"""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
__lowerCAmelCase					: Union[str, Any]        ="""src/transformers"""
__lowerCAmelCase					: int        ="""docs/source/en"""
__lowerCAmelCase					: List[Any]        ="""."""


def     UpperCAmelCase__ (       lowerCAmelCase__  :List[str]      ,	lowerCAmelCase__  :List[Any]      ,	lowerCAmelCase__  :Optional[Any]	)				->					Any:
      '''simple docstring'''


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

      lowercase														=			start_index
      while not lines[end_index].startswith(lowerCAmelCase__	):
            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 |
__lowerCAmelCase					: Union[str, Any]        ="""Model|Encoder|Decoder|ForConditionalGeneration"""
# Regexes that match TF/Flax/PT model names.
__lowerCAmelCase					: Any        =re.compile(R"""TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)""")
__lowerCAmelCase					: Any        =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.
__lowerCAmelCase					: List[str]        =re.compile(R"""(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)""")


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


def     UpperCAmelCase__ (       lowerCAmelCase__  :int	)				->					str:
      '''simple docstring'''


      lowercase														=			re.finditer(""".+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)"""      ,	lowerCAmelCase__	)
      return [m.group(0	) for m in matches]


def     UpperCAmelCase__ (       lowerCAmelCase__  :int      ,	lowerCAmelCase__  :Dict	)				->					List[Any]:
      '''simple docstring'''


      lowercase														=			2 if text == """✅""" or text == """❌""" else len(lowerCAmelCase__	)
      lowercase														=			(width - text_length) // 2
      lowercase														=			width - text_length - left_indent
      return " " * left_indent + text + " " * right_indent


def     UpperCAmelCase__ (       )				->					Any:
      '''simple docstring'''


      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(lowerCAmelCase__	)
      lowercase														=			collections.defaultdict(lowerCAmelCase__	)
      lowercase														=			collections.defaultdict(lowerCAmelCase__	)
      lowercase														=			collections.defaultdict(lowerCAmelCase__	)
      lowercase														=			collections.defaultdict(lowerCAmelCase__	)

      # Let's lookup through all transformers object (once).
      for attr_name in dir(lowerCAmelCase__	):
            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[:-1_3]
            elif _re_tf_models.match(lowerCAmelCase__	) is not None:
                  lowercase														=			tf_models
                  lowercase														=			_re_tf_models.match(lowerCAmelCase__	).groups()[0]
            elif _re_flax_models.match(lowerCAmelCase__	) is not None:
                  lowercase														=			flax_models
                  lowercase														=			_re_flax_models.match(lowerCAmelCase__	).groups()[0]
            elif _re_pt_models.match(lowerCAmelCase__	) is not None:
                  lowercase														=			pt_models
                  lowercase														=			_re_pt_models.match(lowerCAmelCase__	).groups()[0]

            if lookup_dict is not None:
                  while len(lowerCAmelCase__	) > 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(lowerCAmelCase__	)[:-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(lowerCAmelCase__	) + 2 for c in columns]
      lowercase														=			max([len(lowerCAmelCase__	) for name in model_names]	) + 2

      # Build the table per se
      lowercase														=			"""|""" + """|""".join([_center_text(lowerCAmelCase__      ,	lowerCAmelCase__	) for c, w in zip(lowerCAmelCase__      ,	lowerCAmelCase__	)]	) + """|\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(lowerCAmelCase__      ,	lowerCAmelCase__	) for l, w in zip(lowerCAmelCase__      ,	lowerCAmelCase__	)]	) + "|\n"
      return table


def     UpperCAmelCase__ (       lowerCAmelCase__  :Tuple=False	)				->					int:
      '''simple docstring'''


      lowercase							,							lowercase							,							lowercase							,							lowercase														=			_find_text_in_file(
          filename=os.path.join(lowerCAmelCase__      ,	"""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(lowerCAmelCase__      ,	"""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__":
       __lowerCAmelCase					: Tuple        =argparse.ArgumentParser()
       parser.add_argument("""--fix_and_overwrite""", action="""store_true""", help="""Whether to fix inconsistencies.""")
       __lowerCAmelCase					: Union[str, Any]        =parser.parse_args()

       check_model_table(args.fix_and_overwrite)




"""simple docstring"""






import absl  # noqa: F401 # Here to have a nice missing dependency error message early on
import nltk  # noqa: F401 # Here to have a nice missing dependency error message early on
import numpy  # noqa: F401 # Here to have a nice missing dependency error message early on
import six  # noqa: F401 # Here to have a nice missing dependency error message early on
from rouge_score import rouge_scorer, scoring

import datasets


__lowerCAmelCase					: str        ="""\
@inproceedings{lin-2004-rouge,
    title = \"{ROUGE}: A Package for Automatic Evaluation of Summaries\",
    author = \"Lin, Chin-Yew\",
    booktitle = \"Text Summarization Branches Out\",
    month = jul,
    year = \"2004\",
    address = \"Barcelona, Spain\",
    publisher = \"Association for Computational Linguistics\",
    url = \"https://www.aclweb.org/anthology/W04-1013\",
    pages = \"74--81\",
}
"""

__lowerCAmelCase					: str        ="""\
ROUGE, or Recall-Oriented Understudy for Gisting Evaluation, is a set of metrics and a software package used for
evaluating automatic summarization and machine translation software in natural language processing.
The metrics compare an automatically produced summary or translation against a reference or a set of references (human-produced) summary or translation.

Note that ROUGE is case insensitive, meaning that upper case letters are treated the same way as lower case letters.

This metrics is a wrapper around Google Research reimplementation of ROUGE:
https://github.com/google-research/google-research/tree/master/rouge
"""

__lowerCAmelCase					: Optional[int]        ="""
Calculates average rouge scores for a list of hypotheses and references
Args:
    predictions: list of predictions to score. Each prediction
        should be a string with tokens separated by spaces.
    references: list of reference for each prediction. Each
        reference should be a string with tokens separated by spaces.
    rouge_types: A list of rouge types to calculate.
        Valid names:
        `\"rouge{n}\"` (e.g. `\"rouge1\"`, `\"rouge2\"`) where: {n} is the n-gram based scoring,
        `\"rougeL\"`: Longest common subsequence based scoring.
        `\"rougeLSum\"`: rougeLsum splits text using `\"\n\"`.
        See details in https://github.com/huggingface/datasets/issues/617
    use_stemmer: Bool indicating whether Porter stemmer should be used to strip word suffixes.
    use_aggregator: Return aggregates if this is set to True
Returns:
    rouge1: rouge_1 (precision, recall, f1),
    rouge2: rouge_2 (precision, recall, f1),
    rougeL: rouge_l (precision, recall, f1),
    rougeLsum: rouge_lsum (precision, recall, f1)
Examples:

    >>> rouge = datasets.load_metric('rouge')
    >>> predictions = [\"hello there\", \"general kenobi\"]
    >>> references = [\"hello there\", \"general kenobi\"]
    >>> results = rouge.compute(predictions=predictions, references=references)
    >>> print(list(results.keys()))
    ['rouge1', 'rouge2', 'rougeL', 'rougeLsum']
    >>> print(results[\"rouge1\"])
    AggregateScore(low=Score(precision=1.0, recall=1.0, fmeasure=1.0), mid=Score(precision=1.0, recall=1.0, fmeasure=1.0), high=Score(precision=1.0, recall=1.0, fmeasure=1.0))
    >>> print(results[\"rouge1\"].mid.fmeasure)
    1.0
"""





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

      def        A__ (		self		):







            """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/google-research/google-research/tree/master/rouge"""]	, reference_urls=[
                    """https://en.wikipedia.org/wiki/ROUGE_(metric)""",
                    """https://github.com/google-research/google-research/tree/master/rouge""",
                ]	, )







      def        A__ (		self	, __lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase=None	, __lowerCAmelCase=True	, __lowerCAmelCase=False		):







            """simple docstring"""



            if rouge_types is None:
                  lowercase														=			["""rouge1""", """rouge2""", """rougeL""", """rougeLsum"""]

            lowercase														=			rouge_scorer.RougeScorer(rouge_types=__lowerCAmelCase	, use_stemmer=__lowerCAmelCase		)
            if use_aggregator:
                  lowercase														=			scoring.BootstrapAggregator()
            else:
                  lowercase														=			[]

            for ref, pred in zip(__lowerCAmelCase	, __lowerCAmelCase		):
                  lowercase														=			scorer.score(__lowerCAmelCase	, __lowerCAmelCase		)
                  if use_aggregator:
                        aggregator.add_scores(__lowerCAmelCase		)
                  else:
                        scores.append(__lowerCAmelCase		)

            if use_aggregator:
                  lowercase														=			aggregator.aggregate()
            else:
                  lowercase														=			{}
                  for key in scores[0]:
                        lowercase														=			[score[key] for score in scores]

            return result



