





import os
import sys
import unittest


__lowerCamelCase       :     List[str]   =     os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, """utils"""))

import check_dummies  # noqa: E402
from check_dummies import create_dummy_files, create_dummy_object, find_backend, read_init  # noqa: E402


# Align TRANSFORMERS_PATH in check_dummies with the current path
__lowerCamelCase       :     List[Any]   =     os.path.join(git_repo_path, """src""", """transformers""")

__lowerCamelCase       :     Tuple   =     """
{0} = None
"""

__lowerCamelCase       :     List[Any]   =     """
class {0}(metaclass=DummyObject):
    _backends = {1}

    def __init__(self, *args, **kwargs):
        requires_backends(self, {1})
"""


__lowerCamelCase       :     Optional[Any]   =     """
def {0}(*args, **kwargs):
    requires_backends({0}, {1})
"""



class 					A__   (       unittest.TestCase			):





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       UpperCamelCase	:					Tuple       	=   find_backend("    _import_structure[\"models.albert\"].append(\"AlbertTokenizerFast\")"   )
       self.assertIsNone(A_   )

       UpperCamelCase	:					List[Any]       	=   find_backend("    if not is_tokenizers_available():"   )
       self.assertEqual(A_       ,							"tokenizers"   )

       UpperCamelCase	:					Union[str, Any]       	=   find_backend("    if not is_tensorflow_text_available():"   )
       self.assertEqual(A_       ,							"tensorflow_text"   )

       UpperCamelCase	:					int       	=   find_backend("    if not (is_sentencepiece_available() and is_tokenizers_available()):"   )
       self.assertEqual(A_       ,							"sentencepiece_and_tokenizers"   )

       UpperCamelCase	:					Tuple       	=   find_backend(
           "    if not (is_sentencepiece_available() and is_tensorflow_text_available()):"   )
       self.assertEqual(A_       ,							"sentencepiece_and_tensorflow_text"   )

       UpperCamelCase	:					List[Any]       	=   find_backend(
           "    if not (is_sentencepiece_available() and is_tokenizers_available() and is_vision_available()):"   )
       self.assertEqual(A_       ,							"sentencepiece_and_tokenizers_and_vision"   )





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       UpperCamelCase	:					List[str]       	=   read_init()
       # We don't assert on the exact list of keys to allow for smooth grow of backend-specific objects
       self.assertIn("torch"       ,							A_   )
       self.assertIn("tensorflow_text"       ,							A_   )
       self.assertIn("sentencepiece_and_tokenizers"       ,							A_   )

       # Likewise, we can't assert on the exact content of a key
       self.assertIn("BertModel"       ,							objects["torch"]   )
       self.assertIn("TFBertModel"       ,							objects["tf"]   )
       self.assertIn("FlaxBertModel"       ,							objects["flax"]   )
       self.assertIn("BertModel"       ,							objects["torch"]   )
       self.assertIn("TFBertTokenizer"       ,							objects["tensorflow_text"]   )
       self.assertIn("convert_slow_tokenizer"       ,							objects["sentencepiece_and_tokenizers"]   )





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       UpperCamelCase	:					Tuple       	=   create_dummy_object("CONSTANT"       ,							"'torch'"   )
       self.assertEqual(A_       ,							"\nCONSTANT = None\n"   )

       UpperCamelCase	:					Optional[Any]       	=   create_dummy_object("function"       ,							"'torch'"   )
       self.assertEqual(
           A_       ,							"\ndef function(*args, **kwargs):\n    requires_backends(function, 'torch')\n"   )

       UpperCamelCase	:					Optional[Any]       	=   "\nclass FakeClass(metaclass=DummyObject):\n    _backends = 'torch'\n\n    def __init__(self, *args, **kwargs):\n        requires_backends(self, 'torch')\n"
       UpperCamelCase	:					Dict       	=   create_dummy_object("FakeClass"       ,							"'torch'"   )
       self.assertEqual(A_       ,							A_   )





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       UpperCamelCase	:					List[Any]       	=   "# This file is autogenerated by the command `make fix-copies`, do not edit.\nfrom ..utils import DummyObject, requires_backends\n\n\nCONSTANT = None\n\n\ndef function(*args, **kwargs):\n    requires_backends(function, [\"torch\"])\n\n\nclass FakeClass(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n        requires_backends(self, [\"torch\"])\n"
       UpperCamelCase	:					str       	=   create_dummy_files({"torch": ["CONSTANT", "function", "FakeClass"]}   )
       self.assertEqual(dummy_files["torch"]       ,							A_   )





"""simple docstring"""







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        __A       (	datasets.Metric    ):







		'''simple docstring'''





		def 					UpperCAmelCase    (	self						:  Optional[int]						)					->		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 					UpperCAmelCase    (	self						:  str			,_snake_case						:  Dict						)					->		Dict:






				"""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 					UpperCAmelCase    (	self						:  Dict			,_snake_case						:  Dict			,_snake_case						:  List[str]			,_snake_case						:  Tuple=0.9			,_snake_case						:  Optional[int]=3			,_snake_case						:  Union[str, Any]=0.5						)					->		List[str]:






				"""simple docstring"""



				if NLTK_VERSION >= version.Version('''3.6.5'''						):
						lowercase__   :    int				=			[
						    meteor_score.single_meteor_score(
						        word_tokenize(_snake_case						)			,word_tokenize(_snake_case						)			,alpha=_snake_case			,beta=_snake_case			,gamma=_snake_case						)
						    for ref, pred in zip(_snake_case			,_snake_case						)
						]
				else:
						lowercase__   :    Tuple				=			[
						    meteor_score.single_meteor_score(_snake_case			,_snake_case			,alpha=_snake_case			,beta=_snake_case			,gamma=_snake_case						)
						    for ref, pred in zip(_snake_case			,_snake_case						)
						]

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