





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					:


	'''simple docstring'''





	def __init__(						self:						Any,					lowerCamelCase_:						Any,					lowerCamelCase_:						Union[str, Any]=2,					lowerCamelCase_:						Any=3,					lowerCamelCase_:						Dict=4,					lowerCamelCase_:						Dict=2,					lowerCamelCase_:						List[Any]=7,					lowerCamelCase_:						Union[str, Any]=True,					lowerCamelCase_:						List[str]=True,					lowerCamelCase_:						int=True,					lowerCamelCase_:						List[Any]=True,					lowerCamelCase_:						int=99,					lowerCamelCase_:						Dict=36,					lowerCamelCase_:						Tuple=3,					lowerCamelCase_:						Dict=4,					lowerCamelCase_:						Dict=37,					lowerCamelCase_:						Optional[Any]="gelu",					lowerCamelCase_:						Dict=0.1,					lowerCamelCase_:						Union[str, Any]=0.1,					lowerCamelCase_:						Optional[int]=512,					lowerCamelCase_:						List[Any]=16,					lowerCamelCase_:						str=2,					lowerCamelCase_:						Union[str, Any]=0.0_2,					lowerCamelCase_:						Optional[int]=6,					lowerCamelCase_:						Union[str, Any]=6,					lowerCamelCase_:						Optional[int]=3,					lowerCamelCase_:						Union[str, Any]=4,					lowerCamelCase_:						List[Any]=None,					lowerCamelCase_:						List[Any]=1000,					):
			lowercase__					:      Any      =		parent
			lowercase__					:      List[str]      =		batch_size
			lowercase__					:      str      =		num_channels
			lowercase__					:      Dict      =		image_size
			lowercase__					:      Dict      =		patch_size
			lowercase__					:      str      =		text_seq_length
			lowercase__					:      str      =		is_training
			lowercase__					:      Optional[int]      =		use_input_mask
			lowercase__					:      List[Any]      =		use_token_type_ids
			lowercase__					:      List[str]      =		use_labels
			lowercase__					:      str      =		vocab_size
			lowercase__					:      Optional[int]      =		hidden_size
			lowercase__					:      int      =		num_hidden_layers
			lowercase__					:      Any      =		num_attention_heads
			lowercase__					:      Dict      =		intermediate_size
			lowercase__					:      Any      =		hidden_act
			lowercase__					:      str      =		hidden_dropout_prob
			lowercase__					:      Union[str, Any]      =		attention_probs_dropout_prob
			lowercase__					:      List[str]      =		max_position_embeddings
			lowercase__					:      Any      =		type_vocab_size
			lowercase__					:      Union[str, Any]      =		type_sequence_label_size
			lowercase__					:      Any      =		initializer_range
			lowercase__					:      List[str]      =		coordinate_size
			lowercase__					:      List[Any]      =		shape_size
			lowercase__					:      int      =		num_labels
			lowercase__					:      Optional[Any]      =		num_choices
			lowercase__					:      Any      =		scope
			lowercase__					:      Optional[Any]      =		range_bbox

			# LayoutLMv3's sequence length equals the number of text tokens + number of patches + 1 (we add 1 for the CLS token)
			lowercase__					:      Union[str, Any]      =		text_seq_length
			lowercase__					:      Dict      =		(image_size // patch_size) ** 2 + 1
			lowercase__					:      Any      =		self.text_seq_length + self.image_seq_length

	def    snake_case__(						self:						str			):
			lowercase__					:      str      =		ids_tensor([self.batch_size, self.text_seq_length],					self.vocab_size			)

			lowercase__					:      Any      =		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]:
									lowercase__					:      Tuple      =		bbox[i, j, 3]
									lowercase__					:      Dict      =		bbox[i, j, 1]
									lowercase__					:      Optional[int]      =		t
							if bbox[i, j, 2] < bbox[i, j, 0]:
									lowercase__					:      Dict      =		bbox[i, j, 2]
									lowercase__					:      List[Any]      =		bbox[i, j, 0]
									lowercase__					:      List[str]      =		t

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

			lowercase__					:      Tuple      =		None
			if self.use_input_mask:
					lowercase__					:      List[str]      =		random_attention_mask([self.batch_size, self.text_seq_length]			)

			lowercase__					:      Optional[Any]      =		None
			if self.use_token_type_ids:
					lowercase__					:      Optional[int]      =		ids_tensor([self.batch_size, self.text_seq_length],					self.type_vocab_size			)

			lowercase__					:      Optional[Any]      =		None
			lowercase__					:      Any      =		None
			if self.use_labels:
					lowercase__					:      str      =		ids_tensor([self.batch_size],					self.type_sequence_label_size			)
					lowercase__					:      Tuple      =		ids_tensor([self.batch_size, self.text_seq_length],					self.num_labels			)

			lowercase__					:      List[str]      =		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:						Dict,					lowerCamelCase_:						Tuple,					lowerCamelCase_:						List[str],					lowerCamelCase_:						List[Any],					lowerCamelCase_:						Union[str, Any],					lowerCamelCase_:						Optional[int],					lowerCamelCase_:						List[Any],					lowerCamelCase_:						List[str],					lowerCamelCase_:						Any			):
			lowercase__					:      int      =		LayoutLMvaModel(config=lowerCamelCase_			)
			model.to(lowerCamelCase_			)
			model.eval()

			# text + image
			lowercase__					:      List[str]      =		model(lowerCamelCase_,					pixel_values=lowerCamelCase_			)
			lowercase__					:      List[str]      =		model(
			    lowerCamelCase_,					bbox=lowerCamelCase_,					pixel_values=lowerCamelCase_,					attention_mask=lowerCamelCase_,					token_type_ids=lowerCamelCase_			)
			lowercase__					:      Optional[Any]      =		model(lowerCamelCase_,					bbox=lowerCamelCase_,					pixel_values=lowerCamelCase_,					token_type_ids=lowerCamelCase_			)
			lowercase__					:      Optional[Any]      =		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
			lowercase__					:      Tuple      =		model(lowerCamelCase_			)
			self.parent.assertEqual(
			    result.last_hidden_state.shape,					(self.batch_size, self.text_seq_length, self.hidden_size)			)

			# image only
			lowercase__					:      Optional[Any]      =		model(pixel_values=lowerCamelCase_			)
			self.parent.assertEqual(
			    result.last_hidden_state.shape,					(self.batch_size, self.image_seq_length, self.hidden_size)			)

	def    snake_case__(						self:						Any,					lowerCamelCase_:						List[Any],					lowerCamelCase_:						int,					lowerCamelCase_:						Optional[Any],					lowerCamelCase_:						Any,					lowerCamelCase_:						Union[str, Any],					lowerCamelCase_:						Optional[int],					lowerCamelCase_:						Optional[Any],					lowerCamelCase_:						str			):
			lowercase__					:      List[str]      =		self.num_labels
			lowercase__					:      Union[str, Any]      =		LayoutLMvaForSequenceClassification(lowerCamelCase_			)
			model.to(lowerCamelCase_			)
			model.eval()
			lowercase__					:      List[Any]      =		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    snake_case__(						self:						int,					lowerCamelCase_:						Dict,					lowerCamelCase_:						Tuple,					lowerCamelCase_:						Tuple,					lowerCamelCase_:						Optional[Any],					lowerCamelCase_:						Optional[int],					lowerCamelCase_:						Tuple,					lowerCamelCase_:						str,					lowerCamelCase_:						str			):
			lowercase__					:      Optional[Any]      =		self.num_labels
			lowercase__					:      Optional[int]      =		LayoutLMvaForTokenClassification(config=lowerCamelCase_			)
			model.to(lowerCamelCase_			)
			model.eval()
			lowercase__					:      Tuple      =		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    snake_case__(						self:						Optional[Any],					lowerCamelCase_:						Tuple,					lowerCamelCase_:						List[Any],					lowerCamelCase_:						Any,					lowerCamelCase_:						Union[str, Any],					lowerCamelCase_:						Dict,					lowerCamelCase_:						Tuple,					lowerCamelCase_:						int,					lowerCamelCase_:						Optional[Any]			):
			lowercase__					:      Optional[int]      =		LayoutLMvaForQuestionAnswering(config=lowerCamelCase_			)
			model.to(lowerCamelCase_			)
			model.eval()
			lowercase__					:      str      =		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    snake_case__(						self:						int			):
			lowercase__					:      str      =		self.prepare_config_and_inputs()
			(
			    (
			    lowercase__
			)   ,				(
			    lowercase__
			)   ,				(
			    lowercase__
			)   ,				(
			    lowercase__
			)   ,				(
			    lowercase__
			)   ,				(
			    lowercase__
			)   ,				(
			    lowercase__
			)   ,				(
			    lowercase__
			)   ,				
			)					:      Optional[Any]      =		config_and_inputs
			lowercase__					:      str      =		{
			    '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					(       _UpperCamelCase   ,    _UpperCamelCase   ,    unittest.TestCase      ):


	'''simple docstring'''





	_A													=				False
	_A													=				False
	_A													=				False

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

	def    snake_case__(						self:						Dict,					lowerCamelCase_:						Any,					lowerCamelCase_:						Any,					lowerCamelCase_:						Optional[int],					lowerCamelCase_:						List[Any],					lowerCamelCase_:						Dict			):
			# `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:						Tuple			):
			lowercase__					:      Any      =		LayoutLMvaModelTester(self			)
			lowercase__					:      Optional[Any]      =		ConfigTester(self,					config_class=lowerCamelCase_,					hidden_size=37			)

	def    snake_case__(						self:						int,					lowerCamelCase_:						List[str],					lowerCamelCase_:						List[Any],					lowerCamelCase_:						int=False			):
			lowercase__					:      Optional[int]      =		copy.deepcopy(lowerCamelCase_			)
			if model_class in get_values(lowerCamelCase_			):
					lowercase__					:      Tuple      =		{
					    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_			):
							lowercase__					:      Dict      =		torch.ones(self.model_tester.batch_size,					dtype=torch.long,					device=lowerCamelCase_			)
					elif model_class in get_values(lowerCamelCase_			):
							lowercase__					:      Union[str, Any]      =		torch.zeros(
							    self.model_tester.batch_size,					dtype=torch.long,					device=lowerCamelCase_			)
							lowercase__					:      Optional[Any]      =		torch.zeros(
							    self.model_tester.batch_size,					dtype=torch.long,					device=lowerCamelCase_			)
					elif model_class in [
					    *get_values(lowerCamelCase_			),
					]:
							lowercase__					:      List[str]      =		torch.zeros(
							    self.model_tester.batch_size,					dtype=torch.long,					device=lowerCamelCase_			)
					elif model_class in [
					    *get_values(lowerCamelCase_			),
					]:
							lowercase__					:      List[Any]      =		torch.zeros(
							    (self.model_tester.batch_size, self.model_tester.text_seq_length),					dtype=torch.long,					device=lowerCamelCase_,					)

			return inputs_dict

	def    snake_case__(						self:						Union[str, Any]			):
			self.config_tester.run_common_tests()

	def    snake_case__(						self:						Tuple			):
			lowercase__					:      str      =		self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_model(*lowerCamelCase_			)

	def    snake_case__(						self:						List[str]			):
			lowercase__					:      List[str]      =		self.model_tester.prepare_config_and_inputs()
			for type in ["absolute", "relative_key", "relative_key_query"]:
					lowercase__					:      Any      =		type
					self.model_tester.create_and_check_model(*lowerCamelCase_			)

	def    snake_case__(						self:						str			):
			lowercase__					:      Union[str, Any]      =		self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_for_sequence_classification(*lowerCamelCase_			)

	def    snake_case__(						self:						Any			):
			lowercase__					:      List[Any]      =		self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_for_token_classification(*lowerCamelCase_			)

	def    snake_case__(						self:						Union[str, Any]			):
			lowercase__					:      List[Any]      =		self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_for_question_answering(*lowerCamelCase_			)





	@slow
	def    snake_case__(						self:						Tuple			):
			for model_name in LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
					lowercase__					:      int      =		LayoutLMvaModel.from_pretrained(lowerCamelCase_			)
					self.assertIsNotNone(lowerCamelCase_			)


def       SCREAMING_SNAKE_CASE__  (       )   ->							Optional[Any]:




		'''simple docstring'''
		lowercase__					:      Dict      =		Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'			)
		return image





@require_torch
class       __lowerCAmelCase					(       unittest.TestCase      ):


	'''simple docstring'''





	@cached_property
	def    snake_case__(						self:						Tuple			):
			return LayoutLMvaImageProcessor(apply_ocr=lowerCamelCase_			) if is_vision_available() else None





	@slow
	def    snake_case__(						self:						Dict			):
			lowercase__					:      List[str]      =		LayoutLMvaModel.from_pretrained('microsoft/layoutlmv3-base'			).to(lowerCamelCase_			)

			lowercase__					:      Union[str, Any]      =		self.default_image_processor
			lowercase__					:      int      =		prepare_img()
			lowercase__					:      str      =		image_processor(images=lowerCamelCase_,					return_tensors='pt'			).pixel_values.to(lowerCamelCase_			)

			lowercase__					:      Tuple      =		torch.tensor([[1, 2]]			)
			lowercase__					:      Optional[Any]      =		torch.tensor([[1, 2, 3, 4], [5, 6, 7, 8]]			).unsqueeze(0			)

			# forward pass
			lowercase__					:      Any      =		model(
			    input_ids=input_ids.to(lowerCamelCase_			),					bbox=bbox.to(lowerCamelCase_			),					pixel_values=pixel_values.to(lowerCamelCase_			),					)

			# verify the logits
			lowercase__					:      Optional[Any]      =		torch.Size((1, 199, 768)			)
			self.assertEqual(outputs.last_hidden_state.shape,					lowerCamelCase_			)

			lowercase__					:      Any      =		torch.tensor(
			    [[-0.0_5_2_9, 0.3_6_1_8, 0.1_6_3_2], [-0.1_5_8_7, -0.1_6_6_7, -0.0_4_0_0], [-0.1_5_5_7, -0.1_6_7_1, -0.0_5_0_5]]			).to(lowerCamelCase_			)

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









import numpy as np
from cva import destroyAllWindows, imread, imshow, waitKey


class       __lowerCAmelCase					:


	'''simple docstring'''





	def __init__(						self:						List[str],					lowerCamelCase_:						Optional[Any],					lowerCamelCase_:						int,					lowerCamelCase_:						int			):
			if dst_width < 0 or dst_height < 0:
					raise ValueError('Destination width/height should be > 0'			)

			lowercase__					:      str      =		img
			lowercase__					:      Tuple      =		img.shape[1]
			lowercase__					:      str      =		img.shape[0]
			lowercase__					:      Tuple      =		dst_width
			lowercase__					:      Any      =		dst_height

			lowercase__					:      Tuple      =		self.src_w / self.dst_w
			lowercase__					:      Union[str, Any]      =		self.src_h / self.dst_h

			lowercase__					:      List[Any]      =		(
			    np.ones((self.dst_h, self.dst_w, 3),					np.uinta			) * 255
			)

	def    snake_case__(						self:						Dict			):
			for i in range(self.dst_h			):
					for j in range(self.dst_w			):
							lowercase__					:      Tuple      =		self.img[self.get_y(lowerCamelCase_			)][self.get_x(lowerCamelCase_			)]

	def    snake_case__(						self:						List[str],					lowerCamelCase_:						int			):
			return int(self.ratio_x * x			)





	def    snake_case__(						self:						int,					lowerCamelCase_:						int			):
			return int(self.ratio_y * y			)


if __name__ == "__main__":
			__UpperCamelCase,	__UpperCamelCase:	List[Any] = 8_0_0, 6_0_0
			__UpperCamelCase:	Optional[Any] = imread("""image_data/lena.jpg""", 1)
			__UpperCamelCase:	Union[str, Any] = NearestNeighbour(im, dst_w, dst_h)
			n.process()

			imshow(
			    F'Image resized from: {im.shape[1]}x{im.shape[0]} to {dst_w}x{dst_h}', n.output
			)
			waitKey(0)
			destroyAllWindows()


