



'''simple docstring'''


import itertools
import json
import linecache
import os
import pickle
import re
import socket
import string
from collections import Counter
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List

import git
import torch
from torch.utils.data import Dataset

from transformers import BartTokenizer, RagTokenizer, TaTokenizer







def      SCREAMING_SNAKE_CASE				(				lowercase_    :      Union[str, Any] ,  lowercase_    :      Optional[Any] ,  lowercase_    :      Dict ,  lowercase_    :      int ,  lowercase_    :      int=True ,  lowercase_    :      str="pt"						):
      lowercase        =							{"""add_prefix_space""": True} if isinstance(UpperCAmelCase__ ,  UpperCAmelCase__						) and not line.startswith(""" """						) else {}
      lowercase        =							padding_side
      return tokenizer(
          [line] ,  max_length=UpperCAmelCase__ ,  padding="""max_length""" if pad_to_max_length else None ,  truncation=UpperCAmelCase__ ,  return_tensors=UpperCAmelCase__ ,  add_special_tokens=UpperCAmelCase__ ,  **UpperCAmelCase__ ,  )







def      SCREAMING_SNAKE_CASE				(				lowercase_    :      str ,  lowercase_    :      str ,  lowercase_    :      int=None ,  ):
      lowercase        =							input_ids.ne(UpperCAmelCase__						).any(dim=0						)
      if attention_mask is None:
            return input_ids[:, keep_column_mask]
      else:
            return (input_ids[:, keep_column_mask], attention_mask[:, keep_column_mask])




class      __UpperCamelCase       (_a			):







 def __init__(    self ,				_lowerCAmelCase ,				_lowerCAmelCase ,				_lowerCAmelCase ,				_lowerCAmelCase ,				_lowerCAmelCase="train" ,				_lowerCAmelCase=None ,				_lowerCAmelCase=None ,				_lowerCAmelCase=None ,				_lowerCAmelCase="" ,				)    ->    str:
       '''simple docstring'''






       super().__init__()
       lowercase        =							Path(_A						).joinpath(type_path + """.source"""						)
       lowercase        =							Path(_A						).joinpath(type_path + """.target"""						)
       lowercase        =							self.get_char_lens(self.src_file						)
       lowercase        =							max_source_length
       lowercase        =							max_target_length
       assert min(self.src_lens						) > 0, F"""found empty line in {self.src_file}"""
       lowercase        =							tokenizer
       lowercase        =							prefix
       if n_obs is not None:
             lowercase        =							self.src_lens[:n_obs]
       lowercase        =							src_lang
       lowercase        =							tgt_lang







 def __len__(    self						)    ->    List[Any]:
       '''simple docstring'''






       return len(self.src_lens						)







 def __getitem__(    self ,				_lowerCAmelCase						)    ->    Optional[Any]:
       '''simple docstring'''






       lowercase        =							index + 1  # linecache starts at 1
       lowercase        =							self.prefix + linecache.getline(str(self.src_file						) ,				_A						).rstrip("""\n"""						)
       lowercase        =							linecache.getline(str(self.tgt_file						) ,				_A						).rstrip("""\n"""						)
       assert source_line, F"""empty source line for index {index}"""
       assert tgt_line, F"""empty tgt line for index {index}"""

       # Need to add eos token manually for T5
       if isinstance(self.tokenizer ,				_A						):
             source_line += self.tokenizer.eos_token
             tgt_line += self.tokenizer.eos_token

       # Pad source and target to the right
       lowercase        =							(
           self.tokenizer.question_encoder if isinstance(self.tokenizer ,				_A						) else self.tokenizer
       )
       lowercase        =							self.tokenizer.generator if isinstance(self.tokenizer ,				_A						) else self.tokenizer

       lowercase        =							encode_line(_A ,				_A ,				self.max_source_length ,				"""right"""						)
       lowercase        =							encode_line(_A ,				_A ,				self.max_target_length ,				"""right"""						)

       lowercase        =							source_inputs["""input_ids"""].squeeze()
       lowercase        =							target_inputs["""input_ids"""].squeeze()
       lowercase        =							source_inputs["""attention_mask"""].squeeze()
       return {
           "input_ids": source_ids,
           "attention_mask": src_mask,
           "decoder_input_ids": target_ids,
       }







 @staticmethod
 def   _a   (    _lowerCAmelCase						)    ->    Optional[Any]:
       '''simple docstring'''






       return [len(_A						) for x in Path(_A						).open().readlines()]







 def   _a   (    self ,				_lowerCAmelCase						)    ->    Union[str, Any]:
       '''simple docstring'''






       lowercase        =							torch.stack([x["""input_ids"""] for x in batch]						)
       lowercase        =							torch.stack([x["""attention_mask"""] for x in batch]						)
       lowercase        =							torch.stack([x["""decoder_input_ids"""] for x in batch]						)
       lowercase        =							(
           self.tokenizer.generator.pad_token_id
           if isinstance(self.tokenizer ,				_A						)
           else self.tokenizer.pad_token_id
       )
       lowercase        =							(
           self.tokenizer.question_encoder.pad_token_id
           if isinstance(self.tokenizer ,				_A						)
           else self.tokenizer.pad_token_id
       )
       lowercase        =							trim_batch(_A ,				_A						)
       lowercase     ,   lowercase        =							trim_batch(_A ,				_A ,				attention_mask=_A						)
       lowercase        =							{
           """input_ids""": source_ids,
           """attention_mask""": source_mask,
           """decoder_input_ids""": y,
       }
       return batch




lowercase_      :				Dict   					=    getLogger(__name__)







def      SCREAMING_SNAKE_CASE				(				lowercase_    :      List[str]						):
      return list(itertools.chain.from_iterable(UpperCAmelCase__						)						)







def      SCREAMING_SNAKE_CASE				(				lowercase_    :      Optional[int]						):
      lowercase        =							get_git_info()
      save_json(UpperCAmelCase__ ,  os.path.join(UpperCAmelCase__ ,  """git_log.json"""						)						)







def      SCREAMING_SNAKE_CASE				(				lowercase_    :      Tuple ,  lowercase_    :      Dict ,  lowercase_    :      List[str]=4 ,  **lowercase_    :      Optional[Any]						):






      with open(UpperCAmelCase__ ,  """w"""						) as f:
            json.dump(UpperCAmelCase__ ,  UpperCAmelCase__ ,  indent=UpperCAmelCase__ ,  **UpperCAmelCase__						)







def      SCREAMING_SNAKE_CASE				(				lowercase_    :      List[Any]						):






      with open(UpperCAmelCase__						) as f:
            return json.load(UpperCAmelCase__						)







def      SCREAMING_SNAKE_CASE				(				):
      lowercase        =							git.Repo(search_parent_directories=UpperCAmelCase__						)
      lowercase        =							{
          """repo_id""": str(UpperCAmelCase__						),
          """repo_sha""": str(repo.head.object.hexsha						),
          """repo_branch""": str(repo.active_branch						),
          """hostname""": str(socket.gethostname()						),
      }
      return repo_infos







def      SCREAMING_SNAKE_CASE				(				lowercase_    :      Any ,  lowercase_    :      List[str]						):
      return list(map(UpperCAmelCase__ ,  UpperCAmelCase__						)						)







def      SCREAMING_SNAKE_CASE				(				lowercase_    :      str ,  lowercase_    :      Optional[Any]						):






      with open(UpperCAmelCase__ ,  """wb"""						) as f:
            return pickle.dump(UpperCAmelCase__ ,  UpperCAmelCase__						)







def      SCREAMING_SNAKE_CASE				(				lowercase_    :      List[str]						):






      def remove_articles(lowercase_    :      Optional[int]						):
            return re.sub(R"""\b(a|an|the)\b""" ,  """ """ ,  UpperCAmelCase__						)

      def white_space_fix(lowercase_    :      int						):
            return " ".join(text.split()						)

      def remove_punc(lowercase_    :      List[Any]						):
            lowercase        =							set(string.punctuation						)
            return "".join(ch for ch in text if ch not in exclude						)

      def lower(lowercase_    :      Tuple						):
            return text.lower()

      return white_space_fix(remove_articles(remove_punc(lower(UpperCAmelCase__						)						)						)						)







def      SCREAMING_SNAKE_CASE				(				lowercase_    :      Union[str, Any] ,  lowercase_    :      Any						):
      lowercase        =							normalize_answer(UpperCAmelCase__						).split()
      lowercase        =							normalize_answer(UpperCAmelCase__						).split()
      lowercase        =							Counter(UpperCAmelCase__						) & Counter(UpperCAmelCase__						)
      lowercase        =							sum(common.values()						)
      if num_same == 0:
            return 0
      lowercase        =							1.0 * num_same / len(UpperCAmelCase__						)
      lowercase        =							1.0 * num_same / len(UpperCAmelCase__						)
      lowercase        =							(2 * precision * recall) / (precision + recall)
      return fa







def      SCREAMING_SNAKE_CASE				(				lowercase_    :      List[str] ,  lowercase_    :      List[Any]						):
      return normalize_answer(UpperCAmelCase__						) == normalize_answer(UpperCAmelCase__						)







def      SCREAMING_SNAKE_CASE				(				lowercase_    :      int ,  lowercase_    :      List[Any]						):
      assert len(UpperCAmelCase__						) == len(UpperCAmelCase__						)
      lowercase        =							0
      for hypo, pred in zip(UpperCAmelCase__ ,  UpperCAmelCase__						):
            em += exact_match_score(UpperCAmelCase__ ,  UpperCAmelCase__						)
      if len(UpperCAmelCase__						) > 0:
            em /= len(UpperCAmelCase__						)
      return {"em": em}







def      SCREAMING_SNAKE_CASE				(				lowercase_    :      str						):
      return model_prefix.startswith("""rag"""						)







def      SCREAMING_SNAKE_CASE				(				lowercase_    :      Union[str, Any] ,  lowercase_    :      Tuple ,  lowercase_    :      str						):
      lowercase        =							{p: p for p in extra_params}
      # T5 models don't have `dropout` param, they have `dropout_rate` instead
      lowercase        =							"""dropout_rate"""
      for p in extra_params:
            if getattr(UpperCAmelCase__ ,  UpperCAmelCase__ ,  UpperCAmelCase__						):
                  if not hasattr(UpperCAmelCase__ ,  UpperCAmelCase__						) and not hasattr(UpperCAmelCase__ ,  equivalent_param[p]						):
                        logger.info("""config doesn\'t have a `{}` attribute""".format(UpperCAmelCase__						)						)
                        delattr(UpperCAmelCase__ ,  UpperCAmelCase__						)
                        continue
                  lowercase        =							p if hasattr(UpperCAmelCase__ ,  UpperCAmelCase__						) else equivalent_param[p]
                  setattr(UpperCAmelCase__ ,  UpperCAmelCase__ ,  getattr(UpperCAmelCase__ ,  UpperCAmelCase__						)						)
                  delattr(UpperCAmelCase__ ,  UpperCAmelCase__						)
      return hparams, config







'''simple docstring'''


import argparse
import json
from pathlib import Path

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

from transformers import (
    MobileViTConfig,
    MobileViTForImageClassification,
    MobileViTForSemanticSegmentation,
    MobileViTImageProcessor,
)
from transformers.utils import logging


logging.set_verbosity_info()
lowercase_      :				List[str]   					=    logging.get_logger(__name__)







def      SCREAMING_SNAKE_CASE				(				lowercase_    :      int						):
						lowercase        =							MobileViTConfig()

						# size of the architecture
						if "mobilevit_s" in mobilevit_name:
												lowercase        =							[144, 192, 240]
												lowercase        =							[16, 32, 64, 96, 128, 160, 640]
						elif "mobilevit_xs" in mobilevit_name:
												lowercase        =							[96, 120, 144]
												lowercase        =							[16, 32, 48, 64, 80, 96, 384]
						elif "mobilevit_xxs" in mobilevit_name:
												lowercase        =							[64, 80, 96]
												lowercase        =							[16, 16, 24, 48, 64, 80, 320]
												lowercase        =							0.05
												lowercase        =							2.0

						if mobilevit_name.startswith("""deeplabv3_"""						):
												lowercase        =							512
												lowercase        =							16
												lowercase        =							21
												lowercase        =							"""pascal-voc-id2label.json"""
						else:
												lowercase        =							1000
												lowercase        =							"""imagenet-1k-id2label.json"""

						lowercase        =							"""huggingface/label-files"""
						lowercase        =							json.load(open(hf_hub_download(lowercase_ ,  lowercase_ ,  repo_type="""dataset"""						) ,  """r"""						)						)
						lowercase        =							{int(lowercase_						): v for k, v in idalabel.items()}
						lowercase        =							idalabel
						lowercase        =							{v: k for k, v in idalabel.items()}

						return config







def      SCREAMING_SNAKE_CASE				(				lowercase_    :      Any ,  lowercase_    :      Any=False						):
						for i in range(1 ,  6						):
												if F"""layer_{i}.""" in name:
																		lowercase        =							name.replace(F"""layer_{i}.""" ,  F"""encoder.layer.{i - 1}."""						)

						if "conv_1." in name:
												lowercase        =							name.replace("""conv_1.""" ,  """conv_stem."""						)
						if ".block." in name:
												lowercase        =							name.replace(""".block.""" ,  """."""						)

						if "exp_1x1" in name:
												lowercase        =							name.replace("""exp_1x1""" ,  """expand_1x1"""						)
						if "red_1x1" in name:
												lowercase        =							name.replace("""red_1x1""" ,  """reduce_1x1"""						)
						if ".local_rep.conv_3x3." in name:
												lowercase        =							name.replace(""".local_rep.conv_3x3.""" ,  """.conv_kxk."""						)
						if ".local_rep.conv_1x1." in name:
												lowercase        =							name.replace(""".local_rep.conv_1x1.""" ,  """.conv_1x1."""						)
						if ".norm." in name:
												lowercase        =							name.replace(""".norm.""" ,  """.normalization."""						)
						if ".conv." in name:
												lowercase        =							name.replace(""".conv.""" ,  """.convolution."""						)
						if ".conv_proj." in name:
												lowercase        =							name.replace(""".conv_proj.""" ,  """.conv_projection."""						)

						for i in range(0 ,  2						):
												for j in range(0 ,  4						):
																		if F""".{i}.{j}.""" in name:
																								lowercase        =							name.replace(F""".{i}.{j}.""" ,  F""".{i}.layer.{j}."""						)

						for i in range(2 ,  6						):
												for j in range(0 ,  4						):
																		if F""".{i}.{j}.""" in name:
																								lowercase        =							name.replace(F""".{i}.{j}.""" ,  F""".{i}."""						)
																								if "expand_1x1" in name:
																														lowercase        =							name.replace("""expand_1x1""" ,  """downsampling_layer.expand_1x1"""						)
																								if "conv_3x3" in name:
																														lowercase        =							name.replace("""conv_3x3""" ,  """downsampling_layer.conv_3x3"""						)
																								if "reduce_1x1" in name:
																														lowercase        =							name.replace("""reduce_1x1""" ,  """downsampling_layer.reduce_1x1"""						)

						for i in range(2 ,  5						):
												if F""".global_rep.{i}.weight""" in name:
																		lowercase        =							name.replace(F""".global_rep.{i}.weight""" ,  """.layernorm.weight"""						)
												if F""".global_rep.{i}.bias""" in name:
																		lowercase        =							name.replace(F""".global_rep.{i}.bias""" ,  """.layernorm.bias"""						)

						if ".global_rep." in name:
												lowercase        =							name.replace(""".global_rep.""" ,  """.transformer."""						)
						if ".pre_norm_mha.0." in name:
												lowercase        =							name.replace(""".pre_norm_mha.0.""" ,  """.layernorm_before."""						)
						if ".pre_norm_mha.1.out_proj." in name:
												lowercase        =							name.replace(""".pre_norm_mha.1.out_proj.""" ,  """.attention.output.dense."""						)
						if ".pre_norm_ffn.0." in name:
												lowercase        =							name.replace(""".pre_norm_ffn.0.""" ,  """.layernorm_after."""						)
						if ".pre_norm_ffn.1." in name:
												lowercase        =							name.replace(""".pre_norm_ffn.1.""" ,  """.intermediate.dense."""						)
						if ".pre_norm_ffn.4." in name:
												lowercase        =							name.replace(""".pre_norm_ffn.4.""" ,  """.output.dense."""						)
						if ".transformer." in name:
												lowercase        =							name.replace(""".transformer.""" ,  """.transformer.layer."""						)

						if ".aspp_layer." in name:
												lowercase        =							name.replace(""".aspp_layer.""" ,  """."""						)
						if ".aspp_pool." in name:
												lowercase        =							name.replace(""".aspp_pool.""" ,  """."""						)
						if "seg_head." in name:
												lowercase        =							name.replace("""seg_head.""" ,  """segmentation_head."""						)
						if "segmentation_head.classifier.classifier." in name:
												lowercase        =							name.replace("""segmentation_head.classifier.classifier.""" ,  """segmentation_head.classifier."""						)

						if "classifier.fc." in name:
												lowercase        =							name.replace("""classifier.fc.""" ,  """classifier."""						)
						elif (not base_model) and ("segmentation_head." not in name):
												lowercase        =							"""mobilevit.""" + name

						return name







def      SCREAMING_SNAKE_CASE				(				lowercase_    :      Optional[Any] ,  lowercase_    :      List[Any] ,  lowercase_    :      str=False						):
						if base_model:
												lowercase        =							""""""
						else:
												lowercase        =							"""mobilevit."""

						for key in orig_state_dict.copy().keys():
												lowercase        =							orig_state_dict.pop(lowercase_						)

												if key[:8] == "encoder.":
																		lowercase        =							key[8:]

												if "qkv" in key:
																		lowercase        =							key.split("""."""						)
																		lowercase        =							int(key_split[0][6:]						) - 1
																		lowercase        =							int(key_split[3]						)
																		lowercase        =							model.get_submodule(F"""{model_prefix}encoder.layer.{layer_num}"""						)
																		lowercase        =							layer.transformer.layer[transformer_num].attention.attention.all_head_size
																		lowercase        =							(
																		    F"""{model_prefix}encoder.layer.{layer_num}.transformer.layer.{transformer_num}.attention.attention."""
																		)
																		if "weight" in key:
																								lowercase        =							val[:dim, :]
																								lowercase        =							val[dim : dim * 2, :]
																								lowercase        =							val[-dim:, :]
																		else:
																								lowercase        =							val[:dim]
																								lowercase        =							val[dim : dim * 2]
																								lowercase        =							val[-dim:]
												else:
																		lowercase        =							val

						return orig_state_dict







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







@torch.no_grad()
def      SCREAMING_SNAKE_CASE				(				lowercase_    :      Dict ,  lowercase_    :      List[Any] ,  lowercase_    :      Any ,  lowercase_    :      List[str]=False						):
						lowercase        =							get_mobilevit_config(lowercase_						)

						# load original state_dict
						lowercase        =							torch.load(lowercase_ ,  map_location="""cpu"""						)

						# load 🤗 model
						if mobilevit_name.startswith("""deeplabv3_"""						):
												lowercase        =							MobileViTForSemanticSegmentation(lowercase_						).eval()
						else:
												lowercase        =							MobileViTForImageClassification(lowercase_						).eval()

						lowercase        =							convert_state_dict(lowercase_ ,  lowercase_						)
						model.load_state_dict(lowercase_						)

						# Check outputs on an image, prepared by MobileViTImageProcessor
						lowercase        =							MobileViTImageProcessor(crop_size=config.image_size ,  size=config.image_size + 32						)
						lowercase        =							image_processor(images=prepare_img() ,  return_tensors="""pt"""						)
						lowercase        =							model(**lowercase_						)
						lowercase        =							outputs.logits

						if mobilevit_name.startswith("""deeplabv3_"""						):
												assert logits.shape == (1, 21, 32, 32)

												if mobilevit_name == "deeplabv3_mobilevit_s":
																		lowercase        =							torch.tensor(
																		    [
																		        [[6.2_065, 6.1_292, 6.2_070], [6.1_079, 6.1_254, 6.1_747], [6.0_042, 6.1_071, 6.1_034]],
																		        [[-6.9_253, -6.8_653, -7.0_398], [-7.3_218, -7.3_983, -7.3_670], [-7.1_961, -7.2_482, -7.1_569]],
																		        [[-4.4_723, -4.4_348, -4.3_769], [-5.3_629, -5.4_632, -5.4_598], [-5.1_587, -5.3_402, -5.5_059]],
																		    ]						)
												elif mobilevit_name == "deeplabv3_mobilevit_xs":
																		lowercase        =							torch.tensor(
																		    [
																		        [[5.4_449, 5.5_733, 5.6_314], [5.1_815, 5.3_930, 5.5_963], [5.1_656, 5.4_333, 5.4_853]],
																		        [[-9.4_423, -9.7_766, -9.6_714], [-9.1_581, -9.5_720, -9.5_519], [-9.1_006, -9.6_458, -9.5_703]],
																		        [[-7.7_721, -7.3_716, -7.1_583], [-8.4_599, -8.0_624, -7.7_944], [-8.4_172, -7.8_366, -7.5_025]],
																		    ]						)
												elif mobilevit_name == "deeplabv3_mobilevit_xxs":
																		lowercase        =							torch.tensor(
																		    [
																		        [[6.9_811, 6.9_743, 7.3_123], [7.1_777, 7.1_931, 7.3_938], [7.5_633, 7.8_050, 7.8_901]],
																		        [[-10.5_536, -10.2_332, -10.2_924], [-10.2_336, -9.8_624, -9.5_964], [-10.8_840, -10.8_158, -10.6_659]],
																		        [[-3.4_938, -3.0_631, -2.8_620], [-3.4_205, -2.8_135, -2.6_875], [-3.4_179, -2.7_945, -2.8_750]],
																		    ]						)
												else:
																		raise ValueError(F"""Unknown mobilevit_name: {mobilevit_name}"""						)

												assert torch.allclose(logits[0, :3, :3, :3] ,  lowercase_ ,  atol=1E-4						)
						else:
												assert logits.shape == (1, 1000)

												if mobilevit_name == "mobilevit_s":
																		lowercase        =							torch.tensor([-0.9_866, 0.2_392, -1.1_241]						)
												elif mobilevit_name == "mobilevit_xs":
																		lowercase        =							torch.tensor([-2.4_761, -0.9_399, -1.9_587]						)
												elif mobilevit_name == "mobilevit_xxs":
																		lowercase        =							torch.tensor([-1.9_364, -1.2_327, -0.4_653]						)
												else:
																		raise ValueError(F"""Unknown mobilevit_name: {mobilevit_name}"""						)

												assert torch.allclose(logits[0, :3] ,  lowercase_ ,  atol=1E-4						)

						Path(lowercase_						).mkdir(exist_ok=lowercase_						)
						print(F"""Saving model {mobilevit_name} to {pytorch_dump_folder_path}"""						)
						model.save_pretrained(lowercase_						)
						print(F"""Saving image processor to {pytorch_dump_folder_path}"""						)
						image_processor.save_pretrained(lowercase_						)

						if push_to_hub:
												lowercase        =							{
												    """mobilevit_s""": """mobilevit-small""",
												    """mobilevit_xs""": """mobilevit-x-small""",
												    """mobilevit_xxs""": """mobilevit-xx-small""",
												    """deeplabv3_mobilevit_s""": """deeplabv3-mobilevit-small""",
												    """deeplabv3_mobilevit_xs""": """deeplabv3-mobilevit-x-small""",
												    """deeplabv3_mobilevit_xxs""": """deeplabv3-mobilevit-xx-small""",
												}

												print("""Pushing to the hub..."""						)
												lowercase        =							model_mapping[mobilevit_name]
												image_processor.push_to_hub(lowercase_ ,  organization="""apple"""						)
												model.push_to_hub(lowercase_ ,  organization="""apple"""						)


if __name__ == "__main__":
						lowercase_      :				List[str]   					=    argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    '''--mobilevit_name''',
						    default='''mobilevit_s''',
						    type=str,
						    help=(
						        '''Name of the MobileViT model you\'d like to convert. Should be one of \'mobilevit_s\', \'mobilevit_xs\','''
						        ''' \'mobilevit_xxs\', \'deeplabv3_mobilevit_s\', \'deeplabv3_mobilevit_xs\', \'deeplabv3_mobilevit_xxs\'.'''
						    ),
						)
						parser.add_argument(
						    '''--checkpoint_path''', required=True, type=str, help='''Path to the original state dict (.pt file).'''
						)
						parser.add_argument(
						    '''--pytorch_dump_folder_path''', required=True, type=str, help='''Path to the output PyTorch model directory.'''
						)
						parser.add_argument(
						    '''--push_to_hub''', action='''store_true''', help='''Whether or not to push the converted model to the 🤗 hub.'''
						)

						lowercase_      :				List[str]   					=    parser.parse_args()
						convert_movilevit_checkpoint(
						    args.mobilevit_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
						)


