

import argparse

import requests
import torch
from PIL import Image

from transformers import SwinConfig, SwinForMaskedImageModeling, ViTImageProcessor







def       lowercase					(	__A :					Tuple		)				->			Union[str, Any]:





    '''simple docstring'''




    snake_case							:		Optional[int]        =		SwinConfig(image_size=192		)

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

    snake_case							:		Union[str, Any]        =		window_size
    snake_case							:		str        =		embed_dim
    snake_case							:		List[Any]        =		depths
    snake_case							:		Tuple        =		num_heads

    return config







def       lowercase					(	__A :					List[str]		)				->			Union[str, Any]:





    '''simple docstring'''




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

    if name == "encoder.norm.weight":
        snake_case							:		int        =		"""layernorm.weight"""
    if name == "encoder.norm.bias":
        snake_case							:		Union[str, Any]        =		"""layernorm.bias"""

    if "decoder" in name:
        pass
    else:
        snake_case							:		Union[str, Any]        =		"""swin.""" + name

    return name







def       lowercase					(	__A :					List[Any] ,    __A :					Union[str, Any]		)				->			Union[str, Any]:





    '''simple docstring'''




    for key in orig_state_dict.copy().keys():
        snake_case							:		Optional[int]        =		orig_state_dict.pop(__A		)

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

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

    return orig_state_dict







def       lowercase					(	__A :					Tuple ,    __A :					Tuple ,    __A :					Any ,    __A :					int		)				->			Dict:





    '''simple docstring'''




    snake_case							:		Any        =		torch.load(__A ,    map_location="""cpu"""		)["""model"""]

    snake_case							:		List[Any]        =		get_swin_config(__A		)
    snake_case							:		Optional[Any]        =		SwinForMaskedImageModeling(__A		)
    model.eval()

    snake_case							:		str        =		convert_state_dict(__A ,    __A		)
    model.load_state_dict(__A		)

    snake_case							:		List[Any]        =		"""http://images.cocodataset.org/val2017/000000039769.jpg"""

    snake_case							:		Optional[int]        =		ViTImageProcessor(size={"""height""": 192, """width""": 192}		)
    snake_case							:		Any        =		Image.open(requests.get(__A ,    stream=__A		).raw		)
    snake_case							:		Tuple        =		image_processor(images=__A ,    return_tensors="""pt"""		)

    with torch.no_grad():
        snake_case							:		Tuple        =		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__":
     __lowercase     : List[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.'''
     )

     __lowercase     : str       =						parser.parse_args()
     convert_swin_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)











"""simple docstring"""
from __future__ import annotations

from collections.abc import Generator





def 				lowercase__		(					):
				_SCREAMING_SNAKE_CASE   :		dict[int, int]       		=    {}
				_SCREAMING_SNAKE_CASE   :		List[Any]       		=    2
				while True:
								_SCREAMING_SNAKE_CASE   :		List[Any]       		=    factor_map.pop(lowerCamelCase,						lowerCamelCase       )
								if factor:
												_SCREAMING_SNAKE_CASE   :		str       		=    factor + prime
												while x in factor_map:
																x += factor
												_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    factor
								else:
												_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    prime
												yield prime
								prime += 1





def 				lowercase__		(					lowerCamelCase = 1E10       ):
				_SCREAMING_SNAKE_CASE   :		Dict       		=    sieve()

				_SCREAMING_SNAKE_CASE   :		Dict       		=    1
				while True:
								_SCREAMING_SNAKE_CASE   :		int       		=    next(lowerCamelCase       )
								if (2 * prime * n) > limit:
												return n
								# Ignore the next prime as the reminder will be 2.
								next(lowerCamelCase       )
								n += 2


if __name__ == "__main__":
			print(solution())

