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(
  															    [
  															        [[1_7.5_7_9_0, 1_7.7_5_8_1, 1_8.3_3_5_5], [1_8.3_2_5_7, 1_8.4_2_3_0, 1_8.8_9_7_3], [1_8.6_1_6_9, 1_8.8_6_5_0, 1_9.2_1_8_7]],
  															        [[-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 os
  from argparse import ArgumentParser
  from pathlib import Path
  from shutil import copyfile
  from typing import List
  
  from datasets import config
  from datasets.builder import DatasetBuilder
  from datasets.commands import BaseDatasetsCLICommand
  from datasets.download.download_config import DownloadConfig
  from datasets.download.download_manager import DownloadMode
  from datasets.load import dataset_module_factory, import_main_class
  from datasets.utils.info_utils import VerificationMode
  
  
  
  
  
  
  
  def 							UpperCAmelCase				(     UpperCAmelCase				,**UpperCAmelCase      )->		Tuple:
  
  
  
  
  
  
  
  					'''simple docstring'''
  
  
  
  					return RunBeamCommand(
  					    args.dataset				,args.name				,args.cache_dir				,args.beam_pipeline_options				,args.data_dir				,args.all_configs				,args.save_info or args.save_infos				,args.ignore_verifications				,args.force_redownload				,**UpperCAmelCase				,)
  
  
  
  
  
  
  class 						snake_case			(       lowerCAmelCase__	):
  			'''simple docstring'''
  
  			@staticmethod
  			def _lowercase (	lowerCAmelCase_     :     ArgumentParser					)					->					List[str]:
  
  
  
  
  								"""simple docstring"""
  
  
  
  
  
  
  								SCREAMING_SNAKE_CASE_	      =       parser.add_parser('''run_beam'''							,							help='''Run a Beam dataset processing pipeline'''					)
  								run_beam_parser.add_argument('''dataset'''							,							type=lowerCAmelCase_							,							help='''Name of the dataset to download'''					)
  								run_beam_parser.add_argument('''--name'''							,							type=lowerCAmelCase_							,							default=lowerCAmelCase_							,							help='''Dataset config name'''					)
  								run_beam_parser.add_argument(
  								    '''--cache_dir'''							,							type=lowerCAmelCase_							,							default=lowerCAmelCase_							,							help='''Cache directory where the datasets are stored'''							,							)
  								run_beam_parser.add_argument(
  								    '''--beam_pipeline_options'''							,							type=lowerCAmelCase_							,							default=''''''							,							help='''Beam pipeline options, separated by commas. Example:: `--beam_pipeline_options=job_name=my-job,project=my-project`'''							,							)
  								run_beam_parser.add_argument(
  								    '''--data_dir'''							,							type=lowerCAmelCase_							,							default=lowerCAmelCase_							,							help='''Can be used to specify a manual directory to get the files from'''							,							)
  								run_beam_parser.add_argument('''--all_configs'''							,							action='''store_true'''							,							help='''Test all dataset configurations'''					)
  								run_beam_parser.add_argument('''--save_info'''							,							action='''store_true'''							,							help='''Save the dataset infos file'''					)
  								run_beam_parser.add_argument(
  								    '''--ignore_verifications'''							,							action='''store_true'''							,							help='''Run the test without checksums and splits checks'''					)
  								run_beam_parser.add_argument('''--force_redownload'''							,							action='''store_true'''							,							help='''Force dataset redownload'''					)
  								# aliases
  								run_beam_parser.add_argument('''--save_infos'''							,							action='''store_true'''							,							help='''alias for save_info'''					)
  								run_beam_parser.set_defaults(func=lowerCAmelCase_					)
  
  			def __init__(	self     :     Any							,							lowerCAmelCase_     :     str							,							lowerCAmelCase_     :     str							,							lowerCAmelCase_     :     str							,							lowerCAmelCase_     :     str							,							lowerCAmelCase_     :     str							,							lowerCAmelCase_     :     bool							,							lowerCAmelCase_     :     bool							,							lowerCAmelCase_     :     bool							,							lowerCAmelCase_     :     bool							,							**lowerCAmelCase_     :     Any							,							)					->					Tuple:
  
  
  
  
  								"""simple docstring"""
  
  
  
  
  
  
  								SCREAMING_SNAKE_CASE_	      =       dataset
  								SCREAMING_SNAKE_CASE_	      =       name
  								SCREAMING_SNAKE_CASE_	      =       cache_dir
  								SCREAMING_SNAKE_CASE_	      =       beam_pipeline_options
  								SCREAMING_SNAKE_CASE_	      =       data_dir
  								SCREAMING_SNAKE_CASE_	      =       all_configs
  								SCREAMING_SNAKE_CASE_	      =       save_infos
  								SCREAMING_SNAKE_CASE_	      =       ignore_verifications
  								SCREAMING_SNAKE_CASE_	      =       force_redownload
  								SCREAMING_SNAKE_CASE_	      =       config_kwargs
  
  
  
  
  			def _lowercase (	self     :     List[str]					)					->					int:
  
  
  
  
  								"""simple docstring"""
  
  
  
  
  
  
  								import apache_beam as beam
  
  								if self._name is not None and self._all_configs:
  													print('''Both parameters `name` and `all_configs` can\'t be used at once.'''					)
  													exit(1					)
  								SCREAMING_SNAKE_CASE_      ,SCREAMING_SNAKE_CASE_	      =       self._dataset, self._name
  								SCREAMING_SNAKE_CASE_	      =       dataset_module_factory(lowerCAmelCase_					)
  								SCREAMING_SNAKE_CASE_	      =       import_main_class(dataset_module.module_path					)
  								SCREAMING_SNAKE_CASE_	      =       []
  								if self._beam_pipeline_options:
  													SCREAMING_SNAKE_CASE_	      =       beam.options.pipeline_options.PipelineOptions(
  													    flags=[F'''--{opt.strip()}''' for opt in self._beam_pipeline_options.split(''','''					) if opt]					)
  								else:
  													SCREAMING_SNAKE_CASE_	      =       None
  								if self._all_configs and len(builder_cls.BUILDER_CONFIGS					) > 0:
  													for builder_config in builder_cls.BUILDER_CONFIGS:
  																		builders.append(
  																		    builder_cls(
  																		        config_name=builder_config.name							,							data_dir=self._data_dir							,							hash=dataset_module.hash							,							beam_options=lowerCAmelCase_							,							cache_dir=self._cache_dir							,							base_path=dataset_module.builder_kwargs.get('''base_path'''					)							,							)					)
  								else:
  													builders.append(
  													    builder_cls(
  													        config_name=lowerCAmelCase_							,							data_dir=self._data_dir							,							beam_options=lowerCAmelCase_							,							cache_dir=self._cache_dir							,							base_path=dataset_module.builder_kwargs.get('''base_path'''					)							,							**self._config_kwargs							,							)					)
  
  								for builder in builders:
  													builder.download_and_prepare(
  													    download_mode=DownloadMode.REUSE_CACHE_IF_EXISTS
  													    if not self._force_redownload
  													    else DownloadMode.FORCE_REDOWNLOAD							,							download_config=DownloadConfig(cache_dir=config.DOWNLOADED_DATASETS_PATH					)							,							verification_mode=VerificationMode.NO_CHECKS
  													    if self._ignore_verifications
  													    else VerificationMode.ALL_CHECKS							,							try_from_hf_gcs=lowerCAmelCase_							,							)
  													if self._save_infos:
  																		builder._save_infos()
  
  								print('''Apache beam run successful.'''					)
  
  								# If save_infos=True, the dataset infos file is created next to the loaded module file.
  								# Let's move it to the original directory of the dataset script, to allow the user to
  								# upload them on S3 at the same time afterwards.
  								if self._save_infos:
  													SCREAMING_SNAKE_CASE_	      =       os.path.join(builder_cls.get_imported_module_dir()							,							config.DATASETDICT_INFOS_FILENAME					)
  
  													SCREAMING_SNAKE_CASE_	      =       Path(lowerCAmelCase_					).name + '''.py'''
  
  													SCREAMING_SNAKE_CASE_	      =       os.path.join(lowerCAmelCase_							,							lowerCAmelCase_					)
  													if os.path.isfile(lowerCAmelCase_					):
  																		SCREAMING_SNAKE_CASE_	      =       os.path.dirname(lowerCAmelCase_					)
  													elif os.path.isfile(lowerCAmelCase_					):
  																		SCREAMING_SNAKE_CASE_	      =       path
  													else:  # in case of a remote dataset
  																		print(F'''Dataset Infos file saved at {dataset_infos_path}'''					)
  																		exit(1					)
  
  													# Move datasetinfo back to the user
  													SCREAMING_SNAKE_CASE_	      =       os.path.join(lowerCAmelCase_							,							config.DATASETDICT_INFOS_FILENAME					)
  													copyfile(lowerCAmelCase_							,							lowerCAmelCase_					)
  													print(F'''Dataset Infos file saved at {user_dataset_infos_path}'''					)
  
  
  