
import re
import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin





class     lowerCamelCase      (       __lowerCamelCase					):
	UpperCamelCase_  :					Union[str, Any]    		=	['image_processor', 'tokenizer']
	UpperCamelCase_  :					Dict    		=	'AutoImageProcessor'
	UpperCamelCase_  :					List[str]    		=	'AutoTokenizer'







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







				"""simple docstring"""






				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.'''   ,     lowercase   ,     )
							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__(lowercase   ,     lowercase			)
				SCREAMING_SNAKE_CASE     						= self.image_processor
				SCREAMING_SNAKE_CASE     						= False







	def __call__(     self      :Tuple   ,     *lowercase      :Optional[Any]   ,     **lowercase      :str			)		->      List[Any]:







				"""simple docstring"""






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

				SCREAMING_SNAKE_CASE     						= kwargs.pop('''images'''   ,     lowercase			)
				SCREAMING_SNAKE_CASE     						= kwargs.pop('''text'''   ,     lowercase			)
				if len(lowercase			) > 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(lowercase   ,     *lowercase   ,     **lowercase			)
				if text is not None:
							SCREAMING_SNAKE_CASE     						= self.tokenizer(lowercase   ,     **lowercase			)

				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      :Any   ,     *lowercase      :Any   ,     **lowercase      :int			)		->      Union[str, Any]:







				"""simple docstring"""






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







	def  snake_case__     (     self      :Tuple   ,     *lowercase      :int   ,     **lowercase      :Optional[Any]			)		->      Any:







				"""simple docstring"""






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







	@contextmanager
	def  snake_case__     (     self      :Tuple			)		->      Optional[int]:







				"""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     						= True
				SCREAMING_SNAKE_CASE     						= self.tokenizer
				yield
				SCREAMING_SNAKE_CASE     						= self.image_processor
				SCREAMING_SNAKE_CASE     						= False







	def  snake_case__     (     self      :Tuple   ,     lowercase      :Any   ,     lowercase      :Dict=False   ,     lowercase      :Any=None			)		->      Union[str, Any]:







				"""simple docstring"""






				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_(.*?)>'''   ,     lowercase   ,     re.IGNORECASE			)
							if start_token is None:
										break
							SCREAMING_SNAKE_CASE     						= start_token.group(1			)
							SCREAMING_SNAKE_CASE     						= re.search(rf"""</s_{key}>"""   ,     lowercase   ,     re.IGNORECASE			)
							SCREAMING_SNAKE_CASE     						= start_token.group()
							if end_token is None:
										SCREAMING_SNAKE_CASE     						= tokens.replace(lowercase   ,     ''''''			)
							else:
										SCREAMING_SNAKE_CASE     						= end_token.group()
										SCREAMING_SNAKE_CASE     						= re.escape(lowercase			)
										SCREAMING_SNAKE_CASE     						= re.escape(lowercase			)
										SCREAMING_SNAKE_CASE     						= re.search(f"""{start_token_escaped}(.*?){end_token_escaped}"""   ,     lowercase   ,     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(lowercase   ,     is_inner_value=lowercase   ,     added_vocab=lowercase			)
																if value:
																			if len(lowercase			) == 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(lowercase			)
																if len(output[key]			) == 1:
																			SCREAMING_SNAKE_CASE     						= output[key][0]

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

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







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







				"""simple docstring"""






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







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







				"""simple docstring"""






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

import warnings

from ...utils import logging
from .image_processing_yolos import YolosImageProcessor


__lowerCAmelCase					   =       logging.get_logger(__name__)





class     lowerCamelCase      (       __lowerCamelCase					):







	def __init__(     self      :List[Any]   ,     *lowercase      :List[str]   ,     **lowercase      :List[Any]			)		->      None:







				"""simple docstring"""






				warnings.warn(
				    '''The class YolosFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'''
				    ''' use YolosImageProcessor instead.'''   ,     lowercase   ,     )
				super().__init__(*lowercase   ,     **lowercase			)