

'''simple docstring'''





import argparse
import json
from pathlib import Path

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

from transformers import AutoImageProcessor, SwinvaConfig, SwinvaForImageClassification






def 				_lowerCAmelCase			(   __magic_name__						:     Optional[int]						)				->  Dict:
						lowercase :							Dict    		=SwinvaConfig()
						lowercase :							str    		=swinva_name.split('''_'''						)

						lowercase :							Dict    		=name_split[1]
						if "to" in name_split[3]:
												lowercase :							Optional[Any]    		=int(name_split[3][-3:]						)
						else:
												lowercase :							Tuple    		=int(name_split[3]						)
						if "to" in name_split[2]:
												lowercase :							Optional[int]    		=int(name_split[2][-2:]						)
						else:
												lowercase :							Union[str, Any]    		=int(name_split[2][6:]						)

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

						if "to" in swinva_name:
												lowercase :							Any    		=(12, 12, 12, 6)

						if ("22k" in swinva_name) and ("to" not in swinva_name):
												lowercase :							Optional[int]    		=21841
												lowercase :							List[str]    		='''huggingface/label-files'''
												lowercase :							int    		='''imagenet-22k-id2label.json'''
												lowercase :							int    		=json.load(open(hf_hub_download(__magic_name__   , __magic_name__   , repo_type='''dataset'''						)   , '''r'''						)						)
												lowercase :							Any    		={int(__magic_name__						): v for k, v in idalabel.items()}
												lowercase :							Dict    		=idalabel
												lowercase :							Dict    		={v: k for k, v in idalabel.items()}

						else:
												lowercase :							Dict    		=1000
												lowercase :							Optional[Any]    		='''huggingface/label-files'''
												lowercase :							Optional[Any]    		='''imagenet-1k-id2label.json'''
												lowercase :							str    		=json.load(open(hf_hub_download(__magic_name__   , __magic_name__   , repo_type='''dataset'''						)   , '''r'''						)						)
												lowercase :							int    		={int(__magic_name__						): v for k, v in idalabel.items()}
												lowercase :							Any    		=idalabel
												lowercase :							Dict    		={v: k for k, v in idalabel.items()}

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

						return config






def 				_lowerCAmelCase			(   __magic_name__						:     List[Any]						)				->  List[Any]:
						if "patch_embed.proj" in name:
												lowercase :							List[Any]    		=name.replace('''patch_embed.proj'''   , '''embeddings.patch_embeddings.projection'''						)
						if "patch_embed.norm" in name:
												lowercase :							Union[str, Any]    		=name.replace('''patch_embed.norm'''   , '''embeddings.norm'''						)
						if "layers" in name:
												lowercase :							Optional[Any]    		='''encoder.''' + name
						if "attn.proj" in name:
												lowercase :							List[str]    		=name.replace('''attn.proj'''   , '''attention.output.dense'''						)
						if "attn" in name:
												lowercase :							Optional[Any]    		=name.replace('''attn'''   , '''attention.self'''						)
						if "norm1" in name:
												lowercase :							Any    		=name.replace('''norm1'''   , '''layernorm_before'''						)
						if "norm2" in name:
												lowercase :							str    		=name.replace('''norm2'''   , '''layernorm_after'''						)
						if "mlp.fc1" in name:
												lowercase :							Any    		=name.replace('''mlp.fc1'''   , '''intermediate.dense'''						)
						if "mlp.fc2" in name:
												lowercase :							int    		=name.replace('''mlp.fc2'''   , '''output.dense'''						)
						if "q_bias" in name:
												lowercase :							Union[str, Any]    		=name.replace('''q_bias'''   , '''query.bias'''						)
						if "k_bias" in name:
												lowercase :							Optional[int]    		=name.replace('''k_bias'''   , '''key.bias'''						)
						if "v_bias" in name:
												lowercase :							Optional[int]    		=name.replace('''v_bias'''   , '''value.bias'''						)
						if "cpb_mlp" in name:
												lowercase :							Any    		=name.replace('''cpb_mlp'''   , '''continuous_position_bias_mlp'''						)
						if name == "norm.weight":
												lowercase :							Tuple    		='''layernorm.weight'''
						if name == "norm.bias":
												lowercase :							int    		='''layernorm.bias'''

						if "head" in name:
												lowercase :							Any    		=name.replace('''head'''   , '''classifier'''						)
						else:
												lowercase :							int    		='''swinv2.''' + name

						return name






def 				_lowerCAmelCase			(   __magic_name__						:     Union[str, Any]   , __magic_name__						:     Optional[int]						)				->  Union[str, Any]:
						for key in orig_state_dict.copy().keys():
												lowercase :							int    		=orig_state_dict.pop(__magic_name__						)

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

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

						return orig_state_dict






def 				_lowerCAmelCase			(   __magic_name__						:     str   , __magic_name__						:     str						)				->  List[str]:
						lowercase :							Tuple    		=timm.create_model(__magic_name__   , pretrained=__magic_name__						)
						timm_model.eval()

						lowercase :							int    		=get_swinva_config(__magic_name__						)
						lowercase :							List[Any]    		=SwinvaForImageClassification(__magic_name__						)
						model.eval()

						lowercase :							int    		=convert_state_dict(timm_model.state_dict()   , __magic_name__						)
						model.load_state_dict(__magic_name__						)

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

						lowercase :							Optional[int]    		=AutoImageProcessor.from_pretrained('''microsoft/{}'''.format(swinva_name.replace('''_'''   , '''-'''						)						)						)
						lowercase :							Tuple    		=Image.open(requests.get(__magic_name__   , stream=__magic_name__						).raw						)
						lowercase :							str    		=image_processor(images=__magic_name__   , return_tensors='''pt'''						)

						lowercase :							Any    		=timm_model(inputs['''pixel_values''']						)
						lowercase :							Optional[int]    		=model(**__magic_name__						).logits

						assert torch.allclose(__magic_name__   , __magic_name__   , atol=1E-3						)

						print(f'''Saving model {swinva_name} to {pytorch_dump_folder_path}'''						)
						model.save_pretrained(__magic_name__						)

						print(f'''Saving image processor to {pytorch_dump_folder_path}'''						)
						image_processor.save_pretrained(__magic_name__						)

						model.push_to_hub(
						    repo_path_or_name=Path(__magic_name__   , __magic_name__						)   , organization='''nandwalritik'''   , commit_message='''Add model'''   , )


if __name__ == "__main__":
							UpperCamelCase_    						=       argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    """--swinv2_name""",
							    default="""swinv2_tiny_patch4_window8_256""",
							    type=str,
							    help="""Name of the Swinv2 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."""
							)

							UpperCamelCase_    						=       parser.parse_args()
							convert_swinva_checkpoint(args.swinva_name, args.pytorch_dump_folder_path)









def 			lowerCAmelCase__					(     a__:						list			)    ->						list:


							'''simple docstring'''





							if len(a__			) < 2:
														return collection

							def circle_sort_util(a__:						list     ,    a__:						int     ,    a__:						int			) -> bool:
														_UpperCAmelCase											=     False

														if low == high:
																					return swapped

														_UpperCAmelCase											=     low
														_UpperCAmelCase											=     high

														while left < right:
																					if collection[left] > collection[right]:
																												_UpperCAmelCase						,			_UpperCAmelCase											=     (
																												    collection[right],
																												    collection[left],
																												)
																												_UpperCAmelCase											=     True

																					left += 1
																					right -= 1

														if left == right and collection[left] > collection[right + 1]:
																					_UpperCAmelCase						,			_UpperCAmelCase											=     (
																					    collection[right + 1],
																					    collection[left],
																					)

																					_UpperCAmelCase											=     True

														_UpperCAmelCase											=     low + int((high - low) / 2			)
														_UpperCAmelCase											=     circle_sort_util(a__     ,    a__     ,    a__			)
														_UpperCAmelCase											=     circle_sort_util(a__     ,    mid + 1     ,    a__			)

														return swapped or left_swap or right_swap

							_UpperCAmelCase											=     True

							while is_not_sorted is True:
														_UpperCAmelCase											=     circle_sort_util(a__     ,    0     ,    len(a__			) - 1			)

							return collection


if __name__ == "__main__":
		lowerCAmelCase__    :Tuple       =				input('''Enter numbers separated by a comma:\n''').strip()
		lowerCAmelCase__    :List[str]       =				[int(item) for item in user_input.split(''',''')]
		print(circle_sort(unsorted))
