





"""simple docstring"""





from __future__ import annotations

import copy
import tempfile
import unittest

from transformers import CONFIG_MAPPING, AutoConfig, BertConfig, GPTaConfig, TaConfig, TapasConfig, is_tf_available
from transformers.testing_utils import (
    DUMMY_UNKNOWN_IDENTIFIER,
    SMALL_MODEL_IDENTIFIER,
    RequestCounter,
    require_tensorflow_probability,
    require_tf,
    slow,
)

from ..bert.test_modeling_bert import BertModelTester


if is_tf_available():
							from transformers import (
							    TFAutoModel,
							    TFAutoModelForCausalLM,
							    TFAutoModelForMaskedLM,
							    TFAutoModelForPreTraining,
							    TFAutoModelForQuestionAnswering,
							    TFAutoModelForSeqaSeqLM,
							    TFAutoModelForSequenceClassification,
							    TFAutoModelForTableQuestionAnswering,
							    TFAutoModelForTokenClassification,
							    TFAutoModelWithLMHead,
							    TFBertForMaskedLM,
							    TFBertForPreTraining,
							    TFBertForQuestionAnswering,
							    TFBertForSequenceClassification,
							    TFBertModel,
							    TFFunnelBaseModel,
							    TFFunnelModel,
							    TFGPTaLMHeadModel,
							    TFRobertaForMaskedLM,
							    TFTaForConditionalGeneration,
							    TFTapasForQuestionAnswering,
							)
							from transformers.models.auto.modeling_tf_auto import (
							    TF_MODEL_FOR_CAUSAL_LM_MAPPING,
							    TF_MODEL_FOR_MASKED_LM_MAPPING,
							    TF_MODEL_FOR_PRETRAINING_MAPPING,
							    TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING,
							    TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
							    TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
							    TF_MODEL_MAPPING,
							)
							from transformers.models.bert.modeling_tf_bert import TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST
							from transformers.models.gpta.modeling_tf_gpta import TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST
							from transformers.models.ta.modeling_tf_ta import TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST
							from transformers.models.tapas.modeling_tf_tapas import TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST
class 		SCREAMING_SNAKE_CASE_							(    snake_case__   ):
				"""simple docstring"""
				__snake_case   :       List[str]	 =		"""new-model"""


if is_tf_available():
							class 		SCREAMING_SNAKE_CASE_							(    snake_case__   ):
											"""simple docstring"""
											__snake_case   :       Optional[int]	 =		NewModelConfig
@require_tf
class 		SCREAMING_SNAKE_CASE_							(    unittest.TestCase   ):
				"""simple docstring"""




				@slow
				def 					__lowercase  (     self  :Any			):
											__lowerCamelCase      :      Tuple         ='''bert-base-cased'''
											__lowerCamelCase      :      str         =AutoConfig.from_pretrained(__lowercase			)
											self.assertIsNotNone(__lowercase			)
											self.assertIsInstance(__lowercase						,    __lowercase			)

											__lowerCamelCase      :      List[str]         =TFAutoModel.from_pretrained(__lowercase			)
											self.assertIsNotNone(__lowercase			)
											self.assertIsInstance(__lowercase						,    __lowercase			)




				@slow
				def 					__lowercase  (     self  :Union[str, Any]			):
											__lowerCamelCase      :      Optional[int]         ='''bert-base-cased'''
											__lowerCamelCase      :      str         =AutoConfig.from_pretrained(__lowercase			)
											self.assertIsNotNone(__lowercase			)
											self.assertIsInstance(__lowercase						,    __lowercase			)

											__lowerCamelCase      :      List[Any]         =TFAutoModelForPreTraining.from_pretrained(__lowercase			)
											self.assertIsNotNone(__lowercase			)
											self.assertIsInstance(__lowercase						,    __lowercase			)




				@slow
				def 					__lowercase  (     self  :Dict			):
											for model_name in TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																		__lowerCamelCase      :      Tuple         =AutoConfig.from_pretrained(__lowercase			)
																		self.assertIsNotNone(__lowercase			)
																		self.assertIsInstance(__lowercase						,    __lowercase			)

																		__lowerCamelCase      :      List[Any]         =TFAutoModelForCausalLM.from_pretrained(__lowercase			)
																		__lowerCamelCase      :      Optional[Any]         =TFAutoModelForCausalLM.from_pretrained(__lowercase						,    output_loading_info=__lowercase			)
																		self.assertIsNotNone(__lowercase			)
																		self.assertIsInstance(__lowercase						,    __lowercase			)




				@slow
				def 					__lowercase  (     self  :Any			):
											for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																		__lowerCamelCase      :      List[Any]         =AutoConfig.from_pretrained(__lowercase			)
																		self.assertIsNotNone(__lowercase			)
																		self.assertIsInstance(__lowercase						,    __lowercase			)

																		__lowerCamelCase      :      Union[str, Any]         =TFAutoModelWithLMHead.from_pretrained(__lowercase			)
																		self.assertIsNotNone(__lowercase			)
																		self.assertIsInstance(__lowercase						,    __lowercase			)




				@slow
				def 					__lowercase  (     self  :Tuple			):
											for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																		__lowerCamelCase      :      List[str]         =AutoConfig.from_pretrained(__lowercase			)
																		self.assertIsNotNone(__lowercase			)
																		self.assertIsInstance(__lowercase						,    __lowercase			)

																		__lowerCamelCase      :      Dict         =TFAutoModelForMaskedLM.from_pretrained(__lowercase			)
																		__lowerCamelCase      :      Optional[Any]         =TFAutoModelForMaskedLM.from_pretrained(__lowercase						,    output_loading_info=__lowercase			)
																		self.assertIsNotNone(__lowercase			)
																		self.assertIsInstance(__lowercase						,    __lowercase			)




				@slow
				def 					__lowercase  (     self  :List[str]			):
											for model_name in TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																		__lowerCamelCase      :      Dict         =AutoConfig.from_pretrained(__lowercase			)
																		self.assertIsNotNone(__lowercase			)
																		self.assertIsInstance(__lowercase						,    __lowercase			)

																		__lowerCamelCase      :      Tuple         =TFAutoModelForSeqaSeqLM.from_pretrained(__lowercase			)
																		__lowerCamelCase      :      int         =TFAutoModelForSeqaSeqLM.from_pretrained(__lowercase						,    output_loading_info=__lowercase			)
																		self.assertIsNotNone(__lowercase			)
																		self.assertIsInstance(__lowercase						,    __lowercase			)




				@slow
				def 					__lowercase  (     self  :Optional[Any]			):
											# for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
											for model_name in ["bert-base-uncased"]:
																		__lowerCamelCase      :      Optional[int]         =AutoConfig.from_pretrained(__lowercase			)
																		self.assertIsNotNone(__lowercase			)
																		self.assertIsInstance(__lowercase						,    __lowercase			)

																		__lowerCamelCase      :      Optional[Any]         =TFAutoModelForSequenceClassification.from_pretrained(__lowercase			)
																		self.assertIsNotNone(__lowercase			)
																		self.assertIsInstance(__lowercase						,    __lowercase			)




				@slow
				def 					__lowercase  (     self  :int			):
											# for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
											for model_name in ["bert-base-uncased"]:
																		__lowerCamelCase      :      Tuple         =AutoConfig.from_pretrained(__lowercase			)
																		self.assertIsNotNone(__lowercase			)
																		self.assertIsInstance(__lowercase						,    __lowercase			)

																		__lowerCamelCase      :      Union[str, Any]         =TFAutoModelForQuestionAnswering.from_pretrained(__lowercase			)
																		self.assertIsNotNone(__lowercase			)
																		self.assertIsInstance(__lowercase						,    __lowercase			)




				@slow
				@require_tensorflow_probability
				def 					__lowercase  (     self  :Dict			):
											for model_name in TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST[5:6]:
																		__lowerCamelCase      :      Any         =AutoConfig.from_pretrained(__lowercase			)
																		self.assertIsNotNone(__lowercase			)
																		self.assertIsInstance(__lowercase						,    __lowercase			)

																		__lowerCamelCase      :      Tuple         =TFAutoModelForTableQuestionAnswering.from_pretrained(__lowercase			)
																		__lowerCamelCase      :      Optional[int]         =TFAutoModelForTableQuestionAnswering.from_pretrained(
																		    __lowercase						,    output_loading_info=__lowercase			)
																		self.assertIsNotNone(__lowercase			)
																		self.assertIsInstance(__lowercase						,    __lowercase			)




				def 					__lowercase  (     self  :Optional[int]			):
											__lowerCamelCase      :      Optional[Any]         =TFAutoModelWithLMHead.from_pretrained(__lowercase			)
											self.assertIsInstance(__lowercase						,    __lowercase			)
											self.assertEqual(model.num_parameters()						,    1_4410			)
											self.assertEqual(model.num_parameters(only_trainable=__lowercase			)						,    1_4410			)




				def 					__lowercase  (     self  :List[str]			):
											__lowerCamelCase      :      str         =TFAutoModelWithLMHead.from_pretrained(__lowercase			)
											self.assertIsInstance(__lowercase						,    __lowercase			)
											self.assertEqual(model.num_parameters()						,    1_4410			)
											self.assertEqual(model.num_parameters(only_trainable=__lowercase			)						,    1_4410			)




				def 					__lowercase  (     self  :Tuple			):
											# For the auto model mapping, FunnelConfig has two models: FunnelModel and FunnelBaseModel
											__lowerCamelCase      :      Optional[int]         =TFAutoModel.from_pretrained('''sgugger/funnel-random-tiny'''			)
											self.assertIsInstance(__lowercase						,    __lowercase			)

											__lowerCamelCase      :      int         =copy.deepcopy(model.config			)
											__lowerCamelCase      :      Union[str, Any]         =['''FunnelBaseModel''']
											__lowerCamelCase      :      Tuple         =TFAutoModel.from_config(__lowercase			)
											self.assertIsInstance(__lowercase						,    __lowercase			)

											with tempfile.TemporaryDirectory() as tmp_dir:
																		model.save_pretrained(__lowercase			)
																		__lowerCamelCase      :      Optional[Any]         =TFAutoModel.from_pretrained(__lowercase			)
																		self.assertIsInstance(__lowercase						,    __lowercase			)




				def 					__lowercase  (     self  :Union[str, Any]			):
											try:
																		AutoConfig.register('''new-model'''						,    __lowercase			)

																		__lowerCamelCase      :      Optional[Any]         =[
																		    TFAutoModel,
																		    TFAutoModelForCausalLM,
																		    TFAutoModelForMaskedLM,
																		    TFAutoModelForPreTraining,
																		    TFAutoModelForQuestionAnswering,
																		    TFAutoModelForSequenceClassification,
																		    TFAutoModelForTokenClassification,
																		]

																		for auto_class in auto_classes:
																									with self.subTest(auto_class.__name__			):
																																# Wrong config class will raise an error
																																with self.assertRaises(__lowercase			):
																																							auto_class.register(__lowercase						,    __lowercase			)
																																auto_class.register(__lowercase						,    __lowercase			)
																																# Trying to register something existing in the Transformers library will raise an error
																																with self.assertRaises(__lowercase			):
																																							auto_class.register(__lowercase						,    __lowercase			)

																																# Now that the config is registered, it can be used as any other config with the auto-API
																																__lowerCamelCase      :      Union[str, Any]         =BertModelTester(self			).get_config()
																																__lowerCamelCase      :      Optional[int]         =NewModelConfig(**tiny_config.to_dict()			)
																																__lowerCamelCase      :      Tuple         =auto_class.from_config(__lowercase			)
																																self.assertIsInstance(__lowercase						,    __lowercase			)

																																with tempfile.TemporaryDirectory() as tmp_dir:
																																							model.save_pretrained(__lowercase			)
																																							__lowerCamelCase      :      int         =auto_class.from_pretrained(__lowercase			)
																																							self.assertIsInstance(__lowercase						,    __lowercase			)

											finally:
																		if "new-model" in CONFIG_MAPPING._extra_content:
																									del CONFIG_MAPPING._extra_content["new-model"]
																		for mapping in (
																		    TF_MODEL_MAPPING,
																		    TF_MODEL_FOR_PRETRAINING_MAPPING,
																		    TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING,
																		    TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
																		    TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
																		    TF_MODEL_FOR_CAUSAL_LM_MAPPING,
																		    TF_MODEL_FOR_MASKED_LM_MAPPING,
																		):
																									if NewModelConfig in mapping._extra_content:
																																del mapping._extra_content[NewModelConfig]




				def 					__lowercase  (     self  :str			):
											with self.assertRaisesRegex(
											    __lowercase						,    '''bert-base is not a local folder and is not a valid model identifier'''			):
																		__lowerCamelCase      :      int         =TFAutoModel.from_pretrained('''bert-base'''			)




				def 					__lowercase  (     self  :List[str]			):
											with self.assertRaisesRegex(
											    __lowercase						,    r'''aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)'''			):
																		__lowerCamelCase      :      List[str]         =TFAutoModel.from_pretrained(__lowercase						,    revision='''aaaaaa'''			)




				def 					__lowercase  (     self  :Any			):
											with self.assertRaisesRegex(
											    __lowercase						,    '''hf-internal-testing/config-no-model does not appear to have a file named pytorch_model.bin'''						,    ):
																		__lowerCamelCase      :      List[str]         =TFAutoModel.from_pretrained('''hf-internal-testing/config-no-model'''			)




				def 					__lowercase  (     self  :Union[str, Any]			):
											with self.assertRaisesRegex(__lowercase						,    '''Use `from_pt=True` to load this model'''			):
																		__lowerCamelCase      :      Any         =TFAutoModel.from_pretrained('''hf-internal-testing/tiny-bert-pt-only'''			)




				def 					__lowercase  (     self  :Tuple			):
											# Make sure we have cached the model.
											__lowerCamelCase      :      Optional[Any]         =TFAutoModel.from_pretrained('''hf-internal-testing/tiny-random-bert'''			)
											with RequestCounter() as counter:
																		__lowerCamelCase      :      Dict         =TFAutoModel.from_pretrained('''hf-internal-testing/tiny-random-bert'''			)
																		self.assertEqual(counter.get_request_count						,    0			)
																		self.assertEqual(counter.head_request_count						,    1			)
																		self.assertEqual(counter.other_request_count						,    0			)

											# With a sharded checkpoint
											__lowerCamelCase      :      Tuple         =TFAutoModel.from_pretrained('''ArthurZ/tiny-random-bert-sharded'''			)
											with RequestCounter() as counter:
																		__lowerCamelCase      :      str         =TFAutoModel.from_pretrained('''ArthurZ/tiny-random-bert-sharded'''			)
																		self.assertEqual(counter.get_request_count						,    0			)
																		self.assertEqual(counter.head_request_count						,    1			)
																		self.assertEqual(counter.other_request_count						,    0			)











"""simple docstring"""





from typing import Dict, List

from nltk.translate import gleu_score

import datasets
from datasets import MetricInfo


_UpperCamelCase							=      '\\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'

_UpperCamelCase							=      '\\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'

_UpperCamelCase							=      '\\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 		SCREAMING_SNAKE_CASE_							(    datasets.Metric   ):
				"""simple docstring"""




				def 					__lowercase  (     self  :Optional[Any]			):
											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  :Optional[Any]						,    __lowercase  :List[List[List[str]]]						,    __lowercase  :List[List[str]]						,    __lowercase  :int = 1						,    __lowercase  :int = 4						,    ):
											return {
											    "google_bleu": gleu_score.corpus_gleu(
											        list_of_references=__lowercase						,    hypotheses=__lowercase						,    min_len=__lowercase						,    max_len=__lowercase			)
											}




