


'''simple docstring'''







import numpy as np



def 						_UpperCamelCase							( UpperCamelCase__     ,   UpperCamelCase__="utf-8"     ,   UpperCamelCase__="surrogatepass"		):






				"""simple docstring"""



				__magic_name__				:					Optional[Any]						       =       bin(int.from_bytes(text.encode(UpperCamelCase__     ,   UpperCamelCase__		)     ,   "big"		)		)[2:]
				return bits.zfill(8 * ((len(UpperCamelCase__		) + 7) // 8)		)



def 						_UpperCamelCase							( UpperCamelCase__     ,   UpperCamelCase__="utf-8"     ,   UpperCamelCase__="surrogatepass"		):






				"""simple docstring"""



				__magic_name__				:					Optional[int]						       =       int(UpperCamelCase__     ,   2		)
				return n.to_bytes((n.bit_length() + 7) // 8     ,   "big"		).decode(UpperCamelCase__     ,   UpperCamelCase__		) or "\0"



def 						_UpperCamelCase							( UpperCamelCase__     ,   UpperCamelCase__		):






				"""simple docstring"""



				if size_par + len(UpperCamelCase__		) <= 2**size_par - (len(UpperCamelCase__		) - 1):
								raise ValueError("size of parity don't match with size of data"		)

				__magic_name__				:					Tuple						       =       []
				__magic_name__				:					List[str]						       =       []
				__magic_name__				:					List[str]						       =       [bin(UpperCamelCase__		)[2:] for x in range(1     ,   size_par + len(UpperCamelCase__		) + 1		)]

				# sorted information data for the size of the output data
				__magic_name__				:					int						       =       []
				# data position template + parity
				__magic_name__				:					int						       =       []
				# parity bit counter
				__magic_name__				:					Optional[Any]						       =       0
				# counter position of data bits
				__magic_name__				:					Dict						       =       0

				for x in range(1     ,   size_par + len(UpperCamelCase__		) + 1		):
								# Performs a template of bit positions - who should be given,
								# and who should be parity
								if qtd_bp < size_par:
												if (np.log(UpperCamelCase__		) / np.log(2		)).is_integer():
																data_out_gab.append("P"		)
																__magic_name__				:					List[Any]						       =       qtd_bp + 1
												else:
																data_out_gab.append("D"		)
								else:
												data_out_gab.append("D"		)

								# Sorts the data to the new output size
								if data_out_gab[-1] == "D":
												data_ord.append(data[cont_data]		)
												cont_data += 1
								else:
												data_ord.append(UpperCamelCase__		)

    # Calculates parity
				__magic_name__				:					List[str]						       =       0  # parity bit counter
				for bp in range(1     ,   size_par + 1		):
								# Bit counter one for a given parity
								__magic_name__				:					List[str]						       =       0
								# counter to control the loop reading
								__magic_name__				:					List[str]						       =       0
								for x in data_ord:
												if x is not None:
																try:
																				__magic_name__				:					Optional[int]						       =       (bin_pos[cont_loop])[-1 * (bp)]
																except IndexError:
																				__magic_name__				:					str						       =       "0"
																if aux == "1" and x == "1":
																				cont_bo += 1
												cont_loop += 1
								parity.append(cont_bo % 2		)

								qtd_bp += 1

				# Mount the message
				__magic_name__				:					Union[str, Any]						       =       0  # parity bit counter
				for x in range(0     ,   size_par + len(UpperCamelCase__		)		):
								if data_ord[x] is None:
												data_out.append(str(parity[cont_bp]		)		)
												cont_bp += 1
								else:
												data_out.append(data_ord[x]		)

				return data_out



def 						_UpperCamelCase							( UpperCamelCase__     ,   UpperCamelCase__		):






				"""simple docstring"""



				__magic_name__				:					Union[str, Any]						       =       []
				# Parity bit counter
				__magic_name__				:					List[str]						       =       0
				# Counter p data bit reading
				__magic_name__				:					Optional[int]						       =       0
				# list of parity received
				__magic_name__				:					Optional[int]						       =       []
				__magic_name__				:					Dict						       =       []

				for x in range(1     ,   len(UpperCamelCase__		) + 1		):
								# Performs a template of bit positions - who should be given,
								#  and who should be parity
								if qtd_bp < size_par and (np.log(UpperCamelCase__		) / np.log(2		)).is_integer():
												data_out_gab.append("P"		)
												__magic_name__				:					Dict						       =       qtd_bp + 1
								else:
												data_out_gab.append("D"		)

								# Sorts the data to the new output size
								if data_out_gab[-1] == "D":
												data_output.append(data[cont_data]		)
								else:
												parity_received.append(data[cont_data]		)
								cont_data += 1

				# -----------calculates the parity with the data
				__magic_name__				:					Optional[int]						       =       []
				__magic_name__				:					int						       =       []
				__magic_name__				:					Union[str, Any]						       =       [bin(UpperCamelCase__		)[2:] for x in range(1     ,   size_par + len(UpperCamelCase__		) + 1		)]

				#  sorted information data for the size of the output data
				__magic_name__				:					Optional[int]						       =       []
				# Data position feedback + parity
				__magic_name__				:					Union[str, Any]						       =       []
				# Parity bit counter
				__magic_name__				:					List[Any]						       =       0
				# Counter p data bit reading
				__magic_name__				:					Optional[int]						       =       0

				for x in range(1     ,   size_par + len(UpperCamelCase__		) + 1		):
								# Performs a template position of bits - who should be given,
								# and who should be parity
								if qtd_bp < size_par and (np.log(UpperCamelCase__		) / np.log(2		)).is_integer():
												data_out_gab.append("P"		)
												__magic_name__				:					Tuple						       =       qtd_bp + 1
								else:
												data_out_gab.append("D"		)

								# Sorts the data to the new output size
								if data_out_gab[-1] == "D":
												data_ord.append(data_output[cont_data]		)
												cont_data += 1
								else:
												data_ord.append(UpperCamelCase__		)

    # Calculates parity
				__magic_name__				:					str						       =       0  # parity bit counter
				for bp in range(1     ,   size_par + 1		):
								# Bit counter one for a certain parity
								__magic_name__				:					Optional[int]						       =       0
								# Counter to control loop reading
								__magic_name__				:					Optional[Any]						       =       0
								for x in data_ord:
												if x is not None:
																try:
																				__magic_name__				:					int						       =       (bin_pos[cont_loop])[-1 * (bp)]
																except IndexError:
																				__magic_name__				:					Tuple						       =       "0"
																if aux == "1" and x == "1":
																				cont_bo += 1
												cont_loop += 1
								parity.append(str(cont_bo % 2		)		)

								qtd_bp += 1

				# Mount the message
				__magic_name__				:					Optional[int]						       =       0  # Parity bit counter
				for x in range(0     ,   size_par + len(UpperCamelCase__		)		):
								if data_ord[x] is None:
												data_out.append(str(parity[cont_bp]		)		)
												cont_bp += 1
								else:
												data_out.append(data_ord[x]		)

				__magic_name__				:					str						       =       parity_received == parity
				return data_output, ack



'''simple docstring'''







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_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
		import PIL


_SCREAMING_SNAKE_CASE       :   int        = logging.get_logger(__name__)






class 		_snake_case    (				snake_case_		):






						'''simple docstring'''







						__snake_case   =      ["pixel_values"]







						def __init__(  self:	Union[str, Any]      ,							__UpperCamelCase:	bool = True      ,							__UpperCamelCase:	Dict[str, int] = None      ,							__UpperCamelCase:	bool = True      ,							__UpperCamelCase:	Dict[str, int] = None      ,							__UpperCamelCase:	PILImageResampling = PILImageResampling.BICUBIC      ,							__UpperCamelCase:	bool = True      ,							__UpperCamelCase:	Union[int, float] = 1 / 255      ,							__UpperCamelCase:	bool = True      ,							__UpperCamelCase:	Optional[Union[float, List[float]]] = None      ,							__UpperCamelCase:	Optional[Union[float, List[float]]] = None      ,							**__UpperCamelCase:	Union[str, Any]      ,							)							->	None:
										super().__init__(**__UpperCamelCase     )
										__magic_name__				:					Optional[Any]						       =       crop_size if crop_size is not None else {"height": 256, "width": 256}
										__magic_name__				:					Optional[int]						       =       get_size_dict(__UpperCamelCase      ,							param_name="crop_size"     )
										__magic_name__				:					Union[str, Any]						       =       size if size is not None else {"height": 224, "width": 224}
										__magic_name__				:					Tuple						       =       get_size_dict(__UpperCamelCase     )

										__magic_name__				:					Tuple						       =       do_center_crop
										__magic_name__				:					int						       =       crop_size
										__magic_name__				:					Dict						       =       do_resize
										__magic_name__				:					Optional[Any]						       =       size
										__magic_name__				:					Optional[Any]						       =       resample
										__magic_name__				:					str						       =       do_rescale
										__magic_name__				:					int						       =       rescale_factor
										__magic_name__				:					Dict						       =       do_normalize
										__magic_name__				:					Any						       =       image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
										__magic_name__				:					str						       =       image_std if image_std is not None else IMAGENET_DEFAULT_STD







						def lowerCAmelCase__ (  self:	str      ,							__UpperCamelCase:	np.ndarray      ,							__UpperCamelCase:	Dict[str, int]      ,							__UpperCamelCase:	Optional[int] = None      ,							__UpperCamelCase:	Optional[Union[str, ChannelDimension]] = None      ,							**__UpperCamelCase:	List[str]      ,							)							->	np.ndarray:
										__magic_name__				:					List[str]						       =       self.size if size is None else size
										__magic_name__				:					Dict						       =       get_size_dict(__UpperCamelCase     )
										__magic_name__				:					Dict						       =       get_size_dict(__UpperCamelCase      ,							param_name="crop_size"     )

										__magic_name__       ,     __magic_name__				:					Dict						       =       get_image_size(__UpperCamelCase     )
										__magic_name__				:					int						       =       min(__UpperCamelCase      ,							__UpperCamelCase     )
										__magic_name__				:					Dict						       =       (size["height"] / crop_size["height"]) * min_dim
										__magic_name__				:					str						       =       (size["width"] / crop_size["width"]) * min_dim
										return center_crop(__UpperCamelCase      ,							size=(cropped_height, cropped_width)      ,							data_format=__UpperCamelCase      ,							**__UpperCamelCase     )







						def lowerCAmelCase__ (  self:	int      ,							__UpperCamelCase:	np.ndarray      ,							__UpperCamelCase:	Dict[str, int]      ,							__UpperCamelCase:	PILImageResampling = PIL.Image.BICUBIC      ,							__UpperCamelCase:	Optional[Union[str, ChannelDimension]] = None      ,							**__UpperCamelCase:	Any      ,							)							->	np.ndarray:
										__magic_name__				:					Optional[Any]						       =       get_size_dict(__UpperCamelCase     )
										if "height" not in size or "width" not in size:
														raise ValueError(f"""The size dictionary must contain the keys 'height' and 'width'. Got {size.keys()}"""     )
										return resize(
										    __UpperCamelCase      ,							size=(size["height"], size["width"])      ,							resample=__UpperCamelCase      ,							data_format=__UpperCamelCase      ,							**__UpperCamelCase     )







						def lowerCAmelCase__ (  self:	Optional[int]      ,							__UpperCamelCase:	np.ndarray      ,							__UpperCamelCase:	Union[int, float]      ,							__UpperCamelCase:	Optional[Union[str, ChannelDimension]] = None      ,							**__UpperCamelCase:	List[Any]      ,							)							->	List[Any]:
										return rescale(__UpperCamelCase      ,							scale=__UpperCamelCase      ,							data_format=__UpperCamelCase      ,							**__UpperCamelCase     )







						def lowerCAmelCase__ (  self:	Any      ,							__UpperCamelCase:	np.ndarray      ,							__UpperCamelCase:	Union[float, List[float]]      ,							__UpperCamelCase:	Union[float, List[float]]      ,							__UpperCamelCase:	Optional[Union[str, ChannelDimension]] = None      ,							**__UpperCamelCase:	str      ,							)							->	np.ndarray:
										return normalize(__UpperCamelCase      ,							mean=__UpperCamelCase      ,							std=__UpperCamelCase      ,							data_format=__UpperCamelCase      ,							**__UpperCamelCase     )







						def lowerCAmelCase__ (  self:	str      ,							__UpperCamelCase:	ImageInput      ,							__UpperCamelCase:	Optional[bool] = None      ,							__UpperCamelCase:	Optional[Dict[str, int]] = None      ,							__UpperCamelCase:	Optional[bool] = None      ,							__UpperCamelCase:	Optional[Dict[str, int]] = None      ,							__UpperCamelCase:	PILImageResampling = None      ,							__UpperCamelCase:	Optional[bool] = None      ,							__UpperCamelCase:	Optional[float] = None      ,							__UpperCamelCase:	Optional[bool] = None      ,							__UpperCamelCase:	Optional[Union[float, List[float]]] = None      ,							__UpperCamelCase:	Optional[Union[float, List[float]]] = None      ,							__UpperCamelCase:	Optional[Union[str, TensorType]] = None      ,							__UpperCamelCase:	ChannelDimension = ChannelDimension.FIRST      ,							**__UpperCamelCase:	Dict      ,							)							->	PIL.Image.Image:
										__magic_name__				:					Optional[Any]						       =       do_center_crop if do_center_crop is not None else self.do_center_crop
										__magic_name__				:					Optional[Any]						       =       crop_size if crop_size is not None else self.crop_size
										__magic_name__				:					Tuple						       =       get_size_dict(__UpperCamelCase      ,							param_name="crop_size"     )
										__magic_name__				:					int						       =       do_resize if do_resize is not None else self.do_resize
										__magic_name__				:					Optional[Any]						       =       size if size is not None else self.size
										__magic_name__				:					Any						       =       get_size_dict(__UpperCamelCase     )
										__magic_name__				:					Optional[int]						       =       resample if resample is not None else self.resample
										__magic_name__				:					int						       =       do_rescale if do_rescale is not None else self.do_rescale
										__magic_name__				:					int						       =       rescale_factor if rescale_factor is not None else self.rescale_factor
										__magic_name__				:					Any						       =       do_normalize if do_normalize is not None else self.do_normalize
										__magic_name__				:					Dict						       =       image_mean if image_mean is not None else self.image_mean
										__magic_name__				:					Tuple						       =       image_std if image_std is not None else self.image_std

										__magic_name__				:					List[Any]						       =       make_list_of_images(__UpperCamelCase     )

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

										if do_center_crop and crop_size is None:
														raise ValueError("If `do_center_crop` is set to `True`, `crop_size` must be provided."     )

										if do_resize and size is None:
														raise ValueError("Size must be specified if do_resize 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 image standard deviation must be specified if do_normalize is True."     )

										# All transformations expect numpy arrays.
										__magic_name__				:					Optional[Any]						       =       [to_numpy_array(__UpperCamelCase     ) for image in images]

										if do_center_crop:
														__magic_name__				:					Union[str, Any]						       =       [self.center_crop(__UpperCamelCase      ,							__UpperCamelCase      ,							size=__UpperCamelCase     ) for image in images]

										if do_resize:
														__magic_name__				:					List[Any]						       =       [self.resize(image=__UpperCamelCase      ,							size=__UpperCamelCase      ,							resample=__UpperCamelCase     ) for image in images]

										if do_rescale:
														__magic_name__				:					str						       =       [self.rescale(image=__UpperCamelCase      ,							scale=__UpperCamelCase     ) for image in images]

										if do_normalize:
														__magic_name__				:					Optional[int]						       =       [self.normalize(image=__UpperCamelCase      ,							mean=__UpperCamelCase      ,							std=__UpperCamelCase     ) for image in images]

										__magic_name__				:					List[Any]						       =       [to_channel_dimension_format(__UpperCamelCase      ,							__UpperCamelCase     ) for image in images]

										__magic_name__				:					Optional[int]						       =       {"pixel_values": images}
										return BatchFeature(data=__UpperCamelCase      ,							tensor_type=__UpperCamelCase     )