




'''simple docstring'''






from typing import List

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


if is_torch_available():
      import torch






class 					lowercase_			(	lowerCAmelCase_       ):
     A_       							= ["image_processor", "tokenizer"]
     A_       							= "OneFormerImageProcessor"
     A_       							= ("CLIPTokenizer", "CLIPTokenizerFast")





     def __init__(    self    :				int    ,       __lowerCamelCase    :				List[str]=None    ,       __lowerCamelCase    :				Optional[Any]=None    ,       __lowerCamelCase    :				int = 77    ,       __lowerCamelCase    :				int = 77    ,       **__lowerCamelCase    :				List[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`.' )

           snake_case__					:      Optional[int]  			=						max_seq_length
           snake_case__					:      Optional[Any]  			=						task_seq_length

           super().__init__(__lowerCamelCase    ,       __lowerCamelCase )





     def  _lowerCAmelCase			(    self    :				str    ,       __lowerCamelCase    :				List[str]=None    ,       __lowerCamelCase    :				Optional[Any]=77 ):
           if text_list is None:
                 raise ValueError('tokens cannot be None.' )

           snake_case__					:      Union[str, Any]  			=						self.tokenizer(__lowerCamelCase    ,       padding='max_length'    ,       max_length=__lowerCamelCase    ,       truncation=__lowerCamelCase )

           snake_case__			,       snake_case__					:      List[Any]  			=						tokens['attention_mask'], tokens['input_ids']

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

           snake_case__					:      List[Any]  			=						torch.cat(__lowerCamelCase    ,       dim=0 )
           return token_inputs





     def __call__(    self    :				Any    ,       __lowerCamelCase    :				Tuple=None    ,       __lowerCamelCase    :				Dict=None    ,       __lowerCamelCase    :				Dict=None    ,       **__lowerCamelCase    :				Optional[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.' )

           snake_case__					:      Dict  			=						self.image_processor(__lowerCamelCase    ,       __lowerCamelCase    ,       __lowerCamelCase    ,       **__lowerCamelCase )

           if isinstance(__lowerCamelCase    ,       __lowerCamelCase ):
                 snake_case__					:      Any  			=						[task_inputs]

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

           if hasattr(__lowerCamelCase    ,       'text_inputs' ):
                 snake_case__					:      Any  			=						encoded_inputs.text_inputs

                 snake_case__					:      Dict  			=						[]
                 for texts in texts_list:
                       snake_case__					:      Any  			=						self._preprocess_text(__lowerCamelCase    ,       max_length=self.max_seq_length )
                       text_inputs.append(text_input_list.unsqueeze(0 ) )

                 snake_case__					:      Tuple  			=						torch.cat(__lowerCamelCase    ,       dim=0 )

           return encoded_inputs





     def  _lowerCAmelCase			(    self    :				List[Any]    ,       __lowerCamelCase    :				Dict=None    ,       __lowerCamelCase    :				List[str]=None    ,       __lowerCamelCase    :				Dict=None    ,       **__lowerCamelCase    :				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.' )

           snake_case__					:      Dict  			=						self.image_processor.encode_inputs(__lowerCamelCase    ,       __lowerCamelCase    ,       __lowerCamelCase    ,       **__lowerCamelCase )

           if isinstance(__lowerCamelCase    ,       __lowerCamelCase ):
                 snake_case__					:      Optional[Any]  			=						[task_inputs]

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

           if hasattr(__lowerCamelCase    ,       'text_inputs' ):
                 snake_case__					:      Optional[int]  			=						encoded_inputs.text_inputs

                 snake_case__					:      Union[str, Any]  			=						[]
                 for texts in texts_list:
                       snake_case__					:      Optional[Any]  			=						self._preprocess_text(__lowerCamelCase    ,       max_length=self.max_seq_length )
                       text_inputs.append(text_input_list.unsqueeze(0 ) )

                 snake_case__					:      Optional[Any]  			=						torch.cat(__lowerCamelCase    ,       dim=0 )

           return encoded_inputs





     def  _lowerCAmelCase			(    self    :				List[Any]    ,       *__lowerCamelCase    :				Union[str, Any]    ,       **__lowerCamelCase    :				Any ):
           return self.image_processor.post_process_semantic_segmentation(*__lowerCamelCase    ,       **__lowerCamelCase )





     def  _lowerCAmelCase			(    self    :				str    ,       *__lowerCamelCase    :				Union[str, Any]    ,       **__lowerCamelCase    :				Optional[Any] ):
           return self.image_processor.post_process_instance_segmentation(*__lowerCamelCase    ,       **__lowerCamelCase )





     def  _lowerCAmelCase			(    self    :				Tuple    ,       *__lowerCamelCase    :				Tuple    ,       **__lowerCamelCase    :				Tuple ):
           return self.image_processor.post_process_panoptic_segmentation(*__lowerCamelCase    ,       **__lowerCamelCase )






'''simple docstring'''






def 		UpperCamelCase__			(		__SCREAMING_SNAKE_CASE = 1000		)						->      int:
      snake_case__					:      List[str]  			=						0
      snake_case__					:      int  			=						(n - 1) // 3
      total += ((terms) * (6 + (terms - 1) * 3)) // 2  # total of an A.P.
      snake_case__					:      Optional[Any]  			=						(n - 1) // 5
      total += ((terms) * (10 + (terms - 1) * 5)) // 2
      snake_case__					:      Tuple  			=						(n - 1) // 15
      total -= ((terms) * (30 + (terms - 1) * 15)) // 2
      return total


if __name__ == "__main__":
      print(F'{solution() = }')
