



'''simple docstring'''



import copy
import unittest

from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
							import torch

							from transformers import (
							    MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
							    MODEL_FOR_QUESTION_ANSWERING_MAPPING,
							    MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
							    MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
							    LayoutLMvaConfig,
							    LayoutLMvaForQuestionAnswering,
							    LayoutLMvaForSequenceClassification,
							    LayoutLMvaForTokenClassification,
							    LayoutLMvaModel,
							)
							from transformers.models.layoutlmva.modeling_layoutlmva import LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST

if is_vision_available():
							from PIL import Image

							from transformers import LayoutLMvaImageProcessor
class 	lowerCamelCase_							:







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

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







						def 					__lowercase    (  self   :      Union[str, Any]				):

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

													SCREAMING_SNAKE_CASE					:      Tuple     					=			ids_tensor([self.batch_size, self.text_seq_length, 4]				,    self.range_bbox				)
													# 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					:      Dict     					=			bbox[i, j, 3]
																																		SCREAMING_SNAKE_CASE					:      Tuple     					=			bbox[i, j, 1]
																																		SCREAMING_SNAKE_CASE					:      str     					=			t
																											if bbox[i, j, 2] < bbox[i, j, 0]:
																																		SCREAMING_SNAKE_CASE					:      Any     					=			bbox[i, j, 2]
																																		SCREAMING_SNAKE_CASE					:      List[str]     					=			bbox[i, j, 0]
																																		SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			t

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

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

													SCREAMING_SNAKE_CASE					:      Optional[int]     					=			None
													if self.use_token_type_ids:
																				SCREAMING_SNAKE_CASE					:      Dict     					=			ids_tensor([self.batch_size, self.text_seq_length]				,    self.type_vocab_size				)

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

													SCREAMING_SNAKE_CASE					:      Optional[int]     					=			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[str]				,    lowerCAmelCase__   :      List[str]				,    lowerCAmelCase__   :      Optional[Any]				,    lowerCAmelCase__   :      Tuple				,    lowerCAmelCase__   :      int				,    lowerCAmelCase__   :      List[str]				,    lowerCAmelCase__   :      str				,    lowerCAmelCase__   :      int				,    lowerCAmelCase__   :      Union[str, Any]				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      List[Any]     					=			LayoutLMvaModel(config=UpperCamelCase__				)
													model.to(UpperCamelCase__				)
													model.eval()

													# text + image
													SCREAMING_SNAKE_CASE					:      Dict     					=			model(UpperCamelCase__				,    pixel_values=UpperCamelCase__				)
													SCREAMING_SNAKE_CASE					:      List[str]     					=			model(
													    UpperCamelCase__				,    bbox=UpperCamelCase__				,    pixel_values=UpperCamelCase__				,    attention_mask=UpperCamelCase__				,    token_type_ids=UpperCamelCase__				)
													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			model(UpperCamelCase__				,    bbox=UpperCamelCase__				,    pixel_values=UpperCamelCase__				,    token_type_ids=UpperCamelCase__				)
													SCREAMING_SNAKE_CASE					:      Tuple     					=			model(UpperCamelCase__				,    bbox=UpperCamelCase__				,    pixel_values=UpperCamelCase__				)

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

													# text only
													SCREAMING_SNAKE_CASE					:      Any     					=			model(UpperCamelCase__				)
													self.parent.assertEqual(
													    result.last_hidden_state.shape				,    (self.batch_size, self.text_seq_length, self.hidden_size)				)

													# image only
													SCREAMING_SNAKE_CASE					:      List[Any]     					=			model(pixel_values=UpperCamelCase__				)
													self.parent.assertEqual(
													    result.last_hidden_state.shape				,    (self.batch_size, self.image_seq_length, self.hidden_size)				)







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

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Dict     					=			self.num_labels
													SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			LayoutLMvaForSequenceClassification(UpperCamelCase__				)
													model.to(UpperCamelCase__				)
													model.eval()
													SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			model(
													    UpperCamelCase__				,    bbox=UpperCamelCase__				,    pixel_values=UpperCamelCase__				,    attention_mask=UpperCamelCase__				,    token_type_ids=UpperCamelCase__				,    labels=UpperCamelCase__				,    )
													self.parent.assertEqual(result.logits.shape				,    (self.batch_size, self.num_labels)				)







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

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Tuple     					=			self.num_labels
													SCREAMING_SNAKE_CASE					:      Optional[int]     					=			LayoutLMvaForTokenClassification(config=UpperCamelCase__				)
													model.to(UpperCamelCase__				)
													model.eval()
													SCREAMING_SNAKE_CASE					:      List[Any]     					=			model(
													    UpperCamelCase__				,    bbox=UpperCamelCase__				,    pixel_values=UpperCamelCase__				,    attention_mask=UpperCamelCase__				,    token_type_ids=UpperCamelCase__				,    labels=UpperCamelCase__				,    )
													self.parent.assertEqual(result.logits.shape				,    (self.batch_size, self.text_seq_length, self.num_labels)				)







						def 					__lowercase    (  self   :      Optional[int]				,    lowerCAmelCase__   :      Union[str, Any]				,    lowerCAmelCase__   :      Dict				,    lowerCAmelCase__   :      Any				,    lowerCAmelCase__   :      Union[str, Any]				,    lowerCAmelCase__   :      Union[str, Any]				,    lowerCAmelCase__   :      Optional[int]				,    lowerCAmelCase__   :      Optional[Any]				,    lowerCAmelCase__   :      str				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Tuple     					=			LayoutLMvaForQuestionAnswering(config=UpperCamelCase__				)
													model.to(UpperCamelCase__				)
													model.eval()
													SCREAMING_SNAKE_CASE					:      List[str]     					=			model(
													    UpperCamelCase__				,    bbox=UpperCamelCase__				,    pixel_values=UpperCamelCase__				,    attention_mask=UpperCamelCase__				,    token_type_ids=UpperCamelCase__				,    start_positions=UpperCamelCase__				,    end_positions=UpperCamelCase__				,    )
													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   :      List[Any]				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      List[Any]     					=			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       
													)					,       
													)					:      Dict     					=			config_and_inputs
													SCREAMING_SNAKE_CASE					:      Optional[int]     					=			{
													    '''input_ids''': input_ids,
													    '''bbox''': bbox,
													    '''pixel_values''': pixel_values,
													    '''token_type_ids''': token_type_ids,
													    '''attention_mask''': input_mask,
													}
													return config, inputs_dict


@require_torch
class 	lowerCamelCase_							(					snake_case_    ,				snake_case_    ,				unittest.TestCase			):
						_lowerCAmelCase     :   str	     =				False
						_lowerCAmelCase     :   str	     =				False
						_lowerCAmelCase     :   int	     =				False

						_lowerCAmelCase     :   List[Any]	     =				(
						    (
						        LayoutLMvaModel,
						        LayoutLMvaForSequenceClassification,
						        LayoutLMvaForTokenClassification,
						        LayoutLMvaForQuestionAnswering,
						    )
						    if is_torch_available()
						    else ()
						)
						_lowerCAmelCase     :   Union[str, Any]	     =				(
						    {"document-question-answering": LayoutLMvaForQuestionAnswering, "feature-extraction": LayoutLMvaModel}
						    if is_torch_available()
						    else {}
						)







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

													"""simple docstring"""
													return True







						def 					__lowercase    (  self   :      Optional[Any]				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			LayoutLMvaModelTester(self				)
													SCREAMING_SNAKE_CASE					:      str     					=			ConfigTester(self				,    config_class=UpperCamelCase__				,    hidden_size=37				)







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

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Optional[int]     					=			copy.deepcopy(UpperCamelCase__				)
													if model_class in get_values(UpperCamelCase__				):
																				SCREAMING_SNAKE_CASE					:      int     					=			{
																				    k: v.unsqueeze(1				).expand(-1				,    self.model_tester.num_choices				,    -1				).contiguous()
																				    if isinstance(UpperCamelCase__				,    torch.Tensor				) and v.ndim > 1
																				    else v
																				    for k, v in inputs_dict.items()
																				}
													if return_labels:
																				if model_class in get_values(UpperCamelCase__				):
																											SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			torch.ones(self.model_tester.batch_size				,    dtype=torch.long				,    device=UpperCamelCase__				)
																				elif model_class in get_values(UpperCamelCase__				):
																											SCREAMING_SNAKE_CASE					:      str     					=			torch.zeros(
																											    self.model_tester.batch_size				,    dtype=torch.long				,    device=UpperCamelCase__				)
																											SCREAMING_SNAKE_CASE					:      Dict     					=			torch.zeros(
																											    self.model_tester.batch_size				,    dtype=torch.long				,    device=UpperCamelCase__				)
																				elif model_class in [
																				    *get_values(UpperCamelCase__				),
																				]:
																											SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			torch.zeros(
																											    self.model_tester.batch_size				,    dtype=torch.long				,    device=UpperCamelCase__				)
																				elif model_class in [
																				    *get_values(UpperCamelCase__				),
																				]:
																											SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			torch.zeros(
																											    (self.model_tester.batch_size, self.model_tester.text_seq_length)				,    dtype=torch.long				,    device=UpperCamelCase__				,    )

													return inputs_dict







						def 					__lowercase    (  self   :      Optional[int]				):

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







						def 					__lowercase    (  self   :      Dict				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      List[Any]     					=			self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_model(*UpperCamelCase__				)







						def 					__lowercase    (  self   :      Tuple				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			self.model_tester.prepare_config_and_inputs()
													for type in ["absolute", "relative_key", "relative_key_query"]:
																				SCREAMING_SNAKE_CASE					:      Optional[int]     					=			type
																				self.model_tester.create_and_check_model(*UpperCamelCase__				)







						def 					__lowercase    (  self   :      Optional[Any]				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Any     					=			self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_for_sequence_classification(*UpperCamelCase__				)







						def 					__lowercase    (  self   :      Union[str, Any]				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      str     					=			self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_for_token_classification(*UpperCamelCase__				)







						def 					__lowercase    (  self   :      Union[str, Any]				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Optional[int]     					=			self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_for_question_answering(*UpperCamelCase__				)







						@slow
						def 					__lowercase    (  self   :      Optional[int]				):

													"""simple docstring"""





													for model_name in LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																				SCREAMING_SNAKE_CASE					:      Tuple     					=			LayoutLMvaModel.from_pretrained(UpperCamelCase__				)
																				self.assertIsNotNone(UpperCamelCase__				)


def 					UpperCAmelCase   (						):

							'''simple docstring'''





							SCREAMING_SNAKE_CASE					:      int     					=			Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''				)
							return image






@require_torch
class 	lowerCamelCase_							(					unittest.TestCase			):







						@cached_property
						def 					__lowercase    (  self   :      Optional[Any]				):

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







						@slow
						def 					__lowercase    (  self   :      Optional[int]				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			LayoutLMvaModel.from_pretrained('''microsoft/layoutlmv3-base'''				).to(UpperCamelCase__				)

													SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			self.default_image_processor
													SCREAMING_SNAKE_CASE					:      List[str]     					=			prepare_img()
													SCREAMING_SNAKE_CASE					:      Any     					=			image_processor(images=UpperCamelCase__				,    return_tensors='''pt'''				).pixel_values.to(UpperCamelCase__				)

													SCREAMING_SNAKE_CASE					:      List[Any]     					=			torch.tensor([[1, 2]]				)
													SCREAMING_SNAKE_CASE					:      List[Any]     					=			torch.tensor([[1, 2, 3, 4], [5, 6, 7, 8]]				).unsqueeze(0				)

													# forward pass
													SCREAMING_SNAKE_CASE					:      List[str]     					=			model(
													    input_ids=input_ids.to(UpperCamelCase__				)				,    bbox=bbox.to(UpperCamelCase__				)				,    pixel_values=pixel_values.to(UpperCamelCase__				)				,    )

													# verify the logits
													SCREAMING_SNAKE_CASE					:      Any     					=			torch.Size((1, 1_99, 7_68)				)
													self.assertEqual(outputs.last_hidden_state.shape				,    UpperCamelCase__				)

													SCREAMING_SNAKE_CASE					:      List[Any]     					=			torch.tensor(
													    [[-0.0529, 0.3618, 0.1632], [-0.1587, -0.1667, -0.0400], [-0.1557, -0.1671, -0.0505]]				).to(UpperCamelCase__				)

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









'''simple docstring'''



import warnings
from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class 	lowerCamelCase_							(					snake_case_			):
						_lowerCAmelCase     :   int	     =				['image_processor', 'tokenizer']
						_lowerCAmelCase     :   Union[str, Any]	     =				'LayoutLMv3ImageProcessor'
						_lowerCAmelCase     :   List[Any]	     =				('LayoutLMv3Tokenizer', 'LayoutLMv3TokenizerFast')







						def __init__(  self   :      str				,    lowerCAmelCase__   :      int=None				,    lowerCAmelCase__   :      str=None				,    **lowerCAmelCase__   :      List[str]				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Tuple     					=			None
													if "feature_extractor" in kwargs:
																				warnings.warn(
																				    '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
																				    ''' instead.'''				,    lowerCAmelCase__				,    )
																				SCREAMING_SNAKE_CASE					:      List[str]     					=			kwargs.pop('''feature_extractor'''				)

													SCREAMING_SNAKE_CASE					:      Any     					=			image_processor if image_processor is not None else feature_extractor
													if image_processor is None:
																				raise ValueError('''You need to specify an `image_processor`.'''				)
													if tokenizer is None:
																				raise ValueError('''You need to specify a `tokenizer`.'''				)

													super().__init__(lowerCAmelCase__				,    lowerCAmelCase__				)







						def __call__(  self   :      Dict				,    lowerCAmelCase__   :      Any				,    lowerCAmelCase__   :      Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None				,    lowerCAmelCase__   :      Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None				,    lowerCAmelCase__   :      Union[List[List[int]], List[List[List[int]]]] = None				,    lowerCAmelCase__   :      Optional[Union[List[int], List[List[int]]]] = None				,    lowerCAmelCase__   :      bool = True				,    lowerCAmelCase__   :      Union[bool, str, PaddingStrategy] = False				,    lowerCAmelCase__   :      Union[bool, str, TruncationStrategy] = None				,    lowerCAmelCase__   :      Optional[int] = None				,    lowerCAmelCase__   :      int = 0				,    lowerCAmelCase__   :      Optional[int] = None				,    lowerCAmelCase__   :      Optional[bool] = None				,    lowerCAmelCase__   :      Optional[bool] = None				,    lowerCAmelCase__   :      bool = False				,    lowerCAmelCase__   :      bool = False				,    lowerCAmelCase__   :      bool = False				,    lowerCAmelCase__   :      bool = False				,    lowerCAmelCase__   :      bool = True				,    lowerCAmelCase__   :      Optional[Union[str, TensorType]] = None				,    **lowerCAmelCase__   :      Any				,    ):

													"""simple docstring"""
													# verify input
													if self.image_processor.apply_ocr and (boxes is not None):
																				raise ValueError(
																				    '''You cannot provide bounding boxes if you initialized the image processor with apply_ocr set to True.'''				)

													if self.image_processor.apply_ocr and (word_labels is not None):
																				raise ValueError(
																				    '''You cannot provide word labels if you initialized the image processor with apply_ocr set to True.'''				)

													# first, apply the image processor
													SCREAMING_SNAKE_CASE					:      List[Any]     					=			self.image_processor(images=lowerCAmelCase__				,    return_tensors=lowerCAmelCase__				)

													# second, apply the tokenizer
													if text is not None and self.image_processor.apply_ocr and text_pair is None:
																				if isinstance(lowerCAmelCase__				,    lowerCAmelCase__				):
																											SCREAMING_SNAKE_CASE					:      List[Any]     					=			[text]  # add batch dimension (as the image processor always adds a batch dimension)
																				SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			features['''words''']

													SCREAMING_SNAKE_CASE					:      Tuple     					=			self.tokenizer(
													    text=text if text is not None else features['''words''']				,    text_pair=text_pair if text_pair is not None else None				,    boxes=boxes if boxes is not None else features['''boxes''']				,    word_labels=lowerCAmelCase__				,    add_special_tokens=lowerCAmelCase__				,    padding=lowerCAmelCase__				,    truncation=lowerCAmelCase__				,    max_length=lowerCAmelCase__				,    stride=lowerCAmelCase__				,    pad_to_multiple_of=lowerCAmelCase__				,    return_token_type_ids=lowerCAmelCase__				,    return_attention_mask=lowerCAmelCase__				,    return_overflowing_tokens=lowerCAmelCase__				,    return_special_tokens_mask=lowerCAmelCase__				,    return_offsets_mapping=lowerCAmelCase__				,    return_length=lowerCAmelCase__				,    verbose=lowerCAmelCase__				,    return_tensors=lowerCAmelCase__				,    **lowerCAmelCase__				,    )

													# add pixel values
													SCREAMING_SNAKE_CASE					:      List[Any]     					=			features.pop('''pixel_values'''				)
													if return_overflowing_tokens is True:
																				SCREAMING_SNAKE_CASE					:      Any     					=			self.get_overflowing_images(lowerCAmelCase__				,    encoded_inputs['''overflow_to_sample_mapping''']				)
													SCREAMING_SNAKE_CASE					:      Any     					=			images

													return encoded_inputs







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

													"""simple docstring"""
													# in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
													SCREAMING_SNAKE_CASE					:      Dict     					=			[]
													for sample_idx in overflow_to_sample_mapping:
																				images_with_overflow.append(images[sample_idx]				)

													if len(lowerCAmelCase__				) != len(lowerCAmelCase__				):
																				raise ValueError(
																				    '''Expected length of images to be the same as the length of `overflow_to_sample_mapping`, but got'''
																				    F""" {len(lowerCAmelCase__				)} and {len(lowerCAmelCase__				)}"""				)

													return images_with_overflow







						def 					__lowercase    (  self   :      Any				,    *lowerCAmelCase__   :      Union[str, Any]				,    **lowerCAmelCase__   :      List[Any]				):

													"""simple docstring"""
													return self.tokenizer.batch_decode(*lowerCAmelCase__				,    **lowerCAmelCase__				)







						def 					__lowercase    (  self   :      Optional[int]				,    *lowerCAmelCase__   :      Optional[Any]				,    **lowerCAmelCase__   :      Tuple				):

													"""simple docstring"""
													return self.tokenizer.decode(*lowerCAmelCase__				,    **lowerCAmelCase__				)







						@property
						def 					__lowercase    (  self   :      Union[str, Any]				):

													"""simple docstring"""
													return ["input_ids", "bbox", "attention_mask", "pixel_values"]







						@property
						def 					__lowercase    (  self   :      Union[str, Any]				):

													"""simple docstring"""
													warnings.warn(
													    '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'''				,    lowerCAmelCase__				,    )
													return self.image_processor_class







						@property
						def 					__lowercase    (  self   :      List[Any]				):

													"""simple docstring"""
													warnings.warn(
													    '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'''				,    lowerCAmelCase__				,    )
													return self.image_processor




