




"""simple docstring"""





from __future__ import annotations

from random import choice

def        _UpperCamelCase     (      UpperCamelCase				)      ->      Optional[Any]:


			"""simple docstring"""
			return choice(UpperCamelCase				)

def        _UpperCamelCase     (      UpperCamelCase					,							UpperCamelCase				)      ->      int:


			"""simple docstring"""
			__UpperCAmelCase						:			List[Any]												=       random_pivot(UpperCamelCase				)

			# partition based on pivot
			# linear time
			__UpperCAmelCase						:			Optional[Any]												=       [e for e in lst if e < pivot]
			__UpperCAmelCase						:			List[str]												=       [e for e in lst if e > pivot]

			# if we get lucky, pivot might be the element we want.
			# we can easily see this:
			# small (elements smaller than k)
			# + pivot (kth element)
			# + big (elements larger than k)
			if len(UpperCamelCase				) == k - 1:
						return pivot
			# pivot is in elements bigger than k
			elif len(UpperCamelCase				) < k - 1:
						return kth_number(UpperCamelCase					,							k - len(UpperCamelCase				) - 1				)
			# pivot is in elements smaller than k
			else:
						return kth_number(UpperCamelCase					,							UpperCamelCase				)


if __name__ == "__main__":
					import doctest

					doctest.testmod()







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    UpperCAmelCase__:

   '''simple docstring'''




   def __init__(    self	:      Dict      ,					lowerCAmelCase	:      Union[str, Any]      ,					lowerCAmelCase	:      Tuple=2      ,					lowerCAmelCase	:      Any=3      ,					lowerCAmelCase	:      Any=4      ,					lowerCAmelCase	:      Optional[Any]=2      ,					lowerCAmelCase	:      Optional[Any]=7      ,					lowerCAmelCase	:      Tuple=True      ,					lowerCAmelCase	:      Optional[int]=True      ,					lowerCAmelCase	:      int=True      ,					lowerCAmelCase	:      Dict=True      ,					lowerCAmelCase	:      Dict=99      ,					lowerCAmelCase	:      Optional[int]=36      ,					lowerCAmelCase	:      List[Any]=2      ,					lowerCAmelCase	:      Dict=4      ,					lowerCAmelCase	:      int=37      ,					lowerCAmelCase	:      Optional[int]="gelu"      ,					lowerCAmelCase	:      str=0.1      ,					lowerCAmelCase	:      str=0.1      ,					lowerCAmelCase	:      List[Any]=5_12      ,					lowerCAmelCase	:      List[str]=16      ,					lowerCAmelCase	:      Union[str, Any]=2      ,					lowerCAmelCase	:      Dict=0.02      ,					lowerCAmelCase	:      Tuple=6      ,					lowerCAmelCase	:      Tuple=6      ,					lowerCAmelCase	:      Dict=3      ,					lowerCAmelCase	:      List[str]=4      ,					lowerCAmelCase	:      List[str]=None      ,					lowerCAmelCase	:      Optional[Any]=10_00      ,					)						->					Dict:




         """simple docstring"""

         lowercase__  =	parent
         lowercase__  =	batch_size
         lowercase__  =	num_channels
         lowercase__  =	image_size
         lowercase__  =	patch_size
         lowercase__  =	is_training
         lowercase__  =	use_input_mask
         lowercase__  =	use_token_type_ids
         lowercase__  =	use_labels
         lowercase__  =	vocab_size
         lowercase__  =	hidden_size
         lowercase__  =	num_hidden_layers
         lowercase__  =	num_attention_heads
         lowercase__  =	intermediate_size
         lowercase__  =	hidden_act
         lowercase__  =	hidden_dropout_prob
         lowercase__  =	attention_probs_dropout_prob
         lowercase__  =	max_position_embeddings
         lowercase__  =	type_vocab_size
         lowercase__  =	type_sequence_label_size
         lowercase__  =	initializer_range
         lowercase__  =	coordinate_size
         lowercase__  =	shape_size
         lowercase__  =	num_labels
         lowercase__  =	num_choices
         lowercase__  =	scope
         lowercase__  =	range_bbox

         # LayoutLMv3's sequence length equals the number of text tokens + number of patches + 1 (we add 1 for the CLS token)
         lowercase__  =	text_seq_length
         lowercase__  =	(image_size // patch_size) ** 2 + 1
         lowercase__  =	self.text_seq_length + self.image_seq_length


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




         """simple docstring"""

         lowercase__  =	ids_tensor([self.batch_size, self.text_seq_length]      ,					self.vocab_size)

         lowercase__  =	ids_tensor([self.batch_size, self.text_seq_length, 4]      ,					self.range_bbox)
         lowercase__  =	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]:
                           lowercase__  =	bbox[i, j, 3]
                           lowercase__  =	bbox[i, j, 1]
                           lowercase__  =	tmp_coordinate
                     if bbox[i, j, 2] < bbox[i, j, 0]:
                           lowercase__  =	bbox[i, j, 2]
                           lowercase__  =	bbox[i, j, 0]
                           lowercase__  =	tmp_coordinate
         lowercase__  =	tf.constant(lowerCAmelCase)

         lowercase__  =	floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

         lowercase__  =	None
         if self.use_input_mask:
               lowercase__  =	random_attention_mask([self.batch_size, self.text_seq_length])

         lowercase__  =	None
         if self.use_token_type_ids:
               lowercase__  =	ids_tensor([self.batch_size, self.text_seq_length]      ,					self.type_vocab_size)

         lowercase__  =	None
         lowercase__  =	None
         if self.use_labels:
               lowercase__  =	ids_tensor([self.batch_size]      ,					self.type_sequence_label_size)
               lowercase__  =	ids_tensor([self.batch_size, self.text_seq_length]      ,					self.num_labels)

         lowercase__  =	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	:      Optional[Any]      ,					lowerCAmelCase	:      Any      ,					lowerCAmelCase	:      str      ,					lowerCAmelCase	:      int      ,					lowerCAmelCase	:      int      ,					lowerCAmelCase	:      Tuple      ,					lowerCAmelCase	:      List[Any])						->					List[str]:




         """simple docstring"""

         lowercase__  =	TFLayoutLMvaModel(config=lowerCAmelCase)

         # text + image
         lowercase__  =	model(lowerCAmelCase      ,					pixel_values=lowerCAmelCase      ,					training=lowerCAmelCase)
         lowercase__  =	model(
             lowerCAmelCase      ,					bbox=lowerCAmelCase      ,					pixel_values=lowerCAmelCase      ,					attention_mask=lowerCAmelCase      ,					token_type_ids=lowerCAmelCase      ,					training=lowerCAmelCase      ,					)
         lowercase__  =	model(lowerCAmelCase      ,					bbox=lowerCAmelCase      ,					pixel_values=lowerCAmelCase      ,					training=lowerCAmelCase)

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

         # text only
         lowercase__  =	model(lowerCAmelCase      ,					training=lowerCAmelCase)
         self.parent.assertEqual(
             result.last_hidden_state.shape      ,					(self.batch_size, self.text_seq_length, self.hidden_size))

         # image only
         lowercase__  =	model({'pixel_values': pixel_values}      ,					training=lowerCAmelCase)
         self.parent.assertEqual(
             result.last_hidden_state.shape      ,					(self.batch_size, self.image_seq_length, self.hidden_size))


   def       UpperCAmelCase		(    self	:      Any      ,					lowerCAmelCase	:      Optional[int]      ,					lowerCAmelCase	:      int      ,					lowerCAmelCase	:      Dict      ,					lowerCAmelCase	:      List[Any]      ,					lowerCAmelCase	:      Optional[Any]      ,					lowerCAmelCase	:      List[str]      ,					lowerCAmelCase	:      List[str])						->					Union[str, Any]:




         """simple docstring"""

         lowercase__  =	self.num_labels
         lowercase__  =	TFLayoutLMvaForSequenceClassification(config=lowerCAmelCase)
         lowercase__  =	model(
             lowerCAmelCase      ,					bbox=lowerCAmelCase      ,					pixel_values=lowerCAmelCase      ,					attention_mask=lowerCAmelCase      ,					token_type_ids=lowerCAmelCase      ,					labels=lowerCAmelCase      ,					training=lowerCAmelCase      ,					)
         self.parent.assertEqual(result.logits.shape      ,					(self.batch_size, self.num_labels))


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




         """simple docstring"""

         lowercase__  =	self.num_labels
         lowercase__  =	TFLayoutLMvaForTokenClassification(config=lowerCAmelCase)
         lowercase__  =	model(
             lowerCAmelCase      ,					bbox=lowerCAmelCase      ,					pixel_values=lowerCAmelCase      ,					attention_mask=lowerCAmelCase      ,					token_type_ids=lowerCAmelCase      ,					labels=lowerCAmelCase      ,					training=lowerCAmelCase      ,					)
         self.parent.assertEqual(result.logits.shape      ,					(self.batch_size, self.text_seq_length, self.num_labels))


   def       UpperCAmelCase		(    self	:      int      ,					lowerCAmelCase	:      int      ,					lowerCAmelCase	:      List[str]      ,					lowerCAmelCase	:      Dict      ,					lowerCAmelCase	:      int      ,					lowerCAmelCase	:      str      ,					lowerCAmelCase	:      List[str]      ,					lowerCAmelCase	:      List[Any])						->					int:




         """simple docstring"""

         lowercase__  =	2
         lowercase__  =	TFLayoutLMvaForQuestionAnswering(config=lowerCAmelCase)
         lowercase__  =	model(
             lowerCAmelCase      ,					bbox=lowerCAmelCase      ,					pixel_values=lowerCAmelCase      ,					attention_mask=lowerCAmelCase      ,					token_type_ids=lowerCAmelCase      ,					start_positions=lowerCAmelCase      ,					end_positions=lowerCAmelCase      ,					training=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       UpperCAmelCase		(    self	:      Dict)						->					Dict:




         """simple docstring"""

         lowercase__  =	self.prepare_config_and_inputs()
         ((lowercase__),						(lowercase__),						(lowercase__),						(lowercase__),						(lowercase__),						(lowercase__),						(lowercase__),						(lowercase__))  =	config_and_inputs
         lowercase__  =	{
             '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    UpperCAmelCase__(				lowerCamelCase  ,				lowerCamelCase  ,				unittest.TestCase						):

   '''simple docstring'''




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

   A     :	Any												=     False
   A     :	Any												=     False
   A     :	Dict												=     False


   def       UpperCAmelCase		(    self	:      int      ,					lowerCAmelCase	:      int      ,					lowerCAmelCase	:      Dict      ,					lowerCAmelCase	:      Any      ,					lowerCAmelCase	:      List[Any]      ,					lowerCAmelCase	:      Dict)						->					Tuple:




         """simple docstring"""

         return True


   def       UpperCAmelCase		(    self	:      Tuple      ,					lowerCAmelCase	:      Union[str, Any]      ,					lowerCAmelCase	:      Optional[Any]      ,					lowerCAmelCase	:      List[str]=False)						->					dict:




         """simple docstring"""

         lowercase__  =	copy.deepcopy(lowerCAmelCase)

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

         if return_labels:
               if model_class in get_values(lowerCAmelCase):
                     lowercase__  =	tf.ones(self.model_tester.batch_size      ,					dtype=tf.intaa)
               elif model_class in get_values(lowerCAmelCase):
                     lowercase__  =	tf.zeros(self.model_tester.batch_size      ,					dtype=tf.intaa)
                     lowercase__  =	tf.zeros(self.model_tester.batch_size      ,					dtype=tf.intaa)
               elif model_class in get_values(lowerCAmelCase):
                     lowercase__  =	tf.zeros(self.model_tester.batch_size      ,					dtype=tf.intaa)
               elif model_class in get_values(lowerCAmelCase):
                     lowercase__  =	tf.zeros(
                         (self.model_tester.batch_size, self.model_tester.text_seq_length)      ,					dtype=tf.intaa)

         return inputs_dict


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




         """simple docstring"""

         lowercase__  =	TFLayoutLMvaModelTester(self)
         lowercase__  =	ConfigTester(self      ,					config_class=lowerCAmelCase      ,					hidden_size=37)


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




         """simple docstring"""

         self.config_tester.run_common_tests()


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




         """simple docstring"""

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

                     # Test that model correctly compute the loss with kwargs
                     lowercase__  =	self._prepare_for_class(inputs_dict.copy()      ,					lowerCAmelCase      ,					return_labels=lowerCAmelCase)
                     lowercase__  =	prepared_for_class.pop('input_ids')

                     lowercase__  =	model(lowerCAmelCase      ,					**lowerCAmelCase)[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
                     lowercase__  =	self._prepare_for_class(inputs_dict.copy()      ,					lowerCAmelCase      ,					return_labels=lowerCAmelCase)
                     lowercase__  =	prepared_for_class.pop('input_ids')
                     if "labels" in prepared_for_class:
                           lowercase__  =	prepared_for_class['labels'].numpy()
                           if len(labels.shape) > 1 and labels.shape[1] != 1:
                                 lowercase__  =	-1_00
                                 lowercase__  =	tf.convert_to_tensor(lowerCAmelCase)
                                 lowercase__  =	model(lowerCAmelCase      ,					**lowerCAmelCase)[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
                     lowercase__  =	self._prepare_for_class(inputs_dict.copy()      ,					lowerCAmelCase      ,					return_labels=lowerCAmelCase)
                     lowercase__  =	model(lowerCAmelCase)[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
                     lowercase__  =	self._prepare_for_class(inputs_dict.copy()      ,					lowerCAmelCase      ,					return_labels=lowerCAmelCase)

                     # Get keys that were added with the _prepare_for_class function
                     lowercase__  =	prepared_for_class.keys() - inputs_dict.keys()
                     lowercase__  =	inspect.signature(model.call).parameters
                     lowercase__  =	list(signature.keys())

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

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

                     for index, value in sorted_tuple_index_mapping:
                           lowercase__  =	prepared_for_class[value]

                     lowercase__  =	tuple(lowerCAmelCase)

                     # Send to model
                     lowercase__  =	model(tuple_input[:-1])[0]

                     self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1])


   def       UpperCAmelCase		(    self	:      str)						->					Any:




         """simple docstring"""

         (
             (
             lowercase__
         ),						(
             lowercase__
         ),						(
             lowercase__
         ),						(
             lowercase__
         ),						(
             lowercase__
         ),						(
             lowercase__
         ),						(
             lowercase__
         ),						(
             lowercase__
         ),						
         )  =	self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(lowerCAmelCase      ,					lowerCAmelCase      ,					lowerCAmelCase      ,					lowerCAmelCase      ,					lowerCAmelCase      ,					lowerCAmelCase)


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




         """simple docstring"""

         (
             (
             lowercase__
         ),						(
             lowercase__
         ),						(
             lowercase__
         ),						(
             lowercase__
         ),						(
             lowercase__
         ),						(
             lowercase__
         ),						(
             lowercase__
         ),						(
             lowercase__
         ),						
         )  =	self.model_tester.prepare_config_and_inputs()
         for type in ["absolute", "relative_key", "relative_key_query"]:
               lowercase__  =	type
               self.model_tester.create_and_check_model(lowerCAmelCase      ,					lowerCAmelCase      ,					lowerCAmelCase      ,					lowerCAmelCase      ,					lowerCAmelCase      ,					lowerCAmelCase)


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




         """simple docstring"""

         (
             (
             lowercase__
         ),						(
             lowercase__
         ),						(
             lowercase__
         ),						(
             lowercase__
         ),						(
             lowercase__
         ),						(
             lowercase__
         ),						(
             lowercase__
         ),						(
             lowercase__
         ),						
         )  =	self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_sequence_classification(
             lowerCAmelCase      ,					lowerCAmelCase      ,					lowerCAmelCase      ,					lowerCAmelCase      ,					lowerCAmelCase      ,					lowerCAmelCase      ,					lowerCAmelCase)


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




         """simple docstring"""

         (
             (
             lowercase__
         ),						(
             lowercase__
         ),						(
             lowercase__
         ),						(
             lowercase__
         ),						(
             lowercase__
         ),						(
             lowercase__
         ),						(
             lowercase__
         ),						(
             lowercase__
         ),						
         )  =	self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_token_classification(
             lowerCAmelCase      ,					lowerCAmelCase      ,					lowerCAmelCase      ,					lowerCAmelCase      ,					lowerCAmelCase      ,					lowerCAmelCase      ,					lowerCAmelCase)


   def       UpperCAmelCase		(    self	:      int)						->					Dict:




         """simple docstring"""

         (
             (
             lowercase__
         ),						(
             lowercase__
         ),						(
             lowercase__
         ),						(
             lowercase__
         ),						(
             lowercase__
         ),						(
             lowercase__
         ),						(
             lowercase__
         ),						(
             lowercase__
         ),						
         )  =	self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_question_answering(
             lowerCAmelCase      ,					lowerCAmelCase      ,					lowerCAmelCase      ,					lowerCAmelCase      ,					lowerCAmelCase      ,					lowerCAmelCase      ,					lowerCAmelCase)



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




         """simple docstring"""

         for model_name in TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
               lowercase__  =	TFLayoutLMvaModel.from_pretrained(lowerCAmelCase)
               self.assertIsNotNone(lowerCAmelCase)

def 	_lowerCAmelCase (    ):
      lowercase__  =	Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'						)
      return image



@require_tf
class    UpperCAmelCase__(				unittest.TestCase						):

   '''simple docstring'''




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




         """simple docstring"""

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



   @slow
   def       UpperCAmelCase		(    self	:      str)						->					str:




         """simple docstring"""

         lowercase__  =	TFLayoutLMvaModel.from_pretrained('microsoft/layoutlmv3-base')

         lowercase__  =	self.default_image_processor
         lowercase__  =	prepare_img()
         lowercase__  =	image_processor(images=lowerCAmelCase      ,					return_tensors='tf').pixel_values

         lowercase__  =	tf.constant([[1, 2]])
         lowercase__  =	tf.expand_dims(tf.constant([[1, 2, 3, 4], [5, 6, 7, 8]])      ,					axis=0)

         # forward pass
         lowercase__  =	model(input_ids=lowerCAmelCase      ,					bbox=lowerCAmelCase      ,					pixel_values=lowerCAmelCase      ,					training=lowerCAmelCase)

         # verify the logits
         lowercase__  =	(1, 1_99, 7_68)
         self.assertEqual(outputs.last_hidden_state.shape      ,					lowerCAmelCase)

         lowercase__  =	tf.constant(
             [[-0.05_29, 0.36_18, 0.16_32], [-0.15_87, -0.16_67, -0.04_00], [-0.15_57, -0.16_71, -0.05_05]])

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