




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


if is_vision_available():
   import PIL


lowercase__				:      Optional[Any]	   = logging.get_logger(__name__)




class       a__	( UpperCamelCase__				):
     a		:      Tuple		 =     ["""pixel_values"""]







     def __init__(     self  ,		A = True  ,		A = None  ,		A = None  ,		A = PILImageResampling.BILINEAR  ,		A = True  ,		A = 1 / 255  ,		A = True  ,		A = None  ,		A = None  ,		**A  ,		)  ->   None:



          '''simple docstring'''



          super().__init__(**A			)
          a     =				size if size is not None else {"shortest_edge": 384}
          a     =				get_size_dict(A  ,		default_to_square=A			)

          a     =				do_resize
          a     =				size
          # Default value set here for backwards compatibility where the value in config is None
          a     =				crop_pct if crop_pct is not None else 224 / 256
          a     =				resample
          a     =				do_rescale
          a     =				rescale_factor
          a     =				do_normalize
          a     =				image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
          a     =				image_std if image_std is not None else IMAGENET_STANDARD_STD







     def       lowerCAmelCase_    (     self  ,		A  ,		A  ,		A  ,		A = PILImageResampling.BICUBIC  ,		A = None  ,		**A  ,		)  ->   np.ndarray:



          '''simple docstring'''



          a     =				get_size_dict(A  ,		default_to_square=A			)
          if "shortest_edge" not in size:
               raise ValueError(F'''Size dictionary must contain \'shortest_edge\' key. Got {size.keys()}'''			)
          a     =				size["shortest_edge"]

          if shortest_edge < 384:
               # maintain same ratio, resizing shortest edge to shortest_edge/crop_pct
               a     =				int(shortest_edge / crop_pct			)
               a     =				get_resize_output_image_size(A  ,		size=A  ,		default_to_square=A			)
               a     =				resize(image=A  ,		size=A  ,		resample=A  ,		data_format=A  ,		**A			)
               # then crop to (shortest_edge, shortest_edge)
               return center_crop(image=A  ,		size=(shortest_edge, shortest_edge)  ,		data_format=A  ,		**A			)
          else:
               # warping (no cropping) when evaluated at 384 or larger
               return resize(
                   A  ,		size=(shortest_edge, shortest_edge)  ,		resample=A  ,		data_format=A  ,		**A			)







     def       lowerCAmelCase_    (     self  ,		A  ,		A  ,		A = None  ,		**A  ,		)  ->   Tuple:



          '''simple docstring'''



          return rescale(A  ,		scale=A  ,		data_format=A  ,		**A			)







     def       lowerCAmelCase_    (     self  ,		A  ,		A  ,		A  ,		A = None  ,		**A  ,		)  ->   np.ndarray:



          '''simple docstring'''



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







     def       lowerCAmelCase_    (     self  ,		A  ,		A = None  ,		A = None  ,		A = None  ,		A = None  ,		A = None  ,		A = None  ,		A = None  ,		A = None  ,		A = None  ,		A = None  ,		A = ChannelDimension.FIRST  ,		**A  ,		)  ->   PIL.Image.Image:



          '''simple docstring'''



          a     =				do_resize if do_resize is not None else self.do_resize
          a     =				crop_pct if crop_pct is not None else self.crop_pct
          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     =				size if size is not None else self.size
          a     =				get_size_dict(A  ,		default_to_square=A			)

          a     =				make_list_of_images(A			)

          if not valid_images(A			):
               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 or resample is None:
               raise ValueError("Size and resample must be specified if do_resize is True."			)

          if do_resize and size["shortest_edge"] < 384 and crop_pct is None:
               raise ValueError("crop_pct must be specified if size < 384."			)

          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 std must be specified if do_normalize is True."			)

          # All transformations expect numpy arrays.
          a     =				[to_numpy_array(A			) for image in images]

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

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

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

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

          a     =				{"pixel_values": images}
          return BatchFeature(data=A  ,		tensor_type=A			)







lowercase__				:      List[Any]	   = {
    "a": "AAAAA",
    "b": "AAAAB",
    "c": "AAABA",
    "d": "AAABB",
    "e": "AABAA",
    "f": "AABAB",
    "g": "AABBA",
    "h": "AABBB",
    "i": "ABAAA",
    "j": "BBBAA",
    "k": "ABAAB",
    "l": "ABABA",
    "m": "ABABB",
    "n": "ABBAA",
    "o": "ABBAB",
    "p": "ABBBA",
    "q": "ABBBB",
    "r": "BAAAA",
    "s": "BAAAB",
    "t": "BAABA",
    "u": "BAABB",
    "v": "BBBAB",
    "w": "BABAA",
    "x": "BABAB",
    "y": "BABBA",
    "z": "BABBB",
    " ": " ",
}


lowercase__				:      str	   = {value: key for key, value in encode_dict.items()}

def  SCREAMING_SNAKE_CASE							(       __UpperCamelCase)      ->					str:
     a     =				""
     for letter in word.lower():
          if letter.isalpha() or letter == " ":
               encoded += encode_dict[letter]
          else:
               raise Exception("encode() accepts only letters of the alphabet and spaces")
     return encoded

def  SCREAMING_SNAKE_CASE							(       __UpperCamelCase)      ->					str:
     if set(__UpperCamelCase) - {"A", "B", " "} != set():
          raise Exception("decode() accepts only 'A', 'B' and spaces")
     a     =				""
     for word in coded.split():
          while len(__UpperCamelCase) != 0:
               decoded += decode_dict[word[:5]]
               a     =				word[5:]
          decoded += " "
     return decoded.strip()


if __name__ == "__main__":
   from doctest import testmod

   testmod()

