





'''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				(					_A  ):







	'''simple docstring'''

	a			: Tuple      =				["""image_processor""", """tokenizer"""]
	a			: List[str]      =				"""OneFormerImageProcessor"""
	a			: int      =				("""CLIPTokenizer""", """CLIPTokenizerFast""")


	def __init__(self   ,_lowerCamelCase=None   ,_lowerCamelCase=None   ,_lowerCamelCase = 77   ,_lowerCamelCase = 77   ,**_lowerCamelCase       )							->							Tuple:







				'''simple docstring'''





				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`.'''       )

				__lowercase   =							max_seq_length
				__lowercase   =							task_seq_length

				super().__init__(snake_case_   ,snake_case_       )


	def   _UpperCAmelCase   (self   ,_lowerCamelCase=None   ,_lowerCamelCase=77       )							->							Optional[Any]:







				'''simple docstring'''





				if text_list is None:
							raise ValueError('''tokens cannot be None.'''       )

				__lowercase   =							self.tokenizer(snake_case_   ,padding='''max_length'''   ,max_length=snake_case_   ,truncation=snake_case_       )

				__lowercase   =							tokens["""attention_mask"""], tokens["""input_ids"""]

				__lowercase   =							[]
				for attn_mask, input_id in zip(snake_case_   ,snake_case_       ):
							__lowercase   =							torch.tensor(snake_case_       ) * torch.tensor(snake_case_       )
							token_inputs.append(token.unsqueeze(0       )       )

				__lowercase   =							torch.cat(snake_case_   ,dim=0       )
				return token_inputs


	def __call__(self   ,_lowerCamelCase=None   ,_lowerCamelCase=None   ,_lowerCamelCase=None   ,**_lowerCamelCase       )							->							Any:







				'''simple docstring'''





				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.'''       )

				__lowercase   =							self.image_processor(snake_case_   ,snake_case_   ,snake_case_   ,**snake_case_       )

				if isinstance(snake_case_   ,snake_case_       ):
							__lowercase   =							[task_inputs]

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

				if hasattr(snake_case_   ,'''text_inputs'''       ):
							__lowercase   =							encoded_inputs.text_inputs

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

							__lowercase   =							torch.cat(snake_case_   ,dim=0       )

				return encoded_inputs


	def   _UpperCAmelCase   (self   ,_lowerCamelCase=None   ,_lowerCamelCase=None   ,_lowerCamelCase=None   ,**_lowerCamelCase       )							->							Tuple:







				'''simple docstring'''





				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.'''       )

				__lowercase   =							self.image_processor.encode_inputs(snake_case_   ,snake_case_   ,snake_case_   ,**snake_case_       )

				if isinstance(snake_case_   ,snake_case_       ):
							__lowercase   =							[task_inputs]

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

				if hasattr(snake_case_   ,'''text_inputs'''       ):
							__lowercase   =							encoded_inputs.text_inputs

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

							__lowercase   =							torch.cat(snake_case_   ,dim=0       )

				return encoded_inputs


	def   _UpperCAmelCase   (self   ,*_lowerCamelCase   ,**_lowerCamelCase       )							->							List[str]:







				'''simple docstring'''





				return self.image_processor.post_process_semantic_segmentation(*snake_case_   ,**snake_case_       )


	def   _UpperCAmelCase   (self   ,*_lowerCamelCase   ,**_lowerCamelCase       )							->							List[str]:







				'''simple docstring'''





				return self.image_processor.post_process_instance_segmentation(*snake_case_   ,**snake_case_       )


	def   _UpperCAmelCase   (self   ,*_lowerCamelCase   ,**_lowerCamelCase       )							->							List[Any]:







				'''simple docstring'''





				return self.image_processor.post_process_panoptic_segmentation(*snake_case_   ,**snake_case_       )







from math import cos, sin







def 				A__    (  lowerCamelCase  )     ->		list[list[float]]:
				UpperCamelCase_:							int		=  float(lowerCamelCase  )
				return [[scaling_factor * int(x == y  ) for x in range(2  )] for y in range(2  )]







def 				A__    (  lowerCamelCase  )     ->		list[list[float]]:
				UpperCamelCase_,      UpperCamelCase_:							Union[str, Any]		=  cos(lowerCamelCase  ), sin(lowerCamelCase  )
				return [[c, -s], [s, c]]







def 				A__    (  lowerCamelCase  )     ->		list[list[float]]:
				UpperCamelCase_,      UpperCamelCase_:							Any		=  cos(lowerCamelCase  ), sin(lowerCamelCase  )
				UpperCamelCase_:							List[Any]		=  c * s
				return [[c * c, cs], [cs, s * s]]







def 				A__    (  lowerCamelCase  )     ->		list[list[float]]:
				UpperCamelCase_,      UpperCamelCase_:							List[str]		=  cos(lowerCamelCase  ), sin(lowerCamelCase  )
				UpperCamelCase_:							int		=  c * s
				return [[2 * c - 1, 2 * cs], [2 * cs, 2 * s - 1]]


print(F"""    {scaling(5) = }""")
print(F"""  {rotation(45) = }""")
print(F"""{projection(45) = }""")





print(F"""{reflection(45) = }""")






