




"""simple docstring"""




import re
import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin

class   UpperCAmelCase_    (			snake_case		):
		UpperCamelCase							=["image_processor", "tokenizer"]
		UpperCamelCase							="AutoImageProcessor"
		UpperCamelCase							="AutoTokenizer"
		def __init__(						self				,						UpperCamelCase_=None				,						UpperCamelCase_=None				,						**UpperCamelCase_       )						->       List[Any]:
					__lowercase			:  int								=       None
					if "feature_extractor" in kwargs:
								warnings.warn(
								    '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
								    ''' instead.'''				,						UpperCamelCase_				,						)
								__lowercase			:  Union[str, Any]								=       kwargs.pop('''feature_extractor'''       )

					__lowercase			:  Optional[int]								=       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__(UpperCamelCase_				,						UpperCamelCase_       )
					__lowercase			:  Tuple								=       self.image_processor
					__lowercase			:  Tuple								=       False
		def __call__(						self				,						*UpperCamelCase_				,						**UpperCamelCase_       )						->       Tuple:
					# For backward compatibility
					if self._in_target_context_manager:
								return self.current_processor(*UpperCamelCase_				,						**UpperCamelCase_       )

					__lowercase			:  List[Any]								=       kwargs.pop('''images'''				,						UpperCamelCase_       )
					__lowercase			:  str								=       kwargs.pop('''text'''				,						UpperCamelCase_       )
					if len(UpperCamelCase_       ) > 0:
								__lowercase			:  List[Any]								=       args[0]
								__lowercase			:  int								=       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:
								__lowercase			:  Any								=       self.image_processor(UpperCamelCase_				,						*UpperCamelCase_				,						**UpperCamelCase_       )
					if text is not None:
								__lowercase			:  str								=       self.tokenizer(UpperCamelCase_				,						**UpperCamelCase_       )

					if text is None:
								return inputs
					elif images is None:
								return encodings
					else:
								__lowercase			:  int								=       encodings['''input_ids''']
								return inputs
		def 	_lowerCamelCase							(						self				,						*UpperCamelCase_				,						**UpperCamelCase_       )						->       int:
					return self.tokenizer.batch_decode(*UpperCamelCase_				,						**UpperCamelCase_       )
		def 	_lowerCamelCase							(						self				,						*UpperCamelCase_				,						**UpperCamelCase_       )						->       str:
					return self.tokenizer.decode(*UpperCamelCase_				,						**UpperCamelCase_       )
		@contextmanager
		def 	_lowerCamelCase							(						self       )						->       int:
					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.'''       )
					__lowercase			:  int								=       True
					__lowercase			:  Any								=       self.tokenizer
					yield
					__lowercase			:  List[Any]								=       self.image_processor
					__lowercase			:  Dict								=       False
		def 	_lowerCamelCase							(						self				,						UpperCamelCase_				,						UpperCamelCase_=False				,						UpperCamelCase_=None       )						->       Any:
					if added_vocab is None:
								__lowercase			:  int								=       self.tokenizer.get_added_vocab()

					__lowercase			:  Union[str, Any]								=       {}

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

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

					if len(UpperCamelCase_       ):
								return [output] if is_inner_value else output
					else:
								return [] if is_inner_value else {"text_sequence": tokens}
		@property
		def 	_lowerCamelCase							(						self       )						->       Optional[int]:
					warnings.warn(
					    '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'''				,						UpperCamelCase_				,						)
					return self.image_processor_class
		@property
		def 	_lowerCamelCase							(						self       )						->       Optional[Any]:
					warnings.warn(
					    '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'''				,						UpperCamelCase_				,						)
					return self.image_processor











def     a_   (__A      )				->						Dict:



					"""simple docstring"""
					if not head:
										return True
					# split the list to two parts
					__a      ,			__a					:						Any      =					head.next, head
					while fast and fast.next:
										__a					:						Optional[int]      =					fast.next.next
										__a					:						Optional[int]      =					slow.next
					__a					:						Optional[int]      =					slow.next
					__a					:						Tuple      =					None  # Don't forget here! But forget still works!
					# reverse the second part
					__a					:						Any      =					None
					while second:
										__a					:						int      =					second.next
										__a					:						int      =					node
										__a					:						Union[str, Any]      =					second
										__a					:						Union[str, Any]      =					nxt
					# compare two parts
					# second part has the same or one less node
					while node:
										if node.val != head.val:
															return False
										__a					:						int      =					node.next
										__a					:						Dict      =					head.next
					return True







def     a_   (__A      )				->						Union[str, Any]:



					"""simple docstring"""
					if not head or not head.next:
										return True

					# 1. Get the midpoint (slow)
					__a					:						Union[str, Any]      =					head
					while fast and fast.next:
										__a      ,			__a					:						List[str]      =					fast.next.next, slow.next

					# 2. Push the second half into the stack
					__a					:						Dict      =					[slow.val]
					while slow.next:
										__a					:						Optional[Any]      =					slow.next
										stack.append(slow.val      )

					# 3. Comparison
					while stack:
										if stack.pop() != cur.val:
															return False
										__a					:						List[str]      =					cur.next

					return True







def     a_   (__A      )				->						List[str]:



					"""simple docstring"""
					if not head or not head.next:
										return True
					__a					:						Optional[int]      =					{}
					__a					:						Optional[int]      =					0
					while head:
										if head.val in d:
															d[head.val].append(__A      )
										else:
															__a					:						Dict      =					[pos]
										__a					:						Dict      =					head.next
										pos += 1
					__a					:						Dict      =					pos - 1
					__a					:						Optional[int]      =					0
					for v in d.values():
										if len(__A      ) % 2 != 0:
															middle += 1
										else:
															__a					:						Dict      =					0
															for i in range(0				,       len(__A      )      ):
																				if v[i] + v[len(__A      ) - 1 - step] != checksum:
																									return False
																				step += 1
										if middle > 1:
															return False
					return True





