


'''simple docstring'''



import re
import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin





class    UpperCAmelCase_  (_UpperCAmelCase  ):





    """simple docstring"""






    lowerCamelCase  :					Any							     =			['image_processor', 'tokenizer']
    lowerCamelCase  :					List[str]							     =			'AutoImageProcessor'
    lowerCamelCase  :					Tuple							     =			'AutoTokenizer'







    def __init__(							self			,     SCREAMING_SNAKE_CASE_=None			,     SCREAMING_SNAKE_CASE_=None			,     **SCREAMING_SNAKE_CASE_	)				->     Union[str, Any]:
         __lowerCamelCase   :	Dict								=		None
         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_			,     )
              __lowerCamelCase   :	Optional[Any]								=		kwargs.pop('feature_extractor'	)

         __lowerCamelCase   :	str								=		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_	)
         __lowerCamelCase   :	Tuple								=		self.image_processor
         __lowerCamelCase   :	Tuple								=		False







    def __call__(							self			,     *SCREAMING_SNAKE_CASE_			,     **SCREAMING_SNAKE_CASE_	)				->     List[str]:
         # For backward compatibility
         if self._in_target_context_manager:
              return self.current_processor(*SCREAMING_SNAKE_CASE_			,     **SCREAMING_SNAKE_CASE_	)

         __lowerCamelCase   :	Union[str, Any]								=		kwargs.pop('images'			,     SCREAMING_SNAKE_CASE_	)
         __lowerCamelCase   :	List[str]								=		kwargs.pop('text'			,     SCREAMING_SNAKE_CASE_	)
         if len(SCREAMING_SNAKE_CASE_	) > 0:
              __lowerCamelCase   :	Tuple								=		args[0]
              __lowerCamelCase   :	List[Any]								=		args[1:]

         if images is None and text is None:
              raise ValueError('You need to specify either an `images` or `text` input to process.'	)

         if images is not None:
              __lowerCamelCase   :	List[Any]								=		self.image_processor(SCREAMING_SNAKE_CASE_			,     *SCREAMING_SNAKE_CASE_			,     **SCREAMING_SNAKE_CASE_	)
         if text is not None:
              __lowerCamelCase   :	Any								=		self.tokenizer(SCREAMING_SNAKE_CASE_			,     **SCREAMING_SNAKE_CASE_	)

         if text is None:
              return inputs
         elif images is None:
              return encodings
         else:
              __lowerCamelCase   :	Any								=		encodings['input_ids']
              return inputs







    def       lowercase_				(							self			,     *SCREAMING_SNAKE_CASE_			,     **SCREAMING_SNAKE_CASE_	)				->     List[Any]:
         return self.tokenizer.batch_decode(*SCREAMING_SNAKE_CASE_			,     **SCREAMING_SNAKE_CASE_	)







    def       lowercase_				(							self			,     *SCREAMING_SNAKE_CASE_			,     **SCREAMING_SNAKE_CASE_	)				->     Any:
         return self.tokenizer.decode(*SCREAMING_SNAKE_CASE_			,     **SCREAMING_SNAKE_CASE_	)







    @contextmanager
    def       lowercase_				(							self	)				->     Dict:
         warnings.warn(
             '`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your '
             'labels by using the argument `text` of the regular `__call__` method (either in the same call as '
             'your images inputs, or in a separate call.'	)
         __lowerCamelCase   :	Tuple								=		True
         __lowerCamelCase   :	List[str]								=		self.tokenizer
         yield
         __lowerCamelCase   :	Dict								=		self.image_processor
         __lowerCamelCase   :	Tuple								=		False







    def       lowercase_				(							self			,     SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_=False			,     SCREAMING_SNAKE_CASE_=None	)				->     Optional[Any]:
         if added_vocab is None:
              __lowerCamelCase   :	List[str]								=		self.tokenizer.get_added_vocab()

         __lowerCamelCase   :	int								=		{}

         while tokens:
              __lowerCamelCase   :	Optional[Any]								=		re.search(r'<s_(.*?)>'			,     SCREAMING_SNAKE_CASE_			,     re.IGNORECASE	)
              if start_token is None:
                   break
              __lowerCamelCase   :	List[str]								=		start_token.group(1	)
              __lowerCamelCase   :	str								=		re.search(rf'</s_{key}>'			,     SCREAMING_SNAKE_CASE_			,     re.IGNORECASE	)
              __lowerCamelCase   :	str								=		start_token.group()
              if end_token is None:
                   __lowerCamelCase   :	Tuple								=		tokens.replace(SCREAMING_SNAKE_CASE_			,     ''	)
              else:
                   __lowerCamelCase   :	Dict								=		end_token.group()
                   __lowerCamelCase   :	Dict								=		re.escape(SCREAMING_SNAKE_CASE_	)
                   __lowerCamelCase   :	Union[str, Any]								=		re.escape(SCREAMING_SNAKE_CASE_	)
                   __lowerCamelCase   :	Optional[int]								=		re.search(f'{start_token_escaped}(.*?){end_token_escaped}'			,     SCREAMING_SNAKE_CASE_			,     re.IGNORECASE	)
                   if content is not None:
                        __lowerCamelCase   :	List[str]								=		content.group(1	).strip()
                        if r"<s_" in content and r"</s_" in content:  # non-leaf node
                             __lowerCamelCase   :	Optional[int]								=		self.tokenajson(SCREAMING_SNAKE_CASE_			,     is_inner_value=SCREAMING_SNAKE_CASE_			,     added_vocab=SCREAMING_SNAKE_CASE_	)
                             if value:
                                  if len(SCREAMING_SNAKE_CASE_	) == 1:
                                       __lowerCamelCase   :	List[Any]								=		value[0]
                                  __lowerCamelCase   :	List[str]								=		value
                        else:  # leaf nodes
                             __lowerCamelCase   :	List[str]								=		[]
                             for leaf in content.split(r'<sep/>'	):
                                  __lowerCamelCase   :	List[str]								=		leaf.strip()
                                  if leaf in added_vocab and leaf[0] == "<" and leaf[-2:] == "/>":
                                       __lowerCamelCase   :	Optional[int]								=		leaf[1:-2]  # for categorical special tokens
                                  output[key].append(SCREAMING_SNAKE_CASE_	)
                             if len(output[key]	) == 1:
                                  __lowerCamelCase   :	Dict								=		output[key][0]

                   __lowerCamelCase   :	Dict								=		tokens[tokens.find(SCREAMING_SNAKE_CASE_	) + len(SCREAMING_SNAKE_CASE_	) :].strip()
                   if tokens[:6] == r"<sep/>":  # non-leaf nodes
                        return [output] + self.tokenajson(tokens[6:]			,     is_inner_value=SCREAMING_SNAKE_CASE_			,     added_vocab=SCREAMING_SNAKE_CASE_	)

         if len(SCREAMING_SNAKE_CASE_	):
              return [output] if is_inner_value else output
         else:
              return [] if is_inner_value else {"text_sequence": tokens}







    @property
    def       lowercase_				(							self	)				->     str:
         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       lowercase_				(							self	)				->     Optional[Any]:
         warnings.warn(
             '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'			,     SCREAMING_SNAKE_CASE_			,     )
         return self.image_processor










'''simple docstring'''



import unittest

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


if is_torch_available():
      import torch

      from transformers import AutoModelForImageClassification

if is_vision_available():
      from transformers import AutoImageProcessor





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





    """simple docstring"""






    @slow
    def       lowercase_				(							self	)				->     List[str]:
         __lowerCamelCase   :	Any								=		AutoImageProcessor.from_pretrained('microsoft/dit-base-finetuned-rvlcdip'	)
         __lowerCamelCase   :	Tuple								=		AutoModelForImageClassification.from_pretrained('microsoft/dit-base-finetuned-rvlcdip'	)
         model.to(SCREAMING_SNAKE_CASE_	)

         from datasets import load_dataset

         __lowerCamelCase   :	str								=		load_dataset('nielsr/rvlcdip-demo'	)

         __lowerCamelCase   :	List[Any]								=		dataset['train'][0]['image'].convert('RGB'	)

         __lowerCamelCase   :	str								=		image_processor(SCREAMING_SNAKE_CASE_			,     return_tensors='pt'	).to(SCREAMING_SNAKE_CASE_	)

         # forward pass
         with torch.no_grad():
              __lowerCamelCase   :	str								=		model(**SCREAMING_SNAKE_CASE_	)
              __lowerCamelCase   :	int								=		outputs.logits

         __lowerCamelCase   :	List[Any]								=		torch.Size((1, 16)	)
         self.assertEqual(logits.shape			,     SCREAMING_SNAKE_CASE_	)

         __lowerCamelCase   :	Optional[Any]								=		torch.tensor(
             [-0.4_1_5_8, -0.4_0_9_2, -0.4_3_4_7]			,     device=SCREAMING_SNAKE_CASE_			,     dtype=torch.float			,     )
         self.assertTrue(torch.allclose(logits[0, :3]			,     SCREAMING_SNAKE_CASE_			,     atol=1E-4	)	)






