from typing import List

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


if is_torch_available():
    import torch
class 				a_ (						SCREAMING_SNAKE_CASE						):






 '''simple docstring'''






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




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







        '''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__(lowercase__			,    lowercase__)




 def 		__snake_case						(			self					:  Dict			,    lowercase__					:  int=None			,    lowercase__					:  List[Any]=77):







        '''simple docstring'''



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

        lowerCAmelCase__     	=					self.tokenizer(lowercase__			,    padding='max_length'			,    max_length=lowercase__			,    truncation=lowercase__)

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

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

        lowerCAmelCase__     	=					torch.cat(lowercase__			,    dim=0)
        return token_inputs




 def __call__(			self					:  List[Any]			,    lowercase__					:  Optional[int]=None			,    lowercase__					:  List[str]=None			,    lowercase__					:  Optional[int]=None			,    **lowercase__					:  Tuple):







        '''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(lowercase__			,    lowercase__			,    lowercase__			,    **lowercase__)

        if isinstance(lowercase__			,    lowercase__):
               lowerCAmelCase__     	=					[task_inputs]

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

        if hasattr(lowercase__			,    'text_inputs'):
               lowerCAmelCase__     	=					encoded_inputs.text_inputs

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

               lowerCAmelCase__     	=					torch.cat(lowercase__			,    dim=0)

        return encoded_inputs




 def 		__snake_case						(			self					:  Any			,    lowercase__					:  Union[str, Any]=None			,    lowercase__					:  Any=None			,    lowercase__					:  Dict=None			,    **lowercase__					:  int):







        '''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(lowercase__			,    lowercase__			,    lowercase__			,    **lowercase__)

        if isinstance(lowercase__			,    lowercase__):
               lowerCAmelCase__     	=					[task_inputs]

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

        if hasattr(lowercase__			,    'text_inputs'):
               lowerCAmelCase__     	=					encoded_inputs.text_inputs

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

               lowerCAmelCase__     	=					torch.cat(lowercase__			,    dim=0)

        return encoded_inputs




 def 		__snake_case						(			self					:  int			,    *lowercase__					:  Dict			,    **lowercase__					:  Dict):







        '''simple docstring'''



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




 def 		__snake_case						(			self					:  List[Any]			,    *lowercase__					:  int			,    **lowercase__					:  int):







        '''simple docstring'''



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




 def 		__snake_case						(			self					:  Any			,    *lowercase__					:  Union[str, Any]			,    **lowercase__					:  int):







        '''simple docstring'''



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

from ..utils import DummyObject, requires_backends
class 				a_ (						metaclass=SCREAMING_SNAKE_CASE						):






 '''simple docstring'''






 UpperCAmelCase_	 =					['tensorflow_text']




 def __init__(			self					:  Tuple			,    *lowercase__					:  str			,    **lowercase__					:  Union[str, Any]):







        '''simple docstring'''



        requires_backends(self			,    ['tensorflow_text'])
