





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


lowercase     :			Union[str, Any]     						=	logging.get_logger(__name__)


if is_vision_available():
       import PIL

class 			__A(    __UpperCAmelCase			):
     __A							=							["pixel_values"]

     def __init__(   self,							A = True,							A = None,							A = PILImageResampling.BICUBIC,							A = True,							A = None,							A = True,							A = 1 / 255,							A = True,							A = None,							A = None,							A = True,							**A,							):





            """simple docstring"""

            super().__init__(**A   )
            _UpperCamelCase														=    size if size is not None else {'''shortest_edge''': 224}
            _UpperCamelCase														=    get_size_dict(A,							default_to_square=A   )
            _UpperCamelCase														=    crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
            _UpperCamelCase														=    get_size_dict(A   )

            _UpperCamelCase														=    do_resize
            _UpperCamelCase														=    size
            _UpperCamelCase														=    resample
            _UpperCamelCase														=    do_center_crop
            _UpperCamelCase														=    crop_size
            _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,							A,							A,							A = PILImageResampling.BICUBIC,							A = None,							**A,							):





            """simple docstring"""

            _UpperCamelCase														=    get_size_dict(A,							default_to_square=A   )
            _UpperCamelCase														=    get_resize_output_image_size(
                A,							size=(size['''height'''], size['''width''']),							default_to_square=A   )
            return resize(A,							size=A,							resample=A,							data_format=A,							**A   )

     def 						_UpperCamelCase  (   self,							A,							A,							A = None,							**A,							):





            """simple docstring"""

            _UpperCamelCase														=    get_size_dict(A   )
            return center_crop(A,							size=(size['''height'''], size['''width''']),							data_format=A,							**A   )

     def 						_UpperCamelCase  (   self,							A,							A,							A = None,							**A,							):





            """simple docstring"""

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

     def 						_UpperCamelCase  (   self,							A,							A,							A,							A = None,							**A,							):





            """simple docstring"""

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



     def 						_UpperCamelCase  (   self,							A,							A = None,							A = None,							A = None,							A = None,							A = None,							A = None,							A = None,							A = None,							A = None,							A = None,							A = None,							A = None,							A = ChannelDimension.FIRST,							**A,							):





            """simple docstring"""

            _UpperCamelCase														=    do_resize if do_resize is not None else self.do_resize
            _UpperCamelCase														=    size if size is not None else self.size
            _UpperCamelCase														=    get_size_dict(A,							default_to_square=A   )
            _UpperCamelCase														=    resample if resample is not None else self.resample
            _UpperCamelCase														=    do_center_crop if do_center_crop is not None else self.do_center_crop
            _UpperCamelCase														=    crop_size if crop_size is not None else self.crop_size
            _UpperCamelCase														=    get_size_dict(A   )
            _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														=    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.'''   )

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

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

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

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

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

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

            _UpperCamelCase														=    [to_channel_dimension_format(A,							A   ) for image in images]

            _UpperCamelCase														=    {'''pixel_values''': images}
            return BatchFeature(data=A,							tensor_type=A   )













import math
from decimal import Decimal, getcontext


def      SCREAMING_SNAKE_CASE      (   lowerCAmelCase = 4_000_000       ):

       try:
              _UpperCamelCase														=    int(lowerCAmelCase       )
       except (TypeError, ValueError):
              raise TypeError('''Parameter n must be int or castable to int.'''       )
       if n <= 0:
              raise ValueError('''Parameter n must be greater than or equal to one.'''       )
       _UpperCamelCase														=    100
       _UpperCamelCase														=    (Decimal(5       ) ** Decimal(0.5       ) + 1) / Decimal(2       )

       _UpperCamelCase														=    (math.floor(math.log(n * (phi + 2)     ,							lowerCAmelCase       ) - 1       ) // 3) * 3 + 2
       _UpperCamelCase														=    Decimal(round(phi ** Decimal(index + 1       )       )       ) / (phi + 2)
       _UpperCamelCase														=    num // 2
       return int(lowerCAmelCase       )


if __name__ == "__main__":
       print(F'''{solution() = }''')






