




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,
    convert_to_rgb,
    get_resize_output_image_size,
    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


SCREAMING_SNAKE_CASE_						       =	logging.get_logger(__name__)


if is_vision_available():
      import PIL

class 		UpperCamelCase__			(				lowerCAmelCase_       ):



     '''simple docstring'''

     __snake_case   :     Tuple		     =      ["pixel_values"]






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




           '''simple docstring'''


           super().__init__(**lowerCamelCase__	)
           SCREAMING_SNAKE_CASE		=						size if size is not None else {"""shortest_edge""": 224}
           SCREAMING_SNAKE_CASE		=						get_size_dict(lowerCamelCase__						,default_to_square=lowerCamelCase__	)
           SCREAMING_SNAKE_CASE		=						crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
           SCREAMING_SNAKE_CASE		=						get_size_dict(lowerCamelCase__						,default_to_square=lowerCamelCase__						,param_name="""crop_size"""	)

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






     def     SCREAMING_SNAKE_CASE__  (							self						:   Union[str, Any]						,lowerCamelCase__						:   np.ndarray						,lowerCamelCase__						:   Dict[str, int]						,lowerCamelCase__						:   PILImageResampling = PILImageResampling.BICUBIC						,lowerCamelCase__						:   Optional[Union[str, ChannelDimension]] = None						,**lowerCamelCase__						:   Optional[Any]						,)					->		np.ndarray:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						get_size_dict(lowerCamelCase__						,default_to_square=lowerCamelCase__	)
           if "shortest_edge" not in size:
                 raise ValueError(F"""The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}"""	)
           SCREAMING_SNAKE_CASE		=						get_resize_output_image_size(lowerCamelCase__						,size=size["""shortest_edge"""]						,default_to_square=lowerCamelCase__	)
           return resize(lowerCamelCase__						,size=lowerCamelCase__						,resample=lowerCamelCase__						,data_format=lowerCamelCase__						,**lowerCamelCase__	)






     def     SCREAMING_SNAKE_CASE__  (							self						:   List[Any]						,lowerCamelCase__						:   np.ndarray						,lowerCamelCase__						:   Dict[str, int]						,lowerCamelCase__						:   Optional[Union[str, ChannelDimension]] = None						,**lowerCamelCase__						:   Optional[int]						,)					->		np.ndarray:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						get_size_dict(lowerCamelCase__	)
           if "height" not in size or "width" not in size:
                 raise ValueError(F"""The `size` parameter must contain the keys (height, width). Got {size.keys()}"""	)
           return center_crop(lowerCamelCase__						,size=(size["""height"""], size["""width"""])						,data_format=lowerCamelCase__						,**lowerCamelCase__	)






     def     SCREAMING_SNAKE_CASE__  (							self						:   Optional[int]						,lowerCamelCase__						:   np.ndarray						,lowerCamelCase__						:   Union[int, float]						,lowerCamelCase__						:   Optional[Union[str, ChannelDimension]] = None						,**lowerCamelCase__						:   Optional[int]						,)					->		Any:




           '''simple docstring'''


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






     def     SCREAMING_SNAKE_CASE__  (							self						:   Tuple						,lowerCamelCase__						:   np.ndarray						,lowerCamelCase__						:   Union[float, List[float]]						,lowerCamelCase__						:   Union[float, List[float]]						,lowerCamelCase__						:   Optional[Union[str, ChannelDimension]] = None						,**lowerCamelCase__						:   Optional[int]						,)					->		np.ndarray:




           '''simple docstring'''


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






     def     SCREAMING_SNAKE_CASE__  (							self						:   Tuple						,lowerCamelCase__						:   ImageInput						,lowerCamelCase__						:   bool = None						,lowerCamelCase__						:   Dict[str, int] = None						,lowerCamelCase__						:   PILImageResampling = None						,lowerCamelCase__						:   bool = None						,lowerCamelCase__						:   int = None						,lowerCamelCase__						:   bool = None						,lowerCamelCase__						:   float = None						,lowerCamelCase__						:   bool = None						,lowerCamelCase__						:   Optional[Union[float, List[float]]] = None						,lowerCamelCase__						:   Optional[Union[float, List[float]]] = None						,lowerCamelCase__						:   bool = None						,lowerCamelCase__						:   Optional[Union[str, TensorType]] = None						,lowerCamelCase__						:   Optional[ChannelDimension] = ChannelDimension.FIRST						,**lowerCamelCase__						:   str						,)					->		PIL.Image.Image:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						do_resize if do_resize is not None else self.do_resize
           SCREAMING_SNAKE_CASE		=						size if size is not None else self.size
           SCREAMING_SNAKE_CASE		=						get_size_dict(lowerCamelCase__						,param_name="""size"""						,default_to_square=lowerCamelCase__	)
           SCREAMING_SNAKE_CASE		=						resample if resample is not None else self.resample
           SCREAMING_SNAKE_CASE		=						do_center_crop if do_center_crop is not None else self.do_center_crop
           SCREAMING_SNAKE_CASE		=						crop_size if crop_size is not None else self.crop_size
           SCREAMING_SNAKE_CASE		=						get_size_dict(lowerCamelCase__						,param_name="""crop_size"""						,default_to_square=lowerCamelCase__	)
           SCREAMING_SNAKE_CASE		=						do_rescale if do_rescale is not None else self.do_rescale
           SCREAMING_SNAKE_CASE		=						rescale_factor if rescale_factor is not None else self.rescale_factor
           SCREAMING_SNAKE_CASE		=						do_normalize if do_normalize is not None else self.do_normalize
           SCREAMING_SNAKE_CASE		=						image_mean if image_mean is not None else self.image_mean
           SCREAMING_SNAKE_CASE		=						image_std if image_std is not None else self.image_std
           SCREAMING_SNAKE_CASE		=						do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

           SCREAMING_SNAKE_CASE		=						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:
                 raise ValueError("""Size 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."""	)

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

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

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

           if do_center_crop:
                 SCREAMING_SNAKE_CASE		=						[self.center_crop(image=lowerCamelCase__						,size=lowerCamelCase__	) for image in images]

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

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

           SCREAMING_SNAKE_CASE		=						[to_channel_dimension_format(lowerCamelCase__						,lowerCamelCase__	) for image in images]

           SCREAMING_SNAKE_CASE		=						{"""pixel_values""": images}
           return BatchFeature(data=lowerCamelCase__						,tensor_type=lowerCamelCase__	)









from __future__ import annotations



def        __lowercase       (				_SCREAMING_SNAKE_CASE     )			->       list[int]:


      '''simple docstring'''


      SCREAMING_SNAKE_CASE		=						2
      SCREAMING_SNAKE_CASE		=						[]
      while i * i <= n:
            if n % i:
                  i += 1
            else:
                  n //= i
                  factors.append(_SCREAMING_SNAKE_CASE     )
      if n > 1:
            factors.append(_SCREAMING_SNAKE_CASE     )
      return factors


if __name__ == "__main__":
      import doctest

      doctest.testmod()


