code: 
  
  
  
  
  
  import json
  import os
  import re
  import warnings
  from pathlib import Path
  from shutil import copyfile
  from typing import Any, Dict, List, Optional, Tuple, Union
  
  import sentencepiece
  
  from ...tokenization_utils import PreTrainedTokenizer
  from ...utils import logging
  
  
  _UpperCamelCase       = logging.get_logger(__name__)
  
  _UpperCamelCase       = {
      '''source_spm''': '''source.spm''',
      '''target_spm''': '''target.spm''',
      '''vocab''': '''vocab.json''',
      '''target_vocab_file''': '''target_vocab.json''',
      '''tokenizer_config_file''': '''tokenizer_config.json''',
  }
  
  _UpperCamelCase       = {
      '''source_spm''': {
          '''Helsinki-NLP/opus-mt-en-de''': '''https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/source.spm'''
      },
      '''target_spm''': {
          '''Helsinki-NLP/opus-mt-en-de''': '''https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/target.spm'''
      },
      '''vocab''': {
          '''Helsinki-NLP/opus-mt-en-de''': '''https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/vocab.json'''
      },
      '''tokenizer_config_file''': {
          '''Helsinki-NLP/opus-mt-en-de''': (
              '''https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/tokenizer_config.json'''
          )
      },
  }
  
  _UpperCamelCase       = {'''Helsinki-NLP/opus-mt-en-de''': 512}
  _UpperCamelCase       = {}
  
  
  
  
  class 				_lowerCamelCase (    a    ):
  
  
  
        """simple docstring"""
  
  
  
  
  
  
        UpperCAmelCase_						:      str       =VOCAB_FILES_NAMES
        UpperCAmelCase_						:      Optional[int]       =PRETRAINED_VOCAB_FILES_MAP
        UpperCAmelCase_						:      Tuple       =PRETRAINED_INIT_CONFIGURATION
        UpperCAmelCase_						:      Optional[int]       =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
        UpperCAmelCase_						:      Tuple       =["input_ids", "attention_mask"]
        UpperCAmelCase_						:      List[Any]       =re.compile(">>.+<<"    )  # type: re.Pattern
  
  
  
  
  
  
  
        def __init__( self				,						UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase=None				,						UpperCAmelCase=None				,						UpperCAmelCase=None				,						UpperCAmelCase="<unk>"				,						UpperCAmelCase="</s>"				,						UpperCAmelCase="<pad>"				,						UpperCAmelCase=512				,						UpperCAmelCase = None				,						UpperCAmelCase=False				,						**UpperCAmelCase				,						)       ->   None:
  
  
  
  
  
  
          '''simple docstring'''
  
  
  
  
  
  
          __snake_case   :				str        =       {} if sp_model_kwargs is None else sp_model_kwargs
  
          super().__init__(
              # bos_token=bos_token,  unused. Start decoding with config.decoder_start_token_id
              source_lang=UpperCAmelCase				,						target_lang=UpperCAmelCase				,						unk_token=UpperCAmelCase				,						eos_token=UpperCAmelCase				,						pad_token=UpperCAmelCase				,						model_max_length=UpperCAmelCase				,						sp_model_kwargs=self.sp_model_kwargs				,						target_vocab_file=UpperCAmelCase				,						separate_vocabs=UpperCAmelCase				,						**UpperCAmelCase				,						)
          assert Path(UpperCAmelCase   ).exists(), F"""cannot find spm source {source_spm}"""
  
          __snake_case   :				List[Any]        =       separate_vocabs
          __snake_case   :				str        =       load_json(UpperCAmelCase   )
          if self.unk_token not in self.encoder:
            raise KeyError("<unk> token must be in vocab"   )
          assert self.pad_token in self.encoder
  
          if separate_vocabs:
            __snake_case   :				Tuple        =       load_json(UpperCAmelCase   )
            __snake_case   :				int        =       {v: k for k, v in self.target_encoder.items()}
            __snake_case   :				Tuple        =       []
          else:
            __snake_case   :				Any        =       {v: k for k, v in self.encoder.items()}
            __snake_case   :				Dict        =       [k for k in self.encoder if k.startswith(">>"   ) and k.endswith("<<"   )]
  
          __snake_case   :				str        =       source_lang
          __snake_case   :				int        =       target_lang
          __snake_case   :				Dict        =       [source_spm, target_spm]
  
          # load SentencePiece model for pre-processing
          __snake_case   :				List[Any]        =       load_spm(UpperCAmelCase				,						self.sp_model_kwargs   )
          __snake_case   :				int        =       load_spm(UpperCAmelCase				,						self.sp_model_kwargs   )
          __snake_case   :				Optional[Any]        =       self.spm_source
          __snake_case   :				Dict        =       self.encoder
  
          # Multilingual target side: default to using first supported language code.
  
          self._setup_normalizer()
  
  
  
  
  
  
  
        def 						UpperCAmelCase		( self   )       ->   Optional[int]:
  
  
  
  
  
  
          '''simple docstring'''
  
  
  
  
  
  
          try:
            from sacremoses import MosesPunctNormalizer
  
            __snake_case   :				Dict        =       MosesPunctNormalizer(self.source_lang   ).normalize
          except (ImportError, FileNotFoundError):
            warnings.warn("Recommended: pip install sacremoses."   )
            __snake_case   :				Optional[int]        =       lambda UpperCAmelCase   : x
  
  
  
  
  
  
  
        def 						UpperCAmelCase		( self				,						UpperCAmelCase   )       ->   str:
  
  
  
  
  
  
          '''simple docstring'''
  
  
  
  
  
  
          return self.punc_normalizer(UpperCAmelCase   ) if x else ""
  
  
  
  
  
  
  
        def 						UpperCAmelCase		( self				,						UpperCAmelCase   )       ->   List[str]:
  
  
  
  
  
  
          '''simple docstring'''
  
  
  
  
  
  
          return self.current_encoder.get(UpperCAmelCase				,						self.current_encoder[self.unk_token]   )
  
  
  
  
  
  
  
        def 						UpperCAmelCase		( self				,						UpperCAmelCase   )       ->   Union[str, Any]:
  
  
  
  
  
  
          '''simple docstring'''
  
  
  
  
  
  
          __snake_case   :				Tuple        =       self.language_code_re.match(UpperCAmelCase   )
          __snake_case   :				Optional[int]        =       [match.group(0   )] if match else []
          return code, self.language_code_re.sub(""				,						UpperCAmelCase   )
  
  
  
  
  
  
  
        def 						UpperCAmelCase		( self				,						UpperCAmelCase   )       ->   List[str]:
  
  
  
  
  
  
          '''simple docstring'''
  
  
  
  
  
  
          __snake_case    ,   __snake_case   :				Tuple        =       self.remove_language_code(UpperCAmelCase   )
          __snake_case   :				Any        =       self.current_spm.encode(UpperCAmelCase				,						out_type=UpperCAmelCase   )
          return code + pieces
  
  
  
  
  
  
  
        def 						UpperCAmelCase		( self				,						UpperCAmelCase   )       ->   str:
  
  
  
  
  
  
          '''simple docstring'''
  
  
  
  
  
  
          return self.decoder.get(UpperCAmelCase				,						self.unk_token   )
  
  
  
  
  
  
  
        def 						UpperCAmelCase		( self				,						UpperCAmelCase				,						**UpperCAmelCase   )       ->   Any:
  
  
  
  
  
  
          '''simple docstring'''
  
  
  
  
  
  
          return super().batch_decode(UpperCAmelCase				,						**UpperCAmelCase   )
  
  
  
  
  
  
  
        def 						UpperCAmelCase		( self				,						UpperCAmelCase				,						**UpperCAmelCase   )       ->   Union[str, Any]:
  
  
  
  
  
  
          '''simple docstring'''
  
  
  
  
  
  
          return super().decode(UpperCAmelCase				,						**UpperCAmelCase   )
  
  
  
  
  
  
  
        def 						UpperCAmelCase		( self				,						UpperCAmelCase   )       ->   str:
  
  
  
  
  
  
          '''simple docstring'''
  
  
  
  
  
  
          __snake_case   :				Tuple        =       self.spm_source if self._decode_use_source_tokenizer else self.spm_target
          __snake_case   :				Any        =       []
          __snake_case   :				Union[str, Any]        =       ""
          for token in tokens:
            # make sure that special tokens are not decoded using sentencepiece model
            if token in self.all_special_tokens:
              out_string += sp_model.decode_pieces(UpperCAmelCase   ) + token + " "
              __snake_case   :				Union[str, Any]        =       []
            else:
              current_sub_tokens.append(UpperCAmelCase   )
          out_string += sp_model.decode_pieces(UpperCAmelCase   )
          return out_string.strip()
  
  
  
  
  
  
  
        def 						UpperCAmelCase		( self				,						UpperCAmelCase				,						UpperCAmelCase=None   )       ->   List[int]:
  
  
  
  
  
  
          '''simple docstring'''
  
  
  
  
  
  
          if token_ids_a is None:
            return token_ids_a + [self.eos_token_id]
          # We don't expect to process pairs, but leave the pair logic for API consistency
          return token_ids_a + token_ids_a + [self.eos_token_id]
  
  
  
  
  
  
  
        def 						UpperCAmelCase		( self   )       ->   Union[str, Any]:
  
  
  
  
  
  
          '''simple docstring'''
  
  
  
  
  
  
          __snake_case   :				Optional[Any]        =       self.spm_source
          __snake_case   :				List[str]        =       self.encoder
  
  
  
  
  
  
  
        def 						UpperCAmelCase		( self   )       ->   str:
  
  
  
  
  
  
          '''simple docstring'''
  
  
  
  
  
  
          __snake_case   :				List[Any]        =       self.spm_target
          if self.separate_vocabs:
            __snake_case   :				int        =       self.target_encoder
  
  
  
  
  
  
  
        @property
        def 						UpperCAmelCase		( self   )       ->   int:
  
  
  
  
  
  
          '''simple docstring'''
  
  
  
  
  
  
          return len(self.encoder   )
  
  
  
  
  
  
  
        def 						UpperCAmelCase		( self				,						UpperCAmelCase				,						UpperCAmelCase = None   )       ->   Tuple[str]:
  
  
  
  
  
  
          '''simple docstring'''
  
  
  
  
  
  
          if not os.path.isdir(UpperCAmelCase   ):
            logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""   )
            return
          __snake_case   :				Tuple        =       []
  
          if self.separate_vocabs:
            __snake_case   :				Tuple        =       os.path.join(
                UpperCAmelCase				,						(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab"]				,						)
            __snake_case   :				Dict        =       os.path.join(
                UpperCAmelCase				,						(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["target_vocab_file"]				,						)
            save_json(self.encoder				,						UpperCAmelCase   )
            save_json(self.target_encoder				,						UpperCAmelCase   )
            saved_files.append(UpperCAmelCase   )
            saved_files.append(UpperCAmelCase   )
          else:
            __snake_case   :				Optional[int]        =       os.path.join(
                UpperCAmelCase				,						(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab"]   )
            save_json(self.encoder				,						UpperCAmelCase   )
            saved_files.append(UpperCAmelCase   )
  
          for spm_save_filename, spm_orig_path, spm_model in zip(
              [VOCAB_FILES_NAMES["source_spm"], VOCAB_FILES_NAMES["target_spm"]]				,						self.spm_files				,						[self.spm_source, self.spm_target]				,						):
            __snake_case   :				List[str]        =       os.path.join(
                UpperCAmelCase				,						(filename_prefix + "-" if filename_prefix else "") + spm_save_filename   )
            if os.path.abspath(UpperCAmelCase   ) != os.path.abspath(UpperCAmelCase   ) and os.path.isfile(UpperCAmelCase   ):
              copyfile(UpperCAmelCase				,						UpperCAmelCase   )
              saved_files.append(UpperCAmelCase   )
            elif not os.path.isfile(UpperCAmelCase   ):
              with open(UpperCAmelCase				,						"wb"   ) as fi:
                __snake_case   :				List[str]        =       spm_model.serialized_model_proto()
                fi.write(UpperCAmelCase   )
              saved_files.append(UpperCAmelCase   )
  
          return tuple(UpperCAmelCase   )
  
  
  
  
  
  
  
        def 						UpperCAmelCase		( self   )       ->   Dict:
  
  
  
  
  
  
          '''simple docstring'''
  
  
  
  
  
  
          return self.get_src_vocab()
  
  
  
  
  
  
  
        def 						UpperCAmelCase		( self   )       ->   Optional[Any]:
  
  
  
  
  
  
          '''simple docstring'''
  
  
  
  
  
  
          return dict(self.encoder				,						**self.added_tokens_encoder   )
  
  
  
  
  
  
  
        def 						UpperCAmelCase		( self   )       ->   Dict:
  
  
  
  
  
  
          '''simple docstring'''
  
  
  
  
  
  
          return dict(self.target_encoder				,						**self.added_tokens_decoder   )
  
  
  
  
  
  
  
        def __getstate__( self   )       ->   Dict:
  
  
  
  
  
  
          '''simple docstring'''
  
  
  
  
  
  
          __snake_case   :				Dict        =       self.__dict__.copy()
          state.update(
              {k: None for k in ["spm_source", "spm_target", "current_spm", "punc_normalizer", "target_vocab_file"]}   )
          return state
  
  
  
  
  
  
  
        def __setstate__( self				,						UpperCAmelCase   )       ->   None:
  
  
  
  
  
  
          '''simple docstring'''
  
  
  
  
  
  
          __snake_case   :				Tuple        =       d
  
          # for backward compatibility
          if not hasattr(self				,						"sp_model_kwargs"   ):
            __snake_case   :				Optional[Any]        =       {}
  
          __snake_case    ,   __snake_case   :				Optional[int]        =       (load_spm(UpperCAmelCase				,						self.sp_model_kwargs   ) for f in self.spm_files)
          __snake_case   :				Union[str, Any]        =       self.spm_source
          self._setup_normalizer()
  
  
  
  
  
  
  
        def 						UpperCAmelCase		( self				,						*UpperCAmelCase				,						**UpperCAmelCase   )       ->   int:
  
  
  
  
  
  
          '''simple docstring'''
  
  
  
  
  
  
          return 1
  
  
  
  
  
  
  
        def 						UpperCAmelCase		( self				,						UpperCAmelCase   )       ->   int:
  
  
  
  
  
  
          '''simple docstring'''
  
  
  
  
  
  
          __snake_case   :				Tuple        =       set(self.all_special_ids   )  # call it once instead of inside list comp
          all_special_ids.remove(self.unk_token_id   )  # <unk> is only sometimes special
          return [1 if x in all_special_ids else 0 for x in seq]
  
  
  
  
  
  
  
        def 						UpperCAmelCase		( self				,						UpperCAmelCase				,						UpperCAmelCase = None				,						UpperCAmelCase = False   )       ->   List[int]:
  
  
  
  
  
  
          '''simple docstring'''
  
  
  
  
  
  
          if already_has_special_tokens:
            return self._special_token_mask(UpperCAmelCase   )
          elif token_ids_a is None:
            return self._special_token_mask(UpperCAmelCase   ) + [1]
          else:
            return self._special_token_mask(token_ids_a + token_ids_a   ) + [1]
  
  
  
  
  
  
  
  def       lowerCAmelCase__(	lowercase       :    Any   ,     lowercase       :    str				) ->    sentencepiece.SentencePieceProcessor:
    __snake_case   :				str        =       sentencepiece.SentencePieceProcessor(**__snake_case				)
    spm.Load(__snake_case				)
    return spm
  
  
  
  
  
  
  
  def       lowerCAmelCase__(	lowercase       :    Optional[Any]   ,     lowercase       :    List[Any]				) ->    None:
    with open(__snake_case   ,     "w"				) as f:
      json.dump(__snake_case   ,     __snake_case   ,     indent=2				)
  
  
  
  
  
  
  
  def       lowerCAmelCase__(	lowercase       :    Optional[Any]				) ->    Union[Dict, List]:
    with open(__snake_case   ,     "r"				) as f:
      return json.load(__snake_case				)
  
  
style_context: 
  
  
  
  
  
  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()
  _UpperCamelCase       = logging.get_logger(__name__)
  
  
  
  
  
  
  
  def       lowerCAmelCase__(	lowercase       :    Dict				) ->    Union[str, Any]:
    __snake_case   :				List[str]        =       MobileNetVaConfig(layer_norm_eps=0.0_0_1				)
  
    if "quant" in model_name:
      raise ValueError("Quantized models are not supported."				)
  
    __snake_case   :				Dict        =       re.match(R"^.*mobilenet_v2_([^_]*)_([^_]*)$"   ,     lowercase				)
    if matches:
      __snake_case   :				Tuple        =       float(matches[1]				)
      __snake_case   :				List[Any]        =       int(matches[2]				)
  
    if model_name.startswith("deeplabv3_"				):
      __snake_case   :				Any        =       8
      __snake_case   :				Any        =       21
      __snake_case   :				Optional[Any]        =       "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".
      __snake_case   :				Optional[Any]        =       1001
      __snake_case   :				str        =       "imagenet-1k-id2label.json"
  
    __snake_case   :				Any        =       "huggingface/label-files"
    __snake_case   :				str        =       json.load(open(hf_hub_download(lowercase   ,     lowercase   ,     repo_type="dataset"				)   ,     "r"				)				)
  
    if config.num_labels == 1001:
      __snake_case   :				Optional[Any]        =       {int(lowercase				) + 1: v for k, v in idalabel.items()}
      __snake_case   :				Any        =       "background"
    else:
      __snake_case   :				Optional[Any]        =       {int(lowercase				): v for k, v in idalabel.items()}
  
    __snake_case   :				Any        =       idalabel
    __snake_case   :				List[str]        =       {v: k for k, v in idalabel.items()}
  
    return config
  
  
  
  
  
  
  
  def       lowerCAmelCase__(	) ->    Optional[Any]:
    __snake_case   :				List[Any]        =       "http://images.cocodataset.org/val2017/000000039769.jpg"
    __snake_case   :				Union[str, Any]        =       Image.open(requests.get(lowercase   ,     stream=lowercase				).raw				)
    return im
  
  
  
  
  
  
  
  @torch.no_grad()
  def       lowerCAmelCase__(	lowercase       :    str   ,     lowercase       :    List[str]   ,     lowercase       :    int   ,     lowercase       :    List[str]=False				) ->    str:
    __snake_case   :				Dict        =       get_mobilenet_va_config(lowercase				)
  
    # Load 🤗 model
    if model_name.startswith("deeplabv3_"				):
      __snake_case   :				str        =       MobileNetVaForSemanticSegmentation(lowercase				).eval()
    else:
      __snake_case   :				Any        =       MobileNetVaForImageClassification(lowercase				).eval()
  
    # Load weights from TensorFlow checkpoint
    load_tf_weights_in_mobilenet_va(lowercase   ,     lowercase   ,     lowercase				)
  
    # Check outputs on an image, prepared by MobileNetV2ImageProcessor
    __snake_case   :				List[str]        =       MobileNetVaImageProcessor(
        crop_size={"width": config.image_size, "height": config.image_size}   ,     size={"shortest_edge": config.image_size + 32}   ,     )
    __snake_case   :				Any        =       image_processor(images=prepare_img()   ,     return_tensors="pt"				)
    __snake_case   :				Dict        =       model(**lowercase				)
    __snake_case   :				List[Any]        =       outputs.logits
  
    if model_name.startswith("deeplabv3_"				):
      assert logits.shape == (1, 21, 65, 65)
  
      if model_name == "deeplabv3_mobilenet_v2_1.0_513":
        __snake_case   :				Any        =       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]   ,     lowercase   ,     atol=1E-4				)
    else:
      assert logits.shape == (1, 1001)
  
      if model_name == "mobilenet_v2_1.4_224":
        __snake_case   :				List[str]        =       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":
        __snake_case   :				str        =       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":
        __snake_case   :				Dict        =       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":
        __snake_case   :				List[Any]        =       torch.tensor([0.1_4_5_1, -0.4_6_2_4, 0.7_1_9_2]				)
      else:
        __snake_case   :				int        =       None
  
      if expected_logits is not None:
        assert torch.allclose(logits[0, :3]   ,     lowercase   ,     atol=1E-4				)
  
    Path(lowercase				).mkdir(exist_ok=lowercase				)
    print(f"""Saving model {model_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:
      print("Pushing to the hub..."				)
      __snake_case   :				Optional[Any]        =       "google/" + model_name
      image_processor.push_to_hub(lowercase				)
      model.push_to_hub(lowercase				)
  
  
  if __name__ == "__main__":
       _UpperCamelCase       = 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.'''
       )
  
       _UpperCamelCase       = parser.parse_args()
       convert_movilevit_checkpoint(
           args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
       )
  
  