code: 
  
  
  
  
  
  
  import argparse
  
  import requests
  import torch
  from PIL import Image
  
  from transformers import (
      CLIPSegConfig,
      CLIPSegForImageSegmentation,
      CLIPSegProcessor,
      CLIPSegTextConfig,
      CLIPSegVisionConfig,
      CLIPTokenizer,
      ViTImageProcessor,
  )
  
  
  
  
  def 						A_			(  lowercase_  )			->						int:
         _snake_case      :							List[Any]     				=						CLIPSegTextConfig()
         _snake_case      :							Dict     				=						CLIPSegVisionConfig(patch_size=16  )
  
         _snake_case      :							Optional[Any]     				=						True if '''refined''' in model_name else False
         _snake_case      :							int     				=						16 if '''rd16''' in model_name else 64
  
         _snake_case      :							Union[str, Any]     				=						CLIPSegConfig.from_text_vision_configs(
             lowercase_      ,		lowercase_      ,		use_complex_transposed_convolution=lowercase_      ,		reduce_dim=lowercase_      ,		)
         return config
  
  
  
  
  def 						A_			(  lowercase_  )			->						Union[str, Any]:
         # update prefixes
         if "clip_model" in name:
                _snake_case      :							Optional[int]     				=						name.replace('''clip_model'''      ,		'''clip'''  )
         if "transformer" in name:
                if "visual" in name:
                       _snake_case      :							List[str]     				=						name.replace('''visual.transformer'''      ,		'''vision_model'''  )
                else:
                       _snake_case      :							str     				=						name.replace('''transformer'''      ,		'''text_model'''  )
         if "resblocks" in name:
                _snake_case      :							List[str]     				=						name.replace('''resblocks'''      ,		'''encoder.layers'''  )
         if "ln_1" in name:
                _snake_case      :							str     				=						name.replace('''ln_1'''      ,		'''layer_norm1'''  )
         if "ln_2" in name:
                _snake_case      :							List[str]     				=						name.replace('''ln_2'''      ,		'''layer_norm2'''  )
         if "c_fc" in name:
                _snake_case      :							Optional[int]     				=						name.replace('''c_fc'''      ,		'''fc1'''  )
         if "c_proj" in name:
                _snake_case      :							List[Any]     				=						name.replace('''c_proj'''      ,		'''fc2'''  )
         if "attn" in name and "self" not in name:
                _snake_case      :							List[Any]     				=						name.replace('''attn'''      ,		'''self_attn'''  )
         # text encoder
         if "token_embedding" in name:
                _snake_case      :							List[str]     				=						name.replace('''token_embedding'''      ,		'''text_model.embeddings.token_embedding'''  )
         if "positional_embedding" in name and "visual" not in name:
                _snake_case      :							List[Any]     				=						name.replace('''positional_embedding'''      ,		'''text_model.embeddings.position_embedding.weight'''  )
         if "ln_final" in name:
                _snake_case      :							Dict     				=						name.replace('''ln_final'''      ,		'''text_model.final_layer_norm'''  )
         # vision encoder
         if "visual.class_embedding" in name:
                _snake_case      :							Tuple     				=						name.replace('''visual.class_embedding'''      ,		'''vision_model.embeddings.class_embedding'''  )
         if "visual.conv1" in name:
                _snake_case      :							Optional[Any]     				=						name.replace('''visual.conv1'''      ,		'''vision_model.embeddings.patch_embedding'''  )
         if "visual.positional_embedding" in name:
                _snake_case      :							Dict     				=						name.replace('''visual.positional_embedding'''      ,		'''vision_model.embeddings.position_embedding.weight'''  )
         if "visual.ln_pre" in name:
                _snake_case      :							Optional[int]     				=						name.replace('''visual.ln_pre'''      ,		'''vision_model.pre_layrnorm'''  )
         if "visual.ln_post" in name:
                _snake_case      :							str     				=						name.replace('''visual.ln_post'''      ,		'''vision_model.post_layernorm'''  )
         # projection layers
         if "visual.proj" in name:
                _snake_case      :							str     				=						name.replace('''visual.proj'''      ,		'''visual_projection.weight'''  )
         if "text_projection" in name:
                _snake_case      :							List[Any]     				=						name.replace('''text_projection'''      ,		'''text_projection.weight'''  )
         # decoder
         if "trans_conv" in name:
                _snake_case      :							List[str]     				=						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:
                _snake_case      :							List[str]     				=						'''decoder.''' + name
         if "blocks" in name:
                _snake_case      :							Tuple     				=						name.replace('''blocks'''      ,		'''decoder.layers'''  )
         if "linear1" in name:
                _snake_case      :							Union[str, Any]     				=						name.replace('''linear1'''      ,		'''mlp.fc1'''  )
         if "linear2" in name:
                _snake_case      :							Any     				=						name.replace('''linear2'''      ,		'''mlp.fc2'''  )
         if "norm1" in name and "layer_" not in name:
                _snake_case      :							Any     				=						name.replace('''norm1'''      ,		'''layer_norm1'''  )
         if "norm2" in name and "layer_" not in name:
                _snake_case      :							List[Any]     				=						name.replace('''norm2'''      ,		'''layer_norm2'''  )
  
         return name
  
  
  
  
  def 						A_			(  lowercase_      ,		lowercase_  )			->						int:
         for key in orig_state_dict.copy().keys():
                _snake_case      :							Tuple     				=						orig_state_dict.pop(lowercase_  )
  
                if key.startswith('''clip_model'''  ) and "attn.in_proj" in key:
                       _snake_case      :							Tuple     				=						key.split('''.'''  )
                       if "visual" in key:
                              _snake_case      :							Dict     				=						int(key_split[4]  )
                              _snake_case      :							int     				=						config.vision_config.hidden_size
                              _snake_case      :							Any     				=						'''vision_model'''
                       else:
                              _snake_case      :							List[Any]     				=						int(key_split[3]  )
                              _snake_case      :							Optional[int]     				=						config.text_config.hidden_size
                              _snake_case      :							Union[str, Any]     				=						'''text_model'''
  
                       if "weight" in key:
                              _snake_case      :							Dict     				=						val[:dim, :]
                              _snake_case      :							str     				=						val[
                                  dim : dim * 2, :
                              ]
                              _snake_case      :							Tuple     				=						val[-dim:, :]
                       else:
                              _snake_case      :							Optional[int]     				=						val[:dim]
                              _snake_case      :							Optional[int]     				=						val[dim : dim * 2]
                              _snake_case      :							str     				=						val[-dim:]
                elif "self_attn" in key and "out_proj" not in key:
                       _snake_case      :							List[Any]     				=						key.split('''.'''  )
                       _snake_case      :							List[str]     				=						int(key_split[1]  )
                       _snake_case      :							Dict     				=						config.reduce_dim
                       if "weight" in key:
                              _snake_case      :							Optional[int]     				=						val[:dim, :]
                              _snake_case      :							Any     				=						val[dim : dim * 2, :]
                              _snake_case      :							Any     				=						val[-dim:, :]
                       else:
                              _snake_case      :							Any     				=						val[:dim]
                              _snake_case      :							Tuple     				=						val[dim : dim * 2]
                              _snake_case      :							Dict     				=						val[-dim:]
                else:
                       _snake_case      :							int     				=						rename_key(lowercase_  )
                       if "visual_projection" in new_name or "text_projection" in new_name:
                              _snake_case      :							Dict     				=						val.T
                       _snake_case      :							Dict     				=						val
  
         return orig_state_dict
  
  
  
  
  def 						A_			(  )			->						int:
         _snake_case      :							Union[str, Any]     				=						'''http://images.cocodataset.org/val2017/000000039769.jpg'''
         _snake_case      :							Any     				=						Image.open(requests.get(lowercase_      ,		stream=lowercase_  ).raw  )
         return image
  
  
  
  
  def 						A_			(  lowercase_      ,		lowercase_      ,		lowercase_      ,		lowercase_  )			->						List[Any]:
         _snake_case      :							Optional[Any]     				=						get_clipseg_config(lowercase_  )
         _snake_case      :							List[Any]     				=						CLIPSegForImageSegmentation(lowercase_  )
         model.eval()
  
         _snake_case      :							List[str]     				=						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
         _snake_case      :							Union[str, Any]     				=						convert_state_dict(lowercase_      ,		lowercase_  )
         _snake_case      :							Optional[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}'''  )
  
         _snake_case      :							str     				=						ViTImageProcessor(size=352  )
         _snake_case      :							Any     				=						CLIPTokenizer.from_pretrained('''openai/clip-vit-base-patch32'''  )
         _snake_case      :							Dict     				=						CLIPSegProcessor(image_processor=lowercase_      ,		tokenizer=lowercase_  )
  
         _snake_case      :							Optional[int]     				=						prepare_img()
         _snake_case      :							Optional[int]     				=						['''a glass''', '''something to fill''', '''wood''', '''a jar''']
  
         _snake_case      :							int     				=						processor(text=lowercase_      ,		images=[image] * len(lowercase_  )      ,		padding='''max_length'''      ,		return_tensors='''pt'''  )
  
         with torch.no_grad():
                _snake_case      :							Any     				=						model(**lowercase_  )
  
         # verify values
         _snake_case      :							Any     				=						torch.tensor([0.11_10, -0.18_82, 0.16_45]  )
         _snake_case      :							Optional[Any]     				=						torch.tensor([0.26_92, -0.71_97, -0.13_28]  )
         if model_name == "clipseg-rd64-refined":
                _snake_case      :							str     				=						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":
                _snake_case      :							Dict     				=						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":
                _snake_case      :							str     				=						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: 
  
  
  
  
  
  import unittest
  
  from transformers import load_tool
  from transformers.tools.agent_types import AGENT_TYPE_MAPPING
  
  from .test_tools_common import ToolTesterMixin, output_types
  
  class 				A    (unittest.TestCase       ,__UpperCAmelCase				):
  
  
  
        def    __a (					self					)      ->   str:
  
  
  
               '''simple docstring'''
  
               _snake_case      :							List[Any]     				=						load_tool('''translation'''					)
               self.tool.setup()
               _snake_case      :							Any     				=						load_tool('''translation'''     ,  remote=lowercase_					)
  
  
  
        def    __a (					self					)      ->   Optional[int]:
  
  
  
               '''simple docstring'''
  
               _snake_case      :							Tuple     				=						self.tool('''Hey, what\'s up?'''     ,  src_lang='''English'''     ,  tgt_lang='''French'''					)
               self.assertEqual(lowercase_     ,  '''- Hé, comment ça va?'''					)
  
  
  
        def    __a (					self					)      ->   Union[str, Any]:
  
  
  
               '''simple docstring'''
  
               _snake_case      :							Optional[int]     				=						self.remote_tool('''Hey, what\'s up?'''     ,  src_lang='''English'''     ,  tgt_lang='''French'''					)
               self.assertEqual(lowercase_     ,  '''- Hé, comment ça va?'''					)
  
  
  
        def    __a (					self					)      ->   List[Any]:
  
  
  
               '''simple docstring'''
  
               _snake_case      :							Dict     				=						self.tool(text='''Hey, what\'s up?'''     ,  src_lang='''English'''     ,  tgt_lang='''French'''					)
               self.assertEqual(lowercase_     ,  '''- Hé, comment ça va?'''					)
  
  
  
        def    __a (					self					)      ->   Dict:
  
  
  
               '''simple docstring'''
  
               _snake_case      :							List[Any]     				=						self.remote_tool(text='''Hey, what\'s up?'''     ,  src_lang='''English'''     ,  tgt_lang='''French'''					)
               self.assertEqual(lowercase_     ,  '''- Hé, comment ça va?'''					)
  
  
  
        def    __a (					self					)      ->   str:
  
  
  
               '''simple docstring'''
  
               _snake_case      :							Optional[int]     				=						['''Hey, what\'s up?''', '''English''', '''Spanish''']
               _snake_case      :							Dict     				=						self.tool(*lowercase_					)
  
               # There is a single output
               if len(self.tool.outputs					) == 1:
                      _snake_case      :							Optional[Any]     				=						[outputs]
  
               self.assertListEqual(output_types(lowercase_					)     ,  self.tool.outputs					)
  
  
  
        def    __a (					self					)      ->   Tuple:
  
  
  
               '''simple docstring'''
  
               _snake_case      :							Union[str, Any]     				=						['''Hey, what\'s up?''', '''English''', '''Spanish''']
               _snake_case      :							Dict     				=						self.tool(*lowercase_					)
  
               if not isinstance(lowercase_     ,  lowercase_					):
                      _snake_case      :							Optional[Any]     				=						[outputs]
  
               self.assertEqual(len(lowercase_					)     ,  len(self.tool.outputs					)					)
  
               for output, output_type in zip(lowercase_     ,  self.tool.outputs					):
                      _snake_case      :							List[str]     				=						AGENT_TYPE_MAPPING[output_type]
                      self.assertTrue(isinstance(lowercase_     ,  lowercase_					)					)
  
  
  
        def    __a (					self					)      ->   List[Any]:
  
  
  
               '''simple docstring'''
  
               _snake_case      :							Dict     				=						['''Hey, what\'s up?''', '''English''', '''Spanish''']
  
               _snake_case      :							Any     				=						[]
  
               for _input, input_type in zip(lowercase_     ,  self.tool.inputs					):
                      if isinstance(lowercase_     ,  lowercase_					):
                             _inputs.append([AGENT_TYPE_MAPPING[_input_type](_input					) for _input_type in input_type]					)
                      else:
                             _inputs.append(AGENT_TYPE_MAPPING[input_type](_input					)					)
  
          # Should not raise an error
               _snake_case      :							List[Any]     				=						self.tool(*lowercase_					)
  
               if not isinstance(lowercase_     ,  lowercase_					):
                      _snake_case      :							Any     				=						[outputs]
  
               self.assertEqual(len(lowercase_					)     ,  len(self.tool.outputs					)					)
  
  
  
  
  
  
  