



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    lowercase							( SCREAMING_SNAKE_CASE__							:						List[str]					,							SCREAMING_SNAKE_CASE__							:						str					)	->  str:
 _snake_case     :	Any											=       args.log_outputs
 _snake_case     :	int											=       """_""".join(args.dataset.split("""/"""					) + [args.config, args.split]					)

 # load metric
 _snake_case     :	Dict											=       load_metric("""wer"""					)
 _snake_case     :	Dict											=       load_metric("""cer"""					)

 # compute metrics
 _snake_case     :	Dict											=       wer.compute(references=result["""target"""]					,							predictions=result["""prediction"""]					)
 _snake_case     :	List[Any]											=       cer.compute(references=result["""target"""]					,							predictions=result["""prediction"""]					)

 # print & log results
 _snake_case     :	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:
  _snake_case     :	List[str]											=       F'''log_{dataset_id}_predictions.txt'''
  _snake_case     :	List[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(SCREAMING_SNAKE_CASE__							:						Any					,							SCREAMING_SNAKE_CASE__							:						Tuple					):
    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    lowercase							( SCREAMING_SNAKE_CASE__							:						Optional[int]					)	->  List[Any]:
 _snake_case     :	Optional[Any]											=       """[,?.!\-\;\:\"“%‘”�—’…–]"""  # noqa: W605 IMPORTANT: this should correspond to the chars that were ignored during training

 _snake_case     :	List[str]											=       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!
 _snake_case     :	str											=       ["""\n\n""", """\n""", """   """, """  """]

 for t in token_sequences_to_ignore:
  _snake_case     :	Optional[int]											=       """ """.join(text.split(_lowerCAmelCase					)					)

 return text



def    lowercase							( SCREAMING_SNAKE_CASE__							:						List[str]					)	->  List[str]:
 # load dataset
 _snake_case     :	str											=       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
 _snake_case     :	Tuple											=       AutoFeatureExtractor.from_pretrained(args.model_id					)
 _snake_case     :	List[Any]											=       feature_extractor.sampling_rate

 # resample audio
 _snake_case     :	Optional[Any]											=       dataset.cast_column("""audio"""					,							Audio(sampling_rate=_lowerCAmelCase					)					)

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

 # map function to decode audio
 def map_to_pred(SCREAMING_SNAKE_CASE__							:						Optional[Any]					):
  _snake_case     :	Union[str, Any]											=       asr(
      batch["""audio"""]["""array"""]					,							chunk_length_s=args.chunk_length_s					,							stride_length_s=args.stride_length_s					)

  _snake_case     :	List[Any]											=       prediction["""text"""]
  _snake_case     :	int											=       normalize_text(batch["""sentence"""]					)
  return batch

 # run inference on all examples
 _snake_case     :	List[Any]											=       dataset.map(_lowerCAmelCase					,							remove_columns=dataset.column_names					)

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


if __name__ == "__main__":
  a__     =						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.""",
  )
  a__     =						parser.parse_args()

  main(args)


"""simple docstring"""
from collections import OrderedDict
from typing import List, Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


SCREAMING_SNAKE_CASE_					     =	logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_					     =	{
    '''google/efficientnet-b7''': '''https://huggingface.co/google/efficientnet-b7/resolve/main/config.json''',
}
class       lowerCAmelCase_		(				A__ ):



							'''simple docstring'''





							_snake_case						      =		'''efficientnet'''




							def __init__( self    ,				snake_case_ = 3    ,				snake_case_ = 600    ,				snake_case_ = 2.0    ,				snake_case_ = 3.1    ,				snake_case_ = 8    ,				snake_case_ = [3, 3, 5, 3, 5, 5, 3]    ,				snake_case_ = [32, 16, 24, 40, 80, 112, 192]    ,				snake_case_ = [16, 24, 40, 80, 112, 192, 320]    ,				snake_case_ = []    ,				snake_case_ = [1, 2, 2, 2, 1, 2, 1]    ,				snake_case_ = [1, 2, 2, 3, 3, 4, 1]    ,				snake_case_ = [1, 6, 6, 6, 6, 6, 6]    ,				snake_case_ = 0.25    ,				snake_case_ = "swish"    ,				snake_case_ = 2_560    ,				snake_case_ = "mean"    ,				snake_case_ = 0.02    ,				snake_case_ = 0.001    ,				snake_case_ = 0.99    ,				snake_case_ = 0.5    ,				snake_case_ = 0.2    ,				**snake_case_    ,				)       ->  List[str]:
													super().__init__(**snake_case_				)

													__lowerCAmelCase     =  num_channels
													__lowerCAmelCase     =  image_size
													__lowerCAmelCase     =  width_coefficient
													__lowerCAmelCase     =  depth_coefficient
													__lowerCAmelCase     =  depth_divisor
													__lowerCAmelCase     =  kernel_sizes
													__lowerCAmelCase     =  in_channels
													__lowerCAmelCase     =  out_channels
													__lowerCAmelCase     =  depthwise_padding
													__lowerCAmelCase     =  strides
													__lowerCAmelCase     =  num_block_repeats
													__lowerCAmelCase     =  expand_ratios
													__lowerCAmelCase     =  squeeze_expansion_ratio
													__lowerCAmelCase     =  hidden_act
													__lowerCAmelCase     =  hidden_dim
													__lowerCAmelCase     =  pooling_type
													__lowerCAmelCase     =  initializer_range
													__lowerCAmelCase     =  batch_norm_eps
													__lowerCAmelCase     =  batch_norm_momentum
													__lowerCAmelCase     =  dropout_rate
													__lowerCAmelCase     =  drop_connect_rate
													__lowerCAmelCase     =  sum(snake_case_				) * 4
class       lowerCAmelCase_		(				A__ ):



							'''simple docstring'''





							_snake_case						      =		version.parse('''1.11''' )




							@property
							def    A__						( self				)       ->  Mapping[str, Mapping[int, str]]:
													return OrderedDict(
													    [
													        ("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
													    ]				)




							@property
							def    A__						( self				)       ->  float:
													return 1e-5
