






"""simple docstring"""

from math import sqrt

import numpy as np
from sympy import symbols

# Coefficient
# Speed of light (m/s)
lowercase    			=       299792458

# Symbols
lowercase			,      lowercase			,      lowercase			,      lowercase    			=       symbols('''ct x y z''')






def _lowerCAmelCase	(  __lowerCamelCase:float   ):

						'''simple docstring'''






						if velocity > c:
												raise ValueError("Speed must not exceed light speed 299,792,458 [m/s]!"   )
						elif velocity < 1:
												# Usually the speed should be much higher than 1 (c order of magnitude)
												raise ValueError("Speed must be greater than or equal to 1!"   )

						return velocity / c






def _lowerCAmelCase	(  __lowerCamelCase:float   ):

						'''simple docstring'''






						return 1 / sqrt(1 - beta(__lowerCamelCase   ) ** 2   )






def _lowerCAmelCase	(  __lowerCamelCase:float   ):

						'''simple docstring'''






						return np.array(
						    [
						        [gamma(__lowerCamelCase   ), -gamma(__lowerCamelCase   ) * beta(__lowerCamelCase   ), 0, 0],
						        [-gamma(__lowerCamelCase   ) * beta(__lowerCamelCase   ), gamma(__lowerCamelCase   ), 0, 0],
						        [0, 0, 1, 0],
						        [0, 0, 0, 1],
						    ]   )






def _lowerCAmelCase	(  __lowerCamelCase:float ,						__lowerCamelCase:np.ndarray | None = None   ):

						'''simple docstring'''






						if event is None:
												__magic_name__            =     np.array([ct, x, y, z]   )  # Symbolic four vector
						else:
												event[0] *= c  # x0 is ct (speed of light * time)

						return transformation_matrix(__lowerCamelCase   ) @ event


if __name__ == "__main__":
			import doctest

			doctest.testmod()

			# Example of symbolic vector:
			lowercase    			=       transform(29979245)
			print('''Example of four vector: ''')
			print(f'''ct\' = {four_vector[0]}''')
			print(f'''x\' = {four_vector[1]}''')
			print(f'''y\' = {four_vector[2]}''')
			print(f'''z\' = {four_vector[3]}''')

			# Substitute symbols with numerical values
			lowercase    			=       {ct: c, x: 1, y: 1, z: 1}
			lowercase    			=       [four_vector[i].subs(sub_dict) for i in range(4)]

			print(f'''\n{numerical_vector}''')











"""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_							:







				def __init__(							self							:						int						,    __lowerCamelCase							:						int						,    __lowerCamelCase							:						Optional[int]=2						,    __lowerCamelCase							:						Any=3						,    __lowerCamelCase							:						Any=4						,    __lowerCamelCase							:						str=2						,    __lowerCamelCase							:						int=7						,    __lowerCamelCase							:						Union[str, Any]=True						,    __lowerCamelCase							:						Optional[Any]=True						,    __lowerCamelCase							:						List[Any]=True						,    __lowerCamelCase							:						Dict=True						,    __lowerCamelCase							:						Any=9_9						,    __lowerCamelCase							:						int=3_6						,    __lowerCamelCase							:						Dict=2						,    __lowerCamelCase							:						int=4						,    __lowerCamelCase							:						Dict=3_7						,    __lowerCamelCase							:						str="gelu"						,    __lowerCamelCase							:						int=0.1						,    __lowerCamelCase							:						Optional[int]=0.1						,    __lowerCamelCase							:						int=5_1_2						,    __lowerCamelCase							:						Optional[Any]=1_6						,    __lowerCamelCase							:						Any=2						,    __lowerCamelCase							:						Union[str, Any]=0.02						,    __lowerCamelCase							:						List[Any]=6						,    __lowerCamelCase							:						Tuple=6						,    __lowerCamelCase							:						Optional[int]=3						,    __lowerCamelCase							:						Any=4						,    __lowerCamelCase							:						Optional[Any]=None						,    __lowerCamelCase							:						Optional[int]=1_0_0_0						,    )				->					int:
										__magic_name__            =     parent
										__magic_name__            =     batch_size
										__magic_name__            =     num_channels
										__magic_name__            =     image_size
										__magic_name__            =     patch_size
										__magic_name__            =     is_training
										__magic_name__            =     use_input_mask
										__magic_name__            =     use_token_type_ids
										__magic_name__            =     use_labels
										__magic_name__            =     vocab_size
										__magic_name__            =     hidden_size
										__magic_name__            =     num_hidden_layers
										__magic_name__            =     num_attention_heads
										__magic_name__            =     intermediate_size
										__magic_name__            =     hidden_act
										__magic_name__            =     hidden_dropout_prob
										__magic_name__            =     attention_probs_dropout_prob
										__magic_name__            =     max_position_embeddings
										__magic_name__            =     type_vocab_size
										__magic_name__            =     type_sequence_label_size
										__magic_name__            =     initializer_range
										__magic_name__            =     coordinate_size
										__magic_name__            =     shape_size
										__magic_name__            =     num_labels
										__magic_name__            =     num_choices
										__magic_name__            =     scope
										__magic_name__            =     range_bbox

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







				def     _snake_case					(							self							:						Optional[Any]	)				->					List[Any]:
										__magic_name__            =     ids_tensor([self.batch_size, self.text_seq_length]						,    self.vocab_size	)

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

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

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

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

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

										__magic_name__            =     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     _snake_case					(							self							:						Dict						,    __lowerCamelCase							:						Optional[Any]						,    __lowerCamelCase							:						Optional[int]						,    __lowerCamelCase							:						Tuple						,    __lowerCamelCase							:						Optional[Any]						,    __lowerCamelCase							:						Any						,    __lowerCamelCase							:						str	)				->					Union[str, Any]:
										__magic_name__            =     TFLayoutLMvaModel(config=__lowerCamelCase	)

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

										# image only
										__magic_name__            =     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     _snake_case					(							self							:						Any						,    __lowerCamelCase							:						List[Any]						,    __lowerCamelCase							:						List[str]						,    __lowerCamelCase							:						Optional[Any]						,    __lowerCamelCase							:						Optional[Any]						,    __lowerCamelCase							:						Any						,    __lowerCamelCase							:						List[Any]						,    __lowerCamelCase							:						Any	)				->					List[str]:
										__magic_name__            =     self.num_labels
										__magic_name__            =     TFLayoutLMvaForSequenceClassification(config=__lowerCamelCase	)
										__magic_name__            =     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     _snake_case					(							self							:						Any						,    __lowerCamelCase							:						Optional[int]						,    __lowerCamelCase							:						List[str]						,    __lowerCamelCase							:						str						,    __lowerCamelCase							:						List[Any]						,    __lowerCamelCase							:						List[Any]						,    __lowerCamelCase							:						Dict						,    __lowerCamelCase							:						List[Any]	)				->					Dict:
										__magic_name__            =     self.num_labels
										__magic_name__            =     TFLayoutLMvaForTokenClassification(config=__lowerCamelCase	)
										__magic_name__            =     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     _snake_case					(							self							:						Union[str, Any]						,    __lowerCamelCase							:						Union[str, Any]						,    __lowerCamelCase							:						Union[str, Any]						,    __lowerCamelCase							:						Optional[Any]						,    __lowerCamelCase							:						Optional[Any]						,    __lowerCamelCase							:						Tuple						,    __lowerCamelCase							:						Any						,    __lowerCamelCase							:						Optional[int]	)				->					List[Any]:
										__magic_name__            =     2
										__magic_name__            =     TFLayoutLMvaForQuestionAnswering(config=__lowerCamelCase	)
										__magic_name__            =     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     _snake_case					(							self							:						Optional[int]	)				->					str:
										__magic_name__            =     self.prepare_config_and_inputs()
										((__magic_name__)      ,    (__magic_name__)      ,    (__magic_name__)      ,    (__magic_name__)      ,    (__magic_name__)      ,    (__magic_name__)      ,    (__magic_name__)      ,    (__magic_name__))            =     config_and_inputs
										__magic_name__            =     {
										    "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_							(      snake_case_					,							snake_case_					,							unittest.TestCase  ):
				UpperCAmelCase__        =				(
				    (
				        TFLayoutLMvaModel,
				        TFLayoutLMvaForQuestionAnswering,
				        TFLayoutLMvaForSequenceClassification,
				        TFLayoutLMvaForTokenClassification,
				    )
				    if is_tf_available()
				    else ()
				)
				UpperCAmelCase__        =				(
				    {'''document-question-answering''': TFLayoutLMvaForQuestionAnswering, '''feature-extraction''': TFLayoutLMvaModel}
				    if is_tf_available()
				    else {}
				)

				UpperCAmelCase__        =				False
				UpperCAmelCase__        =				False
				UpperCAmelCase__        =				False







				def     _snake_case					(							self							:						str						,    __lowerCamelCase							:						int						,    __lowerCamelCase							:						Optional[int]						,    __lowerCamelCase							:						List[Any]						,    __lowerCamelCase							:						Any						,    __lowerCamelCase							:						Any	)				->					Union[str, Any]:
										return True







				def     _snake_case					(							self							:						Union[str, Any]						,    __lowerCamelCase							:						str						,    __lowerCamelCase							:						Union[str, Any]						,    __lowerCamelCase							:						Optional[int]=False	)				->					dict:
										__magic_name__            =     copy.deepcopy(__lowerCamelCase	)

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

										return inputs_dict







				def     _snake_case					(							self							:						List[str]	)				->					str:
										__magic_name__            =     TFLayoutLMvaModelTester(self	)
										__magic_name__            =     ConfigTester(self						,    config_class=__lowerCamelCase						,    hidden_size=3_7	)







				def     _snake_case					(							self							:						List[Any]	)				->					List[str]:
										self.config_tester.run_common_tests()







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

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

																						__magic_name__            =     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
																						__magic_name__            =     self._prepare_for_class(inputs_dict.copy()						,    __lowerCamelCase						,    return_labels=__lowerCamelCase	)
																						__magic_name__            =     prepared_for_class.pop("input_ids"	)
																						if "labels" in prepared_for_class:
																												__magic_name__            =     prepared_for_class["labels"].numpy()
																												if len(labels.shape	) > 1 and labels.shape[1] != 1:
																																		__magic_name__            =     -1_0_0
																																		__magic_name__            =     tf.convert_to_tensor(__lowerCamelCase	)
																																		__magic_name__            =     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
																						__magic_name__            =     self._prepare_for_class(inputs_dict.copy()						,    __lowerCamelCase						,    return_labels=__lowerCamelCase	)
																						__magic_name__            =     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
																						__magic_name__            =     self._prepare_for_class(inputs_dict.copy()						,    __lowerCamelCase						,    return_labels=__lowerCamelCase	)

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

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

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

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

																						__magic_name__            =     tuple(__lowerCamelCase	)

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

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







				def     _snake_case					(							self							:						Dict	)				->					Dict:
										(
										    (
										    __magic_name__
										)      ,    (
										    __magic_name__
										)      ,    (
										    __magic_name__
										)      ,    (
										    __magic_name__
										)      ,    (
										    __magic_name__
										)      ,    (
										    __magic_name__
										)      ,    (
										    __magic_name__
										)      ,    (
										    __magic_name__
										)      ,    
										)            =     self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(__lowerCamelCase						,    __lowerCamelCase						,    __lowerCamelCase						,    __lowerCamelCase						,    __lowerCamelCase						,    __lowerCamelCase	)







				def     _snake_case					(							self							:						List[str]	)				->					int:
										(
										    (
										    __magic_name__
										)      ,    (
										    __magic_name__
										)      ,    (
										    __magic_name__
										)      ,    (
										    __magic_name__
										)      ,    (
										    __magic_name__
										)      ,    (
										    __magic_name__
										)      ,    (
										    __magic_name__
										)      ,    (
										    __magic_name__
										)      ,    
										)            =     self.model_tester.prepare_config_and_inputs()
										for type in ["absolute", "relative_key", "relative_key_query"]:
																__magic_name__            =     type
																self.model_tester.create_and_check_model(__lowerCamelCase						,    __lowerCamelCase						,    __lowerCamelCase						,    __lowerCamelCase						,    __lowerCamelCase						,    __lowerCamelCase	)







				def     _snake_case					(							self							:						Dict	)				->					List[Any]:
										(
										    (
										    __magic_name__
										)      ,    (
										    __magic_name__
										)      ,    (
										    __magic_name__
										)      ,    (
										    __magic_name__
										)      ,    (
										    __magic_name__
										)      ,    (
										    __magic_name__
										)      ,    (
										    __magic_name__
										)      ,    (
										    __magic_name__
										)      ,    
										)            =     self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_sequence_classification(
										    __lowerCamelCase						,    __lowerCamelCase						,    __lowerCamelCase						,    __lowerCamelCase						,    __lowerCamelCase						,    __lowerCamelCase						,    __lowerCamelCase	)







				def     _snake_case					(							self							:						Any	)				->					List[str]:
										(
										    (
										    __magic_name__
										)      ,    (
										    __magic_name__
										)      ,    (
										    __magic_name__
										)      ,    (
										    __magic_name__
										)      ,    (
										    __magic_name__
										)      ,    (
										    __magic_name__
										)      ,    (
										    __magic_name__
										)      ,    (
										    __magic_name__
										)      ,    
										)            =     self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_token_classification(
										    __lowerCamelCase						,    __lowerCamelCase						,    __lowerCamelCase						,    __lowerCamelCase						,    __lowerCamelCase						,    __lowerCamelCase						,    __lowerCamelCase	)







				def     _snake_case					(							self							:						Tuple	)				->					Optional[int]:
										(
										    (
										    __magic_name__
										)      ,    (
										    __magic_name__
										)      ,    (
										    __magic_name__
										)      ,    (
										    __magic_name__
										)      ,    (
										    __magic_name__
										)      ,    (
										    __magic_name__
										)      ,    (
										    __magic_name__
										)      ,    (
										    __magic_name__
										)      ,    
										)            =     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     _snake_case					(							self							:						int	)				->					Any:
										for model_name in TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																__magic_name__            =     TFLayoutLMvaModel.from_pretrained(__lowerCamelCase	)
																self.assertIsNotNone(__lowerCamelCase	)






def _lowerCAmelCase	(  ):

						'''simple docstring'''






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


@require_tf
class 						A_							(      unittest.TestCase  ):







				@cached_property
				def     _snake_case					(							self							:						List[Any]	)				->					Dict:
										return LayoutLMvaImageProcessor(apply_ocr=__lowerCamelCase	) if is_vision_available() else None







				@slow
				def     _snake_case					(							self							:						Dict	)				->					Dict:
										__magic_name__            =     TFLayoutLMvaModel.from_pretrained("microsoft/layoutlmv3-base"	)

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

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

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

										# verify the logits
										__magic_name__            =     (1, 1_9_9, 7_6_8)
										self.assertEqual(outputs.last_hidden_state.shape						,    __lowerCamelCase	)

										__magic_name__            =     tf.constant(
										    [[-0.0529, 0.3618, 0.1632], [-0.1587, -0.1667, -0.0400], [-0.1557, -0.1671, -0.0505]]	)

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



