






'''simple docstring'''



import json
import pathlib
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
      import torch

if is_vision_available():
      from PIL import Image

      from transformers import YolosImageProcessor
class 					UpperCAmelCase_					(    unittest.TestCase				):




      """simple docstring"""




      def __init__( self			:		Optional[Any]     ,      UpperCAmelCase			:		Any     ,      UpperCAmelCase			:		Optional[Any]=7     ,      UpperCAmelCase			:		Optional[Any]=3     ,      UpperCAmelCase			:		Optional[int]=30     ,      UpperCAmelCase			:		Any=400     ,      UpperCAmelCase			:		str=True     ,      UpperCAmelCase			:		Optional[Any]=None     ,      UpperCAmelCase			:		Dict=True     ,      UpperCAmelCase			:		List[Any]=[0.5, 0.5, 0.5]     ,      UpperCAmelCase			:		Any=[0.5, 0.5, 0.5]     ,      UpperCAmelCase			:		str=True     ,      UpperCAmelCase			:		Optional[Any]=1 / 255     ,      UpperCAmelCase			:		int=True     ,      )  ->  str:


       '''simple docstring'''

       lowercase					:   Optional[Any]				  =size if size is not None else {'''shortest_edge''': 18, '''longest_edge''': 1333}
       lowercase					:   List[str]				  =parent
       lowercase					:   Union[str, Any]				  =batch_size
       lowercase					:   List[Any]				  =num_channels
       lowercase					:   List[str]				  =min_resolution
       lowercase					:   Union[str, Any]				  =max_resolution
       lowercase					:   Union[str, Any]				  =do_resize
       lowercase					:   Optional[Any]				  =size
       lowercase					:   List[Any]				  =do_normalize
       lowercase					:   Tuple				  =image_mean
       lowercase					:   Any				  =image_std
       lowercase					:   Any				  =do_rescale
       lowercase					:   str				  =rescale_factor
       lowercase					:   Union[str, Any]				  =do_pad






      def 				A__      ( self			:		Union[str, Any]		)  ->  List[Any]:


       '''simple docstring'''

       return {
           "do_resize": self.do_resize,
           "size": self.size,
           "do_normalize": self.do_normalize,
           "image_mean": self.image_mean,
           "image_std": self.image_std,
           "do_rescale": self.do_rescale,
           "rescale_factor": self.rescale_factor,
           "do_pad": self.do_pad,
       }






      def 				A__      ( self			:		Dict     ,      UpperCAmelCase			:		str     ,      UpperCAmelCase			:		Tuple=False		)  ->  Tuple:


       '''simple docstring'''

       if not batched:
        lowercase					:   str				  =image_inputs[0]
        if isinstance(UpperCAmelCase     ,      Image.Image		):
         lowercase					:   Dict				  =image.size
        else:
         lowercase					:   int				  =image.shape[1], image.shape[2]
        if w < h:
         lowercase					:   List[Any]				  =int(self.size['''shortest_edge'''] * h / w		)
         lowercase					:   List[Any]				  =self.size['''shortest_edge''']
        elif w > h:
         lowercase					:   str				  =self.size['''shortest_edge''']
         lowercase					:   str				  =int(self.size['''shortest_edge'''] * w / h		)
        else:
         lowercase					:   Union[str, Any]				  =self.size['''shortest_edge''']
         lowercase					:   List[str]				  =self.size['''shortest_edge''']

       else:
        lowercase					:   Optional[Any]				  =[]
        for image in image_inputs:
         lowercase					:   int				  =self.get_expected_values([image]		)
         expected_values.append((expected_height, expected_width)		)
        lowercase					:   Union[str, Any]				  =max(UpperCAmelCase     ,      key=lambda UpperCAmelCase		: item[0]		)[0]
        lowercase					:   Dict				  =max(UpperCAmelCase     ,      key=lambda UpperCAmelCase		: item[1]		)[1]

       return expected_height, expected_width





@require_torch
@require_vision
class 					UpperCAmelCase_					(    __A				,       unittest.TestCase				):




      """simple docstring"""




      UpperCamelCase_								=			YolosImageProcessor if is_vision_available() else None






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


       '''simple docstring'''

       lowercase					:   Dict				  =YolosImageProcessingTester(self		)






      @property
      def 				A__      ( self			:		List[str]		)  ->  Optional[Any]:


       '''simple docstring'''

       return self.image_processor_tester.prepare_image_processor_dict()






      def 				A__      ( self			:		List[Any]		)  ->  Tuple:


       '''simple docstring'''

       lowercase					:   str				  =self.image_processing_class(**self.image_processor_dict		)
       self.assertTrue(hasattr(UpperCAmelCase     ,      '''image_mean'''		)		)
       self.assertTrue(hasattr(UpperCAmelCase     ,      '''image_std'''		)		)
       self.assertTrue(hasattr(UpperCAmelCase     ,      '''do_normalize'''		)		)
       self.assertTrue(hasattr(UpperCAmelCase     ,      '''do_resize'''		)		)
       self.assertTrue(hasattr(UpperCAmelCase     ,      '''size'''		)		)






      def 				A__      ( self			:		Tuple		)  ->  List[Any]:


       '''simple docstring'''

       lowercase					:   int				  =self.image_processing_class.from_dict(self.image_processor_dict		)
       self.assertEqual(image_processor.size     ,      {'''shortest_edge''': 18, '''longest_edge''': 1333}		)
       self.assertEqual(image_processor.do_pad     ,      UpperCAmelCase		)

       lowercase					:   List[Any]				  =self.image_processing_class.from_dict(
           self.image_processor_dict     ,      size=42     ,      max_size=84     ,      pad_and_return_pixel_mask=UpperCAmelCase		)
       self.assertEqual(image_processor.size     ,      {'''shortest_edge''': 42, '''longest_edge''': 84}		)
       self.assertEqual(image_processor.do_pad     ,      UpperCAmelCase		)






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


       '''simple docstring'''

       pass






      def 				A__      ( self			:		Tuple		)  ->  Dict:


       '''simple docstring'''

       lowercase					:   Optional[int]				  =self.image_processing_class(**self.image_processor_dict		)
       # create random PIL images
       lowercase					:   Tuple				  =prepare_image_inputs(self.image_processor_tester     ,      equal_resolution=UpperCAmelCase		)
       for image in image_inputs:
        self.assertIsInstance(UpperCAmelCase     ,      Image.Image		)

       # Test not batched input
       lowercase					:   int				  =image_processing(image_inputs[0]     ,      return_tensors='''pt'''		).pixel_values

       lowercase					:   Union[str, Any]				  =self.image_processor_tester.get_expected_values(UpperCAmelCase		)

       self.assertEqual(
           encoded_images.shape     ,      (1, self.image_processor_tester.num_channels, expected_height, expected_width)     ,      )

       # Test batched
       lowercase					:   Any				  =self.image_processor_tester.get_expected_values(UpperCAmelCase     ,      batched=UpperCAmelCase		)

       lowercase					:   int				  =image_processing(UpperCAmelCase     ,      return_tensors='''pt'''		).pixel_values
       self.assertEqual(
           encoded_images.shape     ,      (
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_channels,
               expected_height,
               expected_width,
           )     ,      )






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


       '''simple docstring'''

       lowercase					:   Optional[Any]				  =self.image_processing_class(**self.image_processor_dict		)
       # create random numpy tensors
       lowercase					:   int				  =prepare_image_inputs(self.image_processor_tester     ,      equal_resolution=UpperCAmelCase     ,      numpify=UpperCAmelCase		)
       for image in image_inputs:
        self.assertIsInstance(UpperCAmelCase     ,      np.ndarray		)

       # Test not batched input
       lowercase					:   int				  =image_processing(image_inputs[0]     ,      return_tensors='''pt'''		).pixel_values

       lowercase					:   str				  =self.image_processor_tester.get_expected_values(UpperCAmelCase		)

       self.assertEqual(
           encoded_images.shape     ,      (1, self.image_processor_tester.num_channels, expected_height, expected_width)     ,      )

       # Test batched
       lowercase					:   int				  =image_processing(UpperCAmelCase     ,      return_tensors='''pt'''		).pixel_values

       lowercase					:   int				  =self.image_processor_tester.get_expected_values(UpperCAmelCase     ,      batched=UpperCAmelCase		)

       self.assertEqual(
           encoded_images.shape     ,      (
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_channels,
               expected_height,
               expected_width,
           )     ,      )






      def 				A__      ( self			:		Any		)  ->  int:


       '''simple docstring'''

       lowercase					:   Any				  =self.image_processing_class(**self.image_processor_dict		)
       # create random PyTorch tensors
       lowercase					:   str				  =prepare_image_inputs(self.image_processor_tester     ,      equal_resolution=UpperCAmelCase     ,      torchify=UpperCAmelCase		)
       for image in image_inputs:
        self.assertIsInstance(UpperCAmelCase     ,      torch.Tensor		)

       # Test not batched input
       lowercase					:   List[Any]				  =image_processing(image_inputs[0]     ,      return_tensors='''pt'''		).pixel_values

       lowercase					:   Optional[int]				  =self.image_processor_tester.get_expected_values(UpperCAmelCase		)

       self.assertEqual(
           encoded_images.shape     ,      (1, self.image_processor_tester.num_channels, expected_height, expected_width)     ,      )

       # Test batched
       lowercase					:   List[Any]				  =image_processing(UpperCAmelCase     ,      return_tensors='''pt'''		).pixel_values

       lowercase					:   Union[str, Any]				  =self.image_processor_tester.get_expected_values(UpperCAmelCase     ,      batched=UpperCAmelCase		)

       self.assertEqual(
           encoded_images.shape     ,      (
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_channels,
               expected_height,
               expected_width,
           )     ,      )






      def 				A__      ( self			:		Optional[Any]		)  ->  Optional[int]:


       '''simple docstring'''

       lowercase					:   Optional[Any]				  =self.image_processing_class(**self.image_processor_dict		)
       lowercase					:   Tuple				  =self.image_processing_class(do_resize=UpperCAmelCase     ,      do_normalize=UpperCAmelCase     ,      do_rescale=UpperCAmelCase		)
       # create random PyTorch tensors
       lowercase					:   str				  =prepare_image_inputs(self.image_processor_tester     ,      equal_resolution=UpperCAmelCase     ,      torchify=UpperCAmelCase		)
       for image in image_inputs:
        self.assertIsInstance(UpperCAmelCase     ,      torch.Tensor		)

       # Test whether the method "pad" and calling the image processor return the same tensors
       lowercase					:   Any				  =image_processing_a.pad(UpperCAmelCase     ,      return_tensors='''pt'''		)
       lowercase					:   Any				  =image_processing_a(UpperCAmelCase     ,      return_tensors='''pt'''		)

       self.assertTrue(
           torch.allclose(encoded_images_with_method['''pixel_values''']     ,      encoded_images['''pixel_values''']     ,      atol=1e-4		)		)






      @slow
      def 				A__      ( self			:		Dict		)  ->  Optional[Any]:


       '''simple docstring'''

       lowercase					:   Optional[int]				  =Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''		)
       with open('''./tests/fixtures/tests_samples/COCO/coco_annotations.txt'''     ,      '''r'''		) as f:
        lowercase					:   Union[str, Any]				  =json.loads(f.read()		)

       lowercase					:   List[Any]				  ={'''image_id''': 3_9769, '''annotations''': target}

       # encode them
       lowercase					:   Any				  =YolosImageProcessor.from_pretrained('''hustvl/yolos-small'''		)
       lowercase					:   Optional[int]				  =image_processing(images=UpperCAmelCase     ,      annotations=UpperCAmelCase     ,      return_tensors='''pt'''		)

       # verify pixel values
       lowercase					:   Optional[int]				  =torch.Size([1, 3, 800, 1066]		)
       self.assertEqual(encoding['''pixel_values'''].shape     ,      UpperCAmelCase		)

       lowercase					:   Union[str, Any]				  =torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1]		)
       self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3]     ,      UpperCAmelCase     ,      atol=1e-4		)		)

       # verify area
       lowercase					:   Optional[Any]				  =torch.tensor([5887.9600, 1_1250.2061, 48_9353.8438, 83_7122.7500, 14_7967.5156, 16_5732.3438]		)
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area''']     ,      UpperCAmelCase		)		)
       # verify boxes
       lowercase					:   Any				  =torch.Size([6, 4]		)
       self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape     ,      UpperCAmelCase		)
       lowercase					:   Optional[Any]				  =torch.tensor([0.5_5_0_3, 0.2_7_6_5, 0.0_6_0_4, 0.2_2_1_5]		)
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0]     ,      UpperCAmelCase     ,      atol=1e-3		)		)
       # verify image_id
       lowercase					:   str				  =torch.tensor([3_9769]		)
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']     ,      UpperCAmelCase		)		)
       # verify is_crowd
       lowercase					:   Dict				  =torch.tensor([0, 0, 0, 0, 0, 0]		)
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']     ,      UpperCAmelCase		)		)
       # verify class_labels
       lowercase					:   Optional[int]				  =torch.tensor([75, 75, 63, 65, 17, 17]		)
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']     ,      UpperCAmelCase		)		)
       # verify orig_size
       lowercase					:   str				  =torch.tensor([480, 640]		)
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']     ,      UpperCAmelCase		)		)
       # verify size
       lowercase					:   Optional[Any]				  =torch.tensor([800, 1066]		)
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']     ,      UpperCAmelCase		)		)






      @slow
      def 				A__      ( self			:		Optional[int]		)  ->  Optional[Any]:


       '''simple docstring'''

       lowercase					:   int				  =Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''		)
       with open('''./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt'''     ,      '''r'''		) as f:
        lowercase					:   Dict				  =json.loads(f.read()		)

       lowercase					:   Any				  ={'''file_name''': '''000000039769.png''', '''image_id''': 3_9769, '''segments_info''': target}

       lowercase					:   Optional[Any]				  =pathlib.Path('''./tests/fixtures/tests_samples/COCO/coco_panoptic'''		)

       # encode them
       lowercase					:   Optional[Any]				  =YolosImageProcessor(format='''coco_panoptic'''		)
       lowercase					:   Optional[int]				  =image_processing(images=UpperCAmelCase     ,      annotations=UpperCAmelCase     ,      masks_path=UpperCAmelCase     ,      return_tensors='''pt'''		)

       # verify pixel values
       lowercase					:   Optional[int]				  =torch.Size([1, 3, 800, 1066]		)
       self.assertEqual(encoding['''pixel_values'''].shape     ,      UpperCAmelCase		)

       lowercase					:   Optional[int]				  =torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1]		)
       self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3]     ,      UpperCAmelCase     ,      atol=1e-4		)		)

       # verify area
       lowercase					:   Dict				  =torch.tensor([14_7979.6875, 16_5527.0469, 48_4638.5938, 1_1292.9375, 5879.6562, 7634.1147]		)
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area''']     ,      UpperCAmelCase		)		)
       # verify boxes
       lowercase					:   Optional[int]				  =torch.Size([6, 4]		)
       self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape     ,      UpperCAmelCase		)
       lowercase					:   Tuple				  =torch.tensor([0.2_6_2_5, 0.5_4_3_7, 0.4_6_8_8, 0.8_6_2_5]		)
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0]     ,      UpperCAmelCase     ,      atol=1e-3		)		)
       # verify image_id
       lowercase					:   List[str]				  =torch.tensor([3_9769]		)
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']     ,      UpperCAmelCase		)		)
       # verify is_crowd
       lowercase					:   Any				  =torch.tensor([0, 0, 0, 0, 0, 0]		)
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']     ,      UpperCAmelCase		)		)
       # verify class_labels
       lowercase					:   Dict				  =torch.tensor([17, 17, 63, 75, 75, 93]		)
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']     ,      UpperCAmelCase		)		)
       # verify masks
       lowercase					:   Optional[int]				  =82_2873
       self.assertEqual(encoding['''labels'''][0]['''masks'''].sum().item()     ,      UpperCAmelCase		)
       # verify orig_size
       lowercase					:   List[Any]				  =torch.tensor([480, 640]		)
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']     ,      UpperCAmelCase		)		)
       # verify size
       lowercase					:   Union[str, Any]				  =torch.tensor([800, 1066]		)
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']     ,      UpperCAmelCase		)		)











'''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 UpperCAmelCase_					(    __A				):




      """simple docstring"""




      UpperCamelCase_								=			['''image_processor''', '''tokenizer''']
      UpperCamelCase_								=			'''LayoutLMv2ImageProcessor'''
      UpperCamelCase_								=			('''LayoutXLMTokenizer''', '''LayoutXLMTokenizerFast''')






      def __init__( self			:		List[str]     ,      UpperCAmelCase			:		Tuple=None     ,      UpperCAmelCase			:		str=None     ,      **UpperCAmelCase			:		Dict		)  ->  Optional[int]:


       '''simple docstring'''

       if "feature_extractor" in kwargs:
        warnings.warn(
            '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
            ''' instead.'''     ,      UpperCAmelCase     ,      )
        lowercase					:   Any				  =kwargs.pop('''feature_extractor'''		)

       lowercase					:   Dict				  =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__(UpperCAmelCase     ,      UpperCAmelCase		)






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


       '''simple docstring'''

       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.'''		)

       if return_overflowing_tokens is True and return_offsets_mapping is False:
        raise ValueError('''You cannot return overflowing tokens without returning the offsets mapping.'''		)

       # first, apply the image processor
       lowercase					:   Tuple				  =self.image_processor(images=UpperCAmelCase     ,      return_tensors=UpperCAmelCase		)

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

       lowercase					:   Optional[Any]				  =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=UpperCAmelCase     ,      add_special_tokens=UpperCAmelCase     ,      padding=UpperCAmelCase     ,      truncation=UpperCAmelCase     ,      max_length=UpperCAmelCase     ,      stride=UpperCAmelCase     ,      pad_to_multiple_of=UpperCAmelCase     ,      return_token_type_ids=UpperCAmelCase     ,      return_attention_mask=UpperCAmelCase     ,      return_overflowing_tokens=UpperCAmelCase     ,      return_special_tokens_mask=UpperCAmelCase     ,      return_offsets_mapping=UpperCAmelCase     ,      return_length=UpperCAmelCase     ,      verbose=UpperCAmelCase     ,      return_tensors=UpperCAmelCase     ,      **UpperCAmelCase     ,      )

       # add pixel values
       lowercase					:   List[str]				  =features.pop('''pixel_values'''		)
       if return_overflowing_tokens is True:
        lowercase					:   str				  =self.get_overflowing_images(UpperCAmelCase     ,      encoded_inputs['''overflow_to_sample_mapping''']		)
       lowercase					:   Dict				  =images

       return encoded_inputs






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


       '''simple docstring'''

       lowercase					:   str				  =[]
       for sample_idx in overflow_to_sample_mapping:
        images_with_overflow.append(images[sample_idx]		)

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

       return images_with_overflow






      def 				A__      ( self			:		Union[str, Any]     ,      *UpperCAmelCase			:		Tuple     ,      **UpperCAmelCase			:		Dict		)  ->  int:


       '''simple docstring'''

       return self.tokenizer.batch_decode(*UpperCAmelCase     ,      **UpperCAmelCase		)






      def 				A__      ( self			:		Dict     ,      *UpperCAmelCase			:		Any     ,      **UpperCAmelCase			:		Dict		)  ->  str:


       '''simple docstring'''

       return self.tokenizer.decode(*UpperCAmelCase     ,      **UpperCAmelCase		)






      @property
      def 				A__      ( self			:		List[str]		)  ->  List[str]:


       '''simple docstring'''

       return ["input_ids", "bbox", "attention_mask", "image"]






      @property
      def 				A__      ( self			:		int		)  ->  List[str]:


       '''simple docstring'''

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






      @property
      def 				A__      ( self			:		Dict		)  ->  List[str]:


       '''simple docstring'''

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



