



import argparse

import requests
import torch
from PIL import Image

from transformers import SwinConfig, SwinForMaskedImageModeling, ViTImageProcessor
def 	snake_case__       (					__lowercase       )		->		Dict:







						"""simple docstring"""

						A__	:							Tuple						    = SwinConfig(image_size=1_9_2       )

						if "base" in model_name:
												A__	:							Optional[int]						    = 6
												A__	:							Union[str, Any]						    = 1_2_8
												A__	:							Dict						    = (2, 2, 1_8, 2)
												A__	:							int						    = (4, 8, 1_6, 3_2)
						elif "large" in model_name:
												A__	:							Optional[Any]						    = 1_2
												A__	:							Optional[int]						    = 1_9_2
												A__	:							List[Any]						    = (2, 2, 1_8, 2)
												A__	:							int						    = (6, 1_2, 2_4, 4_8)
						else:
												raise ValueError("Model not supported, only supports base and large variants"       )

						A__	:							str						    = window_size
						A__	:							List[str]						    = embed_dim
						A__	:							Optional[int]						    = depths
						A__	:							Union[str, Any]						    = num_heads

						return config
def 	snake_case__       (					__lowercase       )		->		Optional[Any]:







						"""simple docstring"""

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

						if name == "encoder.norm.weight":
												A__	:							Any						    = "layernorm.weight"
						if name == "encoder.norm.bias":
												A__	:							str						    = "layernorm.bias"

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

						return name
def 	snake_case__       (					__lowercase							,     __lowercase       )		->		Any:







						"""simple docstring"""

						for key in orig_state_dict.copy().keys():
												A__	:							Tuple						    = orig_state_dict.pop(__lowercase       )

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

																		if "weight" in key:
																								A__	:							Tuple						    = val[:dim, :]
																								A__	:							Any						    = val[
																								    dim : dim * 2, :
																								]
																								A__	:							List[str]						    = val[-dim:, :]
																		else:
																								A__	:							Any						    = val[
																								    :dim
																								]
																								A__	:							List[str]						    = val[
																								    dim : dim * 2
																								]
																								A__	:							Optional[Any]						    = val[
																								    -dim:
																								]
												else:
																		A__	:							Tuple						    = val

						return orig_state_dict
def 	snake_case__       (					__lowercase							,     __lowercase							,     __lowercase							,     __lowercase       )		->		List[Any]:







						"""simple docstring"""

						A__	:							str						    = torch.load(__lowercase							,     map_location="cpu"       )["model"]

						A__	:							str						    = get_swin_config(__lowercase       )
						A__	:							Optional[int]						    = SwinForMaskedImageModeling(__lowercase       )
						model.eval()

						A__	:							List[Any]						    = convert_state_dict(__lowercase							,     __lowercase       )
						model.load_state_dict(__lowercase       )

						A__	:							Union[str, Any]						    = "http://images.cocodataset.org/val2017/000000039769.jpg"

						A__	:							Optional[Any]						    = ViTImageProcessor(size={"height": 1_9_2, "width": 1_9_2}       )
						A__	:							str						    = Image.open(requests.get(__lowercase							,     stream=__lowercase       ).raw       )
						A__	:							Optional[int]						    = image_processor(images=__lowercase							,     return_tensors="pt"       )

						with torch.no_grad():
												A__	:							Tuple						    = model(**__lowercase       ).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(__lowercase       )

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

						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    :						Union[str, 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.'
		)

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




from unittest import TestCase

from datasets import Sequence, Value
from datasets.arrow_dataset import Dataset




class 							lowerCAmelCase__  (	UpperCamelCase						):







					def  _lowercase			(       self      :     List[Any]):
											return [
											    {"col_1": 3, "col_2": "a"},
											    {"col_1": 2, "col_2": "b"},
											    {"col_1": 1, "col_2": "c"},
											    {"col_1": 0, "col_2": "d"},
											]







					def  _lowercase			(       self      :     Tuple):
											A__	:							Any						    = {"col_1": [3, 2, 1, 0], "col_2": ["a", "b", "c", "d"]}
											return Dataset.from_dict(_A)







					def  _lowercase			(       self      :     Union[str, Any]):
											A__	:							str						    = self._create_example_records()
											A__	:							Dict						    = Dataset.from_list(_A)
											self.assertListEqual(dset.column_names			,		["col_1", "col_2"])
											for i, r in enumerate(_A):
																	self.assertDictEqual(_A			,		example_records[i])







					def  _lowercase			(       self      :     List[str]):
											A__	:							List[str]						    = self._create_example_records()
											A__	:							Any						    = Dataset.from_list(_A)
											A__	:							Optional[int]						    = Dataset.from_dict({k: [r[k] for r in example_records] for k in example_records[0]})
											self.assertEqual(dset.info			,		dset_from_dict.info)







					def  _lowercase			(       self      :     Any):  # checks what happens with missing columns
											A__	:							Dict						    = [{"col_1": 1}, {"col_2": "x"}]
											A__	:							Any						    = Dataset.from_list(_A)
											self.assertDictEqual(dset[0]			,		{"col_1": 1})
											self.assertDictEqual(dset[1]			,		{"col_1": None})  # NB: first record is used for columns







					def  _lowercase			(       self      :     str):  # checks if the type can be inferred from the second record
											A__	:							Dict						    = [{"col_1": []}, {"col_1": [1, 2]}]
											A__	:							Dict						    = Dataset.from_list(_A)
											self.assertEqual(dset.info.features["col_1"]			,		Sequence(Value("int64")))







					def  _lowercase			(       self      :     Union[str, Any]):
											A__	:							Dict						    = Dataset.from_list([])
											self.assertEqual(len(_A)			,		0)
											self.assertListEqual(dset.column_names			,		[])