






"""simple docstring"""






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, logging


UpperCAmelCase_   : List[str]   			= logging.get_logger(__name__)




class 							lowerCAmelCase__				(			UpperCAmelCase__ ):






      '''simple docstring'''

      __UpperCamelCase	 =  ["pixel_values"]
      def __init__(						self     :  Tuple						,	lowercase_     :  bool = True						,	lowercase_     :  Optional[Dict[str, int]] = None						,	lowercase_     :  PILImageResampling = PILImageResampling.BILINEAR						,	lowercase_     :  bool = True						,	lowercase_     :  Dict[str, int] = None						,	lowercase_     :  bool = True						,	lowercase_     :  Union[int, float] = 1 / 255						,	lowercase_     :  bool = True						,	lowercase_     :  Optional[Union[float, List[float]]] = None						,	lowercase_     :  Optional[Union[float, List[float]]] = None						,	**lowercase_     :  str						,	):




          '''simple docstring'''


          super().__init__(**lowercase_)
          SCREAMING_SNAKE_CASE_      :  Dict    						=		size if size is not None else {'''shortest_edge''': 256}
          SCREAMING_SNAKE_CASE_      :  str    						=		get_size_dict(lowercase_						,	default_to_square=lowercase_)
          SCREAMING_SNAKE_CASE_      :  Dict    						=		crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
          SCREAMING_SNAKE_CASE_      :  int    						=		get_size_dict(lowercase_)
          SCREAMING_SNAKE_CASE_      :  List[Any]    						=		do_resize
          SCREAMING_SNAKE_CASE_      :  int    						=		size
          SCREAMING_SNAKE_CASE_      :  List[Any]    						=		resample
          SCREAMING_SNAKE_CASE_      :  int    						=		do_center_crop
          SCREAMING_SNAKE_CASE_      :  List[Any]    						=		crop_size
          SCREAMING_SNAKE_CASE_      :  List[Any]    						=		do_rescale
          SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		rescale_factor
          SCREAMING_SNAKE_CASE_      :  str    						=		do_normalize
          SCREAMING_SNAKE_CASE_      :  int    						=		image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
          SCREAMING_SNAKE_CASE_      :  List[Any]    						=		image_std if image_std is not None else IMAGENET_STANDARD_STD
      def _SCREAMING_SNAKE_CASE (						self     :  str						,	lowercase_     :  np.ndarray						,	lowercase_     :  Dict[str, int]						,	lowercase_     :  PILImageResampling = PILImageResampling.BICUBIC						,	lowercase_     :  Optional[Union[str, ChannelDimension]] = None						,	**lowercase_     :  Optional[int]						,	):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  List[Any]    						=		get_size_dict(lowercase_						,	default_to_square=lowercase_)
          if "shortest_edge" not in size:
              raise ValueError(F'The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}')
          SCREAMING_SNAKE_CASE_      :  int    						=		get_resize_output_image_size(lowercase_						,	size=size['''shortest_edge''']						,	default_to_square=lowercase_)
          return resize(lowercase_						,	size=lowercase_						,	resample=lowercase_						,	data_format=lowercase_						,	**lowercase_)
      def _SCREAMING_SNAKE_CASE (						self     :  Optional[int]						,	lowercase_     :  np.ndarray						,	lowercase_     :  Dict[str, int]						,	lowercase_     :  Optional[Union[str, ChannelDimension]] = None						,	**lowercase_     :  int						,	):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  List[str]    						=		get_size_dict(lowercase_)
          return center_crop(lowercase_						,	size=(size['''height'''], size['''width'''])						,	data_format=lowercase_						,	**lowercase_)
      def _SCREAMING_SNAKE_CASE (						self     :  Union[str, Any]						,	lowercase_     :  np.ndarray						,	lowercase_     :  float						,	lowercase_     :  Optional[Union[str, ChannelDimension]] = None						,	**lowercase_     :  List[str]):




          '''simple docstring'''


          return rescale(lowercase_						,	scale=lowercase_						,	data_format=lowercase_						,	**lowercase_)
      def _SCREAMING_SNAKE_CASE (						self     :  int						,	lowercase_     :  np.ndarray						,	lowercase_     :  Union[float, List[float]]						,	lowercase_     :  Union[float, List[float]]						,	lowercase_     :  Optional[Union[str, ChannelDimension]] = None						,	**lowercase_     :  str						,	):




          '''simple docstring'''


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



      def _SCREAMING_SNAKE_CASE (						self     :  int						,	lowercase_     :  ImageInput						,	lowercase_     :  Optional[bool] = None						,	lowercase_     :  Dict[str, int] = None						,	lowercase_     :  PILImageResampling = None						,	lowercase_     :  bool = None						,	lowercase_     :  Dict[str, int] = None						,	lowercase_     :  Optional[bool] = None						,	lowercase_     :  Optional[float] = None						,	lowercase_     :  Optional[bool] = None						,	lowercase_     :  Optional[Union[float, List[float]]] = None						,	lowercase_     :  Optional[Union[float, List[float]]] = None						,	lowercase_     :  Optional[Union[str, TensorType]] = None						,	lowercase_     :  Union[str, ChannelDimension] = ChannelDimension.FIRST						,	**lowercase_     :  Dict						,	):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  Tuple    						=		do_resize if do_resize is not None else self.do_resize
          SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		size if size is not None else self.size
          SCREAMING_SNAKE_CASE_      :  int    						=		get_size_dict(lowercase_						,	default_to_square=lowercase_)
          SCREAMING_SNAKE_CASE_      :  Dict    						=		resample if resample is not None else self.resample
          SCREAMING_SNAKE_CASE_      :  Dict    						=		do_center_crop if do_center_crop is not None else self.do_center_crop
          SCREAMING_SNAKE_CASE_      :  List[str]    						=		crop_size if crop_size is not None else self.crop_size
          SCREAMING_SNAKE_CASE_      :  Any    						=		get_size_dict(lowercase_)
          SCREAMING_SNAKE_CASE_      :  Dict    						=		do_rescale if do_rescale is not None else self.do_rescale
          SCREAMING_SNAKE_CASE_      :  Dict    						=		rescale_factor if rescale_factor is not None else self.rescale_factor
          SCREAMING_SNAKE_CASE_      :  str    						=		do_normalize if do_normalize is not None else self.do_normalize
          SCREAMING_SNAKE_CASE_      :  List[Any]    						=		image_mean if image_mean is not None else self.image_mean
          SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		image_std if image_std is not None else self.image_std

          SCREAMING_SNAKE_CASE_      :  List[Any]    						=		make_list_of_images(lowercase_)

          if not valid_images(lowercase_):
              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.''')

          # All transformations expect numpy arrays.
          SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		[to_numpy_array(lowercase_) for image in images]

          if do_resize:
              SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		[self.resize(image=lowercase_						,	size=lowercase_						,	resample=lowercase_) for image in images]

          if do_center_crop:
              SCREAMING_SNAKE_CASE_      :  Tuple    						=		[self.center_crop(image=lowercase_						,	size=lowercase_) for image in images]

          if do_rescale:
              SCREAMING_SNAKE_CASE_      :  int    						=		[self.rescale(image=lowercase_						,	scale=lowercase_) for image in images]

          if do_normalize:
              SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		[self.normalize(image=lowercase_						,	mean=lowercase_						,	std=lowercase_) for image in images]

          SCREAMING_SNAKE_CASE_      :  Any    						=		[to_channel_dimension_format(lowercase_						,	lowercase_) for image in images]

          SCREAMING_SNAKE_CASE_      :  Dict    						=		{'''pixel_values''': images}
          return BatchFeature(data=lowercase_						,	tensor_type=lowercase_)





def 						_snake_case(    SCREAMING_SNAKE_CASE__			:	float    ,			SCREAMING_SNAKE_CASE__			:	float    ,			SCREAMING_SNAKE_CASE__			:	float    ,			SCREAMING_SNAKE_CASE__			:	float    ,			SCREAMING_SNAKE_CASE__			:	float    ,			)							->      float:





    '''simple docstring'''






    A__          =   [redshift, radiation_density, matter_density, dark_energy]
    if any(p < 0 for p in parameters      ):
        raise ValueError('All input parameters must be positive'      )

    if any(p > 1 for p in parameters[1:4]      ):
        raise ValueError('Relative densities cannot be greater than one'      )
    else:
        A__          =   1 - (matter_density + radiation_density + dark_energy)

        A__          =   (
            radiation_density * (redshift + 1) ** 4
            + matter_density * (redshift + 1) ** 3
            + curvature * (redshift + 1) ** 2
            + dark_energy
        )

        A__          =   hubble_constant * e_a ** (1 / 2)
        return hubble


if __name__ == "__main__":
     import doctest

     # run doctest
     doctest.testmod()

     # demo LCDM approximation
     lowercase_       		=  0.3

     print(
         hubble_parameter(
             hubble_constant=68.3,
             radiation_density=1e-4,
             matter_density=matter_density,
             dark_energy=1 - matter_density,
             redshift=0,
         )
     )

