code: 
  
  
  
  import argparse
  
  import requests
  import torch
  from PIL import Image
  
  from transformers import (
      CLIPSegConfig,
      CLIPSegForImageSegmentation,
      CLIPSegProcessor,
      CLIPSegTextConfig,
      CLIPSegVisionConfig,
      CLIPTokenizer,
      ViTImageProcessor,
  )
  
  
  
  
  def     UpperCAmelCase__ (							__snake_case							)   -> Any:
    _A           =    CLIPSegTextConfig()
    _A           =    CLIPSegVisionConfig(patch_size=16							)
  
    _A           =    True if "refined" in model_name else False
    _A           =    16 if "rd16" in model_name else 64
  
    _A           =    CLIPSegConfig.from_text_vision_configs(
        lowercase__						,				lowercase__						,				use_complex_transposed_convolution=lowercase__						,				reduce_dim=lowercase__						,				)
    return config
  
  
  
  
  def     UpperCAmelCase__ (							__snake_case							)   -> Tuple:
    # update prefixes
    if "clip_model" in name:
      _A           =    name.replace('''clip_model'''						,				'''clip'''							)
    if "transformer" in name:
      if "visual" in name:
        _A           =    name.replace('''visual.transformer'''						,				'''vision_model'''							)
      else:
        _A           =    name.replace('''transformer'''						,				'''text_model'''							)
    if "resblocks" in name:
      _A           =    name.replace('''resblocks'''						,				'''encoder.layers'''							)
    if "ln_1" in name:
      _A           =    name.replace('''ln_1'''						,				'''layer_norm1'''							)
    if "ln_2" in name:
      _A           =    name.replace('''ln_2'''						,				'''layer_norm2'''							)
    if "c_fc" in name:
      _A           =    name.replace('''c_fc'''						,				'''fc1'''							)
    if "c_proj" in name:
      _A           =    name.replace('''c_proj'''						,				'''fc2'''							)
    if "attn" in name and "self" not in name:
      _A           =    name.replace('''attn'''						,				'''self_attn'''							)
    # text encoder
    if "token_embedding" in name:
      _A           =    name.replace('''token_embedding'''						,				'''text_model.embeddings.token_embedding'''							)
    if "positional_embedding" in name and "visual" not in name:
      _A           =    name.replace('''positional_embedding'''						,				'''text_model.embeddings.position_embedding.weight'''							)
    if "ln_final" in name:
      _A           =    name.replace('''ln_final'''						,				'''text_model.final_layer_norm'''							)
    # vision encoder
    if "visual.class_embedding" in name:
      _A           =    name.replace('''visual.class_embedding'''						,				'''vision_model.embeddings.class_embedding'''							)
    if "visual.conv1" in name:
      _A           =    name.replace('''visual.conv1'''						,				'''vision_model.embeddings.patch_embedding'''							)
    if "visual.positional_embedding" in name:
      _A           =    name.replace('''visual.positional_embedding'''						,				'''vision_model.embeddings.position_embedding.weight'''							)
    if "visual.ln_pre" in name:
      _A           =    name.replace('''visual.ln_pre'''						,				'''vision_model.pre_layrnorm'''							)
    if "visual.ln_post" in name:
      _A           =    name.replace('''visual.ln_post'''						,				'''vision_model.post_layernorm'''							)
    # projection layers
    if "visual.proj" in name:
      _A           =    name.replace('''visual.proj'''						,				'''visual_projection.weight'''							)
    if "text_projection" in name:
      _A           =    name.replace('''text_projection'''						,				'''text_projection.weight'''							)
    # decoder
    if "trans_conv" in name:
      _A           =    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:
      _A           =    "decoder." + name
    if "blocks" in name:
      _A           =    name.replace('''blocks'''						,				'''decoder.layers'''							)
    if "linear1" in name:
      _A           =    name.replace('''linear1'''						,				'''mlp.fc1'''							)
    if "linear2" in name:
      _A           =    name.replace('''linear2'''						,				'''mlp.fc2'''							)
    if "norm1" in name and "layer_" not in name:
      _A           =    name.replace('''norm1'''						,				'''layer_norm1'''							)
    if "norm2" in name and "layer_" not in name:
      _A           =    name.replace('''norm2'''						,				'''layer_norm2'''							)
  
    return name
  
  
  
  
  def     UpperCAmelCase__ (							__snake_case						,				__snake_case							)   -> List[Any]:
    for key in orig_state_dict.copy().keys():
      _A           =    orig_state_dict.pop(lowercase__							)
  
      if key.startswith('''clip_model'''							) and "attn.in_proj" in key:
        _A           =    key.split('''.'''							)
        if "visual" in key:
          _A           =    int(key_split[4]							)
          _A           =    config.vision_config.hidden_size
          _A           =    "vision_model"
        else:
          _A           =    int(key_split[3]							)
          _A           =    config.text_config.hidden_size
          _A           =    "text_model"
  
        if "weight" in key:
          _A           =    val[:dim, :]
          _A           =    val[
              dim : dim * 2, :
          ]
          _A           =    val[-dim:, :]
        else:
          _A           =    val[:dim]
          _A           =    val[dim : dim * 2]
          _A           =    val[-dim:]
      elif "self_attn" in key and "out_proj" not in key:
        _A           =    key.split('''.'''							)
        _A           =    int(key_split[1]							)
        _A           =    config.reduce_dim
        if "weight" in key:
          _A           =    val[:dim, :]
          _A           =    val[dim : dim * 2, :]
          _A           =    val[-dim:, :]
        else:
          _A           =    val[:dim]
          _A           =    val[dim : dim * 2]
          _A           =    val[-dim:]
      else:
        _A           =    rename_key(lowercase__							)
        if "visual_projection" in new_name or "text_projection" in new_name:
          _A           =    val.T
        _A           =    val
  
    return orig_state_dict
  
  
  
  
  def     UpperCAmelCase__ (							)   -> Dict:
    _A           =    "http://images.cocodataset.org/val2017/000000039769.jpg"
    _A           =    Image.open(requests.get(lowercase__						,				stream=lowercase__							).raw							)
    return image
  
  
  
  
  def     UpperCAmelCase__ (							__snake_case						,				__snake_case						,				__snake_case						,				__snake_case							)   -> Union[str, Any]:
    _A           =    get_clipseg_config(lowercase__							)
    _A           =    CLIPSegForImageSegmentation(lowercase__							)
    model.eval()
  
    _A           =    torch.load(lowercase__						,				map_location='''cpu'''							)
  
    # remove some keys
    for key in state_dict.copy().keys():
      if key.startswith('''model'''							):
        state_dict.pop(lowercase__						,				lowercase__							)
  
      # rename some keys
    _A           =    convert_state_dict(lowercase__						,				lowercase__							)
    _A           =    model.load_state_dict(lowercase__						,				strict=lowercase__							)
  
    if missing_keys != ["clip.text_model.embeddings.position_ids", "clip.vision_model.embeddings.position_ids"]:
      raise ValueError('''Missing keys that are not expected: {}'''.format(lowercase__							)							)
    if unexpected_keys != ["decoder.reduce.weight", "decoder.reduce.bias"]:
      raise ValueError(F'''Unexpected keys: {unexpected_keys}'''							)
  
    _A           =    ViTImageProcessor(size=352							)
    _A           =    CLIPTokenizer.from_pretrained('''openai/clip-vit-base-patch32'''							)
    _A           =    CLIPSegProcessor(image_processor=lowercase__						,				tokenizer=lowercase__							)
  
    _A           =    prepare_img()
    _A           =    ["a glass", "something to fill", "wood", "a jar"]
  
    _A           =    processor(text=lowercase__						,				images=[image] * len(lowercase__							)						,				padding='''max_length'''						,				return_tensors='''pt'''							)
  
    with torch.no_grad():
      _A           =    model(**lowercase__							)
  
    # verify values
    _A           =    torch.tensor([0.11_10, -0.18_82, 0.16_45]							)
    _A           =    torch.tensor([0.26_92, -0.71_97, -0.13_28]							)
    if model_name == "clipseg-rd64-refined":
      _A           =    torch.tensor(
          [[-10.04_07, -9.94_31, -10.26_46], [-9.97_51, -9.70_64, -9.95_86], [-9.68_91, -9.56_45, -9.96_18]]							)
    elif model_name == "clipseg-rd64":
      _A           =    torch.tensor(
          [[-7.28_77, -7.27_11, -7.24_63], [-7.26_52, -7.27_80, -7.25_20], [-7.22_39, -7.22_04, -7.20_01]]							)
    elif model_name == "clipseg-rd16":
      _A           =    torch.tensor(
          [[-6.39_55, -6.40_55, -6.41_51], [-6.39_11, -6.40_33, -6.41_00], [-6.34_74, -6.37_02, -6.37_62]]							)
    else:
      raise ValueError(F'''Model name {model_name} not supported.'''							)
  
    assert torch.allclose(outputs.logits[0, :3, :3]						,				lowercase__						,				atol=1e-3							)
    assert torch.allclose(outputs.conditional_embeddings[0, :3]						,				lowercase__						,				atol=1e-3							)
    assert torch.allclose(outputs.pooled_output[0, :3]						,				lowercase__						,				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(lowercase__							)
      processor.save_pretrained(lowercase__							)
  
    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__":
      __lowerCamelCase  				=					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."""
      )
  
      __lowerCamelCase  				=					parser.parse_args()
      convert_clipseg_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)
style_context: 
  
  
  
  from sklearn.metrics import accuracy_score
  
  import datasets
  
  
  __lowerCamelCase  				=					"""
  Accuracy is the proportion of correct predictions among the total number of cases processed. It can be computed with:
  Accuracy = (TP + TN) / (TP + TN + FP + FN)
   Where:
  TP: True positive
  TN: True negative
  FP: False positive
  FN: False negative
  """
  
  
  __lowerCamelCase  				=					"""
  Args:
      predictions (`list` of `int`): Predicted labels.
      references (`list` of `int`): Ground truth labels.
      normalize (`boolean`): If set to False, returns the number of correctly classified samples. Otherwise, returns the fraction of correctly classified samples. Defaults to True.
      sample_weight (`list` of `float`): Sample weights Defaults to None.
  
  Returns:
      accuracy (`float` or `int`): Accuracy score. Minimum possible value is 0. Maximum possible value is 1.0, or the number of examples input, if `normalize` is set to `True`.. A higher score means higher accuracy.
  
  Examples:
  
      Example 1-A simple example
          >>> accuracy_metric = datasets.load_metric(\"accuracy\")
          >>> results = accuracy_metric.compute(references=[0, 1, 2, 0, 1, 2], predictions=[0, 1, 1, 2, 1, 0])
          >>> print(results)
          {'accuracy': 0.5}
  
      Example 2-The same as Example 1, except with `normalize` set to `False`.
          >>> accuracy_metric = datasets.load_metric(\"accuracy\")
          >>> results = accuracy_metric.compute(references=[0, 1, 2, 0, 1, 2], predictions=[0, 1, 1, 2, 1, 0], normalize=False)
          >>> print(results)
          {'accuracy': 3.0}
  
      Example 3-The same as Example 1, except with `sample_weight` set.
          >>> accuracy_metric = datasets.load_metric(\"accuracy\")
          >>> results = accuracy_metric.compute(references=[0, 1, 2, 0, 1, 2], predictions=[0, 1, 1, 2, 1, 0], sample_weight=[0.5, 2, 0.7, 0.5, 9, 0.4])
          >>> print(results)
          {'accuracy': 0.8778625954198473}
  """
  
  
  __lowerCamelCase  				=					"""
  @article{scikit-learn,
    title={Scikit-learn: Machine Learning in {P}ython},
    author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.
           and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.
           and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and
           Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},
    journal={Journal of Machine Learning Research},
    volume={12},
    pages={2825--2830},
    year={2011}
  }
  """
  
  
  
  @datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION					,_KWARGS_DESCRIPTION				)
  class 		_snake_case      (			datasets.Metric				):
  
  
  
  
  
   """simple docstring"""
  
  
  
   def lowercase_		(						self					)	->							Optional[Any]:
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     return datasets.MetricInfo(
         description=_DESCRIPTION	,    citation=_CITATION	,    inputs_description=_KWARGS_DESCRIPTION	,    features=datasets.Features(
             {
                 '''predictions''': datasets.Sequence(datasets.Value('''int32'''					)					),
                 '''references''': datasets.Sequence(datasets.Value('''int32'''					)					),
             }
             if self.config_name == '''multilabel'''
             else {
                 '''predictions''': datasets.Value('''int32'''					),
                 '''references''': datasets.Value('''int32'''					),
             }					)	,    reference_urls=['''https://scikit-learn.org/stable/modules/generated/sklearn.metrics.accuracy_score.html''']	,    )
  
  
  
   def lowercase_		(						self	,    a	,    a	,    a=True	,    a=None					)	->							Dict:
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     return {
         "accuracy": float(
             accuracy_score(a	,    a	,    normalize=a	,    sample_weight=a					)					)
     }