



"""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 	__SCREAMING_SNAKE_CASE     :


					'''simple docstring'''





					def __init__(	self							:  List[Any],  lowerCamelCase							:  str,  lowerCamelCase							:  Union[str, Any]=13,  lowerCamelCase							:  Dict=7,  lowerCamelCase							:  Tuple=True,  lowerCamelCase							:  Dict=True,  lowerCamelCase							:  List[str]=True,  lowerCamelCase							:  Dict=True,  lowerCamelCase							:  int=99,  lowerCamelCase							:  Optional[int]=32,  lowerCamelCase							:  Dict=2,  lowerCamelCase							:  Optional[int]=4,  lowerCamelCase							:  int=37,  lowerCamelCase							:  List[Any]="gelu",  lowerCamelCase							:  List[Any]=0.1,  lowerCamelCase							:  Any=0.1,  lowerCamelCase							:  Tuple=512,  lowerCamelCase							:  List[str]=16,  lowerCamelCase							:  Dict=2,  lowerCamelCase							:  Dict=0.02,  lowerCamelCase							:  str=3,  lowerCamelCase							:  Tuple=4,  lowerCamelCase							:  Optional[Any]=None,  lowerCamelCase							:  Any=1000,  )-> Optional[int]:
								lowerCamelCase__		:		Optional[Any]		     =parent
								lowerCamelCase__		:		int		     =batch_size
								lowerCamelCase__		:		Optional[int]		     =seq_length
								lowerCamelCase__		:		str		     =is_training
								lowerCamelCase__		:		List[str]		     =use_input_mask
								lowerCamelCase__		:		Union[str, Any]		     =use_token_type_ids
								lowerCamelCase__		:		Any		     =use_labels
								lowerCamelCase__		:		Optional[int]		     =vocab_size
								lowerCamelCase__		:		Optional[Any]		     =hidden_size
								lowerCamelCase__		:		Union[str, Any]		     =num_hidden_layers
								lowerCamelCase__		:		List[str]		     =num_attention_heads
								lowerCamelCase__		:		List[Any]		     =intermediate_size
								lowerCamelCase__		:		str		     =hidden_act
								lowerCamelCase__		:		Union[str, Any]		     =hidden_dropout_prob
								lowerCamelCase__		:		Dict		     =attention_probs_dropout_prob
								lowerCamelCase__		:		Dict		     =max_position_embeddings
								lowerCamelCase__		:		Dict		     =type_vocab_size
								lowerCamelCase__		:		Union[str, Any]		     =type_sequence_label_size
								lowerCamelCase__		:		List[Any]		     =initializer_range
								lowerCamelCase__		:		Any		     =num_labels
								lowerCamelCase__		:		List[str]		     =num_choices
								lowerCamelCase__		:		Union[str, Any]		     =scope
								lowerCamelCase__		:		Tuple		     =range_bbox



					def 		snake_case	(	self							:  Any					)-> int:
								lowerCamelCase__		:		Dict		     =ids_tensor([self.batch_size, self.seq_length],  self.vocab_size					)

								# convert bbox to numpy since TF does not support item assignment
								lowerCamelCase__		:		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]:
																	lowerCamelCase__		:		Tuple		     =bbox[i, j, 3]
																	lowerCamelCase__		:		str		     =bbox[i, j, 1]
																	lowerCamelCase__		:		List[str]		     =t
														if bbox[i, j, 2] < bbox[i, j, 0]:
																	lowerCamelCase__		:		str		     =bbox[i, j, 2]
																	lowerCamelCase__		:		Tuple		     =bbox[i, j, 0]
																	lowerCamelCase__		:		Optional[int]		     =t
								lowerCamelCase__		:		Optional[Any]		     =tf.convert_to_tensor(lowerCamelCase					)

								lowerCamelCase__		:		int		     =None
								if self.use_input_mask:
											lowerCamelCase__		:		Union[str, Any]		     =random_attention_mask([self.batch_size, self.seq_length]					)

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

								lowerCamelCase__		:		Optional[Any]		     =None
								lowerCamelCase__		:		int		     =None
								lowerCamelCase__		:		Optional[int]		     =None
								if self.use_labels:
											lowerCamelCase__		:		Tuple		     =ids_tensor([self.batch_size],  self.type_sequence_label_size					)
											lowerCamelCase__		:		Optional[Any]		     =ids_tensor([self.batch_size, self.seq_length],  self.num_labels					)
											lowerCamelCase__		:		Dict		     =ids_tensor([self.batch_size],  self.num_choices					)

								lowerCamelCase__		:		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 		snake_case	(	self							:  Any,  lowerCamelCase							:  Any,  lowerCamelCase							:  List[Any],  lowerCamelCase							:  Dict,  lowerCamelCase							:  Union[str, Any],  lowerCamelCase							:  List[Any],  lowerCamelCase							:  Any,  lowerCamelCase							:  List[Any],  lowerCamelCase							:  Optional[int]					)-> Any:
								lowerCamelCase__		:		List[str]		     =TFLayoutLMModel(config=lowerCamelCase					)

								lowerCamelCase__		:		Any		     =model(lowerCamelCase,  lowerCamelCase,  attention_mask=lowerCamelCase,  token_type_ids=lowerCamelCase					)
								lowerCamelCase__		:		Tuple		     =model(lowerCamelCase,  lowerCamelCase,  token_type_ids=lowerCamelCase					)
								lowerCamelCase__		:		Any		     =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 		snake_case	(	self							:  Tuple,  lowerCamelCase							:  Tuple,  lowerCamelCase							:  Tuple,  lowerCamelCase							:  Any,  lowerCamelCase							:  Optional[Any],  lowerCamelCase							:  Any,  lowerCamelCase							:  Dict,  lowerCamelCase							:  Dict,  lowerCamelCase							:  Optional[int]					)-> Dict:
								lowerCamelCase__		:		int		     =TFLayoutLMForMaskedLM(config=lowerCamelCase					)

								lowerCamelCase__		:		List[Any]		     =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 		snake_case	(	self							:  Optional[Any],  lowerCamelCase							:  Optional[int],  lowerCamelCase							:  str,  lowerCamelCase							:  List[str],  lowerCamelCase							:  Union[str, Any],  lowerCamelCase							:  Optional[int],  lowerCamelCase							:  List[Any],  lowerCamelCase							:  Optional[int],  lowerCamelCase							:  Tuple					)-> str:
								lowerCamelCase__		:		Union[str, Any]		     =self.num_labels
								lowerCamelCase__		:		Union[str, Any]		     =TFLayoutLMForSequenceClassification(config=lowerCamelCase					)

								lowerCamelCase__		:		Tuple		     =model(lowerCamelCase,  lowerCamelCase,  attention_mask=lowerCamelCase,  token_type_ids=lowerCamelCase					)
								self.parent.assertEqual(result.logits.shape,  (self.batch_size, self.num_labels)					)



					def 		snake_case	(	self							:  str,  lowerCamelCase							:  Union[str, Any],  lowerCamelCase							:  Union[str, Any],  lowerCamelCase							:  str,  lowerCamelCase							:  Any,  lowerCamelCase							:  Union[str, Any],  lowerCamelCase							:  Union[str, Any],  lowerCamelCase							:  Optional[int],  lowerCamelCase							:  str					)-> Tuple:
								lowerCamelCase__		:		List[str]		     =self.num_labels
								lowerCamelCase__		:		str		     =TFLayoutLMForTokenClassification(config=lowerCamelCase					)

								lowerCamelCase__		:		Any		     =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 		snake_case	(	self							:  Dict,  lowerCamelCase							:  Union[str, Any],  lowerCamelCase							:  List[Any],  lowerCamelCase							:  Tuple,  lowerCamelCase							:  List[Any],  lowerCamelCase							:  Dict,  lowerCamelCase							:  List[Any],  lowerCamelCase							:  Dict,  lowerCamelCase							:  Optional[int]					)-> str:
								lowerCamelCase__		:		Dict		     =TFLayoutLMForQuestionAnswering(config=lowerCamelCase					)

								lowerCamelCase__		:		Tuple		     =model(lowerCamelCase,  lowerCamelCase,  attention_mask=lowerCamelCase,  token_type_ids=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 		snake_case	(	self							:  int					)-> int:
								lowerCamelCase__		:		Optional[Any]		     =self.prepare_config_and_inputs()
								(
								    (
								    lowerCamelCase__
								)     ,       (
								    lowerCamelCase__
								)     ,       (
								    lowerCamelCase__
								)     ,       (
								    lowerCamelCase__
								)     ,       (
								    lowerCamelCase__
								)     ,       (
								    lowerCamelCase__
								)     ,       (
								    lowerCamelCase__
								)     ,       (
								    lowerCamelCase__
								)     ,       
								)		:		Optional[int]		     =config_and_inputs
								lowerCamelCase__		:		Union[str, Any]		     ={
								    '''input_ids''': input_ids,
								    '''bbox''': bbox,
								    '''token_type_ids''': token_type_ids,
								    '''attention_mask''': input_mask,
								}
								return config, inputs_dict





@require_tf
class 	__SCREAMING_SNAKE_CASE     (				lowerCAmelCase_		,       lowerCAmelCase_		,       unittest.TestCase       ):


					'''simple docstring'''





					_a             =							(
					    (
					        TFLayoutLMModel,
					        TFLayoutLMForMaskedLM,
					        TFLayoutLMForTokenClassification,
					        TFLayoutLMForSequenceClassification,
					        TFLayoutLMForQuestionAnswering,
					    )
					    if is_tf_available()
					    else ()
					)
					_a             =							(
					    {
					        'feature-extraction': TFLayoutLMModel,
					        'fill-mask': TFLayoutLMForMaskedLM,
					        'text-classification': TFLayoutLMForSequenceClassification,
					        'token-classification': TFLayoutLMForTokenClassification,
					        'zero-shot': TFLayoutLMForSequenceClassification,
					    }
					    if is_tf_available()
					    else {}
					)
					_a             =							False
					_a             =							True
					_a             =							1_0



					def 		snake_case	(	self							:  str					)-> Any:
								lowerCamelCase__		:		int		     =TFLayoutLMModelTester(self					)
								lowerCamelCase__		:		Tuple		     =ConfigTester(self,  config_class=lowerCamelCase,  hidden_size=37					)



					def 		snake_case	(	self							:  Optional[Any]					)-> int:
								self.config_tester.run_common_tests()



					def 		snake_case	(	self							:  Dict					)-> Optional[Any]:
								lowerCamelCase__		:		Optional[int]		     =self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*lowerCamelCase					)



					def 		snake_case	(	self							:  Tuple					)-> int:
								lowerCamelCase__		:		Union[str, Any]		     =self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_masked_lm(*lowerCamelCase					)



					def 		snake_case	(	self							:  int					)-> List[str]:
								lowerCamelCase__		:		Optional[Any]		     =self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_sequence_classification(*lowerCamelCase					)



					def 		snake_case	(	self							:  List[str]					)-> List[Any]:
								lowerCamelCase__		:		Optional[int]		     =self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_token_classification(*lowerCamelCase					)



					def 		snake_case	(	self							:  str					)-> Any:
								lowerCamelCase__		:		Dict		     =self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_question_answering(*lowerCamelCase					)



					@slow
					def 		snake_case	(	self							:  Union[str, Any]					)-> Optional[int]:
								for model_name in TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
											lowerCamelCase__		:		List[Any]		     =TFLayoutLMModel.from_pretrained(lowerCamelCase					)
											self.assertIsNotNone(lowerCamelCase					)




					@unittest.skip('''Onnx compliancy broke with TF 2.10'''					)
					def 		snake_case	(	self							:  Union[str, Any]					)-> Dict:
								pass






def 					snake_case__       (		):
			"""simple docstring"""
			# Here we prepare a batch of 2 sequences to test a LayoutLM forward pass on:
			# fmt: off
			lowerCamelCase__		:		Optional[int]		     =tf.convert_to_tensor([[101,1019,1014,1016,1037,12849,4747,1004,14246,2278,5439,4524,5002,2930,2193,2930,4341,3208,1005,1055,2171,2848,11300,3531,102],[101,4070,4034,7020,1024,3058,1015,1013,2861,1013,6070,19274,2772,6205,27814,16147,16147,4343,2047,10283,10969,14389,1012,2338,102]]     )  # noqa: E231
			lowerCamelCase__		:		int		     =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
			lowerCamelCase__		:		List[str]		     =tf.convert_to_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]]]     )  # noqa: E231
			lowerCamelCase__		:		Optional[int]		     =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)
			lowerCamelCase__		:		Optional[Any]		     =tf.convert_to_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]]     )  # noqa: E231
			# fmt: on

			return input_ids, attention_mask, bbox, token_type_ids, labels

@require_tf
class 	__SCREAMING_SNAKE_CASE     (				unittest.TestCase       ):


					'''simple docstring'''





					@slow
					def 		snake_case	(	self							:  int					)-> Dict:
								lowerCamelCase__		:		Union[str, Any]		     =TFLayoutLMModel.from_pretrained('''microsoft/layoutlm-base-uncased'''					)

								lowerCamelCase__     ,       lowerCamelCase__     ,       lowerCamelCase__     ,       lowerCamelCase__     ,       lowerCamelCase__		:		Tuple		     =prepare_layoutlm_batch_inputs()

								# forward pass
								lowerCamelCase__		:		str		     =model(input_ids=lowerCamelCase,  bbox=lowerCamelCase,  attention_mask=lowerCamelCase,  token_type_ids=lowerCamelCase					)

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

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

								# test the pooled output on [1, :3]
								lowerCamelCase__		:		Union[str, Any]		     =tf.convert_to_tensor([-0.6_580, -0.0_214, 0.8_552]					)

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



					@slow
					def 		snake_case	(	self							:  List[str]					)-> List[Any]:
								# initialize model with randomly initialized sequence classification head
								lowerCamelCase__		:		Any		     =TFLayoutLMForSequenceClassification.from_pretrained('''microsoft/layoutlm-base-uncased''',  num_labels=2					)

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

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

								# test whether we get a loss as a scalar
								lowerCamelCase__		:		Optional[Any]		     =outputs.loss
								lowerCamelCase__		:		Tuple		     =(2,)
								self.assertEqual(loss.shape,  lowerCamelCase					)

								# test the shape of the logits
								lowerCamelCase__		:		Optional[Any]		     =outputs.logits
								lowerCamelCase__		:		List[str]		     =(2, 2)
								self.assertEqual(logits.shape,  lowerCamelCase					)



					@slow
					def 		snake_case	(	self							:  str					)-> Dict:
								# initialize model with randomly initialized token classification head
								lowerCamelCase__		:		str		     =TFLayoutLMForTokenClassification.from_pretrained('''microsoft/layoutlm-base-uncased''',  num_labels=13					)

								lowerCamelCase__     ,       lowerCamelCase__     ,       lowerCamelCase__     ,       lowerCamelCase__     ,       lowerCamelCase__		:		Optional[int]		     =prepare_layoutlm_batch_inputs()

								# forward pass
								lowerCamelCase__		:		List[Any]		     =model(
								    input_ids=lowerCamelCase,  bbox=lowerCamelCase,  attention_mask=lowerCamelCase,  token_type_ids=lowerCamelCase,  labels=lowerCamelCase					)

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




					@slow
					def 		snake_case	(	self							:  Union[str, Any]					)-> Optional[Any]:
								# initialize model with randomly initialized token classification head
								lowerCamelCase__		:		Any		     =TFLayoutLMForQuestionAnswering.from_pretrained('''microsoft/layoutlm-base-uncased'''					)

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

								# forward pass
								lowerCamelCase__		:		str		     =model(input_ids=lowerCamelCase,  bbox=lowerCamelCase,  attention_mask=lowerCamelCase,  token_type_ids=lowerCamelCase					)

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









"""simple docstring"""






def 					snake_case__       (		__lowerCamelCase    :   int							,		__lowerCamelCase    :   int     ):
			"""simple docstring"""
			return int((input_a, input_a).count(0     ) != 0     )






def 					snake_case__       (		):
			"""simple docstring"""
			assert nand_gate(0							,		0     ) == 1
			assert nand_gate(0							,		1     ) == 1
			assert nand_gate(1							,		0     ) == 1
			assert nand_gate(1							,		1     ) == 0


if __name__ == "__main__":
		print(nand_gate(0, 0))
		print(nand_gate(0, 1))
		print(nand_gate(1, 0))
		print(nand_gate(1, 1))




