



"""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      __UpperCAmelCase      (    __lowerCamelCase	)							->      List[str]:
		lowercase__   :    int				=			SwinvaConfig()
		lowercase__   :    Optional[Any]				=			swinva_name.split('''_'''	)

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

		if model_size == "tiny":
				lowercase__   :    Optional[Any]				=			96
				lowercase__   :    Optional[int]				=			(2, 2, 6, 2)
				lowercase__   :    Union[str, Any]				=			(3, 6, 12, 24)
		elif model_size == "small":
				lowercase__   :    List[str]				=			96
				lowercase__   :    Any				=			(2, 2, 18, 2)
				lowercase__   :    List[Any]				=			(3, 6, 12, 24)
		elif model_size == "base":
				lowercase__   :    Optional[Any]				=			1_28
				lowercase__   :    Dict				=			(2, 2, 18, 2)
				lowercase__   :    List[Any]				=			(4, 8, 16, 32)
		else:
				lowercase__   :    Optional[Any]				=			1_92
				lowercase__   :    Optional[Any]				=			(2, 2, 18, 2)
				lowercase__   :    Any				=			(6, 12, 24, 48)

		if "to" in swinva_name:
				lowercase__   :    List[str]				=			(12, 12, 12, 6)

		if ("22k" in swinva_name) and ("to" not in swinva_name):
				lowercase__   :    Tuple				=			2_18_41
				lowercase__   :    Any				=			'''huggingface/label-files'''
				lowercase__   :    str				=			'''imagenet-22k-id2label.json'''
				lowercase__   :    Union[str, Any]				=			json.load(open(hf_hub_download(__lowerCamelCase						,     __lowerCamelCase						,     repo_type='''dataset'''	)						,     '''r'''	)	)
				lowercase__   :    Tuple				=			{int(__lowerCamelCase	): v for k, v in idalabel.items()}
				lowercase__   :    Union[str, Any]				=			idalabel
				lowercase__   :    Tuple				=			{v: k for k, v in idalabel.items()}

		else:
				lowercase__   :    str				=			10_00
				lowercase__   :    Union[str, Any]				=			'''huggingface/label-files'''
				lowercase__   :    Dict				=			'''imagenet-1k-id2label.json'''
				lowercase__   :    Optional[int]				=			json.load(open(hf_hub_download(__lowerCamelCase						,     __lowerCamelCase						,     repo_type='''dataset'''	)						,     '''r'''	)	)
				lowercase__   :    Any				=			{int(__lowerCamelCase	): v for k, v in idalabel.items()}
				lowercase__   :    List[str]				=			idalabel
				lowercase__   :    Optional[Any]				=			{v: k for k, v in idalabel.items()}

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

		return config



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

		if "head" in name:
				lowercase__   :    Optional[Any]				=			name.replace('''head'''						,     '''classifier'''	)
		else:
				lowercase__   :    List[str]				=			'''swinv2.''' + name

		return name



def      __UpperCAmelCase      (    __lowerCamelCase						,     __lowerCamelCase	)							->      Union[str, Any]:
		for key in orig_state_dict.copy().keys():
				lowercase__   :    str				=			orig_state_dict.pop(__lowerCamelCase	)

				if "mask" in key:
						continue
				elif "qkv" in key:
						lowercase__   :    str				=			key.split('''.'''	)
						lowercase__   :    Tuple				=			int(key_split[1]	)
						lowercase__   :    int				=			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__   :    Tuple				=			val[:dim, :]
								lowercase__   :    Any				=			val[dim : dim * 2, :]
								lowercase__   :    str				=			val[-dim:, :]
						else:
								lowercase__   :    str				=			val[:dim]
								lowercase__   :    int				=			val[
								    dim : dim * 2
								]
								lowercase__   :    Optional[int]				=			val[-dim:]
				else:
						lowercase__   :    int				=			val

		return orig_state_dict



def      __UpperCAmelCase      (    __lowerCamelCase						,     __lowerCamelCase	)							->      Optional[Any]:
		lowercase__   :    Dict				=			timm.create_model(__lowerCamelCase						,     pretrained=__lowerCamelCase	)
		timm_model.eval()

		lowercase__   :    Union[str, Any]				=			get_swinva_config(__lowerCamelCase	)
		lowercase__   :    Tuple				=			SwinvaForImageClassification(__lowerCamelCase	)
		model.eval()

		lowercase__   :    str				=			convert_state_dict(timm_model.state_dict()						,     __lowerCamelCase	)
		model.load_state_dict(__lowerCamelCase	)

		lowercase__   :    Optional[Any]				=			'''http://images.cocodataset.org/val2017/000000039769.jpg'''

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

		lowercase__   :    str				=			timm_model(inputs['''pixel_values''']	)
		lowercase__   :    List[str]				=			model(**__lowerCamelCase	).logits

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

		print(f"""Saving model {swinva_name} to {pytorch_dump_folder_path}"""	)
		model.save_pretrained(__lowerCamelCase	)

		print(f"""Saving image processor to {pytorch_dump_folder_path}"""	)
		image_processor.save_pretrained(__lowerCamelCase	)

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


if __name__ == "__main__":
							lowerCAmelCase_   						=   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.'
							)

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





"""simple docstring"""







import unittest

from accelerate import debug_launcher
from accelerate.test_utils import require_cpu, test_ops, test_script
@require_cpu
class        __A       (	unittest.TestCase    ):







		'''simple docstring'''





		def 					UpperCAmelCase    (	self						:  Optional[int]						)					->		str:






				"""simple docstring"""



				debug_launcher(test_script.main						)







		def 					UpperCAmelCase    (	self						:  Dict						)					->		Tuple:






				"""simple docstring"""



				debug_launcher(test_ops.main						)
