




"""simple docstring"""





from typing import List

from ...processing_utils import ProcessorMixin
from ...utils import is_torch_available


if is_torch_available():
	import torch

class  __lowercase    (     snake_case_		):


				'''simple docstring'''


				_lowerCAmelCase		     =["image_processor", "tokenizer"]
				_lowerCAmelCase		     ="OneFormerImageProcessor"
				_lowerCAmelCase		     =("CLIPTokenizer", "CLIPTokenizerFast")

				def __init__(				self				:       Tuple ,SCREAMING_SNAKE_CASE_				:       Union[str, Any]=None ,SCREAMING_SNAKE_CASE_				:       List[Any]=None ,SCREAMING_SNAKE_CASE_				:       int = 7_7 ,SCREAMING_SNAKE_CASE_				:       int = 7_7 ,**SCREAMING_SNAKE_CASE_				:       Tuple ):
										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`.''' )

										snake_case__    :   Any							     =   max_seq_length
										snake_case__    :   Union[str, Any]							     =   task_seq_length

										super().__init__(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ )

				def  _lowerCamelCase   (				self				:       Optional[Any] ,SCREAMING_SNAKE_CASE_				:       Dict=None ,SCREAMING_SNAKE_CASE_				:       Tuple=7_7 ):
										if text_list is None:
																raise ValueError('''tokens cannot be None.''' )

										snake_case__    :   Dict							     =   self.tokenizer(SCREAMING_SNAKE_CASE_ ,padding='''max_length''' ,max_length=SCREAMING_SNAKE_CASE_ ,truncation=SCREAMING_SNAKE_CASE_ )

										snake_case__    :   int							     =   tokens['''attention_mask'''], tokens['''input_ids''']

										snake_case__    :   Any							     =   []
										for attn_mask, input_id in zip(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ):
																snake_case__    :   List[Any]							     =   torch.tensor(SCREAMING_SNAKE_CASE_ ) * torch.tensor(SCREAMING_SNAKE_CASE_ )
																token_inputs.append(token.unsqueeze(0 ) )

										snake_case__    :   str							     =   torch.cat(SCREAMING_SNAKE_CASE_ ,dim=0 )
										return token_inputs

				def __call__(				self				:       Dict ,SCREAMING_SNAKE_CASE_				:       Any=None ,SCREAMING_SNAKE_CASE_				:       Union[str, Any]=None ,SCREAMING_SNAKE_CASE_				:       Optional[int]=None ,**SCREAMING_SNAKE_CASE_				:       Tuple ):

										if task_inputs is None:
																raise ValueError('''You have to specify the task_input. Found None.''' )
										elif images is None:
																raise ValueError('''You have to specify the image. Found None.''' )

										if not all(task in ['''semantic''', '''instance''', '''panoptic'''] for task in task_inputs ):
																raise ValueError('''task_inputs must be semantic, instance, or panoptic.''' )

										snake_case__    :   str							     =   self.image_processor(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,**SCREAMING_SNAKE_CASE_ )

										if isinstance(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ):
																snake_case__    :   int							     =   [task_inputs]

										if isinstance(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ) and all(isinstance(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ) for task_input in task_inputs ):
																snake_case__    :   Tuple							     =   []
																for task in task_inputs:
																						snake_case__    :   Union[str, Any]							     =   F'''the task is {task}'''
																						task_token_inputs.append(SCREAMING_SNAKE_CASE_ )
																snake_case__    :   Optional[int]							     =   self._preprocess_text(SCREAMING_SNAKE_CASE_ ,max_length=self.task_seq_length )
										else:
																raise TypeError('''Task Inputs should be a string or a list of strings.''' )

										if hasattr(SCREAMING_SNAKE_CASE_ ,'''text_inputs''' ):
																snake_case__    :   Any							     =   encoded_inputs.text_inputs

																snake_case__    :   Tuple							     =   []
																for texts in texts_list:
																						snake_case__    :   Any							     =   self._preprocess_text(SCREAMING_SNAKE_CASE_ ,max_length=self.max_seq_length )
																						text_inputs.append(text_input_list.unsqueeze(0 ) )

																snake_case__    :   List[str]							     =   torch.cat(SCREAMING_SNAKE_CASE_ ,dim=0 )

										return encoded_inputs

				def  _lowerCamelCase   (				self				:       Optional[int] ,SCREAMING_SNAKE_CASE_				:       int=None ,SCREAMING_SNAKE_CASE_				:       str=None ,SCREAMING_SNAKE_CASE_				:       List[str]=None ,**SCREAMING_SNAKE_CASE_				:       Any ):

										if task_inputs is None:
																raise ValueError('''You have to specify the task_input. Found None.''' )
										elif images is None:
																raise ValueError('''You have to specify the image. Found None.''' )

										if not all(task in ['''semantic''', '''instance''', '''panoptic'''] for task in task_inputs ):
																raise ValueError('''task_inputs must be semantic, instance, or panoptic.''' )

										snake_case__    :   Union[str, Any]							     =   self.image_processor.encode_inputs(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,**SCREAMING_SNAKE_CASE_ )

										if isinstance(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ):
																snake_case__    :   Optional[Any]							     =   [task_inputs]

										if isinstance(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ) and all(isinstance(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ) for task_input in task_inputs ):
																snake_case__    :   str							     =   []
																for task in task_inputs:
																						snake_case__    :   str							     =   F'''the task is {task}'''
																						task_token_inputs.append(SCREAMING_SNAKE_CASE_ )
																snake_case__    :   Optional[Any]							     =   self._preprocess_text(SCREAMING_SNAKE_CASE_ ,max_length=self.task_seq_length )
										else:
																raise TypeError('''Task Inputs should be a string or a list of strings.''' )

										if hasattr(SCREAMING_SNAKE_CASE_ ,'''text_inputs''' ):
																snake_case__    :   Optional[Any]							     =   encoded_inputs.text_inputs

																snake_case__    :   Dict							     =   []
																for texts in texts_list:
																						snake_case__    :   Union[str, Any]							     =   self._preprocess_text(SCREAMING_SNAKE_CASE_ ,max_length=self.max_seq_length )
																						text_inputs.append(text_input_list.unsqueeze(0 ) )

																snake_case__    :   List[Any]							     =   torch.cat(SCREAMING_SNAKE_CASE_ ,dim=0 )

										return encoded_inputs

				def  _lowerCamelCase   (				self				:       Tuple ,*SCREAMING_SNAKE_CASE_				:       Union[str, Any] ,**SCREAMING_SNAKE_CASE_				:       Any ):
										return self.image_processor.post_process_semantic_segmentation(*SCREAMING_SNAKE_CASE_ ,**SCREAMING_SNAKE_CASE_ )

				def  _lowerCamelCase   (				self				:       Optional[Any] ,*SCREAMING_SNAKE_CASE_				:       str ,**SCREAMING_SNAKE_CASE_				:       Union[str, Any] ):
										return self.image_processor.post_process_instance_segmentation(*SCREAMING_SNAKE_CASE_ ,**SCREAMING_SNAKE_CASE_ )





				def  _lowerCamelCase   (				self				:       Optional[Any] ,*SCREAMING_SNAKE_CASE_				:       Union[str, Any] ,**SCREAMING_SNAKE_CASE_				:       Any ):
										return self.image_processor.post_process_panoptic_segmentation(*SCREAMING_SNAKE_CASE_ ,**SCREAMING_SNAKE_CASE_ )








"""simple docstring"""





class  __lowercase    :


				'''simple docstring'''


				class  __lowercase    :


								'''simple docstring'''


								_lowerCAmelCase		     ="_prev", "_data", "_next"

								def __init__(				self				:       List[Any] ,SCREAMING_SNAKE_CASE_				:       int ,SCREAMING_SNAKE_CASE_				:       Union[str, Any] ,SCREAMING_SNAKE_CASE_				:       Any ):
														snake_case__    :   Any							     =   link_p
														snake_case__    :   List[str]							     =   element
														snake_case__    :   str							     =   link_n





								def  _lowerCamelCase   (				self				:       Optional[Any] ):
														return (
														    F''' Prev -> {self._prev is not None}, Next -> {self._next is not None}'''
														)







				def __init__(				self				:       Any ):
										snake_case__    :   int							     =   self._Node(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ )
										snake_case__    :   Optional[int]							     =   self._Node(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ )
										snake_case__    :   Union[str, Any]							     =   self._trailer
										snake_case__    :   Union[str, Any]							     =   self._header
										snake_case__    :   Any							     =   0

				def __len__(				self				:       str ):
										return self._size

				def  _lowerCamelCase   (				self				:       Union[str, Any] ):
										return self.__len__() == 0

				def  _lowerCamelCase   (				self				:       Any ,SCREAMING_SNAKE_CASE_				:       List[Any] ,SCREAMING_SNAKE_CASE_				:       List[str] ,SCREAMING_SNAKE_CASE_				:       Dict ):
										# Create new_node by setting it's prev.link -> header
										# setting it's next.link -> trailer
										snake_case__    :   Dict							     =   self._Node(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ )
										snake_case__    :   Any							     =   new_node
										snake_case__    :   str							     =   new_node
										self._size += 1
										return self





				def  _lowerCamelCase   (				self				:       Union[str, Any] ,SCREAMING_SNAKE_CASE_				:       Dict ):
										snake_case__    :   Union[str, Any]							     =   node._prev
										snake_case__    :   Union[str, Any]							     =   node._next

										snake_case__    :   List[Any]							     =   successor
										snake_case__    :   Dict							     =   predecessor
										self._size -= 1
										snake_case__    :   Dict							     =   node._data
										snake_case__    :   List[str]							     =   None
										del node
										return temp







class  __lowercase    (     _DoublyLinkedBase		):


				'''simple docstring'''


				def  _lowerCamelCase   (				self				:       Optional[int] ):
										if self.is_empty():
																raise Exception('''List is empty''' )
										return self._header._next._data

				def  _lowerCamelCase   (				self				:       Union[str, Any] ):
										if self.is_empty():
																raise Exception('''List is empty''' )
										return self._trailer._prev._data

				def  _lowerCamelCase   (				self				:       Any ,SCREAMING_SNAKE_CASE_				:       Dict ):
										return self._insert(self._header ,SCREAMING_SNAKE_CASE_ ,self._header._next )

				def  _lowerCamelCase   (				self				:       Dict ,SCREAMING_SNAKE_CASE_				:       Union[str, Any] ):
										return self._insert(self._trailer._prev ,SCREAMING_SNAKE_CASE_ ,self._trailer )

				def  _lowerCamelCase   (				self				:       Optional[int] ):
										if self.is_empty():
																raise IndexError('''remove_first from empty list''' )
										return self._delete(self._header._next )





				def  _lowerCamelCase   (				self				:       Optional[Any] ):
										if self.is_empty():
																raise IndexError('''remove_first from empty list''' )
										return self._delete(self._trailer._prev )


