





import numpy as np
from nltk.translate import meteor_score

import datasets
from datasets.config import importlib_metadata, version


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


UpperCamelCase_												=	'\\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'

UpperCamelCase_												=	'\\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'

UpperCamelCase_												=	'\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     snake_case_		(   datasets.Metric							):






 '''simple docstring'''





 def  __UpperCAmelCase						(  self		)	->       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,		A_		)	->       Union[str, Any]:
    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,		A_,		A_,		A_=0.9,		A_=3,		A_=0.5		)	->       Union[str, Any]:
    if NLTK_VERSION >= version.Version("3.6.5"		):
       UpperCAmelCase__						=[
           meteor_score.single_meteor_score(
               word_tokenize(A_		),		word_tokenize(A_		),		alpha=A_,		beta=A_,		gamma=A_		)
           for ref, pred in zip(A_,		A_		)
       ]
    else:
       UpperCAmelCase__						=[
           meteor_score.single_meteor_score(A_,		A_,		alpha=A_,		beta=A_,		gamma=A_		)
           for ref, pred in zip(A_,		A_		)
       ]

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










import os
import zipfile

import requests
from get_ci_error_statistics import download_artifact, get_artifacts_links






def 				_UpperCAmelCase			(    A				,							A=7     ):




   '''simple docstring'''

   UpperCAmelCase__						=None
   if token is not None:
      UpperCAmelCase__						={"Accept": "application/vnd.github+json", "Authorization": F"""Bearer {token}"""}

   # The id of a workflow (not of a workflow run)
   UpperCAmelCase__						="636036"

   UpperCAmelCase__						=F"""https://api.github.com/repos/huggingface/transformers/actions/workflows/{workflow_id}/runs"""
   # On `main` branch + event being `schedule` + not returning PRs + only `num_runs` results
   url += F"""?branch=main&event=schedule&exclude_pull_requests=true&per_page={num_runs}"""

   UpperCAmelCase__						=requests.get(A				,							headers=A     ).json()

   return result["workflow_runs"]






def 				_UpperCAmelCase			(    A     ):




   '''simple docstring'''

   UpperCAmelCase__						=get_daily_ci_runs(A     )
   UpperCAmelCase__						=None
   for workflow_run in workflow_runs:
      if workflow_run["status"] == "completed":
         UpperCAmelCase__						=workflow_run["id"]
         break

   return workflow_run_id






def 				_UpperCAmelCase			(    A				,							A				,							A     ):




   '''simple docstring'''

   UpperCAmelCase__						=get_last_daily_ci_runs(A     )
   if workflow_run_id is not None:
      UpperCAmelCase__						=get_artifacts_links(worflow_run_id=A				,							token=A     )
      for artifact_name in artifact_names:
         if artifact_name in artifacts_links:
            UpperCAmelCase__						=artifacts_links[artifact_name]
            download_artifact(
                artifact_name=A				,							artifact_url=A				,							output_dir=A				,							token=A     )






def 				_UpperCAmelCase			(    A				,							A				,							A     ):




   '''simple docstring'''

   get_last_daily_ci_artifacts(A				,							A				,							A     )

   UpperCAmelCase__						={}
   for artifact_name in artifact_names:
      UpperCAmelCase__						=os.path.join(A				,							F"""{artifact_name}.zip"""     )
      if os.path.isfile(A     ):
         UpperCAmelCase__						={}
         with zipfile.ZipFile(A     ) as z:
            for filename in z.namelist():
               if not os.path.isdir(A     ):
                  # read the file
                  with z.open(A     ) as f:
                     UpperCAmelCase__						=f.read().decode("UTF-8"     )

   return results



