



'''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 	lowerCamelCase_							:







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

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Any     					=			parent
													SCREAMING_SNAKE_CASE					:      Optional[int]     					=			batch_size
													SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			num_channels
													SCREAMING_SNAKE_CASE					:      List[Any]     					=			image_size
													SCREAMING_SNAKE_CASE					:      Optional[int]     					=			patch_size
													SCREAMING_SNAKE_CASE					:      List[Any]     					=			is_training
													SCREAMING_SNAKE_CASE					:      Tuple     					=			use_input_mask
													SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			use_token_type_ids
													SCREAMING_SNAKE_CASE					:      int     					=			use_labels
													SCREAMING_SNAKE_CASE					:      List[str]     					=			vocab_size
													SCREAMING_SNAKE_CASE					:      int     					=			hidden_size
													SCREAMING_SNAKE_CASE					:      List[Any]     					=			num_hidden_layers
													SCREAMING_SNAKE_CASE					:      List[Any]     					=			num_attention_heads
													SCREAMING_SNAKE_CASE					:      Optional[int]     					=			intermediate_size
													SCREAMING_SNAKE_CASE					:      List[Any]     					=			hidden_act
													SCREAMING_SNAKE_CASE					:      Optional[int]     					=			hidden_dropout_prob
													SCREAMING_SNAKE_CASE					:      int     					=			attention_probs_dropout_prob
													SCREAMING_SNAKE_CASE					:      Dict     					=			max_position_embeddings
													SCREAMING_SNAKE_CASE					:      str     					=			type_vocab_size
													SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			type_sequence_label_size
													SCREAMING_SNAKE_CASE					:      Optional[int]     					=			initializer_range
													SCREAMING_SNAKE_CASE					:      List[Any]     					=			coordinate_size
													SCREAMING_SNAKE_CASE					:      Optional[int]     					=			shape_size
													SCREAMING_SNAKE_CASE					:      Any     					=			num_labels
													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			num_choices
													SCREAMING_SNAKE_CASE					:      Any     					=			scope
													SCREAMING_SNAKE_CASE					:      Dict     					=			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					:      Union[str, Any]     					=			text_seq_length
													SCREAMING_SNAKE_CASE					:      Optional[int]     					=			(image_size // patch_size) ** 2 + 1
													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			self.text_seq_length + self.image_seq_length







						def 					__lowercase    (  self   :      Optional[int]				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			ids_tensor([self.batch_size, self.text_seq_length]				,    self.vocab_size				)

													SCREAMING_SNAKE_CASE					:      str     					=			ids_tensor([self.batch_size, self.text_seq_length, 4]				,    self.range_bbox				)
													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			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					:      Optional[int]     					=			bbox[i, j, 3]
																																		SCREAMING_SNAKE_CASE					:      List[str]     					=			bbox[i, j, 1]
																																		SCREAMING_SNAKE_CASE					:      int     					=			tmp_coordinate
																											if bbox[i, j, 2] < bbox[i, j, 0]:
																																		SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			bbox[i, j, 2]
																																		SCREAMING_SNAKE_CASE					:      str     					=			bbox[i, j, 0]
																																		SCREAMING_SNAKE_CASE					:      str     					=			tmp_coordinate
													SCREAMING_SNAKE_CASE					:      Optional[int]     					=			tf.constant(_SCREAMING_SNAKE_CASE				)

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

													SCREAMING_SNAKE_CASE					:      str     					=			None
													if self.use_input_mask:
																				SCREAMING_SNAKE_CASE					:      Tuple     					=			random_attention_mask([self.batch_size, self.text_seq_length]				)

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

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

													SCREAMING_SNAKE_CASE					:      Dict     					=			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 					__lowercase    (  self   :      List[Any]				,    lowerCAmelCase__   :      Dict				,    lowerCAmelCase__   :      Optional[Any]				,    lowerCAmelCase__   :      List[Any]				,    lowerCAmelCase__   :      Union[str, Any]				,    lowerCAmelCase__   :      List[str]				,    lowerCAmelCase__   :      str				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Any     					=			TFLayoutLMvaModel(config=_SCREAMING_SNAKE_CASE				)

													# text + image
													SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			model(_SCREAMING_SNAKE_CASE				,    pixel_values=_SCREAMING_SNAKE_CASE				,    training=_SCREAMING_SNAKE_CASE				)
													SCREAMING_SNAKE_CASE					:      List[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				,    )
													SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			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
													SCREAMING_SNAKE_CASE					:      Optional[int]     					=			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
													SCREAMING_SNAKE_CASE					:      Optional[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 					__lowercase    (  self   :      int				,    lowerCAmelCase__   :      Optional[Any]				,    lowerCAmelCase__   :      Dict				,    lowerCAmelCase__   :      Union[str, Any]				,    lowerCAmelCase__   :      Any				,    lowerCAmelCase__   :      Optional[Any]				,    lowerCAmelCase__   :      int				,    lowerCAmelCase__   :      List[str]				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Tuple     					=			self.num_labels
													SCREAMING_SNAKE_CASE					:      Dict     					=			TFLayoutLMvaForSequenceClassification(config=_SCREAMING_SNAKE_CASE				)
													SCREAMING_SNAKE_CASE					:      List[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				,    labels=_SCREAMING_SNAKE_CASE				,    training=_SCREAMING_SNAKE_CASE				,    )
													self.parent.assertEqual(result.logits.shape				,    (self.batch_size, self.num_labels)				)







						def 					__lowercase    (  self   :      Dict				,    lowerCAmelCase__   :      List[str]				,    lowerCAmelCase__   :      Any				,    lowerCAmelCase__   :      Tuple				,    lowerCAmelCase__   :      Any				,    lowerCAmelCase__   :      str				,    lowerCAmelCase__   :      str				,    lowerCAmelCase__   :      List[Any]				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Tuple     					=			self.num_labels
													SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			TFLayoutLMvaForTokenClassification(config=_SCREAMING_SNAKE_CASE				)
													SCREAMING_SNAKE_CASE					:      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 					__lowercase    (  self   :      str				,    lowerCAmelCase__   :      Optional[int]				,    lowerCAmelCase__   :      str				,    lowerCAmelCase__   :      List[Any]				,    lowerCAmelCase__   :      List[str]				,    lowerCAmelCase__   :      Any				,    lowerCAmelCase__   :      Union[str, Any]				,    lowerCAmelCase__   :      List[str]				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Any     					=			2
													SCREAMING_SNAKE_CASE					:      List[Any]     					=			TFLayoutLMvaForQuestionAnswering(config=_SCREAMING_SNAKE_CASE				)
													SCREAMING_SNAKE_CASE					:      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 					__lowercase    (  self   :      Any				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      List[str]     					=			self.prepare_config_and_inputs()
													((SCREAMING_SNAKE_CASE)					,(SCREAMING_SNAKE_CASE)					,(SCREAMING_SNAKE_CASE)					,(SCREAMING_SNAKE_CASE)					,(SCREAMING_SNAKE_CASE)					,(SCREAMING_SNAKE_CASE)					,(SCREAMING_SNAKE_CASE)					,(SCREAMING_SNAKE_CASE))					:      List[Any]     					=			config_and_inputs
													SCREAMING_SNAKE_CASE					:      Dict     					=			{
													    '''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 	lowerCamelCase_							(					lowerCAmelCase__    ,				lowerCAmelCase__    ,				unittest.TestCase			):
						_lowerCAmelCase     :   Optional[int]	     =				(
						    (
						        TFLayoutLMvaModel,
						        TFLayoutLMvaForQuestionAnswering,
						        TFLayoutLMvaForSequenceClassification,
						        TFLayoutLMvaForTokenClassification,
						    )
						    if is_tf_available()
						    else ()
						)
						_lowerCAmelCase     :   Union[str, Any]	     =				(
						    {"document-question-answering": TFLayoutLMvaForQuestionAnswering, "feature-extraction": TFLayoutLMvaModel}
						    if is_tf_available()
						    else {}
						)

						_lowerCAmelCase     :   Dict	     =				False
						_lowerCAmelCase     :   Dict	     =				False
						_lowerCAmelCase     :   Any	     =				False







						def 					__lowercase    (  self   :      List[Any]				,    lowerCAmelCase__   :      Any				,    lowerCAmelCase__   :      Any				,    lowerCAmelCase__   :      Tuple				,    lowerCAmelCase__   :      int				,    lowerCAmelCase__   :      List[str]				):

													"""simple docstring"""
													return True







						def 					__lowercase    (  self   :      Union[str, Any]				,    lowerCAmelCase__   :      List[str]				,    lowerCAmelCase__   :      int				,    lowerCAmelCase__   :      str=False				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      str     					=			copy.deepcopy(_SCREAMING_SNAKE_CASE				)

													if model_class in get_values(_SCREAMING_SNAKE_CASE				):
																				SCREAMING_SNAKE_CASE					:      List[Any]     					=			{
																				    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				):
																											SCREAMING_SNAKE_CASE					:      Tuple     					=			tf.ones(self.model_tester.batch_size				,    dtype=tf.intaa				)
																				elif model_class in get_values(_SCREAMING_SNAKE_CASE				):
																											SCREAMING_SNAKE_CASE					:      Dict     					=			tf.zeros(self.model_tester.batch_size				,    dtype=tf.intaa				)
																											SCREAMING_SNAKE_CASE					:      Any     					=			tf.zeros(self.model_tester.batch_size				,    dtype=tf.intaa				)
																				elif model_class in get_values(_SCREAMING_SNAKE_CASE				):
																											SCREAMING_SNAKE_CASE					:      Optional[int]     					=			tf.zeros(self.model_tester.batch_size				,    dtype=tf.intaa				)
																				elif model_class in get_values(_SCREAMING_SNAKE_CASE				):
																											SCREAMING_SNAKE_CASE					:      Optional[int]     					=			tf.zeros(
																											    (self.model_tester.batch_size, self.model_tester.text_seq_length)				,    dtype=tf.intaa				)

													return inputs_dict







						def 					__lowercase    (  self   :      List[Any]				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Tuple     					=			TFLayoutLMvaModelTester(self				)
													SCREAMING_SNAKE_CASE					:      List[str]     					=			ConfigTester(self				,    config_class=_SCREAMING_SNAKE_CASE				,    hidden_size=37				)







						def 					__lowercase    (  self   :      int				):

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







						def 					__lowercase    (  self   :      List[str]				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					,SCREAMING_SNAKE_CASE					:      Any     					=			self.model_tester.prepare_config_and_inputs_for_common()
													for model_class in self.all_model_classes:
																				SCREAMING_SNAKE_CASE					:      List[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
																											SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			self._prepare_for_class(inputs_dict.copy()				,    _SCREAMING_SNAKE_CASE				,    return_labels=_SCREAMING_SNAKE_CASE				)
																											SCREAMING_SNAKE_CASE					:      Dict     					=			prepared_for_class[
																											    sorted(prepared_for_class.keys() - inputs_dict.keys()				,    reverse=_SCREAMING_SNAKE_CASE				)[0]
																											]
																											SCREAMING_SNAKE_CASE					:      List[str]     					=			added_label.shape.as_list()[:1]

																											# Test that model correctly compute the loss with kwargs
																											SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			self._prepare_for_class(inputs_dict.copy()				,    _SCREAMING_SNAKE_CASE				,    return_labels=_SCREAMING_SNAKE_CASE				)
																											SCREAMING_SNAKE_CASE					:      str     					=			prepared_for_class.pop('''input_ids'''				)

																											SCREAMING_SNAKE_CASE					:      Any     					=			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
																											SCREAMING_SNAKE_CASE					:      str     					=			self._prepare_for_class(inputs_dict.copy()				,    _SCREAMING_SNAKE_CASE				,    return_labels=_SCREAMING_SNAKE_CASE				)
																											SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			prepared_for_class.pop('''input_ids'''				)
																											if "labels" in prepared_for_class:
																																		SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			prepared_for_class['''labels'''].numpy()
																																		if len(labels.shape				) > 1 and labels.shape[1] != 1:
																																									SCREAMING_SNAKE_CASE					:      Dict     					=			-1_00
																																									SCREAMING_SNAKE_CASE					:      List[Any]     					=			tf.convert_to_tensor(_SCREAMING_SNAKE_CASE				)
																																									SCREAMING_SNAKE_CASE					:      Dict     					=			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
																											SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			self._prepare_for_class(inputs_dict.copy()				,    _SCREAMING_SNAKE_CASE				,    return_labels=_SCREAMING_SNAKE_CASE				)
																											SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			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
																											SCREAMING_SNAKE_CASE					:      Any     					=			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
																											SCREAMING_SNAKE_CASE					:      str     					=			prepared_for_class.keys() - inputs_dict.keys()
																											SCREAMING_SNAKE_CASE					:      Optional[int]     					=			inspect.signature(model.call				).parameters
																											SCREAMING_SNAKE_CASE					:      Dict     					=			list(signature.keys()				)

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

																											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					:      List[Any]     					=			prepared_for_class[value]

																											SCREAMING_SNAKE_CASE					:      List[Any]     					=			tuple(_SCREAMING_SNAKE_CASE				)

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

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







						def 					__lowercase    (  self   :      Dict				):

													"""simple docstring"""
													(
													    (
													    SCREAMING_SNAKE_CASE      
													)					,(
													    SCREAMING_SNAKE_CASE      
													)					,(
													    SCREAMING_SNAKE_CASE      
													)					,(
													    SCREAMING_SNAKE_CASE      
													)					,(
													    SCREAMING_SNAKE_CASE      
													)					,(
													    SCREAMING_SNAKE_CASE      
													)					,(
													    SCREAMING_SNAKE_CASE      
													)					,(
													    SCREAMING_SNAKE_CASE      
													)					,      
													)					:      str     					=			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 					__lowercase    (  self   :      int				):

													"""simple docstring"""
													(
													    (
													    SCREAMING_SNAKE_CASE      
													)					,(
													    SCREAMING_SNAKE_CASE      
													)					,(
													    SCREAMING_SNAKE_CASE      
													)					,(
													    SCREAMING_SNAKE_CASE      
													)					,(
													    SCREAMING_SNAKE_CASE      
													)					,(
													    SCREAMING_SNAKE_CASE      
													)					,(
													    SCREAMING_SNAKE_CASE      
													)					,(
													    SCREAMING_SNAKE_CASE      
													)					,      
													)					:      List[str]     					=			self.model_tester.prepare_config_and_inputs()
													for type in ["absolute", "relative_key", "relative_key_query"]:
																				SCREAMING_SNAKE_CASE					:      Tuple     					=			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 					__lowercase    (  self   :      Tuple				):

													"""simple docstring"""
													(
													    (
													    SCREAMING_SNAKE_CASE      
													)					,(
													    SCREAMING_SNAKE_CASE      
													)					,(
													    SCREAMING_SNAKE_CASE      
													)					,(
													    SCREAMING_SNAKE_CASE      
													)					,(
													    SCREAMING_SNAKE_CASE      
													)					,(
													    SCREAMING_SNAKE_CASE      
													)					,(
													    SCREAMING_SNAKE_CASE      
													)					,(
													    SCREAMING_SNAKE_CASE      
													)					,      
													)					:      str     					=			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 					__lowercase    (  self   :      List[Any]				):

													"""simple docstring"""
													(
													    (
													    SCREAMING_SNAKE_CASE      
													)					,(
													    SCREAMING_SNAKE_CASE      
													)					,(
													    SCREAMING_SNAKE_CASE      
													)					,(
													    SCREAMING_SNAKE_CASE      
													)					,(
													    SCREAMING_SNAKE_CASE      
													)					,(
													    SCREAMING_SNAKE_CASE      
													)					,(
													    SCREAMING_SNAKE_CASE      
													)					,(
													    SCREAMING_SNAKE_CASE      
													)					,      
													)					:      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 					__lowercase    (  self   :      Any				):

													"""simple docstring"""
													(
													    (
													    SCREAMING_SNAKE_CASE      
													)					,(
													    SCREAMING_SNAKE_CASE      
													)					,(
													    SCREAMING_SNAKE_CASE      
													)					,(
													    SCREAMING_SNAKE_CASE      
													)					,(
													    SCREAMING_SNAKE_CASE      
													)					,(
													    SCREAMING_SNAKE_CASE      
													)					,(
													    SCREAMING_SNAKE_CASE      
													)					,(
													    SCREAMING_SNAKE_CASE      
													)					,      
													)					:      Union[str, Any]     					=			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 					__lowercase    (  self   :      Tuple				):

													"""simple docstring"""
													for model_name in TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																				SCREAMING_SNAKE_CASE					:      str     					=			TFLayoutLMvaModel.from_pretrained(_SCREAMING_SNAKE_CASE				)
																				self.assertIsNotNone(_SCREAMING_SNAKE_CASE				)


def 					lowerCAmelCase_   (						):
							SCREAMING_SNAKE_CASE					:      Optional[int]     					=			Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''				)
							return image






@require_tf
class 	lowerCamelCase_							(					unittest.TestCase			):







						@cached_property
						def 					__lowercase    (  self   :      Any				):

													"""simple docstring"""
													return LayoutLMvaImageProcessor(apply_ocr=_SCREAMING_SNAKE_CASE				) if is_vision_available() else None







						@slow
						def 					__lowercase    (  self   :      List[str]				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      List[str]     					=			TFLayoutLMvaModel.from_pretrained('''microsoft/layoutlmv3-base'''				)

													SCREAMING_SNAKE_CASE					:      int     					=			self.default_image_processor
													SCREAMING_SNAKE_CASE					:      Any     					=			prepare_img()
													SCREAMING_SNAKE_CASE					:      Any     					=			image_processor(images=_SCREAMING_SNAKE_CASE				,    return_tensors='''tf'''				).pixel_values

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

													# forward pass
													SCREAMING_SNAKE_CASE					:      List[str]     					=			model(input_ids=_SCREAMING_SNAKE_CASE				,    bbox=_SCREAMING_SNAKE_CASE				,    pixel_values=_SCREAMING_SNAKE_CASE				,    training=_SCREAMING_SNAKE_CASE				)

													# verify the logits
													SCREAMING_SNAKE_CASE					:      List[str]     					=			(1, 1_99, 7_68)
													self.assertEqual(outputs.last_hidden_state.shape				,    _SCREAMING_SNAKE_CASE				)

													SCREAMING_SNAKE_CASE					:      int     					=			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]				,    _SCREAMING_SNAKE_CASE				,    atol=1e-4				)				)









'''simple docstring'''



import argparse
import os
from io import BytesIO
from pathlib import Path

import requests
from clip_retrieval.clip_client import ClipClient
from PIL import Image
from tqdm import tqdm
def 					UpperCAmelCase   (						A  :				Union[str, Any]					,		A  :				Optional[int]					,		A  :				Any				):
							SCREAMING_SNAKE_CASE					:      List[str]     					=			1.5
							SCREAMING_SNAKE_CASE					:      str     					=			int(factor * num_class_images				)
							SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			ClipClient(
							    url='''https://knn.laion.ai/knn-service'''					,		indice_name='''laion_400m'''					,		num_images=A					,		aesthetic_weight=0.1				)

							os.makedirs(F"""{class_data_dir}/images"""					,		exist_ok=A				)
							if len(list(Path(F"""{class_data_dir}/images"""				).iterdir()				)				) >= num_class_images:
														return

							while True:
														SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			client.query(text=A				)
														if len(A				) >= factor * num_class_images or num_images > 1e4:
																					break
														else:
																					SCREAMING_SNAKE_CASE					:      Optional[int]     					=			int(factor * num_images				)
																					SCREAMING_SNAKE_CASE					:      str     					=			ClipClient(
																					    url='''https://knn.laion.ai/knn-service'''					,		indice_name='''laion_400m'''					,		num_images=A					,		aesthetic_weight=0.1					,		)

							SCREAMING_SNAKE_CASE					:      Optional[int]     					=			0
							SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			0
							SCREAMING_SNAKE_CASE					:      Tuple     					=			tqdm(desc='''downloading real regularization images'''					,		total=A				)

							with open(F"""{class_data_dir}/caption.txt"""					,		'''w'''				) as fa, open(F"""{class_data_dir}/urls.txt"""					,		'''w'''				) as fa, open(
							    F"""{class_data_dir}/images.txt"""					,		'''w'''				) as fa:
														while total < num_class_images:
																					SCREAMING_SNAKE_CASE					:      int     					=			class_images[count]
																					count += 1
																					try:
																												SCREAMING_SNAKE_CASE					:      int     					=			requests.get(images['''url''']				)
																												if img.status_code == 200:
																																			SCREAMING_SNAKE_CASE					:      List[str]     					=			Image.open(BytesIO(img.content				)				)
																																			with open(F"""{class_data_dir}/images/{total}.jpg"""					,		'''wb'''				) as f:
																																										f.write(img.content				)
																																			fa.write(images['''caption'''] + '''\n'''				)
																																			fa.write(images['''url'''] + '''\n'''				)
																																			fa.write(F"""{class_data_dir}/images/{total}.jpg""" + '''\n'''				)
																																			total += 1
																																			pbar.update(1				)
																												else:
																																			continue
																					except Exception:
																												continue
							return
def 					UpperCAmelCase   (						):
							SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			argparse.ArgumentParser(''''''					,		add_help=A				)
							parser.add_argument('''--class_prompt'''					,		help='''text prompt to retrieve images'''					,		required=A					,		type=A				)
							parser.add_argument('''--class_data_dir'''					,		help='''path to save images'''					,		required=A					,		type=A				)
							parser.add_argument('''--num_class_images'''					,		help='''number of images to download'''					,		default=200					,		type=A				)
							return parser.parse_args()


if __name__ == "__main__":
							lowerCAmelCase_     :       List[str]								=						parse_args()
							retrieve(args.class_prompt, args.class_data_dir, args.num_class_images)




