




'''simple docstring'''







from typing import List

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


if is_torch_available():
						import torch







class 				_a   (a__  ):





					'''simple docstring'''



					lowerCAmelCase_     :    Tuple									=				["""image_processor""", """tokenizer"""]
					lowerCAmelCase_     :    Optional[Any]									=				"""OneFormerImageProcessor"""
					lowerCAmelCase_     :    int									=				("""CLIPTokenizer""", """CLIPTokenizerFast""")







					def __init__(							self       ,__a=None       ,__a=None       ,__a = 77       ,__a = 77       ,**__a       )			->							Optional[Any]:
										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 :			str					   = max_seq_length
										snake_case :			Any					   = task_seq_length

										super().__init__(__a       ,__a       )







					def  snake_case_			(							self       ,__a=None       ,__a=77       )			->							List[str]:
										if text_list is None:
															raise ValueError("""tokens cannot be None."""       )

										snake_case :			int					   = self.tokenizer(__a       ,padding="""max_length"""       ,max_length=__a       ,truncation=__a       )

										snake_case :			int					   = tokens["""attention_mask"""], tokens["""input_ids"""]

										snake_case :			Tuple					   = []
										for attn_mask, input_id in zip(__a       ,__a       ):
															snake_case :			Dict					   = torch.tensor(__a       ) * torch.tensor(__a       )
															token_inputs.append(token.unsqueeze(0       )       )

										snake_case :			Optional[int]					   = torch.cat(__a       ,dim=0       )
										return token_inputs







					def __call__(							self       ,__a=None       ,__a=None       ,__a=None       ,**__a       )			->							Optional[int]:

										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(__a       ,__a       ,__a       ,**__a       )

										if isinstance(__a       ,__a       ):
															snake_case :			Tuple					   = [task_inputs]

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

										if hasattr(__a       ,"""text_inputs"""       ):
															snake_case :			Optional[int]					   = encoded_inputs.text_inputs

															snake_case :			Dict					   = []
															for texts in texts_list:
																				snake_case :			Optional[Any]					   = self._preprocess_text(__a       ,max_length=self.max_seq_length       )
																				text_inputs.append(text_input_list.unsqueeze(0       )       )

															snake_case :			Tuple					   = torch.cat(__a       ,dim=0       )

										return encoded_inputs







					def  snake_case_			(							self       ,__a=None       ,__a=None       ,__a=None       ,**__a       )			->							List[str]:

										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 :			Any					   = self.image_processor.encode_inputs(__a       ,__a       ,__a       ,**__a       )

										if isinstance(__a       ,__a       ):
															snake_case :			Optional[int]					   = [task_inputs]

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

										if hasattr(__a       ,"""text_inputs"""       ):
															snake_case :			Dict					   = encoded_inputs.text_inputs

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

															snake_case :			Union[str, Any]					   = torch.cat(__a       ,dim=0       )

										return encoded_inputs







					def  snake_case_			(							self       ,*__a       ,**__a       )			->							List[str]:
										return self.image_processor.post_process_semantic_segmentation(*__a       ,**__a       )







					def  snake_case_			(							self       ,*__a       ,**__a       )			->							List[Any]:
										return self.image_processor.post_process_instance_segmentation(*__a       ,**__a       )







					def  snake_case_			(							self       ,*__a       ,**__a       )			->							Optional[Any]:
										return self.image_processor.post_process_panoptic_segmentation(*__a       ,**__a       )









'''simple docstring'''







def 					lowerCamelCase__							(			__lowercase    ,      __lowercase = 2							):
     return round((float(__lowercase							) * 9 / 5) + 32    ,      __lowercase							)



def 					lowerCamelCase__							(			__lowercase    ,      __lowercase = 2							):
     return round(float(__lowercase							) + 273.15    ,      __lowercase							)



def 					lowerCamelCase__							(			__lowercase    ,      __lowercase = 2							):
     return round((float(__lowercase							) * 9 / 5) + 491.67    ,      __lowercase							)



def 					lowerCamelCase__							(			__lowercase    ,      __lowercase = 2							):
     return round((float(__lowercase							) - 32) * 5 / 9    ,      __lowercase							)



def 					lowerCamelCase__							(			__lowercase    ,      __lowercase = 2							):
     return round(((float(__lowercase							) - 32) * 5 / 9) + 273.15    ,      __lowercase							)



def 					lowerCamelCase__							(			__lowercase    ,      __lowercase = 2							):
     return round(float(__lowercase							) + 459.67    ,      __lowercase							)



def 					lowerCamelCase__							(			__lowercase    ,      __lowercase = 2							):
     return round(float(__lowercase							) - 273.15    ,      __lowercase							)



def 					lowerCamelCase__							(			__lowercase    ,      __lowercase = 2							):
     return round(((float(__lowercase							) - 273.15) * 9 / 5) + 32    ,      __lowercase							)



def 					lowerCamelCase__							(			__lowercase    ,      __lowercase = 2							):
     return round((float(__lowercase							) * 9 / 5)    ,      __lowercase							)



def 					lowerCamelCase__							(			__lowercase    ,      __lowercase = 2							):
     return round((float(__lowercase							) - 491.67) * 5 / 9    ,      __lowercase							)



def 					lowerCamelCase__							(			__lowercase    ,      __lowercase = 2							):
     return round(float(__lowercase							) - 459.67    ,      __lowercase							)



def 					lowerCamelCase__							(			__lowercase    ,      __lowercase = 2							):
     return round((float(__lowercase							) * 5 / 9)    ,      __lowercase							)



def 					lowerCamelCase__							(			__lowercase    ,      __lowercase = 2							):
     return round((float(__lowercase							) * 1.25 + 273.15)    ,      __lowercase							)



def 					lowerCamelCase__							(			__lowercase    ,      __lowercase = 2							):
     return round((float(__lowercase							) * 2.25 + 32)    ,      __lowercase							)



def 					lowerCamelCase__							(			__lowercase    ,      __lowercase = 2							):
     return round((float(__lowercase							) * 1.25)    ,      __lowercase							)



def 					lowerCamelCase__							(			__lowercase    ,      __lowercase = 2							):
     return round((float(__lowercase							) * 2.25 + 32 + 459.67)    ,      __lowercase							)


if __name__ == "__main__":
      import doctest

      doctest.testmod()



