


import os
import tempfile
import unittest

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

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
							import torch

							from transformers import (
							    MODEL_FOR_PRETRAINING_MAPPING,
							    NezhaForMaskedLM,
							    NezhaForMultipleChoice,
							    NezhaForNextSentencePrediction,
							    NezhaForPreTraining,
							    NezhaForQuestionAnswering,
							    NezhaForSequenceClassification,
							    NezhaForTokenClassification,
							    NezhaModel,
							)
							from transformers.models.nezha.modeling_nezha import NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST







class        UpperCamelCase__   :

					def __init__(     self       :   int							,					UpperCamelCase__       :   Any							,					UpperCamelCase__       :   Optional[Any]=13							,					UpperCamelCase__       :   List[str]=7							,					UpperCamelCase__       :   Optional[int]=True							,					UpperCamelCase__       :   Dict=True							,					UpperCamelCase__       :   Union[str, Any]=True							,					UpperCamelCase__       :   str=True							,					UpperCamelCase__       :   Optional[int]=99							,					UpperCamelCase__       :   Tuple=32							,					UpperCamelCase__       :   Tuple=5							,					UpperCamelCase__       :   List[str]=4							,					UpperCamelCase__       :   Optional[int]=37							,					UpperCamelCase__       :   Union[str, Any]="gelu"							,					UpperCamelCase__       :   Dict=0.1							,					UpperCamelCase__       :   Optional[int]=0.1							,					UpperCamelCase__       :   Optional[int]=128							,					UpperCamelCase__       :   Tuple=32							,					UpperCamelCase__       :   Union[str, Any]=16							,					UpperCamelCase__       :   str=2							,					UpperCamelCase__       :   Dict=0.02							,					UpperCamelCase__       :   Tuple=3							,					UpperCamelCase__       :   str=4							,					UpperCamelCase__       :   List[Any]=None							,					):






										'''simple docstring'''






										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_  							=			hidden_size
										lowercase_  							=			num_hidden_layers
										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   UpperCAmelCase__       (     self       :   Union[str, Any]       ):






										'''simple docstring'''






										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   UpperCAmelCase__       (     self       :   Dict       ):






										'''simple docstring'''






										return NezhaConfig(
										    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							,					is_decoder=UpperCamelCase__							,					initializer_range=self.initializer_range							,					)

					def   UpperCAmelCase__       (     self       :   str       ):






										'''simple docstring'''






										(
										    (
										    lowercase_
										)							,					(
										    lowercase_
										)							,					(
										    lowercase_
										)							,					(
										    lowercase_
										)							,					(
										    lowercase_
										)							,					(
										    lowercase_
										)							,					(
										    lowercase_
										)							,					
										)  							=			self.prepare_config_and_inputs()

										lowercase_  							=			True
										lowercase_  							=			floats_tensor([self.batch_size, self.seq_length, self.hidden_size]       )
										lowercase_  							=			ids_tensor([self.batch_size, self.seq_length]							,					vocab_size=2       )

										return (
										    config,
										    input_ids,
										    token_type_ids,
										    input_mask,
										    sequence_labels,
										    token_labels,
										    choice_labels,
										    encoder_hidden_states,
										    encoder_attention_mask,
										)

					def   UpperCAmelCase__       (     self       :   str							,					UpperCamelCase__       :   List[Any]							,					UpperCamelCase__       :   Union[str, Any]							,					UpperCamelCase__       :   int							,					UpperCamelCase__       :   Dict							,					UpperCamelCase__       :   Optional[int]							,					UpperCamelCase__       :   Union[str, Any]							,					UpperCamelCase__       :   Optional[int]       ):






										'''simple docstring'''






										lowercase_  							=			NezhaModel(config=UpperCamelCase__       )
										model.to(UpperCamelCase__       )
										model.eval()
										lowercase_  							=			model(UpperCamelCase__							,					attention_mask=UpperCamelCase__							,					token_type_ids=UpperCamelCase__       )
										lowercase_  							=			model(UpperCamelCase__							,					token_type_ids=UpperCamelCase__       )
										lowercase_  							=			model(UpperCamelCase__       )
										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       :   int							,					UpperCamelCase__       :   Union[str, Any]							,					UpperCamelCase__       :   Dict							,					UpperCamelCase__       :   Union[str, Any]							,					UpperCamelCase__       :   List[Any]							,					UpperCamelCase__       :   Optional[int]							,					UpperCamelCase__       :   Optional[int]							,					UpperCamelCase__       :   str							,					UpperCamelCase__       :   str							,					UpperCamelCase__       :   Dict							,					):






										'''simple docstring'''






										lowercase_  							=			True
										lowercase_  							=			NezhaModel(UpperCamelCase__       )
										model.to(UpperCamelCase__       )
										model.eval()
										lowercase_  							=			model(
										    UpperCamelCase__							,					attention_mask=UpperCamelCase__							,					token_type_ids=UpperCamelCase__							,					encoder_hidden_states=UpperCamelCase__							,					encoder_attention_mask=UpperCamelCase__							,					)
										lowercase_  							=			model(
										    UpperCamelCase__							,					attention_mask=UpperCamelCase__							,					token_type_ids=UpperCamelCase__							,					encoder_hidden_states=UpperCamelCase__							,					)
										lowercase_  							=			model(UpperCamelCase__							,					attention_mask=UpperCamelCase__							,					token_type_ids=UpperCamelCase__       )
										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       :   Optional[int]							,					UpperCamelCase__       :   str							,					UpperCamelCase__       :   int							,					UpperCamelCase__       :   Dict							,					UpperCamelCase__       :   Union[str, Any]							,					UpperCamelCase__       :   List[Any]							,					UpperCamelCase__       :   Any							,					UpperCamelCase__       :   Union[str, Any]       ):






										'''simple docstring'''






										lowercase_  							=			NezhaForMaskedLM(config=UpperCamelCase__       )
										model.to(UpperCamelCase__       )
										model.eval()
										lowercase_  							=			model(UpperCamelCase__							,					attention_mask=UpperCamelCase__							,					token_type_ids=UpperCamelCase__							,					labels=UpperCamelCase__       )
										self.parent.assertEqual(result.logits.shape							,					(self.batch_size, self.seq_length, self.vocab_size)       )

					def   UpperCAmelCase__       (     self       :   Any							,					UpperCamelCase__       :   int							,					UpperCamelCase__       :   List[str]							,					UpperCamelCase__       :   Optional[Any]							,					UpperCamelCase__       :   Any							,					UpperCamelCase__       :   int							,					UpperCamelCase__       :   Union[str, Any]							,					UpperCamelCase__       :   Optional[int]       ):






										'''simple docstring'''






										lowercase_  							=			NezhaForNextSentencePrediction(config=UpperCamelCase__       )
										model.to(UpperCamelCase__       )
										model.eval()
										lowercase_  							=			model(
										    UpperCamelCase__							,					attention_mask=UpperCamelCase__							,					token_type_ids=UpperCamelCase__							,					labels=UpperCamelCase__							,					)
										self.parent.assertEqual(result.logits.shape							,					(self.batch_size, 2)       )

					def   UpperCAmelCase__       (     self       :   Any							,					UpperCamelCase__       :   Tuple							,					UpperCamelCase__       :   List[str]							,					UpperCamelCase__       :   List[Any]							,					UpperCamelCase__       :   List[str]							,					UpperCamelCase__       :   Union[str, Any]							,					UpperCamelCase__       :   Optional[int]							,					UpperCamelCase__       :   List[Any]       ):






										'''simple docstring'''






										lowercase_  							=			NezhaForPreTraining(config=UpperCamelCase__       )
										model.to(UpperCamelCase__       )
										model.eval()
										lowercase_  							=			model(
										    UpperCamelCase__							,					attention_mask=UpperCamelCase__							,					token_type_ids=UpperCamelCase__							,					labels=UpperCamelCase__							,					next_sentence_label=UpperCamelCase__							,					)
										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       :   Optional[int]							,					UpperCamelCase__       :   List[str]							,					UpperCamelCase__       :   List[str]							,					UpperCamelCase__       :   int							,					UpperCamelCase__       :   Optional[Any]							,					UpperCamelCase__       :   List[str]							,					UpperCamelCase__       :   str							,					UpperCamelCase__       :   Dict       ):






										'''simple docstring'''






										lowercase_  							=			NezhaForQuestionAnswering(config=UpperCamelCase__       )
										model.to(UpperCamelCase__       )
										model.eval()
										lowercase_  							=			model(
										    UpperCamelCase__							,					attention_mask=UpperCamelCase__							,					token_type_ids=UpperCamelCase__							,					start_positions=UpperCamelCase__							,					end_positions=UpperCamelCase__							,					)
										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       :   Any							,					UpperCamelCase__       :   int							,					UpperCamelCase__       :   Dict							,					UpperCamelCase__       :   Dict							,					UpperCamelCase__       :   str							,					UpperCamelCase__       :   List[str]							,					UpperCamelCase__       :   List[Any]							,					UpperCamelCase__       :   int       ):






										'''simple docstring'''






										lowercase_  							=			self.num_labels
										lowercase_  							=			NezhaForSequenceClassification(UpperCamelCase__       )
										model.to(UpperCamelCase__       )
										model.eval()
										lowercase_  							=			model(UpperCamelCase__							,					attention_mask=UpperCamelCase__							,					token_type_ids=UpperCamelCase__							,					labels=UpperCamelCase__       )
										self.parent.assertEqual(result.logits.shape							,					(self.batch_size, self.num_labels)       )

					def   UpperCAmelCase__       (     self       :   Union[str, Any]							,					UpperCamelCase__       :   Optional[Any]							,					UpperCamelCase__       :   int							,					UpperCamelCase__       :   Optional[Any]							,					UpperCamelCase__       :   List[Any]							,					UpperCamelCase__       :   List[str]							,					UpperCamelCase__       :   List[str]							,					UpperCamelCase__       :   List[Any]       ):






										'''simple docstring'''






										lowercase_  							=			self.num_labels
										lowercase_  							=			NezhaForTokenClassification(config=UpperCamelCase__       )
										model.to(UpperCamelCase__       )
										model.eval()
										lowercase_  							=			model(UpperCamelCase__							,					attention_mask=UpperCamelCase__							,					token_type_ids=UpperCamelCase__							,					labels=UpperCamelCase__       )
										self.parent.assertEqual(result.logits.shape							,					(self.batch_size, self.seq_length, self.num_labels)       )

					def   UpperCAmelCase__       (     self       :   Any							,					UpperCamelCase__       :   Dict							,					UpperCamelCase__       :   Dict							,					UpperCamelCase__       :   Dict							,					UpperCamelCase__       :   Dict							,					UpperCamelCase__       :   str							,					UpperCamelCase__       :   List[str]							,					UpperCamelCase__       :   List[str]       ):






										'''simple docstring'''






										lowercase_  							=			self.num_choices
										lowercase_  							=			NezhaForMultipleChoice(config=UpperCamelCase__       )
										model.to(UpperCamelCase__       )
										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(
										    UpperCamelCase__							,					attention_mask=UpperCamelCase__							,					token_type_ids=UpperCamelCase__							,					labels=UpperCamelCase__							,					)
										self.parent.assertEqual(result.logits.shape							,					(self.batch_size, self.num_choices)       )







					def   UpperCAmelCase__       (     self       :   Tuple       ):






										'''simple docstring'''






										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        UpperCamelCase__   (	__magic_name__							,				__magic_name__							,				__magic_name__							,				unittest.TestCase   ):
					__SCREAMING_SNAKE_CASE       :  Optional[int]						=   (
					    (
					        NezhaModel,
					        NezhaForMaskedLM,
					        NezhaForMultipleChoice,
					        NezhaForNextSentencePrediction,
					        NezhaForPreTraining,
					        NezhaForQuestionAnswering,
					        NezhaForSequenceClassification,
					        NezhaForTokenClassification,
					    )
					    if is_torch_available()
					    else ()
					)
					__SCREAMING_SNAKE_CASE       :  List[Any]						=   (
					    {
					        'feature-extraction': NezhaModel,
					        'fill-mask': NezhaForMaskedLM,
					        'question-answering': NezhaForQuestionAnswering,
					        'text-classification': NezhaForSequenceClassification,
					        'token-classification': NezhaForTokenClassification,
					        'zero-shot': NezhaForSequenceClassification,
					    }
					    if is_torch_available()
					    else {}
					)
					__SCREAMING_SNAKE_CASE       :  Dict						=   True

					def   UpperCAmelCase__       (     self       :   Optional[int]							,					UpperCamelCase__       :   List[str]							,					UpperCamelCase__       :   int							,					UpperCamelCase__       :   List[Any]=False       ):






										'''simple docstring'''






										lowercase_  							=			super()._prepare_for_class(UpperCamelCase__							,					UpperCamelCase__							,					return_labels=UpperCamelCase__       )

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

					def   UpperCAmelCase__       (     self       :   Tuple       ):






										'''simple docstring'''






										lowercase_  							=			NezhaModelTester(self       )
										lowercase_  							=			ConfigTester(self							,					config_class=UpperCamelCase__							,					hidden_size=37       )

					def   UpperCAmelCase__       (     self       :   str       ):






										'''simple docstring'''






										self.config_tester.run_common_tests()

					def   UpperCAmelCase__       (     self       :   int       ):






										'''simple docstring'''






										lowercase_  							=			self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*UpperCamelCase__       )

					def   UpperCAmelCase__       (     self       :   str       ):






										'''simple docstring'''






										lowercase_  							=			self.model_tester.prepare_config_and_inputs_for_decoder()
										self.model_tester.create_and_check_model_as_decoder(*UpperCamelCase__       )

					def   UpperCAmelCase__       (     self       :   Tuple       ):






										'''simple docstring'''






										(
										    (
										    lowercase_
										)							,					(
										    lowercase_
										)							,					(
										    lowercase_
										)							,					(
										    lowercase_
										)							,					(
										    lowercase_
										)							,					(
										    lowercase_
										)							,					(
										    lowercase_
										)							,					(
										    lowercase_
										)							,					(
										    lowercase_
										)							,					
										)  							=			self.model_tester.prepare_config_and_inputs_for_decoder()

										lowercase_  							=			None

										self.model_tester.create_and_check_model_as_decoder(
										    UpperCamelCase__							,					UpperCamelCase__							,					UpperCamelCase__							,					UpperCamelCase__							,					UpperCamelCase__							,					UpperCamelCase__							,					UpperCamelCase__							,					UpperCamelCase__							,					UpperCamelCase__							,					)

					def   UpperCAmelCase__       (     self       :   Optional[int]       ):






										'''simple docstring'''






										lowercase_  							=			self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_masked_lm(*UpperCamelCase__       )

					def   UpperCAmelCase__       (     self       :   Tuple       ):






										'''simple docstring'''






										lowercase_  							=			self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_multiple_choice(*UpperCamelCase__       )

					def   UpperCAmelCase__       (     self       :   int       ):






										'''simple docstring'''






										lowercase_  							=			self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_next_sequence_prediction(*UpperCamelCase__       )

					def   UpperCAmelCase__       (     self       :   Optional[Any]       ):






										'''simple docstring'''






										lowercase_  							=			self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_pretraining(*UpperCamelCase__       )

					def   UpperCAmelCase__       (     self       :   Any       ):






										'''simple docstring'''






										lowercase_  							=			self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_question_answering(*UpperCamelCase__       )

					def   UpperCAmelCase__       (     self       :   Tuple       ):






										'''simple docstring'''






										lowercase_  							=			self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_sequence_classification(*UpperCamelCase__       )

					def   UpperCAmelCase__       (     self       :   Any       ):






										'''simple docstring'''






										lowercase_  							=			self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_token_classification(*UpperCamelCase__       )

					@slow
					def   UpperCAmelCase__       (     self       :   Dict       ):






										'''simple docstring'''






										for model_name in NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
															lowercase_  							=			NezhaModel.from_pretrained(UpperCamelCase__       )
															self.assertIsNotNone(UpperCamelCase__       )







					@slow
					@require_torch_gpu
					def   UpperCAmelCase__       (     self       :   Dict       ):






										'''simple docstring'''






										lowercase_							,					lowercase_  							=			self.model_tester.prepare_config_and_inputs_for_common()
										for model_class in self.all_model_classes:
															# NezhaForMultipleChoice behaves incorrectly in JIT environments.
															if model_class == NezhaForMultipleChoice:
																				return

															lowercase_  							=			True
															lowercase_  							=			model_class(config=UpperCamelCase__       )

															lowercase_  							=			self._prepare_for_class(UpperCamelCase__							,					UpperCamelCase__       )
															lowercase_  							=			torch.jit.trace(
															    UpperCamelCase__							,					(inputs_dict["""input_ids"""].to("""cpu"""       ), inputs_dict["""attention_mask"""].to("""cpu"""       ))       )

															with tempfile.TemporaryDirectory() as tmp:
																				torch.jit.save(UpperCamelCase__							,					os.path.join(UpperCamelCase__							,					"""bert.pt"""       )       )
																				lowercase_  							=			torch.jit.load(os.path.join(UpperCamelCase__							,					"""bert.pt"""       )							,					map_location=UpperCamelCase__       )
																				loaded(inputs_dict["""input_ids"""].to(UpperCamelCase__       )							,					inputs_dict["""attention_mask"""].to(UpperCamelCase__       )       )







@require_torch
class        UpperCamelCase__   (	unittest.TestCase   ):

					@slow
					def   UpperCAmelCase__       (     self       :   Tuple       ):






										'''simple docstring'''






										lowercase_  							=			NezhaModel.from_pretrained("""sijunhe/nezha-cn-base"""       )
										lowercase_  							=			torch.tensor([[0, 1, 2, 3, 4, 5]]       )
										lowercase_  							=			torch.tensor([[0, 1, 1, 1, 1, 1]]       )
										with torch.no_grad():
															lowercase_  							=			model(UpperCamelCase__							,					attention_mask=UpperCamelCase__       )[0]
										lowercase_  							=			torch.Size((1, 6, 768)       )
										self.assertEqual(output.shape							,					UpperCamelCase__       )
										lowercase_  							=			torch.tensor([[[0.0_685, 0.2_441, 0.1_102], [0.0_600, 0.1_906, 0.1_349], [0.0_221, 0.0_819, 0.0_586]]]       )

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







					@slow
					def   UpperCAmelCase__       (     self       :   Optional[Any]       ):






										'''simple docstring'''






										lowercase_  							=			NezhaForMaskedLM.from_pretrained("""sijunhe/nezha-cn-base"""       )
										lowercase_  							=			torch.tensor([[0, 1, 2, 3, 4, 5]]       )
										lowercase_  							=			torch.tensor([[1, 1, 1, 1, 1, 1]]       )
										with torch.no_grad():
															lowercase_  							=			model(UpperCamelCase__							,					attention_mask=UpperCamelCase__       )[0]
										lowercase_  							=			torch.Size((1, 6, 21_128)       )
										self.assertEqual(output.shape							,					UpperCamelCase__       )
										lowercase_  							=			torch.tensor(
										    [[-2.7_939, -1.7_902, -2.2_189], [-2.8_585, -1.8_908, -2.3_723], [-2.6_499, -1.7_750, -2.2_558]]       )

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






import torch
import torch.nn as nn
from transformers.modeling_utils import ModuleUtilsMixin
from transformers.models.ta.modeling_ta import TaBlock, TaConfig, TaLayerNorm

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin







class        UpperCamelCase__   (	__magic_name__							,				__magic_name__							,				__magic_name__   ):

					@register_to_config
					def __init__(     self       :   int							,					UpperCamelCase__       :   int							,					UpperCamelCase__       :   int							,					UpperCamelCase__       :   int							,					UpperCamelCase__       :   float							,					UpperCamelCase__       :   int							,					UpperCamelCase__       :   int							,					UpperCamelCase__       :   int							,					UpperCamelCase__       :   int							,					UpperCamelCase__       :   str							,					UpperCamelCase__       :   bool = False							,					):






										'''simple docstring'''






										super().__init__()

										lowercase_  							=			nn.Embedding(UpperCamelCase__							,					UpperCamelCase__       )

										lowercase_  							=			nn.Embedding(UpperCamelCase__							,					UpperCamelCase__       )
										lowercase_  							=			False

										lowercase_  							=			nn.Dropout(p=UpperCamelCase__       )

										lowercase_  							=			TaConfig(
										    vocab_size=UpperCamelCase__							,					d_model=UpperCamelCase__							,					num_heads=UpperCamelCase__							,					d_kv=UpperCamelCase__							,					d_ff=UpperCamelCase__							,					dropout_rate=UpperCamelCase__							,					feed_forward_proj=UpperCamelCase__							,					is_decoder=UpperCamelCase__							,					is_encoder_decoder=UpperCamelCase__							,					)

										lowercase_  							=			nn.ModuleList()
										for lyr_num in range(UpperCamelCase__       ):
															lowercase_  							=			TaBlock(UpperCamelCase__       )
															self.encoders.append(UpperCamelCase__       )

										lowercase_  							=			TaLayerNorm(UpperCamelCase__       )
										lowercase_  							=			nn.Dropout(p=UpperCamelCase__       )







					def   UpperCAmelCase__       (     self       :   Optional[Any]							,					UpperCamelCase__       :   Optional[int]							,					UpperCamelCase__       :   str       ):






										'''simple docstring'''






										lowercase_  							=			self.token_embedder(UpperCamelCase__       )

										lowercase_  							=			encoder_input_tokens.shape[1]
										lowercase_  							=			torch.arange(UpperCamelCase__							,					device=encoder_input_tokens.device       )
										x += self.position_encoding(UpperCamelCase__       )

										lowercase_  							=			self.dropout_pre(UpperCamelCase__       )

										# inverted the attention mask
										lowercase_  							=			encoder_input_tokens.size()
										lowercase_  							=			self.get_extended_attention_mask(UpperCamelCase__							,					UpperCamelCase__       )

										for lyr in self.encoders:
															lowercase_  							=			lyr(UpperCamelCase__							,					UpperCamelCase__       )[0]
										lowercase_  							=			self.layer_norm(UpperCamelCase__       )

										return self.dropout_post(UpperCamelCase__       ), encoder_inputs_mask


