






'''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 _lowercase     :




							def __init__(							self:    Dict      ,							UpperCamelCase__:    int      ,							UpperCamelCase__:    Union[str, Any]=2      ,							UpperCamelCase__:    List[Any]=3      ,							UpperCamelCase__:    List[str]=4      ,							UpperCamelCase__:    Optional[Any]=2      ,							UpperCamelCase__:    List[Any]=7      ,							UpperCamelCase__:    Union[str, Any]=True      ,							UpperCamelCase__:    Tuple=True      ,							UpperCamelCase__:    Union[str, Any]=True      ,							UpperCamelCase__:    List[Any]=True      ,							UpperCamelCase__:    int=99      ,							UpperCamelCase__:    str=36      ,							UpperCamelCase__:    int=3      ,							UpperCamelCase__:    str=4      ,							UpperCamelCase__:    Any=37      ,							UpperCamelCase__:    List[str]="gelu"      ,							UpperCamelCase__:    str=0.1      ,							UpperCamelCase__:    str=0.1      ,							UpperCamelCase__:    Tuple=512      ,							UpperCamelCase__:    str=16      ,							UpperCamelCase__:    Any=2      ,							UpperCamelCase__:    Any=0.02      ,							UpperCamelCase__:    int=6      ,							UpperCamelCase__:    int=6      ,							UpperCamelCase__:    Any=3      ,							UpperCamelCase__:    List[Any]=4      ,							UpperCamelCase__:    Any=None      ,							UpperCamelCase__:    Optional[Any]=1_000      ,							):
														lowerCamelCase__ :     Optional[Any]	  =	parent
														lowerCamelCase__ :     List[str]	  =	batch_size
														lowerCamelCase__ :     str	  =	num_channels
														lowerCamelCase__ :     str	  =	image_size
														lowerCamelCase__ :     Union[str, Any]	  =	patch_size
														lowerCamelCase__ :     str	  =	text_seq_length
														lowerCamelCase__ :     List[str]	  =	is_training
														lowerCamelCase__ :     int	  =	use_input_mask
														lowerCamelCase__ :     Any	  =	use_token_type_ids
														lowerCamelCase__ :     List[Any]	  =	use_labels
														lowerCamelCase__ :     Union[str, Any]	  =	vocab_size
														lowerCamelCase__ :     List[str]	  =	hidden_size
														lowerCamelCase__ :     str	  =	num_hidden_layers
														lowerCamelCase__ :     List[str]	  =	num_attention_heads
														lowerCamelCase__ :     str	  =	intermediate_size
														lowerCamelCase__ :     int	  =	hidden_act
														lowerCamelCase__ :     Optional[Any]	  =	hidden_dropout_prob
														lowerCamelCase__ :     int	  =	attention_probs_dropout_prob
														lowerCamelCase__ :     List[str]	  =	max_position_embeddings
														lowerCamelCase__ :     Dict	  =	type_vocab_size
														lowerCamelCase__ :     int	  =	type_sequence_label_size
														lowerCamelCase__ :     str	  =	initializer_range
														lowerCamelCase__ :     Tuple	  =	coordinate_size
														lowerCamelCase__ :     Tuple	  =	shape_size
														lowerCamelCase__ :     int	  =	num_labels
														lowerCamelCase__ :     Any	  =	num_choices
														lowerCamelCase__ :     Tuple	  =	scope
														lowerCamelCase__ :     List[str]	  =	range_bbox

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




							def 		lowerCamelCase_    (							self:    Dict					):
														lowerCamelCase__ :     Union[str, Any]	  =	ids_tensor([self.batch_size, self.text_seq_length]      ,							self.vocab_size					)

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

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

														lowerCamelCase__ :     Any	  =	None
														if self.use_input_mask:
																					lowerCamelCase__ :     Union[str, Any]	  =	random_attention_mask([self.batch_size, self.text_seq_length]					)

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

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

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

														# text + image
														lowerCamelCase__ :     Any	  =	model(_SCREAMING_SNAKE_CASE      ,							pixel_values=_SCREAMING_SNAKE_CASE					)
														lowerCamelCase__ :     Any	  =	model(
														    _SCREAMING_SNAKE_CASE      ,							bbox=_SCREAMING_SNAKE_CASE      ,							pixel_values=_SCREAMING_SNAKE_CASE      ,							attention_mask=_SCREAMING_SNAKE_CASE      ,							token_type_ids=_SCREAMING_SNAKE_CASE					)
														lowerCamelCase__ :     Union[str, Any]	  =	model(_SCREAMING_SNAKE_CASE      ,							bbox=_SCREAMING_SNAKE_CASE      ,							pixel_values=_SCREAMING_SNAKE_CASE      ,							token_type_ids=_SCREAMING_SNAKE_CASE					)
														lowerCamelCase__ :     List[Any]	  =	model(_SCREAMING_SNAKE_CASE      ,							bbox=_SCREAMING_SNAKE_CASE      ,							pixel_values=_SCREAMING_SNAKE_CASE					)

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

														# text only
														lowerCamelCase__ :     int	  =	model(_SCREAMING_SNAKE_CASE					)
														self.parent.assertEqual(
														    result.last_hidden_state.shape      ,							(self.batch_size, self.text_seq_length, self.hidden_size)					)

														# image only
														lowerCamelCase__ :     List[Any]	  =	model(pixel_values=_SCREAMING_SNAKE_CASE					)
														self.parent.assertEqual(
														    result.last_hidden_state.shape      ,							(self.batch_size, self.image_seq_length, self.hidden_size)					)




							def 		lowerCamelCase_    (							self:    str      ,							UpperCamelCase__:    List[Any]      ,							UpperCamelCase__:    Dict      ,							UpperCamelCase__:    Optional[Any]      ,							UpperCamelCase__:    Dict      ,							UpperCamelCase__:    Union[str, Any]      ,							UpperCamelCase__:    Union[str, Any]      ,							UpperCamelCase__:    Optional[int]      ,							UpperCamelCase__:    int					):
														lowerCamelCase__ :     Union[str, Any]	  =	self.num_labels
														lowerCamelCase__ :     Optional[int]	  =	LayoutLMvaForSequenceClassification(_SCREAMING_SNAKE_CASE					)
														model.to(_SCREAMING_SNAKE_CASE					)
														model.eval()
														lowerCamelCase__ :     str	  =	model(
														    _SCREAMING_SNAKE_CASE      ,							bbox=_SCREAMING_SNAKE_CASE      ,							pixel_values=_SCREAMING_SNAKE_CASE      ,							attention_mask=_SCREAMING_SNAKE_CASE      ,							token_type_ids=_SCREAMING_SNAKE_CASE      ,							labels=_SCREAMING_SNAKE_CASE      ,							)
														self.parent.assertEqual(result.logits.shape      ,							(self.batch_size, self.num_labels)					)




							def 		lowerCamelCase_    (							self:    str      ,							UpperCamelCase__:    int      ,							UpperCamelCase__:    Any      ,							UpperCamelCase__:    Dict      ,							UpperCamelCase__:    Dict      ,							UpperCamelCase__:    Any      ,							UpperCamelCase__:    int      ,							UpperCamelCase__:    Optional[Any]      ,							UpperCamelCase__:    Tuple					):
														lowerCamelCase__ :     Optional[Any]	  =	self.num_labels
														lowerCamelCase__ :     Dict	  =	LayoutLMvaForTokenClassification(config=_SCREAMING_SNAKE_CASE					)
														model.to(_SCREAMING_SNAKE_CASE					)
														model.eval()
														lowerCamelCase__ :     List[str]	  =	model(
														    _SCREAMING_SNAKE_CASE      ,							bbox=_SCREAMING_SNAKE_CASE      ,							pixel_values=_SCREAMING_SNAKE_CASE      ,							attention_mask=_SCREAMING_SNAKE_CASE      ,							token_type_ids=_SCREAMING_SNAKE_CASE      ,							labels=_SCREAMING_SNAKE_CASE      ,							)
														self.parent.assertEqual(result.logits.shape      ,							(self.batch_size, self.text_seq_length, self.num_labels)					)




							def 		lowerCamelCase_    (							self:    int      ,							UpperCamelCase__:    int      ,							UpperCamelCase__:    Optional[Any]      ,							UpperCamelCase__:    Union[str, Any]      ,							UpperCamelCase__:    Dict      ,							UpperCamelCase__:    Union[str, Any]      ,							UpperCamelCase__:    List[str]      ,							UpperCamelCase__:    Union[str, Any]      ,							UpperCamelCase__:    str					):
														lowerCamelCase__ :     List[str]	  =	LayoutLMvaForQuestionAnswering(config=_SCREAMING_SNAKE_CASE					)
														model.to(_SCREAMING_SNAKE_CASE					)
														model.eval()
														lowerCamelCase__ :     int	  =	model(
														    _SCREAMING_SNAKE_CASE      ,							bbox=_SCREAMING_SNAKE_CASE      ,							pixel_values=_SCREAMING_SNAKE_CASE      ,							attention_mask=_SCREAMING_SNAKE_CASE      ,							token_type_ids=_SCREAMING_SNAKE_CASE      ,							start_positions=_SCREAMING_SNAKE_CASE      ,							end_positions=_SCREAMING_SNAKE_CASE      ,							)
														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 		lowerCamelCase_    (							self:    Optional[int]					):
														lowerCamelCase__ :     Dict	  =	self.prepare_config_and_inputs()
														(
														    lowerCamelCase__     
														) :     List[str]	  =	config_and_inputs
														lowerCamelCase__ :     Union[str, Any]	  =	{
														    "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 _lowercase     (    __SCREAMING_SNAKE_CASE    ,				__SCREAMING_SNAKE_CASE    ,				unittest.TestCase		):
							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 		lowerCamelCase_    (							self:    Any      ,							UpperCamelCase__:    Any      ,							UpperCamelCase__:    Union[str, Any]      ,							UpperCamelCase__:    str      ,							UpperCamelCase__:    Any      ,							UpperCamelCase__:    List[str]					):
														return True




							def 		lowerCamelCase_    (							self:    Dict					):
														lowerCamelCase__ :     List[str]	  =	LayoutLMvaModelTester(self					)
														lowerCamelCase__ :     Union[str, Any]	  =	ConfigTester(self      ,							config_class=_SCREAMING_SNAKE_CASE      ,							hidden_size=37					)




							def 		lowerCamelCase_    (							self:    Optional[int]      ,							UpperCamelCase__:    Optional[Any]      ,							UpperCamelCase__:    Union[str, Any]      ,							UpperCamelCase__:    List[Any]=False					):
														lowerCamelCase__ :     int	  =	copy.deepcopy(_SCREAMING_SNAKE_CASE					)
														if model_class in get_values(_SCREAMING_SNAKE_CASE					):
																					lowerCamelCase__ :     Optional[int]	  =	{
																					    k: v.unsqueeze(1					).expand(-1      ,							self.model_tester.num_choices      ,							-1					).contiguous()
																					    if isinstance(_SCREAMING_SNAKE_CASE      ,							torch.Tensor					) and v.ndim > 1
																					    else v
																					    for k, v in inputs_dict.items()
																					}
														if return_labels:
																					if model_class in get_values(_SCREAMING_SNAKE_CASE					):
																												lowerCamelCase__ :     List[Any]	  =	torch.ones(self.model_tester.batch_size      ,							dtype=torch.long      ,							device=_SCREAMING_SNAKE_CASE					)
																					elif model_class in get_values(_SCREAMING_SNAKE_CASE					):
																												lowerCamelCase__ :     Optional[int]	  =	torch.zeros(
																												    self.model_tester.batch_size      ,							dtype=torch.long      ,							device=_SCREAMING_SNAKE_CASE					)
																												lowerCamelCase__ :     List[Any]	  =	torch.zeros(
																												    self.model_tester.batch_size      ,							dtype=torch.long      ,							device=_SCREAMING_SNAKE_CASE					)
																					elif model_class in [
																					    *get_values(_SCREAMING_SNAKE_CASE					),
																					]:
																												lowerCamelCase__ :     int	  =	torch.zeros(
																												    self.model_tester.batch_size      ,							dtype=torch.long      ,							device=_SCREAMING_SNAKE_CASE					)
																					elif model_class in [
																					    *get_values(_SCREAMING_SNAKE_CASE					),
																					]:
																												lowerCamelCase__ :     List[str]	  =	torch.zeros(
																												    (self.model_tester.batch_size, self.model_tester.text_seq_length)      ,							dtype=torch.long      ,							device=_SCREAMING_SNAKE_CASE      ,							)

														return inputs_dict




							def 		lowerCamelCase_    (							self:    str					):
														self.config_tester.run_common_tests()




							def 		lowerCamelCase_    (							self:    List[Any]					):
														lowerCamelCase__ :     Optional[int]	  =	self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_model(*_SCREAMING_SNAKE_CASE					)




							def 		lowerCamelCase_    (							self:    Tuple					):
														lowerCamelCase__ :     int	  =	self.model_tester.prepare_config_and_inputs()
														for type in ["absolute", "relative_key", "relative_key_query"]:
																					lowerCamelCase__ :     int	  =	type
																					self.model_tester.create_and_check_model(*_SCREAMING_SNAKE_CASE					)




							def 		lowerCamelCase_    (							self:    Optional[Any]					):
														lowerCamelCase__ :     str	  =	self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_for_sequence_classification(*_SCREAMING_SNAKE_CASE					)




							def 		lowerCamelCase_    (							self:    int					):
														lowerCamelCase__ :     Dict	  =	self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_for_token_classification(*_SCREAMING_SNAKE_CASE					)




							def 		lowerCamelCase_    (							self:    List[str]					):
														lowerCamelCase__ :     int	  =	self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_for_question_answering(*_SCREAMING_SNAKE_CASE					)




							@slow
							def 		lowerCamelCase_    (							self:    Any					):

														for model_name in LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																					lowerCamelCase__ :     Dict	  =	LayoutLMvaModel.from_pretrained(_SCREAMING_SNAKE_CASE					)
																					self.assertIsNotNone(_SCREAMING_SNAKE_CASE					)





def 			SCREAMING_SNAKE_CASE_	()				->	int:
							lowerCamelCase__ :     Tuple	  =	Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""							)
							return image



@require_torch
class _lowercase     (    unittest.TestCase		):




							@cached_property
							def 		lowerCamelCase_    (							self:    Optional[int]					):
														return LayoutLMvaImageProcessor(apply_ocr=_SCREAMING_SNAKE_CASE					) if is_vision_available() else None




							@slow
							def 		lowerCamelCase_    (							self:    Optional[int]					):
														lowerCamelCase__ :     Dict	  =	LayoutLMvaModel.from_pretrained("""microsoft/layoutlmv3-base"""					).to(_SCREAMING_SNAKE_CASE					)

														lowerCamelCase__ :     List[Any]	  =	self.default_image_processor
														lowerCamelCase__ :     Dict	  =	prepare_img()
														lowerCamelCase__ :     int	  =	image_processor(images=_SCREAMING_SNAKE_CASE      ,							return_tensors="""pt"""					).pixel_values.to(_SCREAMING_SNAKE_CASE					)

														lowerCamelCase__ :     List[Any]	  =	torch.tensor([[1, 2]]					)
														lowerCamelCase__ :     int	  =	torch.tensor([[1, 2, 3, 4], [5, 6, 7, 8]]					).unsqueeze(0					)

														# forward pass
														lowerCamelCase__ :     Optional[Any]	  =	model(
														    input_ids=input_ids.to(_SCREAMING_SNAKE_CASE					)      ,							bbox=bbox.to(_SCREAMING_SNAKE_CASE					)      ,							pixel_values=pixel_values.to(_SCREAMING_SNAKE_CASE					)      ,							)

														# verify the logits
														lowerCamelCase__ :     Union[str, Any]	  =	torch.Size((1, 199, 768)					)
														self.assertEqual(outputs.last_hidden_state.shape      ,							_SCREAMING_SNAKE_CASE					)

														lowerCamelCase__ :     Dict	  =	torch.tensor(
														    [[-0.0_529, 0.3_618, 0.1_632], [-0.1_587, -0.1_667, -0.0_400], [-0.1_557, -0.1_671, -0.0_505]]					).to(_SCREAMING_SNAKE_CASE					)

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









'''simple docstring'''







from __future__ import annotations

import typing
from collections.abc import Iterable

import numpy as np

_A     :      Optional[Any]   					=typing.Union[Iterable[float], Iterable[int], np.ndarray]  # noqa: UP007
_A     :      Optional[int]   					=typing.Union[np.floataa, int, float]  # noqa: UP007





def 			SCREAMING_SNAKE_CASE_	(UpperCamelCase    ,      UpperCamelCase				)				->	VectorOut:
							return np.sqrt(np.sum((np.asarray(UpperCamelCase				) - np.asarray(UpperCamelCase				)) ** 2				)				)





def 			SCREAMING_SNAKE_CASE_	(UpperCamelCase    ,      UpperCamelCase				)				->	VectorOut:
							return sum((va - va) ** 2 for va, va in zip(UpperCamelCase    ,      UpperCamelCase				)				) ** (1 / 2)


if __name__ == "__main__":





			def 			SCREAMING_SNAKE_CASE_	()				->	None:
										from timeit import timeit

										print("""Without Numpy"""				)
										print(
										    timeit(
										        """euclidean_distance_no_np([1, 2, 3], [4, 5, 6])"""    ,      number=10000    ,      globals=globals()    ,      )				)
										print("""With Numpy"""				)
										print(
										    timeit(
										        """euclidean_distance([1, 2, 3], [4, 5, 6])"""    ,      number=10000    ,      globals=globals()    ,      )				)



			benchmark()

