


'''simple docstring'''



import argparse

import requests
import torch
from PIL import Image

from transformers import SwinConfig, SwinForMaskedImageModeling, ViTImageProcessor






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE				:       str       ):
					__a      :	Tuple    =  SwinConfig(image_size=192       )

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

					__a      :	Tuple    =  window_size
					__a      :	Optional[int]    =  embed_dim
					__a      :	str    =  depths
					__a      :	Dict    =  num_heads

					return config






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

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

					if "decoder" in name:
										pass
					else:
										__a      :	Optional[Any]    =  'swin.' + name

					return name






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE				:       int						,				_SCREAMING_SNAKE_CASE				:       List[Any]       ):
					for key in orig_state_dict.copy().keys():
										__a      :	int    =  orig_state_dict.pop(_SCREAMING_SNAKE_CASE       )

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

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

					return orig_state_dict






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE				:       Any						,				_SCREAMING_SNAKE_CASE				:       int						,				_SCREAMING_SNAKE_CASE				:       str						,				_SCREAMING_SNAKE_CASE				:       Optional[int]       ):
					__a      :	int    =  torch.load(_SCREAMING_SNAKE_CASE						,				map_location='cpu'       )['model']

					__a      :	Any    =  get_swin_config(_SCREAMING_SNAKE_CASE       )
					__a      :	List[str]    =  SwinForMaskedImageModeling(_SCREAMING_SNAKE_CASE       )
					model.eval()

					__a      :	List[Any]    =  convert_state_dict(_SCREAMING_SNAKE_CASE						,				_SCREAMING_SNAKE_CASE       )
					model.load_state_dict(_SCREAMING_SNAKE_CASE       )

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

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

					with torch.no_grad():
										__a      :	Optional[Any]    =  model(**_SCREAMING_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(_SCREAMING_SNAKE_CASE       )

										print(F"""Saving image processor to {pytorch_dump_folder_path}"""       )
										image_processor.save_pretrained(_SCREAMING_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__":
	__lowercase     :						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.'
	)

	__lowercase     :						Tuple	=     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 filelock import FileLock


try:
	import nltk

	__lowercase     :						Optional[Any]	=     True
except (ImportError, ModuleNotFoundError):
	__lowercase     :						Dict	=     False

if NLTK_AVAILABLE:
	with FileLock('.lock') as lock:
		nltk.download('punkt', quiet=True)






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE				:       str       ):
					re.sub('<n>'						,				''						,				_SCREAMING_SNAKE_CASE       )  # remove pegasus newline char
					assert NLTK_AVAILABLE, "nltk must be installed to separate newlines between sentences. (pip install nltk)"
					return "\n".join(nltk.sent_tokenize(_SCREAMING_SNAKE_CASE       )       )



