





'''simple docstring'''


import tempfile

import torch

from diffusers import (
    DEISMultistepScheduler,
    DPMSolverMultistepScheduler,
    DPMSolverSinglestepScheduler,
    UniPCMultistepScheduler,
)

from .test_schedulers import SchedulerCommonTest






class       UpperCamelCase__  (		__lowerCAmelCase     ):
	lowerCAmelCase__					:			List[str]								=						(DPMSolverSinglestepScheduler,)
	lowerCAmelCase__					:			Tuple								=						(("num_inference_steps", 25),)







	def 						__a   (				self			:   Optional[int] , **lowerCamelCase			:   Tuple		):




				'''simple docstring'''
				a__     = {
				    "num_train_timesteps": 1_0_0_0,
				    "beta_start": 0.0001,
				    "beta_end": 0.02,
				    "beta_schedule": "linear",
				    "solver_order": 2,
				    "prediction_type": "epsilon",
				    "thresholding": False,
				    "sample_max_value": 1.0,
				    "algorithm_type": "dpmsolver++",
				    "solver_type": "midpoint",
				    "lambda_min_clipped": -float("inf"		),
				    "variance_type": None,
				}

				config.update(**lowerCamelCase		)
				return config







	def 						__a   (				self			:   Tuple , lowerCamelCase			:   List[Any]=0 , **lowerCamelCase			:   List[Any]		):




				'''simple docstring'''
				a__     = dict(self.forward_default_kwargs		)
				a__     = kwargs.pop("num_inference_steps" , lowerCamelCase		)
				a__     = self.dummy_sample
				a__     = 0.1 * sample
				a__     = [residual + 0.2, residual + 0.15, residual + 0.10]

				for scheduler_class in self.scheduler_classes:
							a__     = self.get_scheduler_config(**lowerCamelCase		)
							a__     = scheduler_class(**lowerCamelCase		)
							scheduler.set_timesteps(lowerCamelCase		)
							# copy over dummy past residuals
							a__     = dummy_past_residuals[: scheduler.config.solver_order]

							with tempfile.TemporaryDirectory() as tmpdirname:
										scheduler.save_config(lowerCamelCase		)
										a__     = scheduler_class.from_pretrained(lowerCamelCase		)
										new_scheduler.set_timesteps(lowerCamelCase		)
										# copy over dummy past residuals
										a__     = dummy_past_residuals[: new_scheduler.config.solver_order]

							a__   ,				a__     = sample, sample
							for t in range(lowerCamelCase , time_step + scheduler.config.solver_order + 1		):
										a__     = scheduler.step(lowerCamelCase , lowerCamelCase , lowerCamelCase , **lowerCamelCase		).prev_sample
										a__     = new_scheduler.step(lowerCamelCase , lowerCamelCase , lowerCamelCase , **lowerCamelCase		).prev_sample

										assert torch.sum(torch.abs(output - new_output		)		) < 1e-5, "Scheduler outputs are not identical"







	def 						__a   (				self			:   int		):




				'''simple docstring'''
				pass







	def 						__a   (				self			:   int , lowerCamelCase			:   Dict=0 , **lowerCamelCase			:   Any		):




				'''simple docstring'''
				a__     = dict(self.forward_default_kwargs		)
				a__     = kwargs.pop("num_inference_steps" , lowerCamelCase		)
				a__     = self.dummy_sample
				a__     = 0.1 * sample
				a__     = [residual + 0.2, residual + 0.15, residual + 0.10]

				for scheduler_class in self.scheduler_classes:
							a__     = self.get_scheduler_config()
							a__     = scheduler_class(**lowerCamelCase		)
							scheduler.set_timesteps(lowerCamelCase		)

							# copy over dummy past residuals (must be after setting timesteps)
							a__     = dummy_past_residuals[: scheduler.config.solver_order]

							with tempfile.TemporaryDirectory() as tmpdirname:
										scheduler.save_config(lowerCamelCase		)
										a__     = scheduler_class.from_pretrained(lowerCamelCase		)
										# copy over dummy past residuals
										new_scheduler.set_timesteps(lowerCamelCase		)

										# copy over dummy past residual (must be after setting timesteps)
										a__     = dummy_past_residuals[: new_scheduler.config.solver_order]

							a__     = scheduler.step(lowerCamelCase , lowerCamelCase , lowerCamelCase , **lowerCamelCase		).prev_sample
							a__     = new_scheduler.step(lowerCamelCase , lowerCamelCase , lowerCamelCase , **lowerCamelCase		).prev_sample

							assert torch.sum(torch.abs(output - new_output		)		) < 1e-5, "Scheduler outputs are not identical"







	def 						__a   (				self			:   Optional[int] , lowerCamelCase			:   Optional[int]=None , **lowerCamelCase			:   str		):




				'''simple docstring'''
				if scheduler is None:
							a__     = self.scheduler_classes[0]
							a__     = self.get_scheduler_config(**lowerCamelCase		)
							a__     = scheduler_class(**lowerCamelCase		)

				a__     = self.scheduler_classes[0]
				a__     = self.get_scheduler_config(**lowerCamelCase		)
				a__     = scheduler_class(**lowerCamelCase		)

				a__     = 1_0
				a__     = self.dummy_model()
				a__     = self.dummy_sample_deter
				scheduler.set_timesteps(lowerCamelCase		)

				for i, t in enumerate(scheduler.timesteps		):
							a__     = model(lowerCamelCase , lowerCamelCase		)
							a__     = scheduler.step(lowerCamelCase , lowerCamelCase , lowerCamelCase		).prev_sample

				return sample







	def 						__a   (				self			:   Tuple		):




				'''simple docstring'''
				a__     = DPMSolverSinglestepScheduler(**self.get_scheduler_config()		)
				a__     = 5_0
				a__     = self.dummy_model()
				a__     = self.dummy_sample_deter
				scheduler.set_timesteps(lowerCamelCase		)

				# make sure that the first t is uneven
				for i, t in enumerate(scheduler.timesteps[3:]		):
							a__     = model(lowerCamelCase , lowerCamelCase		)
							a__     = scheduler.step(lowerCamelCase , lowerCamelCase , lowerCamelCase		).prev_sample

				a__     = torch.mean(torch.abs(lowerCamelCase		)		)

				assert abs(result_mean.item() - 0.2574		) < 1e-3







	def 						__a   (				self			:   str		):




				'''simple docstring'''
				for timesteps in [2_5, 5_0, 1_0_0, 9_9_9, 1_0_0_0]:
							self.check_over_configs(num_train_timesteps=lowerCamelCase		)







	def 						__a   (				self			:   Optional[Any]		):




				'''simple docstring'''
				# make sure that iterating over schedulers with same config names gives same results
				# for defaults
				a__     = DPMSolverSinglestepScheduler(**self.get_scheduler_config()		)
				a__     = self.full_loop(scheduler=lowerCamelCase		)
				a__     = torch.mean(torch.abs(lowerCamelCase		)		)

				assert abs(result_mean.item() - 0.2791		) < 1e-3

				a__     = DEISMultistepScheduler.from_config(scheduler.config		)
				a__     = DPMSolverMultistepScheduler.from_config(scheduler.config		)
				a__     = UniPCMultistepScheduler.from_config(scheduler.config		)
				a__     = DPMSolverSinglestepScheduler.from_config(scheduler.config		)

				a__     = self.full_loop(scheduler=lowerCamelCase		)
				a__     = torch.mean(torch.abs(lowerCamelCase		)		)

				assert abs(result_mean.item() - 0.2791		) < 1e-3







	def 						__a   (				self			:   Optional[Any]		):




				'''simple docstring'''
				self.check_over_configs(thresholding=lowerCamelCase		)
				for order in [1, 2, 3]:
							for solver_type in ["midpoint", "heun"]:
										for threshold in [0.5, 1.0, 2.0]:
													for prediction_type in ["epsilon", "sample"]:
																self.check_over_configs(
																    thresholding=lowerCamelCase , prediction_type=lowerCamelCase , sample_max_value=lowerCamelCase , algorithm_type="dpmsolver++" , solver_order=lowerCamelCase , solver_type=lowerCamelCase , )







	def 						__a   (				self			:   Optional[int]		):




				'''simple docstring'''
				for prediction_type in ["epsilon", "v_prediction"]:
							self.check_over_configs(prediction_type=lowerCamelCase		)







	def 						__a   (				self			:   Dict		):




				'''simple docstring'''
				for algorithm_type in ["dpmsolver", "dpmsolver++"]:
							for solver_type in ["midpoint", "heun"]:
										for order in [1, 2, 3]:
													for prediction_type in ["epsilon", "sample"]:
																self.check_over_configs(
																    solver_order=lowerCamelCase , solver_type=lowerCamelCase , prediction_type=lowerCamelCase , algorithm_type=lowerCamelCase , )
																a__     = self.full_loop(
																    solver_order=lowerCamelCase , solver_type=lowerCamelCase , prediction_type=lowerCamelCase , algorithm_type=lowerCamelCase , )
																assert not torch.isnan(lowerCamelCase		).any(), "Samples have nan numbers"







	def 						__a   (				self			:   Dict		):




				'''simple docstring'''
				self.check_over_configs(lower_order_final=lowerCamelCase		)
				self.check_over_configs(lower_order_final=lowerCamelCase		)







	def 						__a   (				self			:   List[Any]		):




				'''simple docstring'''
				self.check_over_configs(lambda_min_clipped=-float("inf"		)		)
				self.check_over_configs(lambda_min_clipped=-5.1		)







	def 						__a   (				self			:   Union[str, Any]		):




				'''simple docstring'''
				self.check_over_configs(variance_type=lowerCamelCase		)
				self.check_over_configs(variance_type="learned_range"		)







	def 						__a   (				self			:   Tuple		):




				'''simple docstring'''
				for num_inference_steps in [1, 2, 3, 5, 1_0, 5_0, 1_0_0, 9_9_9, 1_0_0_0]:
							self.check_over_forward(num_inference_steps=lowerCamelCase , time_step=0		)







	def 						__a   (				self			:   int		):




				'''simple docstring'''
				a__     = self.full_loop()
				a__     = torch.mean(torch.abs(lowerCamelCase		)		)

				assert abs(result_mean.item() - 0.2791		) < 1e-3







	def 						__a   (				self			:   Union[str, Any]		):




				'''simple docstring'''
				a__     = self.full_loop(use_karras_sigmas=lowerCamelCase		)
				a__     = torch.mean(torch.abs(lowerCamelCase		)		)

				assert abs(result_mean.item() - 0.2248		) < 1e-3







	def 						__a   (				self			:   Dict		):




				'''simple docstring'''
				a__     = self.full_loop(prediction_type="v_prediction"		)
				a__     = torch.mean(torch.abs(lowerCamelCase		)		)

				assert abs(result_mean.item() - 0.1453		) < 1e-3







	def 						__a   (				self			:   Optional[Any]		):




				'''simple docstring'''
				a__     = self.full_loop(prediction_type="v_prediction" , use_karras_sigmas=lowerCamelCase		)
				a__     = torch.mean(torch.abs(lowerCamelCase		)		)

				assert abs(result_mean.item() - 0.0649		) < 1e-3







	def 						__a   (				self			:   Tuple		):




				'''simple docstring'''
				a__     = self.scheduler_classes[0]
				a__     = self.get_scheduler_config(thresholding=lowerCamelCase , dynamic_thresholding_ratio=0		)
				a__     = scheduler_class(**lowerCamelCase		)

				a__     = 1_0
				a__     = self.dummy_model()
				a__     = self.dummy_sample_deter.half()
				scheduler.set_timesteps(lowerCamelCase		)

				for i, t in enumerate(scheduler.timesteps		):
							a__     = model(lowerCamelCase , lowerCamelCase		)
							a__     = scheduler.step(lowerCamelCase , lowerCamelCase , lowerCamelCase		).prev_sample

				assert sample.dtype == torch.floataa










'''simple docstring'''


import copy
import unittest

from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

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_MULTIPLE_CHOICE_MAPPING,
		    MODEL_FOR_QUESTION_ANSWERING_MAPPING,
		    MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
		    MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
		    LayoutLMvaConfig,
		    LayoutLMvaForQuestionAnswering,
		    LayoutLMvaForSequenceClassification,
		    LayoutLMvaForTokenClassification,
		    LayoutLMvaModel,
		)
		from transformers.models.layoutlmva.modeling_layoutlmva import LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST

if is_vision_available():
		from PIL import Image

		from transformers import LayoutLMvaImageProcessor






class       UpperCamelCase__  :







	def __init__(				self			:   List[Any] , lowerCamelCase			:   Optional[Any] , lowerCamelCase			:   Any=2 , lowerCamelCase			:   Optional[Any]=3 , lowerCamelCase			:   Optional[Any]=4 , lowerCamelCase			:   Optional[Any]=2 , lowerCamelCase			:   Tuple=7 , lowerCamelCase			:   Dict=True , lowerCamelCase			:   Optional[Any]=True , lowerCamelCase			:   int=True , lowerCamelCase			:   Any=True , lowerCamelCase			:   int=9_9 , lowerCamelCase			:   Tuple=3_6 , lowerCamelCase			:   List[Any]=3 , lowerCamelCase			:   Optional[Any]=4 , lowerCamelCase			:   Any=3_7 , lowerCamelCase			:   List[Any]="gelu" , lowerCamelCase			:   Optional[Any]=0.1 , lowerCamelCase			:   Optional[int]=0.1 , lowerCamelCase			:   Union[str, Any]=5_1_2 , lowerCamelCase			:   Dict=1_6 , lowerCamelCase			:   int=2 , lowerCamelCase			:   Union[str, Any]=0.02 , lowerCamelCase			:   Tuple=6 , lowerCamelCase			:   Tuple=6 , lowerCamelCase			:   int=3 , lowerCamelCase			:   str=4 , lowerCamelCase			:   int=None , lowerCamelCase			:   Optional[int]=1_0_0_0 , ):




				'''simple docstring'''
				a__     = parent
				a__     = batch_size
				a__     = num_channels
				a__     = image_size
				a__     = patch_size
				a__     = text_seq_length
				a__     = is_training
				a__     = use_input_mask
				a__     = use_token_type_ids
				a__     = use_labels
				a__     = vocab_size
				a__     = hidden_size
				a__     = num_hidden_layers
				a__     = num_attention_heads
				a__     = intermediate_size
				a__     = hidden_act
				a__     = hidden_dropout_prob
				a__     = attention_probs_dropout_prob
				a__     = max_position_embeddings
				a__     = type_vocab_size
				a__     = type_sequence_label_size
				a__     = initializer_range
				a__     = coordinate_size
				a__     = shape_size
				a__     = num_labels
				a__     = num_choices
				a__     = scope
				a__     = range_bbox

				# LayoutLMv3's sequence length equals the number of text tokens + number of patches + 1 (we add 1 for the CLS token)
				a__     = text_seq_length
				a__     = (image_size // patch_size) ** 2 + 1
				a__     = self.text_seq_length + self.image_seq_length







	def 						__a   (				self			:   Union[str, Any]		):




				'''simple docstring'''
				a__     = ids_tensor([self.batch_size, self.text_seq_length] , self.vocab_size		)

				a__     = ids_tensor([self.batch_size, self.text_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]:
													a__     = bbox[i, j, 3]
													a__     = bbox[i, j, 1]
													a__     = t
										if bbox[i, j, 2] < bbox[i, j, 0]:
													a__     = bbox[i, j, 2]
													a__     = bbox[i, j, 0]
													a__     = t

				a__     = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]		)

				a__     = None
				if self.use_input_mask:
							a__     = random_attention_mask([self.batch_size, self.text_seq_length]		)

				a__     = None
				if self.use_token_type_ids:
							a__     = ids_tensor([self.batch_size, self.text_seq_length] , self.type_vocab_size		)

				a__     = None
				a__     = None
				if self.use_labels:
							a__     = ids_tensor([self.batch_size] , self.type_sequence_label_size		)
							a__     = ids_tensor([self.batch_size, self.text_seq_length] , self.num_labels		)

				a__     = LayoutLMvaConfig(
				    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 , coordinate_size=self.coordinate_size , shape_size=self.shape_size , input_size=self.image_size , patch_size=self.patch_size , )

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







	def 						__a   (				self			:   Tuple , lowerCamelCase			:   int , lowerCamelCase			:   Dict , lowerCamelCase			:   int , lowerCamelCase			:   str , lowerCamelCase			:   int , lowerCamelCase			:   Tuple , lowerCamelCase			:   Optional[int] , lowerCamelCase			:   Dict		):




				'''simple docstring'''
				a__     = LayoutLMvaModel(config=lowerCamelCase		)
				model.to(lowerCamelCase		)
				model.eval()

				# text + image
				a__     = model(lowerCamelCase , pixel_values=lowerCamelCase		)
				a__     = model(
				    lowerCamelCase , bbox=lowerCamelCase , pixel_values=lowerCamelCase , attention_mask=lowerCamelCase , token_type_ids=lowerCamelCase		)
				a__     = model(lowerCamelCase , bbox=lowerCamelCase , pixel_values=lowerCamelCase , token_type_ids=lowerCamelCase		)
				a__     = model(lowerCamelCase , bbox=lowerCamelCase , pixel_values=lowerCamelCase		)

				self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size)		)

				# text only
				a__     = model(lowerCamelCase		)
				self.parent.assertEqual(
				    result.last_hidden_state.shape , (self.batch_size, self.text_seq_length, self.hidden_size)		)

				# image only
				a__     = model(pixel_values=lowerCamelCase		)
				self.parent.assertEqual(
				    result.last_hidden_state.shape , (self.batch_size, self.image_seq_length, self.hidden_size)		)







	def 						__a   (				self			:   str , lowerCamelCase			:   Tuple , lowerCamelCase			:   str , lowerCamelCase			:   List[str] , lowerCamelCase			:   int , lowerCamelCase			:   Any , lowerCamelCase			:   Union[str, Any] , lowerCamelCase			:   Union[str, Any] , lowerCamelCase			:   Optional[int]		):




				'''simple docstring'''
				a__     = self.num_labels
				a__     = LayoutLMvaForSequenceClassification(lowerCamelCase		)
				model.to(lowerCamelCase		)
				model.eval()
				a__     = model(
				    lowerCamelCase , bbox=lowerCamelCase , pixel_values=lowerCamelCase , attention_mask=lowerCamelCase , token_type_ids=lowerCamelCase , labels=lowerCamelCase , )
				self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels)		)







	def 						__a   (				self			:   int , lowerCamelCase			:   Dict , lowerCamelCase			:   Any , lowerCamelCase			:   str , lowerCamelCase			:   Dict , lowerCamelCase			:   int , lowerCamelCase			:   Tuple , lowerCamelCase			:   Optional[Any] , lowerCamelCase			:   Tuple		):




				'''simple docstring'''
				a__     = self.num_labels
				a__     = LayoutLMvaForTokenClassification(config=lowerCamelCase		)
				model.to(lowerCamelCase		)
				model.eval()
				a__     = model(
				    lowerCamelCase , bbox=lowerCamelCase , pixel_values=lowerCamelCase , attention_mask=lowerCamelCase , token_type_ids=lowerCamelCase , labels=lowerCamelCase , )
				self.parent.assertEqual(result.logits.shape , (self.batch_size, self.text_seq_length, self.num_labels)		)







	def 						__a   (				self			:   Dict , lowerCamelCase			:   Tuple , lowerCamelCase			:   int , lowerCamelCase			:   Any , lowerCamelCase			:   Union[str, Any] , lowerCamelCase			:   List[str] , lowerCamelCase			:   Union[str, Any] , lowerCamelCase			:   Tuple , lowerCamelCase			:   Any		):




				'''simple docstring'''
				a__     = LayoutLMvaForQuestionAnswering(config=lowerCamelCase		)
				model.to(lowerCamelCase		)
				model.eval()
				a__     = model(
				    lowerCamelCase , bbox=lowerCamelCase , pixel_values=lowerCamelCase , attention_mask=lowerCamelCase , token_type_ids=lowerCamelCase , start_positions=lowerCamelCase , end_positions=lowerCamelCase , )
				self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length)		)
				self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length)		)







	def 						__a   (				self			:   Optional[int]		):




				'''simple docstring'''
				a__     = self.prepare_config_and_inputs()
				(
				    (
				    a__
				)   ,				(
				    a__
				)   ,				(
				    a__
				)   ,				(
				    a__
				)   ,				(
				    a__
				)   ,				(
				    a__
				)   ,				(
				    a__
				)   ,				(
				    a__
				)   ,				
				)     = config_and_inputs
				a__     = {
				    "input_ids": input_ids,
				    "bbox": bbox,
				    "pixel_values": pixel_values,
				    "token_type_ids": token_type_ids,
				    "attention_mask": input_mask,
				}
				return config, inputs_dict






@require_torch
class       UpperCamelCase__  (		__lowerCAmelCase	,__lowerCAmelCase	,unittest.TestCase     ):
	lowerCAmelCase__					:			str								=						False
	lowerCAmelCase__					:			Optional[Any]								=						False
	lowerCAmelCase__					:			Any								=						False

	lowerCAmelCase__					:			List[str]								=						(
	    (
	        LayoutLMvaModel,
	        LayoutLMvaForSequenceClassification,
	        LayoutLMvaForTokenClassification,
	        LayoutLMvaForQuestionAnswering,
	    )
	    if is_torch_available()
	    else ()
	)
	lowerCAmelCase__					:			List[Any]								=						(
	    {"document-question-answering": LayoutLMvaForQuestionAnswering, "feature-extraction": LayoutLMvaModel}
	    if is_torch_available()
	    else {}
	)







	def 						__a   (				self			:   List[str] , lowerCamelCase			:   List[str] , lowerCamelCase			:   int , lowerCamelCase			:   int , lowerCamelCase			:   Union[str, Any] , lowerCamelCase			:   List[Any]		):




				'''simple docstring'''
				# `DocumentQuestionAnsweringPipeline` is expected to work with this model, but it combines the text and visual
				# embedding along the sequence dimension (dim 1), which causes an error during post-processing as `p_mask` has
				# the sequence dimension of the text embedding only.
				# (see the line `embedding_output = torch.cat([embedding_output, visual_embeddings], dim=1)`)
				return True







	def 						__a   (				self			:   int		):




				'''simple docstring'''
				a__     = LayoutLMvaModelTester(self		)
				a__     = ConfigTester(self , config_class=lowerCamelCase , hidden_size=3_7		)







	def 						__a   (				self			:   List[str] , lowerCamelCase			:   Optional[int] , lowerCamelCase			:   Any , lowerCamelCase			:   Tuple=False		):




				'''simple docstring'''
				a__     = copy.deepcopy(lowerCamelCase		)
				if model_class in get_values(lowerCamelCase		):
							a__     = {
							    k: v.unsqueeze(1		).expand(-1 , self.model_tester.num_choices , -1		).contiguous()
							    if isinstance(lowerCamelCase , torch.Tensor		) and v.ndim > 1
							    else v
							    for k, v in inputs_dict.items()
							}
				if return_labels:
							if model_class in get_values(lowerCamelCase		):
										a__     = torch.ones(self.model_tester.batch_size , dtype=torch.long , device=lowerCamelCase		)
							elif model_class in get_values(lowerCamelCase		):
										a__     = torch.zeros(
										    self.model_tester.batch_size , dtype=torch.long , device=lowerCamelCase		)
										a__     = torch.zeros(
										    self.model_tester.batch_size , dtype=torch.long , device=lowerCamelCase		)
							elif model_class in [
							    *get_values(lowerCamelCase		),
							]:
										a__     = torch.zeros(
										    self.model_tester.batch_size , dtype=torch.long , device=lowerCamelCase		)
							elif model_class in [
							    *get_values(lowerCamelCase		),
							]:
										a__     = torch.zeros(
										    (self.model_tester.batch_size, self.model_tester.text_seq_length) , dtype=torch.long , device=lowerCamelCase , )

				return inputs_dict







	def 						__a   (				self			:   List[Any]		):




				'''simple docstring'''
				self.config_tester.run_common_tests()







	def 						__a   (				self			:   Optional[Any]		):




				'''simple docstring'''
				a__     = self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_model(*lowerCamelCase		)







	def 						__a   (				self			:   List[Any]		):




				'''simple docstring'''
				a__     = self.model_tester.prepare_config_and_inputs()
				for type in ["absolute", "relative_key", "relative_key_query"]:
							a__     = type
							self.model_tester.create_and_check_model(*lowerCamelCase		)







	def 						__a   (				self			:   Tuple		):




				'''simple docstring'''
				a__     = self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_sequence_classification(*lowerCamelCase		)







	def 						__a   (				self			:   Optional[int]		):




				'''simple docstring'''
				a__     = self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_token_classification(*lowerCamelCase		)







	def 						__a   (				self			:   List[Any]		):




				'''simple docstring'''
				a__     = self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_question_answering(*lowerCamelCase		)







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




				'''simple docstring'''
				for model_name in LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
							a__     = LayoutLMvaModel.from_pretrained(lowerCamelCase		)
							self.assertIsNotNone(lowerCamelCase		)







def    _lowerCamelCase  ()   ->    Dict:
			a__     = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"     )
			return image






@require_torch
class       UpperCamelCase__  (		unittest.TestCase     ):







	@cached_property
	def 						__a   (				self			:   int		):




				'''simple docstring'''
				return LayoutLMvaImageProcessor(apply_ocr=lowerCamelCase		) if is_vision_available() else None







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




				'''simple docstring'''
				a__     = LayoutLMvaModel.from_pretrained("microsoft/layoutlmv3-base"		).to(lowerCamelCase		)

				a__     = self.default_image_processor
				a__     = prepare_img()
				a__     = image_processor(images=lowerCamelCase , return_tensors="pt"		).pixel_values.to(lowerCamelCase		)

				a__     = torch.tensor([[1, 2]]		)
				a__     = torch.tensor([[1, 2, 3, 4], [5, 6, 7, 8]]		).unsqueeze(0		)

				# forward pass
				a__     = model(
				    input_ids=input_ids.to(lowerCamelCase		) , bbox=bbox.to(lowerCamelCase		) , pixel_values=pixel_values.to(lowerCamelCase		) , )

				# verify the logits
				a__     = torch.Size((1, 1_9_9, 7_6_8)		)
				self.assertEqual(outputs.last_hidden_state.shape , lowerCamelCase		)

				a__     = torch.tensor(
				    [[-0.0529, 0.3618, 0.1632], [-0.1587, -0.1667, -0.0400], [-0.1557, -0.1671, -0.0505]]		).to(lowerCamelCase		)

				self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :3, :3] , lowerCamelCase , atol=1e-4		)		)



