




import re
import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin



class       a_       (     SCREAMING_SNAKE_CASE__     ):
	A    							= ['''image_processor''', '''tokenizer''']
	A    							= '''AutoImageProcessor'''
	A    							= '''AutoTokenizer'''





	def __init__(							self      ,     SCREAMING_SNAKE_CASE=None      ,     SCREAMING_SNAKE_CASE=None      ,     **SCREAMING_SNAKE_CASE      )  ->					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.'      ,     SCREAMING_SNAKE_CASE      ,     )
							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__(SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE      )
				SCREAMING_SNAKE_CASE_								=						self.image_processor
				SCREAMING_SNAKE_CASE_								=						False





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




				"""simple docstring"""



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

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

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





	def 						A_(							self      ,     *SCREAMING_SNAKE_CASE      ,     **SCREAMING_SNAKE_CASE      )  ->					str:




				"""simple docstring"""



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





	def 						A_(							self      ,     *SCREAMING_SNAKE_CASE      ,     **SCREAMING_SNAKE_CASE      )  ->					Optional[Any]:




				"""simple docstring"""



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





	@contextmanager
	def 						A_(							self      )  ->					List[str]:




				"""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 						A_(							self      ,     SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE=False      ,     SCREAMING_SNAKE_CASE=None      )  ->					Optional[int]:




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

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

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





	@property
	def 						A_(							self      )  ->					int:




				"""simple docstring"""



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





	@property
	def 						A_(							self      )  ->					Dict:




				"""simple docstring"""



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