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							)   -> str:
    _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(
        __snake_case						,				__snake_case						,				use_complex_transposed_convolution=__snake_case						,				reduce_dim=__snake_case						,				)
    return config
  
  
  
  
  def     UpperCAmelCase__ (							__snake_case							)   -> Optional[Any]:
    # 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							)   -> str:
    for key in orig_state_dict.copy().keys():
      _A           =    orig_state_dict.pop(__snake_case							)
  
      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(__snake_case							)
        if "visual_projection" in new_name or "text_projection" in new_name:
          _A           =    val.T
        _A           =    val
  
    return orig_state_dict
  
  
  
  
  def     UpperCAmelCase__ (							)   -> Any:
    _A           =    '''http://images.cocodataset.org/val2017/000000039769.jpg'''
    _A           =    Image.open(requests.get(__snake_case						,				stream=__snake_case							).raw							)
    return image
  
  
  
  
  def     UpperCAmelCase__ (							__snake_case						,				__snake_case						,				__snake_case						,				__snake_case							)   -> int:
    _A           =    get_clipseg_config(__snake_case							)
    _A           =    CLIPSegForImageSegmentation(__snake_case							)
    model.eval()
  
    _A           =    torch.load(__snake_case						,				map_location='''cpu'''							)
  
    # remove some keys
    for key in state_dict.copy().keys():
      if key.startswith('''model'''							):
        state_dict.pop(__snake_case						,				__snake_case							)
  
      # rename some keys
    _A           =    convert_state_dict(__snake_case						,				__snake_case							)
    _A    ,   _A           =    model.load_state_dict(__snake_case						,				strict=__snake_case							)
  
    if missing_keys != ["clip.text_model.embeddings.position_ids", "clip.vision_model.embeddings.position_ids"]:
      raise ValueError('''Missing keys that are not expected: {}'''.format(__snake_case							)							)
    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=__snake_case						,				tokenizer=__snake_case							)
  
    _A           =    prepare_img()
    _A           =    ['''a glass''', '''something to fill''', '''wood''', '''a jar''']
  
    _A           =    processor(text=__snake_case						,				images=[image] * len(__snake_case							)						,				padding='''max_length'''						,				return_tensors='''pt'''							)
  
    with torch.no_grad():
      _A           =    model(**__snake_case							)
  
    # 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]						,				__snake_case						,				atol=1e-3							)
    assert torch.allclose(outputs.conditional_embeddings[0, :3]						,				__snake_case						,				atol=1e-3							)
    assert torch.allclose(outputs.pooled_output[0, :3]						,				__snake_case						,				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(__snake_case							)
      processor.save_pretrained(__snake_case							)
  
    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: 
  
  
  
  def     UpperCAmelCase__ (							__snake_case						,				__snake_case = 1_000							)   -> str:
    if (not isinstance(__snake_case						,				__snake_case							)) or (digit_position <= 0):
      raise ValueError('''Digit position must be a positive integer'''							)
    elif (not isinstance(__snake_case						,				__snake_case							)) or (precision < 0):
      raise ValueError('''Precision must be a nonnegative integer'''							)
  
    # compute an approximation of (16 ** (n - 1)) * pi whose fractional part is mostly
    # accurate
    _A           =    (
        4 * _subsum(__snake_case						,				1						,				__snake_case							)
        - 2 * _subsum(__snake_case						,				4						,				__snake_case							)
        - _subsum(__snake_case						,				5						,				__snake_case							)
        - _subsum(__snake_case						,				6						,				__snake_case							)
    )
  
    # return the first hex digit of the fractional part of the result
    return hex(int((sum_result % 1) * 16							)							)[2:]
  
  
  
  
  def     UpperCAmelCase__ (							__snake_case						,				__snake_case						,				__snake_case							)   -> float:
    _A           =    0.0
    for sum_index in range(digit_pos_to_extract + precision							):
      _A           =    8 * sum_index + denominator_addend
      if sum_index < digit_pos_to_extract:
        # if the exponential term is an integer and we mod it by the denominator
        # before dividing, only the integer part of the sum will change;
        # the fractional part will not
        _A           =    pow(
            16						,				digit_pos_to_extract - 1 - sum_index						,				__snake_case							)
      else:
        _A           =    pow(16						,				digit_pos_to_extract - 1 - sum_index							)
      total += exponential_term / denominator
    return total
  
  
  if __name__ == "__main__":
      import doctest
  
      doctest.testmod()