code: 
  
  
  import argparse
  import json
  import re
  from pathlib import Path
  
  import requests
  import torch
  from huggingface_hub import hf_hub_download
  from PIL import Image
  
  from transformers import (
      MobileNetVaConfig,
      MobileNetVaForImageClassification,
      MobileNetVaForSemanticSegmentation,
      MobileNetVaImageProcessor,
      load_tf_weights_in_mobilenet_va,
  )
  from transformers.utils import logging
  
  
  logging.set_verbosity_info()
  A_														=      logging.get_logger(__name__)
  
  
  
  
  
  
  
  def 							UpperCAmelCase				(     UpperCAmelCase      )->		Any:
  
  
  
  
  
  
  
       '''simple docstring'''
  
  
  
       SCREAMING_SNAKE_CASE_	      =       MobileNetVaConfig(layer_norm_eps=0.0_0_1      )
  
       if "quant" in model_name:
            raise ValueError('''Quantized models are not supported.'''      )
  
       SCREAMING_SNAKE_CASE_	      =       re.match(R'''^.*mobilenet_v2_([^_]*)_([^_]*)$'''				,UpperCAmelCase      )
       if matches:
            SCREAMING_SNAKE_CASE_	      =       float(matches[1]      )
            SCREAMING_SNAKE_CASE_	      =       int(matches[2]      )
  
       if model_name.startswith('''deeplabv3_'''      ):
            SCREAMING_SNAKE_CASE_	      =       8
            SCREAMING_SNAKE_CASE_	      =       21
            SCREAMING_SNAKE_CASE_	      =       '''pascal-voc-id2label.json'''
       else:
            # The TensorFlow version of MobileNetV2 predicts 1001 classes instead
            # of the usual 1000. The first class (index 0) is "background".
            SCREAMING_SNAKE_CASE_	      =       1001
            SCREAMING_SNAKE_CASE_	      =       '''imagenet-1k-id2label.json'''
  
       SCREAMING_SNAKE_CASE_	      =       '''huggingface/label-files'''
       SCREAMING_SNAKE_CASE_	      =       json.load(open(hf_hub_download(UpperCAmelCase				,UpperCAmelCase				,repo_type='''dataset'''      )				,'''r'''      )      )
  
       if config.num_labels == 1001:
            SCREAMING_SNAKE_CASE_	      =       {int(UpperCAmelCase      ) + 1: v for k, v in idalabel.items()}
            SCREAMING_SNAKE_CASE_	      =       '''background'''
       else:
            SCREAMING_SNAKE_CASE_	      =       {int(UpperCAmelCase      ): v for k, v in idalabel.items()}
  
       SCREAMING_SNAKE_CASE_	      =       idalabel
       SCREAMING_SNAKE_CASE_	      =       {v: k for k, v in idalabel.items()}
  
       return config
  
  
  
  
  
  
  
  def 							UpperCAmelCase				(     )->		List[str]:
  
  
  
  
  
  
  
       '''simple docstring'''
  
  
  
       SCREAMING_SNAKE_CASE_	      =       '''http://images.cocodataset.org/val2017/000000039769.jpg'''
       SCREAMING_SNAKE_CASE_	      =       Image.open(requests.get(UpperCAmelCase				,stream=UpperCAmelCase      ).raw      )
       return im
  
  
  
  
  
  
  
  @torch.no_grad()
  def 							UpperCAmelCase				(     UpperCAmelCase				,UpperCAmelCase				,UpperCAmelCase				,UpperCAmelCase=False      )->		Tuple:
  
  
  
  
  
  
  
       '''simple docstring'''
  
  
  
       SCREAMING_SNAKE_CASE_	      =       get_mobilenet_va_config(UpperCAmelCase      )
  
       # Load 🤗 model
       if model_name.startswith('''deeplabv3_'''      ):
            SCREAMING_SNAKE_CASE_	      =       MobileNetVaForSemanticSegmentation(UpperCAmelCase      ).eval()
       else:
            SCREAMING_SNAKE_CASE_	      =       MobileNetVaForImageClassification(UpperCAmelCase      ).eval()
  
       # Load weights from TensorFlow checkpoint
       load_tf_weights_in_mobilenet_va(UpperCAmelCase				,UpperCAmelCase				,UpperCAmelCase      )
  
       # Check outputs on an image, prepared by MobileNetV2ImageProcessor
       SCREAMING_SNAKE_CASE_	      =       MobileNetVaImageProcessor(
           crop_size={'''width''': config.image_size, '''height''': config.image_size}				,size={'''shortest_edge''': config.image_size + 32}				,)
       SCREAMING_SNAKE_CASE_	      =       image_processor(images=prepare_img()				,return_tensors='''pt'''      )
       SCREAMING_SNAKE_CASE_	      =       model(**UpperCAmelCase      )
       SCREAMING_SNAKE_CASE_	      =       outputs.logits
  
       if model_name.startswith('''deeplabv3_'''      ):
            assert logits.shape == (1, 21, 65, 65)
  
            if model_name == "deeplabv3_mobilenet_v2_1.0_513":
                 SCREAMING_SNAKE_CASE_	      =       torch.tensor(
                     [
                         [[17.5790, 17.7581, 18.3355], [18.3257, 18.4230, 18.8973], [18.6169, 18.8650, 19.2187]],
                         [[-2.1_5_9_5, -2.0_9_7_7, -2.3_7_4_1], [-2.4_2_2_6, -2.3_0_2_8, -2.6_8_3_5], [-2.7_8_1_9, -2.5_9_9_1, -2.7_7_0_6]],
                         [[4.2_0_5_8, 4.8_3_1_7, 4.7_6_3_8], [4.4_1_3_6, 5.0_3_6_1, 4.9_3_8_3], [4.5_0_2_8, 4.9_6_4_4, 4.8_7_3_4]],
                     ]      )
  
            else:
                 raise ValueError(f'''Unknown model name: {model_name}'''      )
  
            assert torch.allclose(logits[0, :3, :3, :3]				,UpperCAmelCase				,atol=1E-4      )
       else:
            assert logits.shape == (1, 1001)
  
            if model_name == "mobilenet_v2_1.4_224":
                 SCREAMING_SNAKE_CASE_	      =       torch.tensor([0.0_1_8_1, -1.0_0_1_5, 0.4_6_8_8]      )
            elif model_name == "mobilenet_v2_1.0_224":
                 SCREAMING_SNAKE_CASE_	      =       torch.tensor([0.2_4_4_5, -1.1_9_9_3, 0.1_9_0_5]      )
            elif model_name == "mobilenet_v2_0.75_160":
                 SCREAMING_SNAKE_CASE_	      =       torch.tensor([0.2_4_8_2, 0.4_1_3_6, 0.6_6_6_9]      )
            elif model_name == "mobilenet_v2_0.35_96":
                 SCREAMING_SNAKE_CASE_	      =       torch.tensor([0.1_4_5_1, -0.4_6_2_4, 0.7_1_9_2]      )
            else:
                 SCREAMING_SNAKE_CASE_	      =       None
  
            if expected_logits is not None:
                 assert torch.allclose(logits[0, :3]				,UpperCAmelCase				,atol=1E-4      )
  
       Path(UpperCAmelCase      ).mkdir(exist_ok=UpperCAmelCase      )
       print(f'''Saving model {model_name} to {pytorch_dump_folder_path}'''      )
       model.save_pretrained(UpperCAmelCase      )
       print(f'''Saving image processor to {pytorch_dump_folder_path}'''      )
       image_processor.save_pretrained(UpperCAmelCase      )
  
       if push_to_hub:
            print('''Pushing to the hub...'''      )
            SCREAMING_SNAKE_CASE_	      =       '''google/''' + model_name
            image_processor.push_to_hub(UpperCAmelCase      )
            model.push_to_hub(UpperCAmelCase      )
  
  
  if __name__ == "__main__":
    A_														=      argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        "--model_name",
        default="mobilenet_v2_1.0_224",
        type=str,
        help="Name of the MobileNetV2 model you'd like to convert. Should in the form 'mobilenet_v2_<depth>_<size>'.",
    )
    parser.add_argument(
        "--checkpoint_path", required=True, type=str, help="Path to the original TensorFlow checkpoint (.ckpt 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."
    )
  
    A_														=      parser.parse_args()
    convert_movilevit_checkpoint(
        args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
    )
  
  
  
style_context: 
  
  
  import tempfile
  import unittest
  
  import torch
  
  from diffusers import UNetaDConditionModel
  from diffusers.training_utils import EMAModel
  from diffusers.utils.testing_utils import enable_full_determinism, skip_mps, torch_device
  
  
  enable_full_determinism()
  
  class 						snake_case			(       unittest.TestCase	):
  			'''simple docstring'''
  
  			UpperCAmelCase       :       Union[str, Any]       = """hf-internal-testing/tiny-stable-diffusion-pipe"""
  			UpperCAmelCase       :       int       = 1
  			UpperCAmelCase       :       Optional[Any]       = 77
  			UpperCAmelCase       :       Any       = 32
  			UpperCAmelCase       :       str       = 4
  			UpperCAmelCase       :       Optional[int]       = 64
  			UpperCAmelCase       :       Tuple       = torch.manual_seed(0	)
  
  			def _lowercase (	self     :     str							,							lowerCAmelCase_     :     str=0.9_999					)					->					int:
  
  
  
  
  								"""simple docstring"""
  
  
  
  
  
  
  								SCREAMING_SNAKE_CASE_	      =       UNetaDConditionModel.from_pretrained(self.model_id							,							subfolder='''unet'''					)
  								SCREAMING_SNAKE_CASE_	      =       unet.to(lowerCAmelCase_					)
  								SCREAMING_SNAKE_CASE_	      =       EMAModel(unet.parameters()							,							decay=lowerCAmelCase_							,							model_cls=lowerCAmelCase_							,							model_config=unet.config					)
  								return unet, ema_unet
  
  			def _lowercase (	self     :     str					)					->					List[str]:
  
  
  
  
  								"""simple docstring"""
  
  
  
  
  
  
  								SCREAMING_SNAKE_CASE_	      =       torch.randn(
  								    self.batch_size							,							self.num_in_channels							,							self.latent_height							,							self.latent_width							,							generator=self.generator					).to(lowerCAmelCase_					)
  								SCREAMING_SNAKE_CASE_	      =       torch.randint(0							,							1_000							,							size=(self.batch_size,)							,							generator=self.generator					).to(lowerCAmelCase_					)
  								SCREAMING_SNAKE_CASE_	      =       torch.randn(
  								    self.batch_size							,							self.prompt_length							,							self.text_encoder_hidden_dim							,							generator=self.generator					).to(lowerCAmelCase_					)
  								return noisy_latents, timesteps, encoder_hidden_states
  
  			def _lowercase (	self     :     str							,							lowerCAmelCase_     :     Tuple					)					->					str:
  
  
  
  
  								"""simple docstring"""
  
  
  
  
  
  
  								SCREAMING_SNAKE_CASE_	      =       {}
  								for k, param in unet.state_dict().items():
  													SCREAMING_SNAKE_CASE_	      =       torch.randn_like(lowerCAmelCase_					) + (param * torch.randn_like(lowerCAmelCase_					))
  													updated_state_dict.update({k: updated_param}					)
  								unet.load_state_dict(lowerCAmelCase_					)
  								return unet
  
  			def _lowercase (	self     :     Dict					)					->					str:
  
  
  
  
  								"""simple docstring"""
  
  
  
  
  
  
  								SCREAMING_SNAKE_CASE_      ,SCREAMING_SNAKE_CASE_	      =       self.get_models()
  								# Take the first (hypothetical) EMA step.
  								ema_unet.step(unet.parameters()					)
  								assert ema_unet.optimization_step == 1
  
  								# Take two more.
  								for _ in range(2					):
  													ema_unet.step(unet.parameters()					)
  								assert ema_unet.optimization_step == 3
  
  			def _lowercase (	self     :     Union[str, Any]					)					->					List[str]:
  
  
  
  
  								"""simple docstring"""
  
  
  
  
  
  
  								SCREAMING_SNAKE_CASE_      ,SCREAMING_SNAKE_CASE_	      =       self.get_models()
  								# Since the `unet` is not being updated (i.e., backprop'd)
  								# there won't be any difference between the `params` of `unet`
  								# and `ema_unet` even if we call `ema_unet.step(unet.parameters())`.
  								ema_unet.step(unet.parameters()					)
  								SCREAMING_SNAKE_CASE_	      =       list(unet.parameters()					)
  								for s_param, param in zip(ema_unet.shadow_params							,							lowerCAmelCase_					):
  													assert torch.allclose(lowerCAmelCase_							,							lowerCAmelCase_					)
  
  								# The above holds true even if we call `ema.step()` multiple times since
  								# `unet` params are still not being updated.
  								for _ in range(4					):
  													ema_unet.step(unet.parameters()					)
  								for s_param, param in zip(ema_unet.shadow_params							,							lowerCAmelCase_					):
  													assert torch.allclose(lowerCAmelCase_							,							lowerCAmelCase_					)
  
  			def _lowercase (	self     :     Any					)					->					Union[str, Any]:
  
  
  
  
  								"""simple docstring"""
  
  
  
  
  
  
  								SCREAMING_SNAKE_CASE_      ,SCREAMING_SNAKE_CASE_	      =       self.get_models()
  								# Here we simulate the parameter updates for `unet`. Since there might
  								# be some parameters which are initialized to zero we take extra care to
  								# initialize their values to something non-zero before the multiplication.
  								SCREAMING_SNAKE_CASE_	      =       self.simulate_backprop(lowerCAmelCase_					)
  
  								# Take the EMA step.
  								ema_unet.step(unet_pseudo_updated_step_one.parameters()					)
  
  								# Now the EMA'd parameters won't be equal to the original model parameters.
  								SCREAMING_SNAKE_CASE_	      =       list(unet_pseudo_updated_step_one.parameters()					)
  								for s_param, param in zip(ema_unet.shadow_params							,							lowerCAmelCase_					):
  													assert ~torch.allclose(lowerCAmelCase_							,							lowerCAmelCase_					)
  
  								# Ensure this is the case when we take multiple EMA steps.
  								for _ in range(4					):
  													ema_unet.step(unet.parameters()					)
  								for s_param, param in zip(ema_unet.shadow_params							,							lowerCAmelCase_					):
  													assert ~torch.allclose(lowerCAmelCase_							,							lowerCAmelCase_					)
  
  			def _lowercase (	self     :     Any					)					->					Tuple:
  
  
  
  
  								"""simple docstring"""
  
  
  
  
  
  
  								SCREAMING_SNAKE_CASE_      ,SCREAMING_SNAKE_CASE_	      =       self.get_models()
  
  								# First backprop + EMA
  								SCREAMING_SNAKE_CASE_	      =       self.simulate_backprop(lowerCAmelCase_					)
  								ema_unet.step(unet_step_one.parameters()					)
  								SCREAMING_SNAKE_CASE_	      =       ema_unet.shadow_params
  
  								# Second backprop + EMA
  								SCREAMING_SNAKE_CASE_	      =       self.simulate_backprop(lowerCAmelCase_					)
  								ema_unet.step(unet_step_two.parameters()					)
  								SCREAMING_SNAKE_CASE_	      =       ema_unet.shadow_params
  
  								for step_one, step_two in zip(lowerCAmelCase_							,							lowerCAmelCase_					):
  													assert ~torch.allclose(lowerCAmelCase_							,							lowerCAmelCase_					)
  
  			def _lowercase (	self     :     str					)					->					Optional[Any]:
  
  
  
  
  								"""simple docstring"""
  
  
  
  
  
  
  								SCREAMING_SNAKE_CASE_      ,SCREAMING_SNAKE_CASE_	      =       self.get_models(decay=0.0					)
  								SCREAMING_SNAKE_CASE_	      =       self.simulate_backprop(lowerCAmelCase_					)
  								ema_unet.step(unet_step_one.parameters()					)
  								SCREAMING_SNAKE_CASE_	      =       ema_unet.shadow_params
  
  								SCREAMING_SNAKE_CASE_	      =       self.simulate_backprop(lowerCAmelCase_					)
  								ema_unet.step(unet_step_two.parameters()					)
  								SCREAMING_SNAKE_CASE_	      =       ema_unet.shadow_params
  
  								for step_one, step_two in zip(lowerCAmelCase_							,							lowerCAmelCase_					):
  													assert torch.allclose(lowerCAmelCase_							,							lowerCAmelCase_					)
  
  
  
  
  			@skip_mps
  			def _lowercase (	self     :     str					)					->					List[Any]:
  
  
  
  
  								"""simple docstring"""
  
  
  
  
  
  
  								SCREAMING_SNAKE_CASE_      ,SCREAMING_SNAKE_CASE_	      =       self.get_models()
  								SCREAMING_SNAKE_CASE_      ,SCREAMING_SNAKE_CASE_      ,SCREAMING_SNAKE_CASE_	      =       self.get_dummy_inputs()
  
  								with tempfile.TemporaryDirectory() as tmpdir:
  													ema_unet.save_pretrained(lowerCAmelCase_					)
  													SCREAMING_SNAKE_CASE_	      =       UNetaDConditionModel.from_pretrained(lowerCAmelCase_							,							model_cls=lowerCAmelCase_					)
  													SCREAMING_SNAKE_CASE_	      =       loaded_unet.to(unet.device					)
  
  								# Since no EMA step has been performed the outputs should match.
  								SCREAMING_SNAKE_CASE_	      =       unet(lowerCAmelCase_							,							lowerCAmelCase_							,							lowerCAmelCase_					).sample
  								SCREAMING_SNAKE_CASE_	      =       loaded_unet(lowerCAmelCase_							,							lowerCAmelCase_							,							lowerCAmelCase_					).sample
  
  								assert torch.allclose(lowerCAmelCase_							,							lowerCAmelCase_							,							atol=1e-4					)
  
  
  