




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 			a__    :






						def __init__(   self		,			_A		,			_A=1_3		,			_A=7		,			_A=True		,			_A=True		,			_A=True		,			_A=True		,			_A=9_9		,			_A=3_2		,			_A=2		,			_A=4		,			_A=3_7		,			_A="gelu"		,			_A=0.1		,			_A=0.1		,			_A=5_1_2		,			_A=1_6		,			_A=2		,			_A=0.02		,			_A=3		,			_A=4		,			_A=None		,			_A=1_0_0_0		,			):




											"""simple docstring"""



											__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 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											__lowerCAmelCase				   =       ids_tensor([self.batch_size, self.seq_length]		,			self.vocab_size			)

											# convert bbox to numpy since TF does not support item assignment
											__lowerCAmelCase				   =       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				   =       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				   =       tf.convert_to_tensor(_A			)

											__lowerCAmelCase				   =       None
											if self.use_input_mask:
																__lowerCAmelCase				   =       random_attention_mask([self.batch_size, self.seq_length]			)

											__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				   =       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 				__SCREAMING_SNAKE_CASE(   self		,			_A		,			_A		,			_A		,			_A		,			_A		,			_A		,			_A		,			_A			):




											"""simple docstring"""



											__lowerCAmelCase				   =       TFLayoutLMModel(config=_A			)

											__lowerCAmelCase				   =       model(_A		,			_A		,			attention_mask=_A		,			token_type_ids=_A			)
											__lowerCAmelCase				   =       model(_A		,			_A		,			token_type_ids=_A			)
											__lowerCAmelCase				   =       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 				__SCREAMING_SNAKE_CASE(   self		,			_A		,			_A		,			_A		,			_A		,			_A		,			_A		,			_A		,			_A			):




											"""simple docstring"""



											__lowerCAmelCase				   =       TFLayoutLMForMaskedLM(config=_A			)

											__lowerCAmelCase				   =       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 				__SCREAMING_SNAKE_CASE(   self		,			_A		,			_A		,			_A		,			_A		,			_A		,			_A		,			_A		,			_A			):




											"""simple docstring"""



											__lowerCAmelCase				   =       self.num_labels
											__lowerCAmelCase				   =       TFLayoutLMForSequenceClassification(config=_A			)

											__lowerCAmelCase				   =       model(_A		,			_A		,			attention_mask=_A		,			token_type_ids=_A			)
											self.parent.assertEqual(result.logits.shape		,			(self.batch_size, self.num_labels)			)






						def 				__SCREAMING_SNAKE_CASE(   self		,			_A		,			_A		,			_A		,			_A		,			_A		,			_A		,			_A		,			_A			):




											"""simple docstring"""



											__lowerCAmelCase				   =       self.num_labels
											__lowerCAmelCase				   =       TFLayoutLMForTokenClassification(config=_A			)

											__lowerCAmelCase				   =       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 				__SCREAMING_SNAKE_CASE(   self		,			_A		,			_A		,			_A		,			_A		,			_A		,			_A		,			_A		,			_A			):




											"""simple docstring"""



											__lowerCAmelCase				   =       TFLayoutLMForQuestionAnswering(config=_A			)

											__lowerCAmelCase				   =       model(_A		,			_A		,			attention_mask=_A		,			token_type_ids=_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 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											__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_tf
class 			a__    (					snake_case__ ,				snake_case__ ,				unittest.TestCase      ):
						_a							:    List[Any]					=    (
						    (
						        TFLayoutLMModel,
						        TFLayoutLMForMaskedLM,
						        TFLayoutLMForTokenClassification,
						        TFLayoutLMForSequenceClassification,
						        TFLayoutLMForQuestionAnswering,
						    )
						    if is_tf_available()
						    else ()
						)
						_a							:    Optional[int]					=    (
						    {
						        """feature-extraction""": TFLayoutLMModel,
						        """fill-mask""": TFLayoutLMForMaskedLM,
						        """text-classification""": TFLayoutLMForSequenceClassification,
						        """token-classification""": TFLayoutLMForTokenClassification,
						        """zero-shot""": TFLayoutLMForSequenceClassification,
						    }
						    if is_tf_available()
						    else {}
						)
						_a							:    Optional[Any]					=    False
						_a							:    Dict					=    True
						_a							:    List[str]					=    1_0






						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											__lowerCAmelCase				   =       TFLayoutLMModelTester(self			)
											__lowerCAmelCase				   =       ConfigTester(self		,			config_class=_A		,			hidden_size=3_7			)






						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											self.config_tester.run_common_tests()






						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											__lowerCAmelCase				   =       self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_model(*_A			)






						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											__lowerCAmelCase				   =       self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_masked_lm(*_A			)






						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											__lowerCAmelCase				   =       self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_sequence_classification(*_A			)






						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											__lowerCAmelCase				   =       self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_token_classification(*_A			)






						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											__lowerCAmelCase				   =       self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_question_answering(*_A			)






						@slow
						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											for model_name in TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																__lowerCAmelCase				   =       TFLayoutLMModel.from_pretrained(_A			)
																self.assertIsNotNone(_A			)






						@unittest.skip("Onnx compliancy broke with TF 2.10"			)
						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											pass







def 	_a      (						):
					# Here we prepare a batch of 2 sequences to test a LayoutLM forward pass on:
					# fmt: off
					__lowerCAmelCase				   =       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
					__lowerCAmelCase				   =       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				   =       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
					__lowerCAmelCase				   =       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				   =       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 			a__    (					unittest.TestCase      ):






						@slow
						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											__lowerCAmelCase				   =       TFLayoutLMModel.from_pretrained("microsoft/layoutlm-base-uncased"			)

											__lowerCAmelCase   ,							__lowerCAmelCase   ,							__lowerCAmelCase   ,							__lowerCAmelCase   ,							__lowerCAmelCase				   =       prepare_layoutlm_batch_inputs()

											# forward pass
											__lowerCAmelCase				   =       model(input_ids=_A		,			bbox=_A		,			attention_mask=_A		,			token_type_ids=_A			)

											# test the sequence output on [0, :3, :3]
											__lowerCAmelCase				   =       tf.convert_to_tensor(
											    [[0.17_85, -0.19_47, -0.04_25], [-0.32_54, -0.28_07, 0.25_53], [-0.53_91, -0.33_22, 0.33_64]]		,			)

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

											# test the pooled output on [1, :3]
											__lowerCAmelCase				   =       tf.convert_to_tensor([-0.65_80, -0.02_14, 0.85_52]			)

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






						@slow
						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



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

											__lowerCAmelCase   ,							__lowerCAmelCase   ,							__lowerCAmelCase   ,							__lowerCAmelCase   ,							__lowerCAmelCase				   =       prepare_layoutlm_batch_inputs()

											# forward pass
											__lowerCAmelCase				   =       model(
											    input_ids=_A		,			bbox=_A		,			attention_mask=_A		,			token_type_ids=_A		,			labels=tf.convert_to_tensor([1, 1]			)		,			)

											# test whether we get a loss as a scalar
											__lowerCAmelCase				   =       outputs.loss
											__lowerCAmelCase				   =       (2,)
											self.assertEqual(loss.shape		,			_A			)

											# test the shape of the logits
											__lowerCAmelCase				   =       outputs.logits
											__lowerCAmelCase				   =       (2, 2)
											self.assertEqual(logits.shape		,			_A			)






						@slow
						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											__lowerCAmelCase				   =       TFLayoutLMForTokenClassification.from_pretrained("microsoft/layoutlm-base-uncased"		,			num_labels=1_3			)

											__lowerCAmelCase   ,							__lowerCAmelCase   ,							__lowerCAmelCase   ,							__lowerCAmelCase   ,							__lowerCAmelCase				   =       prepare_layoutlm_batch_inputs()

											# forward pass
											__lowerCAmelCase				   =       model(
											    input_ids=_A		,			bbox=_A		,			attention_mask=_A		,			token_type_ids=_A		,			labels=_A			)

											# test the shape of the logits
											__lowerCAmelCase				   =       outputs.logits
											__lowerCAmelCase				   =       tf.convert_to_tensor((2, 2_5, 1_3)			)
											self.assertEqual(logits.shape		,			_A			)






						@slow
						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											__lowerCAmelCase				   =       TFLayoutLMForQuestionAnswering.from_pretrained("microsoft/layoutlm-base-uncased"			)

											__lowerCAmelCase   ,							__lowerCAmelCase   ,							__lowerCAmelCase   ,							__lowerCAmelCase   ,							__lowerCAmelCase				   =       prepare_layoutlm_batch_inputs()

											# forward pass
											__lowerCAmelCase				   =       model(input_ids=_A		,			bbox=_A		,			attention_mask=_A		,			token_type_ids=_A			)

											# test the shape of the logits
											__lowerCAmelCase				   =       tf.convert_to_tensor((2, 2_5)			)
											self.assertEqual(outputs.start_logits.shape		,			_A			)
											self.assertEqual(outputs.end_logits.shape		,			_A			)










def      A      (      _SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE    )	-> Any:
       # "extended trapezoidal rule"
       # int(f) = dx/2 * (f1 + 2f2 + ... + fn)
       lowerCamelCase   :      str			 =					(boundary[1] - boundary[0]) / steps
       lowerCamelCase   :      List[str]			 =					boundary[0]
       lowerCamelCase   :      Union[str, Any]			 =					boundary[1]
       lowerCamelCase   :      int			 =					make_points(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE    )
       lowerCamelCase   :      List[str]			 =					0.0
       y += (h / 2.0) * f(_SCREAMING_SNAKE_CASE    )
       for i in x_i:
              # print(i)
              y += h * f(_SCREAMING_SNAKE_CASE    )
       y += (h / 2.0) * f(_SCREAMING_SNAKE_CASE    )
       return y

def      A      (      _SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE    )	-> int:
       lowerCamelCase   :      int			 =					a + h
       while x < (b - h):
              yield x
              lowerCamelCase   :      List[str]			 =					x + h

def      A      (      _SCREAMING_SNAKE_CASE    )	-> Optional[Any]:  # enter your function here
       lowerCamelCase   :      str			 =					(x - 0) * (x - 0)
       return y

def      A      (      )	-> int:
       lowerCamelCase   :      int			 =					0.0  # Lower bound of integration
       lowerCamelCase   :      int			 =					1.0  # Upper bound of integration
       lowerCamelCase   :      Dict			 =					10.0  # define number of steps or resolution
       lowerCamelCase   :      int			 =					[a, b]  # define boundary of integration
       lowerCamelCase   :      str			 =					method_a(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE    )
       print(f'''y = {y}'''    )


if __name__ == "__main__":
       main()






