
import re
import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin







class 			__A( a			):
				snake_case_						   =    ['''image_processor''', '''tokenizer''']
				snake_case_						   =    '''AutoImageProcessor'''
				snake_case_						   =    '''AutoTokenizer'''




				def __init__(     self		,     _snake_case=None		,     _snake_case=None		,     **_snake_case				)    ->      str:


								'''simple docstring'''




								__a										=    None
								if "feature_extractor" in kwargs:
												warnings.warn(
												    '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
												    ''' instead.'''		,     _snake_case		,     )
												__a										=    kwargs.pop('''feature_extractor'''				)

								__a										=    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__(_snake_case		,     _snake_case				)
								__a										=    self.image_processor
								__a										=    False




				def __call__(     self		,     *_snake_case		,     **_snake_case				)    ->      Optional[int]:


								'''simple docstring'''




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

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

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




				def 				SCREAMING_SNAKE_CASE_		(     self		,     *_snake_case		,     **_snake_case				)    ->      List[str]:


								'''simple docstring'''




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




				def 				SCREAMING_SNAKE_CASE_		(     self		,     *_snake_case		,     **_snake_case				)    ->      int:


								'''simple docstring'''




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




				@contextmanager
				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      Optional[Any]:


								'''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.'''				)
								__a										=    True
								__a										=    self.tokenizer
								yield
								__a										=    self.image_processor
								__a										=    False




				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case		,     _snake_case=False		,     _snake_case=None				)    ->      int:


								'''simple docstring'''




								if added_vocab is None:
												__a										=    self.tokenizer.get_added_vocab()

								__a										=    {}

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

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

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




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


								'''simple docstring'''




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




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


								'''simple docstring'''




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

def     __lowerCAmelCase			(			a__						,    a__						,    a__  )       ->  list:
				__a										=    len(a__  )
				__a										=    [[0] * n for i in range(a__  )]
				for i in range(a__  ):
								__a										=    y_points[i]

				for i in range(2						,    a__  ):
								for j in range(a__						,    a__  ):
												__a										=    (
												    (xa - x_points[j - i + 1]) * q[j][i - 1]
												    - (xa - x_points[j]) * q[j - 1][i - 1]
												) / (x_points[j] - x_points[j - i + 1])

				return [q[n - 1][n - 1], q]


if __name__ == "__main__":
					import doctest

					doctest.testmod()