






"""simple docstring"""
import importlib.metadata
import warnings
from copy import deepcopy

from packaging import version

from ..utils import logging
from .import_utils import is_accelerate_available, is_bitsandbytes_available


if is_bitsandbytes_available():
		import bitsandbytes as bnb
		import torch
		import torch.nn as nn

		from ..pytorch_utils import ConvaD

if is_accelerate_available():
		from accelerate import init_empty_weights
		from accelerate.utils import find_tied_parameters

_lowerCAmelCase         =							logging.get_logger(__name__)






def 			__UpperCamelCase	(		snake_case__   ,   snake_case__   ,   snake_case__   ,   snake_case__=None   ,   snake_case__=None					):


	if "." in tensor_name:
		A_		:						Optional[int]	    =   tensor_name.split("""."""					)
		for split in splits[:-1]:
			A_		:						List[Any]	    =   getattr(_lowerCamelCase   ,   _lowerCamelCase					)
			if new_module is None:
				raise ValueError(F"""{module} has no attribute {split}."""					)
			A_		:						Any	    =   new_module
		A_		:						int	    =   splits[-1]

	if tensor_name not in module._parameters and tensor_name not in module._buffers:
		raise ValueError(F"""{module} does not have a parameter or a buffer named {tensor_name}."""					)
	A_		:						int	    =   tensor_name in module._buffers
	A_		:						Tuple	    =   getattr(_lowerCamelCase   ,   _lowerCamelCase					)

	if old_value.device == torch.device("""meta"""					) and device not in ["meta", torch.device("""meta"""					)] and value is None:
		raise ValueError(F"""{tensor_name} is on the meta device, we need a `value` to put in on {device}."""					)

	A_		:						str	    =   False
	A_		:						Optional[Any]	    =   False
	if is_buffer or not is_bitsandbytes_available():
		A_		:						List[Any]	    =   False
		A_		:						int	    =   False
	else:
		A_		:						Optional[Any]	    =   hasattr(bnb.nn   ,   """Params4bit"""					) and isinstance(module._parameters[tensor_name]   ,   bnb.nn.Paramsabit					)
		A_		:						Union[str, Any]	    =   isinstance(module._parameters[tensor_name]   ,   bnb.nn.IntaParams					)

	if is_abit or is_abit:
		A_		:						Union[str, Any]	    =   module._parameters[tensor_name]
		if param.device.type != "cuda":
			if value is None:
				A_		:						Dict	    =   old_value.to(_lowerCamelCase					)
			elif isinstance(_lowerCamelCase   ,   torch.Tensor					):
				A_		:						int	    =   value.to("""cpu"""					)
				if value.dtype == torch.inta:
					A_		:						List[Any]	    =   version.parse(importlib.metadata.version("""bitsandbytes"""					)					) > version.parse(
					    """0.37.2"""					)
					if not is_abit_serializable:
						raise ValueError(
						    """Detected int8 weights but the version of bitsandbytes is not compatible with int8 serialization. """
						    """Make sure to download the latest `bitsandbytes` version. `pip install --upgrade bitsandbytes`."""					)
			else:
				A_		:						Any	    =   torch.tensor(_lowerCamelCase   ,   device="""cpu"""					)

			# Support models using `Conv1D` in place of `nn.Linear` (e.g. gpt2) by transposing the weight matrix prior to quantization.
			# Since weights are saved in the correct "orientation", we skip transposing when loading.
			if issubclass(module.source_cls   ,   _lowerCamelCase					) and fpaa_statistics is None:
				A_		:						Dict	    =   new_value.T

			A_		:						int	    =   old_value.__dict__
			if is_abit:
				A_		:						Optional[int]	    =   bnb.nn.IntaParams(_lowerCamelCase   ,   requires_grad=_lowerCamelCase   ,   **_lowerCamelCase					).to(_lowerCamelCase					)
			elif is_abit:
				A_		:						List[str]	    =   bnb.nn.Paramsabit(_lowerCamelCase   ,   requires_grad=_lowerCamelCase   ,   **_lowerCamelCase					).to(_lowerCamelCase					)

			A_		:						List[str]	    =   new_value
			if fpaa_statistics is not None:
				setattr(module.weight   ,   """SCB"""   ,   fpaa_statistics.to(_lowerCamelCase					)					)

	else:
		if value is None:
			A_		:						List[str]	    =   old_value.to(_lowerCamelCase					)
		elif isinstance(_lowerCamelCase   ,   torch.Tensor					):
			A_		:						Optional[Any]	    =   value.to(_lowerCamelCase					)
		else:
			A_		:						Any	    =   torch.tensor(_lowerCamelCase   ,   device=_lowerCamelCase					)

		if is_buffer:
			A_		:						Union[str, Any]	    =   new_value
		else:
			A_		:						Union[str, Any]	    =   nn.Parameter(_lowerCamelCase   ,   requires_grad=old_value.requires_grad					)
			A_		:						Dict	    =   new_value






def 			__UpperCamelCase	(		snake_case__   ,   snake_case__=None   ,   snake_case__=None   ,   snake_case__=None   ,   snake_case__=False					):


	for name, module in model.named_children():
		if current_key_name is None:
			A_		:						Optional[Any]	    =   []
		current_key_name.append(_lowerCamelCase					)

		if (isinstance(_lowerCamelCase   ,   nn.Linear					) or isinstance(_lowerCamelCase   ,   _lowerCamelCase					)) and name not in modules_to_not_convert:
			# Check if the current key is not in the `modules_to_not_convert`
			if not any(key in """.""".join(_lowerCamelCase					) for key in modules_to_not_convert					):
				with init_empty_weights():
					if isinstance(_lowerCamelCase   ,   _lowerCamelCase					):
						A_		:						List[str]	    =   module.weight.shape
					else:
						A_		:						Any	    =   module.in_features
						A_		:						int	    =   module.out_features

					if quantization_config.quantization_method() == "llm_int8":
						A_		:						str	    =   bnb.nn.LinearabitLt(
						    _lowerCamelCase   ,   _lowerCamelCase   ,   module.bias is not None   ,   has_fpaa_weights=quantization_config.llm_inta_has_fpaa_weight   ,   threshold=quantization_config.llm_inta_threshold   ,   )
						A_		:						Tuple	    =   True
					else:
						if (
						    quantization_config.llm_inta_skip_modules is not None
						    and name in quantization_config.llm_inta_skip_modules
						):
							pass
						else:
							A_		:						Optional[Any]	    =   bnb.nn.Linearabit(
							    _lowerCamelCase   ,   _lowerCamelCase   ,   module.bias is not None   ,   quantization_config.bnb_abit_compute_dtype   ,   compress_statistics=quantization_config.bnb_abit_use_double_quant   ,   quant_type=quantization_config.bnb_abit_quant_type   ,   )
							A_		:						str	    =   True
                    # Store the module class in case we need to transpose the weight later
					A_		:						Tuple	    =   type(_lowerCamelCase					)
					# Force requires grad to False to avoid unexpected errors
					model._modules[name].requires_grad_(_lowerCamelCase					)
		if len(list(module.children()					)					) > 0:
			A_		:						List[str]	    =   _replace_with_bnb_linear(
			    _lowerCamelCase   ,   _lowerCamelCase   ,   _lowerCamelCase   ,   _lowerCamelCase   ,   has_been_replaced=_lowerCamelCase   ,   )
		# Remove the last key for recursion
		current_key_name.pop(-1					)
	return model, has_been_replaced






def 			__UpperCamelCase	(		snake_case__   ,   snake_case__=None   ,   snake_case__=None   ,   snake_case__=None					):
	A_		:						Optional[Any]	    =   ["lm_head"] if modules_to_not_convert is None else modules_to_not_convert
	A_		:						Any	    =   _replace_with_bnb_linear(
	    _lowerCamelCase   ,   _lowerCamelCase   ,   _lowerCamelCase   ,   _lowerCamelCase					)

	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."""
		    """ Please double check your model architecture, or submit an issue on github if you think this is"""
		    """ a bug."""					)

	return model






def 			__UpperCamelCase	(		*snake_case__   ,   **snake_case__					):
	warnings.warn(
	    """`replace_8bit_linear` will be deprecated in a future version, please use `replace_with_bnb_linear` instead"""   ,   _lowerCamelCase   ,   )
	return replace_with_bnb_linear(*_lowerCamelCase   ,   **_lowerCamelCase					)






def 			__UpperCamelCase	(		*snake_case__   ,   **snake_case__					):
	warnings.warn(
	    """`set_module_8bit_tensor_to_device` will be deprecated in a future version, please use `set_module_quantized_tensor_to_device` instead"""   ,   _lowerCamelCase   ,   )
	return set_module_quantized_tensor_to_device(*_lowerCamelCase   ,   **_lowerCamelCase					)






def 			__UpperCamelCase	(		snake_case__					):
	A_		:						int	    =   deepcopy(_lowerCamelCase					)  # this has 0 cost since it is done inside `init_empty_weights` context manager`
	tied_model.tie_weights()

	A_		:						List[str]	    =   find_tied_parameters(_lowerCamelCase					)
	# For compatibility with Accelerate < 0.18
	if isinstance(_lowerCamelCase   ,   _lowerCamelCase					):
		A_		:						Union[str, Any]	    =   sum(list(tied_params.values()					)   ,   []					) + list(tied_params.keys()					)
	else:
		A_		:						Optional[int]	    =   sum(_lowerCamelCase   ,   []					)
	A_		:						int	    =   len(_lowerCamelCase					) > 0

	# Check if it is a base model
	A_		:						Optional[int]	    =   not hasattr(_lowerCamelCase   ,   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
	A_		:						Optional[int]	    =   list(model.named_children()					)
	A_		:						int	    =   [list_modules[-1][0]]

	# add last module together with tied weights
	A_		:						int	    =   set(_lowerCamelCase					) - set(_lowerCamelCase					)
	A_		:						Any	    =   list(set(_lowerCamelCase					)					) + list(_lowerCamelCase					)

	# remove ".weight" from the keys
	A_		:						Dict	    =   [".weight", ".bias"]
	A_		:						str	    =   []
	for name in list_untouched:
		for name_to_remove in names_to_remove:
			if name_to_remove in name:
				A_		:						Any	    =   name.replace(_lowerCamelCase   ,   """"""					)
		filtered_module_names.append(_lowerCamelCase					)

	return filtered_module_names









"""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 timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform

from transformers import (
    BitConfig,
    ViTHybridConfig,
    ViTHybridForImageClassification,
    ViTHybridImageProcessor,
    ViTHybridModel,
)
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


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






def 			__UpperCamelCase	(		snake_case__   ,   snake_case__=False					):
	A_		:						Dict	    =   []

	# fmt: off
	# stem:
	rename_keys.append(("""cls_token""", """vit.embeddings.cls_token""")					)
	rename_keys.append(("""pos_embed""", """vit.embeddings.position_embeddings""")					)

	rename_keys.append(("""patch_embed.proj.weight""", """vit.embeddings.patch_embeddings.projection.weight""")					)
	rename_keys.append(("""patch_embed.proj.bias""", """vit.embeddings.patch_embeddings.projection.bias""")					)

	# backbone
	rename_keys.append(("""patch_embed.backbone.stem.conv.weight""", """vit.embeddings.patch_embeddings.backbone.bit.embedder.convolution.weight""")					)
	rename_keys.append(("""patch_embed.backbone.stem.norm.weight""", """vit.embeddings.patch_embeddings.backbone.bit.embedder.norm.weight""")					)
	rename_keys.append(("""patch_embed.backbone.stem.norm.bias""", """vit.embeddings.patch_embeddings.backbone.bit.embedder.norm.bias""")					)

	for stage_idx in range(len(config.backbone_config.depths					)					):
		for layer_idx in range(config.backbone_config.depths[stage_idx]					):
			rename_keys.append((F"""patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv1.weight""", F"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv1.weight""")					)
			rename_keys.append((F"""patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm1.weight""", F"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm1.weight""")					)
			rename_keys.append((F"""patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm1.bias""", F"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm1.bias""")					)
			rename_keys.append((F"""patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv2.weight""", F"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv2.weight""")					)
			rename_keys.append((F"""patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm2.weight""", F"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm2.weight""")					)
			rename_keys.append((F"""patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm2.bias""", F"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm2.bias""")					)
			rename_keys.append((F"""patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv3.weight""", F"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv3.weight""")					)
			rename_keys.append((F"""patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm3.weight""", F"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm3.weight""")					)
			rename_keys.append((F"""patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm3.bias""", F"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm3.bias""")					)

		rename_keys.append((F"""patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.conv.weight""", F"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.conv.weight""")					)
		rename_keys.append((F"""patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.norm.weight""", F"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.norm.weight""")					)
		rename_keys.append((F"""patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.norm.bias""", F"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.norm.bias""")					)

	# transformer encoder
	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""")					)

	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"
		A_		:						Dict	    =   [(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"""),
		    ]					)
	# fmt: on

	return rename_keys






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






def 			__UpperCamelCase	(		snake_case__					):
	A_		:						Tuple	    =   ["""head.weight""", """head.bias"""]
	for k in ignore_keys:
		state_dict.pop(snake_case__   ,   snake_case__					)






def 			__UpperCamelCase	(		snake_case__   ,   snake_case__   ,   snake_case__					):
	A_		:						Union[str, Any]	    =   dct.pop(snake_case__					)
	A_		:						List[Any]	    =   val






def 			__UpperCamelCase	(		):
	A_		:						Any	    =   """http://images.cocodataset.org/val2017/000000039769.jpg"""
	A_		:						str	    =   Image.open(requests.get(snake_case__   ,   stream=snake_case__					).raw					)
	return im






@torch.no_grad()
def 			__UpperCamelCase	(		snake_case__   ,   snake_case__   ,   snake_case__=False					):
	A_		:						str	    =   BitConfig(
	    global_padding="""same"""   ,   layer_type="""bottleneck"""   ,   depths=(3, 4, 9)   ,   out_features=["""stage3"""]   ,   embedding_dynamic_padding=snake_case__   ,   )
	A_		:						int	    =   ViTHybridConfig(backbone_config=snake_case__   ,   image_size=384   ,   num_labels=1_000					)
	A_		:						Any	    =   False

	# load original model from timm
	A_		:						str	    =   timm.create_model(snake_case__   ,   pretrained=snake_case__					)
	timm_model.eval()

	# load state_dict of original model, remove and rename some keys
	A_		:						Optional[Any]	    =   timm_model.state_dict()
	if base_model:
		remove_classification_head_(snake_case__					)
	A_		:						Dict	    =   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__					)

	A_		:						Any	    =   """huggingface/label-files"""
	A_		:						Any	    =   """imagenet-1k-id2label.json"""
	A_		:						Any	    =   json.load(open(hf_hub_download(snake_case__   ,   snake_case__   ,   repo_type="""dataset"""					)   ,   """r"""					)					)
	A_		:						Optional[int]	    =   {int(snake_case__					): v for k, v in idalabel.items()}
	A_		:						Optional[int]	    =   idalabel
	A_		:						Dict	    =   {v: k for k, v in idalabel.items()}

	# load HuggingFace model
	if vit_name[-5:] == "in21k":
		A_		:						int	    =   ViTHybridModel(snake_case__					).eval()
	else:
		A_		:						Union[str, Any]	    =   ViTHybridForImageClassification(snake_case__					).eval()
	model.load_state_dict(snake_case__					)

	# create image processor
	A_		:						Any	    =   create_transform(**resolve_data_config({}   ,   model=snake_case__					)					)
	A_		:						List[Any]	    =   transform.transforms

	A_		:						int	    =   {
	    """bilinear""": PILImageResampling.BILINEAR,
	    """bicubic""": PILImageResampling.BICUBIC,
	    """nearest""": PILImageResampling.NEAREST,
	}

	A_		:						Optional[int]	    =   ViTHybridImageProcessor(
	    do_resize=snake_case__   ,   size={"""shortest_edge""": timm_transforms[0].size}   ,   resample=pillow_resamplings[timm_transforms[0].interpolation.value]   ,   do_center_crop=snake_case__   ,   crop_size={"""height""": timm_transforms[1].size[0], """width""": timm_transforms[1].size[1]}   ,   do_normalize=snake_case__   ,   image_mean=timm_transforms[-1].mean.tolist()   ,   image_std=timm_transforms[-1].std.tolist()   ,   )

	A_		:						Tuple	    =   prepare_img()
	A_		:						List[str]	    =   transform(snake_case__					).unsqueeze(0					)
	A_		:						int	    =   processor(snake_case__   ,   return_tensors="""pt"""					).pixel_values

	# verify pixel values
	assert torch.allclose(snake_case__   ,   snake_case__					)

	# verify logits
	with torch.no_grad():
		A_		:						List[str]	    =   model(snake_case__					)
		A_		:						Any	    =   outputs.logits

	print("""Predicted class:"""   ,   logits.argmax(-1					).item()					)
	if base_model:
		A_		:						int	    =   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:
		A_		:						Optional[int]	    =   timm_model(snake_case__					)
		assert timm_logits.shape == outputs.logits.shape
		assert torch.allclose(snake_case__   ,   outputs.logits   ,   atol=1E-3					)
	print("""Looks ok!"""					)

	if pytorch_dump_folder_path is not None:
		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 processor to {pytorch_dump_folder_path}"""					)
		processor.save_pretrained(snake_case__					)

	if push_to_hub:
		print(F"""Pushing model and processor to the hub {vit_name}"""					)
		model.push_to_hub(F"""ybelkada/{vit_name}"""					)
		processor.push_to_hub(F"""ybelkada/{vit_name}"""					)


if __name__ == "__main__":
							_lowerCAmelCase         =							argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    "--vit_name",
							    default="vit_base_r50_s16_384",
							    type=str,
							    help="Name of the hybrid 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."
							)
							parser.add_argument(
							    "--push_to_hub", action="store_true", help="Whether to upload the model to the HuggingFace hub."
							)

							_lowerCAmelCase         =							parser.parse_args()
							convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path, args.push_to_hub)

