code: 
  '''simple docstring'''
  
  
  
  import argparse
  
  import requests
  import torch
  from PIL import Image
  
  from transformers import (
      CLIPSegConfig,
      CLIPSegForImageSegmentation,
      CLIPSegProcessor,
      CLIPSegTextConfig,
      CLIPSegVisionConfig,
      CLIPTokenizer,
      ViTImageProcessor,
  )
  
  
  
  
  
  def  _A		(							lowercase__	):
  						lowercase__							 =       CLIPSegTextConfig()
  						lowercase__							 =       CLIPSegVisionConfig(patch_size=16	)
  
  						lowercase__							 =       True if """refined""" in model_name else False
  						lowercase__							 =       16 if """rd16""" in model_name else 64
  
  						lowercase__							 =       CLIPSegConfig.from_text_vision_configs(
  						    lowercase__		,		lowercase__		,		use_complex_transposed_convolution=lowercase__		,		reduce_dim=lowercase__		,		)
  						return config
  
  
  
  
  
  def  _A		(							lowercase__	):
  						# update prefixes
  						if "clip_model" in name:
  												lowercase__							 =       name.replace("""clip_model"""		,		"""clip"""	)
  						if "transformer" in name:
  												if "visual" in name:
  																		lowercase__							 =       name.replace("""visual.transformer"""		,		"""vision_model"""	)
  												else:
  																		lowercase__							 =       name.replace("""transformer"""		,		"""text_model"""	)
  						if "resblocks" in name:
  												lowercase__							 =       name.replace("""resblocks"""		,		"""encoder.layers"""	)
  						if "ln_1" in name:
  												lowercase__							 =       name.replace("""ln_1"""		,		"""layer_norm1"""	)
  						if "ln_2" in name:
  												lowercase__							 =       name.replace("""ln_2"""		,		"""layer_norm2"""	)
  						if "c_fc" in name:
  												lowercase__							 =       name.replace("""c_fc"""		,		"""fc1"""	)
  						if "c_proj" in name:
  												lowercase__							 =       name.replace("""c_proj"""		,		"""fc2"""	)
  						if "attn" in name and "self" not in name:
  												lowercase__							 =       name.replace("""attn"""		,		"""self_attn"""	)
  						# text encoder
  						if "token_embedding" in name:
  												lowercase__							 =       name.replace("""token_embedding"""		,		"""text_model.embeddings.token_embedding"""	)
  						if "positional_embedding" in name and "visual" not in name:
  												lowercase__							 =       name.replace("""positional_embedding"""		,		"""text_model.embeddings.position_embedding.weight"""	)
  						if "ln_final" in name:
  												lowercase__							 =       name.replace("""ln_final"""		,		"""text_model.final_layer_norm"""	)
  						# vision encoder
  						if "visual.class_embedding" in name:
  												lowercase__							 =       name.replace("""visual.class_embedding"""		,		"""vision_model.embeddings.class_embedding"""	)
  						if "visual.conv1" in name:
  												lowercase__							 =       name.replace("""visual.conv1"""		,		"""vision_model.embeddings.patch_embedding"""	)
  						if "visual.positional_embedding" in name:
  												lowercase__							 =       name.replace("""visual.positional_embedding"""		,		"""vision_model.embeddings.position_embedding.weight"""	)
  						if "visual.ln_pre" in name:
  												lowercase__							 =       name.replace("""visual.ln_pre"""		,		"""vision_model.pre_layrnorm"""	)
  						if "visual.ln_post" in name:
  												lowercase__							 =       name.replace("""visual.ln_post"""		,		"""vision_model.post_layernorm"""	)
  						# projection layers
  						if "visual.proj" in name:
  												lowercase__							 =       name.replace("""visual.proj"""		,		"""visual_projection.weight"""	)
  						if "text_projection" in name:
  												lowercase__							 =       name.replace("""text_projection"""		,		"""text_projection.weight"""	)
  						# decoder
  						if "trans_conv" in name:
  												lowercase__							 =       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__							 =       """decoder.""" + name
  						if "blocks" in name:
  												lowercase__							 =       name.replace("""blocks"""		,		"""decoder.layers"""	)
  						if "linear1" in name:
  												lowercase__							 =       name.replace("""linear1"""		,		"""mlp.fc1"""	)
  						if "linear2" in name:
  												lowercase__							 =       name.replace("""linear2"""		,		"""mlp.fc2"""	)
  						if "norm1" in name and "layer_" not in name:
  												lowercase__							 =       name.replace("""norm1"""		,		"""layer_norm1"""	)
  						if "norm2" in name and "layer_" not in name:
  												lowercase__							 =       name.replace("""norm2"""		,		"""layer_norm2"""	)
  
  						return name
  
  
  
  
  
  def  _A		(							lowercase__		,		lowercase__	):
  						for key in orig_state_dict.copy().keys():
  												lowercase__							 =       orig_state_dict.pop(lowercase__	)
  
  												if key.startswith("""clip_model"""	) and "attn.in_proj" in key:
  																		lowercase__							 =       key.split("""."""	)
  																		if "visual" in key:
  																								lowercase__							 =       int(key_split[4]	)
  																								lowercase__							 =       config.vision_config.hidden_size
  																								lowercase__							 =       """vision_model"""
  																		else:
  																								lowercase__							 =       int(key_split[3]	)
  																								lowercase__							 =       config.text_config.hidden_size
  																								lowercase__							 =       """text_model"""
  
  																		if "weight" in key:
  																								lowercase__							 =       val[:dim, :]
  																								lowercase__							 =       val[
  																								    dim : dim * 2, :
  																								]
  																								lowercase__							 =       val[-dim:, :]
  																		else:
  																								lowercase__							 =       val[:dim]
  																								lowercase__							 =       val[dim : dim * 2]
  																								lowercase__							 =       val[-dim:]
  												elif "self_attn" in key and "out_proj" not in key:
  																		lowercase__							 =       key.split("""."""	)
  																		lowercase__							 =       int(key_split[1]	)
  																		lowercase__							 =       config.reduce_dim
  																		if "weight" in key:
  																								lowercase__							 =       val[:dim, :]
  																								lowercase__							 =       val[dim : dim * 2, :]
  																								lowercase__							 =       val[-dim:, :]
  																		else:
  																								lowercase__							 =       val[:dim]
  																								lowercase__							 =       val[dim : dim * 2]
  																								lowercase__							 =       val[-dim:]
  												else:
  																		lowercase__							 =       rename_key(lowercase__	)
  																		if "visual_projection" in new_name or "text_projection" in new_name:
  																								lowercase__							 =       val.T
  																		lowercase__							 =       val
  
  						return orig_state_dict
  
  
  
  
  
  def  _A		(							):
  						lowercase__							 =       """http://images.cocodataset.org/val2017/000000039769.jpg"""
  						lowercase__							 =       Image.open(requests.get(lowercase__		,		stream=lowercase__	).raw	)
  						return image
  
  
  
  
  
  def  _A		(							lowercase__		,		lowercase__		,		lowercase__		,		lowercase__	):
  						lowercase__							 =       get_clipseg_config(lowercase__	)
  						lowercase__							 =       CLIPSegForImageSegmentation(lowercase__	)
  						model.eval()
  
  						lowercase__							 =       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__							 =       convert_state_dict(lowercase__		,		lowercase__	)
  						lowercase__ ,						lowercase__							 =       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__							 =       ViTImageProcessor(size=352	)
  						lowercase__							 =       CLIPTokenizer.from_pretrained("""openai/clip-vit-base-patch32"""	)
  						lowercase__							 =       CLIPSegProcessor(image_processor=lowercase__		,		tokenizer=lowercase__	)
  
  						lowercase__							 =       prepare_img()
  						lowercase__							 =       ["""a glass""", """something to fill""", """wood""", """a jar"""]
  
  						lowercase__							 =       processor(text=lowercase__		,		images=[image] * len(lowercase__	)		,		padding="""max_length"""		,		return_tensors="""pt"""	)
  
  						with torch.no_grad():
  												lowercase__							 =       model(**lowercase__	)
  
  						# verify values
  						lowercase__							 =       torch.tensor([0.1_1_1_0, -0.1_8_8_2, 0.1_6_4_5]	)
  						lowercase__							 =       torch.tensor([0.2_6_9_2, -0.7_1_9_7, -0.1_3_2_8]	)
  						if model_name == "clipseg-rd64-refined":
  												lowercase__							 =       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__							 =       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__							 =       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: 
  '''simple docstring'''
  
  
  
  def  _A		(							lowercase__	):
  
  						# validate:
  						if not isinstance(lowercase__		,		lowercase__	) or not all(
  						    isinstance(lowercase__		,		lowercase__	) and item >= 0 for item in citations	):
  												raise ValueError("""The citations should be a list of non negative integers."""	)
  
  						citations.sort()
  						lowercase__							 =       len(lowercase__	)
  
  						for i in range(lowercase__	):
  												if citations[len_citations - 1 - i] <= i:
  																		return i
  
  						return len_citations
  
  
  if __name__ == "__main__":
  				import doctest
  
  				doctest.testmod()
  
  
  
  
  
  
  