
"""simple docstring"""

from typing import List

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


if is_torch_available():
   import torch
class        _UpperCAmelCase							(						SCREAMING_SNAKE_CASE_							):
      __SCREAMING_SNAKE_CASE				:							List[str]							 =		["image_processor", "tokenizer"]
      __SCREAMING_SNAKE_CASE				:							List[str]							 =		"OneFormerImageProcessor"
      __SCREAMING_SNAKE_CASE				:							int							 =		("CLIPTokenizer", "CLIPTokenizerFast")




      def __init__(							self				,    lowercase_=None				,    lowercase_=None				,    lowercase_ = 7_7				,    lowercase_ = 7_7				,    **lowercase_							)  ->   int:
           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       =   max_seq_length
           UpperCAmelCase       =   task_seq_length

           super().__init__(lowercase_				,    lowercase_							)




      def       a_ (							self				,    lowercase_=None				,    lowercase_=7_7							)  ->   str:
           if text_list is None:
                raise ValueError('tokens cannot be None.'							)

           UpperCAmelCase       =   self.tokenizer(lowercase_				,    padding='max_length'				,    max_length=lowercase_				,    truncation=lowercase_							)

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

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

           UpperCAmelCase       =   torch.cat(lowercase_				,    dim=0							)
           return token_inputs




      def __call__(							self				,    lowercase_=None				,    lowercase_=None				,    lowercase_=None				,    **lowercase_							)  ->   Tuple:

           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       =   self.image_processor(lowercase_				,    lowercase_				,    lowercase_				,    **lowercase_							)

           if isinstance(lowercase_				,    lowercase_							):
                UpperCAmelCase       =   [task_inputs]

           if isinstance(lowercase_				,    lowercase_							) and all(isinstance(lowercase_				,    lowercase_							) for task_input in task_inputs							):
                UpperCAmelCase       =   []
                for task in task_inputs:
                     UpperCAmelCase       =   F"the task is {task}"
                     task_token_inputs.append(lowercase_							)
                UpperCAmelCase       =   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'							):
                UpperCAmelCase       =   encoded_inputs.text_inputs

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

                UpperCAmelCase       =   torch.cat(lowercase_				,    dim=0							)

           return encoded_inputs




      def       a_ (							self				,    lowercase_=None				,    lowercase_=None				,    lowercase_=None				,    **lowercase_							)  ->   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       =   self.image_processor.encode_inputs(lowercase_				,    lowercase_				,    lowercase_				,    **lowercase_							)

           if isinstance(lowercase_				,    lowercase_							):
                UpperCAmelCase       =   [task_inputs]

           if isinstance(lowercase_				,    lowercase_							) and all(isinstance(lowercase_				,    lowercase_							) for task_input in task_inputs							):
                UpperCAmelCase       =   []
                for task in task_inputs:
                     UpperCAmelCase       =   F"the task is {task}"
                     task_token_inputs.append(lowercase_							)
                UpperCAmelCase       =   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'							):
                UpperCAmelCase       =   encoded_inputs.text_inputs

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

                UpperCAmelCase       =   torch.cat(lowercase_				,    dim=0							)

           return encoded_inputs




      def       a_ (							self				,    *lowercase_				,    **lowercase_							)  ->   List[str]:
           return self.image_processor.post_process_semantic_segmentation(*lowercase_				,    **lowercase_							)




      def       a_ (							self				,    *lowercase_				,    **lowercase_							)  ->   Union[str, Any]:
           return self.image_processor.post_process_instance_segmentation(*lowercase_				,    **lowercase_							)




      def       a_ (							self				,    *lowercase_				,    **lowercase_							)  ->   Tuple:
           return self.image_processor.post_process_panoptic_segmentation(*lowercase_				,    **lowercase_							)





"""simple docstring"""

SCREAMING_SNAKE_CASE_         =  {
    0: '''underwear''',
    1: '''pants''',
    2: '''belt''',
    3: '''suit''',
    4: '''shoe''',
    5: '''socks''',
    6: '''shirt''',
    7: '''tie''',
    8: '''watch''',
}

SCREAMING_SNAKE_CASE_         =  [[1, 4], [2, 4], [3], [], [], [4], [2, 7], [3], []]

SCREAMING_SNAKE_CASE_         =  [0 for x in range(len(graph))]
SCREAMING_SNAKE_CASE_         =  []




def       lowercase__  (   lowerCAmelCase			:   Optional[int]	,			lowerCAmelCase			:   Any	)	-> List[str]:






    """simple docstring"""




    UpperCAmelCase       =   1
    while stack:
        UpperCAmelCase       =   stack.pop()
        print(lowerCAmelCase	,			clothes[current_clothing]	)
        order += 1




def       lowercase__  (   lowerCAmelCase			:   List[Any]	,			lowerCAmelCase			:   Any	,			lowerCAmelCase			:   Dict	)	-> Optional[Any]:






    """simple docstring"""




    UpperCAmelCase       =   1
    for v in graph[u]:
        if not visited[v]:
            depth_first_search(lowerCAmelCase	,			lowerCAmelCase	,			lowerCAmelCase	)

    stack.append(lowerCAmelCase	)




def       lowercase__  (   lowerCAmelCase			:   Tuple	,			lowerCAmelCase			:   Dict	)	-> Optional[Any]:






    """simple docstring"""




    for v in range(len(lowerCAmelCase	)	):
        if not visited[v]:
            depth_first_search(lowerCAmelCase	,			lowerCAmelCase	,			lowerCAmelCase	)


if __name__ == "__main__":
   topological_sort(graph, visited)
   print(stack)
   print_stack(stack, clothes)



