




"""simple docstring"""






import argparse

import requests
import torch
from PIL import Image

from transformers import SwinConfig, SwinForMaskedImageModeling, ViTImageProcessor





def 							UpperCAmelCase		(		snake_case			:					int    ):
					_lowerCAmelCase:Any									=  SwinConfig(image_size=192    )

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

					_lowerCAmelCase:Dict									=  window_size
					_lowerCAmelCase:List[Any]									=  embed_dim
					_lowerCAmelCase:int									=  depths
					_lowerCAmelCase:Union[str, Any]									=  num_heads

					return config





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

					if name == "encoder.norm.weight":
										_lowerCAmelCase:Optional[Any]									=  '''layernorm.weight'''
					if name == "encoder.norm.bias":
										_lowerCAmelCase:str									=  '''layernorm.bias'''

					if "decoder" in name:
										pass
					else:
										_lowerCAmelCase:List[str]									=  '''swin.''' + name

					return name





def 							UpperCAmelCase		(		snake_case			:					List[str]      ,       snake_case			:					int    ):
					for key in orig_state_dict.copy().keys():
										_lowerCAmelCase:Union[str, Any]									=  orig_state_dict.pop(snake_case    )

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

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

					return orig_state_dict





def 							UpperCAmelCase		(		snake_case			:					Optional[int]      ,       snake_case			:					Optional[Any]      ,       snake_case			:					str      ,       snake_case			:					int    ):
					_lowerCAmelCase:Tuple									=  torch.load(snake_case      ,       map_location='''cpu'''    )['''model''']

					_lowerCAmelCase:List[str]									=  get_swin_config(snake_case    )
					_lowerCAmelCase:List[Any]									=  SwinForMaskedImageModeling(snake_case    )
					model.eval()

					_lowerCAmelCase:List[str]									=  convert_state_dict(snake_case      ,       snake_case    )
					model.load_state_dict(snake_case    )

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

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

					with torch.no_grad():
										_lowerCAmelCase:List[str]									=  model(**snake_case    ).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(snake_case    )

										print(F'Saving image processor to {pytorch_dump_folder_path}'    )
										image_processor.save_pretrained(snake_case    )

					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)












"""simple docstring"""






import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path

import torch_xla.distributed.xla_multiprocessing as xmp





def 							UpperCAmelCase		(		):
					_lowerCAmelCase:Optional[int]									=  ArgumentParser(
					    description=(
					        '''PyTorch TPU distributed training launch helper utility that will spawn up multiple distributed processes'''
					    )    )

					# Optional arguments for the launch helper
					parser.add_argument('''--num_cores'''      ,       type=snake_case      ,       default=1      ,       help='''Number of TPU cores to use (1 or 8).'''    )

					# positional
					parser.add_argument(
					    '''training_script'''      ,       type=snake_case      ,       help=(
					        '''The full path to the single TPU training '''
					        '''program/script to be launched in parallel, '''
					        '''followed by all the arguments for the '''
					        '''training script'''
					    )      ,       )

					# rest from the training program
					parser.add_argument('''training_script_args'''      ,       nargs=snake_case    )

					return parser.parse_args()





def 							UpperCAmelCase		(		):
					_lowerCAmelCase:int									=  parse_args()

					# Import training_script as a module.
					_lowerCAmelCase:Optional[int]									=  Path(args.training_script    )
					sys.path.append(str(script_fpath.parent.resolve()    )    )
					_lowerCAmelCase:Optional[int]									=  script_fpath.stem
					_lowerCAmelCase:Any									=  importlib.import_module(snake_case    )

					# Patch sys.argv
					_lowerCAmelCase:Dict									=  [args.training_script] + args.training_script_args + ['''--tpu_num_cores''', str(args.num_cores    )]

					xmp.spawn(mod._mp_fn      ,       args=()      ,       nprocs=args.num_cores    )


if __name__ == "__main__":
				main()






