from typing import List

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


if is_torch_available():
		import torch






class 		a__						( A__ ):
					A     	=							['image_processor', 'tokenizer']
					A     	=							'OneFormerImageProcessor'
					A     	=							('CLIPTokenizer', 'CLIPTokenizerFast')

					def __init__(   self				:	Union[str, Any],_A				:	int=None,_A				:	List[str]=None,_A				:	Tuple = 77,_A				:	List[str] = 77,**_A				:	List[str]   ):







							"""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`."   )

							SCREAMING_SNAKE_CASE_  :		Union[str, Any]    				=   max_seq_length
							SCREAMING_SNAKE_CASE_  :		List[Any]    				=   task_seq_length

							super().__init__(snake_case_,snake_case_   )

					def     __UpperCamelCase       (   self				:	Tuple,_A				:	Optional[Any]=None,_A				:	List[str]=77   ):







							"""simple docstring"""






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

							SCREAMING_SNAKE_CASE_  :		int    				=   self.tokenizer(snake_case_,padding="max_length",max_length=snake_case_,truncation=snake_case_   )

							SCREAMING_SNAKE_CASE_				,   SCREAMING_SNAKE_CASE_  :		List[Any]    				=   tokens["attention_mask"], tokens["input_ids"]

							SCREAMING_SNAKE_CASE_  :		Optional[int]    				=   []
							for attn_mask, input_id in zip(snake_case_,snake_case_   ):
									SCREAMING_SNAKE_CASE_  :		List[str]    				=   torch.tensor(snake_case_   ) * torch.tensor(snake_case_   )
									token_inputs.append(token.unsqueeze(0   )   )

							SCREAMING_SNAKE_CASE_  :		Union[str, Any]    				=   torch.cat(snake_case_,dim=0   )
							return token_inputs

					def __call__(   self				:	Any,_A				:	int=None,_A				:	Tuple=None,_A				:	Any=None,**_A				:	List[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."   )

							SCREAMING_SNAKE_CASE_  :		List[str]    				=   self.image_processor(snake_case_,snake_case_,snake_case_,**snake_case_   )

							if isinstance(snake_case_,snake_case_   ):
									SCREAMING_SNAKE_CASE_  :		Any    				=   [task_inputs]

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

									SCREAMING_SNAKE_CASE_  :		Optional[Any]    				=   []
									for texts in texts_list:
											SCREAMING_SNAKE_CASE_  :		List[str]    				=   self._preprocess_text(snake_case_,max_length=self.max_seq_length   )
											text_inputs.append(text_input_list.unsqueeze(0   )   )

									SCREAMING_SNAKE_CASE_  :		Dict    				=   torch.cat(snake_case_,dim=0   )

							return encoded_inputs

					def     __UpperCamelCase       (   self				:	Tuple,_A				:	Tuple=None,_A				:	Dict=None,_A				:	Any=None,**_A				:	List[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."   )

							SCREAMING_SNAKE_CASE_  :		List[Any]    				=   self.image_processor.encode_inputs(snake_case_,snake_case_,snake_case_,**snake_case_   )

							if isinstance(snake_case_,snake_case_   ):
									SCREAMING_SNAKE_CASE_  :		Any    				=   [task_inputs]

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

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

									SCREAMING_SNAKE_CASE_  :		Any    				=   torch.cat(snake_case_,dim=0   )

							return encoded_inputs

					def     __UpperCamelCase       (   self				:	List[str],*_A				:	List[Any],**_A				:	Any   ):







							"""simple docstring"""






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

					def     __UpperCamelCase       (   self				:	Tuple,*_A				:	List[Any],**_A				:	List[str]   ):







							"""simple docstring"""






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






					def     __UpperCamelCase       (   self				:	List[Any],*_A				:	Optional[Any],**_A				:	Optional[Any]   ):







							"""simple docstring"""






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

import warnings

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


__lowerCamelCase :     Tuple          =  logging.get_logger(__name__)






class 		a__						( A__ ):

					def __init__(   self				:	List[Any],*_A				:	Union[str, Any],**_A				:	List[Any]   ):







							"""simple docstring"""






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