import argparse
from collections import defaultdict

import yaml


SCREAMING_SNAKE_CASE :	Any							     =		"""docs/source/en/_toctree.yml"""



def   __A		(			_A					):



 """simple docstring"""


 __a       =  defaultdict(_A					)
 for doc in model_doc:
  counts[doc["local"]] += 1
 __a       =  [key for key, value in counts.items() if value > 1]

 __a       =  []
 for duplicate_key in duplicates:
  __a       =  list({doc["title"] for doc in model_doc if doc["local"] == duplicate_key}					)
  if len(_A					) > 1:
   raise ValueError(
       f"""{duplicate_key} is present several times in the documentation table of content at """
       "`docs/source/en/_toctree.yml` with different *Title* values. Choose one of those and remove the "
       "others."					)
  # Only add this once
  new_doc.append({"local": duplicate_key, "title": titles[0]}					)

 # Add none duplicate-keys
 new_doc.extend([doc for doc in model_doc if counts[doc["local"]] == 1]					)

 # Sort
 return sorted(_A							,				key=lambda _A					: s["title"].lower()					)



def   __A		(			_A=False					):



 """simple docstring"""


 with open(_A							,				encoding="utf-8"					) as f:
  __a       =  yaml.safe_load(f.read()					)

 # Get to the API doc
 __a       =  0
 while content[api_idx]["title"] != "API":
  api_idx += 1
 __a       =  content[api_idx]["sections"]

 # Then to the model doc
 __a       =  0
 while api_doc[model_idx]["title"] != "Models":
  model_idx += 1

 __a       =  api_doc[model_idx]["sections"]

 __a       =  [(idx, section) for idx, section in enumerate(_A					) if "sections" in section]
 __a       =  False
 for idx, modality_doc in modalities_docs:
  __a       =  modality_doc["sections"]
  __a       =  clean_model_doc_toc(_A					)

  if old_modality_doc != new_modality_doc:
   __a       =  True
   if overwrite:
    __a       =  new_modality_doc

 if diff:
  if overwrite:
   __a       =  model_doc
   __a       =  api_doc
   with open(_A							,				"w"							,				encoding="utf-8"					) as f:
    f.write(yaml.dump(_A							,				allow_unicode=_A					)					)
  else:
   raise ValueError(
       "The model doc part of the table of content is not properly sorted, run `make style` to fix this."					)


if __name__ == "__main__":
  SCREAMING_SNAKE_CASE :	Tuple							     =		argparse.ArgumentParser()
  parser.add_argument("""--fix_and_overwrite""", action="""store_true""", help="""Whether to fix inconsistencies.""")
  SCREAMING_SNAKE_CASE :	int							     =		parser.parse_args()

  check_model_doc(args.fix_and_overwrite)



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   __A		(			_A							,				_A					):



 """simple docstring"""


 __a       =  args.log_outputs
 __a       =  "_".join(args.dataset.split("/"					) + [args.config, args.split]					)

 # load metric
 __a       =  load_metric("wer"					)
 __a       =  load_metric("cer"					)

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

 # print & log results
 __a       =  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:
  __a       =  f"""log_{dataset_id}_predictions.txt"""
  __a       =  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   __A		(			_A					):



 """simple docstring"""


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

 __a       =  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!
 __a       =  ["\n\n", "\n", "   ", "  "]

 for t in token_sequences_to_ignore:
  __a       =  " ".join(text.split(_A					)					)

 return text



def   __A		(			_A					):



 """simple docstring"""


 __a       =  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
 __a       =  AutoFeatureExtractor.from_pretrained(args.model_id					)
 __a       =  feature_extractor.sampling_rate

 # resample audio
 __a       =  dataset.cast_column("audio"							,				Audio(sampling_rate=_A					)					)

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

 # map function to decode audio
 def map_to_pred(_A					):
  __a       =  asr(
      batch["audio"]["array"]							,				chunk_length_s=args.chunk_length_s							,				stride_length_s=args.stride_length_s					)

  __a       =  prediction["text"]
  __a       =  normalize_text(batch["sentence"]					)
  return batch

 # run inference on all examples
 __a       =  dataset.map(_A							,				remove_columns=dataset.column_names					)

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


if __name__ == "__main__":
  SCREAMING_SNAKE_CASE :	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.""",
  )
  SCREAMING_SNAKE_CASE :	Optional[Any]							     =		parser.parse_args()

  main(args)


