




import json
import os
import re
import shutil
import tempfile
import unittest
from typing import Tuple

from transformers import AddedToken, BatchEncoding, PerceiverTokenizer
from transformers.utils import cached_property, is_tf_available, is_torch_available

from ...test_tokenization_common import TokenizerTesterMixin


if is_torch_available():
 _lowerCamelCase     					=		'pt'
elif is_tf_available():
 _lowerCamelCase     					=		'tf'
else:
 _lowerCamelCase     					=		'jax'



class      a	(  _A      ,  unittest.TestCase     ):

     '''simple docstring'''





     lowerCAmelCase							:			str  						=    PerceiverTokenizer
     lowerCAmelCase							:			Tuple  						=    False
     def     lowerCamelCase_  (      self  :     Optional[Any]							):
         super().setUp()
         UpperCAmelCase_									=	PerceiverTokenizer()
         tokenizer.save_pretrained(self.tmpdirname							)
     @cached_property
     def     lowerCamelCase_  (      self  :     Tuple							):
         return PerceiverTokenizer.from_pretrained('''deepmind/language-perceiver'''							)
     def     lowerCamelCase_  (      self  :     List[str]   ,       **__snake_case  :     Optional[Any]							):
         return self.tokenizer_class.from_pretrained(self.tmpdirname   ,       **__SCREAMING_SNAKE_CASE							)
     def     lowerCamelCase_  (      self  :     List[str]   ,       __snake_case  :     List[Any]   ,       __snake_case  :     Union[str, Any]=False   ,       __snake_case  :     Union[str, Any]=20   ,       __snake_case  :     List[Any]=5							):
         UpperCAmelCase_									=	[]
         for i in range(len(__SCREAMING_SNAKE_CASE							)							):
             try:
                 UpperCAmelCase_									=	tokenizer.decode([i]   ,       clean_up_tokenization_spaces=__SCREAMING_SNAKE_CASE							)
             except UnicodeDecodeError:
                 pass
             toks.append((i, tok)							)

         UpperCAmelCase_									=	list(filter(lambda __snake_case							: re.match(R'''^[ a-zA-Z]+$'''   ,       t[1]							)   ,       __SCREAMING_SNAKE_CASE							)							)
         UpperCAmelCase_									=	list(filter(lambda __snake_case							: [t[0]] == tokenizer.encode(t[1]   ,       add_special_tokens=__SCREAMING_SNAKE_CASE							)   ,       __SCREAMING_SNAKE_CASE							)							)
         if max_length is not None and len(__SCREAMING_SNAKE_CASE							) > max_length:
             UpperCAmelCase_									=	toks[:max_length]
         if min_length is not None and len(__SCREAMING_SNAKE_CASE							) < min_length and len(__SCREAMING_SNAKE_CASE							) > 0:
             while len(__SCREAMING_SNAKE_CASE							) < min_length:
                 UpperCAmelCase_									=	toks + toks
        # toks_str = [t[1] for t in toks]
         UpperCAmelCase_									=	[t[0] for t in toks]

         # Ensure consistency
         UpperCAmelCase_									=	tokenizer.decode(__SCREAMING_SNAKE_CASE   ,       clean_up_tokenization_spaces=__SCREAMING_SNAKE_CASE							)
         if " " not in output_txt and len(__SCREAMING_SNAKE_CASE							) > 1:
             UpperCAmelCase_									=	(
                 tokenizer.decode([toks_ids[0]]   ,       clean_up_tokenization_spaces=__SCREAMING_SNAKE_CASE							)
                 + ''' '''
                 + tokenizer.decode(toks_ids[1:]   ,       clean_up_tokenization_spaces=__SCREAMING_SNAKE_CASE							)
             )
         if with_prefix_space:
             UpperCAmelCase_									=	''' ''' + output_txt
         UpperCAmelCase_									=	tokenizer.encode(__SCREAMING_SNAKE_CASE   ,       add_special_tokens=__SCREAMING_SNAKE_CASE							)
         return output_txt, output_ids
     def     lowerCamelCase_  (      self  :     List[Any]							):
         UpperCAmelCase_									=	self.perceiver_tokenizer
         UpperCAmelCase_									=	'''Unicode €.'''
         UpperCAmelCase_									=	tokenizer(__SCREAMING_SNAKE_CASE							)
         UpperCAmelCase_									=	[4, 91, 1_16, 1_11, 1_05, 1_17, 1_06, 1_07, 38, 2_32, 1_36, 1_78, 52, 5]
         self.assertEqual(encoded['''input_ids''']   ,       __SCREAMING_SNAKE_CASE							)

         # decoding
         UpperCAmelCase_									=	tokenizer.decode(__SCREAMING_SNAKE_CASE							)
         self.assertEqual(__SCREAMING_SNAKE_CASE   ,       '''[CLS]Unicode €.[SEP]'''							)

         UpperCAmelCase_									=	tokenizer('''e è é ê ë'''							)
         UpperCAmelCase_									=	[4, 1_07, 38, 2_01, 1_74, 38, 2_01, 1_75, 38, 2_01, 1_76, 38, 2_01, 1_77, 5]
         self.assertEqual(encoded['''input_ids''']   ,       __SCREAMING_SNAKE_CASE							)
         # decoding
         UpperCAmelCase_									=	tokenizer.decode(__SCREAMING_SNAKE_CASE							)
         self.assertEqual(__SCREAMING_SNAKE_CASE   ,       '''[CLS]e è é ê ë[SEP]'''							)

         # encode/decode, but with `encode` instead of `__call__`
         self.assertEqual(tokenizer.decode(tokenizer.encode('''e è é ê ë'''							)							)   ,       '''[CLS]e è é ê ë[SEP]'''							)
     def     lowerCamelCase_  (      self  :     int							):
         UpperCAmelCase_									=	self.perceiver_tokenizer
         UpperCAmelCase_									=	['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
         # fmt: off
         UpperCAmelCase_									=	[4, 71, 38, 1_14, 1_17, 1_16, 1_09, 38, 1_18, 1_03, 1_20, 1_03, 1_09, 1_20, 1_03, 1_18, 1_10, 38, 1_08, 1_17, 1_20, 38, 1_21, 1_23, 1_15, 1_15, 1_03, 1_20, 1_11, 1_28, 1_03, 1_22, 1_11, 1_17, 1_16, 52, 5, 0]
         # fmt: on
         UpperCAmelCase_									=	tokenizer(__SCREAMING_SNAKE_CASE   ,       padding=__SCREAMING_SNAKE_CASE   ,       return_tensors=__SCREAMING_SNAKE_CASE							)
         self.assertIsInstance(__SCREAMING_SNAKE_CASE   ,       __SCREAMING_SNAKE_CASE							)

         if FRAMEWORK != "jax":
             UpperCAmelCase_									=	list(batch.input_ids.numpy()[0]							)
         else:
             UpperCAmelCase_									=	list(batch.input_ids.tolist()[0]							)

         self.assertListEqual(__SCREAMING_SNAKE_CASE   ,       __SCREAMING_SNAKE_CASE							)

         self.assertEqual((2, 38)   ,       batch.input_ids.shape							)
         self.assertEqual((2, 38)   ,       batch.attention_mask.shape							)
     def     lowerCamelCase_  (      self  :     List[str]							):
         UpperCAmelCase_									=	self.perceiver_tokenizer
         UpperCAmelCase_									=	['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
         UpperCAmelCase_									=	tokenizer(__SCREAMING_SNAKE_CASE   ,       padding=__SCREAMING_SNAKE_CASE   ,       return_tensors=__SCREAMING_SNAKE_CASE							)
         # check if input_ids are returned and no decoder_input_ids
         self.assertIn('''input_ids'''   ,       __SCREAMING_SNAKE_CASE							)
         self.assertIn('''attention_mask'''   ,       __SCREAMING_SNAKE_CASE							)
         self.assertNotIn('''decoder_input_ids'''   ,       __SCREAMING_SNAKE_CASE							)
         self.assertNotIn('''decoder_attention_mask'''   ,       __SCREAMING_SNAKE_CASE							)
     def     lowerCamelCase_  (      self  :     Dict							):
         UpperCAmelCase_									=	self.perceiver_tokenizer
         UpperCAmelCase_									=	[
             '''Summary of the text.''',
             '''Another summary.''',
         ]
         UpperCAmelCase_									=	tokenizer(
             text_target=__SCREAMING_SNAKE_CASE   ,       max_length=32   ,       padding='''max_length'''   ,       truncation=__SCREAMING_SNAKE_CASE   ,       return_tensors=__SCREAMING_SNAKE_CASE							)
         self.assertEqual(32   ,       targets['''input_ids'''].shape[1]							)
     def     lowerCamelCase_  (      self  :     Optional[int]							):
         UpperCAmelCase_									=	self.get_tokenizers()
         for tokenizer in tokenizers:
             with self.subTest(F'{tokenizer.__class__.__name__}'							):
                 self.assertNotEqual(tokenizer.model_max_length   ,       42							)

        # Now let's start the test
         UpperCAmelCase_									=	self.get_tokenizers()
         for tokenizer in tokenizers:
             with self.subTest(F'{tokenizer.__class__.__name__}'							):
                 # Isolate this from the other tests because we save additional tokens/etc
                 UpperCAmelCase_									=	tempfile.mkdtemp()

                 UpperCAmelCase_									=	''' He is very happy, UNwant\u00E9d,running'''
                 UpperCAmelCase_									=	tokenizer.encode(__SCREAMING_SNAKE_CASE   ,       add_special_tokens=__SCREAMING_SNAKE_CASE							)
                 tokenizer.save_pretrained(__SCREAMING_SNAKE_CASE							)

                 UpperCAmelCase_									=	tokenizer.__class__.from_pretrained(__SCREAMING_SNAKE_CASE							)
                 UpperCAmelCase_									=	after_tokenizer.encode(__SCREAMING_SNAKE_CASE   ,       add_special_tokens=__SCREAMING_SNAKE_CASE							)
                 self.assertListEqual(__SCREAMING_SNAKE_CASE   ,       __SCREAMING_SNAKE_CASE							)

                 shutil.rmtree(__SCREAMING_SNAKE_CASE							)

         UpperCAmelCase_									=	self.get_tokenizers(model_max_length=42							)
         for tokenizer in tokenizers:
             with self.subTest(F'{tokenizer.__class__.__name__}'							):
                 # Isolate this from the other tests because we save additional tokens/etc
                 UpperCAmelCase_									=	tempfile.mkdtemp()

                 UpperCAmelCase_									=	''' He is very happy, UNwant\u00E9d,running'''
                 tokenizer.add_tokens(['''bim''', '''bambam''']							)
                 UpperCAmelCase_									=	tokenizer.additional_special_tokens
                 additional_special_tokens.append('''new_additional_special_token'''							)
                 tokenizer.add_special_tokens({'''additional_special_tokens''': additional_special_tokens}							)
                 UpperCAmelCase_									=	tokenizer.encode(__SCREAMING_SNAKE_CASE   ,       add_special_tokens=__SCREAMING_SNAKE_CASE							)
                 tokenizer.save_pretrained(__SCREAMING_SNAKE_CASE							)

                 UpperCAmelCase_									=	tokenizer.__class__.from_pretrained(__SCREAMING_SNAKE_CASE							)
                 UpperCAmelCase_									=	after_tokenizer.encode(__SCREAMING_SNAKE_CASE   ,       add_special_tokens=__SCREAMING_SNAKE_CASE							)
                 self.assertListEqual(__SCREAMING_SNAKE_CASE   ,       __SCREAMING_SNAKE_CASE							)
                 self.assertIn('''new_additional_special_token'''   ,       after_tokenizer.additional_special_tokens							)
                 self.assertEqual(after_tokenizer.model_max_length   ,       42							)

                 UpperCAmelCase_									=	tokenizer.__class__.from_pretrained(__SCREAMING_SNAKE_CASE   ,       model_max_length=43							)
                 self.assertEqual(tokenizer.model_max_length   ,       43							)

                 shutil.rmtree(__SCREAMING_SNAKE_CASE							)
     def     lowerCamelCase_  (      self  :     Dict							):
         UpperCAmelCase_									=	[]
         if self.test_slow_tokenizer:
             tokenizer_list.append((self.tokenizer_class, self.get_tokenizer())							)

         if self.test_rust_tokenizer:
             tokenizer_list.append((self.rust_tokenizer_class, self.get_rust_tokenizer())							)

         for tokenizer_class, tokenizer_utils in tokenizer_list:
             with tempfile.TemporaryDirectory() as tmp_dir:
                 tokenizer_utils.save_pretrained(__SCREAMING_SNAKE_CASE							)

                 with open(os.path.join(__SCREAMING_SNAKE_CASE   ,       '''special_tokens_map.json'''							)   ,       encoding='''utf-8'''							) as json_file:
                     UpperCAmelCase_									=	json.load(__SCREAMING_SNAKE_CASE							)

                 with open(os.path.join(__SCREAMING_SNAKE_CASE   ,       '''tokenizer_config.json'''							)   ,       encoding='''utf-8'''							) as json_file:
                     UpperCAmelCase_									=	json.load(__SCREAMING_SNAKE_CASE							)

                 UpperCAmelCase_									=	[F'<extra_id_{i}>' for i in range(1_25							)]

                 UpperCAmelCase_									=	added_tokens_extra_ids + [
                     '''an_additional_special_token'''
                 ]
                 UpperCAmelCase_									=	added_tokens_extra_ids + [
                     '''an_additional_special_token'''
                 ]

                 with open(os.path.join(__SCREAMING_SNAKE_CASE   ,       '''special_tokens_map.json'''							)   ,       '''w'''   ,       encoding='''utf-8'''							) as outfile:
                     json.dump(__SCREAMING_SNAKE_CASE   ,       __SCREAMING_SNAKE_CASE							)
                 with open(os.path.join(__SCREAMING_SNAKE_CASE   ,       '''tokenizer_config.json'''							)   ,       '''w'''   ,       encoding='''utf-8'''							) as outfile:
                     json.dump(__SCREAMING_SNAKE_CASE   ,       __SCREAMING_SNAKE_CASE							)

                 # the following checks allow us to verify that our test works as expected, i.e. that the tokenizer takes
                 # into account the new value of additional_special_tokens given in the "tokenizer_config.json" and
                 # "special_tokens_map.json" files
                 UpperCAmelCase_									=	tokenizer_class.from_pretrained(
                     __SCREAMING_SNAKE_CASE   ,       )
                 self.assertIn(
                     '''an_additional_special_token'''   ,       tokenizer_without_change_in_init.additional_special_tokens							)
                 self.assertEqual(
                     ['''an_additional_special_token''']   ,       tokenizer_without_change_in_init.convert_ids_to_tokens(
                         tokenizer_without_change_in_init.convert_tokens_to_ids(['''an_additional_special_token''']							)							)   ,       )

                 # Now we test that we can change the value of additional_special_tokens in the from_pretrained
                 UpperCAmelCase_									=	added_tokens_extra_ids + [AddedToken('''a_new_additional_special_token'''   ,       lstrip=__SCREAMING_SNAKE_CASE							)]
                 UpperCAmelCase_									=	tokenizer_class.from_pretrained(
                     __SCREAMING_SNAKE_CASE   ,       additional_special_tokens=__SCREAMING_SNAKE_CASE   ,       )

                 self.assertIn('''a_new_additional_special_token'''   ,       tokenizer.additional_special_tokens							)
                 self.assertEqual(
                     ['''a_new_additional_special_token''']   ,       tokenizer.convert_ids_to_tokens(
                         tokenizer.convert_tokens_to_ids(['''a_new_additional_special_token''']							)							)   ,       )
     def     lowerCamelCase_  (      self  :     Union[str, Any]							):
         UpperCAmelCase_									=	self.perceiver_tokenizer
         self.assertEqual(tokenizer.decode([1_78]							)   ,       '''�'''							)
     def     lowerCamelCase_  (      self  :     int							):
         pass
     def     lowerCamelCase_  (      self  :     List[str]							):
         pass
     def     lowerCamelCase_  (      self  :     Any							):
         pass
     def     lowerCamelCase_  (      self  :     Optional[Any]							):
         pass




     def     lowerCamelCase_  (      self  :     str							):
         UpperCAmelCase_									=	self.get_tokenizers(fast=__SCREAMING_SNAKE_CASE   ,       do_lower_case=__SCREAMING_SNAKE_CASE							)
         for tokenizer in tokenizers:
             with self.subTest(F'{tokenizer.__class__.__name__}'							):
                 UpperCAmelCase_									=	['''[CLS]''', '''t''', '''h''', '''i''', '''s''', ''' ''', '''i''', '''s''', ''' ''', '''a''', ''' ''', '''t''', '''e''', '''s''', '''t''', '''[SEP]''']
                 UpperCAmelCase_									=	tokenizer.convert_tokens_to_string(__SCREAMING_SNAKE_CASE							)

                 self.assertIsInstance(__SCREAMING_SNAKE_CASE   ,       __SCREAMING_SNAKE_CASE							)








'''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__  ( a   ):



 """simple docstring"""



 lowerCAmelCase__       = ["image_processor", "tokenizer"]
 lowerCAmelCase__       = "LayoutLMv2ImageProcessor"
 lowerCAmelCase__       = ("LayoutXLMTokenizer", "LayoutXLMTokenizerFast")
 def __init__(						self   :			Any   ,				__SCREAMING_SNAKE_CASE   :			Optional[Any]=None   ,				__SCREAMING_SNAKE_CASE   :			List[Any]=None   ,				**__SCREAMING_SNAKE_CASE   :			str     )    ->					List[str]:


      """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."""   ,				__SCREAMING_SNAKE_CASE   ,				)
           __SCREAMING_SNAKE_CASE      = kwargs.pop("""feature_extractor"""     )

      __SCREAMING_SNAKE_CASE      = 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__(__SCREAMING_SNAKE_CASE   ,				__SCREAMING_SNAKE_CASE     )
 def __call__(						self   :			str   ,				__SCREAMING_SNAKE_CASE   :			Optional[int]   ,				__SCREAMING_SNAKE_CASE   :			Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None   ,				__SCREAMING_SNAKE_CASE   :			Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None   ,				__SCREAMING_SNAKE_CASE   :			Union[List[List[int]], List[List[List[int]]]] = None   ,				__SCREAMING_SNAKE_CASE   :			Optional[Union[List[int], List[List[int]]]] = None   ,				__SCREAMING_SNAKE_CASE   :			bool = True   ,				__SCREAMING_SNAKE_CASE   :			Union[bool, str, PaddingStrategy] = False   ,				__SCREAMING_SNAKE_CASE   :			Union[bool, str, TruncationStrategy] = None   ,				__SCREAMING_SNAKE_CASE   :			Optional[int] = None   ,				__SCREAMING_SNAKE_CASE   :			int = 0   ,				__SCREAMING_SNAKE_CASE   :			Optional[int] = None   ,				__SCREAMING_SNAKE_CASE   :			Optional[bool] = None   ,				__SCREAMING_SNAKE_CASE   :			Optional[bool] = None   ,				__SCREAMING_SNAKE_CASE   :			bool = False   ,				__SCREAMING_SNAKE_CASE   :			bool = False   ,				__SCREAMING_SNAKE_CASE   :			bool = False   ,				__SCREAMING_SNAKE_CASE   :			bool = False   ,				__SCREAMING_SNAKE_CASE   :			bool = True   ,				__SCREAMING_SNAKE_CASE   :			Optional[Union[str, TensorType]] = None   ,				**__SCREAMING_SNAKE_CASE   :			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
      __SCREAMING_SNAKE_CASE      = self.image_processor(images=__SCREAMING_SNAKE_CASE   ,				return_tensors=__SCREAMING_SNAKE_CASE     )

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

      __SCREAMING_SNAKE_CASE      = 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=__SCREAMING_SNAKE_CASE   ,				add_special_tokens=__SCREAMING_SNAKE_CASE   ,				padding=__SCREAMING_SNAKE_CASE   ,				truncation=__SCREAMING_SNAKE_CASE   ,				max_length=__SCREAMING_SNAKE_CASE   ,				stride=__SCREAMING_SNAKE_CASE   ,				pad_to_multiple_of=__SCREAMING_SNAKE_CASE   ,				return_token_type_ids=__SCREAMING_SNAKE_CASE   ,				return_attention_mask=__SCREAMING_SNAKE_CASE   ,				return_overflowing_tokens=__SCREAMING_SNAKE_CASE   ,				return_special_tokens_mask=__SCREAMING_SNAKE_CASE   ,				return_offsets_mapping=__SCREAMING_SNAKE_CASE   ,				return_length=__SCREAMING_SNAKE_CASE   ,				verbose=__SCREAMING_SNAKE_CASE   ,				return_tensors=__SCREAMING_SNAKE_CASE   ,				**__SCREAMING_SNAKE_CASE   ,				)

      # add pixel values
      __SCREAMING_SNAKE_CASE      = features.pop("""pixel_values"""     )
      if return_overflowing_tokens is True:
           __SCREAMING_SNAKE_CASE      = self.get_overflowing_images(__SCREAMING_SNAKE_CASE   ,				encoded_inputs["""overflow_to_sample_mapping"""]     )
      __SCREAMING_SNAKE_CASE      = images

      return encoded_inputs
 def  UpperCAmelCase__						(						self   :			List[str]   ,				__SCREAMING_SNAKE_CASE   :			Tuple   ,				__SCREAMING_SNAKE_CASE   :			Dict     )    ->					int:


      """simple docstring"""




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

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

      return images_with_overflow
 def  UpperCAmelCase__						(						self   :			Optional[Any]   ,				*__SCREAMING_SNAKE_CASE   :			str   ,				**__SCREAMING_SNAKE_CASE   :			List[str]     )    ->					List[str]:


      """simple docstring"""




      return self.tokenizer.batch_decode(*__SCREAMING_SNAKE_CASE   ,				**__SCREAMING_SNAKE_CASE     )
 def  UpperCAmelCase__						(						self   :			Union[str, Any]   ,				*__SCREAMING_SNAKE_CASE   :			Dict   ,				**__SCREAMING_SNAKE_CASE   :			Any     )    ->					Union[str, Any]:


      """simple docstring"""




      return self.tokenizer.decode(*__SCREAMING_SNAKE_CASE   ,				**__SCREAMING_SNAKE_CASE     )
 @property
 def  UpperCAmelCase__						(						self   :			int     )    ->					Optional[int]:


      """simple docstring"""




      return ["input_ids", "bbox", "attention_mask", "image"]
 @property
 def  UpperCAmelCase__						(						self   :			Any     )    ->					List[Any]:


      """simple docstring"""




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


 @property
 def  UpperCAmelCase__						(						self   :			Optional[int]     )    ->					Any:


      """simple docstring"""




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






