import argparse

import requests
import torch
from PIL import Image

from transformers import SwinConfig, SwinForMaskedImageModeling, ViTImageProcessor




def     UpperCamelCase						(	__lowercase						:			Any     ):
		'''simple docstring'''




		A_		:    Union[str, Any]         =				SwinConfig(image_size=1_92     )

		if "base" in model_name:
				A_		:    str         =				6
				A_		:    Tuple         =				1_28
				A_		:    Tuple         =				(2, 2, 18, 2)
				A_		:    Union[str, Any]         =				(4, 8, 16, 32)
		elif "large" in model_name:
				A_		:    Union[str, Any]         =				12
				A_		:    Optional[Any]         =				1_92
				A_		:    List[Any]         =				(2, 2, 18, 2)
				A_		:    Union[str, Any]         =				(6, 12, 24, 48)
		else:
				raise ValueError('Model not supported, only supports base and large variants'     )

		A_		:    Any         =				window_size
		A_		:    Optional[Any]         =				embed_dim
		A_		:    int         =				depths
		A_		:    List[str]         =				num_heads

		return config




def     UpperCamelCase						(	__lowercase						:			Dict     ):
		'''simple docstring'''




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

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

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

		return name




def     UpperCamelCase						(	__lowercase						:			Optional[Any]						,__lowercase						:			Dict     ):
		'''simple docstring'''




		for key in orig_state_dict.copy().keys():
				A_		:    Tuple         =				orig_state_dict.pop(__lowercase     )

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

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

		return orig_state_dict




def     UpperCamelCase						(	__lowercase						:			List[str]						,__lowercase						:			Tuple						,__lowercase						:			Union[str, Any]						,__lowercase						:			int     ):
		'''simple docstring'''




		A_		:    Optional[Any]         =				torch.load(__lowercase						,map_location='cpu'     )['model']

		A_		:    Dict         =				get_swin_config(__lowercase     )
		A_		:    int         =				SwinForMaskedImageModeling(__lowercase     )
		model.eval()

		A_		:    Optional[int]         =				convert_state_dict(__lowercase						,__lowercase     )
		model.load_state_dict(__lowercase     )

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

		A_		:    Dict         =				ViTImageProcessor(size={'height': 1_92, 'width': 1_92}     )
		A_		:    Tuple         =				Image.open(requests.get(__lowercase						,stream=__lowercase     ).raw     )
		A_		:    Optional[Any]         =				image_processor(images=__lowercase						,return_tensors='pt'     )

		with torch.no_grad():
				A_		:    Any         =				model(**__lowercase     ).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(__lowercase     )

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

		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)







import sys

from .dependency_versions_table import deps
from .utils.versions import require_version, require_version_core


# define which module versions we always want to check at run time
# (usually the ones defined in `install_requires` in setup.py)
#
# order specific notes:
# - tqdm must be checked before tokenizers

_UpperCAmelCase            =		"""python tqdm regex requests packaging filelock numpy tokenizers""".split()
if sys.version_info < (3, 7):
	pkgs_to_check_at_runtime.append("""dataclasses""")
if sys.version_info < (3, 8):
	pkgs_to_check_at_runtime.append("""importlib_metadata""")

for pkg in pkgs_to_check_at_runtime:
	if pkg in deps:
		if pkg == "tokenizers":
			# must be loaded here, or else tqdm check may fail
			from .utils import is_tokenizers_available

			if not is_tokenizers_available():
				continue  # not required, check version only if installed

		require_version_core(deps[pkg])
	else:
		raise ValueError(F"""can't find {pkg} in {deps.keys()}, check dependency_versions_table.py""")




def     UpperCamelCase						(	__lowercase						:			str						,__lowercase						:			Dict=None     ):
		'''simple docstring'''




		require_version(deps[pkg]						,__lowercase     )






