

"""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     __SCREAMING_SNAKE_CASE    ( A_		,	A_					):
	lowerCAmelCase__      :		str    							=      args.log_outputs
	lowerCAmelCase__      :		Union[str, Any]    							=      '''_'''.join(args.dataset.split('''/'''					) + [args.config, args.split]					)

	# load metric
	lowerCAmelCase__      :		str    							=      load_metric('''wer'''					)
	lowerCAmelCase__      :		str    							=      load_metric('''cer'''					)

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

	# print & log results
	lowerCAmelCase__      :		Any    							=      f'WER: {wer_result}\nCER: {cer_result}'
	print(A_					)

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

	# log all results in text file. Possibly interesting for analysis
	if log_outputs is not None:
		lowerCAmelCase__      :		Optional[int]    							=      f'log_{dataset_id}_predictions.txt'
		lowerCAmelCase__      :		Optional[int]    							=      f'log_{dataset_id}_targets.txt'

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

			result.map(A_		,	with_indices=A_					)

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

	lowerCAmelCase__      :		List[Any]    							=      re.sub(A_		,	''''''		,	text.lower()					)

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

	for t in token_sequences_to_ignore:
		lowerCAmelCase__      :		Dict    							=      ''' '''.join(text.split(A_					)					)

	return text

def     __SCREAMING_SNAKE_CASE    ( A_					):
	# load dataset
	lowerCAmelCase__      :		Union[str, Any]    							=      load_dataset(args.dataset		,	args.config		,	split=args.split		,	use_auth_token=A_					)

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

	# load processor
	lowerCAmelCase__      :		int    							=      AutoFeatureExtractor.from_pretrained(args.model_id					)
	lowerCAmelCase__      :		Union[str, Any]    							=      feature_extractor.sampling_rate

	# resample audio
	lowerCAmelCase__      :		Optional[Any]    							=      dataset.cast_column('''audio'''		,	Audio(sampling_rate=A_					)					)

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

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

		lowerCAmelCase__      :		List[str]    							=      prediction['''text''']
		lowerCAmelCase__      :		List[str]    							=      normalize_text(batch['''sentence''']					)
		return batch

	# run inference on all examples
	lowerCAmelCase__      :		int    							=      dataset.map(A_		,	remove_columns=dataset.column_names					)

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


if __name__ == "__main__":
							__UpperCamelCase    : int				 =						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    : Union[str, Any]				 =						parser.parse_args()

							main(args)







"""simple docstring"""





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__UpperCamelCase    : str				 =						{
    '''configuration_swinv2''': ['''SWINV2_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''Swinv2Config'''],
}


try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__UpperCamelCase    : List[Any]				 =						[
							    '''SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''Swinv2ForImageClassification''',
							    '''Swinv2ForMaskedImageModeling''',
							    '''Swinv2Model''',
							    '''Swinv2PreTrainedModel''',
							]


if TYPE_CHECKING:
							from .configuration_swinva import SWINV2_PRETRAINED_CONFIG_ARCHIVE_MAP, SwinvaConfig

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_swinva import (
														    SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST,
														    SwinvaForImageClassification,
														    SwinvaForMaskedImageModeling,
														    SwinvaModel,
														    SwinvaPreTrainedModel,
														)


else:
							import sys

							__UpperCamelCase    : List[Any]				 =						_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




