code: 
  
  import argparse
  
  import requests
  import torch
  from PIL import Image
  
  from transformers import (
      CLIPSegConfig,
      CLIPSegForImageSegmentation,
      CLIPSegProcessor,
      CLIPSegTextConfig,
      CLIPSegVisionConfig,
      CLIPTokenizer,
      ViTImageProcessor,
  )
  
  
  
  def  _A  (			SCREAMING_SNAKE_CASE			):
  	UpperCAmelCase__:  Optional[Any]		       =   CLIPSegTextConfig()
  	UpperCAmelCase__:  str		       =   CLIPSegVisionConfig(patch_size=1_6			)
  
  	UpperCAmelCase__:  str		       =   True if "refined" in model_name else False
  	UpperCAmelCase__:  Dict		       =   1_6 if "rd16" in model_name else 6_4
  
  	UpperCAmelCase__:  Dict		       =   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  _A  (			SCREAMING_SNAKE_CASE			):
  	# update prefixes
  	if "clip_model" in name:
  		UpperCAmelCase__:  Optional[Any]		       =   name.replace("clip_model"  ,"clip"			)
  	if "transformer" in name:
  		if "visual" in name:
  			UpperCAmelCase__:  Dict		       =   name.replace("visual.transformer"  ,"vision_model"			)
  		else:
  			UpperCAmelCase__:  str		       =   name.replace("transformer"  ,"text_model"			)
  	if "resblocks" in name:
  		UpperCAmelCase__:  Tuple		       =   name.replace("resblocks"  ,"encoder.layers"			)
  	if "ln_1" in name:
  		UpperCAmelCase__:  Tuple		       =   name.replace("ln_1"  ,"layer_norm1"			)
  	if "ln_2" in name:
  		UpperCAmelCase__:  Optional[int]		       =   name.replace("ln_2"  ,"layer_norm2"			)
  	if "c_fc" in name:
  		UpperCAmelCase__:  Tuple		       =   name.replace("c_fc"  ,"fc1"			)
  	if "c_proj" in name:
  		UpperCAmelCase__:  int		       =   name.replace("c_proj"  ,"fc2"			)
  	if "attn" in name and "self" not in name:
  		UpperCAmelCase__:  List[str]		       =   name.replace("attn"  ,"self_attn"			)
  	# text encoder
  	if "token_embedding" in name:
  		UpperCAmelCase__:  List[Any]		       =   name.replace("token_embedding"  ,"text_model.embeddings.token_embedding"			)
  	if "positional_embedding" in name and "visual" not in name:
  		UpperCAmelCase__:  Union[str, Any]		       =   name.replace("positional_embedding"  ,"text_model.embeddings.position_embedding.weight"			)
  	if "ln_final" in name:
  		UpperCAmelCase__:  Any		       =   name.replace("ln_final"  ,"text_model.final_layer_norm"			)
  	# vision encoder
  	if "visual.class_embedding" in name:
  		UpperCAmelCase__:  str		       =   name.replace("visual.class_embedding"  ,"vision_model.embeddings.class_embedding"			)
  	if "visual.conv1" in name:
  		UpperCAmelCase__:  List[str]		       =   name.replace("visual.conv1"  ,"vision_model.embeddings.patch_embedding"			)
  	if "visual.positional_embedding" in name:
  		UpperCAmelCase__:  List[Any]		       =   name.replace("visual.positional_embedding"  ,"vision_model.embeddings.position_embedding.weight"			)
  	if "visual.ln_pre" in name:
  		UpperCAmelCase__:  Union[str, Any]		       =   name.replace("visual.ln_pre"  ,"vision_model.pre_layrnorm"			)
  	if "visual.ln_post" in name:
  		UpperCAmelCase__:  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__:  int		       =   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__:  List[Any]		       =   "decoder." + name
  	if "blocks" in name:
  		UpperCAmelCase__:  Optional[Any]		       =   name.replace("blocks"  ,"decoder.layers"			)
  	if "linear1" in name:
  		UpperCAmelCase__:  Any		       =   name.replace("linear1"  ,"mlp.fc1"			)
  	if "linear2" in name:
  		UpperCAmelCase__:  Dict		       =   name.replace("linear2"  ,"mlp.fc2"			)
  	if "norm1" in name and "layer_" not in name:
  		UpperCAmelCase__:  Dict		       =   name.replace("norm1"  ,"layer_norm1"			)
  	if "norm2" in name and "layer_" not in name:
  		UpperCAmelCase__:  Any		       =   name.replace("norm2"  ,"layer_norm2"			)
  
  	return name
  
  
  
  def  _A  (			SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE			):
  	for key in orig_state_dict.copy().keys():
  		UpperCAmelCase__:  List[str]		       =   orig_state_dict.pop(SCREAMING_SNAKE_CASE			)
  
  		if key.startswith("clip_model"			) and "attn.in_proj" in key:
  			UpperCAmelCase__:  str		       =   key.split("."			)
  			if "visual" in key:
  				UpperCAmelCase__:  Tuple		       =   int(key_split[4]			)
  				UpperCAmelCase__:  Any		       =   config.vision_config.hidden_size
  				UpperCAmelCase__:  str		       =   "vision_model"
  			else:
  				UpperCAmelCase__:  str		       =   int(key_split[3]			)
  				UpperCAmelCase__:  Dict		       =   config.text_config.hidden_size
  				UpperCAmelCase__:  Any		       =   "text_model"
  
  			if "weight" in key:
  				UpperCAmelCase__:  int		       =   val[:dim, :]
  				UpperCAmelCase__:  List[str]		       =   val[
  				    dim : dim * 2, :
  				]
  				UpperCAmelCase__:  Any		       =   val[-dim:, :]
  			else:
  				UpperCAmelCase__:  int		       =   val[:dim]
  				UpperCAmelCase__:  str		       =   val[dim : dim * 2]
  				UpperCAmelCase__:  Dict		       =   val[-dim:]
  		elif "self_attn" in key and "out_proj" not in key:
  			UpperCAmelCase__:  Union[str, Any]		       =   key.split("."			)
  			UpperCAmelCase__:  Dict		       =   int(key_split[1]			)
  			UpperCAmelCase__:  str		       =   config.reduce_dim
  			if "weight" in key:
  				UpperCAmelCase__:  Union[str, Any]		       =   val[:dim, :]
  				UpperCAmelCase__:  Any		       =   val[dim : dim * 2, :]
  				UpperCAmelCase__:  int		       =   val[-dim:, :]
  			else:
  				UpperCAmelCase__:  Optional[Any]		       =   val[:dim]
  				UpperCAmelCase__:  Tuple		       =   val[dim : dim * 2]
  				UpperCAmelCase__:  List[Any]		       =   val[-dim:]
  		else:
  			UpperCAmelCase__:  Optional[Any]		       =   rename_key(SCREAMING_SNAKE_CASE			)
  			if "visual_projection" in new_name or "text_projection" in new_name:
  				UpperCAmelCase__:  Optional[Any]		       =   val.T
  			UpperCAmelCase__:  Tuple		       =   val
  
  	return orig_state_dict
  
  
  
  def  _A  (			):
  	UpperCAmelCase__:  Union[str, Any]		       =   "http://images.cocodataset.org/val2017/000000039769.jpg"
  	UpperCAmelCase__:  int		       =   Image.open(requests.get(SCREAMING_SNAKE_CASE  ,stream=SCREAMING_SNAKE_CASE			).raw			)
  	return image
  
  
  
  def  _A  (			SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE			):
  	UpperCAmelCase__:  Tuple		       =   get_clipseg_config(SCREAMING_SNAKE_CASE			)
  	UpperCAmelCase__:  Dict		       =   CLIPSegForImageSegmentation(SCREAMING_SNAKE_CASE			)
  	model.eval()
  
  	UpperCAmelCase__:  List[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
  	UpperCAmelCase__:  Union[str, Any]		       =   convert_state_dict(SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE			)
  	UpperCAmelCase__       ,	UpperCAmelCase__:  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}"			)
  
  	UpperCAmelCase__:  Union[str, Any]		       =   ViTImageProcessor(size=3_5_2			)
  	UpperCAmelCase__:  List[str]		       =   CLIPTokenizer.from_pretrained("openai/clip-vit-base-patch32"			)
  	UpperCAmelCase__:  Optional[int]		       =   CLIPSegProcessor(image_processor=SCREAMING_SNAKE_CASE  ,tokenizer=SCREAMING_SNAKE_CASE			)
  
  	UpperCAmelCase__:  Optional[int]		       =   prepare_img()
  	UpperCAmelCase__:  Dict		       =   ["a glass", "something to fill", "wood", "a jar"]
  
  	UpperCAmelCase__:  Any		       =   processor(text=SCREAMING_SNAKE_CASE  ,images=[image] * len(SCREAMING_SNAKE_CASE			)  ,padding="max_length"  ,return_tensors="pt"			)
  
  	with torch.no_grad():
  		UpperCAmelCase__:  List[str]		       =   model(**SCREAMING_SNAKE_CASE			)
  
  	# verify values
  	UpperCAmelCase__:  List[str]		       =   torch.tensor([0.11_10, -0.18_82, 0.16_45]			)
  	UpperCAmelCase__:  Optional[int]		       =   torch.tensor([0.26_92, -0.71_97, -0.13_28]			)
  	if model_name == "clipseg-rd64-refined":
  		UpperCAmelCase__:  int		       =   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":
  		UpperCAmelCase__:  Optional[int]		       =   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":
  		UpperCAmelCase__:  Optional[Any]		       =   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]  ,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__":
  					_lowerCAmelCase     :			Optional[Any]    =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     :			Optional[Any]    =parser.parse_args()
  					convert_clipseg_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)
style_context: 
  
  import warnings
  
  import numpy as np
  import pandas as pd
  from matplotlib import pyplot as plt
  from sklearn.metrics import pairwise_distances
  
  warnings.filterwarnings("""ignore""")
  
  _lowerCAmelCase     :			str    ="""K-MEANS-CLUST/ """
  
  
  
  def  _A  (			SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE=None			):
  	if seed is not None:  # useful for obtaining consistent results
  		np.random.seed(SCREAMING_SNAKE_CASE			)
  	UpperCAmelCase__:  Optional[Any]		       =   data.shape[0]  # number of data points
  
  	# Pick K indices from range [0, N).
  	UpperCAmelCase__:  Optional[int]		       =   np.random.randint(0  ,SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE			)
  
  	# Keep centroids as dense format, as many entries will be nonzero due to averaging.
  	# As long as at least one document in a cluster contains a word,
  	# it will carry a nonzero weight in the TF-IDF vector of the centroid.
  	UpperCAmelCase__:  Tuple		       =   data[rand_indices, :]
  
  	return centroids
  
  
  
  def  _A  (			SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE			):
  	return pairwise_distances(SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE  ,metric="euclidean"			)
  
  
  
  def  _A  (			SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE			):
  	# Compute distances between each data point and the set of centroids:
  	# Fill in the blank (RHS only)
  	UpperCAmelCase__:  Union[str, Any]		       =   centroid_pairwise_dist(SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE			)
  
  	# Compute cluster assignments for each data point:
  	# Fill in the blank (RHS only)
  	UpperCAmelCase__:  Dict		       =   np.argmin(SCREAMING_SNAKE_CASE  ,axis=1			)
  
  	return cluster_assignment
  
  
  
  def  _A  (			SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE			):
  	UpperCAmelCase__:  str		       =   []
  	for i in range(SCREAMING_SNAKE_CASE			):
  		# Select all data points that belong to cluster i. Fill in the blank (RHS only)
  		UpperCAmelCase__:  Tuple		       =   data[cluster_assignment == i]
  		# Compute the mean of the data points. Fill in the blank (RHS only)
  		UpperCAmelCase__:  Any		       =   member_data_points.mean(axis=0			)
  		new_centroids.append(SCREAMING_SNAKE_CASE			)
  	UpperCAmelCase__:  List[Any]		       =   np.array(SCREAMING_SNAKE_CASE			)
  
  	return new_centroids
  
  
  
  def  _A  (			SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE			):
  	UpperCAmelCase__:  Tuple		       =   0.0
  	for i in range(SCREAMING_SNAKE_CASE			):
  		# Select all data points that belong to cluster i. Fill in the blank (RHS only)
  		UpperCAmelCase__:  Optional[Any]		       =   data[cluster_assignment == i, :]
  
  		if member_data_points.shape[0] > 0:  # check if i-th cluster is non-empty
  			# Compute distances from centroid to data points (RHS only)
  			UpperCAmelCase__:  int		       =   pairwise_distances(
  			    SCREAMING_SNAKE_CASE  ,[centroids[i]]  ,metric="euclidean"			)
  			UpperCAmelCase__:  int		       =   distances**2
  			heterogeneity += np.sum(SCREAMING_SNAKE_CASE			)
  
  	return heterogeneity
  
  
  
  def  _A  (			SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE			):
  	plt.figure(figsize=(7, 4)			)
  	plt.plot(SCREAMING_SNAKE_CASE  ,linewidth=4			)
  	plt.xlabel("# Iterations"			)
  	plt.ylabel("Heterogeneity"			)
  	plt.title(f"Heterogeneity of clustering over time, K={k:d}"			)
  	plt.rcParams.update({"font.size": 1_6}			)
  	plt.show()
  
  
  
  def  _A  (			SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE=5_0_0  ,SCREAMING_SNAKE_CASE=None  ,SCREAMING_SNAKE_CASE=False			):
  	UpperCAmelCase__:  Optional[int]		       =   initial_centroids[:]
  	UpperCAmelCase__:  List[str]		       =   None
  
  	for itr in range(SCREAMING_SNAKE_CASE			):
  		if verbose:
  			print(SCREAMING_SNAKE_CASE  ,end=""			)
  
  		# 1. Make cluster assignments using nearest centroids
  		UpperCAmelCase__:  Optional[Any]		       =   assign_clusters(SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE			)
  
  		# 2. Compute a new centroid for each of the k clusters, averaging all data
  		#    points assigned to that cluster.
  		UpperCAmelCase__:  Union[str, Any]		       =   revise_centroids(SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE			)
  
  		# Check for convergence: if none of the assignments changed, stop
  		if (
  		    prev_cluster_assignment is not None
  		    and (prev_cluster_assignment == cluster_assignment).all()
  		):
  			break
  
  		# Print number of new assignments
  		if prev_cluster_assignment is not None:
  			UpperCAmelCase__:  Union[str, Any]		       =   np.sum(prev_cluster_assignment != cluster_assignment			)
  			if verbose:
  				print(
  				    f"    {num_changed:5d} elements changed their cluster assignment."			)
  
          # Record heterogeneity convergence metric
  		if record_heterogeneity is not None:
  			# YOUR CODE HERE
  			UpperCAmelCase__:  str		       =   compute_heterogeneity(SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE			)
  			record_heterogeneity.append(SCREAMING_SNAKE_CASE			)
  
  		UpperCAmelCase__:  str		       =   cluster_assignment[:]
  
  	return centroids, cluster_assignment
  
  
  # Mock test below
  if False:  # change to true to run this test case.
  					from sklearn import datasets as ds
  
  					_lowerCAmelCase     :			Optional[Any]    =ds.load_iris()
  					_lowerCAmelCase     :			int    =3
  					_lowerCAmelCase     :			List[str]    =[]
  					_lowerCAmelCase     :			int    =get_initial_centroids(dataset["""data"""], k, seed=0)
  					_lowerCAmelCase   ,  _lowerCAmelCase     :			Union[str, Any]    =kmeans(
  					    dataset["""data"""],
  					    k,
  					    initial_centroids,
  					    maxiter=4_00,
  					    record_heterogeneity=heterogeneity,
  					    verbose=True,
  					)
  					plot_heterogeneity(heterogeneity, k)
  
  
  
  def  _A  (			SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE=None			):
  	# Fill missing values with given rules
  	if fill_missing_report:
  		UpperCAmelCase__:  int		       =   df.fillna(value=SCREAMING_SNAKE_CASE			)
  	UpperCAmelCase__:  Any		       =   1
  	UpperCAmelCase__:  Any		       =   df.select_dtypes(np.number			).columns
  	UpperCAmelCase__:  List[Any]		       =   (
  	    df.groupby(["Cluster"]			)[  # construct report dataframe
  	        numeric_cols
  	    ]  # group by cluster number
  	    .agg(
  	        [
  	            ("sum", np.sum),
  	            ("mean_with_zeros", lambda SCREAMING_SNAKE_CASE			: np.mean(np.nan_to_num(SCREAMING_SNAKE_CASE			)			)),
  	            ("mean_without_zeros", lambda SCREAMING_SNAKE_CASE			: x.replace(0  ,np.NaN			).mean()),
  	            (
  	                "mean_25-75",
  	                lambda SCREAMING_SNAKE_CASE			: np.mean(
  	                    np.nan_to_num(
  	                        sorted(SCREAMING_SNAKE_CASE			)[
  	                            round(len(SCREAMING_SNAKE_CASE			) * 2_5 / 1_0_0			) : round(len(SCREAMING_SNAKE_CASE			) * 7_5 / 1_0_0			)
  	                        ]			)			),
  	            ),
  	            ("mean_with_na", np.mean),
  	            ("min", lambda SCREAMING_SNAKE_CASE			: x.min()),
  	            ("5%", lambda SCREAMING_SNAKE_CASE			: x.quantile(0.05			)),
  	            ("25%", lambda SCREAMING_SNAKE_CASE			: x.quantile(0.25			)),
  	            ("50%", lambda SCREAMING_SNAKE_CASE			: x.quantile(0.50			)),
  	            ("75%", lambda SCREAMING_SNAKE_CASE			: x.quantile(0.75			)),
  	            ("95%", lambda SCREAMING_SNAKE_CASE			: x.quantile(0.95			)),
  	            ("max", lambda SCREAMING_SNAKE_CASE			: x.max()),
  	            ("count", lambda SCREAMING_SNAKE_CASE			: x.count()),
  	            ("stdev", lambda SCREAMING_SNAKE_CASE			: x.std()),
  	            ("mode", lambda SCREAMING_SNAKE_CASE			: x.mode()[0]),
  	            ("median", lambda SCREAMING_SNAKE_CASE			: x.median()),
  	            ("# > 0", lambda SCREAMING_SNAKE_CASE			: (x > 0).sum()),
  	        ]			)
  	    .T.reset_index()
  	    .rename(index=SCREAMING_SNAKE_CASE  ,columns={"level_0": "Features", "level_1": "Type"}			)
  	)  # rename columns
  	# calculate the size of cluster(count of clientID's)
  	UpperCAmelCase__:  str		       =   report[
  	    (report["Features"] == "dummy") & (report["Type"] == "count")
  	].copy()  # avoid SettingWithCopyWarning
  	UpperCAmelCase__:  int		       =   (
  	    "ClusterSize"  # rename created cluster df to match report column names
  	)
  	UpperCAmelCase__:  List[str]		       =   "# of Customers"
  	UpperCAmelCase__:  int		       =   pd.DataFrame(
  	    clustersize.iloc[:, 2:].values
  	    / clustersize.iloc[:, 2:].values.sum()			)
  	UpperCAmelCase__:  Dict		       =   "% of Customers"  # rename created cluster df to match report column names
  	UpperCAmelCase__:  Any		       =   "ClusterProportion"
  	UpperCAmelCase__:  int		       =   clusterproportion.columns.tolist()
  	UpperCAmelCase__:  int		       =   cols[-2:] + cols[:-2]
  	UpperCAmelCase__:  Optional[int]		       =   clusterproportion[cols]  # rearrange columns to match report
  	UpperCAmelCase__:  Union[str, Any]		       =   report.columns
  	UpperCAmelCase__:  int		       =   pd.DataFrame(
  	    abs(
  	        report[report["Type"] == "count"].iloc[:, 2:].values
  	        - clustersize.iloc[:, 2:].values			)			)  # generating df with count of nan values
  	UpperCAmelCase__:  Optional[int]		       =   0
  	UpperCAmelCase__:  Tuple		       =   "# of nan"
  	UpperCAmelCase__:  Any		       =   report[
  	    report["Type"] == "count"
  	].Features.tolist()  # filling values in order to match report
  	UpperCAmelCase__:  int		       =   a.columns.tolist()
  	UpperCAmelCase__:  Tuple		       =   cols[-2:] + cols[:-2]
  	UpperCAmelCase__:  List[str]		       =   a[cols]  # rearrange columns to match report
  	UpperCAmelCase__:  int		       =   report.columns  # rename columns to match report
  	UpperCAmelCase__:  List[Any]		       =   report.drop(
  	    report[report.Type == "count"].index			)  # drop count values except cluster size
  	UpperCAmelCase__:  Optional[Any]		       =   pd.concat(
  	    [report, a, clustersize, clusterproportion]  ,axis=0			)  # concat report with clustert size and nan values
  	UpperCAmelCase__:  Any		       =   report["Features"].isin(SCREAMING_SNAKE_CASE			)
  	UpperCAmelCase__:  Tuple		       =   report.columns.tolist()
  	UpperCAmelCase__:  str		       =   cols[0:2] + cols[-1:] + cols[2:-1]
  	UpperCAmelCase__:  int		       =   report[cols]
  	UpperCAmelCase__:  str		       =   {
  	    "ClusterSize": 9,
  	    "ClusterProportion": 8,
  	    "mean_with_zeros": 7,
  	    "mean_with_na": 6,
  	    "max": 5,
  	    "50%": 4,
  	    "min": 3,
  	    "25%": 2,
  	    "75%": 1,
  	    "# of nan": 0,
  	    "# > 0": -1,
  	    "sum_with_na": -2,
  	}
  	UpperCAmelCase__:  int		       =   (
  	    report.assign(
  	        Sortera=lambda SCREAMING_SNAKE_CASE			: x.Type.map(SCREAMING_SNAKE_CASE			)  ,Sortera=lambda SCREAMING_SNAKE_CASE			: list(reversed(range(len(SCREAMING_SNAKE_CASE			)			)			)			)  ,)
  	    .sort_values(["Sorter1", "Mark", "Sorter2"]  ,ascending=SCREAMING_SNAKE_CASE			)
  	    .drop(["Sorter1", "Sorter2"]  ,axis=1			)
  	)
  	UpperCAmelCase__:  List[str]		       =   ""
  	UpperCAmelCase__:  Any		       =   report.reset_index()
  	UpperCAmelCase__:  Any		       =   report.drop(columns=["index"]			)
  	return report
  
  
  if __name__ == "__main__":
  					import doctest
  
  					doctest.testmod()