

"""simple docstring"""






from __future__ import annotations

import unittest

import numpy as np

from transformers import LayoutLMConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
	import tensorflow as tf

	from transformers.models.layoutlm.modeling_tf_layoutlm import (
	    TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
	    TFLayoutLMForMaskedLM,
	    TFLayoutLMForQuestionAnswering,
	    TFLayoutLMForSequenceClassification,
	    TFLayoutLMForTokenClassification,
	    TFLayoutLMModel,
	)







class       snake_case__  :







			def __init__(			self			: Union[str, Any]			,  lowercase			: Union[str, Any]			,  lowercase			: List[Any]=13			,  lowercase			: str=7			,  lowercase			: Optional[int]=True			,  lowercase			: Optional[Any]=True			,  lowercase			: Tuple=True			,  lowercase			: List[Any]=True			,  lowercase			: List[str]=99			,  lowercase			: Optional[int]=32			,  lowercase			: List[str]=2			,  lowercase			: Dict=4			,  lowercase			: Any=37			,  lowercase			: List[Any]="gelu"			,  lowercase			: str=0.1			,  lowercase			: Tuple=0.1			,  lowercase			: str=5_12			,  lowercase			: Any=16			,  lowercase			: Optional[int]=2			,  lowercase			: Dict=0.0_2			,  lowercase			: int=3			,  lowercase			: Optional[int]=4			,  lowercase			: Union[str, Any]=None			,  lowercase			: int=10_00			,  ):





								'''simple docstring'''



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







			def 			__lowerCAmelCase				(			self			: List[Any]  ):





								'''simple docstring'''



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

								# convert bbox to numpy since TF does not support item assignment
								UpperCAmelCase			:   List[str]      =				ids_tensor([self.batch_size, self.seq_length, 4]			,  self.range_bbox  ).numpy()
								# 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]:
																							UpperCAmelCase			:   Optional[Any]      =				bbox[i, j, 3]
																							UpperCAmelCase			:   List[Any]      =				bbox[i, j, 1]
																							UpperCAmelCase			:   Tuple      =				t
																		if bbox[i, j, 2] < bbox[i, j, 0]:
																							UpperCAmelCase			:   Tuple      =				bbox[i, j, 2]
																							UpperCAmelCase			:   Optional[Any]      =				bbox[i, j, 0]
																							UpperCAmelCase			:   str      =				t
								UpperCAmelCase			:   Any      =				tf.convert_to_tensor(lowercase  )

								UpperCAmelCase			:   int      =				None
								if self.use_input_mask:
													UpperCAmelCase			:   Optional[Any]      =				random_attention_mask([self.batch_size, self.seq_length]  )

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

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

								UpperCAmelCase			:   Optional[Any]      =				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			,  )

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







			def 			__lowerCAmelCase				(			self			: Optional[Any]			,  lowercase			: List[str]			,  lowercase			: List[Any]			,  lowercase			: Optional[Any]			,  lowercase			: Optional[int]			,  lowercase			: Optional[int]			,  lowercase			: Any			,  lowercase			: Any			,  lowercase			: Tuple  ):





								'''simple docstring'''



								UpperCAmelCase			:   Tuple      =				TFLayoutLMModel(config=lowercase  )

								UpperCAmelCase			:   str      =				model(lowercase			,  lowercase			,  attention_mask=lowercase			,  token_type_ids=lowercase  )
								UpperCAmelCase			:   Optional[int]      =				model(lowercase			,  lowercase			,  token_type_ids=lowercase  )
								UpperCAmelCase			:   str      =				model(lowercase			,  lowercase  )
								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 			__lowerCAmelCase				(			self			: Optional[Any]			,  lowercase			: List[str]			,  lowercase			: Dict			,  lowercase			: int			,  lowercase			: Union[str, Any]			,  lowercase			: Union[str, Any]			,  lowercase			: Tuple			,  lowercase			: Optional[int]			,  lowercase			: Tuple  ):





								'''simple docstring'''



								UpperCAmelCase			:   Tuple      =				TFLayoutLMForMaskedLM(config=lowercase  )

								UpperCAmelCase			:   List[Any]      =				model(lowercase			,  lowercase			,  attention_mask=lowercase			,  token_type_ids=lowercase			,  labels=lowercase  )
								self.parent.assertEqual(result.logits.shape			,  (self.batch_size, self.seq_length, self.vocab_size)  )







			def 			__lowerCAmelCase				(			self			: str			,  lowercase			: Optional[Any]			,  lowercase			: Optional[Any]			,  lowercase			: Tuple			,  lowercase			: int			,  lowercase			: List[str]			,  lowercase			: Optional[Any]			,  lowercase			: Optional[Any]			,  lowercase			: Union[str, Any]  ):





								'''simple docstring'''



								UpperCAmelCase			:   Optional[Any]      =				self.num_labels
								UpperCAmelCase			:   int      =				TFLayoutLMForSequenceClassification(config=lowercase  )

								UpperCAmelCase			:   Optional[Any]      =				model(lowercase			,  lowercase			,  attention_mask=lowercase			,  token_type_ids=lowercase  )
								self.parent.assertEqual(result.logits.shape			,  (self.batch_size, self.num_labels)  )







			def 			__lowerCAmelCase				(			self			: List[str]			,  lowercase			: Optional[int]			,  lowercase			: Optional[int]			,  lowercase			: Any			,  lowercase			: List[Any]			,  lowercase			: Union[str, Any]			,  lowercase			: Optional[Any]			,  lowercase			: Any			,  lowercase			: Dict  ):





								'''simple docstring'''



								UpperCAmelCase			:   Tuple      =				self.num_labels
								UpperCAmelCase			:   int      =				TFLayoutLMForTokenClassification(config=lowercase  )

								UpperCAmelCase			:   Tuple      =				model(lowercase			,  lowercase			,  attention_mask=lowercase			,  token_type_ids=lowercase			,  labels=lowercase  )
								self.parent.assertEqual(result.logits.shape			,  (self.batch_size, self.seq_length, self.num_labels)  )







			def 			__lowerCAmelCase				(			self			: Tuple			,  lowercase			: Any			,  lowercase			: List[str]			,  lowercase			: Optional[int]			,  lowercase			: str			,  lowercase			: int			,  lowercase			: Any			,  lowercase			: List[str]			,  lowercase			: Optional[int]  ):





								'''simple docstring'''



								UpperCAmelCase			:   str      =				TFLayoutLMForQuestionAnswering(config=lowercase  )

								UpperCAmelCase			:   Optional[int]      =				model(lowercase			,  lowercase			,  attention_mask=lowercase			,  token_type_ids=lowercase  )
								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			: int  ):





								'''simple docstring'''



								UpperCAmelCase			:   str      =				self.prepare_config_and_inputs()
								(
								    (
								    UpperCAmelCase
								)      ,							(
								    UpperCAmelCase
								)      ,							(
								    UpperCAmelCase
								)      ,							(
								    UpperCAmelCase
								)      ,							(
								    UpperCAmelCase
								)      ,							(
								    UpperCAmelCase
								)      ,							(
								    UpperCAmelCase
								)      ,							(
								    UpperCAmelCase
								)      ,							
								)			:   int      =				config_and_inputs
								UpperCAmelCase			:   int      =				{
								    "input_ids": input_ids,
								    "bbox": bbox,
								    "token_type_ids": token_type_ids,
								    "attention_mask": input_mask,
								}
								return config, inputs_dict







@require_tf
class       snake_case__  ( lowerCAmelCase_					,  lowerCAmelCase_					,  unittest.TestCase     ):
			SCREAMING_SNAKE_CASE__     				=						(
			    (
			        TFLayoutLMModel,
			        TFLayoutLMForMaskedLM,
			        TFLayoutLMForTokenClassification,
			        TFLayoutLMForSequenceClassification,
			        TFLayoutLMForQuestionAnswering,
			    )
			    if is_tf_available()
			    else ()
			)
			SCREAMING_SNAKE_CASE__     				=						(
			    {
			        '''feature-extraction''': TFLayoutLMModel,
			        '''fill-mask''': TFLayoutLMForMaskedLM,
			        '''text-classification''': TFLayoutLMForSequenceClassification,
			        '''token-classification''': TFLayoutLMForTokenClassification,
			        '''zero-shot''': TFLayoutLMForSequenceClassification,
			    }
			    if is_tf_available()
			    else {}
			)
			SCREAMING_SNAKE_CASE__     				=						False
			SCREAMING_SNAKE_CASE__     				=						True
			SCREAMING_SNAKE_CASE__     				=						10







			def 			__lowerCAmelCase				(			self			: str  ):





								'''simple docstring'''



								UpperCAmelCase			:   Union[str, Any]      =				TFLayoutLMModelTester(self  )
								UpperCAmelCase			:   Union[str, Any]      =				ConfigTester(self			,  config_class=lowercase			,  hidden_size=37  )







			def 			__lowerCAmelCase				(			self			: Dict  ):





								'''simple docstring'''



								self.config_tester.run_common_tests()







			def 			__lowerCAmelCase				(			self			: Tuple  ):





								'''simple docstring'''



								UpperCAmelCase			:   List[str]      =				self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*lowercase  )







			def 			__lowerCAmelCase				(			self			: Tuple  ):





								'''simple docstring'''



								UpperCAmelCase			:   Optional[Any]      =				self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_masked_lm(*lowercase  )







			def 			__lowerCAmelCase				(			self			: str  ):





								'''simple docstring'''



								UpperCAmelCase			:   List[str]      =				self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_sequence_classification(*lowercase  )







			def 			__lowerCAmelCase				(			self			: Optional[Any]  ):





								'''simple docstring'''



								UpperCAmelCase			:   Tuple      =				self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_token_classification(*lowercase  )







			def 			__lowerCAmelCase				(			self			: Dict  ):





								'''simple docstring'''



								UpperCAmelCase			:   Union[str, Any]      =				self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_question_answering(*lowercase  )







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





								'''simple docstring'''



								for model_name in TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
													UpperCAmelCase			:   Dict      =				TFLayoutLMModel.from_pretrained(lowercase  )
													self.assertIsNotNone(lowercase  )







			@unittest.skip("Onnx compliancy broke with TF 2.10"  )
			def 			__lowerCAmelCase				(			self			: Union[str, Any]  ):





								'''simple docstring'''



								pass



def   lowercase_	(    ):




					'''simple docstring'''






					UpperCAmelCase			:   int      =				tf.convert_to_tensor([[1_01,10_19,10_14,10_16,10_37,1_28_49,47_47,10_04,1_42_46,22_78,54_39,45_24,50_02,29_30,21_93,29_30,43_41,32_08,10_05,10_55,21_71,28_48,1_13_00,35_31,1_02],[1_01,40_70,40_34,70_20,10_24,30_58,10_15,10_13,28_61,10_13,60_70,1_92_74,27_72,62_05,2_78_14,1_61_47,1_61_47,43_43,20_47,1_02_83,1_09_69,1_43_89,10_12,23_38,1_02]]	)  # noqa: E231
					UpperCAmelCase			:   Union[str, Any]      =				tf.convert_to_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],]	)  # noqa: E231
					UpperCAmelCase			:   Dict      =				tf.convert_to_tensor([[[0,0,0,0],[4_23,2_37,4_40,2_51],[4_27,2_72,4_41,2_87],[4_19,1_15,4_37,1_29],[9_61,8_85,9_92,9_12],[2_56,38,3_30,58],[2_56,38,3_30,58],[3_36,42,3_53,57],[3_60,39,4_01,56],[3_60,39,4_01,56],[4_11,39,4_71,59],[4_79,41,5_28,59],[5_33,39,6_30,60],[67,1_13,1_34,1_31],[1_41,1_15,2_09,1_32],[68,1_49,1_33,1_66],[1_41,1_49,1_87,1_64],[1_95,1_48,2_87,1_65],[1_95,1_48,2_87,1_65],[1_95,1_48,2_87,1_65],[2_95,1_48,3_49,1_65],[4_41,1_49,4_92,1_66],[4_97,1_49,5_46,1_64],[64,2_01,1_25,2_18],[10_00,10_00,10_00,10_00]],[[0,0,0,0],[6_62,1_50,7_54,1_66],[6_65,1_99,7_42,2_11],[5_19,2_13,5_54,2_28],[5_19,2_13,5_54,2_28],[1_34,4_33,1_87,4_54],[1_30,4_67,2_04,4_80],[1_30,4_67,2_04,4_80],[1_30,4_67,2_04,4_80],[1_30,4_67,2_04,4_80],[1_30,4_67,2_04,4_80],[3_14,4_69,3_76,4_82],[5_04,6_84,5_82,7_06],[9_41,8_25,9_73,9_00],[9_41,8_25,9_73,9_00],[9_41,8_25,9_73,9_00],[9_41,8_25,9_73,9_00],[6_10,7_49,6_52,7_65],[1_30,6_59,1_68,6_72],[1_76,6_57,2_37,6_72],[2_38,6_57,3_12,6_72],[4_43,6_53,6_28,6_72],[4_43,6_53,6_28,6_72],[7_16,3_01,8_25,3_17],[10_00,10_00,10_00,10_00]]]	)  # noqa: E231
					UpperCAmelCase			:   str      =				tf.convert_to_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]]	)  # noqa: E231
					# these are sequence labels (i.e. at the token level)
					UpperCAmelCase			:   Optional[int]      =				tf.convert_to_tensor([[-1_00,10,10,10,9,1,-1_00,7,7,-1_00,7,7,4,2,5,2,8,8,-1_00,-1_00,5,0,3,2,-1_00],[-1_00,12,12,12,-1_00,12,10,-1_00,-1_00,-1_00,-1_00,10,12,9,-1_00,-1_00,-1_00,10,10,10,9,12,-1_00,10,-1_00]]	)  # noqa: E231
					# fmt: on

					return input_ids, attention_mask, bbox, token_type_ids, labels







@require_tf
class       snake_case__  ( unittest.TestCase     ):







			@slow
			def 			__lowerCAmelCase				(			self			: str  ):





								'''simple docstring'''



								UpperCAmelCase			:   str      =				TFLayoutLMModel.from_pretrained("microsoft/layoutlm-base-uncased"  )

								UpperCAmelCase      ,							UpperCAmelCase      ,							UpperCAmelCase      ,							UpperCAmelCase      ,							UpperCAmelCase			:   Tuple      =				prepare_layoutlm_batch_inputs()

								# forward pass
								UpperCAmelCase			:   Any      =				model(input_ids=lowercase			,  bbox=lowercase			,  attention_mask=lowercase			,  token_type_ids=lowercase  )

								# test the sequence output on [0, :3, :3]
								UpperCAmelCase			:   Dict      =				tf.convert_to_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]]			,  )

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

								# test the pooled output on [1, :3]
								UpperCAmelCase			:   Optional[int]      =				tf.convert_to_tensor([-0.6_5_8_0, -0.0_2_1_4, 0.8_5_5_2]  )

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







			@slow
			def 			__lowerCAmelCase				(			self			: Any  ):





								'''simple docstring'''



								UpperCAmelCase			:   str      =				TFLayoutLMForSequenceClassification.from_pretrained("microsoft/layoutlm-base-uncased"			,  num_labels=2  )

								UpperCAmelCase      ,							UpperCAmelCase      ,							UpperCAmelCase      ,							UpperCAmelCase      ,							UpperCAmelCase			:   Optional[Any]      =				prepare_layoutlm_batch_inputs()

								# forward pass
								UpperCAmelCase			:   str      =				model(
								    input_ids=lowercase			,  bbox=lowercase			,  attention_mask=lowercase			,  token_type_ids=lowercase			,  labels=tf.convert_to_tensor([1, 1]  )			,  )

								# test whether we get a loss as a scalar
								UpperCAmelCase			:   List[str]      =				outputs.loss
								UpperCAmelCase			:   int      =				(2,)
								self.assertEqual(loss.shape			,  lowercase  )

								# test the shape of the logits
								UpperCAmelCase			:   Optional[Any]      =				outputs.logits
								UpperCAmelCase			:   Union[str, Any]      =				(2, 2)
								self.assertEqual(logits.shape			,  lowercase  )







			@slow
			def 			__lowerCAmelCase				(			self			: str  ):





								'''simple docstring'''



								UpperCAmelCase			:   Optional[Any]      =				TFLayoutLMForTokenClassification.from_pretrained("microsoft/layoutlm-base-uncased"			,  num_labels=13  )

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

								# forward pass
								UpperCAmelCase			:   str      =				model(
								    input_ids=lowercase			,  bbox=lowercase			,  attention_mask=lowercase			,  token_type_ids=lowercase			,  labels=lowercase  )

								# test the shape of the logits
								UpperCAmelCase			:   Any      =				outputs.logits
								UpperCAmelCase			:   Optional[int]      =				tf.convert_to_tensor((2, 25, 13)  )
								self.assertEqual(logits.shape			,  lowercase  )







			@slow
			def 			__lowerCAmelCase				(			self			: int  ):





								'''simple docstring'''



								UpperCAmelCase			:   Union[str, Any]      =				TFLayoutLMForQuestionAnswering.from_pretrained("microsoft/layoutlm-base-uncased"  )

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

								# forward pass
								UpperCAmelCase			:   Optional[Any]      =				model(input_ids=lowercase			,  bbox=lowercase			,  attention_mask=lowercase			,  token_type_ids=lowercase  )

								# test the shape of the logits
								UpperCAmelCase			:   str      =				tf.convert_to_tensor((2, 25)  )
								self.assertEqual(outputs.start_logits.shape			,  lowercase  )
								self.assertEqual(outputs.end_logits.shape			,  lowercase  )





"""simple docstring"""






def   lowercase_	(    _lowercase       :		int							,      _lowercase       :		int	):




					'''simple docstring'''






					return int(input_a == input_a == 0	)



def   lowercase_	(    ):




					'''simple docstring'''






					print("Truth Table of NOR Gate:"	)
					print("|   Input 1   |  Input 2  | Output |"	)
					print(F"""|      0      |    0      |  {nor_gate(0							,      0	)}     |"""	)
					print(F"""|      0      |    1      |  {nor_gate(0							,      1	)}     |"""	)
					print(F"""|      1      |    0      |  {nor_gate(1							,      0	)}     |"""	)
					print(F"""|      1      |    1      |  {nor_gate(1							,      1	)}     |"""	)


if __name__ == "__main__":
	import doctest

	doctest.testmod()
	main()


