






import argparse
import json

import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import AutoImageProcessor, SwinConfig, SwinForImageClassification






def 							A     (       a_					)     ->						Union[str, Any]:
					__UpperCamelCase      :					Union[str, Any]       		=SwinConfig()
					__UpperCamelCase      :					str       		=swin_name.split('_'					)

					__UpperCamelCase      :					List[Any]       		=name_split[1]
					__UpperCamelCase      :					List[Any]       		=int(name_split[4]					)
					__UpperCamelCase      :					Optional[Any]       		=int(name_split[3][-1]					)

					if model_size == "tiny":
										__UpperCamelCase      :					Optional[Any]       		=96
										__UpperCamelCase      :					Optional[int]       		=(2, 2, 6, 2)
										__UpperCamelCase      :					Optional[Any]       		=(3, 6, 12, 24)
					elif model_size == "small":
										__UpperCamelCase      :					Dict       		=96
										__UpperCamelCase      :					List[Any]       		=(2, 2, 18, 2)
										__UpperCamelCase      :					List[str]       		=(3, 6, 12, 24)
					elif model_size == "base":
										__UpperCamelCase      :					Any       		=128
										__UpperCamelCase      :					Optional[Any]       		=(2, 2, 18, 2)
										__UpperCamelCase      :					List[Any]       		=(4, 8, 16, 32)
					else:
										__UpperCamelCase      :					int       		=192
										__UpperCamelCase      :					str       		=(2, 2, 18, 2)
										__UpperCamelCase      :					int       		=(6, 12, 24, 48)

					if "in22k" in swin_name:
										__UpperCamelCase      :					Any       		=21_841
					else:
										__UpperCamelCase      :					Tuple       		=1_000
										__UpperCamelCase      :					Tuple       		='huggingface/label-files'
										__UpperCamelCase      :					List[Any]       		='imagenet-1k-id2label.json'
										__UpperCamelCase      :					Optional[int]       		=json.load(open(hf_hub_download(a_							,a_							,repo_type='dataset'					)							,'r'					)					)
										__UpperCamelCase      :					Optional[Any]       		={int(a_					): v for k, v in idalabel.items()}
										__UpperCamelCase      :					int       		=idalabel
										__UpperCamelCase      :					str       		={v: k for k, v in idalabel.items()}

					__UpperCamelCase      :					Tuple       		=img_size
					__UpperCamelCase      :					Union[str, Any]       		=num_classes
					__UpperCamelCase      :					int       		=embed_dim
					__UpperCamelCase      :					Optional[Any]       		=depths
					__UpperCamelCase      :					Union[str, Any]       		=num_heads
					__UpperCamelCase      :					int       		=window_size

					return config






def 							A     (       a_					)     ->						List[str]:
					if "patch_embed.proj" in name:
										__UpperCamelCase      :					Dict       		=name.replace('patch_embed.proj'							,'embeddings.patch_embeddings.projection'					)
					if "patch_embed.norm" in name:
										__UpperCamelCase      :					Optional[Any]       		=name.replace('patch_embed.norm'							,'embeddings.norm'					)
					if "layers" in name:
										__UpperCamelCase      :					Union[str, Any]       		='encoder.' + name
					if "attn.proj" in name:
										__UpperCamelCase      :					Optional[int]       		=name.replace('attn.proj'							,'attention.output.dense'					)
					if "attn" in name:
										__UpperCamelCase      :					Optional[Any]       		=name.replace('attn'							,'attention.self'					)
					if "norm1" in name:
										__UpperCamelCase      :					Any       		=name.replace('norm1'							,'layernorm_before'					)
					if "norm2" in name:
										__UpperCamelCase      :					str       		=name.replace('norm2'							,'layernorm_after'					)
					if "mlp.fc1" in name:
										__UpperCamelCase      :					Tuple       		=name.replace('mlp.fc1'							,'intermediate.dense'					)
					if "mlp.fc2" in name:
										__UpperCamelCase      :					str       		=name.replace('mlp.fc2'							,'output.dense'					)

					if name == "norm.weight":
										__UpperCamelCase      :					Any       		='layernorm.weight'
					if name == "norm.bias":
										__UpperCamelCase      :					Dict       		='layernorm.bias'

					if "head" in name:
										__UpperCamelCase      :					Union[str, Any]       		=name.replace('head'							,'classifier'					)
					else:
										__UpperCamelCase      :					str       		='swin.' + name

					return name






def 							A     (       a_							,a_					)     ->						List[Any]:
					for key in orig_state_dict.copy().keys():
										__UpperCamelCase      :					List[str]       		=orig_state_dict.pop(a_					)

										if "mask" in key:
															continue
										elif "qkv" in key:
															__UpperCamelCase      :					Union[str, Any]       		=key.split('.'					)
															__UpperCamelCase      :					List[Any]       		=int(key_split[1]					)
															__UpperCamelCase      :					List[str]       		=int(key_split[3]					)
															__UpperCamelCase      :					List[str]       		=model.swin.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size

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

					return orig_state_dict






def 							A     (       a_							,a_					)     ->						int:
					__UpperCamelCase      :					Union[str, Any]       		=timm.create_model(a_							,pretrained=a_					)
					timm_model.eval()

					__UpperCamelCase      :					int       		=get_swin_config(a_					)
					__UpperCamelCase      :					List[str]       		=SwinForImageClassification(a_					)
					model.eval()

					__UpperCamelCase      :					Tuple       		=convert_state_dict(timm_model.state_dict()							,a_					)
					model.load_state_dict(a_					)

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

					__UpperCamelCase      :					List[str]       		=AutoImageProcessor.from_pretrained('microsoft/{}'.format(swin_name.replace('_'							,'-'					)					)					)
					__UpperCamelCase      :					Optional[int]       		=Image.open(requests.get(a_							,stream=a_					).raw					)
					__UpperCamelCase      :					Union[str, Any]       		=image_processor(images=a_							,return_tensors='pt'					)

					__UpperCamelCase      :					Optional[int]       		=timm_model(inputs['pixel_values']					)
					__UpperCamelCase      :					Tuple       		=model(**a_					).logits

					assert torch.allclose(a_							,a_							,atol=1e-3					)

					print(F'Saving model {swin_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 __name__ == "__main__":
							A_       :List[str]       =						argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    '''--swin_name''',
							    default='''swin_tiny_patch4_window7_224''',
							    type=str,
							    help='''Name of the Swin timm model you\'d like to convert.''',
							)
							parser.add_argument(
							    '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model directory.'''
							)

							A_       :Any       =						parser.parse_args()
							convert_swin_checkpoint(args.swin_name, args.pytorch_dump_folder_path)


import gc
import threading
import time

import psutil
import torch




class    _UpperCamelCase      :


			"""simple docstring"""






			def __init__(   self						)     ->					str:

							'''simple docstring'''


							__lowercase 			=							psutil.Process()
							__lowercase 			=							False





			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					str:

							'''simple docstring'''


							__lowercase 			=							-1

							while True:
											__lowercase 			=							max(self.process.memory_info().rss      ,   self.cpu_memory_peak						)

											# can't sleep or will not catch the peak right (this comment is here on purpose)
											if not self.peak_monitoring:
															break





			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					int:

							'''simple docstring'''


							__lowercase 			=							True
							__lowercase 			=							threading.Thread(target=self.peak_monitor						)
							__lowercase 			=							True
							self.thread.start()





			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					str:

							'''simple docstring'''


							__lowercase 			=							False
							self.thread.join()
							return self.cpu_memory_peak


__a  :     List[str]       =  PeakCPUMemory()





def 				UpperCAmelCase						(    ):







				"""simple docstring"""



				__lowercase 			=							{'''time''': time.time()}

				gc.collect()
				torch.cuda.empty_cache()

				# CPU mem
				__lowercase 			=							psutil.Process().memory_info().rss
				cpu_peak_tracker.start()

				# GPU mem
				for i in range(torch.cuda.device_count()							):
								__lowercase 			=							torch.cuda.memory_allocated(lowercase							)
				torch.cuda.reset_peak_memory_stats()

				return measures





def 				UpperCAmelCase						(    lowercase							):







				"""simple docstring"""



				__lowercase 			=							{'''time''': time.time() - start_measures['''time''']}

				gc.collect()
				torch.cuda.empty_cache()

				# CPU mem
				__lowercase 			=							(psutil.Process().memory_info().rss - start_measures['''cpu''']) / 2**20
				__lowercase 			=							(cpu_peak_tracker.stop() - start_measures['''cpu''']) / 2**20

				# GPU mem
				for i in range(torch.cuda.device_count()							):
								__lowercase 			=							(torch.cuda.memory_allocated(lowercase							) - start_measures[str(lowercase							)]) / 2**20
								__lowercase 			=							(torch.cuda.max_memory_allocated(lowercase							) - start_measures[str(lowercase							)]) / 2**20

				return measures





def 				UpperCAmelCase						(    lowercase							,			lowercase							):







				"""simple docstring"""



				print(F"{description}:"							)
				print(F"- Time: {measures['time']:.2f}s"							)
				for i in range(torch.cuda.device_count()							):
								print(F"- GPU {i} allocated: {measures[str(lowercase							)]:.2f}MiB"							)
								__lowercase 			=							measures[F"{i}-peak"]
								print(F"- GPU {i} peak: {peak:.2f}MiB"							)
				print(F"- CPU RAM allocated: {measures['cpu']:.2f}MiB"							)
				print(F"- CPU RAM peak: {measures['cpu-peak']:.2f}MiB"							)