



'''simple docstring'''


import math
from typing import Dict, Iterable, List, Optional, Tuple, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    get_image_size,
    is_torch_available,
    is_torch_tensor,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_torch_available():
  import torch

if is_vision_available():
  import PIL


lowercase							       =			logging.get_logger(__name__)






def  UpperCAmelCase_       (		lowercase__       ,	lowercase__       ,	lowercase__       ,	lowercase__       ):


     '''simple docstring'''







     def constraint_to_multiple_of(lowercase__       ,	lowercase__       ,	lowercase__=0       ,	lowercase__=None       ):
          a_       		=round(val / multiple       ) * multiple

          if max_val is not None and x > max_val:
               a_       		=math.floor(val / multiple       ) * multiple

          if x < min_val:
               a_       		=math.ceil(val / multiple       ) * multiple

          return x

     a_       		=(output_size, output_size) if isinstance(lowercase__       ,	lowercase__       ) else output_size

     a_		,						a_       		=get_image_size(lowercase__       )
     a_		,						a_       		=output_size

     # determine new height and width
     a_       		=output_height / input_height
     a_       		=output_width / input_width

     if keep_aspect_ratio:
          # scale as little as possible
          if abs(1 - scale_width       ) < abs(1 - scale_height       ):
               # fit width
               a_       		=scale_width
          else:
               # fit height
               a_       		=scale_height

     a_       		=constraint_to_multiple_of(scale_height * input_height       ,	multiple=lowercase__       )
     a_       		=constraint_to_multiple_of(scale_width * input_width       ,	multiple=lowercase__       )

     return (new_height, new_width)







class 	UpperCAmelCase	(		__a):


 '''simple docstring'''




 __magic_name__					: Union[str, Any]  =      ["pixel_values"]




 def __init__(   self							,  lowerCAmelCase_ = True							,  lowerCAmelCase_ = None							,  lowerCAmelCase_ = PILImageResampling.BILINEAR							,  lowerCAmelCase_ = False							,  lowerCAmelCase_ = 1							,  lowerCAmelCase_ = True							,  lowerCAmelCase_ = 1 / 2_5_5							,  lowerCAmelCase_ = True							,  lowerCAmelCase_ = None							,  lowerCAmelCase_ = None							,  **lowerCAmelCase_							,  ) ->      None:



      """simple docstring"""







      super().__init__(**lowerCAmelCase_)
      a_       		=size if size is not None else {"height": 3_8_4, "width": 3_8_4}
      a_       		=get_size_dict(lowerCAmelCase_)
      a_       		=do_resize
      a_       		=size
      a_       		=keep_aspect_ratio
      a_       		=ensure_multiple_of
      a_       		=resample
      a_       		=do_rescale
      a_       		=rescale_factor
      a_       		=do_normalize
      a_       		=image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
      a_       		=image_std if image_std is not None else IMAGENET_STANDARD_STD




 def       lowercase_  (   self							,  lowerCAmelCase_							,  lowerCAmelCase_							,  lowerCAmelCase_ = False							,  lowerCAmelCase_ = 1							,  lowerCAmelCase_ = PILImageResampling.BICUBIC							,  lowerCAmelCase_ = None							,  **lowerCAmelCase_							,  ) ->      np.ndarray:



      """simple docstring"""







      a_       		=get_size_dict(lowerCAmelCase_)
      if "height" not in size or "width" not in size:
           raise ValueError(f"""The size dictionary must contain the keys 'height' and 'width'. Got {size.keys()}""")
      a_       		=get_resize_output_image_size(
          lowerCAmelCase_							,  output_size=(size["height"], size["width"])							,  keep_aspect_ratio=lowerCAmelCase_							,  multiple=lowerCAmelCase_							,  )
      return resize(lowerCAmelCase_							,  size=lowerCAmelCase_							,  resample=lowerCAmelCase_							,  data_format=lowerCAmelCase_							,  **lowerCAmelCase_)




 def       lowercase_  (   self							,  lowerCAmelCase_							,  lowerCAmelCase_							,  lowerCAmelCase_ = None							,  **lowerCAmelCase_							,  ) ->      List[Any]:



      """simple docstring"""







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




 def       lowercase_  (   self							,  lowerCAmelCase_							,  lowerCAmelCase_							,  lowerCAmelCase_							,  lowerCAmelCase_ = None							,  **lowerCAmelCase_							,  ) ->      np.ndarray:



      """simple docstring"""







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




 def       lowercase_  (   self							,  lowerCAmelCase_							,  lowerCAmelCase_ = None							,  lowerCAmelCase_ = None							,  lowerCAmelCase_ = None							,  lowerCAmelCase_ = None							,  lowerCAmelCase_ = None							,  lowerCAmelCase_ = None							,  lowerCAmelCase_ = None							,  lowerCAmelCase_ = None							,  lowerCAmelCase_ = None							,  lowerCAmelCase_ = None							,  lowerCAmelCase_ = None							,  lowerCAmelCase_ = ChannelDimension.FIRST							,  **lowerCAmelCase_							,  ) ->      PIL.Image.Image:



      """simple docstring"""







      a_       		=do_resize if do_resize is not None else self.do_resize
      a_       		=size if size is not None else self.size
      a_       		=get_size_dict(lowerCAmelCase_)
      a_       		=keep_aspect_ratio if keep_aspect_ratio is not None else self.keep_aspect_ratio
      a_       		=ensure_multiple_of if ensure_multiple_of is not None else self.ensure_multiple_of
      a_       		=resample if resample is not None else self.resample
      a_       		=do_rescale if do_rescale is not None else self.do_rescale
      a_       		=rescale_factor if rescale_factor is not None else self.rescale_factor
      a_       		=do_normalize if do_normalize is not None else self.do_normalize
      a_       		=image_mean if image_mean is not None else self.image_mean
      a_       		=image_std if image_std is not None else self.image_std

      a_       		=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 or resample is None:
           raise ValueError("Size and resample must be specified if do_resize 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_       		=[to_numpy_array(lowerCAmelCase_) for image in images]

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

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

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

      a_       		=[to_channel_dimension_format(lowerCAmelCase_							,  lowerCAmelCase_) for image in images]

      a_       		={"pixel_values": images}
      return BatchFeature(data=lowerCAmelCase_							,  tensor_type=lowerCAmelCase_)




 def       lowercase_  (   self							,  lowerCAmelCase_							,  lowerCAmelCase_ = None) ->      str:



      """simple docstring"""







      a_       		=outputs.logits

      # Resize logits and compute semantic segmentation maps
      if target_sizes is not None:
           if len(lowerCAmelCase_) != len(lowerCAmelCase_):
                raise ValueError(
                    "Make sure that you pass in as many target sizes as the batch dimension of the logits")

           if is_torch_tensor(lowerCAmelCase_):
                a_       		=target_sizes.numpy()

           a_       		=[]

           for idx in range(len(lowerCAmelCase_)):
                a_       		=torch.nn.functional.interpolate(
                    logits[idx].unsqueeze(dim=0)							,  size=target_sizes[idx]							,  mode="bilinear"							,  align_corners=lowerCAmelCase_)
                a_       		=resized_logits[0].argmax(dim=0)
                semantic_segmentation.append(lowerCAmelCase_)
      else:
           a_       		=logits.argmax(dim=1)
           a_       		=[semantic_segmentation[i] for i in range(semantic_segmentation.shape[0])]

      return semantic_segmentation







'''simple docstring'''


import os
from math import logaa






def  UpperCAmelCase_       (		lowercase__ = "base_exp.txt"       ):


     '''simple docstring'''







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


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


