


"""simple docstring"""





import logging
import os
from typing import Dict, List, Optional, Union

import torch
import torch.nn as nn

from accelerate.utils.imports import (
    is_abit_bnb_available,
    is_abit_bnb_available,
    is_bnb_available,
)

from ..big_modeling import dispatch_model, init_empty_weights
from .dataclasses import BnbQuantizationConfig
from .modeling import (
    find_tied_parameters,
    get_balanced_memory,
    infer_auto_device_map,
    load_checkpoint_in_model,
    offload_weight,
    set_module_tensor_to_device,
)


if is_bnb_available():
 import bitsandbytes as bnb

from copy import deepcopy


snake_case__						:   Dict			 = logging.getLogger(__name__)


def    _snake_case  (  _snake_case :	torch.nn.Module       ,      _snake_case :	BnbQuantizationConfig       ,      _snake_case :	Union[str, os.PathLike] = None       ,      _snake_case :	Optional[Dict[str, Union[int, str, torch.device]]] = None       ,      _snake_case :	Optional[List[str]] = None       ,      _snake_case :	Optional[Dict[Union[int, str], Union[int, str]]] = None       ,      _snake_case :	Optional[Union[str, os.PathLike]] = None       ,      _snake_case :	bool = False       ,      ):
      lowerCAmelCase    :		Any				=      bnb_quantization_config.load_in_abit
      lowerCAmelCase    :		List[str]				=      bnb_quantization_config.load_in_abit

      if load_in_abit and not is_abit_bnb_available():
            raise ImportError(
                '''You have a version of `bitsandbytes` that is not compatible with 8bit quantization,'''
                ''' make sure you have the latest version of `bitsandbytes` installed.'''	)
      if load_in_abit and not is_abit_bnb_available():
            raise ValueError(
                '''You have a version of `bitsandbytes` that is not compatible with 4bit quantization,'''
                '''make sure you have the latest version of `bitsandbytes` installed.'''	)

      lowerCAmelCase    :		Dict				=      []
      # custom device map
      if isinstance(_snake_case       ,      _snake_case	) and len(device_map.keys()	) > 1:
            lowerCAmelCase    :		Any				=      [key for key, value in device_map.items() if value in ['''disk''', '''cpu''']]

      # We keep some modules such as the lm_head in their original dtype for numerical stability reasons
      if bnb_quantization_config.skip_modules is None:
            lowerCAmelCase    :		List[Any]				=      get_keys_to_not_convert(_snake_case	)

      # add cpu modules to skip modules only for 4-bit modules
      if load_in_abit:
            bnb_quantization_config.skip_modules.extend(_snake_case	)
      lowerCAmelCase    :		str				=      bnb_quantization_config.skip_modules

      # We add the modules we want to keep in full precision
      if bnb_quantization_config.keep_in_fpaa_modules is None:
            lowerCAmelCase    :		Optional[Any]				=      []
      lowerCAmelCase    :		int				=      bnb_quantization_config.keep_in_fpaa_modules
      modules_to_not_convert.extend(_snake_case	)

      # compatibility with peft
      lowerCAmelCase    :		str				=      load_in_abit
      lowerCAmelCase    :		str				=      load_in_abit

      lowerCAmelCase    :		str				=      get_parameter_device(_snake_case	)
      if model_device.type != "meta":
            # quantization of an already loaded model
            logger.warning(
                '''It is not recommended to quantize a loaded model. '''
                '''The model should be instantiated under the `init_empty_weights` context manager.'''	)
            lowerCAmelCase    :		Optional[int]				=      replace_with_bnb_layers(_snake_case       ,      _snake_case       ,      modules_to_not_convert=_snake_case	)
            # convert param to the right dtype
            lowerCAmelCase    :		Any				=      bnb_quantization_config.torch_dtype
            for name, param in model.state_dict().items():
                  if any(module_to_keep_in_fpaa in name for module_to_keep_in_fpaa in keep_in_fpaa_modules	):
                        param.to(torch.floataa	)
                        if param.dtype != torch.floataa:
                              lowerCAmelCase    :		Any				=      name.replace('''.weight'''       ,      ''''''	).replace('''.bias'''       ,      ''''''	)
                              lowerCAmelCase    :		Optional[Any]				=      getattr(_snake_case       ,      _snake_case       ,      _snake_case	)
                              if param is not None:
                                    param.to(torch.floataa	)
                  elif torch.is_floating_point(_snake_case	):
                        param.to(_snake_case	)
            if model_device.type == "cuda":
                  # move everything to cpu in the first place because we can't do quantization if the weights are already on cuda
                  model.cuda(torch.cuda.current_device()	)
                  torch.cuda.empty_cache()
            elif torch.cuda.is_available():
                  model.to(torch.cuda.current_device()	)
            else:
                  raise RuntimeError('''No GPU found. A GPU is needed for quantization.'''	)
            logger.info(
                f'''The model device type is {model_device.type}. However, cuda is needed for quantization.'''
                '''We move the model to cuda.'''	)
            return model

      elif weights_location is None:
            raise RuntimeError(
                f'''`weights_location` needs to be the folder path containing the weights of the model, but we found {weights_location} '''	)

      else:
            with init_empty_weights():
                  lowerCAmelCase    :		List[str]				=      replace_with_bnb_layers(
                      _snake_case       ,      _snake_case       ,      modules_to_not_convert=_snake_case	)

            lowerCAmelCase    :		List[str]				=      get_quantized_model_device_map(
                _snake_case       ,      _snake_case       ,      _snake_case       ,      max_memory=_snake_case       ,      no_split_module_classes=_snake_case       ,      )
            if offload_state_dict is None and device_map is not None and "disk" in device_map.values():
                  lowerCAmelCase    :		List[Any]				=      True

            lowerCAmelCase    :		List[str]				=      any(x in list(device_map.values()	) for x in ['''cpu''', '''disk''']	)

            load_checkpoint_in_model(
                _snake_case       ,      _snake_case       ,      _snake_case       ,      dtype=bnb_quantization_config.torch_dtype       ,      offload_folder=_snake_case       ,      offload_state_dict=_snake_case       ,      keep_in_fpaa_modules=bnb_quantization_config.keep_in_fpaa_modules       ,      offload_abit_bnb=load_in_abit and offload       ,      )
            return dispatch_model(_snake_case       ,      device_map=_snake_case       ,      offload_dir=_snake_case	)


def    _snake_case  (  _snake_case :	str       ,      _snake_case :	List[str]       ,      _snake_case :	Tuple=None       ,      _snake_case :	Any=None       ,      _snake_case :	Dict=None	):
      if device_map is None:
            if torch.cuda.is_available():
                  lowerCAmelCase    :		int				=      {'''''': torch.cuda.current_device()}
            else:
                  raise RuntimeError('''No GPU found. A GPU is needed for quantization.'''	)
            logger.info('''The device_map was not initialized.''' '''Setting device_map to `{\'\':torch.cuda.current_device()}`.'''	)

      if isinstance(_snake_case       ,      _snake_case	):
            if device_map not in ["auto", "balanced", "balanced_low_0", "sequential"]:
                  raise ValueError(
                      '''If passing a string for `device_map`, please choose \'auto\', \'balanced\', \'balanced_low_0\' or '''
                      '''\'sequential\'.'''	)

            lowerCAmelCase    :		int				=      {}
            special_dtypes.update(
                {
                    name: bnb_quantization_config.torch_dtype
                    for name, _ in model.named_parameters()
                    if any(m in name for m in bnb_quantization_config.skip_modules	)
                }	)
            special_dtypes.update(
                {
                    name: torch.floataa
                    for name, _ in model.named_parameters()
                    if any(m in name for m in bnb_quantization_config.keep_in_fpaa_modules	)
                }	)

            lowerCAmelCase    :		Tuple				=      {}
            lowerCAmelCase    :		List[Any]				=      special_dtypes
            lowerCAmelCase    :		List[str]				=      no_split_module_classes
            lowerCAmelCase    :		Optional[int]				=      bnb_quantization_config.target_dtype

            # get max_memory for each device.
            if device_map != "sequential":
                  lowerCAmelCase    :		str				=      get_balanced_memory(
                      _snake_case       ,      low_zero=(device_map == '''balanced_low_0''')       ,      max_memory=_snake_case       ,      **_snake_case       ,      )

            lowerCAmelCase    :		Tuple				=      max_memory
            lowerCAmelCase    :		int				=      infer_auto_device_map(_snake_case       ,      **_snake_case	)

      if isinstance(_snake_case       ,      _snake_case	):
            # check if don't have any quantized module on the cpu
            lowerCAmelCase    :		Dict				=      bnb_quantization_config.skip_modules + bnb_quantization_config.keep_in_fpaa_modules

            lowerCAmelCase    :		Tuple				=      {
                key: device_map[key] for key in device_map.keys() if key not in modules_not_to_convert
            }
            for device in ["cpu", "disk"]:
                  if device in device_map_without_some_modules.values():
                        if bnb_quantization_config.load_in_abit:
                              raise ValueError(
                                  '''
                        Some modules are dispatched on the CPU or the disk. Make sure you have enough GPU RAM to fit
                        the quantized model. If you want to dispatch the model on the CPU or the disk while keeping
                        these modules in `torch_dtype`, you need to pass a custom `device_map` to
                        `load_and_quantize_model`. Check
                        https://huggingface.co/docs/accelerate/main/en/usage_guides/quantization#offload-modules-to-cpu-and-disk
                        for more details.
                        '''	)
                        else:
                              logger.info(
                                  '''Some modules are are offloaded to the CPU or the disk. Note that these modules will be converted to 8-bit'''	)
            del device_map_without_some_modules
      return device_map


def    _snake_case  (  _snake_case :	Tuple       ,      _snake_case :	List[Any]       ,      _snake_case :	int=None       ,      _snake_case :	Any=None	):

      if modules_to_not_convert is None:
            lowerCAmelCase    :		str				=      []

      lowerCAmelCase,					lowerCAmelCase    :		List[str]				=      _replace_with_bnb_layers(
          _snake_case       ,      _snake_case       ,      _snake_case       ,      _snake_case	)
      if not has_been_replaced:
            logger.warning(
                '''You are loading your model in 8bit or 4bit but no linear modules were found in your model.'''
                ''' this can happen for some architectures such as gpt2 that uses Conv1D instead of Linear layers.'''
                ''' Please double check your model architecture, or submit an issue on github if you think this is'''
                ''' a bug.'''	)
      return model


def    _snake_case  (  _snake_case :	Dict       ,      _snake_case :	Optional[Any]       ,      _snake_case :	List[Any]=None       ,      _snake_case :	Dict=None       ,      ):
      lowerCAmelCase    :		List[str]				=      False
      for name, module in model.named_children():
            if current_key_name is None:
                  lowerCAmelCase    :		List[Any]				=      []
            current_key_name.append(_snake_case	)
            if isinstance(_snake_case       ,      nn.Linear	) and name not in modules_to_not_convert:
                  # Check if the current key is not in the `modules_to_not_convert`
                  lowerCAmelCase    :		Optional[int]				=      '''.'''.join(_snake_case	)
                  lowerCAmelCase    :		Optional[int]				=      True
                  for key in modules_to_not_convert:
                        if (
                            (key in current_key_name_str) and (key + "." in current_key_name_str)
                        ) or key == current_key_name_str:
                              lowerCAmelCase    :		List[str]				=      False
                              break
                  if proceed:
                        # Load bnb module with empty weight and replace ``nn.Linear` module
                        if bnb_quantization_config.load_in_abit:
                              lowerCAmelCase    :		Any				=      bnb.nn.LinearabitLt(
                                  module.in_features       ,      module.out_features       ,      module.bias is not None       ,      has_fpaa_weights=_snake_case       ,      threshold=bnb_quantization_config.llm_inta_threshold       ,      )
                        elif bnb_quantization_config.load_in_abit:
                              lowerCAmelCase    :		Dict				=      bnb.nn.Linearabit(
                                  module.in_features       ,      module.out_features       ,      module.bias is not None       ,      bnb_quantization_config.bnb_abit_compute_dtype       ,      compress_statistics=bnb_quantization_config.bnb_abit_use_double_quant       ,      quant_type=bnb_quantization_config.bnb_abit_quant_type       ,      )
                        else:
                              raise ValueError('''load_in_8bit and load_in_4bit can\'t be both False'''	)
                        lowerCAmelCase    :		Dict				=      module.weight.data
                        if module.bias is not None:
                              lowerCAmelCase    :		Union[str, Any]				=      module.bias.data
                        bnb_module.requires_grad_(_snake_case	)
                        setattr(_snake_case       ,      _snake_case       ,      _snake_case	)
                        lowerCAmelCase    :		Any				=      True
            if len(list(module.children()	)	) > 0:
                  lowerCAmelCase,					lowerCAmelCase    :		Any				=      _replace_with_bnb_layers(
                      _snake_case       ,      _snake_case       ,      _snake_case       ,      _snake_case	)
                  lowerCAmelCase    :		Optional[Any]				=      has_been_replaced | _has_been_replaced
            # Remove the last key for recursion
            current_key_name.pop(-1	)
      return model, has_been_replaced


def    _snake_case  (  _snake_case :	List[str]	):
      # Create a copy of the model
      with init_empty_weights():
            lowerCAmelCase    :		str				=      deepcopy(_snake_case	)  # this has 0 cost since it is done inside `init_empty_weights` context manager`

      lowerCAmelCase    :		List[Any]				=      find_tied_parameters(_snake_case	)
      # For compatibility with Accelerate < 0.18
      if isinstance(_snake_case       ,      _snake_case	):
            lowerCAmelCase    :		List[Any]				=      sum(list(tied_params.values()	)       ,      []	) + list(tied_params.keys()	)
      else:
            lowerCAmelCase    :		Tuple				=      sum(_snake_case       ,      []	)
      lowerCAmelCase    :		Optional[Any]				=      len(_snake_case	) > 0

      # Check if it is a base model
      lowerCAmelCase    :		Union[str, Any]				=      False
      if hasattr(_snake_case       ,      '''base_model_prefix'''	):
            lowerCAmelCase    :		int				=      not hasattr(_snake_case       ,      model.base_model_prefix	)

      # Ignore this for base models (BertModel, GPT2Model, etc.)
      if (not has_tied_params) and is_base_model:
            return []

      # otherwise they have an attached head
      lowerCAmelCase    :		Union[str, Any]				=      list(model.named_children()	)
      lowerCAmelCase    :		Dict				=      [list_modules[-1][0]]

      # add last module together with tied weights
      lowerCAmelCase    :		Optional[Any]				=      set(_snake_case	) - set(_snake_case	)
      lowerCAmelCase    :		Dict				=      list(set(_snake_case	)	) + list(_snake_case	)

      # remove ".weight" from the keys
      lowerCAmelCase    :		Dict				=      ['''.weight''', '''.bias''']
      lowerCAmelCase    :		List[str]				=      []
      for name in list_untouched:
            for name_to_remove in names_to_remove:
                  if name_to_remove in name:
                        lowerCAmelCase    :		Optional[int]				=      name.replace(_snake_case       ,      ''''''	)
            filtered_module_names.append(_snake_case	)

      return filtered_module_names


def    _snake_case  (  _snake_case :	Any	):
      for m in model.modules():
            if isinstance(_snake_case       ,      bnb.nn.Linearabit	):
                  return True
      return False


def    _snake_case  (  _snake_case :	nn.Module	):
      return next(parameter.parameters()	).device






def    _snake_case  (  _snake_case :	Dict       ,      _snake_case :	str       ,      _snake_case :	List[str]       ,      _snake_case :	List[str]       ,      _snake_case :	List[str]       ,      _snake_case :	str       ,      _snake_case :	Tuple	):
      # if it is not quantized, we quantize and offload the quantized weights and the SCB stats
      if fpaa_statistics is None:
            set_module_tensor_to_device(_snake_case       ,      _snake_case       ,      0       ,      dtype=_snake_case       ,      value=_snake_case	)
            lowerCAmelCase    :		List[str]				=      param_name
            lowerCAmelCase    :		Union[str, Any]				=      model
            if "." in tensor_name:
                  lowerCAmelCase    :		int				=      tensor_name.split('''.'''	)
                  for split in splits[:-1]:
                        lowerCAmelCase    :		str				=      getattr(_snake_case       ,      _snake_case	)
                        if new_module is None:
                              raise ValueError(f'''{module} has no attribute {split}.'''	)
                        lowerCAmelCase    :		List[str]				=      new_module
                  lowerCAmelCase    :		int				=      splits[-1]
            # offload weights
            lowerCAmelCase    :		str				=      False
            offload_weight(module._parameters[tensor_name]       ,      _snake_case       ,      _snake_case       ,      index=_snake_case	)
            if hasattr(module._parameters[tensor_name]       ,      '''SCB'''	):
                  offload_weight(
                      module._parameters[tensor_name].SCB       ,      param_name.replace('''weight'''       ,      '''SCB'''	)       ,      _snake_case       ,      index=_snake_case       ,      )
      else:
            offload_weight(_snake_case       ,      _snake_case       ,      _snake_case       ,      index=_snake_case	)
            offload_weight(_snake_case       ,      param_name.replace('''weight'''       ,      '''SCB'''	)       ,      _snake_case       ,      index=_snake_case	)

      set_module_tensor_to_device(_snake_case       ,      _snake_case       ,      '''meta'''       ,      dtype=_snake_case       ,      value=torch.empty(*param.size()	)	)






"""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()
snake_case__						:   Any			 = logging.get_logger(__name__)


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


def    _snake_case  (  _snake_case :	Tuple	):
      lowerCAmelCase    :		List[Any]				=      ['''head.weight''', '''head.bias''']
      for k in ignore_keys:
            state_dict.pop(_snake_case       ,      _snake_case	)


def    _snake_case  (  _snake_case :	Union[str, Any]       ,      _snake_case :	Any       ,      _snake_case :	List[Any]	):
      lowerCAmelCase    :		Optional[int]				=      dct.pop(_snake_case	)
      lowerCAmelCase    :		Union[str, Any]				=      val


def    _snake_case  (  ):
      lowerCAmelCase    :		Any				=      '''http://images.cocodataset.org/val2017/000000039769.jpg'''
      lowerCAmelCase    :		Any				=      Image.open(requests.get(_snake_case       ,      stream=_snake_case	).raw	)
      return im


@torch.no_grad()
def    _snake_case  (  _snake_case :	Optional[int]       ,      _snake_case :	Optional[Any]	):
      lowerCAmelCase    :		Any				=      ViTConfig()
      lowerCAmelCase    :		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[str]				=      True
            lowerCAmelCase    :		int				=      int(vit_name[-12:-10]	)
            lowerCAmelCase    :		List[Any]				=      int(vit_name[-9:-6]	)
      else:
            lowerCAmelCase    :		str				=      1000
            lowerCAmelCase    :		Optional[int]				=      '''huggingface/label-files'''
            lowerCAmelCase    :		Any				=      '''imagenet-1k-id2label.json'''
            lowerCAmelCase    :		Optional[Any]				=      json.load(open(hf_hub_download(_snake_case       ,      _snake_case       ,      repo_type='''dataset'''	)       ,      '''r'''	)	)
            lowerCAmelCase    :		Optional[Any]				=      {int(_snake_case	): v for k, v in idalabel.items()}
            lowerCAmelCase    :		Dict				=      idalabel
            lowerCAmelCase    :		List[Any]				=      {v: k for k, v in idalabel.items()}
            lowerCAmelCase    :		List[str]				=      int(vit_name[-6:-4]	)
            lowerCAmelCase    :		int				=      int(vit_name[-3:]	)
      # size of the architecture
      if "deit" in vit_name:
            if vit_name[9:].startswith('''tiny'''	):
                  lowerCAmelCase    :		str				=      192
                  lowerCAmelCase    :		int				=      768
                  lowerCAmelCase    :		List[str]				=      12
                  lowerCAmelCase    :		str				=      3
            elif vit_name[9:].startswith('''small'''	):
                  lowerCAmelCase    :		List[str]				=      384
                  lowerCAmelCase    :		Optional[int]				=      1536
                  lowerCAmelCase    :		int				=      12
                  lowerCAmelCase    :		str				=      6
            else:
                  pass
      else:
            if vit_name[4:].startswith('''small'''	):
                  lowerCAmelCase    :		List[str]				=      768
                  lowerCAmelCase    :		Dict				=      2304
                  lowerCAmelCase    :		Dict				=      8
                  lowerCAmelCase    :		Tuple				=      8
            elif vit_name[4:].startswith('''base'''	):
                  pass
            elif vit_name[4:].startswith('''large'''	):
                  lowerCAmelCase    :		Union[str, Any]				=      1024
                  lowerCAmelCase    :		List[Any]				=      4096
                  lowerCAmelCase    :		Union[str, Any]				=      24
                  lowerCAmelCase    :		Any				=      16
            elif vit_name[4:].startswith('''huge'''	):
                  lowerCAmelCase    :		Any				=      1280
                  lowerCAmelCase    :		str				=      5120
                  lowerCAmelCase    :		Tuple				=      32
                  lowerCAmelCase    :		Tuple				=      16

    # load original model from timm
      lowerCAmelCase    :		Any				=      timm.create_model(_snake_case       ,      pretrained=_snake_case	)
      timm_model.eval()

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

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

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

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

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


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

 snake_case__						:   int			 = parser.parse_args()
 convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path)


