






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)












import doctest
import logging
import os
import unittest
from pathlib import Path
from typing import List, Union

import transformers
from transformers.testing_utils import require_tf, require_torch, slow


__UpperCamelCase					:		Optional[Any]	  =						logging.getLogger()


@unittest.skip("Temporarily disable the doc tests."	)
@require_torch
@require_tf
@slow
class   __SCREAMING_SNAKE_CASE(	unittest.TestCase	):
							def lowerCAmelCase_ (     self:      Dict       ,	UpperCamelCase:      Path       ,	UpperCamelCase:      Union[str, None] = None       ,	UpperCamelCase:      Union[List[str], None] = None       ,	UpperCamelCase:      Union[str, List[str], None] = None       ,	UpperCamelCase:      bool = True       ,	)		->		int:
											snake_case__									=     [file for file in os.listdir(UpperCamelCase						) if os.path.isfile(os.path.join(UpperCamelCase       ,	UpperCamelCase						)						)]

											if identifier is not None:
															snake_case__									=     [file for file in files if identifier in file]

											if n_identifier is not None:
															if isinstance(UpperCamelCase       ,	UpperCamelCase						):
																			for n_ in n_identifier:
																							snake_case__									=     [file for file in files if n_ not in file]
															else:
																			snake_case__									=     [file for file in files if n_identifier not in file]

											snake_case__									=     ignore_files or []
											ignore_files.append('__init__.py'						)
											snake_case__									=     [file for file in files if file not in ignore_files]

											for file in files:
															# Open all files
															print('Testing'       ,	UpperCamelCase						)

															if only_modules:
																			snake_case__									=     file.split('.'						)[0]
																			try:
																							snake_case__									=     getattr(UpperCamelCase       ,	UpperCamelCase						)
																							snake_case__									=     doctest.DocTestSuite(UpperCamelCase						)
																							snake_case__									=     unittest.TextTestRunner().run(UpperCamelCase						)
																							self.assertIs(len(result.failures						)       ,	0						)
																			except AttributeError:
																							logger.info(F'''{module_identifier} is not a module.'''						)
															else:
																			snake_case__									=     doctest.testfile(str('..' / directory / file						)       ,	optionflags=doctest.ELLIPSIS						)
																			self.assertIs(result.failed       ,	0						)
							def lowerCAmelCase_ (     self:      Tuple						)		->		int:
											snake_case__									=     Path('src/transformers'						)
											snake_case__									=     'modeling'
											snake_case__									=     [
											    'modeling_ctrl.py',
											    'modeling_tf_ctrl.py',
											]
											self.analyze_directory(UpperCamelCase       ,	identifier=UpperCamelCase       ,	ignore_files=UpperCamelCase						)
							def lowerCAmelCase_ (     self:      Optional[int]						)		->		str:
											snake_case__									=     Path('src/transformers'						)
											snake_case__									=     'tokenization'
											self.analyze_directory(UpperCamelCase       ,	identifier=UpperCamelCase						)
							def lowerCAmelCase_ (     self:      Optional[Any]						)		->		int:
											snake_case__									=     Path('src/transformers'						)
											snake_case__									=     'configuration'
											self.analyze_directory(UpperCamelCase       ,	identifier=UpperCamelCase						)
							def lowerCAmelCase_ (     self:      Dict						)		->		List[Any]:
											snake_case__									=     Path('src/transformers'						)
											snake_case__									=     ['configuration', 'modeling', 'tokenization']
											self.analyze_directory(UpperCamelCase       ,	n_identifier=UpperCamelCase						)


							def lowerCAmelCase_ (     self:      Tuple						)		->		Union[str, Any]:
											snake_case__									=     Path('docs/source'						)
											snake_case__									=     ['favicon.ico']
											self.analyze_directory(UpperCamelCase       ,	ignore_files=UpperCamelCase       ,	only_modules=UpperCamelCase						)




