




import argparse
import shutil
import time
from json import JSONDecodeError
from logging import getLogger
from pathlib import Path
from typing import Dict, List

import torch
from torch.utils.data import DataLoader
from tqdm import tqdm

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from utils import (
    SeqaSeqDataset,
    calculate_bleu,
    calculate_rouge,
    chunks,
    lmap,
    load_json,
    parse_numeric_n_bool_cl_kwargs,
    save_json,
    use_task_specific_params,
    write_txt_file,
)


_snake_case										=       getLogger(__name__)







def 				lowerCAmelCase_	(     snake_case_,snake_case_,snake_case_,snake_case_ = 8,snake_case_ = 1024,snake_case_="val",snake_case_=None,snake_case_=False,snake_case_="summarization",snake_case_=None,snake_case_=1,snake_case_ = None,snake_case_="",**snake_case_,):
      _A							:     Dict			=						str(snake_case_     )
      assert local_rank is not None
      torch.distributed.init_process_group(backend="""nccl""",rank=snake_case_     )

      _A							:     Tuple			=						Path(snake_case_     )
      _A							:     List[Any]			=						save_dir.joinpath(f'''rank_{local_rank}_output.json'''     )
      torch.cuda.set_device(snake_case_     )
      _A							:     Union[str, Any]			=						AutoModelForSeqaSeqLM.from_pretrained(snake_case_     ).cuda()
      if fpaa:
            _A							:     Any			=						model.half()
      # determine if we need to increase num_beams
      use_task_specific_params(snake_case_,snake_case_     )  # update config with task specific params
      _A							:     str			=						generate_kwargs.pop("""num_beams""",model.config.num_beams     )  # AttributeError risk?
      if num_return_sequences > num_beams:
            _A							:     int			=						num_return_sequences

      _A							:     Optional[Any]			=						AutoTokenizer.from_pretrained(snake_case_     )
      logger.info(f'''Inferred tokenizer type: {tokenizer.__class__}'''     )  # if this is wrong, check config.model_type.

      if max_source_length is None:
            _A							:     Optional[int]			=						tokenizer.model_max_length
      if prefix is None:
            _A							:     Tuple			=						prefix or getattr(model.config,"""prefix""",""""""     ) or """"""
      _A							:     Optional[int]			=						SeqaSeqDataset(
          snake_case_,snake_case_,snake_case_,max_target_length=1024,type_path=snake_case_,n_obs=snake_case_,prefix=snake_case_,**snake_case_,)
      # I set shuffle=True for a more accurate progress bar.
      # If all the longest samples are first, the prog bar estimate is too high at the beginning.
      _A							:     Optional[int]			=						ds.make_sortish_sampler(snake_case_,distributed=snake_case_,add_extra_examples=snake_case_,shuffle=snake_case_     )
      _A							:     Dict			=						DataLoader(snake_case_,sampler=snake_case_,batch_size=snake_case_,collate_fn=ds.collate_fn     )
      _A							:     Optional[Any]			=						[]
      for batch in tqdm(snake_case_     ):
            _A							:     Tuple			=						model.generate(
                input_ids=batch["""input_ids"""].to(model.device     ),attention_mask=batch["""attention_mask"""].to(model.device     ),num_return_sequences=snake_case_,num_beams=snake_case_,**snake_case_,)
            _A							:     Any			=						tokenizer.batch_decode(snake_case_,skip_special_tokens=snake_case_,clean_up_tokenization_spaces=snake_case_     )
            _A							:     Dict			=						batch["""ids"""]
            if num_return_sequences > 1:
                  _A							:     Any			=						chunks(snake_case_,snake_case_     )  # batch size chunks, each of size num_return_seq
            for i, pred in enumerate(snake_case_     ):
                  results.append({"""pred""": pred, """id""": ids[i].item()}     )
      save_json(snake_case_,snake_case_     )
      return results, sampler.num_replicas







def 				lowerCAmelCase_	(     ):
      _A							:     Tuple			=						argparse.ArgumentParser(
          epilog="""Unspecified args like --num_beams=2 --decoder_start_token_id=4 are passed to model.generate"""     )
      parser.add_argument("""--data_dir""",type=snake_case_,help="""like cnn_dm/test.source"""     )
      parser.add_argument(
          """--model_name""",type=snake_case_,help="""like facebook/bart-large-cnn,t5-base, etc.""",default="""sshleifer/distilbart-xsum-12-3""",)
      parser.add_argument("""--save_dir""",type=snake_case_,help="""where to save""",default="""tmp_gen"""     )
      parser.add_argument("""--max_source_length""",type=snake_case_,default=snake_case_     )
      parser.add_argument(
          """--type_path""",type=snake_case_,default="""test""",help="""which subset to evaluate typically train/val/test"""     )
      parser.add_argument("""--task""",type=snake_case_,default="""summarization""",help="""used for task_specific_params + metrics"""     )
      parser.add_argument("""--bs""",type=snake_case_,default=8,required=snake_case_,help="""batch size"""     )
      parser.add_argument(
          """--local_rank""",type=snake_case_,default=-1,required=snake_case_,help="""should be passed by distributed.launch"""     )

      parser.add_argument(
          """--n_obs""",type=snake_case_,default=snake_case_,required=snake_case_,help="""How many observations. Defaults to all."""     )
      parser.add_argument(
          """--num_return_sequences""",type=snake_case_,default=1,required=snake_case_,help="""How many sequences to return"""     )
      parser.add_argument(
          """--sync_timeout""",type=snake_case_,default=600,required=snake_case_,help="""How long should master process wait for other processes to finish.""",)
      parser.add_argument("""--src_lang""",type=snake_case_,default=snake_case_,required=snake_case_     )
      parser.add_argument("""--tgt_lang""",type=snake_case_,default=snake_case_,required=snake_case_     )
      parser.add_argument(
          """--prefix""",type=snake_case_,required=snake_case_,default=snake_case_,help="""will be added to the begininng of src examples"""     )
      parser.add_argument("""--fp16""",action="""store_true"""     )
      parser.add_argument("""--debug""",action="""store_true"""     )
      _A							:     Union[str, Any]			=						time.time()
      _A			,   _A							:     List[str]			=						parser.parse_known_args()
      _A							:     List[str]			=						parse_numeric_n_bool_cl_kwargs(snake_case_     )
      if generate_kwargs and args.local_rank <= 0:
            print(f'''parsed the following generate kwargs: {generate_kwargs}'''     )
      _A							:     Dict			=						Path(args.save_dir + """_tmp"""     )
      Path(snake_case_     ).mkdir(exist_ok=snake_case_     )  # this handles locking.
      _A							:     int			=						list(json_save_dir.glob("""rank_*.json"""     )     )
      if intermediate_files:
            raise ValueError(f'''Found files at {json_save_dir} please move or remove them.'''     )
            # In theory, a node could finish and save before another node hits this. If this happens, we can address later.
      _A							:     Any			=						{}
      if args.src_lang is not None:
            _A							:     int			=						args.src_lang
      if args.tgt_lang is not None:
            _A							:     Dict			=						args.tgt_lang

      Path(args.save_dir     ).mkdir(exist_ok=snake_case_     )
      _A			,   _A							:     str			=						eval_data_dir(
          args.data_dir,snake_case_,args.model_name,type_path=args.type_path,bs=args.bs,fpaa=args.fpaa,task=args.task,local_rank=args.local_rank,n_obs=args.n_obs,max_source_length=args.max_source_length,num_return_sequences=args.num_return_sequences,prefix=args.prefix,dataset_kwargs=snake_case_,**snake_case_,)

      if args.local_rank <= 0:
            _A							:     List[Any]			=						Path(args.save_dir     )
            save_dir.mkdir(exist_ok=snake_case_     )
            _A							:     Tuple			=						gather_results_from_each_node(snake_case_,snake_case_,args.sync_timeout     )
            _A							:     Optional[int]			=						combine_partial_results(snake_case_     )
            if args.num_return_sequences > 1:
                  _A							:     Optional[Any]			=						save_dir.joinpath("""pseudolabel_results.json"""     )
                  print(f'''Saving aggregated results at {save_path}, intermediate in {json_save_dir}/'''     )
                  save_json(snake_case_,snake_case_     )
                  return
            _A							:     List[str]			=						Path(args.data_dir     ).joinpath(args.type_path + """.target"""     )
            with open(snake_case_     ) as f:
                  _A							:     int			=						[x.rstrip() for x in f.readlines()][: len(snake_case_     )]

            # Calculate metrics, save metrics,  and save _generations.txt
            _A							:     Dict			=						"""translation""" in args.task
            _A							:     Optional[Any]			=						calculate_bleu if calc_bleu else calculate_rouge
            _A							:     Tuple			=						"""bleu""" if calc_bleu else """rouge"""
            _A							:     Dict			=						score_fn(snake_case_,snake_case_     )
            _A							:     List[Any]			=						len(snake_case_     )
            _A							:     Optional[int]			=						time.time() - start_time
            _A							:     Dict			=						round(runtime / metrics["""n_obs"""],4     )
            _A							:     Dict			=						num_replicas
            # TODO(@stas00): add whatever metadata to metrics
            _A							:     Any			=						save_dir.joinpath(f'''{args.type_path}_{metric_name}.json'''     )
            save_json(snake_case_,snake_case_,indent=snake_case_     )
            print(snake_case_     )
            write_txt_file(snake_case_,save_dir.joinpath(f'''{args.type_path}_generations.txt'''     )     )
            if args.debug:
                  write_txt_file(snake_case_,save_dir.joinpath(f'''{args.type_path}.target'''     )     )
            else:
                  shutil.rmtree(snake_case_     )







def 				lowerCAmelCase_	(     snake_case_     ):
      _A							:     Dict			=						[]
      for partial_result in partial_results:
            records.extend(snake_case_     )
      _A							:     Optional[Any]			=						sorted(snake_case_,key=lambda snake_case_     : x["id"]     )
      _A							:     List[str]			=						[x["""pred"""] for x in records]
      return preds







def 				lowerCAmelCase_	(     snake_case_,snake_case_,snake_case_     ):
      # WAIT FOR lots of .json files
      _A							:     Optional[Any]			=						time.time()
      logger.info("""waiting for all nodes to finish"""     )
      _A							:     List[str]			=						None
      while (time.time() - start_wait) < timeout:
            _A							:     str			=						list(save_dir.glob("""rank_*.json"""     )     )
            if len(snake_case_     ) < num_replicas:
                  continue
            try:
                  # make sure all json files are fully saved
                  _A							:     List[str]			=						lmap(snake_case_,snake_case_     )
                  return json_data
            except JSONDecodeError:
                  continue
      else:
            raise TimeoutError("""Rank 0 gave up on waiting for other processes"""     )
      # Unreachable


if __name__ == "__main__":
  # Usage for MT:
  run_generate()







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()
_snake_case										=       logging.get_logger(__name__)







def 				lowerCAmelCase_	(     snake_case_     ):
      print("""Loading config file..."""     )

      def flatten_yaml_as_dict(snake_case_,snake_case_="",snake_case_="."     ):
            _A							:     Union[str, Any]			=						[]
            for k, v in d.items():
                  _A							:     Optional[int]			=						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_     )

      _A							:     List[Any]			=						argparse.Namespace()
      with open(snake_case_,"""r"""     ) as yaml_file:
            try:
                  _A							:     List[Any]			=						yaml.load(snake_case_,Loader=yaml.FullLoader     )

                  _A							:     Optional[int]			=						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 				lowerCAmelCase_	(     snake_case_,snake_case_     ):
      _A							:     Optional[Any]			=						MobileViTVaConfig()

      _A							:     Tuple			=						False

      # dataset
      if task_name.startswith("""imagenet1k_"""     ):
            _A							:     Dict			=						1000
            if int(task_name.strip().split("""_"""     )[-1]     ) == 384:
                  _A							:     int			=						384
            else:
                  _A							:     int			=						256
            _A							:     List[str]			=						"""imagenet-1k-id2label.json"""
      elif task_name.startswith("""imagenet21k_to_1k_"""     ):
            _A							:     Union[str, Any]			=						21000
            if int(task_name.strip().split("""_"""     )[-1]     ) == 384:
                  _A							:     str			=						384
            else:
                  _A							:     List[Any]			=						256
            _A							:     List[str]			=						"""imagenet-22k-id2label.json"""
      elif task_name.startswith("""ade20k_"""     ):
            _A							:     int			=						151
            _A							:     int			=						512
            _A							:     Optional[int]			=						"""ade20k-id2label.json"""
            _A							:     Any			=						True
      elif task_name.startswith("""voc_"""     ):
            _A							:     List[Any]			=						21
            _A							:     Dict			=						512
            _A							:     Dict			=						"""pascal-voc-id2label.json"""
            _A							:     int			=						True

      # orig_config
      _A							:     Any			=						load_orig_config_file(snake_case_     )
      assert getattr(snake_case_,"""model.classification.name""",-1     ) == "mobilevit_v2", "Invalid model"
      _A							:     List[Any]			=						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"
      _A							:     str			=						getattr(snake_case_,"""model.classification.activation.name""","""swish"""     )
      # config.image_size == getattr(orig_config,  'sampler.bs.crop_size_width', 256)

      if is_segmentation_model:
            _A							:     Optional[int]			=						getattr(snake_case_,"""model.segmentation.output_stride""",16     )
            if "_deeplabv3" in task_name:
                  _A							:     int			=						getattr(snake_case_,"""model.segmentation.deeplabv3.aspp_rates""",[12, 24, 36]     )
                  _A							:     int			=						getattr(snake_case_,"""model.segmentation.deeplabv3.aspp_out_channels""",512     )
                  _A							:     str			=						getattr(snake_case_,"""model.segmentation.deeplabv3.aspp_dropout""",0.1     )

    # id2label
      _A							:     List[Any]			=						"""huggingface/label-files"""
      _A							:     List[Any]			=						json.load(open(hf_hub_download(snake_case_,snake_case_,repo_type="""dataset"""     ),"""r"""     )     )
      _A							:     str			=						{int(snake_case_     ): v for k, v in idalabel.items()}
      _A							:     str			=						idalabel
      _A							:     Dict			=						{v: k for k, v in idalabel.items()}

      return config







def 				lowerCAmelCase_	(     snake_case_,snake_case_,snake_case_     ):
      _A							:     Any			=						dct.pop(snake_case_     )
      _A							:     Union[str, Any]			=						val







def 				lowerCAmelCase_	(     snake_case_,snake_case_=False     ):
      if base_model:
            _A							:     Optional[int]			=						""""""
      else:
            _A							:     Dict			=						"""mobilevitv2."""

      _A							:     int			=						[]
      for k in state_dict.keys():
            if k[:8] == "encoder.":
                  _A							:     Any			=						k[8:]
            else:
                  _A							:     List[str]			=						k

            if ".block." in k:
                  _A							:     Any			=						k_new.replace(""".block.""","""."""     )
            if ".conv." in k:
                  _A							:     List[Any]			=						k_new.replace(""".conv.""",""".convolution."""     )
            if ".norm." in k:
                  _A							:     Any			=						k_new.replace(""".norm.""",""".normalization."""     )

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

            for i in [3, 4, 5]:
                  if f'''layer_{i}.0.''' in k:
                        _A							:     Optional[int]			=						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:
                        _A							:     Union[str, Any]			=						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:
                        _A							:     str			=						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:
                        _A							:     Optional[int]			=						[0, 1]
                  elif i == 4:
                        _A							:     Union[str, Any]			=						[0, 1, 2, 3]
                  elif i == 5:
                        _A							:     Optional[Any]			=						[0, 1, 2]

                  for j in j_in:
                        if f'''layer_{i}.1.global_rep.{j}.''' in k:
                              _A							:     Union[str, Any]			=						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:
                        _A							:     List[str]			=						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:
                        _A							:     Optional[Any]			=						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:
                  _A							:     Optional[Any]			=						k_new.replace("""pre_norm_attn.0.""","""layernorm_before."""     )
            if "pre_norm_attn.1." in k:
                  _A							:     str			=						k_new.replace("""pre_norm_attn.1.""","""attention."""     )
            if "pre_norm_ffn.0." in k:
                  _A							:     Optional[Any]			=						k_new.replace("""pre_norm_ffn.0.""","""layernorm_after."""     )
            if "pre_norm_ffn.1." in k:
                  _A							:     Dict			=						k_new.replace("""pre_norm_ffn.1.""","""ffn.conv1."""     )
            if "pre_norm_ffn.3." in k:
                  _A							:     List[str]			=						k_new.replace("""pre_norm_ffn.3.""","""ffn.conv2."""     )

            if "classifier.1." in k:
                  _A							:     List[str]			=						k_new.replace("""classifier.1.""","""classifier."""     )

            if "seg_head." in k:
                  _A							:     List[Any]			=						k_new.replace("""seg_head.""","""segmentation_head."""     )
            if ".aspp_layer." in k:
                  _A							:     List[Any]			=						k_new.replace(""".aspp_layer.""","""."""     )
            if ".aspp_pool." in k:
                  _A							:     Optional[Any]			=						k_new.replace(""".aspp_pool.""","""."""     )

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







def 				lowerCAmelCase_	(     snake_case_     ):
      _A							:     Tuple			=						[]
      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 				lowerCAmelCase_	(     ):
      _A							:     Dict			=						"""http://images.cocodataset.org/val2017/000000039769.jpg"""
      # url = "https://cdn.britannica.com/86/141086-050-9D7C75EE/Gulfstream-G450-business-jet-passengers.jpg"
      _A							:     List[Any]			=						Image.open(requests.get(snake_case_,stream=snake_case_     ).raw     )
      return im







@torch.no_grad()
def 				lowerCAmelCase_	(     snake_case_,snake_case_,snake_case_,snake_case_     ):
      _A							:     List[Any]			=						get_mobilevitva_config(snake_case_,snake_case_     )

      # load original state_dict
      _A							:     Tuple			=						torch.load(snake_case_,map_location="""cpu"""     )

      # load huggingface model
      if task_name.startswith("""ade20k_"""     ) or task_name.startswith("""voc_"""     ):
            _A							:     Optional[Any]			=						MobileViTVaForSemanticSegmentation(snake_case_     ).eval()
            _A							:     str			=						False
      else:
            _A							:     int			=						MobileViTVaForImageClassification(snake_case_     ).eval()
            _A							:     List[Any]			=						False

      # remove and rename some keys of load the original model
      _A							:     List[Any]			=						checkpoint
      remove_unused_keys(snake_case_     )
      _A							:     Optional[Any]			=						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
      _A							:     str			=						MobileViTImageProcessor(crop_size=config.image_size,size=config.image_size + 32     )
      _A							:     List[Any]			=						image_processor(images=prepare_img(),return_tensors="""pt"""     )
      _A							:     Optional[Any]			=						model(**snake_case_     )

      # verify classification model
      if task_name.startswith("""imagenet"""     ):
            _A							:     List[Any]			=						outputs.logits
            _A							:     Optional[int]			=						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
                  _A							:     int			=						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__":
  _snake_case										=       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."
  )

  _snake_case										=       parser.parse_args()
  convert_mobilevitva_checkpoint(
      args.task, args.orig_checkpoint_path, args.orig_config_path, args.pytorch_dump_folder_path
  )

