

lowerCAmelCase       :				Union[str, Any]         =					[0.33, 0.44, 0.55, 0.44, 0.33]
lowerCAmelCase       :				List[str]         =					3
lowerCAmelCase       :				int         =					5

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

# Machine Time
lowerCAmelCase       :				Union[str, Any]         =					0

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

      # Choosing Dynamical Systems (All)
      for key, value in enumerate(a					):
            # Evolution Parameter
            SCREAMING_SNAKE_CASE_			:					Union[str, Any]          =       float(seed / value					)

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

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

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

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

      # Machine Time
      machine_time += 1

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

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

      # Choosing Dynamical Systems (Increment)
      SCREAMING_SNAKE_CASE_			:					Optional[Any]          =       machine_time % m

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

            # Modification (Transition Function) - Flow
            SCREAMING_SNAKE_CASE_			:					Union[str, Any]          =       round(float(r * value * (1 - value)					)   ,				1_0					)
            SCREAMING_SNAKE_CASE_			:					Tuple          =       (machine_time * 0.01 + r * 1.01) % 1 + 3

      # Choosing Chaotic Data
      SCREAMING_SNAKE_CASE_			:					Union[str, Any]          =       int(buffer_space[(key + 2) % m] * (1_0**1_0)					)
      SCREAMING_SNAKE_CASE_			:					List[Any]          =       int(buffer_space[(key - 2) % m] * (1_0**1_0)					)

      # Machine Time
      machine_time += 1

      return xorshift(a   ,				a					) % 0xff_fff_fff

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

      SCREAMING_SNAKE_CASE_			:					Dict          =       K
      SCREAMING_SNAKE_CASE_			:					Union[str, Any]          =       [0] * m
      SCREAMING_SNAKE_CASE_			:					Optional[Any]          =       0


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

    # Pushing Data (Input)
    import random

    lowerCAmelCase       :				Optional[Any]         =					random.sample(range(0XF_F_F_F_F_F_F_F), 1_00)
    for chunk in message:
        push(chunk)

    # for controlling
    lowerCAmelCase       :				List[Any]         =					''

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









from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import center_crop, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
    import PIL


lowerCAmelCase       :				Dict         =					logging.get_logger(__name__)
class 	_A						(					__magic_name__):
  SCREAMING_SNAKE_CASE      : Any    =					['''pixel_values''']

  def __init__(			self	,   _SCREAMING_SNAKE_CASE = True	,   _SCREAMING_SNAKE_CASE = None	,   _SCREAMING_SNAKE_CASE = PIL.Image.NEAREST	,   _SCREAMING_SNAKE_CASE = False	,   _SCREAMING_SNAKE_CASE = None	,   _SCREAMING_SNAKE_CASE = 1 / 255	,   _SCREAMING_SNAKE_CASE = False	,   _SCREAMING_SNAKE_CASE = True	,   _SCREAMING_SNAKE_CASE = True	,   _SCREAMING_SNAKE_CASE = None	,   _SCREAMING_SNAKE_CASE = None	,   _SCREAMING_SNAKE_CASE = True	,   **_SCREAMING_SNAKE_CASE	,   ):





        """simple docstring"""







        super().__init__(**_SCREAMING_SNAKE_CASE   )
        SCREAMING_SNAKE_CASE_			:					str          =       size if size is not None else {'height': 346, 'width': 346}
        SCREAMING_SNAKE_CASE_			:					str          =       get_size_dict(_SCREAMING_SNAKE_CASE   )
        SCREAMING_SNAKE_CASE_			:					int          =       crop_size if crop_size is not None else {'height': 289, 'width': 289}
        SCREAMING_SNAKE_CASE_			:					Optional[Any]          =       get_size_dict(_SCREAMING_SNAKE_CASE	,   param_name='crop_size'   )

        SCREAMING_SNAKE_CASE_			:					List[str]          =       do_resize
        SCREAMING_SNAKE_CASE_			:					int          =       size
        SCREAMING_SNAKE_CASE_			:					Tuple          =       resample
        SCREAMING_SNAKE_CASE_			:					int          =       do_center_crop
        SCREAMING_SNAKE_CASE_			:					List[str]          =       crop_size
        SCREAMING_SNAKE_CASE_			:					Optional[Any]          =       do_rescale
        SCREAMING_SNAKE_CASE_			:					Optional[int]          =       rescale_factor
        SCREAMING_SNAKE_CASE_			:					Optional[Any]          =       rescale_offset
        SCREAMING_SNAKE_CASE_			:					Dict          =       do_normalize
        SCREAMING_SNAKE_CASE_			:					str          =       image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
        SCREAMING_SNAKE_CASE_			:					List[str]          =       image_std if image_std is not None else IMAGENET_STANDARD_STD
        SCREAMING_SNAKE_CASE_			:					Union[str, Any]          =       include_top

  def 	UpperCAmelCase      (			self	,   _SCREAMING_SNAKE_CASE	,   _SCREAMING_SNAKE_CASE	,   _SCREAMING_SNAKE_CASE = PIL.Image.NEAREST	,   _SCREAMING_SNAKE_CASE = None	,   **_SCREAMING_SNAKE_CASE	,   ):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_			:					str          =       get_size_dict(_SCREAMING_SNAKE_CASE   )
        if "height" not in size or "width" not in size:
              raise ValueError(f"The size dictionary must have keys 'height' and 'width'. Got {size.keys()}"   )
        return resize(
            _SCREAMING_SNAKE_CASE	,   size=(size['height'], size['width'])	,   resample=_SCREAMING_SNAKE_CASE	,   data_format=_SCREAMING_SNAKE_CASE	,   **_SCREAMING_SNAKE_CASE   )

  def 	UpperCAmelCase      (			self	,   _SCREAMING_SNAKE_CASE	,   _SCREAMING_SNAKE_CASE	,   _SCREAMING_SNAKE_CASE = None	,   **_SCREAMING_SNAKE_CASE	,   ):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_			:					Tuple          =       get_size_dict(_SCREAMING_SNAKE_CASE   )
        if "height" not in size or "width" not in size:
              raise ValueError(f"The size dictionary must have keys 'height' and 'width'. Got {size.keys()}"   )
        return center_crop(_SCREAMING_SNAKE_CASE	,   size=(size['height'], size['width'])	,   data_format=_SCREAMING_SNAKE_CASE	,   **_SCREAMING_SNAKE_CASE   )

  def 	UpperCAmelCase      (			self	,   _SCREAMING_SNAKE_CASE	,   _SCREAMING_SNAKE_CASE	,   _SCREAMING_SNAKE_CASE = True	,   _SCREAMING_SNAKE_CASE = None	,   **_SCREAMING_SNAKE_CASE	,   ):





        """simple docstring"""







        if offset:
              SCREAMING_SNAKE_CASE_			:					Dict          =       (image - 127.5) * scale
              if data_format is not None:
                    SCREAMING_SNAKE_CASE_			:					Tuple          =       to_channel_dimension_format(_SCREAMING_SNAKE_CASE	,   _SCREAMING_SNAKE_CASE   )
              SCREAMING_SNAKE_CASE_			:					int          =       rescaled_image.astype(np.floataa   )
        else:
              SCREAMING_SNAKE_CASE_			:					Optional[Any]          =       rescale(_SCREAMING_SNAKE_CASE	,   scale=_SCREAMING_SNAKE_CASE	,   data_format=_SCREAMING_SNAKE_CASE	,   **_SCREAMING_SNAKE_CASE   )
        return rescaled_image

  def 	UpperCAmelCase      (			self	,   _SCREAMING_SNAKE_CASE	,   _SCREAMING_SNAKE_CASE	,   _SCREAMING_SNAKE_CASE	,   _SCREAMING_SNAKE_CASE = None	,   **_SCREAMING_SNAKE_CASE	,   ):





        """simple docstring"""







        return normalize(_SCREAMING_SNAKE_CASE	,   mean=_SCREAMING_SNAKE_CASE	,   std=_SCREAMING_SNAKE_CASE	,   data_format=_SCREAMING_SNAKE_CASE	,   **_SCREAMING_SNAKE_CASE   )



  def 	UpperCAmelCase      (			self	,   _SCREAMING_SNAKE_CASE	,   _SCREAMING_SNAKE_CASE = None	,   _SCREAMING_SNAKE_CASE = None	,   _SCREAMING_SNAKE_CASE=None	,   _SCREAMING_SNAKE_CASE = None	,   _SCREAMING_SNAKE_CASE = None	,   _SCREAMING_SNAKE_CASE = None	,   _SCREAMING_SNAKE_CASE = None	,   _SCREAMING_SNAKE_CASE = None	,   _SCREAMING_SNAKE_CASE = None	,   _SCREAMING_SNAKE_CASE = None	,   _SCREAMING_SNAKE_CASE = None	,   _SCREAMING_SNAKE_CASE = None	,   _SCREAMING_SNAKE_CASE = None	,   _SCREAMING_SNAKE_CASE = ChannelDimension.FIRST	,   **_SCREAMING_SNAKE_CASE	,   ):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_			:					List[Any]          =       do_resize if do_resize is not None else self.do_resize
        SCREAMING_SNAKE_CASE_			:					Union[str, Any]          =       resample if resample is not None else self.resample
        SCREAMING_SNAKE_CASE_			:					Optional[Any]          =       do_center_crop if do_center_crop is not None else self.do_center_crop
        SCREAMING_SNAKE_CASE_			:					Optional[int]          =       do_rescale if do_rescale is not None else self.do_rescale
        SCREAMING_SNAKE_CASE_			:					int          =       rescale_factor if rescale_factor is not None else self.rescale_factor
        SCREAMING_SNAKE_CASE_			:					Tuple          =       rescale_offset if rescale_offset is not None else self.rescale_offset
        SCREAMING_SNAKE_CASE_			:					Dict          =       do_normalize if do_normalize is not None else self.do_normalize
        SCREAMING_SNAKE_CASE_			:					str          =       image_mean if image_mean is not None else self.image_mean
        SCREAMING_SNAKE_CASE_			:					List[Any]          =       image_std if image_std is not None else self.image_std
        SCREAMING_SNAKE_CASE_			:					List[Any]          =       include_top if include_top is not None else self.include_top

        SCREAMING_SNAKE_CASE_			:					Optional[int]          =       size if size is not None else self.size
        SCREAMING_SNAKE_CASE_			:					Tuple          =       get_size_dict(_SCREAMING_SNAKE_CASE   )
        SCREAMING_SNAKE_CASE_			:					Any          =       crop_size if crop_size is not None else self.crop_size
        SCREAMING_SNAKE_CASE_			:					Optional[int]          =       get_size_dict(_SCREAMING_SNAKE_CASE	,   param_name='crop_size'   )

        SCREAMING_SNAKE_CASE_			:					List[str]          =       make_list_of_images(_SCREAMING_SNAKE_CASE   )

        if not valid_images(_SCREAMING_SNAKE_CASE   ):
              raise ValueError(
                  'Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '
                  'torch.Tensor, tf.Tensor or jax.ndarray.'   )

        if do_resize and size is None or resample is None:
              raise ValueError('Size and resample must be specified if do_resize is True.'   )

        if do_center_crop and crop_size is None:
              raise ValueError('Crop size must be specified if do_center_crop is True.'   )

        if do_rescale and rescale_factor is None:
              raise ValueError('Rescale factor must be specified if do_rescale is True.'   )

        if do_normalize and (image_mean is None or image_std is None):
              raise ValueError('Image mean and std must be specified if do_normalize is True.'   )

        # All transformations expect numpy arrays.
        SCREAMING_SNAKE_CASE_			:					int          =       [to_numpy_array(_SCREAMING_SNAKE_CASE   ) for image in images]

        if do_resize:
              SCREAMING_SNAKE_CASE_			:					Dict          =       [self.resize(image=_SCREAMING_SNAKE_CASE	,   size=_SCREAMING_SNAKE_CASE	,   resample=_SCREAMING_SNAKE_CASE   ) for image in images]

        if do_center_crop:
              SCREAMING_SNAKE_CASE_			:					str          =       [self.center_crop(image=_SCREAMING_SNAKE_CASE	,   size=_SCREAMING_SNAKE_CASE   ) for image in images]

        if do_rescale:
              SCREAMING_SNAKE_CASE_			:					Tuple          =       [self.rescale(image=_SCREAMING_SNAKE_CASE	,   scale=_SCREAMING_SNAKE_CASE	,   offset=_SCREAMING_SNAKE_CASE   ) for image in images]

        if do_normalize:
              SCREAMING_SNAKE_CASE_			:					Optional[Any]          =       [self.normalize(image=_SCREAMING_SNAKE_CASE	,   mean=_SCREAMING_SNAKE_CASE	,   std=_SCREAMING_SNAKE_CASE   ) for image in images]

        if include_top:
              SCREAMING_SNAKE_CASE_			:					Dict          =       [self.normalize(image=_SCREAMING_SNAKE_CASE	,   mean=[0, 0, 0]	,   std=_SCREAMING_SNAKE_CASE   ) for image in images]

        SCREAMING_SNAKE_CASE_			:					Dict          =       [to_channel_dimension_format(_SCREAMING_SNAKE_CASE	,   _SCREAMING_SNAKE_CASE   ) for image in images]

        SCREAMING_SNAKE_CASE_			:					Any          =       {'pixel_values': images}
        return BatchFeature(data=_SCREAMING_SNAKE_CASE	,   tensor_type=_SCREAMING_SNAKE_CASE   )






