


'''simple docstring'''
import argparse

import requests
import torch
from PIL import Image

from transformers import SwinConfig, SwinForMaskedImageModeling, ViTImageProcessor


def       a				(      __a       )  ->     Optional[Any]:



					'''simple docstring'''






					UpperCamelCase__       :List[Any]							 =			SwinConfig(image_size=192       )

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

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

					return config


def       a				(      __a       )  ->     Optional[int]:



					'''simple docstring'''






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

					if name == "encoder.norm.weight":
										UpperCamelCase__       :str							 =			'''layernorm.weight'''
					if name == "encoder.norm.bias":
										UpperCamelCase__       :int							 =			'''layernorm.bias'''

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

					return name


def       a				(      __a			, __a       )  ->     Dict:



					'''simple docstring'''






					for key in orig_state_dict.copy().keys():
										UpperCamelCase__       :Dict							 =			orig_state_dict.pop(__a       )

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

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

					return orig_state_dict


def       a				(      __a			, __a			, __a			, __a       )  ->     Optional[Any]:



					'''simple docstring'''






					UpperCamelCase__       :List[Any]							 =			torch.load(__a			, map_location='''cpu'''       )['''model''']

					UpperCamelCase__       :str							 =			get_swin_config(__a       )
					UpperCamelCase__       :Any							 =			SwinForMaskedImageModeling(__a       )
					model.eval()

					UpperCamelCase__       :int							 =			convert_state_dict(__a			, __a       )
					model.load_state_dict(__a       )

					UpperCamelCase__       :Optional[Any]							 =			'''http://images.cocodataset.org/val2017/000000039769.jpg'''

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

					with torch.no_grad():
										UpperCamelCase__       :int							 =			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__":
		__snake_case											=					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.'''
		)

		__snake_case											=					parser.parse_args()
		convert_swin_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)



'''simple docstring'''
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import argparse

from .config import config_command_parser
from .config_args import default_config_file, load_config_from_file  # noqa: F401
from .default import default_command_parser
from .update import update_command_parser


def       a				(      __a=None       )  ->     List[str]:



					'''simple docstring'''






					UpperCamelCase__       :Optional[Any]							 =			argparse.ArgumentParser(add_help=__a			, allow_abbrev=__a       )
					# The main config parser
					UpperCamelCase__       :str							 =			config_command_parser(__a       )
					# The subparser to add commands to
					UpperCamelCase__       :Union[str, Any]							 =			config_parser.add_subparsers(title='''subcommands'''			, dest='''subcommand'''       )

					# Then add other parsers with the parent parser
					default_command_parser(__a			, parents=[parent_parser]       )
					update_command_parser(__a			, parents=[parent_parser]       )

					return config_parser


def       a				(      )  ->     Any:



					'''simple docstring'''






					UpperCamelCase__       :int							 =			get_config_parser()
					UpperCamelCase__       :List[Any]							 =			config_parser.parse_args()

					if not hasattr(__a			, '''func'''       ):
										config_parser.print_help()
										exit(1       )

					# Run
					args.func(__a       )


if __name__ == "__main__":
		main()