

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 convert_to_rgb, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_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


lowerCAmelCase__       = logging.get_logger(__name__)






class 						__magic_name__    (       _snake_case    ):
 UpperCAmelCase							    =						["""pixel_values"""]


 def __init__(						self		: List[Any]			,						lowerCAmelCase__		: bool = True			,						lowerCAmelCase__		: Dict[str, int] = None			,						lowerCAmelCase__		: PILImageResampling = PILImageResampling.BICUBIC			,						lowerCAmelCase__		: bool = True			,						lowerCAmelCase__		: Union[int, float] = 1 / 2_5_5			,						lowerCAmelCase__		: bool = True			,						lowerCAmelCase__		: Optional[Union[float, List[float]]] = None			,						lowerCAmelCase__		: Optional[Union[float, List[float]]] = None			,						lowerCAmelCase__		: bool = True			,						**lowerCAmelCase__		: Any			,						)    ->      None:
   super().__init__(**lowerCAmelCase__    )
   UpperCAmelCase					      =  size if size is not None else {"height": 3_8_4, "width": 3_8_4}
   UpperCAmelCase					      =  get_size_dict(lowerCAmelCase__			,						default_to_square=lowerCAmelCase__    )

   UpperCAmelCase					      =  do_resize
   UpperCAmelCase					      =  size
   UpperCAmelCase					      =  resample
   UpperCAmelCase					      =  do_rescale
   UpperCAmelCase					      =  rescale_factor
   UpperCAmelCase					      =  do_normalize
   UpperCAmelCase					      =  image_mean if image_mean is not None else OPENAI_CLIP_MEAN
   UpperCAmelCase					      =  image_std if image_std is not None else OPENAI_CLIP_STD
   UpperCAmelCase					      =  do_convert_rgb


 def _UpperCamelCase	(						self		: int			,						lowerCAmelCase__		: np.ndarray			,						lowerCAmelCase__		: Dict[str, int]			,						lowerCAmelCase__		: PILImageResampling = PILImageResampling.BICUBIC			,						lowerCAmelCase__		: Optional[Union[str, ChannelDimension]] = None			,						**lowerCAmelCase__		: List[Any]			,						)    ->      np.ndarray:
   UpperCAmelCase					      =  get_size_dict(lowerCAmelCase__			,						default_to_square=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()}"    )
   UpperCAmelCase					      =  (size["height"], size["width"])
   return resize(lowerCAmelCase__			,						size=lowerCAmelCase__			,						resample=lowerCAmelCase__			,						data_format=lowerCAmelCase__			,						**lowerCAmelCase__    )


 def _UpperCamelCase	(						self		: Union[str, Any]			,						lowerCAmelCase__		: np.ndarray			,						lowerCAmelCase__		: Union[int, float]			,						lowerCAmelCase__		: Optional[Union[str, ChannelDimension]] = None			,						**lowerCAmelCase__		: Optional[int]			,						)    ->      Optional[Any]:
   return rescale(lowerCAmelCase__			,						scale=lowerCAmelCase__			,						data_format=lowerCAmelCase__			,						**lowerCAmelCase__    )


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


 def _UpperCamelCase	(						self		: Union[str, Any]			,						lowerCAmelCase__		: ImageInput			,						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__		: bool = None			,						lowerCAmelCase__		: ChannelDimension = ChannelDimension.FIRST			,						**lowerCAmelCase__		: int			,						)    ->      PIL.Image.Image:
   UpperCAmelCase					      =  do_resize if do_resize is not None else self.do_resize
   UpperCAmelCase					      =  resample if resample is not None else self.resample
   UpperCAmelCase					      =  do_rescale if do_rescale is not None else self.do_rescale
   UpperCAmelCase					      =  rescale_factor if rescale_factor is not None else self.rescale_factor
   UpperCAmelCase					      =  do_normalize if do_normalize is not None else self.do_normalize
   UpperCAmelCase					      =  image_mean if image_mean is not None else self.image_mean
   UpperCAmelCase					      =  image_std if image_std is not None else self.image_std
   UpperCAmelCase					      =  do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

   UpperCAmelCase					      =  size if size is not None else self.size
   UpperCAmelCase					      =  get_size_dict(lowerCAmelCase__			,						default_to_square=lowerCAmelCase__    )

   UpperCAmelCase					      =  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_resize and size is None or resample is None:
     raise ValueError("Size and resample 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 std must be specified if do_normalize is True."    )

   # PIL RGBA images are converted to RGB
   if do_convert_rgb:
     UpperCAmelCase					      =  [convert_to_rgb(lowerCAmelCase__    ) for image in images]

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

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

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

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

   UpperCAmelCase					      =  [to_channel_dimension_format(lowerCAmelCase__			,						lowerCAmelCase__    ) for image in images]

   UpperCAmelCase					      =  BatchFeature(data={"pixel_values": images}			,						tensor_type=lowerCAmelCase__    )

   return encoded_outputs






lowerCAmelCase__       = {
    "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",
    " ": " ",
}


lowerCAmelCase__       = {value: key for key, value in encode_dict.items()}
def      _lowerCAmelCase(	__A			):
  UpperCAmelCase					      =  ""
  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      _lowerCAmelCase(	__A			):
  if set(__A			) - {"A", "B", " "} != set():
    raise Exception("decode() accepts only 'A', 'B' and spaces"			)
  UpperCAmelCase					      =  ""
  for word in coded.split():
    while len(__A			) != 0:
      decoded += decode_dict[word[:5]]
      UpperCAmelCase					      =  word[5:]
    decoded += " "
  return decoded.strip()


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

      testmod()



