






import argparse

import requests
import torch
from PIL import Image

from transformers import SwinConfig, SwinForMaskedImageModeling, ViTImageProcessor
def 	lowerCamelCase   (		a_      )      ->     Optional[Any]:
							lowerCAmelCase_									=	SwinConfig(image_size=192      )

							if "base" in model_name:
														lowerCAmelCase_									=	6
														lowerCAmelCase_									=	128
														lowerCAmelCase_									=	(2, 2, 18, 2)
														lowerCAmelCase_									=	(4, 8, 16, 32)
							elif "large" in model_name:
														lowerCAmelCase_									=	12
														lowerCAmelCase_									=	192
														lowerCAmelCase_									=	(2, 2, 18, 2)
														lowerCAmelCase_									=	(6, 12, 24, 48)
							else:
														raise ValueError('Model not supported, only supports base and large variants'      )

							lowerCAmelCase_									=	window_size
							lowerCAmelCase_									=	embed_dim
							lowerCAmelCase_									=	depths
							lowerCAmelCase_									=	num_heads

							return config
def 	lowerCamelCase   (		a_      )      ->     Dict:
							if "encoder.mask_token" in name:
														lowerCAmelCase_									=	name.replace('encoder.mask_token'					,		'embeddings.mask_token'      )
							if "encoder.patch_embed.proj" in name:
														lowerCAmelCase_									=	name.replace('encoder.patch_embed.proj'					,		'embeddings.patch_embeddings.projection'      )
							if "encoder.patch_embed.norm" in name:
														lowerCAmelCase_									=	name.replace('encoder.patch_embed.norm'					,		'embeddings.norm'      )
							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 name == "encoder.norm.weight":
														lowerCAmelCase_									=	'layernorm.weight'
							if name == "encoder.norm.bias":
														lowerCAmelCase_									=	'layernorm.bias'

							if "decoder" in name:
														pass
							else:
														lowerCAmelCase_									=	'swin.' + name

							return name
def 	lowerCamelCase   (		a_					,		a_      )      ->     Any:
							for key in orig_state_dict.copy().keys():
														lowerCAmelCase_									=	orig_state_dict.pop(_UpperCamelCase      )

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

																					if "weight" in key:
																												lowerCAmelCase_									=	val[:dim, :]
																												lowerCAmelCase_									=	val[
																												    dim : dim * 2, :
																												]
																												lowerCAmelCase_									=	val[-dim:, :]
																					else:
																												lowerCAmelCase_									=	val[
																												    :dim
																												]
																												lowerCAmelCase_									=	val[
																												    dim : dim * 2
																												]
																												lowerCAmelCase_									=	val[
																												    -dim:
																												]
														else:
																					lowerCAmelCase_									=	val

							return orig_state_dict
def 	lowerCamelCase   (		a_					,		a_					,		a_					,		a_      )      ->     List[str]:
							lowerCAmelCase_									=	torch.load(_UpperCamelCase					,		map_location='cpu'      )['model']

							lowerCAmelCase_									=	get_swin_config(_UpperCamelCase      )
							lowerCAmelCase_									=	SwinForMaskedImageModeling(_UpperCamelCase      )
							model.eval()

							lowerCAmelCase_									=	convert_state_dict(_UpperCamelCase					,		_UpperCamelCase      )
							model.load_state_dict(_UpperCamelCase      )

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

							lowerCAmelCase_									=	ViTImageProcessor(size={'height': 192, 'width': 192}      )
							lowerCAmelCase_									=	Image.open(requests.get(_UpperCamelCase					,		stream=_UpperCamelCase      ).raw      )
							lowerCAmelCase_									=	image_processor(images=_UpperCamelCase					,		return_tensors='pt'      )

							with torch.no_grad():
														lowerCAmelCase_									=	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__":
		lowerCamelCase_			=    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."""
		)

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








import math





def 		a__      (		_UpperCamelCase      :			float			,_UpperCamelCase      :			float				):
   if (
       not isinstance(_UpperCamelCase			,(int, float)				)
       or power_factor < -1
       or power_factor > 1
   ):
      raise ValueError('''power_factor must be a valid float value between -1 and 1.'''				)
   return apparent_power * power_factor





def 		a__      (		_UpperCamelCase      :			float			,_UpperCamelCase      :			float				):
   if (
       not isinstance(_UpperCamelCase			,(int, float)				)
       or power_factor < -1
       or power_factor > 1
   ):
      raise ValueError('''power_factor must be a valid float value between -1 and 1.'''				)
   return apparent_power * math.sqrt(1 - power_factor**2				)


if __name__ == "__main__":
  import doctest

  doctest.testmod()






