



'''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__:     Optional[Any]										=    random.Random()


def 				snake_case_  (      _lowerCAmelCase						:		Optional[int]     ,   _lowerCAmelCase						:		int=1.0     ,   _lowerCAmelCase						:		Dict=None     ,   _lowerCAmelCase						:		List[str]=None						)   ->     Tuple:



							if rng is None:
														UpperCAmelCase				: Dict											=	global_rng

							UpperCAmelCase				: str											=	[]
							for batch_idx in range(shape[0]						):
														values.append([]						)
														for _ in range(shape[1]						):
																					values[-1].append(rng.random() * scale						)

							return values

class        SCREAMING_SNAKE_CASE(							unittest.TestCase    ):







	"""simple docstring"""






	def __init__(		self    :  int	,    __snake_case    :  int	,    __snake_case    :  Any=7	,    __snake_case    :  str=400	,    __snake_case    :  List[str]=2000	,    __snake_case    :  Union[str, Any]=1	,    __snake_case    :  Optional[int]=0.0	,    __snake_case    :  List[Any]=16000	,    __snake_case    :  Optional[int]=True	,    __snake_case    :  Optional[Any]=True	,    )    ->	int:
								UpperCAmelCase				: Optional[int]											=	parent
								UpperCAmelCase				: List[Any]											=	batch_size
								UpperCAmelCase				: Tuple											=	min_seq_length
								UpperCAmelCase				: List[str]											=	max_seq_length
								UpperCAmelCase				: Union[str, Any]											=	(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
								UpperCAmelCase				: int											=	feature_size
								UpperCAmelCase				: Union[str, Any]											=	padding_value
								UpperCAmelCase				: str											=	sampling_rate
								UpperCAmelCase				: Dict											=	return_attention_mask
								UpperCAmelCase				: List[Any]											=	do_normalize







	def   A			(		self    :  Dict	)    ->	str:
								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   A			(		self    :  str	,    __snake_case    :  Dict=False	,    __snake_case    :  Optional[Any]=False	)    ->	Tuple:
								def _flatten(__snake_case    :  List[Any]	):
															return list(itertools.chain(*__UpperCamelCase	)	)

								if equal_length:
															UpperCAmelCase				: Optional[Any]											=	floats_list((self.batch_size, self.max_seq_length)	)
								else:
															# make sure that inputs increase in size
															UpperCAmelCase				: Optional[Any]											=	[
															    _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:
															UpperCAmelCase				: List[str]											=	[np.asarray(__UpperCamelCase	) for x in speech_inputs]

								return speech_inputs

class        SCREAMING_SNAKE_CASE(							A__							,	unittest.TestCase    ):







	"""simple docstring"""






	lowerCamelCase__    				= WavaVecaFeatureExtractor







	def   A			(		self    :  str	)    ->	Optional[Any]:
								UpperCAmelCase				: List[str]											=	WavaVecaFeatureExtractionTester(self	)







	def   A			(		self    :  Optional[Any]	,    __snake_case    :  str	)    ->	Dict:
								self.assertTrue(np.all(np.mean(__UpperCamelCase	,    axis=0	) < 1E-3	)	)
								self.assertTrue(np.all(np.abs(np.var(__UpperCamelCase	,    axis=0	) - 1	) < 1E-3	)	)







	def   A			(		self    :  Optional[Any]	)    ->	int:
								# Tests that all call wrap to encode_plus and batch_encode_plus
								UpperCAmelCase				: List[str]											=	self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()	)
								# create three inputs of length 800, 1000, and 1200
								UpperCAmelCase				: List[Any]											=	[floats_list((1, x)	)[0] for x in range(800	,    1400	,    200	)]
								UpperCAmelCase				: List[str]											=	[np.asarray(__UpperCamelCase	) for speech_input in speech_inputs]

								# Test not batched input
								UpperCAmelCase				: List[str]											=	feat_extract(speech_inputs[0]	,    return_tensors='''np'''	).input_values
								UpperCAmelCase				: Any											=	feat_extract(np_speech_inputs[0]	,    return_tensors='''np'''	).input_values
								self.assertTrue(np.allclose(__UpperCamelCase	,    __UpperCamelCase	,    atol=1E-3	)	)

								# Test batched
								UpperCAmelCase				: Optional[Any]											=	feat_extract(__UpperCamelCase	,    return_tensors='''np'''	).input_values
								UpperCAmelCase				: str											=	feat_extract(__UpperCamelCase	,    return_tensors='''np'''	).input_values
								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.
								UpperCAmelCase				: int											=	[floats_list((1, x)	)[0] for x in (800, 800, 800)]
								UpperCAmelCase				: str											=	np.asarray(__UpperCamelCase	)
								UpperCAmelCase				: str											=	feat_extract(__UpperCamelCase	,    return_tensors='''np'''	).input_values
								UpperCAmelCase				: Any											=	feat_extract(__UpperCamelCase	,    return_tensors='''np'''	).input_values
								for enc_seq_a, enc_seq_a in zip(__UpperCamelCase	,    __UpperCamelCase	):
															self.assertTrue(np.allclose(__UpperCamelCase	,    __UpperCamelCase	,    atol=1E-3	)	)







	def   A			(		self    :  Optional[Any]	)    ->	Any:
								UpperCAmelCase				: Optional[int]											=	self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()	)
								UpperCAmelCase				: Any											=	[floats_list((1, x)	)[0] for x in range(800	,    1400	,    200	)]

								UpperCAmelCase				: List[str]											=	['''longest''', '''max_length''', '''do_not_pad''']
								UpperCAmelCase				: str											=	[None, 1600, None]
								for max_length, padding in zip(__UpperCamelCase	,    __UpperCamelCase	):
															UpperCAmelCase				: Optional[Any]											=	feat_extract(__UpperCamelCase	,    padding=__UpperCamelCase	,    max_length=__UpperCamelCase	,    return_tensors='''np'''	)
															UpperCAmelCase				: Dict											=	processed.input_values

															self._check_zero_mean_unit_variance(input_values[0][:800]	)
															self.assertTrue(input_values[0][800:].sum() < 1E-6	)
															self._check_zero_mean_unit_variance(input_values[1][:1000]	)
															self.assertTrue(input_values[0][1000:].sum() < 1E-6	)
															self._check_zero_mean_unit_variance(input_values[2][:1200]	)







	def   A			(		self    :  Optional[int]	)    ->	Tuple:
								UpperCAmelCase				: int											=	self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()	)
								UpperCAmelCase				: List[Any]											=	range(800	,    1400	,    200	)
								UpperCAmelCase				: Dict											=	[floats_list((1, x)	)[0] for x in lengths]

								UpperCAmelCase				: List[str]											=	['''longest''', '''max_length''', '''do_not_pad''']
								UpperCAmelCase				: Union[str, Any]											=	[None, 1600, None]

								for max_length, padding in zip(__UpperCamelCase	,    __UpperCamelCase	):
															UpperCAmelCase				: Tuple											=	feat_extract(__UpperCamelCase	,    max_length=__UpperCamelCase	,    padding=__UpperCamelCase	)
															UpperCAmelCase				: List[str]											=	processed.input_values

															self._check_zero_mean_unit_variance(input_values[0][:800]	)
															self._check_zero_mean_unit_variance(input_values[1][:1000]	)
															self._check_zero_mean_unit_variance(input_values[2][:1200]	)







	def   A			(		self    :  Dict	)    ->	str:
								UpperCAmelCase				: Tuple											=	self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()	)
								UpperCAmelCase				: List[str]											=	[floats_list((1, x)	)[0] for x in range(800	,    1400	,    200	)]
								UpperCAmelCase				: Any											=	feat_extract(
								    __UpperCamelCase	,    truncation=__UpperCamelCase	,    max_length=1000	,    padding='''max_length'''	,    return_tensors='''np'''	)
								UpperCAmelCase				: List[str]											=	processed.input_values

								self._check_zero_mean_unit_variance(input_values[0, :800]	)
								self._check_zero_mean_unit_variance(input_values[1]	)
								self._check_zero_mean_unit_variance(input_values[2]	)







	def   A			(		self    :  Optional[int]	)    ->	str:
								UpperCAmelCase				: str											=	self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()	)
								UpperCAmelCase				: str											=	[floats_list((1, x)	)[0] for x in range(800	,    1400	,    200	)]
								UpperCAmelCase				: Tuple											=	feat_extract(
								    __UpperCamelCase	,    truncation=__UpperCamelCase	,    max_length=1000	,    padding='''longest'''	,    return_tensors='''np'''	)
								UpperCAmelCase				: Optional[Any]											=	processed.input_values

								self._check_zero_mean_unit_variance(input_values[0, :800]	)
								self._check_zero_mean_unit_variance(input_values[1, :1000]	)
								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, 1000)	)

								UpperCAmelCase				: int											=	[floats_list((1, x)	)[0] for x in range(800	,    1400	,    200	)]
								UpperCAmelCase				: Tuple											=	feat_extract(
								    __UpperCamelCase	,    truncation=__UpperCamelCase	,    max_length=2000	,    padding='''longest'''	,    return_tensors='''np'''	)
								UpperCAmelCase				: Tuple											=	processed.input_values

								self._check_zero_mean_unit_variance(input_values[0, :800]	)
								self._check_zero_mean_unit_variance(input_values[1, :1000]	)
								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, 1200)	)







	@require_torch
	def   A			(		self    :  Optional[int]	)    ->	str:
								import torch

								UpperCAmelCase				: str											=	self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()	)
								UpperCAmelCase				: List[str]											=	np.random.rand(100	).astype(np.floataa	)
								UpperCAmelCase				: str											=	np_speech_inputs.tolist()

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







	@slow
	@require_torch
	def   A			(		self    :  int	)    ->	Any:
								# 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:
															UpperCAmelCase				: Optional[Any]											=	WavaVecaConfig.from_pretrained(__UpperCamelCase	)
															UpperCAmelCase				: str											=	WavaVecaFeatureExtractor.from_pretrained(__UpperCamelCase	)

															# only "layer" feature extraction norm should make use of
															# attention_mask
															self.assertEqual(feat_extract.return_attention_mask	,    config.feat_extract_norm == '''layer'''	)










'''simple docstring'''






import argparse
import re
from typing import Dict

import torch
from datasets import Audio, Dataset, load_dataset, load_metric

from transformers import AutoFeatureExtractor, pipeline


def 				snake_case_  (      _lowerCAmelCase						:		Dataset     ,   _lowerCAmelCase						:		Dict[str, str]						)   ->     str:
							UpperCAmelCase				: Optional[int]											=	args.log_outputs
							UpperCAmelCase				: Any											=	'''_'''.join(args.dataset.split('''/'''						) + [args.config, args.split]						)

							# load metric
							UpperCAmelCase				: Union[str, Any]											=	load_metric('''wer'''						)
							UpperCAmelCase				: Dict											=	load_metric('''cer'''						)

							# compute metrics
							UpperCAmelCase				: Any											=	wer.compute(references=result['''target''']     ,   predictions=result['''prediction''']						)
							UpperCAmelCase				: str											=	cer.compute(references=result['''target''']     ,   predictions=result['''prediction''']						)

							# print & log results
							UpperCAmelCase				: Tuple											=	f"""WER: {wer_result}\nCER: {cer_result}"""
							print(_lowerCAmelCase						)

							with open(f"""{dataset_id}_eval_results.txt"""     ,   '''w'''						) as f:
														f.write(_lowerCAmelCase						)

							# log all results in text file. Possibly interesting for analysis
							if log_outputs is not None:
														UpperCAmelCase				: Union[str, Any]											=	f"""log_{dataset_id}_predictions.txt"""
														UpperCAmelCase				: Optional[Any]											=	f"""log_{dataset_id}_targets.txt"""

														with open(_lowerCAmelCase     ,   '''w'''						) as p, open(_lowerCAmelCase     ,   '''w'''						) as t:
																					# mapping function to write output
																					def write_to_file(_lowerCAmelCase						:		int     ,   _lowerCAmelCase						:		str						):
																												p.write(f"""{i}""" + '''\n'''						)
																												p.write(batch['''prediction'''] + '''\n'''						)
																												t.write(f"""{i}""" + '''\n'''						)
																												t.write(batch['''target'''] + '''\n'''						)

																					result.map(_lowerCAmelCase     ,   with_indices=_lowerCAmelCase						)


def 				snake_case_  (      _lowerCAmelCase						:		str						)   ->     str:
							UpperCAmelCase				: Optional[int]											=	'''[,?.!\-\;\:"“%‘”�—’…–]'''  # noqa: W605 IMPORTANT: this should correspond to the chars that were ignored during training

							UpperCAmelCase				: List[Any]											=	re.sub(_lowerCAmelCase     ,   ''''''     ,   text.lower()						)

							# In addition, we can normalize the target text, e.g. removing new lines characters etc...
							# note that order is important here!
							UpperCAmelCase				: str											=	['''\n\n''', '''\n''', '''   ''', '''  ''']

							for t in token_sequences_to_ignore:
														UpperCAmelCase				: List[Any]											=	''' '''.join(text.split(_lowerCAmelCase						)						)

							return text


def 				snake_case_  (      _lowerCAmelCase						:		Optional[Any]						)   ->     Union[str, Any]:
							# load dataset
							UpperCAmelCase				: Optional[int]											=	load_dataset(args.dataset     ,   args.config     ,   split=args.split     ,   use_auth_token=_lowerCAmelCase						)

							# for testing: only process the first two examples as a test
							# dataset = dataset.select(range(10))

							# load processor
							UpperCAmelCase				: Any											=	AutoFeatureExtractor.from_pretrained(args.model_id						)
							UpperCAmelCase				: Optional[Any]											=	feature_extractor.sampling_rate

							# resample audio
							UpperCAmelCase				: Tuple											=	dataset.cast_column('''audio'''     ,   Audio(sampling_rate=_lowerCAmelCase						)						)

							# load eval pipeline
							if args.device is None:
														UpperCAmelCase				: List[Any]											=	0 if torch.cuda.is_available() else -1
							UpperCAmelCase				: List[Any]											=	pipeline('''automatic-speech-recognition'''     ,   model=args.model_id     ,   device=args.device						)

							# map function to decode audio
							def map_to_pred(_lowerCAmelCase						:		List[str]						):
														UpperCAmelCase				: Optional[int]											=	asr(
														    batch['''audio''']['''array''']     ,   chunk_length_s=args.chunk_length_s     ,   stride_length_s=args.stride_length_s						)

														UpperCAmelCase				: str											=	prediction['''text''']
														UpperCAmelCase				: int											=	normalize_text(batch['''sentence''']						)
														return batch

							# run inference on all examples
							UpperCAmelCase				: Tuple											=	dataset.map(_lowerCAmelCase     ,   remove_columns=dataset.column_names						)

							# compute and log_results
							# do not change function below
							log_results(_lowerCAmelCase     ,   _lowerCAmelCase						)


if __name__ == "__main__":
						UpperCamelCase__:     List[Any]										=    argparse.ArgumentParser()

						parser.add_argument(
						    "--model_id", type=str, required=True, help="Model identifier. Should be loadable with 🤗 Transformers"
						)
						parser.add_argument(
						    "--dataset",
						    type=str,
						    required=True,
						    help="Dataset name to evaluate the `model_id`. Should be loadable with 🤗 Datasets",
						)
						parser.add_argument(
						    "--config", type=str, required=True, help="Config of the dataset. *E.g.* `'en'`  for Common Voice"
						)
						parser.add_argument("--split", type=str, required=True, help="Split of the dataset. *E.g.* `'test'`")
						parser.add_argument(
						    "--chunk_length_s", type=float, default=None, help="Chunk length in seconds. Defaults to 5 seconds."
						)
						parser.add_argument(
						    "--stride_length_s", type=float, default=None, help="Stride of the audio chunks. Defaults to 1 second."
						)
						parser.add_argument(
						    "--log_outputs", action="store_true", help="If defined, write outputs to log file for analysis."
						)
						parser.add_argument(
						    "--device",
						    type=int,
						    default=None,
						    help="The device to run the pipeline on. -1 for CPU (default), 0 for the first GPU and so on.",
						)
						UpperCamelCase__:     List[str]										=    parser.parse_args()

						main(args)





