






"""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(							_UpperCamelCase	):
						lowerCAmelCase_		:				Optional[int]=							["""image_processor""", """tokenizer"""]
						lowerCAmelCase_		:				List[str]=							"""OneFormerImageProcessor"""
						lowerCAmelCase_		:				Dict=							("""CLIPTokenizer""", """CLIPTokenizerFast""")






						def __init__(       self     ,    _SCREAMING_SNAKE_CASE=None     ,    _SCREAMING_SNAKE_CASE=None     ,    _SCREAMING_SNAKE_CASE = 77     ,    _SCREAMING_SNAKE_CASE = 77     ,    **_SCREAMING_SNAKE_CASE)      ->	int:




											"""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__(_SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE)






						def 		lowerCAmelCase (       self     ,    _SCREAMING_SNAKE_CASE=None     ,    _SCREAMING_SNAKE_CASE=77)      ->	List[Any]:




											"""simple docstring"""





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

											lowerCamelCase__					=self.tokenizer(_SCREAMING_SNAKE_CASE     ,    padding='max_length'     ,    max_length=_SCREAMING_SNAKE_CASE     ,    truncation=_SCREAMING_SNAKE_CASE)

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

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

											lowerCamelCase__					=torch.cat(_SCREAMING_SNAKE_CASE     ,    dim=0)
											return token_inputs






						def __call__(       self     ,    _SCREAMING_SNAKE_CASE=None     ,    _SCREAMING_SNAKE_CASE=None     ,    _SCREAMING_SNAKE_CASE=None     ,    **_SCREAMING_SNAKE_CASE)      ->	Union[str, 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.')

											lowerCamelCase__					=self.image_processor(_SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE     ,    **_SCREAMING_SNAKE_CASE)

											if isinstance(_SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE):
																lowerCamelCase__					=[task_inputs]

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

											if hasattr(_SCREAMING_SNAKE_CASE     ,    'text_inputs'):
																lowerCamelCase__					=encoded_inputs.text_inputs

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

																lowerCamelCase__					=torch.cat(_SCREAMING_SNAKE_CASE     ,    dim=0)

											return encoded_inputs






						def 		lowerCAmelCase (       self     ,    _SCREAMING_SNAKE_CASE=None     ,    _SCREAMING_SNAKE_CASE=None     ,    _SCREAMING_SNAKE_CASE=None     ,    **_SCREAMING_SNAKE_CASE)      ->	Union[str, 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.')

											lowerCamelCase__					=self.image_processor.encode_inputs(_SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE     ,    **_SCREAMING_SNAKE_CASE)

											if isinstance(_SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE):
																lowerCamelCase__					=[task_inputs]

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

											if hasattr(_SCREAMING_SNAKE_CASE     ,    'text_inputs'):
																lowerCamelCase__					=encoded_inputs.text_inputs

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

																lowerCamelCase__					=torch.cat(_SCREAMING_SNAKE_CASE     ,    dim=0)

											return encoded_inputs






						def 		lowerCAmelCase (       self     ,    *_SCREAMING_SNAKE_CASE     ,    **_SCREAMING_SNAKE_CASE)      ->	List[str]:




											"""simple docstring"""





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






						def 		lowerCAmelCase (       self     ,    *_SCREAMING_SNAKE_CASE     ,    **_SCREAMING_SNAKE_CASE)      ->	Tuple:




											"""simple docstring"""





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






						def 		lowerCAmelCase (       self     ,    *_SCREAMING_SNAKE_CASE     ,    **_SCREAMING_SNAKE_CASE)      ->	Tuple:




											"""simple docstring"""





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







"""simple docstring"""



import warnings

from ...utils import logging
from .image_processing_vit import ViTImageProcessor


_snake_case							:   Tuple	  =       logging.get_logger(__name__)




class 							__lowercase(							_UpperCamelCase	):






						def __init__(       self     ,    *_SCREAMING_SNAKE_CASE     ,    **_SCREAMING_SNAKE_CASE)      ->	None:




											"""simple docstring"""





											warnings.warn(
											    'The class ViTFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'
											    ' use ViTImageProcessor instead.'     ,    _SCREAMING_SNAKE_CASE     ,    )
											super().__init__(*_SCREAMING_SNAKE_CASE     ,    **_SCREAMING_SNAKE_CASE)