


"""simple docstring"""






from __future__ import annotations

import copy
import inspect
import unittest

import numpy as np

from transformers import is_tf_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_tf, slow
from transformers.utils import cached_property

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


if is_tf_available():
					import tensorflow as tf

					from transformers import (
					    TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST,
					    TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
					    TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING,
					    TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
					    TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
					    LayoutLMvaConfig,
					    TFLayoutLMvaForQuestionAnswering,
					    TFLayoutLMvaForSequenceClassification,
					    TFLayoutLMvaForTokenClassification,
					    TFLayoutLMvaModel,
					)

if is_vision_available():
					from PIL import Image

					from transformers import LayoutLMvaImageProcessor






class      A:

		"""simple docstring"""





		def __init__( self					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__=2					,				SCREAMING_SNAKE_CASE__=3					,				SCREAMING_SNAKE_CASE__=4					,				SCREAMING_SNAKE_CASE__=2					,				SCREAMING_SNAKE_CASE__=7					,				SCREAMING_SNAKE_CASE__=True					,				SCREAMING_SNAKE_CASE__=True					,				SCREAMING_SNAKE_CASE__=True					,				SCREAMING_SNAKE_CASE__=True					,				SCREAMING_SNAKE_CASE__=99					,				SCREAMING_SNAKE_CASE__=36					,				SCREAMING_SNAKE_CASE__=2					,				SCREAMING_SNAKE_CASE__=4					,				SCREAMING_SNAKE_CASE__=37					,				SCREAMING_SNAKE_CASE__="gelu"					,				SCREAMING_SNAKE_CASE__=0.1					,				SCREAMING_SNAKE_CASE__=0.1					,				SCREAMING_SNAKE_CASE__=5_12					,				SCREAMING_SNAKE_CASE__=16					,				SCREAMING_SNAKE_CASE__=2					,				SCREAMING_SNAKE_CASE__=0.0_2					,				SCREAMING_SNAKE_CASE__=6					,				SCREAMING_SNAKE_CASE__=6					,				SCREAMING_SNAKE_CASE__=3					,				SCREAMING_SNAKE_CASE__=4					,				SCREAMING_SNAKE_CASE__=None					,				SCREAMING_SNAKE_CASE__=10_00					,				)      ->	List[str]:

					"""simple docstring"""



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

					# LayoutLMv3's sequence length equals the number of text tokens + number of patches + 1 (we add 1 for the CLS token)
					_UpperCamelCase					:Optional[int]					     =	text_seq_length
					_UpperCamelCase					:List[str]					     =	(image_size // patch_size) ** 2 + 1
					_UpperCamelCase					:Union[str, Any]					     =	self.text_seq_length + self.image_seq_length
		def   _UpperCamelCase( self   )      ->	List[str]:

					"""simple docstring"""



					_UpperCamelCase					:Dict					     =	ids_tensor([self.batch_size, self.text_seq_length]					,				self.vocab_size   )

					_UpperCamelCase					:Any					     =	ids_tensor([self.batch_size, self.text_seq_length, 4]					,				self.range_bbox   )
					_UpperCamelCase					:Dict					     =	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					:Dict					     =	bbox[i, j, 3]
														_UpperCamelCase					:List[Any]					     =	bbox[i, j, 1]
														_UpperCamelCase					:str					     =	tmp_coordinate
											if bbox[i, j, 2] < bbox[i, j, 0]:
														_UpperCamelCase					:Dict					     =	bbox[i, j, 2]
														_UpperCamelCase					:List[Any]					     =	bbox[i, j, 0]
														_UpperCamelCase					:Tuple					     =	tmp_coordinate
					_UpperCamelCase					:Dict					     =	tf.constant(SCREAMING_SNAKE_CASE__   )

					_UpperCamelCase					:Dict					     =	floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]   )

					_UpperCamelCase					:str					     =	None
					if self.use_input_mask:
								_UpperCamelCase					:Optional[Any]					     =	random_attention_mask([self.batch_size, self.text_seq_length]   )

					_UpperCamelCase					:Union[str, Any]					     =	None
					if self.use_token_type_ids:
								_UpperCamelCase					:int					     =	ids_tensor([self.batch_size, self.text_seq_length]					,				self.type_vocab_size   )

					_UpperCamelCase					:Any					     =	None
					_UpperCamelCase					:Optional[int]					     =	None
					if self.use_labels:
								_UpperCamelCase					:str					     =	ids_tensor([self.batch_size]					,				self.type_sequence_label_size   )
								_UpperCamelCase					:Optional[Any]					     =	ids_tensor([self.batch_size, self.text_seq_length]					,				self.num_labels   )

					_UpperCamelCase					:Any					     =	LayoutLMvaConfig(
					    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					,				coordinate_size=self.coordinate_size					,				shape_size=self.shape_size					,				input_size=self.image_size					,				patch_size=self.patch_size					,				)

					return config, input_ids, bbox, pixel_values, token_type_ids, input_mask, sequence_labels, token_labels
		def   _UpperCamelCase( self					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__   )      ->	str:

					"""simple docstring"""



					_UpperCamelCase					:List[Any]					     =	TFLayoutLMvaModel(config=SCREAMING_SNAKE_CASE__   )

					# text + image
					_UpperCamelCase					:Union[str, Any]					     =	model(SCREAMING_SNAKE_CASE__					,				pixel_values=SCREAMING_SNAKE_CASE__					,				training=SCREAMING_SNAKE_CASE__   )
					_UpperCamelCase					:str					     =	model(
					    SCREAMING_SNAKE_CASE__					,				bbox=SCREAMING_SNAKE_CASE__					,				pixel_values=SCREAMING_SNAKE_CASE__					,				attention_mask=SCREAMING_SNAKE_CASE__					,				token_type_ids=SCREAMING_SNAKE_CASE__					,				training=SCREAMING_SNAKE_CASE__					,				)
					_UpperCamelCase					:Optional[int]					     =	model(SCREAMING_SNAKE_CASE__					,				bbox=SCREAMING_SNAKE_CASE__					,				pixel_values=SCREAMING_SNAKE_CASE__					,				training=SCREAMING_SNAKE_CASE__   )

					self.parent.assertEqual(result.last_hidden_state.shape					,				(self.batch_size, self.seq_length, self.hidden_size)   )

					# text only
					_UpperCamelCase					:Dict					     =	model(SCREAMING_SNAKE_CASE__					,				training=SCREAMING_SNAKE_CASE__   )
					self.parent.assertEqual(
					    result.last_hidden_state.shape					,				(self.batch_size, self.text_seq_length, self.hidden_size)   )

					# image only
					_UpperCamelCase					:Any					     =	model({'''pixel_values''': pixel_values}					,				training=SCREAMING_SNAKE_CASE__   )
					self.parent.assertEqual(
					    result.last_hidden_state.shape					,				(self.batch_size, self.image_seq_length, self.hidden_size)   )
		def   _UpperCamelCase( self					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__   )      ->	Tuple:

					"""simple docstring"""



					_UpperCamelCase					:List[str]					     =	self.num_labels
					_UpperCamelCase					:Union[str, Any]					     =	TFLayoutLMvaForSequenceClassification(config=SCREAMING_SNAKE_CASE__   )
					_UpperCamelCase					:List[Any]					     =	model(
					    SCREAMING_SNAKE_CASE__					,				bbox=SCREAMING_SNAKE_CASE__					,				pixel_values=SCREAMING_SNAKE_CASE__					,				attention_mask=SCREAMING_SNAKE_CASE__					,				token_type_ids=SCREAMING_SNAKE_CASE__					,				labels=SCREAMING_SNAKE_CASE__					,				training=SCREAMING_SNAKE_CASE__					,				)
					self.parent.assertEqual(result.logits.shape					,				(self.batch_size, self.num_labels)   )
		def   _UpperCamelCase( self					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__   )      ->	Any:

					"""simple docstring"""



					_UpperCamelCase					:Any					     =	self.num_labels
					_UpperCamelCase					:Any					     =	TFLayoutLMvaForTokenClassification(config=SCREAMING_SNAKE_CASE__   )
					_UpperCamelCase					:Union[str, Any]					     =	model(
					    SCREAMING_SNAKE_CASE__					,				bbox=SCREAMING_SNAKE_CASE__					,				pixel_values=SCREAMING_SNAKE_CASE__					,				attention_mask=SCREAMING_SNAKE_CASE__					,				token_type_ids=SCREAMING_SNAKE_CASE__					,				labels=SCREAMING_SNAKE_CASE__					,				training=SCREAMING_SNAKE_CASE__					,				)
					self.parent.assertEqual(result.logits.shape					,				(self.batch_size, self.text_seq_length, self.num_labels)   )
		def   _UpperCamelCase( self					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__   )      ->	str:

					"""simple docstring"""



					_UpperCamelCase					:str					     =	2
					_UpperCamelCase					:List[str]					     =	TFLayoutLMvaForQuestionAnswering(config=SCREAMING_SNAKE_CASE__   )
					_UpperCamelCase					:Optional[int]					     =	model(
					    SCREAMING_SNAKE_CASE__					,				bbox=SCREAMING_SNAKE_CASE__					,				pixel_values=SCREAMING_SNAKE_CASE__					,				attention_mask=SCREAMING_SNAKE_CASE__					,				token_type_ids=SCREAMING_SNAKE_CASE__					,				start_positions=SCREAMING_SNAKE_CASE__					,				end_positions=SCREAMING_SNAKE_CASE__					,				training=SCREAMING_SNAKE_CASE__					,				)
					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   )      ->	Dict:

					"""simple docstring"""



					_UpperCamelCase					:Union[str, Any]					     =	self.prepare_config_and_inputs()
					((_UpperCamelCase)     ,      (_UpperCamelCase)     ,      (_UpperCamelCase)     ,      (_UpperCamelCase)     ,      (_UpperCamelCase)     ,      (_UpperCamelCase)     ,      (_UpperCamelCase)     ,      (_UpperCamelCase))					:List[Any]					     =	config_and_inputs
					_UpperCamelCase					:Optional[int]					     =	{
					    '''input_ids''': input_ids,
					    '''bbox''': bbox,
					    '''pixel_values''': pixel_values,
					    '''token_type_ids''': token_type_ids,
					    '''attention_mask''': input_mask,
					}
					return config, inputs_dict






@require_tf
class      A(  lowerCamelCase__				,							lowerCamelCase__				,							unittest.TestCase		):

		"""simple docstring"""





		A    =							(
		    (
		        TFLayoutLMvaModel,
		        TFLayoutLMvaForQuestionAnswering,
		        TFLayoutLMvaForSequenceClassification,
		        TFLayoutLMvaForTokenClassification,
		    )
		    if is_tf_available()
		    else ()
		)
		A    =							(
		    {"document-question-answering": TFLayoutLMvaForQuestionAnswering, "feature-extraction": TFLayoutLMvaModel}
		    if is_tf_available()
		    else {}
		)

		A    =							False
		A    =							False
		A    =							False
		def   _UpperCamelCase( self					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__   )      ->	int:

					"""simple docstring"""



					return True
		def   _UpperCamelCase( self					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__=False   )      ->	dict:

					"""simple docstring"""



					_UpperCamelCase					:List[str]					     =	copy.deepcopy(SCREAMING_SNAKE_CASE__   )

					if model_class in get_values(SCREAMING_SNAKE_CASE__   ):
								_UpperCamelCase					:Tuple					     =	{
								    k: tf.tile(tf.expand_dims(SCREAMING_SNAKE_CASE__					,				1   )					,				(1, self.model_tester.num_choices) + (1,) * (v.ndim - 1)   )
								    if isinstance(SCREAMING_SNAKE_CASE__					,				tf.Tensor   ) and v.ndim > 0
								    else v
								    for k, v in inputs_dict.items()
								}

					if return_labels:
								if model_class in get_values(SCREAMING_SNAKE_CASE__   ):
											_UpperCamelCase					:Optional[Any]					     =	tf.ones(self.model_tester.batch_size					,				dtype=tf.intaa   )
								elif model_class in get_values(SCREAMING_SNAKE_CASE__   ):
											_UpperCamelCase					:List[Any]					     =	tf.zeros(self.model_tester.batch_size					,				dtype=tf.intaa   )
											_UpperCamelCase					:int					     =	tf.zeros(self.model_tester.batch_size					,				dtype=tf.intaa   )
								elif model_class in get_values(SCREAMING_SNAKE_CASE__   ):
											_UpperCamelCase					:Optional[int]					     =	tf.zeros(self.model_tester.batch_size					,				dtype=tf.intaa   )
								elif model_class in get_values(SCREAMING_SNAKE_CASE__   ):
											_UpperCamelCase					:int					     =	tf.zeros(
											    (self.model_tester.batch_size, self.model_tester.text_seq_length)					,				dtype=tf.intaa   )

					return inputs_dict
		def   _UpperCamelCase( self   )      ->	List[Any]:

					"""simple docstring"""



					_UpperCamelCase					:Dict					     =	TFLayoutLMvaModelTester(self   )
					_UpperCamelCase					:Union[str, Any]					     =	ConfigTester(self					,				config_class=SCREAMING_SNAKE_CASE__					,				hidden_size=37   )
		def   _UpperCamelCase( self   )      ->	Union[str, Any]:

					"""simple docstring"""



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

					"""simple docstring"""



					_UpperCamelCase     ,      _UpperCamelCase					:Tuple					     =	self.model_tester.prepare_config_and_inputs_for_common()
					for model_class in self.all_model_classes:
								_UpperCamelCase					:Union[str, Any]					     =	model_class(SCREAMING_SNAKE_CASE__   )
								if getattr(SCREAMING_SNAKE_CASE__					,				'''hf_compute_loss'''					,				SCREAMING_SNAKE_CASE__   ):
											# The number of elements in the loss should be the same as the number of elements in the label
											_UpperCamelCase					:Tuple					     =	self._prepare_for_class(inputs_dict.copy()					,				SCREAMING_SNAKE_CASE__					,				return_labels=SCREAMING_SNAKE_CASE__   )
											_UpperCamelCase					:Dict					     =	prepared_for_class[
											    sorted(prepared_for_class.keys() - inputs_dict.keys()					,				reverse=SCREAMING_SNAKE_CASE__   )[0]
											]
											_UpperCamelCase					:Optional[Any]					     =	added_label.shape.as_list()[:1]

											# Test that model correctly compute the loss with kwargs
											_UpperCamelCase					:str					     =	self._prepare_for_class(inputs_dict.copy()					,				SCREAMING_SNAKE_CASE__					,				return_labels=SCREAMING_SNAKE_CASE__   )
											_UpperCamelCase					:Any					     =	prepared_for_class.pop('''input_ids'''   )

											_UpperCamelCase					:Tuple					     =	model(SCREAMING_SNAKE_CASE__					,				**SCREAMING_SNAKE_CASE__   )[0]
											self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1]   )

											# Test that model correctly compute the loss when we mask some positions
											_UpperCamelCase					:List[str]					     =	self._prepare_for_class(inputs_dict.copy()					,				SCREAMING_SNAKE_CASE__					,				return_labels=SCREAMING_SNAKE_CASE__   )
											_UpperCamelCase					:str					     =	prepared_for_class.pop('''input_ids'''   )
											if "labels" in prepared_for_class:
														_UpperCamelCase					:Dict					     =	prepared_for_class['''labels'''].numpy()
														if len(labels.shape   ) > 1 and labels.shape[1] != 1:
																	_UpperCamelCase					:Tuple					     =	-1_00
																	_UpperCamelCase					:Optional[Any]					     =	tf.convert_to_tensor(SCREAMING_SNAKE_CASE__   )
																	_UpperCamelCase					:Optional[int]					     =	model(SCREAMING_SNAKE_CASE__					,				**SCREAMING_SNAKE_CASE__   )[0]
																	self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1]   )
																	self.assertTrue(not np.any(np.isnan(loss.numpy()   )   )   )

                # Test that model correctly compute the loss with a dict
											_UpperCamelCase					:Dict					     =	self._prepare_for_class(inputs_dict.copy()					,				SCREAMING_SNAKE_CASE__					,				return_labels=SCREAMING_SNAKE_CASE__   )
											_UpperCamelCase					:str					     =	model(SCREAMING_SNAKE_CASE__   )[0]
											self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1]   )

											# Test that model correctly compute the loss with a tuple
											_UpperCamelCase					:Tuple					     =	self._prepare_for_class(inputs_dict.copy()					,				SCREAMING_SNAKE_CASE__					,				return_labels=SCREAMING_SNAKE_CASE__   )

											# Get keys that were added with the _prepare_for_class function
											_UpperCamelCase					:Any					     =	prepared_for_class.keys() - inputs_dict.keys()
											_UpperCamelCase					:Union[str, Any]					     =	inspect.signature(model.call   ).parameters
											_UpperCamelCase					:Union[str, Any]					     =	list(signature.keys()   )

											# Create a dictionary holding the location of the tensors in the tuple
											_UpperCamelCase					:Optional[Any]					     =	{0: '''input_ids'''}
											for label_key in label_keys:
														_UpperCamelCase					:Tuple					     =	signature_names.index(SCREAMING_SNAKE_CASE__   )
														_UpperCamelCase					:str					     =	label_key
											_UpperCamelCase					:Optional[int]					     =	sorted(tuple_index_mapping.items()   )
											# Initialize a list with their default values, update the values and convert to a tuple
											_UpperCamelCase					:str					     =	[]

											for name in signature_names:
														if name != "kwargs":
																	list_input.append(signature[name].default   )

											for index, value in sorted_tuple_index_mapping:
														_UpperCamelCase					:int					     =	prepared_for_class[value]

											_UpperCamelCase					:Tuple					     =	tuple(SCREAMING_SNAKE_CASE__   )

											# Send to model
											_UpperCamelCase					:Union[str, Any]					     =	model(tuple_input[:-1]   )[0]

											self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1]   )
		def   _UpperCamelCase( self   )      ->	Union[str, Any]:

					"""simple docstring"""



					(
					    (
					    _UpperCamelCase
					)     ,      (
					    _UpperCamelCase
					)     ,      (
					    _UpperCamelCase
					)     ,      (
					    _UpperCamelCase
					)     ,      (
					    _UpperCamelCase
					)     ,      (
					    _UpperCamelCase
					)     ,      (
					    _UpperCamelCase
					)     ,      (
					    _UpperCamelCase
					)     ,      
					)					:Optional[Any]					     =	self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_model(SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__   )
		def   _UpperCamelCase( self   )      ->	Optional[Any]:

					"""simple docstring"""



					(
					    (
					    _UpperCamelCase
					)     ,      (
					    _UpperCamelCase
					)     ,      (
					    _UpperCamelCase
					)     ,      (
					    _UpperCamelCase
					)     ,      (
					    _UpperCamelCase
					)     ,      (
					    _UpperCamelCase
					)     ,      (
					    _UpperCamelCase
					)     ,      (
					    _UpperCamelCase
					)     ,      
					)					:int					     =	self.model_tester.prepare_config_and_inputs()
					for type in ["absolute", "relative_key", "relative_key_query"]:
								_UpperCamelCase					:Dict					     =	type
								self.model_tester.create_and_check_model(SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__   )
		def   _UpperCamelCase( self   )      ->	List[Any]:

					"""simple docstring"""



					(
					    (
					    _UpperCamelCase
					)     ,      (
					    _UpperCamelCase
					)     ,      (
					    _UpperCamelCase
					)     ,      (
					    _UpperCamelCase
					)     ,      (
					    _UpperCamelCase
					)     ,      (
					    _UpperCamelCase
					)     ,      (
					    _UpperCamelCase
					)     ,      (
					    _UpperCamelCase
					)     ,      
					)					:Optional[Any]					     =	self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_sequence_classification(
					    SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__   )
		def   _UpperCamelCase( self   )      ->	Dict:

					"""simple docstring"""



					(
					    (
					    _UpperCamelCase
					)     ,      (
					    _UpperCamelCase
					)     ,      (
					    _UpperCamelCase
					)     ,      (
					    _UpperCamelCase
					)     ,      (
					    _UpperCamelCase
					)     ,      (
					    _UpperCamelCase
					)     ,      (
					    _UpperCamelCase
					)     ,      (
					    _UpperCamelCase
					)     ,      
					)					:Dict					     =	self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_token_classification(
					    SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__   )
		def   _UpperCamelCase( self   )      ->	Optional[int]:

					"""simple docstring"""



					(
					    (
					    _UpperCamelCase
					)     ,      (
					    _UpperCamelCase
					)     ,      (
					    _UpperCamelCase
					)     ,      (
					    _UpperCamelCase
					)     ,      (
					    _UpperCamelCase
					)     ,      (
					    _UpperCamelCase
					)     ,      (
					    _UpperCamelCase
					)     ,      (
					    _UpperCamelCase
					)     ,      
					)					:Optional[int]					     =	self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_question_answering(
					    SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__   )





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

					"""simple docstring"""



					for model_name in TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
								_UpperCamelCase					:Tuple					     =	TFLayoutLMvaModel.from_pretrained(SCREAMING_SNAKE_CASE__   )
								self.assertIsNotNone(SCREAMING_SNAKE_CASE__   )





def 				A_       ( )					->  List[Any]:
			_UpperCamelCase					:Tuple					     =	Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''		)
			return image






@require_tf
class      A(  unittest.TestCase		):

		"""simple docstring"""





		@cached_property
		def   _UpperCamelCase( self   )      ->	Optional[int]:

					"""simple docstring"""



					return LayoutLMvaImageProcessor(apply_ocr=SCREAMING_SNAKE_CASE__   ) if is_vision_available() else None





		@slow
		def   _UpperCamelCase( self   )      ->	Union[str, Any]:

					"""simple docstring"""



					_UpperCamelCase					:int					     =	TFLayoutLMvaModel.from_pretrained('''microsoft/layoutlmv3-base'''   )

					_UpperCamelCase					:str					     =	self.default_image_processor
					_UpperCamelCase					:int					     =	prepare_img()
					_UpperCamelCase					:List[Any]					     =	image_processor(images=SCREAMING_SNAKE_CASE__					,				return_tensors='''tf'''   ).pixel_values

					_UpperCamelCase					:int					     =	tf.constant([[1, 2]]   )
					_UpperCamelCase					:List[str]					     =	tf.expand_dims(tf.constant([[1, 2, 3, 4], [5, 6, 7, 8]]   )					,				axis=0   )

					# forward pass
					_UpperCamelCase					:Dict					     =	model(input_ids=SCREAMING_SNAKE_CASE__					,				bbox=SCREAMING_SNAKE_CASE__					,				pixel_values=SCREAMING_SNAKE_CASE__					,				training=SCREAMING_SNAKE_CASE__   )

					# verify the logits
					_UpperCamelCase					:Dict					     =	(1, 1_99, 7_68)
					self.assertEqual(outputs.last_hidden_state.shape					,				SCREAMING_SNAKE_CASE__   )

					_UpperCamelCase					:List[str]					     =	tf.constant(
					    [[-0.0_5_2_9, 0.3_6_1_8, 0.1_6_3_2], [-0.1_5_8_7, -0.1_6_6_7, -0.0_4_0_0], [-0.1_5_5_7, -0.1_6_7_1, -0.0_5_0_5]]   )

					self.assertTrue(np.allclose(outputs.last_hidden_state[0, :3, :3]					,				SCREAMING_SNAKE_CASE__					,				atol=1E-4   )   )





"""simple docstring"""






def 				A_       ( snake_case__       ,					snake_case__ = " "		)					->  list:
			_UpperCamelCase					:List[str]					     =	[]

			_UpperCamelCase					:int					     =	0
			for index, char in enumerate(snake_case__		):
						if char == separator:
									split_words.append(string[last_index:index]		)
									_UpperCamelCase					:Dict					     =	index + 1
						elif index + 1 == len(snake_case__		):
									split_words.append(string[last_index : index + 1]		)
			return split_words


if __name__ == "__main__":
					from doctest import testmod

					testmod()

