"""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    __UpperCAmelCase						:

				def __init__(      self	, _lowerCamelCase	, _lowerCamelCase=13	, _lowerCamelCase=7	, _lowerCamelCase=True	, _lowerCamelCase=True	, _lowerCamelCase=True	, _lowerCamelCase=True	, _lowerCamelCase=99	, _lowerCamelCase=32	, _lowerCamelCase=5	, _lowerCamelCase=4	, _lowerCamelCase=37	, _lowerCamelCase="gelu"	, _lowerCamelCase=0.1	, _lowerCamelCase=0.1	, _lowerCamelCase=512	, _lowerCamelCase=16	, _lowerCamelCase=2	, _lowerCamelCase=0.0_2	, _lowerCamelCase=3	, _lowerCamelCase=4	, _lowerCamelCase=None	, _lowerCamelCase=1000	, ):
										lowerCamelCase__					=parent
										lowerCamelCase__					=batch_size
										lowerCamelCase__					=seq_length
										lowerCamelCase__					=is_training
										lowerCamelCase__					=use_input_mask
										lowerCamelCase__					=use_token_type_ids
										lowerCamelCase__					=use_labels
										lowerCamelCase__					=vocab_size
										lowerCamelCase__					=hidden_size
										lowerCamelCase__					=num_hidden_layers
										lowerCamelCase__					=num_attention_heads
										lowerCamelCase__					=intermediate_size
										lowerCamelCase__					=hidden_act
										lowerCamelCase__					=hidden_dropout_prob
										lowerCamelCase__					=attention_probs_dropout_prob
										lowerCamelCase__					=max_position_embeddings
										lowerCamelCase__					=type_vocab_size
										lowerCamelCase__					=type_sequence_label_size
										lowerCamelCase__					=initializer_range
										lowerCamelCase__					=num_labels
										lowerCamelCase__					=num_choices
										lowerCamelCase__					=scope
										lowerCamelCase__					=range_bbox

				def 		_a	(      self      ):
										lowerCamelCase__					=ids_tensor([self.batch_size, self.seq_length]	, self.vocab_size      )

										lowerCamelCase__					=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]:
																												lowerCamelCase__					=bbox[i, j, 3]
																												lowerCamelCase__					=bbox[i, j, 1]
																												lowerCamelCase__					=t
																						if bbox[i, j, 2] < bbox[i, j, 0]:
																												lowerCamelCase__					=bbox[i, j, 2]
																												lowerCamelCase__					=bbox[i, j, 0]
																												lowerCamelCase__					=t

										lowerCamelCase__					=None
										if self.use_input_mask:
																lowerCamelCase__					=ids_tensor([self.batch_size, self.seq_length]	, vocab_size=2      )

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

										lowerCamelCase__					=None
										lowerCamelCase__					=None
										lowerCamelCase__					=None
										if self.use_labels:
																lowerCamelCase__					=ids_tensor([self.batch_size]	, self.type_sequence_label_size      )
																lowerCamelCase__					=ids_tensor([self.batch_size, self.seq_length]	, self.num_labels      )
																lowerCamelCase__					=ids_tensor([self.batch_size]	, self.num_choices      )

										lowerCamelCase__					=self.get_config()

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

				def 		_a	(      self      ):
										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 		_a	(      self	, _lowerCamelCase	, _lowerCamelCase	, _lowerCamelCase	, _lowerCamelCase	, _lowerCamelCase	, _lowerCamelCase	, _lowerCamelCase	, _lowerCamelCase      ):
										lowerCamelCase__					=LayoutLMModel(config=_lowerCamelCase      )
										model.to(_lowerCamelCase      )
										model.eval()
										lowerCamelCase__					=model(_lowerCamelCase	, _lowerCamelCase	, attention_mask=_lowerCamelCase	, token_type_ids=_lowerCamelCase      )
										lowerCamelCase__					=model(_lowerCamelCase	, _lowerCamelCase	, token_type_ids=_lowerCamelCase      )
										lowerCamelCase__					=model(_lowerCamelCase	, _lowerCamelCase      )
										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 		_a	(      self	, _lowerCamelCase	, _lowerCamelCase	, _lowerCamelCase	, _lowerCamelCase	, _lowerCamelCase	, _lowerCamelCase	, _lowerCamelCase	, _lowerCamelCase      ):
										lowerCamelCase__					=LayoutLMForMaskedLM(config=_lowerCamelCase      )
										model.to(_lowerCamelCase      )
										model.eval()
										lowerCamelCase__					=model(_lowerCamelCase	, _lowerCamelCase	, attention_mask=_lowerCamelCase	, token_type_ids=_lowerCamelCase	, labels=_lowerCamelCase      )
										self.parent.assertEqual(result.logits.shape	, (self.batch_size, self.seq_length, self.vocab_size)      )

				def 		_a	(      self	, _lowerCamelCase	, _lowerCamelCase	, _lowerCamelCase	, _lowerCamelCase	, _lowerCamelCase	, _lowerCamelCase	, _lowerCamelCase	, _lowerCamelCase      ):
										lowerCamelCase__					=self.num_labels
										lowerCamelCase__					=LayoutLMForSequenceClassification(_lowerCamelCase      )
										model.to(_lowerCamelCase      )
										model.eval()
										lowerCamelCase__					=model(
										    _lowerCamelCase	, _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	, _lowerCamelCase	, _lowerCamelCase	, _lowerCamelCase	, _lowerCamelCase	, _lowerCamelCase	, _lowerCamelCase	, _lowerCamelCase	, _lowerCamelCase      ):
										lowerCamelCase__					=self.num_labels
										lowerCamelCase__					=LayoutLMForTokenClassification(config=_lowerCamelCase      )
										model.to(_lowerCamelCase      )
										model.eval()
										lowerCamelCase__					=model(_lowerCamelCase	, _lowerCamelCase	, attention_mask=_lowerCamelCase	, token_type_ids=_lowerCamelCase	, labels=_lowerCamelCase      )
										self.parent.assertEqual(result.logits.shape	, (self.batch_size, self.seq_length, self.num_labels)      )

				def 		_a	(      self	, _lowerCamelCase	, _lowerCamelCase	, _lowerCamelCase	, _lowerCamelCase	, _lowerCamelCase	, _lowerCamelCase	, _lowerCamelCase	, _lowerCamelCase      ):
										lowerCamelCase__					=LayoutLMForQuestionAnswering(config=_lowerCamelCase      )
										model.to(_lowerCamelCase      )
										model.eval()
										lowerCamelCase__					=model(
										    _lowerCamelCase	, bbox=_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      ):
										lowerCamelCase__					=self.prepare_config_and_inputs()
										(
										    (
										    lowerCamelCase__
										)					,      (
										    lowerCamelCase__
										)					,      (
										    lowerCamelCase__
										)					,      (
										    lowerCamelCase__
										)					,      (
										    lowerCamelCase__
										)					,      (
										    lowerCamelCase__
										)					,      (
										    lowerCamelCase__
										)					,      (
										    lowerCamelCase__
										)					,      
										)					=config_and_inputs
										lowerCamelCase__					={
										    "input_ids": input_ids,
										    "bbox": bbox,
										    "token_type_ids": token_type_ids,
										    "attention_mask": input_mask,
										}
										return config, inputs_dict






@require_torch
class    __UpperCAmelCase						(	__lowerCAmelCase      ,      __lowerCAmelCase      ,      unittest.TestCase	):
				A__      :   Optional[int]  							=       (
				    (
				        LayoutLMModel,
				        LayoutLMForMaskedLM,
				        LayoutLMForSequenceClassification,
				        LayoutLMForTokenClassification,
				        LayoutLMForQuestionAnswering,
				    )
				    if is_torch_available()
				    else None
				)
				A__      :   Optional[Any]  							=       (
				    {
				        '''document-question-answering''': LayoutLMForQuestionAnswering,
				        '''feature-extraction''': LayoutLMModel,
				        '''fill-mask''': LayoutLMForMaskedLM,
				        '''text-classification''': LayoutLMForSequenceClassification,
				        '''token-classification''': LayoutLMForTokenClassification,
				        '''zero-shot''': LayoutLMForSequenceClassification,
				    }
				    if is_torch_available()
				    else {}
				)
				A__      :   str  							=       True

				def 		_a	(      self      ):
										lowerCamelCase__					=LayoutLMModelTester(self      )
										lowerCamelCase__					=ConfigTester(self	, config_class=_lowerCamelCase	, hidden_size=37      )

				def 		_a	(      self      ):
										self.config_tester.run_common_tests()

				def 		_a	(      self      ):
										lowerCamelCase__					=self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*_lowerCamelCase      )

				def 		_a	(      self      ):
										lowerCamelCase__					=self.model_tester.prepare_config_and_inputs()
										for type in ["absolute", "relative_key", "relative_key_query"]:
																lowerCamelCase__					=type
																self.model_tester.create_and_check_model(*_lowerCamelCase      )

				def 		_a	(      self      ):
										lowerCamelCase__					=self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_masked_lm(*_lowerCamelCase      )

				def 		_a	(      self      ):
										lowerCamelCase__					=self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_sequence_classification(*_lowerCamelCase      )

				def 		_a	(      self      ):
										lowerCamelCase__					=self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_token_classification(*_lowerCamelCase      )

				def 		_a	(      self      ):
										lowerCamelCase__					=self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_question_answering(*_lowerCamelCase      )






				@unittest.skip("Will be fixed soon by reducing the size of the model used for common tests."      )
				def 		_a	(      self      ):
										pass





def 			lowerCamelCase_ (					)							->					Optional[Any]:




						'''simple docstring'''



						lowerCamelCase__					=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=__lowerCAmelCase				)  # noqa: E231
						lowerCamelCase__					=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=__lowerCAmelCase				)  # noqa: E231
						lowerCamelCase__					=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=__lowerCAmelCase				)  # noqa: E231
						lowerCamelCase__					=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=__lowerCAmelCase				)  # noqa: E231
						# these are sequence labels (i.e. at the token level)
						lowerCamelCase__					=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=__lowerCAmelCase				)  # noqa: E231
						# fmt: on

						return input_ids, attention_mask, bbox, token_type_ids, labels






@require_torch
class    __UpperCAmelCase						(	unittest.TestCase	):

				@slow
				def 		_a	(      self      ):
										lowerCamelCase__					=LayoutLMModel.from_pretrained("microsoft/layoutlm-base-uncased"      ).to(_lowerCamelCase      )

										lowerCamelCase__					,      lowerCamelCase__					,      lowerCamelCase__					,      lowerCamelCase__					,      lowerCamelCase__					=prepare_layoutlm_batch_inputs()

										# forward pass
										lowerCamelCase__					=model(input_ids=_lowerCamelCase	, bbox=_lowerCamelCase	, attention_mask=_lowerCamelCase	, token_type_ids=_lowerCamelCase      )

										# test the sequence output on [0, :3, :3]
										lowerCamelCase__					=torch.tensor(
										    [[0.1_7_8_5, -0.1_9_4_7, -0.0_4_2_5], [-0.3_2_5_4, -0.2_8_0_7, 0.2_5_5_3], [-0.5_3_9_1, -0.3_3_2_2, 0.3_3_6_4]]	, device=_lowerCamelCase	, )

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

										# test the pooled output on [1, :3]
										lowerCamelCase__					=torch.tensor([-0.6_5_8_0, -0.0_2_1_4, 0.8_5_5_2]	, device=_lowerCamelCase      )

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

				@slow
				def 		_a	(      self      ):
										# initialize model with randomly initialized sequence classification head
										lowerCamelCase__					=LayoutLMForSequenceClassification.from_pretrained("microsoft/layoutlm-base-uncased"	, num_labels=2      ).to(
										    _lowerCamelCase      )

										lowerCamelCase__					,      lowerCamelCase__					,      lowerCamelCase__					,      lowerCamelCase__					,      lowerCamelCase__					=prepare_layoutlm_batch_inputs()

										# forward pass
										lowerCamelCase__					=model(
										    input_ids=_lowerCamelCase	, bbox=_lowerCamelCase	, attention_mask=_lowerCamelCase	, token_type_ids=_lowerCamelCase	, labels=torch.tensor([1, 1]	, device=_lowerCamelCase      )	, )

										# test whether we get a loss as a scalar
										lowerCamelCase__					=outputs.loss
										lowerCamelCase__					=torch.Size([]      )
										self.assertEqual(loss.shape	, _lowerCamelCase      )

										# test the shape of the logits
										lowerCamelCase__					=outputs.logits
										lowerCamelCase__					=torch.Size((2, 2)      )
										self.assertEqual(logits.shape	, _lowerCamelCase      )

				@slow
				def 		_a	(      self      ):
										# initialize model with randomly initialized token classification head
										lowerCamelCase__					=LayoutLMForTokenClassification.from_pretrained("microsoft/layoutlm-base-uncased"	, num_labels=13      ).to(
										    _lowerCamelCase      )

										lowerCamelCase__					,      lowerCamelCase__					,      lowerCamelCase__					,      lowerCamelCase__					,      lowerCamelCase__					=prepare_layoutlm_batch_inputs()

										# forward pass
										lowerCamelCase__					=model(
										    input_ids=_lowerCamelCase	, bbox=_lowerCamelCase	, attention_mask=_lowerCamelCase	, token_type_ids=_lowerCamelCase	, labels=_lowerCamelCase      )

										# 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
										lowerCamelCase__					=outputs.logits
										lowerCamelCase__					=torch.Size((2, 25, 13)      )
										self.assertEqual(logits.shape	, _lowerCamelCase      )






				@slow
				def 		_a	(      self      ):
										# initialize model with randomly initialized token classification head
										lowerCamelCase__					=LayoutLMForQuestionAnswering.from_pretrained("microsoft/layoutlm-base-uncased"      ).to(_lowerCamelCase      )

										lowerCamelCase__					,      lowerCamelCase__					,      lowerCamelCase__					,      lowerCamelCase__					,      lowerCamelCase__					=prepare_layoutlm_batch_inputs()

										# forward pass
										lowerCamelCase__					=model(input_ids=_lowerCamelCase	, bbox=_lowerCamelCase	, attention_mask=_lowerCamelCase	, token_type_ids=_lowerCamelCase      )

										# test the shape of the logits
										lowerCamelCase__					=torch.Size((2, 25)      )
										self.assertEqual(outputs.start_logits.shape	, _lowerCamelCase      )
										self.assertEqual(outputs.end_logits.shape	, _lowerCamelCase      )



"""simple docstring"""


def 			lowerCamelCase_ (					__lowerCAmelCase				)							->					str:




						'''simple docstring'''



						lowerCamelCase__					=1
						lowerCamelCase__					=0
						lowerCamelCase__					=1
						lowerCamelCase__					=1
						lowerCamelCase__					=3
						lowerCamelCase__					=3
						lowerCamelCase__					=limit
						lowerCamelCase__					=0

						lowerCamelCase__					=""
						while counter != decimal + 1:
												if 4 * q + r - t < n * t:
																		result += str(__lowerCAmelCase				)
																		if counter == 0:
																								result += "."

																		if decimal == counter:
																								break

																		counter += 1
																		lowerCamelCase__					=10 * (r - n * t)
																		lowerCamelCase__					=((10 * (3 * q + r)) // t) - 10 * n
																		q *= 10
																		lowerCamelCase__					=nr
												else:
																		lowerCamelCase__					=(2 * q + r) * l
																		lowerCamelCase__					=(q * (7 * k) + 2 + (r * l)) // (t * l)
																		q *= k
																		t *= l
																		l += 2
																		k += 1
																		lowerCamelCase__					=nn
																		lowerCamelCase__					=nr
						return result





def 			lowerCamelCase_ (					)							->					None:




						'''simple docstring'''



						print(F'''{calculate_pi(50				) = }'''				)
						import doctest

						doctest.testmod()


if __name__ == "__main__":
	main()


