'''simple docstring'''


from jiwer import compute_measures

import datasets


__snake_case						  =		"""\
@inproceedings{inproceedings,
    author = {Morris, Andrew and Maier, Viktoria and Green, Phil},
    year = {2004},
    month = {01},
    pages = {},
    title = {From WER and RIL to MER and WIL: improved evaluation measures for connected speech recognition.}
}
"""

__snake_case						  =		"""\
Word error rate (WER) is a common metric of the performance of an automatic speech recognition system.

The general difficulty of measuring performance lies in the fact that the recognized word sequence can have a different length from the reference word sequence (supposedly the correct one). The WER is derived from the Levenshtein distance, working at the word level instead of the phoneme level. The WER is a valuable tool for comparing different systems as well as for evaluating improvements within one system. This kind of measurement, however, provides no details on the nature of translation errors and further work is therefore required to identify the main source(s) of error and to focus any research effort.

This problem is solved by first aligning the recognized word sequence with the reference (spoken) word sequence using dynamic string alignment. Examination of this issue is seen through a theory called the power law that states the correlation between perplexity and word error rate.

Word error rate can then be computed as:

WER = (S + D + I) / N = (S + D + I) / (S + D + C)

where

S is the number of substitutions,
D is the number of deletions,
I is the number of insertions,
C is the number of correct words,
N is the number of words in the reference (N=S+D+C).

This value indicates the average number of errors per reference word. The lower the value, the better the
performance of the ASR system with a WER of 0 being a perfect score.
"""

__snake_case						  =		"""
Compute WER score of transcribed segments against references.

Args:
    references: List of references for each speech input.
    predictions: List of transcriptions to score.
    concatenate_texts (bool, default=False): Whether to concatenate all input texts or compute WER iteratively.

Returns:
    (float): the word error rate

Examples:

    >>> predictions = [\"this is the prediction\", \"there is an other sample\"]
    >>> references = [\"this is the reference\", \"there is another one\"]
    >>> wer = datasets.load_metric(\"wer\")
    >>> wer_score = wer.compute(predictions=predictions, references=references)
    >>> print(wer_score)
    0.5
"""





@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION ,				_KWARGS_DESCRIPTION      )
class       _a    (		datasets.Metric      ):


  """simple docstring"""

  def 		lowerCamelCase__  (		self      :       Optional[Any]      ):







    '''simple docstring'''


    return datasets.MetricInfo(
        description=_DESCRIPTION   ,				citation=_CITATION   ,				inputs_description=_KWARGS_DESCRIPTION   ,				features=datasets.Features(
            {
                """predictions""": datasets.Value("""string"""   ,				id="""sequence"""      ),
                """references""": datasets.Value("""string"""   ,				id="""sequence"""      ),
            }      )   ,				codebase_urls=["""https://github.com/jitsi/jiwer/"""]   ,				reference_urls=[
            """https://en.wikipedia.org/wiki/Word_error_rate""",
        ]   ,				)





  def 		lowerCamelCase__  (		self      :       int   ,				lowercase_      :       Tuple=None   ,				lowercase_      :       List[str]=None   ,				lowercase_      :       Any=False      ):







    '''simple docstring'''


    if concatenate_texts:
      return compute_measures(lowercase_   ,				lowercase_      )["wer"]
    else:
      lowercase_     =       0
      lowercase_     =       0
      for prediction, reference in zip(lowercase_   ,				lowercase_      ):
        lowercase_     =       compute_measures(lowercase_   ,				lowercase_      )
        incorrect += measures["substitutions"] + measures["deletions"] + measures["insertions"]
        total += measures["substitutions"] + measures["deletions"] + measures["hits"]
      return incorrect / total


'''simple docstring'''


import argparse
import collections
import os
import re

from transformers.utils import direct_transformers_import


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_table.py
__snake_case						  =		"""src/transformers"""
__snake_case						  =		"""docs/source/en"""
__snake_case						  =		"""."""

def   A_      (       SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_						)			->Tuple:
  with open(SCREAMING_SNAKE_CASE_ ,	"""r""" ,	encoding="""utf-8""" ,	newline="""\n"""						) as f:
    lowercase_     =       f.readlines()
  # Find the start prompt.
  lowercase_     =       0
  while not lines[start_index].startswith(SCREAMING_SNAKE_CASE_						):
    start_index += 1
  start_index += 1

  lowercase_     =       start_index
  while not lines[end_index].startswith(SCREAMING_SNAKE_CASE_						):
    end_index += 1
  end_index -= 1

  while len(lines[start_index]						) <= 1:
    start_index += 1
  while len(lines[end_index]						) <= 1:
    end_index -= 1
  end_index += 1
  return "".join(lines[start_index:end_index]						), start_index, end_index, lines


# Add here suffixes that are used to identify models, separated by |
__snake_case						  =		"""Model|Encoder|Decoder|ForConditionalGeneration"""
# Regexes that match TF/Flax/PT model names.
__snake_case						  =		re.compile(r"""TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)""")
__snake_case						  =		re.compile(r"""Flax(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)""")
# Will match any TF or Flax model too so need to be in an else branch afterthe two previous regexes.
__snake_case						  =		re.compile(r"""(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)""")


# This is to make sure the transformers module imported is the one in the repo.
__snake_case						  =		direct_transformers_import(TRANSFORMERS_PATH)

def   A_      (       SCREAMING_SNAKE_CASE_						)			->Dict:
  lowercase_     =       re.finditer(""".+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)""" ,	SCREAMING_SNAKE_CASE_						)
  return [m.group(0						) for m in matches]

def   A_      (       SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_						)			->Optional[Any]:
  lowercase_     =       2 if text == """✅""" or text == """❌""" else len(SCREAMING_SNAKE_CASE_						)
  lowercase_     =       (width - text_length) // 2
  lowercase_     =       width - text_length - left_indent
  return " " * left_indent + text + " " * right_indent

def   A_      (       )			->Tuple:
  lowercase_     =       transformers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
  lowercase_     =       {
      name: config_maping_names[code]
      for code, name in transformers_module.MODEL_NAMES_MAPPING.items()
      if code in config_maping_names
  }
  lowercase_     =       {name: config.replace("""Config""" ,	""""""						) for name, config in model_name_to_config.items()}

  # Dictionaries flagging if each model prefix has a slow/fast tokenizer, backend in PT/TF/Flax.
  lowercase_     =       collections.defaultdict(SCREAMING_SNAKE_CASE_						)
  lowercase_     =       collections.defaultdict(SCREAMING_SNAKE_CASE_						)
  lowercase_     =       collections.defaultdict(SCREAMING_SNAKE_CASE_						)
  lowercase_     =       collections.defaultdict(SCREAMING_SNAKE_CASE_						)
  lowercase_     =       collections.defaultdict(SCREAMING_SNAKE_CASE_						)

  # Let's lookup through all transformers object (once).
  for attr_name in dir(SCREAMING_SNAKE_CASE_						):
    lowercase_     =       None
    if attr_name.endswith("""Tokenizer"""						):
      lowercase_     =       slow_tokenizers
      lowercase_     =       attr_name[:-9]
    elif attr_name.endswith("""TokenizerFast"""						):
      lowercase_     =       fast_tokenizers
      lowercase_     =       attr_name[:-13]
    elif _re_tf_models.match(SCREAMING_SNAKE_CASE_						) is not None:
      lowercase_     =       tf_models
      lowercase_     =       _re_tf_models.match(SCREAMING_SNAKE_CASE_						).groups()[0]
    elif _re_flax_models.match(SCREAMING_SNAKE_CASE_						) is not None:
      lowercase_     =       flax_models
      lowercase_     =       _re_flax_models.match(SCREAMING_SNAKE_CASE_						).groups()[0]
    elif _re_pt_models.match(SCREAMING_SNAKE_CASE_						) is not None:
      lowercase_     =       pt_models
      lowercase_     =       _re_pt_models.match(SCREAMING_SNAKE_CASE_						).groups()[0]

    if lookup_dict is not None:
      while len(SCREAMING_SNAKE_CASE_						) > 0:
        if attr_name in model_name_to_prefix.values():
          lowercase_     =       True
          break
        # Try again after removing the last word in the name
        lowercase_     =       """""".join(camel_case_split(SCREAMING_SNAKE_CASE_						)[:-1]						)

    # Let's build that table!
  lowercase_     =       list(model_name_to_config.keys()						)
  model_names.sort(key=str.lower						)
  lowercase_     =       ["""Model""", """Tokenizer slow""", """Tokenizer fast""", """PyTorch support""", """TensorFlow support""", """Flax Support"""]
  # We'll need widths to properly display everything in the center (+2 is to leave one extra space on each side).
  lowercase_     =       [len(SCREAMING_SNAKE_CASE_						) + 2 for c in columns]
  lowercase_     =       max([len(SCREAMING_SNAKE_CASE_						) for name in model_names]						) + 2

  # Build the table per se
  lowercase_     =       """|""" + """|""".join([_center_text(SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_						) for c, w in zip(SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_						)]						) + """|\n"""
  # Use ":-----:" format to center-aligned table cell texts
  table += "|" + "|".join([""":""" + """-""" * (w - 2) + """:""" for w in widths]						) + "|\n"

  lowercase_     =       {True: """✅""", False: """❌"""}
  for name in model_names:
    lowercase_     =       model_name_to_prefix[name]
    lowercase_     =       [
        name,
        check[slow_tokenizers[prefix]],
        check[fast_tokenizers[prefix]],
        check[pt_models[prefix]],
        check[tf_models[prefix]],
        check[flax_models[prefix]],
    ]
    table += "|" + "|".join([_center_text(SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_						) for l, w in zip(SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_						)]						) + "|\n"
  return table

def   A_      (       SCREAMING_SNAKE_CASE_=False						)			->Dict:
  lowercase_     ,	lowercase_     ,	lowercase_     ,	lowercase_     =       _find_text_in_file(
      filename=os.path.join(SCREAMING_SNAKE_CASE_ ,	"""index.md"""						) ,	start_prompt="""<!--This table is updated automatically from the auto modules""" ,	end_prompt="""<!-- End table-->""" ,	)
  lowercase_     =       get_model_table_from_auto_modules()

  if current_table != new_table:
    if overwrite:
      with open(os.path.join(SCREAMING_SNAKE_CASE_ ,	"""index.md"""						) ,	"""w""" ,	encoding="""utf-8""" ,	newline="""\n"""						) as f:
        f.writelines(lines[:start_index] + [new_table] + lines[end_index:]						)
    else:
      raise ValueError(
          """The model table in the `index.md` has not been updated. Run `make fix-copies` to fix this."""						)


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

       check_model_table(args.fix_and_overwrite)

