

from typing import List

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


if is_torch_available():
					import torch





class 			snake_case_     (	__A      ):







			'''simple docstring'''

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







			def __init__(  self				:       Tuple							,     _UpperCamelCase				:       List[str]=None							,     _UpperCamelCase				:       Optional[int]=None							,     _UpperCamelCase				:       int = 7_7							,     _UpperCamelCase				:       int = 7_7							,     **_UpperCamelCase				:       Tuple							)   ->Dict:
									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_     =			max_seq_length
									snake_case_     =			task_seq_length

									super().__init__(_UpperCamelCase							,     _UpperCamelCase							)







			def        snake_case__(  self				:       Any							,     _UpperCamelCase				:       Optional[int]=None							,     _UpperCamelCase				:       List[Any]=7_7							)   ->Optional[int]:
									if text_list is None:
															raise ValueError('''tokens cannot be None.'''							)

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

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

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

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







			def __call__(  self				:       Union[str, Any]							,     _UpperCamelCase				:       Tuple=None							,     _UpperCamelCase				:       Tuple=None							,     _UpperCamelCase				:       Any=None							,     **_UpperCamelCase				:       Optional[Any]							)   ->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_     =			self.image_processor(_UpperCamelCase							,     _UpperCamelCase							,     _UpperCamelCase							,     **_UpperCamelCase							)

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

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

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

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

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

									return encoded_inputs







			def        snake_case__(  self				:       Dict							,     _UpperCamelCase				:       str=None							,     _UpperCamelCase				:       Optional[Any]=None							,     _UpperCamelCase				:       Any=None							,     **_UpperCamelCase				:       Any							)   ->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_     =			self.image_processor.encode_inputs(_UpperCamelCase							,     _UpperCamelCase							,     _UpperCamelCase							,     **_UpperCamelCase							)

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

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

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

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

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

									return encoded_inputs







			def        snake_case__(  self				:       Optional[Any]							,     *_UpperCamelCase				:       str							,     **_UpperCamelCase				:       int							)   ->int:
									return self.image_processor.post_process_semantic_segmentation(*_UpperCamelCase							,     **_UpperCamelCase							)







			def        snake_case__(  self				:       List[Any]							,     *_UpperCamelCase				:       Optional[Any]							,     **_UpperCamelCase				:       str							)   ->str:
									return self.image_processor.post_process_instance_segmentation(*_UpperCamelCase							,     **_UpperCamelCase							)







			def        snake_case__(  self				:       Optional[int]							,     *_UpperCamelCase				:       Optional[int]							,     **_UpperCamelCase				:       Any							)   ->Union[str, Any]:
									return self.image_processor.post_process_panoptic_segmentation(*_UpperCamelCase							,     **_UpperCamelCase							)


def 						__SCREAMING_SNAKE_CASE      (SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__ ):
						return (input_a, input_a).count(0 ) % 2


def 						__SCREAMING_SNAKE_CASE      ():
						assert xor_gate(0						,	0 ) == 0
						assert xor_gate(0						,	1 ) == 1
						assert xor_gate(1						,	0 ) == 1
						assert xor_gate(1						,	1 ) == 0


if __name__ == "__main__":
					print(xor_gate(0, 0))
					print(xor_gate(0, 1))