


import argparse
import os.path as osp
import re

import torch
from safetensors.torch import load_file, save_file


# =================#
# UNet Conversion #
# =================#

lowerCamelCase						=		[
    # (stable-diffusion, HF Diffusers)
    ('''time_embed.0.weight''', '''time_embedding.linear_1.weight'''),
    ('''time_embed.0.bias''', '''time_embedding.linear_1.bias'''),
    ('''time_embed.2.weight''', '''time_embedding.linear_2.weight'''),
    ('''time_embed.2.bias''', '''time_embedding.linear_2.bias'''),
    ('''input_blocks.0.0.weight''', '''conv_in.weight'''),
    ('''input_blocks.0.0.bias''', '''conv_in.bias'''),
    ('''out.0.weight''', '''conv_norm_out.weight'''),
    ('''out.0.bias''', '''conv_norm_out.bias'''),
    ('''out.2.weight''', '''conv_out.weight'''),
    ('''out.2.bias''', '''conv_out.bias'''),
]

lowerCamelCase						=		[
    # (stable-diffusion, HF Diffusers)
    ('''in_layers.0''', '''norm1'''),
    ('''in_layers.2''', '''conv1'''),
    ('''out_layers.0''', '''norm2'''),
    ('''out_layers.3''', '''conv2'''),
    ('''emb_layers.1''', '''time_emb_proj'''),
    ('''skip_connection''', '''conv_shortcut'''),
]

lowerCamelCase						=		[]
# hardcoded number of downblocks and resnets/attentions...
# would need smarter logic for other networks.
for i in range(4):
		# loop over downblocks/upblocks

		for j in range(2):
				# loop over resnets/attentions for downblocks
				lowerCamelCase						=		f'''down_blocks.{i}.resnets.{j}.'''
				lowerCamelCase						=		f'''input_blocks.{3*i + j + 1}.0.'''
				unet_conversion_map_layer.append((sd_down_res_prefix, hf_down_res_prefix))

				if i < 3:
						# no attention layers in down_blocks.3
						lowerCamelCase						=		f'''down_blocks.{i}.attentions.{j}.'''
						lowerCamelCase						=		f'''input_blocks.{3*i + j + 1}.1.'''
						unet_conversion_map_layer.append((sd_down_atn_prefix, hf_down_atn_prefix))

		for j in range(3):
				# loop over resnets/attentions for upblocks
				lowerCamelCase						=		f'''up_blocks.{i}.resnets.{j}.'''
				lowerCamelCase						=		f'''output_blocks.{3*i + j}.0.'''
				unet_conversion_map_layer.append((sd_up_res_prefix, hf_up_res_prefix))

				if i > 0:
						# no attention layers in up_blocks.0
						lowerCamelCase						=		f'''up_blocks.{i}.attentions.{j}.'''
						lowerCamelCase						=		f'''output_blocks.{3*i + j}.1.'''
						unet_conversion_map_layer.append((sd_up_atn_prefix, hf_up_atn_prefix))

		if i < 3:
				# no downsample in down_blocks.3
				lowerCamelCase						=		f'''down_blocks.{i}.downsamplers.0.conv.'''
				lowerCamelCase						=		f'''input_blocks.{3*(i+1)}.0.op.'''
				unet_conversion_map_layer.append((sd_downsample_prefix, hf_downsample_prefix))

				# no upsample in up_blocks.3
				lowerCamelCase						=		f'''up_blocks.{i}.upsamplers.0.'''
				lowerCamelCase						=		f'''output_blocks.{3*i + 2}.{1 if i == 0 else 2}.'''
				unet_conversion_map_layer.append((sd_upsample_prefix, hf_upsample_prefix))

lowerCamelCase						=		'''mid_block.attentions.0.'''
lowerCamelCase						=		'''middle_block.1.'''
unet_conversion_map_layer.append((sd_mid_atn_prefix, hf_mid_atn_prefix))

for j in range(2):
		lowerCamelCase						=		f'''mid_block.resnets.{j}.'''
		lowerCamelCase						=		f'''middle_block.{2*j}.'''
		unet_conversion_map_layer.append((sd_mid_res_prefix, hf_mid_res_prefix))






def 					lowerCamelCase_   (	_a	):




		"""simple docstring"""






		lowerCAmelCase__       :      Tuple   					= {k: k for k in unet_state_dict.keys()}
		for sd_name, hf_name in unet_conversion_map:
				lowerCAmelCase__       :      Union[str, Any]   					= sd_name
		for k, v in mapping.items():
				if "resnets" in k:
						for sd_part, hf_part in unet_conversion_map_resnet:
								lowerCAmelCase__       :      Optional[Any]   					= v.replace(_a     ,       _a	)
						lowerCAmelCase__       :      List[Any]   					= v
		for k, v in mapping.items():
				for sd_part, hf_part in unet_conversion_map_layer:
						lowerCAmelCase__       :      int   					= v.replace(_a     ,       _a	)
				lowerCAmelCase__       :      Dict   					= v
		lowerCAmelCase__       :      str   					= {v: unet_state_dict[k] for k, v in mapping.items()}
		return new_state_dict


# ================#
# VAE Conversion #
# ================#

lowerCamelCase						=		[
    # (stable-diffusion, HF Diffusers)
    ('''nin_shortcut''', '''conv_shortcut'''),
    ('''norm_out''', '''conv_norm_out'''),
    ('''mid.attn_1.''', '''mid_block.attentions.0.'''),
]

for i in range(4):
		# down_blocks have two resnets
		for j in range(2):
				lowerCamelCase						=		f'''encoder.down_blocks.{i}.resnets.{j}.'''
				lowerCamelCase						=		f'''encoder.down.{i}.block.{j}.'''
				vae_conversion_map.append((sd_down_prefix, hf_down_prefix))

		if i < 3:
				lowerCamelCase						=		f'''down_blocks.{i}.downsamplers.0.'''
				lowerCamelCase						=		f'''down.{i}.downsample.'''
				vae_conversion_map.append((sd_downsample_prefix, hf_downsample_prefix))

				lowerCamelCase						=		f'''up_blocks.{i}.upsamplers.0.'''
				lowerCamelCase						=		f'''up.{3-i}.upsample.'''
				vae_conversion_map.append((sd_upsample_prefix, hf_upsample_prefix))

		# up_blocks have three resnets
		# also, up blocks in hf are numbered in reverse from sd
		for j in range(3):
				lowerCamelCase						=		f'''decoder.up_blocks.{i}.resnets.{j}.'''
				lowerCamelCase						=		f'''decoder.up.{3-i}.block.{j}.'''
				vae_conversion_map.append((sd_up_prefix, hf_up_prefix))

# this part accounts for mid blocks in both the encoder and the decoder
for i in range(2):
		lowerCamelCase						=		f'''mid_block.resnets.{i}.'''
		lowerCamelCase						=		f'''mid.block_{i+1}.'''
		vae_conversion_map.append((sd_mid_res_prefix, hf_mid_res_prefix))


lowerCamelCase						=		[
    # (stable-diffusion, HF Diffusers)
    ('''norm.''', '''group_norm.'''),
    ('''q.''', '''query.'''),
    ('''k.''', '''key.'''),
    ('''v.''', '''value.'''),
    ('''proj_out.''', '''proj_attn.'''),
]






def 					lowerCamelCase_   (	_a	):




		"""simple docstring"""






		return w.reshape(*w.shape     ,       1     ,       1	)






def 					lowerCamelCase_   (	_a	):




		"""simple docstring"""






		lowerCAmelCase__       :      List[Any]   					= {k: k for k in vae_state_dict.keys()}
		for k, v in mapping.items():
				for sd_part, hf_part in vae_conversion_map:
						lowerCAmelCase__       :      str   					= v.replace(_a     ,       _a	)
				lowerCAmelCase__       :      Dict   					= v
		for k, v in mapping.items():
				if "attentions" in k:
						for sd_part, hf_part in vae_conversion_map_attn:
								lowerCAmelCase__       :      Optional[int]   					= v.replace(_a     ,       _a	)
						lowerCAmelCase__       :      str   					= v
		lowerCAmelCase__       :      List[Any]   					= {v: vae_state_dict[k] for k, v in mapping.items()}
		lowerCAmelCase__       :      str   					= ['''q''', '''k''', '''v''', '''proj_out''']
		for k, v in new_state_dict.items():
				for weight_name in weights_to_convert:
						if f'mid.attn_1.{weight_name}.weight' in k:
								print(f'Reshaping {k} for SD format'	)
								lowerCAmelCase__       :      List[Any]   					= reshape_weight_for_sd(_a	)
		return new_state_dict


# =========================#
# Text Encoder Conversion #
# =========================#


lowerCamelCase						=		[
    # (stable-diffusion, HF Diffusers)
    ('''resblocks.''', '''text_model.encoder.layers.'''),
    ('''ln_1''', '''layer_norm1'''),
    ('''ln_2''', '''layer_norm2'''),
    ('''.c_fc.''', '''.fc1.'''),
    ('''.c_proj.''', '''.fc2.'''),
    ('''.attn''', '''.self_attn'''),
    ('''ln_final.''', '''transformer.text_model.final_layer_norm.'''),
    ('''token_embedding.weight''', '''transformer.text_model.embeddings.token_embedding.weight'''),
    ('''positional_embedding''', '''transformer.text_model.embeddings.position_embedding.weight'''),
]
lowerCamelCase						=		{re.escape(x[1]): x[0] for x in textenc_conversion_lst}
lowerCamelCase						=		re.compile('''|'''.join(protected.keys()))

# Ordering is from https://github.com/pytorch/pytorch/blob/master/test/cpp/api/modules.cpp
lowerCamelCase						=		{'''q''': 0, '''k''': 1, '''v''': 2}






def 					lowerCamelCase_   (	_a	):




		"""simple docstring"""






		lowerCAmelCase__       :      int   					= {}
		lowerCAmelCase__       :      Tuple   					= {}
		lowerCAmelCase__       :      int   					= {}
		for k, v in text_enc_dict.items():
				if (
				    k.endswith('''.self_attn.q_proj.weight'''	)
				    or k.endswith('''.self_attn.k_proj.weight'''	)
				    or k.endswith('''.self_attn.v_proj.weight'''	)
				):
						lowerCAmelCase__       :      Dict   					= k[: -len('''.q_proj.weight'''	)]
						lowerCAmelCase__       :      Any   					= k[-len('''q_proj.weight'''	)]
						if k_pre not in capture_qkv_weight:
								lowerCAmelCase__       :      Union[str, Any]   					= [None, None, None]
						lowerCAmelCase__       :      int   					= v
						continue

				if (
				    k.endswith('''.self_attn.q_proj.bias'''	)
				    or k.endswith('''.self_attn.k_proj.bias'''	)
				    or k.endswith('''.self_attn.v_proj.bias'''	)
				):
						lowerCAmelCase__       :      str   					= k[: -len('''.q_proj.bias'''	)]
						lowerCAmelCase__       :      List[str]   					= k[-len('''q_proj.bias'''	)]
						if k_pre not in capture_qkv_bias:
								lowerCAmelCase__       :      Dict   					= [None, None, None]
						lowerCAmelCase__       :      Union[str, Any]   					= v
						continue

				lowerCAmelCase__       :      Any   					= textenc_pattern.sub(lambda _a	: protected[re.escape(m.group(0	)	)]     ,       _a	)
				lowerCAmelCase__       :      Any   					= v

		for k_pre, tensors in capture_qkv_weight.items():
				if None in tensors:
						raise Exception('''CORRUPTED MODEL: one of the q-k-v values for the text encoder was missing'''	)
				lowerCAmelCase__       :      Any   					= textenc_pattern.sub(lambda _a	: protected[re.escape(m.group(0	)	)]     ,       _a	)
				lowerCAmelCase__       :      List[Any]   					= torch.cat(_a	)

		for k_pre, tensors in capture_qkv_bias.items():
				if None in tensors:
						raise Exception('''CORRUPTED MODEL: one of the q-k-v values for the text encoder was missing'''	)
				lowerCAmelCase__       :      str   					= textenc_pattern.sub(lambda _a	: protected[re.escape(m.group(0	)	)]     ,       _a	)
				lowerCAmelCase__       :      Union[str, Any]   					= torch.cat(_a	)

		return new_state_dict






def 					lowerCamelCase_   (	_a	):




		"""simple docstring"""






		return text_enc_dict


if __name__ == "__main__":
		lowerCamelCase						=		argparse.ArgumentParser()

		parser.add_argument('''--model_path''', default=None, type=str, required=True, help='''Path to the model to convert.''')
		parser.add_argument('''--checkpoint_path''', default=None, type=str, required=True, help='''Path to the output model.''')
		parser.add_argument('''--half''', action='''store_true''', help='''Save weights in half precision.''')
		parser.add_argument(
		    '''--use_safetensors''', action='''store_true''', help='''Save weights use safetensors, default is ckpt.'''
		)

		lowerCamelCase						=		parser.parse_args()

		assert args.model_path is not None, "Must provide a model path!"

		assert args.checkpoint_path is not None, "Must provide a checkpoint path!"

		# Path for safetensors
		lowerCamelCase						=		osp.join(args.model_path, '''unet''', '''diffusion_pytorch_model.safetensors''')
		lowerCamelCase						=		osp.join(args.model_path, '''vae''', '''diffusion_pytorch_model.safetensors''')
		lowerCamelCase						=		osp.join(args.model_path, '''text_encoder''', '''model.safetensors''')

		# Load models from safetensors if it exists, if it doesn't pytorch
		if osp.exists(unet_path):
				lowerCamelCase						=		load_file(unet_path, device='''cpu''')
		else:
				lowerCamelCase						=		osp.join(args.model_path, '''unet''', '''diffusion_pytorch_model.bin''')
				lowerCamelCase						=		torch.load(unet_path, map_location='''cpu''')

		if osp.exists(vae_path):
				lowerCamelCase						=		load_file(vae_path, device='''cpu''')
		else:
				lowerCamelCase						=		osp.join(args.model_path, '''vae''', '''diffusion_pytorch_model.bin''')
				lowerCamelCase						=		torch.load(vae_path, map_location='''cpu''')

		if osp.exists(text_enc_path):
				lowerCamelCase						=		load_file(text_enc_path, device='''cpu''')
		else:
				lowerCamelCase						=		osp.join(args.model_path, '''text_encoder''', '''pytorch_model.bin''')
				lowerCamelCase						=		torch.load(text_enc_path, map_location='''cpu''')

		# Convert the UNet model
		lowerCamelCase						=		convert_unet_state_dict(unet_state_dict)
		lowerCamelCase						=		{'''model.diffusion_model.''' + k: v for k, v in unet_state_dict.items()}

		# Convert the VAE model
		lowerCamelCase						=		convert_vae_state_dict(vae_state_dict)
		lowerCamelCase						=		{'''first_stage_model.''' + k: v for k, v in vae_state_dict.items()}

		# Easiest way to identify v2.0 model seems to be that the text encoder (OpenCLIP) is deeper
		lowerCamelCase						=		'''text_model.encoder.layers.22.layer_norm2.bias''' in text_enc_dict

		if is_vaa_model:
				# Need to add the tag 'transformer' in advance so we can knock it out from the final layer-norm
				lowerCamelCase						=		{'''transformer.''' + k: v for k, v in text_enc_dict.items()}
				lowerCamelCase						=		convert_text_enc_state_dict_vaa(text_enc_dict)
				lowerCamelCase						=		{'''cond_stage_model.model.''' + k: v for k, v in text_enc_dict.items()}
		else:
				lowerCamelCase						=		convert_text_enc_state_dict(text_enc_dict)
				lowerCamelCase						=		{'''cond_stage_model.transformer.''' + k: v for k, v in text_enc_dict.items()}

		# Put together new checkpoint
		lowerCamelCase						=		{**unet_state_dict, **vae_state_dict, **text_enc_dict}
		if args.half:
				lowerCamelCase						=		{k: v.half() for k, v in state_dict.items()}

		if args.use_safetensors:
				save_file(state_dict, args.checkpoint_path)
		else:
				lowerCamelCase						=		{'''state_dict''': state_dict}
				torch.save(state_dict, args.checkpoint_path)








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 DeiTImageProcessor, ViTConfig, ViTForImageClassification, ViTImageProcessor, ViTModel
from transformers.utils import logging


logging.set_verbosity_info()
lowerCamelCase						=		logging.get_logger(__name__)






def 					lowerCamelCase_   (	_a     ,       _a=False	):




		"""simple docstring"""






		lowerCAmelCase__       :      int   					= []
		for i in range(config.num_hidden_layers	):
				# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
				rename_keys.append((f'blocks.{i}.norm1.weight', f'vit.encoder.layer.{i}.layernorm_before.weight')	)
				rename_keys.append((f'blocks.{i}.norm1.bias', f'vit.encoder.layer.{i}.layernorm_before.bias')	)
				rename_keys.append((f'blocks.{i}.attn.proj.weight', f'vit.encoder.layer.{i}.attention.output.dense.weight')	)
				rename_keys.append((f'blocks.{i}.attn.proj.bias', f'vit.encoder.layer.{i}.attention.output.dense.bias')	)
				rename_keys.append((f'blocks.{i}.norm2.weight', f'vit.encoder.layer.{i}.layernorm_after.weight')	)
				rename_keys.append((f'blocks.{i}.norm2.bias', f'vit.encoder.layer.{i}.layernorm_after.bias')	)
				rename_keys.append((f'blocks.{i}.mlp.fc1.weight', f'vit.encoder.layer.{i}.intermediate.dense.weight')	)
				rename_keys.append((f'blocks.{i}.mlp.fc1.bias', f'vit.encoder.layer.{i}.intermediate.dense.bias')	)
				rename_keys.append((f'blocks.{i}.mlp.fc2.weight', f'vit.encoder.layer.{i}.output.dense.weight')	)
				rename_keys.append((f'blocks.{i}.mlp.fc2.bias', f'vit.encoder.layer.{i}.output.dense.bias')	)

		# projection layer + position embeddings
		rename_keys.extend(
		    [
		        ('''cls_token''', '''vit.embeddings.cls_token'''),
		        ('''patch_embed.proj.weight''', '''vit.embeddings.patch_embeddings.projection.weight'''),
		        ('''patch_embed.proj.bias''', '''vit.embeddings.patch_embeddings.projection.bias'''),
		        ('''pos_embed''', '''vit.embeddings.position_embeddings'''),
		    ]	)

		if base_model:
				# layernorm + pooler
				rename_keys.extend(
				    [
				        ('''norm.weight''', '''layernorm.weight'''),
				        ('''norm.bias''', '''layernorm.bias'''),
				        ('''pre_logits.fc.weight''', '''pooler.dense.weight'''),
				        ('''pre_logits.fc.bias''', '''pooler.dense.bias'''),
				    ]	)

				# if just the base model, we should remove "vit" from all keys that start with "vit"
				lowerCAmelCase__       :      Tuple   					= [(pair[0], pair[1][4:]) if pair[1].startswith('''vit'''	) else pair for pair in rename_keys]
		else:
				# layernorm + classification head
				rename_keys.extend(
				    [
				        ('''norm.weight''', '''vit.layernorm.weight'''),
				        ('''norm.bias''', '''vit.layernorm.bias'''),
				        ('''head.weight''', '''classifier.weight'''),
				        ('''head.bias''', '''classifier.bias'''),
				    ]	)

		return rename_keys






def 					lowerCamelCase_   (	_a     ,       _a     ,       _a=False	):




		"""simple docstring"""






		for i in range(config.num_hidden_layers	):
				if base_model:
						lowerCAmelCase__       :      Dict   					= ''''''
				else:
						lowerCAmelCase__       :      List[str]   					= '''vit.'''
				# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
				lowerCAmelCase__       :      Dict   					= state_dict.pop(f'blocks.{i}.attn.qkv.weight'	)
				lowerCAmelCase__       :      Dict   					= state_dict.pop(f'blocks.{i}.attn.qkv.bias'	)
				# next, add query, keys and values (in that order) to the state dict
				lowerCAmelCase__       :      Any   					= in_proj_weight[
				    : config.hidden_size, :
				]
				lowerCAmelCase__       :      Optional[Any]   					= in_proj_bias[: config.hidden_size]
				lowerCAmelCase__       :      int   					= in_proj_weight[
				    config.hidden_size : config.hidden_size * 2, :
				]
				lowerCAmelCase__       :      Dict   					= in_proj_bias[
				    config.hidden_size : config.hidden_size * 2
				]
				lowerCAmelCase__       :      Dict   					= in_proj_weight[
				    -config.hidden_size :, :
				]
				lowerCAmelCase__       :      Any   					= in_proj_bias[-config.hidden_size :]






def 					lowerCamelCase_   (	_a	):




		"""simple docstring"""






		lowerCAmelCase__       :      Union[str, Any]   					= ['''head.weight''', '''head.bias''']
		for k in ignore_keys:
				state_dict.pop(_a     ,       _a	)






def 					lowerCamelCase_   (	_a     ,       _a     ,       _a	):




		"""simple docstring"""






		lowerCAmelCase__       :      Any   					= dct.pop(_a	)
		lowerCAmelCase__       :      Optional[Any]   					= val






def 					lowerCamelCase_   (	):




		"""simple docstring"""






		lowerCAmelCase__       :      Dict   					= '''http://images.cocodataset.org/val2017/000000039769.jpg'''
		lowerCAmelCase__       :      Tuple   					= Image.open(requests.get(_a     ,       stream=_a	).raw	)
		return im






@torch.no_grad()
def 					lowerCamelCase_   (	_a     ,       _a	):




		"""simple docstring"""






		lowerCAmelCase__       :      List[str]   					= ViTConfig()
		lowerCAmelCase__       :      Optional[Any]   					= False
		# dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
		if vit_name[-5:] == "in21k":
				lowerCAmelCase__       :      List[Any]   					= True
				lowerCAmelCase__       :      Any   					= int(vit_name[-12:-10]	)
				lowerCAmelCase__       :      int   					= int(vit_name[-9:-6]	)
		else:
				lowerCAmelCase__       :      Dict   					= 1_000
				lowerCAmelCase__       :      str   					= '''huggingface/label-files'''
				lowerCAmelCase__       :      Dict   					= '''imagenet-1k-id2label.json'''
				lowerCAmelCase__       :      str   					= json.load(open(hf_hub_download(_a     ,       _a     ,       repo_type='''dataset'''	)     ,       '''r'''	)	)
				lowerCAmelCase__       :      Any   					= {int(_a	): v for k, v in idalabel.items()}
				lowerCAmelCase__       :      Optional[Any]   					= idalabel
				lowerCAmelCase__       :      List[str]   					= {v: k for k, v in idalabel.items()}
				lowerCAmelCase__       :      Tuple   					= int(vit_name[-6:-4]	)
				lowerCAmelCase__       :      Union[str, Any]   					= int(vit_name[-3:]	)
		# size of the architecture
		if "deit" in vit_name:
				if vit_name[9:].startswith('''tiny'''	):
						lowerCAmelCase__       :      List[str]   					= 192
						lowerCAmelCase__       :      Tuple   					= 768
						lowerCAmelCase__       :      Optional[int]   					= 12
						lowerCAmelCase__       :      List[Any]   					= 3
				elif vit_name[9:].startswith('''small'''	):
						lowerCAmelCase__       :      Any   					= 384
						lowerCAmelCase__       :      Optional[int]   					= 1_536
						lowerCAmelCase__       :      List[Any]   					= 12
						lowerCAmelCase__       :      Tuple   					= 6
				else:
						pass
		else:
				if vit_name[4:].startswith('''small'''	):
						lowerCAmelCase__       :      List[str]   					= 768
						lowerCAmelCase__       :      Tuple   					= 2_304
						lowerCAmelCase__       :      Any   					= 8
						lowerCAmelCase__       :      Union[str, Any]   					= 8
				elif vit_name[4:].startswith('''base'''	):
						pass
				elif vit_name[4:].startswith('''large'''	):
						lowerCAmelCase__       :      str   					= 1_024
						lowerCAmelCase__       :      Optional[Any]   					= 4_096
						lowerCAmelCase__       :      Optional[int]   					= 24
						lowerCAmelCase__       :      Tuple   					= 16
				elif vit_name[4:].startswith('''huge'''	):
						lowerCAmelCase__       :      Tuple   					= 1_280
						lowerCAmelCase__       :      Tuple   					= 5_120
						lowerCAmelCase__       :      Optional[int]   					= 32
						lowerCAmelCase__       :      List[Any]   					= 16

    # load original model from timm
		lowerCAmelCase__       :      Tuple   					= timm.create_model(_a     ,       pretrained=_a	)
		timm_model.eval()

		# load state_dict of original model, remove and rename some keys
		lowerCAmelCase__       :      Any   					= timm_model.state_dict()
		if base_model:
				remove_classification_head_(_a	)
		lowerCAmelCase__       :      List[Any]   					= create_rename_keys(_a     ,       _a	)
		for src, dest in rename_keys:
				rename_key(_a     ,       _a     ,       _a	)
		read_in_q_k_v(_a     ,       _a     ,       _a	)

		# load HuggingFace model
		if vit_name[-5:] == "in21k":
				lowerCAmelCase__       :      List[str]   					= ViTModel(_a	).eval()
		else:
				lowerCAmelCase__       :      Any   					= ViTForImageClassification(_a	).eval()
		model.load_state_dict(_a	)

		# Check outputs on an image, prepared by ViTImageProcessor/DeiTImageProcessor
		if "deit" in vit_name:
				lowerCAmelCase__       :      Dict   					= DeiTImageProcessor(size=config.image_size	)
		else:
				lowerCAmelCase__       :      int   					= ViTImageProcessor(size=config.image_size	)
		lowerCAmelCase__       :      Optional[Any]   					= image_processor(images=prepare_img()     ,       return_tensors='''pt'''	)
		lowerCAmelCase__       :      List[str]   					= encoding['''pixel_values''']
		lowerCAmelCase__       :      List[Any]   					= model(_a	)

		if base_model:
				lowerCAmelCase__       :      Optional[Any]   					= timm_model.forward_features(_a	)
				assert timm_pooled_output.shape == outputs.pooler_output.shape
				assert torch.allclose(_a     ,       outputs.pooler_output     ,       atol=1e-3	)
		else:
				lowerCAmelCase__       :      Union[str, Any]   					= timm_model(_a	)
				assert timm_logits.shape == outputs.logits.shape
				assert torch.allclose(_a     ,       outputs.logits     ,       atol=1e-3	)

		Path(_a	).mkdir(exist_ok=_a	)
		print(f'Saving model {vit_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__":
		lowerCamelCase						=		argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '''--vit_name''',
		    default='''vit_base_patch16_224''',
		    type=str,
		    help='''Name of the ViT 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_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path)




