code: 
  '''simple docstring'''
  
  import argparse
  
  import requests
  import torch
  from PIL import Image
  
  from transformers import (
      CLIPSegConfig,
      CLIPSegForImageSegmentation,
      CLIPSegProcessor,
      CLIPSegTextConfig,
      CLIPSegVisionConfig,
      CLIPTokenizer,
      ViTImageProcessor,
  )
  
  
  
  
  
  
  
  def 						snake_case__	(      UpperCamelCase	) ->      Dict:
  		_UpperCamelCase       : Optional[int]    =      CLIPSegTextConfig()
  		_UpperCamelCase       : Dict    =      CLIPSegVisionConfig(patch_size=16	)
  
  		_UpperCamelCase       : List[Any]    =      True if '''refined''' in model_name else False
  		_UpperCamelCase       : Any    =      16 if '''rd16''' in model_name else 64
  
  		_UpperCamelCase       : Optional[int]    =      CLIPSegConfig.from_text_vision_configs(
  		    UpperCamelCase			,UpperCamelCase			,use_complex_transposed_convolution=UpperCamelCase			,reduce_dim=UpperCamelCase			,)
  		return config
  
  
  
  
  
  
  
  def 						snake_case__	(      UpperCamelCase	) ->      Tuple:
  		# update prefixes
  		if "clip_model" in name:
  				_UpperCamelCase       : Any    =      name.replace('''clip_model'''			,'''clip'''	)
  		if "transformer" in name:
  				if "visual" in name:
  						_UpperCamelCase       : str    =      name.replace('''visual.transformer'''			,'''vision_model'''	)
  				else:
  						_UpperCamelCase       : List[str]    =      name.replace('''transformer'''			,'''text_model'''	)
  		if "resblocks" in name:
  				_UpperCamelCase       : Tuple    =      name.replace('''resblocks'''			,'''encoder.layers'''	)
  		if "ln_1" in name:
  				_UpperCamelCase       : int    =      name.replace('''ln_1'''			,'''layer_norm1'''	)
  		if "ln_2" in name:
  				_UpperCamelCase       : int    =      name.replace('''ln_2'''			,'''layer_norm2'''	)
  		if "c_fc" in name:
  				_UpperCamelCase       : List[Any]    =      name.replace('''c_fc'''			,'''fc1'''	)
  		if "c_proj" in name:
  				_UpperCamelCase       : Any    =      name.replace('''c_proj'''			,'''fc2'''	)
  		if "attn" in name and "self" not in name:
  				_UpperCamelCase       : Tuple    =      name.replace('''attn'''			,'''self_attn'''	)
  		# text encoder
  		if "token_embedding" in name:
  				_UpperCamelCase       : Dict    =      name.replace('''token_embedding'''			,'''text_model.embeddings.token_embedding'''	)
  		if "positional_embedding" in name and "visual" not in name:
  				_UpperCamelCase       : Optional[Any]    =      name.replace('''positional_embedding'''			,'''text_model.embeddings.position_embedding.weight'''	)
  		if "ln_final" in name:
  				_UpperCamelCase       : int    =      name.replace('''ln_final'''			,'''text_model.final_layer_norm'''	)
  		# vision encoder
  		if "visual.class_embedding" in name:
  				_UpperCamelCase       : List[str]    =      name.replace('''visual.class_embedding'''			,'''vision_model.embeddings.class_embedding'''	)
  		if "visual.conv1" in name:
  				_UpperCamelCase       : Union[str, Any]    =      name.replace('''visual.conv1'''			,'''vision_model.embeddings.patch_embedding'''	)
  		if "visual.positional_embedding" in name:
  				_UpperCamelCase       : Optional[Any]    =      name.replace('''visual.positional_embedding'''			,'''vision_model.embeddings.position_embedding.weight'''	)
  		if "visual.ln_pre" in name:
  				_UpperCamelCase       : str    =      name.replace('''visual.ln_pre'''			,'''vision_model.pre_layrnorm'''	)
  		if "visual.ln_post" in name:
  				_UpperCamelCase       : List[Any]    =      name.replace('''visual.ln_post'''			,'''vision_model.post_layernorm'''	)
  		# projection layers
  		if "visual.proj" in name:
  				_UpperCamelCase       : List[Any]    =      name.replace('''visual.proj'''			,'''visual_projection.weight'''	)
  		if "text_projection" in name:
  				_UpperCamelCase       : str    =      name.replace('''text_projection'''			,'''text_projection.weight'''	)
  		# decoder
  		if "trans_conv" in name:
  				_UpperCamelCase       : Tuple    =      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:
  				_UpperCamelCase       : Union[str, Any]    =      '''decoder.''' + name
  		if "blocks" in name:
  				_UpperCamelCase       : List[str]    =      name.replace('''blocks'''			,'''decoder.layers'''	)
  		if "linear1" in name:
  				_UpperCamelCase       : Tuple    =      name.replace('''linear1'''			,'''mlp.fc1'''	)
  		if "linear2" in name:
  				_UpperCamelCase       : Any    =      name.replace('''linear2'''			,'''mlp.fc2'''	)
  		if "norm1" in name and "layer_" not in name:
  				_UpperCamelCase       : str    =      name.replace('''norm1'''			,'''layer_norm1'''	)
  		if "norm2" in name and "layer_" not in name:
  				_UpperCamelCase       : str    =      name.replace('''norm2'''			,'''layer_norm2'''	)
  
  		return name
  
  
  
  
  
  
  
  def 						snake_case__	(      UpperCamelCase			,UpperCamelCase	) ->      str:
  		for key in orig_state_dict.copy().keys():
  				_UpperCamelCase       : int    =      orig_state_dict.pop(UpperCamelCase	)
  
  				if key.startswith('''clip_model'''	) and "attn.in_proj" in key:
  						_UpperCamelCase       : int    =      key.split('''.'''	)
  						if "visual" in key:
  								_UpperCamelCase       : List[Any]    =      int(key_split[4]	)
  								_UpperCamelCase       : Tuple    =      config.vision_config.hidden_size
  								_UpperCamelCase       : Tuple    =      '''vision_model'''
  						else:
  								_UpperCamelCase       : Tuple    =      int(key_split[3]	)
  								_UpperCamelCase       : List[Any]    =      config.text_config.hidden_size
  								_UpperCamelCase       : List[Any]    =      '''text_model'''
  
  						if "weight" in key:
  								_UpperCamelCase       : List[str]    =      val[:dim, :]
  								_UpperCamelCase       : Optional[int]    =      val[
  								    dim : dim * 2, :
  								]
  								_UpperCamelCase       : Optional[int]    =      val[-dim:, :]
  						else:
  								_UpperCamelCase       : Optional[Any]    =      val[:dim]
  								_UpperCamelCase       : Tuple    =      val[dim : dim * 2]
  								_UpperCamelCase       : Tuple    =      val[-dim:]
  				elif "self_attn" in key and "out_proj" not in key:
  						_UpperCamelCase       : Dict    =      key.split('''.'''	)
  						_UpperCamelCase       : Optional[Any]    =      int(key_split[1]	)
  						_UpperCamelCase       : Any    =      config.reduce_dim
  						if "weight" in key:
  								_UpperCamelCase       : int    =      val[:dim, :]
  								_UpperCamelCase       : List[str]    =      val[dim : dim * 2, :]
  								_UpperCamelCase       : Optional[int]    =      val[-dim:, :]
  						else:
  								_UpperCamelCase       : Union[str, Any]    =      val[:dim]
  								_UpperCamelCase       : Any    =      val[dim : dim * 2]
  								_UpperCamelCase       : str    =      val[-dim:]
  				else:
  						_UpperCamelCase       : List[Any]    =      rename_key(UpperCamelCase	)
  						if "visual_projection" in new_name or "text_projection" in new_name:
  								_UpperCamelCase       : Tuple    =      val.T
  						_UpperCamelCase       : List[Any]    =      val
  
  		return orig_state_dict
  
  
  
  
  
  
  
  def 						snake_case__	(      ) ->      List[Any]:
  		_UpperCamelCase       : Optional[int]    =      '''http://images.cocodataset.org/val2017/000000039769.jpg'''
  		_UpperCamelCase       : Dict    =      Image.open(requests.get(UpperCamelCase			,stream=UpperCamelCase	).raw	)
  		return image
  
  
  
  
  
  
  
  def 						snake_case__	(      UpperCamelCase			,UpperCamelCase			,UpperCamelCase			,UpperCamelCase	) ->      List[str]:
  		_UpperCamelCase       : List[Any]    =      get_clipseg_config(UpperCamelCase	)
  		_UpperCamelCase       : Optional[Any]    =      CLIPSegForImageSegmentation(UpperCamelCase	)
  		model.eval()
  
  		_UpperCamelCase       : str    =      torch.load(UpperCamelCase			,map_location='''cpu'''	)
  
  		# remove some keys
  		for key in state_dict.copy().keys():
  				if key.startswith('''model'''	):
  						state_dict.pop(UpperCamelCase			,UpperCamelCase	)
  
      # rename some keys
  		_UpperCamelCase       : Optional[int]    =      convert_state_dict(UpperCamelCase			,UpperCamelCase	)
  		_UpperCamelCase,							_UpperCamelCase       : List[str]    =      model.load_state_dict(UpperCamelCase			,strict=UpperCamelCase	)
  
  		if missing_keys != ["clip.text_model.embeddings.position_ids", "clip.vision_model.embeddings.position_ids"]:
  				raise ValueError('''Missing keys that are not expected: {}'''.format(UpperCamelCase	)	)
  		if unexpected_keys != ["decoder.reduce.weight", "decoder.reduce.bias"]:
  				raise ValueError(f'''Unexpected keys: {unexpected_keys}'''	)
  
  		_UpperCamelCase       : str    =      ViTImageProcessor(size=3_52	)
  		_UpperCamelCase       : List[str]    =      CLIPTokenizer.from_pretrained('''openai/clip-vit-base-patch32'''	)
  		_UpperCamelCase       : Union[str, Any]    =      CLIPSegProcessor(image_processor=UpperCamelCase			,tokenizer=UpperCamelCase	)
  
  		_UpperCamelCase       : Any    =      prepare_img()
  		_UpperCamelCase       : Tuple    =      ['''a glass''', '''something to fill''', '''wood''', '''a jar''']
  
  		_UpperCamelCase       : List[Any]    =      processor(text=UpperCamelCase			,images=[image] * len(UpperCamelCase	)			,padding='''max_length'''			,return_tensors='''pt'''	)
  
  		with torch.no_grad():
  				_UpperCamelCase       : Optional[int]    =      model(**UpperCamelCase	)
  
  		# verify values
  		_UpperCamelCase       : Optional[Any]    =      torch.tensor([0.1110, -0.1882, 0.1645]	)
  		_UpperCamelCase       : List[str]    =      torch.tensor([0.2692, -0.7197, -0.1328]	)
  		if model_name == "clipseg-rd64-refined":
  				_UpperCamelCase       : Optional[Any]    =      torch.tensor(
  				    [[-10.0407, -9.9431, -10.2646], [-9.9751, -9.7064, -9.9586], [-9.6891, -9.5645, -9.9618]]	)
  		elif model_name == "clipseg-rd64":
  				_UpperCamelCase       : Optional[int]    =      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":
  				_UpperCamelCase       : int    =      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]			,UpperCamelCase			,atol=1e-3	)
  		assert torch.allclose(outputs.conditional_embeddings[0, :3]			,UpperCamelCase			,atol=1e-3	)
  		assert torch.allclose(outputs.pooled_output[0, :3]			,UpperCamelCase			,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(UpperCamelCase	)
  				processor.save_pretrained(UpperCamelCase	)
  
  		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__":
  	_UpperCAmelCase   :      List[str] 						=  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."""
  	)
  
  	_UpperCAmelCase   :      Tuple 						=  parser.parse_args()
  	convert_clipseg_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)
  
  
  
  
  
  
style_context: 
  '''simple docstring'''
  
  import os
  
  
  
  
  
  
  
  def 						snake_case__	(      ) ->      Tuple:
  		_UpperCamelCase       : Dict    =      os.path.dirname(os.path.realpath(UpperCamelCase	)	)
  		_UpperCamelCase       : str    =      os.path.join(UpperCamelCase			,'''triangle.txt'''	)
  
  		with open(UpperCamelCase	) as f:
  				_UpperCamelCase       : Optional[Any]    =      f.readlines()
  
  		_UpperCamelCase       : List[str]    =      [[int(UpperCamelCase	) for y in x.rstrip('''\r\n'''	).split(''' '''	)] for x in triangle]
  
  		for i in range(1			,len(UpperCamelCase	)	):
  				for j in range(len(a[i]	)	):
  						_UpperCamelCase       : str    =      a[i - 1][j] if j != len(a[i - 1]	) else 0
  						_UpperCamelCase       : Any    =      a[i - 1][j - 1] if j > 0 else 0
  						a[i][j] += max(UpperCamelCase			,UpperCamelCase	)
  		return max(a[-1]	)
  
  
  if __name__ == "__main__":
  	print(solution())
  
  
  
  
  
  