


'''simple docstring'''

import argparse
import os.path as osp
import re

import torch
from safetensors.torch import load_file, save_file


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

a_  :				int						= [
    # (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"""),
]

a_  :				Optional[Any]						= [
    # (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"""),
]

a_  :				Union[str, Any]						= []
# 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
          a_  :				Union[str, Any]						= f'''down_blocks.{i}.resnets.{j}.'''
          a_  :				str						= 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
               a_  :				Optional[Any]						= f'''down_blocks.{i}.attentions.{j}.'''
               a_  :				Any						= 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
          a_  :				Optional[int]						= f'''up_blocks.{i}.resnets.{j}.'''
          a_  :				str						= 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
               a_  :				str						= f'''up_blocks.{i}.attentions.{j}.'''
               a_  :				int						= 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
          a_  :				Union[str, Any]						= f'''down_blocks.{i}.downsamplers.0.conv.'''
          a_  :				str						= 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
          a_  :				Optional[int]						= f'''up_blocks.{i}.upsamplers.0.'''
          a_  :				Any						= f'''output_blocks.{3*i + 2}.{1 if i == 0 else 2}.'''
          unet_conversion_map_layer.append((sd_upsample_prefix, hf_upsample_prefix))

a_  :				Optional[Any]						= """mid_block.attentions.0."""
a_  :				Any						= """middle_block.1."""
unet_conversion_map_layer.append((sd_mid_atn_prefix, hf_mid_atn_prefix))

for j in range(2):
     a_  :				Dict						= f'''mid_block.resnets.{j}.'''
     a_  :				Union[str, Any]						= f'''middle_block.{2*j}.'''
     unet_conversion_map_layer.append((sd_mid_res_prefix, hf_mid_res_prefix))





def __snake_case					(      UpperCAmelCase_						:						Optional[Any]		):
 # buyer beware: this is a *brittle* function,
 # and correct output requires that all of these pieces interact in
 # the exact order in which I have arranged them.
 lowerCamelCase_      =  {k: k for k in unet_state_dict.keys()}
 for sd_name, hf_name in unet_conversion_map:
  lowerCamelCase_      =  sd_name
 for k, v in mapping.items():
  if "resnets" in k:
   for sd_part, hf_part in unet_conversion_map_resnet:
    lowerCamelCase_      =  v.replace(UpperCAmelCase_					,				UpperCAmelCase_		)
   lowerCamelCase_      =  v
 for k, v in mapping.items():
  for sd_part, hf_part in unet_conversion_map_layer:
   lowerCamelCase_      =  v.replace(UpperCAmelCase_					,				UpperCAmelCase_		)
  lowerCamelCase_      =  v
 lowerCamelCase_      =  {v: unet_state_dict[k] for k, v in mapping.items()}
 return new_state_dict


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

a_  :				int						= [
    # (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):
          a_  :				Dict						= f'''encoder.down_blocks.{i}.resnets.{j}.'''
          a_  :				List[str]						= f'''encoder.down.{i}.block.{j}.'''
          vae_conversion_map.append((sd_down_prefix, hf_down_prefix))

     if i < 3:
          a_  :				Union[str, Any]						= f'''down_blocks.{i}.downsamplers.0.'''
          a_  :				List[Any]						= f'''down.{i}.downsample.'''
          vae_conversion_map.append((sd_downsample_prefix, hf_downsample_prefix))

          a_  :				str						= f'''up_blocks.{i}.upsamplers.0.'''
          a_  :				List[Any]						= 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):
          a_  :				List[Any]						= f'''decoder.up_blocks.{i}.resnets.{j}.'''
          a_  :				Dict						= 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):
     a_  :				Optional[int]						= f'''mid_block.resnets.{i}.'''
     a_  :				List[Any]						= f'''mid.block_{i+1}.'''
     vae_conversion_map.append((sd_mid_res_prefix, hf_mid_res_prefix))


a_  :				str						= [
    # (stable-diffusion, HF Diffusers)
    ("""norm.""", """group_norm."""),
    ("""q.""", """query."""),
    ("""k.""", """key."""),
    ("""v.""", """value."""),
    ("""proj_out.""", """proj_attn."""),
]





def __snake_case					(      UpperCAmelCase_						:						Optional[int]		):
 # convert HF linear weights to SD conv2d weights
 return w.reshape(*w.shape					,				1					,				1		)





def __snake_case					(      UpperCAmelCase_						:						Optional[Any]		):
 lowerCamelCase_      =  {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_      =  v.replace(UpperCAmelCase_					,				UpperCAmelCase_		)
  lowerCamelCase_      =  v
 for k, v in mapping.items():
  if "attentions" in k:
   for sd_part, hf_part in vae_conversion_map_attn:
    lowerCamelCase_      =  v.replace(UpperCAmelCase_					,				UpperCAmelCase_		)
   lowerCamelCase_      =  v
 lowerCamelCase_      =  {v: vae_state_dict[k] for k, v in mapping.items()}
 lowerCamelCase_      =  ["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_      =  reshape_weight_for_sd(UpperCAmelCase_		)
 return new_state_dict


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


a_  :				int						= [
    # (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"""),
]
a_  :				str						= {re.escape(x[1]): x[0] for x in textenc_conversion_lst}
a_  :				int						= re.compile("""|""".join(protected.keys()))

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





def __snake_case					(      UpperCAmelCase_						:						Tuple		):
 lowerCamelCase_      =  {}
 lowerCamelCase_      =  {}
 lowerCamelCase_      =  {}
 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_      =  k[: -len(".q_proj.weight"		)]
   lowerCamelCase_      =  k[-len("q_proj.weight"		)]
   if k_pre not in capture_qkv_weight:
    lowerCamelCase_      =  [None, None, None]
   lowerCamelCase_      =  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_      =  k[: -len(".q_proj.bias"		)]
   lowerCamelCase_      =  k[-len("q_proj.bias"		)]
   if k_pre not in capture_qkv_bias:
    lowerCamelCase_      =  [None, None, None]
   lowerCamelCase_      =  v
   continue

  lowerCamelCase_      =  textenc_pattern.sub(lambda UpperCAmelCase_		: protected[re.escape(m.group(0		)		)]					,				UpperCAmelCase_		)
  lowerCamelCase_      =  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_      =  textenc_pattern.sub(lambda UpperCAmelCase_		: protected[re.escape(m.group(0		)		)]					,				UpperCAmelCase_		)
  lowerCamelCase_      =  torch.cat(UpperCAmelCase_		)

 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_      =  textenc_pattern.sub(lambda UpperCAmelCase_		: protected[re.escape(m.group(0		)		)]					,				UpperCAmelCase_		)
  lowerCamelCase_      =  torch.cat(UpperCAmelCase_		)

 return new_state_dict





def __snake_case					(      UpperCAmelCase_						:						List[Any]		):
 return text_enc_dict


if __name__ == "__main__":
     a_  :				List[str]						= 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."""
     )

     a_  :				List[Any]						= 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
     a_  :				str						= osp.join(args.model_path, """unet""", """diffusion_pytorch_model.safetensors""")
     a_  :				Optional[int]						= osp.join(args.model_path, """vae""", """diffusion_pytorch_model.safetensors""")
     a_  :				Optional[Any]						= 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):
          a_  :				int						= load_file(unet_path, device="""cpu""")
     else:
          a_  :				str						= osp.join(args.model_path, """unet""", """diffusion_pytorch_model.bin""")
          a_  :				int						= torch.load(unet_path, map_location="""cpu""")

     if osp.exists(vae_path):
          a_  :				List[Any]						= load_file(vae_path, device="""cpu""")
     else:
          a_  :				Dict						= osp.join(args.model_path, """vae""", """diffusion_pytorch_model.bin""")
          a_  :				Tuple						= torch.load(vae_path, map_location="""cpu""")

     if osp.exists(text_enc_path):
          a_  :				Union[str, Any]						= load_file(text_enc_path, device="""cpu""")
     else:
          a_  :				Tuple						= osp.join(args.model_path, """text_encoder""", """pytorch_model.bin""")
          a_  :				int						= torch.load(text_enc_path, map_location="""cpu""")

     # Convert the UNet model
     a_  :				List[str]						= convert_unet_state_dict(unet_state_dict)
     a_  :				Tuple						= {"""model.diffusion_model.""" + k: v for k, v in unet_state_dict.items()}

     # Convert the VAE model
     a_  :				Any						= convert_vae_state_dict(vae_state_dict)
     a_  :				List[Any]						= {"""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
     a_  :				List[Any]						= """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
          a_  :				List[Any]						= {"""transformer.""" + k: v for k, v in text_enc_dict.items()}
          a_  :				List[Any]						= convert_text_enc_state_dict_vaa(text_enc_dict)
          a_  :				str						= {"""cond_stage_model.model.""" + k: v for k, v in text_enc_dict.items()}
     else:
          a_  :				List[str]						= convert_text_enc_state_dict(text_enc_dict)
          a_  :				Tuple						= {"""cond_stage_model.transformer.""" + k: v for k, v in text_enc_dict.items()}

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

     if args.use_safetensors:
          save_file(state_dict, args.checkpoint_path)
     else:
          a_  :				Optional[int]						= {"""state_dict""": state_dict}
          torch.save(state_dict, args.checkpoint_path)







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


logging.set_verbosity_info()
a_  :				int						= logging.get_logger(__name__)





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





def __snake_case					(      UpperCAmelCase_						:						int		):
 lowerCamelCase_      =  ["head.weight", "head.bias"]
 for k in ignore_keys:
  state_dict.pop(UpperCAmelCase_					,				UpperCAmelCase_		)





def __snake_case					(      UpperCAmelCase_						:						List[str]					,				UpperCAmelCase_						:						Union[str, Any]					,				UpperCAmelCase_						:						int		):
 lowerCamelCase_      =  dct.pop(UpperCAmelCase_		)
 lowerCamelCase_      =  val





def __snake_case					(      ):
 lowerCamelCase_      =  "http://images.cocodataset.org/val2017/000000039769.jpg"
 lowerCamelCase_      =  Image.open(requests.get(UpperCAmelCase_					,				stream=UpperCAmelCase_		).raw		)
 return im





@torch.no_grad()
def __snake_case					(      UpperCAmelCase_						:						Any					,				UpperCAmelCase_						:						Optional[int]		):
 lowerCamelCase_      =  ViTConfig()
 lowerCamelCase_      =  False
 # dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
 if vit_name[-5:] == "in21k":
  lowerCamelCase_      =  True
  lowerCamelCase_      =  int(vit_name[-12:-10]		)
  lowerCamelCase_      =  int(vit_name[-9:-6]		)
 else:
  lowerCamelCase_      =  1000
  lowerCamelCase_      =  "huggingface/label-files"
  lowerCamelCase_      =  "imagenet-1k-id2label.json"
  lowerCamelCase_      =  json.load(open(hf_hub_download(UpperCAmelCase_					,				UpperCAmelCase_					,				repo_type="dataset"		)					,				"r"		)		)
  lowerCamelCase_      =  {int(UpperCAmelCase_		): v for k, v in idalabel.items()}
  lowerCamelCase_      =  idalabel
  lowerCamelCase_      =  {v: k for k, v in idalabel.items()}
  lowerCamelCase_      =  int(vit_name[-6:-4]		)
  lowerCamelCase_      =  int(vit_name[-3:]		)
 # size of the architecture
 if "deit" in vit_name:
  if vit_name[9:].startswith("tiny"		):
   lowerCamelCase_      =  192
   lowerCamelCase_      =  768
   lowerCamelCase_      =  12
   lowerCamelCase_      =  3
  elif vit_name[9:].startswith("small"		):
   lowerCamelCase_      =  384
   lowerCamelCase_      =  1536
   lowerCamelCase_      =  12
   lowerCamelCase_      =  6
  else:
   pass
 else:
  if vit_name[4:].startswith("small"		):
   lowerCamelCase_      =  768
   lowerCamelCase_      =  2304
   lowerCamelCase_      =  8
   lowerCamelCase_      =  8
  elif vit_name[4:].startswith("base"		):
   pass
  elif vit_name[4:].startswith("large"		):
   lowerCamelCase_      =  1024
   lowerCamelCase_      =  4096
   lowerCamelCase_      =  24
   lowerCamelCase_      =  16
  elif vit_name[4:].startswith("huge"		):
   lowerCamelCase_      =  1280
   lowerCamelCase_      =  5120
   lowerCamelCase_      =  32
   lowerCamelCase_      =  16

    # load original model from timm
 lowerCamelCase_      =  timm.create_model(UpperCAmelCase_					,				pretrained=UpperCAmelCase_		)
 timm_model.eval()

 # load state_dict of original model, remove and rename some keys
 lowerCamelCase_      =  timm_model.state_dict()
 if base_model:
  remove_classification_head_(UpperCAmelCase_		)
 lowerCamelCase_      =  create_rename_keys(UpperCAmelCase_					,				UpperCAmelCase_		)
 for src, dest in rename_keys:
  rename_key(UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_		)
 read_in_q_k_v(UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_		)

 # load HuggingFace model
 if vit_name[-5:] == "in21k":
  lowerCamelCase_      =  ViTModel(UpperCAmelCase_		).eval()
 else:
  lowerCamelCase_      =  ViTForImageClassification(UpperCAmelCase_		).eval()
 model.load_state_dict(UpperCAmelCase_		)

 # Check outputs on an image, prepared by ViTImageProcessor/DeiTImageProcessor
 if "deit" in vit_name:
  lowerCamelCase_      =  DeiTImageProcessor(size=config.image_size		)
 else:
  lowerCamelCase_      =  ViTImageProcessor(size=config.image_size		)
 lowerCamelCase_      =  image_processor(images=prepare_img()					,				return_tensors="pt"		)
 lowerCamelCase_      =  encoding["pixel_values"]
 lowerCamelCase_      =  model(UpperCAmelCase_		)

 if base_model:
  lowerCamelCase_      =  timm_model.forward_features(UpperCAmelCase_		)
  assert timm_pooled_output.shape == outputs.pooler_output.shape
  assert torch.allclose(UpperCAmelCase_					,				outputs.pooler_output					,				atol=1E-3		)
 else:
  lowerCamelCase_      =  timm_model(UpperCAmelCase_		)
  assert timm_logits.shape == outputs.logits.shape
  assert torch.allclose(UpperCAmelCase_					,				outputs.logits					,				atol=1E-3		)

 Path(UpperCAmelCase_		).mkdir(exist_ok=UpperCAmelCase_		)
 print(F'''Saving model {vit_name} to {pytorch_dump_folder_path}'''		)
 model.save_pretrained(UpperCAmelCase_		)
 print(F'''Saving image processor to {pytorch_dump_folder_path}'''		)
 image_processor.save_pretrained(UpperCAmelCase_		)


if __name__ == "__main__":
     a_  :				Union[str, Any]						= 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."""
     )

     a_  :				List[str]						= parser.parse_args()
     convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path)



