





import re
import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin





class 				a			(      lowerCAmelCase__			):
				SCREAMING_SNAKE_CASE__     :   int		      =     ['''image_processor''', '''tokenizer''']
				SCREAMING_SNAKE_CASE__     :   Tuple		      =     '''AutoImageProcessor'''
				SCREAMING_SNAKE_CASE__     :   Union[str, Any]		      =     '''AutoTokenizer'''






				def __init__(  self			, _lowerCAmelCase=None			, _lowerCAmelCase=None			, **_lowerCAmelCase					):

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE:				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.'''			, __lowerCAmelCase			, )
																__SCREAMING_SNAKE_CASE:				List[Any]           =   kwargs.pop('''feature_extractor'''					)

										__SCREAMING_SNAKE_CASE:				Optional[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					)
										__SCREAMING_SNAKE_CASE:				Optional[Any]           =   self.image_processor
										__SCREAMING_SNAKE_CASE:				List[str]           =   False






				def __call__(  self			, *_lowerCAmelCase			, **_lowerCAmelCase					):

										"""simple docstring"""




										if self._in_target_context_manager:
																return self.current_processor(*__lowerCAmelCase			, **__lowerCAmelCase					)

										__SCREAMING_SNAKE_CASE:				Union[str, Any]           =   kwargs.pop('''images'''			, __lowerCAmelCase					)
										__SCREAMING_SNAKE_CASE:				Union[str, Any]           =   kwargs.pop('''text'''			, __lowerCAmelCase					)
										if len(__lowerCAmelCase					) > 0:
																__SCREAMING_SNAKE_CASE:				List[str]           =   args[0]
																__SCREAMING_SNAKE_CASE:				Tuple           =   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:				Optional[Any]           =   self.image_processor(__lowerCAmelCase			, *__lowerCAmelCase			, **__lowerCAmelCase					)
										if text is not None:
																__SCREAMING_SNAKE_CASE:				Any           =   self.tokenizer(__lowerCAmelCase			, **__lowerCAmelCase					)

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






				def 			snake_case_    (  self			, *_lowerCAmelCase			, **_lowerCAmelCase					):

										"""simple docstring"""




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






				def 			snake_case_    (  self			, *_lowerCAmelCase			, **_lowerCAmelCase					):

										"""simple docstring"""




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






				@contextmanager
				def 			snake_case_    (  self					):

										"""simple docstring"""




										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:				str           =   True
										__SCREAMING_SNAKE_CASE:				List[str]           =   self.tokenizer
										yield
										__SCREAMING_SNAKE_CASE:				Dict           =   self.image_processor
										__SCREAMING_SNAKE_CASE:				Dict           =   False






				def 			snake_case_    (  self			, _lowerCAmelCase			, _lowerCAmelCase=False			, _lowerCAmelCase=None					):

										"""simple docstring"""




										if added_vocab is None:
																__SCREAMING_SNAKE_CASE:				Optional[Any]           =   self.tokenizer.get_added_vocab()

										__SCREAMING_SNAKE_CASE:				int           =   {}

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

																						__SCREAMING_SNAKE_CASE:				Optional[int]           =   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					):

										"""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 			snake_case_    (  self					):

										"""simple docstring"""




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




'''simple docstring'''




import os
from distutils.util import strtobool






def 							snake_case__				(     _A:   int ,  _A:   List[Any]					)       ->      Union[str, Any]:

		'''simple docstring'''


		for e in env_keys:
				lowerCAmelCase     =    int(os.environ.get(_A ,  -1					)					)
				if val >= 0:
						return val
		return default






def 							snake_case__				(     _A:   str ,  _A:   List[Any]=False					)       ->      int:

		'''simple docstring'''


		lowerCAmelCase     =    os.environ.get(_A ,  str(_A					)					)
		return strtobool(_A					) == 1  # As its name indicates `strtobool` actually returns an int...






def 							snake_case__				(     _A:   Dict ,  _A:   List[Any]="no"					)       ->      str:

		'''simple docstring'''


		lowerCAmelCase     =    os.environ.get(_A ,  str(_A					)					)
		return value




