
"""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				(   lowerCAmelCase_						):
			lowercase			     =    ['image_processor', 'tokenizer']
			lowercase			     =    'OneFormerImageProcessor'
			lowercase			     =    ('CLIPTokenizer', 'CLIPTokenizerFast')






			def __init__(self     :  int			,SCREAMING_SNAKE_CASE_     :  str=None			,SCREAMING_SNAKE_CASE_     :  Any=None			,SCREAMING_SNAKE_CASE_     :  List[Any] = 77			,SCREAMING_SNAKE_CASE_     :  List[str] = 77			,**SCREAMING_SNAKE_CASE_     :  Optional[int] )					->  Dict:





				"""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`.''' )

				lowerCAmelCase				       =     max_seq_length
				lowerCAmelCase				       =     task_seq_length

				super().__init__(lowerCamelCase_			,lowerCamelCase_ )






			def 				UpperCAmelCase       (self     :  str			,SCREAMING_SNAKE_CASE_     :  Union[str, Any]=None			,SCREAMING_SNAKE_CASE_     :  Optional[Any]=77 )					->  Union[str, Any]:





				"""simple docstring"""


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

				lowerCAmelCase				       =     self.tokenizer(lowerCamelCase_			,padding='''max_length'''			,max_length=lowerCamelCase_			,truncation=lowerCamelCase_ )

				lowerCAmelCase       ,					lowerCAmelCase				       =     tokens['''attention_mask'''], tokens['''input_ids''']

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

				lowerCAmelCase				       =     torch.cat(lowerCamelCase_			,dim=0 )
				return token_inputs






			def __call__(self     :  Any			,SCREAMING_SNAKE_CASE_     :  List[str]=None			,SCREAMING_SNAKE_CASE_     :  Tuple=None			,SCREAMING_SNAKE_CASE_     :  List[str]=None			,**SCREAMING_SNAKE_CASE_     :  List[str] )					->  str:





				"""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.''' )

				lowerCAmelCase				       =     self.image_processor(lowerCamelCase_			,lowerCamelCase_			,lowerCamelCase_			,**lowerCamelCase_ )

				if isinstance(lowerCamelCase_			,lowerCamelCase_ ):
					lowerCAmelCase				       =     [task_inputs]

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

				if hasattr(lowerCamelCase_			,'''text_inputs''' ):
					lowerCAmelCase				       =     encoded_inputs.text_inputs

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

					lowerCAmelCase				       =     torch.cat(lowerCamelCase_			,dim=0 )

				return encoded_inputs






			def 				UpperCAmelCase       (self     :  Optional[int]			,SCREAMING_SNAKE_CASE_     :  int=None			,SCREAMING_SNAKE_CASE_     :  Tuple=None			,SCREAMING_SNAKE_CASE_     :  Tuple=None			,**SCREAMING_SNAKE_CASE_     :  Dict )					->  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.''' )

				lowerCAmelCase				       =     self.image_processor.encode_inputs(lowerCamelCase_			,lowerCamelCase_			,lowerCamelCase_			,**lowerCamelCase_ )

				if isinstance(lowerCamelCase_			,lowerCamelCase_ ):
					lowerCAmelCase				       =     [task_inputs]

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

				if hasattr(lowerCamelCase_			,'''text_inputs''' ):
					lowerCAmelCase				       =     encoded_inputs.text_inputs

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

					lowerCAmelCase				       =     torch.cat(lowerCamelCase_			,dim=0 )

				return encoded_inputs






			def 				UpperCAmelCase       (self     :  List[str]			,*SCREAMING_SNAKE_CASE_     :  List[str]			,**SCREAMING_SNAKE_CASE_     :  Optional[Any] )					->  Tuple:





				"""simple docstring"""


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






			def 				UpperCAmelCase       (self     :  Optional[int]			,*SCREAMING_SNAKE_CASE_     :  Any			,**SCREAMING_SNAKE_CASE_     :  str )					->  str:





				"""simple docstring"""


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






			def 				UpperCAmelCase       (self     :  Union[str, Any]			,*SCREAMING_SNAKE_CASE_     :  Any			,**SCREAMING_SNAKE_CASE_     :  Optional[Any] )					->  List[Any]:





				"""simple docstring"""


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




"""simple docstring"""






def __magic_name__  (							_lowerCamelCase:		int,				_lowerCamelCase:		int )				->	int:

	'''simple docstring'''




	lowerCAmelCase				       =     1.0
	for i in range(1,				r + 1 ):
		ret *= (n + 1 - i) / i
	return round(_lowerCamelCase )




def __magic_name__  (							_lowerCamelCase:		int )				->	int:

	'''simple docstring'''




	return choose(8 + n,				_lowerCamelCase ) + choose(9 + n,				_lowerCamelCase ) - 10




def __magic_name__  (							_lowerCamelCase:		int )				->	int:

	'''simple docstring'''




	return sum(non_bouncy_exact(_lowerCamelCase ) for i in range(1,				n + 1 ) )




def __magic_name__  (							_lowerCamelCase:		int = 100 )				->	int:

	'''simple docstring'''




	return non_bouncy_upto(_lowerCamelCase )


if __name__ == "__main__":
					print(f"""{solution() = }""")


