



'''simple docstring'''





from collections.abc import Sequence




def lowerCAmelCase    (__A = 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(__A)):
          _a           =    nums[i]
          _a           =    max(__A    ,      ans + num    ,      __A)

     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))








import argparse

import requests
import torch
from PIL import Image

from transformers import ViTMAEConfig, ViTMAEForPreTraining, ViTMAEImageProcessor





def 		a__      (		_UpperCamelCase      :			List[Any]				):
   if "cls_token" in name:
      __lowerCamelCase    				=     name.replace('''cls_token'''			,'''vit.embeddings.cls_token'''				)
   if "mask_token" in name:
      __lowerCamelCase    				=     name.replace('''mask_token'''			,'''decoder.mask_token'''				)
   if "decoder_pos_embed" in name:
      __lowerCamelCase    				=     name.replace('''decoder_pos_embed'''			,'''decoder.decoder_pos_embed'''				)
   if "pos_embed" in name and "decoder" not in name:
      __lowerCamelCase    				=     name.replace('''pos_embed'''			,'''vit.embeddings.position_embeddings'''				)
   if "patch_embed.proj" in name:
      __lowerCamelCase    				=     name.replace('''patch_embed.proj'''			,'''vit.embeddings.patch_embeddings.projection'''				)
   if "patch_embed.norm" in name:
      __lowerCamelCase    				=     name.replace('''patch_embed.norm'''			,'''vit.embeddings.norm'''				)
   if "decoder_blocks" in name:
      __lowerCamelCase    				=     name.replace('''decoder_blocks'''			,'''decoder.decoder_layers'''				)
   if "blocks" in name:
      __lowerCamelCase    				=     name.replace('''blocks'''			,'''vit.encoder.layer'''				)
   if "attn.proj" in name:
      __lowerCamelCase    				=     name.replace('''attn.proj'''			,'''attention.output.dense'''				)
   if "attn" in name:
      __lowerCamelCase    				=     name.replace('''attn'''			,'''attention.self'''				)
   if "norm1" in name:
      __lowerCamelCase    				=     name.replace('''norm1'''			,'''layernorm_before'''				)
   if "norm2" in name:
      __lowerCamelCase    				=     name.replace('''norm2'''			,'''layernorm_after'''				)
   if "mlp.fc1" in name:
      __lowerCamelCase    				=     name.replace('''mlp.fc1'''			,'''intermediate.dense'''				)
   if "mlp.fc2" in name:
      __lowerCamelCase    				=     name.replace('''mlp.fc2'''			,'''output.dense'''				)
   if "decoder_embed" in name:
      __lowerCamelCase    				=     name.replace('''decoder_embed'''			,'''decoder.decoder_embed'''				)
   if "decoder_norm" in name:
      __lowerCamelCase    				=     name.replace('''decoder_norm'''			,'''decoder.decoder_norm'''				)
   if "decoder_pred" in name:
      __lowerCamelCase    				=     name.replace('''decoder_pred'''			,'''decoder.decoder_pred'''				)
   if "norm.weight" in name and "decoder" not in name:
      __lowerCamelCase    				=     name.replace('''norm.weight'''			,'''vit.layernorm.weight'''				)
   if "norm.bias" in name and "decoder" not in name:
      __lowerCamelCase    				=     name.replace('''norm.bias'''			,'''vit.layernorm.bias'''				)

   return name





def 		a__      (		_UpperCamelCase      :			Tuple			,_UpperCamelCase      :			Dict				):
   for key in orig_state_dict.copy().keys():
      __lowerCamelCase    				=     orig_state_dict.pop(_UpperCamelCase				)

      if "qkv" in key:
         __lowerCamelCase    				=     key.split('''.'''				)
         __lowerCamelCase    				=     int(key_split[1]				)
         if "decoder_blocks" in key:
            __lowerCamelCase    				=     config.decoder_hidden_size
            __lowerCamelCase    				=     '''decoder.decoder_layers.'''
            if "weight" in key:
               __lowerCamelCase    				=     val[:dim, :]
               __lowerCamelCase    				=     val[dim : dim * 2, :]
               __lowerCamelCase    				=     val[-dim:, :]
            elif "bias" in key:
               __lowerCamelCase    				=     val[:dim]
               __lowerCamelCase    				=     val[dim : dim * 2]
               __lowerCamelCase    				=     val[-dim:]
         else:
            __lowerCamelCase    				=     config.hidden_size
            __lowerCamelCase    				=     '''vit.encoder.layer.'''
            if "weight" in key:
               __lowerCamelCase    				=     val[:dim, :]
               __lowerCamelCase    				=     val[dim : dim * 2, :]
               __lowerCamelCase    				=     val[-dim:, :]
            elif "bias" in key:
               __lowerCamelCase    				=     val[:dim]
               __lowerCamelCase    				=     val[dim : dim * 2]
               __lowerCamelCase    				=     val[-dim:]

      else:
         __lowerCamelCase    				=     val

   return orig_state_dict





def 		a__      (		_UpperCamelCase      :			Dict			,_UpperCamelCase      :			Any				):
   __lowerCamelCase    				=     ViTMAEConfig()
   if "large" in checkpoint_url:
      __lowerCamelCase    				=     10_24
      __lowerCamelCase    				=     40_96
      __lowerCamelCase    				=     24
      __lowerCamelCase    				=     16
   elif "huge" in checkpoint_url:
      __lowerCamelCase    				=     14
      __lowerCamelCase    				=     12_80
      __lowerCamelCase    				=     51_20
      __lowerCamelCase    				=     32
      __lowerCamelCase    				=     16

   __lowerCamelCase    				=     ViTMAEForPreTraining(_UpperCamelCase				)

   __lowerCamelCase    				=     torch.hub.load_state_dict_from_url(_UpperCamelCase			,map_location='''cpu'''				)['''model''']

   __lowerCamelCase    				=     ViTMAEImageProcessor(size=config.image_size				)

   __lowerCamelCase    				=     convert_state_dict(_UpperCamelCase			,_UpperCamelCase				)

   model.load_state_dict(_UpperCamelCase				)
   model.eval()

   __lowerCamelCase    				=     '''https://user-images.githubusercontent.com/11435359/147738734-196fd92f-9260-48d5-ba7e-bf103d29364d.jpg'''

   __lowerCamelCase    				=     Image.open(requests.get(_UpperCamelCase			,stream=_UpperCamelCase				).raw				)
   __lowerCamelCase    				=     ViTMAEImageProcessor(size=config.image_size				)
   __lowerCamelCase    				=     image_processor(images=_UpperCamelCase			,return_tensors='''pt'''				)

   # forward pass
   torch.manual_seed(2				)
   __lowerCamelCase    				=     model(**_UpperCamelCase				)
   __lowerCamelCase    				=     outputs.logits

   if "large" in checkpoint_url:
      __lowerCamelCase    				=     torch.tensor(
          [[-0.7_309, -0.7_128, -1.0_169], [-1.0_161, -0.9_058, -1.1_878], [-1.0_478, -0.9_411, -1.1_911]]				)
   elif "huge" in checkpoint_url:
      __lowerCamelCase    				=     torch.tensor(
          [[-1.1_599, -0.9_199, -1.2_221], [-1.1_952, -0.9_269, -1.2_307], [-1.2_143, -0.9_337, -1.2_262]]				)
   else:
      __lowerCamelCase    				=     torch.tensor(
          [[-0.9_192, -0.8_481, -1.1_259], [-1.1_349, -1.0_034, -1.2_599], [-1.1_757, -1.0_429, -1.2_726]]				)

   # verify logits
   assert torch.allclose(logits[0, :3, :3]			,_UpperCamelCase			,atol=1e-4				)

   print(F"""Saving model to {pytorch_dump_folder_path}"""				)
   model.save_pretrained(_UpperCamelCase				)

   print(F"""Saving image processor to {pytorch_dump_folder_path}"""				)
   image_processor.save_pretrained(_UpperCamelCase				)


if __name__ == "__main__":
  a_								=       argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      """--checkpoint_url""",
      default="""https://dl.fbaipublicfiles.com/mae/visualize/mae_visualize_vit_base.pth""",
      type=str,
      help="""URL of the checkpoint you'd like to convert.""",
  )
  parser.add_argument(
      """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model directory."""
  )

  a_								=       parser.parse_args()
  convert_vit_mae_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)






