

"""simple docstring"""





from typing import List

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


if is_torch_available():
   import torch




class    _lowerCAmelCase    (      lowercase  ):







 """simple docstring"""







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


 def __init__(     self				:     Optional[int],   UpperCAmelCase__				:     List[str]=None,   UpperCAmelCase__				:     Optional[int]=None,   UpperCAmelCase__				:     int = 7_7,   UpperCAmelCase__				:     int = 7_7,   **UpperCAmelCase__				:     Optional[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`."      )

    __lowercase								=		max_seq_length
    __lowercase								=		task_seq_length

    super().__init__(UpperCAmelCase__,   UpperCAmelCase__      )


 def    _lowercase					(     self				:     List[Any],   UpperCAmelCase__				:     Union[str, Any]=None,   UpperCAmelCase__				:     int=7_7      ):
    if text_list is None:
       raise ValueError("tokens cannot be None."      )

    __lowercase								=		self.tokenizer(UpperCAmelCase__,   padding="max_length",   max_length=UpperCAmelCase__,   truncation=UpperCAmelCase__      )

    __lowercase						,__lowercase								=		tokens["attention_mask"], tokens["input_ids"]

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

    __lowercase								=		torch.cat(UpperCAmelCase__,   dim=0      )
    return token_inputs


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

    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."      )

    __lowercase								=		self.image_processor(UpperCAmelCase__,   UpperCAmelCase__,   UpperCAmelCase__,   **UpperCAmelCase__      )

    if isinstance(UpperCAmelCase__,   UpperCAmelCase__      ):
       __lowercase								=		[task_inputs]

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

    if hasattr(UpperCAmelCase__,   "text_inputs"      ):
       __lowercase								=		encoded_inputs.text_inputs

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

       __lowercase								=		torch.cat(UpperCAmelCase__,   dim=0      )

    return encoded_inputs


 def    _lowercase					(     self				:     List[Any],   UpperCAmelCase__				:     Tuple=None,   UpperCAmelCase__				:     Optional[Any]=None,   UpperCAmelCase__				:     str=None,   **UpperCAmelCase__				:     Union[str, 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."      )

    __lowercase								=		self.image_processor.encode_inputs(UpperCAmelCase__,   UpperCAmelCase__,   UpperCAmelCase__,   **UpperCAmelCase__      )

    if isinstance(UpperCAmelCase__,   UpperCAmelCase__      ):
       __lowercase								=		[task_inputs]

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

    if hasattr(UpperCAmelCase__,   "text_inputs"      ):
       __lowercase								=		encoded_inputs.text_inputs

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

       __lowercase								=		torch.cat(UpperCAmelCase__,   dim=0      )

    return encoded_inputs


 def    _lowercase					(     self				:     Optional[int],   *UpperCAmelCase__				:     List[str],   **UpperCAmelCase__				:     Union[str, Any]      ):
    return self.image_processor.post_process_semantic_segmentation(*UpperCAmelCase__,   **UpperCAmelCase__      )


 def    _lowercase					(     self				:     int,   *UpperCAmelCase__				:     Tuple,   **UpperCAmelCase__				:     str      ):
    return self.image_processor.post_process_instance_segmentation(*UpperCAmelCase__,   **UpperCAmelCase__      )



 def    _lowercase					(     self				:     List[str],   *UpperCAmelCase__				:     Tuple,   **UpperCAmelCase__				:     Any      ):
    return self.image_processor.post_process_panoptic_segmentation(*UpperCAmelCase__,   **UpperCAmelCase__      )







"""simple docstring"""





def     _A    (    UpperCamelCase_	:			list)      ->		list:
   '''simple docstring'''


   for i, _ in enumerate(UpperCamelCase_):
      if (i % 2 == 1) == (nums[i - 1] > nums[i]):
         __lowercase						,__lowercase								=		nums[i], nums[i - 1]

   return nums


if __name__ == "__main__":
   print('Enter the array elements:')
   _a					 =				list(map(int, input().split()))
   print('The unsorted array is:')
   print(array)
   print('Array after Wiggle sort:')
   print(wiggle_sort(array))




