code: import argparse
  
  import requests
  import torch
  from PIL import Image
  
  from transformers import (
      CLIPSegConfig,
      CLIPSegForImageSegmentation,
      CLIPSegProcessor,
      CLIPSegTextConfig,
      CLIPSegVisionConfig,
      CLIPTokenizer,
      ViTImageProcessor,
  )
  def 				__lowercase	(						lowerCamelCase__					)				->Optional[int]:
  
    '''simple docstring'''
  
  
  
  
    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(
        _A ,				_A ,				use_complex_transposed_convolution=_A ,				reduce_dim=_A ,				)
    return config
  def 				__lowercase	(						lowerCamelCase__					)				->List[str]:
  
    '''simple docstring'''
  
  
  
  
    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 				__lowercase	(						lowerCamelCase__ ,				lowerCamelCase__					)				->Any:
  
    '''simple docstring'''
  
  
  
  
    for key in orig_state_dict.copy().keys():
      a__     		=     orig_state_dict.pop(_A					)
  
      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(_A					)
        if "visual_projection" in new_name or "text_projection" in new_name:
          a__     		=     val.T
        a__     		=     val
  
    return orig_state_dict
  def 				__lowercase	(						)				->Union[str, Any]:
  
    '''simple docstring'''
  
  
  
  
    a__     		=     "http://images.cocodataset.org/val2017/000000039769.jpg"
    a__     		=     Image.open(requests.get(_A ,				stream=_A					).raw					)
    return image
  def 				__lowercase	(						lowerCamelCase__ ,				lowerCamelCase__ ,				lowerCamelCase__ ,				lowerCamelCase__					)				->Any:
  
    '''simple docstring'''
  
  
  
  
    a__     		=     get_clipseg_config(_A					)
    a__     		=     CLIPSegForImageSegmentation(_A					)
    model.eval()
  
    a__     		=     torch.load(_A ,				map_location="cpu"					)
  
    # remove some keys
    for key in state_dict.copy().keys():
      if key.startswith("model"					):
        state_dict.pop(_A ,				_A					)
  
      # rename some keys
    a__     		=     convert_state_dict(_A ,				_A					)
    a__		,a__     		=     model.load_state_dict(_A ,				strict=_A					)
  
    if missing_keys != ["clip.text_model.embeddings.position_ids", "clip.vision_model.embeddings.position_ids"]:
      raise ValueError("Missing keys that are not expected: {}".format(_A					)					)
    if unexpected_keys != ["decoder.reduce.weight", "decoder.reduce.bias"]:
      raise ValueError(f"Unexpected keys: {unexpected_keys}"					)
  
    a__     		=     ViTImageProcessor(size=3_52					)
    a__     		=     CLIPTokenizer.from_pretrained("openai/clip-vit-base-patch32"					)
    a__     		=     CLIPSegProcessor(image_processor=_A ,				tokenizer=_A					)
  
    a__     		=     prepare_img()
    a__     		=     ["a glass", "something to fill", "wood", "a jar"]
  
    a__     		=     processor(text=_A ,				images=[image] * len(_A					) ,				padding="max_length" ,				return_tensors="pt"					)
  
    with torch.no_grad():
      a__     		=     model(**_A					)
  
    # verify values
    a__     		=     torch.tensor([0.1110, -0.1882, 0.1645]					)
    a__     		=     torch.tensor([0.2692, -0.7197, -0.1328]					)
    if model_name == "clipseg-rd64-refined":
      a__     		=     torch.tensor(
          [[-10.04_07, -9.9431, -10.26_46], [-9.9751, -9.7064, -9.9586], [-9.6891, -9.5645, -9.9618]]					)
    elif model_name == "clipseg-rd64":
      a__     		=     torch.tensor(
          [[-7.2877, -7.2711, -7.2463], [-7.2652, -7.2780, -7.2520], [-7.2239, -7.2204, -7.2001]]					)
    elif model_name == "clipseg-rd16":
      a__     		=     torch.tensor(
          [[-6.3955, -6.4055, -6.4151], [-6.3911, -6.4033, -6.4100], [-6.3474, -6.3702, -6.3762]]					)
    else:
      raise ValueError(f"Model name {model_name} not supported."					)
  
    assert torch.allclose(outputs.logits[0, :3, :3] ,				_A ,				atol=1E-3					)
    assert torch.allclose(outputs.conditional_embeddings[0, :3] ,				_A ,				atol=1E-3					)
    assert torch.allclose(outputs.pooled_output[0, :3] ,				_A ,				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(_A					)
      processor.save_pretrained(_A					)
  
    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__":
       _SCREAMING_SNAKE_CASE   						=    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."""
       )
  
       _SCREAMING_SNAKE_CASE   						=    parser.parse_args()
       convert_clipseg_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)
  
  
style_context: from typing import TYPE_CHECKING
  
  from ...utils import (
      OptionalDependencyNotAvailable,
      _LazyModule,
      is_sentencepiece_available,
      is_tokenizers_available,
      is_torch_available,
  )
  
  
  _SCREAMING_SNAKE_CASE   						=    {
      """configuration_open_llama""": ["""OPEN_LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """OpenLlamaConfig"""],
  }
  
  try:
       if not is_sentencepiece_available():
            raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
       pass
  else:
       _SCREAMING_SNAKE_CASE   						=    ["""LlamaTokenizer"""]
  
  try:
       if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
       pass
  else:
       _SCREAMING_SNAKE_CASE   						=    ["""LlamaTokenizerFast"""]
  
  try:
       if not is_torch_available():
            raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
       pass
  else:
       _SCREAMING_SNAKE_CASE   						=    [
           """OpenLlamaForCausalLM""",
           """OpenLlamaModel""",
           """OpenLlamaPreTrainedModel""",
           """OpenLlamaForSequenceClassification""",
       ]
  
  
  if TYPE_CHECKING:
       from .configuration_open_llama import OPEN_LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP, OpenLlamaConfig
  
       try:
            if not is_sentencepiece_available():
                 raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
            pass
       else:
            from transformers import LlamaTokenizer
  
       try:
            if not is_tokenizers_available():
                 raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
            pass
       else:
            from transformers import LlamaTokenizerFast
  
       try:
            if not is_torch_available():
                 raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
            pass
       else:
            from .modeling_open_llama import (
                OpenLlamaForCausalLM,
                OpenLlamaForSequenceClassification,
                OpenLlamaModel,
                OpenLlamaPreTrainedModel,
            )
  
  
  else:
       import sys
  
       _SCREAMING_SNAKE_CASE   						=    _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
  
  