






from __future__ import annotations

import unittest

from transformers import BlenderbotSmallConfig, BlenderbotSmallTokenizer, is_tf_available
from transformers.testing_utils import require_tf, require_tokenizers, slow
from transformers.utils import cached_property

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
     import tensorflow as tf

     from transformers import TFAutoModelForSeqaSeqLM, TFBlenderbotSmallForConditionalGeneration, TFBlenderbotSmallModel






@require_tf
class 		lowercase_					:






      '''simple docstring'''




      __snake_case       =  BlenderbotSmallConfig
      __snake_case       =  {}
      __snake_case       =  '''gelu'''







      def __init__(      self      :						Dict ,					__UpperCAmelCase      :						List[Any] ,					__UpperCAmelCase      :						Optional[int]=13 ,					__UpperCAmelCase      :						Union[str, Any]=7 ,					__UpperCAmelCase      :						Optional[Any]=True ,					__UpperCAmelCase      :						Optional[int]=False ,					__UpperCAmelCase      :						Dict=99 ,					__UpperCAmelCase      :						Union[str, Any]=32 ,					__UpperCAmelCase      :						Any=2 ,					__UpperCAmelCase      :						List[Any]=4 ,					__UpperCAmelCase      :						Optional[int]=37 ,					__UpperCAmelCase      :						Tuple=0.1 ,					__UpperCAmelCase      :						Union[str, Any]=0.1 ,					__UpperCAmelCase      :						List[Any]=20 ,					__UpperCAmelCase      :						Optional[int]=2 ,					__UpperCAmelCase      :						str=1 ,					__UpperCAmelCase      :						str=0 ,					)    ->List[str]:




       """simple docstring"""


       a		  =      parent
       a		  =      batch_size
       a		  =      seq_length
       a		  =      is_training
       a		  =      use_labels
       a		  =      vocab_size
       a		  =      hidden_size
       a		  =      num_hidden_layers
       a		  =      num_attention_heads
       a		  =      intermediate_size

       a		  =      hidden_dropout_prob
       a		  =      attention_probs_dropout_prob
       a		  =      max_position_embeddings
       a		  =      eos_token_id
       a		  =      pad_token_id
       a		  =      bos_token_id







      def       __lowerCAmelCase					(      self      :						Any  )    ->Union[str, Any]:




       """simple docstring"""


       a		  =      ids_tensor([self.batch_size, self.seq_length - 1] ,					self.vocab_size  )
       a		  =      tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size  ) ,					1  )
       a		  =      tf.concat([input_ids, eos_tensor] ,					axis=1  )

       a		  =      ids_tensor([self.batch_size, self.seq_length] ,					self.vocab_size  )

       a		  =      self.config_cls(
           vocab_size=self.vocab_size ,					d_model=self.hidden_size ,					encoder_layers=self.num_hidden_layers ,					decoder_layers=self.num_hidden_layers ,					encoder_attention_heads=self.num_attention_heads ,					decoder_attention_heads=self.num_attention_heads ,					encoder_ffn_dim=self.intermediate_size ,					decoder_ffn_dim=self.intermediate_size ,					dropout=self.hidden_dropout_prob ,					attention_dropout=self.attention_probs_dropout_prob ,					max_position_embeddings=self.max_position_embeddings ,					eos_token_ids=[2] ,					bos_token_id=self.bos_token_id ,					pad_token_id=self.pad_token_id ,					decoder_start_token_id=self.pad_token_id ,					**self.config_updates ,					)
       a		  =      prepare_blenderbot_small_inputs_dict(__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase  )
       return config, inputs_dict







      def       __lowerCAmelCase					(      self      :						int ,					__UpperCAmelCase      :						Any ,					__UpperCAmelCase      :						Union[str, Any]  )    ->Dict:




       """simple docstring"""


       a		  =      TFBlenderbotSmallModel(config=__UpperCAmelCase  ).get_decoder()
       a		  =      inputs_dict['''input_ids''']

       a		  =      input_ids[:1, :]
       a		  =      inputs_dict['''attention_mask'''][:1, :]
       a		  =      inputs_dict['''head_mask''']
       a		  =      1

       # first forward pass
       a		  =      model(__UpperCAmelCase ,					attention_mask=__UpperCAmelCase ,					head_mask=__UpperCAmelCase ,					use_cache=__UpperCAmelCase  )

       a  ,     a		  =      outputs.to_tuple()

       # create hypothetical next token and extent to next_input_ids
       a		  =      ids_tensor((self.batch_size, 3) ,					config.vocab_size  )
       a		  =      tf.cast(ids_tensor((self.batch_size, 3) ,					2  ) ,					tf.inta  )

       # append to next input_ids and
       a		  =      tf.concat([input_ids, next_tokens] ,					axis=-1  )
       a		  =      tf.concat([attention_mask, next_attn_mask] ,					axis=-1  )

       a		  =      model(__UpperCAmelCase ,					attention_mask=__UpperCAmelCase  )[0]
       a		  =      model(__UpperCAmelCase ,					attention_mask=__UpperCAmelCase ,					past_key_values=__UpperCAmelCase  )[0]

       self.parent.assertEqual(next_tokens.shape[1] ,					output_from_past.shape[1]  )

       # select random slice
       a		  =      int(ids_tensor((1,) ,					output_from_past.shape[-1]  )  )
       a		  =      output_from_no_past[:, -3:, random_slice_idx]
       a		  =      output_from_past[:, :, random_slice_idx]

       # test that outputs are equal for slice
       tf.debugging.assert_near(__UpperCAmelCase ,					__UpperCAmelCase ,					rtol=1e-3  )
def 						_a    (      a :int	,							a :List[str]	,							a :Optional[int]	,							a :str=None	,							a :Optional[Any]=None	,							a :List[str]=None	,							a :Tuple=None	,							a :List[Any]=None	,							)							->			Optional[int]:
 if attention_mask is None:
  a		  =      tf.cast(tf.math.not_equal(a	,							config.pad_token_id )	,							tf.inta )
 if decoder_attention_mask is None:
  a		  =      tf.concat(
      [
          tf.ones(decoder_input_ids[:, :1].shape	,							dtype=tf.inta ),
          tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:]	,							config.pad_token_id )	,							tf.inta ),
      ]	,							axis=-1	,							)
 if head_mask is None:
  a		  =      tf.ones((config.encoder_layers, config.encoder_attention_heads) )
 if decoder_head_mask is None:
  a		  =      tf.ones((config.decoder_layers, config.decoder_attention_heads) )
 if cross_attn_head_mask is None:
  a		  =      tf.ones((config.decoder_layers, config.decoder_attention_heads) )
 return {
     "input_ids": input_ids,
     "decoder_input_ids": decoder_input_ids,
     "attention_mask": attention_mask,
     "decoder_attention_mask": decoder_attention_mask,
     "head_mask": head_mask,
     "decoder_head_mask": decoder_head_mask,
     "cross_attn_head_mask": cross_attn_head_mask,
 }






@require_tf
class 		lowercase_					(	lowercase ,  lowercase ,  unittest.TestCase   ):






      '''simple docstring'''




      __snake_case       =  (
          (TFBlenderbotSmallForConditionalGeneration, TFBlenderbotSmallModel) if is_tf_available() else ()
      )
      __snake_case       =  (TFBlenderbotSmallForConditionalGeneration,) if is_tf_available() else ()
      __snake_case       =  (
          {
              '''conversational''': TFBlenderbotSmallForConditionalGeneration,
              '''feature-extraction''': TFBlenderbotSmallModel,
              '''summarization''': TFBlenderbotSmallForConditionalGeneration,
              '''text2text-generation''': TFBlenderbotSmallForConditionalGeneration,
              '''translation''': TFBlenderbotSmallForConditionalGeneration,
          }
          if is_tf_available()
          else {}
      )
      __snake_case       =  True
      __snake_case       =  False
      __snake_case       =  False







      def       __lowerCAmelCase					(      self      :						str  )    ->List[Any]:




       """simple docstring"""


       a		  =      TFBlenderbotSmallModelTester(self  )
       a		  =      ConfigTester(self ,					config_class=__UpperCAmelCase  )







      def       __lowerCAmelCase					(      self      :						Union[str, Any]  )    ->int:




       """simple docstring"""


       self.config_tester.run_common_tests()







      def       __lowerCAmelCase					(      self      :						int  )    ->Optional[int]:




       """simple docstring"""


       a		  =      self.model_tester.prepare_config_and_inputs_for_common()
       self.model_tester.check_decoder_model_past_large_inputs(*__UpperCAmelCase  )






@require_tokenizers
@require_tf
class 		lowercase_					(	unittest.TestCase   ):






      '''simple docstring'''




      __snake_case       =  [
          '''Social anxiety\nWow, I am never shy. Do you have anxiety?\nYes. I end up sweating and blushing and feel like '''
          '''  i\'m going to throw up.\nand why is that?'''
      ]
      __snake_case       =  '''facebook/blenderbot_small-90M'''







      @cached_property
      def       __lowerCAmelCase					(      self      :						List[Any]  )    ->Optional[int]:




       """simple docstring"""


       return BlenderbotSmallTokenizer.from_pretrained('''facebook/blenderbot-90M'''  )







      @cached_property
      def       __lowerCAmelCase					(      self      :						List[str]  )    ->str:




       """simple docstring"""


       a		  =      TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name  )
       return model







      @slow
      def       __lowerCAmelCase					(      self      :						List[Any]  )    ->Optional[Any]:




       """simple docstring"""


       a		  =      self.tokenizer(self.src_text ,					return_tensors='''tf'''  )
       a		  =      self.model.generate(
           model_inputs.input_ids ,					attention_mask=model_inputs.attention_mask ,					num_beams=2 ,					use_cache=__UpperCAmelCase ,					)
       a		  =      self.tokenizer.batch_decode(generated_ids.numpy() ,					skip_special_tokens=__UpperCAmelCase  )[0]
       assert generated_words in (
           "i don't know. i just feel like i'm going to throw up. it's not fun.",
           "i'm not sure. i just feel like i've been feeling like i have to be in a certain place",
           "i'm not sure. i just feel like i've been in a bad situation.",
       )






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()
UpperCAmelCase__   	=							logging.get_logger(__name__)
def 				UpperCAmelCase_					(							__snake_case						)     ->			Optional[Any]:







 """simple docstring"""


 _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(__snake_case     ,						__snake_case     ,						repo_type='''dataset'''						)     ,						'''r'''						)						)
 _lowercase							      ={int(__snake_case						): v for k, v in idalabel.items()}
 _lowercase							      =idalabel
 _lowercase							      ={v: k for k, v in idalabel.items()}

 return config
def 				UpperCAmelCase_					(							__snake_case     ,						__snake_case=False						)     ->			Tuple:







 """simple docstring"""


 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 				UpperCAmelCase_					(							__snake_case     ,						__snake_case     ,						__snake_case=False						)     ->			Optional[Any]:







 """simple docstring"""


 if base_model:
  _lowercase							      =''''''
 else:
  _lowercase							      ='''mobilevit.'''

 for key in orig_state_dict.copy().keys():
  _lowercase							      =orig_state_dict.pop(__snake_case						)

  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 				UpperCAmelCase_					(							)     ->			Union[str, Any]:







 """simple docstring"""


 _lowercase							      ='''http://images.cocodataset.org/val2017/000000039769.jpg'''
 _lowercase							      =Image.open(requests.get(__snake_case     ,						stream=__snake_case						).raw						)
 return im
@torch.no_grad()
def 				UpperCAmelCase_					(							__snake_case     ,						__snake_case     ,						__snake_case     ,						__snake_case=False						)     ->			int:







 """simple docstring"""


 _lowercase							      =get_mobilevit_config(__snake_case						)

 # load original state_dict
 _lowercase							      =torch.load(__snake_case     ,						map_location='''cpu'''						)

 # load 🤗 model
 if mobilevit_name.startswith('''deeplabv3_'''						):
  _lowercase							      =MobileViTForSemanticSegmentation(__snake_case						).eval()
 else:
  _lowercase							      =MobileViTForImageClassification(__snake_case						).eval()

 _lowercase							      =convert_state_dict(__snake_case     ,						__snake_case						)
 model.load_state_dict(__snake_case						)

 # 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(**__snake_case						)
 _lowercase							      =outputs.logits

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

  if mobilevit_name == "deeplabv3_mobilevit_s":
   _lowercase							      =torch.tensor(
       [
           [[6.20_65, 6.12_92, 6.20_70], [6.10_79, 6.12_54, 6.17_47], [6.00_42, 6.10_71, 6.10_34]],
           [[-6.92_53, -6.86_53, -7.03_98], [-7.32_18, -7.39_83, -7.36_70], [-7.19_61, -7.24_82, -7.15_69]],
           [[-4.47_23, -4.43_48, -4.37_69], [-5.36_29, -5.46_32, -5.45_98], [-5.15_87, -5.34_02, -5.50_59]],
       ]						)
  elif mobilevit_name == "deeplabv3_mobilevit_xs":
   _lowercase							      =torch.tensor(
       [
           [[5.44_49, 5.57_33, 5.63_14], [5.18_15, 5.39_30, 5.59_63], [5.16_56, 5.43_33, 5.48_53]],
           [[-9.44_23, -9.77_66, -9.67_14], [-9.15_81, -9.57_20, -9.55_19], [-9.10_06, -9.64_58, -9.57_03]],
           [[-7.77_21, -7.37_16, -7.15_83], [-8.45_99, -8.06_24, -7.79_44], [-8.41_72, -7.83_66, -7.50_25]],
       ]						)
  elif mobilevit_name == "deeplabv3_mobilevit_xxs":
   _lowercase							      =torch.tensor(
       [
           [[6.98_11, 6.97_43, 7.31_23], [7.17_77, 7.19_31, 7.39_38], [7.56_33, 7.80_50, 7.89_01]],
           [[-10.55_36, -10.23_32, -10.29_24], [-10.23_36, -9.86_24, -9.59_64], [-10.88_40, -10.81_58, -10.66_59]],
           [[-3.49_38, -3.06_31, -2.86_20], [-3.42_05, -2.81_35, -2.68_75], [-3.41_79, -2.79_45, -2.87_50]],
       ]						)
  else:
   raise ValueError(F"Unknown mobilevit_name: {mobilevit_name}"						)

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

  if mobilevit_name == "mobilevit_s":
   _lowercase							      =torch.tensor([-0.98_66, 0.23_92, -1.12_41]						)
  elif mobilevit_name == "mobilevit_xs":
   _lowercase							      =torch.tensor([-2.47_61, -0.93_99, -1.95_87]						)
  elif mobilevit_name == "mobilevit_xxs":
   _lowercase							      =torch.tensor([-1.93_64, -1.23_27, -0.46_53]						)
  else:
   raise ValueError(F"Unknown mobilevit_name: {mobilevit_name}"						)

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

 Path(__snake_case						).mkdir(exist_ok=__snake_case						)
 print(F"Saving model {mobilevit_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 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(__snake_case     ,						organization='''apple'''						)
  model.push_to_hub(__snake_case     ,						organization='''apple'''						)


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

  UpperCAmelCase__   	=							parser.parse_args()
  convert_movilevit_checkpoint(
      args.mobilevit_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
  )

