"""simple docstring"""




import unittest

from transformers import LayoutLMConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

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


if is_torch_available():
					import torch

					from transformers import (
					    LayoutLMForMaskedLM,
					    LayoutLMForQuestionAnswering,
					    LayoutLMForSequenceClassification,
					    LayoutLMForTokenClassification,
					    LayoutLMModel,
					)

class        __SCREAMING_SNAKE_CASE     :







				def __init__(    self    :Union[str, Any]      ,__UpperCAmelCase    :str      ,__UpperCAmelCase    :List[Any]=13      ,__UpperCAmelCase    :Optional[int]=7      ,__UpperCAmelCase    :List[str]=True      ,__UpperCAmelCase    :str=True      ,__UpperCAmelCase    :Optional[int]=True      ,__UpperCAmelCase    :Optional[Any]=True      ,__UpperCAmelCase    :Union[str, Any]=99      ,__UpperCAmelCase    :Optional[int]=32      ,__UpperCAmelCase    :str=5      ,__UpperCAmelCase    :Tuple=4      ,__UpperCAmelCase    :Optional[Any]=37      ,__UpperCAmelCase    :Optional[int]="gelu"      ,__UpperCAmelCase    :Dict=0.1      ,__UpperCAmelCase    :int=0.1      ,__UpperCAmelCase    :List[str]=5_12      ,__UpperCAmelCase    :Optional[Any]=16      ,__UpperCAmelCase    :str=2      ,__UpperCAmelCase    :Optional[int]=0.02      ,__UpperCAmelCase    :Dict=3      ,__UpperCAmelCase    :Optional[int]=4      ,__UpperCAmelCase    :Optional[Any]=None      ,__UpperCAmelCase    :Dict=10_00      ,)      ->					int:




									"""simple docstring"""



									lowerCamelCase__		:   Union[str, Any]  				=					parent
									lowerCamelCase__		:   int  				=					batch_size
									lowerCamelCase__		:   Dict  				=					seq_length
									lowerCamelCase__		:   List[Any]  				=					is_training
									lowerCamelCase__		:   List[Any]  				=					use_input_mask
									lowerCamelCase__		:   int  				=					use_token_type_ids
									lowerCamelCase__		:   int  				=					use_labels
									lowerCamelCase__		:   Optional[Any]  				=					vocab_size
									lowerCamelCase__		:   List[Any]  				=					hidden_size
									lowerCamelCase__		:   Dict  				=					num_hidden_layers
									lowerCamelCase__		:   Tuple  				=					num_attention_heads
									lowerCamelCase__		:   Optional[Any]  				=					intermediate_size
									lowerCamelCase__		:   Union[str, Any]  				=					hidden_act
									lowerCamelCase__		:   Tuple  				=					hidden_dropout_prob
									lowerCamelCase__		:   Tuple  				=					attention_probs_dropout_prob
									lowerCamelCase__		:   Tuple  				=					max_position_embeddings
									lowerCamelCase__		:   str  				=					type_vocab_size
									lowerCamelCase__		:   Any  				=					type_sequence_label_size
									lowerCamelCase__		:   Union[str, Any]  				=					initializer_range
									lowerCamelCase__		:   List[str]  				=					num_labels
									lowerCamelCase__		:   Tuple  				=					num_choices
									lowerCamelCase__		:   str  				=					scope
									lowerCamelCase__		:   Union[str, Any]  				=					range_bbox







				def   lowercase_		(    self    :Optional[Any]  )      ->					Dict:




									"""simple docstring"""



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

									lowerCamelCase__		:   Optional[Any]  				=					ids_tensor([self.batch_size, self.seq_length, 4]      ,self.range_bbox  )
									# Ensure that bbox is legal
									for i in range(bbox.shape[0]  ):
														for j in range(bbox.shape[1]  ):
																			if bbox[i, j, 3] < bbox[i, j, 1]:
																								lowerCamelCase__		:   Dict  				=					bbox[i, j, 3]
																								lowerCamelCase__		:   int  				=					bbox[i, j, 1]
																								lowerCamelCase__		:   Dict  				=					t
																			if bbox[i, j, 2] < bbox[i, j, 0]:
																								lowerCamelCase__		:   str  				=					bbox[i, j, 2]
																								lowerCamelCase__		:   Optional[Any]  				=					bbox[i, j, 0]
																								lowerCamelCase__		:   Union[str, Any]  				=					t

									lowerCamelCase__		:   List[Any]  				=					None
									if self.use_input_mask:
														lowerCamelCase__		:   Union[str, Any]  				=					ids_tensor([self.batch_size, self.seq_length]      ,vocab_size=2  )

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

									lowerCamelCase__		:   Dict  				=					None
									lowerCamelCase__		:   str  				=					None
									lowerCamelCase__		:   int  				=					None
									if self.use_labels:
														lowerCamelCase__		:   int  				=					ids_tensor([self.batch_size]      ,self.type_sequence_label_size  )
														lowerCamelCase__		:   Optional[Any]  				=					ids_tensor([self.batch_size, self.seq_length]      ,self.num_labels  )
														lowerCamelCase__		:   Any  				=					ids_tensor([self.batch_size]      ,self.num_choices  )

									lowerCamelCase__		:   Any  				=					self.get_config()

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







				def   lowercase_		(    self    :int  )      ->					str:




									"""simple docstring"""



									return LayoutLMConfig(
									    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      ,)







				def   lowercase_		(    self    :str      ,__UpperCAmelCase    :List[Any]      ,__UpperCAmelCase    :Tuple      ,__UpperCAmelCase    :Optional[int]      ,__UpperCAmelCase    :int      ,__UpperCAmelCase    :Optional[int]      ,__UpperCAmelCase    :Dict      ,__UpperCAmelCase    :List[Any]      ,__UpperCAmelCase    :Optional[Any]  )      ->					Dict:




									"""simple docstring"""



									lowerCamelCase__		:   List[str]  				=					LayoutLMModel(config=__UpperCAmelCase  )
									model.to(__UpperCAmelCase  )
									model.eval()
									lowerCamelCase__		:   Tuple  				=					model(__UpperCAmelCase      ,__UpperCAmelCase      ,attention_mask=__UpperCAmelCase      ,token_type_ids=__UpperCAmelCase  )
									lowerCamelCase__		:   int  				=					model(__UpperCAmelCase      ,__UpperCAmelCase      ,token_type_ids=__UpperCAmelCase  )
									lowerCamelCase__		:   Any  				=					model(__UpperCAmelCase      ,__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   lowercase_		(    self    :Any      ,__UpperCAmelCase    :List[str]      ,__UpperCAmelCase    :Optional[Any]      ,__UpperCAmelCase    :Union[str, Any]      ,__UpperCAmelCase    :Optional[int]      ,__UpperCAmelCase    :Dict      ,__UpperCAmelCase    :List[str]      ,__UpperCAmelCase    :Dict      ,__UpperCAmelCase    :Optional[Any]  )      ->					List[Any]:




									"""simple docstring"""



									lowerCamelCase__		:   Union[str, Any]  				=					LayoutLMForMaskedLM(config=__UpperCAmelCase  )
									model.to(__UpperCAmelCase  )
									model.eval()
									lowerCamelCase__		:   Optional[Any]  				=					model(__UpperCAmelCase      ,__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   lowercase_		(    self    :List[str]      ,__UpperCAmelCase    :Union[str, Any]      ,__UpperCAmelCase    :List[Any]      ,__UpperCAmelCase    :int      ,__UpperCAmelCase    :Any      ,__UpperCAmelCase    :Tuple      ,__UpperCAmelCase    :Optional[Any]      ,__UpperCAmelCase    :Dict      ,__UpperCAmelCase    :Tuple  )      ->					Any:




									"""simple docstring"""



									lowerCamelCase__		:   str  				=					self.num_labels
									lowerCamelCase__		:   List[Any]  				=					LayoutLMForSequenceClassification(__UpperCAmelCase  )
									model.to(__UpperCAmelCase  )
									model.eval()
									lowerCamelCase__		:   List[Any]  				=					model(
									    __UpperCAmelCase      ,__UpperCAmelCase      ,attention_mask=__UpperCAmelCase      ,token_type_ids=__UpperCAmelCase      ,labels=__UpperCAmelCase  )
									self.parent.assertEqual(result.logits.shape      ,(self.batch_size, self.num_labels)  )







				def   lowercase_		(    self    :Dict      ,__UpperCAmelCase    :Optional[Any]      ,__UpperCAmelCase    :Dict      ,__UpperCAmelCase    :Dict      ,__UpperCAmelCase    :Any      ,__UpperCAmelCase    :Dict      ,__UpperCAmelCase    :str      ,__UpperCAmelCase    :List[str]      ,__UpperCAmelCase    :Dict  )      ->					List[Any]:




									"""simple docstring"""



									lowerCamelCase__		:   int  				=					self.num_labels
									lowerCamelCase__		:   int  				=					LayoutLMForTokenClassification(config=__UpperCAmelCase  )
									model.to(__UpperCAmelCase  )
									model.eval()
									lowerCamelCase__		:   Any  				=					model(__UpperCAmelCase      ,__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   lowercase_		(    self    :Union[str, Any]      ,__UpperCAmelCase    :Union[str, Any]      ,__UpperCAmelCase    :List[str]      ,__UpperCAmelCase    :Dict      ,__UpperCAmelCase    :Optional[int]      ,__UpperCAmelCase    :str      ,__UpperCAmelCase    :int      ,__UpperCAmelCase    :int      ,__UpperCAmelCase    :Dict  )      ->					int:




									"""simple docstring"""



									lowerCamelCase__		:   Optional[Any]  				=					LayoutLMForQuestionAnswering(config=__UpperCAmelCase  )
									model.to(__UpperCAmelCase  )
									model.eval()
									lowerCamelCase__		:   List[Any]  				=					model(
									    __UpperCAmelCase      ,bbox=__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   lowercase_		(    self    :Dict  )      ->					Optional[int]:




									"""simple docstring"""



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





@require_torch
class        __SCREAMING_SNAKE_CASE     (   _lowerCAmelCase						,       _lowerCAmelCase						,       unittest.TestCase							):
				UpperCAmelCase	=      (
				    (
				        LayoutLMModel,
				        LayoutLMForMaskedLM,
				        LayoutLMForSequenceClassification,
				        LayoutLMForTokenClassification,
				        LayoutLMForQuestionAnswering,
				    )
				    if is_torch_available()
				    else None
				)
				UpperCAmelCase	=      (
				    {
				        '''document-question-answering''': LayoutLMForQuestionAnswering,
				        '''feature-extraction''': LayoutLMModel,
				        '''fill-mask''': LayoutLMForMaskedLM,
				        '''text-classification''': LayoutLMForSequenceClassification,
				        '''token-classification''': LayoutLMForTokenClassification,
				        '''zero-shot''': LayoutLMForSequenceClassification,
				    }
				    if is_torch_available()
				    else {}
				)
				UpperCAmelCase	=      True







				def   lowercase_		(    self    :Optional[Any]  )      ->					Union[str, Any]:




									"""simple docstring"""



									lowerCamelCase__		:   List[str]  				=					LayoutLMModelTester(self  )
									lowerCamelCase__		:   int  				=					ConfigTester(self      ,config_class=__UpperCAmelCase      ,hidden_size=37  )







				def   lowercase_		(    self    :Union[str, Any]  )      ->					Optional[Any]:




									"""simple docstring"""



									self.config_tester.run_common_tests()







				def   lowercase_		(    self    :Tuple  )      ->					Tuple:




									"""simple docstring"""



									lowerCamelCase__		:   Optional[Any]  				=					self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(*__UpperCAmelCase  )







				def   lowercase_		(    self    :List[str]  )      ->					Dict:




									"""simple docstring"""



									lowerCamelCase__		:   Dict  				=					self.model_tester.prepare_config_and_inputs()
									for type in ["absolute", "relative_key", "relative_key_query"]:
														lowerCamelCase__		:   Optional[Any]  				=					type
														self.model_tester.create_and_check_model(*__UpperCAmelCase  )







				def   lowercase_		(    self    :List[Any]  )      ->					Any:




									"""simple docstring"""



									lowerCamelCase__		:   str  				=					self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_masked_lm(*__UpperCAmelCase  )







				def   lowercase_		(    self    :List[str]  )      ->					List[Any]:




									"""simple docstring"""



									lowerCamelCase__		:   List[str]  				=					self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_sequence_classification(*__UpperCAmelCase  )







				def   lowercase_		(    self    :int  )      ->					Any:




									"""simple docstring"""



									lowerCamelCase__		:   str  				=					self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_token_classification(*__UpperCAmelCase  )







				def   lowercase_		(    self    :List[Any]  )      ->					List[str]:




									"""simple docstring"""



									lowerCamelCase__		:   Any  				=					self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_question_answering(*__UpperCAmelCase  )







				@unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.'''  )
				def   lowercase_		(    self    :Tuple  )      ->					Dict:




									"""simple docstring"""



									pass





def 				__a       (			):







					"""simple docstring"""





					lowerCamelCase__		:   Union[str, Any]  				=					torch.tensor([[101,1019,1014,1016,1037,12849,4747,1004,14246,2278,5439,4524,5002,2930,2193,2930,4341,3208,1005,1055,2171,2848,11300,3531,102],[101,4070,4034,7020,1024,3058,1015,1013,2861,1013,6070,19274,2772,6205,27814,16147,16147,4343,2047,10283,10969,14389,1012,2338,102]]							, device=_lowercase						)  # noqa: E231
					lowerCamelCase__		:   int  				=					torch.tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],]							, device=_lowercase						)  # noqa: E231
					lowerCamelCase__		:   str  				=					torch.tensor([[[0,0,0,0],[423,237,440,251],[427,272,441,287],[419,115,437,129],[961,885,992,912],[256,38,330,58],[256,38,330,58],[336,42,353,57],[360,39,401,56],[360,39,401,56],[411,39,471,59],[479,41,528,59],[533,39,630,60],[67,113,134,131],[141,115,209,132],[68,149,133,166],[141,149,187,164],[195,148,287,165],[195,148,287,165],[195,148,287,165],[295,148,349,165],[441,149,492,166],[497,149,546,164],[64,201,125,218],[1000,1000,1000,1000]],[[0,0,0,0],[662,150,754,166],[665,199,742,211],[519,213,554,228],[519,213,554,228],[134,433,187,454],[130,467,204,480],[130,467,204,480],[130,467,204,480],[130,467,204,480],[130,467,204,480],[314,469,376,482],[504,684,582,706],[941,825,973,900],[941,825,973,900],[941,825,973,900],[941,825,973,900],[610,749,652,765],[130,659,168,672],[176,657,237,672],[238,657,312,672],[443,653,628,672],[443,653,628,672],[716,301,825,317],[1000,1000,1000,1000]]]							, device=_lowercase						)  # noqa: E231
					lowerCamelCase__		:   List[Any]  				=					torch.tensor([[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]]							, device=_lowercase						)  # noqa: E231
					# these are sequence labels (i.e. at the token level)
					lowerCamelCase__		:   str  				=					torch.tensor([[-100,10,10,10,9,1,-100,7,7,-100,7,7,4,2,5,2,8,8,-100,-100,5,0,3,2,-100],[-100,12,12,12,-100,12,10,-100,-100,-100,-100,10,12,9,-100,-100,-100,10,10,10,9,12,-100,10,-100]]							, device=_lowercase						)  # noqa: E231
					# fmt: on

					return input_ids, attention_mask, bbox, token_type_ids, labels





@require_torch
class        __SCREAMING_SNAKE_CASE     (   unittest.TestCase							):







				@slow
				def   lowercase_		(    self    :List[Any]  )      ->					List[str]:




									"""simple docstring"""



									lowerCamelCase__		:   Optional[Any]  				=					LayoutLMModel.from_pretrained('''microsoft/layoutlm-base-uncased'''  ).to(__UpperCAmelCase  )

									lowerCamelCase__				,						lowerCamelCase__				,						lowerCamelCase__				,						lowerCamelCase__				,						lowerCamelCase__		:   str  				=					prepare_layoutlm_batch_inputs()

									# forward pass
									lowerCamelCase__		:   Optional[Any]  				=					model(input_ids=__UpperCAmelCase      ,bbox=__UpperCAmelCase      ,attention_mask=__UpperCAmelCase      ,token_type_ids=__UpperCAmelCase  )

									# test the sequence output on [0, :3, :3]
									lowerCamelCase__		:   str  				=					torch.tensor(
									    [[0.1_785, -0.1_947, -0.0_425], [-0.3_254, -0.2_807, 0.2_553], [-0.5_391, -0.3_322, 0.3_364]]      ,device=__UpperCAmelCase      ,)

									self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :3, :3]      ,__UpperCAmelCase      ,atol=1E-3  )  )

									# test the pooled output on [1, :3]
									lowerCamelCase__		:   Optional[int]  				=					torch.tensor([-0.6_580, -0.0_214, 0.8_552]      ,device=__UpperCAmelCase  )

									self.assertTrue(torch.allclose(outputs.pooler_output[1, :3]      ,__UpperCAmelCase      ,atol=1E-3  )  )







				@slow
				def   lowercase_		(    self    :int  )      ->					List[str]:




									"""simple docstring"""



									lowerCamelCase__		:   Any  				=					LayoutLMForSequenceClassification.from_pretrained('''microsoft/layoutlm-base-uncased'''      ,num_labels=2  ).to(
									    __UpperCAmelCase  )

									lowerCamelCase__				,						lowerCamelCase__				,						lowerCamelCase__				,						lowerCamelCase__				,						lowerCamelCase__		:   Dict  				=					prepare_layoutlm_batch_inputs()

									# forward pass
									lowerCamelCase__		:   Optional[Any]  				=					model(
									    input_ids=__UpperCAmelCase      ,bbox=__UpperCAmelCase      ,attention_mask=__UpperCAmelCase      ,token_type_ids=__UpperCAmelCase      ,labels=torch.tensor([1, 1]      ,device=__UpperCAmelCase  )      ,)

									# test whether we get a loss as a scalar
									lowerCamelCase__		:   Any  				=					outputs.loss
									lowerCamelCase__		:   Optional[int]  				=					torch.Size([]  )
									self.assertEqual(loss.shape      ,__UpperCAmelCase  )

									# test the shape of the logits
									lowerCamelCase__		:   List[Any]  				=					outputs.logits
									lowerCamelCase__		:   int  				=					torch.Size((2, 2)  )
									self.assertEqual(logits.shape      ,__UpperCAmelCase  )







				@slow
				def   lowercase_		(    self    :Union[str, Any]  )      ->					Optional[Any]:




									"""simple docstring"""



									lowerCamelCase__		:   Optional[Any]  				=					LayoutLMForTokenClassification.from_pretrained('''microsoft/layoutlm-base-uncased'''      ,num_labels=13  ).to(
									    __UpperCAmelCase  )

									lowerCamelCase__				,						lowerCamelCase__				,						lowerCamelCase__				,						lowerCamelCase__				,						lowerCamelCase__		:   Tuple  				=					prepare_layoutlm_batch_inputs()

									# forward pass
									lowerCamelCase__		:   str  				=					model(
									    input_ids=__UpperCAmelCase      ,bbox=__UpperCAmelCase      ,attention_mask=__UpperCAmelCase      ,token_type_ids=__UpperCAmelCase      ,labels=__UpperCAmelCase  )

									# test the loss calculation to be around 2.65
									# expected_loss = torch.tensor(2.65, device=torch_device)

									# The loss is currently somewhat random and can vary between 0.1-0.3 atol.
									# self.assertTrue(torch.allclose(outputs.loss, expected_loss, atol=0.1))

									# test the shape of the logits
									lowerCamelCase__		:   Any  				=					outputs.logits
									lowerCamelCase__		:   int  				=					torch.Size((2, 25, 13)  )
									self.assertEqual(logits.shape      ,__UpperCAmelCase  )







				@slow
				def   lowercase_		(    self    :Optional[int]  )      ->					Any:




									"""simple docstring"""



									lowerCamelCase__		:   Union[str, Any]  				=					LayoutLMForQuestionAnswering.from_pretrained('''microsoft/layoutlm-base-uncased'''  ).to(__UpperCAmelCase  )

									lowerCamelCase__				,						lowerCamelCase__				,						lowerCamelCase__				,						lowerCamelCase__				,						lowerCamelCase__		:   Optional[int]  				=					prepare_layoutlm_batch_inputs()

									# forward pass
									lowerCamelCase__		:   Optional[int]  				=					model(input_ids=__UpperCAmelCase      ,bbox=__UpperCAmelCase      ,attention_mask=__UpperCAmelCase      ,token_type_ids=__UpperCAmelCase  )

									# test the shape of the logits
									lowerCamelCase__		:   Dict  				=					torch.Size((2, 25)  )
									self.assertEqual(outputs.start_logits.shape      ,__UpperCAmelCase  )
									self.assertEqual(outputs.end_logits.shape      ,__UpperCAmelCase  )







"""simple docstring"""




import os
import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device


if is_torch_available():
					import torch

					from transformers import GPTaLMHeadModel

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







				@slow
				@unittest.skip('''Model is not available.'''  )
				def   lowercase_		(    self    :Union[str, Any]  )      ->					Optional[int]:




									"""simple docstring"""



									lowerCamelCase__		:   Any  				=					'''nvidia/megatron-gpt2-345m/'''
									if "MYDIR" in os.environ:
														lowerCamelCase__		:   str  				=					os.path.join(os.environ['''MYDIR''']      ,__UpperCAmelCase  )
									lowerCamelCase__		:   Dict  				=					GPTaLMHeadModel.from_pretrained(__UpperCAmelCase  )
									model.to(__UpperCAmelCase  )
									model.half()

									lowerCamelCase__		:   Dict  				=					torch.tensor(
									    [[1_01, 71_10, 10_05, 10_56, 20_23, 1_13_33, 1_74_13, 10_29, 1_02]]      ,device=__UpperCAmelCase      ,dtype=torch.long      ,)

									with torch.no_grad():
														lowerCamelCase__		:   Tuple  				=					model(__UpperCAmelCase  ).logits

									lowerCamelCase__		:   List[str]  				=					torch.Size((1, 9, 5_02_57)  )
									self.assertEqual(output.shape      ,__UpperCAmelCase  )

									lowerCamelCase__		:   Tuple  				=					torch.tensor(
									    [
									        4.9_414,
									        -0.2_920,
									        -1.2_148,
									        -4.0_273,
									        -0.5_161,
									        -5.2_109,
									        -1.2_412,
									        -1.8_301,
									        -1.7_734,
									        -4.7_148,
									        -0.2_317,
									        -1.0_811,
									        -2.1_777,
									        0.4_141,
									        -3.7_969,
									        -4.0_586,
									        -2.5_332,
									        -3.3_809,
									        4.3_867,
									    ]      ,device=__UpperCAmelCase      ,dtype=torch.half      ,)

									for i in range(19  ):
														lowerCamelCase__				,						lowerCamelCase__		:   Dict  				=					8 * i // 17, 27_92 * i  # along the diagonal
														lowerCamelCase__				,						lowerCamelCase__		:   Optional[int]  				=					output[0, r, c], expected_diag[i]
														lowerCamelCase__		:   Dict  				=					F"""row={r} col={c} computed={computed} expected={expected}"""
														self.assertAlmostEqual(__UpperCAmelCase      ,__UpperCAmelCase      ,delta=1E-4      ,msg=__UpperCAmelCase  )






