

'''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_			):
							_lowerCamelCase		:   Optional[Any]= ["image_processor", "tokenizer"]
							_lowerCamelCase		:   str= "OneFormerImageProcessor"
							_lowerCamelCase		:   List[Any]= ("CLIPTokenizer", "CLIPTokenizerFast")


							def __init__(      self     ,      _snake_case=None     ,      _snake_case=None     ,      _snake_case = 77     ,      _snake_case = 77     ,      **_snake_case)				->					str:
								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`.')

								UpperCAmelCase_		:   Optional[Any]													=       max_seq_length
								UpperCAmelCase_		:   List[str]													=       task_seq_length

								super().__init__(_snake_case     ,      _snake_case)


							def     _snake_case				(      self     ,      _snake_case=None     ,      _snake_case=77)				->					str:
								if text_list is None:
									raise ValueError('tokens cannot be None.')

								UpperCAmelCase_		:   Dict													=       self.tokenizer(_snake_case     ,      padding='max_length'     ,      max_length=_snake_case     ,      truncation=_snake_case)

								UpperCAmelCase_						,     UpperCAmelCase_		:   Any													=       tokens['attention_mask'], tokens['input_ids']

								UpperCAmelCase_		:   int													=       []
								for attn_mask, input_id in zip(_snake_case     ,      _snake_case):
									UpperCAmelCase_		:   Tuple													=       torch.tensor(_snake_case) * torch.tensor(_snake_case)
									token_inputs.append(token.unsqueeze(0))

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


							def __call__(      self     ,      _snake_case=None     ,      _snake_case=None     ,      _snake_case=None     ,      **_snake_case)				->					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.')

								UpperCAmelCase_		:   int													=       self.image_processor(_snake_case     ,      _snake_case     ,      _snake_case     ,      **_snake_case)

								if isinstance(_snake_case     ,      _snake_case):
									UpperCAmelCase_		:   Union[str, Any]													=       [task_inputs]

								if isinstance(_snake_case     ,      _snake_case) and all(isinstance(_snake_case     ,      _snake_case) for task_input in task_inputs):
									UpperCAmelCase_		:   List[Any]													=       []
									for task in task_inputs:
										UpperCAmelCase_		:   Optional[int]													=       F"""the task is {task}"""
										task_token_inputs.append(_snake_case)
									UpperCAmelCase_		:   List[Any]													=       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'):
									UpperCAmelCase_		:   Tuple													=       encoded_inputs.text_inputs

									UpperCAmelCase_		:   int													=       []
									for texts in texts_list:
										UpperCAmelCase_		:   Any													=       self._preprocess_text(_snake_case     ,      max_length=self.max_seq_length)
										text_inputs.append(text_input_list.unsqueeze(0))

									UpperCAmelCase_		:   Any													=       torch.cat(_snake_case     ,      dim=0)

								return encoded_inputs


							def     _snake_case				(      self     ,      _snake_case=None     ,      _snake_case=None     ,      _snake_case=None     ,      **_snake_case)				->					Dict:

								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.')

								UpperCAmelCase_		:   Optional[Any]													=       self.image_processor.encode_inputs(_snake_case     ,      _snake_case     ,      _snake_case     ,      **_snake_case)

								if isinstance(_snake_case     ,      _snake_case):
									UpperCAmelCase_		:   str													=       [task_inputs]

								if isinstance(_snake_case     ,      _snake_case) and all(isinstance(_snake_case     ,      _snake_case) for task_input in task_inputs):
									UpperCAmelCase_		:   Dict													=       []
									for task in task_inputs:
										UpperCAmelCase_		:   Tuple													=       F"""the task is {task}"""
										task_token_inputs.append(_snake_case)
									UpperCAmelCase_		:   Dict													=       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'):
									UpperCAmelCase_		:   Optional[Any]													=       encoded_inputs.text_inputs

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

									UpperCAmelCase_		:   Dict													=       torch.cat(_snake_case     ,      dim=0)

								return encoded_inputs


							def     _snake_case				(      self     ,      *_snake_case     ,      **_snake_case)				->					Dict:
								return self.image_processor.post_process_semantic_segmentation(*_snake_case     ,      **_snake_case)


							def     _snake_case				(      self     ,      *_snake_case     ,      **_snake_case)				->					str:
								return self.image_processor.post_process_instance_segmentation(*_snake_case     ,      **_snake_case)





							def     _snake_case				(      self     ,      *_snake_case     ,      **_snake_case)				->					Tuple:
								return self.image_processor.post_process_panoptic_segmentation(*_snake_case     ,      **_snake_case)









'''simple docstring'''


from itertools import combinations, product







def     SCREAMING_SNAKE_CASE(	UpperCamelCase ,UpperCamelCase ,UpperCamelCase ,UpperCamelCase	)     ->     bool:
	if xa == ya == 0 or xa == ya == 0:
		return False
	UpperCAmelCase_		:   int													=       xa * xa + ya * ya
	UpperCAmelCase_		:   List[str]													=       xa * xa + ya * ya
	UpperCAmelCase_		:   Optional[Any]													=       (xa - xa) * (xa - xa) + (ya - ya) * (ya - ya)
	return (
	    a_square + b_square == c_square
	    or a_square + c_square == b_square
	    or b_square + c_square == a_square
	)







def     SCREAMING_SNAKE_CASE(	UpperCamelCase = 5_0	)     ->     int:
	return sum(
	    1
	    for pta, pta in combinations(product(range(limit + 1	) ,repeat=2	) ,2	)
	    if is_right(*UpperCamelCase ,*UpperCamelCase	)	)


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






