
import argparse

import requests
import torch
from PIL import Image

from transformers import SwinConfig, SwinForMaskedImageModeling, ViTImageProcessor

def 				SCREAMING_SNAKE_CASE     (      __UpperCamelCase :						List[str]       ):

				"""simple docstring"""







				A__  :	Dict         =							SwinConfig(image_size=1_92       )

				if "base" in model_name:
								A__  :	Dict         =							6
								A__  :	Optional[Any]         =							1_28
								A__  :	str         =							(2, 2, 18, 2)
								A__  :	Dict         =							(4, 8, 16, 32)
				elif "large" in model_name:
								A__  :	str         =							12
								A__  :	Optional[int]         =							1_92
								A__  :	Any         =							(2, 2, 18, 2)
								A__  :	int         =							(6, 12, 24, 48)
				else:
								raise ValueError('''Model not supported, only supports base and large variants'''       )

				A__  :	Any         =							window_size
				A__  :	Any         =							embed_dim
				A__  :	Dict         =							depths
				A__  :	Union[str, Any]         =							num_heads

				return config

def 				SCREAMING_SNAKE_CASE     (      __UpperCamelCase :						Optional[Any]       ):

				"""simple docstring"""







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

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

				if "decoder" in name:
								pass
				else:
								A__  :	str         =							"swin." + name

				return name

def 				SCREAMING_SNAKE_CASE     (      __UpperCamelCase :						int							,						__UpperCamelCase :						List[Any]       ):

				"""simple docstring"""







				for key in orig_state_dict.copy().keys():
								A__  :	Optional[int]         =							orig_state_dict.pop(__SCREAMING_SNAKE_CASE       )

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

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

				return orig_state_dict

def 				SCREAMING_SNAKE_CASE     (      __UpperCamelCase :						Optional[int]							,						__UpperCamelCase :						List[str]							,						__UpperCamelCase :						List[str]							,						__UpperCamelCase :						Tuple       ):

				"""simple docstring"""







				A__  :	Union[str, Any]         =							torch.load(__SCREAMING_SNAKE_CASE							,						map_location='''cpu'''       )["model"]

				A__  :	Any         =							get_swin_config(__SCREAMING_SNAKE_CASE       )
				A__  :	Optional[int]         =							SwinForMaskedImageModeling(__SCREAMING_SNAKE_CASE       )
				model.eval()

				A__  :	Tuple         =							convert_state_dict(__SCREAMING_SNAKE_CASE							,						__SCREAMING_SNAKE_CASE       )
				model.load_state_dict(__SCREAMING_SNAKE_CASE       )

				A__  :	Tuple         =							"http://images.cocodataset.org/val2017/000000039769.jpg"

				A__  :	Optional[int]         =							ViTImageProcessor(size={'''height''': 1_92, '''width''': 1_92}       )
				A__  :	Any         =							Image.open(requests.get(__SCREAMING_SNAKE_CASE							,						stream=__SCREAMING_SNAKE_CASE       ).raw       )
				A__  :	Optional[int]         =							image_processor(images=__SCREAMING_SNAKE_CASE							,						return_tensors='''pt'''       )

				with torch.no_grad():
								A__  :	Tuple         =							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__":
					_SCREAMING_SNAKE_CASE    :      Tuple          =			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.'
					)

					_SCREAMING_SNAKE_CASE    :      int          =			parser.parse_args()
					convert_swin_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)

import inspect
import os
import unittest

import torch

import accelerate
from accelerate import debug_launcher
from accelerate.test_utils import (
    execute_subprocess_async,
    require_cpu,
    require_huggingface_suite,
    require_multi_gpu,
    require_single_gpu,
)
from accelerate.utils import patch_environment







@require_huggingface_suite
class  UpperCamelCase__		(    unittest.TestCase   ):







	'''simple docstring'''





	def   __snake_case				(			self		):
					A__  :	Dict         =							inspect.getfile(accelerate.test_utils		)
					A__  :	Any         =							os.path.sep.join(
					    mod_file.split(os.path.sep		)[:-1] + ['''scripts''', '''external_deps''', '''test_metrics.py''']		)

					from accelerate.test_utils.scripts.external_deps import test_metrics  # noqa: F401

					A__  :	Tuple         =							test_metrics





	@require_cpu
	def   __snake_case				(			self		):
					debug_launcher(self.test_metrics.main				,       num_processes=1		)





	@require_cpu
	def   __snake_case				(			self		):
					debug_launcher(self.test_metrics.main		)





	@require_single_gpu
	def   __snake_case				(			self		):
					self.test_metrics.main()





	@require_multi_gpu
	def   __snake_case				(			self		):
					print(F"Found {torch.cuda.device_count()} devices."		)
					A__  :	int         =							['''torchrun''', F"--nproc_per_node={torch.cuda.device_count()}", self.test_file_path]
					with patch_environment(omp_num_threads=1		):
									execute_subprocess_async(UpperCamelCase__				,       env=os.environ.copy()		)