from typing import Dict, Iterable, 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_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


SCREAMING_SNAKE_CASE__		:					str						=					logging.get_logger(__name__)

class       snake_case					(      _UpperCAmelCase						):
  lowercase_			=     ['''pixel_values''']




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




       """simple docstring"""
       super().__init__(**A_				)
       __A				:				List[str]			=		size if size is not None else {'shortest_edge': 224}
       __A				:				List[Any]			=		get_size_dict(A_     , default_to_square=A_				)
       __A				:				Optional[int]			=		crop_size if crop_size is not None else {'height': 224, 'width': 224}
       __A				:				str			=		get_size_dict(A_     , param_name='crop_size'				)

       __A				:				Dict			=		do_resize
       __A				:				Optional[int]			=		size
       __A				:				List[Any]			=		resample
       __A				:				List[Any]			=		do_center_crop
       __A				:				Optional[int]			=		crop_size
       __A				:				int			=		do_rescale
       __A				:				str			=		rescale_factor
       __A				:				Any			=		do_normalize
       __A				:				int			=		image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
       __A				:				int			=		image_std if image_std is not None else IMAGENET_DEFAULT_STD




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




       """simple docstring"""
       __A				:				int			=		get_size_dict(A_     , default_to_square=A_				)
       # size_dict is a dict with either keys "height" and "width" or "shortest_edge"
       if "shortest_edge" in size:
            __A				:				int			=		int((256 / 224) * size['shortest_edge']				)
            __A				:				List[Any]			=		get_resize_output_image_size(A_     , size=A_     , default_to_square=A_				)
            __A				:				List[Any]			=		{'height': output_size[0], 'width': output_size[1]}
       if "height" not in size_dict or "width" not in size_dict:
            raise ValueError(
                F'''Size dict must have keys \'height\' and \'width\' or \'shortest_edge\'. Got {size_dict.keys()}'''				)
       return resize(
           A_     , size=(size_dict['height'], size_dict['width'])     , resample=A_     , data_format=A_     , **A_				)




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




       """simple docstring"""
       __A				:				List[str]			=		get_size_dict(A_				)
       if "height" not in size or "width" not in size:
            raise ValueError(F'''Size dict must have keys \'height\' and \'width\'. Got {size.keys()}'''				)
       return center_crop(A_     , size=(size['height'], size['width'])     , data_format=A_     , **A_				)




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




       """simple docstring"""
       return rescale(A_     , scale=A_     , data_format=A_     , **A_				)




  def 	__lowercase(      self     :	Dict     , a_     :	np.ndarray     , a_     :	Union[float, List[float]]     , a_     :	Union[float, List[float]]     , a_     :	Optional[Union[str, ChannelDimension]] = None     , **a_     :	Optional[int]     , )->       np.ndarray:




       """simple docstring"""
       return normalize(A_     , mean=A_     , std=A_     , data_format=A_     , **A_				)




  def 	__lowercase(      self     :	Union[str, Any]     , a_     :	ImageInput     , a_     :	Optional[bool] = None     , a_     :	Optional[Dict[str, int]] = None     , a_     :	PILImageResampling = None     , a_     :	Optional[bool] = None     , a_     :	Optional[Dict[str, int]] = None     , a_     :	Optional[bool] = None     , a_     :	Optional[float] = None     , a_     :	Optional[bool] = None     , a_     :	Optional[Union[float, Iterable[float]]] = None     , a_     :	Optional[Union[float, Iterable[float]]] = None     , a_     :	Optional[TensorType] = None     , a_     :	ChannelDimension = ChannelDimension.FIRST     , **a_     :	Any     , )->       BatchFeature:




       """simple docstring"""
       __A				:				int			=		do_resize if do_resize is not None else self.do_resize
       __A				:				int			=		resample if resample is not None else self.resample
       __A				:				List[str]			=		do_center_crop if do_center_crop is not None else self.do_center_crop
       __A				:				List[str]			=		do_rescale if do_rescale is not None else self.do_rescale
       __A				:				Tuple			=		rescale_factor if rescale_factor is not None else self.rescale_factor
       __A				:				Any			=		do_normalize if do_normalize is not None else self.do_normalize
       __A				:				int			=		image_mean if image_mean is not None else self.image_mean
       __A				:				List[str]			=		image_std if image_std is not None else self.image_std

       __A				:				int			=		size if size is not None else self.size
       __A				:				str			=		get_size_dict(A_     , default_to_square=A_				)
       __A				:				List[str]			=		crop_size if crop_size is not None else self.crop_size
       __A				:				Union[str, Any]			=		get_size_dict(A_     , param_name='crop_size'				)

       __A				:				Dict			=		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:
            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.
       __A				:				Any			=		[to_numpy_array(A_				) for image in images]

       if do_resize:
            __A				:				Optional[Any]			=		[self.resize(A_     , A_     , A_				) for image in images]

       if do_center_crop:
            __A				:				int			=		[self.center_crop(A_     , A_				) for image in images]

       if do_rescale:
            __A				:				Optional[Any]			=		[self.rescale(A_     , A_				) for image in images]

       if do_normalize:
            __A				:				Optional[Any]			=		[self.normalize(A_     , A_     , A_				) for image in images]

       __A				:				List[str]			=		[to_channel_dimension_format(A_     , A_				) for image in images]

       __A				:				Optional[int]			=		{'pixel_values': images}
       return BatchFeature(data=A_     , tensor_type=A_				)




import math







def     _a				(      lowercase__  :			int ):







     '''simple docstring'''


     assert isinstance(lowercase__     ,  lowercase__ ) and (
         number >= 0
     ), "'number' must been an int and positive"

     if 1 < number < 4:
          # 2 and 3 are primes
          return True
     elif number < 2 or not number % 2:
          # Negatives, 0, 1 and all even numbers are not primes
          return False

     SCREAMING_SNAKE_CASE__				:				Tuple			=		range(3     ,  int(math.sqrt(lowercase__ ) + 1 )     ,  2 )
     return not any(not number % i for i in odd_numbers )







def     _a				(      lowercase__  :			List[str]     ,  lowercase__  :			Any=1     ,  **lowercase__  :			Any ):







     '''simple docstring'''


     SCREAMING_SNAKE_CASE__				:				int			=		factor * value
     SCREAMING_SNAKE_CASE__				:				Dict			=		value

     while not is_prime(lowercase__ ):
          value += 1 if not ("desc" in kwargs and kwargs["desc"] is True) else -1

     if value == first_value_val:
          return next_prime(value + 1     ,  **lowercase__ )
     return value



