
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

lowercase__							:							Union[str, Any]									=			logging.get_logger(__name__)




def 	A_ (							snake_case							:   List[Any] ,      snake_case							:   int ,      snake_case							:   List[Any] ,      snake_case							:   Dict=None ,      snake_case							:   Optional[int]=None )      ->    Dict:



						'''simple docstring'''

						if "." in tensor_name:
												__UpperCamelCase       =			tensor_name.split('''.''' )
												for split in splits[:-1]:
																		__UpperCamelCase       =			getattr(snake_case ,      snake_case )
																		if new_module is None:
																								raise ValueError(f"{module} has no attribute {split}." )
																		__UpperCamelCase       =			new_module
												__UpperCamelCase       =			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}." )
						__UpperCamelCase       =			tensor_name in module._buffers
						__UpperCamelCase       =			getattr(snake_case ,      snake_case )

						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}." )

						__UpperCamelCase       =			False
						__UpperCamelCase       =			False
						if is_buffer or not is_bitsandbytes_available():
												__UpperCamelCase       =			False
												__UpperCamelCase       =			False
						else:
												__UpperCamelCase       =			hasattr(bnb.nn ,      '''Params4bit''' ) and isinstance(module._parameters[tensor_name] ,      bnb.nn.Paramsabit )
												__UpperCamelCase       =			isinstance(module._parameters[tensor_name] ,      bnb.nn.IntaParams )

						if is_abit or is_abit:
												__UpperCamelCase       =			module._parameters[tensor_name]
												if param.device.type != "cuda":
																		if value is None:
																								__UpperCamelCase       =			old_value.to(snake_case )
																		elif isinstance(snake_case ,      torch.Tensor ):
																								__UpperCamelCase       =			value.to('''cpu''' )
																								if value.dtype == torch.inta:
																														__UpperCamelCase       =			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:
																								__UpperCamelCase       =			torch.tensor(snake_case ,      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 ,      snake_case ) and fpaa_statistics is None:
																								__UpperCamelCase       =			new_value.T

																		__UpperCamelCase       =			old_value.__dict__
																		if is_abit:
																								__UpperCamelCase       =			bnb.nn.IntaParams(snake_case ,      requires_grad=snake_case ,      **snake_case ).to(snake_case )
																		elif is_abit:
																								__UpperCamelCase       =			bnb.nn.Paramsabit(snake_case ,      requires_grad=snake_case ,      **snake_case ).to(snake_case )

																		__UpperCamelCase       =			new_value
																		if fpaa_statistics is not None:
																								setattr(module.weight ,      '''SCB''' ,      fpaa_statistics.to(snake_case ) )

						else:
												if value is None:
																		__UpperCamelCase       =			old_value.to(snake_case )
												elif isinstance(snake_case ,      torch.Tensor ):
																		__UpperCamelCase       =			value.to(snake_case )
												else:
																		__UpperCamelCase       =			torch.tensor(snake_case ,      device=snake_case )

												if is_buffer:
																		__UpperCamelCase       =			new_value
												else:
																		__UpperCamelCase       =			nn.Parameter(snake_case ,      requires_grad=old_value.requires_grad )
																		__UpperCamelCase       =			new_value




def 	A_ (							snake_case							:   List[Any] ,      snake_case							:   List[str]=None ,      snake_case							:   str=None ,      snake_case							:   Union[str, Any]=None ,      snake_case							:   Union[str, Any]=False )      ->    Any:



						'''simple docstring'''

						for name, module in model.named_children():
												if current_key_name is None:
																		__UpperCamelCase       =			[]
												current_key_name.append(snake_case )

												if (isinstance(snake_case ,      nn.Linear ) or isinstance(snake_case ,      snake_case )) 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(snake_case ) for key in modules_to_not_convert ):
																								with init_empty_weights():
																														if isinstance(snake_case ,      snake_case ):
																																				__UpperCamelCase  ,							__UpperCamelCase       =			module.weight.shape
																														else:
																																				__UpperCamelCase       =			module.in_features
																																				__UpperCamelCase       =			module.out_features

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




def 	A_ (							snake_case							:   Any ,      snake_case							:   List[str]=None ,      snake_case							:   str=None ,      snake_case							:   List[Any]=None )      ->    Optional[int]:



						'''simple docstring'''

						__UpperCamelCase       =			['''lm_head'''] if modules_to_not_convert is None else modules_to_not_convert
						__UpperCamelCase  ,							__UpperCamelCase       =			_replace_with_bnb_linear(
						    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.'''
												    ''' Please double check your model architecture, or submit an issue on github if you think this is'''
												    ''' a bug.''' )

						return model




def 	A_ (							*snake_case							:   Tuple ,      **snake_case							:   Tuple )      ->    Optional[Any]:



						'''simple docstring'''

						warnings.warn(
						    '''`replace_8bit_linear` will be deprecated in a future version, please use `replace_with_bnb_linear` instead''' ,      snake_case ,      )
						return replace_with_bnb_linear(*snake_case ,      **snake_case )




def 	A_ (							*snake_case							:   Optional[Any] ,      **snake_case							:   List[Any] )      ->    Dict:



						'''simple docstring'''

						warnings.warn(
						    '''`set_module_8bit_tensor_to_device` will be deprecated in a future version, please use `set_module_quantized_tensor_to_device` instead''' ,      snake_case ,      )
						return set_module_quantized_tensor_to_device(*snake_case ,      **snake_case )




def 	A_ (							snake_case							:   Any )      ->    str:



						'''simple docstring'''

						__UpperCamelCase       =			deepcopy(snake_case )  # this has 0 cost since it is done inside `init_empty_weights` context manager`
						tied_model.tie_weights()

						__UpperCamelCase       =			find_tied_parameters(snake_case )
						# For compatibility with Accelerate < 0.18
						if isinstance(snake_case ,      snake_case ):
												__UpperCamelCase       =			sum(list(tied_params.values() ) ,      [] ) + list(tied_params.keys() )
						else:
												__UpperCamelCase       =			sum(snake_case ,      [] )
						__UpperCamelCase       =			len(snake_case ) > 0

						# Check if it is a base model
						__UpperCamelCase       =			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
						__UpperCamelCase       =			list(model.named_children() )
						__UpperCamelCase       =			[list_modules[-1][0]]

						# add last module together with tied weights
						__UpperCamelCase       =			set(snake_case ) - set(snake_case )
						__UpperCamelCase       =			list(set(snake_case ) ) + list(snake_case )

						# remove ".weight" from the keys
						__UpperCamelCase       =			['''.weight''', '''.bias''']
						__UpperCamelCase       =			[]
						for name in list_untouched:
												for name_to_remove in names_to_remove:
																		if name_to_remove in name:
																								__UpperCamelCase       =			name.replace(snake_case ,      '''''' )
												filtered_module_names.append(snake_case )

						return filtered_module_names






import argparse
import collections
import json
from pathlib import Path

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

from transformers import (
    MobileViTImageProcessor,
    MobileViTVaConfig,
    MobileViTVaForImageClassification,
    MobileViTVaForSemanticSegmentation,
)
from transformers.utils import logging


logging.set_verbosity_info()
lowercase__							:							Union[str, Any]									=			logging.get_logger(__name__)




def 	A_ (							snake_case							:   List[str] )      ->    List[str]:



						'''simple docstring'''

						print('''Loading config file...''' )

						def flatten_yaml_as_dict(snake_case							:   Optional[int] ,      snake_case							:   List[Any]="" ,      snake_case							:   str="." ):
												__UpperCamelCase       =			[]
												for k, v in d.items():
																		__UpperCamelCase       =			parent_key + sep + k if parent_key else k
																		if isinstance(snake_case ,      collections.abc.MutableMapping ):
																								items.extend(flatten_yaml_as_dict(snake_case ,      snake_case ,      sep=snake_case ).items() )
																		else:
																								items.append((new_key, v) )
												return dict(snake_case )

						__UpperCamelCase       =			argparse.Namespace()
						with open(snake_case ,      '''r''' ) as yaml_file:
												try:
																		__UpperCamelCase       =			yaml.load(snake_case ,      Loader=yaml.FullLoader )

																		__UpperCamelCase       =			flatten_yaml_as_dict(snake_case )
																		for k, v in flat_cfg.items():
																								setattr(snake_case ,      snake_case ,      snake_case )
												except yaml.YAMLError as exc:
																		logger.error('''Error while loading config file: {}. Error message: {}'''.format(snake_case ,      str(snake_case ) ) )
						return config




def 	A_ (							snake_case							:   List[Any] ,      snake_case							:   Optional[Any] )      ->    Union[str, Any]:



						'''simple docstring'''

						__UpperCamelCase       =			MobileViTVaConfig()

						__UpperCamelCase       =			False

						# dataset
						if task_name.startswith('''imagenet1k_''' ):
												__UpperCamelCase       =			1000
												if int(task_name.strip().split('''_''' )[-1] ) == 384:
																		__UpperCamelCase       =			384
												else:
																		__UpperCamelCase       =			256
												__UpperCamelCase       =			'''imagenet-1k-id2label.json'''
						elif task_name.startswith('''imagenet21k_to_1k_''' ):
												__UpperCamelCase       =			21000
												if int(task_name.strip().split('''_''' )[-1] ) == 384:
																		__UpperCamelCase       =			384
												else:
																		__UpperCamelCase       =			256
												__UpperCamelCase       =			'''imagenet-22k-id2label.json'''
						elif task_name.startswith('''ade20k_''' ):
												__UpperCamelCase       =			151
												__UpperCamelCase       =			512
												__UpperCamelCase       =			'''ade20k-id2label.json'''
												__UpperCamelCase       =			True
						elif task_name.startswith('''voc_''' ):
												__UpperCamelCase       =			21
												__UpperCamelCase       =			512
												__UpperCamelCase       =			'''pascal-voc-id2label.json'''
												__UpperCamelCase       =			True

						# orig_config
						__UpperCamelCase       =			load_orig_config_file(snake_case )
						assert getattr(snake_case ,      '''model.classification.name''' ,      -1 ) == "mobilevit_v2", "Invalid model"
						__UpperCamelCase       =			getattr(snake_case ,      '''model.classification.mitv2.width_multiplier''' ,      1.0 )
						assert (
						    getattr(snake_case ,      '''model.classification.mitv2.attn_norm_layer''' ,      -1 ) == "layer_norm_2d"
						), "Norm layers other than layer_norm_2d is not supported"
						__UpperCamelCase       =			getattr(snake_case ,      '''model.classification.activation.name''' ,      '''swish''' )
						# config.image_size == getattr(orig_config,  'sampler.bs.crop_size_width', 256)

						if is_segmentation_model:
												__UpperCamelCase       =			getattr(snake_case ,      '''model.segmentation.output_stride''' ,      16 )
												if "_deeplabv3" in task_name:
																		__UpperCamelCase       =			getattr(snake_case ,      '''model.segmentation.deeplabv3.aspp_rates''' ,      [12, 24, 36] )
																		__UpperCamelCase       =			getattr(snake_case ,      '''model.segmentation.deeplabv3.aspp_out_channels''' ,      512 )
																		__UpperCamelCase       =			getattr(snake_case ,      '''model.segmentation.deeplabv3.aspp_dropout''' ,      0.1 )

    # id2label
						__UpperCamelCase       =			'''huggingface/label-files'''
						__UpperCamelCase       =			json.load(open(hf_hub_download(snake_case ,      snake_case ,      repo_type='''dataset''' ) ,      '''r''' ) )
						__UpperCamelCase       =			{int(snake_case ): v for k, v in idalabel.items()}
						__UpperCamelCase       =			idalabel
						__UpperCamelCase       =			{v: k for k, v in idalabel.items()}

						return config




def 	A_ (							snake_case							:   List[Any] ,      snake_case							:   int ,      snake_case							:   Any )      ->    str:



						'''simple docstring'''

						__UpperCamelCase       =			dct.pop(snake_case )
						__UpperCamelCase       =			val




def 	A_ (							snake_case							:   int ,      snake_case							:   List[Any]=False )      ->    Optional[Any]:



						'''simple docstring'''

						if base_model:
												__UpperCamelCase       =			''''''
						else:
												__UpperCamelCase       =			'''mobilevitv2.'''

						__UpperCamelCase       =			[]
						for k in state_dict.keys():
												if k[:8] == "encoder.":
																		__UpperCamelCase       =			k[8:]
												else:
																		__UpperCamelCase       =			k

												if ".block." in k:
																		__UpperCamelCase       =			k_new.replace('''.block.''' ,      '''.''' )
												if ".conv." in k:
																		__UpperCamelCase       =			k_new.replace('''.conv.''' ,      '''.convolution.''' )
												if ".norm." in k:
																		__UpperCamelCase       =			k_new.replace('''.norm.''' ,      '''.normalization.''' )

												if "conv_1." in k:
																		__UpperCamelCase       =			k_new.replace('''conv_1.''' ,      f"{model_prefix}conv_stem." )
												for i in [1, 2]:
																		if f"layer_{i}." in k:
																								__UpperCamelCase       =			k_new.replace(f"layer_{i}." ,      f"{model_prefix}encoder.layer.{i-1}.layer." )
												if ".exp_1x1." in k:
																		__UpperCamelCase       =			k_new.replace('''.exp_1x1.''' ,      '''.expand_1x1.''' )
												if ".red_1x1." in k:
																		__UpperCamelCase       =			k_new.replace('''.red_1x1.''' ,      '''.reduce_1x1.''' )

												for i in [3, 4, 5]:
																		if f"layer_{i}.0." in k:
																								__UpperCamelCase       =			k_new.replace(f"layer_{i}.0." ,      f"{model_prefix}encoder.layer.{i-1}.downsampling_layer." )
																		if f"layer_{i}.1.local_rep.0." in k:
																								__UpperCamelCase       =			k_new.replace(f"layer_{i}.1.local_rep.0." ,      f"{model_prefix}encoder.layer.{i-1}.conv_kxk." )
																		if f"layer_{i}.1.local_rep.1." in k:
																								__UpperCamelCase       =			k_new.replace(f"layer_{i}.1.local_rep.1." ,      f"{model_prefix}encoder.layer.{i-1}.conv_1x1." )

												for i in [3, 4, 5]:
																		if i == 3:
																								__UpperCamelCase       =			[0, 1]
																		elif i == 4:
																								__UpperCamelCase       =			[0, 1, 2, 3]
																		elif i == 5:
																								__UpperCamelCase       =			[0, 1, 2]

																		for j in j_in:
																								if f"layer_{i}.1.global_rep.{j}." in k:
																														__UpperCamelCase       =			k_new.replace(
																														    f"layer_{i}.1.global_rep.{j}." ,      f"{model_prefix}encoder.layer.{i-1}.transformer.layer.{j}." )
																		if f"layer_{i}.1.global_rep.{j+1}." in k:
																								__UpperCamelCase       =			k_new.replace(
																								    f"layer_{i}.1.global_rep.{j+1}." ,      f"{model_prefix}encoder.layer.{i-1}.layernorm." )

																		if f"layer_{i}.1.conv_proj." in k:
																								__UpperCamelCase       =			k_new.replace(f"layer_{i}.1.conv_proj." ,      f"{model_prefix}encoder.layer.{i-1}.conv_projection." )

												if "pre_norm_attn.0." in k:
																		__UpperCamelCase       =			k_new.replace('''pre_norm_attn.0.''' ,      '''layernorm_before.''' )
												if "pre_norm_attn.1." in k:
																		__UpperCamelCase       =			k_new.replace('''pre_norm_attn.1.''' ,      '''attention.''' )
												if "pre_norm_ffn.0." in k:
																		__UpperCamelCase       =			k_new.replace('''pre_norm_ffn.0.''' ,      '''layernorm_after.''' )
												if "pre_norm_ffn.1." in k:
																		__UpperCamelCase       =			k_new.replace('''pre_norm_ffn.1.''' ,      '''ffn.conv1.''' )
												if "pre_norm_ffn.3." in k:
																		__UpperCamelCase       =			k_new.replace('''pre_norm_ffn.3.''' ,      '''ffn.conv2.''' )

												if "classifier.1." in k:
																		__UpperCamelCase       =			k_new.replace('''classifier.1.''' ,      '''classifier.''' )

												if "seg_head." in k:
																		__UpperCamelCase       =			k_new.replace('''seg_head.''' ,      '''segmentation_head.''' )
												if ".aspp_layer." in k:
																		__UpperCamelCase       =			k_new.replace('''.aspp_layer.''' ,      '''.''' )
												if ".aspp_pool." in k:
																		__UpperCamelCase       =			k_new.replace('''.aspp_pool.''' ,      '''.''' )

												rename_keys.append((k, k_new) )
						return rename_keys




def 	A_ (							snake_case							:   List[str] )      ->    str:



						'''simple docstring'''

						__UpperCamelCase       =			[]
						for k in state_dict.keys():
												if k.startswith('''seg_head.aux_head.''' ):
																		keys_to_ignore.append(snake_case )
						for k in keys_to_ignore:
												state_dict.pop(snake_case ,      snake_case )




def 	A_ (							)      ->    str:



						'''simple docstring'''

						__UpperCamelCase       =			'''http://images.cocodataset.org/val2017/000000039769.jpg'''
						# url = "https://cdn.britannica.com/86/141086-050-9D7C75EE/Gulfstream-G450-business-jet-passengers.jpg"
						__UpperCamelCase       =			Image.open(requests.get(snake_case ,      stream=snake_case ).raw )
						return im




@torch.no_grad()
def 	A_ (							snake_case							:   Dict ,      snake_case							:   List[str] ,      snake_case							:   Optional[Any] ,      snake_case							:   Optional[int] )      ->    int:



						'''simple docstring'''

						__UpperCamelCase       =			get_mobilevitva_config(snake_case ,      snake_case )

						# load original state_dict
						__UpperCamelCase       =			torch.load(snake_case ,      map_location='''cpu''' )

						# load huggingface model
						if task_name.startswith('''ade20k_''' ) or task_name.startswith('''voc_''' ):
												__UpperCamelCase       =			MobileViTVaForSemanticSegmentation(snake_case ).eval()
												__UpperCamelCase       =			False
						else:
												__UpperCamelCase       =			MobileViTVaForImageClassification(snake_case ).eval()
												__UpperCamelCase       =			False

						# remove and rename some keys of load the original model
						__UpperCamelCase       =			checkpoint
						remove_unused_keys(snake_case )
						__UpperCamelCase       =			create_rename_keys(snake_case ,      base_model=snake_case )
						for rename_key_src, rename_key_dest in rename_keys:
												rename_key(snake_case ,      snake_case ,      snake_case )

						# load modified state_dict
						model.load_state_dict(snake_case )

						# Check outputs on an image, prepared by MobileViTImageProcessor
						__UpperCamelCase       =			MobileViTImageProcessor(crop_size=config.image_size ,      size=config.image_size + 32 )
						__UpperCamelCase       =			image_processor(images=prepare_img() ,      return_tensors='''pt''' )
						__UpperCamelCase       =			model(**snake_case )

						# verify classification model
						if task_name.startswith('''imagenet''' ):
												__UpperCamelCase       =			outputs.logits
												__UpperCamelCase       =			logits.argmax(-1 ).item()
												print('''Predicted class:''' ,      model.config.idalabel[predicted_class_idx] )
												if task_name.startswith('''imagenet1k_256''' ) and config.width_multiplier == 1.0:
																		# expected_logits for base variant
																		__UpperCamelCase       =			torch.tensor([-1.63_36e00, -7.32_04e-02, -5.18_83e-01] )
																		assert torch.allclose(logits[0, :3] ,      snake_case ,      atol=1e-4 )

						Path(snake_case ).mkdir(exist_ok=snake_case )
						print(f"Saving model {task_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__":
				lowercase__							:							Any									=			argparse.ArgumentParser()
				# Required parameters
				parser.add_argument(
				    "--task",
				    default="imagenet1k_256",
				    type=str,
				    help=(
				        "Name of the task for which the MobileViTV2 model you'd like to convert is trained on . "
				        "\n                Classification (ImageNet-1k)\n                    - MobileViTV2 (256x256) : imagenet1k_256\n                    - MobileViTV2 (Trained on 256x256 and Finetuned on 384x384) : imagenet1k_384\n                    - MobileViTV2 (Trained on ImageNet-21k and Finetuned on ImageNet-1k 256x256) :\n                      imagenet21k_to_1k_256\n                    - MobileViTV2 (Trained on ImageNet-21k, Finetuned on ImageNet-1k 256x256, and Finetuned on\n                      ImageNet-1k 384x384) : imagenet21k_to_1k_384\n                Segmentation\n                    - ADE20K Dataset : ade20k_deeplabv3\n                    - Pascal VOC 2012 Dataset: voc_deeplabv3\n            "
				    ),
				    choices=[
				        "imagenet1k_256",
				        "imagenet1k_384",
				        "imagenet21k_to_1k_256",
				        "imagenet21k_to_1k_384",
				        "ade20k_deeplabv3",
				        "voc_deeplabv3",
				    ],
				)

				parser.add_argument(
				    "--orig_checkpoint_path", required=True, type=str, help="Path to the original state dict (.pt file)."
				)
				parser.add_argument("--orig_config_path", required=True, type=str, help="Path to the original config file.")
				parser.add_argument(
				    "--pytorch_dump_folder_path", required=True, type=str, help="Path to the output PyTorch model directory."
				)

				lowercase__							:							Tuple									=			parser.parse_args()
				convert_mobilevitva_checkpoint(
				    args.task, args.orig_checkpoint_path, args.orig_config_path, args.pytorch_dump_folder_path
				)




