
"""simple docstring"""





import re
import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin
class 						SCREAMING_SNAKE_CASE_ (						__a ):




		"""simple docstring"""





		__lowercase							:  str						   =		['''image_processor''', '''tokenizer''']
		__lowercase							:  Optional[Any]						   =		'''AutoImageProcessor'''
		__lowercase							:  int						   =		'''AutoTokenizer'''




		def __init__(     self							,  lowerCAmelCase__=None							,  lowerCAmelCase__=None							,  **lowerCAmelCase__):
				__SCREAMING_SNAKE_CASE            = 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            = 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__(lowerCAmelCase__							,  lowerCAmelCase__)
				__SCREAMING_SNAKE_CASE            = self.image_processor
				__SCREAMING_SNAKE_CASE            = False




		def __call__(     self							,  *lowerCAmelCase__							,  **lowerCAmelCase__):
				# For backward compatibility
				if self._in_target_context_manager:
						return self.current_processor(*lowerCAmelCase__							,  **lowerCAmelCase__)

				__SCREAMING_SNAKE_CASE            = kwargs.pop("""images"""							,  lowerCAmelCase__)
				__SCREAMING_SNAKE_CASE            = kwargs.pop("""text"""							,  lowerCAmelCase__)
				if len(lowerCAmelCase__) > 0:
						__SCREAMING_SNAKE_CASE            = args[0]
						__SCREAMING_SNAKE_CASE            = 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:
						__SCREAMING_SNAKE_CASE            = self.image_processor(lowerCAmelCase__							,  *lowerCAmelCase__							,  **lowerCAmelCase__)
				if text is not None:
						__SCREAMING_SNAKE_CASE            = self.tokenizer(lowerCAmelCase__							,  **lowerCAmelCase__)

				if text is None:
						return inputs
				elif images is None:
						return encodings
				else:
						__SCREAMING_SNAKE_CASE            = encodings["""input_ids"""]
						return inputs




		def 	snake_case_     (     self							,  *lowerCAmelCase__							,  **lowerCAmelCase__):
				return self.tokenizer.batch_decode(*lowerCAmelCase__							,  **lowerCAmelCase__)




		def 	snake_case_     (     self							,  *lowerCAmelCase__							,  **lowerCAmelCase__):
				return self.tokenizer.decode(*lowerCAmelCase__							,  **lowerCAmelCase__)




		@contextmanager
		def 	snake_case_     (     self):
				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.""")
				__SCREAMING_SNAKE_CASE            = True
				__SCREAMING_SNAKE_CASE            = self.tokenizer
				yield
				__SCREAMING_SNAKE_CASE            = self.image_processor
				__SCREAMING_SNAKE_CASE            = False




		def 	snake_case_     (     self							,  lowerCAmelCase__							,  lowerCAmelCase__=False							,  lowerCAmelCase__=None):
				if added_vocab is None:
						__SCREAMING_SNAKE_CASE            = self.tokenizer.get_added_vocab()

				__SCREAMING_SNAKE_CASE            = {}

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

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

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




		@property
		def 	snake_case_     (     self):
				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 	snake_case_     (     self):
				warnings.warn(
				    """`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."""							,  lowerCAmelCase__							,  )
				return self.image_processor








def 							_UpperCAmelCase				(UpperCamelCase__     :  str				,							UpperCamelCase__     :  bool = False						):

       if not isinstance(UpperCamelCase__				,							UpperCamelCase__						):
              _A      :       Optional[Any]				    = f"Expected string as input, found {type(UpperCamelCase__						)}"
              raise ValueError(UpperCamelCase__						)
       if not isinstance(UpperCamelCase__				,							UpperCamelCase__						):
              _A      :       Union[str, Any]				    = f"Expected boolean as use_pascal parameter, found {type(UpperCamelCase__						)}"
              raise ValueError(UpperCamelCase__						)

       _A      :       int				    = input_str.split("_"						)

       _A      :       str				    = 0 if use_pascal else 1

       _A      :       str				    = words[start_index:]

       _A      :       Optional[Any]				    = [word[0].upper() + word[1:] for word in words_to_capitalize]

       _A      :       Any				    = "" if use_pascal else words[0]

       return "".join([initial_word, *capitalized_words]						)


if __name__ == "__main__":
    from doctest import testmod

    testmod()
