


'''simple docstring'''
import argparse

import requests
import torch
from PIL import Image

from transformers import SwinConfig, SwinForMaskedImageModeling, ViTImageProcessor


def     a   (    __a     )						->			int:



					'''simple docstring'''






					UpperCamelCase__     :Dict							      =				SwinConfig(image_size=192     )

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

					UpperCamelCase__     :Tuple							      =				window_size
					UpperCamelCase__     :Union[str, Any]							      =				embed_dim
					UpperCamelCase__     :int							      =				depths
					UpperCamelCase__     :Tuple							      =				num_heads

					return config


def     a   (    __a     )						->			Optional[int]:



					'''simple docstring'''






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

					if name == "encoder.norm.weight":
										UpperCamelCase__     :int							      =				"layernorm.weight"
					if name == "encoder.norm.bias":
										UpperCamelCase__     :str							      =				"layernorm.bias"

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

					return name


def     a   (    __a				,      __a     )						->			Tuple:



					'''simple docstring'''






					for key in orig_state_dict.copy().keys():
										UpperCamelCase__     :List[Any]							      =				orig_state_dict.pop(__a     )

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

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

					return orig_state_dict


def     a   (    __a				,      __a				,      __a				,      __a     )						->			List[Any]:



					'''simple docstring'''






					UpperCamelCase__     :str							      =				torch.load(__a				,      map_location='''cpu'''     )["model"]

					UpperCamelCase__     :Union[str, Any]							      =				get_swin_config(__a     )
					UpperCamelCase__     :Optional[Any]							      =				SwinForMaskedImageModeling(__a     )
					model.eval()

					UpperCamelCase__     :List[str]							      =				convert_state_dict(__a				,      __a     )
					model.load_state_dict(__a     )

					UpperCamelCase__     :Tuple							      =				"http://images.cocodataset.org/val2017/000000039769.jpg"

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

					with torch.no_grad():
										UpperCamelCase__     :str							      =				model(**__a     ).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(__a     )

										print(f'''Saving image processor to {pytorch_dump_folder_path}'''     )
										image_processor.save_pretrained(__a     )

					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__":
		__snake_case							   =  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.'''
		)

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




'''simple docstring'''
# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch

import math
from typing import Union

import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import randn_tensor
from .scheduling_utils import SchedulerMixin
class      lowercase			(							A__			,	A__      ):

		"""simple docstring"""

		_a									=       1




		@register_to_config
		def __init__(	self					,     UpperCamelCase_=2000					,     UpperCamelCase_=0.1					,     UpperCamelCase_=20					,     UpperCamelCase_=1e-3							):

							'''simple docstring'''


							UpperCamelCase__     :Dict							      =				None
							UpperCamelCase__     :Any							      =				None
							UpperCamelCase__     :List[str]							      =				None




		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_					,     UpperCamelCase_ = None							):

							'''simple docstring'''


							UpperCamelCase__     :Union[str, Any]							      =				torch.linspace(1					,     self.config.sampling_eps					,     UpperCamelCase_					,     device=UpperCamelCase_							)




		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_=None							):

							'''simple docstring'''


							if self.timesteps is None:
												raise ValueError(
												    '''`self.timesteps` is not set, you need to run \'set_timesteps\' after creating the scheduler'''							)

							# TODO(Patrick) better comments + non-PyTorch
							# postprocess model score
							UpperCamelCase__     :Optional[int]							      =				(
							    -0.25 * t**2 * (self.config.beta_max - self.config.beta_min) - 0.5 * t * self.config.beta_min
							)
							UpperCamelCase__     :Tuple							      =				torch.sqrt(1.0 - torch.exp(2.0 * log_mean_coeff							)							)
							UpperCamelCase__     :Tuple							      =				std.flatten()
							while len(std.shape							) < len(score.shape							):
												UpperCamelCase__     :int							      =				std.unsqueeze(-1							)
							UpperCamelCase__     :List[Any]							      =				-score / std

							# compute
							UpperCamelCase__     :List[str]							      =				-1.0 / len(self.timesteps							)

							UpperCamelCase__     :Optional[int]							      =				self.config.beta_min + t * (self.config.beta_max - self.config.beta_min)
							UpperCamelCase__     :Any							      =				beta_t.flatten()
							while len(beta_t.shape							) < len(x.shape							):
												UpperCamelCase__     :Any							      =				beta_t.unsqueeze(-1							)
							UpperCamelCase__     :Optional[int]							      =				-0.5 * beta_t * x

							UpperCamelCase__     :int							      =				torch.sqrt(UpperCamelCase_							)
							UpperCamelCase__     :List[str]							      =				drift - diffusion**2 * score
							UpperCamelCase__     :Dict							      =				x + drift * dt

							# add noise
							UpperCamelCase__     :List[Any]							      =				randn_tensor(x.shape					,     layout=x.layout					,     generator=UpperCamelCase_					,     device=x.device					,     dtype=x.dtype							)
							UpperCamelCase__     :int							      =				x_mean + diffusion * math.sqrt(-dt							) * noise

							return x, x_mean




		def __len__(	self							):

							'''simple docstring'''


							return self.config.num_train_timesteps