



import re
import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin


class 							lowerCamelCase_(   A__    ):





					'''simple docstring'''



					lowercase__   :       List[Any]										= ['image_processor', 'tokenizer']
					lowercase__   :       Tuple										= 'AutoImageProcessor'
					lowercase__   :       Optional[int]										= 'AutoTokenizer'

					def __init__(    self				,       lowerCamelCase__=None				,       lowerCamelCase__=None				,       **lowerCamelCase__   ):
									_lowerCamelCase        =				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__				,       )
													_lowerCamelCase        =				kwargs.pop('''feature_extractor'''   )

									_lowerCamelCase        =				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__   )
									_lowerCamelCase        =				self.image_processor
									_lowerCamelCase        =				False

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

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

									if text is None:
													return inputs
									elif images is None:
													return encodings
									else:
													_lowerCamelCase        =				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.'''   )
									_lowerCamelCase        =				True
									_lowerCamelCase        =				self.tokenizer
									yield
									_lowerCamelCase        =				self.image_processor
									_lowerCamelCase        =				False

					def 			snake_case__      (    self				,       lowerCamelCase__				,       lowerCamelCase__=False				,       lowerCamelCase__=None   ):
									if added_vocab is None:
													_lowerCamelCase        =				self.tokenizer.get_added_vocab()

									_lowerCamelCase        =				{}

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

																	_lowerCamelCase        =				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





"""simple docstring"""
def        lowerCAmelCase_(   lowercase_     :							str						,   lowercase_     :							str							)	->       bool:
				_lowerCamelCase        =				len(lowercase_							)
				_lowerCamelCase        =				len(lowercase_							)
				_lowerCamelCase        =				[[False for _ in range(m + 1							)] for _ in range(n + 1							)]
				_lowerCamelCase        =				True
				for i in range(lowercase_							):
								for j in range(m + 1							):
												if dp[i][j]:
																if j < m and a[i].upper() == b[j]:
																				_lowerCamelCase        =				True
																if a[i].islower():
																				_lowerCamelCase        =				True
				return dp[n][m]


if __name__ == "__main__":
			import doctest

			doctest.testmod()
