






"""simple docstring"""



import argparse

import requests
import torch
from PIL import Image

from transformers import SwinConfig, SwinForMaskedImageModeling, ViTImageProcessor



def 						lowercase    (				__snake_case				: int	):
		lowercase_ : Tuple							       =   SwinConfig(image_size=1_9_2	)

		if "base" in model_name:
				lowercase_ : Union[str, Any]							       =   6
				lowercase_ : Dict							       =   1_2_8
				lowercase_ : Any							       =   (2, 2, 1_8, 2)
				lowercase_ : Tuple							       =   (4, 8, 1_6, 3_2)
		elif "large" in model_name:
				lowercase_ : Any							       =   1_2
				lowercase_ : Tuple							       =   1_9_2
				lowercase_ : List[str]							       =   (2, 2, 1_8, 2)
				lowercase_ : Union[str, Any]							       =   (6, 1_2, 2_4, 4_8)
		else:
				raise ValueError('''Model not supported, only supports base and large variants'''	)

		lowercase_ : str							       =   window_size
		lowercase_ : Dict							       =   embed_dim
		lowercase_ : Tuple							       =   depths
		lowercase_ : str							       =   num_heads

		return config



def 						lowercase    (				__snake_case				: Union[str, Any]	):
		if "encoder.mask_token" in name:
				lowercase_ : Any							       =   name.replace('''encoder.mask_token'''		,					'''embeddings.mask_token'''	)
		if "encoder.patch_embed.proj" in name:
				lowercase_ : List[Any]							       =   name.replace('''encoder.patch_embed.proj'''		,					'''embeddings.patch_embeddings.projection'''	)
		if "encoder.patch_embed.norm" in name:
				lowercase_ : str							       =   name.replace('''encoder.patch_embed.norm'''		,					'''embeddings.norm'''	)
		if "attn.proj" in name:
				lowercase_ : List[Any]							       =   name.replace('''attn.proj'''		,					'''attention.output.dense'''	)
		if "attn" in name:
				lowercase_ : List[Any]							       =   name.replace('''attn'''		,					'''attention.self'''	)
		if "norm1" in name:
				lowercase_ : Tuple							       =   name.replace('''norm1'''		,					'''layernorm_before'''	)
		if "norm2" in name:
				lowercase_ : int							       =   name.replace('''norm2'''		,					'''layernorm_after'''	)
		if "mlp.fc1" in name:
				lowercase_ : Optional[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_ : Optional[int]							       =   """layernorm.weight"""
		if name == "encoder.norm.bias":
				lowercase_ : str							       =   """layernorm.bias"""

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

		return name



def 						lowercase    (				__snake_case				: Optional[int]		,					__snake_case				: Union[str, Any]	):
		for key in orig_state_dict.copy().keys():
				lowercase_ : Tuple							       =   orig_state_dict.pop(__snake_case	)

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

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

		return orig_state_dict



def 						lowercase    (				__snake_case				: List[Any]		,					__snake_case				: Optional[Any]		,					__snake_case				: str		,					__snake_case				: str	):
		lowercase_ : Optional[int]							       =   torch.load(__snake_case		,					map_location='''cpu'''	)["""model"""]

		lowercase_ : List[str]							       =   get_swin_config(__snake_case	)
		lowercase_ : str							       =   SwinForMaskedImageModeling(__snake_case	)
		model.eval()

		lowercase_ : List[Any]							       =   convert_state_dict(__snake_case		,					__snake_case	)
		model.load_state_dict(__snake_case	)

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

		lowercase_ : Dict							       =   ViTImageProcessor(size={'''height''': 1_9_2, '''width''': 1_9_2}	)
		lowercase_ : Dict							       =   Image.open(requests.get(__snake_case		,					stream=__snake_case	).raw	)
		lowercase_ : Union[str, Any]							       =   image_processor(images=__snake_case		,					return_tensors='''pt'''	)

		with torch.no_grad():
				lowercase_ : List[Any]							       =   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__":
						__A		:       Union[str, Any]  				=		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.'''
						)

						__A		:       int  				=		parser.parse_args()
						convert_swin_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)











'''simple docstring'''







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available


_A	:			Dict         ={'''tokenization_herbert''': ['''HerbertTokenizer''']}

try:
			if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_A	:			Optional[int]         =['''HerbertTokenizerFast''']


if TYPE_CHECKING:
			from .tokenization_herbert import HerbertTokenizer

			try:
						if not is_tokenizers_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_herbert_fast import HerbertTokenizerFast

else:
			import sys

			_A	:			Optional[int]         =_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)

