code: 
  
  
  '''simple docstring'''
  
  
  
  import argparse
  
  import requests
  import torch
  from PIL import Image
  
  from transformers import (
      CLIPSegConfig,
      CLIPSegForImageSegmentation,
      CLIPSegProcessor,
      CLIPSegTextConfig,
      CLIPSegVisionConfig,
      CLIPTokenizer,
      ViTImageProcessor,
  )
  
  
  
  
  
  
  def 						lowerCamelCase							(_SCREAMING_SNAKE_CASE   :     Optional[int]     ):
  					__a    : Optional[int]      					=						CLIPSegTextConfig()
  					__a    : Dict      					=						CLIPSegVisionConfig(patch_size=16     )
  
  					__a    : List[Any]      					=						True if '''refined''' in model_name else False
  					__a    : Any      					=						16 if '''rd16''' in model_name else 64
  
  					__a    : Optional[int]      					=						CLIPSegConfig.from_text_vision_configs(
  					    _SCREAMING_SNAKE_CASE		,   _SCREAMING_SNAKE_CASE		,   use_complex_transposed_convolution=_SCREAMING_SNAKE_CASE		,   reduce_dim=_SCREAMING_SNAKE_CASE		,   )
  					return config
  
  
  
  
  
  
  def 						lowerCamelCase							(_SCREAMING_SNAKE_CASE   :     Any     ):
  					# update prefixes
  					if "clip_model" in name:
  										__a    : Any      					=						name.replace('clip_model'		,   'clip'     )
  					if "transformer" in name:
  										if "visual" in name:
  															__a    : str      					=						name.replace('visual.transformer'		,   'vision_model'     )
  										else:
  															__a    : List[str]      					=						name.replace('transformer'		,   'text_model'     )
  					if "resblocks" in name:
  										__a    : Tuple      					=						name.replace('resblocks'		,   'encoder.layers'     )
  					if "ln_1" in name:
  										__a    : int      					=						name.replace('ln_1'		,   'layer_norm1'     )
  					if "ln_2" in name:
  										__a    : int      					=						name.replace('ln_2'		,   'layer_norm2'     )
  					if "c_fc" in name:
  										__a    : List[Any]      					=						name.replace('c_fc'		,   'fc1'     )
  					if "c_proj" in name:
  										__a    : Any      					=						name.replace('c_proj'		,   'fc2'     )
  					if "attn" in name and "self" not in name:
  										__a    : Tuple      					=						name.replace('attn'		,   'self_attn'     )
  					# text encoder
  					if "token_embedding" in name:
  										__a    : Dict      					=						name.replace('token_embedding'		,   'text_model.embeddings.token_embedding'     )
  					if "positional_embedding" in name and "visual" not in name:
  										__a    : Optional[Any]      					=						name.replace('positional_embedding'		,   'text_model.embeddings.position_embedding.weight'     )
  					if "ln_final" in name:
  										__a    : int      					=						name.replace('ln_final'		,   'text_model.final_layer_norm'     )
  					# vision encoder
  					if "visual.class_embedding" in name:
  										__a    : List[str]      					=						name.replace('visual.class_embedding'		,   'vision_model.embeddings.class_embedding'     )
  					if "visual.conv1" in name:
  										__a    : Union[str, Any]      					=						name.replace('visual.conv1'		,   'vision_model.embeddings.patch_embedding'     )
  					if "visual.positional_embedding" in name:
  										__a    : Optional[Any]      					=						name.replace('visual.positional_embedding'		,   'vision_model.embeddings.position_embedding.weight'     )
  					if "visual.ln_pre" in name:
  										__a    : str      					=						name.replace('visual.ln_pre'		,   'vision_model.pre_layrnorm'     )
  					if "visual.ln_post" in name:
  										__a    : List[Any]      					=						name.replace('visual.ln_post'		,   'vision_model.post_layernorm'     )
  					# projection layers
  					if "visual.proj" in name:
  										__a    : List[Any]      					=						name.replace('visual.proj'		,   'visual_projection.weight'     )
  					if "text_projection" in name:
  										__a    : str      					=						name.replace('text_projection'		,   'text_projection.weight'     )
  					# decoder
  					if "trans_conv" in name:
  										__a    : 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:
  										__a    : Union[str, Any]      					=						'''decoder.''' + name
  					if "blocks" in name:
  										__a    : List[str]      					=						name.replace('blocks'		,   'decoder.layers'     )
  					if "linear1" in name:
  										__a    : Tuple      					=						name.replace('linear1'		,   'mlp.fc1'     )
  					if "linear2" in name:
  										__a    : Any      					=						name.replace('linear2'		,   'mlp.fc2'     )
  					if "norm1" in name and "layer_" not in name:
  										__a    : str      					=						name.replace('norm1'		,   'layer_norm1'     )
  					if "norm2" in name and "layer_" not in name:
  										__a    : str      					=						name.replace('norm2'		,   'layer_norm2'     )
  
  					return name
  
  
  
  
  
  
  def 						lowerCamelCase							(_SCREAMING_SNAKE_CASE   :     Any		,   _SCREAMING_SNAKE_CASE   :     Union[str, Any]     ):
  					for key in orig_state_dict.copy().keys():
  										__a    : int      					=						orig_state_dict.pop(_SCREAMING_SNAKE_CASE     )
  
  										if key.startswith('clip_model'     ) and "attn.in_proj" in key:
  															__a    : int      					=						key.split('.'     )
  															if "visual" in key:
  																				__a    : List[Any]      					=						int(key_split[4]     )
  																				__a    : Tuple      					=						config.vision_config.hidden_size
  																				__a    : Tuple      					=						'''vision_model'''
  															else:
  																				__a    : Tuple      					=						int(key_split[3]     )
  																				__a    : List[Any]      					=						config.text_config.hidden_size
  																				__a    : List[Any]      					=						'''text_model'''
  
  															if "weight" in key:
  																				__a    : List[str]      					=						val[:dim, :]
  																				__a    : Optional[int]      					=						val[
  																				    dim : dim * 2, :
  																				]
  																				__a    : Optional[int]      					=						val[-dim:, :]
  															else:
  																				__a    : Optional[Any]      					=						val[:dim]
  																				__a    : Tuple      					=						val[dim : dim * 2]
  																				__a    : Tuple      					=						val[-dim:]
  										elif "self_attn" in key and "out_proj" not in key:
  															__a    : Dict      					=						key.split('.'     )
  															__a    : Optional[Any]      					=						int(key_split[1]     )
  															__a    : Any      					=						config.reduce_dim
  															if "weight" in key:
  																				__a    : int      					=						val[:dim, :]
  																				__a    : List[str]      					=						val[dim : dim * 2, :]
  																				__a    : Optional[int]      					=						val[-dim:, :]
  															else:
  																				__a    : Union[str, Any]      					=						val[:dim]
  																				__a    : Any      					=						val[dim : dim * 2]
  																				__a    : str      					=						val[-dim:]
  										else:
  															__a    : List[Any]      					=						rename_key(_SCREAMING_SNAKE_CASE     )
  															if "visual_projection" in new_name or "text_projection" in new_name:
  																				__a    : Tuple      					=						val.T
  															__a    : List[Any]      					=						val
  
  					return orig_state_dict
  
  
  
  
  
  
  def 						lowerCamelCase							():
  					__a    : Optional[int]      					=						'''http://images.cocodataset.org/val2017/000000039769.jpg'''
  					__a    : Dict      					=						Image.open(requests.get(_SCREAMING_SNAKE_CASE		,   stream=_SCREAMING_SNAKE_CASE     ).raw     )
  					return image
  
  
  
  
  
  
  def 						lowerCamelCase							(_SCREAMING_SNAKE_CASE   :     Tuple		,   _SCREAMING_SNAKE_CASE   :     List[Any]		,   _SCREAMING_SNAKE_CASE   :     Union[str, Any]		,   _SCREAMING_SNAKE_CASE   :     List[Any]     ):
  					__a    : List[Any]      					=						get_clipseg_config(_SCREAMING_SNAKE_CASE     )
  					__a    : Optional[Any]      					=						CLIPSegForImageSegmentation(_SCREAMING_SNAKE_CASE     )
  					model.eval()
  
  					__a    : str      					=						torch.load(_SCREAMING_SNAKE_CASE		,   map_location='cpu'     )
  
  					# remove some keys
  					for key in state_dict.copy().keys():
  										if key.startswith('model'     ):
  															state_dict.pop(_SCREAMING_SNAKE_CASE		,   _SCREAMING_SNAKE_CASE     )
  
      # rename some keys
  					__a    : Optional[int]      					=						convert_state_dict(_SCREAMING_SNAKE_CASE		,   _SCREAMING_SNAKE_CASE     )
  					__a    : List[str]      					=						model.load_state_dict(_SCREAMING_SNAKE_CASE		,   strict=_SCREAMING_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(_SCREAMING_SNAKE_CASE     )     )
  					if unexpected_keys != ["decoder.reduce.weight", "decoder.reduce.bias"]:
  										raise ValueError(F"""Unexpected keys: {unexpected_keys}"""     )
  
  					__a    : str      					=						ViTImageProcessor(size=352     )
  					__a    : List[str]      					=						CLIPTokenizer.from_pretrained('openai/clip-vit-base-patch32'     )
  					__a    : Union[str, Any]      					=						CLIPSegProcessor(image_processor=_SCREAMING_SNAKE_CASE		,   tokenizer=_SCREAMING_SNAKE_CASE     )
  
  					__a    : Any      					=						prepare_img()
  					__a    : Tuple      					=						['''a glass''', '''something to fill''', '''wood''', '''a jar''']
  
  					__a    : List[Any]      					=						processor(text=_SCREAMING_SNAKE_CASE		,   images=[image] * len(_SCREAMING_SNAKE_CASE     )		,   padding='max_length'		,   return_tensors='pt'     )
  
  					with torch.no_grad():
  										__a    : Optional[int]      					=						model(**_SCREAMING_SNAKE_CASE     )
  
  					# verify values
  					__a    : Optional[Any]      					=						torch.tensor([0.1_1_1_0, -0.1_8_8_2, 0.1_6_4_5]     )
  					__a    : List[str]      					=						torch.tensor([0.2_6_9_2, -0.7_1_9_7, -0.1_3_2_8]     )
  					if model_name == "clipseg-rd64-refined":
  										__a    : Optional[Any]      					=						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":
  										__a    : Optional[int]      					=						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":
  										__a    : int      					=						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]		,   _SCREAMING_SNAKE_CASE		,   atol=1e-3     )
  					assert torch.allclose(outputs.conditional_embeddings[0, :3]		,   _SCREAMING_SNAKE_CASE		,   atol=1e-3     )
  					assert torch.allclose(outputs.pooled_output[0, :3]		,   _SCREAMING_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(_SCREAMING_SNAKE_CASE     )
  										processor.save_pretrained(_SCREAMING_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__":
  		__lowercase			:		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.'
  		)
  
  		__lowercase			:		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'''
  
  
  
  def 						lowerCamelCase							(_SCREAMING_SNAKE_CASE   :     int = 30     ):
  					__a    : Dict      					=						exponent + 2
  					__a    : List[Any]      					=						(1 + 5**0.5) / 2
  					__a    : Any      					=						(phi**fibonacci_index - (phi - 1) ** fibonacci_index) / 5**0.5
  
  					return int(_SCREAMING_SNAKE_CASE     )
  
  
  if __name__ == "__main__":
  	print(f'''{solution() = }''')
  
  
  
  