'''simple docstring'''

import argparse

import requests
import torch
from PIL import Image

from transformers import SwinConfig, SwinForMaskedImageModeling, ViTImageProcessor
def       __UpperCamelCase	(       UpperCAmelCase					):
 lowercase__    :   int						= SwinConfig(image_size=192					)

 if "base" in model_name:
  lowercase__    :   Optional[int]						= 6
  lowercase__    :   Any						= 128
  lowercase__    :   Optional[Any]						= (2, 2, 18, 2)
  lowercase__    :   List[Any]						= (4, 8, 16, 32)
 elif "large" in model_name:
  lowercase__    :   Optional[Any]						= 12
  lowercase__    :   int						= 192
  lowercase__    :   Tuple						= (2, 2, 18, 2)
  lowercase__    :   Union[str, Any]						= (6, 12, 24, 48)
 else:
  raise ValueError('''Model not supported, only supports base and large variants'''					)

 lowercase__    :   List[str]						= window_size
 lowercase__    :   Any						= embed_dim
 lowercase__    :   int						= depths
 lowercase__    :   List[Any]						= num_heads

 return config
def       __UpperCamelCase	(       UpperCAmelCase					):
 if "encoder.mask_token" in name:
  lowercase__    :   Dict						= name.replace('''encoder.mask_token'''		,					'''embeddings.mask_token'''					)
 if "encoder.patch_embed.proj" in name:
  lowercase__    :   List[str]						= name.replace('''encoder.patch_embed.proj'''		,					'''embeddings.patch_embeddings.projection'''					)
 if "encoder.patch_embed.norm" in name:
  lowercase__    :   str						= name.replace('''encoder.patch_embed.norm'''		,					'''embeddings.norm'''					)
 if "attn.proj" in name:
  lowercase__    :   Any						= name.replace('''attn.proj'''		,					'''attention.output.dense'''					)
 if "attn" in name:
  lowercase__    :   Optional[Any]						= name.replace('''attn'''		,					'''attention.self'''					)
 if "norm1" in name:
  lowercase__    :   Optional[int]						= name.replace('''norm1'''		,					'''layernorm_before'''					)
 if "norm2" in name:
  lowercase__    :   List[Any]						= name.replace('''norm2'''		,					'''layernorm_after'''					)
 if "mlp.fc1" in name:
  lowercase__    :   List[Any]						= name.replace('''mlp.fc1'''		,					'''intermediate.dense'''					)
 if "mlp.fc2" in name:
  lowercase__    :   Any						= name.replace('''mlp.fc2'''		,					'''output.dense'''					)

 if name == "encoder.norm.weight":
  lowercase__    :   Union[str, Any]						= '''layernorm.weight'''
 if name == "encoder.norm.bias":
  lowercase__    :   List[Any]						= '''layernorm.bias'''

 if "decoder" in name:
  pass
 else:
  lowercase__    :   List[Any]						= '''swin.''' + name

 return name
def       __UpperCamelCase	(       UpperCAmelCase		,					UpperCAmelCase					):
 for key in orig_state_dict.copy().keys():
  lowercase__    :   str						= orig_state_dict.pop(UpperCAmelCase					)

  if "attn_mask" in key:
   pass
  elif "qkv" in key:
   lowercase__    :   Dict						= key.split('''.'''					)
   lowercase__    :   Dict						= int(key_split[2]					)
   lowercase__    :   List[str]						= int(key_split[4]					)
   lowercase__    :   Optional[Any]						= model.swin.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size

   if "weight" in key:
    lowercase__    :   Optional[int]						= val[:dim, :]
    lowercase__    :   Optional[int]						= val[
        dim : dim * 2, :
    ]
    lowercase__    :   Optional[int]						= val[-dim:, :]
   else:
    lowercase__    :   Dict						= val[
        :dim
    ]
    lowercase__    :   Optional[Any]						= val[
        dim : dim * 2
    ]
    lowercase__    :   List[str]						= val[
        -dim:
    ]
  else:
   lowercase__    :   int						= val

 return orig_state_dict
def       __UpperCamelCase	(       UpperCAmelCase		,					UpperCAmelCase		,					UpperCAmelCase		,					UpperCAmelCase					):
 lowercase__    :   str						= torch.load(UpperCAmelCase		,					map_location='''cpu'''					)['''model''']

 lowercase__    :   Optional[int]						= get_swin_config(UpperCAmelCase					)
 lowercase__    :   str						= SwinForMaskedImageModeling(UpperCAmelCase					)
 model.eval()

 lowercase__    :   Any						= convert_state_dict(UpperCAmelCase		,					UpperCAmelCase					)
 model.load_state_dict(UpperCAmelCase					)

 lowercase__    :   Tuple						= '''http://images.cocodataset.org/val2017/000000039769.jpg'''

 lowercase__    :   Tuple						= ViTImageProcessor(size={'''height''': 192, '''width''': 192}					)
 lowercase__    :   List[str]						= Image.open(requests.get(UpperCAmelCase		,					stream=UpperCAmelCase					).raw					)
 lowercase__    :   List[Any]						= image_processor(images=UpperCAmelCase		,					return_tensors='''pt'''					)

 with torch.no_grad():
  lowercase__    :   str						= model(**UpperCAmelCase					).logits

 print(outputs.keys()					)
 print('''Looks ok!'''					)

 if pytorch_dump_folder_path is not None:
  print(F"""Saving model {model_name} 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 push_to_hub:
  print(F"""Pushing model and image processor for {model_name} to hub"""					)
  model.push_to_hub(F"""microsoft/{model_name}"""					)
  image_processor.push_to_hub(F"""microsoft/{model_name}"""					)


if __name__ == "__main__":
  __a:			Tuple	       =       argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      """--model_name""",
      default="""swin-base-simmim-window6-192""",
      type=str,
      choices=["""swin-base-simmim-window6-192""", """swin-large-simmim-window12-192"""],
      help="""Name of the Swin SimMIM model you'd like to convert.""",
  )
  parser.add_argument(
      """--checkpoint_path""",
      default="""/Users/nielsrogge/Documents/SwinSimMIM/simmim_pretrain__swin_base__img192_window6__100ep.pth""",
      type=str,
      help="""Path to the original PyTorch checkpoint (.pth file).""",
  )
  parser.add_argument(
      """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model directory."""
  )
  parser.add_argument(
      """--push_to_hub""", action="""store_true""", help="""Whether or not to push the converted model to the 🤗 hub."""
  )

  __a:			List[str]	       =       parser.parse_args()
  convert_swin_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)


'''simple docstring'''

def       __UpperCamelCase	(       UpperCAmelCase		,					UpperCAmelCase					):
 lowercase__    :   Dict						= len(UpperCAmelCase					)
 print('''The following activities are selected:'''					)

 # The first activity is always selected
 lowercase__    :   str						= 0
 print(UpperCAmelCase		,					end=''','''					)

 # Consider rest of the activities
 for j in range(UpperCAmelCase					):
  # If this activity has start time greater than
  # or equal to the finish time of previously
  # selected activity, then select it
  if start[j] >= finish[i]:
   print(UpperCAmelCase		,					end=''','''					)
   lowercase__    :   str						= j


if __name__ == "__main__":
  import doctest

  doctest.testmod()

  __a:			str	       =       [1, 3, 0, 5, 8, 5]
  __a:			Optional[Any]	       =       [2, 4, 6, 7, 9, 9]
  print_max_activities(start, finish)

