






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


UpperCamelCase__						:      Tuple								=					logging.get_logger(__name__)







class 				lowerCamelCase_     (	a_     ):
							SCREAMING_SNAKE_CASE_						     =			['pixel_values']
							def __init__(						self		:					Optional[int]							,__lowerCamelCase		:					bool = True							,__lowerCamelCase		:					Dict[str, int] = None							,__lowerCamelCase		:					bool = True							,__lowerCamelCase		:					Dict[str, int] = None							,__lowerCamelCase		:					PILImageResampling = PILImageResampling.BICUBIC							,__lowerCamelCase		:					bool = True							,__lowerCamelCase		:					Union[int, float] = 1 / 2_55							,__lowerCamelCase		:					bool = True							,__lowerCamelCase		:					Optional[Union[float, List[float]]] = None							,__lowerCamelCase		:					Optional[Union[float, List[float]]] = None							,**__lowerCamelCase		:					int							,):





								'''simple docstring'''



								super().__init__(**__lowerCamelCase					)
								a		=			crop_size if crop_size is not None else {'''height''': 2_56, '''width''': 2_56}
								a		=			get_size_dict(__lowerCamelCase							,param_name='''crop_size'''					)
								a		=			size if size is not None else {'''height''': 2_24, '''width''': 2_24}
								a		=			get_size_dict(__lowerCamelCase					)

								a		=			do_center_crop
								a		=			crop_size
								a		=			do_resize
								a		=			size
								a		=			resample
								a		=			do_rescale
								a		=			rescale_factor
								a		=			do_normalize
								a		=			image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
								a		=			image_std if image_std is not None else IMAGENET_DEFAULT_STD
							def  SCREAMING_SNAKE_CASE_ (						self		:					Any							,__lowerCamelCase		:					np.ndarray							,__lowerCamelCase		:					Dict[str, int]							,__lowerCamelCase		:					Optional[int] = None							,__lowerCamelCase		:					Optional[Union[str, ChannelDimension]] = None							,**__lowerCamelCase		:					Optional[Any]							,):





								'''simple docstring'''



								a		=			self.size if size is None else size
								a		=			get_size_dict(__lowerCamelCase					)
								a		=			get_size_dict(__lowerCamelCase							,param_name='''crop_size'''					)

								a	, a		=			get_image_size(__lowerCamelCase					)
								a		=			min(__lowerCamelCase							,__lowerCamelCase					)
								a		=			(size['''height'''] / crop_size['''height''']) * min_dim
								a		=			(size['''width'''] / crop_size['''width''']) * min_dim
								return center_crop(__lowerCamelCase							,size=(cropped_height, cropped_width)							,data_format=__lowerCamelCase							,**__lowerCamelCase					)
							def  SCREAMING_SNAKE_CASE_ (						self		:					List[str]							,__lowerCamelCase		:					np.ndarray							,__lowerCamelCase		:					Dict[str, int]							,__lowerCamelCase		:					PILImageResampling = PIL.Image.BICUBIC							,__lowerCamelCase		:					Optional[Union[str, ChannelDimension]] = None							,**__lowerCamelCase		:					List[str]							,):





								'''simple docstring'''



								a		=			get_size_dict(__lowerCamelCase					)
								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(
								    __lowerCamelCase							,size=(size['''height'''], size['''width'''])							,resample=__lowerCamelCase							,data_format=__lowerCamelCase							,**__lowerCamelCase					)
							def  SCREAMING_SNAKE_CASE_ (						self		:					Dict							,__lowerCamelCase		:					np.ndarray							,__lowerCamelCase		:					Union[int, float]							,__lowerCamelCase		:					Optional[Union[str, ChannelDimension]] = None							,**__lowerCamelCase		:					Union[str, Any]							,):





								'''simple docstring'''



								return rescale(__lowerCamelCase							,scale=__lowerCamelCase							,data_format=__lowerCamelCase							,**__lowerCamelCase					)
							def  SCREAMING_SNAKE_CASE_ (						self		:					Dict							,__lowerCamelCase		:					np.ndarray							,__lowerCamelCase		:					Union[float, List[float]]							,__lowerCamelCase		:					Union[float, List[float]]							,__lowerCamelCase		:					Optional[Union[str, ChannelDimension]] = None							,**__lowerCamelCase		:					List[str]							,):





								'''simple docstring'''



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




							def  SCREAMING_SNAKE_CASE_ (						self		:					str							,__lowerCamelCase		:					ImageInput							,__lowerCamelCase		:					Optional[bool] = None							,__lowerCamelCase		:					Optional[Dict[str, int]] = None							,__lowerCamelCase		:					Optional[bool] = None							,__lowerCamelCase		:					Optional[Dict[str, int]] = None							,__lowerCamelCase		:					PILImageResampling = None							,__lowerCamelCase		:					Optional[bool] = None							,__lowerCamelCase		:					Optional[float] = None							,__lowerCamelCase		:					Optional[bool] = None							,__lowerCamelCase		:					Optional[Union[float, List[float]]] = None							,__lowerCamelCase		:					Optional[Union[float, List[float]]] = None							,__lowerCamelCase		:					Optional[Union[str, TensorType]] = None							,__lowerCamelCase		:					ChannelDimension = ChannelDimension.FIRST							,**__lowerCamelCase		:					str							,):





								'''simple docstring'''



								a		=			do_center_crop if do_center_crop is not None else self.do_center_crop
								a		=			crop_size if crop_size is not None else self.crop_size
								a		=			get_size_dict(__lowerCamelCase							,param_name='''crop_size'''					)
								a		=			do_resize if do_resize is not None else self.do_resize
								a		=			size if size is not None else self.size
								a		=			get_size_dict(__lowerCamelCase					)
								a		=			resample if resample is not None else self.resample
								a		=			do_rescale if do_rescale is not None else self.do_rescale
								a		=			rescale_factor if rescale_factor is not None else self.rescale_factor
								a		=			do_normalize if do_normalize is not None else self.do_normalize
								a		=			image_mean if image_mean is not None else self.image_mean
								a		=			image_std if image_std is not None else self.image_std

								a		=			make_list_of_images(__lowerCamelCase					)

								if not valid_images(__lowerCamelCase					):
									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.
								a		=			[to_numpy_array(__lowerCamelCase					) for image in images]

								if do_center_crop:
									a		=			[self.center_crop(__lowerCamelCase							,__lowerCamelCase							,size=__lowerCamelCase					) for image in images]

								if do_resize:
									a		=			[self.resize(image=__lowerCamelCase							,size=__lowerCamelCase							,resample=__lowerCamelCase					) for image in images]

								if do_rescale:
									a		=			[self.rescale(image=__lowerCamelCase							,scale=__lowerCamelCase					) for image in images]

								if do_normalize:
									a		=			[self.normalize(image=__lowerCamelCase							,mean=__lowerCamelCase							,std=__lowerCamelCase					) for image in images]

								a		=			[to_channel_dimension_format(__lowerCamelCase							,__lowerCamelCase					) for image in images]

								a		=			{'''pixel_values''': images}
								return BatchFeature(data=__lowerCamelCase							,tensor_type=__lowerCamelCase					)













import numpy as np





def   SCREAMING_SNAKE_CASE__						(   snake_case_,  snake_case_="utf-8",  snake_case_="surrogatepass"  )   ->  List[str]:


	"""simple docstring"""



	a		=			bin(int.from_bytes(text.encode(snake_case_,  snake_case_  ),  '''big'''  )  )[2:]
	return bits.zfill(8 * ((len(snake_case_  ) + 7) // 8)  )





def   SCREAMING_SNAKE_CASE__						(   snake_case_,  snake_case_="utf-8",  snake_case_="surrogatepass"  )   ->  int:


	"""simple docstring"""



	a		=			int(snake_case_,  2  )
	return n.to_bytes((n.bit_length() + 7) // 8,  '''big'''  ).decode(snake_case_,  snake_case_  ) or "\0"





def   SCREAMING_SNAKE_CASE__						(   snake_case_,  snake_case_  )   ->  List[Any]:


	"""simple docstring"""



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

	a		=			[]
	a		=			[]
	a		=			[bin(snake_case_  )[2:] for x in range(1,  size_par + len(snake_case_  ) + 1  )]

	# sorted information data for the size of the output data
	a		=			[]
	# data position template + parity
	a		=			[]
	# parity bit counter
	a		=			0
	# counter position of data bits
	a		=			0

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

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

		qtd_bp += 1

	# Mount the message
	a		=			0  # parity bit counter
	for x in range(0,  size_par + len(snake_case_  )  ):
		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   SCREAMING_SNAKE_CASE__						(   snake_case_,  snake_case_  )   ->  str:


	"""simple docstring"""



	a		=			[]
	# Parity bit counter
	a		=			0
	# Counter p data bit reading
	a		=			0
	# list of parity received
	a		=			[]
	a		=			[]

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

	#  sorted information data for the size of the output data
	a		=			[]
	# Data position feedback + parity
	a		=			[]
	# Parity bit counter
	a		=			0
	# Counter p data bit reading
	a		=			0

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

    # Calculates parity
	a		=			0  # parity bit counter
	for bp in range(1,  size_par + 1  ):
		# Bit counter one for a certain parity
		a		=			0
		# Counter to control loop reading
		a		=			0
		for x in data_ord:
			if x is not None:
				try:
					a		=			(bin_pos[cont_loop])[-1 * (bp)]
				except IndexError:
					a		=			'''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
	a		=			0  # Parity bit counter
	for x in range(0,  size_par + len(snake_case_  )  ):
		if data_ord[x] is None:
			data_out.append(str(parity[cont_bp]  )  )
			cont_bp += 1
		else:
			data_out.append(data_ord[x]  )

	a		=			parity_received == parity
	return data_output, ack





