"""simple docstring"""







from typing import List

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


if is_torch_available():
     import torch






class     _SCREAMING_SNAKE_CASE       ( UpperCAmelCase							):



   '''simple docstring'''



   __UpperCAmelCase  :       Any						=						["image_processor", "tokenizer"]
   __UpperCAmelCase  :       Optional[Any]						=						"OneFormerImageProcessor"
   __UpperCAmelCase  :       Tuple						=						("CLIPTokenizer", "CLIPTokenizerFast")







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


      '''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       _A							(  self   ,							_SCREAMING_SNAKE_CASE=None   ,							_SCREAMING_SNAKE_CASE=77  ):


      '''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  ):


      '''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       _A							(  self   ,							_SCREAMING_SNAKE_CASE=None   ,							_SCREAMING_SNAKE_CASE=None   ,							_SCREAMING_SNAKE_CASE=None   ,							**_SCREAMING_SNAKE_CASE  ):


      '''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       _A							(  self   ,							*_SCREAMING_SNAKE_CASE   ,							**_SCREAMING_SNAKE_CASE  ):


      '''simple docstring'''

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







   def       _A							(  self   ,							*_SCREAMING_SNAKE_CASE   ,							**_SCREAMING_SNAKE_CASE  ):


      '''simple docstring'''

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







   def       _A							(  self   ,							*_SCREAMING_SNAKE_CASE   ,							**_SCREAMING_SNAKE_CASE  ):


      '''simple docstring'''

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







"""simple docstring"""







from typing import TYPE_CHECKING

from ...utils import _LazyModule


_SCREAMING_SNAKE_CASE  :		List[Any]        =      {'''tokenization_phobert''': ['''PhobertTokenizer''']}


if TYPE_CHECKING:
     from .tokenization_phobert import PhobertTokenizer

else:
     import sys

     _SCREAMING_SNAKE_CASE  :		List[Any]        =      _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)






