

'''simple docstring'''





import argparse

import requests
import torch
from PIL import Image

from transformers import SwinConfig, SwinForMaskedImageModeling, ViTImageProcessor






def 				_lowerCAmelCase			(   __magic_name__						:     Any						)				->  Optional[Any]:
						lowercase :							Dict    		=SwinConfig(image_size=192						)

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

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

						return config






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

						if name == "encoder.norm.weight":
												lowercase :							List[str]    		='''layernorm.weight'''
						if name == "encoder.norm.bias":
												lowercase :							List[str]    		='''layernorm.bias'''

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

						return name






def 				_lowerCAmelCase			(   __magic_name__						:     Optional[int]   , __magic_name__						:     Any						)				->  Optional[int]:
						for key in orig_state_dict.copy().keys():
												lowercase :							str    		=orig_state_dict.pop(__magic_name__						)

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

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

						return orig_state_dict






def 				_lowerCAmelCase			(   __magic_name__						:     List[Any]   , __magic_name__						:     Any   , __magic_name__						:     Optional[int]   , __magic_name__						:     int						)				->  Tuple:
						lowercase :							Tuple    		=torch.load(__magic_name__   , map_location='''cpu'''						)['''model''']

						lowercase :							Union[str, Any]    		=get_swin_config(__magic_name__						)
						lowercase :							int    		=SwinForMaskedImageModeling(__magic_name__						)
						model.eval()

						lowercase :							List[Any]    		=convert_state_dict(__magic_name__   , __magic_name__						)
						model.load_state_dict(__magic_name__						)

						lowercase :							List[Any]    		='''http://images.cocodataset.org/val2017/000000039769.jpg'''

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

						with torch.no_grad():
												lowercase :							List[str]    		=model(**__magic_name__						).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(__magic_name__						)

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

						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 re

from flax.core.frozen_dict import freeze
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.experimental import PartitionSpec as P


# Sentinels
UpperCamelCase_    						=       object()

# For specifying empty leaf dict `{}`
UpperCamelCase_    						=       object()






def 				_lowerCAmelCase			(   __magic_name__						:     int   , __magic_name__						:     int						)				->  Optional[int]:
						lowercase :							Optional[Any]    		=tuple((re.compile(x + '''$'''						) for x in qs)						)
						for i in range(len(__magic_name__						) - len(__magic_name__						) + 1						):
												lowercase :							Union[str, Any]    		=[x.match(__magic_name__						) for x, y in zip(__magic_name__   , ks[i:]						)]
												if matches and all(__magic_name__						):
																		return True
						return False






def 				_lowerCAmelCase			(   __magic_name__						:     Dict						)				->  List[str]:
						def replace(__magic_name__						:     Optional[int]   , __magic_name__						:     Optional[Any]						):
												for rule, replacement in rules:
																		if _match(__magic_name__   , __magic_name__						):
																								return replacement
												return val

						return replace






def 				_lowerCAmelCase			(   )				->  int:
						return [
						    # embeddings
						    (("transformer", "wpe", "embedding"), P('''mp'''   , __magic_name__						)),
						    (("transformer", "wte", "embedding"), P('''mp'''   , __magic_name__						)),
						    # atention
						    (("attention", "(q_proj|k_proj|v_proj)", "kernel"), P(__magic_name__   , '''mp'''						)),
						    (("attention", "out_proj", "kernel"), P('''mp'''   , __magic_name__						)),
						    (("attention", "out_proj", "bias"), None),
						    # mlp
						    (("mlp", "c_fc", "kernel"), P(__magic_name__   , '''mp'''						)),
						    (("mlp", "c_fc", "bias"), P('''mp'''						)),
						    (("mlp", "c_proj", "kernel"), P('''mp'''   , __magic_name__						)),
						    (("mlp", "c_proj", "bias"), None),
						    # layer norms
						    ((r"ln_\d+", "bias"), None),
						    ((r"\d+", r"ln_\d+", "scale"), None),
						    (("ln_f", "bias"), None),
						    (("ln_f", "scale"), None),
						]







def 				_lowerCAmelCase			(   __magic_name__						:     str						)				->  int:
						lowercase :							int    		=_get_partition_rules()
						lowercase :							Tuple    		=_replacement_rules(__magic_name__						)
						lowercase :							Any    		={k: _unmatched for k in flatten_dict(__magic_name__						)}
						lowercase :							Any    		={k: replace(__magic_name__   , __magic_name__						) for k, v in initd.items()}
						assert _unmatched not in result.values(), "Incomplete partition spec."
						return freeze(unflatten_dict(__magic_name__						)						)





