
"""simple docstring"""




import logging
import os
import sys
from dataclasses import dataclass, field
from importlib import import_module
from typing import Dict, List, Optional, Tuple

import numpy as np
from seqeval.metrics import accuracy_score, fa_score, precision_score, recall_score
from torch import nn
from utils_ner import Split, TokenClassificationDataset, TokenClassificationTask

import transformers
from transformers import (
    AutoConfig,
    AutoModelForTokenClassification,
    AutoTokenizer,
    DataCollatorWithPadding,
    EvalPrediction,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    set_seed,
)
from transformers.trainer_utils import is_main_process


UpperCAmelCase__      :    Union[str, Any]    							= logging.getLogger(__name__)



@dataclass
class 	lowerCAmelCase_						:


				"""simple docstring"""



				__UpperCamelCase      :						str					       =   field(
				    metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''}  )
				__UpperCamelCase      :						Optional[str]					       =   field(
				    default=a__ ,					metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''}  )
				__UpperCamelCase      :						Optional[str]					       =   field(
				    default='''NER''' ,					metadata={'''help''': '''Task type to fine tune in training (e.g. NER, POS, etc)'''}  )
				__UpperCamelCase      :						Optional[str]					       =   field(
				    default=a__ ,					metadata={'''help''': '''Pretrained tokenizer name or path if not the same as model_name'''}  )
				__UpperCamelCase      :						bool					       =   field(default=a__ ,					metadata={'''help''': '''Set this flag to use fast tokenization.'''}  )
				# If you want to tweak more attributes on your tokenizer, you should do it in a distinct script,
				# or just modify its tokenizer_config.json.
				__UpperCamelCase      :						Optional[str]					       =   field(
				    default=a__ ,					metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''} ,					)






@dataclass
class 	lowerCAmelCase_						:


				"""simple docstring"""



				__UpperCamelCase      :						str					       =   field(
				    metadata={'''help''': '''The input data dir. Should contain the .txt files for a CoNLL-2003-formatted task.'''}  )
				__UpperCamelCase      :						Optional[str]					       =   field(
				    default=a__ ,					metadata={'''help''': '''Path to a file containing all labels. If not specified, CoNLL-2003 labels are used.'''} ,					)
				__UpperCamelCase      :						int					       =   field(
				    default=128 ,					metadata={
				        '''help''': (
				            '''The maximum total input sequence length after tokenization. Sequences longer '''
				            '''than this will be truncated, sequences shorter will be padded.'''
				        )
				    } ,					)
				__UpperCamelCase      :						bool					       =   field(
				    default=a__ ,					metadata={'''help''': '''Overwrite the cached training and evaluation sets'''}  )






def lowercase_  (   ):
							# 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.

							SCREAMING_SNAKE_CASE__						:       Optional[int]           =   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.
														SCREAMING_SNAKE_CASE__      ,      SCREAMING_SNAKE_CASE__      ,      SCREAMING_SNAKE_CASE__						:       List[Any]           =   parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]						)						)
							else:
														SCREAMING_SNAKE_CASE__      ,      SCREAMING_SNAKE_CASE__      ,      SCREAMING_SNAKE_CASE__						:       str           =   parser.parse_args_into_dataclasses()

							if (
							    os.path.exists(training_args.output_dir						)
							    and os.listdir(training_args.output_dir						)
							    and training_args.do_train
							    and not training_args.overwrite_output_dir
							):
														raise ValueError(
														    f'''Output directory ({training_args.output_dir}) already exists and is not empty. Use'''
														    """ --overwrite_output_dir to overcome."""						)

							SCREAMING_SNAKE_CASE__						:       Any           =   import_module("""tasks"""						)
							try:
														SCREAMING_SNAKE_CASE__						:       int           =   getattr(_snake_case   ,model_args.task_type						)
														SCREAMING_SNAKE_CASE__						:       TokenClassificationTask           =   token_classification_task_clazz()
							except AttributeError:
														raise ValueError(
														    f'''Task {model_args.task_type} needs to be defined as a TokenClassificationTask subclass in {module}. '''
														    f'''Available tasks classes are: {TokenClassificationTask.__subclasses__()}'''						)

							# Setup logging
							logging.basicConfig(
							    format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s"""   ,datefmt="""%m/%d/%Y %H:%M:%S"""   ,level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN   ,)
							logger.warning(
							    """Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s"""   ,training_args.local_rank   ,training_args.device   ,training_args.n_gpu   ,bool(training_args.local_rank != -1						)   ,training_args.fpaa   ,)
							# Set the verbosity to info of the Transformers logger (on main process only):
							if is_main_process(training_args.local_rank						):
														transformers.utils.logging.set_verbosity_info()
														transformers.utils.logging.enable_default_handler()
														transformers.utils.logging.enable_explicit_format()
							logger.info("""Training/evaluation parameters %s"""   ,_snake_case						)

							# Set seed
							set_seed(training_args.seed						)

							# Prepare CONLL-2003 task
							SCREAMING_SNAKE_CASE__						:       Dict           =   token_classification_task.get_labels(data_args.labels						)
							SCREAMING_SNAKE_CASE__						:       Dict[int, str]           =   dict(enumerate(_snake_case						)						)
							SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   len(_snake_case						)

							# Load pretrained model and tokenizer
							#
							# Distributed training:
							# The .from_pretrained methods guarantee that only one local process can concurrently
							# download model & vocab.

							SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   AutoConfig.from_pretrained(
							    model_args.config_name if model_args.config_name else model_args.model_name_or_path   ,num_labels=_snake_case   ,idalabel=_snake_case   ,labelaid={label: i for i, label in enumerate(_snake_case						)}   ,cache_dir=model_args.cache_dir   ,)
							SCREAMING_SNAKE_CASE__						:       str           =   AutoTokenizer.from_pretrained(
							    model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path   ,cache_dir=model_args.cache_dir   ,use_fast=model_args.use_fast   ,)
							SCREAMING_SNAKE_CASE__						:       List[str]           =   AutoModelForTokenClassification.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   ,)

							# Get datasets
							SCREAMING_SNAKE_CASE__						:       Tuple           =   (
							    TokenClassificationDataset(
							        token_classification_task=_snake_case   ,data_dir=data_args.data_dir   ,tokenizer=_snake_case   ,labels=_snake_case   ,model_type=config.model_type   ,max_seq_length=data_args.max_seq_length   ,overwrite_cache=data_args.overwrite_cache   ,mode=Split.train   ,)
							    if training_args.do_train
							    else None
							)
							SCREAMING_SNAKE_CASE__						:       Any           =   (
							    TokenClassificationDataset(
							        token_classification_task=_snake_case   ,data_dir=data_args.data_dir   ,tokenizer=_snake_case   ,labels=_snake_case   ,model_type=config.model_type   ,max_seq_length=data_args.max_seq_length   ,overwrite_cache=data_args.overwrite_cache   ,mode=Split.dev   ,)
							    if training_args.do_eval
							    else None
							)

							def align_predictions(_snake_case   ,_snake_case						) -> Tuple[List[int], List[int]]:
														SCREAMING_SNAKE_CASE__						:       Dict           =   np.argmax(_snake_case   ,axis=2						)

														SCREAMING_SNAKE_CASE__      ,      SCREAMING_SNAKE_CASE__						:       Any           =   preds.shape

														SCREAMING_SNAKE_CASE__						:       int           =   [[] for _ in range(_snake_case						)]
														SCREAMING_SNAKE_CASE__						:       List[str]           =   [[] for _ in range(_snake_case						)]

														for i in range(_snake_case						):
																					for j in range(_snake_case						):
																												if label_ids[i, j] != nn.CrossEntropyLoss().ignore_index:
																																			out_label_list[i].append(label_map[label_ids[i][j]]						)
																																			preds_list[i].append(label_map[preds[i][j]]						)

														return preds_list, out_label_list

							def compute_metrics(_snake_case						) -> Dict:
														SCREAMING_SNAKE_CASE__      ,      SCREAMING_SNAKE_CASE__						:       List[Any]           =   align_predictions(p.predictions   ,p.label_ids						)
														return {
														    "accuracy_score": accuracy_score(_snake_case   ,_snake_case						),
														    "precision": precision_score(_snake_case   ,_snake_case						),
														    "recall": recall_score(_snake_case   ,_snake_case						),
														    "f1": fa_score(_snake_case   ,_snake_case						),
														}

							# Data collator
							SCREAMING_SNAKE_CASE__						:       List[Any]           =   DataCollatorWithPadding(_snake_case   ,pad_to_multiple_of=8						) if training_args.fpaa else None

							# Initialize our Trainer
							SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   Trainer(
							    model=_snake_case   ,args=_snake_case   ,train_dataset=_snake_case   ,eval_dataset=_snake_case   ,compute_metrics=_snake_case   ,data_collator=_snake_case   ,)

							# Training
							if training_args.do_train:
														trainer.train(
														    model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path						) else None						)
														trainer.save_model()
														# For convenience, we also re-save the tokenizer to the same directory,
														# so that you can share your model easily on huggingface.co/models =)
														if trainer.is_world_process_zero():
																					tokenizer.save_pretrained(training_args.output_dir						)

    # Evaluation
							SCREAMING_SNAKE_CASE__						:       Tuple           =   {}
							if training_args.do_eval:
														logger.info("""*** Evaluate ***"""						)

														SCREAMING_SNAKE_CASE__						:       Dict           =   trainer.evaluate()

														SCREAMING_SNAKE_CASE__						:       Optional[int]           =   os.path.join(training_args.output_dir   ,"""eval_results.txt"""						)
														if trainer.is_world_process_zero():
																					with open(_snake_case   ,"""w"""						) as writer:
																												logger.info("""***** Eval results *****"""						)
																												for key, value in result.items():
																																			logger.info("""  %s = %s"""   ,_snake_case   ,_snake_case						)
																																			writer.write("""%s = %s\n""" % (key, value)						)

																					results.update(_snake_case						)

    # Predict
							if training_args.do_predict:
														SCREAMING_SNAKE_CASE__						:       List[str]           =   TokenClassificationDataset(
														    token_classification_task=_snake_case   ,data_dir=data_args.data_dir   ,tokenizer=_snake_case   ,labels=_snake_case   ,model_type=config.model_type   ,max_seq_length=data_args.max_seq_length   ,overwrite_cache=data_args.overwrite_cache   ,mode=Split.test   ,)

														SCREAMING_SNAKE_CASE__      ,      SCREAMING_SNAKE_CASE__      ,      SCREAMING_SNAKE_CASE__						:       Dict           =   trainer.predict(_snake_case						)
														SCREAMING_SNAKE_CASE__      ,      SCREAMING_SNAKE_CASE__						:       Optional[int]           =   align_predictions(_snake_case   ,_snake_case						)

														SCREAMING_SNAKE_CASE__						:       List[Any]           =   os.path.join(training_args.output_dir   ,"""test_results.txt"""						)
														if trainer.is_world_process_zero():
																					with open(_snake_case   ,"""w"""						) as writer:
																												for key, value in metrics.items():
																																			logger.info("""  %s = %s"""   ,_snake_case   ,_snake_case						)
																																			writer.write("""%s = %s\n""" % (key, value)						)

        # Save predictions
														SCREAMING_SNAKE_CASE__						:       Dict           =   os.path.join(training_args.output_dir   ,"""test_predictions.txt"""						)
														if trainer.is_world_process_zero():
																					with open(_snake_case   ,"""w"""						) as writer:
																												with open(os.path.join(data_args.data_dir   ,"""test.txt"""						)   ,"""r"""						) as f:
																																			token_classification_task.write_predictions_to_file(_snake_case   ,_snake_case   ,_snake_case						)

							return results

def lowercase_  (   _snake_case						):
							# For xla_spawn (TPUs)
							main()


if __name__ == "__main__":
					main()










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

__A        =    random.Random()







def 			lowerCamelCase_		(       UpperCamelCase__				:      Optional[int] ,     UpperCamelCase__				:      List[str]=1.0 ,     UpperCamelCase__				:      Union[str, Any]=None ,     UpperCamelCase__				:      Optional[int]=None      )     ->       Optional[Any]:





				"""simple docstring"""


				if rng is None:
								__lowerCamelCase		       =    global_rng

				__lowerCamelCase		       =    []
				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     __lowerCAmelCase (       unittest.TestCase ):

			"""simple docstring"""







			def __init__(      self			,						lowerCamelCase__			,						lowerCamelCase__=7			,						lowerCamelCase__=400			,						lowerCamelCase__=2_000			,						lowerCamelCase__=10			,						lowerCamelCase__=160			,						lowerCamelCase__=8			,						lowerCamelCase__=0.0			,						lowerCamelCase__=4_000			,						lowerCamelCase__=False			,						lowerCamelCase__=True			,						)    ->      List[str]:



							'''simple docstring'''
							__lowerCamelCase		       =    parent
							__lowerCamelCase		       =    batch_size
							__lowerCamelCase		       =    min_seq_length
							__lowerCamelCase		       =    max_seq_length
							__lowerCamelCase		       =    (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
							__lowerCamelCase		       =    padding_value
							__lowerCamelCase		       =    sampling_rate
							__lowerCamelCase		       =    return_attention_mask
							__lowerCamelCase		       =    do_normalize
							__lowerCamelCase		       =    feature_size
							__lowerCamelCase		       =    chunk_length
							__lowerCamelCase		       =    hop_length


			def       lowercase_    (      self							)    ->      Any:



							'''simple docstring'''
							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       lowercase_    (      self			,						lowerCamelCase__=False			,						lowerCamelCase__=False							)    ->      Optional[int]:



							'''simple docstring'''
							def _flatten(lowerCamelCase__							):
											return list(itertools.chain(*lowerCamelCase__							)							)

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







@require_torch
@require_torchaudio
class     __lowerCAmelCase (       __magic_name__			,				unittest.TestCase ):

			"""simple docstring"""







			snake_case_   		=       WhisperFeatureExtractor if is_speech_available() else None


			def       lowercase_    (      self							)    ->      Any:



							'''simple docstring'''
							__lowerCamelCase		       =    WhisperFeatureExtractionTester(self							)


			def       lowercase_    (      self							)    ->      List[str]:



							'''simple docstring'''
							__lowerCamelCase		       =    self.feature_extraction_class(**self.feat_extract_dict							)

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

							__lowerCamelCase		       =    feat_extract_first.to_dict()
							__lowerCamelCase		       =    feat_extract_second.to_dict()
							__lowerCamelCase		       =    feat_extract_first.mel_filters
							__lowerCamelCase		       =    feat_extract_second.mel_filters
							self.assertTrue(np.allclose(lowerCamelCase__			,						lowerCamelCase__							)							)
							self.assertEqual(lowerCamelCase__			,						lowerCamelCase__							)


			def       lowercase_    (      self							)    ->      Dict:



							'''simple docstring'''
							__lowerCamelCase		       =    self.feature_extraction_class(**self.feat_extract_dict							)

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

							__lowerCamelCase		       =    feat_extract_first.to_dict()
							__lowerCamelCase		       =    feat_extract_second.to_dict()
							__lowerCamelCase		       =    feat_extract_first.mel_filters
							__lowerCamelCase		       =    feat_extract_second.mel_filters
							self.assertTrue(np.allclose(lowerCamelCase__			,						lowerCamelCase__							)							)
							self.assertEqual(lowerCamelCase__			,						lowerCamelCase__							)


			def       lowercase_    (      self							)    ->      Dict:



							'''simple docstring'''
							# Tests that all call wrap to encode_plus and batch_encode_plus
							__lowerCamelCase		       =    self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()							)
							# create three inputs of length 800, 1000, and 1200
							__lowerCamelCase		       =    [floats_list((1, x)							)[0] for x in range(800			,						1_400			,						200							)]
							__lowerCamelCase		       =    [np.asarray(lowerCamelCase__							) for speech_input in speech_inputs]

							# Test feature size
							__lowerCamelCase		       =    feature_extractor(lowerCamelCase__			,						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
							__lowerCamelCase		       =    feature_extractor(speech_inputs[0]			,						return_tensors='np'							).input_features
							__lowerCamelCase		       =    feature_extractor(np_speech_inputs[0]			,						return_tensors='np'							).input_features
							self.assertTrue(np.allclose(lowerCamelCase__			,						lowerCamelCase__			,						atol=1e-3							)							)

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

							# Test 2-D numpy arrays are batched.
							__lowerCamelCase		       =    [floats_list((1, x)							)[0] for x in (800, 800, 800)]
							__lowerCamelCase		       =    np.asarray(lowerCamelCase__							)
							__lowerCamelCase		       =    feature_extractor(lowerCamelCase__			,						return_tensors='np'							).input_features
							__lowerCamelCase		       =    feature_extractor(lowerCamelCase__			,						return_tensors='np'							).input_features
							for enc_seq_a, enc_seq_a in zip(lowerCamelCase__			,						lowerCamelCase__							):
											self.assertTrue(np.allclose(lowerCamelCase__			,						lowerCamelCase__			,						atol=1e-3							)							)

							# Test truncation required
							__lowerCamelCase		       =    [floats_list((1, x)							)[0] for x in range(200			,						(feature_extractor.n_samples + 500)			,						200							)]
							__lowerCamelCase		       =    [np.asarray(lowerCamelCase__							) for speech_input in speech_inputs]

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

							__lowerCamelCase		       =    feature_extractor(lowerCamelCase__			,						return_tensors='np'							).input_features
							__lowerCamelCase		       =    feature_extractor(lowerCamelCase__			,						return_tensors='np'							).input_features
							for enc_seq_a, enc_seq_a in zip(lowerCamelCase__			,						lowerCamelCase__							):
											self.assertTrue(np.allclose(lowerCamelCase__			,						lowerCamelCase__			,						atol=1e-3							)							)


			def       lowercase_    (      self							)    ->      List[str]:



							'''simple docstring'''
							import torch

							__lowerCamelCase		       =    self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()							)
							__lowerCamelCase		       =    np.random.rand(100			,						32							).astype(np.floataa							)
							__lowerCamelCase		       =    np_speech_inputs.tolist()

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


			def       lowercase_    (      self			,						lowerCamelCase__							)    ->      List[str]:



							'''simple docstring'''
							__lowerCamelCase		       =    load_dataset('hf-internal-testing/librispeech_asr_dummy'			,						'clean'			,						split='validation'							)
							# automatic decoding with librispeech
							__lowerCamelCase		       =    ds.sort('id'							).select(range(lowerCamelCase__							)							)[:num_samples]['audio']

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


			def       lowercase_    (      self							)    ->      Tuple:



							'''simple docstring'''
							# fmt: off
							__lowerCamelCase		       =    torch.tensor(
							    [
							        0.11_93, -0.09_46, -0.10_98, -0.01_96, 0.02_25, -0.06_90, -0.17_36, 0.09_51,
							        0.09_71, -0.08_17, -0.07_02, 0.01_62, 0.02_60, 0.00_17, -0.01_92, -0.16_78,
							        0.07_09, -0.18_67, -0.06_55, -0.02_74, -0.02_34, -0.18_84, -0.05_16, -0.05_54,
							        -0.02_74, -0.14_25, -0.14_23, 0.08_37, 0.03_77, -0.08_54
							    ]							)
							# fmt: on

							__lowerCamelCase		       =    self._load_datasamples(1							)
							__lowerCamelCase		       =    WhisperFeatureExtractor()
							__lowerCamelCase		       =    feature_extractor(lowerCamelCase__			,						return_tensors='pt'							).input_features
							self.assertEqual(input_features.shape			,						(1, 80, 3_000)							)
							self.assertTrue(torch.allclose(input_features[0, 0, :30]			,						lowerCamelCase__			,						atol=1e-4							)							)






			def       lowercase_    (      self							)    ->      List[Any]:



							'''simple docstring'''
							__lowerCamelCase		       =    self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()							)
							__lowerCamelCase		       =    self._load_datasamples(1							)[0]
							__lowerCamelCase		       =    ((audio - audio.min()) / (audio.max() - audio.min())) * 65_535  # Rescale to [0, 65535] to show issue
							__lowerCamelCase		       =    feat_extract.zero_mean_unit_var_norm([audio]			,						attention_mask=lowerCamelCase__							)[0]

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


