






"""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 (
    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


_UpperCamelCase :    Optional[int]      =			logging.get_logger(__name__)


if is_vision_available():
   import PIL






class __a ( snake_case_					):
  __lowercase     :     List[str]       						=       ['pixel_values']



  def __init__(self	:		Union[str, Any]					,      lowercase_	:		bool = True					,      lowercase_	:		Dict[str, int] = None					,      lowercase_	:		PILImageResampling = PILImageResampling.BILINEAR					,      lowercase_	:		bool = True					,      lowercase_	:		bool = False					,      lowercase_	:		bool = True					,      lowercase_	:		bool = True					,      lowercase_	:		Union[int, float] = 1 / 255					,      lowercase_	:		bool = True					,      lowercase_	:		Optional[Union[float, List[float]]] = None					,      lowercase_	:		Optional[Union[float, List[float]]] = None					,      **lowercase_	:		int					,      )		->				None:
   super().__init__(**lowercase_ )

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

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



  def     A_					(self	:		List[str]					,      lowercase_	:		np.ndarray					,      lowercase_	:		Dict[str, int]					,      lowercase_	:		Optional[Union[str, ChannelDimension]] = None )		->				np.ndarray:
   __A     ,       __A						:							int      =						get_image_size(lowercase_ )
   __A     ,       __A						:							str      =						size['height'], size['width']

   if (output_width < output_height and input_width > input_height) or (
       output_width > output_height and input_width < input_height
   ):
    __A						:							Optional[int]      =						np.rotaa(lowercase_					,      3 )

   if data_format is not None:
    __A						:							str      =						to_channel_dimension_format(lowercase_					,      lowercase_ )

   return image



  def     A_					(self	:		str					,      lowercase_	:		np.ndarray					,      lowercase_	:		Dict[str, int]					,      lowercase_	:		bool = False					,      lowercase_	:		Optional[Union[str, ChannelDimension]] = None					,      )		->				np.ndarray:
   __A     ,       __A						:							Dict      =						size['height'], size['width']
   __A     ,       __A						:							int      =						get_image_size(lowercase_ )

   __A						:							Optional[int]      =						output_width - input_width
   __A						:							Tuple      =						output_height - input_height

   if random_padding:
    __A						:							Tuple      =						np.random.randint(low=0					,      high=delta_height + 1 )
    __A						:							str      =						np.random.randint(low=0					,      high=delta_width + 1 )
   else:
    __A						:							Any      =						delta_height // 2
    __A						:							Any      =						delta_width // 2

   __A						:							Union[str, Any]      =						delta_height - pad_top
   __A						:							Union[str, Any]      =						delta_width - pad_left

   __A						:							Dict      =						((pad_top, pad_bottom), (pad_left, pad_right))
   return pad(lowercase_					,      lowercase_					,      data_format=lowercase_ )



  def     A_					(self	:		Tuple					,      *lowercase_	:		Any					,      **lowercase_	:		int )		->				Tuple:
   logger.info('pad is deprecated and will be removed in version 4.27. Please use pad_image instead.' )
   return self.pad_image(*lowercase_					,      **lowercase_ )



  def     A_					(self	:		Any					,      lowercase_	:		np.ndarray					,      lowercase_	:		Dict[str, int]					,      lowercase_	:		PILImageResampling = PILImageResampling.BICUBIC					,      lowercase_	:		Optional[Union[str, ChannelDimension]] = None					,      **lowercase_	:		str					,      )		->				np.ndarray:
   __A     ,       __A						:							Optional[Any]      =						get_image_size(lowercase_ )
   __A     ,       __A						:							Any      =						size['height'], size['width']

   # We always resize to the smallest of either the input or output size.
   __A						:							int      =						min(lowercase_					,      lowercase_ )
   __A						:							Any      =						min(lowercase_					,      lowercase_ )

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

   if input_height > input_width:
    __A						:							Dict      =						int(input_width * height / input_height )
   elif input_width > input_height:
    __A						:							Optional[Any]      =						int(input_height * width / input_width )

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



  def     A_					(self	:		Dict					,      lowercase_	:		np.ndarray					,      lowercase_	:		Dict[str, int]					,      lowercase_	:		PILImageResampling = PILImageResampling.BICUBIC					,      lowercase_	:		Optional[Union[str, ChannelDimension]] = None					,      **lowercase_	:		Dict					,      )		->				np.ndarray:
   __A						:							Optional[int]      =						get_size_dict(lowercase_ )
   __A						:							Union[str, Any]      =						min(size['height']					,      size['width'] )
   __A						:							Any      =						get_resize_output_image_size(lowercase_					,      size=lowercase_					,      default_to_square=lowercase_ )
   __A						:							Optional[Any]      =						resize(lowercase_					,      size=lowercase_					,      resample=lowercase_					,      data_format=lowercase_					,      **lowercase_ )
   return resized_image



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



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



  def     A_					(self	:		int					,      lowercase_	:		ImageInput					,      lowercase_	:		bool = None					,      lowercase_	:		Dict[str, int] = None					,      lowercase_	:		PILImageResampling = None					,      lowercase_	:		bool = None					,      lowercase_	:		bool = None					,      lowercase_	:		bool = None					,      lowercase_	:		bool = False					,      lowercase_	:		bool = None					,      lowercase_	:		float = None					,      lowercase_	:		bool = None					,      lowercase_	:		Optional[Union[float, List[float]]] = None					,      lowercase_	:		Optional[Union[float, List[float]]] = None					,      lowercase_	:		Optional[Union[str, TensorType]] = None					,      lowercase_	:		Optional[ChannelDimension] = ChannelDimension.FIRST					,      **lowercase_	:		str					,      )		->				PIL.Image.Image:
   __A						:							int      =						do_resize if do_resize is not None else self.do_resize
   __A						:							Optional[int]      =						size if size is not None else self.size
   if isinstance(lowercase_					,      (tuple, list) ):
    # Previous feature extractor had size in (width, height) format
    __A						:							Tuple      =						size[::-1]
   __A						:							Tuple      =						get_size_dict(lowercase_ )
   __A						:							List[str]      =						resample if resample is not None else self.resample
   __A						:							str      =						do_thumbnail if do_thumbnail is not None else self.do_thumbnail
   __A						:							int      =						do_align_long_axis if do_align_long_axis is not None else self.do_align_long_axis
   __A						:							Tuple      =						do_pad if do_pad is not None else self.do_pad
   __A						:							Any      =						do_rescale if do_rescale is not None else self.do_rescale
   __A						:							int      =						rescale_factor if rescale_factor is not None else self.rescale_factor
   __A						:							Optional[Any]      =						do_normalize if do_normalize is not None else self.do_normalize
   __A						:							Dict      =						image_mean if image_mean is not None else self.image_mean
   __A						:							str      =						image_std if image_std is not None else self.image_std

   __A						:							str      =						make_list_of_images(lowercase_ )

   if not valid_images(lowercase_ ):
    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.
   __A						:							str      =						[to_numpy_array(lowercase_ ) for image in images]

   if do_align_long_axis:
    __A						:							Union[str, Any]      =						[self.align_long_axis(lowercase_					,      size=lowercase_ ) for image in images]

   if do_resize:
    __A						:							int      =						[self.resize(image=lowercase_					,      size=lowercase_					,      resample=lowercase_ ) for image in images]

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

   if do_pad:
    __A						:							List[Any]      =						[self.pad_image(image=lowercase_					,      size=lowercase_					,      random_padding=lowercase_ ) for image in images]

   if do_rescale:
    __A						:							Optional[Any]      =						[self.rescale(image=lowercase_					,      scale=lowercase_ ) for image in images]

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

   __A						:							List[Any]      =						[to_channel_dimension_format(lowercase_					,      lowercase_ ) for image in images]

   __A						:							List[Any]      =						{'pixel_values': images}
   return BatchFeature(data=lowercase_					,      tensor_type=lowercase_ )













"""simple docstring"""


import numpy as np

_UpperCamelCase :    int      =			[
    ["""a""", """b""", """c""", """d""", """e"""],
    ["""f""", """g""", """h""", """i""", """k"""],
    ["""l""", """m""", """n""", """o""", """p"""],
    ["""q""", """r""", """s""", """t""", """u"""],
    ["""v""", """w""", """x""", """y""", """z"""],
]






class __a :



  def __init__(self	:		Any )		->				None:
   __A						:							Any      =						np.array(lowercase_ )



  def     A_					(self	:		Dict					,      lowercase_	:		str )		->				np.ndarray:
   __A     ,       __A						:							Any      =						np.where(letter == self.SQUARE )
   __A						:							Tuple      =						np.concatenate([indexa + 1, indexa + 1] )
   return indexes



  def     A_					(self	:		Tuple					,      lowercase_	:		int					,      lowercase_	:		int )		->				str:
   return self.SQUARE[indexa - 1, indexa - 1]



  def     A_					(self	:		Union[str, Any]					,      lowercase_	:		str )		->				str:
   __A						:							Any      =						message.lower()
   __A						:							Optional[Any]      =						message.replace('j'					,      'i' )

   __A						:							Union[str, Any]      =						''
   for letter_index in range(len(lowercase_ ) ):
    if message[letter_index] != " ":
     __A						:							Union[str, Any]      =						self.letter_to_numbers(message[letter_index] )
     __A						:							Union[str, Any]      =						encoded_message + str(numbers[0] ) + str(numbers[1] )
    elif message[letter_index] == " ":
     __A						:							Optional[Any]      =						encoded_message + ' '

   return encoded_message



  def     A_					(self	:		Dict					,      lowercase_	:		str )		->				str:
   __A						:							List[str]      =						message.replace(' '					,      '  ' )
   __A						:							List[Any]      =						''
   for numbers_index in range(int(len(lowercase_ ) / 2 ) ):
    if message[numbers_index * 2] != " ":
     __A						:							Any      =						message[numbers_index * 2]
     __A						:							Tuple      =						message[numbers_index * 2 + 1]

     __A						:							Dict      =						self.numbers_to_letter(int(lowercase_ )					,      int(lowercase_ ) )
     __A						:							Union[str, Any]      =						decoded_message + letter
    elif message[numbers_index * 2] == " ":
     __A						:							int      =						decoded_message + ' '

   return decoded_message





