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       lowerCAmelCase_     (		lowercase:							Optional[Any]  ,					lowercase:							str		)    ->				Dict:







	'''simple docstring'''
	_UpperCamelCase:     Dict							= args.log_outputs
	_UpperCamelCase:     Optional[int]							= '''_'''.join(args.dataset.split('''/'''		) + [args.config, args.split]		)

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

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

	# print & log results
	_UpperCamelCase:     Optional[Any]							= F"""WER: {wer_result}\nCER: {cer_result}"""
	print(lowercase		)

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

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

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

			result.map(lowercase  ,					with_indices=lowercase		)




def       lowerCAmelCase_     (		lowercase:							List[str]		)    ->				str:







	'''simple docstring'''
	_UpperCamelCase:     List[Any]							= '''[,?.!\-\;\:"“%‘”�—’…–]'''  # noqa: W605 IMPORTANT: this should correspond to the chars that were ignored during training

	_UpperCamelCase:     Optional[Any]							= re.sub(lowercase  ,					''''''  ,					text.lower()		)

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

	for t in token_sequences_to_ignore:
		_UpperCamelCase:     Optional[int]							= ''' '''.join(text.split(lowercase		)		)

	return text




def       lowerCAmelCase_     (		lowercase:							str		)    ->				List[str]:







	'''simple docstring'''
	_UpperCamelCase:     Dict							= load_dataset(args.dataset  ,					args.config  ,					split=args.split  ,					use_auth_token=lowercase		)

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

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

	# resample audio
	_UpperCamelCase:     Tuple							= dataset.cast_column('''audio'''  ,					Audio(sampling_rate=lowercase		)		)

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

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

		_UpperCamelCase:     List[Any]							= prediction['''text''']
		_UpperCamelCase:     List[Any]							= normalize_text(batch['''sentence''']		)
		return batch

	# run inference on all examples
	_UpperCamelCase:     str							= dataset.map(lowercase  ,					remove_columns=dataset.column_names		)

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


if __name__ == "__main__":
		UpperCAmelCase_			    =							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_			    =							parser.parse_args()

		main(args)
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


UpperCAmelCase_			    =							{
    '''configuration_blenderbot''': [
        '''BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''BlenderbotConfig''',
        '''BlenderbotOnnxConfig''',
    ],
    '''tokenization_blenderbot''': ['''BlenderbotTokenizer'''],
}

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase_			    =							['''BlenderbotTokenizerFast''']

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase_			    =							[
      '''BLENDERBOT_PRETRAINED_MODEL_ARCHIVE_LIST''',
      '''BlenderbotForCausalLM''',
      '''BlenderbotForConditionalGeneration''',
      '''BlenderbotModel''',
      '''BlenderbotPreTrainedModel''',
  ]


try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase_			    =							[
      '''TFBlenderbotForConditionalGeneration''',
      '''TFBlenderbotModel''',
      '''TFBlenderbotPreTrainedModel''',
  ]


try:
  if not is_flax_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase_			    =							[
      '''FlaxBlenderbotForConditionalGeneration''',
      '''FlaxBlenderbotModel''',
      '''FlaxBlenderbotPreTrainedModel''',
  ]


if TYPE_CHECKING:
  from .configuration_blenderbot import (
      BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_MAP,
      BlenderbotConfig,
      BlenderbotOnnxConfig,
  )
  from .tokenization_blenderbot import BlenderbotTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_blenderbot_fast import BlenderbotTokenizerFast

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_blenderbot import (
        BLENDERBOT_PRETRAINED_MODEL_ARCHIVE_LIST,
        BlenderbotForCausalLM,
        BlenderbotForConditionalGeneration,
        BlenderbotModel,
        BlenderbotPreTrainedModel,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_blenderbot import (
        TFBlenderbotForConditionalGeneration,
        TFBlenderbotModel,
        TFBlenderbotPreTrainedModel,
    )

  try:
    if not is_flax_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_flax_blenderbot import (
        FlaxBlenderbotForConditionalGeneration,
        FlaxBlenderbotModel,
        FlaxBlenderbotPreTrainedModel,
    )

else:
  import sys

  UpperCAmelCase_			    =							_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)