
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_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


__snake_case							 =  logging.get_logger(__name__)







class       UpperCAmelCase_						( lowercase			):



      """simple docstring"""
      UpperCamelCase_    : Dict									=['pixel_values']
      def __init__(    self   ,    SCREAMING_SNAKE_CASE_ = True   ,    SCREAMING_SNAKE_CASE_ = None   ,    SCREAMING_SNAKE_CASE_ = PIL.Image.NEAREST   ,    SCREAMING_SNAKE_CASE_ = False   ,    SCREAMING_SNAKE_CASE_ = None   ,    SCREAMING_SNAKE_CASE_ = 1 / 255   ,    SCREAMING_SNAKE_CASE_ = False   ,    SCREAMING_SNAKE_CASE_ = True   ,    SCREAMING_SNAKE_CASE_ = True   ,    SCREAMING_SNAKE_CASE_ = None   ,    SCREAMING_SNAKE_CASE_ = None   ,    SCREAMING_SNAKE_CASE_ = True   ,    **SCREAMING_SNAKE_CASE_   ,    )    ->				None:
          super().__init__(**SCREAMING_SNAKE_CASE_			)
          UpperCamelCase	:List[str]     							=       size if size is not None else {'''height''': 346, '''width''': 346}
          UpperCamelCase	:Any     							=       get_size_dict(SCREAMING_SNAKE_CASE_			)
          UpperCamelCase	:Any     							=       crop_size if crop_size is not None else {'''height''': 289, '''width''': 289}
          UpperCamelCase	:Optional[int]     							=       get_size_dict(SCREAMING_SNAKE_CASE_   ,    param_name='''crop_size'''			)

          UpperCamelCase	:Optional[int]     							=       do_resize
          UpperCamelCase	:List[str]     							=       size
          UpperCamelCase	:List[Any]     							=       resample
          UpperCamelCase	:Tuple     							=       do_center_crop
          UpperCamelCase	:Any     							=       crop_size
          UpperCamelCase	:Tuple     							=       do_rescale
          UpperCamelCase	:int     							=       rescale_factor
          UpperCamelCase	:Optional[int]     							=       rescale_offset
          UpperCamelCase	:Union[str, Any]     							=       do_normalize
          UpperCamelCase	:Dict     							=       image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
          UpperCamelCase	:Optional[int]     							=       image_std if image_std is not None else IMAGENET_STANDARD_STD
          UpperCamelCase	:Union[str, Any]     							=       include_top
      def    UpperCAmelCase						(    self   ,    SCREAMING_SNAKE_CASE_   ,    SCREAMING_SNAKE_CASE_   ,    SCREAMING_SNAKE_CASE_ = PIL.Image.NEAREST   ,    SCREAMING_SNAKE_CASE_ = None   ,    **SCREAMING_SNAKE_CASE_   ,    )    ->				np.ndarray:
          UpperCamelCase	:Dict     							=       get_size_dict(SCREAMING_SNAKE_CASE_			)
          if "height" not in size or "width" not in size:
              raise ValueError(F'''The size dictionary must have keys \'height\' and \'width\'. Got {size.keys()}'''			)
          return resize(
              SCREAMING_SNAKE_CASE_   ,    size=(size['''height'''], size['''width'''])   ,    resample=SCREAMING_SNAKE_CASE_   ,    data_format=SCREAMING_SNAKE_CASE_   ,    **SCREAMING_SNAKE_CASE_			)
      def    UpperCAmelCase						(    self   ,    SCREAMING_SNAKE_CASE_   ,    SCREAMING_SNAKE_CASE_   ,    SCREAMING_SNAKE_CASE_ = None   ,    **SCREAMING_SNAKE_CASE_   ,    )    ->				np.ndarray:
          UpperCamelCase	:List[str]     							=       get_size_dict(SCREAMING_SNAKE_CASE_			)
          if "height" not in size or "width" not in size:
              raise ValueError(F'''The size dictionary must have keys \'height\' and \'width\'. Got {size.keys()}'''			)
          return center_crop(SCREAMING_SNAKE_CASE_   ,    size=(size['''height'''], size['''width'''])   ,    data_format=SCREAMING_SNAKE_CASE_   ,    **SCREAMING_SNAKE_CASE_			)
      def    UpperCAmelCase						(    self   ,    SCREAMING_SNAKE_CASE_   ,    SCREAMING_SNAKE_CASE_   ,    SCREAMING_SNAKE_CASE_ = True   ,    SCREAMING_SNAKE_CASE_ = None   ,    **SCREAMING_SNAKE_CASE_   ,    )    ->				Optional[Any]:
          if offset:
              UpperCamelCase	:List[Any]     							=       (image - 127.5) * scale
              if data_format is not None:
                  UpperCamelCase	:Any     							=       to_channel_dimension_format(SCREAMING_SNAKE_CASE_   ,    SCREAMING_SNAKE_CASE_			)
              UpperCamelCase	:Union[str, Any]     							=       rescaled_image.astype(np.floataa			)
          else:
              UpperCamelCase	:List[str]     							=       rescale(SCREAMING_SNAKE_CASE_   ,    scale=SCREAMING_SNAKE_CASE_   ,    data_format=SCREAMING_SNAKE_CASE_   ,    **SCREAMING_SNAKE_CASE_			)
          return rescaled_image
      def    UpperCAmelCase						(    self   ,    SCREAMING_SNAKE_CASE_   ,    SCREAMING_SNAKE_CASE_   ,    SCREAMING_SNAKE_CASE_   ,    SCREAMING_SNAKE_CASE_ = None   ,    **SCREAMING_SNAKE_CASE_   ,    )    ->				np.ndarray:
          return normalize(SCREAMING_SNAKE_CASE_   ,    mean=SCREAMING_SNAKE_CASE_   ,    std=SCREAMING_SNAKE_CASE_   ,    data_format=SCREAMING_SNAKE_CASE_   ,    **SCREAMING_SNAKE_CASE_			)




      def    UpperCAmelCase						(    self   ,    SCREAMING_SNAKE_CASE_   ,    SCREAMING_SNAKE_CASE_ = None   ,    SCREAMING_SNAKE_CASE_ = None   ,    SCREAMING_SNAKE_CASE_=None   ,    SCREAMING_SNAKE_CASE_ = None   ,    SCREAMING_SNAKE_CASE_ = None   ,    SCREAMING_SNAKE_CASE_ = None   ,    SCREAMING_SNAKE_CASE_ = None   ,    SCREAMING_SNAKE_CASE_ = None   ,    SCREAMING_SNAKE_CASE_ = None   ,    SCREAMING_SNAKE_CASE_ = None   ,    SCREAMING_SNAKE_CASE_ = None   ,    SCREAMING_SNAKE_CASE_ = None   ,    SCREAMING_SNAKE_CASE_ = None   ,    SCREAMING_SNAKE_CASE_ = ChannelDimension.FIRST   ,    **SCREAMING_SNAKE_CASE_   ,    )    ->				PIL.Image.Image:
          UpperCamelCase	:Tuple     							=       do_resize if do_resize is not None else self.do_resize
          UpperCamelCase	:str     							=       resample if resample is not None else self.resample
          UpperCamelCase	:Optional[int]     							=       do_center_crop if do_center_crop is not None else self.do_center_crop
          UpperCamelCase	:int     							=       do_rescale if do_rescale is not None else self.do_rescale
          UpperCamelCase	:List[Any]     							=       rescale_factor if rescale_factor is not None else self.rescale_factor
          UpperCamelCase	:Optional[Any]     							=       rescale_offset if rescale_offset is not None else self.rescale_offset
          UpperCamelCase	:List[Any]     							=       do_normalize if do_normalize is not None else self.do_normalize
          UpperCamelCase	:Union[str, Any]     							=       image_mean if image_mean is not None else self.image_mean
          UpperCamelCase	:Tuple     							=       image_std if image_std is not None else self.image_std
          UpperCamelCase	:Union[str, Any]     							=       include_top if include_top is not None else self.include_top

          UpperCamelCase	:Tuple     							=       size if size is not None else self.size
          UpperCamelCase	:List[str]     							=       get_size_dict(SCREAMING_SNAKE_CASE_			)
          UpperCamelCase	:Any     							=       crop_size if crop_size is not None else self.crop_size
          UpperCamelCase	:Optional[int]     							=       get_size_dict(SCREAMING_SNAKE_CASE_   ,    param_name='''crop_size'''			)

          UpperCamelCase	:Any     							=       make_list_of_images(SCREAMING_SNAKE_CASE_			)

          if not valid_images(SCREAMING_SNAKE_CASE_			):
              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_center_crop and crop_size is None:
              raise ValueError('''Crop size must be specified if do_center_crop 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.'''			)

          # All transformations expect numpy arrays.
          UpperCamelCase	:int     							=       [to_numpy_array(SCREAMING_SNAKE_CASE_			) for image in images]

          if do_resize:
              UpperCamelCase	:Optional[int]     							=       [self.resize(image=SCREAMING_SNAKE_CASE_   ,    size=SCREAMING_SNAKE_CASE_   ,    resample=SCREAMING_SNAKE_CASE_			) for image in images]

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

          if do_rescale:
              UpperCamelCase	:Dict     							=       [self.rescale(image=SCREAMING_SNAKE_CASE_   ,    scale=SCREAMING_SNAKE_CASE_   ,    offset=SCREAMING_SNAKE_CASE_			) for image in images]

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

          if include_top:
              UpperCamelCase	:Any     							=       [self.normalize(image=SCREAMING_SNAKE_CASE_   ,    mean=[0, 0, 0]   ,    std=SCREAMING_SNAKE_CASE_			) for image in images]

          UpperCamelCase	:List[str]     							=       [to_channel_dimension_format(SCREAMING_SNAKE_CASE_   ,    SCREAMING_SNAKE_CASE_			) for image in images]

          UpperCamelCase	:Union[str, Any]     							=       {'''pixel_values''': images}
          return BatchFeature(data=SCREAMING_SNAKE_CASE_   ,    tensor_type=SCREAMING_SNAKE_CASE_			)













from __future__ import annotations

from enum import Enum







class       UpperCAmelCase_						( lowercase			):



      """simple docstring"""
      UpperCamelCase_    : Optional[Any]									=24
      UpperCamelCase_    : Any									=21
      UpperCamelCase_    : Union[str, Any]									=18
      UpperCamelCase_    : Tuple									=15
      UpperCamelCase_    : List[Any]									=12
      UpperCamelCase_    : Optional[Any]									=9
      UpperCamelCase_    : List[str]									=6
      UpperCamelCase_    : List[str]									=3
      UpperCamelCase_    : Any									=2
      UpperCamelCase_    : List[str]									=1
      UpperCamelCase_    : Optional[Any]									=-1
      UpperCamelCase_    : str									=-2
      UpperCamelCase_    : Any									=-3
      UpperCamelCase_    : Optional[int]									=-6
      UpperCamelCase_    : int									=-9
      UpperCamelCase_    : Optional[int]									=-12
      UpperCamelCase_    : Optional[Any]									=-15
      UpperCamelCase_    : str									=-18
      UpperCamelCase_    : int									=-21
      UpperCamelCase_    : Union[str, Any]									=-24







class       UpperCAmelCase_						( lowercase			):



      """simple docstring"""
      UpperCamelCase_    : Tuple									=8
      UpperCamelCase_    : str									=7
      UpperCamelCase_    : List[str]									=6
      UpperCamelCase_    : List[str]									=5
      UpperCamelCase_    : int									=4
      UpperCamelCase_    : Optional[Any]									=3
      UpperCamelCase_    : Optional[Any]									=2
      UpperCamelCase_    : List[str]									=1







def   _A       (    SCREAMING_SNAKE_CASE__  :     float					,  SCREAMING_SNAKE_CASE__  :     str | SIUnit					,  SCREAMING_SNAKE_CASE__  :     str | SIUnit					,  ):
    if isinstance(SCREAMING_SNAKE_CASE__					,  SCREAMING_SNAKE_CASE__	):
        UpperCamelCase	:str     							=       SIUnit[known_prefix.lower()]
    if isinstance(SCREAMING_SNAKE_CASE__					,  SCREAMING_SNAKE_CASE__	):
        UpperCamelCase	:Any     							=       SIUnit[unknown_prefix.lower()]
    UpperCamelCase	:float     							=       known_amount * (
        10 ** (known_prefix.value - unknown_prefix.value)
    )
    return unknown_amount


def   _A       (    SCREAMING_SNAKE_CASE__  :     float					,  SCREAMING_SNAKE_CASE__  :     str | BinaryUnit					,  SCREAMING_SNAKE_CASE__  :     str | BinaryUnit					,  ):
    if isinstance(SCREAMING_SNAKE_CASE__					,  SCREAMING_SNAKE_CASE__	):
        UpperCamelCase	:Dict     							=       BinaryUnit[known_prefix.lower()]
    if isinstance(SCREAMING_SNAKE_CASE__					,  SCREAMING_SNAKE_CASE__	):
        UpperCamelCase	:List[str]     							=       BinaryUnit[unknown_prefix.lower()]
    UpperCamelCase	:float     							=       known_amount * (
        2 ** ((known_prefix.value - unknown_prefix.value) * 10)
    )
    return unknown_amount


if __name__ == "__main__":
 import doctest

 doctest.testmod()





