



"""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
__snake_case							:     Optional[int]   =					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')

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


def     _lowercase       (						__snake_case			)		->     Optional[Any]:
			with open(snake_case_      ,"rb"			) as f:
						__lowerCAmelCase   :						List[Any]				= Image.open(snake_case_			)
						return im.convert("RGB"			)



@dataclass
class     A__     :




	'''simple docstring'''





	SCREAMING_SNAKE_CASE					   =			field(
	    default=UpperCamelCase_						,	metadata={
	        'help': 'Name of a dataset from the hub (could be your own, possibly private dataset hosted on the hub).'
	    }						,	)
	SCREAMING_SNAKE_CASE					   =			field(
	    default=UpperCamelCase_						,	metadata={'help': 'The configuration name of the dataset to use (via the datasets library).'}							)
	SCREAMING_SNAKE_CASE					   =			field(default=UpperCamelCase_						,	metadata={'help': 'A folder containing the training data.'}							)
	SCREAMING_SNAKE_CASE					   =			field(default=UpperCamelCase_						,	metadata={'help': 'A folder containing the validation data.'}							)
	SCREAMING_SNAKE_CASE					   =			field(
	    default=0.15						,	metadata={'help': 'Percent to split off of train for validation.'}							)
	SCREAMING_SNAKE_CASE					   =			field(
	    default=UpperCamelCase_						,	metadata={
	        'help': (
	            'For debugging purposes or quicker training, truncate the number of training examples to this '
	            'value if set.'
	        )
	    }						,	)
	SCREAMING_SNAKE_CASE					   =			field(
	    default=UpperCamelCase_						,	metadata={
	        'help': (
	            'For debugging purposes or quicker training, truncate the number of evaluation examples to this '
	            'value if set.'
	        )
	    }						,	)

	def 							_SCREAMING_SNAKE_CASE     (  self:	Optional[int])       -> Tuple:

				"""simple docstring"""

				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     A__     :




	'''simple docstring'''





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






def     _lowercase       (						__snake_case			)		->     List[Any]:
			__lowerCAmelCase   :						List[str]				= torch.stack([example["pixel_values"] for example in examples]			)
			__lowerCAmelCase   :						List[Any]				= torch.tensor([example["labels"] for example in examples]			)
			return {"pixel_values": pixel_values, "labels": labels}


def     _lowercase       (						)		->     List[str]:
			# 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.

			__lowerCAmelCase   :						Tuple				= 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.
						__lowerCAmelCase   :						Optional[int]				= parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]			)			)
			else:
						__lowerCAmelCase   :						Optional[int]				= 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()

			__lowerCAmelCase   :						Tuple				= 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.
			__lowerCAmelCase   :						int				= None
			if os.path.isdir(training_args.output_dir			) and training_args.do_train and not training_args.overwrite_output_dir:
						__lowerCAmelCase   :						int				= 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:
						__lowerCAmelCase   :						List[str]				= 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:
						__lowerCAmelCase   :						Any				= {}
						if data_args.train_dir is not None:
									__lowerCAmelCase   :						str				= os.path.join(data_args.train_dir      ,"**"			)
						if data_args.validation_dir is not None:
									__lowerCAmelCase   :						Tuple				= os.path.join(data_args.validation_dir      ,"**"			)
						__lowerCAmelCase   :						int				= 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.
			__lowerCAmelCase   :						Tuple				= 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:
						__lowerCAmelCase   :						Optional[int]				= dataset['''train'''].train_test_split(data_args.train_val_split			)
						__lowerCAmelCase   :						List[Any]				= split['''train''']
						__lowerCAmelCase   :						List[Any]				= split['''test''']

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

			# Load the accuracy metric from the datasets package
			__lowerCAmelCase   :						List[Any]				= 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			):
						return metric.compute(predictions=np.argmax(p.predictions      ,axis=1			)      ,references=p.label_ids			)

			__lowerCAmelCase   :						int				= 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      ,)
			__lowerCAmelCase   :						int				= 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      ,)
			__lowerCAmelCase   :						Optional[int]				= 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:
						__lowerCAmelCase   :						Optional[Any]				= image_processor.size['''shortest_edge''']
			else:
						__lowerCAmelCase   :						Optional[Any]				= (image_processor.size['''height'''], image_processor.size['''width'''])
			__lowerCAmelCase   :						int				= Normalize(mean=image_processor.image_mean      ,std=image_processor.image_std			)
			__lowerCAmelCase   :						Tuple				= Compose(
			    [
			        RandomResizedCrop(snake_case_			),
			        RandomHorizontalFlip(),
			        ToTensor(),
			        normalize,
			    ]			)
			__lowerCAmelCase   :						Union[str, Any]				= Compose(
			    [
			        Resize(snake_case_			),
			        CenterCrop(snake_case_			),
			        ToTensor(),
			        normalize,
			    ]			)

			def train_transforms(__snake_case			):
						__lowerCAmelCase   :						Any				= [
						    _train_transforms(pil_img.convert("RGB"			)			) for pil_img in example_batch['''image''']
						]
						return example_batch

			def val_transforms(__snake_case			):
						__lowerCAmelCase   :						int				= [_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:
									__lowerCAmelCase   :						Optional[Any]				= (
									    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:
									__lowerCAmelCase   :						List[str]				= (
									    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
			__lowerCAmelCase   :						Optional[Any]				= 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:
						__lowerCAmelCase   :						List[str]				= None
						if training_args.resume_from_checkpoint is not None:
									__lowerCAmelCase   :						str				= training_args.resume_from_checkpoint
						elif last_checkpoint is not None:
									__lowerCAmelCase   :						List[Any]				= last_checkpoint
						__lowerCAmelCase   :						str				= 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:
						__lowerCAmelCase   :						Optional[int]				= trainer.evaluate()
						trainer.log_metrics("eval"      ,snake_case_			)
						trainer.save_metrics("eval"      ,snake_case_			)

			# Write model card and (optionally) push to hub
			__lowerCAmelCase   :						List[Any]				= {
			    '''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 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

__snake_case							:     Union[str, Any]   =					random.Random()


def     _lowercase       (						__snake_case      ,__snake_case=1.0      ,__snake_case=None      ,__snake_case=None			)		->     List[Any]:
			if rng is None:
						__lowerCAmelCase   :						Dict				= global_rng

			__lowerCAmelCase   :						Tuple				= []
			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     A__     (						unittest.TestCase							):




	'''simple docstring'''





	def __init__(  self:	Optional[Any] ,    _SCREAMING_SNAKE_CASE:	Any ,    _SCREAMING_SNAKE_CASE:	List[Any]=7 ,    _SCREAMING_SNAKE_CASE:	int=400 ,    _SCREAMING_SNAKE_CASE:	List[str]=2000 ,    _SCREAMING_SNAKE_CASE:	Optional[Any]=24 ,    _SCREAMING_SNAKE_CASE:	Dict=24 ,    _SCREAMING_SNAKE_CASE:	Optional[int]=0.0 ,    _SCREAMING_SNAKE_CASE:	Any=1_6000 ,    _SCREAMING_SNAKE_CASE:	int=True ,    _SCREAMING_SNAKE_CASE:	Union[str, Any]=True ,    )       -> List[Any]:

				"""simple docstring"""

				__lowerCAmelCase   :						Any				= parent
				__lowerCAmelCase   :						str				= batch_size
				__lowerCAmelCase   :						List[str]				= min_seq_length
				__lowerCAmelCase   :						Any				= max_seq_length
				__lowerCAmelCase   :						Dict				= (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
				__lowerCAmelCase   :						Union[str, Any]				= feature_size
				__lowerCAmelCase   :						int				= num_mel_bins
				__lowerCAmelCase   :						Optional[Any]				= padding_value
				__lowerCAmelCase   :						List[Any]				= sampling_rate
				__lowerCAmelCase   :						Dict				= return_attention_mask
				__lowerCAmelCase   :						int				= do_normalize

	def 							_SCREAMING_SNAKE_CASE     (  self:	Any)       -> Optional[int]:

				"""simple docstring"""

				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 							_SCREAMING_SNAKE_CASE     (  self:	Tuple ,    _SCREAMING_SNAKE_CASE:	Tuple=False ,    _SCREAMING_SNAKE_CASE:	int=False)       -> Optional[int]:

				"""simple docstring"""

				def _flatten(_SCREAMING_SNAKE_CASE:	Optional[Any]):
							return list(itertools.chain(*_SCREAMING_SNAKE_CASE))

				if equal_length:
							__lowerCAmelCase   :						Tuple				= [floats_list((self.max_seq_length, self.feature_size)) for _ in range(self.batch_size)]
				else:
							# make sure that inputs increase in size
							__lowerCAmelCase   :						List[Any]				= [
							    floats_list((x, self.feature_size))
							    for x in range(self.min_seq_length ,    self.max_seq_length ,    self.seq_length_diff)
							]
				if numpify:
							__lowerCAmelCase   :						Any				= [np.asarray(_SCREAMING_SNAKE_CASE) for x in speech_inputs]
				return speech_inputs






@require_torch
@require_torchaudio
class     A__     (						__SCREAMING_SNAKE_CASE						,	unittest.TestCase							):




	'''simple docstring'''





	SCREAMING_SNAKE_CASE					   =			SpeechaTextFeatureExtractor if is_speech_available() else None

	def 							_SCREAMING_SNAKE_CASE     (  self:	List[Any])       -> Tuple:

				"""simple docstring"""

				__lowerCAmelCase   :						Tuple				= SpeechaTextFeatureExtractionTester(self)

	def 							_SCREAMING_SNAKE_CASE     (  self:	Optional[Any] ,    _SCREAMING_SNAKE_CASE:	Optional[Any])       -> str:

				"""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 							_SCREAMING_SNAKE_CASE     (  self:	Union[str, Any])       -> int:

				"""simple docstring"""

				__lowerCAmelCase   :						Dict				= self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
				# create three inputs of length 800, 1000, and 1200
				__lowerCAmelCase   :						Tuple				= [floats_list((1, x))[0] for x in range(800 ,    1400 ,    200)]
				__lowerCAmelCase   :						Optional[int]				= [np.asarray(_SCREAMING_SNAKE_CASE) for speech_input in speech_inputs]

				# Test feature size
				__lowerCAmelCase   :						Optional[int]				= feature_extractor(_SCREAMING_SNAKE_CASE ,    padding=_SCREAMING_SNAKE_CASE ,    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
				__lowerCAmelCase   :						Optional[Any]				= feature_extractor(speech_inputs[0] ,    return_tensors="np").input_features
				__lowerCAmelCase   :						Any				= feature_extractor(np_speech_inputs[0] ,    return_tensors="np").input_features
				self.assertTrue(np.allclose(_SCREAMING_SNAKE_CASE ,    _SCREAMING_SNAKE_CASE ,    atol=1e-3))

				# Test batched
				__lowerCAmelCase   :						Tuple				= feature_extractor(_SCREAMING_SNAKE_CASE ,    return_tensors="np").input_features
				__lowerCAmelCase   :						Tuple				= feature_extractor(_SCREAMING_SNAKE_CASE ,    return_tensors="np").input_features
				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.
				__lowerCAmelCase   :						List[str]				= [floats_list((1, x))[0] for x in (800, 800, 800)]
				__lowerCAmelCase   :						Dict				= np.asarray(_SCREAMING_SNAKE_CASE)
				__lowerCAmelCase   :						Optional[int]				= feature_extractor(_SCREAMING_SNAKE_CASE ,    return_tensors="np").input_features
				__lowerCAmelCase   :						Optional[Any]				= feature_extractor(_SCREAMING_SNAKE_CASE ,    return_tensors="np").input_features
				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 							_SCREAMING_SNAKE_CASE     (  self:	Union[str, Any])       -> List[Any]:

				"""simple docstring"""

				__lowerCAmelCase   :						Tuple				= self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
				__lowerCAmelCase   :						Tuple				= [floats_list((1, x))[0] for x in range(800 ,    1400 ,    200)]

				__lowerCAmelCase   :						Optional[Any]				= ["longest", "max_length", "do_not_pad"]
				__lowerCAmelCase   :						str				= [None, 16, None]
				for max_length, padding in zip(_SCREAMING_SNAKE_CASE ,    _SCREAMING_SNAKE_CASE):
							__lowerCAmelCase   :						str				= feature_extractor(
							    _SCREAMING_SNAKE_CASE ,    padding=_SCREAMING_SNAKE_CASE ,    max_length=_SCREAMING_SNAKE_CASE ,    return_attention_mask=_SCREAMING_SNAKE_CASE)
							__lowerCAmelCase   :						Optional[Any]				= inputs.input_features
							__lowerCAmelCase   :						Optional[Any]				= inputs.attention_mask
							__lowerCAmelCase   :						Tuple				= [np.sum(_SCREAMING_SNAKE_CASE) 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 							_SCREAMING_SNAKE_CASE     (  self:	Any)       -> Optional[Any]:

				"""simple docstring"""

				__lowerCAmelCase   :						str				= self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
				__lowerCAmelCase   :						Any				= [floats_list((1, x))[0] for x in range(800 ,    1400 ,    200)]

				__lowerCAmelCase   :						Dict				= ["longest", "max_length", "do_not_pad"]
				__lowerCAmelCase   :						List[Any]				= [None, 16, None]
				for max_length, padding in zip(_SCREAMING_SNAKE_CASE ,    _SCREAMING_SNAKE_CASE):
							__lowerCAmelCase   :						List[str]				= feature_extractor(
							    _SCREAMING_SNAKE_CASE ,    max_length=_SCREAMING_SNAKE_CASE ,    padding=_SCREAMING_SNAKE_CASE ,    return_tensors="np" ,    return_attention_mask=_SCREAMING_SNAKE_CASE)
							__lowerCAmelCase   :						List[str]				= inputs.input_features
							__lowerCAmelCase   :						Dict				= inputs.attention_mask
							__lowerCAmelCase   :						Dict				= [np.sum(_SCREAMING_SNAKE_CASE) 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 							_SCREAMING_SNAKE_CASE     (  self:	str)       -> Any:

				"""simple docstring"""

				__lowerCAmelCase   :						str				= self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
				__lowerCAmelCase   :						str				= [floats_list((1, x))[0] for x in range(800 ,    1400 ,    200)]
				__lowerCAmelCase   :						str				= feature_extractor(
				    _SCREAMING_SNAKE_CASE ,    padding="max_length" ,    max_length=4 ,    truncation=_SCREAMING_SNAKE_CASE ,    return_tensors="np" ,    return_attention_mask=_SCREAMING_SNAKE_CASE ,    )
				__lowerCAmelCase   :						List[str]				= inputs.input_features
				__lowerCAmelCase   :						Dict				= inputs.attention_mask
				__lowerCAmelCase   :						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 							_SCREAMING_SNAKE_CASE     (  self:	int)       -> List[str]:

				"""simple docstring"""

				__lowerCAmelCase   :						Union[str, Any]				= self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
				__lowerCAmelCase   :						List[Any]				= [floats_list((1, x))[0] for x in range(800 ,    1400 ,    200)]
				__lowerCAmelCase   :						Any				= feature_extractor(
				    _SCREAMING_SNAKE_CASE ,    padding="longest" ,    max_length=4 ,    truncation=_SCREAMING_SNAKE_CASE ,    return_tensors="np" ,    return_attention_mask=_SCREAMING_SNAKE_CASE ,    )
				__lowerCAmelCase   :						List[str]				= inputs.input_features
				__lowerCAmelCase   :						Optional[Any]				= inputs.attention_mask
				__lowerCAmelCase   :						Optional[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, : 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))

				__lowerCAmelCase   :						List[str]				= [floats_list((1, x))[0] for x in range(800 ,    1400 ,    200)]
				__lowerCAmelCase   :						List[str]				= feature_extractor(
				    _SCREAMING_SNAKE_CASE ,    padding="longest" ,    max_length=16 ,    truncation=_SCREAMING_SNAKE_CASE ,    return_tensors="np" ,    return_attention_mask=_SCREAMING_SNAKE_CASE ,    )
				__lowerCAmelCase   :						Optional[Any]				= inputs.input_features
				__lowerCAmelCase   :						Optional[int]				= inputs.attention_mask
				__lowerCAmelCase   :						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 							_SCREAMING_SNAKE_CASE     (  self:	Any)       -> Dict:

				"""simple docstring"""

				import torch

				__lowerCAmelCase   :						Dict				= self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
				__lowerCAmelCase   :						List[str]				= np.random.rand(100 ,    32).astype(np.floataa)
				__lowerCAmelCase   :						List[Any]				= np_speech_inputs.tolist()

				for inputs in [py_speech_inputs, np_speech_inputs]:
							__lowerCAmelCase   :						Any				= feature_extractor.pad([{"input_features": inputs}] ,    return_tensors="np")
							self.assertTrue(np_processed.input_features.dtype == np.floataa)
							__lowerCAmelCase   :						List[Any]				= feature_extractor.pad([{"input_features": inputs}] ,    return_tensors="pt")
							self.assertTrue(pt_processed.input_features.dtype == torch.floataa)

	def 							_SCREAMING_SNAKE_CASE     (  self:	str ,    _SCREAMING_SNAKE_CASE:	List[Any])       -> Optional[Any]:

				"""simple docstring"""

				from datasets import load_dataset

				__lowerCAmelCase   :						Any				= load_dataset("hf-internal-testing/librispeech_asr_dummy" ,    "clean" ,    split="validation")
				# automatic decoding with librispeech
				__lowerCAmelCase   :						List[Any]				= ds.sort("id").select(range(_SCREAMING_SNAKE_CASE))[:num_samples]["audio"]

				return [x["array"] for x in speech_samples]







	def 							_SCREAMING_SNAKE_CASE     (  self:	List[str])       -> Any:

				"""simple docstring"""

				__lowerCAmelCase   :						str				= np.array([
				    -1.5745, -1.7713, -1.7020, -1.6069, -1.2250, -1.1105, -0.9072, -0.8241,
				    -1.2310, -0.8098, -0.3320, -0.4101, -0.7985, -0.4996, -0.8213, -0.9128,
				    -1.0420, -1.1286, -1.0440, -0.7999, -0.8405, -1.2275, -1.5443, -1.4625,
				])
				# fmt: on

				__lowerCAmelCase   :						str				= self._load_datasamples(1)
				__lowerCAmelCase   :						Any				= self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
				__lowerCAmelCase   :						List[str]				= feature_extractor(_SCREAMING_SNAKE_CASE ,    return_tensors="pt").input_features
				self.assertEquals(input_features.shape ,    (1, 584, 24))
				self.assertTrue(np.allclose(input_features[0, 0, :30] ,    _SCREAMING_SNAKE_CASE ,    atol=1e-4))