





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 SCREAMING_SNAKE_CASE_  :






						def __init__( self : Union[str, Any]						,				_A : Union[str, Any]						,				_A : List[Any]=2						,				_A : Dict=3						,				_A : int=4						,				_A : Union[str, Any]=2						,				_A : int=7						,				_A : Tuple=True						,				_A : Union[str, Any]=True						,				_A : Optional[int]=True						,				_A : Dict=True						,				_A : List[str]=99						,				_A : str=36						,				_A : Dict=3						,				_A : str=4						,				_A : Union[str, Any]=37						,				_A : Optional[Any]="gelu"						,				_A : int=0.1						,				_A : Optional[int]=0.1						,				_A : Any=512						,				_A : Optional[int]=16						,				_A : List[str]=2						,				_A : Tuple=0.0_2						,				_A : List[Any]=6						,				_A : str=6						,				_A : List[Any]=3						,				_A : Any=4						,				_A : Optional[int]=None						,				_A : List[Any]=1000						,				)							->      Union[str, Any]:







							"""simple docstring"""


							snake_case_						:     Tuple     =		parent
							snake_case_						:     int     =		batch_size
							snake_case_						:     Tuple     =		num_channels
							snake_case_						:     Union[str, Any]     =		image_size
							snake_case_						:     int     =		patch_size
							snake_case_						:     Any     =		text_seq_length
							snake_case_						:     str     =		is_training
							snake_case_						:     Union[str, Any]     =		use_input_mask
							snake_case_						:     int     =		use_token_type_ids
							snake_case_						:     Optional[Any]     =		use_labels
							snake_case_						:     Union[str, Any]     =		vocab_size
							snake_case_						:     Tuple     =		hidden_size
							snake_case_						:     Any     =		num_hidden_layers
							snake_case_						:     Dict     =		num_attention_heads
							snake_case_						:     Any     =		intermediate_size
							snake_case_						:     int     =		hidden_act
							snake_case_						:     str     =		hidden_dropout_prob
							snake_case_						:     Any     =		attention_probs_dropout_prob
							snake_case_						:     Optional[Any]     =		max_position_embeddings
							snake_case_						:     List[str]     =		type_vocab_size
							snake_case_						:     List[str]     =		type_sequence_label_size
							snake_case_						:     Tuple     =		initializer_range
							snake_case_						:     Dict     =		coordinate_size
							snake_case_						:     List[str]     =		shape_size
							snake_case_						:     Optional[int]     =		num_labels
							snake_case_						:     Optional[Any]     =		num_choices
							snake_case_						:     Union[str, Any]     =		scope
							snake_case_						:     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)
							snake_case_						:     int     =		text_seq_length
							snake_case_						:     Tuple     =		(image_size // patch_size) ** 2 + 1
							snake_case_						:     List[str]     =		self.text_seq_length + self.image_seq_length






						def UpperCAmelCase_			( self : List[str]		)							->      Any:







							"""simple docstring"""


							snake_case_						:     Optional[int]     =		ids_tensor([self.batch_size, self.text_seq_length]						,				self.vocab_size		)

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

							snake_case_						:     Union[str, Any]     =		floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]		)

							snake_case_						:     Optional[int]     =		None
							if self.use_input_mask:
								snake_case_						:     List[str]     =		random_attention_mask([self.batch_size, self.text_seq_length]		)

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

							snake_case_						:     Optional[int]     =		None
							snake_case_						:     List[str]     =		None
							if self.use_labels:
								snake_case_						:     Optional[Any]     =		ids_tensor([self.batch_size]						,				self.type_sequence_label_size		)
								snake_case_						:     Dict     =		ids_tensor([self.batch_size, self.text_seq_length]						,				self.num_labels		)

							snake_case_						:     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 UpperCAmelCase_			( self : List[str]						,				_A : Union[str, Any]						,				_A : Optional[Any]						,				_A : List[Any]						,				_A : List[Any]						,				_A : List[str]						,				_A : List[str]						,				_A : List[str]						,				_A : str		)							->      List[Any]:







							"""simple docstring"""


							snake_case_						:     List[Any]     =		LayoutLMvaModel(config=_A		)
							model.to(_A		)
							model.eval()

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

							# image only
							snake_case_						:     str     =		model(pixel_values=_A		)
							self.parent.assertEqual(
							    result.last_hidden_state.shape						,				(self.batch_size, self.image_seq_length, self.hidden_size)		)






						def UpperCAmelCase_			( self : int						,				_A : Union[str, Any]						,				_A : List[Any]						,				_A : Union[str, Any]						,				_A : Tuple						,				_A : Dict						,				_A : Optional[Any]						,				_A : int						,				_A : List[Any]		)							->      List[Any]:







							"""simple docstring"""


							snake_case_						:     List[str]     =		self.num_labels
							snake_case_						:     List[Any]     =		LayoutLMvaForSequenceClassification(_A		)
							model.to(_A		)
							model.eval()
							snake_case_						:     Optional[Any]     =		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 UpperCAmelCase_			( self : Any						,				_A : List[Any]						,				_A : Any						,				_A : Optional[Any]						,				_A : List[str]						,				_A : Dict						,				_A : List[Any]						,				_A : str						,				_A : Optional[int]		)							->      str:







							"""simple docstring"""


							snake_case_						:     Optional[int]     =		self.num_labels
							snake_case_						:     List[Any]     =		LayoutLMvaForTokenClassification(config=_A		)
							model.to(_A		)
							model.eval()
							snake_case_						:     Any     =		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 UpperCAmelCase_			( self : Any						,				_A : Tuple						,				_A : str						,				_A : List[str]						,				_A : Any						,				_A : Union[str, Any]						,				_A : Union[str, Any]						,				_A : Any						,				_A : List[str]		)							->      Tuple:







							"""simple docstring"""


							snake_case_						:     Any     =		LayoutLMvaForQuestionAnswering(config=_A		)
							model.to(_A		)
							model.eval()
							snake_case_						:     Optional[int]     =		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 UpperCAmelCase_			( self : Optional[int]		)							->      Tuple:







							"""simple docstring"""


							snake_case_						:     Dict     =		self.prepare_config_and_inputs()
							(
							    (
							    snake_case_
							)  ,(
							    snake_case_
							)  ,(
							    snake_case_
							)  ,(
							    snake_case_
							)  ,(
							    snake_case_
							)  ,(
							    snake_case_
							)  ,(
							    snake_case_
							)  ,(
							    snake_case_
							)  ,
							)						:     Optional[Any]     =		config_and_inputs
							snake_case_						:     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 SCREAMING_SNAKE_CASE_  (   snake_case_    ,			snake_case_    ,			unittest.TestCase   ):
						__magic_name__:     Optional[int]	 =    False
						__magic_name__:     Union[str, Any]	 =    False
						__magic_name__:     Union[str, Any]	 =    False

						__magic_name__:     List[str]	 =    (
						    (
						        LayoutLMvaModel,
						        LayoutLMvaForSequenceClassification,
						        LayoutLMvaForTokenClassification,
						        LayoutLMvaForQuestionAnswering,
						    )
						    if is_torch_available()
						    else ()
						)
						__magic_name__:     str	 =    (
						    {"document-question-answering": LayoutLMvaForQuestionAnswering, "feature-extraction": LayoutLMvaModel}
						    if is_torch_available()
						    else {}
						)






						def UpperCAmelCase_			( self : Dict						,				_A : List[Any]						,				_A : int						,				_A : Optional[int]						,				_A : Optional[Any]						,				_A : Any		)							->      str:







							"""simple docstring"""


							return True






						def UpperCAmelCase_			( self : Any		)							->      Any:







							"""simple docstring"""


							snake_case_						:     Any     =		LayoutLMvaModelTester(self		)
							snake_case_						:     Any     =		ConfigTester(self						,				config_class=_A						,				hidden_size=37		)






						def UpperCAmelCase_			( self : Optional[Any]						,				_A : Union[str, Any]						,				_A : List[Any]						,				_A : Dict=False		)							->      List[str]:







							"""simple docstring"""


							snake_case_						:     List[str]     =		copy.deepcopy(_A		)
							if model_class in get_values(_A		):
								snake_case_						:     Optional[int]     =		{
								    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		):
									snake_case_						:     Any     =		torch.ones(self.model_tester.batch_size						,				dtype=torch.long						,				device=_A		)
								elif model_class in get_values(_A		):
									snake_case_						:     List[Any]     =		torch.zeros(
									    self.model_tester.batch_size						,				dtype=torch.long						,				device=_A		)
									snake_case_						:     str     =		torch.zeros(
									    self.model_tester.batch_size						,				dtype=torch.long						,				device=_A		)
								elif model_class in [
								    *get_values(_A		),
								]:
									snake_case_						:     List[str]     =		torch.zeros(
									    self.model_tester.batch_size						,				dtype=torch.long						,				device=_A		)
								elif model_class in [
								    *get_values(_A		),
								]:
									snake_case_						:     List[Any]     =		torch.zeros(
									    (self.model_tester.batch_size, self.model_tester.text_seq_length)						,				dtype=torch.long						,				device=_A						,				)

							return inputs_dict






						def UpperCAmelCase_			( self : str		)							->      Optional[Any]:







							"""simple docstring"""


							self.config_tester.run_common_tests()






						def UpperCAmelCase_			( self : List[Any]		)							->      Dict:







							"""simple docstring"""


							snake_case_						:     str     =		self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*_A		)






						def UpperCAmelCase_			( self : Tuple		)							->      str:







							"""simple docstring"""


							snake_case_						:     Tuple     =		self.model_tester.prepare_config_and_inputs()
							for type in ["absolute", "relative_key", "relative_key_query"]:
								snake_case_						:     str     =		type
								self.model_tester.create_and_check_model(*_A		)






						def UpperCAmelCase_			( self : Tuple		)							->      int:







							"""simple docstring"""


							snake_case_						:     List[str]     =		self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_sequence_classification(*_A		)






						def UpperCAmelCase_			( self : Optional[int]		)							->      List[str]:







							"""simple docstring"""


							snake_case_						:     Optional[int]     =		self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_token_classification(*_A		)






						def UpperCAmelCase_			( self : List[Any]		)							->      Union[str, Any]:







							"""simple docstring"""


							snake_case_						:     Optional[int]     =		self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_question_answering(*_A		)






						@slow
						def UpperCAmelCase_			( self : str		)							->      Any:







							"""simple docstring"""


							for model_name in LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
								snake_case_						:     str     =		LayoutLMvaModel.from_pretrained(_A		)
								self.assertIsNotNone(_A		)


def 							SCREAMING_SNAKE_CASE__  (      ):
	snake_case_						:     List[Any]     =		Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'				)
	return image


@require_torch
class SCREAMING_SNAKE_CASE_  (   unittest.TestCase   ):






						@cached_property
						def UpperCAmelCase_			( self : int		)							->      List[Any]:







							"""simple docstring"""


							return LayoutLMvaImageProcessor(apply_ocr=_A		) if is_vision_available() else None






						@slow
						def UpperCAmelCase_			( self : Union[str, Any]		)							->      str:







							"""simple docstring"""


							snake_case_						:     int     =		LayoutLMvaModel.from_pretrained('microsoft/layoutlmv3-base'		).to(_A		)

							snake_case_						:     Optional[int]     =		self.default_image_processor
							snake_case_						:     Optional[int]     =		prepare_img()
							snake_case_						:     Optional[Any]     =		image_processor(images=_A						,				return_tensors='pt'		).pixel_values.to(_A		)

							snake_case_						:     str     =		torch.tensor([[1, 2]]		)
							snake_case_						:     Union[str, Any]     =		torch.tensor([[1, 2, 3, 4], [5, 6, 7, 8]]		).unsqueeze(0		)

							# forward pass
							snake_case_						:     int     =		model(
							    input_ids=input_ids.to(_A		)						,				bbox=bbox.to(_A		)						,				pixel_values=pixel_values.to(_A		)						,				)

							# verify the logits
							snake_case_						:     Any     =		torch.Size((1, 199, 768)		)
							self.assertEqual(outputs.last_hidden_state.shape						,				_A		)

							snake_case_						:     Optional[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(_A		)

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








def 							SCREAMING_SNAKE_CASE__  (      __a = 60_08_51_47_51_43				):

	try:
		snake_case_						:     Optional[Any]     =		int(__a				)
	except (TypeError, ValueError):
		raise TypeError('Parameter n must be int or castable to int.'				)
	if n <= 0:
		raise ValueError('Parameter n must be greater than or equal to one.'				)
	snake_case_						:     Any     =		2
	snake_case_						:     Any     =		0
	if n == 2:
		return 2
	while n > 2:
		while n % i != 0:
			i += 1
		snake_case_						:     Tuple     =		i
		while n % i == 0:
			snake_case_						:     List[str]     =		n // i
		i += 1
	return int(__a				)


if __name__ == "__main__":
						print(F'''{solution() = }''')

