




"""simple docstring"""






import argparse
import os
import re

import packaging.version


lowercase__     						=						"""examples/"""
lowercase__     						=						{
    """examples""": (re.compile(R"""^check_min_version\(\"[^\"]+\"\)\s*$""", re.MULTILINE), """check_min_version(\"VERSION\")\n"""),
    """init""": (re.compile(R"""^__version__\s+=\s+\"([^\"]+)\"\s*$""", re.MULTILINE), """__version__ = \"VERSION\"\n"""),
    """setup""": (re.compile(R"""^(\s*)version\s*=\s*\"[^\"]+\",""", re.MULTILINE), R"""\1version=\"VERSION\","""),
    """doc""": (re.compile(R"""^(\s*)release\s*=\s*\"[^\"]+\"$""", re.MULTILINE), """release = \"VERSION\"\n"""),
}
lowercase__     						=						{
    """init""": """src/transformers/__init__.py""",
    """setup""": """setup.py""",
}
lowercase__     						=						"""README.md"""







def      _snake_case			(				lowercase__							,    lowercase__							,    lowercase__		):
				with open(lowercase__							,    'r'							,    encoding='utf-8'							,    newline='\n'		) as f:
								_lowerCamelCase     :			Optional[int]    =    f.read()
				_lowerCamelCase, _lowerCamelCase     :			Optional[Any]    =    REPLACE_PATTERNS[pattern]
				_lowerCamelCase     :			Dict    =    replace.replace('VERSION'							,    lowercase__		)
				_lowerCamelCase     :			int    =    re_pattern.sub(lowercase__							,    lowercase__		)
				with open(lowercase__							,    'w'							,    encoding='utf-8'							,    newline='\n'		) as f:
								f.write(lowercase__		)







def      _snake_case			(				lowercase__		):
				for folder, directories, fnames in os.walk(lowercase__		):
								# Removing some of the folders with non-actively maintained examples from the walk
								if "research_projects" in directories:
												directories.remove('research_projects'		)
								if "legacy" in directories:
												directories.remove('legacy'		)
								for fname in fnames:
												if fname.endswith('.py'		):
																update_version_in_file(os.path.join(lowercase__							,    lowercase__		)							,    lowercase__							,    pattern='examples'		)







def      _snake_case			(				lowercase__							,    lowercase__=False		):
				for pattern, fname in REPLACE_FILES.items():
								update_version_in_file(lowercase__							,    lowercase__							,    lowercase__		)
				if not patch:
								update_version_in_examples(lowercase__		)







def      _snake_case			(				):
				_lowerCamelCase     :			int    =    '🤗 Transformers currently provides the following architectures'
				_lowerCamelCase     :			Tuple    =    '1. Want to contribute a new model?'
				with open(lowercase__							,    'r'							,    encoding='utf-8'							,    newline='\n'		) as f:
								_lowerCamelCase     :			int    =    f.readlines()

				# Find the start of the list.
				_lowerCamelCase     :			List[str]    =    0
				while not lines[start_index].startswith(_start_prompt		):
								start_index += 1
				start_index += 1

				_lowerCamelCase     :			List[str]    =    start_index
				# Update the lines in the model list.
				while not lines[index].startswith(_end_prompt		):
								if lines[index].startswith('1.'		):
												_lowerCamelCase     :			List[Any]    =    lines[index].replace(
												    'https://huggingface.co/docs/transformers/main/model_doc'							,    'https://huggingface.co/docs/transformers/model_doc'							,    )
								index += 1

				with open(lowercase__							,    'w'							,    encoding='utf-8'							,    newline='\n'		) as f:
								f.writelines(lowercase__		)







def      _snake_case			(				):
				with open(REPLACE_FILES['init']							,    'r'		) as f:
								_lowerCamelCase     :			Tuple    =    f.read()
				_lowerCamelCase     :			Union[str, Any]    =    REPLACE_PATTERNS['init'][0].search(lowercase__		).groups()[0]
				return packaging.version.parse(lowercase__		)







def      _snake_case			(				lowercase__=False		):
				_lowerCamelCase     :			Optional[int]    =    get_version()
				if patch and default_version.is_devrelease:
								raise ValueError('Can\'t create a patch version from the dev branch, checkout a released version!'		)
				if default_version.is_devrelease:
								_lowerCamelCase     :			str    =    default_version.base_version
				elif patch:
								_lowerCamelCase     :			Union[str, Any]    =    f'''{default_version.major}.{default_version.minor}.{default_version.micro + 1}'''
				else:
								_lowerCamelCase     :			Any    =    f'''{default_version.major}.{default_version.minor + 1}.0'''

				# Now let's ask nicely if that's the right one.
				_lowerCamelCase     :			int    =    input(f'''Which version are you releasing? [{default_version}]'''		)
				if len(lowercase__		) == 0:
								_lowerCamelCase     :			Tuple    =    default_version

				print(f'''Updating version to {version}.'''		)
				global_version_update(lowercase__							,    patch=lowercase__		)
				if not patch:
								print('Cleaning main README, don\'t forget to run `make fix-copies`.'		)
								clean_main_ref_in_model_list()







def      _snake_case			(				):
				_lowerCamelCase     :			Optional[Any]    =    get_version()
				_lowerCamelCase     :			Optional[int]    =    f'''{current_version.major}.{current_version.minor + 1}.0.dev0'''
				_lowerCamelCase     :			str    =    current_version.base_version

				# Check with the user we got that right.
				_lowerCamelCase     :			List[str]    =    input(f'''Which version are we developing now? [{dev_version}]'''		)
				if len(lowercase__		) == 0:
								_lowerCamelCase     :			List[str]    =    dev_version

				print(f'''Updating version to {version}.'''		)
				global_version_update(lowercase__		)
				print('Cleaning main README, don\'t forget to run `make fix-copies`.'		)
				clean_main_ref_in_model_list()


if __name__ == "__main__":
			lowercase__     						=						argparse.ArgumentParser()
			parser.add_argument("""--post_release""", action="""store_true""", help="""Whether this is pre or post release.""")
			parser.add_argument("""--patch""", action="""store_true""", help="""Whether or not this is a patch release.""")
			lowercase__     						=						parser.parse_args()
			if not args.post_release:
						pre_release_work(patch=args.patch)
			elif args.patch:
						print("""Nothing to do after a patch :-)""")
			else:
						post_release_work()







"""simple docstring"""





import numpy as np
from nltk.translate import meteor_score

import datasets
from datasets.config import importlib_metadata, version


_SCREAMING_SNAKE_CASE					:				Tuple					    =						version.parse(importlib_metadata.version('''nltk'''))
if NLTK_VERSION >= version.Version('''3.6.4'''):
  from nltk import word_tokenize


_SCREAMING_SNAKE_CASE					:				Tuple					    =						'''\
@inproceedings{banarjee2005,
  title     = {{METEOR}: An Automatic Metric for {MT} Evaluation with Improved Correlation with Human Judgments},
  author    = {Banerjee, Satanjeev  and Lavie, Alon},
  booktitle = {Proceedings of the {ACL} Workshop on Intrinsic and Extrinsic Evaluation Measures for Machine Translation and/or Summarization},
  month     = jun,
  year      = {2005},
  address   = {Ann Arbor, Michigan},
  publisher = {Association for Computational Linguistics},
  url       = {https://www.aclweb.org/anthology/W05-0909},
  pages     = {65--72},
}
'''

_SCREAMING_SNAKE_CASE					:				int					    =						'''\
METEOR, an automatic metric for machine translation evaluation
that is based on a generalized concept of unigram matching between the
machine-produced translation and human-produced reference translations.
Unigrams can be matched based on their surface forms, stemmed forms,
and meanings; furthermore, METEOR can be easily extended to include more
advanced matching strategies. Once all generalized unigram matches
between the two strings have been found, METEOR computes a score for
this matching using a combination of unigram-precision, unigram-recall, and
a measure of fragmentation that is designed to directly capture how
well-ordered the matched words in the machine translation are in relation
to the reference.

METEOR gets an R correlation value of 0.347 with human evaluation on the Arabic
data and 0.331 on the Chinese data. This is shown to be an improvement on
using simply unigram-precision, unigram-recall and their harmonic F1
combination.
'''

_SCREAMING_SNAKE_CASE					:				List[Any]					    =						'''
Computes METEOR score of translated segments against one or more 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.
    alpha: Parameter for controlling relative weights of precision and recall. default: 0.9
    beta: Parameter for controlling shape of penalty as a function of fragmentation. default: 3
    gamma: Relative weight assigned to fragmentation penalty. default: 0.5
Returns:
    \'meteor\': meteor score.
Examples:

    >>> meteor = datasets.load_metric(\'meteor\')
    >>> predictions = ["It is a guide to action which ensures that the military always obeys the commands of the party"]
    >>> references = ["It is a guide to action that ensures that the military will forever heed Party commands"]
    >>> results = meteor.compute(predictions=predictions, references=references)
    >>> print(round(results["meteor"], 4))
    0.6944
'''






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






    def    UpperCamelCase       (       self     :							List[str]					)     ->		List[str]:
      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/nltk/nltk/blob/develop/nltk/translate/meteor_score.py']       ,						reference_urls=[
              'https://www.nltk.org/api/nltk.translate.html#module-nltk.translate.meteor_score',
              'https://en.wikipedia.org/wiki/METEOR',
          ]       ,						)






    def    UpperCamelCase       (       self     :							Optional[int]       ,						__SCREAMING_SNAKE_CASE     :							Union[str, Any]					)     ->		Dict:
      import nltk

      nltk.download('wordnet'					)
      if NLTK_VERSION >= version.Version('3.6.5'					):
        nltk.download('punkt'					)
      if NLTK_VERSION >= version.Version('3.6.6'					):
        nltk.download('omw-1.4'					)






    def    UpperCamelCase       (       self     :							Optional[int]       ,						__SCREAMING_SNAKE_CASE     :							Any       ,						__SCREAMING_SNAKE_CASE     :							Any       ,						__SCREAMING_SNAKE_CASE     :							List[Any]=0.9       ,						__SCREAMING_SNAKE_CASE     :							Dict=3       ,						__SCREAMING_SNAKE_CASE     :							Union[str, Any]=0.5					)     ->		int:
      if NLTK_VERSION >= version.Version('3.6.5'					):
        lowerCamelCase_         =						[
            meteor_score.single_meteor_score(
                word_tokenize(__SCREAMING_SNAKE_CASE					)       ,						word_tokenize(__SCREAMING_SNAKE_CASE					)       ,						alpha=__SCREAMING_SNAKE_CASE       ,						beta=__SCREAMING_SNAKE_CASE       ,						gamma=__SCREAMING_SNAKE_CASE					)
            for ref, pred in zip(__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE					)
        ]
      else:
        lowerCamelCase_         =						[
            meteor_score.single_meteor_score(__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE       ,						alpha=__SCREAMING_SNAKE_CASE       ,						beta=__SCREAMING_SNAKE_CASE       ,						gamma=__SCREAMING_SNAKE_CASE					)
            for ref, pred in zip(__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE					)
        ]

      return {"meteor": np.mean(__SCREAMING_SNAKE_CASE					)}




