




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        __lowerCAmelCase					:







			def __init__(			self       :     List[Any]       ,       snake_case__       :     Any       ,       snake_case__       :     Optional[Any]=13       ,       snake_case__       :     Optional[Any]=7       ,       snake_case__       :     List[str]=True       ,       snake_case__       :     Optional[int]=True       ,       snake_case__       :     int=True       ,       snake_case__       :     Tuple=True       ,       snake_case__       :     int=99       ,       snake_case__       :     Any=32       ,       snake_case__       :     Union[str, Any]=5       ,       snake_case__       :     Optional[int]=4       ,       snake_case__       :     Optional[int]=37       ,       snake_case__       :     int="gelu"       ,       snake_case__       :     int=0.1       ,       snake_case__       :     str=0.1       ,       snake_case__       :     int=512       ,       snake_case__       :     str=16       ,       snake_case__       :     List[str]=2       ,       snake_case__       :     Union[str, Any]=0.02       ,       snake_case__       :     Tuple=3       ,       snake_case__       :     Dict=4       ,       snake_case__       :     List[str]=None       ,       snake_case__       :     Dict=1_000       ,       ):




									"""simple docstring"""


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







			def UpperCamelCase     (			self       :     int ):




									"""simple docstring"""


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

									_UpperCAmelCase						=	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]:
																											_UpperCAmelCase						=	bbox[i, j, 3]
																											_UpperCAmelCase						=	bbox[i, j, 1]
																											_UpperCAmelCase						=	t
																					if bbox[i, j, 2] < bbox[i, j, 0]:
																											_UpperCAmelCase						=	bbox[i, j, 2]
																											_UpperCAmelCase						=	bbox[i, j, 0]
																											_UpperCAmelCase						=	t

									_UpperCAmelCase						=	None
									if self.use_input_mask:
															_UpperCAmelCase						=	ids_tensor([self.batch_size, self.seq_length]       ,       vocab_size=2 )

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

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

									_UpperCAmelCase						=	self.get_config()

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







			def UpperCamelCase     (			self       :     Tuple ):




									"""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 UpperCamelCase     (			self       :     Optional[Any]       ,       snake_case__       :     Optional[Any]       ,       snake_case__       :     Tuple       ,       snake_case__       :     Optional[int]       ,       snake_case__       :     List[str]       ,       snake_case__       :     str       ,       snake_case__       :     Dict       ,       snake_case__       :     Optional[Any]       ,       snake_case__       :     Dict ):




									"""simple docstring"""


									_UpperCAmelCase						=	LayoutLMModel(config=snake_case__ )
									model.to(snake_case__ )
									model.eval()
									_UpperCAmelCase						=	model(snake_case__       ,       snake_case__       ,       attention_mask=snake_case__       ,       token_type_ids=snake_case__ )
									_UpperCAmelCase						=	model(snake_case__       ,       snake_case__       ,       token_type_ids=snake_case__ )
									_UpperCAmelCase						=	model(snake_case__       ,       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       :     Optional[Any]       ,       snake_case__       :     List[Any]       ,       snake_case__       :     Dict       ,       snake_case__       :     Optional[Any]       ,       snake_case__       :     str       ,       snake_case__       :     str       ,       snake_case__       :     List[Any]       ,       snake_case__       :     List[Any]       ,       snake_case__       :     Tuple ):




									"""simple docstring"""


									_UpperCAmelCase						=	LayoutLMForMaskedLM(config=snake_case__ )
									model.to(snake_case__ )
									model.eval()
									_UpperCAmelCase						=	model(snake_case__       ,       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__       :     Union[str, Any]       ,       snake_case__       :     Union[str, Any]       ,       snake_case__       :     List[Any]       ,       snake_case__       :     Tuple       ,       snake_case__       :     List[str]       ,       snake_case__       :     Any       ,       snake_case__       :     str       ,       snake_case__       :     str ):




									"""simple docstring"""


									_UpperCAmelCase						=	self.num_labels
									_UpperCAmelCase						=	LayoutLMForSequenceClassification(snake_case__ )
									model.to(snake_case__ )
									model.eval()
									_UpperCAmelCase						=	model(
									    snake_case__       ,       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       :     Optional[Any]       ,       snake_case__       :     str       ,       snake_case__       :     List[Any]       ,       snake_case__       :     Union[str, Any]       ,       snake_case__       :     List[Any]       ,       snake_case__       :     Optional[int]       ,       snake_case__       :     List[str]       ,       snake_case__       :     Optional[Any]       ,       snake_case__       :     Union[str, Any] ):




									"""simple docstring"""


									_UpperCAmelCase						=	self.num_labels
									_UpperCAmelCase						=	LayoutLMForTokenClassification(config=snake_case__ )
									model.to(snake_case__ )
									model.eval()
									_UpperCAmelCase						=	model(snake_case__       ,       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       :     Union[str, Any]       ,       snake_case__       :     Tuple       ,       snake_case__       :     List[Any]       ,       snake_case__       :     List[str]       ,       snake_case__       :     List[str]       ,       snake_case__       :     Dict       ,       snake_case__       :     List[Any]       ,       snake_case__       :     Union[str, Any]       ,       snake_case__       :     Tuple ):




									"""simple docstring"""


									_UpperCAmelCase						=	LayoutLMForQuestionAnswering(config=snake_case__ )
									model.to(snake_case__ )
									model.eval()
									_UpperCAmelCase						=	model(
									    snake_case__       ,       bbox=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       :     Optional[int] ):




									"""simple docstring"""


									_UpperCAmelCase						=	self.prepare_config_and_inputs()
									(
									    (
									    _UpperCAmelCase
									)				,					(
									    _UpperCAmelCase
									)				,					(
									    _UpperCAmelCase
									)				,					(
									    _UpperCAmelCase
									)				,					(
									    _UpperCAmelCase
									)				,					(
									    _UpperCAmelCase
									)				,					(
									    _UpperCAmelCase
									)				,					(
									    _UpperCAmelCase
									)				,					
									)						=	config_and_inputs
									_UpperCAmelCase						=	{
									    "input_ids": input_ids,
									    "bbox": bbox,
									    "token_type_ids": token_type_ids,
									    "attention_mask": input_mask,
									}
									return config, inputs_dict






@require_torch
class        __lowerCAmelCase					(			UpperCAmelCase__   ,						UpperCAmelCase__   ,						unittest.TestCase					):
			snake_case_ :					Tuple						=	(
			    (
			        LayoutLMModel,
			        LayoutLMForMaskedLM,
			        LayoutLMForSequenceClassification,
			        LayoutLMForTokenClassification,
			        LayoutLMForQuestionAnswering,
			    )
			    if is_torch_available()
			    else None
			)
			snake_case_ :					Optional[int]						=	(
			    {
			        "document-question-answering": LayoutLMForQuestionAnswering,
			        "feature-extraction": LayoutLMModel,
			        "fill-mask": LayoutLMForMaskedLM,
			        "text-classification": LayoutLMForSequenceClassification,
			        "token-classification": LayoutLMForTokenClassification,
			        "zero-shot": LayoutLMForSequenceClassification,
			    }
			    if is_torch_available()
			    else {}
			)
			snake_case_ :					List[str]						=	True







			def UpperCamelCase     (			self       :     int ):




									"""simple docstring"""


									_UpperCAmelCase						=	LayoutLMModelTester(self )
									_UpperCAmelCase						=	ConfigTester(self       ,       config_class=snake_case__       ,       hidden_size=37 )







			def UpperCamelCase     (			self       :     Dict ):




									"""simple docstring"""


									self.config_tester.run_common_tests()







			def UpperCamelCase     (			self       :     Dict ):




									"""simple docstring"""


									_UpperCAmelCase						=	self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(*snake_case__ )







			def UpperCamelCase     (			self       :     List[str] ):




									"""simple docstring"""


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







			def UpperCamelCase     (			self       :     Optional[int] ):




									"""simple docstring"""


									_UpperCAmelCase						=	self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_masked_lm(*snake_case__ )







			def UpperCamelCase     (			self       :     int ):




									"""simple docstring"""


									_UpperCAmelCase						=	self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_sequence_classification(*snake_case__ )







			def UpperCamelCase     (			self       :     List[str] ):




									"""simple docstring"""


									_UpperCAmelCase						=	self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_token_classification(*snake_case__ )







			def UpperCamelCase     (			self       :     Any ):




									"""simple docstring"""


									_UpperCAmelCase						=	self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_question_answering(*snake_case__ )







			@unittest.skip("Will be fixed soon by reducing the size of the model used for common tests." )
			def UpperCamelCase     (			self       :     List[str] ):




									"""simple docstring"""


									pass




def 				__SCREAMING_SNAKE_CASE    (     ):

						'''simple docstring'''


						_UpperCAmelCase						=	torch.tensor([[101,1019,1014,1016,1037,1_2849,4747,1004,1_4246,2278,5439,4524,5002,2930,2193,2930,4341,3208,1005,1055,2171,2848,1_1300,3531,102],[101,4070,4034,7020,1024,3058,1015,1013,2861,1013,6070,1_9274,2772,6205,2_7814,1_6147,1_6147,4343,2047,1_0283,1_0969,1_4389,1012,2338,102]]      ,       device=snake_case_		)  # noqa: E231
						_UpperCAmelCase						=	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=snake_case_		)  # noqa: E231
						_UpperCAmelCase						=	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=snake_case_		)  # noqa: E231
						_UpperCAmelCase						=	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=snake_case_		)  # noqa: E231
						# these are sequence labels (i.e. at the token level)
						_UpperCAmelCase						=	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=snake_case_		)  # noqa: E231
						# fmt: on

						return input_ids, attention_mask, bbox, token_type_ids, labels






@require_torch
class        __lowerCAmelCase					(			unittest.TestCase					):







			@slow
			def UpperCamelCase     (			self       :     Optional[int] ):




									"""simple docstring"""


									_UpperCAmelCase						=	LayoutLMModel.from_pretrained("microsoft/layoutlm-base-uncased" ).to(snake_case__ )

									_UpperCAmelCase				,					_UpperCAmelCase				,					_UpperCAmelCase				,					_UpperCAmelCase				,					_UpperCAmelCase						=	prepare_layoutlm_batch_inputs()

									# forward pass
									_UpperCAmelCase						=	model(input_ids=snake_case__       ,       bbox=snake_case__       ,       attention_mask=snake_case__       ,       token_type_ids=snake_case__ )

									# test the sequence output on [0, :3, :3]
									_UpperCAmelCase						=	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=snake_case__       ,       )

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

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

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







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




									"""simple docstring"""


									_UpperCAmelCase						=	LayoutLMForSequenceClassification.from_pretrained("microsoft/layoutlm-base-uncased"       ,       num_labels=2 ).to(
									    snake_case__ )

									_UpperCAmelCase				,					_UpperCAmelCase				,					_UpperCAmelCase				,					_UpperCAmelCase				,					_UpperCAmelCase						=	prepare_layoutlm_batch_inputs()

									# forward pass
									_UpperCAmelCase						=	model(
									    input_ids=snake_case__       ,       bbox=snake_case__       ,       attention_mask=snake_case__       ,       token_type_ids=snake_case__       ,       labels=torch.tensor([1, 1]       ,       device=snake_case__ )       ,       )

									# test whether we get a loss as a scalar
									_UpperCAmelCase						=	outputs.loss
									_UpperCAmelCase						=	torch.Size([] )
									self.assertEqual(loss.shape       ,       snake_case__ )

									# test the shape of the logits
									_UpperCAmelCase						=	outputs.logits
									_UpperCAmelCase						=	torch.Size((2, 2) )
									self.assertEqual(logits.shape       ,       snake_case__ )







			@slow
			def UpperCamelCase     (			self       :     List[Any] ):




									"""simple docstring"""


									_UpperCAmelCase						=	LayoutLMForTokenClassification.from_pretrained("microsoft/layoutlm-base-uncased"       ,       num_labels=13 ).to(
									    snake_case__ )

									_UpperCAmelCase				,					_UpperCAmelCase				,					_UpperCAmelCase				,					_UpperCAmelCase				,					_UpperCAmelCase						=	prepare_layoutlm_batch_inputs()

									# forward pass
									_UpperCAmelCase						=	model(
									    input_ids=snake_case__       ,       bbox=snake_case__       ,       attention_mask=snake_case__       ,       token_type_ids=snake_case__       ,       labels=snake_case__ )

									# 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
									_UpperCAmelCase						=	outputs.logits
									_UpperCAmelCase						=	torch.Size((2, 25, 13) )
									self.assertEqual(logits.shape       ,       snake_case__ )







			@slow
			def UpperCamelCase     (			self       :     Dict ):




									"""simple docstring"""


									_UpperCAmelCase						=	LayoutLMForQuestionAnswering.from_pretrained("microsoft/layoutlm-base-uncased" ).to(snake_case__ )

									_UpperCAmelCase				,					_UpperCAmelCase				,					_UpperCAmelCase				,					_UpperCAmelCase				,					_UpperCAmelCase						=	prepare_layoutlm_batch_inputs()

									# forward pass
									_UpperCAmelCase						=	model(input_ids=snake_case__       ,       bbox=snake_case__       ,       attention_mask=snake_case__       ,       token_type_ids=snake_case__ )

									# test the shape of the logits
									_UpperCAmelCase						=	torch.Size((2, 25) )
									self.assertEqual(outputs.start_logits.shape       ,       snake_case__ )
									self.assertEqual(outputs.end_logits.shape       ,       snake_case__ )










import torch
import torch.nn.functional as F

from diffusers import VQDiffusionScheduler

from .test_schedulers import SchedulerCommonTest






class        __lowerCAmelCase					(			UpperCAmelCase__					):
			snake_case_ :					List[str]						=	(VQDiffusionScheduler,)







			def UpperCamelCase     (			self       :     Dict       ,       **snake_case__       :     int ):




									"""simple docstring"""


									_UpperCAmelCase						=	{
									    "num_vec_classes": 4_097,
									    "num_train_timesteps": 100,
									}

									config.update(**snake_case__ )
									return config







			def UpperCamelCase     (			self       :     Optional[int]       ,       snake_case__       :     Any ):




									"""simple docstring"""


									_UpperCAmelCase						=	4
									_UpperCAmelCase						=	8
									_UpperCAmelCase						=	8

									_UpperCAmelCase						=	torch.randint(0       ,       snake_case__       ,       (batch_size, height * width) )

									return sample







			@property
			def UpperCamelCase     (			self       :     str ):




									"""simple docstring"""


									assert False







			def UpperCamelCase     (			self       :     List[str]       ,       snake_case__       :     Optional[Any] ):




									"""simple docstring"""


									def model(snake_case__       :     Dict       ,       snake_case__       :     Tuple       ,       *snake_case__       :     str ):
															_UpperCAmelCase				,					_UpperCAmelCase						=	sample.shape
															_UpperCAmelCase						=	torch.rand((batch_size, num_vec_classes - 1, num_latent_pixels) )
															_UpperCAmelCase						=	F.log_softmax(logits.double()       ,       dim=1 ).float()
															return return_value

									return model







			def UpperCamelCase     (			self       :     Optional[int] ):




									"""simple docstring"""


									for timesteps in [2, 5, 100, 1_000]:
															self.check_over_configs(num_train_timesteps=snake_case__ )







			def UpperCamelCase     (			self       :     Optional[int] ):




									"""simple docstring"""


									for num_vec_classes in [5, 100, 1_000, 4_000]:
															self.check_over_configs(num_vec_classes=snake_case__ )







			def UpperCamelCase     (			self       :     Optional[Any] ):




									"""simple docstring"""


									for t in [0, 50, 99]:
															self.check_over_forward(time_step=snake_case__ )







			def UpperCamelCase     (			self       :     Tuple ):




									"""simple docstring"""


									pass




