


'''simple docstring'''




import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin




class  _lowercase   (  __lowercase       ):
   _SCREAMING_SNAKE_CASE     :     Union[str, Any]									=  ["image_processor", "tokenizer"]
   _SCREAMING_SNAKE_CASE     :     List[Any]									=  "AutoImageProcessor"
   _SCREAMING_SNAKE_CASE     :     List[Any]									=  "AutoTokenizer"
   def __init__(	self					:			List[str]					,      SCREAMING_SNAKE_CASE_					:			Any=None					,      SCREAMING_SNAKE_CASE_					:			List[Any]=None					,      **SCREAMING_SNAKE_CASE_					:			Tuple			)   ->       Optional[int]:
        __snake_case    			=	None
        if "feature_extractor" in kwargs:
             warnings.warn(
                 'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
                 ' instead.'					,      SCREAMING_SNAKE_CASE_					,      )
             __snake_case    			=	kwargs.pop('feature_extractor'			)

        __snake_case    			=	image_processor if image_processor is not None else feature_extractor
        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`.'			)

        super().__init__(SCREAMING_SNAKE_CASE_					,      SCREAMING_SNAKE_CASE_			)
        __snake_case    			=	self.image_processor
        __snake_case    			=	False
   def __call__(	self					:			Dict					,      *SCREAMING_SNAKE_CASE_					:			List[Any]					,      **SCREAMING_SNAKE_CASE_					:			Dict			)   ->       Optional[Any]:
        # For backward compatibility
        if self._in_target_context_manager:
             return self.current_processor(*SCREAMING_SNAKE_CASE_					,      **SCREAMING_SNAKE_CASE_			)

        __snake_case    			=	kwargs.pop('images'					,      SCREAMING_SNAKE_CASE_			)
        __snake_case    			=	kwargs.pop('text'					,      SCREAMING_SNAKE_CASE_			)
        if len(SCREAMING_SNAKE_CASE_			) > 0:
             __snake_case    			=	args[0]
             __snake_case    			=	args[1:]

        if images is None and text is None:
             raise ValueError('You need to specify either an `images` or `text` input to process.'			)

        if images is not None:
             __snake_case    			=	self.image_processor(SCREAMING_SNAKE_CASE_					,      *SCREAMING_SNAKE_CASE_					,      **SCREAMING_SNAKE_CASE_			)
        if text is not None:
             __snake_case    			=	self.tokenizer(SCREAMING_SNAKE_CASE_					,      **SCREAMING_SNAKE_CASE_			)

        if text is None:
             return inputs
        elif images is None:
             return encodings
        else:
             __snake_case    			=	encodings['input_ids']
             return inputs
   def a     (	self					:			Optional[int]					,      *SCREAMING_SNAKE_CASE_					:			Optional[Any]					,      **SCREAMING_SNAKE_CASE_					:			Optional[int]			)   ->       int:
        return self.tokenizer.batch_decode(*SCREAMING_SNAKE_CASE_					,      **SCREAMING_SNAKE_CASE_			)
   def a     (	self					:			Any					,      *SCREAMING_SNAKE_CASE_					:			Optional[Any]					,      **SCREAMING_SNAKE_CASE_					:			Optional[Any]			)   ->       Optional[Any]:
        return self.tokenizer.decode(*SCREAMING_SNAKE_CASE_					,      **SCREAMING_SNAKE_CASE_			)
   @contextmanager
   def a     (	self					:			Union[str, Any]			)   ->       Optional[Any]:
        warnings.warn(
            '`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your '
            'labels by using the argument `text` of the regular `__call__` method (either in the same call as '
            'your images inputs, or in a separate call.'			)
        __snake_case    			=	True
        __snake_case    			=	self.tokenizer
        yield
        __snake_case    			=	self.image_processor
        __snake_case    			=	False
   @property
   def a     (	self					:			Optional[Any]			)   ->       str:
        warnings.warn(
            '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'					,      SCREAMING_SNAKE_CASE_					,      )
        return self.image_processor_class





   @property
   def a     (	self					:			Tuple			)   ->       Tuple:
        warnings.warn(
            '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'					,      SCREAMING_SNAKE_CASE_					,      )
        return self.image_processor








'''simple docstring'''




_a      :      List[Any]       				=      [0.33, 0.44, 0.55, 0.44, 0.33]
_a      :      Union[str, Any]       				=      3
_a      :      Optional[Any]       				=      5

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

# Machine Time
_a      :      str       				=      0





def      _a		(lowercase__ :      Optional[int]	) -> List[str]:






     """simple docstring"""
     global buffer_space, params_space, machine_time, K, m, t

     # Choosing Dynamical Systems (All)
     for key, value in enumerate(lowercase__	):
          # Evolution Parameter
          __snake_case    			=	float(seed / value	)

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

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

          # Modification (Transition Function) - Jumps
          __snake_case    			=	round(float(r * value * (1 - value)	)       ,      1_0	)
          __snake_case    			=	r  # Saving to Parameters Space

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

     # Machine Time
     machine_time += 1





def      _a		() -> Tuple:






     """simple docstring"""
     global buffer_space, params_space, machine_time, K, m, t

     # PRNG (Xorshift by George Marsaglia)
     def xorshift(lowercase__ :      Tuple       ,      lowercase__ :      int	):
          x ^= y >> 1_3
          y ^= x << 1_7
          x ^= y >> 5
          return x

     # Choosing Dynamical Systems (Increment)
     __snake_case    			=	machine_time % m

     # Evolution (Time Length)
     for _ in range(0       ,      lowercase__	):
          # Variables (Position + Parameters)
          __snake_case    			=	params_space[key]
          __snake_case    			=	buffer_space[key]

          # Modification (Transition Function) - Flow
          __snake_case    			=	round(float(r * value * (1 - value)	)       ,      1_0	)
          __snake_case    			=	(machine_time * 0.01 + r * 1.01) % 1 + 3

     # Choosing Chaotic Data
     __snake_case    			=	int(buffer_space[(key + 2) % m] * (1_0**1_0)	)
     __snake_case    			=	int(buffer_space[(key - 2) % m] * (1_0**1_0)	)

     # Machine Time
     machine_time += 1

     return xorshift(lowercase__       ,      lowercase__	) % 0xF_F_F_F_F_F_F_F





def      _a		() -> List[str]:






     """simple docstring"""
     global buffer_space, params_space, machine_time, K, m, t

     __snake_case    			=	K
     __snake_case    			=	[0] * m
     __snake_case    			=	0


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

       # Pushing Data (Input)
       import random

       _a      :      Tuple       				=      random.sample(range(0xFFFF_FFFF), 100)
       for chunk in message:
              push(chunk)

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

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




