


'''simple docstring'''



import json
import os
import unittest

from transformers.models.roc_bert.tokenization_roc_bert import (
    VOCAB_FILES_NAMES,
    RoCBertBasicTokenizer,
    RoCBertTokenizer,
    RoCBertWordpieceTokenizer,
    _is_control,
    _is_punctuation,
    _is_whitespace,
)
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin, filter_non_english





@require_tokenizers
class 							__UpperCamelCase				(		lowerCAmelCase_		,      unittest.TestCase					):
	A_				=    RoCBertTokenizer
	A_				=    None
	A_				=    False
	A_				=    True
	A_				=    filter_non_english




	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						super().setUp()

						__a			:       List[Any]					    =  ['[UNK]', '[CLS]', '[SEP]', '[PAD]', '[MASK]', '你', '好', '是', '谁', 'a', 'b', 'c', 'd']
						__a			:       Tuple					    =  {}
						__a			:       Tuple					    =  {}
						for i, value in enumerate(__a							):
											__a			:       str					    =  i
											__a			:       int					    =  i
						__a			:       List[Any]					    =  os.path.join(self.tmpdirname						,				VOCAB_FILES_NAMES['vocab_file']							)
						__a			:       int					    =  os.path.join(self.tmpdirname						,				VOCAB_FILES_NAMES['word_shape_file']							)
						__a			:       Tuple					    =  os.path.join(self.tmpdirname						,				VOCAB_FILES_NAMES['word_pronunciation_file']							)
						with open(self.vocab_file						,				'w'						,				encoding='utf-8'							) as vocab_writer:
											vocab_writer.write(''.join([x + '\n' for x in vocab_tokens]							)							)
						with open(self.word_shape_file						,				'w'						,				encoding='utf-8'							) as word_shape_writer:
											json.dump(__a						,				__a						,				ensure_ascii=__a							)
						with open(self.word_pronunciation_file						,				'w'						,				encoding='utf-8'							) as word_pronunciation_writer:
											json.dump(__a						,				__a						,				ensure_ascii=__a							)




	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						__a			:       Any					    =  self.tokenizer_class(self.vocab_file						,				self.word_shape_file						,				self.word_pronunciation_file							)

						__a			:       List[str]					    =  tokenizer.tokenize('你好[SEP]你是谁'							)
						self.assertListEqual(__a						,				['你', '好', '[SEP]', '你', '是', '谁']							)
						self.assertListEqual(tokenizer.convert_tokens_to_ids(__a							)						,				[5, 6, 2, 5, 7, 8]							)
						self.assertListEqual(tokenizer.convert_tokens_to_shape_ids(__a							)						,				[5, 6, 2, 5, 7, 8]							)
						self.assertListEqual(tokenizer.convert_tokens_to_pronunciation_ids(__a							)						,				[5, 6, 2, 5, 7, 8]							)




	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						__a			:       List[Any]					    =  RoCBertBasicTokenizer()

						self.assertListEqual(tokenizer.tokenize('ah\u535A\u63A8zz'							)						,				['ah', '\u535A', '\u63A8', 'zz']							)




	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						__a			:       Tuple					    =  RoCBertBasicTokenizer(do_lower_case=__a							)

						self.assertListEqual(
						    tokenizer.tokenize(' \tHeLLo!how  \n Are yoU?  '							)						,				['hello', '!', 'how', 'are', 'you', '?']							)
						self.assertListEqual(tokenizer.tokenize('H\u00E9llo'							)						,				['hello']							)




	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						__a			:       Tuple					    =  RoCBertBasicTokenizer(do_lower_case=__a						,				strip_accents=__a							)

						self.assertListEqual(
						    tokenizer.tokenize(' \tHäLLo!how  \n Are yoU?  '							)						,				['hällo', '!', 'how', 'are', 'you', '?']							)
						self.assertListEqual(tokenizer.tokenize('H\u00E9llo'							)						,				['h\u00E9llo']							)




	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						__a			:       Union[str, Any]					    =  RoCBertBasicTokenizer(do_lower_case=__a						,				strip_accents=__a							)

						self.assertListEqual(
						    tokenizer.tokenize(' \tHäLLo!how  \n Are yoU?  '							)						,				['hallo', '!', 'how', 'are', 'you', '?']							)
						self.assertListEqual(tokenizer.tokenize('H\u00E9llo'							)						,				['hello']							)




	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						__a			:       str					    =  RoCBertBasicTokenizer(do_lower_case=__a							)

						self.assertListEqual(
						    tokenizer.tokenize(' \tHäLLo!how  \n Are yoU?  '							)						,				['hallo', '!', 'how', 'are', 'you', '?']							)
						self.assertListEqual(tokenizer.tokenize('H\u00E9llo'							)						,				['hello']							)




	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						__a			:       int					    =  RoCBertBasicTokenizer(do_lower_case=__a							)

						self.assertListEqual(
						    tokenizer.tokenize(' \tHeLLo!how  \n Are yoU?  '							)						,				['HeLLo', '!', 'how', 'Are', 'yoU', '?']							)




	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						__a			:       Union[str, Any]					    =  RoCBertBasicTokenizer(do_lower_case=__a						,				strip_accents=__a							)

						self.assertListEqual(
						    tokenizer.tokenize(' \tHäLLo!how  \n Are yoU?  '							)						,				['HäLLo', '!', 'how', 'Are', 'yoU', '?']							)




	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						__a			:       str					    =  RoCBertBasicTokenizer(do_lower_case=__a						,				strip_accents=__a							)

						self.assertListEqual(
						    tokenizer.tokenize(' \tHäLLo!how  \n Are yoU?  '							)						,				['HaLLo', '!', 'how', 'Are', 'yoU', '?']							)




	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						__a			:       Union[str, Any]					    =  RoCBertBasicTokenizer(do_lower_case=__a						,				never_split=['[UNK]']							)

						self.assertListEqual(
						    tokenizer.tokenize(' \tHeLLo!how  \n Are yoU? [UNK]'							)						,				['HeLLo', '!', 'how', 'Are', 'yoU', '?', '[UNK]']							)




	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						__a			:       str					    =  ['[UNK]', '[CLS]', '[SEP]', 'want', '##want', '##ed', 'wa', 'un', 'runn', '##ing']

						__a			:       Union[str, Any]					    =  {}
						for i, token in enumerate(__a							):
											__a			:       Any					    =  i
						__a			:       Union[str, Any]					    =  RoCBertWordpieceTokenizer(vocab=__a						,				unk_token='[UNK]'							)

						self.assertListEqual(tokenizer.tokenize(''							)						,				[]							)

						self.assertListEqual(tokenizer.tokenize('unwanted running'							)						,				['un', '##want', '##ed', 'runn', '##ing']							)

						self.assertListEqual(tokenizer.tokenize('unwantedX running'							)						,				['[UNK]', 'runn', '##ing']							)




	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						self.assertTrue(_is_whitespace(' '							)							)
						self.assertTrue(_is_whitespace('\t'							)							)
						self.assertTrue(_is_whitespace('\r'							)							)
						self.assertTrue(_is_whitespace('\n'							)							)
						self.assertTrue(_is_whitespace('\u00A0'							)							)

						self.assertFalse(_is_whitespace('A'							)							)
						self.assertFalse(_is_whitespace('-'							)							)




	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						self.assertTrue(_is_control('\u0005'							)							)

						self.assertFalse(_is_control('A'							)							)
						self.assertFalse(_is_control(' '							)							)
						self.assertFalse(_is_control('\t'							)							)
						self.assertFalse(_is_control('\r'							)							)




	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						self.assertTrue(_is_punctuation('-'							)							)
						self.assertTrue(_is_punctuation('$'							)							)
						self.assertTrue(_is_punctuation('`'							)							)
						self.assertTrue(_is_punctuation('.'							)							)

						self.assertFalse(_is_punctuation('A'							)							)
						self.assertFalse(_is_punctuation(' '							)							)




	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						__a			:       int					    =  self.get_tokenizer()

						# Example taken from the issue https://github.com/huggingface/tokenizers/issues/340
						self.assertListEqual([tokenizer.tokenize(__a							) for t in ['Test', '\xad', 'test']]						,				[['[UNK]'], [], ['[UNK]']]							)

						if self.test_rust_tokenizer:
											__a			:       str					    =  self.get_rust_tokenizer()
											self.assertListEqual(
											    [rust_tokenizer.tokenize(__a							) for t in ['Test', '\xad', 'test']]						,				[['[UNK]'], [], ['[UNK]']]							)




	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
											with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""							):
																__a			:       Union[str, Any]					    =  self.rust_tokenizer_class.from_pretrained(__a						,				**__a							)

																__a			:       Optional[Any]					    =  f"""A, naïve {tokenizer_r.mask_token} AllenNLP sentence."""
																__a			:       Union[str, Any]					    =  tokenizer_r.encode_plus(
																    __a						,				return_attention_mask=__a						,				return_token_type_ids=__a						,				return_offsets_mapping=__a						,				add_special_tokens=__a						,				)

																__a			:       Optional[Any]					    =  tokenizer_r.do_lower_case if hasattr(__a						,				'do_lower_case'							) else False
																__a			:       Any					    =  (
																    [
																        ((0, 0), tokenizer_r.cls_token),
																        ((0, 1), 'A'),
																        ((1, 2), ','),
																        ((3, 5), 'na'),
																        ((5, 6), '##ï'),
																        ((6, 8), '##ve'),
																        ((9, 15), tokenizer_r.mask_token),
																        ((16, 21), 'Allen'),
																        ((21, 23), '##NL'),
																        ((23, 24), '##P'),
																        ((25, 33), 'sentence'),
																        ((33, 34), '.'),
																        ((0, 0), tokenizer_r.sep_token),
																    ]
																    if not do_lower_case
																    else [
																        ((0, 0), tokenizer_r.cls_token),
																        ((0, 1), 'a'),
																        ((1, 2), ','),
																        ((3, 8), 'naive'),
																        ((9, 15), tokenizer_r.mask_token),
																        ((16, 21), 'allen'),
																        ((21, 23), '##nl'),
																        ((23, 24), '##p'),
																        ((25, 33), 'sentence'),
																        ((33, 34), '.'),
																        ((0, 0), tokenizer_r.sep_token),
																    ]
																)

																self.assertEqual(
																    [e[1] for e in expected_results]						,				tokenizer_r.convert_ids_to_tokens(tokens['input_ids']							)							)
																self.assertEqual([e[0] for e in expected_results]						,				tokens['offset_mapping']							)




	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						__a			:       List[Any]					    =  ['的', '人', '有']
						__a			:       Optional[int]					    =  ''.join(__a							)
						for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
											with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""							):
																__a			:       Optional[int]					    =  True
																__a			:       Tuple					    =  self.tokenizer_class.from_pretrained(__a						,				**__a							)
																__a			:       Optional[Any]					    =  self.rust_tokenizer_class.from_pretrained(__a						,				**__a							)

																__a			:       Optional[int]					    =  tokenizer_p.encode(__a						,				add_special_tokens=__a							)
																__a			:       Optional[int]					    =  tokenizer_r.encode(__a						,				add_special_tokens=__a							)

																__a			:       int					    =  tokenizer_r.convert_ids_to_tokens(__a							)
																__a			:       Union[str, Any]					    =  tokenizer_p.convert_ids_to_tokens(__a							)

																# it is expected that each Chinese character is not preceded by "##"
																self.assertListEqual(__a						,				__a							)
																self.assertListEqual(__a						,				__a							)

																__a			:       List[str]					    =  False
																__a			:       Union[str, Any]					    =  self.rust_tokenizer_class.from_pretrained(__a						,				**__a							)
																__a			:       Any					    =  self.tokenizer_class.from_pretrained(__a						,				**__a							)

																__a			:       Any					    =  tokenizer_r.encode(__a						,				add_special_tokens=__a							)
																__a			:       Optional[int]					    =  tokenizer_p.encode(__a						,				add_special_tokens=__a							)

																__a			:       int					    =  tokenizer_r.convert_ids_to_tokens(__a							)
																__a			:       Union[str, Any]					    =  tokenizer_p.convert_ids_to_tokens(__a							)

																# it is expected that only the first Chinese character is not preceded by "##".
																__a			:       Optional[int]					    =  [
																    f"""##{token}""" if idx != 0 else token for idx, token in enumerate(__a							)
																]
																self.assertListEqual(__a						,				__a							)
																self.assertListEqual(__a						,				__a							)




	@slow
	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						__a			:       Optional[Any]					    =  self.tokenizer_class(self.vocab_file						,				self.word_shape_file						,				self.word_pronunciation_file							)

						__a			:       List[str]					    =  tokenizer.encode('你好'						,				add_special_tokens=__a							)
						__a			:       Optional[int]					    =  tokenizer.encode('你是谁'						,				add_special_tokens=__a							)

						__a			:       Optional[Any]					    =  tokenizer.build_inputs_with_special_tokens(__a							)
						__a			:       Optional[Any]					    =  tokenizer.build_inputs_with_special_tokens(__a						,				__a							)

						assert encoded_sentence == [1] + text + [2]
						assert encoded_pair == [1] + text + [2] + text_a + [2]





	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						__a			:       Optional[Any]					    =  self.get_tokenizers(do_lower_case=__a							)
						for tokenizer in tokenizers:
											with self.subTest(f"""{tokenizer.__class__.__name__}"""							):
																__a			:       Any					    =  '你好，你是谁'
																__a			:       List[Any]					    =  tokenizer.tokenize(__a							)
																__a			:       List[Any]					    =  tokenizer.convert_tokens_to_ids(__a							)
																__a			:       Tuple					    =  tokenizer.convert_tokens_to_shape_ids(__a							)
																__a			:       Optional[Any]					    =  tokenizer.convert_tokens_to_pronunciation_ids(__a							)
																__a			:       List[Any]					    =  tokenizer.prepare_for_model(
																    __a						,				__a						,				__a						,				add_special_tokens=__a							)

																__a			:       str					    =  tokenizer.encode_plus(__a						,				add_special_tokens=__a							)

																self.assertEqual(__a						,				__a							)








from typing import Dict, List

from nltk.translate import gleu_score

import datasets
from datasets import MetricInfo


a_   :Any        =      "\\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"

a_   :List[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"

a_   :List[str]        =      "\\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 					snake_case__						(       datasets.Metric     ):






     """simple docstring"""


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






