






'''simple docstring'''





import argparse

import requests
import torch
from PIL import Image

from transformers import SwinConfig, SwinForMaskedImageModeling, ViTImageProcessor
def      a_ (      _A  )			->			List[str]:







				"""simple docstring"""
				snake_case__									=     SwinConfig(image_size=192  )

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

				snake_case__									=     window_size
				snake_case__									=     embed_dim
				snake_case__									=     depths
				snake_case__									=     num_heads

				return config
def      a_ (      _A  )			->			List[Any]:







				"""simple docstring"""
				if "encoder.mask_token" in name:
								snake_case__									=     name.replace('encoder.mask_token'      ,   'embeddings.mask_token'  )
				if "encoder.patch_embed.proj" in name:
								snake_case__									=     name.replace('encoder.patch_embed.proj'      ,   'embeddings.patch_embeddings.projection'  )
				if "encoder.patch_embed.norm" in name:
								snake_case__									=     name.replace('encoder.patch_embed.norm'      ,   'embeddings.norm'  )
				if "attn.proj" in name:
								snake_case__									=     name.replace('attn.proj'      ,   'attention.output.dense'  )
				if "attn" in name:
								snake_case__									=     name.replace('attn'      ,   'attention.self'  )
				if "norm1" in name:
								snake_case__									=     name.replace('norm1'      ,   'layernorm_before'  )
				if "norm2" in name:
								snake_case__									=     name.replace('norm2'      ,   'layernorm_after'  )
				if "mlp.fc1" in name:
								snake_case__									=     name.replace('mlp.fc1'      ,   'intermediate.dense'  )
				if "mlp.fc2" in name:
								snake_case__									=     name.replace('mlp.fc2'      ,   'output.dense'  )

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

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

				return name
def      a_ (      _A      ,   _A  )			->			List[Any]:







				"""simple docstring"""
				for key in orig_state_dict.copy().keys():
								snake_case__									=     orig_state_dict.pop(_A  )

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

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

				return orig_state_dict
def      a_ (      _A      ,   _A      ,   _A      ,   _A  )			->			Any:







				"""simple docstring"""
				snake_case__									=     torch.load(_A      ,   map_location='cpu'  )['model']

				snake_case__									=     get_swin_config(_A  )
				snake_case__									=     SwinForMaskedImageModeling(_A  )
				model.eval()

				snake_case__									=     convert_state_dict(_A      ,   _A  )
				model.load_state_dict(_A  )

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

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

				with torch.no_grad():
								snake_case__									=     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__":
							__UpperCamelCase					:		List[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."""
							)

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












from __future__ import annotations

from collections.abc import Iterator
from typing import Any


class   __SCREAMING_SNAKE_CASE:
							def __init__(     self:      int       ,	UpperCamelCase:      Any						)		->		List[Any]:
											snake_case__									=     data
											snake_case__									=     None


class   __SCREAMING_SNAKE_CASE:
							def __init__(     self:      Dict						)		->		Optional[Any]:
											snake_case__									=     None
											snake_case__									=     None
							def __iter__(     self:      Optional[int]						)		->		Iterator[Any]:
											snake_case__									=     self.head
											while self.head:
															yield node.data
															snake_case__									=     node.next
															if node == self.head:
																			break
							def __len__(     self:      int						)		->		int:
											return sum(1 for _ in self						)
							def __repr__(     self:      int						)		->		Optional[int]:
											return "->".join(str(UpperCamelCase						) for item in iter(self						)						)
							def lowerCAmelCase_ (     self:      Union[str, Any]       ,	UpperCamelCase:      Any						)		->		None:
											self.insert_nth(len(self						)       ,	UpperCamelCase						)
							def lowerCAmelCase_ (     self:      Tuple       ,	UpperCamelCase:      Any						)		->		None:
											self.insert_nth(0       ,	UpperCamelCase						)
							def lowerCAmelCase_ (     self:      int       ,	UpperCamelCase:      int       ,	UpperCamelCase:      Any						)		->		None:
											if index < 0 or index > len(self						):
															raise IndexError('list index out of range.'						)
											snake_case__									=     Node(UpperCamelCase						)
											if self.head is None:
															snake_case__									=     new_node  # first node points itself
															snake_case__									=     snake_case__									=     new_node
											elif index == 0:  # insert at head
															snake_case__									=     self.head
															snake_case__									=     snake_case__									=     new_node
											else:
															snake_case__									=     self.head
															for _ in range(index - 1						):
																			snake_case__									=     temp.next
															snake_case__									=     temp.next
															snake_case__									=     new_node
															if index == len(self						) - 1:  # insert at tail
																			snake_case__									=     new_node
							def lowerCAmelCase_ (     self:      Optional[int]						)		->		Union[str, Any]:
											return self.delete_nth(0						)
							def lowerCAmelCase_ (     self:      Union[str, Any]						)		->		Any:
											return self.delete_nth(len(self						) - 1						)
							def lowerCAmelCase_ (     self:      str       ,	UpperCamelCase:      int = 0						)		->		Any:
											if not 0 <= index < len(self						):
															raise IndexError('list index out of range.'						)
											snake_case__									=     self.head
											if self.head == self.tail:  # just one node
															snake_case__									=     snake_case__									=     None
											elif index == 0:  # delete head node
															snake_case__									=     self.tail.next.next
															snake_case__									=     self.head.next
											else:
															snake_case__									=     self.head
															for _ in range(index - 1						):
																			snake_case__									=     temp.next
															snake_case__									=     temp.next
															snake_case__									=     temp.next.next
															if index == len(self						) - 1:  # delete at tail
																			snake_case__									=     temp
											return delete_node.data


							def lowerCAmelCase_ (     self:      Union[str, Any]						)		->		bool:
											return len(self						) == 0


def      a_ (      )			->			None:







				"""simple docstring"""
				snake_case__									=     CircularLinkedList()
				assert len(_A  ) == 0
				assert circular_linked_list.is_empty() is True
				assert str(_A  ) == ""

				try:
								circular_linked_list.delete_front()
								raise AssertionError  # This should not happen
				except IndexError:
								assert True  # This should happen

				try:
								circular_linked_list.delete_tail()
								raise AssertionError  # This should not happen
				except IndexError:
								assert True  # This should happen

				try:
								circular_linked_list.delete_nth(-1  )
								raise AssertionError
				except IndexError:
								assert True

				try:
								circular_linked_list.delete_nth(0  )
								raise AssertionError
				except IndexError:
								assert True

				assert circular_linked_list.is_empty() is True
				for i in range(5  ):
								assert len(_A  ) == i
								circular_linked_list.insert_nth(_A      ,   i + 1  )
				assert str(_A  ) == "->".join(str(_A  ) for i in range(1      ,   6  )  )

				circular_linked_list.insert_tail(6  )
				assert str(_A  ) == "->".join(str(_A  ) for i in range(1      ,   7  )  )
				circular_linked_list.insert_head(0  )
				assert str(_A  ) == "->".join(str(_A  ) for i in range(0      ,   7  )  )

				assert circular_linked_list.delete_front() == 0
				assert circular_linked_list.delete_tail() == 6
				assert str(_A  ) == "->".join(str(_A  ) for i in range(1      ,   6  )  )
				assert circular_linked_list.delete_nth(2  ) == 3

				circular_linked_list.insert_nth(2      ,   3  )
				assert str(_A  ) == "->".join(str(_A  ) for i in range(1      ,   6  )  )

				assert circular_linked_list.is_empty() is False


if __name__ == "__main__":
							import doctest

							doctest.testmod()




