'''simple docstring'''






import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional

import evaluate
import numpy as np
import torch
from datasets import load_dataset
from PIL import Image
from torchvision.transforms import (
    CenterCrop,
    Compose,
    Normalize,
    RandomHorizontalFlip,
    RandomResizedCrop,
    Resize,
    ToTensor,
)

import transformers
from transformers import (
    MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
    AutoConfig,
    AutoImageProcessor,
    AutoModelForImageClassification,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    set_seed,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version
_UpperCAmelCase    :     Union[str, Any]         =       logging.getLogger(__name__)

# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version('''4.31.0''')

require_version('''datasets>=1.8.0''', '''To fix: pip install -r examples/pytorch/image-classification/requirements.txt''')

_UpperCAmelCase    :     Union[str, Any]         =       list(MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING.keys())
_UpperCAmelCase    :     List[str]         =       tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)





def  _SCREAMING_SNAKE_CASE		(      __snake_case							:			str				):
     with open(__snake_case   ,		'rb'				) as f:
          _A    		=  Image.open(__snake_case				)
          return im.convert('RGB'				)







@dataclass
class lowercase_       :







   """simple docstring"""

   __lowerCAmelCase         =   field(
       default=_UpperCamelCase    ,    metadata={
           "help": "Name of a dataset from the hub (could be your own, possibly private dataset hosted on the hub)."
       }    ,    )
   __lowerCAmelCase         =   field(
       default=_UpperCamelCase    ,    metadata={"help": "The configuration name of the dataset to use (via the datasets library)."}			)
   __lowerCAmelCase         =   field(default=_UpperCamelCase    ,    metadata={"help": "A folder containing the training data."}			)
   __lowerCAmelCase         =   field(default=_UpperCamelCase    ,    metadata={"help": "A folder containing the validation data."}			)
   __lowerCAmelCase         =   field(
       default=0.1_5    ,    metadata={"help": "Percent to split off of train for validation."}			)
   __lowerCAmelCase         =   field(
       default=_UpperCamelCase    ,    metadata={
           "help": (
               "For debugging purposes or quicker training, truncate the number of training examples to this "
               "value if set."
           )
       }    ,    )
   __lowerCAmelCase         =   field(
       default=_UpperCamelCase    ,    metadata={
           "help": (
               "For debugging purposes or quicker training, truncate the number of evaluation examples to this "
               "value if set."
           )
       }    ,    )
   def 	__UpperCAmelCase   (      self      :			Optional[int]     )  ->			int:
        if self.dataset_name is None and (self.train_dir is None and self.validation_dir is None):
             raise ValueError(
                 'You must specify either a dataset name from the hub or a train and/or validation directory.'     )







@dataclass
class lowercase_       :







   """simple docstring"""

   __lowerCAmelCase         =   field(
       default="google/vit-base-patch16-224-in21k"    ,    metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}    ,    )
   __lowerCAmelCase         =   field(
       default=_UpperCamelCase    ,    metadata={"help": "If training from scratch, pass a model type from the list: " + ", ".join(_UpperCamelCase			)}    ,    )
   __lowerCAmelCase         =   field(
       default=_UpperCamelCase    ,    metadata={"help": "Pretrained config name or path if not the same as model_name"}			)
   __lowerCAmelCase         =   field(
       default=_UpperCamelCase    ,    metadata={"help": "Where do you want to store the pretrained models downloaded from s3"}			)
   __lowerCAmelCase         =   field(
       default="main"    ,    metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."}    ,    )
   __lowerCAmelCase         =   field(default=_UpperCamelCase    ,    metadata={"help": "Name or path of preprocessor config."}			)
   __lowerCAmelCase         =   field(
       default=_UpperCamelCase    ,    metadata={
           "help": (
               "Will use the token generated when running `huggingface-cli login` (necessary to use this script "
               "with private models)."
           )
       }    ,    )
   __lowerCAmelCase         =   field(
       default=_UpperCamelCase    ,    metadata={"help": "Will enable to load a pretrained model whose head dimensions are different."}    ,    )






def  _SCREAMING_SNAKE_CASE		(      __snake_case							:			Optional[int]				):
     _A    		=  torch.stack([example['pixel_values'] for example in examples]				)
     _A    		=  torch.tensor([example['labels'] for example in examples]				)
     return {"pixel_values": pixel_values, "labels": labels}





def  _SCREAMING_SNAKE_CASE		(      ):
     # See all possible arguments in src/transformers/training_args.py
     # or by passing the --help flag to this script.
     # We now keep distinct sets of args, for a cleaner separation of concerns.

     _A    		=  HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)				)
     if len(sys.argv				) == 2 and sys.argv[1].endswith('.json'				):
          # If we pass only one argument to the script and it's the path to a json file,
          # let's parse it to get our arguments.
          _A		,       _A		,       _A    		=  parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]				)				)
     else:
          _A		,       _A		,       _A    		=  parser.parse_args_into_dataclasses()

     # Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
     # information sent is the one passed as arguments along with your Python/PyTorch versions.
     send_example_telemetry('run_image_classification'   ,		__snake_case   ,		__snake_case				)

     # Setup logging
     logging.basicConfig(
         format='%(asctime)s - %(levelname)s - %(name)s - %(message)s'   ,		datefmt='%m/%d/%Y %H:%M:%S'   ,		handlers=[logging.StreamHandler(sys.stdout				)]   ,		)

     if training_args.should_log:
          # The default of training_args.log_level is passive, so we set log level at info here to have that default.
          transformers.utils.logging.set_verbosity_info()

     _A    		=  training_args.get_process_log_level()
     logger.setLevel(__snake_case				)
     transformers.utils.logging.set_verbosity(__snake_case				)
     transformers.utils.logging.enable_default_handler()
     transformers.utils.logging.enable_explicit_format()

     # Log on each process the small summary:
     logger.warning(
         F'Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}'
         + F'distributed training: {bool(training_args.local_rank != -1				)}, 16-bits training: {training_args.fpaa}'				)
     logger.info(F'Training/evaluation parameters {training_args}'				)

     # Detecting last checkpoint.
     _A    		=  None
     if os.path.isdir(training_args.output_dir				) and training_args.do_train and not training_args.overwrite_output_dir:
          _A    		=  get_last_checkpoint(training_args.output_dir				)
          if last_checkpoint is None and len(os.listdir(training_args.output_dir				)				) > 0:
               raise ValueError(
                   F'Output directory ({training_args.output_dir}) already exists and is not empty. '
                   'Use --overwrite_output_dir to overcome.'				)
          elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
               logger.info(
                   F'Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change '
                   'the `--output_dir` or add `--overwrite_output_dir` to train from scratch.'				)

    # Set seed before initializing model.
     set_seed(training_args.seed				)

     # Initialize our dataset and prepare it for the 'image-classification' task.
     if data_args.dataset_name is not None:
          _A    		=  load_dataset(
              data_args.dataset_name   ,		data_args.dataset_config_name   ,		cache_dir=model_args.cache_dir   ,		task='image-classification'   ,		use_auth_token=True if model_args.use_auth_token else None   ,		)
     else:
          _A    		=  {}
          if data_args.train_dir is not None:
               _A    		=  os.path.join(data_args.train_dir   ,		'**'				)
          if data_args.validation_dir is not None:
               _A    		=  os.path.join(data_args.validation_dir   ,		'**'				)
          _A    		=  load_dataset(
              'imagefolder'   ,		data_files=__snake_case   ,		cache_dir=model_args.cache_dir   ,		task='image-classification'   ,		)

     # If we don't have a validation split, split off a percentage of train as validation.
     _A    		=  None if 'validation' in dataset.keys() else data_args.train_val_split
     if isinstance(data_args.train_val_split   ,		__snake_case				) and data_args.train_val_split > 0.0:
          _A    		=  dataset['train'].train_test_split(data_args.train_val_split				)
          _A    		=  split['train']
          _A    		=  split['test']

     # Prepare label mappings.
     # We'll include these in the model's config to get human readable labels in the Inference API.
     _A    		=  dataset['train'].features['labels'].names
     _A		,       _A    		=  {}, {}
     for i, label in enumerate(__snake_case				):
          _A    		=  str(__snake_case				)
          _A    		=  label

     # Load the accuracy metric from the datasets package
     _A    		=  evaluate.load('accuracy'				)

     # Define our compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with a
     # predictions and label_ids field) and has to return a dictionary string to float.
     def compute_metrics(__snake_case							:			Optional[Any]				):
          return metric.compute(predictions=np.argmax(p.predictions   ,		axis=1				)   ,		references=p.label_ids				)

     _A    		=  AutoConfig.from_pretrained(
         model_args.config_name or model_args.model_name_or_path   ,		num_labels=len(__snake_case				)   ,		labelaid=__snake_case   ,		idalabel=__snake_case   ,		finetuning_task='image-classification'   ,		cache_dir=model_args.cache_dir   ,		revision=model_args.model_revision   ,		use_auth_token=True if model_args.use_auth_token else None   ,		)
     _A    		=  AutoModelForImageClassification.from_pretrained(
         model_args.model_name_or_path   ,		from_tf=bool('.ckpt' in model_args.model_name_or_path				)   ,		config=__snake_case   ,		cache_dir=model_args.cache_dir   ,		revision=model_args.model_revision   ,		use_auth_token=True if model_args.use_auth_token else None   ,		ignore_mismatched_sizes=model_args.ignore_mismatched_sizes   ,		)
     _A    		=  AutoImageProcessor.from_pretrained(
         model_args.image_processor_name or model_args.model_name_or_path   ,		cache_dir=model_args.cache_dir   ,		revision=model_args.model_revision   ,		use_auth_token=True if model_args.use_auth_token else None   ,		)

     # Define torchvision transforms to be applied to each image.
     if "shortest_edge" in image_processor.size:
          _A    		=  image_processor.size['shortest_edge']
     else:
          _A    		=  (image_processor.size['height'], image_processor.size['width'])
     _A    		=  Normalize(mean=image_processor.image_mean   ,		std=image_processor.image_std				)
     _A    		=  Compose(
         [
             RandomResizedCrop(__snake_case				),
             RandomHorizontalFlip(),
             ToTensor(),
             normalize,
         ]				)
     _A    		=  Compose(
         [
             Resize(__snake_case				),
             CenterCrop(__snake_case				),
             ToTensor(),
             normalize,
         ]				)

     def train_transforms(__snake_case							:			Optional[int]				):
          _A    		=  [
              _train_transforms(pil_img.convert('RGB'				)				) for pil_img in example_batch['image']
          ]
          return example_batch

     def val_transforms(__snake_case							:			Union[str, Any]				):
          _A    		=  [_val_transforms(pil_img.convert('RGB'				)				) for pil_img in example_batch['image']]
          return example_batch

     if training_args.do_train:
          if "train" not in dataset:
               raise ValueError('--do_train requires a train dataset'				)
          if data_args.max_train_samples is not None:
               _A    		=  (
                   dataset['train'].shuffle(seed=training_args.seed				).select(range(data_args.max_train_samples				)				)
               )
          # Set the training transforms
          dataset["train"].set_transform(__snake_case				)

     if training_args.do_eval:
          if "validation" not in dataset:
               raise ValueError('--do_eval requires a validation dataset'				)
          if data_args.max_eval_samples is not None:
               _A    		=  (
                   dataset['validation'].shuffle(seed=training_args.seed				).select(range(data_args.max_eval_samples				)				)
               )
          # Set the validation transforms
          dataset["validation"].set_transform(__snake_case				)

     # Initalize our trainer
     _A    		=  Trainer(
         model=__snake_case   ,		args=__snake_case   ,		train_dataset=dataset['train'] if training_args.do_train else None   ,		eval_dataset=dataset['validation'] if training_args.do_eval else None   ,		compute_metrics=__snake_case   ,		tokenizer=__snake_case   ,		data_collator=__snake_case   ,		)

     # Training
     if training_args.do_train:
          _A    		=  None
          if training_args.resume_from_checkpoint is not None:
               _A    		=  training_args.resume_from_checkpoint
          elif last_checkpoint is not None:
               _A    		=  last_checkpoint
          _A    		=  trainer.train(resume_from_checkpoint=__snake_case				)
          trainer.save_model()
          trainer.log_metrics('train'   ,		train_result.metrics				)
          trainer.save_metrics('train'   ,		train_result.metrics				)
          trainer.save_state()

     # Evaluation
     if training_args.do_eval:
          _A    		=  trainer.evaluate()
          trainer.log_metrics('eval'   ,		__snake_case				)
          trainer.save_metrics('eval'   ,		__snake_case				)

     # Write model card and (optionally) push to hub
     _A    		=  {
         'finetuned_from': model_args.model_name_or_path,
         'tasks': 'image-classification',
         'dataset': data_args.dataset_name,
         'tags': ['image-classification', 'vision'],
     }
     if training_args.push_to_hub:
          trainer.push_to_hub(**__snake_case				)
     else:
          trainer.create_model_card(**__snake_case				)


if __name__ == "__main__":
   main()




'''simple docstring'''






import itertools
import os
import random
import tempfile
import unittest

import numpy as np
from datasets import load_dataset

from transformers import is_speech_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_speech_available():
   from transformers import WhisperFeatureExtractor

if is_torch_available():
   import torch

_UpperCAmelCase    :     List[Any]         =       random.Random()





def  _SCREAMING_SNAKE_CASE		(      __snake_case							:			int   ,		__snake_case							:			Dict=1.0   ,		__snake_case							:			List[Any]=None   ,		__snake_case							:			List[str]=None				):
     if rng is None:
          _A    		=  global_rng

     _A    		=  []
     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 lowercase_       (		unittest.TestCase			):







   """simple docstring"""

   def __init__(      self      :			Tuple, UpperCamelCase__      :			Tuple, UpperCamelCase__      :			Union[str, Any]=7, UpperCamelCase__      :			List[str]=4_00, UpperCamelCase__      :			Any=20_00, UpperCamelCase__      :			int=10, UpperCamelCase__      :			Tuple=1_60, UpperCamelCase__      :			Tuple=8, UpperCamelCase__      :			List[str]=0.0, UpperCamelCase__      :			Optional[Any]=40_00, UpperCamelCase__      :			List[str]=False, UpperCamelCase__      :			List[Any]=True, )  ->			Dict:
        _A    		=  parent
        _A    		=  batch_size
        _A    		=  min_seq_length
        _A    		=  max_seq_length
        _A    		=  (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
        _A    		=  padding_value
        _A    		=  sampling_rate
        _A    		=  return_attention_mask
        _A    		=  do_normalize
        _A    		=  feature_size
        _A    		=  chunk_length
        _A    		=  hop_length
   def 	__UpperCAmelCase   (      self      :			Tuple     )  ->			Optional[Any]:
        return {
            "feature_size": self.feature_size,
            "hop_length": self.hop_length,
            "chunk_length": self.chunk_length,
            "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      :			Optional[Any], UpperCamelCase__      :			List[Any]=False, UpperCamelCase__      :			str=False     )  ->			Any:
        def _flatten(UpperCamelCase__      :			List[str]     ):
             return list(itertools.chain(*UpperCamelCase__     )     )

        if equal_length:
             _A    		=  [floats_list((self.max_seq_length, self.feature_size)     ) for _ in range(self.batch_size     )]
        else:
             # make sure that inputs increase in size
             _A    		=  [
                 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    		=  [np.asarray(UpperCamelCase__     ) for x in speech_inputs]
        return speech_inputs







@require_torch
@require_torchaudio
class lowercase_       (		_UpperCamelCase    ,    unittest.TestCase			):







   """simple docstring"""

   __lowerCAmelCase         =   WhisperFeatureExtractor if is_speech_available() else None
   def 	__UpperCAmelCase   (      self      :			Dict     )  ->			Optional[Any]:
        _A    		=  WhisperFeatureExtractionTester(self     )
   def 	__UpperCAmelCase   (      self      :			List[Any]     )  ->			int:
        _A    		=  self.feature_extraction_class(**self.feat_extract_dict     )

        with tempfile.TemporaryDirectory() as tmpdirname:
             _A    		=  feat_extract_first.save_pretrained(UpperCamelCase__     )[0]
             check_json_file_has_correct_format(UpperCamelCase__     )
             _A    		=  self.feature_extraction_class.from_pretrained(UpperCamelCase__     )

        _A    		=  feat_extract_first.to_dict()
        _A    		=  feat_extract_second.to_dict()
        _A    		=  feat_extract_first.mel_filters
        _A    		=  feat_extract_second.mel_filters
        self.assertTrue(np.allclose(UpperCamelCase__, UpperCamelCase__     )     )
        self.assertEqual(UpperCamelCase__, UpperCamelCase__     )
   def 	__UpperCAmelCase   (      self      :			Optional[int]     )  ->			List[Any]:
        _A    		=  self.feature_extraction_class(**self.feat_extract_dict     )

        with tempfile.TemporaryDirectory() as tmpdirname:
             _A    		=  os.path.join(UpperCamelCase__, 'feat_extract.json'     )
             feat_extract_first.to_json_file(UpperCamelCase__     )
             _A    		=  self.feature_extraction_class.from_json_file(UpperCamelCase__     )

        _A    		=  feat_extract_first.to_dict()
        _A    		=  feat_extract_second.to_dict()
        _A    		=  feat_extract_first.mel_filters
        _A    		=  feat_extract_second.mel_filters
        self.assertTrue(np.allclose(UpperCamelCase__, UpperCamelCase__     )     )
        self.assertEqual(UpperCamelCase__, UpperCamelCase__     )
   def 	__UpperCAmelCase   (      self      :			Union[str, Any]     )  ->			List[Any]:
        # Tests that all call wrap to encode_plus and batch_encode_plus
        _A    		=  self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()     )
        # create three inputs of length 800, 1000, and 1200
        _A    		=  [floats_list((1, x)     )[0] for x in range(8_00, 14_00, 2_00     )]
        _A    		=  [np.asarray(UpperCamelCase__     ) for speech_input in speech_inputs]

        # Test feature size
        _A    		=  feature_extractor(UpperCamelCase__, padding='max_length', return_tensors='np'     ).input_features
        self.assertTrue(input_features.ndim == 3     )
        self.assertTrue(input_features.shape[-1] == feature_extractor.nb_max_frames     )
        self.assertTrue(input_features.shape[-2] == feature_extractor.feature_size     )

        # Test not batched input
        _A    		=  feature_extractor(speech_inputs[0], return_tensors='np'     ).input_features
        _A    		=  feature_extractor(np_speech_inputs[0], return_tensors='np'     ).input_features
        self.assertTrue(np.allclose(UpperCamelCase__, UpperCamelCase__, atol=1e-3     )     )

        # Test batched
        _A    		=  feature_extractor(UpperCamelCase__, return_tensors='np'     ).input_features
        _A    		=  feature_extractor(UpperCamelCase__, return_tensors='np'     ).input_features
        for enc_seq_a, enc_seq_a in zip(UpperCamelCase__, UpperCamelCase__     ):
             self.assertTrue(np.allclose(UpperCamelCase__, UpperCamelCase__, atol=1e-3     )     )

        # Test 2-D numpy arrays are batched.
        _A    		=  [floats_list((1, x)     )[0] for x in (8_00, 8_00, 8_00)]
        _A    		=  np.asarray(UpperCamelCase__     )
        _A    		=  feature_extractor(UpperCamelCase__, return_tensors='np'     ).input_features
        _A    		=  feature_extractor(UpperCamelCase__, return_tensors='np'     ).input_features
        for enc_seq_a, enc_seq_a in zip(UpperCamelCase__, UpperCamelCase__     ):
             self.assertTrue(np.allclose(UpperCamelCase__, UpperCamelCase__, atol=1e-3     )     )

        # Test truncation required
        _A    		=  [floats_list((1, x)     )[0] for x in range(2_00, (feature_extractor.n_samples + 5_00), 2_00     )]
        _A    		=  [np.asarray(UpperCamelCase__     ) for speech_input in speech_inputs]

        _A    		=  [x[: feature_extractor.n_samples] for x in speech_inputs]
        _A    		=  [np.asarray(UpperCamelCase__     ) for speech_input in speech_inputs_truncated]

        _A    		=  feature_extractor(UpperCamelCase__, return_tensors='np'     ).input_features
        _A    		=  feature_extractor(UpperCamelCase__, return_tensors='np'     ).input_features
        for enc_seq_a, enc_seq_a in zip(UpperCamelCase__, UpperCamelCase__     ):
             self.assertTrue(np.allclose(UpperCamelCase__, UpperCamelCase__, atol=1e-3     )     )
   def 	__UpperCAmelCase   (      self      :			Any     )  ->			Any:
        import torch

        _A    		=  self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()     )
        _A    		=  np.random.rand(1_00, 32     ).astype(np.floataa     )
        _A    		=  np_speech_inputs.tolist()

        for inputs in [py_speech_inputs, np_speech_inputs]:
             _A    		=  feature_extractor.pad([{'input_features': inputs}], return_tensors='np'     )
             self.assertTrue(np_processed.input_features.dtype == np.floataa     )
             _A    		=  feature_extractor.pad([{'input_features': inputs}], return_tensors='pt'     )
             self.assertTrue(pt_processed.input_features.dtype == torch.floataa     )
   def 	__UpperCAmelCase   (      self      :			int, UpperCamelCase__      :			int     )  ->			Optional[Any]:
        _A    		=  load_dataset('hf-internal-testing/librispeech_asr_dummy', 'clean', split='validation'     )
        # automatic decoding with librispeech
        _A    		=  ds.sort('id'     ).select(range(UpperCamelCase__     )     )[:num_samples]['audio']

        return [x["array"] for x in speech_samples]
   def 	__UpperCAmelCase   (      self      :			Dict     )  ->			List[Any]:
        # fmt: off
        _A    		=  torch.tensor(
            [
                0.1_193, -0.0_946, -0.1_098, -0.0_196, 0.0_225, -0.0_690, -0.1_736, 0.0_951,
                0.0_971, -0.0_817, -0.0_702, 0.0_162, 0.0_260, 0.0_017, -0.0_192, -0.1_678,
                0.0_709, -0.1_867, -0.0_655, -0.0_274, -0.0_234, -0.1_884, -0.0_516, -0.0_554,
                -0.0_274, -0.1_425, -0.1_423, 0.0_837, 0.0_377, -0.0_854
            ]     )
        # fmt: on

        _A    		=  self._load_datasamples(1     )
        _A    		=  WhisperFeatureExtractor()
        _A    		=  feature_extractor(UpperCamelCase__, return_tensors='pt'     ).input_features
        self.assertEqual(input_features.shape, (1, 80, 30_00)     )
        self.assertTrue(torch.allclose(input_features[0, 0, :30], UpperCamelCase__, atol=1e-4     )     )

   def 	__UpperCAmelCase   (      self      :			int     )  ->			Any:
        _A    		=  self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()     )
        _A    		=  self._load_datasamples(1     )[0]
        _A    		=  ((audio - audio.min()) / (audio.max() - audio.min())) * 6_55_35  # Rescale to [0, 65535] to show issue
        _A    		=  feat_extract.zero_mean_unit_var_norm([audio], attention_mask=UpperCamelCase__     )[0]

        self.assertTrue(np.all(np.mean(UpperCamelCase__     ) < 1e-3     )     )
        self.assertTrue(np.all(np.abs(np.var(UpperCamelCase__     ) - 1     ) < 1e-3     )     )



