
import argparse

import requests
import torch
from PIL import Image

from transformers import SwinConfig, SwinForMaskedImageModeling, ViTImageProcessor


def 						UpperCAmelCase			(	a_						)		->	Optional[int]:







				"""simple docstring"""



				__A   			= SwinConfig(image_size=1_9_2						)

				if "base" in model_name:
								__A   			= 6
								__A   			= 1_2_8
								__A   			= (2, 2, 1_8, 2)
								__A   			= (4, 8, 1_6, 3_2)
				elif "large" in model_name:
								__A   			= 1_2
								__A   			= 1_9_2
								__A   			= (2, 2, 1_8, 2)
								__A   			= (6, 1_2, 2_4, 4_8)
				else:
								raise ValueError("Model not supported, only supports base and large variants"						)

				__A   			= window_size
				__A   			= embed_dim
				__A   			= depths
				__A   			= num_heads

				return config


def 						UpperCAmelCase			(	a_						)		->	int:







				"""simple docstring"""



				if "encoder.mask_token" in name:
								__A   			= name.replace("encoder.mask_token"		,   "embeddings.mask_token"						)
				if "encoder.patch_embed.proj" in name:
								__A   			= name.replace("encoder.patch_embed.proj"		,   "embeddings.patch_embeddings.projection"						)
				if "encoder.patch_embed.norm" in name:
								__A   			= name.replace("encoder.patch_embed.norm"		,   "embeddings.norm"						)
				if "attn.proj" in name:
								__A   			= name.replace("attn.proj"		,   "attention.output.dense"						)
				if "attn" in name:
								__A   			= name.replace("attn"		,   "attention.self"						)
				if "norm1" in name:
								__A   			= name.replace("norm1"		,   "layernorm_before"						)
				if "norm2" in name:
								__A   			= name.replace("norm2"		,   "layernorm_after"						)
				if "mlp.fc1" in name:
								__A   			= name.replace("mlp.fc1"		,   "intermediate.dense"						)
				if "mlp.fc2" in name:
								__A   			= name.replace("mlp.fc2"		,   "output.dense"						)

				if name == "encoder.norm.weight":
								__A   			= "layernorm.weight"
				if name == "encoder.norm.bias":
								__A   			= "layernorm.bias"

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

				return name


def 						UpperCAmelCase			(	a_		,   a_						)		->	List[str]:







				"""simple docstring"""



				for key in orig_state_dict.copy().keys():
								__A   			= orig_state_dict.pop(a_						)

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

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

				return orig_state_dict


def 						UpperCAmelCase			(	a_		,   a_		,   a_		,   a_						)		->	int:







				"""simple docstring"""



				__A   			= torch.load(a_		,   map_location="cpu"						)["model"]

				__A   			= get_swin_config(a_						)
				__A   			= SwinForMaskedImageModeling(a_						)
				model.eval()

				__A   			= convert_state_dict(a_		,   a_						)
				model.load_state_dict(a_						)

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

				__A   			= ViTImageProcessor(size={"height": 1_9_2, "width": 1_9_2}						)
				__A   			= Image.open(requests.get(a_		,   stream=a_						).raw						)
				__A   			= image_processor(images=a_		,   return_tensors="pt"						)

				with torch.no_grad():
								__A   			= 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__":
					SCREAMING_SNAKE_CASE							: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.'
					)

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


import argparse
import json
import os

import torch
from transformers.file_utils import has_file

from diffusers import UNetaDConditionModel, UNetaDModel


SCREAMING_SNAKE_CASE							:Union[str, Any]							 =				False
SCREAMING_SNAKE_CASE							:Any							 =				True
SCREAMING_SNAKE_CASE							:Tuple							 =				False


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE							:Tuple							 =				argparse.ArgumentParser()

					parser.add_argument(
					    '--repo_path',
					    default=None,
					    type=str,
					    required=True,
					    help='The config json file corresponding to the architecture.',
					)

					parser.add_argument('--dump_path', default=None, type=str, required=True, help='Path to the output model.')

					SCREAMING_SNAKE_CASE							:Union[str, Any]							 =				parser.parse_args()

					SCREAMING_SNAKE_CASE							:Dict							 =				{
					    'image_size': 'sample_size',
					    'num_res_blocks': 'layers_per_block',
					    'block_channels': 'block_out_channels',
					    'down_blocks': 'down_block_types',
					    'up_blocks': 'up_block_types',
					    'downscale_freq_shift': 'freq_shift',
					    'resnet_num_groups': 'norm_num_groups',
					    'resnet_act_fn': 'act_fn',
					    'resnet_eps': 'norm_eps',
					    'num_head_channels': 'attention_head_dim',
					}

					SCREAMING_SNAKE_CASE							:Optional[int]							 =				{
					    'time_steps': 'time_proj',
					    'mid': 'mid_block',
					    'downsample_blocks': 'down_blocks',
					    'upsample_blocks': 'up_blocks',
					}

					SCREAMING_SNAKE_CASE							:int							 =				'' if has_file(args.repo_path, 'config.json') else 'unet'

					with open(os.path.join(args.repo_path, subfolder, 'config.json'), 'r', encoding='utf-8') as reader:
										SCREAMING_SNAKE_CASE							:Dict							 =				reader.read()
										SCREAMING_SNAKE_CASE							:List[str]							 =				json.loads(text)

					if do_only_config:
										for key in config_parameters_to_change.keys():
															config.pop(key, None)

					if has_file(args.repo_path, 'config.json'):
										SCREAMING_SNAKE_CASE							:Optional[int]							 =				UNetaDModel(**config)
					else:
										SCREAMING_SNAKE_CASE							:Optional[Any]							 =				UNetaDConditionModel if 'ldm-text2im-large-256' in args.repo_path else UNetaDModel
										SCREAMING_SNAKE_CASE							:List[str]							 =				class_name(**config)

					if do_only_config:
										model.save_config(os.path.join(args.repo_path, subfolder))

					SCREAMING_SNAKE_CASE							:List[str]							 =				dict(model.config)

					if do_only_renaming:
										for key, value in config_parameters_to_change.items():
															if key in config:
																				SCREAMING_SNAKE_CASE							:Optional[Any]							 =				config[key]
																				del config[key]

										SCREAMING_SNAKE_CASE							:Optional[Any]							 =				[k.replace('UNetRes', '') for k in config['down_block_types']]
										SCREAMING_SNAKE_CASE							:List[Any]							 =				[k.replace('UNetRes', '') for k in config['up_block_types']]

					if do_only_weights:
										SCREAMING_SNAKE_CASE							:Tuple							 =				torch.load(os.path.join(args.repo_path, subfolder, 'diffusion_pytorch_model.bin'))

										SCREAMING_SNAKE_CASE							:Any							 =				{}
										for param_key, param_value in state_dict.items():
															if param_key.endswith('.op.bias') or param_key.endswith('.op.weight'):
																				continue
															SCREAMING_SNAKE_CASE							:List[str]							 =				False
															for key, new_key in key_parameters_to_change.items():
																				if not has_changed and param_key.split('.')[0] == key:
																									SCREAMING_SNAKE_CASE							:List[Any]							 =				param_value
																									SCREAMING_SNAKE_CASE							:str							 =				True
															if not has_changed:
																				SCREAMING_SNAKE_CASE							:List[str]							 =				param_value

										model.load_state_dict(new_state_dict)
										model.save_pretrained(os.path.join(args.repo_path, subfolder))
