



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 :



	def __init__(						self							:    List[Any]				,	snake_case_							:    List[str]				,	snake_case_							:    Optional[Any]=13				,	snake_case_							:    Any=7				,	snake_case_							:    List[Any]=True				,	snake_case_							:    Union[str, Any]=True				,	snake_case_							:    int=True				,	snake_case_							:    Tuple=True				,	snake_case_							:    Dict=99				,	snake_case_							:    Optional[Any]=64				,	snake_case_							:    int=32				,	snake_case_							:    int=5				,	snake_case_							:    str=4				,	snake_case_							:    str=37				,	snake_case_							:    Optional[Any]="gelu"				,	snake_case_							:    Optional[Any]=0.1				,	snake_case_							:    List[Any]=0.1				,	snake_case_							:    Tuple=5_12				,	snake_case_							:    Optional[Any]=16				,	snake_case_							:    List[str]=2				,	snake_case_							:    Any=0.0_2				,	snake_case_							:    Dict=3				,	snake_case_							:    Optional[Any]=4				,	snake_case_							:    Optional[Any]=None				,	)->							List[str]:
						__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 		UpperCamelCase							(						self							:    Any)->							List[Any]:
						__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 		UpperCamelCase							(						self							:    List[str])->							List[Any]:
						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=snake_case_				,	initializer_range=self.initializer_range				,	)



	def 		UpperCamelCase							(						self							:    Optional[Any]				,	snake_case_							:    Tuple				,	snake_case_							:    Any				,	snake_case_							:    Any				,	snake_case_							:    Tuple				,	snake_case_							:    List[Any]				,	snake_case_							:    str				,	snake_case_							:    Optional[Any])->							Any:
						__lowerCAmelCase	   =MegatronBertModel(config=snake_case_)
						model.to(snake_case_)
						model.eval()
						__lowerCAmelCase	   =model(snake_case_				,	attention_mask=snake_case_				,	token_type_ids=snake_case_)
						__lowerCAmelCase	   =model(snake_case_				,	token_type_ids=snake_case_)
						__lowerCAmelCase	   =model(snake_case_)

						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 		UpperCamelCase							(						self							:    Tuple				,	snake_case_							:    str				,	snake_case_							:    Union[str, Any]				,	snake_case_							:    Tuple				,	snake_case_							:    List[Any]				,	snake_case_							:    int				,	snake_case_							:    Optional[Any]				,	snake_case_							:    str)->							Any:
						__lowerCAmelCase	   =MegatronBertForMaskedLM(config=snake_case_)
						model.to(snake_case_)
						model.eval()
						__lowerCAmelCase	   =model(snake_case_				,	attention_mask=snake_case_				,	token_type_ids=snake_case_				,	labels=snake_case_)
						self.parent.assertEqual(result.logits.shape				,	(self.batch_size, self.seq_length, self.vocab_size))



	def 		UpperCamelCase							(						self							:    Tuple				,	snake_case_							:    List[Any]				,	snake_case_							:    str				,	snake_case_							:    int				,	snake_case_							:    Dict				,	snake_case_							:    Optional[int]				,	snake_case_							:    Union[str, Any]				,	snake_case_							:    Tuple)->							Any:
						__lowerCAmelCase	   =MegatronBertForCausalLM(config=snake_case_)
						model.to(snake_case_)
						model.eval()
						__lowerCAmelCase	   =model(snake_case_				,	attention_mask=snake_case_				,	token_type_ids=snake_case_				,	labels=snake_case_)
						self.parent.assertEqual(result.logits.shape				,	(self.batch_size, self.seq_length, self.vocab_size))



	def 		UpperCamelCase							(						self							:    str				,	snake_case_							:    List[str]				,	snake_case_							:    Optional[Any]				,	snake_case_							:    List[str]				,	snake_case_							:    List[str]				,	snake_case_							:    str				,	snake_case_							:    List[str]				,	snake_case_							:    List[Any])->							Union[str, Any]:
						__lowerCAmelCase	   =MegatronBertForNextSentencePrediction(config=snake_case_)
						model.to(snake_case_)
						model.eval()
						__lowerCAmelCase	   =model(
						    snake_case_				,	attention_mask=snake_case_				,	token_type_ids=snake_case_				,	labels=snake_case_				,	)
						self.parent.assertEqual(result.logits.shape				,	(self.batch_size, 2))



	def 		UpperCamelCase							(						self							:    Optional[Any]				,	snake_case_							:    Optional[int]				,	snake_case_							:    int				,	snake_case_							:    Any				,	snake_case_							:    str				,	snake_case_							:    Optional[Any]				,	snake_case_							:    List[Any]				,	snake_case_							:    List[str])->							Tuple:
						__lowerCAmelCase	   =MegatronBertForPreTraining(config=snake_case_)
						model.to(snake_case_)
						model.eval()
						__lowerCAmelCase	   =model(
						    snake_case_				,	attention_mask=snake_case_				,	token_type_ids=snake_case_				,	labels=snake_case_				,	next_sentence_label=snake_case_				,	)
						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 		UpperCamelCase							(						self							:    List[Any]				,	snake_case_							:    str				,	snake_case_							:    Optional[Any]				,	snake_case_							:    Optional[Any]				,	snake_case_							:    List[str]				,	snake_case_							:    Union[str, Any]				,	snake_case_							:    Any				,	snake_case_							:    str)->							Dict:
						__lowerCAmelCase	   =MegatronBertForQuestionAnswering(config=snake_case_)
						model.to(snake_case_)
						model.eval()
						__lowerCAmelCase	   =model(
						    snake_case_				,	attention_mask=snake_case_				,	token_type_ids=snake_case_				,	start_positions=snake_case_				,	end_positions=snake_case_				,	)
						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 		UpperCamelCase							(						self							:    str				,	snake_case_							:    Optional[Any]				,	snake_case_							:    Union[str, Any]				,	snake_case_							:    Union[str, Any]				,	snake_case_							:    int				,	snake_case_							:    Optional[int]				,	snake_case_							:    int				,	snake_case_							:    int)->							Optional[Any]:
						__lowerCAmelCase	   =self.num_labels
						__lowerCAmelCase	   =MegatronBertForSequenceClassification(snake_case_)
						model.to(snake_case_)
						model.eval()
						__lowerCAmelCase	   =model(snake_case_				,	attention_mask=snake_case_				,	token_type_ids=snake_case_				,	labels=snake_case_)
						self.parent.assertEqual(result.logits.shape				,	(self.batch_size, self.num_labels))



	def 		UpperCamelCase							(						self							:    Any				,	snake_case_							:    str				,	snake_case_							:    Any				,	snake_case_							:    Union[str, Any]				,	snake_case_							:    Dict				,	snake_case_							:    List[Any]				,	snake_case_							:    List[Any]				,	snake_case_							:    List[Any])->							Optional[int]:
						__lowerCAmelCase	   =self.num_labels
						__lowerCAmelCase	   =MegatronBertForTokenClassification(config=snake_case_)
						model.to(snake_case_)
						model.eval()
						__lowerCAmelCase	   =model(snake_case_				,	attention_mask=snake_case_				,	token_type_ids=snake_case_				,	labels=snake_case_)
						self.parent.assertEqual(result.logits.shape				,	(self.batch_size, self.seq_length, self.num_labels))



	def 		UpperCamelCase							(						self							:    int				,	snake_case_							:    Any				,	snake_case_							:    Optional[int]				,	snake_case_							:    List[Any]				,	snake_case_							:    Dict				,	snake_case_							:    Dict				,	snake_case_							:    int				,	snake_case_							:    str)->							List[Any]:
						__lowerCAmelCase	   =self.num_choices
						__lowerCAmelCase	   =MegatronBertForMultipleChoice(config=snake_case_)
						model.to(snake_case_)
						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(
						    snake_case_				,	attention_mask=snake_case_				,	token_type_ids=snake_case_				,	labels=snake_case_				,	)
						self.parent.assertEqual(result.logits.shape				,	(self.batch_size, self.num_choices))



	def 		UpperCamelCase							(						self							:    Dict)->							Tuple:
						__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 (     SCREAMING_SNAKE_CASE	,    SCREAMING_SNAKE_CASE	,    unittest.TestCase				):
	SCREAMING_SNAKE_CASE       =       (
	    (
	        MegatronBertModel,
	        MegatronBertForMaskedLM,
	        MegatronBertForCausalLM,
	        MegatronBertForMultipleChoice,
	        MegatronBertForNextSentencePrediction,
	        MegatronBertForPreTraining,
	        MegatronBertForQuestionAnswering,
	        MegatronBertForSequenceClassification,
	        MegatronBertForTokenClassification,
	    )
	    if is_torch_available()
	    else ()
	)
	SCREAMING_SNAKE_CASE       =       (
	    {
	        "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 {}
	)
	SCREAMING_SNAKE_CASE       =       True
	# test_resize_embeddings = False
	SCREAMING_SNAKE_CASE       =       False



	def 		UpperCamelCase							(						self							:    Optional[int]				,	snake_case_							:    str				,	snake_case_							:    Any				,	snake_case_							:    Optional[Any]=False)->							Tuple:
						__lowerCAmelCase	   =super()._prepare_for_class(snake_case_				,	snake_case_				,	return_labels=snake_case_)

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



	def 		UpperCamelCase							(						self							:    Dict)->							str:
						__lowerCAmelCase	   =MegatronBertModelTester(self)
						__lowerCAmelCase	   =ConfigTester(self				,	config_class=snake_case_				,	hidden_size=37)



	def 		UpperCamelCase							(						self							:    Any)->							Tuple:
						self.config_tester.run_common_tests()



	def 		UpperCamelCase							(						self							:    Any)->							Tuple:
						__lowerCAmelCase	   =self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_megatron_bert_model(*snake_case_)



	def 		UpperCamelCase							(						self							:    Dict)->							int:
						__lowerCAmelCase	   =self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_megatron_bert_for_masked_lm(*snake_case_)



	def 		UpperCamelCase							(						self							:    List[Any])->							Union[str, Any]:
						__lowerCAmelCase	   =self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_megatron_bert_for_multiple_choice(*snake_case_)



	def 		UpperCamelCase							(						self							:    List[Any])->							List[str]:
						__lowerCAmelCase	   =self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_megatron_bert_for_next_sequence_prediction(*snake_case_)



	def 		UpperCamelCase							(						self							:    str)->							Tuple:
						__lowerCAmelCase	   =self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_megatron_bert_for_pretraining(*snake_case_)



	def 		UpperCamelCase							(						self							:    Dict)->							str:
						__lowerCAmelCase	   =self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_megatron_bert_for_question_answering(*snake_case_)



	def 		UpperCamelCase							(						self							:    Any)->							Optional[Any]:
						__lowerCAmelCase	   =self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_megatron_bert_for_sequence_classification(*snake_case_)



	def 		UpperCamelCase							(						self							:    Tuple)->							List[str]:
						__lowerCAmelCase	   =self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_megatron_bert_for_token_classification(*snake_case_)






def 					__lowerCAmelCase		(  __lowerCamelCase	:    List[str]	)  ->						Union[str, Any]:
					return torch.tensor(
					    __lowerCamelCase				,   dtype=torch.long				,   device=__lowerCamelCase				,   )


lowercase_	       =			1E-4






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



	@slow
	@unittest.skip("""Model is not available.""")
	def 		UpperCamelCase							(						self							:    Tuple)->							Optional[Any]:
						__lowerCAmelCase	   ="""nvidia/megatron-bert-uncased-345m"""
						if "MYDIR" in os.environ:
											__lowerCAmelCase	   =os.path.join(os.environ["""MYDIR"""]				,	snake_case_)
						__lowerCAmelCase	   =MegatronBertModel.from_pretrained(snake_case_)
						model.to(snake_case_)
						model.half()
						__lowerCAmelCase	   =_long_tensor([[1_01, 71_10, 10_05, 10_56, 20_23, 1_13_33, 1_74_13, 10_29, 1_02]])
						with torch.no_grad():
											__lowerCAmelCase	   =model(snake_case_)[0]
						__lowerCAmelCase	   =torch.Size((1, 9, 10_24))
						self.assertEqual(output.shape				,	snake_case_)

						__lowerCAmelCase	   =[-0.6_0_4_0, -0.2_5_1_7, -0.1_0_2_5, 0.3_4_2_0, -0.6_7_5_8, -0.0_0_1_7, -0.1_0_8_9, -0.1_9_9_0, 0.5_7_2_8]
						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(snake_case_				,	snake_case_				,	snake_case_				,	snake_case_)
																self.assertTrue(math.isclose(snake_case_				,	snake_case_				,	rel_tol=snake_case_				,	abs_tol=snake_case_)				,	msg=snake_case_)









from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


lowercase_	       =			{
    '''configuration_encodec''': [
        '''ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''EncodecConfig''',
    ],
    '''feature_extraction_encodec''': ['''EncodecFeatureExtractor'''],
}

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				lowercase_	       =			[
				    '''ENCODEC_PRETRAINED_MODEL_ARCHIVE_LIST''',
				    '''EncodecModel''',
				    '''EncodecPreTrainedModel''',
				]

if TYPE_CHECKING:
				from .configuration_encodec import (
				    ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP,
				    EncodecConfig,
				)
				from .feature_extraction_encodec import EncodecFeatureExtractor

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_encodec import (
								    ENCODEC_PRETRAINED_MODEL_ARCHIVE_LIST,
								    EncodecModel,
								    EncodecPreTrainedModel,
								)

else:
				import sys

				lowercase_	       =			_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




