





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 			UpperCamelCase_		:




					def __init__(		self				,						lowerCAmelCase_				,						lowerCAmelCase_=13				,						lowerCAmelCase_=7				,						lowerCAmelCase_=True				,						lowerCAmelCase_=True				,						lowerCAmelCase_=True				,						lowerCAmelCase_=True				,						lowerCAmelCase_=99				,						lowerCAmelCase_=32				,						lowerCAmelCase_=2				,						lowerCAmelCase_=4				,						lowerCAmelCase_=37				,						lowerCAmelCase_="gelu"				,						lowerCAmelCase_=0.1				,						lowerCAmelCase_=0.1				,						lowerCAmelCase_=512				,						lowerCAmelCase_=16				,						lowerCAmelCase_=2				,						lowerCAmelCase_=0.02				,						lowerCAmelCase_=3				,						lowerCAmelCase_=4				,						lowerCAmelCase_=None				,						lowerCAmelCase_=1000				,						)		->							Any:
										_snake_case									=				parent
										_snake_case									=				batch_size
										_snake_case									=				seq_length
										_snake_case									=				is_training
										_snake_case									=				use_input_mask
										_snake_case									=				use_token_type_ids
										_snake_case									=				use_labels
										_snake_case									=				vocab_size
										_snake_case									=				hidden_size
										_snake_case									=				num_hidden_layers
										_snake_case									=				num_attention_heads
										_snake_case									=				intermediate_size
										_snake_case									=				hidden_act
										_snake_case									=				hidden_dropout_prob
										_snake_case									=				attention_probs_dropout_prob
										_snake_case									=				max_position_embeddings
										_snake_case									=				type_vocab_size
										_snake_case									=				type_sequence_label_size
										_snake_case									=				initializer_range
										_snake_case									=				num_labels
										_snake_case									=				num_choices
										_snake_case									=				scope
										_snake_case									=				range_bbox




					def 	lowerCAmelCase			(		self  )		->							str:
										_snake_case									=				ids_tensor([self.batch_size, self.seq_length]				,						self.vocab_size  )

										# convert bbox to numpy since TF does not support item assignment
										_snake_case									=				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]:
																									_snake_case									=				bbox[i, j, 3]
																									_snake_case									=				bbox[i, j, 1]
																									_snake_case									=				t
																				if bbox[i, j, 2] < bbox[i, j, 0]:
																									_snake_case									=				bbox[i, j, 2]
																									_snake_case									=				bbox[i, j, 0]
																									_snake_case									=				t
										_snake_case									=				tf.convert_to_tensor(lowerCAmelCase_  )

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

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

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

										_snake_case									=				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				,						lowerCAmelCase_				,						lowerCAmelCase_				,						lowerCAmelCase_				,						lowerCAmelCase_				,						lowerCAmelCase_				,						lowerCAmelCase_				,						lowerCAmelCase_				,						lowerCAmelCase_  )		->							Union[str, Any]:
										_snake_case									=				TFLayoutLMModel(config=lowerCAmelCase_  )

										_snake_case									=				model(lowerCAmelCase_				,						lowerCAmelCase_				,						attention_mask=lowerCAmelCase_				,						token_type_ids=lowerCAmelCase_  )
										_snake_case									=				model(lowerCAmelCase_				,						lowerCAmelCase_				,						token_type_ids=lowerCAmelCase_  )
										_snake_case									=				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 	lowerCAmelCase			(		self				,						lowerCAmelCase_				,						lowerCAmelCase_				,						lowerCAmelCase_				,						lowerCAmelCase_				,						lowerCAmelCase_				,						lowerCAmelCase_				,						lowerCAmelCase_				,						lowerCAmelCase_  )		->							int:
										_snake_case									=				TFLayoutLMForMaskedLM(config=lowerCAmelCase_  )

										_snake_case									=				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 	lowerCAmelCase			(		self				,						lowerCAmelCase_				,						lowerCAmelCase_				,						lowerCAmelCase_				,						lowerCAmelCase_				,						lowerCAmelCase_				,						lowerCAmelCase_				,						lowerCAmelCase_				,						lowerCAmelCase_  )		->							Any:
										_snake_case									=				self.num_labels
										_snake_case									=				TFLayoutLMForSequenceClassification(config=lowerCAmelCase_  )

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




					def 	lowerCAmelCase			(		self				,						lowerCAmelCase_				,						lowerCAmelCase_				,						lowerCAmelCase_				,						lowerCAmelCase_				,						lowerCAmelCase_				,						lowerCAmelCase_				,						lowerCAmelCase_				,						lowerCAmelCase_  )		->							Optional[Any]:
										_snake_case									=				self.num_labels
										_snake_case									=				TFLayoutLMForTokenClassification(config=lowerCAmelCase_  )

										_snake_case									=				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 	lowerCAmelCase			(		self				,						lowerCAmelCase_				,						lowerCAmelCase_				,						lowerCAmelCase_				,						lowerCAmelCase_				,						lowerCAmelCase_				,						lowerCAmelCase_				,						lowerCAmelCase_				,						lowerCAmelCase_  )		->							Tuple:
										_snake_case									=				TFLayoutLMForQuestionAnswering(config=lowerCAmelCase_  )

										_snake_case									=				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 	lowerCAmelCase			(		self  )		->							Any:
										_snake_case									=				self.prepare_config_and_inputs()
										(
										    (
										    _snake_case     
										)							,     (
										    _snake_case     
										)							,     (
										    _snake_case     
										)							,     (
										    _snake_case     
										)							,     (
										    _snake_case     
										)							,     (
										    _snake_case     
										)							,     (
										    _snake_case     
										)							,     (
										    _snake_case     
										)							,          
										)									=				config_and_inputs
										_snake_case									=				{
										    'input_ids': input_ids,
										    'bbox': bbox,
										    'token_type_ids': token_type_ids,
										    'attention_mask': input_mask,
										}
										return config, inputs_dict





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




					def 	lowerCAmelCase			(		self  )		->							Dict:
										_snake_case									=				TFLayoutLMModelTester(self  )
										_snake_case									=				ConfigTester(self				,						config_class=lowerCAmelCase_				,						hidden_size=37  )




					def 	lowerCAmelCase			(		self  )		->							int:
										self.config_tester.run_common_tests()




					def 	lowerCAmelCase			(		self  )		->							Any:
										_snake_case									=				self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*lowerCAmelCase_  )




					def 	lowerCAmelCase			(		self  )		->							Union[str, Any]:
										_snake_case									=				self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_masked_lm(*lowerCAmelCase_  )




					def 	lowerCAmelCase			(		self  )		->							Any:
										_snake_case									=				self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_sequence_classification(*lowerCAmelCase_  )




					def 	lowerCAmelCase			(		self  )		->							Dict:
										_snake_case									=				self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_token_classification(*lowerCAmelCase_  )




					def 	lowerCAmelCase			(		self  )		->							List[Any]:
										_snake_case									=				self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_question_answering(*lowerCAmelCase_  )




					@slow
					def 	lowerCAmelCase			(		self  )		->							Optional[int]:
										for model_name in TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
															_snake_case									=				TFLayoutLMModel.from_pretrained(lowerCAmelCase_  )
															self.assertIsNotNone(lowerCAmelCase_  )






					@unittest.skip('Onnx compliancy broke with TF 2.10'  )
					def 	lowerCAmelCase			(		self  )		->							Dict:
										pass





def 						lowerCamelCase__     (     )			-> Tuple:



					'''simple docstring'''




					_snake_case									=				tf.convert_to_tensor([[101,1_019,1_014,1_016,1_037,12_849,4_747,1_004,14_246,2_278,5_439,4_524,5_002,2_930,2_193,2_930,4_341,3_208,1_005,1_055,2_171,2_848,11_300,3_531,102],[101,4_070,4_034,7_020,1_024,3_058,1_015,1_013,2_861,1_013,6_070,19_274,2_772,6_205,27_814,16_147,16_147,4_343,2_047,10_283,10_969,14_389,1_012,2_338,102]]						)  # noqa: E231
					_snake_case									=				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
					_snake_case									=				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],[1_000,1_000,1_000,1_000]],[[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],[1_000,1_000,1_000,1_000]]]						)  # noqa: E231
					_snake_case									=				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)
					_snake_case									=				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 			UpperCamelCase_		(  unittest.TestCase					):




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

										_snake_case							,     _snake_case							,     _snake_case							,     _snake_case							,     _snake_case									=				prepare_layoutlm_batch_inputs()

										# forward pass
										_snake_case									=				model(input_ids=lowerCAmelCase_				,						bbox=lowerCAmelCase_				,						attention_mask=lowerCAmelCase_				,						token_type_ids=lowerCAmelCase_  )

										# test the sequence output on [0, :3, :3]
										_snake_case									=				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]				,						lowerCAmelCase_				,						atol=1E-3  )  )

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

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




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

										_snake_case							,     _snake_case							,     _snake_case							,     _snake_case							,     _snake_case									=				prepare_layoutlm_batch_inputs()

										# forward pass
										_snake_case									=				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
										_snake_case									=				outputs.loss
										_snake_case									=				(2,)
										self.assertEqual(loss.shape				,						lowerCAmelCase_  )

										# test the shape of the logits
										_snake_case									=				outputs.logits
										_snake_case									=				(2, 2)
										self.assertEqual(logits.shape				,						lowerCAmelCase_  )




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

										_snake_case							,     _snake_case							,     _snake_case							,     _snake_case							,     _snake_case									=				prepare_layoutlm_batch_inputs()

										# forward pass
										_snake_case									=				model(
										    input_ids=lowerCAmelCase_				,						bbox=lowerCAmelCase_				,						attention_mask=lowerCAmelCase_				,						token_type_ids=lowerCAmelCase_				,						labels=lowerCAmelCase_  )

										# test the shape of the logits
										_snake_case									=				outputs.logits
										_snake_case									=				tf.convert_to_tensor((2, 25, 13)  )
										self.assertEqual(logits.shape				,						lowerCAmelCase_  )






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

										_snake_case							,     _snake_case							,     _snake_case							,     _snake_case							,     _snake_case									=				prepare_layoutlm_batch_inputs()

										# forward pass
										_snake_case									=				model(input_ids=lowerCAmelCase_				,						bbox=lowerCAmelCase_				,						attention_mask=lowerCAmelCase_				,						token_type_ids=lowerCAmelCase_  )

										# test the shape of the logits
										_snake_case									=				tf.convert_to_tensor((2, 25)  )
										self.assertEqual(outputs.start_logits.shape				,						lowerCAmelCase_  )
										self.assertEqual(outputs.end_logits.shape				,						lowerCAmelCase_  )










from collections import deque

from .hash_table import HashTable
class 			UpperCamelCase_		(  _lowerCamelCase					):




					def __init__(		self				,						*lowerCAmelCase_				,						**lowerCAmelCase_  )		->							Any:
										super().__init__(*lowerCAmelCase_				,						**lowerCAmelCase_  )




					def 	lowerCAmelCase			(		self				,						lowerCAmelCase_				,						lowerCAmelCase_  )		->							Optional[int]:
										_snake_case									=				deque([]  ) if self.values[key] is None else self.values[key]
										self.values[key].appendleft(lowerCAmelCase_  )
										_snake_case									=				self.values[key]




					def 	lowerCAmelCase			(		self  )		->							Optional[int]:
										return (
										    sum(self.charge_factor - len(lowerCAmelCase_  ) for slot in self.values  )
										    / self.size_table
										    * self.charge_factor
										)






					def 	lowerCAmelCase			(		self				,						lowerCAmelCase_				,						lowerCAmelCase_=None  )		->							str:
										if not (
										    len(self.values[key]  ) == self.charge_factor and self.values.count(lowerCAmelCase_  ) == 0
										):
															return key
										return super()._collision_resolution(lowerCAmelCase_				,						lowerCAmelCase_  )



