






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		:







			def __init__(	self:  Any      ,	UpperCAmelCase_:  Union[str, Any]      ,	UpperCAmelCase_:  Any=2      ,	UpperCAmelCase_:  int=3      ,	UpperCAmelCase_:  str=4      ,	UpperCAmelCase_:  Union[str, Any]=2      ,	UpperCAmelCase_:  List[str]=7      ,	UpperCAmelCase_:  str=True      ,	UpperCAmelCase_:  List[Any]=True      ,	UpperCAmelCase_:  Any=True      ,	UpperCAmelCase_:  Dict=True      ,	UpperCAmelCase_:  Any=99      ,	UpperCAmelCase_:  Dict=36      ,	UpperCAmelCase_:  Optional[int]=2      ,	UpperCAmelCase_:  Any=4      ,	UpperCAmelCase_:  Optional[Any]=37      ,	UpperCAmelCase_:  Dict="gelu"      ,	UpperCAmelCase_:  int=0.1      ,	UpperCAmelCase_:  Optional[int]=0.1      ,	UpperCAmelCase_:  Tuple=512      ,	UpperCAmelCase_:  str=16      ,	UpperCAmelCase_:  Optional[Any]=2      ,	UpperCAmelCase_:  Optional[Any]=0.02      ,	UpperCAmelCase_:  Union[str, Any]=6      ,	UpperCAmelCase_:  str=6      ,	UpperCAmelCase_:  Any=3      ,	UpperCAmelCase_:  Optional[int]=4      ,	UpperCAmelCase_:  Optional[Any]=None      ,	UpperCAmelCase_:  Optional[Any]=1_000      ,	):







									'''simple docstring'''




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

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







			def 							UpperCamelCase      (	self:  List[str]     ):







									'''simple docstring'''




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

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

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

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

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

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

									_SCREAMING_SNAKE_CASE	      =  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:  Tuple      ,	UpperCAmelCase_:  Optional[Any]      ,	UpperCAmelCase_:  str      ,	UpperCAmelCase_:  List[str]      ,	UpperCAmelCase_:  Tuple      ,	UpperCAmelCase_:  str      ,	UpperCAmelCase_:  List[Any]     ):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE	      =  TFLayoutLMvaModel(config=__lowerCamelCase     )

									# text + image
									_SCREAMING_SNAKE_CASE	      =  model(__lowerCamelCase      ,	pixel_values=__lowerCamelCase      ,	training=__lowerCamelCase     )
									_SCREAMING_SNAKE_CASE	      =  model(
									    __lowerCamelCase      ,	bbox=__lowerCamelCase      ,	pixel_values=__lowerCamelCase      ,	attention_mask=__lowerCamelCase      ,	token_type_ids=__lowerCamelCase      ,	training=__lowerCamelCase      ,	)
									_SCREAMING_SNAKE_CASE	      =  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
									_SCREAMING_SNAKE_CASE	      =  model(__lowerCamelCase      ,	training=__lowerCamelCase     )
									self.parent.assertEqual(
									    result.last_hidden_state.shape      ,	(self.batch_size, self.text_seq_length, self.hidden_size)     )

									# image only
									_SCREAMING_SNAKE_CASE	      =  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:  List[Any]      ,	UpperCAmelCase_:  List[str]      ,	UpperCAmelCase_:  str      ,	UpperCAmelCase_:  Tuple      ,	UpperCAmelCase_:  int      ,	UpperCAmelCase_:  str      ,	UpperCAmelCase_:  Union[str, Any]      ,	UpperCAmelCase_:  Any     ):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE	      =  self.num_labels
									_SCREAMING_SNAKE_CASE	      =  TFLayoutLMvaForSequenceClassification(config=__lowerCamelCase     )
									_SCREAMING_SNAKE_CASE	      =  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:  Optional[int]      ,	UpperCAmelCase_:  Optional[Any]      ,	UpperCAmelCase_:  Dict      ,	UpperCAmelCase_:  List[str]      ,	UpperCAmelCase_:  Optional[Any]      ,	UpperCAmelCase_:  Union[str, Any]      ,	UpperCAmelCase_:  Optional[int]      ,	UpperCAmelCase_:  Optional[Any]     ):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE	      =  self.num_labels
									_SCREAMING_SNAKE_CASE	      =  TFLayoutLMvaForTokenClassification(config=__lowerCamelCase     )
									_SCREAMING_SNAKE_CASE	      =  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:  Tuple      ,	UpperCAmelCase_:  int      ,	UpperCAmelCase_:  Any      ,	UpperCAmelCase_:  int      ,	UpperCAmelCase_:  int      ,	UpperCAmelCase_:  List[Any]      ,	UpperCAmelCase_:  str      ,	UpperCAmelCase_:  Union[str, Any]     ):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE	      =  2
									_SCREAMING_SNAKE_CASE	      =  TFLayoutLMvaForQuestionAnswering(config=__lowerCamelCase     )
									_SCREAMING_SNAKE_CASE	      =  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:  Any     ):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE	      =  self.prepare_config_and_inputs()
									(_SCREAMING_SNAKE_CASE)	      =  config_and_inputs
									_SCREAMING_SNAKE_CASE	      =  {
									    "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		(lowercase__       ,lowercase__       ,unittest.TestCase    ):
			__snake_case				:      Optional[int] 						=							(
			    (
			        TFLayoutLMvaModel,
			        TFLayoutLMvaForQuestionAnswering,
			        TFLayoutLMvaForSequenceClassification,
			        TFLayoutLMvaForTokenClassification,
			    )
			    if is_tf_available()
			    else ()
			)
			__snake_case				:      str 						=							(
			    {"document-question-answering": TFLayoutLMvaForQuestionAnswering, "feature-extraction": TFLayoutLMvaModel}
			    if is_tf_available()
			    else {}
			)

			__snake_case				:      Optional[int] 						=							False
			__snake_case				:      List[Any] 						=							False
			__snake_case				:      Optional[int] 						=							False







			def 							UpperCamelCase      (	self:  str      ,	UpperCAmelCase_:  List[str]      ,	UpperCAmelCase_:  List[str]      ,	UpperCAmelCase_:  Union[str, Any]      ,	UpperCAmelCase_:  Optional[Any]      ,	UpperCAmelCase_:  Tuple     ):







									'''simple docstring'''




									return True







			def 							UpperCamelCase      (	self:  int      ,	UpperCAmelCase_:  str      ,	UpperCAmelCase_:  List[Any]      ,	UpperCAmelCase_:  List[Any]=False     ):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE	      =  copy.deepcopy(__lowerCamelCase     )

									if model_class in get_values(__lowerCamelCase     ):
															_SCREAMING_SNAKE_CASE	      =  {
															    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     ):
																					_SCREAMING_SNAKE_CASE	      =  tf.ones(self.model_tester.batch_size      ,	dtype=tf.intaa     )
															elif model_class in get_values(__lowerCamelCase     ):
																					_SCREAMING_SNAKE_CASE	      =  tf.zeros(self.model_tester.batch_size      ,	dtype=tf.intaa     )
																					_SCREAMING_SNAKE_CASE	      =  tf.zeros(self.model_tester.batch_size      ,	dtype=tf.intaa     )
															elif model_class in get_values(__lowerCamelCase     ):
																					_SCREAMING_SNAKE_CASE	      =  tf.zeros(self.model_tester.batch_size      ,	dtype=tf.intaa     )
															elif model_class in get_values(__lowerCamelCase     ):
																					_SCREAMING_SNAKE_CASE	      =  tf.zeros(
																					    (self.model_tester.batch_size, self.model_tester.text_seq_length)      ,	dtype=tf.intaa     )

									return inputs_dict







			def 							UpperCamelCase      (	self:  Optional[int]     ):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE	      =  TFLayoutLMvaModelTester(self     )
									_SCREAMING_SNAKE_CASE	      =  ConfigTester(self      ,	config_class=__lowerCamelCase      ,	hidden_size=37     )







			def 							UpperCamelCase      (	self:  str     ):







									'''simple docstring'''




									self.config_tester.run_common_tests()







			def 							UpperCamelCase      (	self:  str     ):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE	      =  self.model_tester.prepare_config_and_inputs_for_common()
									for model_class in self.all_model_classes:
															_SCREAMING_SNAKE_CASE	      =  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
																					_SCREAMING_SNAKE_CASE	      =  self._prepare_for_class(inputs_dict.copy()      ,	__lowerCamelCase      ,	return_labels=__lowerCamelCase     )
																					_SCREAMING_SNAKE_CASE	      =  prepared_for_class[
																					    sorted(prepared_for_class.keys() - inputs_dict.keys()      ,	reverse=__lowerCamelCase     )[0]
																					]
																					_SCREAMING_SNAKE_CASE	      =  added_label.shape.as_list()[:1]

																					# Test that model correctly compute the loss with kwargs
																					_SCREAMING_SNAKE_CASE	      =  self._prepare_for_class(inputs_dict.copy()      ,	__lowerCamelCase      ,	return_labels=__lowerCamelCase     )
																					_SCREAMING_SNAKE_CASE	      =  prepared_for_class.pop("""input_ids"""     )

																					_SCREAMING_SNAKE_CASE	      =  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
																					_SCREAMING_SNAKE_CASE	      =  self._prepare_for_class(inputs_dict.copy()      ,	__lowerCamelCase      ,	return_labels=__lowerCamelCase     )
																					_SCREAMING_SNAKE_CASE	      =  prepared_for_class.pop("""input_ids"""     )
																					if "labels" in prepared_for_class:
																											_SCREAMING_SNAKE_CASE	      =  prepared_for_class["labels"].numpy()
																											if len(labels.shape     ) > 1 and labels.shape[1] != 1:
																																	_SCREAMING_SNAKE_CASE	      =  -100
																																	_SCREAMING_SNAKE_CASE	      =  tf.convert_to_tensor(__lowerCamelCase     )
																																	_SCREAMING_SNAKE_CASE	      =  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
																					_SCREAMING_SNAKE_CASE	      =  self._prepare_for_class(inputs_dict.copy()      ,	__lowerCamelCase      ,	return_labels=__lowerCamelCase     )
																					_SCREAMING_SNAKE_CASE	      =  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
																					_SCREAMING_SNAKE_CASE	      =  self._prepare_for_class(inputs_dict.copy()      ,	__lowerCamelCase      ,	return_labels=__lowerCamelCase     )

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

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

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

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

																					_SCREAMING_SNAKE_CASE	      =  tuple(__lowerCamelCase     )

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

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







			def 							UpperCamelCase      (	self:  Optional[int]     ):







									'''simple docstring'''




									(
									    _SCREAMING_SNAKE_CASE   
									)	      =  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]     ):







									'''simple docstring'''




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







			def 							UpperCamelCase      (	self:  Dict     ):







									'''simple docstring'''




									(
									    _SCREAMING_SNAKE_CASE   
									)	      =  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[Any]     ):







									'''simple docstring'''




									(
									    _SCREAMING_SNAKE_CASE   
									)	      =  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:  Optional[int]     ):







									'''simple docstring'''




									(
									    _SCREAMING_SNAKE_CASE   
									)	      =  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:  int     ):







									'''simple docstring'''




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

def        __lowerCamelCase			(		)							->  str:






						"""simple docstring"""





						_SCREAMING_SNAKE_CASE	      =  Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""		)
						return image




@require_tf
class       __UpperCAmelCase		(unittest.TestCase    ):







			@cached_property
			def 							UpperCamelCase      (	self:  int     ):







									'''simple docstring'''




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







			@slow
			def 							UpperCamelCase      (	self:  str     ):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE	      =  TFLayoutLMvaModel.from_pretrained("""microsoft/layoutlmv3-base"""     )

									_SCREAMING_SNAKE_CASE	      =  self.default_image_processor
									_SCREAMING_SNAKE_CASE	      =  prepare_img()
									_SCREAMING_SNAKE_CASE	      =  image_processor(images=__lowerCamelCase      ,	return_tensors="""tf"""     ).pixel_values

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

									# forward pass
									_SCREAMING_SNAKE_CASE	      =  model(input_ids=__lowerCamelCase      ,	bbox=__lowerCamelCase      ,	pixel_values=__lowerCamelCase      ,	training=__lowerCamelCase     )

									# verify the logits
									_SCREAMING_SNAKE_CASE	      =  (1, 199, 768)
									self.assertEqual(outputs.last_hidden_state.shape      ,	__lowerCamelCase     )

									_SCREAMING_SNAKE_CASE	      =  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     )     )





import math
import random
from typing import Any

from .hill_climbing import SearchProblem

def       lowercase_		(		_A			:    Optional[int]    ,					_A			:    bool = True    ,					_A			:    float = math.inf    ,					_A			:    float = -math.inf    ,					_A			:    float = math.inf    ,					_A			:    float = -math.inf    ,					_A			:    bool = False    ,					_A			:    float = 100    ,					_A			:    float = 0.01    ,					_A			:    float = 1    ,					):

      """simple docstring"""




      lowerCamelCase__		:				List[Any]    =  False
      lowerCamelCase__		:				Optional[Any]    =  search_prob
      lowerCamelCase__		:				List[str]    =  start_temperate
      lowerCamelCase__		:				Optional[int]    =  []
      lowerCamelCase__		:				Any    =  0
      lowerCamelCase__		:				Optional[Any]    =  None

      while not search_end:
            lowerCamelCase__		:				Optional[Any]    =  current_state.score()
            if best_state is None or current_score > best_state.score():
                  lowerCamelCase__		:				List[Any]    =  current_state
            scores.append(_A						)
            iterations += 1
            lowerCamelCase__		:				List[Any]    =  None
            lowerCamelCase__		:				Optional[Any]    =  current_state.get_neighbors()
            while (
                next_state is None and neighbors
            ):  # till we do not find a neighbor that we can move to
                  lowerCamelCase__		:				Any    =  random.randint(0    ,					len(_A						) - 1						)  # picking a random neighbor
                  lowerCamelCase__		:				List[Any]    =  neighbors.pop(_A						)
                  lowerCamelCase__		:				Dict    =  picked_neighbor.score() - current_score

                  if (
                      picked_neighbor.x > max_x
                      or picked_neighbor.x < min_x
                      or picked_neighbor.y > max_y
                      or picked_neighbor.y < min_y
                  ):
                        continue  # neighbor outside our bounds

                  if not find_max:
                        lowerCamelCase__		:				Dict    =  change * -1  # in case we are finding minimum
                  if change > 0:  # improves the solution
                        lowerCamelCase__		:				Optional[Any]    =  picked_neighbor
                  else:
                        lowerCamelCase__		:				str    =  (math.e) ** (
                            change / current_temp
                        )  # probability generation function
                        if random.random() < probability:  # random number within probability
                              lowerCamelCase__		:				Optional[int]    =  picked_neighbor
            lowerCamelCase__		:				Any    =  current_temp - (current_temp * rate_of_decrease)

            if current_temp < threshold_temp or next_state is None:
                  # temperature below threshold, or could not find a suitable neighbor
                  lowerCamelCase__		:				int    =  True
            else:
                  lowerCamelCase__		:				Optional[int]    =  next_state

      if visualization:
            from matplotlib import pyplot as plt

            plt.plot(range(_A						)    ,					_A						)
            plt.xlabel("Iterations"						)
            plt.ylabel("Function values"						)
            plt.show()
      return best_state


if __name__ == "__main__":

    def       lowercase_		(		_A			:    List[str]    ,					_A			:    int						):

          """simple docstring"""




          return (x**2) + (y**2)

    # starting the problem with initial coordinates (12, 47)
    A      :    Any		=					SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_fa)
    A      :    Dict		=					simulated_annealing(
        prob, find_max=False, max_x=100, min_x=5, max_y=50, min_y=-5, visualization=True
    )
    print(
        "The minimum score for f(x, y) = x^2 + y^2 with the domain 100 > x > 5 "
        f'and 50 > y > - 5 found via hill climbing: {local_min.score()}'
    )

    # starting the problem with initial coordinates (12, 47)
    A      :    str		=					SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_fa)
    A      :    int		=					simulated_annealing(
        prob, find_max=True, max_x=100, min_x=5, max_y=50, min_y=-5, visualization=True
    )
    print(
        "The maximum score for f(x, y) = x^2 + y^2 with the domain 100 > x > 5 "
        f'and 50 > y > - 5 found via hill climbing: {local_min.score()}'
    )

    def       lowercase_		(		_A			:    int    ,					_A			:    Any						):

          """simple docstring"""




          return (3 * x**2) - (6 * y)

    A      :    Optional[int]		=					SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
    A      :    Union[str, Any]		=					simulated_annealing(prob, find_max=False, visualization=True)
    print(
        "The minimum score for f(x, y) = 3*x^2 - 6*y found via hill climbing: "
        f'{local_min.score()}'
    )

    A      :    Any		=					SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
    A      :    Optional[int]		=					simulated_annealing(prob, find_max=True, visualization=True)


    print(
        "The maximum score for f(x, y) = 3*x^2 - 6*y found via hill climbing: "
        f'{local_min.score()}'
    )


