
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 						UpperCAmelCase			(	a_						)		->	Optional[Any]:







				"""simple docstring"""



				__A   			= SwinvaConfig()
				__A   			= swinva_name.split("_"						)

				__A   			= name_split[1]
				if "to" in name_split[3]:
								__A   			= int(name_split[3][-3:]						)
				else:
								__A   			= int(name_split[3]						)
				if "to" in name_split[2]:
								__A   			= int(name_split[2][-2:]						)
				else:
								__A   			= int(name_split[2][6:]						)

				if model_size == "tiny":
								__A   			= 9_6
								__A   			= (2, 2, 6, 2)
								__A   			= (3, 6, 1_2, 2_4)
				elif model_size == "small":
								__A   			= 9_6
								__A   			= (2, 2, 1_8, 2)
								__A   			= (3, 6, 1_2, 2_4)
				elif model_size == "base":
								__A   			= 1_2_8
								__A   			= (2, 2, 1_8, 2)
								__A   			= (4, 8, 1_6, 3_2)
				else:
								__A   			= 1_9_2
								__A   			= (2, 2, 1_8, 2)
								__A   			= (6, 1_2, 2_4, 4_8)

				if "to" in swinva_name:
								__A   			= (1_2, 1_2, 1_2, 6)

				if ("22k" in swinva_name) and ("to" not in swinva_name):
								__A   			= 2_1_8_4_1
								__A   			= "huggingface/label-files"
								__A   			= "imagenet-22k-id2label.json"
								__A   			= json.load(open(hf_hub_download(a_		,   a_		,   repo_type="dataset"						)		,   "r"						)						)
								__A   			= {int(a_						): v for k, v in idalabel.items()}
								__A   			= idalabel
								__A   			= {v: k for k, v in idalabel.items()}

				else:
								__A   			= 1_0_0_0
								__A   			= "huggingface/label-files"
								__A   			= "imagenet-1k-id2label.json"
								__A   			= json.load(open(hf_hub_download(a_		,   a_		,   repo_type="dataset"						)		,   "r"						)						)
								__A   			= {int(a_						): v for k, v in idalabel.items()}
								__A   			= idalabel
								__A   			= {v: k for k, v in idalabel.items()}

				__A   			= img_size
				__A   			= num_classes
				__A   			= embed_dim
				__A   			= depths
				__A   			= num_heads
				__A   			= window_size

				return config


def 						UpperCAmelCase			(	a_						)		->	Optional[Any]:







				"""simple docstring"""



				if "patch_embed.proj" in name:
								__A   			= name.replace("patch_embed.proj"		,   "embeddings.patch_embeddings.projection"						)
				if "patch_embed.norm" in name:
								__A   			= name.replace("patch_embed.norm"		,   "embeddings.norm"						)
				if "layers" in name:
								__A   			= "encoder." + name
				if "attn.proj" in name:
								__A   			= name.replace("attn.proj"		,   "attention.output.dense"						)
				if "attn" in name:
								__A   			= name.replace("attn"		,   "attention.self"						)
				if "norm1" in name:
								__A   			= name.replace("norm1"		,   "layernorm_before"						)
				if "norm2" in name:
								__A   			= name.replace("norm2"		,   "layernorm_after"						)
				if "mlp.fc1" in name:
								__A   			= name.replace("mlp.fc1"		,   "intermediate.dense"						)
				if "mlp.fc2" in name:
								__A   			= name.replace("mlp.fc2"		,   "output.dense"						)
				if "q_bias" in name:
								__A   			= name.replace("q_bias"		,   "query.bias"						)
				if "k_bias" in name:
								__A   			= name.replace("k_bias"		,   "key.bias"						)
				if "v_bias" in name:
								__A   			= name.replace("v_bias"		,   "value.bias"						)
				if "cpb_mlp" in name:
								__A   			= name.replace("cpb_mlp"		,   "continuous_position_bias_mlp"						)
				if name == "norm.weight":
								__A   			= "layernorm.weight"
				if name == "norm.bias":
								__A   			= "layernorm.bias"

				if "head" in name:
								__A   			= name.replace("head"		,   "classifier"						)
				else:
								__A   			= "swinv2." + name

				return name


def 						UpperCAmelCase			(	a_		,   a_						)		->	List[str]:







				"""simple docstring"""



				for key in orig_state_dict.copy().keys():
								__A   			= orig_state_dict.pop(a_						)

								if "mask" in key:
												continue
								elif "qkv" in key:
												__A   			= key.split("."						)
												__A   			= int(key_split[1]						)
												__A   			= int(key_split[3]						)
												__A   			= model.swinva.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size

												if "weight" in key:
																__A   			= val[:dim, :]
																__A   			= val[dim : dim * 2, :]
																__A   			= val[-dim:, :]
												else:
																__A   			= val[:dim]
																__A   			= val[
																    dim : dim * 2
																]
																__A   			= val[-dim:]
								else:
												__A   			= val

				return orig_state_dict


def 						UpperCAmelCase			(	a_		,   a_						)		->	Tuple:







				"""simple docstring"""



				__A   			= timm.create_model(a_		,   pretrained=a_						)
				timm_model.eval()

				__A   			= get_swinva_config(a_						)
				__A   			= SwinvaForImageClassification(a_						)
				model.eval()

				__A   			= convert_state_dict(timm_model.state_dict()		,   a_						)
				model.load_state_dict(a_						)

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

				__A   			= AutoImageProcessor.from_pretrained("microsoft/{}".format(swinva_name.replace("_"		,   "-"						)						)						)
				__A   			= Image.open(requests.get(a_		,   stream=a_						).raw						)
				__A   			= image_processor(images=a_		,   return_tensors="pt"						)

				__A   			= timm_model(inputs["pixel_values"]						)
				__A   			= model(**a_						).logits

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

				print(F'''Saving model {swinva_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_						)

				model.push_to_hub(
				    repo_path_or_name=Path(a_		,   a_						)		,   organization="nandwalritik"		,   commit_message="Add model"		,   )


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE							:Dict							 =				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.'
					)

					SCREAMING_SNAKE_CASE							:List[Any]							 =				parser.parse_args()
					convert_swinva_checkpoint(args.swinva_name, args.pytorch_dump_folder_path)




from ..utils import DummyObject, requires_backends


class        _a							(			metaclass=UpperCamelCase__			):
							_lowercase	:			Any        = ['''torch''', '''scipy''']

							def __init__(							self:	int    ,						*UpperCamelCase_:	Any    ,						**UpperCamelCase_:	Optional[Any]							)			->   List[str]:





									"""simple docstring"""







									requires_backends(self    ,						['''torch''', '''scipy''']							)

							@classmethod
							def       lowerCamelCase_ (							cls:	Optional[int]    ,						*UpperCamelCase_:	Any    ,						**UpperCamelCase_:	List[Any]							)			->   int:





									"""simple docstring"""







									requires_backends(cls    ,						['''torch''', '''scipy''']							)


							@classmethod
							def       lowerCamelCase_ (							cls:	Any    ,						*UpperCamelCase_:	Any    ,						**UpperCamelCase_:	Dict							)			->   Optional[int]:





									"""simple docstring"""







									requires_backends(cls    ,						['''torch''', '''scipy''']							)


