
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__				:  int       					=				logging.get_logger(__name__)



class 			UpperCamelCase_			(						lowerCamelCase_   ):




							'''simple docstring'''

							UpperCamelCase_						   =							["""pixel_values"""]
							def __init__(				self     ,						UpperCamelCase = True     ,						UpperCamelCase = None     ,						UpperCamelCase = True     ,						UpperCamelCase = None     ,						UpperCamelCase = PILImageResampling.BICUBIC     ,						UpperCamelCase = True     ,						UpperCamelCase = 1 / 2_55     ,						UpperCamelCase = True     ,						UpperCamelCase = None     ,						UpperCamelCase = None     ,						**UpperCamelCase     ,						) ->	Dict:
										super().__init__(**UpperCamelCase)
										UpperCamelCase__    :		int     =				crop_size if crop_size is not None else {"height": 2_56, "width": 2_56}
										UpperCamelCase__    :		Union[str, Any]     =				get_size_dict(UpperCamelCase     ,						param_name='crop_size')
										UpperCamelCase__    :		Tuple     =				size if size is not None else {"height": 2_24, "width": 2_24}
										UpperCamelCase__    :		Any     =				get_size_dict(UpperCamelCase)

										UpperCamelCase__    :		Optional[Any]     =				do_center_crop
										UpperCamelCase__    :		Tuple     =				crop_size
										UpperCamelCase__    :		List[str]     =				do_resize
										UpperCamelCase__    :		Tuple     =				size
										UpperCamelCase__    :		Tuple     =				resample
										UpperCamelCase__    :		Tuple     =				do_rescale
										UpperCamelCase__    :		Union[str, Any]     =				rescale_factor
										UpperCamelCase__    :		Dict     =				do_normalize
										UpperCamelCase__    :		Optional[Any]     =				image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
										UpperCamelCase__    :		Union[str, Any]     =				image_std if image_std is not None else IMAGENET_DEFAULT_STD
							def        lowerCAmelCase__ (				self     ,						UpperCamelCase     ,						UpperCamelCase     ,						UpperCamelCase = None     ,						UpperCamelCase = None     ,						**UpperCamelCase     ,						) ->	Dict:
										UpperCamelCase__    :		Optional[int]     =				self.size if size is None else size
										UpperCamelCase__    :		List[Any]     =				get_size_dict(UpperCamelCase)
										UpperCamelCase__    :		List[Any]     =				get_size_dict(UpperCamelCase     ,						param_name='crop_size')

										UpperCamelCase__    :		Any     =				get_image_size(UpperCamelCase)
										UpperCamelCase__    :		Dict     =				min(UpperCamelCase     ,						UpperCamelCase)
										UpperCamelCase__    :		List[str]     =				(size["height"] / crop_size["height"]) * min_dim
										UpperCamelCase__    :		Dict     =				(size["width"] / crop_size["width"]) * min_dim
										return center_crop(UpperCamelCase     ,						size=(cropped_height, cropped_width)     ,						data_format=UpperCamelCase     ,						**UpperCamelCase)
							def        lowerCAmelCase__ (				self     ,						UpperCamelCase     ,						UpperCamelCase     ,						UpperCamelCase = PIL.Image.BICUBIC     ,						UpperCamelCase = None     ,						**UpperCamelCase     ,						) ->	List[Any]:
										UpperCamelCase__    :		int     =				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     ,						UpperCamelCase     ,						UpperCamelCase     ,						UpperCamelCase = None     ,						**UpperCamelCase     ,						) ->	Dict:
										return rescale(UpperCamelCase     ,						scale=UpperCamelCase     ,						data_format=UpperCamelCase     ,						**UpperCamelCase)
							def        lowerCAmelCase__ (				self     ,						UpperCamelCase     ,						UpperCamelCase     ,						UpperCamelCase     ,						UpperCamelCase = None     ,						**UpperCamelCase     ,						) ->	Optional[int]:
										return normalize(UpperCamelCase     ,						mean=UpperCamelCase     ,						std=UpperCamelCase     ,						data_format=UpperCamelCase     ,						**UpperCamelCase)






							def        lowerCAmelCase__ (				self     ,						UpperCamelCase     ,						UpperCamelCase = None     ,						UpperCamelCase = None     ,						UpperCamelCase = None     ,						UpperCamelCase = None     ,						UpperCamelCase = None     ,						UpperCamelCase = None     ,						UpperCamelCase = None     ,						UpperCamelCase = None     ,						UpperCamelCase = None     ,						UpperCamelCase = None     ,						UpperCamelCase = None     ,						UpperCamelCase = ChannelDimension.FIRST     ,						**UpperCamelCase     ,						) ->	Any:
										UpperCamelCase__    :		Union[str, Any]     =				do_center_crop if do_center_crop is not None else self.do_center_crop
										UpperCamelCase__    :		Optional[Any]     =				crop_size if crop_size is not None else self.crop_size
										UpperCamelCase__    :		Optional[Any]     =				get_size_dict(UpperCamelCase     ,						param_name='crop_size')
										UpperCamelCase__    :		Any     =				do_resize if do_resize is not None else self.do_resize
										UpperCamelCase__    :		List[str]     =				size if size is not None else self.size
										UpperCamelCase__    :		int     =				get_size_dict(UpperCamelCase)
										UpperCamelCase__    :		List[str]     =				resample if resample is not None else self.resample
										UpperCamelCase__    :		List[Any]     =				do_rescale if do_rescale is not None else self.do_rescale
										UpperCamelCase__    :		int     =				rescale_factor if rescale_factor is not None else self.rescale_factor
										UpperCamelCase__    :		List[Any]     =				do_normalize if do_normalize is not None else self.do_normalize
										UpperCamelCase__    :		List[Any]     =				image_mean if image_mean is not None else self.image_mean
										UpperCamelCase__    :		Optional[int]     =				image_std if image_std is not None else self.image_std

										UpperCamelCase__    :		Union[str, 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.
										UpperCamelCase__    :		int     =				[to_numpy_array(UpperCamelCase) for image in images]

										if do_center_crop:
													UpperCamelCase__    :		Optional[int]     =				[self.center_crop(UpperCamelCase     ,						UpperCamelCase     ,						size=UpperCamelCase) for image in images]

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

										if do_rescale:
													UpperCamelCase__    :		List[Any]     =				[self.rescale(image=UpperCamelCase     ,						scale=UpperCamelCase) for image in images]

										if do_normalize:
													UpperCamelCase__    :		Union[str, Any]     =				[self.normalize(image=UpperCamelCase     ,						mean=UpperCamelCase     ,						std=UpperCamelCase) for image in images]

										UpperCamelCase__    :		List[Any]     =				[to_channel_dimension_format(UpperCamelCase     ,						UpperCamelCase) for image in images]

										UpperCamelCase__    :		Dict     =				{"pixel_values": images}
										return BatchFeature(data=UpperCamelCase     ,						tensor_type=UpperCamelCase)





import numpy as np

UpperCAmelCase__				:  str       					=				[
    ['''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 			UpperCamelCase_			:




							'''simple docstring'''

							def __init__(				self) ->	None:
										UpperCamelCase__    :		Any     =				np.array(UpperCamelCase)
							def        lowerCAmelCase__ (				self     ,						UpperCamelCase) ->	np.ndarray:
										UpperCamelCase__   ,			UpperCamelCase__    :		Optional[int]     =				np.where(letter == self.SQUARE)
										UpperCamelCase__    :		int     =				np.concatenate([indexa + 1, indexa + 1])
										return indexes
							def        lowerCAmelCase__ (				self     ,						UpperCamelCase     ,						UpperCamelCase) ->	str:
										return self.SQUARE[indexa - 1, indexa - 1]
							def        lowerCAmelCase__ (				self     ,						UpperCamelCase) ->	str:
										UpperCamelCase__    :		str     =				message.lower()
										UpperCamelCase__    :		int     =				message.replace('j'     ,						'i')

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

										return encoded_message






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

																UpperCamelCase__    :		Tuple     =				self.numbers_to_letter(int(UpperCamelCase)     ,						int(UpperCamelCase))
																UpperCamelCase__    :		List[str]     =				decoded_message + letter
													elif message[numbers_index * 2] == " ":
																UpperCamelCase__    :		str     =				decoded_message + ' '

										return decoded_message



