


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    (	__lowerCamelCase    ):
     __SCREAMING_SNAKE_CASE    				=						['image_processor', 'tokenizer']
     __SCREAMING_SNAKE_CASE    				=						'OneFormerImageProcessor'
     __SCREAMING_SNAKE_CASE    				=						('CLIPTokenizer', 'CLIPTokenizerFast')


     def __init__(     self       ,				A_=None       ,				A_=None       ,				A_ = 77       ,				A_ = 77       ,				**A_						)     ->   Union[str, Any]:






       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`."""						)

       _UpperCAmelCase		:       List[Any]       =    max_seq_length
       _UpperCAmelCase		:       Dict       =    task_seq_length

       super().__init__(A_       ,				A_						)


     def 					__snake_case(     self       ,				A_=None       ,				A_=77						)     ->   Any:






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

       _UpperCAmelCase		:       List[Any]       =    self.tokenizer(A_       ,				padding="""max_length"""       ,				max_length=A_       ,				truncation=A_						)

       _UpperCAmelCase,_UpperCAmelCase		:       Tuple       =    tokens["""attention_mask"""], tokens["""input_ids"""]

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

       _UpperCAmelCase		:       Tuple       =    torch.cat(A_       ,				dim=0						)
       return token_inputs


     def __call__(     self       ,				A_=None       ,				A_=None       ,				A_=None       ,				**A_						)     ->   Dict:






       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."""						)

       _UpperCAmelCase		:       List[Any]       =    self.image_processor(A_       ,				A_       ,				A_       ,				**A_						)

       if isinstance(A_       ,				A_						):
         _UpperCAmelCase		:       Any       =    [task_inputs]

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

       if hasattr(A_       ,				"""text_inputs"""						):
         _UpperCAmelCase		:       int       =    encoded_inputs.text_inputs

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

         _UpperCAmelCase		:       Optional[Any]       =    torch.cat(A_       ,				dim=0						)

       return encoded_inputs


     def 					__snake_case(     self       ,				A_=None       ,				A_=None       ,				A_=None       ,				**A_						)     ->   List[Any]:






       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."""						)

       _UpperCAmelCase		:       Optional[int]       =    self.image_processor.encode_inputs(A_       ,				A_       ,				A_       ,				**A_						)

       if isinstance(A_       ,				A_						):
         _UpperCAmelCase		:       Optional[Any]       =    [task_inputs]

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

       if hasattr(A_       ,				"""text_inputs"""						):
         _UpperCAmelCase		:       Optional[int]       =    encoded_inputs.text_inputs

         _UpperCAmelCase		:       Any       =    []
         for texts in texts_list:
           _UpperCAmelCase		:       Optional[int]       =    self._preprocess_text(A_       ,				max_length=self.max_seq_length						)
           text_inputs.append(text_input_list.unsqueeze(0						)						)

         _UpperCAmelCase		:       Union[str, Any]       =    torch.cat(A_       ,				dim=0						)

       return encoded_inputs


     def 					__snake_case(     self       ,				*A_       ,				**A_						)     ->   Dict:
       return self.image_processor.post_process_semantic_segmentation(*A_       ,				**A_						)


     def 					__snake_case(     self       ,				*A_       ,				**A_						)     ->   List[Any]:
       return self.image_processor.post_process_instance_segmentation(*A_       ,				**A_						)




     def 					__snake_case(     self       ,				*A_       ,				**A_						)     ->   Optional[int]:
       return self.image_processor.post_process_panoptic_segmentation(*A_       ,				**A_						)









from typing import Any


class 		_SCREAMING_SNAKE_CASE    :


     def __init__(     self						):
       _UpperCAmelCase		:       Union[str, Any]       =    []
       _UpperCAmelCase		:       Dict       =    0


     def __str__(     self						):
       _UpperCAmelCase		:       Union[str, Any]       =    """<""" + str(self.stack						)[1:-1] + """>"""
       return printed


     def 					__snake_case(     self       ,				A_						):
       self.stack.append(A_						)
       _UpperCAmelCase		:       Optional[Any]       =    self.length + 1


     def 					__snake_case(     self						):
       self.rotate(1						)
       _UpperCAmelCase		:       List[Any]       =    self.stack[self.length - 1]
       _UpperCAmelCase		:       Optional[Any]       =    self.stack[:-1]
       self.rotate(self.length - 1						)
       _UpperCAmelCase		:       Union[str, Any]       =    self.length - 1
       return dequeued


     def 					__snake_case(     self       ,				A_						):
       for _ in range(A_						):
         _UpperCAmelCase		:       Dict       =    self.stack[0]
         _UpperCAmelCase		:       List[Any]       =    self.stack[1:]
         self.put(A_						)
         _UpperCAmelCase		:       Dict       =    self.length - 1


     def 					__snake_case(     self						):
       _UpperCAmelCase		:       Tuple       =    self.get()
       self.put(A_						)
       self.rotate(self.length - 1						)
       return front


     def 					__snake_case(     self						):
       return self.length





