




'''simple docstring'''







lowercase			:      Union[str, Any]			=					[0.3_3, 0.4_4, 0.5_5, 0.4_4, 0.3_3]
lowercase			:      Dict			=					3
lowercase			:      List[Any]			=					5

# Buffer Space (with Parameters Space)
lowercase			:      list[float]			=					[]
lowercase			:      list[float]			=					[]

# Machine Time
lowercase			:      Any			=					0



def 					lowerCamelCase__							(			__lowercase							):
     global buffer_space, params_space, machine_time, K, m, t

     # Choosing Dynamical Systems (All)
     for key, value in enumerate(__lowercase							):
          # Evolution Parameter
          snake_case :			List[str]					   = float(seed / value							)

          # Control Theory: Orbit Change
          snake_case :			List[Any]					   = (buffer_space[(key + 1) % m] + e) % 1

          # Control Theory: Trajectory Change
          snake_case :			int					   = (params_space[key] + e) % 1 + 3

          # Modification (Transition Function) - Jumps
          snake_case :			List[str]					   = round(float(r * value * (1 - value)							)    ,      10							)
          snake_case :			List[Any]					   = r  # Saving to Parameters Space

     # Logistic Map
     assert max(__lowercase							) < 1
     assert max(__lowercase							) < 4

     # Machine Time
     machine_time += 1



def 					lowerCamelCase__							(			):
     global buffer_space, params_space, machine_time, K, m, t

     # PRNG (Xorshift by George Marsaglia)
     def xorshift(__lowercase    ,      __lowercase							):
          x ^= y >> 13
          y ^= x << 17
          x ^= y >> 5
          return x

     # Choosing Dynamical Systems (Increment)
     snake_case :			Optional[int]					   = machine_time % m

     # Evolution (Time Length)
     for _ in range(0    ,      __lowercase							):
          # Variables (Position + Parameters)
          snake_case :			Dict					   = params_space[key]
          snake_case :			Any					   = buffer_space[key]

          # Modification (Transition Function) - Flow
          snake_case :			Dict					   = round(float(r * value * (1 - value)							)    ,      10							)
          snake_case :			Union[str, Any]					   = (machine_time * 0.01 + r * 1.01) % 1 + 3

     # Choosing Chaotic Data
     snake_case :			List[Any]					   = int(buffer_space[(key + 2) % m] * (10**10)							)
     snake_case :			Optional[int]					   = int(buffer_space[(key - 2) % m] * (10**10)							)

     # Machine Time
     machine_time += 1

     return xorshift(__lowercase    ,      __lowercase							) % 0XF_F_F_F_F_F_F_F



def 					lowerCamelCase__							(			):
     global buffer_space, params_space, machine_time, K, m, t

     snake_case :			Tuple					   = K
     snake_case :			Optional[int]					   = [0] * m
     snake_case :			Any					   = 0


if __name__ == "__main__":
      # Initialization
      reset()

      # Pushing Data (Input)
      import random

      lowercase			:      List[Any]			=					random.sample(range(0Xff_fff_fff), 100)
      for chunk in message:
            push(chunk)

      # for controlling
      lowercase			:      List[Any]			=					""""""

      # Pulling Data (Output)
      while inp in ("e", "E"):
            print(F"""{format(pull(), '#04x')}""")
            print(buffer_space)
            print(params_space)
            lowercase			:      List[str]			=					input("""(e)exit? """).strip()









'''simple docstring'''







from typing import List

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


if is_torch_available():
      import torch







class 				_a   (a__  ):





     '''simple docstring'''



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







     def __init__(							self       ,__a=None       ,__a=None       ,__a = 77       ,__a = 77       ,**__a       )			->							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`."""       )

          snake_case :			str					   = max_seq_length
          snake_case :			Any					   = task_seq_length

          super().__init__(__a       ,__a       )







     def  snake_case_			(							self       ,__a=None       ,__a=77       )			->							List[str]:
          if text_list is None:
               raise ValueError("""tokens cannot be None."""       )

          snake_case :			int					   = self.tokenizer(__a       ,padding="""max_length"""       ,max_length=__a       ,truncation=__a       )

          snake_case   ,		snake_case :			int					   = tokens["""attention_mask"""], tokens["""input_ids"""]

          snake_case :			Tuple					   = []
          for attn_mask, input_id in zip(__a       ,__a       ):
               snake_case :			Dict					   = torch.tensor(__a       ) * torch.tensor(__a       )
               token_inputs.append(token.unsqueeze(0       )       )

          snake_case :			Optional[int]					   = torch.cat(__a       ,dim=0       )
          return token_inputs







     def __call__(							self       ,__a=None       ,__a=None       ,__a=None       ,**__a       )			->							Optional[int]:

          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 :			Union[str, Any]					   = self.image_processor(__a       ,__a       ,__a       ,**__a       )

          if isinstance(__a       ,__a       ):
               snake_case :			Tuple					   = [task_inputs]

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

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

               snake_case :			Tuple					   = torch.cat(__a       ,dim=0       )

          return encoded_inputs







     def  snake_case_			(							self       ,__a=None       ,__a=None       ,__a=None       ,**__a       )			->							List[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."""       )

          snake_case :			Any					   = self.image_processor.encode_inputs(__a       ,__a       ,__a       ,**__a       )

          if isinstance(__a       ,__a       ):
               snake_case :			Optional[int]					   = [task_inputs]

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

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

               snake_case :			Union[str, Any]					   = torch.cat(__a       ,dim=0       )

          return encoded_inputs







     def  snake_case_			(							self       ,*__a       ,**__a       )			->							List[str]:
          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[Any]:
          return self.image_processor.post_process_panoptic_segmentation(*__a       ,**__a       )



