import random






class 		a__						:

					def __init__(   self				:	int,_A				:	list[list[float]],_A				:	list[int],_A				:	float = 0.01,_A				:	int = 1000,_A				:	float = -1,):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		int    				=   sample
							if len(self.sample   ) == 0:
									raise ValueError("Sample data can not be empty"   )
							SCREAMING_SNAKE_CASE_  :		Any    				=   target
							if len(self.target   ) == 0:
									raise ValueError("Target data can not be empty"   )
							if len(self.sample   ) != len(self.target   ):
									raise ValueError("Sample data and Target data do not have matching lengths"   )
							SCREAMING_SNAKE_CASE_  :		Any    				=   learning_rate
							SCREAMING_SNAKE_CASE_  :		str    				=   epoch_number
							SCREAMING_SNAKE_CASE_  :		Union[str, Any]    				=   bias
							SCREAMING_SNAKE_CASE_  :		Tuple    				=   len(_A   )
							SCREAMING_SNAKE_CASE_  :		Any    				=   len(sample[0]   )  # number of columns in dataset
							SCREAMING_SNAKE_CASE_  :		list    				=   []

					def     __UpperCamelCase       (   self				:	Any   ):







							"""simple docstring"""






							for sample in self.sample:
									sample.insert(0,self.bias   )

							for _ in range(self.col_sample   ):
									self.weight.append(random.random()   )

							self.weight.insert(0,self.bias   )

							SCREAMING_SNAKE_CASE_  :		List[Any]    				=   0

							while True:
									SCREAMING_SNAKE_CASE_  :		Tuple    				=   False
									for i in range(self.number_sample   ):
											SCREAMING_SNAKE_CASE_  :		Union[str, Any]    				=   0
											for j in range(self.col_sample + 1   ):
													SCREAMING_SNAKE_CASE_  :		str    				=   u + self.weight[j] * self.sample[i][j]
											SCREAMING_SNAKE_CASE_  :		int    				=   self.sign(_A   )
											if y != self.target[i]:
													for j in range(self.col_sample + 1   ):
															SCREAMING_SNAKE_CASE_  :		int    				=   (
															    self.weight[j]
															    + self.learning_rate
															    * (self.target[i] - y)
															    * self.sample[i][j]
															)
													SCREAMING_SNAKE_CASE_  :		Optional[int]    				=   True
            # print('Epoch: \n',epoch_count)
									SCREAMING_SNAKE_CASE_  :		Any    				=   epoch_count + 1
									# if you want control the epoch or just by error
									if not has_misclassified:
											print(("\nEpoch:\n", epoch_count)   )
											print("------------------------\n"   )
											# if epoch_count > self.epoch_number or not error:
											break

					def     __UpperCamelCase       (   self				:	Tuple,_A				:	list[float]   ):







							"""simple docstring"""






							if len(self.sample   ) == 0:
									raise ValueError("Sample data can not be empty"   )
							sample.insert(0,self.bias   )
							SCREAMING_SNAKE_CASE_  :		str    				=   0
							for i in range(self.col_sample + 1   ):
									SCREAMING_SNAKE_CASE_  :		Union[str, Any]    				=   u + self.weight[i] * sample[i]

							SCREAMING_SNAKE_CASE_  :		Union[str, Any]    				=   self.sign(_A   )

							if y == -1:
									print(("Sample: ", sample)   )
									print("classification: P1"   )
							else:
									print(("Sample: ", sample)   )
									print("classification: P2"   )






					def     __UpperCamelCase       (   self				:	str,_A				:	float   ):







							"""simple docstring"""






							return 1 if u >= 0 else -1







__lowerCamelCase :     Any          =  [
    [-0.6508, 0.1097, 4.0009],
    [-1.4492, 0.8896, 4.4005],
    [2.0850, 0.6876, 12.0710],
    [0.2626, 1.1476, 7.7985],
    [0.6418, 1.0234, 7.0427],
    [0.2569, 0.6730, 8.3265],
    [1.1155, 0.6043, 7.4446],
    [0.0914, 0.3399, 7.0677],
    [0.0121, 0.5256, 4.6316],
    [-0.0429, 0.4660, 5.4323],
    [0.4340, 0.6870, 8.2287],
    [0.2735, 1.0287, 7.1934],
    [0.4839, 0.4851, 7.4850],
    [0.4089, -0.1267, 5.5019],
    [1.4391, 0.1614, 8.5843],
    [-0.9115, -0.1973, 2.1962],
    [0.3654, 1.0475, 7.4858],
    [0.2144, 0.7515, 7.1699],
    [0.2013, 1.0014, 6.5489],
    [0.6483, 0.2183, 5.8991],
    [-0.1147, 0.2242, 7.2435],
    [-0.7970, 0.8795, 3.8762],
    [-1.0625, 0.6366, 2.4707],
    [0.5307, 0.1285, 5.6883],
    [-1.2200, 0.7777, 1.7252],
    [0.3957, 0.1076, 5.6623],
    [-0.1013, 0.5989, 7.1812],
    [2.4482, 0.9455, 11.2095],
    [2.0149, 0.6192, 10.9263],
    [0.2012, 0.2611, 5.4631],
]

__lowerCamelCase :     Union[str, Any]          =  [
    -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,
]


if __name__ == "__main__":
		import doctest

		doctest.testmod()

		__lowerCamelCase :     Dict          =  Perceptron(
		    sample=samples, target=target, learning_rate=0.01, epoch_number=10_00, bias=-1
		)
		network.training()
		print('''Finished training perceptron''')
		print('''Enter values to predict or q to exit''')
		while True:
				__lowerCamelCase :     list          =  []
				for i in range(len(samples[0])):
						__lowerCamelCase :     Optional[int]          =  input('''value: ''').strip()
						if user_input == "q":
								break
						__lowerCamelCase :     Any          =  float(user_input)
						sample.insert(i, observation)
				network.sort(sample)

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 		a__						:

					def __init__(   self				:	Optional[int],_A				:	List[str],_A				:	Union[str, Any]=13,_A				:	Union[str, Any]=7,_A				:	Tuple=True,_A				:	Dict=True,_A				:	Optional[int]=True,_A				:	List[Any]=True,_A				:	Optional[int]=99,_A				:	List[str]=32,_A				:	List[Any]=5,_A				:	Dict=4,_A				:	Union[str, Any]=37,_A				:	Dict="gelu",_A				:	Optional[Any]=0.1,_A				:	Dict=0.1,_A				:	str=512,_A				:	Tuple=16,_A				:	str=2,_A				:	str=0.02,_A				:	Tuple=3,_A				:	Optional[Any]=4,_A				:	str=None,_A				:	Tuple=1000,):







							"""simple docstring"""






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

					def     __UpperCamelCase       (   self				:	str   ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Any    				=   ids_tensor([self.batch_size, self.seq_length],self.vocab_size   )

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

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

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

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

							SCREAMING_SNAKE_CASE_  :		str    				=   self.get_config()

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

					def     __UpperCamelCase       (   self				:	List[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     __UpperCamelCase       (   self				:	Any,_A				:	Union[str, Any],_A				:	Union[str, Any],_A				:	str,_A				:	Dict,_A				:	Any,_A				:	Any,_A				:	Tuple,_A				:	Dict   ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Any    				=   LayoutLMModel(config=_A   )
							model.to(_A   )
							model.eval()
							SCREAMING_SNAKE_CASE_  :		List[str]    				=   model(_A,_A,attention_mask=_A,token_type_ids=_A   )
							SCREAMING_SNAKE_CASE_  :		Any    				=   model(_A,_A,token_type_ids=_A   )
							SCREAMING_SNAKE_CASE_  :		Tuple    				=   model(_A,_A   )
							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     __UpperCamelCase       (   self				:	Optional[int],_A				:	Optional[int],_A				:	Tuple,_A				:	Dict,_A				:	List[str],_A				:	Dict,_A				:	Union[str, Any],_A				:	Dict,_A				:	Dict   ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Any    				=   LayoutLMForMaskedLM(config=_A   )
							model.to(_A   )
							model.eval()
							SCREAMING_SNAKE_CASE_  :		Union[str, Any]    				=   model(_A,_A,attention_mask=_A,token_type_ids=_A,labels=_A   )
							self.parent.assertEqual(result.logits.shape,(self.batch_size, self.seq_length, self.vocab_size)   )

					def     __UpperCamelCase       (   self				:	Optional[int],_A				:	List[str],_A				:	Optional[Any],_A				:	Optional[int],_A				:	int,_A				:	int,_A				:	int,_A				:	int,_A				:	Union[str, Any]   ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		List[Any]    				=   self.num_labels
							SCREAMING_SNAKE_CASE_  :		Any    				=   LayoutLMForSequenceClassification(_A   )
							model.to(_A   )
							model.eval()
							SCREAMING_SNAKE_CASE_  :		Any    				=   model(
							    _A,_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				:	Union[str, Any],_A				:	int,_A				:	List[Any],_A				:	Any,_A				:	Any,_A				:	Optional[int],_A				:	List[Any],_A				:	Union[str, Any],_A				:	Dict   ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		List[Any]    				=   self.num_labels
							SCREAMING_SNAKE_CASE_  :		int    				=   LayoutLMForTokenClassification(config=_A   )
							model.to(_A   )
							model.eval()
							SCREAMING_SNAKE_CASE_  :		int    				=   model(_A,_A,attention_mask=_A,token_type_ids=_A,labels=_A   )
							self.parent.assertEqual(result.logits.shape,(self.batch_size, self.seq_length, self.num_labels)   )

					def     __UpperCamelCase       (   self				:	Any,_A				:	int,_A				:	Dict,_A				:	List[Any],_A				:	List[Any],_A				:	Any,_A				:	Union[str, Any],_A				:	str,_A				:	List[str]   ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Union[str, Any]    				=   LayoutLMForQuestionAnswering(config=_A   )
							model.to(_A   )
							model.eval()
							SCREAMING_SNAKE_CASE_  :		Any    				=   model(
							    _A,bbox=_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				:	int   ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Union[str, Any]    				=   self.prepare_config_and_inputs()
							(
							    (
							    SCREAMING_SNAKE_CASE_
							)				,   (
							    SCREAMING_SNAKE_CASE_
							)				,   (
							    SCREAMING_SNAKE_CASE_
							)				,   (
							    SCREAMING_SNAKE_CASE_
							)				,   (
							    SCREAMING_SNAKE_CASE_
							)				,   (
							    SCREAMING_SNAKE_CASE_
							)				,   (
							    SCREAMING_SNAKE_CASE_
							)				,   (
							    SCREAMING_SNAKE_CASE_
							)				,   
							)  :		Dict    				=   config_and_inputs
							SCREAMING_SNAKE_CASE_  :		Any    				=   {
							    "input_ids": input_ids,
							    "bbox": bbox,
							    "token_type_ids": token_type_ids,
							    "attention_mask": input_mask,
							}
							return config, inputs_dict







@require_torch
class 		a__						( A__   ,		A__   ,		unittest.TestCase ):
					A     	=							(
					    (
					        LayoutLMModel,
					        LayoutLMForMaskedLM,
					        LayoutLMForSequenceClassification,
					        LayoutLMForTokenClassification,
					        LayoutLMForQuestionAnswering,
					    )
					    if is_torch_available()
					    else None
					)
					A     	=							(
					    {
					        '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     	=							True

					def     __UpperCamelCase       (   self				:	List[Any]   ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		List[str]    				=   LayoutLMModelTester(self   )
							SCREAMING_SNAKE_CASE_  :		str    				=   ConfigTester(self,config_class=_A,hidden_size=37   )

					def     __UpperCamelCase       (   self				:	str   ):







							"""simple docstring"""






							self.config_tester.run_common_tests()

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







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Dict    				=   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*_A   )

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







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Optional[Any]    				=   self.model_tester.prepare_config_and_inputs()
							for type in ["absolute", "relative_key", "relative_key_query"]:
									SCREAMING_SNAKE_CASE_  :		Optional[int]    				=   type
									self.model_tester.create_and_check_model(*_A   )

					def     __UpperCamelCase       (   self				:	List[str]   ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Dict    				=   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_masked_lm(*_A   )

					def     __UpperCamelCase       (   self				:	Dict   ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		List[Any]    				=   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_sequence_classification(*_A   )

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







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Union[str, Any]    				=   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_token_classification(*_A   )

					def     __UpperCamelCase       (   self				:	Dict   ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		int    				=   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_question_answering(*_A   )






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







							"""simple docstring"""






							pass







def 				_snake_case     (       ):
		"""simple docstring"""







		SCREAMING_SNAKE_CASE_  :		Tuple    				=   torch.tensor([[1_0_1,1_0_1_9,1_0_1_4,1_0_1_6,1_0_3_7,1_2_8_4_9,4_7_4_7,1_0_0_4,1_4_2_4_6,2_2_7_8,5_4_3_9,4_5_2_4,5_0_0_2,2_9_3_0,2_1_9_3,2_9_3_0,4_3_4_1,3_2_0_8,1_0_0_5,1_0_5_5,2_1_7_1,2_8_4_8,1_1_3_0_0,3_5_3_1,1_0_2],[1_0_1,4_0_7_0,4_0_3_4,7_0_2_0,1_0_2_4,3_0_5_8,1_0_1_5,1_0_1_3,2_8_6_1,1_0_1_3,6_0_7_0,1_9_2_7_4,2_7_7_2,6_2_0_5,2_7_8_1_4,1_6_1_4_7,1_6_1_4_7,4_3_4_3,2_0_4_7,1_0_2_8_3,1_0_9_6_9,1_4_3_8_9,1_0_1_2,2_3_3_8,1_0_2]]       ,			device=lowerCAmelCase     )  # noqa: E231
		SCREAMING_SNAKE_CASE_  :		Optional[int]    				=   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
		SCREAMING_SNAKE_CASE_  :		Dict    				=   torch.tensor([[[0,0,0,0],[4_2_3,2_3_7,4_4_0,2_5_1],[4_2_7,2_7_2,4_4_1,2_8_7],[4_1_9,1_1_5,4_3_7,1_2_9],[9_6_1,8_8_5,9_9_2,9_1_2],[2_5_6,3_8,3_3_0,5_8],[2_5_6,3_8,3_3_0,5_8],[3_3_6,4_2,3_5_3,5_7],[3_6_0,3_9,4_0_1,5_6],[3_6_0,3_9,4_0_1,5_6],[4_1_1,3_9,4_7_1,5_9],[4_7_9,4_1,5_2_8,5_9],[5_3_3,3_9,6_3_0,6_0],[6_7,1_1_3,1_3_4,1_3_1],[1_4_1,1_1_5,2_0_9,1_3_2],[6_8,1_4_9,1_3_3,1_6_6],[1_4_1,1_4_9,1_8_7,1_6_4],[1_9_5,1_4_8,2_8_7,1_6_5],[1_9_5,1_4_8,2_8_7,1_6_5],[1_9_5,1_4_8,2_8_7,1_6_5],[2_9_5,1_4_8,3_4_9,1_6_5],[4_4_1,1_4_9,4_9_2,1_6_6],[4_9_7,1_4_9,5_4_6,1_6_4],[6_4,2_0_1,1_2_5,2_1_8],[1_0_0_0,1_0_0_0,1_0_0_0,1_0_0_0]],[[0,0,0,0],[6_6_2,1_5_0,7_5_4,1_6_6],[6_6_5,1_9_9,7_4_2,2_1_1],[5_1_9,2_1_3,5_5_4,2_2_8],[5_1_9,2_1_3,5_5_4,2_2_8],[1_3_4,4_3_3,1_8_7,4_5_4],[1_3_0,4_6_7,2_0_4,4_8_0],[1_3_0,4_6_7,2_0_4,4_8_0],[1_3_0,4_6_7,2_0_4,4_8_0],[1_3_0,4_6_7,2_0_4,4_8_0],[1_3_0,4_6_7,2_0_4,4_8_0],[3_1_4,4_6_9,3_7_6,4_8_2],[5_0_4,6_8_4,5_8_2,7_0_6],[9_4_1,8_2_5,9_7_3,9_0_0],[9_4_1,8_2_5,9_7_3,9_0_0],[9_4_1,8_2_5,9_7_3,9_0_0],[9_4_1,8_2_5,9_7_3,9_0_0],[6_1_0,7_4_9,6_5_2,7_6_5],[1_3_0,6_5_9,1_6_8,6_7_2],[1_7_6,6_5_7,2_3_7,6_7_2],[2_3_8,6_5_7,3_1_2,6_7_2],[4_4_3,6_5_3,6_2_8,6_7_2],[4_4_3,6_5_3,6_2_8,6_7_2],[7_1_6,3_0_1,8_2_5,3_1_7],[1_0_0_0,1_0_0_0,1_0_0_0,1_0_0_0]]]       ,			device=lowerCAmelCase     )  # noqa: E231
		SCREAMING_SNAKE_CASE_  :		List[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=lowerCAmelCase     )  # noqa: E231
		# these are sequence labels (i.e. at the token level)
		SCREAMING_SNAKE_CASE_  :		List[Any]    				=   torch.tensor([[-1_0_0,1_0,1_0,1_0,9,1,-1_0_0,7,7,-1_0_0,7,7,4,2,5,2,8,8,-1_0_0,-1_0_0,5,0,3,2,-1_0_0],[-1_0_0,1_2,1_2,1_2,-1_0_0,1_2,1_0,-1_0_0,-1_0_0,-1_0_0,-1_0_0,1_0,1_2,9,-1_0_0,-1_0_0,-1_0_0,1_0,1_0,1_0,9,1_2,-1_0_0,1_0,-1_0_0]]       ,			device=lowerCAmelCase     )  # noqa: E231
		# fmt: on

		return input_ids, attention_mask, bbox, token_type_ids, labels







@require_torch
class 		a__						( unittest.TestCase ):

					@slow
					def     __UpperCamelCase       (   self				:	Dict   ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		List[Any]    				=   LayoutLMModel.from_pretrained("microsoft/layoutlm-base-uncased"   ).to(_A   )

							SCREAMING_SNAKE_CASE_				,   SCREAMING_SNAKE_CASE_				,   SCREAMING_SNAKE_CASE_				,   SCREAMING_SNAKE_CASE_				,   SCREAMING_SNAKE_CASE_  :		Union[str, Any]    				=   prepare_layoutlm_batch_inputs()

							# forward pass
							SCREAMING_SNAKE_CASE_  :		Tuple    				=   model(input_ids=_A,bbox=_A,attention_mask=_A,token_type_ids=_A   )

							# test the sequence output on [0, :3, :3]
							SCREAMING_SNAKE_CASE_  :		Optional[int]    				=   torch.tensor(
							    [[0.1785, -0.1947, -0.0425], [-0.3254, -0.2807, 0.2553], [-0.5391, -0.3322, 0.3364]],device=_A,)

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

							# test the pooled output on [1, :3]
							SCREAMING_SNAKE_CASE_  :		List[Any]    				=   torch.tensor([-0.6580, -0.0214, 0.8552],device=_A   )

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

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







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		str    				=   LayoutLMForSequenceClassification.from_pretrained("microsoft/layoutlm-base-uncased",num_labels=2   ).to(
							    _A   )

							SCREAMING_SNAKE_CASE_				,   SCREAMING_SNAKE_CASE_				,   SCREAMING_SNAKE_CASE_				,   SCREAMING_SNAKE_CASE_				,   SCREAMING_SNAKE_CASE_  :		Dict    				=   prepare_layoutlm_batch_inputs()

							# forward pass
							SCREAMING_SNAKE_CASE_  :		Tuple    				=   model(
							    input_ids=_A,bbox=_A,attention_mask=_A,token_type_ids=_A,labels=torch.tensor([1, 1],device=_A   ),)

							# test whether we get a loss as a scalar
							SCREAMING_SNAKE_CASE_  :		Dict    				=   outputs.loss
							SCREAMING_SNAKE_CASE_  :		str    				=   torch.Size([]   )
							self.assertEqual(loss.shape,_A   )

							# test the shape of the logits
							SCREAMING_SNAKE_CASE_  :		int    				=   outputs.logits
							SCREAMING_SNAKE_CASE_  :		Tuple    				=   torch.Size((2, 2)   )
							self.assertEqual(logits.shape,_A   )

					@slow
					def     __UpperCamelCase       (   self				:	Dict   ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Tuple    				=   LayoutLMForTokenClassification.from_pretrained("microsoft/layoutlm-base-uncased",num_labels=13   ).to(
							    _A   )

							SCREAMING_SNAKE_CASE_				,   SCREAMING_SNAKE_CASE_				,   SCREAMING_SNAKE_CASE_				,   SCREAMING_SNAKE_CASE_				,   SCREAMING_SNAKE_CASE_  :		Tuple    				=   prepare_layoutlm_batch_inputs()

							# forward pass
							SCREAMING_SNAKE_CASE_  :		Any    				=   model(
							    input_ids=_A,bbox=_A,attention_mask=_A,token_type_ids=_A,labels=_A   )

							# 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
							SCREAMING_SNAKE_CASE_  :		str    				=   outputs.logits
							SCREAMING_SNAKE_CASE_  :		int    				=   torch.Size((2, 25, 13)   )
							self.assertEqual(logits.shape,_A   )






					@slow
					def     __UpperCamelCase       (   self				:	Any   ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Optional[int]    				=   LayoutLMForQuestionAnswering.from_pretrained("microsoft/layoutlm-base-uncased"   ).to(_A   )

							SCREAMING_SNAKE_CASE_				,   SCREAMING_SNAKE_CASE_				,   SCREAMING_SNAKE_CASE_				,   SCREAMING_SNAKE_CASE_				,   SCREAMING_SNAKE_CASE_  :		str    				=   prepare_layoutlm_batch_inputs()

							# forward pass
							SCREAMING_SNAKE_CASE_  :		Optional[int]    				=   model(input_ids=_A,bbox=_A,attention_mask=_A,token_type_ids=_A   )

							# test the shape of the logits
							SCREAMING_SNAKE_CASE_  :		Optional[Any]    				=   torch.Size((2, 25)   )
							self.assertEqual(outputs.start_logits.shape,_A   )
							self.assertEqual(outputs.end_logits.shape,_A   )
