'''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, is_vision_available, logging


if is_vision_available():
    import PIL


SCREAMING_SNAKE_CASE_:     Tuple  =logging.get_logger(__name__)




class        __A      ( UpperCamelCase__	):
    a__       :						Optional[Any]          =  ["""pixel_values"""]



    def __init__(self    :    int   ,			__a    :    bool = True   ,			__a    :    Dict[str, int] = None   ,			__a    :    float = None   ,			__a    :    PILImageResampling = PILImageResampling.BILINEAR   ,			__a    :    bool = True   ,			__a    :    Union[int, float] = 1 / 255   ,			__a    :    bool = True   ,			__a    :    Optional[Union[float, List[float]]] = None   ,			__a    :    Optional[Union[float, List[float]]] = None   ,			**__a    :    Any   ,			):
         super().__init__(**__a )
         UpperCAmelCase_           =   size if size is not None else {"shortest_edge": 384}
         UpperCAmelCase_           =   get_size_dict(__a   ,			default_to_square=__a )

         UpperCAmelCase_           =   do_resize
         UpperCAmelCase_           =   size
         # Default value set here for backwards compatibility where the value in config is None
         UpperCAmelCase_           =   crop_pct if crop_pct is not None else 224 / 256
         UpperCAmelCase_           =   resample
         UpperCAmelCase_           =   do_rescale
         UpperCAmelCase_           =   rescale_factor
         UpperCAmelCase_           =   do_normalize
         UpperCAmelCase_           =   image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
         UpperCAmelCase_           =   image_std if image_std is not None else IMAGENET_STANDARD_STD



    def 					_lowercase						(self    :    str   ,			__a    :    np.ndarray   ,			__a    :    Dict[str, int]   ,			__a    :    float   ,			__a    :    PILImageResampling = PILImageResampling.BICUBIC   ,			__a    :    Optional[Union[str, ChannelDimension]] = None   ,			**__a    :    Tuple   ,			):
         UpperCAmelCase_           =   get_size_dict(__a   ,			default_to_square=__a )
         if "shortest_edge" not in size:
              raise ValueError(f"""Size dictionary must contain 'shortest_edge' key. Got {size.keys()}""" )
         UpperCAmelCase_           =   size["shortest_edge"]

         if shortest_edge < 384:
              # maintain same ratio, resizing shortest edge to shortest_edge/crop_pct
              UpperCAmelCase_           =   int(shortest_edge / crop_pct )
              UpperCAmelCase_           =   get_resize_output_image_size(__a   ,			size=__a   ,			default_to_square=__a )
              UpperCAmelCase_           =   resize(image=__a   ,			size=__a   ,			resample=__a   ,			data_format=__a   ,			**__a )
              # then crop to (shortest_edge, shortest_edge)
              return center_crop(image=__a   ,			size=(shortest_edge, shortest_edge)   ,			data_format=__a   ,			**__a )
         else:
              # warping (no cropping) when evaluated at 384 or larger
              return resize(
                  __a   ,			size=(shortest_edge, shortest_edge)   ,			resample=__a   ,			data_format=__a   ,			**__a )



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



    def 					_lowercase						(self    :    Optional[int]   ,			__a    :    np.ndarray   ,			__a    :    Union[float, List[float]]   ,			__a    :    Union[float, List[float]]   ,			__a    :    Optional[Union[str, ChannelDimension]] = None   ,			**__a    :    Any   ,			):
         return normalize(__a   ,			mean=__a   ,			std=__a   ,			data_format=__a   ,			**__a )






    def 					_lowercase						(self    :    Optional[int]   ,			__a    :    ImageInput   ,			__a    :    bool = None   ,			__a    :    Dict[str, int] = None   ,			__a    :    float = None   ,			__a    :    PILImageResampling = None   ,			__a    :    bool = None   ,			__a    :    float = None   ,			__a    :    bool = None   ,			__a    :    Optional[Union[float, List[float]]] = None   ,			__a    :    Optional[Union[float, List[float]]] = None   ,			__a    :    Optional[Union[str, TensorType]] = None   ,			__a    :    ChannelDimension = ChannelDimension.FIRST   ,			**__a    :    Tuple   ,			):
         UpperCAmelCase_           =   do_resize if do_resize is not None else self.do_resize
         UpperCAmelCase_           =   crop_pct if crop_pct is not None else self.crop_pct
         UpperCAmelCase_           =   resample if resample is not None else self.resample
         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_           =   size if size is not None else self.size
         UpperCAmelCase_           =   get_size_dict(__a   ,			default_to_square=__a )

         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 or resample is None:
              raise ValueError("Size and resample must be specified if do_resize is True." )

         if do_resize and size["shortest_edge"] < 384 and crop_pct is None:
              raise ValueError("crop_pct must be specified if size < 384." )

         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_           =   [to_numpy_array(__a ) for image in images]

         if do_resize:
              UpperCAmelCase_           =   [self.resize(image=__a   ,			size=__a   ,			crop_pct=__a   ,			resample=__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 )





'''simple docstring'''





import os
from math import logaa





def 	lowerCAmelCase_				(					snake_case_       : str = "base_exp.txt"			)    ->      int:


     '''simple docstring'''




     UpperCAmelCase_           =   0
     UpperCAmelCase_           =   0
     for i, line in enumerate(open(os.path.join(os.path.dirname(snake_case_			)					,    snake_case_			)			)			):
          UpperCAmelCase_    , UpperCAmelCase_           =   list(map(snake_case_					,    line.split(","			)			)			)
          if x * logaa(snake_case_			) > largest:
               UpperCAmelCase_           =   x * logaa(snake_case_			)
               UpperCAmelCase_           =   i + 1
     return result


if __name__ == "__main__":
    print(solution())




