





"""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        _lowerCamelCase      :


						def __init__(self   ,							__a   ,							__a=2   ,							__a=3   ,							__a=4   ,							__a=2   ,							__a=7   ,							__a=True   ,							__a=True   ,							__a=True   ,							__a=True   ,							__a=99   ,							__a=36   ,							__a=3   ,							__a=4   ,							__a=37   ,							__a="gelu"   ,							__a=0.1   ,							__a=0.1   ,							__a=5_12   ,							__a=16   ,							__a=2   ,							__a=0.02   ,							__a=6   ,							__a=6   ,							__a=3   ,							__a=4   ,							__a=None   ,							__a=10_00   ,							) ->   str:
								UpperCamelCase   =	parent
								UpperCamelCase   =	batch_size
								UpperCamelCase   =	num_channels
								UpperCamelCase   =	image_size
								UpperCamelCase   =	patch_size
								UpperCamelCase   =	text_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   =	coordinate_size
								UpperCamelCase   =	shape_size
								UpperCamelCase   =	num_labels
								UpperCamelCase   =	num_choices
								UpperCamelCase   =	scope
								UpperCamelCase   =	range_bbox

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


						def     snake_case_   (self		) ->   Any:
								UpperCamelCase   =	ids_tensor([self.batch_size, self.text_seq_length]   ,							self.vocab_size		)

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

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

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

								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.text_seq_length]   ,							self.num_labels		)

								UpperCamelCase   =	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     snake_case_   (self   ,							__a   ,							__a   ,							__a   ,							__a   ,							__a   ,							__a   ,							__a   ,							__a		) ->   Dict:
								UpperCamelCase   =	LayoutLMvaModel(config=__a		)
								model.to(__a		)
								model.eval()

								# text + image
								UpperCamelCase   =	model(__a   ,							pixel_values=__a		)
								UpperCamelCase   =	model(
								    __a   ,							bbox=__a   ,							pixel_values=__a   ,							attention_mask=__a   ,							token_type_ids=__a		)
								UpperCamelCase   =	model(__a   ,							bbox=__a   ,							pixel_values=__a   ,							token_type_ids=__a		)
								UpperCamelCase   =	model(__a   ,							bbox=__a   ,							pixel_values=__a		)

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

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

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


						def     snake_case_   (self   ,							__a   ,							__a   ,							__a   ,							__a   ,							__a   ,							__a   ,							__a   ,							__a		) ->   int:
								UpperCamelCase   =	self.num_labels
								UpperCamelCase   =	LayoutLMvaForSequenceClassification(__a		)
								model.to(__a		)
								model.eval()
								UpperCamelCase   =	model(
								    __a   ,							bbox=__a   ,							pixel_values=__a   ,							attention_mask=__a   ,							token_type_ids=__a   ,							labels=__a   ,							)
								self.parent.assertEqual(result.logits.shape   ,							(self.batch_size, self.num_labels)		)


						def     snake_case_   (self   ,							__a   ,							__a   ,							__a   ,							__a   ,							__a   ,							__a   ,							__a   ,							__a		) ->   Dict:
								UpperCamelCase   =	self.num_labels
								UpperCamelCase   =	LayoutLMvaForTokenClassification(config=__a		)
								model.to(__a		)
								model.eval()
								UpperCamelCase   =	model(
								    __a   ,							bbox=__a   ,							pixel_values=__a   ,							attention_mask=__a   ,							token_type_ids=__a   ,							labels=__a   ,							)
								self.parent.assertEqual(result.logits.shape   ,							(self.batch_size, self.text_seq_length, self.num_labels)		)


						def     snake_case_   (self   ,							__a   ,							__a   ,							__a   ,							__a   ,							__a   ,							__a   ,							__a   ,							__a		) ->   List[Any]:
								UpperCamelCase   =	LayoutLMvaForQuestionAnswering(config=__a		)
								model.to(__a		)
								model.eval()
								UpperCamelCase   =	model(
								    __a   ,							bbox=__a   ,							pixel_values=__a   ,							attention_mask=__a   ,							token_type_ids=__a   ,							start_positions=__a   ,							end_positions=__a   ,							)
								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     snake_case_   (self		) ->   int:
								UpperCamelCase   =	self.prepare_config_and_inputs()
								(
								    (
								    UpperCamelCase
								)   ,				(
								    UpperCamelCase
								)   ,				(
								    UpperCamelCase
								)   ,				(
								    UpperCamelCase
								)   ,				(
								    UpperCamelCase
								)   ,				(
								    UpperCamelCase
								)   ,				(
								    UpperCamelCase
								)   ,				(
								    UpperCamelCase
								)   ,				
								)   =	config_and_inputs
								UpperCamelCase   =	{
								    "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        _lowerCamelCase      (       _lowercase  ,			_lowercase  ,			unittest.TestCase ):
						UpperCAmelCase_ =			False
						UpperCAmelCase_ =			False
						UpperCAmelCase_ =			False

						UpperCAmelCase_ =			(
						    (
						        LayoutLMvaModel,
						        LayoutLMvaForSequenceClassification,
						        LayoutLMvaForTokenClassification,
						        LayoutLMvaForQuestionAnswering,
						    )
						    if is_torch_available()
						    else ()
						)
						UpperCAmelCase_ =			(
						    {"document-question-answering": LayoutLMvaForQuestionAnswering, "feature-extraction": LayoutLMvaModel}
						    if is_torch_available()
						    else {}
						)


						def     snake_case_   (self   ,							__a   ,							__a   ,							__a   ,							__a   ,							__a		) ->   Any:
								# `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     snake_case_   (self		) ->   List[Any]:
								UpperCamelCase   =	LayoutLMvaModelTester(self		)
								UpperCamelCase   =	ConfigTester(self   ,							config_class=__a   ,							hidden_size=37		)


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

								return inputs_dict


						def     snake_case_   (self		) ->   List[str]:
								self.config_tester.run_common_tests()


						def     snake_case_   (self		) ->   List[str]:
								UpperCamelCase   =	self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*__a		)


						def     snake_case_   (self		) ->   Optional[Any]:
								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(*__a		)


						def     snake_case_   (self		) ->   Union[str, Any]:
								UpperCamelCase   =	self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_sequence_classification(*__a		)


						def     snake_case_   (self		) ->   List[Any]:
								UpperCamelCase   =	self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_token_classification(*__a		)


						def     snake_case_   (self		) ->   Dict:
								UpperCamelCase   =	self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_question_answering(*__a		)




						@slow
						def     snake_case_   (self		) ->   Tuple:
								for model_name in LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
										UpperCamelCase   =	LayoutLMvaModel.from_pretrained(__a		)
										self.assertIsNotNone(__a		)



def       a__					(					):




		"""simple docstring"""


		UpperCamelCase   =	Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"    )
		return image






@require_torch
class        _lowerCamelCase      (       unittest.TestCase ):


						@cached_property
						def     snake_case_   (self		) ->   Union[str, Any]:
								return LayoutLMvaImageProcessor(apply_ocr=__a		) if is_vision_available() else None




						@slow
						def     snake_case_   (self		) ->   Any:
								UpperCamelCase   =	LayoutLMvaModel.from_pretrained("microsoft/layoutlmv3-base"		).to(__a		)

								UpperCamelCase   =	self.default_image_processor
								UpperCamelCase   =	prepare_img()
								UpperCamelCase   =	image_processor(images=__a   ,							return_tensors="pt"		).pixel_values.to(__a		)

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

								# forward pass
								UpperCamelCase   =	model(
								    input_ids=input_ids.to(__a		)   ,							bbox=bbox.to(__a		)   ,							pixel_values=pixel_values.to(__a		)   ,							)

								# verify the logits
								UpperCamelCase   =	torch.Size((1, 1_99, 7_68)		)
								self.assertEqual(outputs.last_hidden_state.shape   ,							__a		)

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

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













"""simple docstring"""





import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import CONFIG_MAPPING


lowerCAmelCase__     			=			logging.get_logger(__name__)





class        _lowerCamelCase      (       _lowercase ):
						UpperCAmelCase_ =			"upernet"


						def __init__(self   ,							__a=None   ,							__a=5_12   ,							__a=0.02   ,							__a=[1, 2, 3, 6]   ,							__a=True   ,							__a=0.4   ,							__a=3_84   ,							__a=2_56   ,							__a=1   ,							__a=False   ,							__a=2_55   ,							**__a   ,							) ->   Tuple:
								super().__init__(**__a		)

								if backbone_config is None:
										logger.info("`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone."		)
										UpperCamelCase   =	CONFIG_MAPPING["resnet"](out_features=["stage1", "stage2", "stage3", "stage4"]		)
								elif isinstance(__a   ,							__a		):
										UpperCamelCase   =	backbone_config.get("model_type"		)
										UpperCamelCase   =	CONFIG_MAPPING[backbone_model_type]
										UpperCamelCase   =	config_class.from_dict(__a		)

								UpperCamelCase   =	backbone_config
								UpperCamelCase   =	hidden_size
								UpperCamelCase   =	initializer_range
								UpperCamelCase   =	pool_scales
								UpperCamelCase   =	use_auxiliary_head
								UpperCamelCase   =	auxiliary_loss_weight
								UpperCamelCase   =	auxiliary_in_channels
								UpperCamelCase   =	auxiliary_channels
								UpperCamelCase   =	auxiliary_num_convs
								UpperCamelCase   =	auxiliary_concat_input
								UpperCamelCase   =	loss_ignore_index




						def     snake_case_   (self		) ->   Optional[int]:
								UpperCamelCase   =	copy.deepcopy(self.__dict__		)
								UpperCamelCase   =	self.backbone_config.to_dict()
								UpperCamelCase   =	self.__class__.model_type
								return output






