



'''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,
    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							       =			logging.get_logger(__name__)


if is_vision_available():
		import PIL







class 	UpperCAmelCase	(		__a):


	'''simple docstring'''




	__magic_name__					: Tuple  =      ["pixel_values"]




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



						"""simple docstring"""







						super().__init__(**lowerCAmelCase_)
						a_       		=size if size is not None else {"shortest_edge": 2_2_4}
						a_       		=get_size_dict(lowerCAmelCase_							,  default_to_square=lowerCAmelCase_)
						a_       		=crop_size if crop_size is not None else {"height": 2_2_4, "width": 2_2_4}
						a_       		=get_size_dict(lowerCAmelCase_							,  default_to_square=lowerCAmelCase_							,  param_name="crop_size")

						a_       		=do_resize
						a_       		=size
						a_       		=resample
						a_       		=do_center_crop
						a_       		=crop_size
						a_       		=do_rescale
						a_       		=rescale_factor
						a_       		=do_normalize
						a_       		=image_mean if image_mean is not None else OPENAI_CLIP_MEAN
						a_       		=image_std if image_std is not None else OPENAI_CLIP_STD
						a_       		=do_convert_rgb




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



						"""simple docstring"""







						a_       		=get_size_dict(lowerCAmelCase_							,  default_to_square=lowerCAmelCase_)
						if "shortest_edge" not in size:
											raise ValueError(f"""The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}""")
						a_       		=get_resize_output_image_size(lowerCAmelCase_							,  size=size["shortest_edge"]							,  default_to_square=lowerCAmelCase_)
						return resize(lowerCAmelCase_							,  size=lowerCAmelCase_							,  resample=lowerCAmelCase_							,  data_format=lowerCAmelCase_							,  **lowerCAmelCase_)




	def       lowercase_  (   self							,  lowerCAmelCase_							,  lowerCAmelCase_							,  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` parameter must contain the keys (height, width). Got {size.keys()}""")
						return center_crop(lowerCAmelCase_							,  size=(size["height"], size["width"])							,  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_ = 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_							,  param_name="size"							,  default_to_square=lowerCAmelCase_)
						a_       		=resample if resample is not None else self.resample
						a_       		=do_center_crop if do_center_crop is not None else self.do_center_crop
						a_       		=crop_size if crop_size is not None else self.crop_size
						a_       		=get_size_dict(lowerCAmelCase_							,  param_name="crop_size"							,  default_to_square=lowerCAmelCase_)
						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_       		=do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

						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:
											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:
											a_       		=[convert_to_rgb(lowerCAmelCase_) for image in images]

						# 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_center_crop:
											a_       		=[self.center_crop(image=lowerCAmelCase_							,  size=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_)







'''simple docstring'''


from collections.abc import Sequence






def  UpperCAmelCase_       (		lowercase__ = None       ):


     '''simple docstring'''







     if nums is None or not nums:
          raise ValueError("Input sequence should not be empty"       )

     a_       		=nums[0]
     for i in range(1       ,	len(lowercase__       )       ):
          a_       		=nums[i]
          a_       		=max(lowercase__       ,	ans + num       ,	lowercase__       )

     return ans


if __name__ == "__main__":
  import doctest

  doctest.testmod()

  # Try on a sample input from the user
  lowercase							       =			int(input('''Enter number of elements : ''').strip())
  lowercase							       =			list(map(int, input('''\nEnter the numbers : ''').strip().split()))[:n]
  print(max_subsequence_sum(array))


