


import re
import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin







class 			_lowercase (							lowercase__):


       """simple docstring"""




       A__											=			["image_processor", "tokenizer"]
       A__											=			"AutoImageProcessor"
       A__											=			"AutoTokenizer"





       def __init__(   self							:			List[str]					,      __lowerCamelCase							:			Union[str, Any]=None					,      __lowerCamelCase							:			List[str]=None					,      **__lowerCamelCase							:			str       ):


             '''simple docstring'''



             lowerCamelCase__		:				List[str]    =  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__		:				Union[str, Any]    =  kwargs.pop("feature_extractor"       )

             lowerCamelCase__		:				List[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       )
             lowerCamelCase__		:				Dict    =  self.image_processor
             lowerCamelCase__		:				Dict    =  False





       def __call__(   self							:			Tuple					,      *__lowerCamelCase							:			Union[str, Any]					,      **__lowerCamelCase							:			Any       ):


             '''simple docstring'''



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

             lowerCamelCase__		:				Tuple    =  kwargs.pop("images"					,      __lowerCamelCase       )
             lowerCamelCase__		:				Optional[int]    =  kwargs.pop("text"					,      __lowerCamelCase       )
             if len(__lowerCamelCase       ) > 0:
                   lowerCamelCase__		:				Tuple    =  args[0]
                   lowerCamelCase__		:				Union[str, Any]    =  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__		:				Tuple    =  self.image_processor(__lowerCamelCase					,      *__lowerCamelCase					,      **__lowerCamelCase       )
             if text is not None:
                   lowerCamelCase__		:				Any    =  self.tokenizer(__lowerCamelCase					,      **__lowerCamelCase       )

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





       def 						lowerCAmelCase     (   self							:			Optional[int]					,      *__lowerCamelCase							:			List[str]					,      **__lowerCamelCase							:			List[str]       ):


             '''simple docstring'''



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





       def 						lowerCAmelCase     (   self							:			str					,      *__lowerCamelCase							:			List[Any]					,      **__lowerCamelCase							:			Optional[int]       ):


             '''simple docstring'''



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





       @contextmanager
       def 						lowerCAmelCase     (   self							:			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."       )
             lowerCamelCase__		:				Dict    =  True
             lowerCamelCase__		:				int    =  self.tokenizer
             yield
             lowerCamelCase__		:				Dict    =  self.image_processor
             lowerCamelCase__		:				Optional[Any]    =  False





       def 						lowerCAmelCase     (   self							:			Union[str, Any]					,      __lowerCamelCase							:			Union[str, Any]					,      __lowerCamelCase							:			str=False					,      __lowerCamelCase							:			List[str]=None       ):


             '''simple docstring'''



             if added_vocab is None:
                   lowerCamelCase__		:				Optional[int]    =  self.tokenizer.get_added_vocab()

             lowerCamelCase__		:				Tuple    =  {}

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

                         lowerCamelCase__		:				List[Any]    =  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 						lowerCAmelCase     (   self							:			Optional[int]       ):


             '''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 						lowerCAmelCase     (   self							:			str       ):


             '''simple docstring'''



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






from __future__ import annotations

from typing import Any

def       lowercase_		(		_A			:    list[Any]						):

      """simple docstring"""




      create_state_space_tree(_A    ,					[]    ,					0						)

def       lowercase_		(		_A			:    list[Any]    ,					_A			:    list[Any]    ,					_A			:    int						):

      """simple docstring"""




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

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


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

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


