
"""simple docstring"""





import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
				import torch

if is_vision_available():
				from PIL import Image

				from transformers import LevitImageProcessor






class    _UpperCAmelCase(   unittest.TestCase     ):



		def __init__( self    ,		__a    ,		__a=7    ,		__a=3    ,		__a=18    ,		__a=30    ,		__a=4_00    ,		__a=True    ,		__a=None    ,		__a=True    ,		__a=None    ,		__a=True    ,		__a=[0.5, 0.5, 0.5]    ,		__a=[0.5, 0.5, 0.5]    ,		)	->	int:






							'''simple docstring'''
							_UpperCamelCase										=  size if size is not None else {'''shortest_edge''': 18}
							_UpperCamelCase										=  crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}
							_UpperCamelCase										=  parent
							_UpperCamelCase										=  batch_size
							_UpperCamelCase										=  num_channels
							_UpperCamelCase										=  image_size
							_UpperCamelCase										=  min_resolution
							_UpperCamelCase										=  max_resolution
							_UpperCamelCase										=  do_resize
							_UpperCamelCase										=  size
							_UpperCamelCase										=  do_center_crop
							_UpperCamelCase										=  crop_size
							_UpperCamelCase										=  do_normalize
							_UpperCamelCase										=  image_mean
							_UpperCamelCase										=  image_std




		def 						UpperCAmelCase							( self)	->	Optional[int]:






							'''simple docstring'''
							return {
							    "image_mean": self.image_mean,
							    "image_std": self.image_std,
							    "do_normalize": self.do_normalize,
							    "do_resize": self.do_resize,
							    "do_center_crop": self.do_center_crop,
							    "size": self.size,
							    "crop_size": self.crop_size,
							}






@require_torch
@require_vision
class    _UpperCAmelCase(   A__				,					unittest.TestCase     ):
		lowercase__      =				LevitImageProcessor if is_vision_available() else None



		def 						UpperCAmelCase							( self)	->	Union[str, Any]:






							'''simple docstring'''
							_UpperCamelCase										=  LevitImageProcessingTester(self)



		@property
		def 						UpperCAmelCase							( self)	->	Tuple:






							'''simple docstring'''
							return self.image_processor_tester.prepare_image_processor_dict()



		def 						UpperCAmelCase							( self)	->	List[Any]:






							'''simple docstring'''
							_UpperCamelCase										=  self.image_processing_class(**self.image_processor_dict)
							self.assertTrue(hasattr(lowerCamelCase__    ,		'''image_mean'''))
							self.assertTrue(hasattr(lowerCamelCase__    ,		'''image_std'''))
							self.assertTrue(hasattr(lowerCamelCase__    ,		'''do_normalize'''))
							self.assertTrue(hasattr(lowerCamelCase__    ,		'''do_resize'''))
							self.assertTrue(hasattr(lowerCamelCase__    ,		'''do_center_crop'''))
							self.assertTrue(hasattr(lowerCamelCase__    ,		'''size'''))



		def 						UpperCAmelCase							( self)	->	Optional[Any]:






							'''simple docstring'''
							_UpperCamelCase										=  self.image_processing_class.from_dict(self.image_processor_dict)
							self.assertEqual(image_processor.size    ,		{'''shortest_edge''': 18})
							self.assertEqual(image_processor.crop_size    ,		{'''height''': 18, '''width''': 18})

							_UpperCamelCase										=  self.image_processing_class.from_dict(self.image_processor_dict    ,		size=42    ,		crop_size=84)
							self.assertEqual(image_processor.size    ,		{'''shortest_edge''': 42})
							self.assertEqual(image_processor.crop_size    ,		{'''height''': 84, '''width''': 84})



		def 						UpperCAmelCase							( self)	->	int:






							'''simple docstring'''
							pass



		def 						UpperCAmelCase							( self)	->	Dict:






							'''simple docstring'''
							# Initialize image_processing
							_UpperCamelCase										=  self.image_processing_class(**self.image_processor_dict)
							# create random PIL images
							_UpperCamelCase										=  prepare_image_inputs(self.image_processor_tester    ,		equal_resolution=lowerCamelCase__)
							for image in image_inputs:
												self.assertIsInstance(lowerCamelCase__    ,		Image.Image)

							# Test not batched input
							_UpperCamelCase										=  image_processing(image_inputs[0]    ,		return_tensors='''pt''').pixel_values
							self.assertEqual(
							    encoded_images.shape    ,		(
							        1,
							        self.image_processor_tester.num_channels,
							        self.image_processor_tester.crop_size['''height'''],
							        self.image_processor_tester.crop_size['''width'''],
							    )    ,		)

							# Test batched
							_UpperCamelCase										=  image_processing(lowerCamelCase__    ,		return_tensors='''pt''').pixel_values
							self.assertEqual(
							    encoded_images.shape    ,		(
							        self.image_processor_tester.batch_size,
							        self.image_processor_tester.num_channels,
							        self.image_processor_tester.crop_size['''height'''],
							        self.image_processor_tester.crop_size['''width'''],
							    )    ,		)



		def 						UpperCAmelCase							( self)	->	Optional[Any]:






							'''simple docstring'''
							# Initialize image_processing
							_UpperCamelCase										=  self.image_processing_class(**self.image_processor_dict)
							# create random numpy tensors
							_UpperCamelCase										=  prepare_image_inputs(self.image_processor_tester    ,		equal_resolution=lowerCamelCase__    ,		numpify=lowerCamelCase__)
							for image in image_inputs:
												self.assertIsInstance(lowerCamelCase__    ,		np.ndarray)

							# Test not batched input
							_UpperCamelCase										=  image_processing(image_inputs[0]    ,		return_tensors='''pt''').pixel_values
							self.assertEqual(
							    encoded_images.shape    ,		(
							        1,
							        self.image_processor_tester.num_channels,
							        self.image_processor_tester.crop_size['''height'''],
							        self.image_processor_tester.crop_size['''width'''],
							    )    ,		)

							# Test batched
							_UpperCamelCase										=  image_processing(lowerCamelCase__    ,		return_tensors='''pt''').pixel_values
							self.assertEqual(
							    encoded_images.shape    ,		(
							        self.image_processor_tester.batch_size,
							        self.image_processor_tester.num_channels,
							        self.image_processor_tester.crop_size['''height'''],
							        self.image_processor_tester.crop_size['''width'''],
							    )    ,		)




		def 						UpperCAmelCase							( self)	->	List[str]:






							'''simple docstring'''
							# Initialize image_processing
							_UpperCamelCase										=  self.image_processing_class(**self.image_processor_dict)
							# create random PyTorch tensors
							_UpperCamelCase										=  prepare_image_inputs(self.image_processor_tester    ,		equal_resolution=lowerCamelCase__    ,		torchify=lowerCamelCase__)
							for image in image_inputs:
												self.assertIsInstance(lowerCamelCase__    ,		torch.Tensor)

							# Test not batched input
							_UpperCamelCase										=  image_processing(image_inputs[0]    ,		return_tensors='''pt''').pixel_values
							self.assertEqual(
							    encoded_images.shape    ,		(
							        1,
							        self.image_processor_tester.num_channels,
							        self.image_processor_tester.crop_size['''height'''],
							        self.image_processor_tester.crop_size['''width'''],
							    )    ,		)

							# Test batched
							_UpperCamelCase										=  image_processing(lowerCamelCase__    ,		return_tensors='''pt''').pixel_values
							self.assertEqual(
							    encoded_images.shape    ,		(
							        self.image_processor_tester.batch_size,
							        self.image_processor_tester.num_channels,
							        self.image_processor_tester.crop_size['''height'''],
							        self.image_processor_tester.crop_size['''width'''],
							    )    ,		)








"""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    _UpperCAmelCase:



		def __init__( self    ,		__a    ,		__a=13    ,		__a=7    ,		__a=True    ,		__a=True    ,		__a=True    ,		__a=True    ,		__a=99    ,		__a=32    ,		__a=2    ,		__a=4    ,		__a=37    ,		__a="gelu"    ,		__a=0.1    ,		__a=0.1    ,		__a=5_12    ,		__a=16    ,		__a=2    ,		__a=0.02    ,		__a=3    ,		__a=4    ,		__a=None    ,		__a=10_00    ,		)	->	str:






							'''simple docstring'''
							_UpperCamelCase										=  parent
							_UpperCamelCase										=  batch_size
							_UpperCamelCase										=  seq_length
							_UpperCamelCase										=  is_training
							_UpperCamelCase										=  use_input_mask
							_UpperCamelCase										=  use_token_type_ids
							_UpperCamelCase										=  use_labels
							_UpperCamelCase										=  vocab_size
							_UpperCamelCase										=  hidden_size
							_UpperCamelCase										=  num_hidden_layers
							_UpperCamelCase										=  num_attention_heads
							_UpperCamelCase										=  intermediate_size
							_UpperCamelCase										=  hidden_act
							_UpperCamelCase										=  hidden_dropout_prob
							_UpperCamelCase										=  attention_probs_dropout_prob
							_UpperCamelCase										=  max_position_embeddings
							_UpperCamelCase										=  type_vocab_size
							_UpperCamelCase										=  type_sequence_label_size
							_UpperCamelCase										=  initializer_range
							_UpperCamelCase										=  num_labels
							_UpperCamelCase										=  num_choices
							_UpperCamelCase										=  scope
							_UpperCamelCase										=  range_bbox



		def 						UpperCAmelCase							( self)	->	Tuple:






							'''simple docstring'''
							_UpperCamelCase										=  ids_tensor([self.batch_size, self.seq_length]    ,		self.vocab_size)

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

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

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

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

							_UpperCamelCase										=  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 						UpperCAmelCase							( self    ,		__a    ,		__a    ,		__a    ,		__a    ,		__a    ,		__a    ,		__a    ,		__a)	->	List[str]:






							'''simple docstring'''
							_UpperCamelCase										=  TFLayoutLMModel(config=__a)

							_UpperCamelCase										=  model(__a    ,		__a    ,		attention_mask=__a    ,		token_type_ids=__a)
							_UpperCamelCase										=  model(__a    ,		__a    ,		token_type_ids=__a)
							_UpperCamelCase										=  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    ,		__a    ,		__a    ,		__a    ,		__a    ,		__a    ,		__a    ,		__a    ,		__a)	->	Union[str, Any]:






							'''simple docstring'''
							_UpperCamelCase										=  TFLayoutLMForMaskedLM(config=__a)

							_UpperCamelCase										=  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    ,		__a    ,		__a    ,		__a    ,		__a    ,		__a    ,		__a    ,		__a    ,		__a)	->	int:






							'''simple docstring'''
							_UpperCamelCase										=  self.num_labels
							_UpperCamelCase										=  TFLayoutLMForSequenceClassification(config=__a)

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



		def 						UpperCAmelCase							( self    ,		__a    ,		__a    ,		__a    ,		__a    ,		__a    ,		__a    ,		__a    ,		__a)	->	Union[str, Any]:






							'''simple docstring'''
							_UpperCamelCase										=  self.num_labels
							_UpperCamelCase										=  TFLayoutLMForTokenClassification(config=__a)

							_UpperCamelCase										=  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    ,		__a    ,		__a    ,		__a    ,		__a    ,		__a    ,		__a    ,		__a    ,		__a)	->	Union[str, Any]:






							'''simple docstring'''
							_UpperCamelCase										=  TFLayoutLMForQuestionAnswering(config=__a)

							_UpperCamelCase										=  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 						UpperCAmelCase							( self)	->	List[Any]:






							'''simple docstring'''
							_UpperCamelCase										=  self.prepare_config_and_inputs()
							(
							    (
							    _UpperCamelCase
							)			,  (
							    _UpperCamelCase
							)			,  (
							    _UpperCamelCase
							)			,  (
							    _UpperCamelCase
							)			,  (
							    _UpperCamelCase
							)			,  (
							    _UpperCamelCase
							)			,  (
							    _UpperCamelCase
							)			,  (
							    _UpperCamelCase
							)			,  
							)										=  config_and_inputs
							_UpperCamelCase										=  {
							    '''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     ):
		lowercase__      =				(
		    (
		        TFLayoutLMModel,
		        TFLayoutLMForMaskedLM,
		        TFLayoutLMForTokenClassification,
		        TFLayoutLMForSequenceClassification,
		        TFLayoutLMForQuestionAnswering,
		    )
		    if is_tf_available()
		    else ()
		)
		lowercase__      =				(
		    {
		        'feature-extraction': TFLayoutLMModel,
		        'fill-mask': TFLayoutLMForMaskedLM,
		        'text-classification': TFLayoutLMForSequenceClassification,
		        'token-classification': TFLayoutLMForTokenClassification,
		        'zero-shot': TFLayoutLMForSequenceClassification,
		    }
		    if is_tf_available()
		    else {}
		)
		lowercase__      =				False
		lowercase__      =				True
		lowercase__      =				10



		def 						UpperCAmelCase							( self)	->	Union[str, Any]:






							'''simple docstring'''
							_UpperCamelCase										=  TFLayoutLMModelTester(self)
							_UpperCamelCase										=  ConfigTester(self    ,		config_class=__a    ,		hidden_size=37)



		def 						UpperCAmelCase							( self)	->	str:






							'''simple docstring'''
							self.config_tester.run_common_tests()



		def 						UpperCAmelCase							( self)	->	int:






							'''simple docstring'''
							_UpperCamelCase										=  self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*__a)



		def 						UpperCAmelCase							( self)	->	Dict:






							'''simple docstring'''
							_UpperCamelCase										=  self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_masked_lm(*__a)



		def 						UpperCAmelCase							( self)	->	int:






							'''simple docstring'''
							_UpperCamelCase										=  self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_sequence_classification(*__a)



		def 						UpperCAmelCase							( self)	->	Optional[int]:






							'''simple docstring'''
							_UpperCamelCase										=  self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_token_classification(*__a)



		def 						UpperCAmelCase							( self)	->	Tuple:






							'''simple docstring'''
							_UpperCamelCase										=  self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_question_answering(*__a)



		@slow
		def 						UpperCAmelCase							( self)	->	str:






							'''simple docstring'''
							for model_name in TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
												_UpperCamelCase										=  TFLayoutLMModel.from_pretrained(__a)
												self.assertIsNotNone(__a)




		@unittest.skip('''Onnx compliancy broke with TF 2.10''')
		def 						UpperCAmelCase							( self)	->	List[str]:






							'''simple docstring'''
							pass





def 				lowerCamelCase__		(      )							->		Union[str, Any]:


					"""simple docstring"""





					_UpperCamelCase										=  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										=  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										=  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										=  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										=  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    _UpperCAmelCase(   unittest.TestCase     ):



		@slow
		def 						UpperCAmelCase							( self)	->	Optional[Any]:






							'''simple docstring'''
							_UpperCamelCase										=  TFLayoutLMModel.from_pretrained('''microsoft/layoutlm-base-uncased''')

							_UpperCamelCase			,  _UpperCamelCase			,  _UpperCamelCase			,  _UpperCamelCase			,  _UpperCamelCase										=  prepare_layoutlm_batch_inputs()

							# forward pass
							_UpperCamelCase										=  model(input_ids=__a    ,		bbox=__a    ,		attention_mask=__a    ,		token_type_ids=__a)

							# test the sequence output on [0, :3, :3]
							_UpperCamelCase										=  tf.convert_to_tensor(
							    [[0.1785, -0.1947, -0.0425], [-0.3254, -0.2807, 0.2553], [-0.5391, -0.3322, 0.3364]]    ,		)

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

							# test the pooled output on [1, :3]
							_UpperCamelCase										=  tf.convert_to_tensor([-0.6580, -0.0214, 0.8552])

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



		@slow
		def 						UpperCAmelCase							( self)	->	Any:






							'''simple docstring'''
							# initialize model with randomly initialized sequence classification head
							_UpperCamelCase										=  TFLayoutLMForSequenceClassification.from_pretrained('''microsoft/layoutlm-base-uncased'''    ,		num_labels=2)

							_UpperCamelCase			,  _UpperCamelCase			,  _UpperCamelCase			,  _UpperCamelCase			,  _UpperCamelCase										=  prepare_layoutlm_batch_inputs()

							# forward pass
							_UpperCamelCase										=  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
							_UpperCamelCase										=  outputs.loss
							_UpperCamelCase										=  (2,)
							self.assertEqual(loss.shape    ,		__a)

							# test the shape of the logits
							_UpperCamelCase										=  outputs.logits
							_UpperCamelCase										=  (2, 2)
							self.assertEqual(logits.shape    ,		__a)



		@slow
		def 						UpperCAmelCase							( self)	->	List[str]:






							'''simple docstring'''
							# initialize model with randomly initialized token classification head
							_UpperCamelCase										=  TFLayoutLMForTokenClassification.from_pretrained('''microsoft/layoutlm-base-uncased'''    ,		num_labels=13)

							_UpperCamelCase			,  _UpperCamelCase			,  _UpperCamelCase			,  _UpperCamelCase			,  _UpperCamelCase										=  prepare_layoutlm_batch_inputs()

							# forward pass
							_UpperCamelCase										=  model(
							    input_ids=__a    ,		bbox=__a    ,		attention_mask=__a    ,		token_type_ids=__a    ,		labels=__a)

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




		@slow
		def 						UpperCAmelCase							( self)	->	Dict:






							'''simple docstring'''
							# initialize model with randomly initialized token classification head
							_UpperCamelCase										=  TFLayoutLMForQuestionAnswering.from_pretrained('''microsoft/layoutlm-base-uncased''')

							_UpperCamelCase			,  _UpperCamelCase			,  _UpperCamelCase			,  _UpperCamelCase			,  _UpperCamelCase										=  prepare_layoutlm_batch_inputs()

							# forward pass
							_UpperCamelCase										=  model(input_ids=__a    ,		bbox=__a    ,		attention_mask=__a    ,		token_type_ids=__a)

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






