





'''simple docstring'''


import logging

import torch
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss

from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.bert.modeling_bert import (
    BERT_INPUTS_DOCSTRING,
    BERT_START_DOCSTRING,
    BertEncoder,
    BertModel,
    BertPreTrainedModel,
)


lowerCAmelCase_   :   Any       =					logging.getLogger(__name__)






class       UpperCamelCase__  (		__lowerCAmelCase     ):







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




				'''simple docstring'''
				a__     = self.layer[current_layer](lowerCamelCase , lowerCamelCase , head_mask[current_layer]		)

				a__     = layer_outputs[0]

				return hidden_states






@add_start_docstrings(
    "The bare Bert Model transformer with PABEE outputting raw hidden-states without any specific head on top."	,__lowerCAmelCase	,)
class       UpperCamelCase__  (		__lowerCAmelCase     ):







	def __init__(				self			:   Any , lowerCamelCase			:   Dict		):




				'''simple docstring'''
				super().__init__(lowerCamelCase		)

				a__     = BertEncoderWithPabee(lowerCamelCase		)

				self.init_weights()
				a__     = 0
				a__     = 0
				a__     = 0

				a__     = 0







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




				'''simple docstring'''
				a__     = threshold







	def 						__a   (				self			:   str , lowerCamelCase			:   Tuple		):




				'''simple docstring'''
				a__     = patience







	def 						__a   (				self			:   int		):




				'''simple docstring'''
				a__     = 0
				a__     = 0







	def 						__a   (				self			:   int		):




				'''simple docstring'''
				a__     = self.inference_layers_num / self.inference_instances_num
				a__     = (
				    F'''*** Patience = {self.patience} Avg. Inference Layers = {avg_inf_layers:.2f} Speed Up ='''
				    F''' {1 - avg_inf_layers / self.config.num_hidden_layers:.2f} ***'''
				)
				print(lowerCamelCase		)







	@add_start_docstrings_to_model_forward(lowerCamelCase		)
	def 						__a   (				self			:   Tuple , lowerCamelCase			:   str=None , lowerCamelCase			:   Dict=None , lowerCamelCase			:   str=None , lowerCamelCase			:   Tuple=None , lowerCamelCase			:   List[Any]=None , lowerCamelCase			:   List[Any]=None , lowerCamelCase			:   Dict=None , lowerCamelCase			:   Dict=None , lowerCamelCase			:   List[str]=None , lowerCamelCase			:   Optional[int]=None , lowerCamelCase			:   List[Any]=False , ):




				'''simple docstring'''

				if input_ids is not None and inputs_embeds is not None:
							raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time"		)
				elif input_ids is not None:
							a__     = input_ids.size()
				elif inputs_embeds is not None:
							a__     = inputs_embeds.size()[:-1]
				else:
							raise ValueError("You have to specify either input_ids or inputs_embeds"		)

				a__     = input_ids.device if input_ids is not None else inputs_embeds.device

				if attention_mask is None:
							a__     = torch.ones(lowerCamelCase , device=lowerCamelCase		)
				if token_type_ids is None:
							a__     = torch.zeros(lowerCamelCase , dtype=torch.long , device=lowerCamelCase		)

				# We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
				# ourselves in which case we just need to make it broadcastable to all heads.
				a__     = self.get_extended_attention_mask(lowerCamelCase , lowerCamelCase , lowerCamelCase		)

				# If a 2D ou 3D attention mask is provided for the cross-attention
				# we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length]
				if self.config.is_decoder and encoder_hidden_states is not None:
							a__   ,				a__   ,				a__     = encoder_hidden_states.size()
							a__     = (encoder_batch_size, encoder_sequence_length)
							if encoder_attention_mask is None:
										a__     = torch.ones(lowerCamelCase , device=lowerCamelCase		)
							a__     = self.invert_attention_mask(lowerCamelCase		)
				else:
							a__     = None

				# Prepare head mask if needed
				# 1.0 in head_mask indicate we keep the head
				# attention_probs has shape bsz x n_heads x N x N
				# input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
				# and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
				a__     = self.get_head_mask(lowerCamelCase , self.config.num_hidden_layers		)

				a__     = self.embeddings(
				    input_ids=lowerCamelCase , position_ids=lowerCamelCase , token_type_ids=lowerCamelCase , inputs_embeds=lowerCamelCase		)
				a__     = embedding_output

				if self.training:
							a__     = []
							for i in range(self.config.num_hidden_layers		):
										a__     = self.encoder.adaptive_forward(
										    lowerCamelCase , current_layer=lowerCamelCase , attention_mask=lowerCamelCase , head_mask=lowerCamelCase		)

										a__     = self.pooler(lowerCamelCase		)
										a__     = output_layers[i](output_dropout(lowerCamelCase		)		)
										res.append(lowerCamelCase		)
				elif self.patience == 0:  # Use all layers for inference
							a__     = self.encoder(
							    lowerCamelCase , attention_mask=lowerCamelCase , head_mask=lowerCamelCase , encoder_hidden_states=lowerCamelCase , encoder_attention_mask=lowerCamelCase , )
							a__     = self.pooler(encoder_outputs[0]		)
							a__     = [output_layers[self.config.num_hidden_layers - 1](lowerCamelCase		)]
				else:
							a__     = 0
							a__     = None
							a__     = 0
							for i in range(self.config.num_hidden_layers		):
										calculated_layer_num += 1
										a__     = self.encoder.adaptive_forward(
										    lowerCamelCase , current_layer=lowerCamelCase , attention_mask=lowerCamelCase , head_mask=lowerCamelCase		)

										a__     = self.pooler(lowerCamelCase		)
										a__     = output_layers[i](lowerCamelCase		)
										if regression:
													a__     = logits.detach()
													if patient_result is not None:
																a__     = patient_result.detach()
													if (patient_result is not None) and torch.abs(patient_result - labels		) < self.regression_threshold:
																patient_counter += 1
													else:
																a__     = 0
										else:
													a__     = logits.detach().argmax(dim=1		)
													if patient_result is not None:
																a__     = patient_result.detach().argmax(dim=1		)
													if (patient_result is not None) and torch.all(labels.eq(lowerCamelCase		)		):
																patient_counter += 1
													else:
																a__     = 0

										a__     = logits
										if patient_counter == self.patience:
													break
							a__     = [patient_result]
							self.inference_layers_num += calculated_layer_num
							self.inference_instances_num += 1

				return res






@add_start_docstrings(
    "Bert Model transformer with PABEE and a sequence classification/regression head on top (a linear layer on top of\n    the pooled output) e.g. for GLUE tasks. "	,__lowerCAmelCase	,)
class       UpperCamelCase__  (		__lowerCAmelCase     ):







	def __init__(				self			:   Union[str, Any] , lowerCamelCase			:   Tuple		):




				'''simple docstring'''
				super().__init__(lowerCamelCase		)
				a__     = config.num_labels

				a__     = BertModelWithPabee(lowerCamelCase		)
				a__     = nn.Dropout(config.hidden_dropout_prob		)
				a__     = nn.ModuleList(
				    [nn.Linear(config.hidden_size , self.config.num_labels		) for _ in range(config.num_hidden_layers		)]		)

				self.init_weights()







	@add_start_docstrings_to_model_forward(lowerCamelCase		)
	def 						__a   (				self			:   Dict , lowerCamelCase			:   Dict=None , lowerCamelCase			:   List[str]=None , lowerCamelCase			:   List[Any]=None , lowerCamelCase			:   List[Any]=None , lowerCamelCase			:   int=None , lowerCamelCase			:   List[str]=None , lowerCamelCase			:   List[str]=None , ):




				'''simple docstring'''
				a__     = self.bert(
				    input_ids=lowerCamelCase , attention_mask=lowerCamelCase , token_type_ids=lowerCamelCase , position_ids=lowerCamelCase , head_mask=lowerCamelCase , inputs_embeds=lowerCamelCase , output_dropout=self.dropout , output_layers=self.classifiers , regression=self.num_labels == 1 , )

				a__     = (logits[-1],)

				if labels is not None:
							a__     = None
							a__     = 0
							for ix, logits_item in enumerate(lowerCamelCase		):
										if self.num_labels == 1:
													#  We are doing regression
													a__     = MSELoss()
													a__     = loss_fct(logits_item.view(-1		) , labels.view(-1		)		)
										else:
													a__     = CrossEntropyLoss()
													a__     = loss_fct(logits_item.view(-1 , self.num_labels		) , labels.view(-1		)		)
										if total_loss is None:
													a__     = loss
										else:
													total_loss += loss * (ix + 1)
										total_weights += ix + 1
							a__     = (total_loss / total_weights,) + outputs

				return outputs










'''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		)		)



