






import numpy as np
from nltk.translate import meteor_score

import datasets
from datasets.config import importlib_metadata, version


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


__a      :       List[str]				  =   "\\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"

__a      :       Dict				  =   "\\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"

__a      :       Optional[int]				  =   "\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       __lowercase      (	datasets.Metric  ):







 '''simple docstring'''




 def 		lowerCAmelCase_     (				self       :			str				):


     """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/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 		lowerCAmelCase_     (				self       :			Tuple ,	UpperCamelCase_       :			Optional[int]				):


     """simple docstring"""



     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 		lowerCAmelCase_     (				self       :			List[str] ,	UpperCamelCase_       :			int ,	UpperCamelCase_       :			Optional[Any] ,	UpperCamelCase_       :			Tuple=0.9 ,	UpperCamelCase_       :			Dict=3 ,	UpperCamelCase_       :			Any=0.5				):


     """simple docstring"""



     if NLTK_VERSION >= version.Version("""3.6.5"""				):
         __A         =	[
             meteor_score.single_meteor_score(
                 word_tokenize(UpperCamelCase_				) ,	word_tokenize(UpperCamelCase_				) ,	alpha=UpperCamelCase_ ,	beta=UpperCamelCase_ ,	gamma=UpperCamelCase_				)
             for ref, pred in zip(UpperCamelCase_ ,	UpperCamelCase_				)
         ]
     else:
         __A         =	[
             meteor_score.single_meteor_score(UpperCamelCase_ ,	UpperCamelCase_ ,	alpha=UpperCamelCase_ ,	beta=UpperCamelCase_ ,	gamma=UpperCamelCase_				)
             for ref, pred in zip(UpperCamelCase_ ,	UpperCamelCase_				)
         ]

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












import unittest

from transformers.testing_utils import CaptureStdout
from transformers.tools.python_interpreter import evaluate



def 			_SCREAMING_SNAKE_CASE						( __lowercase   :			Optional[int]    )						->      List[str]:





    """simple docstring"""

    return x + 2






class       __lowercase      (	unittest.TestCase  ):







     '''simple docstring'''




     def 		lowerCAmelCase_     (				self       :			List[Any]				):


         """simple docstring"""



         __A         =	"""x = 3"""
         __A         =	{}
         __A         =	evaluate(UpperCamelCase_ ,	{} ,	state=UpperCamelCase_				)
         assert result == 3
         self.assertDictEqual(UpperCamelCase_ ,	{"""x""": 3}				)

         __A         =	"""x = y"""
         __A         =	{"""y""": 5}
         __A         =	evaluate(UpperCamelCase_ ,	{} ,	state=UpperCamelCase_				)
         # evaluate returns the value of the last assignment.
         assert result == 5
         self.assertDictEqual(UpperCamelCase_ ,	{"""x""": 5, """y""": 5}				)




     def 		lowerCAmelCase_     (				self       :			List[Any]				):


         """simple docstring"""



         __A         =	"""y = add_two(x)"""
         __A         =	{"""x""": 3}
         __A         =	evaluate(UpperCamelCase_ ,	{"""add_two""": add_two} ,	state=UpperCamelCase_				)
         assert result == 5
         self.assertDictEqual(UpperCamelCase_ ,	{"""x""": 3, """y""": 5}				)

         # Won't work without the tool
         with CaptureStdout() as out:
             __A         =	evaluate(UpperCamelCase_ ,	{} ,	state=UpperCamelCase_				)
         assert result is None
         assert "tried to execute add_two" in out.out




     def 		lowerCAmelCase_     (				self       :			Optional[Any]				):


         """simple docstring"""



         __A         =	"""x = 3"""
         __A         =	{}
         __A         =	evaluate(UpperCamelCase_ ,	{} ,	state=UpperCamelCase_				)
         assert result == 3
         self.assertDictEqual(UpperCamelCase_ ,	{"""x""": 3}				)




     def 		lowerCAmelCase_     (				self       :			Any				):


         """simple docstring"""



         __A         =	"""test_dict = {'x': x, 'y': add_two(x)}"""
         __A         =	{"""x""": 3}
         __A         =	evaluate(UpperCamelCase_ ,	{"""add_two""": add_two} ,	state=UpperCamelCase_				)
         self.assertDictEqual(UpperCamelCase_ ,	{"""x""": 3, """y""": 5}				)
         self.assertDictEqual(UpperCamelCase_ ,	{"""x""": 3, """test_dict""": {"""x""": 3, """y""": 5}}				)




     def 		lowerCAmelCase_     (				self       :			str				):


         """simple docstring"""



         __A         =	"""x = 3\ny = 5"""
         __A         =	{}
         __A         =	evaluate(UpperCamelCase_ ,	{} ,	state=UpperCamelCase_				)
         # evaluate returns the value of the last assignment.
         assert result == 5
         self.assertDictEqual(UpperCamelCase_ ,	{"""x""": 3, """y""": 5}				)




     def 		lowerCAmelCase_     (				self       :			Optional[int]				):


         """simple docstring"""



         __A         =	"""text = f'This is x: {x}.'"""
         __A         =	{"""x""": 3}
         __A         =	evaluate(UpperCamelCase_ ,	{} ,	state=UpperCamelCase_				)
         # evaluate returns the value of the last assignment.
         assert result == "This is x: 3."
         self.assertDictEqual(UpperCamelCase_ ,	{"""x""": 3, """text""": """This is x: 3."""}				)




     def 		lowerCAmelCase_     (				self       :			List[str]				):


         """simple docstring"""



         __A         =	"""if x <= 3:\n    y = 2\nelse:\n    y = 5"""
         __A         =	{"""x""": 3}
         __A         =	evaluate(UpperCamelCase_ ,	{} ,	state=UpperCamelCase_				)
         # evaluate returns the value of the last assignment.
         assert result == 2
         self.assertDictEqual(UpperCamelCase_ ,	{"""x""": 3, """y""": 2}				)

         __A         =	{"""x""": 8}
         __A         =	evaluate(UpperCamelCase_ ,	{} ,	state=UpperCamelCase_				)
         # evaluate returns the value of the last assignment.
         assert result == 5
         self.assertDictEqual(UpperCamelCase_ ,	{"""x""": 8, """y""": 5}				)




     def 		lowerCAmelCase_     (				self       :			Optional[int]				):


         """simple docstring"""



         __A         =	"""test_list = [x, add_two(x)]"""
         __A         =	{"""x""": 3}
         __A         =	evaluate(UpperCamelCase_ ,	{"""add_two""": add_two} ,	state=UpperCamelCase_				)
         self.assertListEqual(UpperCamelCase_ ,	[3, 5]				)
         self.assertDictEqual(UpperCamelCase_ ,	{"""x""": 3, """test_list""": [3, 5]}				)




     def 		lowerCAmelCase_     (				self       :			Dict				):


         """simple docstring"""



         __A         =	"""y = x"""
         __A         =	{"""x""": 3}
         __A         =	evaluate(UpperCamelCase_ ,	{} ,	state=UpperCamelCase_				)
         assert result == 3
         self.assertDictEqual(UpperCamelCase_ ,	{"""x""": 3, """y""": 3}				)




     def 		lowerCAmelCase_     (				self       :			List[Any]				):


         """simple docstring"""



         __A         =	"""test_list = [x, add_two(x)]\ntest_list[1]"""
         __A         =	{"""x""": 3}
         __A         =	evaluate(UpperCamelCase_ ,	{"""add_two""": add_two} ,	state=UpperCamelCase_				)
         assert result == 5
         self.assertDictEqual(UpperCamelCase_ ,	{"""x""": 3, """test_list""": [3, 5]}				)

         __A         =	"""test_dict = {'x': x, 'y': add_two(x)}\ntest_dict['y']"""
         __A         =	{"""x""": 3}
         __A         =	evaluate(UpperCamelCase_ ,	{"""add_two""": add_two} ,	state=UpperCamelCase_				)
         assert result == 5
         self.assertDictEqual(UpperCamelCase_ ,	{"""x""": 3, """test_dict""": {"""x""": 3, """y""": 5}}				)





     def 		lowerCAmelCase_     (				self       :			Any				):


         """simple docstring"""



         __A         =	"""x = 0\nfor i in range(3):\n    x = i"""
         __A         =	{}
         __A         =	evaluate(UpperCamelCase_ ,	{"""range""": range} ,	state=UpperCamelCase_				)
         assert result == 2
         self.assertDictEqual(UpperCamelCase_ ,	{"""x""": 2, """i""": 2}				)




