




from typing import List

from packaging import version
from sklearn.metrics import fa_score

import datasets
from datasets.config import PY_VERSION


if PY_VERSION < version.parse('3.8'):
   import importlib_metadata
else:
   import importlib.metadata as importlib_metadata


# TODO(Patrick/Anton)
UpperCAmelCase__						:       List[str]							=    '\\n'

UpperCAmelCase__						:       Any							=    '\\n    XTREME-S is a benchmark to evaluate universal cross-lingual speech representations in many languages.\n    XTREME-S covers four task families: speech recognition, classification, speech-to-text translation and retrieval.\n'

UpperCAmelCase__						:       Any							=    '\nCompute XTREME-S evaluation metric associated to each XTREME-S dataset.\nArgs:\n    predictions: list of predictions to score.\n        Each translation should be tokenized into a list of tokens.\n    references: list of lists of references for each translation.\n        Each reference should be tokenized into a list of tokens.\n    bleu_kwargs: optional dict of keywords to be passed when computing \'bleu\'.\n        Keywords include Dict can be one of \'smooth_method\', \'smooth_value\', \'force\', \'lowercase\',\n        \'tokenize\', \'use_effective_order\'.\n    wer_kwargs: optional dict of keywords to be passed when computing \'wer\' and \'cer\'.\n        Keywords include \'concatenate_texts\'.\nReturns: depending on the XTREME-S task, one or several of:\n    "accuracy": Accuracy - for \'fleurs-lang_id\', \'minds14\'\n    "f1": F1 score - for \'minds14\'\n    "wer": Word error rate - for \'mls\', \'fleurs-asr\', \'voxpopuli\', \'babel\'\n    "cer": Character error rate - for \'mls\', \'fleurs-asr\', \'voxpopuli\', \'babel\'\n    "bleu": BLEU score according to the `sacrebleu` metric - for \'covost2\'\nExamples:\n\n    >>> xtreme_s_metric = datasets.load_metric(\'xtreme_s\', \'mls\')  # \'mls\', \'voxpopuli\', \'fleurs-asr\' or \'babel\'\n    >>> references = ["it is sunny here", "paper and pen are essentials"]\n    >>> predictions = ["it\'s sunny", "paper pen are essential"]\n    >>> results = xtreme_s_metric.compute(predictions=predictions, references=references)\n    >>> print({k: round(v, 2) for k, v in results.items()})\n    {\'wer\': 0.56, \'cer\': 0.27}\n\n    >>> xtreme_s_metric = datasets.load_metric(\'xtreme_s\', \'covost2\')\n    >>> references = ["bonjour paris", "il est necessaire de faire du sport de temps en temp"]\n    >>> predictions = ["bonjour paris", "il est important de faire du sport souvent"]\n    >>> results = xtreme_s_metric.compute(predictions=predictions, references=references)\n    >>> print({k: round(v, 2) for k, v in results.items()})\n    {\'bleu\': 31.65}\n\n    >>> xtreme_s_metric = datasets.load_metric(\'xtreme_s\', \'fleurs-lang_id\')\n    >>> references = [0, 1, 0, 0, 1]\n    >>> predictions = [0, 1, 1, 0, 0]\n    >>> results = xtreme_s_metric.compute(predictions=predictions, references=references)\n    >>> print({k: round(v, 2) for k, v in results.items()})\n    {\'accuracy\': 0.6}\n\n    >>> xtreme_s_metric = datasets.load_metric(\'xtreme_s\', \'minds14\')\n    >>> references = [0, 1, 0, 0, 1]\n    >>> predictions = [0, 1, 1, 0, 0]\n    >>> results = xtreme_s_metric.compute(predictions=predictions, references=references)\n    >>> print({k: round(v, 2) for k, v in results.items()})\n    {\'f1\': 0.58, \'accuracy\': 0.6}\n'

UpperCAmelCase__						:       Tuple							=    ['fleurs-asr', 'mls', 'voxpopuli', 'babel', 'covost2', 'fleurs-lang_id', 'minds14']
UpperCAmelCase__						:       Optional[Any]							=    ''

try:
   from jiwer import transforms as tr

   UpperCAmelCase__						:       Union[str, Any]							=    True
except ImportError:
   UpperCAmelCase__						:       str							=    False

if _jiwer_available and version.parse(importlib_metadata.version('jiwer')) < version.parse('2.3.0'):

   class      UpperCAmelCase						(			tr.AbstractTransform   ):






      def __init__(	self   :       int						,					UpperCamelCase_   :       str = " "     )->			Any:
        _lowercase: Any						     =    sentence_delimiter






      def  _a						(	self   :       List[Any]						,					UpperCamelCase_   :       str     )->			Optional[Any]:
        return list(UpperCamelCase_     )






      def  _a						(	self   :       Optional[Any]						,					UpperCamelCase_   :       List[str]     )->			Union[str, Any]:
        _lowercase: List[Any]						     =    []
        for sent_idx, sentence in enumerate(UpperCamelCase_     ):
          chars.extend(self.process_string(UpperCamelCase_     )     )
          if self.sentence_delimiter is not None and self.sentence_delimiter != "" and sent_idx < len(UpperCamelCase_     ) - 1:
            chars.append(self.sentence_delimiter     )
        return chars
   UpperCAmelCase__						:       Dict							=    tr.Compose(
       [tr.RemoveMultipleSpaces(), tr.Strip(), SentencesToListOfCharacters(SENTENCE_DELIMITER)]
   )
elif _jiwer_available:
   UpperCAmelCase__						:       Any							=    tr.Compose(
       [
           tr.RemoveMultipleSpaces(),
           tr.Strip(),
           tr.ReduceToSingleSentence(SENTENCE_DELIMITER),
           tr.ReduceToListOfListOfChars(),
       ]
   )
else:
   UpperCAmelCase__						:       Any							=    None


def 		_lowerCamelCase				(					UpperCamelCase					,     UpperCamelCase							)			->		Tuple:





  """simple docstring"""





  return float((preds == labels).mean()							)


def 		_lowerCamelCase				(					UpperCamelCase					,     UpperCamelCase							)			->		List[Any]:





  """simple docstring"""





  return {
      "f1": float(fa_score(y_true=UpperCamelCase					,     y_pred=UpperCamelCase					,     average='macro'							)							),
      "accuracy": simple_accuracy(UpperCamelCase					,     UpperCamelCase							),
  }


def 		_lowerCamelCase				(					UpperCamelCase					,     UpperCamelCase					,     UpperCamelCase="exp"					,     UpperCamelCase=None					,     UpperCamelCase=False					,     UpperCamelCase=False					,     UpperCamelCase=None					,     UpperCamelCase=False					,     )			->		str:





  """simple docstring"""





  _lowercase: Tuple						     =    [[label] for label in labels]
  _lowercase: Any						     =    list(UpperCamelCase							)
  try:
    import sacrebleu as scb
  except ImportError:
    raise ValueError(
        'sacrebleu has to be installed in order to apply the bleu metric for covost2.'
        'You can install it via `pip install sacrebleu`.'							)

  if version.parse(scb.__version__							) < version.parse('1.4.12'							):
    raise ImportWarning(
        'To use `sacrebleu`, the module `sacrebleu>=1.4.12` is required, and the current version of `sacrebleu` doesn\'t match this condition.\n'
        'You can install it with `pip install "sacrebleu>=1.4.12"`.'							)

  _lowercase: Optional[Any]						     =    len(labels[0]							)
  if any(len(UpperCamelCase							) != references_per_prediction for refs in labels							):
    raise ValueError('Sacrebleu requires the same number of references for each prediction'							)
  _lowercase: str						     =    [[refs[i] for refs in labels] for i in range(UpperCamelCase							)]
  _lowercase: Any						     =    scb.corpus_bleu(
      UpperCamelCase					,     UpperCamelCase					,     smooth_method=UpperCamelCase					,     smooth_value=UpperCamelCase					,     force=UpperCamelCase					,     lowercase=UpperCamelCase					,     use_effective_order=UpperCamelCase					,     **({'tokenize': tokenize} if tokenize else {})					,     )
  return {"bleu": output.score}


def 		_lowerCamelCase				(					UpperCamelCase					,     UpperCamelCase					,     UpperCamelCase					,     UpperCamelCase							)			->		List[str]:





  """simple docstring"""





  try:
    from jiwer import compute_measures
  except ImportError:
    raise ValueError(
        F'jiwer has to be installed in order to apply the wer metric for {config_name}.'
        'You can install it via `pip install jiwer`.'							)

  if concatenate_texts:
    _lowercase: Tuple						     =    compute_measures(UpperCamelCase					,     UpperCamelCase							)['wer']

    _lowercase: Tuple						     =    compute_measures(UpperCamelCase					,     UpperCamelCase					,     truth_transform=UpperCamelCase					,     hypothesis_transform=UpperCamelCase							)['wer']
    return {"wer": wer, "cer": cer}
  else:

    def compute_score(UpperCamelCase					,     UpperCamelCase					,     UpperCamelCase="wer"							):
      _lowercase: int						     =    0
      _lowercase: str						     =    0
      for prediction, reference in zip(UpperCamelCase					,     UpperCamelCase							):
        if score_type == "wer":
          _lowercase: List[str]						     =    compute_measures(UpperCamelCase					,     UpperCamelCase							)
        elif score_type == "cer":
          _lowercase: List[Any]						     =    compute_measures(
              UpperCamelCase					,     UpperCamelCase					,     truth_transform=UpperCamelCase					,     hypothesis_transform=UpperCamelCase							)
        incorrect += measures["substitutions"] + measures["deletions"] + measures["insertions"]
        total += measures["substitutions"] + measures["deletions"] + measures["hits"]
      return incorrect / total

    return {"wer": compute_score(UpperCamelCase					,     UpperCamelCase					,     'wer'							), "cer": compute_score(UpperCamelCase					,     UpperCamelCase					,     'cer'							)}





@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION   ,					_KWARGS_DESCRIPTION   )
class      UpperCAmelCase						(			datasets.Metric   ):






   def  _a						(	self   :       List[Any]     )->			int:
     if self.config_name not in _CONFIG_NAMES:
       raise KeyError(f'You should supply a configuration name selected in {_CONFIG_NAMES}'     )

     _lowercase: List[Any]						     =    'int64' if self.config_name in ['fleurs-lang_id', 'minds14'] else 'string'

     return datasets.MetricInfo(
         description=_DESCRIPTION						,					citation=_CITATION						,					inputs_description=_KWARGS_DESCRIPTION						,					features=datasets.Features(
             {'predictions': datasets.Value(UpperCamelCase_     ), 'references': datasets.Value(UpperCamelCase_     )}     )						,					codebase_urls=[]						,					reference_urls=[]						,					format='numpy'						,					)






   def  _a						(	self   :       Union[str, Any]						,					UpperCamelCase_   :       Any						,					UpperCamelCase_   :       List[str]						,					UpperCamelCase_   :       Optional[Any]=None						,					UpperCamelCase_   :       int=None     )->			str:
     _lowercase: List[Any]						     =    bleu_kwargs if bleu_kwargs is not None else {}
     _lowercase: List[Any]						     =    wer_kwargs if wer_kwargs is not None else {}

     if self.config_name == "fleurs-lang_id":
       return {"accuracy": simple_accuracy(UpperCamelCase_						,					UpperCamelCase_     )}
     elif self.config_name == "minds14":
       return fa_and_simple_accuracy(UpperCamelCase_						,					UpperCamelCase_     )
     elif self.config_name == "covost2":
       _lowercase: Dict						     =    bleu_kwargs.pop('smooth_method'						,					'exp'     )
       _lowercase: Optional[int]						     =    bleu_kwargs.pop('smooth_value'						,					UpperCamelCase_     )
       _lowercase: Any						     =    bleu_kwargs.pop('force'						,					UpperCamelCase_     )
       _lowercase: int						     =    bleu_kwargs.pop('lowercase'						,					UpperCamelCase_     )
       _lowercase: Dict						     =    bleu_kwargs.pop('tokenize'						,					UpperCamelCase_     )
       _lowercase: Dict						     =    bleu_kwargs.pop('use_effective_order'						,					UpperCamelCase_     )
       return bleu(
           preds=UpperCamelCase_						,					labels=UpperCamelCase_						,					smooth_method=UpperCamelCase_						,					smooth_value=UpperCamelCase_						,					force=UpperCamelCase_						,					lowercase=UpperCamelCase_						,					tokenize=UpperCamelCase_						,					use_effective_order=UpperCamelCase_						,					)
     elif self.config_name in ["fleurs-asr", "mls", "voxpopuli", "babel"]:
       _lowercase: Union[str, Any]						     =    wer_kwargs.pop('concatenate_texts'						,					UpperCamelCase_     )
       return wer_and_cer(UpperCamelCase_						,					UpperCamelCase_						,					UpperCamelCase_						,					self.config_name     )
     else:
       raise KeyError(f'You should supply a configuration name selected in {_CONFIG_NAMES}'     )






import argparse

import requests
import torch
from PIL import Image

from transformers import (
    CLIPSegConfig,
    CLIPSegForImageSegmentation,
    CLIPSegProcessor,
    CLIPSegTextConfig,
    CLIPSegVisionConfig,
    CLIPTokenizer,
    ViTImageProcessor,
)


def 		_lowerCamelCase				(					UpperCamelCase							)			->		str:





  """simple docstring"""





  _lowercase: Any						     =    CLIPSegTextConfig()
  _lowercase: Optional[Any]						     =    CLIPSegVisionConfig(patch_size=16							)

  _lowercase: Union[str, Any]						     =    True if 'refined' in model_name else False
  _lowercase: str						     =    16 if 'rd16' in model_name else 64

  _lowercase: Any						     =    CLIPSegConfig.from_text_vision_configs(
      UpperCamelCase					,     UpperCamelCase					,     use_complex_transposed_convolution=UpperCamelCase					,     reduce_dim=UpperCamelCase					,     )
  return config


def 		_lowerCamelCase				(					UpperCamelCase							)			->		Any:





  """simple docstring"""





  if "clip_model" in name:
    _lowercase: Tuple						     =    name.replace('clip_model'					,     'clip'							)
  if "transformer" in name:
    if "visual" in name:
      _lowercase: Union[str, Any]						     =    name.replace('visual.transformer'					,     'vision_model'							)
    else:
      _lowercase: Optional[int]						     =    name.replace('transformer'					,     'text_model'							)
  if "resblocks" in name:
    _lowercase: List[Any]						     =    name.replace('resblocks'					,     'encoder.layers'							)
  if "ln_1" in name:
    _lowercase: Union[str, Any]						     =    name.replace('ln_1'					,     'layer_norm1'							)
  if "ln_2" in name:
    _lowercase: Union[str, Any]						     =    name.replace('ln_2'					,     'layer_norm2'							)
  if "c_fc" in name:
    _lowercase: str						     =    name.replace('c_fc'					,     'fc1'							)
  if "c_proj" in name:
    _lowercase: List[Any]						     =    name.replace('c_proj'					,     'fc2'							)
  if "attn" in name and "self" not in name:
    _lowercase: Dict						     =    name.replace('attn'					,     'self_attn'							)
  # text encoder
  if "token_embedding" in name:
    _lowercase: Optional[int]						     =    name.replace('token_embedding'					,     'text_model.embeddings.token_embedding'							)
  if "positional_embedding" in name and "visual" not in name:
    _lowercase: int						     =    name.replace('positional_embedding'					,     'text_model.embeddings.position_embedding.weight'							)
  if "ln_final" in name:
    _lowercase: Optional[Any]						     =    name.replace('ln_final'					,     'text_model.final_layer_norm'							)
  # vision encoder
  if "visual.class_embedding" in name:
    _lowercase: Union[str, Any]						     =    name.replace('visual.class_embedding'					,     'vision_model.embeddings.class_embedding'							)
  if "visual.conv1" in name:
    _lowercase: int						     =    name.replace('visual.conv1'					,     'vision_model.embeddings.patch_embedding'							)
  if "visual.positional_embedding" in name:
    _lowercase: List[Any]						     =    name.replace('visual.positional_embedding'					,     'vision_model.embeddings.position_embedding.weight'							)
  if "visual.ln_pre" in name:
    _lowercase: Dict						     =    name.replace('visual.ln_pre'					,     'vision_model.pre_layrnorm'							)
  if "visual.ln_post" in name:
    _lowercase: Dict						     =    name.replace('visual.ln_post'					,     'vision_model.post_layernorm'							)
  # projection layers
  if "visual.proj" in name:
    _lowercase: List[str]						     =    name.replace('visual.proj'					,     'visual_projection.weight'							)
  if "text_projection" in name:
    _lowercase: Any						     =    name.replace('text_projection'					,     'text_projection.weight'							)
  # decoder
  if "trans_conv" in name:
    _lowercase: List[Any]						     =    name.replace('trans_conv'					,     'transposed_convolution'							)
  if "film_mul" in name or "film_add" in name or "reduce" in name or "transposed_convolution" in name:
    _lowercase: Any						     =    'decoder.' + name
  if "blocks" in name:
    _lowercase: Optional[int]						     =    name.replace('blocks'					,     'decoder.layers'							)
  if "linear1" in name:
    _lowercase: Optional[Any]						     =    name.replace('linear1'					,     'mlp.fc1'							)
  if "linear2" in name:
    _lowercase: int						     =    name.replace('linear2'					,     'mlp.fc2'							)
  if "norm1" in name and "layer_" not in name:
    _lowercase: Optional[int]						     =    name.replace('norm1'					,     'layer_norm1'							)
  if "norm2" in name and "layer_" not in name:
    _lowercase: Tuple						     =    name.replace('norm2'					,     'layer_norm2'							)

  return name


def 		_lowerCamelCase				(					UpperCamelCase					,     UpperCamelCase							)			->		Optional[Any]:





  """simple docstring"""





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

    if key.startswith('clip_model'							) and "attn.in_proj" in key:
      _lowercase: Union[str, Any]						     =    key.split('.'							)
      if "visual" in key:
        _lowercase: List[str]						     =    int(key_split[4]							)
        _lowercase: Dict						     =    config.vision_config.hidden_size
        _lowercase: Union[str, Any]						     =    'vision_model'
      else:
        _lowercase: str						     =    int(key_split[3]							)
        _lowercase: Optional[Any]						     =    config.text_config.hidden_size
        _lowercase: List[str]						     =    'text_model'

      if "weight" in key:
        _lowercase: Optional[int]						     =    val[:dim, :]
        _lowercase: List[Any]						     =    val[
            dim : dim * 2, :
        ]
        _lowercase: Tuple						     =    val[-dim:, :]
      else:
        _lowercase: Any						     =    val[:dim]
        _lowercase: List[str]						     =    val[dim : dim * 2]
        _lowercase: Optional[int]						     =    val[-dim:]
    elif "self_attn" in key and "out_proj" not in key:
      _lowercase: str						     =    key.split('.'							)
      _lowercase: Union[str, Any]						     =    int(key_split[1]							)
      _lowercase: List[str]						     =    config.reduce_dim
      if "weight" in key:
        _lowercase: Union[str, Any]						     =    val[:dim, :]
        _lowercase: Tuple						     =    val[dim : dim * 2, :]
        _lowercase: Union[str, Any]						     =    val[-dim:, :]
      else:
        _lowercase: Union[str, Any]						     =    val[:dim]
        _lowercase: Dict						     =    val[dim : dim * 2]
        _lowercase: Any						     =    val[-dim:]
    else:
      _lowercase: Dict						     =    rename_key(UpperCamelCase							)
      if "visual_projection" in new_name or "text_projection" in new_name:
        _lowercase: str						     =    val.T
      _lowercase: Tuple						     =    val

  return orig_state_dict


def 		_lowerCamelCase				(					)			->		Optional[Any]:





  """simple docstring"""





  _lowercase: Any						     =    'http://images.cocodataset.org/val2017/000000039769.jpg'
  _lowercase: str						     =    Image.open(requests.get(UpperCamelCase					,     stream=UpperCamelCase							).raw							)
  return image


def 		_lowerCamelCase				(					UpperCamelCase					,     UpperCamelCase					,     UpperCamelCase					,     UpperCamelCase							)			->		Optional[int]:





  """simple docstring"""





  _lowercase: Optional[Any]						     =    get_clipseg_config(UpperCamelCase							)
  _lowercase: Optional[Any]						     =    CLIPSegForImageSegmentation(UpperCamelCase							)
  model.eval()

  _lowercase: Dict						     =    torch.load(UpperCamelCase					,     map_location='cpu'							)

  # remove some keys
  for key in state_dict.copy().keys():
    if key.startswith('model'							):
      state_dict.pop(UpperCamelCase					,     UpperCamelCase							)

    # rename some keys
  _lowercase: List[str]						     =    convert_state_dict(UpperCamelCase					,     UpperCamelCase							)
  _lowercase     ,					_lowercase: List[Any]						     =    model.load_state_dict(UpperCamelCase					,     strict=UpperCamelCase							)

  if missing_keys != ["clip.text_model.embeddings.position_ids", "clip.vision_model.embeddings.position_ids"]:
    raise ValueError('Missing keys that are not expected: {}'.format(UpperCamelCase							)							)
  if unexpected_keys != ["decoder.reduce.weight", "decoder.reduce.bias"]:
    raise ValueError(F'Unexpected keys: {unexpected_keys}'							)

  _lowercase: Tuple						     =    ViTImageProcessor(size=352							)
  _lowercase: Union[str, Any]						     =    CLIPTokenizer.from_pretrained('openai/clip-vit-base-patch32'							)
  _lowercase: Optional[Any]						     =    CLIPSegProcessor(image_processor=UpperCamelCase					,     tokenizer=UpperCamelCase							)

  _lowercase: Union[str, Any]						     =    prepare_img()
  _lowercase: List[Any]						     =    ['a glass', 'something to fill', 'wood', 'a jar']

  _lowercase: str						     =    processor(text=UpperCamelCase					,     images=[image] * len(UpperCamelCase							)					,     padding='max_length'					,     return_tensors='pt'							)

  with torch.no_grad():
    _lowercase: Optional[Any]						     =    model(**UpperCamelCase							)

  # verify values
  _lowercase: int						     =    torch.tensor([0.1_110, -0.1_882, 0.1_645]							)
  _lowercase: Tuple						     =    torch.tensor([0.2_692, -0.7_197, -0.1_328]							)
  if model_name == "clipseg-rd64-refined":
    _lowercase: str						     =    torch.tensor(
        [[-10.0_407, -9.9_431, -10.2_646], [-9.9_751, -9.7_064, -9.9_586], [-9.6_891, -9.5_645, -9.9_618]]							)
  elif model_name == "clipseg-rd64":
    _lowercase: Dict						     =    torch.tensor(
        [[-7.2_877, -7.2_711, -7.2_463], [-7.2_652, -7.2_780, -7.2_520], [-7.2_239, -7.2_204, -7.2_001]]							)
  elif model_name == "clipseg-rd16":
    _lowercase: Optional[int]						     =    torch.tensor(
        [[-6.3_955, -6.4_055, -6.4_151], [-6.3_911, -6.4_033, -6.4_100], [-6.3_474, -6.3_702, -6.3_762]]							)
  else:
    raise ValueError(F'Model name {model_name} not supported.'							)

  assert torch.allclose(outputs.logits[0, :3, :3]					,     UpperCamelCase					,     atol=1E-3							)
  assert torch.allclose(outputs.conditional_embeddings[0, :3]					,     UpperCamelCase					,     atol=1E-3							)
  assert torch.allclose(outputs.pooled_output[0, :3]					,     UpperCamelCase					,     atol=1E-3							)
  print('Looks ok!'							)

  if pytorch_dump_folder_path is not None:
    print(F'Saving model and processor to {pytorch_dump_folder_path}'							)
    model.save_pretrained(UpperCamelCase							)
    processor.save_pretrained(UpperCamelCase							)

  if push_to_hub:
    print(F'Pushing model and processor for {model_name} to the hub'							)
    model.push_to_hub(F'CIDAS/{model_name}'							)
    processor.push_to_hub(F'CIDAS/{model_name}'							)


if __name__ == "__main__":
   UpperCAmelCase__						:       int							=    argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       '--model_name',
       default='clipseg-rd64',
       type=str,
       choices=['clipseg-rd16', 'clipseg-rd64', 'clipseg-rd64-refined'],
       help=(
           'Name of the model. Supported models are: clipseg-rd64, clipseg-rd16 and clipseg-rd64-refined (rd meaning'
           ' reduce dimension)'
       ),
   )
   parser.add_argument(
       '--checkpoint_path',
       default='/Users/nielsrogge/Documents/CLIPSeg/clip_plus_rd64-uni.pth',
       type=str,
       help=(
           'Path to the original checkpoint. Note that the script assumes that the checkpoint includes both CLIP and'
           ' the decoder weights.'
       ),
   )
   parser.add_argument(
       '--pytorch_dump_folder_path', default=None, 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__						:       str							=    parser.parse_args()
   convert_clipseg_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)
