code: 
  
  
  '''simple docstring'''
  
  
  
  
  
  import argparse
  
  import requests
  import torch
  from PIL import Image
  
  from transformers import (
      CLIPSegConfig,
      CLIPSegForImageSegmentation,
      CLIPSegProcessor,
      CLIPSegTextConfig,
      CLIPSegVisionConfig,
      CLIPTokenizer,
      ViTImageProcessor,
  )
  
  
  
  
  def 			__lowercase	(						_UpperCamelCase     )    ->Tuple:
  
  
  
  
  
  
  
        """simple docstring"""
  
        lowercase			:   Optional[Any]					   =		CLIPSegTextConfig()
        lowercase			:   Any					   =		CLIPSegVisionConfig(patch_size=16     )
  
        lowercase			:   Tuple					   =		True if "refined" in model_name else False
        lowercase			:   Optional[int]					   =		16 if "rd16" in model_name else 64
  
        lowercase			:   Any					   =		CLIPSegConfig.from_text_vision_configs(
            lowercase__,    lowercase__,    use_complex_transposed_convolution=lowercase__,    reduce_dim=lowercase__,    )
        return config
  
  
  
  
  def 			__lowercase	(						_UpperCamelCase     )    ->Any:
  
  
  
  
  
  
  
        """simple docstring"""
  
        if "clip_model" in name:
              lowercase			:   str					   =		name.replace('''clip_model''',    '''clip'''     )
        if "transformer" in name:
              if "visual" in name:
                    lowercase			:   List[str]					   =		name.replace('''visual.transformer''',    '''vision_model'''     )
              else:
                    lowercase			:   Union[str, Any]					   =		name.replace('''transformer''',    '''text_model'''     )
        if "resblocks" in name:
              lowercase			:   int					   =		name.replace('''resblocks''',    '''encoder.layers'''     )
        if "ln_1" in name:
              lowercase			:   Dict					   =		name.replace('''ln_1''',    '''layer_norm1'''     )
        if "ln_2" in name:
              lowercase			:   Tuple					   =		name.replace('''ln_2''',    '''layer_norm2'''     )
        if "c_fc" in name:
              lowercase			:   Union[str, Any]					   =		name.replace('''c_fc''',    '''fc1'''     )
        if "c_proj" in name:
              lowercase			:   int					   =		name.replace('''c_proj''',    '''fc2'''     )
        if "attn" in name and "self" not in name:
              lowercase			:   str					   =		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			:   Optional[int]					   =		name.replace('''positional_embedding''',    '''text_model.embeddings.position_embedding.weight'''     )
        if "ln_final" in name:
              lowercase			:   str					   =		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			:   Tuple					   =		name.replace('''visual.ln_pre''',    '''vision_model.pre_layrnorm'''     )
        if "visual.ln_post" in name:
              lowercase			:   Optional[int]					   =		name.replace('''visual.ln_post''',    '''vision_model.post_layernorm'''     )
        # projection layers
        if "visual.proj" in name:
              lowercase			:   int					   =		name.replace('''visual.proj''',    '''visual_projection.weight'''     )
        if "text_projection" in name:
              lowercase			:   str					   =		name.replace('''text_projection''',    '''text_projection.weight'''     )
        # decoder
        if "trans_conv" in name:
              lowercase			:   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			:   Optional[int]					   =		"decoder." + name
        if "blocks" in name:
              lowercase			:   Optional[Any]					   =		name.replace('''blocks''',    '''decoder.layers'''     )
        if "linear1" in name:
              lowercase			:   Dict					   =		name.replace('''linear1''',    '''mlp.fc1'''     )
        if "linear2" in name:
              lowercase			:   Tuple					   =		name.replace('''linear2''',    '''mlp.fc2'''     )
        if "norm1" in name and "layer_" not in name:
              lowercase			:   List[str]					   =		name.replace('''norm1''',    '''layer_norm1'''     )
        if "norm2" in name and "layer_" not in name:
              lowercase			:   str					   =		name.replace('''norm2''',    '''layer_norm2'''     )
  
        return name
  
  
  
  
  def 			__lowercase	(						_UpperCamelCase,    _UpperCamelCase     )    ->Union[str, Any]:
  
  
  
  
  
  
  
        """simple docstring"""
  
        for key in orig_state_dict.copy().keys():
              lowercase			:   Dict					   =		orig_state_dict.pop(lowercase__     )
  
              if key.startswith('''clip_model'''     ) and "attn.in_proj" in key:
                    lowercase			:   Union[str, Any]					   =		key.split('''.'''     )
                    if "visual" in key:
                          lowercase			:   List[Any]					   =		int(key_split[4]     )
                          lowercase			:   Optional[Any]					   =		config.vision_config.hidden_size
                          lowercase			:   Dict					   =		"vision_model"
                    else:
                          lowercase			:   Dict					   =		int(key_split[3]     )
                          lowercase			:   Optional[int]					   =		config.text_config.hidden_size
                          lowercase			:   List[Any]					   =		"text_model"
  
                    if "weight" in key:
                          lowercase			:   Optional[Any]					   =		val[:dim, :]
                          lowercase			:   Optional[int]					   =		val[
                              dim : dim * 2, :
                          ]
                          lowercase			:   Optional[int]					   =		val[-dim:, :]
                    else:
                          lowercase			:   Any					   =		val[:dim]
                          lowercase			:   Optional[Any]					   =		val[dim : dim * 2]
                          lowercase			:   Optional[Any]					   =		val[-dim:]
              elif "self_attn" in key and "out_proj" not in key:
                    lowercase			:   Tuple					   =		key.split('''.'''     )
                    lowercase			:   Optional[int]					   =		int(key_split[1]     )
                    lowercase			:   Optional[int]					   =		config.reduce_dim
                    if "weight" in key:
                          lowercase			:   Dict					   =		val[:dim, :]
                          lowercase			:   Tuple					   =		val[dim : dim * 2, :]
                          lowercase			:   str					   =		val[-dim:, :]
                    else:
                          lowercase			:   List[str]					   =		val[:dim]
                          lowercase			:   Dict					   =		val[dim : dim * 2]
                          lowercase			:   Tuple					   =		val[-dim:]
              else:
                    lowercase			:   Union[str, Any]					   =		rename_key(lowercase__     )
                    if "visual_projection" in new_name or "text_projection" in new_name:
                          lowercase			:   List[str]					   =		val.T
                    lowercase			:   Dict					   =		val
  
        return orig_state_dict
  
  
  
  
  def 			__lowercase	(						)    ->str:
  
  
  
  
  
  
  
        """simple docstring"""
  
        lowercase			:   Any					   =		"http://images.cocodataset.org/val2017/000000039769.jpg"
        lowercase			:   List[str]					   =		Image.open(requests.get(lowercase__,    stream=lowercase__     ).raw     )
        return image
  
  
  
  
  def 			__lowercase	(						_UpperCamelCase,    _UpperCamelCase,    _UpperCamelCase,    _UpperCamelCase     )    ->Dict:
  
  
  
  
  
  
  
        """simple docstring"""
  
        lowercase			:   str					   =		get_clipseg_config(lowercase__     )
        lowercase			:   str					   =		CLIPSegForImageSegmentation(lowercase__     )
        model.eval()
  
        lowercase			:   List[Any]					   =		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
        lowercase			:   Tuple					   =		convert_state_dict(lowercase__,    lowercase__     )
        lowercase			:   List[Any]					   =		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}"""     )
  
        lowercase			:   str					   =		ViTImageProcessor(size=352     )
        lowercase			:   Union[str, Any]					   =		CLIPTokenizer.from_pretrained('''openai/clip-vit-base-patch32'''     )
        lowercase			:   List[str]					   =		CLIPSegProcessor(image_processor=lowercase__,    tokenizer=lowercase__     )
  
        lowercase			:   List[Any]					   =		prepare_img()
        lowercase			:   Union[str, Any]					   =		["a glass", "something to fill", "wood", "a jar"]
  
        lowercase			:   Dict					   =		processor(text=lowercase__,    images=[image] * len(lowercase__     ),    padding='''max_length''',    return_tensors='''pt'''     )
  
        with torch.no_grad():
              lowercase			:   Optional[int]					   =		model(**lowercase__     )
  
        # verify values
        lowercase			:   int					   =		torch.tensor([0.1_1_1_0, -0.1_8_8_2, 0.1_6_4_5]     )
        lowercase			:   List[Any]					   =		torch.tensor([0.2_6_9_2, -0.7_1_9_7, -0.1_3_2_8]     )
        if model_name == "clipseg-rd64-refined":
              lowercase			:   Tuple					   =		torch.tensor(
                  [[-1_0.0_4_0_7, -9.9_4_3_1, -1_0.2_6_4_6], [-9.9_7_5_1, -9.7_0_6_4, -9.9_5_8_6], [-9.6_8_9_1, -9.5_6_4_5, -9.9_6_1_8]]     )
        elif model_name == "clipseg-rd64":
              lowercase			:   str					   =		torch.tensor(
                  [[-7.2_8_7_7, -7.2_7_1_1, -7.2_4_6_3], [-7.2_6_5_2, -7.2_7_8_0, -7.2_5_2_0], [-7.2_2_3_9, -7.2_2_0_4, -7.2_0_0_1]]     )
        elif model_name == "clipseg-rd16":
              lowercase			:   Any					   =		torch.tensor(
                  [[-6.3_9_5_5, -6.4_0_5_5, -6.4_1_5_1], [-6.3_9_1_1, -6.4_0_3_3, -6.4_1_0_0], [-6.3_4_7_4, -6.3_7_0_2, -6.3_7_6_2]]     )
        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__":
    __a   				=      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.'''
    )
  
    __a   				=      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_tokenizers_available, is_torch_available
  
  
  __a   				=      {'''configuration_yoso''': ['''YOSO_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''YosoConfig''']}
  
  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    __a   				=      [
        '''YOSO_PRETRAINED_MODEL_ARCHIVE_LIST''',
        '''YosoForMaskedLM''',
        '''YosoForMultipleChoice''',
        '''YosoForQuestionAnswering''',
        '''YosoForSequenceClassification''',
        '''YosoForTokenClassification''',
        '''YosoLayer''',
        '''YosoModel''',
        '''YosoPreTrainedModel''',
    ]
  
  
  if TYPE_CHECKING:
    from .configuration_yoso import YOSO_PRETRAINED_CONFIG_ARCHIVE_MAP, YosoConfig
  
    try:
      if not is_torch_available():
        raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
      pass
    else:
      from .modeling_yoso import (
          YOSO_PRETRAINED_MODEL_ARCHIVE_LIST,
          YosoForMaskedLM,
          YosoForMultipleChoice,
          YosoForQuestionAnswering,
          YosoForSequenceClassification,
          YosoForTokenClassification,
          YosoLayer,
          YosoModel,
          YosoPreTrainedModel,
      )
  
  
  else:
    import sys
  
    __a   				=      _LazyModule(__name__, globals()['''__file__'''], _import_structure)
  
  