"""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	(     _lowerCamelCase		:			Dataset     ,_lowerCamelCase		:			Dict[str, str]      )			-> Union[str, Any]:
  _lowerCAmelCase		:			Optional[Any]         =	args.log_outputs
  _lowerCAmelCase		:			Optional[int]         =	"""_""".join(args.dataset.split("""/"""      ) + [args.config, args.split]      )

  # load metric
  _lowerCAmelCase		:			str         =	load_metric("""wer"""      )
  _lowerCAmelCase		:			List[str]         =	load_metric("""cer"""      )

  # compute metrics
  _lowerCAmelCase		:			str         =	wer.compute(references=result["""target"""]     ,predictions=result["""prediction"""]      )
  _lowerCAmelCase		:			str         =	cer.compute(references=result["""target"""]     ,predictions=result["""prediction"""]      )

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

  _lowerCAmelCase		:			Tuple         =	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!
  _lowerCAmelCase		:			Tuple         =	["""\n\n""", """\n""", """   """, """  """]

  for t in token_sequences_to_ignore:
    _lowerCAmelCase		:			Union[str, Any]         =	""" """.join(text.split(lowerCAmelCase_      )      )

  return text





def 				SCREAMING_SNAKE_CASE	(     _lowerCamelCase		:			int      )			-> Tuple:
  _lowerCAmelCase		:			List[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
  _lowerCAmelCase		:			List[Any]         =	AutoFeatureExtractor.from_pretrained(args.model_id      )
  _lowerCAmelCase		:			str         =	feature_extractor.sampling_rate

  # resample audio
  _lowerCAmelCase		:			Dict         =	dataset.cast_column("""audio"""     ,Audio(sampling_rate=lowerCAmelCase_      )      )

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

  # map function to decode audio
  def map_to_pred(_lowerCamelCase		:			Any      ):
    _lowerCAmelCase		:			List[str]         =	asr(
        batch["""audio"""]["""array"""]     ,chunk_length_s=args.chunk_length_s     ,stride_length_s=args.stride_length_s      )

    _lowerCAmelCase		:			List[str]         =	prediction["""text"""]
    _lowerCAmelCase		:			Optional[Any]         =	normalize_text(batch["""sentence"""]      )
    return batch

  # run inference on all examples
  _lowerCAmelCase		:			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 :						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.',
     )
     _a :						Optional[int]			       =  parser.parse_args()

     main(args)








import functools
import operator

from ...configuration_utils import PretrainedConfig
from ...utils import logging


SCREAMING_SNAKE_CASE						:List[str]			=  logging.get_logger(__name__)

SCREAMING_SNAKE_CASE						:Dict			=  {
    '''facebook/wav2vec2-base-960h''': '''https://huggingface.co/facebook/wav2vec2-base-960h/resolve/main/config.json''',
    # See all Wav2Vec2 models at https://huggingface.co/models?filter=wav2vec2
}


class      __lowerCAmelCase  (			a       ):






  """simple docstring"""





  _SCREAMING_SNAKE_CASE		     =							'wav2vec2'



  def __init__(  self							:							Any     ,					_lowerCAmelCase							:							Optional[Any]=3_2     ,					_lowerCAmelCase							:							List[Any]=7_6_8     ,					_lowerCAmelCase							:							Dict=1_2     ,					_lowerCAmelCase							:							Tuple=1_2     ,					_lowerCAmelCase							:							Optional[int]=3_0_7_2     ,					_lowerCAmelCase							:							Any="gelu"     ,					_lowerCAmelCase							:							List[str]=0.1     ,					_lowerCAmelCase							:							Dict=0.1     ,					_lowerCAmelCase							:							Union[str, Any]=0.1     ,					_lowerCAmelCase							:							Dict=0.0     ,					_lowerCAmelCase							:							Optional[int]=0.0     ,					_lowerCAmelCase							:							List[Any]=0.1     ,					_lowerCAmelCase							:							List[Any]=0.1     ,					_lowerCAmelCase							:							Dict=0.02     ,					_lowerCAmelCase							:							Any=1e-5     ,					_lowerCAmelCase							:							Any="group"     ,					_lowerCAmelCase							:							Union[str, Any]="gelu"     ,					_lowerCAmelCase							:							str=(5_1_2, 5_1_2, 5_1_2, 5_1_2, 5_1_2, 5_1_2, 5_1_2)     ,					_lowerCAmelCase							:							Optional[Any]=(5, 2, 2, 2, 2, 2, 2)     ,					_lowerCAmelCase							:							Optional[Any]=(1_0, 3, 3, 3, 3, 2, 2)     ,					_lowerCAmelCase							:							int=False     ,					_lowerCAmelCase							:							int=1_2_8     ,					_lowerCAmelCase							:							Union[str, Any]=1_6     ,					_lowerCAmelCase							:							Union[str, Any]=False     ,					_lowerCAmelCase							:							List[Any]=True     ,					_lowerCAmelCase							:							Dict=0.05     ,					_lowerCAmelCase							:							List[str]=1_0     ,					_lowerCAmelCase							:							Tuple=2     ,					_lowerCAmelCase							:							str=0.0     ,					_lowerCAmelCase							:							Union[str, Any]=1_0     ,					_lowerCAmelCase							:							int=0     ,					_lowerCAmelCase							:							Optional[int]=3_2_0     ,					_lowerCAmelCase							:							Optional[Any]=2     ,					_lowerCAmelCase							:							str=0.1     ,					_lowerCAmelCase							:							Optional[Any]=1_0_0     ,					_lowerCAmelCase							:							Tuple=2_5_6     ,					_lowerCAmelCase							:							Union[str, Any]=2_5_6     ,					_lowerCAmelCase							:							Union[str, Any]=0.1     ,					_lowerCAmelCase							:							Optional[Any]="sum"     ,					_lowerCAmelCase							:							List[str]=False     ,					_lowerCAmelCase							:							List[Any]=False     ,					_lowerCAmelCase							:							List[str]=2_5_6     ,					_lowerCAmelCase							:							List[Any]=(5_1_2, 5_1_2, 5_1_2, 5_1_2, 1_5_0_0)     ,					_lowerCAmelCase							:							List[str]=(5, 3, 3, 1, 1)     ,					_lowerCAmelCase							:							Union[str, Any]=(1, 2, 3, 1, 1)     ,					_lowerCAmelCase							:							Optional[Any]=5_1_2     ,					_lowerCAmelCase							:							Any=0     ,					_lowerCAmelCase							:							Optional[int]=1     ,					_lowerCAmelCase							:							List[str]=2     ,					_lowerCAmelCase							:							Tuple=False     ,					_lowerCAmelCase							:							Any=3     ,					_lowerCAmelCase							:							List[Any]=2     ,					_lowerCAmelCase							:							Optional[Any]=3     ,					_lowerCAmelCase							:							List[str]=None     ,					_lowerCAmelCase							:							str=None     ,					**_lowerCAmelCase							:							Tuple     ,					) ->			Optional[int]:




     """simple docstring"""
     super().__init__(**_lowerCAmelCase     ,					pad_token_id=_lowerCAmelCase     ,					bos_token_id=_lowerCAmelCase     ,					eos_token_id=_lowerCAmelCase	)
     snake_case_            =       hidden_size
     snake_case_            =       feat_extract_norm
     snake_case_            =       feat_extract_activation
     snake_case_            =       list(_lowerCAmelCase	)
     snake_case_            =       list(_lowerCAmelCase	)
     snake_case_            =       list(_lowerCAmelCase	)
     snake_case_            =       conv_bias
     snake_case_            =       num_conv_pos_embeddings
     snake_case_            =       num_conv_pos_embedding_groups
     snake_case_            =       len(self.conv_dim	)
     snake_case_            =       num_hidden_layers
     snake_case_            =       intermediate_size
     snake_case_            =       hidden_act
     snake_case_            =       num_attention_heads
     snake_case_            =       hidden_dropout
     snake_case_            =       attention_dropout
     snake_case_            =       activation_dropout
     snake_case_            =       feat_proj_dropout
     snake_case_            =       final_dropout
     snake_case_            =       layerdrop
     snake_case_            =       layer_norm_eps
     snake_case_            =       initializer_range
     snake_case_            =       vocab_size
     snake_case_            =       do_stable_layer_norm
     snake_case_            =       use_weighted_layer_sum

     if (
         (len(self.conv_stride	) != self.num_feat_extract_layers)
         or (len(self.conv_kernel	) != self.num_feat_extract_layers)
         or (len(self.conv_dim	) != self.num_feat_extract_layers)
     ):
        raise ValueError(
            "Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` =="
            " `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) ="
            F''' {len(self.conv_dim	)}`, `len(config.conv_stride) = {len(self.conv_stride	)}`,'''
            F''' `len(config.conv_kernel) = {len(self.conv_kernel	)}`.'''	)

     # fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
     snake_case_            =       apply_spec_augment
     snake_case_            =       mask_time_prob
     snake_case_            =       mask_time_length
     snake_case_            =       mask_time_min_masks
     snake_case_            =       mask_feature_prob
     snake_case_            =       mask_feature_length
     snake_case_            =       mask_feature_min_masks

     # parameters for pretraining with codevector quantized representations
     snake_case_            =       num_codevectors_per_group
     snake_case_            =       num_codevector_groups
     snake_case_            =       contrastive_logits_temperature
     snake_case_            =       feat_quantizer_dropout
     snake_case_            =       num_negatives
     snake_case_            =       codevector_dim
     snake_case_            =       proj_codevector_dim
     snake_case_            =       diversity_loss_weight

     # ctc loss
     snake_case_            =       ctc_loss_reduction
     snake_case_            =       ctc_zero_infinity

     # adapter
     snake_case_            =       add_adapter
     snake_case_            =       adapter_kernel_size
     snake_case_            =       adapter_stride
     snake_case_            =       num_adapter_layers
     snake_case_            =       output_hidden_size or hidden_size
     snake_case_            =       adapter_attn_dim

     # SequenceClassification-specific parameter. Feel free to ignore for other classes.
     snake_case_            =       classifier_proj_size

     # XVector-specific parameters. Feel free to ignore for other classes.
     snake_case_            =       list(_lowerCAmelCase	)
     snake_case_            =       list(_lowerCAmelCase	)
     snake_case_            =       list(_lowerCAmelCase	)
     snake_case_            =       xvector_output_dim



  @property
  def        lowerCAmelCase__	(  self							:							str	) ->			int:




     """simple docstring"""
     return functools.reduce(operator.mul     ,					self.conv_stride     ,					1	)




