
"""simple docstring"""
import re
import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin


class  _lowerCAmelCase    (    SCREAMING_SNAKE_CASE__    ):
							lowercase_  :  Dict      =				['''image_processor''', '''tokenizer''']
							lowercase_  :  Union[str, Any]      =				'''AutoImageProcessor'''
							lowercase_  :  int      =				'''AutoTokenizer'''



							def __init__(   self			,    a_=None			,    a_=None			,    **a_ )				->				Any:
								_UpperCAmelCase			=    None
								if "feature_extractor" in kwargs:
									warnings.warn(
									    "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
									    " instead."			,    a_			,    )
									_UpperCAmelCase			=    kwargs.pop("feature_extractor" )

								_UpperCAmelCase			=    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__(a_			,    a_ )
								_UpperCAmelCase			=    self.image_processor
								_UpperCAmelCase			=    False



							def __call__(   self			,    *a_			,    **a_ )				->				Union[str, Any]:



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

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

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



							def    _a (   self			,    *a_			,    **a_ )				->				str:
								return self.tokenizer.batch_decode(*a_			,    **a_ )



							def    _a (   self			,    *a_			,    **a_ )				->				Optional[Any]:
								return self.tokenizer.decode(*a_			,    **a_ )



							@contextmanager
							def    _a (   self )				->				List[str]:
								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." )
								_UpperCAmelCase			=    True
								_UpperCAmelCase			=    self.tokenizer
								yield
								_UpperCAmelCase			=    self.image_processor
								_UpperCAmelCase			=    False



							def    _a (   self			,    a_			,    a_=False			,    a_=None )				->				Optional[int]:



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

								_UpperCAmelCase			=    {}

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

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

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



							@property
							def    _a (   self )				->				int:
								warnings.warn(
								    "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."			,    a_			,    )
								return self.image_processor_class



							@property
							def    _a (   self )				->				Dict:
								warnings.warn(
								    "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."			,    a_			,    )
								return self.image_processor










from __future__ import annotations

from typing import Any

def lowercase    (  SCREAMING_SNAKE_CASE )			->							None:


			'''simple docstring'''







			create_state_space_tree(SCREAMING_SNAKE_CASE       ,    []       ,    0 )

def lowercase    (  SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE )			->							None:


			'''simple docstring'''







			if index == len(SCREAMING_SNAKE_CASE ):
						print(SCREAMING_SNAKE_CASE )
						return

			create_state_space_tree(SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE       ,    index + 1 )
			current_subsequence.append(sequence[index] )
			create_state_space_tree(SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE       ,    index + 1 )
			current_subsequence.pop()


if __name__ == "__main__":
				SCREAMING_SNAKE_CASE__     :     list[Any]		    =       [3, 1, 2, 4]
				generate_all_subsequences(seq)

				seq.clear()
				seq.extend(["A", "B", "C"])
				generate_all_subsequences(seq)
