






import argparse

import requests
import torch
from PIL import Image

from transformers import SwinConfig, SwinForMaskedImageModeling, ViTImageProcessor






def 							lowerCamelCase__ (       __lowerCamelCase     :	List[Any]						):
  __UpperCAmelCase				:      Any	 =    SwinConfig(image_size=192						)

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

  __UpperCAmelCase				:      int	 =    window_size
  __UpperCAmelCase				:      str	 =    embed_dim
  __UpperCAmelCase				:      int	 =    depths
  __UpperCAmelCase				:      List[str]	 =    num_heads

  return config






def 							lowerCamelCase__ (       __lowerCamelCase     :	List[Any]						):





  if "encoder.mask_token" in name:
    __UpperCAmelCase				:      Any	 =    name.replace("""encoder.mask_token"""    ,						"""embeddings.mask_token"""						)
  if "encoder.patch_embed.proj" in name:
    __UpperCAmelCase				:      int	 =    name.replace("""encoder.patch_embed.proj"""    ,						"""embeddings.patch_embeddings.projection"""						)
  if "encoder.patch_embed.norm" in name:
    __UpperCAmelCase				:      Dict	 =    name.replace("""encoder.patch_embed.norm"""    ,						"""embeddings.norm"""						)
  if "attn.proj" in name:
    __UpperCAmelCase				:      List[str]	 =    name.replace("""attn.proj"""    ,						"""attention.output.dense"""						)
  if "attn" in name:
    __UpperCAmelCase				:      Tuple	 =    name.replace("""attn"""    ,						"""attention.self"""						)
  if "norm1" in name:
    __UpperCAmelCase				:      int	 =    name.replace("""norm1"""    ,						"""layernorm_before"""						)
  if "norm2" in name:
    __UpperCAmelCase				:      Tuple	 =    name.replace("""norm2"""    ,						"""layernorm_after"""						)
  if "mlp.fc1" in name:
    __UpperCAmelCase				:      Tuple	 =    name.replace("""mlp.fc1"""    ,						"""intermediate.dense"""						)
  if "mlp.fc2" in name:
    __UpperCAmelCase				:      Tuple	 =    name.replace("""mlp.fc2"""    ,						"""output.dense"""						)

  if name == "encoder.norm.weight":
    __UpperCAmelCase				:      Union[str, Any]	 =    "layernorm.weight"
  if name == "encoder.norm.bias":
    __UpperCAmelCase				:      Dict	 =    "layernorm.bias"

  if "decoder" in name:
    pass
  else:
    __UpperCAmelCase				:      str	 =    "swin." + name

  return name






def 							lowerCamelCase__ (       __lowerCamelCase     :	Optional[Any]    ,						__lowerCamelCase     :	Optional[int]						):





  for key in orig_state_dict.copy().keys():
    __UpperCAmelCase				:      Optional[int]	 =    orig_state_dict.pop(__lowerCamelCase						)

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

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

  return orig_state_dict






def 							lowerCamelCase__ (       __lowerCamelCase     :	List[str]    ,						__lowerCamelCase     :	int    ,						__lowerCamelCase     :	Tuple    ,						__lowerCamelCase     :	str						):
  __UpperCAmelCase				:      int	 =    torch.load(__lowerCamelCase    ,						map_location="""cpu"""						)["model"]

  __UpperCAmelCase				:      Union[str, Any]	 =    get_swin_config(__lowerCamelCase						)
  __UpperCAmelCase				:      str	 =    SwinForMaskedImageModeling(__lowerCamelCase						)
  model.eval()

  __UpperCAmelCase				:      Dict	 =    convert_state_dict(__lowerCamelCase    ,						__lowerCamelCase						)
  model.load_state_dict(__lowerCamelCase						)

  __UpperCAmelCase				:      Optional[Any]	 =    "http://images.cocodataset.org/val2017/000000039769.jpg"

  __UpperCAmelCase				:      Dict	 =    ViTImageProcessor(size={"""height""": 192, """width""": 192}						)
  __UpperCAmelCase				:      str	 =    Image.open(requests.get(__lowerCamelCase    ,						stream=__lowerCamelCase						).raw						)
  __UpperCAmelCase				:      Any	 =    image_processor(images=__lowerCamelCase    ,						return_tensors="""pt"""						)

  with torch.no_grad():
    __UpperCAmelCase				:      str	 =    model(**__lowerCamelCase						).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(__lowerCamelCase						)

    print(f"""Saving image processor to {pytorch_dump_folder_path}"""						)
    image_processor.save_pretrained(__lowerCamelCase						)

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






'''simple docstring'''


import json
import os

import torch

from diffusers import UNetaDModel


os.makedirs("""hub/hopper-medium-v2/unet/hor32""", exist_ok=True)
os.makedirs("""hub/hopper-medium-v2/unet/hor128""", exist_ok=True)

os.makedirs("""hub/hopper-medium-v2/value_function""", exist_ok=True)


def 		_lowercase (SCREAMING_SNAKE_CASE							):




     '''simple docstring'''





     if hor == 128:
          __A	:       int      =							("DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D")
          __A	:       Union[str, Any]      =							(32, 128, 256)
          __A	:       str      =							("UpResnetBlock1D", "UpResnetBlock1D")

     elif hor == 32:
          __A	:       List[Any]      =							("DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D")
          __A	:       Optional[Any]      =							(32, 64, 128, 256)
          __A	:       Dict      =							("UpResnetBlock1D", "UpResnetBlock1D", "UpResnetBlock1D")
     __A	:       Union[str, Any]      =							torch.load(f"/Users/bglickenhaus/Documents/diffuser/temporal_unet-hopper-mediumv2-hor{hor}.torch"							)
     __A	:       List[str]      =							model.state_dict()
     __A	:       Any      =							{
         "down_block_types": down_block_types,
         "block_out_channels": block_out_channels,
         "up_block_types": up_block_types,
         "layers_per_block": 1,
         "use_timestep_embedding": True,
         "out_block_type": "OutConv1DBlock",
         "norm_num_groups": 8,
         "downsample_each_block": False,
         "in_channels": 14,
         "out_channels": 14,
         "extra_in_channels": 0,
         "time_embedding_type": "positional",
         "flip_sin_to_cos": False,
         "freq_shift": 1,
         "sample_size": 6_5536,
         "mid_block_type": "MidResTemporalBlock1D",
         "act_fn": "mish",
     }
     __A	:       Union[str, Any]      =							UNetaDModel(**SCREAMING_SNAKE_CASE							)
     print(f"length of state dict: {len(state_dict.keys()							)}"							)
     print(f"length of value function dict: {len(hf_value_function.state_dict().keys()							)}"							)
     __A	:       Dict      =							dict(zip(model.state_dict().keys()       ,					hf_value_function.state_dict().keys()							)							)
     for k, v in mapping.items():
          __A	:       Optional[int]      =							state_dict.pop(SCREAMING_SNAKE_CASE							)
     hf_value_function.load_state_dict(SCREAMING_SNAKE_CASE							)

     torch.save(hf_value_function.state_dict()       ,					f"hub/hopper-medium-v2/unet/hor{hor}/diffusion_pytorch_model.bin"							)
     with open(f"hub/hopper-medium-v2/unet/hor{hor}/config.json"       ,					"w"							) as f:
          json.dump(SCREAMING_SNAKE_CASE       ,					SCREAMING_SNAKE_CASE							)


def 		_lowercase ():




     '''simple docstring'''





     __A	:       Optional[Any]      =							{
         "in_channels": 14,
         "down_block_types": ("DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D"),
         "up_block_types": (),
         "out_block_type": "ValueFunction",
         "mid_block_type": "ValueFunctionMidBlock1D",
         "block_out_channels": (32, 64, 128, 256),
         "layers_per_block": 1,
         "downsample_each_block": True,
         "sample_size": 6_5536,
         "out_channels": 14,
         "extra_in_channels": 0,
         "time_embedding_type": "positional",
         "use_timestep_embedding": True,
         "flip_sin_to_cos": False,
         "freq_shift": 1,
         "norm_num_groups": 8,
         "act_fn": "mish",
     }

     __A	:       Optional[int]      =							torch.load("/Users/bglickenhaus/Documents/diffuser/value_function-hopper-mediumv2-hor32.torch"							)
     __A	:       Dict      =							model
     __A	:       Union[str, Any]      =							UNetaDModel(**SCREAMING_SNAKE_CASE							)
     print(f"length of state dict: {len(state_dict.keys()							)}"							)
     print(f"length of value function dict: {len(hf_value_function.state_dict().keys()							)}"							)

     __A	:       int      =							dict(zip(state_dict.keys()       ,					hf_value_function.state_dict().keys()							)							)
     for k, v in mapping.items():
          __A	:       str      =							state_dict.pop(SCREAMING_SNAKE_CASE							)

     hf_value_function.load_state_dict(SCREAMING_SNAKE_CASE							)

     torch.save(hf_value_function.state_dict()       ,					"hub/hopper-medium-v2/value_function/diffusion_pytorch_model.bin"							)
     with open("hub/hopper-medium-v2/value_function/config.json"       ,					"w"							) as f:
          json.dump(SCREAMING_SNAKE_CASE       ,					SCREAMING_SNAKE_CASE							)


if __name__ == "__main__":
      unet(32)
      # unet(128)
      value_function()

