




'''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    (		UpperCAmelCase_    ):
							lowercase_							=				['image_processor', 'tokenizer']
							lowercase_							=				'OneFormerImageProcessor'
							lowercase_							=				('CLIPTokenizer', 'CLIPTokenizerFast')







							def __init__(  self       :  str							,			lowerCAmelCase_       :  Any=None							,			lowerCAmelCase_       :  Optional[int]=None							,			lowerCAmelCase_       :  Dict = 77							,			lowerCAmelCase_       :  Optional[int] = 77							,			**lowerCAmelCase_       :  Optional[int]					)			->							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`.'''					)

														_a	     =				max_seq_length
														_a	     =				task_seq_length

														super().__init__(__UpperCamelCase							,			__UpperCamelCase					)







							def    __lowerCAmelCase			(  self       :  List[Any]							,			lowerCAmelCase_       :  str=None							,			lowerCAmelCase_       :  Optional[int]=77					)			->							List[str]:


														"""simple docstring"""


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

														_a	     =				self.tokenizer(__UpperCamelCase							,			padding='''max_length'''							,			max_length=__UpperCamelCase							,			truncation=__UpperCamelCase					)

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

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

														_a	     =				torch.cat(__UpperCamelCase							,			dim=0					)
														return token_inputs







							def __call__(  self       :  Union[str, Any]							,			lowerCAmelCase_       :  int=None							,			lowerCAmelCase_       :  Union[str, Any]=None							,			lowerCAmelCase_       :  Dict=None							,			**lowerCAmelCase_       :  str					)			->							List[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.'''					)

														_a	     =				self.image_processor(__UpperCamelCase							,			__UpperCamelCase							,			__UpperCamelCase							,			**__UpperCamelCase					)

														if isinstance(__UpperCamelCase							,			__UpperCamelCase					):
																					_a	     =				[task_inputs]

														if isinstance(__UpperCamelCase							,			__UpperCamelCase					) and all(isinstance(__UpperCamelCase							,			__UpperCamelCase					) for task_input in task_inputs					):
																					_a	     =				[]
																					for task in task_inputs:
																												_a	     =				F'the task is {task}'
																												task_token_inputs.append(__UpperCamelCase					)
																					_a	     =				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'''					):
																					_a	     =				encoded_inputs.text_inputs

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

																					_a	     =				torch.cat(__UpperCamelCase							,			dim=0					)

														return encoded_inputs







							def    __lowerCAmelCase			(  self       :  List[Any]							,			lowerCAmelCase_       :  Tuple=None							,			lowerCAmelCase_       :  Optional[Any]=None							,			lowerCAmelCase_       :  List[Any]=None							,			**lowerCAmelCase_       :  Dict					)			->							Optional[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.'''					)

														_a	     =				self.image_processor.encode_inputs(__UpperCamelCase							,			__UpperCamelCase							,			__UpperCamelCase							,			**__UpperCamelCase					)

														if isinstance(__UpperCamelCase							,			__UpperCamelCase					):
																					_a	     =				[task_inputs]

														if isinstance(__UpperCamelCase							,			__UpperCamelCase					) and all(isinstance(__UpperCamelCase							,			__UpperCamelCase					) for task_input in task_inputs					):
																					_a	     =				[]
																					for task in task_inputs:
																												_a	     =				F'the task is {task}'
																												task_token_inputs.append(__UpperCamelCase					)
																					_a	     =				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'''					):
																					_a	     =				encoded_inputs.text_inputs

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

																					_a	     =				torch.cat(__UpperCamelCase							,			dim=0					)

														return encoded_inputs







							def    __lowerCAmelCase			(  self       :  List[Any]							,			*lowerCAmelCase_       :  List[Any]							,			**lowerCAmelCase_       :  Union[str, Any]					)			->							Optional[int]:


														"""simple docstring"""


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







							def    __lowerCAmelCase			(  self       :  Dict							,			*lowerCAmelCase_       :  List[Any]							,			**lowerCAmelCase_       :  Union[str, Any]					)			->							List[Any]:


														"""simple docstring"""


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







							def    __lowerCAmelCase			(  self       :  Dict							,			*lowerCAmelCase_       :  str							,			**lowerCAmelCase_       :  Tuple					)			->							str:


														"""simple docstring"""


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







'''simple docstring'''







def 		_lowercase		(	lowercase_  ):



		'''simple docstring'''







		return "".join(chr(ord(lowercase_  ) + 32  ) if "A" <= char <= "Z" else char for char in word  )


if __name__ == "__main__":
						from doctest import testmod

						testmod()





