






import argparse

import requests
import torch
from PIL import Image

from transformers import SwinConfig, SwinForMaskedImageModeling, ViTImageProcessor


def 				__lowerCamelCase     (							__lowerCAmelCase							:					Tuple							)     ->  Dict:
   __UpperCamelCase	:			List[str]					=					SwinConfig(image_size=192							)

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

   __UpperCamelCase	:			Optional[int]					=					window_size
   __UpperCamelCase	:			List[Any]					=					embed_dim
   __UpperCamelCase	:			Optional[int]					=					depths
   __UpperCamelCase	:			Dict					=					num_heads

   return config


def 				__lowerCamelCase     (							__lowerCAmelCase							:					Optional[Any]							)     ->  Optional[int]:
   if "encoder.mask_token" in name:
      __UpperCamelCase	:			List[Any]					=					name.replace("""encoder.mask_token"""					,							"""embeddings.mask_token"""							)
   if "encoder.patch_embed.proj" in name:
      __UpperCamelCase	:			Union[str, Any]					=					name.replace("""encoder.patch_embed.proj"""					,							"""embeddings.patch_embeddings.projection"""							)
   if "encoder.patch_embed.norm" in name:
      __UpperCamelCase	:			Union[str, Any]					=					name.replace("""encoder.patch_embed.norm"""					,							"""embeddings.norm"""							)
   if "attn.proj" in name:
      __UpperCamelCase	:			Optional[int]					=					name.replace("""attn.proj"""					,							"""attention.output.dense"""							)
   if "attn" in name:
      __UpperCamelCase	:			str					=					name.replace("""attn"""					,							"""attention.self"""							)
   if "norm1" in name:
      __UpperCamelCase	:			Tuple					=					name.replace("""norm1"""					,							"""layernorm_before"""							)
   if "norm2" in name:
      __UpperCamelCase	:			Tuple					=					name.replace("""norm2"""					,							"""layernorm_after"""							)
   if "mlp.fc1" in name:
      __UpperCamelCase	:			Optional[int]					=					name.replace("""mlp.fc1"""					,							"""intermediate.dense"""							)
   if "mlp.fc2" in name:
      __UpperCamelCase	:			str					=					name.replace("""mlp.fc2"""					,							"""output.dense"""							)

   if name == "encoder.norm.weight":
      __UpperCamelCase	:			Optional[Any]					=					"""layernorm.weight"""
   if name == "encoder.norm.bias":
      __UpperCamelCase	:			Optional[int]					=					"""layernorm.bias"""

   if "decoder" in name:
      pass
   else:
      __UpperCamelCase	:			Tuple					=					"""swin.""" + name

   return name


def 				__lowerCamelCase     (							__lowerCAmelCase							:					str					,							__lowerCAmelCase							:					int							)     ->  Any:
   for key in orig_state_dict.copy().keys():
      __UpperCamelCase	:			Tuple					=					orig_state_dict.pop(__lowerCAmelCase							)

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

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

   return orig_state_dict


def 				__lowerCamelCase     (							__lowerCAmelCase							:					Optional[Any]					,							__lowerCAmelCase							:					Any					,							__lowerCAmelCase							:					List[str]					,							__lowerCAmelCase							:					Union[str, Any]							)     ->  List[str]:
   __UpperCamelCase	:			List[str]					=					torch.load(__lowerCAmelCase					,							map_location="""cpu"""							)["""model"""]

   __UpperCamelCase	:			List[Any]					=					get_swin_config(__lowerCAmelCase							)
   __UpperCamelCase	:			Union[str, Any]					=					SwinForMaskedImageModeling(__lowerCAmelCase							)
   model.eval()

   __UpperCamelCase	:			List[str]					=					convert_state_dict(__lowerCAmelCase					,							__lowerCAmelCase							)
   model.load_state_dict(__lowerCAmelCase							)

   __UpperCamelCase	:			Dict					=					"""http://images.cocodataset.org/val2017/000000039769.jpg"""

   __UpperCamelCase	:			Optional[int]					=					ViTImageProcessor(size={"""height""": 192, """width""": 192}							)
   __UpperCamelCase	:			Tuple					=					Image.open(requests.get(__lowerCAmelCase					,							stream=__lowerCAmelCase							).raw							)
   __UpperCamelCase	:			int					=					image_processor(images=__lowerCAmelCase					,							return_tensors="""pt"""							)

   with torch.no_grad():
      __UpperCamelCase	:			List[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__":
       UpperCamelCase    							=      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.'
       )

       UpperCamelCase    							=      parser.parse_args()
       convert_swin_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)











UpperCamelCase    							=      8.3_144_598


def 				__lowerCamelCase     (							__lowerCAmelCase							:					float					,							__lowerCAmelCase							:					float							)     ->  float:
   if temperature < 0:
      raise Exception("""Temperature cannot be less than 0 K"""							)
   if molar_mass <= 0:
      raise Exception("""Molar mass cannot be less than or equal to 0 kg/mol"""							)
   else:
      return (3 * UNIVERSAL_GAS_CONSTANT * temperature / molar_mass) ** 0.5


if __name__ == "__main__":
       import doctest

       # run doctest
       doctest.testmod()

       # example
       UpperCamelCase    							=      300
       UpperCamelCase    							=      28
       UpperCamelCase    							=      rms_speed_of_molecule(temperature, molar_mass)
       print(F"""Vrms of Nitrogen gas at 300 K is {vrms} m/s""")



