





import argparse

import requests
import torch
from PIL import Image

from transformers import SwinConfig, SwinForMaskedImageModeling, ViTImageProcessor



def  SCREAMING_SNAKE_CASE				(      snake_case							)		->    Dict:
		__lowercase											=     SwinConfig(image_size=192							)

		if "base" in model_name:
				__lowercase											=     6
				__lowercase											=     128
				__lowercase											=     (2, 2, 18, 2)
				__lowercase											=     (4, 8, 16, 32)
		elif "large" in model_name:
				__lowercase											=     12
				__lowercase											=     192
				__lowercase											=     (2, 2, 18, 2)
				__lowercase											=     (6, 12, 24, 48)
		else:
				raise ValueError('Model not supported, only supports base and large variants'							)

		__lowercase											=     window_size
		__lowercase											=     embed_dim
		__lowercase											=     depths
		__lowercase											=     num_heads

		return config



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

		if name == "encoder.norm.weight":
				__lowercase											=     'layernorm.weight'
		if name == "encoder.norm.bias":
				__lowercase											=     'layernorm.bias'

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

		return name



def  SCREAMING_SNAKE_CASE				(      snake_case						,   snake_case							)		->    Any:
		for key in orig_state_dict.copy().keys():
				__lowercase											=     orig_state_dict.pop(snake_case							)

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

						if "weight" in key:
								__lowercase											=     val[:dim, :]
								__lowercase											=     val[
								    dim : dim * 2, :
								]
								__lowercase											=     val[-dim:, :]
						else:
								__lowercase											=     val[
								    :dim
								]
								__lowercase											=     val[
								    dim : dim * 2
								]
								__lowercase											=     val[
								    -dim:
								]
				else:
						__lowercase											=     val

		return orig_state_dict



def  SCREAMING_SNAKE_CASE				(      snake_case						,   snake_case						,   snake_case						,   snake_case							)		->    List[str]:
		__lowercase											=     torch.load(snake_case						,   map_location='cpu'							)['model']

		__lowercase											=     get_swin_config(snake_case							)
		__lowercase											=     SwinForMaskedImageModeling(snake_case							)
		model.eval()

		__lowercase											=     convert_state_dict(snake_case						,   snake_case							)
		model.load_state_dict(snake_case							)

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

		__lowercase											=     ViTImageProcessor(size={'height': 192, 'width': 192}							)
		__lowercase											=     Image.open(requests.get(snake_case						,   stream=snake_case							).raw							)
		__lowercase											=     image_processor(images=snake_case						,   return_tensors='pt'							)

		with torch.no_grad():
				__lowercase											=     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__":
		SCREAMING_SNAKE_CASE_   :      int			=   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.'''
		)

		SCREAMING_SNAKE_CASE_   :      Optional[int]			=   parser.parse_args()
		convert_swin_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)











def  SCREAMING_SNAKE_CASE				(      snake_case						,   snake_case = False							)		->    str:

		if not isinstance(snake_case						,   snake_case							):
				__lowercase											=     F"Expected string as input, found {type(snake_case							)}"
				raise ValueError(snake_case							)
		if not isinstance(snake_case						,   snake_case							):
				__lowercase											=     F"Expected boolean as use_pascal parameter, found {type(snake_case							)}"
				raise ValueError(snake_case							)

		__lowercase											=     input_str.split('_'							)

		__lowercase											=     0 if use_pascal else 1

		__lowercase											=     words[start_index:]

		__lowercase											=     [word[0].upper() + word[1:] for word in words_to_capitalize]

		__lowercase											=     '' if use_pascal else words[0]

		return "".join([initial_word, *capitalized_words]							)


if __name__ == "__main__":
		from doctest import testmod

		testmod()




