from collections import Counter
from pathlib import Path
from typing import Optional, Tuple

import yaml



class __A     (      yaml.SafeLoader						):

							'''simple docstring'''

							def 	SCREAMING_SNAKE_CASE__			(				self   ,	_snake_case ):
												_lowerCAmelCase			:		Dict          = [self.constructed_objects[key_node] for key_node, _ in node.value]
												_lowerCAmelCase			:		int          = [tuple(__A ) if isinstance(__A   ,	__A ) else key for key in keys]
												_lowerCAmelCase			:		Union[str, Any]          = Counter(__A )
												_lowerCAmelCase			:		Optional[int]          = [key for key in counter if counter[key] > 1]
												if duplicate_keys:
																	raise TypeError(F"""Got duplicate yaml keys: {duplicate_keys}""" )






							def 	SCREAMING_SNAKE_CASE__			(				self   ,	_snake_case   ,	_snake_case=False ):
												_lowerCAmelCase			:		Tuple          = super().construct_mapping(__A   ,	deep=__A )
												self._check_no_duplicates_on_constructed_node(__A )
												return mapping






def        UpperCamelCase_							(			lowerCAmelCase__  ):



					"""simple docstring"""

					_lowerCAmelCase			:		Tuple          = list(readme_content.splitlines()  )
					if full_content and full_content[0] == "---" and "---" in full_content[1:]:
										_lowerCAmelCase			:		Any          = full_content[1:].index("---"  ) + 1
										_lowerCAmelCase			:		Union[str, Any]          = '''\n'''.join(full_content[1:sep_idx]  )
										return yamlblock, "\n".join(full_content[sep_idx + 1 :]  )

					return None, "\n".join(UpperCAmelCase_  )



class __A     (      snake_case__						):

							'''simple docstring'''

							a_     =					{'''train_eval_index'''}  # train-eval-index in the YAML metadata






							@classmethod
							def 	SCREAMING_SNAKE_CASE__			(				cls   ,	_snake_case ):
												with open(__A   ,	encoding="utf-8" ) as readme_file:
																	_lowerCAmelCase			:		Union[str, Any]          = _split_yaml_from_readme(readme_file.read() )
												if yaml_string is not None:
																	return cls.from_yaml_string(__A )
												else:
																	return cls()






							def 	SCREAMING_SNAKE_CASE__			(				self   ,	_snake_case ):
												if path.exists():
																	with open(__A   ,	encoding="utf-8" ) as readme_file:
																						_lowerCAmelCase			:		Union[str, Any]          = readme_file.read()
												else:
																	_lowerCAmelCase			:		int          = None
												_lowerCAmelCase			:		Tuple          = self._to_readme(__A )
												with open(__A   ,	"w"   ,	encoding="utf-8" ) as readme_file:
																	readme_file.write(__A )






							def 	SCREAMING_SNAKE_CASE__			(				self   ,	_snake_case = None ):
												if readme_content is not None:
																	_lowerCAmelCase			:		str          = _split_yaml_from_readme(__A )
																	_lowerCAmelCase			:		Tuple          = '''---\n''' + self.to_yaml_string() + '''---\n''' + content
												else:
																	_lowerCAmelCase			:		Tuple          = '''---\n''' + self.to_yaml_string() + '''---\n'''
												return full_content






							@classmethod
							def 	SCREAMING_SNAKE_CASE__			(				cls   ,	_snake_case ):
												_lowerCAmelCase			:		str          = yaml.load(__A   ,	Loader=_NoDuplicateSafeLoader ) or {}

												# Convert the YAML keys to DatasetMetadata fields
												_lowerCAmelCase			:		Optional[Any]          = {
												    (key.replace("-"   ,	"_" ) if key.replace("-"   ,	"_" ) in cls._FIELDS_WITH_DASHES else key): value
												    for key, value in metadata_dict.items()
												}
												return cls(**__A )






							def 	SCREAMING_SNAKE_CASE__			(				self ):
												return yaml.safe_dump(
												    {
												        (key.replace("_"   ,	"-" ) if key in self._FIELDS_WITH_DASHES else key): value
												        for key, value in self.items()
												    }   ,	sort_keys=__A   ,	allow_unicode=__A   ,	encoding="utf-8"   ,	).decode("utf-8" )
snake_case      =    {
    "image-classification": [],
    "translation": [],
    "image-segmentation": [],
    "fill-mask": [],
    "automatic-speech-recognition": [],
    "token-classification": [],
    "sentence-similarity": [],
    "audio-classification": [],
    "question-answering": [],
    "summarization": [],
    "zero-shot-classification": [],
    "table-to-text": [],
    "feature-extraction": [],
    "other": [],
    "multiple-choice": [],
    "text-classification": [],
    "text-to-image": [],
    "text2text-generation": [],
    "zero-shot-image-classification": [],
    "tabular-classification": [],
    "tabular-regression": [],
    "image-to-image": [],
    "tabular-to-text": [],
    "unconditional-image-generation": [],
    "text-retrieval": [],
    "text-to-speech": [],
    "object-detection": [],
    "audio-to-audio": [],
    "text-generation": [],
    "conversational": [],
    "table-question-answering": [],
    "visual-question-answering": [],
    "image-to-text": [],
    "reinforcement-learning": [],
    "voice-activity-detection": [],
    "time-series-forecasting": [],
    "document-question-answering": [],
}


if __name__ == "__main__":
						from argparse import ArgumentParser

						snake_case      =    ArgumentParser(usage="Validate the yaml metadata block of a README.md file.")
						ap.add_argument("readme_filepath")
						snake_case      =    ap.parse_args()

						snake_case      =    Path(args.readme_filepath)
						snake_case      =    DatasetMetadata.from_readme(readme_filepath)
						print(dataset_metadata)
						dataset_metadata.to_readme(readme_filepath)








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


_lowercase											=    """\
@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\",
}
"""

_lowercase											=    """\
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
"""

_lowercase											=    """
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 			lowercase_		(      datasets.Metric       ):


       def 	_snake_case				(    self				)						->    Union[str, Any]:
              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 	_snake_case				(    self						,       __A						,       __A						,       __A=None						,       __A=True						,       __A=False				)						->    List[str]:
              if rouge_types is None:
                     SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=['''rouge1''', '''rouge2''', '''rougeL''', '''rougeLsum''']

              SCREAMING_SNAKE_CASE_							:				Tuple		=rouge_scorer.RougeScorer(rouge_types=__A						,       use_stemmer=__A				)
              if use_aggregator:
                     SCREAMING_SNAKE_CASE_							:				List[str]		=scoring.BootstrapAggregator()
              else:
                     SCREAMING_SNAKE_CASE_							:				Tuple		=[]

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

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

              return result
