





import doctest
import logging
import os
import unittest
from pathlib import Path
from typing import List, Union

import transformers
from transformers.testing_utils import require_tf, require_torch, slow


__lowercase       :Tuple       =  logging.getLogger()



@unittest.skip("Temporarily disable the doc tests."							)
@require_torch
@require_tf
@slow
class    _a     (				unittest.TestCase							):






							"""simple docstring"""

							def      A_  (      self				:     List[str]							,	a				:     Path							,	a				:     Union[str, None] = None							,	a				:     Union[List[str], None] = None							,	a				:     Union[str, List[str], None] = None							,	a				:     bool = True							,	)	->List[str]:
													SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					[file for file in os.listdir(a    ) if os.path.isfile(os.path.join(a							,	a    )    )]

													if identifier is not None:
																			SCREAMING_SNAKE_CASE__     :				Dict   =					[file for file in files if identifier in file]

													if n_identifier is not None:
																			if isinstance(a							,	a    ):
																									for n_ in n_identifier:
																															SCREAMING_SNAKE_CASE__     :				Tuple   =					[file for file in files if n_ not in file]
																			else:
																									SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					[file for file in files if n_identifier not in file]

													SCREAMING_SNAKE_CASE__     :				int   =					ignore_files or []
													ignore_files.append("__init__.py"    )
													SCREAMING_SNAKE_CASE__     :				Dict   =					[file for file in files if file not in ignore_files]

													for file in files:
																			# Open all files
																			print("Testing"							,	a    )

																			if only_modules:
																									SCREAMING_SNAKE_CASE__     :				Optional[int]   =					file.split("."    )[0]
																									try:
																															SCREAMING_SNAKE_CASE__     :				str   =					getattr(a							,	a    )
																															SCREAMING_SNAKE_CASE__     :				Dict   =					doctest.DocTestSuite(a    )
																															SCREAMING_SNAKE_CASE__     :				Dict   =					unittest.TextTestRunner().run(a    )
																															self.assertIs(len(result.failures    )							,	0    )
																									except AttributeError:
																															logger.info(f"""{module_identifier} is not a module."""    )
																			else:
																									SCREAMING_SNAKE_CASE__     :				List[str]   =					doctest.testfile(str(".." / directory / file    )							,	optionflags=doctest.ELLIPSIS    )
																									self.assertIs(result.failed							,	0    )

							def      A_  (      self				:     Union[str, Any]    )	->Tuple:
													SCREAMING_SNAKE_CASE__     :				List[Any]   =					Path("src/transformers"    )
													SCREAMING_SNAKE_CASE__     :				List[Any]   =					"modeling"
													SCREAMING_SNAKE_CASE__     :				Tuple   =					[
													    "modeling_ctrl.py",
													    "modeling_tf_ctrl.py",
													]
													self.analyze_directory(a							,	identifier=a							,	ignore_files=a    )

							def      A_  (      self				:     Any    )	->Tuple:
													SCREAMING_SNAKE_CASE__     :				List[Any]   =					Path("src/transformers"    )
													SCREAMING_SNAKE_CASE__     :				List[Any]   =					"tokenization"
													self.analyze_directory(a							,	identifier=a    )

							def      A_  (      self				:     Any    )	->Tuple:
													SCREAMING_SNAKE_CASE__     :				Optional[int]   =					Path("src/transformers"    )
													SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					"configuration"
													self.analyze_directory(a							,	identifier=a    )

							def      A_  (      self				:     str    )	->Optional[Any]:
													SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					Path("src/transformers"    )
													SCREAMING_SNAKE_CASE__     :				Optional[int]   =					["configuration", "modeling", "tokenization"]
													self.analyze_directory(a							,	n_identifier=a    )






							def      A_  (      self				:     Optional[int]    )	->Any:
													SCREAMING_SNAKE_CASE__     :				Any   =					Path("docs/source"    )
													SCREAMING_SNAKE_CASE__     :				List[str]   =					["favicon.ico"]
													self.analyze_directory(a							,	ignore_files=a							,	only_modules=a    )






from typing import Dict, List

from nltk.translate import gleu_score

import datasets
from datasets import MetricInfo


__lowercase       :Tuple       =  "\\n@misc{wu2016googles,\n      title={Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation},\n      author={Yonghui Wu and Mike Schuster and Zhifeng Chen and Quoc V. Le and Mohammad Norouzi and Wolfgang Macherey\n              and Maxim Krikun and Yuan Cao and Qin Gao and Klaus Macherey and Jeff Klingner and Apurva Shah and Melvin\n              Johnson and Xiaobing Liu and Łukasz Kaiser and Stephan Gouws and Yoshikiyo Kato and Taku Kudo and Hideto\n              Kazawa and Keith Stevens and George Kurian and Nishant Patil and Wei Wang and Cliff Young and\n              Jason Smith and Jason Riesa and Alex Rudnick and Oriol Vinyals and Greg Corrado and Macduff Hughes\n              and Jeffrey Dean},\n      year={2016},\n      eprint={1609.08144},\n      archivePrefix={arXiv},\n      primaryClass={cs.CL}\n}\n"

__lowercase       :str       =  "\\nThe BLEU score has some undesirable properties when used for single\nsentences, as it was designed to be a corpus measure. We therefore\nuse a slightly different score for our RL experiments which we call\nthe 'GLEU score'. For the GLEU score, we record all sub-sequences of\n1, 2, 3 or 4 tokens in output and target sequence (n-grams). We then\ncompute a recall, which is the ratio of the number of matching n-grams\nto the number of total n-grams in the target (ground truth) sequence,\nand a precision, which is the ratio of the number of matching n-grams\nto the number of total n-grams in the generated output sequence. Then\nGLEU score is simply the minimum of recall and precision. This GLEU\nscore's range is always between 0 (no matches) and 1 (all match) and\nit is symmetrical when switching output and target. According to\nour experiments, GLEU score correlates quite well with the BLEU\nmetric on a corpus level but does not have its drawbacks for our per\nsentence reward objective.\n"

__lowercase       :List[Any]       =  "\\nComputes corpus-level Google BLEU (GLEU) score of translated segments against one or more references.\nInstead of averaging the sentence level GLEU scores (i.e. macro-average precision), Wu et al. (2016) sum up the matching\ntokens and the max of hypothesis and reference tokens for each sentence, then compute using the aggregate values.\n\nArgs:\n    predictions (list of str): list of translations to score.\n        Each translation should be tokenized into a list of tokens.\n    references (list of list of str): list of lists of references for each translation.\n        Each reference should be tokenized into a list of tokens.\n    min_len (int): The minimum order of n-gram this function should extract. Defaults to 1.\n    max_len (int): The maximum order of n-gram this function should extract. Defaults to 4.\n\nReturns:\n    'google_bleu': google_bleu score\n\nExamples:\n    Example 1:\n        >>> hyp1 = ['It', 'is', 'a', 'guide', 'to', 'action', 'which',\n        ...         'ensures', 'that', 'the', 'rubber', 'duck', 'always',\n        ...         'disobeys', 'the', 'commands', 'of', 'the', 'cat']\n        >>> ref1a = ['It', 'is', 'the', 'guiding', 'principle', 'which',\n        ...          'guarantees', 'the', 'rubber', 'duck', 'forces', 'never',\n        ...          'being', 'under', 'the', 'command', 'of', 'the', 'cat']\n\n        >>> hyp2 = ['he', 'read', 'the', 'book', 'because', 'he', 'was',\n        ...         'interested', 'in', 'world', 'history']\n        >>> ref2a = ['he', 'was', 'interested', 'in', 'world', 'history',\n        ...          'because', 'he', 'read', 'the', 'book']\n\n        >>> list_of_references = [[ref1a], [ref2a]]\n        >>> hypotheses = [hyp1, hyp2]\n        >>> google_bleu = datasets.load_metric(\"google_bleu\")\n        >>> results = google_bleu.compute(predictions=hypotheses, references=list_of_references)\n        >>> print(round(results[\"google_bleu\"], 2))\n        0.44\n\n    Example 2:\n        >>> hyp1 = ['It', 'is', 'a', 'guide', 'to', 'action', 'which',\n        ...         'ensures', 'that', 'the', 'rubber', 'duck', 'always',\n        ...         'disobeys', 'the', 'commands', 'of', 'the', 'cat']\n        >>> ref1a = ['It', 'is', 'the', 'guiding', 'principle', 'which',\n        ...          'guarantees', 'the', 'rubber', 'duck', 'forces', 'never',\n        ...          'being', 'under', 'the', 'command', 'of', 'the', 'cat']\n        >>> ref1b = ['It', 'is', 'a', 'guide', 'to', 'action', 'that',\n        ...          'ensures', 'that', 'the', 'rubber', 'duck', 'will', 'never',\n        ...          'heed', 'the', 'cat', 'commands']\n        >>> ref1c = ['It', 'is', 'the', 'practical', 'guide', 'for', 'the',\n        ...          'rubber', 'duck', 'army', 'never', 'to', 'heed', 'the', 'directions',\n        ...          'of', 'the', 'cat']\n\n        >>> hyp2 = ['he', 'read', 'the', 'book', 'because', 'he', 'was',\n        ...         'interested', 'in', 'world', 'history']\n        >>> ref2a = ['he', 'was', 'interested', 'in', 'world', 'history',\n        ...          'because', 'he', 'read', 'the', 'book']\n\n        >>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]]\n        >>> hypotheses = [hyp1, hyp2]\n        >>> google_bleu = datasets.load_metric(\"google_bleu\")\n        >>> results = google_bleu.compute(predictions=hypotheses, references=list_of_references)\n        >>> print(round(results[\"google_bleu\"], 2))\n        0.61\n\n    Example 3:\n        >>> hyp1 = ['It', 'is', 'a', 'guide', 'to', 'action', 'which',\n        ...         'ensures', 'that', 'the', 'rubber', 'duck', 'always',\n        ...         'disobeys', 'the', 'commands', 'of', 'the', 'cat']\n        >>> ref1a = ['It', 'is', 'the', 'guiding', 'principle', 'which',\n        ...          'guarantees', 'the', 'rubber', 'duck', 'forces', 'never',\n        ...          'being', 'under', 'the', 'command', 'of', 'the', 'cat']\n        >>> ref1b = ['It', 'is', 'a', 'guide', 'to', 'action', 'that',\n        ...          'ensures', 'that', 'the', 'rubber', 'duck', 'will', 'never',\n        ...          'heed', 'the', 'cat', 'commands']\n        >>> ref1c = ['It', 'is', 'the', 'practical', 'guide', 'for', 'the',\n        ...          'rubber', 'duck', 'army', 'never', 'to', 'heed', 'the', 'directions',\n        ...          'of', 'the', 'cat']\n\n        >>> hyp2 = ['he', 'read', 'the', 'book', 'because', 'he', 'was',\n        ...         'interested', 'in', 'world', 'history']\n        >>> ref2a = ['he', 'was', 'interested', 'in', 'world', 'history',\n        ...          'because', 'he', 'read', 'the', 'book']\n\n        >>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]]\n        >>> hypotheses = [hyp1, hyp2]\n        >>> google_bleu = datasets.load_metric(\"google_bleu\")\n        >>> results = google_bleu.compute(predictions=hypotheses, references=list_of_references, min_len=2)\n        >>> print(round(results[\"google_bleu\"], 2))\n        0.53\n\n    Example 4:\n        >>> hyp1 = ['It', 'is', 'a', 'guide', 'to', 'action', 'which',\n        ...         'ensures', 'that', 'the', 'rubber', 'duck', 'always',\n        ...         'disobeys', 'the', 'commands', 'of', 'the', 'cat']\n        >>> ref1a = ['It', 'is', 'the', 'guiding', 'principle', 'which',\n        ...          'guarantees', 'the', 'rubber', 'duck', 'forces', 'never',\n        ...          'being', 'under', 'the', 'command', 'of', 'the', 'cat']\n        >>> ref1b = ['It', 'is', 'a', 'guide', 'to', 'action', 'that',\n        ...          'ensures', 'that', 'the', 'rubber', 'duck', 'will', 'never',\n        ...          'heed', 'the', 'cat', 'commands']\n        >>> ref1c = ['It', 'is', 'the', 'practical', 'guide', 'for', 'the',\n        ...          'rubber', 'duck', 'army', 'never', 'to', 'heed', 'the', 'directions',\n        ...          'of', 'the', 'cat']\n\n        >>> hyp2 = ['he', 'read', 'the', 'book', 'because', 'he', 'was',\n        ...         'interested', 'in', 'world', 'history']\n        >>> ref2a = ['he', 'was', 'interested', 'in', 'world', 'history',\n        ...          'because', 'he', 'read', 'the', 'book']\n\n        >>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]]\n        >>> hypotheses = [hyp1, hyp2]\n        >>> google_bleu = datasets.load_metric(\"google_bleu\")\n        >>> results = google_bleu.compute(predictions=hypotheses,references=list_of_references, min_len=2, max_len=6)\n        >>> print(round(results[\"google_bleu\"], 2))\n        0.4\n"



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






							"""simple docstring"""

							def      A_  (      self				:     List[Any]    )	->MetricInfo:
													return datasets.MetricInfo(
													    description=_DESCRIPTION							,	citation=_CITATION							,	inputs_description=_KWARGS_DESCRIPTION							,	features=datasets.Features(
													        {
													            "predictions": datasets.Sequence(datasets.Value("string"							,	id="token"    )							,	id="sequence"    ),
													            "references": datasets.Sequence(
													                datasets.Sequence(datasets.Value("string"							,	id="token"    )							,	id="sequence"    )							,	id="references"    ),
													        }    )							,	)






							def      A_  (      self				:     str							,	a				:     List[List[List[str]]]							,	a				:     List[List[str]]							,	a				:     int = 1							,	a				:     int = 4							,	)	->Dict[str, float]:
													return {
													    "google_bleu": gleu_score.corpus_gleu(
													        list_of_references=a							,	hypotheses=a							,	min_len=a							,	max_len=a    )
													}