





'''simple docstring'''






import unittest

from transformers import LayoutLMConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
		import torch

		from transformers import (
		    LayoutLMForMaskedLM,
		    LayoutLMForQuestionAnswering,
		    LayoutLMForSequenceClassification,
		    LayoutLMForTokenClassification,
		    LayoutLMModel,
		)







class       lowerCamelCase       :


					def __init__(       self						:	Optional[Any]							,  __snake_case						:	Tuple							,  __snake_case						:	str=13							,  __snake_case						:	Optional[Any]=7							,  __snake_case						:	str=True							,  __snake_case						:	Tuple=True							,  __snake_case						:	Any=True							,  __snake_case						:	Optional[Any]=True							,  __snake_case						:	str=99							,  __snake_case						:	Any=32							,  __snake_case						:	Optional[int]=5							,  __snake_case						:	Optional[Any]=4							,  __snake_case						:	int=37							,  __snake_case						:	str="gelu"							,  __snake_case						:	List[Any]=0.1							,  __snake_case						:	Optional[int]=0.1							,  __snake_case						:	Any=5_12							,  __snake_case						:	int=16							,  __snake_case						:	Dict=2							,  __snake_case						:	Any=0.02							,  __snake_case						:	Dict=3							,  __snake_case						:	Union[str, Any]=4							,  __snake_case						:	Dict=None							,  __snake_case						:	List[str]=10_00							,  ):





											'''simple docstring'''





											_snake_case: Optional[int]	       =		parent
											_snake_case: int	       =		batch_size
											_snake_case: Tuple	       =		seq_length
											_snake_case: Union[str, Any]	       =		is_training
											_snake_case: int	       =		use_input_mask
											_snake_case: Dict	       =		use_token_type_ids
											_snake_case: int	       =		use_labels
											_snake_case: str	       =		vocab_size
											_snake_case: List[str]	       =		hidden_size
											_snake_case: List[str]	       =		num_hidden_layers
											_snake_case: str	       =		num_attention_heads
											_snake_case: Any	       =		intermediate_size
											_snake_case: Dict	       =		hidden_act
											_snake_case: Union[str, Any]	       =		hidden_dropout_prob
											_snake_case: Optional[Any]	       =		attention_probs_dropout_prob
											_snake_case: List[str]	       =		max_position_embeddings
											_snake_case: str	       =		type_vocab_size
											_snake_case: Any	       =		type_sequence_label_size
											_snake_case: List[str]	       =		initializer_range
											_snake_case: Union[str, Any]	       =		num_labels
											_snake_case: Any	       =		num_choices
											_snake_case: Optional[int]	       =		scope
											_snake_case: Tuple	       =		range_bbox


					def  SCREAMING_SNAKE_CASE_		(       self						:	Union[str, Any]							):





											'''simple docstring'''





											_snake_case: str	       =		ids_tensor([self.batch_size, self.seq_length]							,  self.vocab_size							)

											_snake_case: Optional[Any]	       =		ids_tensor([self.batch_size, self.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: int	       =		bbox[i, j, 3]
																													_snake_case: List[str]	       =		bbox[i, j, 1]
																													_snake_case: Any	       =		t
																							if bbox[i, j, 2] < bbox[i, j, 0]:
																													_snake_case: Any	       =		bbox[i, j, 2]
																													_snake_case: List[str]	       =		bbox[i, j, 0]
																													_snake_case: str	       =		t

											_snake_case: Union[str, Any]	       =		None
											if self.use_input_mask:
																	_snake_case: Optional[Any]	       =		ids_tensor([self.batch_size, self.seq_length]							,  vocab_size=2							)

											_snake_case: Optional[int]	       =		None
											if self.use_token_type_ids:
																	_snake_case: List[Any]	       =		ids_tensor([self.batch_size, self.seq_length]							,  self.type_vocab_size							)

											_snake_case: List[Any]	       =		None
											_snake_case: List[str]	       =		None
											_snake_case: Union[str, Any]	       =		None
											if self.use_labels:
																	_snake_case: List[str]	       =		ids_tensor([self.batch_size]							,  self.type_sequence_label_size							)
																	_snake_case: List[Any]	       =		ids_tensor([self.batch_size, self.seq_length]							,  self.num_labels							)
																	_snake_case: int	       =		ids_tensor([self.batch_size]							,  self.num_choices							)

											_snake_case: Union[str, Any]	       =		self.get_config()

											return config, input_ids, bbox, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels


					def  SCREAMING_SNAKE_CASE_		(       self						:	Any							):





											'''simple docstring'''





											return LayoutLMConfig(
											    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							,  )


					def  SCREAMING_SNAKE_CASE_		(       self						:	List[str]							,  __snake_case						:	Any							,  __snake_case						:	str							,  __snake_case						:	str							,  __snake_case						:	str							,  __snake_case						:	List[str]							,  __snake_case						:	Any							,  __snake_case						:	Tuple							,  __snake_case						:	Any							):





											'''simple docstring'''





											_snake_case: List[str]	       =		LayoutLMModel(config=__snake_case							)
											model.to(__snake_case							)
											model.eval()
											_snake_case: Dict	       =		model(__snake_case							,  __snake_case							,  attention_mask=__snake_case							,  token_type_ids=__snake_case							)
											_snake_case: Tuple	       =		model(__snake_case							,  __snake_case							,  token_type_ids=__snake_case							)
											_snake_case: Tuple	       =		model(__snake_case							,  __snake_case							)
											self.parent.assertEqual(result.last_hidden_state.shape							,  (self.batch_size, self.seq_length, self.hidden_size)							)
											self.parent.assertEqual(result.pooler_output.shape							,  (self.batch_size, self.hidden_size)							)


					def  SCREAMING_SNAKE_CASE_		(       self						:	Any							,  __snake_case						:	str							,  __snake_case						:	Dict							,  __snake_case						:	List[Any]							,  __snake_case						:	int							,  __snake_case						:	Optional[Any]							,  __snake_case						:	Optional[Any]							,  __snake_case						:	Optional[Any]							,  __snake_case						:	Optional[Any]							):





											'''simple docstring'''





											_snake_case: List[Any]	       =		LayoutLMForMaskedLM(config=__snake_case							)
											model.to(__snake_case							)
											model.eval()
											_snake_case: List[Any]	       =		model(__snake_case							,  __snake_case							,  attention_mask=__snake_case							,  token_type_ids=__snake_case							,  labels=__snake_case							)
											self.parent.assertEqual(result.logits.shape							,  (self.batch_size, self.seq_length, self.vocab_size)							)


					def  SCREAMING_SNAKE_CASE_		(       self						:	Optional[int]							,  __snake_case						:	Any							,  __snake_case						:	str							,  __snake_case						:	Optional[int]							,  __snake_case						:	Tuple							,  __snake_case						:	Any							,  __snake_case						:	Union[str, Any]							,  __snake_case						:	List[Any]							,  __snake_case						:	Tuple							):





											'''simple docstring'''





											_snake_case: List[str]	       =		self.num_labels
											_snake_case: Dict	       =		LayoutLMForSequenceClassification(__snake_case							)
											model.to(__snake_case							)
											model.eval()
											_snake_case: Dict	       =		model(
											    __snake_case							,  __snake_case							,  attention_mask=__snake_case							,  token_type_ids=__snake_case							,  labels=__snake_case							)
											self.parent.assertEqual(result.logits.shape							,  (self.batch_size, self.num_labels)							)


					def  SCREAMING_SNAKE_CASE_		(       self						:	Dict							,  __snake_case						:	Optional[int]							,  __snake_case						:	List[str]							,  __snake_case						:	List[Any]							,  __snake_case						:	List[str]							,  __snake_case						:	Tuple							,  __snake_case						:	Tuple							,  __snake_case						:	Any							,  __snake_case						:	Tuple							):





											'''simple docstring'''





											_snake_case: Union[str, Any]	       =		self.num_labels
											_snake_case: Any	       =		LayoutLMForTokenClassification(config=__snake_case							)
											model.to(__snake_case							)
											model.eval()
											_snake_case: Tuple	       =		model(__snake_case							,  __snake_case							,  attention_mask=__snake_case							,  token_type_ids=__snake_case							,  labels=__snake_case							)
											self.parent.assertEqual(result.logits.shape							,  (self.batch_size, self.seq_length, self.num_labels)							)


					def  SCREAMING_SNAKE_CASE_		(       self						:	Any							,  __snake_case						:	Tuple							,  __snake_case						:	Optional[Any]							,  __snake_case						:	Any							,  __snake_case						:	Optional[Any]							,  __snake_case						:	Any							,  __snake_case						:	int							,  __snake_case						:	List[str]							,  __snake_case						:	str							):





											'''simple docstring'''





											_snake_case: Optional[int]	       =		LayoutLMForQuestionAnswering(config=__snake_case							)
											model.to(__snake_case							)
											model.eval()
											_snake_case: Tuple	       =		model(
											    __snake_case							,  bbox=__snake_case							,  attention_mask=__snake_case							,  token_type_ids=__snake_case							,  start_positions=__snake_case							,  end_positions=__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  SCREAMING_SNAKE_CASE_		(       self						:	List[Any]							):





											'''simple docstring'''





											_snake_case: List[Any]	       =		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: str	       =		{
											    'input_ids': input_ids,
											    'bbox': bbox,
											    'token_type_ids': token_type_ids,
											    'attention_mask': input_mask,
											}
											return config, inputs_dict







@require_torch
class       lowerCamelCase       (    __UpperCAmelCase  ,				__UpperCAmelCase  ,				unittest.TestCase	):
					_SCREAMING_SNAKE_CASE				 =     (
					    (
					        LayoutLMModel,
					        LayoutLMForMaskedLM,
					        LayoutLMForSequenceClassification,
					        LayoutLMForTokenClassification,
					        LayoutLMForQuestionAnswering,
					    )
					    if is_torch_available()
					    else None
					)
					_SCREAMING_SNAKE_CASE				 =     (
					    {
					        "document-question-answering": LayoutLMForQuestionAnswering,
					        "feature-extraction": LayoutLMModel,
					        "fill-mask": LayoutLMForMaskedLM,
					        "text-classification": LayoutLMForSequenceClassification,
					        "token-classification": LayoutLMForTokenClassification,
					        "zero-shot": LayoutLMForSequenceClassification,
					    }
					    if is_torch_available()
					    else {}
					)
					_SCREAMING_SNAKE_CASE				 =     True


					def  SCREAMING_SNAKE_CASE_		(       self						:	int							):





											'''simple docstring'''





											_snake_case: Union[str, Any]	       =		LayoutLMModelTester(self							)
											_snake_case: List[str]	       =		ConfigTester(self							,  config_class=__snake_case							,  hidden_size=37							)


					def  SCREAMING_SNAKE_CASE_		(       self						:	Tuple							):





											'''simple docstring'''





											self.config_tester.run_common_tests()


					def  SCREAMING_SNAKE_CASE_		(       self						:	List[str]							):





											'''simple docstring'''





											_snake_case: List[str]	       =		self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_model(*__snake_case							)


					def  SCREAMING_SNAKE_CASE_		(       self						:	List[str]							):





											'''simple docstring'''





											_snake_case: Tuple	       =		self.model_tester.prepare_config_and_inputs()
											for type in ["absolute", "relative_key", "relative_key_query"]:
																	_snake_case: Union[str, Any]	       =		type
																	self.model_tester.create_and_check_model(*__snake_case							)


					def  SCREAMING_SNAKE_CASE_		(       self						:	Any							):





											'''simple docstring'''





											_snake_case: Optional[int]	       =		self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_masked_lm(*__snake_case							)


					def  SCREAMING_SNAKE_CASE_		(       self						:	Tuple							):





											'''simple docstring'''





											_snake_case: Optional[int]	       =		self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_sequence_classification(*__snake_case							)


					def  SCREAMING_SNAKE_CASE_		(       self						:	List[str]							):





											'''simple docstring'''





											_snake_case: Tuple	       =		self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_token_classification(*__snake_case							)


					def  SCREAMING_SNAKE_CASE_		(       self						:	Tuple							):





											'''simple docstring'''





											_snake_case: List[Any]	       =		self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_question_answering(*__snake_case							)




					@unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.'							)
					def  SCREAMING_SNAKE_CASE_		(       self						:	int							):





											'''simple docstring'''





											pass





def    lowercase_   (		) ->List[str]:
						# Here we prepare a batch of 2 sequences to test a LayoutLM forward pass on:
						# fmt: off
						_snake_case: Optional[Any]	       =		torch.tensor([[101,1019,1014,1016,1037,1_2849,4747,1004,1_4246,2278,5439,4524,5002,2930,2193,2930,4341,3208,1005,1055,2171,2848,1_1300,3531,102],[101,4070,4034,7020,1024,3058,1015,1013,2861,1013,6070,1_9274,2772,6205,2_7814,1_6147,1_6147,4343,2047,1_0283,1_0969,1_4389,1012,2338,102]]					,	device=lowercase__    )  # noqa: E231
						_snake_case: Dict	       =		torch.tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],]					,	device=lowercase__    )  # noqa: E231
						_snake_case: List[str]	       =		torch.tensor([[[0,0,0,0],[423,237,440,251],[427,272,441,287],[419,115,437,129],[961,885,992,912],[256,38,330,58],[256,38,330,58],[336,42,353,57],[360,39,401,56],[360,39,401,56],[411,39,471,59],[479,41,528,59],[533,39,630,60],[67,113,134,131],[141,115,209,132],[68,149,133,166],[141,149,187,164],[195,148,287,165],[195,148,287,165],[195,148,287,165],[295,148,349,165],[441,149,492,166],[497,149,546,164],[64,201,125,218],[1000,1000,1000,1000]],[[0,0,0,0],[662,150,754,166],[665,199,742,211],[519,213,554,228],[519,213,554,228],[134,433,187,454],[130,467,204,480],[130,467,204,480],[130,467,204,480],[130,467,204,480],[130,467,204,480],[314,469,376,482],[504,684,582,706],[941,825,973,900],[941,825,973,900],[941,825,973,900],[941,825,973,900],[610,749,652,765],[130,659,168,672],[176,657,237,672],[238,657,312,672],[443,653,628,672],[443,653,628,672],[716,301,825,317],[1000,1000,1000,1000]]]					,	device=lowercase__    )  # noqa: E231
						_snake_case: Optional[Any]	       =		torch.tensor([[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]]					,	device=lowercase__    )  # noqa: E231
						# these are sequence labels (i.e. at the token level)
						_snake_case: int	       =		torch.tensor([[-100,10,10,10,9,1,-100,7,7,-100,7,7,4,2,5,2,8,8,-100,-100,5,0,3,2,-100],[-100,12,12,12,-100,12,10,-100,-100,-100,-100,10,12,9,-100,-100,-100,10,10,10,9,12,-100,10,-100]]					,	device=lowercase__    )  # noqa: E231
						# fmt: on

						return input_ids, attention_mask, bbox, token_type_ids, labels







@require_torch
class       lowerCamelCase       (    unittest.TestCase	):


					@slow
					def  SCREAMING_SNAKE_CASE_		(       self						:	Optional[int]							):





											'''simple docstring'''





											_snake_case: List[str]	       =		LayoutLMModel.from_pretrained('microsoft/layoutlm-base-uncased'							).to(__snake_case							)

											_snake_case       ,    _snake_case       ,    _snake_case       ,    _snake_case       ,    _snake_case: List[Any]	       =		prepare_layoutlm_batch_inputs()

											# forward pass
											_snake_case: str	       =		model(input_ids=__snake_case							,  bbox=__snake_case							,  attention_mask=__snake_case							,  token_type_ids=__snake_case							)

											# test the sequence output on [0, :3, :3]
											_snake_case: Dict	       =		torch.tensor(
											    [[0.1_785, -0.1_947, -0.0_425], [-0.3_254, -0.2_807, 0.2_553], [-0.5_391, -0.3_322, 0.3_364]]							,  device=__snake_case							,  )

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

											# test the pooled output on [1, :3]
											_snake_case: List[str]	       =		torch.tensor([-0.6_580, -0.0_214, 0.8_552]							,  device=__snake_case							)

											self.assertTrue(torch.allclose(outputs.pooler_output[1, :3]							,  __snake_case							,  atol=1e-3							)							)


					@slow
					def  SCREAMING_SNAKE_CASE_		(       self						:	Any							):





											'''simple docstring'''





											_snake_case: List[str]	       =		LayoutLMForSequenceClassification.from_pretrained('microsoft/layoutlm-base-uncased'							,  num_labels=2							).to(
											    __snake_case							)

											_snake_case       ,    _snake_case       ,    _snake_case       ,    _snake_case       ,    _snake_case: Tuple	       =		prepare_layoutlm_batch_inputs()

											# forward pass
											_snake_case: Any	       =		model(
											    input_ids=__snake_case							,  bbox=__snake_case							,  attention_mask=__snake_case							,  token_type_ids=__snake_case							,  labels=torch.tensor([1, 1]							,  device=__snake_case							)							,  )

											# test whether we get a loss as a scalar
											_snake_case: Optional[int]	       =		outputs.loss
											_snake_case: Optional[int]	       =		torch.Size([]							)
											self.assertEqual(loss.shape							,  __snake_case							)

											# test the shape of the logits
											_snake_case: List[str]	       =		outputs.logits
											_snake_case: Tuple	       =		torch.Size((2, 2)							)
											self.assertEqual(logits.shape							,  __snake_case							)


					@slow
					def  SCREAMING_SNAKE_CASE_		(       self						:	str							):





											'''simple docstring'''





											_snake_case: Union[str, Any]	       =		LayoutLMForTokenClassification.from_pretrained('microsoft/layoutlm-base-uncased'							,  num_labels=13							).to(
											    __snake_case							)

											_snake_case       ,    _snake_case       ,    _snake_case       ,    _snake_case       ,    _snake_case: List[str]	       =		prepare_layoutlm_batch_inputs()

											# forward pass
											_snake_case: str	       =		model(
											    input_ids=__snake_case							,  bbox=__snake_case							,  attention_mask=__snake_case							,  token_type_ids=__snake_case							,  labels=__snake_case							)

											# test the loss calculation to be around 2.65
											# expected_loss = torch.tensor(2.65, device=torch_device)

											# The loss is currently somewhat random and can vary between 0.1-0.3 atol.
											# self.assertTrue(torch.allclose(outputs.loss, expected_loss, atol=0.1))

											# test the shape of the logits
											_snake_case: Optional[int]	       =		outputs.logits
											_snake_case: Optional[Any]	       =		torch.Size((2, 25, 13)							)
											self.assertEqual(logits.shape							,  __snake_case							)




					@slow
					def  SCREAMING_SNAKE_CASE_		(       self						:	Optional[Any]							):





											'''simple docstring'''





											_snake_case: List[Any]	       =		LayoutLMForQuestionAnswering.from_pretrained('microsoft/layoutlm-base-uncased'							).to(__snake_case							)

											_snake_case       ,    _snake_case       ,    _snake_case       ,    _snake_case       ,    _snake_case: str	       =		prepare_layoutlm_batch_inputs()

											# forward pass
											_snake_case: Any	       =		model(input_ids=__snake_case							,  bbox=__snake_case							,  attention_mask=__snake_case							,  token_type_ids=__snake_case							)

											# test the shape of the logits
											_snake_case: Tuple	       =		torch.Size((2, 25)							)
											self.assertEqual(outputs.start_logits.shape							,  __snake_case							)
											self.assertEqual(outputs.end_logits.shape							,  __snake_case							)












'''simple docstring'''






def    lowercase_   (		lowercase__ = 400_0000    ) ->int:

						if n <= 1:
												return 0
						_snake_case: Union[str, Any]	       =		0
						_snake_case: List[Any]	       =		2
						_snake_case: Any	       =		0
						while 4 * b + a <= n:
												_snake_case       ,    _snake_case: int	       =		b, 4 * b + a
												count += a
						return count + b


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





