
from typing import Dict, List

from nltk.translate import gleu_score

import datasets
from datasets import MetricInfo


lowercase__		:List[str]			  =   "\\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__		:int			  =   "\\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     lowercase		( datasets.Metric		):




							def 			A__				(    self):
												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       ,A__       ,A__       ,A__ = 1       ,A__ = 4       ,):
												return {
												    "google_bleu": gleu_score.corpus_gleu(
												        list_of_references=A__       ,hypotheses=A__       ,min_len=A__       ,max_len=A__)
												}






import unittest

from transformers import AlbertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
							import torch

							from transformers import (
							    MODEL_FOR_PRETRAINING_MAPPING,
							    AlbertForMaskedLM,
							    AlbertForMultipleChoice,
							    AlbertForPreTraining,
							    AlbertForQuestionAnswering,
							    AlbertForSequenceClassification,
							    AlbertForTokenClassification,
							    AlbertModel,
							)
							from transformers.models.albert.modeling_albert import ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST







class     lowercase		:




							def __init__(    self       ,A__       ,A__=1_3       ,A__=7       ,A__=True       ,A__=True       ,A__=True       ,A__=True       ,A__=9_9       ,A__=1_6       ,A__=3_6       ,A__=6       ,A__=6       ,A__=6       ,A__=3_7       ,A__="gelu"       ,A__=0.1       ,A__=0.1       ,A__=5_1_2       ,A__=1_6       ,A__=2       ,A__=0.02       ,A__=3       ,A__=4       ,A__=None       ,):
												lowercase						     =						parent
												lowercase						     =						batch_size
												lowercase						     =						seq_length
												lowercase						     =						is_training
												lowercase						     =						use_input_mask
												lowercase						     =						use_token_type_ids
												lowercase						     =						use_labels
												lowercase						     =						vocab_size
												lowercase						     =						embedding_size
												lowercase						     =						hidden_size
												lowercase						     =						num_hidden_layers
												lowercase						     =						num_hidden_groups
												lowercase						     =						num_attention_heads
												lowercase						     =						intermediate_size
												lowercase						     =						hidden_act
												lowercase						     =						hidden_dropout_prob
												lowercase						     =						attention_probs_dropout_prob
												lowercase						     =						max_position_embeddings
												lowercase						     =						type_vocab_size
												lowercase						     =						type_sequence_label_size
												lowercase						     =						initializer_range
												lowercase						     =						num_labels
												lowercase						     =						num_choices
												lowercase						     =						scope




							def 			A__				(    self):
												lowercase						     =						ids_tensor([self.batch_size, self.seq_length]       ,self.vocab_size)

												lowercase						     =						None
												if self.use_input_mask:
																	lowercase						     =						random_attention_mask([self.batch_size, self.seq_length])

												lowercase						     =						None
												if self.use_token_type_ids:
																	lowercase						     =						ids_tensor([self.batch_size, self.seq_length]       ,self.type_vocab_size)

												lowercase						     =						None
												lowercase						     =						None
												lowercase						     =						None
												if self.use_labels:
																	lowercase						     =						ids_tensor([self.batch_size]       ,self.type_sequence_label_size)
																	lowercase						     =						ids_tensor([self.batch_size, self.seq_length]       ,self.num_labels)
																	lowercase						     =						ids_tensor([self.batch_size]       ,self.num_choices)

												lowercase						     =						self.get_config()

												return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels




							def 			A__				(    self):
												return AlbertConfig(
												    vocab_size=self.vocab_size       ,hidden_size=self.hidden_size       ,num_hidden_layers=self.num_hidden_layers       ,num_attention_heads=self.num_attention_heads       ,intermediate_size=self.intermediate_size       ,hidden_act=self.hidden_act       ,hidden_dropout_prob=self.hidden_dropout_prob       ,attention_probs_dropout_prob=self.attention_probs_dropout_prob       ,max_position_embeddings=self.max_position_embeddings       ,type_vocab_size=self.type_vocab_size       ,initializer_range=self.initializer_range       ,num_hidden_groups=self.num_hidden_groups       ,)




							def 			A__				(    self       ,A__       ,A__       ,A__       ,A__       ,A__       ,A__       ,A__):
												lowercase						     =						AlbertModel(config=A__)
												model.to(A__)
												model.eval()
												lowercase						     =						model(A__       ,attention_mask=A__       ,token_type_ids=A__)
												lowercase						     =						model(A__       ,token_type_ids=A__)
												lowercase						     =						model(A__)
												self.parent.assertEqual(result.last_hidden_state.shape       ,(self.batch_size, self.seq_length, self.hidden_size))
												self.parent.assertEqual(result.pooler_output.shape       ,(self.batch_size, self.hidden_size))




							def 			A__				(    self       ,A__       ,A__       ,A__       ,A__       ,A__       ,A__       ,A__):
												lowercase						     =						AlbertForPreTraining(config=A__)
												model.to(A__)
												model.eval()
												lowercase						     =						model(
												    A__       ,attention_mask=A__       ,token_type_ids=A__       ,labels=A__       ,sentence_order_label=A__       ,)
												self.parent.assertEqual(result.prediction_logits.shape       ,(self.batch_size, self.seq_length, self.vocab_size))
												self.parent.assertEqual(result.sop_logits.shape       ,(self.batch_size, config.num_labels))




							def 			A__				(    self       ,A__       ,A__       ,A__       ,A__       ,A__       ,A__       ,A__):
												lowercase						     =						AlbertForMaskedLM(config=A__)
												model.to(A__)
												model.eval()
												lowercase						     =						model(A__       ,attention_mask=A__       ,token_type_ids=A__       ,labels=A__)
												self.parent.assertEqual(result.logits.shape       ,(self.batch_size, self.seq_length, self.vocab_size))




							def 			A__				(    self       ,A__       ,A__       ,A__       ,A__       ,A__       ,A__       ,A__):
												lowercase						     =						AlbertForQuestionAnswering(config=A__)
												model.to(A__)
												model.eval()
												lowercase						     =						model(
												    A__       ,attention_mask=A__       ,token_type_ids=A__       ,start_positions=A__       ,end_positions=A__       ,)
												self.parent.assertEqual(result.start_logits.shape       ,(self.batch_size, self.seq_length))
												self.parent.assertEqual(result.end_logits.shape       ,(self.batch_size, self.seq_length))




							def 			A__				(    self       ,A__       ,A__       ,A__       ,A__       ,A__       ,A__       ,A__):
												lowercase						     =						self.num_labels
												lowercase						     =						AlbertForSequenceClassification(A__)
												model.to(A__)
												model.eval()
												lowercase						     =						model(A__       ,attention_mask=A__       ,token_type_ids=A__       ,labels=A__)
												self.parent.assertEqual(result.logits.shape       ,(self.batch_size, self.num_labels))




							def 			A__				(    self       ,A__       ,A__       ,A__       ,A__       ,A__       ,A__       ,A__):
												lowercase						     =						self.num_labels
												lowercase						     =						AlbertForTokenClassification(config=A__)
												model.to(A__)
												model.eval()
												lowercase						     =						model(A__       ,attention_mask=A__       ,token_type_ids=A__       ,labels=A__)
												self.parent.assertEqual(result.logits.shape       ,(self.batch_size, self.seq_length, self.num_labels))




							def 			A__				(    self       ,A__       ,A__       ,A__       ,A__       ,A__       ,A__       ,A__):
												lowercase						     =						self.num_choices
												lowercase						     =						AlbertForMultipleChoice(config=A__)
												model.to(A__)
												model.eval()
												lowercase						     =						input_ids.unsqueeze(1).expand(-1       ,self.num_choices       ,-1).contiguous()
												lowercase						     =						token_type_ids.unsqueeze(1).expand(-1       ,self.num_choices       ,-1).contiguous()
												lowercase						     =						input_mask.unsqueeze(1).expand(-1       ,self.num_choices       ,-1).contiguous()
												lowercase						     =						model(
												    A__       ,attention_mask=A__       ,token_type_ids=A__       ,labels=A__       ,)
												self.parent.assertEqual(result.logits.shape       ,(self.batch_size, self.num_choices))





							def 			A__				(    self):
												lowercase						     =						self.prepare_config_and_inputs()
												(
												    (
												    lowercase
												)     ,						(
												    lowercase
												)     ,						(
												    lowercase
												)     ,						(
												    lowercase
												)     ,						(
												    lowercase
												)     ,						(
												    lowercase
												)     ,						(
												    lowercase
												)     ,						
												)						     =						config_and_inputs
												lowercase						     =						{'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
												return config, inputs_dict







@require_torch
class     lowercase		( SCREAMING_SNAKE_CASE__	,  SCREAMING_SNAKE_CASE__	,  unittest.TestCase		):
							lowercase_	:	Union[str, Any]	 =(
							    (
							        AlbertModel,
							        AlbertForPreTraining,
							        AlbertForMaskedLM,
							        AlbertForMultipleChoice,
							        AlbertForSequenceClassification,
							        AlbertForTokenClassification,
							        AlbertForQuestionAnswering,
							    )
							    if is_torch_available()
							    else ()
							)
							lowercase_	:	int	 =(
							    {
							        '''feature-extraction''': AlbertModel,
							        '''fill-mask''': AlbertForMaskedLM,
							        '''question-answering''': AlbertForQuestionAnswering,
							        '''text-classification''': AlbertForSequenceClassification,
							        '''token-classification''': AlbertForTokenClassification,
							        '''zero-shot''': AlbertForSequenceClassification,
							    }
							    if is_torch_available()
							    else {}
							)
							lowercase_	:	str	 =True




							def 			A__				(    self       ,A__       ,A__       ,A__=False):
												lowercase						     =						super()._prepare_for_class(A__       ,A__       ,return_labels=A__)

												if return_labels:
																	if model_class in get_values(A__):
																						lowercase						     =						torch.zeros(
																						    (self.model_tester.batch_size, self.model_tester.seq_length)       ,dtype=torch.long       ,device=A__)
																						lowercase						     =						torch.zeros(
																						    self.model_tester.batch_size       ,dtype=torch.long       ,device=A__)
												return inputs_dict




							def 			A__				(    self):
												lowercase						     =						AlbertModelTester(self)
												lowercase						     =						ConfigTester(self       ,config_class=A__       ,hidden_size=3_7)




							def 			A__				(    self):
												self.config_tester.run_common_tests()




							def 			A__				(    self):
												lowercase						     =						self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_model(*A__)




							def 			A__				(    self):
												lowercase						     =						self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_pretraining(*A__)




							def 			A__				(    self):
												lowercase						     =						self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_masked_lm(*A__)




							def 			A__				(    self):
												lowercase						     =						self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_multiple_choice(*A__)




							def 			A__				(    self):
												lowercase						     =						self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_question_answering(*A__)




							def 			A__				(    self):
												lowercase						     =						self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_sequence_classification(*A__)




							def 			A__				(    self):
												lowercase						     =						self.model_tester.prepare_config_and_inputs()
												for type in ["absolute", "relative_key", "relative_key_query"]:
																	lowercase						     =						type
																	self.model_tester.create_and_check_model(*A__)





							@slow
							def 			A__				(    self):
												for model_name in ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																	lowercase						     =						AlbertModel.from_pretrained(A__)
																	self.assertIsNotNone(A__)







@require_torch
class     lowercase		( unittest.TestCase		):




							@slow
							def 			A__				(    self):
												lowercase						     =						AlbertModel.from_pretrained('''albert-base-v2''')
												lowercase						     =						torch.tensor([[0, 3_4_5, 2_3_2, 3_2_8, 7_4_0, 1_4_0, 1_6_9_5, 6_9, 6_0_7_8, 1_5_8_8, 2]])
												lowercase						     =						torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]])
												with torch.no_grad():
																	lowercase						     =						model(A__       ,attention_mask=A__)[0]
												lowercase						     =						torch.Size((1, 1_1, 7_6_8))
												self.assertEqual(output.shape       ,A__)
												lowercase						     =						torch.tensor(
												    [[[-0.6513, 1.5035, -0.2766], [-0.6515, 1.5046, -0.2780], [-0.6512, 1.5049, -0.2784]]])

												self.assertTrue(torch.allclose(output[:, 1:4, 1:4]       ,A__       ,atol=1E-4))




