


"""simple docstring"""






import argparse
import json
import os
from pathlib import Path

import requests
import torch

from transformers import JukeboxConfig, JukeboxModel
from transformers.utils import logging


logging.set_verbosity_info()
UpperCAmelCase    :     Optional[int]   						=		logging.get_logger(__name__)


UpperCAmelCase    :     str   						=		'https://openaipublic.azureedge.net/jukebox/models/'
UpperCAmelCase    :     Tuple   						=		{
    'jukebox-1b-lyrics': [
        '5b/vqvae.pth.tar',
        '5b/prior_level_0.pth.tar',
        '5b/prior_level_1.pth.tar',
        '1b_lyrics/prior_level_2.pth.tar',
    ],
    'jukebox-5b-lyrics': [
        '5b/vqvae.pth.tar',
        '5b/prior_level_0.pth.tar',
        '5b/prior_level_1.pth.tar',
        '5b_lyrics/prior_level_2.pth.tar',
    ],
}
def      lowerCamelCase       (     _UpperCamelCase		:      List[Any]							)		->       str:

							'''simple docstring'''



							if key.endswith(""".model.1.bias"""							) and len(key.split("""."""							)							) > 1_0:
														__UpperCAmelCase   :						Optional[Any]      							=					key.replace(""".model.1.bias"""       ,			""".conv1d_1.bias"""							)
							elif key.endswith(""".model.1.weight"""							) and len(key.split("""."""							)							) > 1_0:
														__UpperCAmelCase   :						Optional[Any]      							=					key.replace(""".model.1.weight"""       ,			""".conv1d_1.weight"""							)
							elif key.endswith(""".model.3.bias"""							) and len(key.split("""."""							)							) > 1_0:
														__UpperCAmelCase   :						Any      							=					key.replace(""".model.3.bias"""       ,			""".conv1d_2.bias"""							)
							elif key.endswith(""".model.3.weight"""							) and len(key.split("""."""							)							) > 1_0:
														__UpperCAmelCase   :						str      							=					key.replace(""".model.3.weight"""       ,			""".conv1d_2.weight"""							)

							if "conditioner_blocks.0." in key:
														__UpperCAmelCase   :						Optional[Any]      							=					key.replace("""conditioner_blocks.0"""       ,			"""conditioner_blocks"""							)

							if "prime_prior" in key:
														__UpperCAmelCase   :						int      							=					key.replace("""prime_prior"""       ,			"""encoder"""							)

							if ".emb." in key and "total" not in key and "absolute" not in key and "relative" not in key:
														__UpperCAmelCase   :						Dict      							=					key.replace(""".emb."""       ,			"""."""							)

							if key.endswith("""k"""							):  # replace vqvae.X.k with vqvae.X.codebook
														return key.replace(""".k"""       ,			""".codebook"""							)
							if "y_emb." in key:
														return key.replace("""y_emb."""       ,			"""metadata_embedding."""							)

							if "x_emb.emb." in key:
														__UpperCAmelCase   :						Optional[Any]      							=					key.replace("""0.x_emb.emb"""       ,			"""embed_tokens"""							)

							if "prime_state_ln" in key:
														return key.replace("""prime_state_ln"""       ,			"""encoder.final_layer_norm"""							)
							if ".ln" in key:
														return key.replace(""".ln"""       ,			""".layer_norm"""							)
							if "_ln" in key:
														return key.replace("""_ln"""       ,			"""_layer_norm"""							)

							if "prime_state_proj" in key:
														return key.replace("""prime_state_proj"""       ,			"""encoder.proj_in"""							)
							if "prime_x_out" in key:
														return key.replace("""prime_x_out"""       ,			"""encoder.lm_head"""							)
							if "prior.x_out" in key:
														return key.replace("""x_out"""       ,			"""fc_proj_out"""							)
							if "x_emb" in key:
														return key.replace("""x_emb"""       ,			"""embed_tokens"""							)

							return key
def      lowerCamelCase       (     _UpperCamelCase		:      Any       ,			_UpperCamelCase		:      str       ,			_UpperCamelCase		:      Dict       ,			_UpperCamelCase		:      int							)		->       Optional[int]:

							'''simple docstring'''



							__UpperCAmelCase   :						Optional[Any]      							=					{}
							import re

							__UpperCAmelCase   :						Optional[Any]      							=					re.compile(R"""encoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).(bias|weight)"""							)
							__UpperCAmelCase   :						Optional[int]      							=					re.compile(
							    R"""encoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)"""							)
							__UpperCAmelCase   :						Union[str, Any]      							=					re.compile(R"""encoders.(\d*).level_blocks.(\d*).model.(\d*).(bias|weight)"""							)

							__UpperCAmelCase   :						Any      							=					re.compile(R"""decoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).(bias|weight)"""							)
							__UpperCAmelCase   :						Dict      							=					re.compile(
							    R"""decoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)"""							)
							__UpperCAmelCase   :						str      							=					re.compile(R"""decoders.(\d*).level_blocks.(\d*).model.(\d*).(bias|weight)"""							)

							__UpperCAmelCase   :						Union[str, Any]      							=					re.compile(R"""conditioner_blocks.(\d*).cond.model.(\d*).(\d).(bias|weight)"""							)
							__UpperCAmelCase   :						Dict      							=					re.compile(
							    R"""conditioner_blocks.(\d*).cond.model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)"""							)
							__UpperCAmelCase   :						Dict      							=					re.compile(R"""conditioner_blocks.(\d*).cond.model.(\d*).(bias|weight)"""							)

							for original_key, value in state_dict.items():
														# rename vqvae.encoder keys
														if re_encoder_block_conv_in.fullmatch(_UpperCamelCase							):
																					__UpperCAmelCase   :						Optional[Any]      							=					re_encoder_block_conv_in.match(_UpperCamelCase							)
																					__UpperCAmelCase   :						int      							=					regex_match.groups()
																					__UpperCAmelCase   :						List[str]      							=					int(groups[2]							) * 2 + int(groups[3]							)
																					__UpperCAmelCase   :						Union[str, Any]      							=					f'''encoders.{groups[0]}.level_blocks.{groups[1]}.downsample_block.{block_index}.{groups[-1]}'''
																					__UpperCAmelCase   :						Union[str, Any]      							=					re_encoder_block_conv_in.sub(_UpperCamelCase       ,			_UpperCamelCase							)

														elif re_encoder_block_resnet.fullmatch(_UpperCamelCase							):
																					__UpperCAmelCase   :						List[Any]      							=					re_encoder_block_resnet.match(_UpperCamelCase							)
																					__UpperCAmelCase   :						int      							=					regex_match.groups()
																					__UpperCAmelCase   :						Optional[int]      							=					int(groups[2]							) * 2 + int(groups[3]							)
																					__UpperCAmelCase   :						int      							=					{"""1""": 1, """3""": 2}[groups[-2]]
																					__UpperCAmelCase   :						Optional[int]      							=					f'''encoders.{groups[0]}.level_blocks.{groups[1]}.downsample_block.{block_index}.'''
																					__UpperCAmelCase   :						int      							=					f'''resnet_block.{groups[-3]}.conv1d_{conv_index}.{groups[-1]}'''
																					__UpperCAmelCase   :						Any      							=					prefix + resnet_block
																					__UpperCAmelCase   :						str      							=					re_encoder_block_resnet.sub(_UpperCamelCase       ,			_UpperCamelCase							)

														elif re_encoder_block_proj_out.fullmatch(_UpperCamelCase							):
																					__UpperCAmelCase   :						Tuple      							=					re_encoder_block_proj_out.match(_UpperCamelCase							)
																					__UpperCAmelCase   :						Optional[int]      							=					regex_match.groups()
																					__UpperCAmelCase   :						List[Any]      							=					f'''encoders.{groups[0]}.level_blocks.{groups[1]}.proj_out.{groups[-1]}'''
																					__UpperCAmelCase   :						Dict      							=					re_encoder_block_proj_out.sub(_UpperCamelCase       ,			_UpperCamelCase							)

														# rename vqvae.decoder keys
														elif re_decoder_block_conv_out.fullmatch(_UpperCamelCase							):
																					__UpperCAmelCase   :						Union[str, Any]      							=					re_decoder_block_conv_out.match(_UpperCamelCase							)
																					__UpperCAmelCase   :						List[str]      							=					regex_match.groups()
																					__UpperCAmelCase   :						Union[str, Any]      							=					int(groups[2]							) * 2 + int(groups[3]							) - 2
																					__UpperCAmelCase   :						Dict      							=					f'''decoders.{groups[0]}.level_blocks.{groups[1]}.upsample_block.{block_index}.{groups[-1]}'''
																					__UpperCAmelCase   :						Optional[int]      							=					re_decoder_block_conv_out.sub(_UpperCamelCase       ,			_UpperCamelCase							)

														elif re_decoder_block_resnet.fullmatch(_UpperCamelCase							):
																					__UpperCAmelCase   :						str      							=					re_decoder_block_resnet.match(_UpperCamelCase							)
																					__UpperCAmelCase   :						List[str]      							=					regex_match.groups()
																					__UpperCAmelCase   :						List[Any]      							=					int(groups[2]							) * 2 + int(groups[3]							) - 2
																					__UpperCAmelCase   :						Optional[int]      							=					{"""1""": 1, """3""": 2}[groups[-2]]
																					__UpperCAmelCase   :						int      							=					f'''decoders.{groups[0]}.level_blocks.{groups[1]}.upsample_block.{block_index}.'''
																					__UpperCAmelCase   :						List[Any]      							=					f'''resnet_block.{groups[-3]}.conv1d_{conv_index}.{groups[-1]}'''
																					__UpperCAmelCase   :						Tuple      							=					prefix + resnet_block
																					__UpperCAmelCase   :						Dict      							=					re_decoder_block_resnet.sub(_UpperCamelCase       ,			_UpperCamelCase							)

														elif re_decoder_block_proj_in.fullmatch(_UpperCamelCase							):
																					__UpperCAmelCase   :						Optional[Any]      							=					re_decoder_block_proj_in.match(_UpperCamelCase							)
																					__UpperCAmelCase   :						List[Any]      							=					regex_match.groups()
																					__UpperCAmelCase   :						Optional[int]      							=					f'''decoders.{groups[0]}.level_blocks.{groups[1]}.proj_in.{groups[-1]}'''
																					__UpperCAmelCase   :						Dict      							=					re_decoder_block_proj_in.sub(_UpperCamelCase       ,			_UpperCamelCase							)

														# rename prior cond.model to upsampler.upsample_block and resnet
														elif re_prior_cond_conv_out.fullmatch(_UpperCamelCase							):
																					__UpperCAmelCase   :						List[str]      							=					re_prior_cond_conv_out.match(_UpperCamelCase							)
																					__UpperCAmelCase   :						Union[str, Any]      							=					regex_match.groups()
																					__UpperCAmelCase   :						Dict      							=					int(groups[1]							) * 2 + int(groups[2]							) - 2
																					__UpperCAmelCase   :						List[str]      							=					f'''conditioner_blocks.upsampler.upsample_block.{block_index}.{groups[-1]}'''
																					__UpperCAmelCase   :						Tuple      							=					re_prior_cond_conv_out.sub(_UpperCamelCase       ,			_UpperCamelCase							)

														elif re_prior_cond_resnet.fullmatch(_UpperCamelCase							):
																					__UpperCAmelCase   :						Optional[int]      							=					re_prior_cond_resnet.match(_UpperCamelCase							)
																					__UpperCAmelCase   :						Optional[int]      							=					regex_match.groups()
																					__UpperCAmelCase   :						List[str]      							=					int(groups[1]							) * 2 + int(groups[2]							) - 2
																					__UpperCAmelCase   :						List[str]      							=					{"""1""": 1, """3""": 2}[groups[-2]]
																					__UpperCAmelCase   :						Dict      							=					f'''conditioner_blocks.upsampler.upsample_block.{block_index}.'''
																					__UpperCAmelCase   :						Optional[Any]      							=					f'''resnet_block.{groups[-3]}.conv1d_{conv_index}.{groups[-1]}'''
																					__UpperCAmelCase   :						List[Any]      							=					prefix + resnet_block
																					__UpperCAmelCase   :						str      							=					re_prior_cond_resnet.sub(_UpperCamelCase       ,			_UpperCamelCase							)

														elif re_prior_cond_proj_in.fullmatch(_UpperCamelCase							):
																					__UpperCAmelCase   :						Optional[int]      							=					re_prior_cond_proj_in.match(_UpperCamelCase							)
																					__UpperCAmelCase   :						Dict      							=					regex_match.groups()
																					__UpperCAmelCase   :						Union[str, Any]      							=					f'''conditioner_blocks.upsampler.proj_in.{groups[-1]}'''
																					__UpperCAmelCase   :						Union[str, Any]      							=					re_prior_cond_proj_in.sub(_UpperCamelCase       ,			_UpperCamelCase							)

														# keep original key
														else:
																					__UpperCAmelCase   :						Dict      							=					original_key

														__UpperCAmelCase   :						Union[str, Any]      							=					replace_key(_UpperCamelCase							)

														if f'''{key_prefix}.{key}''' not in model_state_dict or key is None:
																					print(f'''failed converting {original_key} to {key}, does not match'''							)

														# handle missmatched shape
														elif value.shape != model_state_dict[f'''{key_prefix}.{key}'''].shape:
																					__UpperCAmelCase   :						Tuple      							=					model_state_dict[f'''{key_prefix}.{key}''']
																					print(f'''{original_key}-> {key} : \nshape {val.shape} and { value.shape}, do not match'''							)
																					__UpperCAmelCase   :						Optional[int]      							=					original_key

														__UpperCAmelCase   :						List[str]      							=					original_key
														__UpperCAmelCase   :						Dict      							=					value

							return new_dict
@torch.no_grad()
def      lowerCamelCase       (     _UpperCamelCase		:      Optional[int]=None       ,			_UpperCamelCase		:      Optional[Any]=None							)		->       List[Any]:

							'''simple docstring'''



							for file in MODEL_MAPPING[model_name]:
														if not os.path.isfile(f'''{pytorch_dump_folder_path}/{file.split("/"							)[-1]}'''							):
																					__UpperCAmelCase   :						List[str]      							=					requests.get(f'''{PREFIX}{file}'''       ,			allow_redirects=_UpperCamelCase							)
																					os.makedirs(f'''{pytorch_dump_folder_path}/'''       ,			exist_ok=_UpperCamelCase							)
																					open(f'''{pytorch_dump_folder_path}/{file.split("/"							)[-1]}'''       ,			"""wb"""							).write(r.content							)

							__UpperCAmelCase   :						Optional[Any]      							=					MODEL_MAPPING[model_name.split("""/"""							)[-1]]

							__UpperCAmelCase   :						Dict      							=					JukeboxConfig.from_pretrained(_UpperCamelCase							)
							__UpperCAmelCase   :						List[Any]      							=					JukeboxModel(_UpperCamelCase							)

							__UpperCAmelCase   :						int      							=					[]
							__UpperCAmelCase   :						Any      							=					{}
							for i, dict_name in enumerate(_UpperCamelCase							):
														__UpperCAmelCase   :						int      							=					torch.load(f'''{pytorch_dump_folder_path}/{dict_name.split("/"							)[-1]}'''							)["""model"""]

														__UpperCAmelCase   :						Dict      							=					{}
														for k in old_dic.keys():
																					if k.endswith(""".b"""							):
																												__UpperCAmelCase   :						Union[str, Any]      							=					old_dic[k]
																					elif k.endswith(""".w"""							):
																												__UpperCAmelCase   :						Any      							=					old_dic[k]
																					elif "level_2" not in dict_name and "cond.model." in k:
																												__UpperCAmelCase   :						List[str]      							=					old_dic[k]
																					else:
																												__UpperCAmelCase   :						Any      							=					old_dic[k]

														__UpperCAmelCase   :						Optional[int]      							=					"""vqvae""" if i == 0 else f'''priors.{3 - i}'''
														__UpperCAmelCase   :						Tuple      							=					fix_jukebox_keys(_UpperCamelCase       ,			model.state_dict()       ,			_UpperCamelCase       ,			_UpperCamelCase							)
														weight_dict.append(_UpperCamelCase							)

							__UpperCAmelCase   :						Dict      							=					weight_dict.pop(0							)
							model.vqvae.load_state_dict(_UpperCamelCase							)
							for i in range(len(_UpperCamelCase							)							):
														model.priors[i].load_state_dict(weight_dict[2 - i]							)

							Path(_UpperCamelCase							).mkdir(exist_ok=_UpperCamelCase							)
							with open(f'''{pytorch_dump_folder_path}/mapping.json'''       ,			"""w"""							) as txtfile:
														json.dump(_UpperCamelCase       ,			_UpperCamelCase							)

							print(f'''Saving model {model_name} to {pytorch_dump_folder_path}'''							)
							model.save_pretrained(_UpperCamelCase							)

							return weight_dict


if __name__ == "__main__":
							UpperCAmelCase    :     Tuple   						=		argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    '--model_name',
							    default='jukebox-5b-lyrics',
							    type=str,
							    help='Name of the model you\'d like to convert.',
							)
							parser.add_argument(
							    '--pytorch_dump_folder_path',
							    default='jukebox-5b-lyrics-converted',
							    type=str,
							    help='Path to the output PyTorch model directory.',
							)
							UpperCAmelCase    :     Optional[int]   						=		parser.parse_args()
							convert_openai_checkpoint(args.model_name, args.pytorch_dump_folder_path)




"""simple docstring"""






import argparse
import json
from pathlib import Path

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

from transformers import (
    SwiftFormerConfig,
    SwiftFormerForImageClassification,
    ViTImageProcessor,
)
from transformers.utils import logging


logging.set_verbosity_info()
UpperCAmelCase    :     Dict   						=		logging.get_logger(__name__)

UpperCAmelCase    :     Union[str, Any]   						=		torch.device('cpu')
def      lowerCamelCase       (     )		->       str:

							'''simple docstring'''



							__UpperCAmelCase   :						Optional[int]      							=					"""http://images.cocodataset.org/val2017/000000039769.jpg"""
							__UpperCAmelCase   :						Optional[Any]      							=					Image.open(requests.get(_UpperCamelCase       ,			stream=_UpperCamelCase							).raw							)
							return im
def      lowerCamelCase       (     _UpperCamelCase		:      Union[str, Any]							)		->       Union[str, Any]:

							'''simple docstring'''



							if swiftformer_name == "swiftformer_xs":
														return torch.tensor([-2.1_703E00, 2.1_107E00, -2.0_811E00, 8.8_685E-01, 2.4_360E-01]							)

							elif swiftformer_name == "swiftformer_s":
														return torch.tensor([3.9_636E-01, 2.3_478E-01, -1.6_963E00, -1.7_381E00, -8.6_337E-01]							)

							elif swiftformer_name == "swiftformer_l1":
														return torch.tensor([-4.2_768E-01, -4.7_429E-01, -1.0_897E00, -1.0_248E00, 3.5_523E-02]							)

							elif swiftformer_name == "swiftformer_l3":
														return torch.tensor([-2.5_330E-01, 2.4_211E-01, -6.0_185E-01, -8.2_789E-01, -6.0_446E-02]							)
def      lowerCamelCase       (     _UpperCamelCase		:      Union[str, Any]       ,			_UpperCamelCase		:      Optional[int]       ,			_UpperCamelCase		:      List[Any]							)		->       Union[str, Any]:

							'''simple docstring'''



							__UpperCAmelCase   :						Any      							=					dct.pop(_UpperCamelCase							)
							__UpperCAmelCase   :						List[str]      							=					val
def      lowerCamelCase       (     _UpperCamelCase		:      List[Any]							)		->       List[Any]:

							'''simple docstring'''



							__UpperCAmelCase   :						Tuple      							=					[]
							for k in state_dict.keys():
														__UpperCAmelCase   :						int      							=					k
														if ".pwconv" in k:
																					__UpperCAmelCase   :						Optional[int]      							=					k_new.replace(""".pwconv"""       ,			""".point_wise_conv"""							)
														if ".dwconv" in k:
																					__UpperCAmelCase   :						List[Any]      							=					k_new.replace(""".dwconv"""       ,			""".depth_wise_conv"""							)
														if ".Proj." in k:
																					__UpperCAmelCase   :						str      							=					k_new.replace(""".Proj."""       ,			""".proj."""							)
														if "patch_embed" in k_new:
																					__UpperCAmelCase   :						int      							=					k_new.replace("""patch_embed"""       ,			"""swiftformer.patch_embed.patch_embedding"""							)
														if "network" in k_new:
																					__UpperCAmelCase   :						str      							=					k_new.split("""."""							)
																					if ls[2].isdigit():
																												__UpperCAmelCase   :						Dict      							=					"""swiftformer.encoder.network.""" + ls[1] + """.blocks.""" + ls[2] + """.""" + """.""".join(ls[3:]							)
																					else:
																												__UpperCAmelCase   :						List[str]      							=					k_new.replace("""network"""       ,			"""swiftformer.encoder.network"""							)
														rename_keys.append((k, k_new)							)
							return rename_keys
@torch.no_grad()
def      lowerCamelCase       (     _UpperCamelCase		:      int       ,			_UpperCamelCase		:      List[Any]       ,			_UpperCamelCase		:      int							)		->       Union[str, Any]:

							'''simple docstring'''



							__UpperCAmelCase   :						Optional[int]      							=					SwiftFormerConfig()

							# dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
							__UpperCAmelCase   :						Optional[int]      							=					1_0_0_0
							__UpperCAmelCase   :						Optional[int]      							=					"""huggingface/label-files"""
							__UpperCAmelCase   :						str      							=					"""imagenet-1k-id2label.json"""
							__UpperCAmelCase   :						List[Any]      							=					json.load(open(hf_hub_download(_UpperCamelCase       ,			_UpperCamelCase       ,			repo_type="""dataset"""							)       ,			"""r"""							)							)
							__UpperCAmelCase   :						Any      							=					{int(_UpperCamelCase							): v for k, v in idalabel.items()}
							__UpperCAmelCase   :						Optional[Any]      							=					idalabel
							__UpperCAmelCase   :						Optional[int]      							=					{v: k for k, v in idalabel.items()}

							# size of the architecture
							if swiftformer_name == "swiftformer_xs":
														__UpperCAmelCase   :						Tuple      							=					[3, 3, 6, 4]
														__UpperCAmelCase   :						List[Any]      							=					[4_8, 5_6, 1_1_2, 2_2_0]

							elif swiftformer_name == "swiftformer_s":
														__UpperCAmelCase   :						Optional[int]      							=					[3, 3, 9, 6]
														__UpperCAmelCase   :						Tuple      							=					[4_8, 6_4, 1_6_8, 2_2_4]

							elif swiftformer_name == "swiftformer_l1":
														__UpperCAmelCase   :						Dict      							=					[4, 3, 1_0, 5]
														__UpperCAmelCase   :						Optional[Any]      							=					[4_8, 9_6, 1_9_2, 3_8_4]

							elif swiftformer_name == "swiftformer_l3":
														__UpperCAmelCase   :						str      							=					[4, 4, 1_2, 6]
														__UpperCAmelCase   :						str      							=					[6_4, 1_2_8, 3_2_0, 5_1_2]

							# load state_dict of original model, remove and rename some keys
							if original_ckpt:
														if original_ckpt.startswith("""https"""							):
																					__UpperCAmelCase   :						str      							=					torch.hub.load_state_dict_from_url(_UpperCamelCase       ,			map_location="""cpu"""       ,			check_hash=_UpperCamelCase							)
														else:
																					__UpperCAmelCase   :						Dict      							=					torch.load(_UpperCamelCase       ,			map_location="""cpu"""							)
							__UpperCAmelCase   :						str      							=					checkpoint

							__UpperCAmelCase   :						Union[str, Any]      							=					create_rename_keys(_UpperCamelCase							)
							for rename_key_src, rename_key_dest in rename_keys:
														rename_key(_UpperCamelCase       ,			_UpperCamelCase       ,			_UpperCamelCase							)

							# load HuggingFace model
							__UpperCAmelCase   :						int      							=					SwiftFormerForImageClassification(_UpperCamelCase							).eval()
							hf_model.load_state_dict(_UpperCamelCase							)

							# prepare test inputs
							__UpperCAmelCase   :						List[Any]      							=					prepare_img()
							__UpperCAmelCase   :						int      							=					ViTImageProcessor.from_pretrained("""preprocessor_config"""							)
							__UpperCAmelCase   :						Optional[Any]      							=					processor(images=_UpperCamelCase       ,			return_tensors="""pt"""							)

							# compare outputs from both models
							__UpperCAmelCase   :						Tuple      							=					get_expected_output(_UpperCamelCase							)
							__UpperCAmelCase   :						Dict      							=					hf_model(inputs["""pixel_values"""]							).logits

							assert hf_logits.shape == torch.Size([1, 1_0_0_0]							)
							assert torch.allclose(hf_logits[0, 0:5]       ,			_UpperCamelCase       ,			atol=1E-3							)

							Path(_UpperCamelCase							).mkdir(exist_ok=_UpperCamelCase							)
							print(f'''Saving model {swiftformer_name} to {pytorch_dump_folder_path}'''							)
							hf_model.save_pretrained(_UpperCamelCase							)


if __name__ == "__main__":
							UpperCAmelCase    :     Optional[Any]   						=		argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    '--swiftformer_name',
							    default='swiftformer_xs',
							    choices=['swiftformer_xs', 'swiftformer_s', 'swiftformer_l1', 'swiftformer_l3'],
							    type=str,
							    help='Name of the SwiftFormer model you\'d like to convert.',
							)
							parser.add_argument(
							    '--pytorch_dump_folder_path',
							    default='./converted_outputs/',
							    type=str,
							    help='Path to the output PyTorch model directory.',
							)
							parser.add_argument('--original_ckpt', default=None, type=str, help='Path to the original model checkpoint.')

							UpperCAmelCase    :     Tuple   						=		parser.parse_args()
							convert_swiftformer_checkpoint(args.swiftformer_name, args.pytorch_dump_folder_path, args.original_ckpt)
