

import argparse
import os
import re

import packaging.version


lowerCAmelCase__       = "examples/"
lowerCAmelCase__       = {
    "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"),
}
lowerCAmelCase__       = {
    "init": "src/transformers/__init__.py",
    "setup": "setup.py",
}
lowerCAmelCase__       = "README.md"
def      _lowerCAmelCase(	__A   ,       __A   ,       __A			):





  with open(UpperCamelCase__   ,       "r"   ,       encoding="utf-8"   ,       newline="\n"			) as f:
    UpperCAmelCase					      =  f.read()
  UpperCAmelCase      ,	UpperCAmelCase					      =  REPLACE_PATTERNS[pattern]
  UpperCAmelCase					      =  replace.replace("VERSION"   ,       UpperCamelCase__			)
  UpperCAmelCase					      =  re_pattern.sub(UpperCamelCase__   ,       UpperCamelCase__			)
  with open(UpperCamelCase__   ,       "w"   ,       encoding="utf-8"   ,       newline="\n"			) as f:
    f.write(UpperCamelCase__			)
def      _lowerCAmelCase(	__A			):





  for folder, directories, fnames in os.walk(UpperCamelCase__			):
    # 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(UpperCamelCase__   ,       UpperCamelCase__			)   ,       UpperCamelCase__   ,       pattern="examples"			)
def      _lowerCAmelCase(	__A   ,       __A=False			):





  for pattern, fname in REPLACE_FILES.items():
    update_version_in_file(UpperCamelCase__   ,       UpperCamelCase__   ,       UpperCamelCase__			)
  if not patch:
    update_version_in_examples(UpperCamelCase__			)
def      _lowerCAmelCase(	):
  UpperCAmelCase					      =  "🤗 Transformers currently provides the following architectures"
  UpperCAmelCase					      =  "1. Want to contribute a new model?"
  with open(UpperCamelCase__   ,       "r"   ,       encoding="utf-8"   ,       newline="\n"			) as f:
    UpperCAmelCase					      =  f.readlines()

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

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

  with open(UpperCamelCase__   ,       "w"   ,       encoding="utf-8"   ,       newline="\n"			) as f:
    f.writelines(UpperCamelCase__			)
def      _lowerCAmelCase(	):





  with open(REPLACE_FILES["init"]   ,       "r"			) as f:
    UpperCAmelCase					      =  f.read()
  UpperCAmelCase					      =  REPLACE_PATTERNS["init"][0].search(UpperCamelCase__			).groups()[0]
  return packaging.version.parse(UpperCamelCase__			)
def      _lowerCAmelCase(	__A=False			):
  UpperCAmelCase					      =  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:
    UpperCAmelCase					      =  default_version.base_version
  elif patch:
    UpperCAmelCase					      =  F"{default_version.major}.{default_version.minor}.{default_version.micro + 1}"
  else:
    UpperCAmelCase					      =  F"{default_version.major}.{default_version.minor + 1}.0"

  # Now let's ask nicely if that's the right one.
  UpperCAmelCase					      =  input(F"Which version are you releasing? [{default_version}]"			)
  if len(UpperCamelCase__			) == 0:
    UpperCAmelCase					      =  default_version

  print(F"Updating version to {version}."			)
  global_version_update(UpperCamelCase__   ,       patch=UpperCamelCase__			)
  if not patch:
    print("Cleaning main README, don\'t forget to run `make fix-copies`."			)
    clean_main_ref_in_model_list()
def      _lowerCAmelCase(	):
  UpperCAmelCase					      =  get_version()
  UpperCAmelCase					      =  F"{current_version.major}.{current_version.minor + 1}.0.dev0"
  UpperCAmelCase					      =  current_version.base_version

  # Check with the user we got that right.
  UpperCAmelCase					      =  input(F"Which version are we developing now? [{dev_version}]"			)
  if len(UpperCamelCase__			) == 0:
    UpperCAmelCase					      =  dev_version

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


if __name__ == "__main__":
      lowerCAmelCase__       = 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.")
      lowerCAmelCase__       = 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()






import numpy as np
from nltk.translate import meteor_score

import datasets
from datasets.config import importlib_metadata, version


lowerCAmelCase__       = version.parse(importlib_metadata.version("nltk"))
if NLTK_VERSION >= version.Version("3.6.4"):
      from nltk import word_tokenize


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

lowerCAmelCase__       = "\\nMETEOR, an automatic metric for machine translation evaluation\nthat is based on a generalized concept of unigram matching between the\nmachine-produced translation and human-produced reference translations.\nUnigrams can be matched based on their surface forms, stemmed forms,\nand meanings; furthermore, METEOR can be easily extended to include more\nadvanced matching strategies. Once all generalized unigram matches\nbetween the two strings have been found, METEOR computes a score for\nthis matching using a combination of unigram-precision, unigram-recall, and\na measure of fragmentation that is designed to directly capture how\nwell-ordered the matched words in the machine translation are in relation\nto the reference.\n\nMETEOR gets an R correlation value of 0.347 with human evaluation on the Arabic\ndata and 0.331 on the Chinese data. This is shown to be an improvement on\nusing simply unigram-precision, unigram-recall and their harmonic F1\ncombination.\n"

lowerCAmelCase__       = "\nComputes METEOR score of translated segments against one or more references.\nArgs:\n    predictions: list of predictions to score. Each prediction\n        should be a string with tokens separated by spaces.\n    references: list of reference for each prediction. Each\n        reference should be a string with tokens separated by spaces.\n    alpha: Parameter for controlling relative weights of precision and recall. default: 0.9\n    beta: Parameter for controlling shape of penalty as a function of fragmentation. default: 3\n    gamma: Relative weight assigned to fragmentation penalty. default: 0.5\nReturns:\n    'meteor': meteor score.\nExamples:\n\n    >>> meteor = datasets.load_metric('meteor')\n    >>> predictions = [\"It is a guide to action which ensures that the military always obeys the commands of the party\"]\n    >>> references = [\"It is a guide to action that ensures that the military will forever heed Party commands\"]\n    >>> results = meteor.compute(predictions=predictions, references=references)\n    >>> print(round(results[\"meteor\"], 4))\n    0.6944\n"






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


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

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



