





from typing import Dict, List

from nltk.translate import gleu_score

import datasets
from datasets import MetricInfo


_a 						=      "\\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 						=      "\\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 						=      "\\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 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				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 		__lowerCamelCase				(    self       ,  __lowerCAmelCase       ,  __lowerCAmelCase       ,  __lowerCAmelCase = 1       ,  __lowerCAmelCase = 4       ,  ):



				'''simple docstring'''







				return {
				    "google_bleu": gleu_score.corpus_gleu(
				        list_of_references=__lowerCAmelCase       ,  hypotheses=__lowerCAmelCase       ,  min_len=__lowerCAmelCase       ,  max_len=__lowerCAmelCase							)
				}










import math
import os
import unittest

from transformers import MegatronBertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_sentencepiece, require_tokenizers, 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,
			    MegatronBertForCausalLM,
			    MegatronBertForMaskedLM,
			    MegatronBertForMultipleChoice,
			    MegatronBertForNextSentencePrediction,
			    MegatronBertForPreTraining,
			    MegatronBertForQuestionAnswering,
			    MegatronBertForSequenceClassification,
			    MegatronBertForTokenClassification,
			    MegatronBertModel,
			)






class 					__A    :

			'''simple docstring'''







			def __init__(    self       ,  __lowerCAmelCase       ,  __lowerCAmelCase=1_3       ,  __lowerCAmelCase=7       ,  __lowerCAmelCase=True       ,  __lowerCAmelCase=True       ,  __lowerCAmelCase=True       ,  __lowerCAmelCase=True       ,  __lowerCAmelCase=9_9       ,  __lowerCAmelCase=6_4       ,  __lowerCAmelCase=3_2       ,  __lowerCAmelCase=5       ,  __lowerCAmelCase=4       ,  __lowerCAmelCase=3_7       ,  __lowerCAmelCase="gelu"       ,  __lowerCAmelCase=0.1       ,  __lowerCAmelCase=0.1       ,  __lowerCAmelCase=5_1_2       ,  __lowerCAmelCase=1_6       ,  __lowerCAmelCase=2       ,  __lowerCAmelCase=0.02       ,  __lowerCAmelCase=3       ,  __lowerCAmelCase=4       ,  __lowerCAmelCase=None       ,  ):



				'''simple docstring'''







				lowerCamelCase__								=		parent
				lowerCamelCase__								=		batch_size
				lowerCamelCase__								=		seq_length
				lowerCamelCase__								=		is_training
				lowerCamelCase__								=		use_input_mask
				lowerCamelCase__								=		use_token_type_ids
				lowerCamelCase__								=		use_labels
				lowerCamelCase__								=		vocab_size
				lowerCamelCase__								=		hidden_size
				lowerCamelCase__								=		embedding_size
				lowerCamelCase__								=		num_hidden_layers
				lowerCamelCase__								=		num_attention_heads
				lowerCamelCase__								=		intermediate_size
				lowerCamelCase__								=		hidden_act
				lowerCamelCase__								=		hidden_dropout_prob
				lowerCamelCase__								=		attention_probs_dropout_prob
				lowerCamelCase__								=		max_position_embeddings
				lowerCamelCase__								=		type_vocab_size
				lowerCamelCase__								=		type_sequence_label_size
				lowerCamelCase__								=		initializer_range
				lowerCamelCase__								=		num_labels
				lowerCamelCase__								=		num_choices
				lowerCamelCase__								=		scope





			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				lowerCamelCase__								=		ids_tensor([self.batch_size, self.seq_length]       ,  self.vocab_size							)

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

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

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

				lowerCamelCase__								=		self.get_config()

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





			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				return MegatronBertConfig(
				    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       ,  embedding_size=self.embedding_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       ,  is_decoder=__lowerCAmelCase       ,  initializer_range=self.initializer_range       ,  )





			def 		__lowerCamelCase				(    self       ,  __lowerCAmelCase       ,  __lowerCAmelCase       ,  __lowerCAmelCase       ,  __lowerCAmelCase       ,  __lowerCAmelCase       ,  __lowerCAmelCase       ,  __lowerCAmelCase							):



				'''simple docstring'''







				lowerCamelCase__								=		MegatronBertModel(config=__lowerCAmelCase							)
				model.to(__lowerCAmelCase							)
				model.eval()
				lowerCamelCase__								=		model(__lowerCAmelCase       ,  attention_mask=__lowerCAmelCase       ,  token_type_ids=__lowerCAmelCase							)
				lowerCamelCase__								=		model(__lowerCAmelCase       ,  token_type_ids=__lowerCAmelCase							)
				lowerCamelCase__								=		model(__lowerCAmelCase							)

				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 		__lowerCamelCase				(    self       ,  __lowerCAmelCase       ,  __lowerCAmelCase       ,  __lowerCAmelCase       ,  __lowerCAmelCase       ,  __lowerCAmelCase       ,  __lowerCAmelCase       ,  __lowerCAmelCase							):



				'''simple docstring'''







				lowerCamelCase__								=		MegatronBertForMaskedLM(config=__lowerCAmelCase							)
				model.to(__lowerCAmelCase							)
				model.eval()
				lowerCamelCase__								=		model(__lowerCAmelCase       ,  attention_mask=__lowerCAmelCase       ,  token_type_ids=__lowerCAmelCase       ,  labels=__lowerCAmelCase							)
				self.parent.assertEqual(result.logits.shape       ,  (self.batch_size, self.seq_length, self.vocab_size)							)





			def 		__lowerCamelCase				(    self       ,  __lowerCAmelCase       ,  __lowerCAmelCase       ,  __lowerCAmelCase       ,  __lowerCAmelCase       ,  __lowerCAmelCase       ,  __lowerCAmelCase       ,  __lowerCAmelCase							):



				'''simple docstring'''







				lowerCamelCase__								=		MegatronBertForCausalLM(config=__lowerCAmelCase							)
				model.to(__lowerCAmelCase							)
				model.eval()
				lowerCamelCase__								=		model(__lowerCAmelCase       ,  attention_mask=__lowerCAmelCase       ,  token_type_ids=__lowerCAmelCase       ,  labels=__lowerCAmelCase							)
				self.parent.assertEqual(result.logits.shape       ,  (self.batch_size, self.seq_length, self.vocab_size)							)





			def 		__lowerCamelCase				(    self       ,  __lowerCAmelCase       ,  __lowerCAmelCase       ,  __lowerCAmelCase       ,  __lowerCAmelCase       ,  __lowerCAmelCase       ,  __lowerCAmelCase       ,  __lowerCAmelCase							):



				'''simple docstring'''







				lowerCamelCase__								=		MegatronBertForNextSentencePrediction(config=__lowerCAmelCase							)
				model.to(__lowerCAmelCase							)
				model.eval()
				lowerCamelCase__								=		model(
				    __lowerCAmelCase       ,  attention_mask=__lowerCAmelCase       ,  token_type_ids=__lowerCAmelCase       ,  labels=__lowerCAmelCase       ,  )
				self.parent.assertEqual(result.logits.shape       ,  (self.batch_size, 2)							)





			def 		__lowerCamelCase				(    self       ,  __lowerCAmelCase       ,  __lowerCAmelCase       ,  __lowerCAmelCase       ,  __lowerCAmelCase       ,  __lowerCAmelCase       ,  __lowerCAmelCase       ,  __lowerCAmelCase							):



				'''simple docstring'''







				lowerCamelCase__								=		MegatronBertForPreTraining(config=__lowerCAmelCase							)
				model.to(__lowerCAmelCase							)
				model.eval()
				lowerCamelCase__								=		model(
				    __lowerCAmelCase       ,  attention_mask=__lowerCAmelCase       ,  token_type_ids=__lowerCAmelCase       ,  labels=__lowerCAmelCase       ,  next_sentence_label=__lowerCAmelCase       ,  )
				self.parent.assertEqual(result.prediction_logits.shape       ,  (self.batch_size, self.seq_length, self.vocab_size)							)
				self.parent.assertEqual(result.seq_relationship_logits.shape       ,  (self.batch_size, 2)							)





			def 		__lowerCamelCase				(    self       ,  __lowerCAmelCase       ,  __lowerCAmelCase       ,  __lowerCAmelCase       ,  __lowerCAmelCase       ,  __lowerCAmelCase       ,  __lowerCAmelCase       ,  __lowerCAmelCase							):



				'''simple docstring'''







				lowerCamelCase__								=		MegatronBertForQuestionAnswering(config=__lowerCAmelCase							)
				model.to(__lowerCAmelCase							)
				model.eval()
				lowerCamelCase__								=		model(
				    __lowerCAmelCase       ,  attention_mask=__lowerCAmelCase       ,  token_type_ids=__lowerCAmelCase       ,  start_positions=__lowerCAmelCase       ,  end_positions=__lowerCAmelCase       ,  )
				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 		__lowerCamelCase				(    self       ,  __lowerCAmelCase       ,  __lowerCAmelCase       ,  __lowerCAmelCase       ,  __lowerCAmelCase       ,  __lowerCAmelCase       ,  __lowerCAmelCase       ,  __lowerCAmelCase							):



				'''simple docstring'''







				lowerCamelCase__								=		self.num_labels
				lowerCamelCase__								=		MegatronBertForSequenceClassification(__lowerCAmelCase							)
				model.to(__lowerCAmelCase							)
				model.eval()
				lowerCamelCase__								=		model(__lowerCAmelCase       ,  attention_mask=__lowerCAmelCase       ,  token_type_ids=__lowerCAmelCase       ,  labels=__lowerCAmelCase							)
				self.parent.assertEqual(result.logits.shape       ,  (self.batch_size, self.num_labels)							)





			def 		__lowerCamelCase				(    self       ,  __lowerCAmelCase       ,  __lowerCAmelCase       ,  __lowerCAmelCase       ,  __lowerCAmelCase       ,  __lowerCAmelCase       ,  __lowerCAmelCase       ,  __lowerCAmelCase							):



				'''simple docstring'''







				lowerCamelCase__								=		self.num_labels
				lowerCamelCase__								=		MegatronBertForTokenClassification(config=__lowerCAmelCase							)
				model.to(__lowerCAmelCase							)
				model.eval()
				lowerCamelCase__								=		model(__lowerCAmelCase       ,  attention_mask=__lowerCAmelCase       ,  token_type_ids=__lowerCAmelCase       ,  labels=__lowerCAmelCase							)
				self.parent.assertEqual(result.logits.shape       ,  (self.batch_size, self.seq_length, self.num_labels)							)





			def 		__lowerCamelCase				(    self       ,  __lowerCAmelCase       ,  __lowerCAmelCase       ,  __lowerCAmelCase       ,  __lowerCAmelCase       ,  __lowerCAmelCase       ,  __lowerCAmelCase       ,  __lowerCAmelCase							):



				'''simple docstring'''







				lowerCamelCase__								=		self.num_choices
				lowerCamelCase__								=		MegatronBertForMultipleChoice(config=__lowerCAmelCase							)
				model.to(__lowerCAmelCase							)
				model.eval()
				lowerCamelCase__								=		input_ids.unsqueeze(1							).expand(-1       ,  self.num_choices       ,  -1							).contiguous()
				lowerCamelCase__								=		token_type_ids.unsqueeze(1							).expand(-1       ,  self.num_choices       ,  -1							).contiguous()
				lowerCamelCase__								=		input_mask.unsqueeze(1							).expand(-1       ,  self.num_choices       ,  -1							).contiguous()
				lowerCamelCase__								=		model(
				    __lowerCAmelCase       ,  attention_mask=__lowerCAmelCase       ,  token_type_ids=__lowerCAmelCase       ,  labels=__lowerCAmelCase       ,  )
				self.parent.assertEqual(result.logits.shape       ,  (self.batch_size, self.num_choices)							)





			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







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






@require_torch
class 					__A    (    lowerCAmelCase			,				lowerCAmelCase			,				unittest.TestCase ):

			'''simple docstring'''







			lowerCAmelCase_							     =						(
			    (
			        MegatronBertModel,
			        MegatronBertForMaskedLM,
			        MegatronBertForCausalLM,
			        MegatronBertForMultipleChoice,
			        MegatronBertForNextSentencePrediction,
			        MegatronBertForPreTraining,
			        MegatronBertForQuestionAnswering,
			        MegatronBertForSequenceClassification,
			        MegatronBertForTokenClassification,
			    )
			    if is_torch_available()
			    else ()
			)
			lowerCAmelCase_							     =						(
			    {
			        """feature-extraction""": MegatronBertModel,
			        """fill-mask""": MegatronBertForMaskedLM,
			        """question-answering""": MegatronBertForQuestionAnswering,
			        """text-classification""": MegatronBertForSequenceClassification,
			        """text-generation""": MegatronBertForCausalLM,
			        """token-classification""": MegatronBertForTokenClassification,
			        """zero-shot""": MegatronBertForSequenceClassification,
			    }
			    if is_torch_available()
			    else {}
			)
			lowerCAmelCase_							     =						True
			# test_resize_embeddings = False
			lowerCAmelCase_							     =						False





			def 		__lowerCamelCase				(    self       ,  __lowerCAmelCase       ,  __lowerCAmelCase       ,  __lowerCAmelCase=False							):



				'''simple docstring'''







				lowerCamelCase__								=		super()._prepare_for_class(__lowerCAmelCase       ,  __lowerCAmelCase       ,  return_labels=__lowerCAmelCase							)

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





			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				lowerCamelCase__								=		MegatronBertModelTester(self							)
				lowerCamelCase__								=		ConfigTester(self       ,  config_class=__lowerCAmelCase       ,  hidden_size=3_7							)





			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				self.config_tester.run_common_tests()





			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				lowerCamelCase__								=		self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_megatron_bert_model(*__lowerCAmelCase							)





			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				lowerCamelCase__								=		self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_megatron_bert_for_masked_lm(*__lowerCAmelCase							)





			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				lowerCamelCase__								=		self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_megatron_bert_for_multiple_choice(*__lowerCAmelCase							)





			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				lowerCamelCase__								=		self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_megatron_bert_for_next_sequence_prediction(*__lowerCAmelCase							)





			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				lowerCamelCase__								=		self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_megatron_bert_for_pretraining(*__lowerCAmelCase							)





			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				lowerCamelCase__								=		self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_megatron_bert_for_question_answering(*__lowerCAmelCase							)





			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				lowerCamelCase__								=		self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_megatron_bert_for_sequence_classification(*__lowerCAmelCase							)





			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				lowerCamelCase__								=		self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_megatron_bert_for_token_classification(*__lowerCAmelCase							)





def lowerCAmelCase__(__snake_case							)				->      List[str]:







	'''simple docstring'''







	return torch.tensor(
	    __snake_case       ,dtype=torch.long       ,device=__snake_case       ,)


_a 						=      1e-4






@require_torch
@require_sentencepiece
@require_tokenizers
class 					__A    (    unittest.TestCase ):

			'''simple docstring'''







			@slow
			@unittest.skip('''Model is not available.'''							)
			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				lowerCamelCase__								=		'''nvidia/megatron-bert-uncased-345m'''
				if "MYDIR" in os.environ:
					lowerCamelCase__								=		os.path.join(os.environ['''MYDIR''']       ,  __lowerCAmelCase							)
				lowerCamelCase__								=		MegatronBertModel.from_pretrained(__lowerCAmelCase							)
				model.to(__lowerCAmelCase							)
				model.half()
				lowerCamelCase__								=		_long_tensor([[1_0_1, 7_1_1_0, 1_0_0_5, 1_0_5_6, 2_0_2_3, 1_1_3_3_3, 1_7_4_1_3, 1_0_2_9, 1_0_2]]							)
				with torch.no_grad():
					lowerCamelCase__								=		model(__lowerCAmelCase							)[0]
				lowerCamelCase__								=		torch.Size((1, 9, 1_0_2_4)							)
				self.assertEqual(output.shape       ,  __lowerCAmelCase							)

				lowerCamelCase__								=		[-0.6040, -0.2517, -0.1025, 0.3420, -0.6758, -0.0017, -0.1089, -0.1990, 0.5728]
				for ii in range(3							):
					for jj in range(3							):
						lowerCamelCase__								=		output[0, ii, jj]
						lowerCamelCase__								=		expected[3 * ii + jj]
						lowerCamelCase__								=		'''ii={} jj={} a={} b={}'''.format(__lowerCAmelCase       ,  __lowerCAmelCase       ,  __lowerCAmelCase       ,  __lowerCAmelCase							)
						self.assertTrue(math.isclose(__lowerCAmelCase       ,  __lowerCAmelCase       ,  rel_tol=__lowerCAmelCase       ,  abs_tol=__lowerCAmelCase							)       ,  msg=__lowerCAmelCase							)



