

'''simple docstring'''


def    _lowerCamelCase  (__lowerCamelCase  :     Union[str, Any]					,  __lowerCamelCase  :     Dict     )   ->    bool:
			a__     = len(_lowerCAmelCase     )
			a__     = len(_lowerCAmelCase     )
			a__     = [[False for _ in range(m + 1     )] for _ in range(n + 1     )]
			a__     = True
			for i in range(_lowerCAmelCase     ):
						for j in range(m + 1     ):
									if dp[i][j]:
												if j < m and a[i].upper() == b[j]:
															a__     = True
												if a[i].islower():
															a__     = True
			return dp[n][m]


if __name__ == "__main__":
		import doctest

		doctest.testmod()










'''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		)		)



