in array format A: List[str] = padded_inputs.get('''input_features''' ) if isinstance(input_features[0] , SCREAMING_SNAKE_CASE_ ): A: Optional[Any] = [np.asarray(SCREAMING_SNAKE_CASE_ , dtype=np.floataa ) for feature in input_features] A: List[Any] = padded_inputs.get('''attention_mask''' ) if attention_mask is not None: A: Dict = [np.asarray(SCREAMING_SNAKE_CASE_ , dtype=np.intaa ) for array in attention_mask] # Utterance-level cepstral mean and variance normalization if self.do_ceptral_normalize: A: Dict = ( np.array(SCREAMING_SNAKE_CASE_ , dtype=np.intaa ) if self._get_padding_strategies(SCREAMING_SNAKE_CASE_ , max_length=SCREAMING_SNAKE_CASE_ ) is not PaddingStrategy.DO_NOT_PAD else None ) A: List[Any] = self.normalize( padded_inputs['''input_features'''] , attention_mask=SCREAMING_SNAKE_CASE_ ) if return_tensors is not None: A: Dict = padded_inputs.convert_to_tensors(SCREAMING_SNAKE_CASE_ ) return padded_inputs
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'''simple docstring''' import argparse import json from pathlib import Path import requests import torch from huggingface_hub import hf_hub_download from PIL import Image from transformers import YolosConfig, YolosForObjectDetection, YolosImageProcessor from transformers.utils import logging logging.set_verbosity_info() UpperCamelCase = logging.get_logger(__name__) def SCREAMING_SNAKE_CASE( __lowercase ) -> YolosConfig: A: List[Any] = YolosConfig() # size of the architecture if "yolos_ti" in yolos_name: A: List[str] = 1_9_2 A: Dict = 7_6_8 A: Optional[Any] = 1_2 A: str = 3 A: Optional[int] = [8_0_0, 1_3_3_3] A: Union[str, Any] = False elif yolos_name == "yolos_s_dWr": A: Dict = 3_3_0 A: Any = 1_4 A: Union[str, Any] = 6 A: int = 1_3_2_0 elif "yolos_s" in yolos_name: A: Optional[int] = 3_8_4 A: List[str] = 1_5_3_6 A: Dict = 1_2 A: Dict = 6 elif "yolos_b" in yolos_name: A: Dict = [8_0_0, 1_3_4_4] A: List[Any] = 9_1 A: Union[str, Any] = '''huggingface/label-files''' A: Dict = '''coco-detection-id2label.json''' A: Optional[Any] = json.load(open(hf_hub_download(__lowercase , __lowercase , repo_type='''dataset''' ) , '''r''' ) ) A: int = {int(__lowercase ): v for k, v in idalabel.items()} A: List[Any] = idalabel A: List[Any] = {v: k for k, v in idalabel.items()} return config def SCREAMING_SNAKE_CASE( __lowercase , __lowercase , __lowercase = False ) -> str: for i in range(config.num_hidden_layers ): # read in weights + bias of input projection layer (in timm, this is a single matrix + bias) A: Optional[int] = state_dict.pop(F"""blocks.{i}.attn.qkv.weight""" ) A: int = state_dict.pop(F"""blocks.{i}.attn.qkv.bias""" ) # next, add query, keys and values (in that order) to the state dict A: Optional[Any] = in_proj_weight[: config.hidden_size, :] A: List[str] = in_proj_bias[: config.hidden_size] A: Dict = in_proj_weight[ config.hidden_size : config.hidden_size * 2, : ] A: List[str] = in_proj_bias[ config.hidden_size : config.hidden_size * 2 ] A: Dict = in_proj_weight[-config.hidden_size :, :] A: List[Any] = in_proj_bias[-config.hidden_size :] def SCREAMING_SNAKE_CASE( __lowercase ) -> str: if "backbone" in name: A: List[str] = name.replace('''backbone''' , '''vit''' ) if "cls_token" in name: A: str = name.replace('''cls_token''' , '''embeddings.cls_token''' ) if "det_token" in name: A: Union[str, Any] = name.replace('''det_token''' , '''embeddings.detection_tokens''' ) if "mid_pos_embed" in name: A: Any = name.replace('''mid_pos_embed''' , '''encoder.mid_position_embeddings''' ) if "pos_embed" in name: A: str = name.replace('''pos_embed''' , '''embeddings.position_embeddings''' ) if "patch_embed.proj" in name: A: List[Any] = name.replace('''patch_embed.proj''' , '''embeddings.patch_embeddings.projection''' ) if "blocks" in name: A: str = name.replace('''blocks''' , '''encoder.layer''' ) if "attn.proj" in name: A: int = name.replace('''attn.proj''' , '''attention.output.dense''' ) if "attn" in name: A: Dict = name.replace('''attn''' , '''attention.self''' ) if "norm1" in name: A: Tuple = name.replace('''norm1''' , '''layernorm_before''' ) if "norm2" in name: A: List[Any] = name.replace('''norm2''' , '''layernorm_after''' ) if "mlp.fc1" in name: A: Union[str, Any] = name.replace('''mlp.fc1''' , '''intermediate.dense''' ) if "mlp.fc2" in name: A: List[Any] = name.replace('''mlp.fc2''' , '''output.dense''' ) if "class_embed" in name: A: List[str] = name.replace('''class_embed''' , '''class_labels_classifier''' ) if "bbox_embed" in name: A: Tuple = name.replace('''bbox_embed''' , '''bbox_predictor''' ) if "vit.norm" in name: A: Tuple = name.replace('''vit.norm''' , '''vit.layernorm''' ) return name def SCREAMING_SNAKE_CASE( __lowercase , __lowercase ) -> dict: for key in orig_state_dict.copy().keys(): A: Optional[Any] = orig_state_dict.pop(__lowercase ) if "qkv" in key: A: Any = key.split('''.''' ) A: Optional[int] = int(key_split[2] ) A: Union[str, Any] = model.vit.encoder.layer[layer_num].attention.attention.all_head_size if "weight" in key: A: str = val[:dim, :] A: int = val[ dim : dim * 2, : ] A: Union[str, Any] = val[-dim:, :] else: A: Any = val[:dim] A: Optional[Any] = val[dim : dim * 2] A: Dict = val[-dim:] else: A: Dict = val return orig_state_dict def SCREAMING_SNAKE_CASE( ) -> torch.Tensor: A: int = '''http://images.cocodataset.org/val2017/000000039769.jpg''' A: int = Image.open(requests.get(__lowercase , stream=__lowercase ).raw ) return im @torch.no_grad() def SCREAMING_SNAKE_CASE( __lowercase , __lowercase , __lowercase , __lowercase = False ) -> Optional[int]: A: int = get_yolos_config(__lowercase ) # load original state_dict A: Tuple = torch.load(__lowercase , map_location='''cpu''' )['''model'''] # load 🤗 model A: List[str] = YolosForObjectDetection(__lowercase ) model.eval() A: List[Any] = convert_state_dict(__lowercase , __lowercase ) model.load_state_dict(__lowercase ) # Check outputs on an image, prepared by YolosImageProcessor A: Tuple = 8_0_0 if yolos_name != '''yolos_ti''' else 5_1_2 A: Any = YolosImageProcessor(format='''coco_detection''' , size=__lowercase ) A: Union[str, Any] = image_processor(images=prepare_img() , return_tensors='''pt''' ) A: Union[str, Any] = model(**__lowercase ) A , A: Dict = outputs.logits, outputs.pred_boxes A , A: Tuple = None, None if yolos_name == "yolos_ti": A: List[Any] = torch.tensor( [[-3_9.5_0_2_2, -1_1.9_8_2_0, -1_7.6_8_8_8], [-2_9.9_5_7_4, -9.9_7_6_9, -1_7.7_6_9_1], [-4_2.3_2_8_1, -2_0.7_2_0_0, -3_0.6_2_9_4]] ) A: List[Any] = torch.tensor( [[0.4_0_2_1, 0.0_8_3_6, 0.7_9_7_9], [0.0_1_8_4, 0.2_6_0_9, 0.0_3_6_4], [0.1_7_8_1, 0.2_0_0_4, 0.2_0_9_5]] ) elif yolos_name == "yolos_s_200_pre": A: Tuple = torch.tensor( [[-2_4.0_2_4_8, -1_0.3_0_2_4, -1_4.8_2_9_0], [-4_2.0_3_9_2, -1_6.8_2_0_0, -2_7.4_3_3_4], [-2_7.2_7_4_3, -1_1.8_1_5_4, -1_8.7_1_4_8]] ) A: List[str] = torch.tensor( [[0.2_5_5_9, 0.5_4_5_5, 0.4_7_0_6], [0.2_9_8_9, 0.7_2_7_9, 0.1_8_7_5], [0.7_7_3_2, 0.4_0_1_7, 0.4_4_6_2]] ) elif yolos_name == "yolos_s_300_pre": A: Optional[int] = torch.tensor( [[-3_6.2_2_2_0, -1_4.4_3_8_5, -2_3.5_4_5_7], [-3_5.6_9_7_0, -1_4.7_5_8_3, -2_1.3_9_3_5], [-3_1.5_9_3_9, -1_3.6_0_4_2, -1_6.8_0_4_9]] ) A: Optional[int] = torch.tensor( [[0.7_6_1_4, 0.2_3_1_6, 0.4_7_2_8], [0.7_1_6_8, 0.4_4_9_5, 0.3_8_5_5], [0.4_9_9_6, 0.1_4_6_6, 0.9_9_9_6]] ) elif yolos_name == "yolos_s_dWr": A: Dict = torch.tensor( [[-4_2.8_6_6_8, -2_4.1_0_4_9, -4_1.1_6_9_0], [-3_4.7_4_5_6, -1_4.1_2_7_4, -2_4.9_1_9_4], [-3_3.7_8_9_8, -1_2.1_9_4_6, -2_5.6_4_9_5]] ) A: List[Any] = torch.tensor( [[0.5_5_8_7, 0.2_7_7_3, 0.0_6_0_5], [0.5_0_0_4, 0.3_0_1_4, 0.9_9_9_4], [0.4_9_9_9, 0.1_5_4_8, 0.9_9_9_4]] ) elif yolos_name == "yolos_base": A: Any = torch.tensor( [[-4_0.6_0_6_4, -2_4.3_0_8_4, -3_2.6_4_4_7], [-5_5.1_9_9_0, -3_0.7_7_1_9, -3_5.5_8_7_7], [-5_1.4_3_1_1, -3_3.3_5_0_7, -3_5.6_4_6_2]] ) A: str = torch.tensor( [[0.5_5_5_5, 0.2_7_9_4, 0.0_6_5_5], [0.9_0_4_9, 0.2_6_6_4, 0.1_8_9_4], [0.9_1_8_3, 0.1_9_8_4, 0.1_6_3_5]] ) else: raise ValueError(F"""Unknown yolos_name: {yolos_name}""" ) assert torch.allclose(logits[0, :3, :3] , __lowercase , atol=1E-4 ) assert torch.allclose(pred_boxes[0, :3, :3] , __lowercase , atol=1E-4 ) Path(__lowercase ).mkdir(exist_ok=__lowercase ) print(F"""Saving model {yolos_name} to {pytorch_dump_folder_path}""" ) model.save_pretrained(__lowercase ) print(F"""Saving image processor to {pytorch_dump_folder_path}""" ) image_processor.save_pretrained(__lowercase ) if push_to_hub: A: List[Any] = { '''yolos_ti''': '''yolos-tiny''', '''yolos_s_200_pre''': '''yolos-small''', '''yolos_s_300_pre''': '''yolos-small-300''', '''yolos_s_dWr''': '''yolos-small-dwr''', '''yolos_base''': '''yolos-base''', } print('''Pushing to the hub...''' ) A: Union[str, Any] = model_mapping[yolos_name] image_processor.push_to_hub(__lowercase , organization='''hustvl''' ) model.push_to_hub(__lowercase , organization='''hustvl''' ) if __name__ == "__main__": UpperCamelCase = argparse.ArgumentParser() # Required parameters parser.add_argument( '''--yolos_name''', default='''yolos_s_200_pre''', type=str, help=( '''Name of the YOLOS model you\'d like to convert. Should be one of \'yolos_ti\', \'yolos_s_200_pre\',''' ''' \'yolos_s_300_pre\', \'yolos_s_dWr\', \'yolos_base\'.''' ), ) parser.add_argument( '''--checkpoint_path''', default=None, type=str, help='''Path to the original state dict (.pth file).''' ) parser.add_argument( '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model directory.''' ) parser.add_argument( '''--push_to_hub''', action='''store_true''', help='''Whether or not to push the converted model to the 🤗 hub.''' ) UpperCamelCase = parser.parse_args() convert_yolos_checkpoint(args.yolos_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)
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'''simple docstring''' import json import os import unittest from transformers import DebertaTokenizer, DebertaTokenizerFast from transformers.models.deberta.tokenization_deberta import VOCAB_FILES_NAMES from transformers.testing_utils import slow from ...test_tokenization_common import TokenizerTesterMixin class lowerCAmelCase_ ( UpperCAmelCase_ , unittest.TestCase ): '''simple docstring''' UpperCamelCase_ : Optional[Any] = DebertaTokenizer UpperCamelCase_ : List[str] = True UpperCamelCase_ : int = DebertaTokenizerFast def _snake_case ( self : Optional[int] ) -> Dict: '''simple docstring''' super().setUp() # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt A: Optional[int] = [ '''l''', '''o''', '''w''', '''e''', '''r''', '''s''', '''t''', '''i''', '''d''', '''n''', '''\u0120''', '''\u0120l''', '''\u0120n''', '''\u0120lo''', '''\u0120low''', '''er''', '''\u0120lowest''', '''\u0120newer''', '''\u0120wider''', '''[UNK]''', ] A: int = dict(zip(SCREAMING_SNAKE_CASE_ , range(len(SCREAMING_SNAKE_CASE_ ) ) ) ) A: Union[str, Any] = ['''#version: 0.2''', '''\u0120 l''', '''\u0120l o''', '''\u0120lo w''', '''e r''', ''''''] A: Union[str, Any] = {'''unk_token''': '''[UNK]'''} A: List[str] = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['''vocab_file'''] ) A: str = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['''merges_file'''] ) with open(self.vocab_file , '''w''' , encoding='''utf-8''' ) as fp: fp.write(json.dumps(SCREAMING_SNAKE_CASE_ ) + '''\n''' ) with open(self.merges_file , '''w''' , encoding='''utf-8''' ) as fp: fp.write('''\n'''.join(SCREAMING_SNAKE_CASE_ ) ) def _snake_case ( self : int , **SCREAMING_SNAKE_CASE_ : List[str] ) -> Tuple: '''simple docstring''' kwargs.update(self.special_tokens_map ) return self.tokenizer_class.from_pretrained(self.tmpdirname , **SCREAMING_SNAKE_CASE_ ) def _snake_case ( self : Tuple , SCREAMING_SNAKE_CASE_ : Dict ) -> Union[str, Any]: '''simple docstring''' A: Optional[int] = '''lower newer''' A: str = '''lower newer''' return input_text, output_text def _snake_case ( self : Union[str, Any] ) -> Dict: '''simple docstring''' A: str = self.get_tokenizer() A: Any = '''lower newer''' A: Dict = ['''l''', '''o''', '''w''', '''er''', '''\u0120''', '''n''', '''e''', '''w''', '''er'''] A: int = tokenizer.tokenize(SCREAMING_SNAKE_CASE_ ) self.assertListEqual(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) A: List[Any] = tokens + [tokenizer.unk_token] A: int = [0, 1, 2, 15, 10, 9, 3, 2, 15, 19] self.assertListEqual(tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE_ ) , SCREAMING_SNAKE_CASE_ ) def _snake_case ( self : List[Any] ) -> Any: '''simple docstring''' A: str = self.get_tokenizer() A: List[str] = tokenizer('''Hello''' , '''World''' ) A: Union[str, Any] = [0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1] self.assertListEqual(tokd['''token_type_ids'''] , SCREAMING_SNAKE_CASE_ ) @slow def _snake_case ( self : Tuple ) -> Optional[int]: '''simple docstring''' A: Union[str, Any] = self.tokenizer_class.from_pretrained('''microsoft/deberta-base''' ) A: Any = tokenizer.encode('''sequence builders''' , add_special_tokens=SCREAMING_SNAKE_CASE_ ) A: Union[str, Any] = tokenizer.encode('''multi-sequence build''' , add_special_tokens=SCREAMING_SNAKE_CASE_ ) A: Dict = tokenizer.encode( '''sequence builders''' , add_special_tokens=SCREAMING_SNAKE_CASE_ , add_prefix_space=SCREAMING_SNAKE_CASE_ ) A: Dict = tokenizer.encode( '''sequence builders''' , '''multi-sequence build''' , add_special_tokens=SCREAMING_SNAKE_CASE_ , add_prefix_space=SCREAMING_SNAKE_CASE_ ) A: List[Any] = tokenizer.build_inputs_with_special_tokens(SCREAMING_SNAKE_CASE_ ) A: int = tokenizer.build_inputs_with_special_tokens(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) assert encoded_sentence == encoded_text_from_decode assert encoded_pair == encoded_pair_from_decode @slow def _snake_case ( self : Tuple ) -> Dict: '''simple docstring''' A: int = [self.tokenizer_class] if self.test_rust_tokenizer: tokenizer_classes.append(self.rust_tokenizer_class ) for tokenizer_class in tokenizer_classes: A: List[Any] = tokenizer_class.from_pretrained('''microsoft/deberta-base''' ) A: Dict = [ '''ALBERT: A Lite BERT for Self-supervised Learning of Language Representations''', '''ALBERT incorporates two parameter reduction techniques''', '''The first one is a factorized embedding parameterization. By decomposing the large vocabulary''' ''' embedding matrix into two small matrices, we separate the size of the hidden layers from the size of''' ''' vocabulary embedding.''', ] A: Dict = tokenizer(SCREAMING_SNAKE_CASE_ , padding=SCREAMING_SNAKE_CASE_ ) A: Any = [tokenizer.decode(SCREAMING_SNAKE_CASE_ , skip_special_tokens=SCREAMING_SNAKE_CASE_ ) for seq in encoding['''input_ids''']] # fmt: off A: Any = { '''input_ids''': [ [1, 21_18, 1_11_26, 5_65, 35, 83, 2_51_91, 1_63, 1_88_54, 13, 1_21_56, 12, 1_61_01, 2_53_76, 1_38_07, 9, 2_22_05, 2_78_93, 16_35, 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], [1, 21_18, 1_11_26, 5_65, 2_45_36, 80, 4_37_97, 48_78, 73_73, 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], [1, 1_33, 78, 65, 16, 10, 37_24, 15_38, 3_31_83, 1_13_03, 4_37_97, 19_38, 4, 8_70, 2_41_65, 2_91_05, 5, 7_39, 3_26_44, 3_31_83, 1_13_03, 3_61_73, 88, 80, 6_50, 78_21, 4_59_40, 6, 52, 25_59, 5, 18_36, 9, 5, 73_97, 1_31_71, 31, 5, 18_36, 9, 3_26_44, 3_31_83, 1_13_03, 4, 2] ], '''token_type_ids''': [ [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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, 0, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1] ] } # fmt: on A: Optional[int] = [ '''ALBERT: A Lite BERT for Self-supervised Learning of Language Representations''', '''ALBERT incorporates two parameter reduction techniques''', '''The first one is a factorized embedding parameterization. By decomposing the large vocabulary''' ''' embedding matrix into two small matrices, we separate the size of the hidden layers from the size of''' ''' vocabulary embedding.''', ] self.assertDictEqual(encoding.data , SCREAMING_SNAKE_CASE_ ) for expected, decoded in zip(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ): self.assertEqual(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
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'''simple docstring''' import math import numpy as np import qiskit from qiskit import Aer, ClassicalRegister, QuantumCircuit, QuantumRegister, execute def SCREAMING_SNAKE_CASE( __lowercase = 3 ) -> qiskit.result.counts.Counts: if isinstance(__lowercase , __lowercase ): raise TypeError('''number of qubits must be a integer.''' ) if number_of_qubits <= 0: raise ValueError('''number of qubits must be > 0.''' ) if math.floor(__lowercase ) != number_of_qubits: raise ValueError('''number of qubits must be exact integer.''' ) if number_of_qubits > 1_0: raise ValueError('''number of qubits too large to simulate(>10).''' ) A: Optional[int] = QuantumRegister(__lowercase , '''qr''' ) A: Tuple = ClassicalRegister(__lowercase , '''cr''' ) A: Union[str, Any] = QuantumCircuit(__lowercase , __lowercase ) A: Union[str, Any] = number_of_qubits for i in range(__lowercase ): quantum_circuit.h(number_of_qubits - i - 1 ) counter -= 1 for j in range(__lowercase ): quantum_circuit.cp(np.pi / 2 ** (counter - j) , __lowercase , __lowercase ) for k in range(number_of_qubits // 2 ): quantum_circuit.swap(__lowercase , number_of_qubits - k - 1 ) # measure all the qubits quantum_circuit.measure(__lowercase , __lowercase ) # simulate with 10000 shots A: List[Any] = Aer.get_backend('''qasm_simulator''' ) A: List[str] = execute(__lowercase , __lowercase , shots=1_0_0_0_0 ) return job.result().get_counts(__lowercase ) if __name__ == "__main__": print( f'Total count for quantum fourier transform state is: \ {quantum_fourier_transform(3)}' )
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'''simple docstring''' import requests UpperCamelCase = '''https://newsapi.org/v1/articles?source=bbc-news&sortBy=top&apiKey=''' def SCREAMING_SNAKE_CASE( __lowercase ) -> None: # fetching a list of articles in json format A: Tuple = requests.get(_NEWS_API + bbc_news_api_key ).json() # each article in the list is a dict for i, article in enumerate(bbc_news_page['''articles'''] , 1 ): print(F"""{i}.) {article['title']}""" ) if __name__ == "__main__": fetch_bbc_news(bbc_news_api_key='''<Your BBC News API key goes here>''')
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'''simple docstring''' from typing import TYPE_CHECKING from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available UpperCamelCase = { '''configuration_upernet''': ['''UperNetConfig'''], } try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: UpperCamelCase = [ '''UperNetForSemanticSegmentation''', '''UperNetPreTrainedModel''', ] if TYPE_CHECKING: from .configuration_upernet import UperNetConfig try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_upernet import UperNetForSemanticSegmentation, UperNetPreTrainedModel else: import sys UpperCamelCase = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
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'''simple docstring''' import os from shutil import copyfile from typing import List, Optional, Tuple from ...tokenization_utils import AddedToken from ...tokenization_utils_fast import PreTrainedTokenizerFast from ...utils import is_sentencepiece_available, logging if is_sentencepiece_available(): from .tokenization_camembert import CamembertTokenizer else: UpperCamelCase = None UpperCamelCase = logging.get_logger(__name__) UpperCamelCase = {'''vocab_file''': '''sentencepiece.bpe.model''', '''tokenizer_file''': '''tokenizer.json'''} UpperCamelCase = { '''vocab_file''': { '''camembert-base''': '''https://huggingface.co/camembert-base/resolve/main/sentencepiece.bpe.model''', }, '''tokenizer_file''': { '''camembert-base''': '''https://huggingface.co/camembert-base/resolve/main/tokenizer.json''', }, } UpperCamelCase = { '''camembert-base''': 512, } UpperCamelCase = '''▁''' class lowerCAmelCase_ ( UpperCAmelCase_ ): '''simple docstring''' UpperCamelCase_ : Tuple = VOCAB_FILES_NAMES UpperCamelCase_ : Optional[int] = PRETRAINED_VOCAB_FILES_MAP UpperCamelCase_ : Union[str, Any] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES UpperCamelCase_ : str = ["""input_ids""", """attention_mask"""] UpperCamelCase_ : int = CamembertTokenizer def __init__( self : Tuple , SCREAMING_SNAKE_CASE_ : int=None , SCREAMING_SNAKE_CASE_ : List[str]=None , SCREAMING_SNAKE_CASE_ : str="<s>" , SCREAMING_SNAKE_CASE_ : Optional[Any]="</s>" , SCREAMING_SNAKE_CASE_ : List[Any]="</s>" , SCREAMING_SNAKE_CASE_ : int="<s>" , SCREAMING_SNAKE_CASE_ : Union[str, Any]="<unk>" , SCREAMING_SNAKE_CASE_ : str="<pad>" , SCREAMING_SNAKE_CASE_ : List[str]="<mask>" , SCREAMING_SNAKE_CASE_ : Union[str, Any]=["<s>NOTUSED", "</s>NOTUSED"] , **SCREAMING_SNAKE_CASE_ : Any , ) -> Any: '''simple docstring''' A: Tuple = AddedToken(SCREAMING_SNAKE_CASE_ , lstrip=SCREAMING_SNAKE_CASE_ , rstrip=SCREAMING_SNAKE_CASE_ ) if isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) else mask_token super().__init__( SCREAMING_SNAKE_CASE_ , tokenizer_file=SCREAMING_SNAKE_CASE_ , bos_token=SCREAMING_SNAKE_CASE_ , eos_token=SCREAMING_SNAKE_CASE_ , sep_token=SCREAMING_SNAKE_CASE_ , cls_token=SCREAMING_SNAKE_CASE_ , unk_token=SCREAMING_SNAKE_CASE_ , pad_token=SCREAMING_SNAKE_CASE_ , mask_token=SCREAMING_SNAKE_CASE_ , additional_special_tokens=SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ , ) A: Any = vocab_file A: Any = False if not self.vocab_file else True def _snake_case ( self : int , SCREAMING_SNAKE_CASE_ : List[int] , SCREAMING_SNAKE_CASE_ : Optional[List[int]] = None ) -> List[int]: '''simple docstring''' if token_ids_a is None: return [self.cls_token_id] + token_ids_a + [self.sep_token_id] A: List[str] = [self.cls_token_id] A: List[str] = [self.sep_token_id] return cls + token_ids_a + sep + sep + token_ids_a + sep def _snake_case ( self : int , SCREAMING_SNAKE_CASE_ : List[int] , SCREAMING_SNAKE_CASE_ : Optional[List[int]] = None ) -> List[int]: '''simple docstring''' A: List[str] = [self.sep_token_id] A: Optional[int] = [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] def _snake_case ( self : int , SCREAMING_SNAKE_CASE_ : str , SCREAMING_SNAKE_CASE_ : Optional[str] = None ) -> Tuple[str]: '''simple docstring''' if not self.can_save_slow_tokenizer: raise ValueError( '''Your fast tokenizer does not have the necessary information to save the vocabulary for a slow ''' '''tokenizer.''' ) if not os.path.isdir(SCREAMING_SNAKE_CASE_ ): logger.error(f"""Vocabulary path ({save_directory}) should be a directory""" ) return A: Dict = os.path.join( SCREAMING_SNAKE_CASE_ , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] ) if os.path.abspath(self.vocab_file ) != os.path.abspath(SCREAMING_SNAKE_CASE_ ): copyfile(self.vocab_file , SCREAMING_SNAKE_CASE_ ) return (out_vocab_file,)
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'''simple docstring''' from ..utils import is_flax_available, is_torch_available if is_torch_available(): from .autoencoder_kl import AutoencoderKL from .controlnet import ControlNetModel from .dual_transformer_ad import DualTransformeraDModel from .modeling_utils import ModelMixin from .prior_transformer import PriorTransformer from .ta_film_transformer import TaFilmDecoder from .transformer_ad import TransformeraDModel from .unet_ad import UNetaDModel from .unet_ad import UNetaDModel from .unet_ad_condition import UNetaDConditionModel from .unet_ad_condition import UNetaDConditionModel from .vq_model import VQModel if is_flax_available(): from .controlnet_flax import FlaxControlNetModel from .unet_ad_condition_flax import FlaxUNetaDConditionModel from .vae_flax import FlaxAutoencoderKL
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'''simple docstring''' import os from distutils.util import strtobool def SCREAMING_SNAKE_CASE( __lowercase , __lowercase ) -> List[Any]: for e in env_keys: A: Dict = int(os.environ.get(__lowercase , -1 ) ) if val >= 0: return val return default def SCREAMING_SNAKE_CASE( __lowercase , __lowercase=False ) -> List[str]: A: str = os.environ.get(__lowercase , str(__lowercase ) ) return strtobool(__lowercase ) == 1 # As its name indicates `strtobool` actually returns an int... def SCREAMING_SNAKE_CASE( __lowercase , __lowercase="no" ) -> str: A: Optional[int] = os.environ.get(__lowercase , str(__lowercase ) ) return value
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'''simple docstring''' import json import logging import os import socket import git import numpy as np import torch logging.basicConfig( format='''%(asctime)s - %(levelname)s - %(name)s - PID: %(process)d - %(message)s''', datefmt='''%m/%d/%Y %H:%M:%S''', level=logging.INFO, ) UpperCamelCase = logging.getLogger(__name__) def SCREAMING_SNAKE_CASE( __lowercase ) -> Union[str, Any]: A: Tuple = git.Repo(search_parent_directories=__lowercase ) A: List[str] = { '''repo_id''': str(__lowercase ), '''repo_sha''': str(repo.head.object.hexsha ), '''repo_branch''': str(repo.active_branch ), } with open(os.path.join(__lowercase , '''git_log.json''' ) , '''w''' ) as f: json.dump(__lowercase , __lowercase , indent=4 ) def SCREAMING_SNAKE_CASE( __lowercase ) -> int: if params.n_gpu <= 0: A: Dict = 0 A: Any = -1 A: List[str] = True A: List[Any] = False return assert torch.cuda.is_available() logger.info('''Initializing GPUs''' ) if params.n_gpu > 1: assert params.local_rank != -1 A: Any = int(os.environ['''WORLD_SIZE'''] ) A: Tuple = int(os.environ['''N_GPU_NODE'''] ) A: int = int(os.environ['''RANK'''] ) # number of nodes / node ID A: Any = params.world_size // params.n_gpu_per_node A: List[Any] = params.global_rank // params.n_gpu_per_node A: Union[str, Any] = True assert params.n_nodes == int(os.environ['''N_NODES'''] ) assert params.node_id == int(os.environ['''NODE_RANK'''] ) # local job (single GPU) else: assert params.local_rank == -1 A: Any = 1 A: List[Any] = 0 A: int = 0 A: Any = 0 A: Optional[int] = 1 A: List[str] = 1 A: Any = False # sanity checks assert params.n_nodes >= 1 assert 0 <= params.node_id < params.n_nodes assert 0 <= params.local_rank <= params.global_rank < params.world_size assert params.world_size == params.n_nodes * params.n_gpu_per_node # define whether this is the master process / if we are in multi-node distributed mode A: str = params.node_id == 0 and params.local_rank == 0 A: List[Any] = params.n_nodes > 1 # summary A: Tuple = F"""--- Global rank: {params.global_rank} - """ logger.info(PREFIX + '''Number of nodes: %i''' % params.n_nodes ) logger.info(PREFIX + '''Node ID : %i''' % params.node_id ) logger.info(PREFIX + '''Local rank : %i''' % params.local_rank ) logger.info(PREFIX + '''World size : %i''' % params.world_size ) logger.info(PREFIX + '''GPUs per node : %i''' % params.n_gpu_per_node ) logger.info(PREFIX + '''Master : %s''' % str(params.is_master ) ) logger.info(PREFIX + '''Multi-node : %s''' % str(params.multi_node ) ) logger.info(PREFIX + '''Multi-GPU : %s''' % str(params.multi_gpu ) ) logger.info(PREFIX + '''Hostname : %s''' % socket.gethostname() ) # set GPU device torch.cuda.set_device(params.local_rank ) # initialize multi-GPU if params.multi_gpu: logger.info('''Initializing PyTorch distributed''' ) torch.distributed.init_process_group( init_method='''env://''' , backend='''nccl''' , ) def SCREAMING_SNAKE_CASE( __lowercase ) -> Dict: np.random.seed(args.seed ) torch.manual_seed(args.seed ) if args.n_gpu > 0: torch.cuda.manual_seed_all(args.seed )
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'''simple docstring''' import argparse import torch from transformers import ( EncodecConfig, EncodecFeatureExtractor, EncodecModel, logging, ) # checkpoints downloaded from: # https://dl.fbaipublicfiles.com/encodec/v0/encodec_24khz-d7cc33bc.th # https://huggingface.co/facebook/musicgen-small/resolve/main/compression_state_dict.bin # https://dl.fbaipublicfiles.com/encodec/v0/encodec_48khz-7e698e3e.th logging.set_verbosity_info() UpperCamelCase = logging.get_logger('''transformers.models.encodec''') UpperCamelCase = { '''quantizer.vq.layers.*._codebook.inited''': '''quantizer.layers.*.codebook.inited''', '''quantizer.vq.layers.*._codebook.cluster_size''': '''quantizer.layers.*.codebook.cluster_size''', '''quantizer.vq.layers.*._codebook.embed''': '''quantizer.layers.*.codebook.embed''', '''quantizer.vq.layers.*._codebook.embed_avg''': '''quantizer.layers.*.codebook.embed_avg''', } UpperCamelCase = { '''encoder.model.0.conv.conv''': '''encoder.layers.0.conv''', '''encoder.model.1.block.1.conv.conv''': '''encoder.layers.1.block.1.conv''', '''encoder.model.1.block.3.conv.conv''': '''encoder.layers.1.block.3.conv''', '''encoder.model.1.shortcut.conv.conv''': '''encoder.layers.1.shortcut.conv''', '''encoder.model.3.conv.conv''': '''encoder.layers.3.conv''', '''encoder.model.4.block.1.conv.conv''': '''encoder.layers.4.block.1.conv''', '''encoder.model.4.block.3.conv.conv''': '''encoder.layers.4.block.3.conv''', '''encoder.model.4.shortcut.conv.conv''': '''encoder.layers.4.shortcut.conv''', '''encoder.model.6.conv.conv''': '''encoder.layers.6.conv''', '''encoder.model.7.block.1.conv.conv''': '''encoder.layers.7.block.1.conv''', '''encoder.model.7.block.3.conv.conv''': '''encoder.layers.7.block.3.conv''', '''encoder.model.7.shortcut.conv.conv''': '''encoder.layers.7.shortcut.conv''', '''encoder.model.9.conv.conv''': '''encoder.layers.9.conv''', '''encoder.model.10.block.1.conv.conv''': '''encoder.layers.10.block.1.conv''', '''encoder.model.10.block.3.conv.conv''': '''encoder.layers.10.block.3.conv''', '''encoder.model.10.shortcut.conv.conv''': '''encoder.layers.10.shortcut.conv''', '''encoder.model.12.conv.conv''': '''encoder.layers.12.conv''', '''encoder.model.13.lstm''': '''encoder.layers.13.lstm''', '''encoder.model.15.conv.conv''': '''encoder.layers.15.conv''', } UpperCamelCase = { '''encoder.model.0.conv.norm''': '''encoder.layers.0.norm''', '''encoder.model.1.block.1.conv.norm''': '''encoder.layers.1.block.1.norm''', '''encoder.model.1.block.3.conv.norm''': '''encoder.layers.1.block.3.norm''', '''encoder.model.1.shortcut.conv.norm''': '''encoder.layers.1.shortcut.norm''', '''encoder.model.3.conv.norm''': '''encoder.layers.3.norm''', '''encoder.model.4.block.1.conv.norm''': '''encoder.layers.4.block.1.norm''', '''encoder.model.4.block.3.conv.norm''': '''encoder.layers.4.block.3.norm''', '''encoder.model.4.shortcut.conv.norm''': '''encoder.layers.4.shortcut.norm''', '''encoder.model.6.conv.norm''': '''encoder.layers.6.norm''', '''encoder.model.7.block.1.conv.norm''': '''encoder.layers.7.block.1.norm''', '''encoder.model.7.block.3.conv.norm''': '''encoder.layers.7.block.3.norm''', '''encoder.model.7.shortcut.conv.norm''': '''encoder.layers.7.shortcut.norm''', '''encoder.model.9.conv.norm''': '''encoder.layers.9.norm''', '''encoder.model.10.block.1.conv.norm''': '''encoder.layers.10.block.1.norm''', '''encoder.model.10.block.3.conv.norm''': '''encoder.layers.10.block.3.norm''', '''encoder.model.10.shortcut.conv.norm''': '''encoder.layers.10.shortcut.norm''', '''encoder.model.12.conv.norm''': '''encoder.layers.12.norm''', '''encoder.model.15.conv.norm''': '''encoder.layers.15.norm''', } UpperCamelCase = { '''decoder.model.0.conv.conv''': '''decoder.layers.0.conv''', '''decoder.model.1.lstm''': '''decoder.layers.1.lstm''', '''decoder.model.3.convtr.convtr''': '''decoder.layers.3.conv''', '''decoder.model.4.block.1.conv.conv''': '''decoder.layers.4.block.1.conv''', '''decoder.model.4.block.3.conv.conv''': '''decoder.layers.4.block.3.conv''', '''decoder.model.4.shortcut.conv.conv''': '''decoder.layers.4.shortcut.conv''', '''decoder.model.6.convtr.convtr''': '''decoder.layers.6.conv''', '''decoder.model.7.block.1.conv.conv''': '''decoder.layers.7.block.1.conv''', '''decoder.model.7.block.3.conv.conv''': '''decoder.layers.7.block.3.conv''', '''decoder.model.7.shortcut.conv.conv''': '''decoder.layers.7.shortcut.conv''', '''decoder.model.9.convtr.convtr''': '''decoder.layers.9.conv''', '''decoder.model.10.block.1.conv.conv''': '''decoder.layers.10.block.1.conv''', '''decoder.model.10.block.3.conv.conv''': '''decoder.layers.10.block.3.conv''', '''decoder.model.10.shortcut.conv.conv''': '''decoder.layers.10.shortcut.conv''', '''decoder.model.12.convtr.convtr''': '''decoder.layers.12.conv''', '''decoder.model.13.block.1.conv.conv''': '''decoder.layers.13.block.1.conv''', '''decoder.model.13.block.3.conv.conv''': '''decoder.layers.13.block.3.conv''', '''decoder.model.13.shortcut.conv.conv''': '''decoder.layers.13.shortcut.conv''', '''decoder.model.15.conv.conv''': '''decoder.layers.15.conv''', } UpperCamelCase = { '''decoder.model.0.conv.norm''': '''decoder.layers.0.norm''', '''decoder.model.3.convtr.norm''': '''decoder.layers.3.norm''', '''decoder.model.4.block.1.conv.norm''': '''decoder.layers.4.block.1.norm''', '''decoder.model.4.block.3.conv.norm''': '''decoder.layers.4.block.3.norm''', '''decoder.model.4.shortcut.conv.norm''': '''decoder.layers.4.shortcut.norm''', '''decoder.model.6.convtr.norm''': '''decoder.layers.6.norm''', '''decoder.model.7.block.1.conv.norm''': '''decoder.layers.7.block.1.norm''', '''decoder.model.7.block.3.conv.norm''': '''decoder.layers.7.block.3.norm''', '''decoder.model.7.shortcut.conv.norm''': '''decoder.layers.7.shortcut.norm''', '''decoder.model.9.convtr.norm''': '''decoder.layers.9.norm''', '''decoder.model.10.block.1.conv.norm''': '''decoder.layers.10.block.1.norm''', '''decoder.model.10.block.3.conv.norm''': '''decoder.layers.10.block.3.norm''', '''decoder.model.10.shortcut.conv.norm''': '''decoder.layers.10.shortcut.norm''', '''decoder.model.12.convtr.norm''': '''decoder.layers.12.norm''', '''decoder.model.13.block.1.conv.norm''': '''decoder.layers.13.block.1.norm''', '''decoder.model.13.block.3.conv.norm''': '''decoder.layers.13.block.3.norm''', '''decoder.model.13.shortcut.conv.norm''': '''decoder.layers.13.shortcut.norm''', '''decoder.model.15.conv.norm''': '''decoder.layers.15.norm''', } UpperCamelCase = { **MAPPING_QUANTIZER, **MAPPING_ENCODER, **MAPPING_DECODER, } UpperCamelCase = { **MAPPING_QUANTIZER, **MAPPING_ENCODER, **MAPPING_ENCODER_48K, **MAPPING_DECODER, **MAPPING_DECODER_48K, } UpperCamelCase = [] UpperCamelCase = [] def SCREAMING_SNAKE_CASE( __lowercase , __lowercase , __lowercase , __lowercase , __lowercase ) -> Dict: for attribute in key.split('''.''' ): A: Union[str, Any] = getattr(__lowercase , __lowercase ) if weight_type is not None: A: Tuple = getattr(__lowercase , __lowercase ).shape else: A: str = 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": A: Dict = value elif weight_type == "weight_g": A: Tuple = value elif weight_type == "weight_v": A: Any = value elif weight_type == "bias": A: str = value elif weight_type == "running_mean": A: List[Any] = value elif weight_type == "running_var": A: Dict = value elif weight_type == "num_batches_tracked": A: List[str] = value elif weight_type == "weight_ih_l0": A: Dict = value elif weight_type == "weight_hh_l0": A: Optional[int] = value elif weight_type == "bias_ih_l0": A: List[Any] = value elif weight_type == "bias_hh_l0": A: str = value elif weight_type == "weight_ih_l1": A: Optional[int] = value elif weight_type == "weight_hh_l1": A: int = value elif weight_type == "bias_ih_l1": A: Optional[Any] = value elif weight_type == "bias_hh_l1": A: str = value else: A: Optional[int] = value logger.info(F"""{key + ('.' + weight_type if weight_type is not None else '')} was initialized from {full_name}.""" ) def SCREAMING_SNAKE_CASE( __lowercase , __lowercase ) -> Optional[Any]: for key in ignore_keys: if key.endswith('''.*''' ): if name.startswith(key[:-1] ): return True elif ".*." in key: A , A: Any = key.split('''.*.''' ) if prefix in name and suffix in name: return True elif key in name: return True return False def SCREAMING_SNAKE_CASE( __lowercase , __lowercase , __lowercase ) -> Tuple: A: Any = [] if model_name == "encodec_24khz" or "encodec_32khz": A: List[str] = MAPPING_24K elif model_name == "encodec_48khz": A: List[Any] = MAPPING_48K else: raise ValueError(F"""Unsupported model: {model_name}""" ) for name, value in orig_dict.items(): if should_ignore(__lowercase , __lowercase ): logger.info(F"""{name} was ignored""" ) continue A: Optional[int] = False for key, mapped_key in MAPPING.items(): if "*" in key: A , A: Optional[int] = key.split('''.*.''' ) if prefix in name and suffix in name: A: str = suffix if key in name: # HACK otherwise .embed gets initialized with .embed_avg too if key.endswith('''embed''' ) and name.endswith('''embed_avg''' ): continue A: Optional[Any] = True if "*" in mapped_key: A: Any = name.split(__lowercase )[0].split('''.''' )[-2] A: Tuple = mapped_key.replace('''*''' , __lowercase ) if "weight_g" in name: A: str = '''weight_g''' elif "weight_v" in name: A: List[Any] = '''weight_v''' elif "weight_ih_l0" in name: A: Dict = '''weight_ih_l0''' elif "weight_hh_l0" in name: A: int = '''weight_hh_l0''' elif "bias_ih_l0" in name: A: Union[str, Any] = '''bias_ih_l0''' elif "bias_hh_l0" in name: A: Tuple = '''bias_hh_l0''' elif "weight_ih_l1" in name: A: int = '''weight_ih_l1''' elif "weight_hh_l1" in name: A: Optional[Any] = '''weight_hh_l1''' elif "bias_ih_l1" in name: A: Dict = '''bias_ih_l1''' elif "bias_hh_l1" in name: A: str = '''bias_hh_l1''' elif "bias" in name: A: Union[str, Any] = '''bias''' elif "weight" in name: A: Dict = '''weight''' elif "running_mean" in name: A: Tuple = '''running_mean''' elif "running_var" in name: A: Any = '''running_var''' elif "num_batches_tracked" in name: A: str = '''num_batches_tracked''' else: A: Tuple = None set_recursively(__lowercase , __lowercase , __lowercase , __lowercase , __lowercase ) continue if not is_used: unused_weights.append(__lowercase ) logger.warning(F"""Unused weights: {unused_weights}""" ) @torch.no_grad() def SCREAMING_SNAKE_CASE( __lowercase , __lowercase , __lowercase , __lowercase=None , __lowercase=None , ) -> Dict: if config_path is not None: A: Tuple = EncodecConfig.from_pretrained(__lowercase ) else: A: Union[str, Any] = EncodecConfig() if model_name == "encodec_24khz": pass # config is already correct elif model_name == "encodec_32khz": A: Union[str, Any] = [8, 5, 4, 4] A: Dict = [2.2] A: List[Any] = 6_4 A: Optional[Any] = 3_2_0_0_0 A: List[Any] = 2_0_4_8 A: Optional[Any] = False A: int = False A: Union[str, Any] = False elif model_name == "encodec_48khz": A: Optional[int] = [8, 5, 4, 2] A: List[Any] = [3.0, 6.0, 1_2.0, 2_4.0] A: List[Any] = 4_8_0_0_0 A: int = 2 A: List[Any] = False A: Any = '''time_group_norm''' A: Optional[Any] = True A: Any = 1.0 A: Any = 0.0_1 else: raise ValueError(F"""Unknown model name: {model_name}""" ) A: str = EncodecModel(__lowercase ) A: Optional[Any] = EncodecFeatureExtractor( feature_size=config.audio_channels , sampling_rate=config.sampling_rate , chunk_length_s=config.chunk_length_s , overlap=config.overlap , ) feature_extractor.save_pretrained(__lowercase ) A: Union[str, Any] = torch.load(__lowercase ) if "best_state" in original_checkpoint: # we might have a training state saved, in which case discard the yaml results and just retain the weights A: Optional[int] = original_checkpoint['''best_state'''] recursively_load_weights(__lowercase , __lowercase , __lowercase ) model.save_pretrained(__lowercase ) if repo_id: print('''Pushing to the hub...''' ) feature_extractor.push_to_hub(__lowercase ) model.push_to_hub(__lowercase ) if __name__ == "__main__": UpperCamelCase = argparse.ArgumentParser() parser.add_argument( '''--model''', default='''encodec_24khz''', type=str, help='''The model to convert. Should be one of \'encodec_24khz\', \'encodec_32khz\', \'encodec_48khz\'.''', ) parser.add_argument('''--checkpoint_path''', required=True, default=None, type=str, help='''Path to original checkpoint''') 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.''' ) UpperCamelCase = parser.parse_args() convert_checkpoint( args.model, args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.push_to_hub, )
334
1
'''simple docstring''' from typing import TYPE_CHECKING from ...utils import _LazyModule UpperCamelCase = {'''tokenization_wav2vec2_phoneme''': ['''Wav2Vec2PhonemeCTCTokenizer''']} if TYPE_CHECKING: from .tokenization_wavaveca_phoneme import WavaVecaPhonemeCTCTokenizer else: import sys UpperCamelCase = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
334
'''simple docstring''' from typing import TYPE_CHECKING from ...utils import ( OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_tf_available, is_torch_available, ) UpperCamelCase = { '''configuration_vision_encoder_decoder''': ['''VisionEncoderDecoderConfig''', '''VisionEncoderDecoderOnnxConfig'''] } try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: UpperCamelCase = ['''VisionEncoderDecoderModel'''] try: if not is_tf_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: UpperCamelCase = ['''TFVisionEncoderDecoderModel'''] try: if not is_flax_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: UpperCamelCase = ['''FlaxVisionEncoderDecoderModel'''] if TYPE_CHECKING: from .configuration_vision_encoder_decoder import VisionEncoderDecoderConfig, VisionEncoderDecoderOnnxConfig try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_vision_encoder_decoder import VisionEncoderDecoderModel try: if not is_tf_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_tf_vision_encoder_decoder import TFVisionEncoderDecoderModel try: if not is_flax_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_flax_vision_encoder_decoder import FlaxVisionEncoderDecoderModel else: import sys UpperCamelCase = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
334
1
'''simple docstring''' def SCREAMING_SNAKE_CASE( __lowercase ) -> str: if not all(char in '''01''' for char in bin_string ): raise ValueError('''Non-binary value was passed to the function''' ) if not bin_string: raise ValueError('''Empty string was passed to the function''' ) A: List[str] = '''''' while len(__lowercase ) % 3 != 0: A: Optional[Any] = '''0''' + bin_string A: Dict = [ bin_string[index : index + 3] for index in range(len(__lowercase ) ) if index % 3 == 0 ] for bin_group in bin_string_in_3_list: A: Optional[int] = 0 for index, val in enumerate(__lowercase ): oct_val += int(2 ** (2 - index) * int(__lowercase ) ) oct_string += str(__lowercase ) return oct_string if __name__ == "__main__": from doctest import testmod testmod()
334
'''simple docstring''' def SCREAMING_SNAKE_CASE( __lowercase ) -> list[list[float]]: A: list[list[float]] = [] for data in source_data: for i, el in enumerate(__lowercase ): if len(__lowercase ) < i + 1: data_lists.append([] ) data_lists[i].append(float(__lowercase ) ) return data_lists def SCREAMING_SNAKE_CASE( __lowercase , __lowercase ) -> list[list[float]]: A: list[list[float]] = [] for dlist, weight in zip(__lowercase , __lowercase ): A: List[str] = min(__lowercase ) A: Union[str, Any] = max(__lowercase ) A: list[float] = [] # for weight 0 score is 1 - actual score if weight == 0: for item in dlist: try: score.append(1 - ((item - mind) / (maxd - mind)) ) except ZeroDivisionError: score.append(1 ) elif weight == 1: for item in dlist: try: score.append((item - mind) / (maxd - mind) ) except ZeroDivisionError: score.append(0 ) # weight not 0 or 1 else: A: List[str] = F"""Invalid weight of {weight:f} provided""" raise ValueError(__lowercase ) score_lists.append(__lowercase ) return score_lists def SCREAMING_SNAKE_CASE( __lowercase ) -> list[float]: A: list[float] = [0 for i in range(len(score_lists[0] ) )] for slist in score_lists: for j, ele in enumerate(__lowercase ): A: str = final_scores[j] + ele return final_scores def SCREAMING_SNAKE_CASE( __lowercase , __lowercase ) -> list[list[float]]: A: Any = get_data(__lowercase ) A: str = calculate_each_score(__lowercase , __lowercase ) A: int = generate_final_scores(__lowercase ) # append scores to source data for i, ele in enumerate(__lowercase ): source_data[i].append(__lowercase ) return source_data
334
1
'''simple docstring''' def SCREAMING_SNAKE_CASE( __lowercase ) -> bool: if number < 0: raise ValueError('''number must not be negative''' ) return number & (number - 1) == 0 if __name__ == "__main__": import doctest doctest.testmod()
334
'''simple docstring''' import collections from typing import List, Optional, Union from ...tokenization_utils_base import BatchEncoding from ...utils import TensorType, add_end_docstrings, add_start_docstrings, logging from ..bert.tokenization_bert_fast import BertTokenizerFast from .tokenization_dpr import DPRContextEncoderTokenizer, DPRQuestionEncoderTokenizer, DPRReaderTokenizer UpperCamelCase = logging.get_logger(__name__) UpperCamelCase = {'''vocab_file''': '''vocab.txt''', '''tokenizer_file''': '''tokenizer.json'''} UpperCamelCase = { '''vocab_file''': { '''facebook/dpr-ctx_encoder-single-nq-base''': ( '''https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/vocab.txt''' ), '''facebook/dpr-ctx_encoder-multiset-base''': ( '''https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/vocab.txt''' ), }, '''tokenizer_file''': { '''facebook/dpr-ctx_encoder-single-nq-base''': ( '''https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/tokenizer.json''' ), '''facebook/dpr-ctx_encoder-multiset-base''': ( '''https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/tokenizer.json''' ), }, } UpperCamelCase = { '''vocab_file''': { '''facebook/dpr-question_encoder-single-nq-base''': ( '''https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/vocab.txt''' ), '''facebook/dpr-question_encoder-multiset-base''': ( '''https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/vocab.txt''' ), }, '''tokenizer_file''': { '''facebook/dpr-question_encoder-single-nq-base''': ( '''https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/tokenizer.json''' ), '''facebook/dpr-question_encoder-multiset-base''': ( '''https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/tokenizer.json''' ), }, } UpperCamelCase = { '''vocab_file''': { '''facebook/dpr-reader-single-nq-base''': ( '''https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/vocab.txt''' ), '''facebook/dpr-reader-multiset-base''': ( '''https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/vocab.txt''' ), }, '''tokenizer_file''': { '''facebook/dpr-reader-single-nq-base''': ( '''https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/tokenizer.json''' ), '''facebook/dpr-reader-multiset-base''': ( '''https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/tokenizer.json''' ), }, } UpperCamelCase = { '''facebook/dpr-ctx_encoder-single-nq-base''': 512, '''facebook/dpr-ctx_encoder-multiset-base''': 512, } UpperCamelCase = { '''facebook/dpr-question_encoder-single-nq-base''': 512, '''facebook/dpr-question_encoder-multiset-base''': 512, } UpperCamelCase = { '''facebook/dpr-reader-single-nq-base''': 512, '''facebook/dpr-reader-multiset-base''': 512, } UpperCamelCase = { '''facebook/dpr-ctx_encoder-single-nq-base''': {'''do_lower_case''': True}, '''facebook/dpr-ctx_encoder-multiset-base''': {'''do_lower_case''': True}, } UpperCamelCase = { '''facebook/dpr-question_encoder-single-nq-base''': {'''do_lower_case''': True}, '''facebook/dpr-question_encoder-multiset-base''': {'''do_lower_case''': True}, } UpperCamelCase = { '''facebook/dpr-reader-single-nq-base''': {'''do_lower_case''': True}, '''facebook/dpr-reader-multiset-base''': {'''do_lower_case''': True}, } class lowerCAmelCase_ ( UpperCAmelCase_ ): '''simple docstring''' UpperCamelCase_ : Union[str, Any] = VOCAB_FILES_NAMES UpperCamelCase_ : Union[str, Any] = CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP UpperCamelCase_ : Union[str, Any] = CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES UpperCamelCase_ : Optional[Any] = CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION UpperCamelCase_ : Any = DPRContextEncoderTokenizer class lowerCAmelCase_ ( UpperCAmelCase_ ): '''simple docstring''' UpperCamelCase_ : Dict = VOCAB_FILES_NAMES UpperCamelCase_ : List[str] = QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP UpperCamelCase_ : List[Any] = QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES UpperCamelCase_ : Tuple = QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION UpperCamelCase_ : Optional[int] = DPRQuestionEncoderTokenizer UpperCamelCase = collections.namedtuple( '''DPRSpanPrediction''', ['''span_score''', '''relevance_score''', '''doc_id''', '''start_index''', '''end_index''', '''text'''] ) UpperCamelCase = collections.namedtuple('''DPRReaderOutput''', ['''start_logits''', '''end_logits''', '''relevance_logits''']) UpperCamelCase = R''' Return a dictionary with the token ids of the input strings and other information to give to `.decode_best_spans`. It converts the strings of a question and different passages (title and text) in a sequence of IDs (integers), using the tokenizer and vocabulary. The resulting `input_ids` is a matrix of size `(n_passages, sequence_length)` with the format: [CLS] <question token ids> [SEP] <titles ids> [SEP] <texts ids> Args: questions (`str` or `List[str]`): The questions to be encoded. You can specify one question for many passages. In this case, the question will be duplicated like `[questions] * n_passages`. Otherwise you have to specify as many questions as in `titles` or `texts`. titles (`str` or `List[str]`): The passages titles to be encoded. This can be a string or a list of strings if there are several passages. texts (`str` or `List[str]`): The passages texts to be encoded. This can be a string or a list of strings if there are several passages. padding (`bool`, `str` or [`~utils.PaddingStrategy`], *optional*, defaults to `False`): Activates and controls padding. Accepts the following values: - `True` or `\'longest\'`: Pad to the longest sequence in the batch (or no padding if only a single sequence if provided). - `\'max_length\'`: Pad to a maximum length specified with the argument `max_length` or to the maximum acceptable input length for the model if that argument is not provided. - `False` or `\'do_not_pad\'` (default): No padding (i.e., can output a batch with sequences of different lengths). truncation (`bool`, `str` or [`~tokenization_utils_base.TruncationStrategy`], *optional*, defaults to `False`): Activates and controls truncation. Accepts the following values: - `True` or `\'longest_first\'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum acceptable input length for the model if that argument is not provided. This will truncate token by token, removing a token from the longest sequence in the pair if a pair of sequences (or a batch of pairs) is provided. - `\'only_first\'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum acceptable input length for the model if that argument is not provided. This will only truncate the first sequence of a pair if a pair of sequences (or a batch of pairs) is provided. - `\'only_second\'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum acceptable input length for the model if that argument is not provided. This will only truncate the second sequence of a pair if a pair of sequences (or a batch of pairs) is provided. - `False` or `\'do_not_truncate\'` (default): No truncation (i.e., can output batch with sequence lengths greater than the model maximum admissible input size). max_length (`int`, *optional*): Controls the maximum length to use by one of the truncation/padding parameters. If left unset or set to `None`, this will use the predefined model maximum length if a maximum length is required by one of the truncation/padding parameters. If the model has no specific maximum input length (like XLNet) truncation/padding to a maximum length will be deactivated. return_tensors (`str` or [`~utils.TensorType`], *optional*): If set, will return tensors instead of list of python integers. Acceptable values are: - `\'tf\'`: Return TensorFlow `tf.constant` objects. - `\'pt\'`: Return PyTorch `torch.Tensor` objects. - `\'np\'`: Return Numpy `np.ndarray` objects. return_attention_mask (`bool`, *optional*): Whether or not to return the attention mask. If not set, will return the attention mask according to the specific tokenizer\'s default, defined by the `return_outputs` attribute. [What are attention masks?](../glossary#attention-mask) Return: `Dict[str, List[List[int]]]`: A dictionary with the following keys: - `input_ids`: List of token ids to be fed to a model. - `attention_mask`: List of indices specifying which tokens should be attended to by the model. ''' @add_start_docstrings(UpperCAmelCase_ ) class lowerCAmelCase_ : '''simple docstring''' def __call__( self : Dict , SCREAMING_SNAKE_CASE_ : Union[str, Any] , SCREAMING_SNAKE_CASE_ : Optional[str] = None , SCREAMING_SNAKE_CASE_ : Optional[str] = None , SCREAMING_SNAKE_CASE_ : Union[bool, str] = False , SCREAMING_SNAKE_CASE_ : Union[bool, str] = False , SCREAMING_SNAKE_CASE_ : Optional[int] = None , SCREAMING_SNAKE_CASE_ : Optional[Union[str, TensorType]] = None , SCREAMING_SNAKE_CASE_ : Optional[bool] = None , **SCREAMING_SNAKE_CASE_ : Dict , ) -> BatchEncoding: '''simple docstring''' if titles is None and texts is None: return super().__call__( SCREAMING_SNAKE_CASE_ , padding=SCREAMING_SNAKE_CASE_ , truncation=SCREAMING_SNAKE_CASE_ , max_length=SCREAMING_SNAKE_CASE_ , return_tensors=SCREAMING_SNAKE_CASE_ , return_attention_mask=SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ , ) elif titles is None or texts is None: A: Union[str, Any] = titles if texts is None else texts return super().__call__( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , padding=SCREAMING_SNAKE_CASE_ , truncation=SCREAMING_SNAKE_CASE_ , max_length=SCREAMING_SNAKE_CASE_ , return_tensors=SCREAMING_SNAKE_CASE_ , return_attention_mask=SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ , ) A: Union[str, Any] = titles if not isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) else [titles] A: Optional[Any] = texts if not isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) else [texts] A: str = len(SCREAMING_SNAKE_CASE_ ) A: List[Any] = questions if not isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) else [questions] * n_passages assert len(SCREAMING_SNAKE_CASE_ ) == len( SCREAMING_SNAKE_CASE_ ), f"""There should be as many titles than texts but got {len(SCREAMING_SNAKE_CASE_ )} titles and {len(SCREAMING_SNAKE_CASE_ )} texts.""" A: Union[str, Any] = super().__call__(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , padding=SCREAMING_SNAKE_CASE_ , truncation=SCREAMING_SNAKE_CASE_ )['''input_ids'''] A: Dict = super().__call__(SCREAMING_SNAKE_CASE_ , add_special_tokens=SCREAMING_SNAKE_CASE_ , padding=SCREAMING_SNAKE_CASE_ , truncation=SCREAMING_SNAKE_CASE_ )['''input_ids'''] A: str = { '''input_ids''': [ (encoded_question_and_title + encoded_text)[:max_length] if max_length is not None and truncation else encoded_question_and_title + encoded_text for encoded_question_and_title, encoded_text in zip(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) ] } if return_attention_mask is not False: A: Union[str, Any] = [] for input_ids in encoded_inputs["input_ids"]: attention_mask.append([int(input_id != self.pad_token_id ) for input_id in input_ids] ) A: Optional[Any] = attention_mask return self.pad(SCREAMING_SNAKE_CASE_ , padding=SCREAMING_SNAKE_CASE_ , max_length=SCREAMING_SNAKE_CASE_ , return_tensors=SCREAMING_SNAKE_CASE_ ) def _snake_case ( self : int , SCREAMING_SNAKE_CASE_ : BatchEncoding , SCREAMING_SNAKE_CASE_ : DPRReaderOutput , SCREAMING_SNAKE_CASE_ : int = 16 , SCREAMING_SNAKE_CASE_ : int = 64 , SCREAMING_SNAKE_CASE_ : int = 4 , ) -> List[DPRSpanPrediction]: '''simple docstring''' A: Any = reader_input['''input_ids'''] A , A , A: str = reader_output[:3] A: str = len(SCREAMING_SNAKE_CASE_ ) A: Union[str, Any] = sorted(range(SCREAMING_SNAKE_CASE_ ) , reverse=SCREAMING_SNAKE_CASE_ , key=relevance_logits.__getitem__ ) A: List[DPRReaderOutput] = [] for doc_id in sorted_docs: A: List[str] = list(input_ids[doc_id] ) # assuming question & title information is at the beginning of the sequence A: Dict = sequence_ids.index(self.sep_token_id , 2 ) + 1 # second sep id if sequence_ids[-1] == self.pad_token_id: A: Union[str, Any] = sequence_ids.index(self.pad_token_id ) else: A: int = len(SCREAMING_SNAKE_CASE_ ) A: Dict = self._get_best_spans( start_logits=start_logits[doc_id][passage_offset:sequence_len] , end_logits=end_logits[doc_id][passage_offset:sequence_len] , max_answer_length=SCREAMING_SNAKE_CASE_ , top_spans=SCREAMING_SNAKE_CASE_ , ) for start_index, end_index in best_spans: start_index += passage_offset end_index += passage_offset nbest_spans_predictions.append( DPRSpanPrediction( span_score=start_logits[doc_id][start_index] + end_logits[doc_id][end_index] , relevance_score=relevance_logits[doc_id] , doc_id=SCREAMING_SNAKE_CASE_ , start_index=SCREAMING_SNAKE_CASE_ , end_index=SCREAMING_SNAKE_CASE_ , text=self.decode(sequence_ids[start_index : end_index + 1] ) , ) ) if len(SCREAMING_SNAKE_CASE_ ) >= num_spans: break return nbest_spans_predictions[:num_spans] def _snake_case ( self : Union[str, Any] , SCREAMING_SNAKE_CASE_ : List[int] , SCREAMING_SNAKE_CASE_ : List[int] , SCREAMING_SNAKE_CASE_ : int , SCREAMING_SNAKE_CASE_ : int , ) -> List[DPRSpanPrediction]: '''simple docstring''' A: Union[str, Any] = [] for start_index, start_score in enumerate(SCREAMING_SNAKE_CASE_ ): for answer_length, end_score in enumerate(end_logits[start_index : start_index + max_answer_length] ): scores.append(((start_index, start_index + answer_length), start_score + end_score) ) A: Any = sorted(SCREAMING_SNAKE_CASE_ , key=lambda SCREAMING_SNAKE_CASE_ : x[1] , reverse=SCREAMING_SNAKE_CASE_ ) A: Dict = [] for (start_index, end_index), score in scores: assert start_index <= end_index, f"""Wrong span indices: [{start_index}:{end_index}]""" A: int = end_index - start_index + 1 assert length <= max_answer_length, f"""Span is too long: {length} > {max_answer_length}""" if any( start_index <= prev_start_index <= prev_end_index <= end_index or prev_start_index <= start_index <= end_index <= prev_end_index for (prev_start_index, prev_end_index) in chosen_span_intervals ): continue chosen_span_intervals.append((start_index, end_index) ) if len(SCREAMING_SNAKE_CASE_ ) == top_spans: break return chosen_span_intervals @add_end_docstrings(UpperCAmelCase_ ) class lowerCAmelCase_ ( UpperCAmelCase_ , UpperCAmelCase_ ): '''simple docstring''' UpperCamelCase_ : Tuple = VOCAB_FILES_NAMES UpperCamelCase_ : List[Any] = READER_PRETRAINED_VOCAB_FILES_MAP UpperCamelCase_ : Union[str, Any] = READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES UpperCamelCase_ : Dict = READER_PRETRAINED_INIT_CONFIGURATION UpperCamelCase_ : Any = ["""input_ids""", """attention_mask"""] UpperCamelCase_ : Optional[Any] = DPRReaderTokenizer
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1
'''simple docstring''' import unittest from transformers import SPIECE_UNDERLINE from transformers.models.speechta import SpeechTaTokenizer from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow from transformers.tokenization_utils import AddedToken from ...test_tokenization_common import TokenizerTesterMixin UpperCamelCase = get_tests_dir('''fixtures/test_sentencepiece_bpe_char.model''') @require_sentencepiece @require_tokenizers class lowerCAmelCase_ ( UpperCAmelCase_ , unittest.TestCase ): '''simple docstring''' UpperCamelCase_ : List[Any] = SpeechTaTokenizer UpperCamelCase_ : Tuple = False UpperCamelCase_ : Any = True def _snake_case ( self : List[str] ) -> Optional[Any]: '''simple docstring''' super().setUp() # We have a SentencePiece fixture for testing A: List[str] = SpeechTaTokenizer(SCREAMING_SNAKE_CASE_ ) A: Dict = AddedToken('''<mask>''' , lstrip=SCREAMING_SNAKE_CASE_ , rstrip=SCREAMING_SNAKE_CASE_ ) A: Union[str, Any] = mask_token tokenizer.add_special_tokens({'''mask_token''': mask_token} ) tokenizer.add_tokens(['''<ctc_blank>'''] ) tokenizer.save_pretrained(self.tmpdirname ) def _snake_case ( self : Tuple , SCREAMING_SNAKE_CASE_ : Dict ) -> List[Any]: '''simple docstring''' A: Any = '''this is a test''' A: str = '''this is a test''' return input_text, output_text def _snake_case ( self : Optional[Any] , SCREAMING_SNAKE_CASE_ : Optional[Any] , SCREAMING_SNAKE_CASE_ : str=False , SCREAMING_SNAKE_CASE_ : List[str]=20 , SCREAMING_SNAKE_CASE_ : Optional[Any]=5 ) -> int: '''simple docstring''' A , A: List[str] = self.get_input_output_texts(SCREAMING_SNAKE_CASE_ ) A: Optional[int] = tokenizer.encode(SCREAMING_SNAKE_CASE_ , add_special_tokens=SCREAMING_SNAKE_CASE_ ) A: str = tokenizer.decode(SCREAMING_SNAKE_CASE_ , clean_up_tokenization_spaces=SCREAMING_SNAKE_CASE_ ) return text, ids def _snake_case ( self : int ) -> List[str]: '''simple docstring''' A: Optional[Any] = '''<pad>''' A: Any = 1 self.assertEqual(self.get_tokenizer()._convert_token_to_id(SCREAMING_SNAKE_CASE_ ) , SCREAMING_SNAKE_CASE_ ) self.assertEqual(self.get_tokenizer()._convert_id_to_token(SCREAMING_SNAKE_CASE_ ) , SCREAMING_SNAKE_CASE_ ) def _snake_case ( self : List[Any] ) -> List[Any]: '''simple docstring''' A: List[str] = list(self.get_tokenizer().get_vocab().keys() ) self.assertEqual(vocab_keys[0] , '''<s>''' ) self.assertEqual(vocab_keys[1] , '''<pad>''' ) self.assertEqual(vocab_keys[-4] , '''œ''' ) self.assertEqual(vocab_keys[-2] , '''<mask>''' ) self.assertEqual(vocab_keys[-1] , '''<ctc_blank>''' ) self.assertEqual(len(SCREAMING_SNAKE_CASE_ ) , 81 ) def _snake_case ( self : int ) -> Optional[int]: '''simple docstring''' self.assertEqual(self.get_tokenizer().vocab_size , 79 ) def _snake_case ( self : Any ) -> Tuple: '''simple docstring''' A: int = self.get_tokenizers(do_lower_case=SCREAMING_SNAKE_CASE_ ) for tokenizer in tokenizers: with self.subTest(f"""{tokenizer.__class__.__name__}""" ): A: Dict = tokenizer.vocab_size A: List[Any] = len(SCREAMING_SNAKE_CASE_ ) self.assertNotEqual(SCREAMING_SNAKE_CASE_ , 0 ) # We usually have added tokens from the start in tests because our vocab fixtures are # smaller than the original vocabs - let's not assert this # self.assertEqual(vocab_size, all_size) A: str = ['''aaaaa bbbbbb''', '''cccccccccdddddddd'''] A: Optional[int] = tokenizer.add_tokens(SCREAMING_SNAKE_CASE_ ) A: int = tokenizer.vocab_size A: Dict = len(SCREAMING_SNAKE_CASE_ ) self.assertNotEqual(SCREAMING_SNAKE_CASE_ , 0 ) self.assertEqual(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) self.assertEqual(SCREAMING_SNAKE_CASE_ , len(SCREAMING_SNAKE_CASE_ ) ) self.assertEqual(SCREAMING_SNAKE_CASE_ , all_size + len(SCREAMING_SNAKE_CASE_ ) ) A: Dict = tokenizer.encode('''aaaaa bbbbbb low cccccccccdddddddd l''' , add_special_tokens=SCREAMING_SNAKE_CASE_ ) self.assertGreaterEqual(len(SCREAMING_SNAKE_CASE_ ) , 4 ) self.assertGreater(tokens[0] , tokenizer.vocab_size - 1 ) self.assertGreater(tokens[-3] , tokenizer.vocab_size - 1 ) A: Union[str, Any] = {'''eos_token''': '''>>>>|||<||<<|<<''', '''pad_token''': '''<<<<<|||>|>>>>|>'''} A: List[str] = tokenizer.add_special_tokens(SCREAMING_SNAKE_CASE_ ) A: Dict = tokenizer.vocab_size A: List[Any] = len(SCREAMING_SNAKE_CASE_ ) self.assertNotEqual(SCREAMING_SNAKE_CASE_ , 0 ) self.assertEqual(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) self.assertEqual(SCREAMING_SNAKE_CASE_ , len(SCREAMING_SNAKE_CASE_ ) ) self.assertEqual(SCREAMING_SNAKE_CASE_ , all_size_a + len(SCREAMING_SNAKE_CASE_ ) ) A: Any = tokenizer.encode( '''>>>>|||<||<<|<< aaaaabbbbbb low cccccccccdddddddd <<<<<|||>|>>>>|> l''' , add_special_tokens=SCREAMING_SNAKE_CASE_ ) self.assertGreaterEqual(len(SCREAMING_SNAKE_CASE_ ) , 6 ) self.assertGreater(tokens[0] , tokenizer.vocab_size - 1 ) self.assertGreater(tokens[0] , tokens[1] ) self.assertGreater(tokens[-3] , tokenizer.vocab_size - 1 ) self.assertGreater(tokens[-3] , tokens[-4] ) self.assertEqual(tokens[0] , tokenizer.eos_token_id ) self.assertEqual(tokens[-3] , tokenizer.pad_token_id ) def _snake_case ( self : List[str] ) -> List[str]: '''simple docstring''' pass def _snake_case ( self : List[str] ) -> Optional[int]: '''simple docstring''' pass def _snake_case ( self : Any ) -> int: '''simple docstring''' A: Any = self.get_tokenizer() A: Dict = tokenizer.tokenize('''This is a test''' ) # fmt: off self.assertListEqual(SCREAMING_SNAKE_CASE_ , [SPIECE_UNDERLINE, '''T''', '''h''', '''i''', '''s''', SPIECE_UNDERLINE, '''i''', '''s''', SPIECE_UNDERLINE, '''a''', SPIECE_UNDERLINE, '''t''', '''e''', '''s''', '''t'''] ) # fmt: on self.assertListEqual( tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE_ ) , [4, 32, 11, 10, 12, 4, 10, 12, 4, 7, 4, 6, 5, 12, 6] , ) A: Optional[Any] = tokenizer.tokenize('''I was born in 92000, and this is falsé.''' ) self.assertListEqual( SCREAMING_SNAKE_CASE_ , [SPIECE_UNDERLINE, '''I''', SPIECE_UNDERLINE, '''w''', '''a''', '''s''', SPIECE_UNDERLINE, '''b''', '''o''', '''r''', '''n''', SPIECE_UNDERLINE, '''i''', '''n''', SPIECE_UNDERLINE, '''92000''', ''',''', SPIECE_UNDERLINE, '''a''', '''n''', '''d''', SPIECE_UNDERLINE, '''t''', '''h''', '''i''', '''s''', SPIECE_UNDERLINE, '''i''', '''s''', SPIECE_UNDERLINE, '''f''', '''a''', '''l''', '''s''', '''é''', '''.'''] ) A: str = tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE_ ) # fmt: off self.assertListEqual(SCREAMING_SNAKE_CASE_ , [4, 30, 4, 20, 7, 12, 4, 25, 8, 13, 9, 4, 10, 9, 4, 3, 23, 4, 7, 9, 14, 4, 6, 11, 10, 12, 4, 10, 12, 4, 19, 7, 15, 12, 73, 26] ) # fmt: on A: Optional[int] = tokenizer.convert_ids_to_tokens(SCREAMING_SNAKE_CASE_ ) self.assertListEqual( SCREAMING_SNAKE_CASE_ , [SPIECE_UNDERLINE, '''I''', SPIECE_UNDERLINE, '''w''', '''a''', '''s''', SPIECE_UNDERLINE, '''b''', '''o''', '''r''', '''n''', SPIECE_UNDERLINE, '''i''', '''n''', SPIECE_UNDERLINE, '''<unk>''', ''',''', SPIECE_UNDERLINE, '''a''', '''n''', '''d''', SPIECE_UNDERLINE, '''t''', '''h''', '''i''', '''s''', SPIECE_UNDERLINE, '''i''', '''s''', SPIECE_UNDERLINE, '''f''', '''a''', '''l''', '''s''', '''é''', '''.'''] ) @slow def _snake_case ( self : Any ) -> Tuple: '''simple docstring''' A: Any = [ '''Transformers (formerly known as pytorch-transformers and pytorch-pretrained-bert) provides ''' '''general-purpose architectures (BERT, GPT, RoBERTa, XLM, DistilBert, XLNet...) for Natural ''' '''Language Understanding (NLU) and Natural Language Generation (NLG) with over thirty-two pretrained ''' '''models in one hundred plus languages and deep interoperability between Jax, PyTorch and TensorFlow.''', '''BERT is designed to pre-train deep bidirectional representations from unlabeled text by jointly ''' '''conditioning on both left and right context in all layers.''', '''The quick brown fox jumps over the lazy dog.''', ] # fmt: off A: Any = { '''input_ids''': [ [4, 32, 13, 7, 9, 12, 19, 8, 13, 18, 5, 13, 12, 4, 64, 19, 8, 13, 18, 5, 13, 15, 22, 4, 28, 9, 8, 20, 9, 4, 7, 12, 4, 24, 22, 6, 8, 13, 17, 11, 39, 6, 13, 7, 9, 12, 19, 8, 13, 18, 5, 13, 12, 4, 7, 9, 14, 4, 24, 22, 6, 8, 13, 17, 11, 39, 24, 13, 5, 6, 13, 7, 10, 9, 5, 14, 39, 25, 5, 13, 6, 63, 4, 24, 13, 8, 27, 10, 14, 5, 12, 4, 21, 5, 9, 5, 13, 7, 15, 39, 24, 16, 13, 24, 8, 12, 5, 4, 7, 13, 17, 11, 10, 6, 5, 17, 6, 16, 13, 5, 12, 4, 64, 40, 47, 54, 32, 23, 4, 53, 49, 32, 23, 4, 54, 8, 40, 47, 54, 32, 7, 23, 4, 69, 52, 43, 23, 4, 51, 10, 12, 6, 10, 15, 40, 5, 13, 6, 23, 4, 69, 52, 48, 5, 6, 26, 26, 26, 63, 4, 19, 8, 13, 4, 48, 7, 6, 16, 13, 7, 15, 4, 52, 7, 9, 21, 16, 7, 21, 5, 4, 61, 9, 14, 5, 13, 12, 6, 7, 9, 14, 10, 9, 21, 4, 64, 48, 52, 61, 63, 4, 7, 9, 14, 4, 48, 7, 6, 16, 13, 7, 15, 4, 52, 7, 9, 21, 16, 7, 21, 5, 4, 53, 5, 9, 5, 13, 7, 6, 10, 8, 9, 4, 64, 48, 52, 53, 63, 4, 20, 10, 6, 11, 4, 8, 27, 5, 13, 4, 6, 11, 10, 13, 6, 22, 39, 6, 20, 8, 4, 24, 13, 5, 6, 13, 7, 10, 9, 5, 14, 4, 18, 8, 14, 5, 15, 12, 4, 10, 9, 4, 8, 9, 5, 4, 11, 16, 9, 14, 13, 5, 14, 4, 24, 15, 16, 12, 4, 15, 7, 9, 21, 16, 7, 21, 5, 12, 4, 7, 9, 14, 4, 14, 5, 5, 24, 4, 10, 9, 6, 5, 13, 8, 24, 5, 13, 7, 25, 10, 15, 10, 6, 22, 4, 25, 5, 6, 20, 5, 5, 9, 4, 58, 7, 37, 23, 4, 49, 22, 32, 8, 13, 17, 11, 4, 7, 9, 14, 4, 32, 5, 9, 12, 8, 13, 55, 15, 8, 20, 26, 2], [4, 40, 47, 54, 32, 4, 10, 12, 4, 14, 5, 12, 10, 21, 9, 5, 14, 4, 6, 8, 4, 24, 13, 5, 39, 6, 13, 7, 10, 9, 4, 14, 5, 5, 24, 4, 25, 10, 14, 10, 13, 5, 17, 6, 10, 8, 9, 7, 15, 4, 13, 5, 24, 13, 5, 12, 5, 9, 6, 7, 6, 10, 8, 9, 12, 4, 19, 13, 8, 18, 4, 16, 9, 15, 7, 25, 5, 15, 5, 14, 4, 6, 5, 37, 6, 4, 25, 22, 4, 46, 8, 10, 9, 6, 15, 22, 4, 17, 8, 9, 14, 10, 6, 10, 8, 9, 10, 9, 21, 4, 8, 9, 4, 25, 8, 6, 11, 4, 15, 5, 19, 6, 4, 7, 9, 14, 4, 13, 10, 21, 11, 6, 4, 17, 8, 9, 6, 5, 37, 6, 4, 10, 9, 4, 7, 15, 15, 4, 15, 7, 22, 5, 13, 12, 26, 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, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [4, 32, 11, 5, 4, 45, 16, 10, 17, 28, 4, 25, 13, 8, 20, 9, 4, 19, 8, 37, 4, 46, 16, 18, 24, 12, 4, 8, 27, 5, 13, 4, 6, 11, 5, 4, 15, 7, 57, 22, 4, 14, 8, 21, 26, 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, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 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, 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1, 1, 1, 1, 1, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], ] } # fmt: on self.tokenizer_integration_test_util( expected_encoding=SCREAMING_SNAKE_CASE_ , model_name='''microsoft/speecht5_asr''' , revision='''c5ef64c71905caeccde0e4462ef3f9077224c524''' , sequences=SCREAMING_SNAKE_CASE_ , )
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'''simple docstring''' from typing import TYPE_CHECKING from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available UpperCamelCase = {} try: if not is_sentencepiece_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: UpperCamelCase = ['''GPTSw3Tokenizer'''] if TYPE_CHECKING: try: if not is_sentencepiece_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .tokenization_gpt_swa import GPTSwaTokenizer else: import sys UpperCamelCase = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
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'''simple docstring''' import argparse import logging import os import re import tensorflow as tf from transformers import ( AutoConfig, AutoTokenizer, DataCollatorForLanguageModeling, PushToHubCallback, TFAutoModelForMaskedLM, create_optimizer, ) UpperCamelCase = logging.getLogger(__name__) UpperCamelCase = tf.data.AUTOTUNE def SCREAMING_SNAKE_CASE( ) -> Union[str, Any]: A: List[Any] = argparse.ArgumentParser(description='''Train a masked language model on TPU.''' ) parser.add_argument( '''--pretrained_model_config''' , type=__lowercase , default='''roberta-base''' , help='''The model config to use. Note that we don\'t copy the model\'s weights, only the config!''' , ) parser.add_argument( '''--tokenizer''' , type=__lowercase , default='''unigram-tokenizer-wikitext''' , help='''The name of the tokenizer to load. We use the pretrained tokenizer to initialize the model\'s vocab size.''' , ) parser.add_argument( '''--per_replica_batch_size''' , type=__lowercase , default=8 , help='''Batch size per TPU core.''' , ) parser.add_argument( '''--no_tpu''' , action='''store_true''' , help='''If set, run on CPU and don\'t try to initialize a TPU. Useful for debugging on non-TPU instances.''' , ) parser.add_argument( '''--tpu_name''' , type=__lowercase , help='''Name of TPU resource to initialize. Should be blank on Colab, and \'local\' on TPU VMs.''' , default='''local''' , ) parser.add_argument( '''--tpu_zone''' , type=__lowercase , help='''Google cloud zone that TPU resource is located in. Only used for non-Colab TPU nodes.''' , ) parser.add_argument( '''--gcp_project''' , type=__lowercase , help='''Google cloud project name. Only used for non-Colab TPU nodes.''' ) parser.add_argument( '''--bfloat16''' , action='''store_true''' , help='''Use mixed-precision bfloat16 for training. This is the recommended lower-precision format for TPU.''' , ) parser.add_argument( '''--train_dataset''' , type=__lowercase , help='''Path to training dataset to load. If the path begins with `gs://`''' ''' then the dataset will be loaded from a Google Cloud Storage bucket.''' , ) parser.add_argument( '''--shuffle_buffer_size''' , type=__lowercase , default=2**1_8 , help='''Size of the shuffle buffer (in samples)''' , ) parser.add_argument( '''--eval_dataset''' , type=__lowercase , help='''Path to evaluation dataset to load. If the path begins with `gs://`''' ''' then the dataset will be loaded from a Google Cloud Storage bucket.''' , ) parser.add_argument( '''--num_epochs''' , type=__lowercase , default=1 , help='''Number of epochs to train for.''' , ) parser.add_argument( '''--learning_rate''' , type=__lowercase , default=1E-4 , help='''Learning rate to use for training.''' , ) parser.add_argument( '''--weight_decay_rate''' , type=__lowercase , default=1E-3 , help='''Weight decay rate to use for training.''' , ) parser.add_argument( '''--max_length''' , type=__lowercase , default=5_1_2 , help='''Maximum length of tokenized sequences. Should match the setting used in prepare_tfrecord_shards.py''' , ) parser.add_argument( '''--mlm_probability''' , type=__lowercase , default=0.1_5 , help='''Fraction of tokens to mask during training.''' , ) parser.add_argument('''--output_dir''' , type=__lowercase , required=__lowercase , help='''Path to save model checkpoints to.''' ) parser.add_argument('''--hub_model_id''' , type=__lowercase , help='''Model ID to upload to on the Hugging Face Hub.''' ) A: Dict = parser.parse_args() return args def SCREAMING_SNAKE_CASE( __lowercase ) -> Optional[int]: try: if args.tpu_name: A: Optional[int] = tf.distribute.cluster_resolver.TPUClusterResolver( args.tpu_name , zone=args.tpu_zone , project=args.gcp_project ) else: A: int = tf.distribute.cluster_resolver.TPUClusterResolver() except ValueError: raise RuntimeError( '''Couldn\'t connect to TPU! Most likely you need to specify --tpu_name, --tpu_zone, or ''' '''--gcp_project. When running on a TPU VM, use --tpu_name local.''' ) tf.config.experimental_connect_to_cluster(__lowercase ) tf.tpu.experimental.initialize_tpu_system(__lowercase ) return tpu def SCREAMING_SNAKE_CASE( __lowercase ) -> Optional[Any]: A: str = 0 for file in file_list: A: Any = file.split('''/''' )[-1] A: Any = re.search(r'''-\d+-(\d+)\.tfrecord''' , __lowercase ).group(1 ) A: int = int(__lowercase ) num_samples += sample_count return num_samples def SCREAMING_SNAKE_CASE( __lowercase , __lowercase , __lowercase , __lowercase , __lowercase , __lowercase=None ) -> Dict: A: List[Any] = count_samples(__lowercase ) A: Tuple = tf.data.Dataset.from_tensor_slices(__lowercase ) if shuffle: A: str = dataset.shuffle(len(__lowercase ) ) A: List[str] = tf.data.TFRecordDataset(__lowercase , num_parallel_reads=__lowercase ) # TF can't infer the total sample count because it doesn't read all the records yet, so we assert it here A: Optional[Any] = dataset.apply(tf.data.experimental.assert_cardinality(__lowercase ) ) A: Optional[int] = dataset.map(__lowercase , num_parallel_calls=__lowercase ) if shuffle: assert shuffle_buffer_size is not None A: Optional[Any] = dataset.shuffle(args.shuffle_buffer_size ) A: List[str] = dataset.batch(__lowercase , drop_remainder=__lowercase ) A: int = dataset.map(__lowercase , num_parallel_calls=__lowercase ) A: List[str] = dataset.prefetch(__lowercase ) return dataset def SCREAMING_SNAKE_CASE( __lowercase ) -> Tuple: if not args.no_tpu: A: List[str] = initialize_tpu(__lowercase ) A: Dict = tf.distribute.TPUStrategy(__lowercase ) else: A: List[Any] = tf.distribute.OneDeviceStrategy(device='''/gpu:0''' ) if args.bfloataa: tf.keras.mixed_precision.set_global_policy('''mixed_bfloat16''' ) A: List[Any] = AutoTokenizer.from_pretrained(args.tokenizer ) A: List[Any] = AutoConfig.from_pretrained(args.pretrained_model_config ) A: Optional[int] = tokenizer.vocab_size A: Optional[int] = tf.io.gfile.glob(os.path.join(args.train_dataset , '''*.tfrecord''' ) ) if not training_records: raise ValueError(F"""No .tfrecord files found in {args.train_dataset}.""" ) A: Union[str, Any] = tf.io.gfile.glob(os.path.join(args.eval_dataset , '''*.tfrecord''' ) ) if not eval_records: raise ValueError(F"""No .tfrecord files found in {args.eval_dataset}.""" ) A: int = count_samples(__lowercase ) A: int = num_train_samples // (args.per_replica_batch_size * strategy.num_replicas_in_sync) A: Tuple = steps_per_epoch * args.num_epochs with strategy.scope(): A: Optional[Any] = TFAutoModelForMaskedLM.from_config(__lowercase ) model(model.dummy_inputs ) # Pass some dummy inputs through the model to ensure all the weights are built A , A: int = create_optimizer( num_train_steps=__lowercase , num_warmup_steps=total_train_steps // 2_0 , init_lr=args.learning_rate , weight_decay_rate=args.weight_decay_rate , ) # Transformers models compute the right loss for their task by default when labels are passed, and will # use this for training unless you specify your own loss function in compile(). model.compile(optimizer=__lowercase , metrics=['''accuracy'''] ) def decode_fn(__lowercase ): A: Dict = { '''input_ids''': tf.io.FixedLenFeature(dtype=tf.intaa , shape=(args.max_length,) ), '''attention_mask''': tf.io.FixedLenFeature(dtype=tf.intaa , shape=(args.max_length,) ), } return tf.io.parse_single_example(__lowercase , __lowercase ) # Many of the data collators in Transformers are TF-compilable when return_tensors == "tf", so we can # use their methods in our data pipeline. A: Optional[int] = DataCollatorForLanguageModeling( tokenizer=__lowercase , mlm_probability=args.mlm_probability , mlm=__lowercase , return_tensors='''tf''' ) def mask_with_collator(__lowercase ): # TF really needs an isin() function A: Dict = ( ~tf.cast(batch['''attention_mask'''] , tf.bool ) | (batch['''input_ids'''] == tokenizer.cls_token_id) | (batch['''input_ids'''] == tokenizer.sep_token_id) ) A , A: Tuple = data_collator.tf_mask_tokens( batch['''input_ids'''] , vocab_size=len(__lowercase ) , mask_token_id=tokenizer.mask_token_id , special_tokens_mask=__lowercase , ) return batch A: Union[str, Any] = args.per_replica_batch_size * strategy.num_replicas_in_sync A: List[str] = prepare_dataset( __lowercase , decode_fn=__lowercase , mask_fn=__lowercase , batch_size=__lowercase , shuffle=__lowercase , shuffle_buffer_size=args.shuffle_buffer_size , ) A: Any = prepare_dataset( __lowercase , decode_fn=__lowercase , mask_fn=__lowercase , batch_size=__lowercase , shuffle=__lowercase , ) A: int = [] if args.hub_model_id: callbacks.append( PushToHubCallback(output_dir=args.output_dir , hub_model_id=args.hub_model_id , tokenizer=__lowercase ) ) model.fit( __lowercase , validation_data=__lowercase , epochs=args.num_epochs , callbacks=__lowercase , ) model.save_pretrained(args.output_dir ) if __name__ == "__main__": UpperCamelCase = parse_args() main(args)
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'''simple docstring''' from __future__ import annotations from typing import Any class lowerCAmelCase_ ( UpperCAmelCase_ ): '''simple docstring''' pass class lowerCAmelCase_ : '''simple docstring''' def __init__( self : List[Any] , SCREAMING_SNAKE_CASE_ : Any ) -> None: '''simple docstring''' A: Any = data A: Node | None = None def __iter__( self : Optional[int] ) -> List[str]: '''simple docstring''' A: List[str] = self A: Dict = [] while node: if node in visited: raise ContainsLoopError visited.append(SCREAMING_SNAKE_CASE_ ) yield node.data A: str = node.next_node @property def _snake_case ( self : List[str] ) -> bool: '''simple docstring''' try: list(self ) return False except ContainsLoopError: return True if __name__ == "__main__": UpperCamelCase = Node(1) UpperCamelCase = Node(2) UpperCamelCase = Node(3) UpperCamelCase = Node(4) print(root_node.has_loop) # False UpperCamelCase = root_node.next_node print(root_node.has_loop) # True UpperCamelCase = Node(5) UpperCamelCase = Node(6) UpperCamelCase = Node(5) UpperCamelCase = Node(6) print(root_node.has_loop) # False UpperCamelCase = Node(1) print(root_node.has_loop) # False
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'''simple docstring''' from typing import List, Optional, Tuple, Union import torch from ...models import UNetaDModel from ...schedulers import KarrasVeScheduler from ...utils import randn_tensor from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput class lowerCAmelCase_ ( UpperCAmelCase_ ): '''simple docstring''' UpperCamelCase_ : UNetaDModel UpperCamelCase_ : KarrasVeScheduler def __init__( self : Any , SCREAMING_SNAKE_CASE_ : UNetaDModel , SCREAMING_SNAKE_CASE_ : KarrasVeScheduler ) -> Optional[int]: '''simple docstring''' super().__init__() self.register_modules(unet=SCREAMING_SNAKE_CASE_ , scheduler=SCREAMING_SNAKE_CASE_ ) @torch.no_grad() def __call__( self : str , SCREAMING_SNAKE_CASE_ : int = 1 , SCREAMING_SNAKE_CASE_ : int = 50 , SCREAMING_SNAKE_CASE_ : Optional[Union[torch.Generator, List[torch.Generator]]] = None , SCREAMING_SNAKE_CASE_ : Optional[str] = "pil" , SCREAMING_SNAKE_CASE_ : bool = True , **SCREAMING_SNAKE_CASE_ : Optional[int] , ) -> Union[Tuple, ImagePipelineOutput]: '''simple docstring''' A: Any = self.unet.config.sample_size A: List[Any] = (batch_size, 3, img_size, img_size) A: Optional[Any] = self.unet # sample x_0 ~ N(0, sigma_0^2 * I) A: Union[str, Any] = randn_tensor(SCREAMING_SNAKE_CASE_ , generator=SCREAMING_SNAKE_CASE_ , device=self.device ) * self.scheduler.init_noise_sigma self.scheduler.set_timesteps(SCREAMING_SNAKE_CASE_ ) for t in self.progress_bar(self.scheduler.timesteps ): # here sigma_t == t_i from the paper A: Optional[int] = self.scheduler.schedule[t] A: Optional[int] = self.scheduler.schedule[t - 1] if t > 0 else 0 # 1. Select temporarily increased noise level sigma_hat # 2. Add new noise to move from sample_i to sample_hat A , A: Optional[Any] = self.scheduler.add_noise_to_input(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , generator=SCREAMING_SNAKE_CASE_ ) # 3. Predict the noise residual given the noise magnitude `sigma_hat` # The model inputs and output are adjusted by following eq. (213) in [1]. A: Dict = (sigma_hat / 2) * model((sample_hat + 1) / 2 , sigma_hat / 2 ).sample # 4. Evaluate dx/dt at sigma_hat # 5. Take Euler step from sigma to sigma_prev A: List[str] = self.scheduler.step(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) if sigma_prev != 0: # 6. Apply 2nd order correction # The model inputs and output are adjusted by following eq. (213) in [1]. A: Union[str, Any] = (sigma_prev / 2) * model((step_output.prev_sample + 1) / 2 , sigma_prev / 2 ).sample A: List[Any] = self.scheduler.step_correct( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , step_output.prev_sample , step_output['''derivative'''] , ) A: int = step_output.prev_sample A: Tuple = (sample / 2 + 0.5).clamp(0 , 1 ) A: Dict = sample.cpu().permute(0 , 2 , 3 , 1 ).numpy() if output_type == "pil": A: str = self.numpy_to_pil(SCREAMING_SNAKE_CASE_ ) if not return_dict: return (image,) return ImagePipelineOutput(images=SCREAMING_SNAKE_CASE_ )
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'''simple docstring''' from __future__ import annotations def SCREAMING_SNAKE_CASE( __lowercase = 4 ) -> list[list[int]]: A: Tuple = abs(__lowercase ) or 4 return [[1 + x + y * row_size for x in range(__lowercase )] for y in range(__lowercase )] def SCREAMING_SNAKE_CASE( __lowercase ) -> list[list[int]]: return reverse_row(transpose(__lowercase ) ) # OR.. transpose(reverse_column(matrix)) def SCREAMING_SNAKE_CASE( __lowercase ) -> list[list[int]]: return reverse_row(reverse_column(__lowercase ) ) # OR.. reverse_column(reverse_row(matrix)) def SCREAMING_SNAKE_CASE( __lowercase ) -> list[list[int]]: return reverse_column(transpose(__lowercase ) ) # OR.. transpose(reverse_row(matrix)) def SCREAMING_SNAKE_CASE( __lowercase ) -> list[list[int]]: A: Union[str, Any] = [list(__lowercase ) for x in zip(*__lowercase )] return matrix def SCREAMING_SNAKE_CASE( __lowercase ) -> list[list[int]]: A: Optional[int] = matrix[::-1] return matrix def SCREAMING_SNAKE_CASE( __lowercase ) -> list[list[int]]: A: Optional[Any] = [x[::-1] for x in matrix] return matrix def SCREAMING_SNAKE_CASE( __lowercase ) -> None: for i in matrix: print(*__lowercase ) if __name__ == "__main__": UpperCamelCase = make_matrix() print('''\norigin:\n''') print_matrix(matrix) print('''\nrotate 90 counterclockwise:\n''') print_matrix(rotate_aa(matrix)) UpperCamelCase = make_matrix() print('''\norigin:\n''') print_matrix(matrix) print('''\nrotate 180:\n''') print_matrix(rotate_aaa(matrix)) UpperCamelCase = make_matrix() print('''\norigin:\n''') print_matrix(matrix) print('''\nrotate 270 counterclockwise:\n''') print_matrix(rotate_aaa(matrix))
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'''simple docstring''' from __future__ import annotations def SCREAMING_SNAKE_CASE( __lowercase , __lowercase ) -> float: A: str = sorted(numsa + numsa ) A , A: List[str] = divmod(len(__lowercase ) , 2 ) if mod == 1: return all_numbers[div] else: return (all_numbers[div] + all_numbers[div - 1]) / 2 if __name__ == "__main__": import doctest doctest.testmod() UpperCamelCase = [float(x) for x in input('''Enter the elements of first array: ''').split()] UpperCamelCase = [float(x) for x in input('''Enter the elements of second array: ''').split()] print(f'The median of two arrays is: {median_of_two_arrays(array_a, array_a)}')
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'''simple docstring''' from __future__ import annotations import numpy as np def SCREAMING_SNAKE_CASE( __lowercase ) -> Dict: return np.maximum(0 , __lowercase ) if __name__ == "__main__": print(np.array(relu([-1, 0, 5]))) # --> [0, 0, 5]
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'''simple docstring''' from __future__ import annotations import random # Maximum size of the population. Bigger could be faster but is more memory expensive. UpperCamelCase = 200 # Number of elements selected in every generation of evolution. The selection takes # place from best to worst of that generation and must be smaller than N_POPULATION. UpperCamelCase = 50 # Probability that an element of a generation can mutate, changing one of its genes. # This will guarantee that all genes will be used during evolution. UpperCamelCase = 0.4 # Just a seed to improve randomness required by the algorithm. random.seed(random.randint(0, 1000)) def SCREAMING_SNAKE_CASE( __lowercase , __lowercase ) -> tuple[str, float]: A: Tuple = len([g for position, g in enumerate(__lowercase ) if g == main_target[position]] ) return (item, float(__lowercase )) def SCREAMING_SNAKE_CASE( __lowercase , __lowercase ) -> tuple[str, str]: A: Optional[Any] = random.randint(0 , len(__lowercase ) - 1 ) A: int = parent_a[:random_slice] + parent_a[random_slice:] A: Tuple = parent_a[:random_slice] + parent_a[random_slice:] return (child_a, child_a) def SCREAMING_SNAKE_CASE( __lowercase , __lowercase ) -> str: A: Optional[int] = list(__lowercase ) if random.uniform(0 , 1 ) < MUTATION_PROBABILITY: A: Any = random.choice(__lowercase ) return "".join(__lowercase ) def SCREAMING_SNAKE_CASE( __lowercase , __lowercase , __lowercase , ) -> list[str]: A: List[str] = [] # Generate more children proportionally to the fitness score. A: List[str] = int(parent_a[1] * 1_0_0 ) + 1 A: Tuple = 1_0 if child_n >= 1_0 else child_n for _ in range(__lowercase ): A: List[str] = population_score[random.randint(0 , __lowercase )][0] A , A: List[str] = crossover(parent_a[0] , __lowercase ) # Append new string to the population list. pop.append(mutate(__lowercase , __lowercase ) ) pop.append(mutate(__lowercase , __lowercase ) ) return pop def SCREAMING_SNAKE_CASE( __lowercase , __lowercase , __lowercase = True ) -> tuple[int, int, str]: # Verify if N_POPULATION is bigger than N_SELECTED if N_POPULATION < N_SELECTED: A: str = F"""{N_POPULATION} must be bigger than {N_SELECTED}""" raise ValueError(__lowercase ) # Verify that the target contains no genes besides the ones inside genes variable. A: List[str] = sorted({c for c in target if c not in genes} ) if not_in_genes_list: A: Any = F"""{not_in_genes_list} is not in genes list, evolution cannot converge""" raise ValueError(__lowercase ) # Generate random starting population. A: Optional[Any] = [] for _ in range(__lowercase ): population.append(''''''.join([random.choice(__lowercase ) for i in range(len(__lowercase ) )] ) ) # Just some logs to know what the algorithms is doing. A , A: Optional[Any] = 0, 0 # This loop will end when we find a perfect match for our target. while True: generation += 1 total_population += len(__lowercase ) # Random population created. Now it's time to evaluate. # Adding a bit of concurrency can make everything faster, # # import concurrent.futures # population_score: list[tuple[str, float]] = [] # with concurrent.futures.ThreadPoolExecutor( # max_workers=NUM_WORKERS) as executor: # futures = {executor.submit(evaluate, item) for item in population} # concurrent.futures.wait(futures) # population_score = [item.result() for item in futures] # # but with a simple algorithm like this, it will probably be slower. # We just need to call evaluate for every item inside the population. A: Optional[int] = [evaluate(__lowercase , __lowercase ) for item in population] # Check if there is a matching evolution. A: Tuple = sorted(__lowercase , key=lambda __lowercase : x[1] , reverse=__lowercase ) if population_score[0][0] == target: return (generation, total_population, population_score[0][0]) # Print the best result every 10 generation. # Just to know that the algorithm is working. if debug and generation % 1_0 == 0: print( F"""\nGeneration: {generation}""" F"""\nTotal Population:{total_population}""" F"""\nBest score: {population_score[0][1]}""" F"""\nBest string: {population_score[0][0]}""" ) # Flush the old population, keeping some of the best evolutions. # Keeping this avoid regression of evolution. A: Union[str, Any] = population[: int(N_POPULATION / 3 )] population.clear() population.extend(__lowercase ) # Normalize population score to be between 0 and 1. A: Union[str, Any] = [ (item, score / len(__lowercase )) for item, score in population_score ] # This is selection for i in range(__lowercase ): population.extend(select(population_score[int(__lowercase )] , __lowercase , __lowercase ) ) # Check if the population has already reached the maximum value and if so, # break the cycle. If this check is disabled, the algorithm will take # forever to compute large strings, but will also calculate small strings in # a far fewer generations. if len(__lowercase ) > N_POPULATION: break if __name__ == "__main__": UpperCamelCase = ( '''This is a genetic algorithm to evaluate, combine, evolve, and mutate a string!''' ) UpperCamelCase = list( ''' ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklm''' '''nopqrstuvwxyz.,;!?+-*#@^\'èéòà€ù=)(&%$£/\\''' ) UpperCamelCase , UpperCamelCase , UpperCamelCase = basic(target_str, genes_list) print( f'\nGeneration: {generation}\nTotal Population: {population}\nTarget: {target}' )
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'''simple docstring''' from typing import TYPE_CHECKING from ...utils import ( OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available, is_speech_available, is_tf_available, is_torch_available, ) UpperCamelCase = { '''configuration_speech_to_text''': ['''SPEECH_TO_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''Speech2TextConfig'''], '''processing_speech_to_text''': ['''Speech2TextProcessor'''], } try: if not is_sentencepiece_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: UpperCamelCase = ['''Speech2TextTokenizer'''] try: if not is_speech_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: UpperCamelCase = ['''Speech2TextFeatureExtractor'''] try: if not is_tf_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: UpperCamelCase = [ '''TF_SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST''', '''TFSpeech2TextForConditionalGeneration''', '''TFSpeech2TextModel''', '''TFSpeech2TextPreTrainedModel''', ] try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: UpperCamelCase = [ '''SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST''', '''Speech2TextForConditionalGeneration''', '''Speech2TextModel''', '''Speech2TextPreTrainedModel''', ] if TYPE_CHECKING: from .configuration_speech_to_text import SPEECH_TO_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP, SpeechaTextConfig from .processing_speech_to_text import SpeechaTextProcessor try: if not is_sentencepiece_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .tokenization_speech_to_text import SpeechaTextTokenizer try: if not is_speech_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .feature_extraction_speech_to_text import SpeechaTextFeatureExtractor try: if not is_tf_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_tf_speech_to_text import ( TF_SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST, TFSpeechaTextForConditionalGeneration, TFSpeechaTextModel, TFSpeechaTextPreTrainedModel, ) try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_speech_to_text import ( SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST, SpeechaTextForConditionalGeneration, SpeechaTextModel, SpeechaTextPreTrainedModel, ) else: import sys UpperCamelCase = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
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'''simple docstring''' import warnings from typing import Dict, List, Optional, Tuple from ...tokenization_utils import AddedToken, PreTrainedTokenizer from ...utils import logging UpperCamelCase = logging.get_logger(__name__) class lowerCAmelCase_ ( UpperCAmelCase_ ): '''simple docstring''' UpperCamelCase_ : List[Any] = ["""input_ids""", """attention_mask"""] def __init__( self : Optional[Any] , SCREAMING_SNAKE_CASE_ : Union[str, Any]="</s>" , SCREAMING_SNAKE_CASE_ : Any="<unk>" , SCREAMING_SNAKE_CASE_ : int="<pad>" , SCREAMING_SNAKE_CASE_ : Optional[Any]=1_25 , SCREAMING_SNAKE_CASE_ : Union[str, Any]=None , **SCREAMING_SNAKE_CASE_ : List[str] , ) -> None: '''simple docstring''' if extra_ids > 0 and additional_special_tokens is None: A: List[Any] = [f"""<extra_id_{i}>""" for i in range(SCREAMING_SNAKE_CASE_ )] elif extra_ids > 0 and additional_special_tokens is not None: # Check that we have the right number of extra_id special tokens A: int = len(set(filter(lambda SCREAMING_SNAKE_CASE_ : bool('''extra_id''' in str(SCREAMING_SNAKE_CASE_ ) ) , SCREAMING_SNAKE_CASE_ ) ) ) if extra_tokens != extra_ids: raise ValueError( f"""Both extra_ids ({extra_ids}) and additional_special_tokens ({additional_special_tokens}) are""" ''' provided to ByT5Tokenizer. In this case the additional_special_tokens must include the''' ''' extra_ids tokens''' ) A: Optional[Any] = AddedToken(SCREAMING_SNAKE_CASE_ , lstrip=SCREAMING_SNAKE_CASE_ , rstrip=SCREAMING_SNAKE_CASE_ ) if isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) else pad_token A: List[str] = AddedToken(SCREAMING_SNAKE_CASE_ , lstrip=SCREAMING_SNAKE_CASE_ , rstrip=SCREAMING_SNAKE_CASE_ ) if isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) else eos_token A: Optional[Any] = AddedToken(SCREAMING_SNAKE_CASE_ , lstrip=SCREAMING_SNAKE_CASE_ , rstrip=SCREAMING_SNAKE_CASE_ ) if isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) else unk_token super().__init__( eos_token=SCREAMING_SNAKE_CASE_ , unk_token=SCREAMING_SNAKE_CASE_ , pad_token=SCREAMING_SNAKE_CASE_ , extra_ids=SCREAMING_SNAKE_CASE_ , additional_special_tokens=SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ , ) A: Union[str, Any] = extra_ids A: Tuple = 2**8 # utf is 8 bits # define special tokens dict A: Dict[int, str] = { self.pad_token: 0, self.eos_token: 1, self.unk_token: 2, } A: Union[str, Any] = len(self.special_tokens_encoder ) A: Dict = len(SCREAMING_SNAKE_CASE_ ) for i, token in enumerate(SCREAMING_SNAKE_CASE_ ): A: Union[str, Any] = self.vocab_size + i - n A: Dict[str, int] = {v: k for k, v in self.special_tokens_encoder.items()} @property def _snake_case ( self : List[str] ) -> int: '''simple docstring''' return self._utf_vocab_size + self._num_special_tokens + self._extra_ids def _snake_case ( self : int , SCREAMING_SNAKE_CASE_ : List[int] , SCREAMING_SNAKE_CASE_ : Optional[List[int]] = None , SCREAMING_SNAKE_CASE_ : bool = False ) -> List[int]: '''simple docstring''' if already_has_special_tokens: return super().get_special_tokens_mask( token_ids_a=SCREAMING_SNAKE_CASE_ , token_ids_a=SCREAMING_SNAKE_CASE_ , already_has_special_tokens=SCREAMING_SNAKE_CASE_ ) # normal case: some special tokens if token_ids_a is None: return ([0] * len(SCREAMING_SNAKE_CASE_ )) + [1] return ([0] * len(SCREAMING_SNAKE_CASE_ )) + [1] + ([0] * len(SCREAMING_SNAKE_CASE_ )) + [1] def _snake_case ( self : List[Any] , SCREAMING_SNAKE_CASE_ : List[int] ) -> List[int]: '''simple docstring''' if len(SCREAMING_SNAKE_CASE_ ) > 0 and token_ids[-1] == self.eos_token_id: warnings.warn( f"""This sequence already has {self.eos_token}. In future versions this behavior may lead to duplicated""" ''' eos tokens being added.''' ) return token_ids else: return token_ids + [self.eos_token_id] def _snake_case ( self : int , SCREAMING_SNAKE_CASE_ : List[int] , SCREAMING_SNAKE_CASE_ : Optional[List[int]] = None ) -> List[int]: '''simple docstring''' A: str = [self.eos_token_id] if token_ids_a is None: return len(token_ids_a + eos ) * [0] return len(token_ids_a + eos + token_ids_a + eos ) * [0] def _snake_case ( self : Tuple , SCREAMING_SNAKE_CASE_ : List[int] , SCREAMING_SNAKE_CASE_ : Optional[List[int]] = None ) -> List[int]: '''simple docstring''' A: List[str] = self._add_eos_if_not_present(SCREAMING_SNAKE_CASE_ ) if token_ids_a is None: return token_ids_a else: A: int = self._add_eos_if_not_present(SCREAMING_SNAKE_CASE_ ) return token_ids_a + token_ids_a def _snake_case ( self : int , SCREAMING_SNAKE_CASE_ : str ) -> List[str]: '''simple docstring''' A: Tuple = [chr(SCREAMING_SNAKE_CASE_ ) for i in text.encode('''utf-8''' )] return tokens def _snake_case ( self : List[str] , SCREAMING_SNAKE_CASE_ : int ) -> List[str]: '''simple docstring''' if token in self.special_tokens_encoder: A: str = self.special_tokens_encoder[token] elif token in self.added_tokens_encoder: A: Any = self.added_tokens_encoder[token] elif len(SCREAMING_SNAKE_CASE_ ) != 1: A: List[Any] = self.unk_token_id else: A: Any = ord(SCREAMING_SNAKE_CASE_ ) + self._num_special_tokens return token_id def _snake_case ( self : str , SCREAMING_SNAKE_CASE_ : Optional[int] ) -> Union[str, Any]: '''simple docstring''' if index in self.special_tokens_decoder: A: int = self.special_tokens_decoder[index] else: A: Union[str, Any] = chr(index - self._num_special_tokens ) return token def _snake_case ( self : List[str] , SCREAMING_SNAKE_CASE_ : Tuple ) -> int: '''simple docstring''' A: Tuple = b'''''' for token in tokens: if token in self.special_tokens_decoder: A: Optional[int] = self.special_tokens_decoder[token].encode('''utf-8''' ) elif token in self.added_tokens_decoder: A: Union[str, Any] = self.special_tokens_decoder[token].encode('''utf-8''' ) elif token in self.special_tokens_encoder: A: Optional[Any] = token.encode('''utf-8''' ) elif token in self.added_tokens_encoder: A: int = token.encode('''utf-8''' ) else: A: List[Any] = bytes([ord(SCREAMING_SNAKE_CASE_ )] ) bstring += tok_string A: Tuple = bstring.decode('''utf-8''' , errors='''ignore''' ) return string def _snake_case ( self : str , SCREAMING_SNAKE_CASE_ : str , SCREAMING_SNAKE_CASE_ : Optional[str] = None ) -> Tuple[str]: '''simple docstring''' return ()
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'''simple docstring''' from ..utils import is_flax_available, is_torch_available if is_torch_available(): from .autoencoder_kl import AutoencoderKL from .controlnet import ControlNetModel from .dual_transformer_ad import DualTransformeraDModel from .modeling_utils import ModelMixin from .prior_transformer import PriorTransformer from .ta_film_transformer import TaFilmDecoder from .transformer_ad import TransformeraDModel from .unet_ad import UNetaDModel from .unet_ad import UNetaDModel from .unet_ad_condition import UNetaDConditionModel from .unet_ad_condition import UNetaDConditionModel from .vq_model import VQModel if is_flax_available(): from .controlnet_flax import FlaxControlNetModel from .unet_ad_condition_flax import FlaxUNetaDConditionModel from .vae_flax import FlaxAutoencoderKL
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'''simple docstring''' import pytest from datasets.splits import SplitDict, SplitInfo from datasets.utils.py_utils import asdict @pytest.mark.parametrize( '''split_dict''' , [ SplitDict(), SplitDict({'''train''': SplitInfo(name='''train''' , num_bytes=1_3_3_7 , num_examples=4_2 , dataset_name='''my_dataset''' )} ), SplitDict({'''train''': SplitInfo(name='''train''' , num_bytes=1_3_3_7 , num_examples=4_2 )} ), SplitDict({'''train''': SplitInfo()} ), ] , ) def SCREAMING_SNAKE_CASE( __lowercase ) -> Any: A: List[str] = split_dict._to_yaml_list() assert len(__lowercase ) == len(__lowercase ) A: Optional[Any] = SplitDict._from_yaml_list(__lowercase ) for split_name, split_info in split_dict.items(): # dataset_name field is deprecated, and is therefore not part of the YAML dump A: Tuple = None # the split name of split_dict takes over the name of the split info object A: int = split_name assert split_dict == reloaded @pytest.mark.parametrize( '''split_info''' , [SplitInfo(), SplitInfo(dataset_name=__lowercase ), SplitInfo(dataset_name='''my_dataset''' )] ) def SCREAMING_SNAKE_CASE( __lowercase ) -> Any: # For backward compatibility, we need asdict(split_dict) to return split info dictrionaries with the "dataset_name" # field even if it's deprecated. This way old versionso of `datasets` can still reload dataset_infos.json files A: Any = asdict(SplitDict({'''train''': split_info} ) ) assert "dataset_name" in split_dict_asdict["train"] assert split_dict_asdict["train"]["dataset_name"] == split_info.dataset_name
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'''simple docstring''' from dataclasses import dataclass from typing import Optional, Tuple, Union import torch import torch.nn as nn from ..configuration_utils import ConfigMixin, register_to_config from ..utils import BaseOutput from .embeddings import GaussianFourierProjection, TimestepEmbedding, Timesteps from .modeling_utils import ModelMixin from .unet_ad_blocks import get_down_block, get_mid_block, get_out_block, get_up_block @dataclass class lowerCAmelCase_ ( UpperCAmelCase_ ): '''simple docstring''' UpperCamelCase_ : torch.FloatTensor class lowerCAmelCase_ ( UpperCAmelCase_ , UpperCAmelCase_ ): '''simple docstring''' @register_to_config def __init__( self : str , SCREAMING_SNAKE_CASE_ : int = 6_55_36 , SCREAMING_SNAKE_CASE_ : Optional[int] = None , SCREAMING_SNAKE_CASE_ : int = 2 , SCREAMING_SNAKE_CASE_ : int = 2 , SCREAMING_SNAKE_CASE_ : int = 0 , SCREAMING_SNAKE_CASE_ : str = "fourier" , SCREAMING_SNAKE_CASE_ : bool = True , SCREAMING_SNAKE_CASE_ : bool = False , SCREAMING_SNAKE_CASE_ : float = 0.0 , SCREAMING_SNAKE_CASE_ : Tuple[str] = ("DownBlock1DNoSkip", "DownBlock1D", "AttnDownBlock1D") , SCREAMING_SNAKE_CASE_ : Tuple[str] = ("AttnUpBlock1D", "UpBlock1D", "UpBlock1DNoSkip") , SCREAMING_SNAKE_CASE_ : Tuple[str] = "UNetMidBlock1D" , SCREAMING_SNAKE_CASE_ : str = None , SCREAMING_SNAKE_CASE_ : Tuple[int] = (32, 32, 64) , SCREAMING_SNAKE_CASE_ : str = None , SCREAMING_SNAKE_CASE_ : int = 8 , SCREAMING_SNAKE_CASE_ : int = 1 , SCREAMING_SNAKE_CASE_ : bool = False , ) -> Tuple: '''simple docstring''' super().__init__() A: Optional[Any] = sample_size # time if time_embedding_type == "fourier": A: Tuple = GaussianFourierProjection( embedding_size=8 , set_W_to_weight=SCREAMING_SNAKE_CASE_ , log=SCREAMING_SNAKE_CASE_ , flip_sin_to_cos=SCREAMING_SNAKE_CASE_ ) A: List[str] = 2 * block_out_channels[0] elif time_embedding_type == "positional": A: str = Timesteps( block_out_channels[0] , flip_sin_to_cos=SCREAMING_SNAKE_CASE_ , downscale_freq_shift=SCREAMING_SNAKE_CASE_ ) A: Any = block_out_channels[0] if use_timestep_embedding: A: Optional[Any] = block_out_channels[0] * 4 A: List[Any] = TimestepEmbedding( in_channels=SCREAMING_SNAKE_CASE_ , time_embed_dim=SCREAMING_SNAKE_CASE_ , act_fn=SCREAMING_SNAKE_CASE_ , out_dim=block_out_channels[0] , ) A: Optional[Any] = nn.ModuleList([] ) A: str = None A: str = nn.ModuleList([] ) A: Tuple = None # down A: Any = in_channels for i, down_block_type in enumerate(SCREAMING_SNAKE_CASE_ ): A: Optional[int] = output_channel A: List[Any] = block_out_channels[i] if i == 0: input_channel += extra_in_channels A: List[Any] = i == len(SCREAMING_SNAKE_CASE_ ) - 1 A: Optional[int] = get_down_block( SCREAMING_SNAKE_CASE_ , num_layers=SCREAMING_SNAKE_CASE_ , in_channels=SCREAMING_SNAKE_CASE_ , out_channels=SCREAMING_SNAKE_CASE_ , temb_channels=block_out_channels[0] , add_downsample=not is_final_block or downsample_each_block , ) self.down_blocks.append(SCREAMING_SNAKE_CASE_ ) # mid A: Union[str, Any] = get_mid_block( SCREAMING_SNAKE_CASE_ , in_channels=block_out_channels[-1] , mid_channels=block_out_channels[-1] , out_channels=block_out_channels[-1] , embed_dim=block_out_channels[0] , num_layers=SCREAMING_SNAKE_CASE_ , add_downsample=SCREAMING_SNAKE_CASE_ , ) # up A: Optional[Any] = list(reversed(SCREAMING_SNAKE_CASE_ ) ) A: List[str] = reversed_block_out_channels[0] if out_block_type is None: A: int = out_channels else: A: Union[str, Any] = block_out_channels[0] for i, up_block_type in enumerate(SCREAMING_SNAKE_CASE_ ): A: List[Any] = output_channel A: int = ( reversed_block_out_channels[i + 1] if i < len(SCREAMING_SNAKE_CASE_ ) - 1 else final_upsample_channels ) A: Optional[int] = i == len(SCREAMING_SNAKE_CASE_ ) - 1 A: Optional[Any] = get_up_block( SCREAMING_SNAKE_CASE_ , num_layers=SCREAMING_SNAKE_CASE_ , in_channels=SCREAMING_SNAKE_CASE_ , out_channels=SCREAMING_SNAKE_CASE_ , temb_channels=block_out_channels[0] , add_upsample=not is_final_block , ) self.up_blocks.append(SCREAMING_SNAKE_CASE_ ) A: Any = output_channel # out A: List[str] = norm_num_groups if norm_num_groups is not None else min(block_out_channels[0] // 4 , 32 ) A: Optional[int] = get_out_block( out_block_type=SCREAMING_SNAKE_CASE_ , num_groups_out=SCREAMING_SNAKE_CASE_ , embed_dim=block_out_channels[0] , out_channels=SCREAMING_SNAKE_CASE_ , act_fn=SCREAMING_SNAKE_CASE_ , fc_dim=block_out_channels[-1] // 4 , ) def _snake_case ( self : List[Any] , SCREAMING_SNAKE_CASE_ : torch.FloatTensor , SCREAMING_SNAKE_CASE_ : Union[torch.Tensor, float, int] , SCREAMING_SNAKE_CASE_ : bool = True , ) -> Union[UNetaDOutput, Tuple]: '''simple docstring''' A: Any = timestep if not torch.is_tensor(SCREAMING_SNAKE_CASE_ ): A: Union[str, Any] = torch.tensor([timesteps] , dtype=torch.long , device=sample.device ) elif torch.is_tensor(SCREAMING_SNAKE_CASE_ ) and len(timesteps.shape ) == 0: A: List[str] = timesteps[None].to(sample.device ) A: int = self.time_proj(SCREAMING_SNAKE_CASE_ ) if self.config.use_timestep_embedding: A: List[Any] = self.time_mlp(SCREAMING_SNAKE_CASE_ ) else: A: str = timestep_embed[..., None] A: Union[str, Any] = timestep_embed.repeat([1, 1, sample.shape[2]] ).to(sample.dtype ) A: Tuple = timestep_embed.broadcast_to((sample.shape[:1] + timestep_embed.shape[1:]) ) # 2. down A: List[str] = () for downsample_block in self.down_blocks: A , A: Optional[int] = downsample_block(hidden_states=SCREAMING_SNAKE_CASE_ , temb=SCREAMING_SNAKE_CASE_ ) down_block_res_samples += res_samples # 3. mid if self.mid_block: A: Dict = self.mid_block(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) # 4. up for i, upsample_block in enumerate(self.up_blocks ): A: List[Any] = down_block_res_samples[-1:] A: List[str] = down_block_res_samples[:-1] A: Optional[int] = upsample_block(SCREAMING_SNAKE_CASE_ , res_hidden_states_tuple=SCREAMING_SNAKE_CASE_ , temb=SCREAMING_SNAKE_CASE_ ) # 5. post-process if self.out_block: A: Any = self.out_block(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) if not return_dict: return (sample,) return UNetaDOutput(sample=SCREAMING_SNAKE_CASE_ )
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'''simple docstring''' from ...configuration_utils import PretrainedConfig from ...utils import logging UpperCamelCase = logging.get_logger(__name__) UpperCamelCase = { '''transfo-xl-wt103''': '''https://huggingface.co/transfo-xl-wt103/resolve/main/config.json''', } class lowerCAmelCase_ ( UpperCAmelCase_ ): '''simple docstring''' UpperCamelCase_ : Union[str, Any] = """transfo-xl""" UpperCamelCase_ : List[str] = ["""mems"""] UpperCamelCase_ : str = { """n_token""": """vocab_size""", """hidden_size""": """d_model""", """num_attention_heads""": """n_head""", """num_hidden_layers""": """n_layer""", } def __init__( self : List[str] , SCREAMING_SNAKE_CASE_ : Union[str, Any]=26_77_35 , SCREAMING_SNAKE_CASE_ : List[Any]=[2_00_00, 4_00_00, 20_00_00] , SCREAMING_SNAKE_CASE_ : int=10_24 , SCREAMING_SNAKE_CASE_ : Optional[int]=10_24 , SCREAMING_SNAKE_CASE_ : Optional[Any]=16 , SCREAMING_SNAKE_CASE_ : Union[str, Any]=64 , SCREAMING_SNAKE_CASE_ : Tuple=40_96 , SCREAMING_SNAKE_CASE_ : int=4 , SCREAMING_SNAKE_CASE_ : Union[str, Any]=False , SCREAMING_SNAKE_CASE_ : Tuple=18 , SCREAMING_SNAKE_CASE_ : Dict=16_00 , SCREAMING_SNAKE_CASE_ : str=10_00 , SCREAMING_SNAKE_CASE_ : List[str]=True , SCREAMING_SNAKE_CASE_ : List[Any]=True , SCREAMING_SNAKE_CASE_ : Optional[Any]=0 , SCREAMING_SNAKE_CASE_ : Union[str, Any]=-1 , SCREAMING_SNAKE_CASE_ : List[Any]=True , SCREAMING_SNAKE_CASE_ : Dict=0.1 , SCREAMING_SNAKE_CASE_ : str=0.0 , SCREAMING_SNAKE_CASE_ : Dict=True , SCREAMING_SNAKE_CASE_ : List[Any]="normal" , SCREAMING_SNAKE_CASE_ : Dict=0.01 , SCREAMING_SNAKE_CASE_ : Any=0.01 , SCREAMING_SNAKE_CASE_ : Dict=0.02 , SCREAMING_SNAKE_CASE_ : Any=1E-5 , SCREAMING_SNAKE_CASE_ : int=0 , **SCREAMING_SNAKE_CASE_ : List[Any] , ) -> Optional[Any]: '''simple docstring''' A: Optional[Any] = vocab_size A: Optional[Any] = [] self.cutoffs.extend(SCREAMING_SNAKE_CASE_ ) if proj_share_all_but_first: A: Union[str, Any] = [False] + [True] * len(self.cutoffs ) else: A: Any = [False] + [False] * len(self.cutoffs ) A: Tuple = d_model A: Union[str, Any] = d_embed A: Tuple = d_head A: Optional[int] = d_inner A: str = div_val A: Any = pre_lnorm A: Dict = n_layer A: Dict = n_head A: Union[str, Any] = mem_len A: str = same_length A: str = attn_type A: Optional[Any] = clamp_len A: Any = sample_softmax A: int = adaptive A: List[str] = dropout A: Tuple = dropatt A: List[str] = untie_r A: Optional[Any] = init A: Optional[int] = init_range A: Dict = proj_init_std A: Optional[int] = init_std A: Dict = layer_norm_epsilon super().__init__(eos_token_id=SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ ) @property def _snake_case ( self : Tuple ) -> Any: '''simple docstring''' logger.info(f"""The model {self.model_type} is one of the few models that has no sequence length limit.""" ) return -1 @max_position_embeddings.setter def _snake_case ( self : int , SCREAMING_SNAKE_CASE_ : Dict ) -> int: '''simple docstring''' raise NotImplementedError( f"""The model {self.model_type} is one of the few models that has no sequence length limit.""" )
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'''simple docstring''' import warnings from ...utils import logging from .image_processing_segformer import SegformerImageProcessor UpperCamelCase = logging.get_logger(__name__) class lowerCAmelCase_ ( UpperCAmelCase_ ): '''simple docstring''' def __init__( self : int , *SCREAMING_SNAKE_CASE_ : str , **SCREAMING_SNAKE_CASE_ : Dict ) -> None: '''simple docstring''' warnings.warn( '''The class SegformerFeatureExtractor is deprecated and will be removed in version 5 of Transformers.''' ''' Please use SegformerImageProcessor instead.''' , SCREAMING_SNAKE_CASE_ , ) super().__init__(*SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
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'''simple docstring''' from typing import TYPE_CHECKING from ...utils import ( OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_tf_available, is_torch_available, ) UpperCamelCase = { '''configuration_vision_encoder_decoder''': ['''VisionEncoderDecoderConfig''', '''VisionEncoderDecoderOnnxConfig'''] } try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: UpperCamelCase = ['''VisionEncoderDecoderModel'''] try: if not is_tf_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: UpperCamelCase = ['''TFVisionEncoderDecoderModel'''] try: if not is_flax_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: UpperCamelCase = ['''FlaxVisionEncoderDecoderModel'''] if TYPE_CHECKING: from .configuration_vision_encoder_decoder import VisionEncoderDecoderConfig, VisionEncoderDecoderOnnxConfig try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_vision_encoder_decoder import VisionEncoderDecoderModel try: if not is_tf_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_tf_vision_encoder_decoder import TFVisionEncoderDecoderModel try: if not is_flax_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_flax_vision_encoder_decoder import FlaxVisionEncoderDecoderModel else: import sys UpperCamelCase = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
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'''simple docstring''' from collections import deque class lowerCAmelCase_ : '''simple docstring''' def __init__( self : Optional[Any] , SCREAMING_SNAKE_CASE_ : str , SCREAMING_SNAKE_CASE_ : int , SCREAMING_SNAKE_CASE_ : int ) -> None: '''simple docstring''' A: Union[str, Any] = process_name # process name A: List[str] = arrival_time # arrival time of the process # completion time of finished process or last interrupted time A: Dict = arrival_time A: Optional[Any] = burst_time # remaining burst time A: Any = 0 # total time of the process wait in ready queue A: Any = 0 # time from arrival time to completion time class lowerCAmelCase_ : '''simple docstring''' def __init__( self : int , SCREAMING_SNAKE_CASE_ : int , SCREAMING_SNAKE_CASE_ : list[int] , SCREAMING_SNAKE_CASE_ : deque[Process] , SCREAMING_SNAKE_CASE_ : int , ) -> None: '''simple docstring''' A: Dict = number_of_queues # time slice of queues that round robin algorithm applied A: int = time_slices # unfinished process is in this ready_queue A: Tuple = queue # current time A: int = current_time # finished process is in this sequence queue A: deque[Process] = deque() def _snake_case ( self : List[Any] ) -> list[str]: '''simple docstring''' A: str = [] for i in range(len(self.finish_queue ) ): sequence.append(self.finish_queue[i].process_name ) return sequence def _snake_case ( self : List[Any] , SCREAMING_SNAKE_CASE_ : list[Process] ) -> list[int]: '''simple docstring''' A: Optional[int] = [] for i in range(len(SCREAMING_SNAKE_CASE_ ) ): waiting_times.append(queue[i].waiting_time ) return waiting_times def _snake_case ( self : Any , SCREAMING_SNAKE_CASE_ : list[Process] ) -> list[int]: '''simple docstring''' A: Any = [] for i in range(len(SCREAMING_SNAKE_CASE_ ) ): turnaround_times.append(queue[i].turnaround_time ) return turnaround_times def _snake_case ( self : str , SCREAMING_SNAKE_CASE_ : list[Process] ) -> list[int]: '''simple docstring''' A: List[Any] = [] for i in range(len(SCREAMING_SNAKE_CASE_ ) ): completion_times.append(queue[i].stop_time ) return completion_times def _snake_case ( self : Tuple , SCREAMING_SNAKE_CASE_ : deque[Process] ) -> list[int]: '''simple docstring''' return [q.burst_time for q in queue] def _snake_case ( self : int , SCREAMING_SNAKE_CASE_ : Process ) -> int: '''simple docstring''' process.waiting_time += self.current_time - process.stop_time return process.waiting_time def _snake_case ( self : List[str] , SCREAMING_SNAKE_CASE_ : deque[Process] ) -> deque[Process]: '''simple docstring''' A: deque[Process] = deque() # sequence deque of finished process while len(SCREAMING_SNAKE_CASE_ ) != 0: A: Optional[Any] = ready_queue.popleft() # current process # if process's arrival time is later than current time, update current time if self.current_time < cp.arrival_time: self.current_time += cp.arrival_time # update waiting time of current process self.update_waiting_time(SCREAMING_SNAKE_CASE_ ) # update current time self.current_time += cp.burst_time # finish the process and set the process's burst-time 0 A: Any = 0 # set the process's turnaround time because it is finished A: int = self.current_time - cp.arrival_time # set the completion time A: List[str] = self.current_time # add the process to queue that has finished queue finished.append(SCREAMING_SNAKE_CASE_ ) self.finish_queue.extend(SCREAMING_SNAKE_CASE_ ) # add finished process to finish queue # FCFS will finish all remaining processes return finished def _snake_case ( self : List[Any] , SCREAMING_SNAKE_CASE_ : deque[Process] , SCREAMING_SNAKE_CASE_ : int ) -> tuple[deque[Process], deque[Process]]: '''simple docstring''' A: deque[Process] = deque() # sequence deque of terminated process # just for 1 cycle and unfinished processes will go back to queue for _ in range(len(SCREAMING_SNAKE_CASE_ ) ): A: Dict = ready_queue.popleft() # current process # if process's arrival time is later than current time, update current time if self.current_time < cp.arrival_time: self.current_time += cp.arrival_time # update waiting time of unfinished processes self.update_waiting_time(SCREAMING_SNAKE_CASE_ ) # if the burst time of process is bigger than time-slice if cp.burst_time > time_slice: # use CPU for only time-slice self.current_time += time_slice # update remaining burst time cp.burst_time -= time_slice # update end point time A: Optional[Any] = self.current_time # locate the process behind the queue because it is not finished ready_queue.append(SCREAMING_SNAKE_CASE_ ) else: # use CPU for remaining burst time self.current_time += cp.burst_time # set burst time 0 because the process is finished A: int = 0 # set the finish time A: Union[str, Any] = self.current_time # update the process' turnaround time because it is finished A: Tuple = self.current_time - cp.arrival_time # add the process to queue that has finished queue finished.append(SCREAMING_SNAKE_CASE_ ) self.finish_queue.extend(SCREAMING_SNAKE_CASE_ ) # add finished process to finish queue # return finished processes queue and remaining processes queue return finished, ready_queue def _snake_case ( self : Optional[Any] ) -> deque[Process]: '''simple docstring''' for i in range(self.number_of_queues - 1 ): A , A: Optional[Any] = self.round_robin( self.ready_queue , self.time_slices[i] ) # the last queue has first_come_first_served algorithm self.first_come_first_served(self.ready_queue ) return self.finish_queue if __name__ == "__main__": import doctest UpperCamelCase = Process('''P1''', 0, 53) UpperCamelCase = Process('''P2''', 0, 17) UpperCamelCase = Process('''P3''', 0, 68) UpperCamelCase = Process('''P4''', 0, 24) UpperCamelCase = 3 UpperCamelCase = [17, 25] UpperCamelCase = deque([Pa, Pa, Pa, Pa]) if len(time_slices) != number_of_queues - 1: raise SystemExit(0) doctest.testmod(extraglobs={'''queue''': deque([Pa, Pa, Pa, Pa])}) UpperCamelCase = Process('''P1''', 0, 53) UpperCamelCase = Process('''P2''', 0, 17) UpperCamelCase = Process('''P3''', 0, 68) UpperCamelCase = Process('''P4''', 0, 24) UpperCamelCase = 3 UpperCamelCase = [17, 25] UpperCamelCase = deque([Pa, Pa, Pa, Pa]) UpperCamelCase = MLFQ(number_of_queues, time_slices, queue, 0) UpperCamelCase = mlfq.multi_level_feedback_queue() # print total waiting times of processes(P1, P2, P3, P4) print( f'waiting time:\ \t\t\t{MLFQ.calculate_waiting_time(mlfq, [Pa, Pa, Pa, Pa])}' ) # print completion times of processes(P1, P2, P3, P4) print( f'completion time:\ \t\t{MLFQ.calculate_completion_time(mlfq, [Pa, Pa, Pa, Pa])}' ) # print total turnaround times of processes(P1, P2, P3, P4) print( f'turnaround time:\ \t\t{MLFQ.calculate_turnaround_time(mlfq, [Pa, Pa, Pa, Pa])}' ) # print sequence of finished processes print( f'sequence of finished processes:\ {mlfq.calculate_sequence_of_finish_queue()}' )
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'''simple docstring''' import itertools import random import unittest import numpy as np from transformers import WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST, WavaVecaConfig, WavaVecaFeatureExtractor from transformers.testing_utils import require_torch, slow from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin UpperCamelCase = random.Random() def SCREAMING_SNAKE_CASE( __lowercase , __lowercase=1.0 , __lowercase=None , __lowercase=None ) -> List[str]: if rng is None: A: List[Any] = global_rng A: Any = [] for batch_idx in range(shape[0] ): values.append([] ) for _ in range(shape[1] ): values[-1].append(rng.random() * scale ) return values class lowerCAmelCase_ ( unittest.TestCase ): '''simple docstring''' def __init__( self : str , SCREAMING_SNAKE_CASE_ : List[Any] , SCREAMING_SNAKE_CASE_ : Optional[int]=7 , SCREAMING_SNAKE_CASE_ : Optional[Any]=4_00 , SCREAMING_SNAKE_CASE_ : List[Any]=20_00 , SCREAMING_SNAKE_CASE_ : Tuple=1 , SCREAMING_SNAKE_CASE_ : Dict=0.0 , SCREAMING_SNAKE_CASE_ : int=1_60_00 , SCREAMING_SNAKE_CASE_ : Tuple=True , SCREAMING_SNAKE_CASE_ : Any=True , ) -> Union[str, Any]: '''simple docstring''' A: Optional[Any] = parent A: Tuple = batch_size A: List[Any] = min_seq_length A: Union[str, Any] = max_seq_length A: Dict = (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1) A: List[Any] = feature_size A: Tuple = padding_value A: Dict = sampling_rate A: Tuple = return_attention_mask A: List[str] = do_normalize def _snake_case ( self : int ) -> List[str]: '''simple docstring''' return { "feature_size": self.feature_size, "padding_value": self.padding_value, "sampling_rate": self.sampling_rate, "return_attention_mask": self.return_attention_mask, "do_normalize": self.do_normalize, } def _snake_case ( self : List[str] , SCREAMING_SNAKE_CASE_ : str=False , SCREAMING_SNAKE_CASE_ : Tuple=False ) -> Dict: '''simple docstring''' def _flatten(SCREAMING_SNAKE_CASE_ : Optional[Any] ): return list(itertools.chain(*SCREAMING_SNAKE_CASE_ ) ) if equal_length: A: int = floats_list((self.batch_size, self.max_seq_length) ) else: # make sure that inputs increase in size A: int = [ _flatten(floats_list((x, self.feature_size) ) ) for x in range(self.min_seq_length , self.max_seq_length , self.seq_length_diff ) ] if numpify: A: int = [np.asarray(SCREAMING_SNAKE_CASE_ ) for x in speech_inputs] return speech_inputs class lowerCAmelCase_ ( UpperCAmelCase_ , unittest.TestCase ): '''simple docstring''' UpperCamelCase_ : Union[str, Any] = WavaVecaFeatureExtractor def _snake_case ( self : Optional[int] ) -> Any: '''simple docstring''' A: List[str] = WavaVecaFeatureExtractionTester(self ) def _snake_case ( self : Any , SCREAMING_SNAKE_CASE_ : int ) -> Any: '''simple docstring''' self.assertTrue(np.all(np.mean(SCREAMING_SNAKE_CASE_ , axis=0 ) < 1E-3 ) ) self.assertTrue(np.all(np.abs(np.var(SCREAMING_SNAKE_CASE_ , axis=0 ) - 1 ) < 1E-3 ) ) def _snake_case ( self : Tuple ) -> Union[str, Any]: '''simple docstring''' A: str = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() ) # create three inputs of length 800, 1000, and 1200 A: Union[str, Any] = [floats_list((1, x) )[0] for x in range(8_00 , 14_00 , 2_00 )] A: Any = [np.asarray(SCREAMING_SNAKE_CASE_ ) for speech_input in speech_inputs] # Test not batched input A: List[Any] = feat_extract(speech_inputs[0] , return_tensors='''np''' ).input_values A: str = feat_extract(np_speech_inputs[0] , return_tensors='''np''' ).input_values self.assertTrue(np.allclose(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , atol=1E-3 ) ) # Test batched A: Any = feat_extract(SCREAMING_SNAKE_CASE_ , return_tensors='''np''' ).input_values A: List[str] = feat_extract(SCREAMING_SNAKE_CASE_ , return_tensors='''np''' ).input_values for enc_seq_a, enc_seq_a in zip(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ): self.assertTrue(np.allclose(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , atol=1E-3 ) ) # Test 2-D numpy arrays are batched. A: int = [floats_list((1, x) )[0] for x in (8_00, 8_00, 8_00)] A: List[str] = np.asarray(SCREAMING_SNAKE_CASE_ ) A: str = feat_extract(SCREAMING_SNAKE_CASE_ , return_tensors='''np''' ).input_values A: List[str] = feat_extract(SCREAMING_SNAKE_CASE_ , return_tensors='''np''' ).input_values for enc_seq_a, enc_seq_a in zip(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ): self.assertTrue(np.allclose(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , atol=1E-3 ) ) def _snake_case ( self : Union[str, Any] ) -> Optional[Any]: '''simple docstring''' A: Optional[Any] = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() ) A: Optional[Any] = [floats_list((1, x) )[0] for x in range(8_00 , 14_00 , 2_00 )] A: List[Any] = ['''longest''', '''max_length''', '''do_not_pad'''] A: Tuple = [None, 16_00, None] for max_length, padding in zip(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ): A: Union[str, Any] = feat_extract(SCREAMING_SNAKE_CASE_ , padding=SCREAMING_SNAKE_CASE_ , max_length=SCREAMING_SNAKE_CASE_ , return_tensors='''np''' ) A: Tuple = processed.input_values self._check_zero_mean_unit_variance(input_values[0][:8_00] ) self.assertTrue(input_values[0][8_00:].sum() < 1E-6 ) self._check_zero_mean_unit_variance(input_values[1][:10_00] ) self.assertTrue(input_values[0][10_00:].sum() < 1E-6 ) self._check_zero_mean_unit_variance(input_values[2][:12_00] ) def _snake_case ( self : Any ) -> Optional[Any]: '''simple docstring''' A: Optional[Any] = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() ) A: Dict = range(8_00 , 14_00 , 2_00 ) A: Optional[int] = [floats_list((1, x) )[0] for x in lengths] A: Tuple = ['''longest''', '''max_length''', '''do_not_pad'''] A: Union[str, Any] = [None, 16_00, None] for max_length, padding in zip(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ): A: Tuple = feat_extract(SCREAMING_SNAKE_CASE_ , max_length=SCREAMING_SNAKE_CASE_ , padding=SCREAMING_SNAKE_CASE_ ) A: List[Any] = processed.input_values self._check_zero_mean_unit_variance(input_values[0][:8_00] ) self._check_zero_mean_unit_variance(input_values[1][:10_00] ) self._check_zero_mean_unit_variance(input_values[2][:12_00] ) def _snake_case ( self : Any ) -> Any: '''simple docstring''' A: List[str] = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() ) A: List[str] = [floats_list((1, x) )[0] for x in range(8_00 , 14_00 , 2_00 )] A: Optional[Any] = feat_extract( SCREAMING_SNAKE_CASE_ , truncation=SCREAMING_SNAKE_CASE_ , max_length=10_00 , padding='''max_length''' , return_tensors='''np''' ) A: Tuple = processed.input_values self._check_zero_mean_unit_variance(input_values[0, :8_00] ) self._check_zero_mean_unit_variance(input_values[1] ) self._check_zero_mean_unit_variance(input_values[2] ) def _snake_case ( self : Optional[int] ) -> Dict: '''simple docstring''' A: int = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() ) A: Any = [floats_list((1, x) )[0] for x in range(8_00 , 14_00 , 2_00 )] A: int = feat_extract( SCREAMING_SNAKE_CASE_ , truncation=SCREAMING_SNAKE_CASE_ , max_length=10_00 , padding='''longest''' , return_tensors='''np''' ) A: List[str] = processed.input_values self._check_zero_mean_unit_variance(input_values[0, :8_00] ) self._check_zero_mean_unit_variance(input_values[1, :10_00] ) self._check_zero_mean_unit_variance(input_values[2] ) # make sure that if max_length < longest -> then pad to max_length self.assertTrue(input_values.shape == (3, 10_00) ) A: int = [floats_list((1, x) )[0] for x in range(8_00 , 14_00 , 2_00 )] A: int = feat_extract( SCREAMING_SNAKE_CASE_ , truncation=SCREAMING_SNAKE_CASE_ , max_length=20_00 , padding='''longest''' , return_tensors='''np''' ) A: str = processed.input_values self._check_zero_mean_unit_variance(input_values[0, :8_00] ) self._check_zero_mean_unit_variance(input_values[1, :10_00] ) self._check_zero_mean_unit_variance(input_values[2] ) # make sure that if max_length > longest -> then pad to longest self.assertTrue(input_values.shape == (3, 12_00) ) @require_torch def _snake_case ( self : Optional[int] ) -> List[str]: '''simple docstring''' import torch A: Union[str, Any] = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() ) A: str = np.random.rand(1_00 ).astype(np.floataa ) A: List[Any] = np_speech_inputs.tolist() for inputs in [py_speech_inputs, np_speech_inputs]: A: Optional[int] = feature_extractor.pad([{'''input_values''': inputs}] , return_tensors='''np''' ) self.assertTrue(np_processed.input_values.dtype == np.floataa ) A: List[str] = feature_extractor.pad([{'''input_values''': inputs}] , return_tensors='''pt''' ) self.assertTrue(pt_processed.input_values.dtype == torch.floataa ) @slow @require_torch def _snake_case ( self : Dict ) -> Tuple: '''simple docstring''' for model_id in WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST: A: Tuple = WavaVecaConfig.from_pretrained(SCREAMING_SNAKE_CASE_ ) A: str = WavaVecaFeatureExtractor.from_pretrained(SCREAMING_SNAKE_CASE_ ) # only "layer" feature extraction norm should make use of # attention_mask self.assertEqual(feat_extract.return_attention_mask , config.feat_extract_norm == '''layer''' )
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'''simple docstring''' import argparse import json from dataclasses import dataclass, field from functools import partial from pathlib import Path from typing import List import timm import torch import torch.nn as nn from huggingface_hub import hf_hub_download from torch import Tensor from transformers import AutoImageProcessor, ResNetConfig, ResNetForImageClassification from transformers.utils import logging logging.set_verbosity_info() UpperCamelCase = logging.get_logger() @dataclass class lowerCAmelCase_ : '''simple docstring''' UpperCamelCase_ : nn.Module UpperCamelCase_ : List[nn.Module] = field(default_factory=UpperCAmelCase_ ) UpperCamelCase_ : list = field(default_factory=UpperCAmelCase_ ) def _snake_case ( self : str , SCREAMING_SNAKE_CASE_ : Union[str, Any] , SCREAMING_SNAKE_CASE_ : Tensor , SCREAMING_SNAKE_CASE_ : Tensor ) -> int: '''simple docstring''' A: List[str] = len(list(m.modules() ) ) == 1 or isinstance(SCREAMING_SNAKE_CASE_ , nn.Convad ) or isinstance(SCREAMING_SNAKE_CASE_ , nn.BatchNormad ) if has_not_submodules: self.traced.append(SCREAMING_SNAKE_CASE_ ) def __call__( self : List[Any] , SCREAMING_SNAKE_CASE_ : Tensor ) -> Dict: '''simple docstring''' for m in self.module.modules(): self.handles.append(m.register_forward_hook(self._forward_hook ) ) self.module(SCREAMING_SNAKE_CASE_ ) [x.remove() for x in self.handles] return self @property def _snake_case ( self : Optional[Any] ) -> Optional[int]: '''simple docstring''' return list(filter(lambda SCREAMING_SNAKE_CASE_ : len(list(x.state_dict().keys() ) ) > 0 , self.traced ) ) @dataclass class lowerCAmelCase_ : '''simple docstring''' UpperCamelCase_ : nn.Module UpperCamelCase_ : nn.Module UpperCamelCase_ : int = 0 UpperCamelCase_ : List = field(default_factory=UpperCAmelCase_ ) UpperCamelCase_ : List = field(default_factory=UpperCAmelCase_ ) def __call__( self : Any , SCREAMING_SNAKE_CASE_ : Tensor ) -> Optional[Any]: '''simple docstring''' A: Dict = Tracker(self.dest )(SCREAMING_SNAKE_CASE_ ).parametrized A: Tuple = Tracker(self.src )(SCREAMING_SNAKE_CASE_ ).parametrized A: str = list(filter(lambda SCREAMING_SNAKE_CASE_ : type(SCREAMING_SNAKE_CASE_ ) not in self.src_skip , SCREAMING_SNAKE_CASE_ ) ) A: str = list(filter(lambda SCREAMING_SNAKE_CASE_ : type(SCREAMING_SNAKE_CASE_ ) not in self.dest_skip , SCREAMING_SNAKE_CASE_ ) ) if len(SCREAMING_SNAKE_CASE_ ) != len(SCREAMING_SNAKE_CASE_ ): raise Exception( f"""Numbers of operations are different. Source module has {len(SCREAMING_SNAKE_CASE_ )} operations while""" f""" destination module has {len(SCREAMING_SNAKE_CASE_ )}.""" ) for dest_m, src_m in zip(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ): dest_m.load_state_dict(src_m.state_dict() ) if self.verbose == 1: print(f"""Transfered from={src_m} to={dest_m}""" ) def SCREAMING_SNAKE_CASE( __lowercase , __lowercase , __lowercase , __lowercase = True ) -> Any: print(F"""Converting {name}...""" ) with torch.no_grad(): A: Union[str, Any] = timm.create_model(__lowercase , pretrained=__lowercase ).eval() A: List[str] = ResNetForImageClassification(__lowercase ).eval() A: int = ModuleTransfer(src=__lowercase , dest=__lowercase ) A: List[str] = torch.randn((1, 3, 2_2_4, 2_2_4) ) module_transfer(__lowercase ) assert torch.allclose(from_model(__lowercase ) , our_model(__lowercase ).logits ), "The model logits don't match the original one." A: str = F"""resnet{'-'.join(name.split('resnet' ) )}""" print(__lowercase ) if push_to_hub: our_model.push_to_hub( repo_path_or_name=save_directory / checkpoint_name , commit_message='''Add model''' , use_temp_dir=__lowercase , ) # we can use the convnext one A: Any = AutoImageProcessor.from_pretrained('''facebook/convnext-base-224-22k-1k''' ) image_processor.push_to_hub( repo_path_or_name=save_directory / checkpoint_name , commit_message='''Add image processor''' , use_temp_dir=__lowercase , ) print(F"""Pushed {checkpoint_name}""" ) def SCREAMING_SNAKE_CASE( __lowercase , __lowercase = None , __lowercase = True ) -> List[Any]: A: Union[str, Any] = '''imagenet-1k-id2label.json''' A: Union[str, Any] = 1_0_0_0 A: Optional[int] = (1, num_labels) A: Dict = '''huggingface/label-files''' A: Any = num_labels A: Union[str, Any] = json.load(open(hf_hub_download(__lowercase , __lowercase , repo_type='''dataset''' ) , '''r''' ) ) A: Optional[int] = {int(__lowercase ): v for k, v in idalabel.items()} A: Optional[int] = idalabel A: List[str] = {v: k for k, v in idalabel.items()} A: str = partial(__lowercase , num_labels=__lowercase , idalabel=__lowercase , labelaid=__lowercase ) A: Optional[Any] = { '''resnet18''': ImageNetPreTrainedConfig( depths=[2, 2, 2, 2] , hidden_sizes=[6_4, 1_2_8, 2_5_6, 5_1_2] , layer_type='''basic''' ), '''resnet26''': ImageNetPreTrainedConfig( depths=[2, 2, 2, 2] , hidden_sizes=[2_5_6, 5_1_2, 1_0_2_4, 2_0_4_8] , layer_type='''bottleneck''' ), '''resnet34''': ImageNetPreTrainedConfig( depths=[3, 4, 6, 3] , hidden_sizes=[6_4, 1_2_8, 2_5_6, 5_1_2] , layer_type='''basic''' ), '''resnet50''': ImageNetPreTrainedConfig( depths=[3, 4, 6, 3] , hidden_sizes=[2_5_6, 5_1_2, 1_0_2_4, 2_0_4_8] , layer_type='''bottleneck''' ), '''resnet101''': ImageNetPreTrainedConfig( depths=[3, 4, 2_3, 3] , hidden_sizes=[2_5_6, 5_1_2, 1_0_2_4, 2_0_4_8] , layer_type='''bottleneck''' ), '''resnet152''': ImageNetPreTrainedConfig( depths=[3, 8, 3_6, 3] , hidden_sizes=[2_5_6, 5_1_2, 1_0_2_4, 2_0_4_8] , layer_type='''bottleneck''' ), } if model_name: convert_weight_and_push(__lowercase , names_to_config[model_name] , __lowercase , __lowercase ) else: for model_name, config in names_to_config.items(): convert_weight_and_push(__lowercase , __lowercase , __lowercase , __lowercase ) return config, expected_shape if __name__ == "__main__": UpperCamelCase = argparse.ArgumentParser() # Required parameters parser.add_argument( '''--model_name''', default=None, type=str, help=( '''The name of the model you wish to convert, it must be one of the supported resnet* architecture,''' ''' currently: resnet18,26,34,50,101,152. If `None`, all of them will the converted.''' ), ) parser.add_argument( '''--pytorch_dump_folder_path''', default=None, type=Path, required=True, help='''Path to the output PyTorch model directory.''', ) parser.add_argument( '''--push_to_hub''', default=True, type=bool, required=False, help='''If True, push model and image processor to the hub.''', ) UpperCamelCase = parser.parse_args() UpperCamelCase = args.pytorch_dump_folder_path pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True) convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)
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'''simple docstring''' import unittest import numpy as np def SCREAMING_SNAKE_CASE( __lowercase , __lowercase , __lowercase , __lowercase = None , ) -> np.ndarray: A: List[str] = np.shape(__lowercase ) A: str = np.shape(__lowercase ) A: Tuple = np.shape(__lowercase ) if shape_a[0] != shape_b[0]: A: Any = ( '''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(__lowercase ) if shape_b[1] != shape_c[1]: A: str = ( '''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(__lowercase ) A: Dict = pseudo_inv if a_inv is None: try: A: int = np.linalg.inv(__lowercase ) 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 lowerCAmelCase_ ( unittest.TestCase ): '''simple docstring''' def _snake_case ( self : Union[str, Any] ) -> None: '''simple docstring''' A: str = np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]] ) A: Dict = np.array([[0, 3], [3, 0], [2, 3]] ) A: Any = np.array([[2, 1], [6, 3]] ) A: str = schur_complement(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) A: Any = np.block([[a, b], [b.T, c]] ) A: Any = np.linalg.det(SCREAMING_SNAKE_CASE_ ) A: str = np.linalg.det(SCREAMING_SNAKE_CASE_ ) A: str = np.linalg.det(SCREAMING_SNAKE_CASE_ ) self.assertAlmostEqual(SCREAMING_SNAKE_CASE_ , det_a * det_s ) def _snake_case ( self : str ) -> None: '''simple docstring''' A: List[Any] = np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]] ) A: Tuple = np.array([[0, 3], [3, 0], [2, 3]] ) A: Union[str, Any] = np.array([[2, 1], [6, 3]] ) with self.assertRaises(SCREAMING_SNAKE_CASE_ ): schur_complement(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) def _snake_case ( self : Any ) -> None: '''simple docstring''' A: str = np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]] ) A: int = np.array([[0, 3], [3, 0], [2, 3]] ) A: Optional[int] = np.array([[2, 1, 3], [6, 3, 5]] ) with self.assertRaises(SCREAMING_SNAKE_CASE_ ): schur_complement(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) if __name__ == "__main__": import doctest doctest.testmod() unittest.main()
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'''simple docstring''' from __future__ import annotations from collections.abc import Sequence from typing import Literal def SCREAMING_SNAKE_CASE( __lowercase , __lowercase ) -> str | Literal[False]: A: List[str] = list(__lowercase ) A: Optional[Any] = list(__lowercase ) A: int = 0 for i in range(len(__lowercase ) ): if lista[i] != lista[i]: count += 1 A: Optional[Any] = '''_''' if count > 1: return False else: return "".join(__lowercase ) def SCREAMING_SNAKE_CASE( __lowercase ) -> list[str]: A: Any = [] while True: A: Dict = ['''$'''] * len(__lowercase ) A: Union[str, Any] = [] for i in range(len(__lowercase ) ): for j in range(i + 1 , len(__lowercase ) ): A: Any = compare_string(binary[i] , binary[j] ) if k is False: A: Any = '''*''' A: List[Any] = '''*''' temp.append('''X''' ) for i in range(len(__lowercase ) ): if checka[i] == "$": pi.append(binary[i] ) if len(__lowercase ) == 0: return pi A: List[Any] = list(set(__lowercase ) ) def SCREAMING_SNAKE_CASE( __lowercase , __lowercase ) -> list[str]: A: Optional[int] = [] for minterm in minterms: A: Optional[int] = '''''' for _ in range(__lowercase ): A: List[Any] = str(minterm % 2 ) + string minterm //= 2 temp.append(__lowercase ) return temp def SCREAMING_SNAKE_CASE( __lowercase , __lowercase , __lowercase ) -> bool: A: Union[str, Any] = list(__lowercase ) A: Union[str, Any] = list(__lowercase ) A: Optional[int] = 0 for i in range(len(__lowercase ) ): if lista[i] != lista[i]: count_n += 1 return count_n == count def SCREAMING_SNAKE_CASE( __lowercase , __lowercase ) -> list[str]: A: List[Any] = [] A: Dict = [0] * len(__lowercase ) for i in range(len(chart[0] ) ): A: List[str] = 0 A: str = -1 for j in range(len(__lowercase ) ): if chart[j][i] == 1: count += 1 A: Any = j if count == 1: A: Any = 1 for i in range(len(__lowercase ) ): if select[i] == 1: for j in range(len(chart[0] ) ): if chart[i][j] == 1: for k in range(len(__lowercase ) ): A: Optional[int] = 0 temp.append(prime_implicants[i] ) while True: A: Dict = 0 A: Optional[int] = -1 A: Dict = 0 for i in range(len(__lowercase ) ): A: str = chart[i].count(1 ) if count_n > max_n: A: Tuple = count_n A: Optional[Any] = i if max_n == 0: return temp temp.append(prime_implicants[rem] ) for i in range(len(chart[0] ) ): if chart[rem][i] == 1: for j in range(len(__lowercase ) ): A: Any = 0 def SCREAMING_SNAKE_CASE( __lowercase , __lowercase ) -> list[list[int]]: A: str = [[0 for x in range(len(__lowercase ) )] for x in range(len(__lowercase ) )] for i in range(len(__lowercase ) ): A: Tuple = prime_implicants[i].count('''_''' ) for j in range(len(__lowercase ) ): if is_for_table(prime_implicants[i] , binary[j] , __lowercase ): A: Optional[Any] = 1 return chart def SCREAMING_SNAKE_CASE( ) -> None: A: int = int(input('''Enter the no. of variables\n''' ) ) A: Optional[int] = [ float(__lowercase ) for x in input( '''Enter the decimal representation of Minterms \'Spaces Separated\'\n''' ).split() ] A: List[str] = decimal_to_binary(__lowercase , __lowercase ) A: str = check(__lowercase ) print('''Prime Implicants are:''' ) print(__lowercase ) A: List[Any] = prime_implicant_chart(__lowercase , __lowercase ) A: Any = selection(__lowercase , __lowercase ) print('''Essential Prime Implicants are:''' ) print(__lowercase ) if __name__ == "__main__": import doctest doctest.testmod() main()
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'''simple docstring''' import warnings from ...utils import logging from .image_processing_flava import FlavaImageProcessor UpperCamelCase = logging.get_logger(__name__) class lowerCAmelCase_ ( UpperCAmelCase_ ): '''simple docstring''' def __init__( self : Optional[int] , *SCREAMING_SNAKE_CASE_ : Dict , **SCREAMING_SNAKE_CASE_ : int ) -> None: '''simple docstring''' warnings.warn( '''The class FlavaFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please''' ''' use FlavaImageProcessor instead.''' , SCREAMING_SNAKE_CASE_ , ) super().__init__(*SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
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'''simple docstring''' def SCREAMING_SNAKE_CASE( __lowercase ) -> Tuple: A: Tuple = len(__lowercase ) for i in range(length - 1 ): A: Dict = i for k in range(i + 1 , __lowercase ): if collection[k] < collection[least]: A: List[str] = k if least != i: A , A: Tuple = (collection[i], collection[least]) return collection if __name__ == "__main__": UpperCamelCase = input('''Enter numbers separated by a comma:\n''').strip() UpperCamelCase = [int(item) for item in user_input.split(''',''')] print(selection_sort(unsorted))
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'''simple docstring''' def SCREAMING_SNAKE_CASE( __lowercase , __lowercase ) -> float: return price * (1 + tax_rate) if __name__ == "__main__": print(f'{price_plus_tax(100, 0.25) = }') print(f'{price_plus_tax(1_25.50, 0.05) = }')
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'''simple docstring''' class lowerCAmelCase_ : '''simple docstring''' def __init__( self : Any , SCREAMING_SNAKE_CASE_ : str , SCREAMING_SNAKE_CASE_ : Optional[int] , SCREAMING_SNAKE_CASE_ : List[str] ) -> int: '''simple docstring''' A: Tuple = None A: Dict = None A: Optional[int] = graph self._normalize_graph(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) A: str = len(SCREAMING_SNAKE_CASE_ ) A: Optional[Any] = None def _snake_case ( self : Tuple , SCREAMING_SNAKE_CASE_ : Optional[int] , SCREAMING_SNAKE_CASE_ : Dict ) -> str: '''simple docstring''' if sources is int: A: Union[str, Any] = [sources] if sinks is int: A: Tuple = [sinks] if len(SCREAMING_SNAKE_CASE_ ) == 0 or len(SCREAMING_SNAKE_CASE_ ) == 0: return A: List[str] = sources[0] A: Optional[int] = sinks[0] # make fake vertex if there are more # than one source or sink if len(SCREAMING_SNAKE_CASE_ ) > 1 or len(SCREAMING_SNAKE_CASE_ ) > 1: A: Any = 0 for i in sources: max_input_flow += sum(self.graph[i] ) A: Dict = len(self.graph ) + 1 for room in self.graph: room.insert(0 , 0 ) self.graph.insert(0 , [0] * size ) for i in sources: A: Optional[Any] = max_input_flow A: Optional[Any] = 0 A: str = len(self.graph ) + 1 for room in self.graph: room.append(0 ) self.graph.append([0] * size ) for i in sinks: A: Optional[Any] = max_input_flow A: str = size - 1 def _snake_case ( self : List[Any] ) -> Union[str, Any]: '''simple docstring''' if self.maximum_flow_algorithm is None: raise Exception('''You need to set maximum flow algorithm before.''' ) if self.source_index is None or self.sink_index is None: return 0 self.maximum_flow_algorithm.execute() return self.maximum_flow_algorithm.getMaximumFlow() def _snake_case ( self : Tuple , SCREAMING_SNAKE_CASE_ : Union[str, Any] ) -> Tuple: '''simple docstring''' A: Optional[Any] = algorithm(self ) class lowerCAmelCase_ : '''simple docstring''' def __init__( self : int , SCREAMING_SNAKE_CASE_ : List[Any] ) -> Optional[Any]: '''simple docstring''' A: str = flow_network A: List[str] = flow_network.verticesCount A: Dict = flow_network.sourceIndex A: Any = flow_network.sinkIndex # it's just a reference, so you shouldn't change # it in your algorithms, use deep copy before doing that A: str = flow_network.graph A: str = False def _snake_case ( self : int ) -> Union[str, Any]: '''simple docstring''' if not self.executed: self._algorithm() A: str = True def _snake_case ( self : Tuple ) -> Optional[Any]: '''simple docstring''' pass class lowerCAmelCase_ ( UpperCAmelCase_ ): '''simple docstring''' def __init__( self : List[str] , SCREAMING_SNAKE_CASE_ : List[str] ) -> Optional[int]: '''simple docstring''' super().__init__(SCREAMING_SNAKE_CASE_ ) # use this to save your result A: Any = -1 def _snake_case ( self : List[str] ) -> Optional[Any]: '''simple docstring''' if not self.executed: raise Exception('''You should execute algorithm before using its result!''' ) return self.maximum_flow class lowerCAmelCase_ ( UpperCAmelCase_ ): '''simple docstring''' def __init__( self : int , SCREAMING_SNAKE_CASE_ : Any ) -> Optional[int]: '''simple docstring''' super().__init__(SCREAMING_SNAKE_CASE_ ) A: Optional[int] = [[0] * self.verticies_count for i in range(self.verticies_count )] A: Any = [0] * self.verticies_count A: Optional[Any] = [0] * self.verticies_count def _snake_case ( self : str ) -> Optional[Any]: '''simple docstring''' A: Any = self.verticies_count # push some substance to graph for nextvertex_index, bandwidth in enumerate(self.graph[self.source_index] ): self.preflow[self.source_index][nextvertex_index] += bandwidth self.preflow[nextvertex_index][self.source_index] -= bandwidth self.excesses[nextvertex_index] += bandwidth # Relabel-to-front selection rule A: str = [ i for i in range(self.verticies_count ) if i != self.source_index and i != self.sink_index ] # move through list A: Dict = 0 while i < len(SCREAMING_SNAKE_CASE_ ): A: Any = vertices_list[i] A: str = self.heights[vertex_index] self.process_vertex(SCREAMING_SNAKE_CASE_ ) if self.heights[vertex_index] > previous_height: # if it was relabeled, swap elements # and start from 0 index vertices_list.insert(0 , vertices_list.pop(SCREAMING_SNAKE_CASE_ ) ) A: Tuple = 0 else: i += 1 A: Tuple = sum(self.preflow[self.source_index] ) def _snake_case ( self : int , SCREAMING_SNAKE_CASE_ : List[str] ) -> str: '''simple docstring''' while self.excesses[vertex_index] > 0: for neighbour_index in range(self.verticies_count ): # if it's neighbour and current vertex is higher if ( self.graph[vertex_index][neighbour_index] - self.preflow[vertex_index][neighbour_index] > 0 and self.heights[vertex_index] > self.heights[neighbour_index] ): self.push(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) self.relabel(SCREAMING_SNAKE_CASE_ ) def _snake_case ( self : Union[str, Any] , SCREAMING_SNAKE_CASE_ : Union[str, Any] , SCREAMING_SNAKE_CASE_ : Optional[Any] ) -> int: '''simple docstring''' A: Optional[int] = min( self.excesses[from_index] , self.graph[from_index][to_index] - self.preflow[from_index][to_index] , ) self.preflow[from_index][to_index] += preflow_delta self.preflow[to_index][from_index] -= preflow_delta self.excesses[from_index] -= preflow_delta self.excesses[to_index] += preflow_delta def _snake_case ( self : Optional[int] , SCREAMING_SNAKE_CASE_ : str ) -> int: '''simple docstring''' A: Optional[Any] = None for to_index in range(self.verticies_count ): if ( self.graph[vertex_index][to_index] - self.preflow[vertex_index][to_index] > 0 ) and (min_height is None or self.heights[to_index] < min_height): A: List[Any] = self.heights[to_index] if min_height is not None: A: int = min_height + 1 if __name__ == "__main__": UpperCamelCase = [0] UpperCamelCase = [3] # graph = [ # [0, 0, 4, 6, 0, 0], # [0, 0, 5, 2, 0, 0], # [0, 0, 0, 0, 4, 4], # [0, 0, 0, 0, 6, 6], # [0, 0, 0, 0, 0, 0], # [0, 0, 0, 0, 0, 0], # ] UpperCamelCase = [[0, 7, 0, 0], [0, 0, 6, 0], [0, 0, 0, 8], [9, 0, 0, 0]] # prepare our network UpperCamelCase = FlowNetwork(graph, entrances, exits) # set algorithm flow_network.set_maximum_flow_algorithm(PushRelabelExecutor) # and calculate UpperCamelCase = flow_network.find_maximum_flow() print(f'maximum flow is {maximum_flow}')
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'''simple docstring''' UpperCamelCase = 256 # Modulus to hash a string UpperCamelCase = 1000003 def SCREAMING_SNAKE_CASE( __lowercase , __lowercase ) -> bool: A: Dict = len(__lowercase ) A: List[str] = len(__lowercase ) if p_len > t_len: return False A: Optional[int] = 0 A: List[Any] = 0 A: Union[str, Any] = 1 # Calculating the hash of pattern and substring of text for i in range(__lowercase ): A: Optional[Any] = (ord(pattern[i] ) + p_hash * alphabet_size) % modulus A: str = (ord(text[i] ) + text_hash * alphabet_size) % modulus if i == p_len - 1: continue A: Dict = (modulus_power * alphabet_size) % modulus for i in range(0 , t_len - p_len + 1 ): if text_hash == p_hash and text[i : i + p_len] == pattern: return True if i == t_len - p_len: continue # Calculate the https://en.wikipedia.org/wiki/Rolling_hash A: Tuple = ( (text_hash - ord(text[i] ) * modulus_power) * alphabet_size + ord(text[i + p_len] ) ) % modulus return False def SCREAMING_SNAKE_CASE( ) -> None: A: Optional[Any] = '''abc1abc12''' A: Optional[Any] = '''alskfjaldsabc1abc1abc12k23adsfabcabc''' A: Optional[Any] = '''alskfjaldsk23adsfabcabc''' assert rabin_karp(__lowercase , __lowercase ) and not rabin_karp(__lowercase , __lowercase ) # Test 2) A: int = '''ABABX''' A: str = '''ABABZABABYABABX''' assert rabin_karp(__lowercase , __lowercase ) # Test 3) A: Dict = '''AAAB''' A: str = '''ABAAAAAB''' assert rabin_karp(__lowercase , __lowercase ) # Test 4) A: List[str] = '''abcdabcy''' A: Optional[int] = '''abcxabcdabxabcdabcdabcy''' assert rabin_karp(__lowercase , __lowercase ) # Test 5) A: Any = '''Lü''' A: Dict = '''Lüsai''' assert rabin_karp(__lowercase , __lowercase ) A: int = '''Lue''' assert not rabin_karp(__lowercase , __lowercase ) print('''Success.''' ) if __name__ == "__main__": test_rabin_karp()
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'''simple docstring''' from typing import List, Optional, Union import numpy as np import torch import torchaudio.compliance.kaldi as ta_kaldi from ...feature_extraction_sequence_utils import SequenceFeatureExtractor from ...feature_extraction_utils import BatchFeature from ...utils import PaddingStrategy, TensorType, logging UpperCamelCase = logging.get_logger(__name__) class lowerCAmelCase_ ( UpperCAmelCase_ ): '''simple docstring''' UpperCamelCase_ : Optional[Any] = ["""input_features""", """attention_mask"""] def __init__( self : List[Any] , SCREAMING_SNAKE_CASE_ : Tuple=80 , SCREAMING_SNAKE_CASE_ : Union[str, Any]=1_60_00 , SCREAMING_SNAKE_CASE_ : int=80 , SCREAMING_SNAKE_CASE_ : Union[str, Any]=0.0 , SCREAMING_SNAKE_CASE_ : Any=True , SCREAMING_SNAKE_CASE_ : Tuple=True , SCREAMING_SNAKE_CASE_ : Union[str, Any]=True , **SCREAMING_SNAKE_CASE_ : List[str] , ) -> List[Any]: '''simple docstring''' super().__init__(feature_size=SCREAMING_SNAKE_CASE_ , sampling_rate=SCREAMING_SNAKE_CASE_ , padding_value=SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ ) A: Union[str, Any] = num_mel_bins A: str = do_ceptral_normalize A: int = normalize_means A: List[Any] = normalize_vars A: Any = True def _snake_case ( self : Any , SCREAMING_SNAKE_CASE_ : np.ndarray , ) -> np.ndarray: '''simple docstring''' A: Optional[int] = waveform * (2**15) # Kaldi compliance: 16-bit signed integers A: Optional[int] = torch.from_numpy(SCREAMING_SNAKE_CASE_ ).unsqueeze(0 ) A: List[Any] = ta_kaldi.fbank(SCREAMING_SNAKE_CASE_ , num_mel_bins=self.num_mel_bins , sample_frequency=self.sampling_rate ) return features.numpy() @staticmethod def _snake_case ( SCREAMING_SNAKE_CASE_ : np.ndarray , SCREAMING_SNAKE_CASE_ : int , SCREAMING_SNAKE_CASE_ : Optional[bool] = True , SCREAMING_SNAKE_CASE_ : Optional[bool] = True , SCREAMING_SNAKE_CASE_ : float = 0.0 , ) -> np.ndarray: '''simple docstring''' if normalize_means: A: str = x[:input_length].mean(axis=0 ) A: Dict = np.subtract(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) if normalize_vars: A: Tuple = x[:input_length].std(axis=0 ) A: List[Any] = np.divide(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) if input_length < x.shape[0]: A: Optional[int] = padding_value # make sure array is in float32 A: Optional[Any] = x.astype(np.floataa ) return x def _snake_case ( self : Optional[Any] , SCREAMING_SNAKE_CASE_ : List[np.ndarray] , SCREAMING_SNAKE_CASE_ : Optional[np.ndarray] = None ) -> List[np.ndarray]: '''simple docstring''' A: int = attention_mask.sum(-1 ) if attention_mask is not None else [x.shape[0] for x in input_features] return [ self.utterance_cmvn(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , self.normalize_means , self.normalize_vars , self.padding_value ) for x, n in zip(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) ] def __call__( self : Tuple , SCREAMING_SNAKE_CASE_ : Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]] , SCREAMING_SNAKE_CASE_ : Union[bool, str, PaddingStrategy] = False , SCREAMING_SNAKE_CASE_ : Optional[int] = None , SCREAMING_SNAKE_CASE_ : bool = False , SCREAMING_SNAKE_CASE_ : Optional[int] = None , SCREAMING_SNAKE_CASE_ : Optional[Union[str, TensorType]] = None , SCREAMING_SNAKE_CASE_ : Optional[int] = None , SCREAMING_SNAKE_CASE_ : Optional[bool] = None , **SCREAMING_SNAKE_CASE_ : Dict , ) -> 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 `raw_speech` 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.''' ) A: Any = isinstance(SCREAMING_SNAKE_CASE_ , np.ndarray ) and len(raw_speech.shape ) > 1 if is_batched_numpy and len(raw_speech.shape ) > 2: raise ValueError(f"""Only mono-channel audio is supported for input to {self}""" ) A: Optional[Any] = is_batched_numpy or ( isinstance(SCREAMING_SNAKE_CASE_ , (list, tuple) ) and (isinstance(raw_speech[0] , (np.ndarray, tuple, list) )) ) if is_batched: A: Optional[int] = [np.asarray(SCREAMING_SNAKE_CASE_ , dtype=np.floataa ) for speech in raw_speech] elif not is_batched and not isinstance(SCREAMING_SNAKE_CASE_ , np.ndarray ): A: int = np.asarray(SCREAMING_SNAKE_CASE_ , dtype=np.floataa ) elif isinstance(SCREAMING_SNAKE_CASE_ , np.ndarray ) and raw_speech.dtype is np.dtype(np.floataa ): A: Any = raw_speech.astype(np.floataa ) # always return batch if not is_batched: A: Union[str, Any] = [raw_speech] # extract fbank features A: str = [self._extract_fbank_features(SCREAMING_SNAKE_CASE_ ) for waveform in raw_speech] # convert into correct format for padding A: int = BatchFeature({'''input_features''': features} ) A: int = self.pad( SCREAMING_SNAKE_CASE_ , padding=SCREAMING_SNAKE_CASE_ , max_length=SCREAMING_SNAKE_CASE_ , truncation=SCREAMING_SNAKE_CASE_ , pad_to_multiple_of=SCREAMING_SNAKE_CASE_ , return_attention_mask=SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ , ) # make sure list is in array format A: List[str] = padded_inputs.get('''input_features''' ) if isinstance(input_features[0] , SCREAMING_SNAKE_CASE_ ): A: Optional[Any] = [np.asarray(SCREAMING_SNAKE_CASE_ , dtype=np.floataa ) for feature in input_features] A: List[Any] = padded_inputs.get('''attention_mask''' ) if attention_mask is not None: A: Dict = [np.asarray(SCREAMING_SNAKE_CASE_ , dtype=np.intaa ) for array in attention_mask] # Utterance-level cepstral mean and variance normalization if self.do_ceptral_normalize: A: Dict = ( np.array(SCREAMING_SNAKE_CASE_ , dtype=np.intaa ) if self._get_padding_strategies(SCREAMING_SNAKE_CASE_ , max_length=SCREAMING_SNAKE_CASE_ ) is not PaddingStrategy.DO_NOT_PAD else None ) A: List[Any] = self.normalize( padded_inputs['''input_features'''] , attention_mask=SCREAMING_SNAKE_CASE_ ) if return_tensors is not None: A: Dict = padded_inputs.convert_to_tensors(SCREAMING_SNAKE_CASE_ ) return padded_inputs
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'''simple docstring''' from collections import OrderedDict from typing import Mapping from packaging import version from ...configuration_utils import PretrainedConfig from ...onnx import OnnxConfig from ...utils import logging UpperCamelCase = logging.get_logger(__name__) UpperCamelCase = { '''google/vit-base-patch16-224''': '''https://huggingface.co/vit-base-patch16-224/resolve/main/config.json''', # See all ViT models at https://huggingface.co/models?filter=vit } class lowerCAmelCase_ ( UpperCAmelCase_ ): '''simple docstring''' UpperCamelCase_ : Union[str, Any] = """vit""" def __init__( self : Any , SCREAMING_SNAKE_CASE_ : Tuple=7_68 , SCREAMING_SNAKE_CASE_ : Optional[int]=12 , SCREAMING_SNAKE_CASE_ : Dict=12 , SCREAMING_SNAKE_CASE_ : List[Any]=30_72 , SCREAMING_SNAKE_CASE_ : Tuple="gelu" , SCREAMING_SNAKE_CASE_ : int=0.0 , SCREAMING_SNAKE_CASE_ : Optional[int]=0.0 , SCREAMING_SNAKE_CASE_ : List[str]=0.02 , SCREAMING_SNAKE_CASE_ : List[str]=1E-12 , SCREAMING_SNAKE_CASE_ : Union[str, Any]=2_24 , SCREAMING_SNAKE_CASE_ : List[Any]=16 , SCREAMING_SNAKE_CASE_ : Union[str, Any]=3 , SCREAMING_SNAKE_CASE_ : str=True , SCREAMING_SNAKE_CASE_ : int=16 , **SCREAMING_SNAKE_CASE_ : Union[str, Any] , ) -> Dict: '''simple docstring''' super().__init__(**SCREAMING_SNAKE_CASE_ ) A: Any = hidden_size A: List[Any] = num_hidden_layers A: str = num_attention_heads A: str = intermediate_size A: Union[str, Any] = hidden_act A: int = hidden_dropout_prob A: Optional[int] = attention_probs_dropout_prob A: List[Any] = initializer_range A: Optional[int] = layer_norm_eps A: Dict = image_size A: Union[str, Any] = patch_size A: Any = num_channels A: List[str] = qkv_bias A: Tuple = encoder_stride class lowerCAmelCase_ ( UpperCAmelCase_ ): '''simple docstring''' UpperCamelCase_ : Tuple = version.parse("""1.11""" ) @property def _snake_case ( self : Any ) -> Mapping[str, Mapping[int, str]]: '''simple docstring''' return OrderedDict( [ ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}), ] ) @property def _snake_case ( self : List[str] ) -> float: '''simple docstring''' return 1E-4
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'''simple docstring''' import json import os import unittest from transformers import DebertaTokenizer, DebertaTokenizerFast from transformers.models.deberta.tokenization_deberta import VOCAB_FILES_NAMES from transformers.testing_utils import slow from ...test_tokenization_common import TokenizerTesterMixin class lowerCAmelCase_ ( UpperCAmelCase_ , unittest.TestCase ): '''simple docstring''' UpperCamelCase_ : Optional[Any] = DebertaTokenizer UpperCamelCase_ : List[str] = True UpperCamelCase_ : int = DebertaTokenizerFast def _snake_case ( self : Optional[int] ) -> Dict: '''simple docstring''' super().setUp() # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt A: Optional[int] = [ '''l''', '''o''', '''w''', '''e''', '''r''', '''s''', '''t''', '''i''', '''d''', '''n''', '''\u0120''', '''\u0120l''', '''\u0120n''', '''\u0120lo''', '''\u0120low''', '''er''', '''\u0120lowest''', '''\u0120newer''', '''\u0120wider''', '''[UNK]''', ] A: int = dict(zip(SCREAMING_SNAKE_CASE_ , range(len(SCREAMING_SNAKE_CASE_ ) ) ) ) A: Union[str, Any] = ['''#version: 0.2''', '''\u0120 l''', '''\u0120l o''', '''\u0120lo w''', '''e r''', ''''''] A: Union[str, Any] = {'''unk_token''': '''[UNK]'''} A: List[str] = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['''vocab_file'''] ) A: str = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['''merges_file'''] ) with open(self.vocab_file , '''w''' , encoding='''utf-8''' ) as fp: fp.write(json.dumps(SCREAMING_SNAKE_CASE_ ) + '''\n''' ) with open(self.merges_file , '''w''' , encoding='''utf-8''' ) as fp: fp.write('''\n'''.join(SCREAMING_SNAKE_CASE_ ) ) def _snake_case ( self : int , **SCREAMING_SNAKE_CASE_ : List[str] ) -> Tuple: '''simple docstring''' kwargs.update(self.special_tokens_map ) return self.tokenizer_class.from_pretrained(self.tmpdirname , **SCREAMING_SNAKE_CASE_ ) def _snake_case ( self : Tuple , SCREAMING_SNAKE_CASE_ : Dict ) -> Union[str, Any]: '''simple docstring''' A: Optional[int] = '''lower newer''' A: str = '''lower newer''' return input_text, output_text def _snake_case ( self : Union[str, Any] ) -> Dict: '''simple docstring''' A: str = self.get_tokenizer() A: Any = '''lower newer''' A: Dict = ['''l''', '''o''', '''w''', '''er''', '''\u0120''', '''n''', '''e''', '''w''', '''er'''] A: int = tokenizer.tokenize(SCREAMING_SNAKE_CASE_ ) self.assertListEqual(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) A: List[Any] = tokens + [tokenizer.unk_token] A: int = [0, 1, 2, 15, 10, 9, 3, 2, 15, 19] self.assertListEqual(tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE_ ) , SCREAMING_SNAKE_CASE_ ) def _snake_case ( self : List[Any] ) -> Any: '''simple docstring''' A: str = self.get_tokenizer() A: List[str] = tokenizer('''Hello''' , '''World''' ) A: Union[str, Any] = [0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1] self.assertListEqual(tokd['''token_type_ids'''] , SCREAMING_SNAKE_CASE_ ) @slow def _snake_case ( self : Tuple ) -> Optional[int]: '''simple docstring''' A: Union[str, Any] = self.tokenizer_class.from_pretrained('''microsoft/deberta-base''' ) A: Any = tokenizer.encode('''sequence builders''' , add_special_tokens=SCREAMING_SNAKE_CASE_ ) A: Union[str, Any] = tokenizer.encode('''multi-sequence build''' , add_special_tokens=SCREAMING_SNAKE_CASE_ ) A: Dict = tokenizer.encode( '''sequence builders''' , add_special_tokens=SCREAMING_SNAKE_CASE_ , add_prefix_space=SCREAMING_SNAKE_CASE_ ) A: Dict = tokenizer.encode( '''sequence builders''' , '''multi-sequence build''' , add_special_tokens=SCREAMING_SNAKE_CASE_ , add_prefix_space=SCREAMING_SNAKE_CASE_ ) A: List[Any] = tokenizer.build_inputs_with_special_tokens(SCREAMING_SNAKE_CASE_ ) A: int = tokenizer.build_inputs_with_special_tokens(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) assert encoded_sentence == encoded_text_from_decode assert encoded_pair == encoded_pair_from_decode @slow def _snake_case ( self : Tuple ) -> Dict: '''simple docstring''' A: int = [self.tokenizer_class] if self.test_rust_tokenizer: tokenizer_classes.append(self.rust_tokenizer_class ) for tokenizer_class in tokenizer_classes: A: List[Any] = tokenizer_class.from_pretrained('''microsoft/deberta-base''' ) A: Dict = [ '''ALBERT: A Lite BERT for Self-supervised Learning of Language Representations''', '''ALBERT incorporates two parameter reduction techniques''', '''The first one is a factorized embedding parameterization. By decomposing the large vocabulary''' ''' embedding matrix into two small matrices, we separate the size of the hidden layers from the size of''' ''' vocabulary embedding.''', ] A: Dict = tokenizer(SCREAMING_SNAKE_CASE_ , padding=SCREAMING_SNAKE_CASE_ ) A: Any = [tokenizer.decode(SCREAMING_SNAKE_CASE_ , skip_special_tokens=SCREAMING_SNAKE_CASE_ ) for seq in encoding['''input_ids''']] # fmt: off A: Any = { '''input_ids''': [ [1, 21_18, 1_11_26, 5_65, 35, 83, 2_51_91, 1_63, 1_88_54, 13, 1_21_56, 12, 1_61_01, 2_53_76, 1_38_07, 9, 2_22_05, 2_78_93, 16_35, 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], [1, 21_18, 1_11_26, 5_65, 2_45_36, 80, 4_37_97, 48_78, 73_73, 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], [1, 1_33, 78, 65, 16, 10, 37_24, 15_38, 3_31_83, 1_13_03, 4_37_97, 19_38, 4, 8_70, 2_41_65, 2_91_05, 5, 7_39, 3_26_44, 3_31_83, 1_13_03, 3_61_73, 88, 80, 6_50, 78_21, 4_59_40, 6, 52, 25_59, 5, 18_36, 9, 5, 73_97, 1_31_71, 31, 5, 18_36, 9, 3_26_44, 3_31_83, 1_13_03, 4, 2] ], '''token_type_ids''': [ [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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, 0, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1] ] } # fmt: on A: Optional[int] = [ '''ALBERT: A Lite BERT for Self-supervised Learning of Language Representations''', '''ALBERT incorporates two parameter reduction techniques''', '''The first one is a factorized embedding parameterization. By decomposing the large vocabulary''' ''' embedding matrix into two small matrices, we separate the size of the hidden layers from the size of''' ''' vocabulary embedding.''', ] self.assertDictEqual(encoding.data , SCREAMING_SNAKE_CASE_ ) for expected, decoded in zip(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ): self.assertEqual(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
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'''simple docstring''' from collections import OrderedDict from typing import Mapping from ...configuration_utils import PretrainedConfig from ...onnx import OnnxConfig from ...utils import logging UpperCamelCase = logging.get_logger(__name__) UpperCamelCase = { '''facebook/xmod-base''': '''https://huggingface.co/facebook/xmod-base/resolve/main/config.json''', '''facebook/xmod-large-prenorm''': '''https://huggingface.co/facebook/xmod-large-prenorm/resolve/main/config.json''', '''facebook/xmod-base-13-125k''': '''https://huggingface.co/facebook/xmod-base-13-125k/resolve/main/config.json''', '''facebook/xmod-base-30-125k''': '''https://huggingface.co/facebook/xmod-base-30-125k/resolve/main/config.json''', '''facebook/xmod-base-30-195k''': '''https://huggingface.co/facebook/xmod-base-30-195k/resolve/main/config.json''', '''facebook/xmod-base-60-125k''': '''https://huggingface.co/facebook/xmod-base-60-125k/resolve/main/config.json''', '''facebook/xmod-base-60-265k''': '''https://huggingface.co/facebook/xmod-base-60-265k/resolve/main/config.json''', '''facebook/xmod-base-75-125k''': '''https://huggingface.co/facebook/xmod-base-75-125k/resolve/main/config.json''', '''facebook/xmod-base-75-269k''': '''https://huggingface.co/facebook/xmod-base-75-269k/resolve/main/config.json''', } class lowerCAmelCase_ ( UpperCAmelCase_ ): '''simple docstring''' UpperCamelCase_ : Optional[Any] = """xmod""" def __init__( self : List[str] , SCREAMING_SNAKE_CASE_ : Optional[int]=3_05_22 , SCREAMING_SNAKE_CASE_ : Tuple=7_68 , SCREAMING_SNAKE_CASE_ : Tuple=12 , SCREAMING_SNAKE_CASE_ : Optional[int]=12 , SCREAMING_SNAKE_CASE_ : Dict=30_72 , SCREAMING_SNAKE_CASE_ : Union[str, Any]="gelu" , SCREAMING_SNAKE_CASE_ : str=0.1 , SCREAMING_SNAKE_CASE_ : List[Any]=0.1 , SCREAMING_SNAKE_CASE_ : Optional[Any]=5_12 , SCREAMING_SNAKE_CASE_ : Dict=2 , SCREAMING_SNAKE_CASE_ : int=0.02 , SCREAMING_SNAKE_CASE_ : Optional[Any]=1E-12 , SCREAMING_SNAKE_CASE_ : List[Any]=1 , SCREAMING_SNAKE_CASE_ : str=0 , SCREAMING_SNAKE_CASE_ : int=2 , SCREAMING_SNAKE_CASE_ : str="absolute" , SCREAMING_SNAKE_CASE_ : Tuple=True , SCREAMING_SNAKE_CASE_ : Dict=None , SCREAMING_SNAKE_CASE_ : Any=False , SCREAMING_SNAKE_CASE_ : Optional[Any]=2 , SCREAMING_SNAKE_CASE_ : Tuple=False , SCREAMING_SNAKE_CASE_ : str=True , SCREAMING_SNAKE_CASE_ : Dict=True , SCREAMING_SNAKE_CASE_ : List[str]=("en_XX",) , SCREAMING_SNAKE_CASE_ : int=None , **SCREAMING_SNAKE_CASE_ : Optional[Any] , ) -> Any: '''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_ ) A: Optional[int] = vocab_size A: str = hidden_size A: Tuple = num_hidden_layers A: Any = num_attention_heads A: List[Any] = hidden_act A: Optional[int] = intermediate_size A: Optional[Any] = hidden_dropout_prob A: str = attention_probs_dropout_prob A: List[str] = max_position_embeddings A: Any = type_vocab_size A: Tuple = initializer_range A: Dict = layer_norm_eps A: List[Any] = position_embedding_type A: Optional[int] = use_cache A: Dict = classifier_dropout A: Optional[int] = pre_norm A: Tuple = adapter_reduction_factor A: List[str] = adapter_layer_norm A: List[str] = adapter_reuse_layer_norm A: Optional[int] = ln_before_adapter A: Optional[int] = list(SCREAMING_SNAKE_CASE_ ) A: Tuple = default_language class lowerCAmelCase_ ( UpperCAmelCase_ ): '''simple docstring''' @property def _snake_case ( self : Dict ) -> Mapping[str, Mapping[int, str]]: '''simple docstring''' if self.task == "multiple-choice": A: Optional[int] = {0: '''batch''', 1: '''choice''', 2: '''sequence'''} else: A: Optional[int] = {0: '''batch''', 1: '''sequence'''} return OrderedDict( [ ('''input_ids''', dynamic_axis), ('''attention_mask''', dynamic_axis), ] )
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'''simple docstring''' import requests UpperCamelCase = '''https://newsapi.org/v1/articles?source=bbc-news&sortBy=top&apiKey=''' def SCREAMING_SNAKE_CASE( __lowercase ) -> None: # fetching a list of articles in json format A: Tuple = requests.get(_NEWS_API + bbc_news_api_key ).json() # each article in the list is a dict for i, article in enumerate(bbc_news_page['''articles'''] , 1 ): print(F"""{i}.) {article['title']}""" ) if __name__ == "__main__": fetch_bbc_news(bbc_news_api_key='''<Your BBC News API key goes here>''')
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'''simple docstring''' def SCREAMING_SNAKE_CASE( __lowercase , __lowercase , __lowercase , __lowercase ) -> int: A , A: Any = len(__lowercase ), len(grid[0] ) if ( min(__lowercase , __lowercase ) < 0 or row == row_length or col == col_length or (row, col) in visit or grid[row][col] == 1 ): return 0 if row == row_length - 1 and col == col_length - 1: return 1 visit.add((row, col) ) A: List[Any] = 0 count += depth_first_search(__lowercase , row + 1 , __lowercase , __lowercase ) count += depth_first_search(__lowercase , row - 1 , __lowercase , __lowercase ) count += depth_first_search(__lowercase , __lowercase , col + 1 , __lowercase ) count += depth_first_search(__lowercase , __lowercase , col - 1 , __lowercase ) visit.remove((row, col) ) return count if __name__ == "__main__": import doctest doctest.testmod()
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'''simple docstring''' import os from shutil import copyfile from typing import List, Optional, Tuple from ...tokenization_utils import AddedToken from ...tokenization_utils_fast import PreTrainedTokenizerFast from ...utils import is_sentencepiece_available, logging if is_sentencepiece_available(): from .tokenization_camembert import CamembertTokenizer else: UpperCamelCase = None UpperCamelCase = logging.get_logger(__name__) UpperCamelCase = {'''vocab_file''': '''sentencepiece.bpe.model''', '''tokenizer_file''': '''tokenizer.json'''} UpperCamelCase = { '''vocab_file''': { '''camembert-base''': '''https://huggingface.co/camembert-base/resolve/main/sentencepiece.bpe.model''', }, '''tokenizer_file''': { '''camembert-base''': '''https://huggingface.co/camembert-base/resolve/main/tokenizer.json''', }, } UpperCamelCase = { '''camembert-base''': 512, } UpperCamelCase = '''▁''' class lowerCAmelCase_ ( UpperCAmelCase_ ): '''simple docstring''' UpperCamelCase_ : Tuple = VOCAB_FILES_NAMES UpperCamelCase_ : Optional[int] = PRETRAINED_VOCAB_FILES_MAP UpperCamelCase_ : Union[str, Any] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES UpperCamelCase_ : str = ["""input_ids""", """attention_mask"""] UpperCamelCase_ : int = CamembertTokenizer def __init__( self : Tuple , SCREAMING_SNAKE_CASE_ : int=None , SCREAMING_SNAKE_CASE_ : List[str]=None , SCREAMING_SNAKE_CASE_ : str="<s>" , SCREAMING_SNAKE_CASE_ : Optional[Any]="</s>" , SCREAMING_SNAKE_CASE_ : List[Any]="</s>" , SCREAMING_SNAKE_CASE_ : int="<s>" , SCREAMING_SNAKE_CASE_ : Union[str, Any]="<unk>" , SCREAMING_SNAKE_CASE_ : str="<pad>" , SCREAMING_SNAKE_CASE_ : List[str]="<mask>" , SCREAMING_SNAKE_CASE_ : Union[str, Any]=["<s>NOTUSED", "</s>NOTUSED"] , **SCREAMING_SNAKE_CASE_ : Any , ) -> Any: '''simple docstring''' A: Tuple = AddedToken(SCREAMING_SNAKE_CASE_ , lstrip=SCREAMING_SNAKE_CASE_ , rstrip=SCREAMING_SNAKE_CASE_ ) if isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) else mask_token super().__init__( SCREAMING_SNAKE_CASE_ , tokenizer_file=SCREAMING_SNAKE_CASE_ , bos_token=SCREAMING_SNAKE_CASE_ , eos_token=SCREAMING_SNAKE_CASE_ , sep_token=SCREAMING_SNAKE_CASE_ , cls_token=SCREAMING_SNAKE_CASE_ , unk_token=SCREAMING_SNAKE_CASE_ , pad_token=SCREAMING_SNAKE_CASE_ , mask_token=SCREAMING_SNAKE_CASE_ , additional_special_tokens=SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ , ) A: Any = vocab_file A: Any = False if not self.vocab_file else True def _snake_case ( self : int , SCREAMING_SNAKE_CASE_ : List[int] , SCREAMING_SNAKE_CASE_ : Optional[List[int]] = None ) -> List[int]: '''simple docstring''' if token_ids_a is None: return [self.cls_token_id] + token_ids_a + [self.sep_token_id] A: List[str] = [self.cls_token_id] A: List[str] = [self.sep_token_id] return cls + token_ids_a + sep + sep + token_ids_a + sep def _snake_case ( self : int , SCREAMING_SNAKE_CASE_ : List[int] , SCREAMING_SNAKE_CASE_ : Optional[List[int]] = None ) -> List[int]: '''simple docstring''' A: List[str] = [self.sep_token_id] A: Optional[int] = [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] def _snake_case ( self : int , SCREAMING_SNAKE_CASE_ : str , SCREAMING_SNAKE_CASE_ : Optional[str] = None ) -> Tuple[str]: '''simple docstring''' if not self.can_save_slow_tokenizer: raise ValueError( '''Your fast tokenizer does not have the necessary information to save the vocabulary for a slow ''' '''tokenizer.''' ) if not os.path.isdir(SCREAMING_SNAKE_CASE_ ): logger.error(f"""Vocabulary path ({save_directory}) should be a directory""" ) return A: Dict = os.path.join( SCREAMING_SNAKE_CASE_ , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] ) if os.path.abspath(self.vocab_file ) != os.path.abspath(SCREAMING_SNAKE_CASE_ ): copyfile(self.vocab_file , SCREAMING_SNAKE_CASE_ ) return (out_vocab_file,)
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'''simple docstring''' UpperCamelCase = '''0.18.2''' from .configuration_utils import ConfigMixin from .utils import ( OptionalDependencyNotAvailable, is_flax_available, is_inflect_available, is_invisible_watermark_available, is_k_diffusion_available, is_k_diffusion_version, is_librosa_available, is_note_seq_available, is_onnx_available, is_scipy_available, is_torch_available, is_torchsde_available, is_transformers_available, is_transformers_version, is_unidecode_available, logging, ) try: if not is_onnx_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: from .utils.dummy_onnx_objects import * # noqa F403 else: from .pipelines import OnnxRuntimeModel try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: from .utils.dummy_pt_objects import * # noqa F403 else: from .models import ( AutoencoderKL, ControlNetModel, ModelMixin, PriorTransformer, TaFilmDecoder, TransformeraDModel, UNetaDModel, UNetaDConditionModel, UNetaDModel, UNetaDConditionModel, VQModel, ) from .optimization import ( get_constant_schedule, get_constant_schedule_with_warmup, get_cosine_schedule_with_warmup, get_cosine_with_hard_restarts_schedule_with_warmup, get_linear_schedule_with_warmup, get_polynomial_decay_schedule_with_warmup, get_scheduler, ) from .pipelines import ( AudioPipelineOutput, ConsistencyModelPipeline, DanceDiffusionPipeline, DDIMPipeline, DDPMPipeline, DiffusionPipeline, DiTPipeline, ImagePipelineOutput, KarrasVePipeline, LDMPipeline, LDMSuperResolutionPipeline, PNDMPipeline, RePaintPipeline, ScoreSdeVePipeline, ) from .schedulers import ( CMStochasticIterativeScheduler, DDIMInverseScheduler, DDIMParallelScheduler, DDIMScheduler, DDPMParallelScheduler, DDPMScheduler, DEISMultistepScheduler, DPMSolverMultistepInverseScheduler, DPMSolverMultistepScheduler, DPMSolverSinglestepScheduler, EulerAncestralDiscreteScheduler, EulerDiscreteScheduler, HeunDiscreteScheduler, IPNDMScheduler, KarrasVeScheduler, KDPMaAncestralDiscreteScheduler, KDPMaDiscreteScheduler, PNDMScheduler, RePaintScheduler, SchedulerMixin, ScoreSdeVeScheduler, UnCLIPScheduler, UniPCMultistepScheduler, VQDiffusionScheduler, ) from .training_utils import EMAModel 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 .schedulers 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 .schedulers import DPMSolverSDEScheduler try: if not (is_torch_available() and is_transformers_available()): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: from .utils.dummy_torch_and_transformers_objects import * # noqa F403 else: from .pipelines import ( AltDiffusionImgaImgPipeline, AltDiffusionPipeline, AudioLDMPipeline, CycleDiffusionPipeline, IFImgaImgPipeline, IFImgaImgSuperResolutionPipeline, IFInpaintingPipeline, IFInpaintingSuperResolutionPipeline, IFPipeline, IFSuperResolutionPipeline, ImageTextPipelineOutput, KandinskyImgaImgPipeline, KandinskyInpaintPipeline, KandinskyPipeline, KandinskyPriorPipeline, KandinskyVaaControlnetImgaImgPipeline, KandinskyVaaControlnetPipeline, KandinskyVaaImgaImgPipeline, KandinskyVaaInpaintPipeline, KandinskyVaaPipeline, KandinskyVaaPriorEmbaEmbPipeline, KandinskyVaaPriorPipeline, LDMTextToImagePipeline, PaintByExamplePipeline, SemanticStableDiffusionPipeline, ShapEImgaImgPipeline, ShapEPipeline, StableDiffusionAttendAndExcitePipeline, StableDiffusionControlNetImgaImgPipeline, StableDiffusionControlNetInpaintPipeline, StableDiffusionControlNetPipeline, StableDiffusionDepthaImgPipeline, StableDiffusionDiffEditPipeline, StableDiffusionImageVariationPipeline, StableDiffusionImgaImgPipeline, StableDiffusionInpaintPipeline, StableDiffusionInpaintPipelineLegacy, StableDiffusionInstructPixaPixPipeline, StableDiffusionLatentUpscalePipeline, StableDiffusionLDMaDPipeline, StableDiffusionModelEditingPipeline, StableDiffusionPanoramaPipeline, StableDiffusionParadigmsPipeline, StableDiffusionPipeline, StableDiffusionPipelineSafe, StableDiffusionPixaPixZeroPipeline, StableDiffusionSAGPipeline, StableDiffusionUpscalePipeline, StableUnCLIPImgaImgPipeline, StableUnCLIPPipeline, TextToVideoSDPipeline, TextToVideoZeroPipeline, UnCLIPImageVariationPipeline, UnCLIPPipeline, UniDiffuserModel, UniDiffuserPipeline, UniDiffuserTextDecoder, VersatileDiffusionDualGuidedPipeline, VersatileDiffusionImageVariationPipeline, VersatileDiffusionPipeline, VersatileDiffusionTextToImagePipeline, VideoToVideoSDPipeline, VQDiffusionPipeline, ) try: if not (is_torch_available() and is_transformers_available() and is_invisible_watermark_available()): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: from .utils.dummy_torch_and_transformers_and_invisible_watermark_objects import * # noqa F403 else: from .pipelines import StableDiffusionXLImgaImgPipeline, StableDiffusionXLPipeline try: if not (is_torch_available() and is_transformers_available() and is_k_diffusion_available()): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: from .utils.dummy_torch_and_transformers_and_k_diffusion_objects import * # noqa F403 else: from .pipelines import StableDiffusionKDiffusionPipeline try: if not (is_torch_available() and is_transformers_available() and is_onnx_available()): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: from .utils.dummy_torch_and_transformers_and_onnx_objects import * # noqa F403 else: from .pipelines import ( OnnxStableDiffusionImgaImgPipeline, OnnxStableDiffusionInpaintPipeline, OnnxStableDiffusionInpaintPipelineLegacy, OnnxStableDiffusionPipeline, OnnxStableDiffusionUpscalePipeline, StableDiffusionOnnxPipeline, ) try: if not (is_torch_available() and is_librosa_available()): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: from .utils.dummy_torch_and_librosa_objects import * # noqa F403 else: from .pipelines import AudioDiffusionPipeline, Mel try: if not (is_transformers_available() and is_torch_available() and is_note_seq_available()): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: from .utils.dummy_transformers_and_torch_and_note_seq_objects import * # noqa F403 else: from .pipelines import SpectrogramDiffusionPipeline try: if not is_flax_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: from .utils.dummy_flax_objects import * # noqa F403 else: from .models.controlnet_flax import FlaxControlNetModel from .models.modeling_flax_utils import FlaxModelMixin from .models.unet_ad_condition_flax import FlaxUNetaDConditionModel from .models.vae_flax import FlaxAutoencoderKL from .pipelines import FlaxDiffusionPipeline from .schedulers import ( FlaxDDIMScheduler, FlaxDDPMScheduler, FlaxDPMSolverMultistepScheduler, FlaxKarrasVeScheduler, FlaxLMSDiscreteScheduler, FlaxPNDMScheduler, FlaxSchedulerMixin, FlaxScoreSdeVeScheduler, ) try: if not (is_flax_available() and is_transformers_available()): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: from .utils.dummy_flax_and_transformers_objects import * # noqa F403 else: from .pipelines import ( FlaxStableDiffusionControlNetPipeline, FlaxStableDiffusionImgaImgPipeline, FlaxStableDiffusionInpaintPipeline, FlaxStableDiffusionPipeline, ) try: if not (is_note_seq_available()): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: from .utils.dummy_note_seq_objects import * # noqa F403 else: from .pipelines import MidiProcessor
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'''simple docstring''' import os from distutils.util import strtobool def SCREAMING_SNAKE_CASE( __lowercase , __lowercase ) -> List[Any]: for e in env_keys: A: Dict = int(os.environ.get(__lowercase , -1 ) ) if val >= 0: return val return default def SCREAMING_SNAKE_CASE( __lowercase , __lowercase=False ) -> List[str]: A: str = os.environ.get(__lowercase , str(__lowercase ) ) return strtobool(__lowercase ) == 1 # As its name indicates `strtobool` actually returns an int... def SCREAMING_SNAKE_CASE( __lowercase , __lowercase="no" ) -> str: A: Optional[int] = os.environ.get(__lowercase , str(__lowercase ) ) return value
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1
'''simple docstring''' import tempfile import torch from diffusers import ( DEISMultistepScheduler, DPMSolverMultistepScheduler, DPMSolverSinglestepScheduler, UniPCMultistepScheduler, ) from .test_schedulers import SchedulerCommonTest class lowerCAmelCase_ ( UpperCAmelCase_ ): '''simple docstring''' UpperCamelCase_ : Any = (DPMSolverSinglestepScheduler,) UpperCamelCase_ : Optional[int] = (("""num_inference_steps""", 25),) def _snake_case ( self : Optional[Any] , **SCREAMING_SNAKE_CASE_ : Optional[Any] ) -> List[Any]: '''simple docstring''' A: List[Any] = { '''num_train_timesteps''': 10_00, '''beta_start''': 0.0001, '''beta_end''': 0.02, '''beta_schedule''': '''linear''', '''solver_order''': 2, '''prediction_type''': '''epsilon''', '''thresholding''': False, '''sample_max_value''': 1.0, '''algorithm_type''': '''dpmsolver++''', '''solver_type''': '''midpoint''', '''lambda_min_clipped''': -float('''inf''' ), '''variance_type''': None, } config.update(**SCREAMING_SNAKE_CASE_ ) return config def _snake_case ( self : List[str] , SCREAMING_SNAKE_CASE_ : Tuple=0 , **SCREAMING_SNAKE_CASE_ : Any ) -> int: '''simple docstring''' A: str = dict(self.forward_default_kwargs ) A: Optional[Any] = kwargs.pop('''num_inference_steps''' , SCREAMING_SNAKE_CASE_ ) A: Optional[Any] = self.dummy_sample A: Any = 0.1 * sample A: Tuple = [residual + 0.2, residual + 0.15, residual + 0.10] for scheduler_class in self.scheduler_classes: A: List[Any] = self.get_scheduler_config(**SCREAMING_SNAKE_CASE_ ) A: Optional[int] = scheduler_class(**SCREAMING_SNAKE_CASE_ ) scheduler.set_timesteps(SCREAMING_SNAKE_CASE_ ) # copy over dummy past residuals A: List[str] = dummy_past_residuals[: scheduler.config.solver_order] with tempfile.TemporaryDirectory() as tmpdirname: scheduler.save_config(SCREAMING_SNAKE_CASE_ ) A: str = scheduler_class.from_pretrained(SCREAMING_SNAKE_CASE_ ) new_scheduler.set_timesteps(SCREAMING_SNAKE_CASE_ ) # copy over dummy past residuals A: Any = dummy_past_residuals[: new_scheduler.config.solver_order] A , A: str = sample, sample for t in range(SCREAMING_SNAKE_CASE_ , time_step + scheduler.config.solver_order + 1 ): A: Union[str, Any] = scheduler.step(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ ).prev_sample A: Dict = 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 _snake_case ( self : str ) -> Dict: '''simple docstring''' pass def _snake_case ( self : str , SCREAMING_SNAKE_CASE_ : Dict=0 , **SCREAMING_SNAKE_CASE_ : List[str] ) -> Tuple: '''simple docstring''' A: List[Any] = dict(self.forward_default_kwargs ) A: int = kwargs.pop('''num_inference_steps''' , SCREAMING_SNAKE_CASE_ ) A: Tuple = self.dummy_sample A: int = 0.1 * sample A: List[str] = [residual + 0.2, residual + 0.15, residual + 0.10] for scheduler_class in self.scheduler_classes: A: int = self.get_scheduler_config() A: List[str] = scheduler_class(**SCREAMING_SNAKE_CASE_ ) scheduler.set_timesteps(SCREAMING_SNAKE_CASE_ ) # copy over dummy past residuals (must be after setting timesteps) A: Union[str, Any] = dummy_past_residuals[: scheduler.config.solver_order] with tempfile.TemporaryDirectory() as tmpdirname: scheduler.save_config(SCREAMING_SNAKE_CASE_ ) A: Tuple = 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) A: List[Any] = dummy_past_residuals[: new_scheduler.config.solver_order] A: Union[str, Any] = scheduler.step(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ ).prev_sample A: Dict = 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 _snake_case ( self : Optional[Any] , SCREAMING_SNAKE_CASE_ : List[Any]=None , **SCREAMING_SNAKE_CASE_ : List[str] ) -> Optional[Any]: '''simple docstring''' if scheduler is None: A: str = self.scheduler_classes[0] A: Union[str, Any] = self.get_scheduler_config(**SCREAMING_SNAKE_CASE_ ) A: Optional[int] = scheduler_class(**SCREAMING_SNAKE_CASE_ ) A: Tuple = self.scheduler_classes[0] A: int = self.get_scheduler_config(**SCREAMING_SNAKE_CASE_ ) A: List[str] = scheduler_class(**SCREAMING_SNAKE_CASE_ ) A: Optional[Any] = 10 A: Union[str, Any] = self.dummy_model() A: int = self.dummy_sample_deter scheduler.set_timesteps(SCREAMING_SNAKE_CASE_ ) for i, t in enumerate(scheduler.timesteps ): A: Dict = model(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) A: Tuple = scheduler.step(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ).prev_sample return sample def _snake_case ( self : Tuple ) -> int: '''simple docstring''' A: List[str] = DPMSolverSinglestepScheduler(**self.get_scheduler_config() ) A: Tuple = 50 A: List[str] = self.dummy_model() A: str = self.dummy_sample_deter scheduler.set_timesteps(SCREAMING_SNAKE_CASE_ ) # make sure that the first t is uneven for i, t in enumerate(scheduler.timesteps[3:] ): A: Dict = model(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) A: List[str] = scheduler.step(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ).prev_sample A: Union[str, Any] = torch.mean(torch.abs(SCREAMING_SNAKE_CASE_ ) ) assert abs(result_mean.item() - 0.2574 ) < 1E-3 def _snake_case ( self : List[str] ) -> Union[str, Any]: '''simple docstring''' for timesteps in [25, 50, 1_00, 9_99, 10_00]: self.check_over_configs(num_train_timesteps=SCREAMING_SNAKE_CASE_ ) def _snake_case ( self : Optional[int] ) -> Optional[Any]: '''simple docstring''' A: str = DPMSolverSinglestepScheduler(**self.get_scheduler_config() ) A: str = self.full_loop(scheduler=SCREAMING_SNAKE_CASE_ ) A: int = torch.mean(torch.abs(SCREAMING_SNAKE_CASE_ ) ) assert abs(result_mean.item() - 0.2791 ) < 1E-3 A: int = DEISMultistepScheduler.from_config(scheduler.config ) A: Optional[int] = DPMSolverMultistepScheduler.from_config(scheduler.config ) A: Dict = UniPCMultistepScheduler.from_config(scheduler.config ) A: Tuple = DPMSolverSinglestepScheduler.from_config(scheduler.config ) A: List[Any] = self.full_loop(scheduler=SCREAMING_SNAKE_CASE_ ) A: str = torch.mean(torch.abs(SCREAMING_SNAKE_CASE_ ) ) assert abs(result_mean.item() - 0.2791 ) < 1E-3 def _snake_case ( self : Optional[int] ) -> Optional[int]: '''simple docstring''' self.check_over_configs(thresholding=SCREAMING_SNAKE_CASE_ ) for order in [1, 2, 3]: for solver_type in ["midpoint", "heun"]: 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_ , algorithm_type='''dpmsolver++''' , solver_order=SCREAMING_SNAKE_CASE_ , solver_type=SCREAMING_SNAKE_CASE_ , ) def _snake_case ( self : Dict ) -> Optional[Any]: '''simple docstring''' for prediction_type in ["epsilon", "v_prediction"]: self.check_over_configs(prediction_type=SCREAMING_SNAKE_CASE_ ) def _snake_case ( self : str ) -> Optional[Any]: '''simple docstring''' for algorithm_type in ["dpmsolver", "dpmsolver++"]: for solver_type in ["midpoint", "heun"]: 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_ , algorithm_type=SCREAMING_SNAKE_CASE_ , ) A: List[Any] = self.full_loop( solver_order=SCREAMING_SNAKE_CASE_ , solver_type=SCREAMING_SNAKE_CASE_ , prediction_type=SCREAMING_SNAKE_CASE_ , algorithm_type=SCREAMING_SNAKE_CASE_ , ) assert not torch.isnan(SCREAMING_SNAKE_CASE_ ).any(), "Samples have nan numbers" def _snake_case ( self : Dict ) -> Any: '''simple docstring''' self.check_over_configs(lower_order_final=SCREAMING_SNAKE_CASE_ ) self.check_over_configs(lower_order_final=SCREAMING_SNAKE_CASE_ ) def _snake_case ( self : Any ) -> List[Any]: '''simple docstring''' self.check_over_configs(lambda_min_clipped=-float('''inf''' ) ) self.check_over_configs(lambda_min_clipped=-5.1 ) def _snake_case ( self : Optional[int] ) -> Dict: '''simple docstring''' self.check_over_configs(variance_type=SCREAMING_SNAKE_CASE_ ) self.check_over_configs(variance_type='''learned_range''' ) def _snake_case ( self : int ) -> Optional[int]: '''simple docstring''' for num_inference_steps in [1, 2, 3, 5, 10, 50, 1_00, 9_99, 10_00]: self.check_over_forward(num_inference_steps=SCREAMING_SNAKE_CASE_ , time_step=0 ) def _snake_case ( self : Optional[Any] ) -> List[str]: '''simple docstring''' A: Optional[Any] = self.full_loop() A: int = torch.mean(torch.abs(SCREAMING_SNAKE_CASE_ ) ) assert abs(result_mean.item() - 0.2791 ) < 1E-3 def _snake_case ( self : Tuple ) -> Any: '''simple docstring''' A: Dict = self.full_loop(use_karras_sigmas=SCREAMING_SNAKE_CASE_ ) A: str = torch.mean(torch.abs(SCREAMING_SNAKE_CASE_ ) ) assert abs(result_mean.item() - 0.2248 ) < 1E-3 def _snake_case ( self : Optional[Any] ) -> str: '''simple docstring''' A: str = self.full_loop(prediction_type='''v_prediction''' ) A: Optional[Any] = torch.mean(torch.abs(SCREAMING_SNAKE_CASE_ ) ) assert abs(result_mean.item() - 0.1453 ) < 1E-3 def _snake_case ( self : str ) -> Optional[Any]: '''simple docstring''' A: List[str] = self.full_loop(prediction_type='''v_prediction''' , use_karras_sigmas=SCREAMING_SNAKE_CASE_ ) A: List[Any] = torch.mean(torch.abs(SCREAMING_SNAKE_CASE_ ) ) assert abs(result_mean.item() - 0.0649 ) < 1E-3 def _snake_case ( self : Tuple ) -> int: '''simple docstring''' A: Any = self.scheduler_classes[0] A: Optional[Any] = self.get_scheduler_config(thresholding=SCREAMING_SNAKE_CASE_ , dynamic_thresholding_ratio=0 ) A: Union[str, Any] = scheduler_class(**SCREAMING_SNAKE_CASE_ ) A: Union[str, Any] = 10 A: Tuple = self.dummy_model() A: Tuple = self.dummy_sample_deter.half() scheduler.set_timesteps(SCREAMING_SNAKE_CASE_ ) for i, t in enumerate(scheduler.timesteps ): A: Any = model(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) A: List[Any] = scheduler.step(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ).prev_sample assert sample.dtype == torch.floataa
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'''simple docstring''' import argparse import torch from transformers import ( EncodecConfig, EncodecFeatureExtractor, EncodecModel, logging, ) # checkpoints downloaded from: # https://dl.fbaipublicfiles.com/encodec/v0/encodec_24khz-d7cc33bc.th # https://huggingface.co/facebook/musicgen-small/resolve/main/compression_state_dict.bin # https://dl.fbaipublicfiles.com/encodec/v0/encodec_48khz-7e698e3e.th logging.set_verbosity_info() UpperCamelCase = logging.get_logger('''transformers.models.encodec''') UpperCamelCase = { '''quantizer.vq.layers.*._codebook.inited''': '''quantizer.layers.*.codebook.inited''', '''quantizer.vq.layers.*._codebook.cluster_size''': '''quantizer.layers.*.codebook.cluster_size''', '''quantizer.vq.layers.*._codebook.embed''': '''quantizer.layers.*.codebook.embed''', '''quantizer.vq.layers.*._codebook.embed_avg''': '''quantizer.layers.*.codebook.embed_avg''', } UpperCamelCase = { '''encoder.model.0.conv.conv''': '''encoder.layers.0.conv''', '''encoder.model.1.block.1.conv.conv''': '''encoder.layers.1.block.1.conv''', '''encoder.model.1.block.3.conv.conv''': '''encoder.layers.1.block.3.conv''', '''encoder.model.1.shortcut.conv.conv''': '''encoder.layers.1.shortcut.conv''', '''encoder.model.3.conv.conv''': '''encoder.layers.3.conv''', '''encoder.model.4.block.1.conv.conv''': '''encoder.layers.4.block.1.conv''', '''encoder.model.4.block.3.conv.conv''': '''encoder.layers.4.block.3.conv''', '''encoder.model.4.shortcut.conv.conv''': '''encoder.layers.4.shortcut.conv''', '''encoder.model.6.conv.conv''': '''encoder.layers.6.conv''', '''encoder.model.7.block.1.conv.conv''': '''encoder.layers.7.block.1.conv''', '''encoder.model.7.block.3.conv.conv''': '''encoder.layers.7.block.3.conv''', '''encoder.model.7.shortcut.conv.conv''': '''encoder.layers.7.shortcut.conv''', '''encoder.model.9.conv.conv''': '''encoder.layers.9.conv''', '''encoder.model.10.block.1.conv.conv''': '''encoder.layers.10.block.1.conv''', '''encoder.model.10.block.3.conv.conv''': '''encoder.layers.10.block.3.conv''', '''encoder.model.10.shortcut.conv.conv''': '''encoder.layers.10.shortcut.conv''', '''encoder.model.12.conv.conv''': '''encoder.layers.12.conv''', '''encoder.model.13.lstm''': '''encoder.layers.13.lstm''', '''encoder.model.15.conv.conv''': '''encoder.layers.15.conv''', } UpperCamelCase = { '''encoder.model.0.conv.norm''': '''encoder.layers.0.norm''', '''encoder.model.1.block.1.conv.norm''': '''encoder.layers.1.block.1.norm''', '''encoder.model.1.block.3.conv.norm''': '''encoder.layers.1.block.3.norm''', '''encoder.model.1.shortcut.conv.norm''': '''encoder.layers.1.shortcut.norm''', '''encoder.model.3.conv.norm''': '''encoder.layers.3.norm''', '''encoder.model.4.block.1.conv.norm''': '''encoder.layers.4.block.1.norm''', '''encoder.model.4.block.3.conv.norm''': '''encoder.layers.4.block.3.norm''', '''encoder.model.4.shortcut.conv.norm''': '''encoder.layers.4.shortcut.norm''', '''encoder.model.6.conv.norm''': '''encoder.layers.6.norm''', '''encoder.model.7.block.1.conv.norm''': '''encoder.layers.7.block.1.norm''', '''encoder.model.7.block.3.conv.norm''': '''encoder.layers.7.block.3.norm''', '''encoder.model.7.shortcut.conv.norm''': '''encoder.layers.7.shortcut.norm''', '''encoder.model.9.conv.norm''': '''encoder.layers.9.norm''', '''encoder.model.10.block.1.conv.norm''': '''encoder.layers.10.block.1.norm''', '''encoder.model.10.block.3.conv.norm''': '''encoder.layers.10.block.3.norm''', '''encoder.model.10.shortcut.conv.norm''': '''encoder.layers.10.shortcut.norm''', '''encoder.model.12.conv.norm''': '''encoder.layers.12.norm''', '''encoder.model.15.conv.norm''': '''encoder.layers.15.norm''', } UpperCamelCase = { '''decoder.model.0.conv.conv''': '''decoder.layers.0.conv''', '''decoder.model.1.lstm''': '''decoder.layers.1.lstm''', '''decoder.model.3.convtr.convtr''': '''decoder.layers.3.conv''', '''decoder.model.4.block.1.conv.conv''': '''decoder.layers.4.block.1.conv''', '''decoder.model.4.block.3.conv.conv''': '''decoder.layers.4.block.3.conv''', '''decoder.model.4.shortcut.conv.conv''': '''decoder.layers.4.shortcut.conv''', '''decoder.model.6.convtr.convtr''': '''decoder.layers.6.conv''', '''decoder.model.7.block.1.conv.conv''': '''decoder.layers.7.block.1.conv''', '''decoder.model.7.block.3.conv.conv''': '''decoder.layers.7.block.3.conv''', '''decoder.model.7.shortcut.conv.conv''': '''decoder.layers.7.shortcut.conv''', '''decoder.model.9.convtr.convtr''': '''decoder.layers.9.conv''', '''decoder.model.10.block.1.conv.conv''': '''decoder.layers.10.block.1.conv''', '''decoder.model.10.block.3.conv.conv''': '''decoder.layers.10.block.3.conv''', '''decoder.model.10.shortcut.conv.conv''': '''decoder.layers.10.shortcut.conv''', '''decoder.model.12.convtr.convtr''': '''decoder.layers.12.conv''', '''decoder.model.13.block.1.conv.conv''': '''decoder.layers.13.block.1.conv''', '''decoder.model.13.block.3.conv.conv''': '''decoder.layers.13.block.3.conv''', '''decoder.model.13.shortcut.conv.conv''': '''decoder.layers.13.shortcut.conv''', '''decoder.model.15.conv.conv''': '''decoder.layers.15.conv''', } UpperCamelCase = { '''decoder.model.0.conv.norm''': '''decoder.layers.0.norm''', '''decoder.model.3.convtr.norm''': '''decoder.layers.3.norm''', '''decoder.model.4.block.1.conv.norm''': '''decoder.layers.4.block.1.norm''', '''decoder.model.4.block.3.conv.norm''': '''decoder.layers.4.block.3.norm''', '''decoder.model.4.shortcut.conv.norm''': '''decoder.layers.4.shortcut.norm''', '''decoder.model.6.convtr.norm''': '''decoder.layers.6.norm''', '''decoder.model.7.block.1.conv.norm''': '''decoder.layers.7.block.1.norm''', '''decoder.model.7.block.3.conv.norm''': '''decoder.layers.7.block.3.norm''', '''decoder.model.7.shortcut.conv.norm''': '''decoder.layers.7.shortcut.norm''', '''decoder.model.9.convtr.norm''': '''decoder.layers.9.norm''', '''decoder.model.10.block.1.conv.norm''': '''decoder.layers.10.block.1.norm''', '''decoder.model.10.block.3.conv.norm''': '''decoder.layers.10.block.3.norm''', '''decoder.model.10.shortcut.conv.norm''': '''decoder.layers.10.shortcut.norm''', '''decoder.model.12.convtr.norm''': '''decoder.layers.12.norm''', '''decoder.model.13.block.1.conv.norm''': '''decoder.layers.13.block.1.norm''', '''decoder.model.13.block.3.conv.norm''': '''decoder.layers.13.block.3.norm''', '''decoder.model.13.shortcut.conv.norm''': '''decoder.layers.13.shortcut.norm''', '''decoder.model.15.conv.norm''': '''decoder.layers.15.norm''', } UpperCamelCase = { **MAPPING_QUANTIZER, **MAPPING_ENCODER, **MAPPING_DECODER, } UpperCamelCase = { **MAPPING_QUANTIZER, **MAPPING_ENCODER, **MAPPING_ENCODER_48K, **MAPPING_DECODER, **MAPPING_DECODER_48K, } UpperCamelCase = [] UpperCamelCase = [] def SCREAMING_SNAKE_CASE( __lowercase , __lowercase , __lowercase , __lowercase , __lowercase ) -> Dict: for attribute in key.split('''.''' ): A: Union[str, Any] = getattr(__lowercase , __lowercase ) if weight_type is not None: A: Tuple = getattr(__lowercase , __lowercase ).shape else: A: str = 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": A: Dict = value elif weight_type == "weight_g": A: Tuple = value elif weight_type == "weight_v": A: Any = value elif weight_type == "bias": A: str = value elif weight_type == "running_mean": A: List[Any] = value elif weight_type == "running_var": A: Dict = value elif weight_type == "num_batches_tracked": A: List[str] = value elif weight_type == "weight_ih_l0": A: Dict = value elif weight_type == "weight_hh_l0": A: Optional[int] = value elif weight_type == "bias_ih_l0": A: List[Any] = value elif weight_type == "bias_hh_l0": A: str = value elif weight_type == "weight_ih_l1": A: Optional[int] = value elif weight_type == "weight_hh_l1": A: int = value elif weight_type == "bias_ih_l1": A: Optional[Any] = value elif weight_type == "bias_hh_l1": A: str = value else: A: Optional[int] = value logger.info(F"""{key + ('.' + weight_type if weight_type is not None else '')} was initialized from {full_name}.""" ) def SCREAMING_SNAKE_CASE( __lowercase , __lowercase ) -> Optional[Any]: for key in ignore_keys: if key.endswith('''.*''' ): if name.startswith(key[:-1] ): return True elif ".*." in key: A , A: Any = key.split('''.*.''' ) if prefix in name and suffix in name: return True elif key in name: return True return False def SCREAMING_SNAKE_CASE( __lowercase , __lowercase , __lowercase ) -> Tuple: A: Any = [] if model_name == "encodec_24khz" or "encodec_32khz": A: List[str] = MAPPING_24K elif model_name == "encodec_48khz": A: List[Any] = MAPPING_48K else: raise ValueError(F"""Unsupported model: {model_name}""" ) for name, value in orig_dict.items(): if should_ignore(__lowercase , __lowercase ): logger.info(F"""{name} was ignored""" ) continue A: Optional[int] = False for key, mapped_key in MAPPING.items(): if "*" in key: A , A: Optional[int] = key.split('''.*.''' ) if prefix in name and suffix in name: A: str = suffix if key in name: # HACK otherwise .embed gets initialized with .embed_avg too if key.endswith('''embed''' ) and name.endswith('''embed_avg''' ): continue A: Optional[Any] = True if "*" in mapped_key: A: Any = name.split(__lowercase )[0].split('''.''' )[-2] A: Tuple = mapped_key.replace('''*''' , __lowercase ) if "weight_g" in name: A: str = '''weight_g''' elif "weight_v" in name: A: List[Any] = '''weight_v''' elif "weight_ih_l0" in name: A: Dict = '''weight_ih_l0''' elif "weight_hh_l0" in name: A: int = '''weight_hh_l0''' elif "bias_ih_l0" in name: A: Union[str, Any] = '''bias_ih_l0''' elif "bias_hh_l0" in name: A: Tuple = '''bias_hh_l0''' elif "weight_ih_l1" in name: A: int = '''weight_ih_l1''' elif "weight_hh_l1" in name: A: Optional[Any] = '''weight_hh_l1''' elif "bias_ih_l1" in name: A: Dict = '''bias_ih_l1''' elif "bias_hh_l1" in name: A: str = '''bias_hh_l1''' elif "bias" in name: A: Union[str, Any] = '''bias''' elif "weight" in name: A: Dict = '''weight''' elif "running_mean" in name: A: Tuple = '''running_mean''' elif "running_var" in name: A: Any = '''running_var''' elif "num_batches_tracked" in name: A: str = '''num_batches_tracked''' else: A: Tuple = None set_recursively(__lowercase , __lowercase , __lowercase , __lowercase , __lowercase ) continue if not is_used: unused_weights.append(__lowercase ) logger.warning(F"""Unused weights: {unused_weights}""" ) @torch.no_grad() def SCREAMING_SNAKE_CASE( __lowercase , __lowercase , __lowercase , __lowercase=None , __lowercase=None , ) -> Dict: if config_path is not None: A: Tuple = EncodecConfig.from_pretrained(__lowercase ) else: A: Union[str, Any] = EncodecConfig() if model_name == "encodec_24khz": pass # config is already correct elif model_name == "encodec_32khz": A: Union[str, Any] = [8, 5, 4, 4] A: Dict = [2.2] A: List[Any] = 6_4 A: Optional[Any] = 3_2_0_0_0 A: List[Any] = 2_0_4_8 A: Optional[Any] = False A: int = False A: Union[str, Any] = False elif model_name == "encodec_48khz": A: Optional[int] = [8, 5, 4, 2] A: List[Any] = [3.0, 6.0, 1_2.0, 2_4.0] A: List[Any] = 4_8_0_0_0 A: int = 2 A: List[Any] = False A: Any = '''time_group_norm''' A: Optional[Any] = True A: Any = 1.0 A: Any = 0.0_1 else: raise ValueError(F"""Unknown model name: {model_name}""" ) A: str = EncodecModel(__lowercase ) A: Optional[Any] = EncodecFeatureExtractor( feature_size=config.audio_channels , sampling_rate=config.sampling_rate , chunk_length_s=config.chunk_length_s , overlap=config.overlap , ) feature_extractor.save_pretrained(__lowercase ) A: Union[str, Any] = torch.load(__lowercase ) if "best_state" in original_checkpoint: # we might have a training state saved, in which case discard the yaml results and just retain the weights A: Optional[int] = original_checkpoint['''best_state'''] recursively_load_weights(__lowercase , __lowercase , __lowercase ) model.save_pretrained(__lowercase ) if repo_id: print('''Pushing to the hub...''' ) feature_extractor.push_to_hub(__lowercase ) model.push_to_hub(__lowercase ) if __name__ == "__main__": UpperCamelCase = argparse.ArgumentParser() parser.add_argument( '''--model''', default='''encodec_24khz''', type=str, help='''The model to convert. Should be one of \'encodec_24khz\', \'encodec_32khz\', \'encodec_48khz\'.''', ) parser.add_argument('''--checkpoint_path''', required=True, default=None, type=str, help='''Path to original checkpoint''') 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.''' ) UpperCamelCase = parser.parse_args() convert_checkpoint( args.model, args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.push_to_hub, )
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'''simple docstring''' def SCREAMING_SNAKE_CASE( __lowercase ) -> bool: return sum(i for i in range(1 , number // 2 + 1 ) if number % i == 0 ) == number if __name__ == "__main__": print('''Program to check whether a number is a Perfect number or not...''') UpperCamelCase = int(input('''Enter number: ''').strip()) print(f'{number} is {"" if perfect(number) else "not "}a Perfect Number.')
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'''simple docstring''' from typing import TYPE_CHECKING from ...utils import ( OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_tf_available, is_torch_available, ) UpperCamelCase = { '''configuration_vision_encoder_decoder''': ['''VisionEncoderDecoderConfig''', '''VisionEncoderDecoderOnnxConfig'''] } try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: UpperCamelCase = ['''VisionEncoderDecoderModel'''] try: if not is_tf_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: UpperCamelCase = ['''TFVisionEncoderDecoderModel'''] try: if not is_flax_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: UpperCamelCase = ['''FlaxVisionEncoderDecoderModel'''] if TYPE_CHECKING: from .configuration_vision_encoder_decoder import VisionEncoderDecoderConfig, VisionEncoderDecoderOnnxConfig try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_vision_encoder_decoder import VisionEncoderDecoderModel try: if not is_tf_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_tf_vision_encoder_decoder import TFVisionEncoderDecoderModel try: if not is_flax_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_flax_vision_encoder_decoder import FlaxVisionEncoderDecoderModel else: import sys UpperCamelCase = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
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