

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 (
    get_resize_output_image_size,
    normalize,
    pad,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging
from ...utils.import_utils import is_vision_available


_SCREAMING_SNAKE_CASE							 =			logging.get_logger(__name__)


if is_vision_available():
      import PIL


class      lowerCamelCase_ (			_lowerCAmelCase):
 _lowerCAmelCase									=	['''pixel_values''']




 def __init__(			self  ,  _UpperCAmelCase = True  ,  _UpperCAmelCase = None  ,  _UpperCAmelCase = PILImageResampling.BILINEAR  ,  _UpperCAmelCase = True  ,  _UpperCAmelCase = False  ,  _UpperCAmelCase = True  ,  _UpperCAmelCase = True  ,  _UpperCAmelCase = 1 / 255  ,  _UpperCAmelCase = True  ,  _UpperCAmelCase = None  ,  _UpperCAmelCase = None  ,  **_UpperCAmelCase  ,  ):
   super().__init__(**_UpperCAmelCase							)

   __UpperCAmelCase	:  List[str]		=     size if size is not None else {'height': 2_560, 'width': 1_920}
   if isinstance(_UpperCAmelCase  ,  (tuple, list)							):
     # The previous feature extractor size parameter was in (width, height) format
     __UpperCAmelCase	:  Union[str, Any]		=     size[::-1]
   __UpperCAmelCase	:  Any		=     get_size_dict(_UpperCAmelCase							)

   __UpperCAmelCase	:  List[str]		=     do_resize
   __UpperCAmelCase	:  List[Any]		=     size
   __UpperCAmelCase	:  Any		=     resample
   __UpperCAmelCase	:  List[str]		=     do_thumbnail
   __UpperCAmelCase	:  Tuple		=     do_align_long_axis
   __UpperCAmelCase	:  List[str]		=     do_pad
   __UpperCAmelCase	:  Union[str, Any]		=     do_rescale
   __UpperCAmelCase	:  Union[str, Any]		=     rescale_factor
   __UpperCAmelCase	:  str		=     do_normalize
   __UpperCAmelCase	:  Any		=     image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
   __UpperCAmelCase	:  Any		=     image_std if image_std is not None else IMAGENET_STANDARD_STD




 def     a       (			self  ,  _UpperCAmelCase  ,  _UpperCAmelCase  ,  _UpperCAmelCase = None							):
   __UpperCAmelCase,			__UpperCAmelCase	:  List[Any]		=     get_image_size(_UpperCAmelCase							)
   __UpperCAmelCase,			__UpperCAmelCase	:  Tuple		=     size['height'], size['width']

   if (output_width < output_height and input_width > input_height) or (
       output_width > output_height and input_width < input_height
   ):
     __UpperCAmelCase	:  Any		=     np.rotaa(_UpperCAmelCase  ,  3							)

   if data_format is not None:
     __UpperCAmelCase	:  Optional[Any]		=     to_channel_dimension_format(_UpperCAmelCase  ,  _UpperCAmelCase							)

   return image




 def     a       (			self  ,  _UpperCAmelCase  ,  _UpperCAmelCase  ,  _UpperCAmelCase = False  ,  _UpperCAmelCase = None  ,  ):
   __UpperCAmelCase,			__UpperCAmelCase	:  Dict		=     size['height'], size['width']
   __UpperCAmelCase,			__UpperCAmelCase	:  str		=     get_image_size(_UpperCAmelCase							)

   __UpperCAmelCase	:  Optional[Any]		=     output_width - input_width
   __UpperCAmelCase	:  Optional[int]		=     output_height - input_height

   if random_padding:
     __UpperCAmelCase	:  Optional[Any]		=     np.random.randint(low=0  ,  high=delta_height + 1							)
     __UpperCAmelCase	:  Optional[int]		=     np.random.randint(low=0  ,  high=delta_width + 1							)
   else:
     __UpperCAmelCase	:  Dict		=     delta_height // 2
     __UpperCAmelCase	:  List[Any]		=     delta_width // 2

   __UpperCAmelCase	:  Optional[Any]		=     delta_height - pad_top
   __UpperCAmelCase	:  Any		=     delta_width - pad_left

   __UpperCAmelCase	:  int		=     ((pad_top, pad_bottom), (pad_left, pad_right))
   return pad(_UpperCAmelCase  ,  _UpperCAmelCase  ,  data_format=_UpperCAmelCase							)




 def     a       (			self  ,  *_UpperCAmelCase  ,  **_UpperCAmelCase							):
   logger.info('pad is deprecated and will be removed in version 4.27. Please use pad_image instead.'							)
   return self.pad_image(*_UpperCAmelCase  ,  **_UpperCAmelCase							)




 def     a       (			self  ,  _UpperCAmelCase  ,  _UpperCAmelCase  ,  _UpperCAmelCase = PILImageResampling.BICUBIC  ,  _UpperCAmelCase = None  ,  **_UpperCAmelCase  ,  ):
   __UpperCAmelCase,			__UpperCAmelCase	:  Tuple		=     get_image_size(_UpperCAmelCase							)
   __UpperCAmelCase,			__UpperCAmelCase	:  Optional[int]		=     size['height'], size['width']

   # We always resize to the smallest of either the input or output size.
   __UpperCAmelCase	:  List[Any]		=     min(_UpperCAmelCase  ,  _UpperCAmelCase							)
   __UpperCAmelCase	:  Optional[Any]		=     min(_UpperCAmelCase  ,  _UpperCAmelCase							)

   if height == input_height and width == input_width:
     return image

   if input_height > input_width:
     __UpperCAmelCase	:  Optional[Any]		=     int(input_width * height / input_height							)
   elif input_width > input_height:
     __UpperCAmelCase	:  List[str]		=     int(input_height * width / input_width							)

   return resize(
       _UpperCAmelCase  ,  size=(height, width)  ,  resample=_UpperCAmelCase  ,  reducing_gap=2.0  ,  data_format=_UpperCAmelCase  ,  **_UpperCAmelCase							)




 def     a       (			self  ,  _UpperCAmelCase  ,  _UpperCAmelCase  ,  _UpperCAmelCase = PILImageResampling.BICUBIC  ,  _UpperCAmelCase = None  ,  **_UpperCAmelCase  ,  ):
   __UpperCAmelCase	:  List[str]		=     get_size_dict(_UpperCAmelCase							)
   __UpperCAmelCase	:  List[str]		=     min(size['height']  ,  size['width']							)
   __UpperCAmelCase	:  List[str]		=     get_resize_output_image_size(_UpperCAmelCase  ,  size=_UpperCAmelCase  ,  default_to_square=_UpperCAmelCase							)
   __UpperCAmelCase	:  Optional[Any]		=     resize(_UpperCAmelCase  ,  size=_UpperCAmelCase  ,  resample=_UpperCAmelCase  ,  data_format=_UpperCAmelCase  ,  **_UpperCAmelCase							)
   return resized_image




 def     a       (			self  ,  _UpperCAmelCase  ,  _UpperCAmelCase  ,  _UpperCAmelCase = None  ,  **_UpperCAmelCase  ,  ):
   return rescale(_UpperCAmelCase  ,  scale=_UpperCAmelCase  ,  data_format=_UpperCAmelCase  ,  **_UpperCAmelCase							)




 def     a       (			self  ,  _UpperCAmelCase  ,  _UpperCAmelCase  ,  _UpperCAmelCase  ,  _UpperCAmelCase = None  ,  **_UpperCAmelCase  ,  ):
   return normalize(_UpperCAmelCase  ,  mean=_UpperCAmelCase  ,  std=_UpperCAmelCase  ,  data_format=_UpperCAmelCase  ,  **_UpperCAmelCase							)




 def     a       (			self  ,  _UpperCAmelCase  ,  _UpperCAmelCase = None  ,  _UpperCAmelCase = None  ,  _UpperCAmelCase = None  ,  _UpperCAmelCase = None  ,  _UpperCAmelCase = None  ,  _UpperCAmelCase = None  ,  _UpperCAmelCase = False  ,  _UpperCAmelCase = None  ,  _UpperCAmelCase = None  ,  _UpperCAmelCase = None  ,  _UpperCAmelCase = None  ,  _UpperCAmelCase = None  ,  _UpperCAmelCase = None  ,  _UpperCAmelCase = ChannelDimension.FIRST  ,  **_UpperCAmelCase  ,  ):
   __UpperCAmelCase	:  str		=     do_resize if do_resize is not None else self.do_resize
   __UpperCAmelCase	:  str		=     size if size is not None else self.size
   if isinstance(_UpperCAmelCase  ,  (tuple, list)							):
     # Previous feature extractor had size in (width, height) format
     __UpperCAmelCase	:  Union[str, Any]		=     size[::-1]
   __UpperCAmelCase	:  Dict		=     get_size_dict(_UpperCAmelCase							)
   __UpperCAmelCase	:  Optional[Any]		=     resample if resample is not None else self.resample
   __UpperCAmelCase	:  List[str]		=     do_thumbnail if do_thumbnail is not None else self.do_thumbnail
   __UpperCAmelCase	:  Optional[int]		=     do_align_long_axis if do_align_long_axis is not None else self.do_align_long_axis
   __UpperCAmelCase	:  Optional[int]		=     do_pad if do_pad is not None else self.do_pad
   __UpperCAmelCase	:  List[Any]		=     do_rescale if do_rescale is not None else self.do_rescale
   __UpperCAmelCase	:  Any		=     rescale_factor if rescale_factor is not None else self.rescale_factor
   __UpperCAmelCase	:  str		=     do_normalize if do_normalize is not None else self.do_normalize
   __UpperCAmelCase	:  Dict		=     image_mean if image_mean is not None else self.image_mean
   __UpperCAmelCase	:  Dict		=     image_std if image_std is not None else self.image_std

   __UpperCAmelCase	:  Optional[int]		=     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_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_pad and size is None:
     raise ValueError('Size must be specified if do_pad 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.
   __UpperCAmelCase	:  List[str]		=     [to_numpy_array(_UpperCAmelCase							) for image in images]

   if do_align_long_axis:
     __UpperCAmelCase	:  Dict		=     [self.align_long_axis(_UpperCAmelCase  ,  size=_UpperCAmelCase							) for image in images]

   if do_resize:
     __UpperCAmelCase	:  List[str]		=     [self.resize(image=_UpperCAmelCase  ,  size=_UpperCAmelCase  ,  resample=_UpperCAmelCase							) for image in images]

   if do_thumbnail:
     __UpperCAmelCase	:  Tuple		=     [self.thumbnail(image=_UpperCAmelCase  ,  size=_UpperCAmelCase							) for image in images]

   if do_pad:
     __UpperCAmelCase	:  str		=     [self.pad_image(image=_UpperCAmelCase  ,  size=_UpperCAmelCase  ,  random_padding=_UpperCAmelCase							) for image in images]

   if do_rescale:
     __UpperCAmelCase	:  Optional[int]		=     [self.rescale(image=_UpperCAmelCase  ,  scale=_UpperCAmelCase							) for image in images]

   if do_normalize:
     __UpperCAmelCase	:  List[str]		=     [self.normalize(image=_UpperCAmelCase  ,  mean=_UpperCAmelCase  ,  std=_UpperCAmelCase							) for image in images]

   __UpperCAmelCase	:  List[str]		=     [to_channel_dimension_format(_UpperCAmelCase  ,  _UpperCAmelCase							) for image in images]

   __UpperCAmelCase	:  List[str]		=     {'pixel_values': images}
   return BatchFeature(data=_UpperCAmelCase  ,  tensor_type=_UpperCAmelCase							)










import numpy as np

def 			a		( _UpperCAmelCase				:   str					,  _UpperCAmelCase				:   Any="utf-8"					,  _UpperCAmelCase				:   List[Any]="surrogatepass"		):







		'''simple docstring'''





		__UpperCAmelCase       :	Union[str, Any]						=      bin(int.from_bytes(text.encode(_UpperCAmelCase					,  _UpperCAmelCase		)					,  '''big'''		)		)[2:]
		return bits.zfill(8 * ((len(_UpperCAmelCase		) + 7) // 8)		)

def 			a		( _UpperCAmelCase				:   Dict					,  _UpperCAmelCase				:   Any="utf-8"					,  _UpperCAmelCase				:   str="surrogatepass"		):







		'''simple docstring'''





		__UpperCAmelCase       :	List[str]						=      int(_UpperCAmelCase					,  2		)
		return n.to_bytes((n.bit_length() + 7) // 8					,  '''big'''		).decode(_UpperCAmelCase					,  _UpperCAmelCase		) or "\0"

def 			a		( _UpperCAmelCase				:   List[Any]					,  _UpperCAmelCase				:   List[str]		):







		'''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'''		)

		__UpperCAmelCase       :	str						=      []
		__UpperCAmelCase       :	Any						=      []
		__UpperCAmelCase       :	Any						=      [bin(_UpperCAmelCase		)[2:] for x in range(1					,  size_par + len(_UpperCAmelCase		) + 1		)]

		# sorted information data for the size of the output data
		__UpperCAmelCase       :	Optional[Any]						=      []
		# data position template + parity
		__UpperCAmelCase       :	Any						=      []
		# parity bit counter
		__UpperCAmelCase       :	Tuple						=      0
		# counter position of data bits
		__UpperCAmelCase       :	Optional[int]						=      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'''		)
								__UpperCAmelCase       :	Union[str, 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
		__UpperCAmelCase       :	Union[str, Any]						=      0  # parity bit counter
		for bp in range(1					,  size_par + 1		):
				# Bit counter one for a given parity
				__UpperCAmelCase       :	List[str]						=      0
				# counter to control the loop reading
				__UpperCAmelCase       :	str						=      0
				for x in data_ord:
						if x is not None:
								try:
										__UpperCAmelCase       :	Tuple						=      (bin_pos[cont_loop])[-1 * (bp)]
								except IndexError:
										__UpperCAmelCase       :	List[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
		__UpperCAmelCase       :	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]		)

		return data_out

def 			a		( _UpperCAmelCase				:   List[str]					,  _UpperCAmelCase				:   str		):







		'''simple docstring'''





		__UpperCAmelCase       :	str						=      []
		# Parity bit counter
		__UpperCAmelCase       :	List[str]						=      0
		# Counter p data bit reading
		__UpperCAmelCase       :	int						=      0
		# list of parity received
		__UpperCAmelCase       :	int						=      []
		__UpperCAmelCase       :	List[Any]						=      []

		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'''		)
						__UpperCAmelCase       :	Union[str, Any]						=      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
		__UpperCAmelCase       :	Optional[int]						=      []
		__UpperCAmelCase       :	Dict						=      []
		__UpperCAmelCase       :	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
		__UpperCAmelCase       :	str						=      []
		# Data position feedback + parity
		__UpperCAmelCase       :	Any						=      []
		# Parity bit counter
		__UpperCAmelCase       :	int						=      0
		# Counter p data bit reading
		__UpperCAmelCase       :	List[Any]						=      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'''		)
						__UpperCAmelCase       :	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_ord.append(data_output[cont_data]		)
						cont_data += 1
				else:
						data_ord.append(_UpperCAmelCase		)

    # Calculates parity
		__UpperCAmelCase       :	Union[str, Any]						=      0  # parity bit counter
		for bp in range(1					,  size_par + 1		):
				# Bit counter one for a certain parity
				__UpperCAmelCase       :	Union[str, Any]						=      0
				# Counter to control loop reading
				__UpperCAmelCase       :	Optional[Any]						=      0
				for x in data_ord:
						if x is not None:
								try:
										__UpperCAmelCase       :	int						=      (bin_pos[cont_loop])[-1 * (bp)]
								except IndexError:
										__UpperCAmelCase       :	int						=      '''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
		__UpperCAmelCase       :	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]		)

		__UpperCAmelCase       :	Optional[Any]						=      parity_received == parity
		return data_output, ack





