code: 
  
  
  
  
  
  
  """simple docstring"""
  
  import argparse
  import json
  from pathlib import Path
  
  import requests
  import torch
  from huggingface_hub import hf_hub_download
  from PIL import Image
  
  from transformers import ConvNextConfig, ConvNextForImageClassification, ConvNextImageProcessor
  from transformers.utils import logging
  
  
  logging.set_verbosity_info()
  lowercase__      =				logging.get_logger(__name__)
  
  def 	__lowerCamelCase			(						__UpperCamelCase )		->					List[Any]:
  
  		"""simple docstring"""
  
  
  
  
  
  
  
  		lowerCAmelCase_     :							List[Any]             =    ConvNextConfig()
  
  		if "tiny" in checkpoint_url:
  				lowerCAmelCase_     :							str             =    [3, 3, 9, 3]
  				lowerCAmelCase_     :							str             =    [96, 192, 384, 768]
  		if "small" in checkpoint_url:
  				lowerCAmelCase_     :							Optional[Any]             =    [3, 3, 27, 3]
  				lowerCAmelCase_     :							Optional[int]             =    [96, 192, 384, 768]
  		if "base" in checkpoint_url:
  				lowerCAmelCase_     :							Optional[int]             =    [3, 3, 27, 3]
  				lowerCAmelCase_     :							int             =    [128, 256, 512, 1024]
  		if "large" in checkpoint_url:
  				lowerCAmelCase_     :							List[str]             =    [3, 3, 27, 3]
  				lowerCAmelCase_     :							Optional[Any]             =    [192, 384, 768, 1536]
  		if "xlarge" in checkpoint_url:
  				lowerCAmelCase_     :							int             =    [3, 3, 27, 3]
  				lowerCAmelCase_     :							Optional[int]             =    [256, 512, 1024, 2048]
  
  		if "1k" in checkpoint_url:
  				lowerCAmelCase_     :							int             =    1000
  				lowerCAmelCase_     :							Dict             =    "imagenet-1k-id2label.json"
  				lowerCAmelCase_     :							Optional[int]             =    (1, 1000)
  		else:
  				lowerCAmelCase_     :							int             =    21841
  				lowerCAmelCase_     :							Optional[int]             =    "imagenet-22k-id2label.json"
  				lowerCAmelCase_     :							Optional[Any]             =    (1, 21841)
  
  		lowerCAmelCase_     :							List[Any]             =    "huggingface/label-files"
  		lowerCAmelCase_     :							List[Any]             =    num_labels
  		lowerCAmelCase_     :							Optional[int]             =    json.load(open(hf_hub_download(__UpperCamelCase   , __UpperCamelCase   , repo_type="dataset" )   , "r" ) )
  		lowerCAmelCase_     :							Union[str, Any]             =    {int(__UpperCamelCase ): v for k, v in idalabel.items()}
  		if "1k" not in checkpoint_url:
  				# this dataset contains 21843 labels but the model only has 21841
  				# we delete the classes as mentioned in https://github.com/google-research/big_transfer/issues/18
  				del idalabel[9205]
  				del idalabel[15027]
  		lowerCAmelCase_     :							List[Any]             =    idalabel
  		lowerCAmelCase_     :							Optional[int]             =    {v: k for k, v in idalabel.items()}
  		lowerCAmelCase_     :							int             =    hidden_sizes
  		lowerCAmelCase_     :							List[str]             =    depths
  
  		return config, expected_shape
  
  def 	__lowerCamelCase			(						__UpperCamelCase )		->					Optional[Any]:
  
  		"""simple docstring"""
  
  
  
  
  
  
  
  		if "downsample_layers.0.0" in name:
  				lowerCAmelCase_     :							Tuple             =    name.replace("downsample_layers.0.0"   , "embeddings.patch_embeddings" )
  		if "downsample_layers.0.1" in name:
  				lowerCAmelCase_     :							Any             =    name.replace("downsample_layers.0.1"   , "embeddings.norm" )  # we rename to layernorm later on
  		if "downsample_layers.1.0" in name:
  				lowerCAmelCase_     :							int             =    name.replace("downsample_layers.1.0"   , "stages.1.downsampling_layer.0" )
  		if "downsample_layers.1.1" in name:
  				lowerCAmelCase_     :							Optional[int]             =    name.replace("downsample_layers.1.1"   , "stages.1.downsampling_layer.1" )
  		if "downsample_layers.2.0" in name:
  				lowerCAmelCase_     :							int             =    name.replace("downsample_layers.2.0"   , "stages.2.downsampling_layer.0" )
  		if "downsample_layers.2.1" in name:
  				lowerCAmelCase_     :							Tuple             =    name.replace("downsample_layers.2.1"   , "stages.2.downsampling_layer.1" )
  		if "downsample_layers.3.0" in name:
  				lowerCAmelCase_     :							List[Any]             =    name.replace("downsample_layers.3.0"   , "stages.3.downsampling_layer.0" )
  		if "downsample_layers.3.1" in name:
  				lowerCAmelCase_     :							str             =    name.replace("downsample_layers.3.1"   , "stages.3.downsampling_layer.1" )
  		if "stages" in name and "downsampling_layer" not in name:
  				# stages.0.0. for instance should be renamed to stages.0.layers.0.
  				lowerCAmelCase_     :							List[Any]             =    name[: len("stages.0" )] + ".layers" + name[len("stages.0" ) :]
  		if "stages" in name:
  				lowerCAmelCase_     :							str             =    name.replace("stages"   , "encoder.stages" )
  		if "norm" in name:
  				lowerCAmelCase_     :							str             =    name.replace("norm"   , "layernorm" )
  		if "gamma" in name:
  				lowerCAmelCase_     :							List[Any]             =    name.replace("gamma"   , "layer_scale_parameter" )
  		if "head" in name:
  				lowerCAmelCase_     :							Dict             =    name.replace("head"   , "classifier" )
  
  		return name
  
  def 	__lowerCamelCase			(						)		->					Union[str, Any]:
  
  		"""simple docstring"""
  
  
  
  
  
  
  
  		lowerCAmelCase_     :							Tuple             =    "http://images.cocodataset.org/val2017/000000039769.jpg"
  		lowerCAmelCase_     :							Tuple             =    Image.open(requests.get(__UpperCamelCase   , stream=__UpperCamelCase ).raw )
  		return im
  
  @torch.no_grad()
  def 	__lowerCamelCase			(						__UpperCamelCase   , __UpperCamelCase )		->					Union[str, Any]:
  
  		"""simple docstring"""
  
  
  
  
  
  
  
  		lowerCAmelCase_       ,			lowerCAmelCase_     :							Dict             =    get_convnext_config(__UpperCamelCase )
  		# load original state_dict from URL
  		lowerCAmelCase_     :							Optional[int]             =    torch.hub.load_state_dict_from_url(__UpperCamelCase )["model"]
  		# rename keys
  		for key in state_dict.copy().keys():
  				lowerCAmelCase_     :							str             =    state_dict.pop(__UpperCamelCase )
  				lowerCAmelCase_     :							int             =    val
  		# add prefix to all keys expect classifier head
  		for key in state_dict.copy().keys():
  				lowerCAmelCase_     :							str             =    state_dict.pop(__UpperCamelCase )
  				if not key.startswith("classifier" ):
  						lowerCAmelCase_     :							str             =    "convnext." + key
  				lowerCAmelCase_     :							List[str]             =    val
  
  		# load HuggingFace model
  		lowerCAmelCase_     :							Tuple             =    ConvNextForImageClassification(__UpperCamelCase )
  		model.load_state_dict(__UpperCamelCase )
  		model.eval()
  
  		# Check outputs on an image, prepared by ConvNextImageProcessor
  		lowerCAmelCase_     :							Any             =    224 if "224" in checkpoint_url else 384
  		lowerCAmelCase_     :							List[str]             =    ConvNextImageProcessor(size=__UpperCamelCase )
  		lowerCAmelCase_     :							str             =    image_processor(images=prepare_img()   , return_tensors="pt" ).pixel_values
  
  		lowerCAmelCase_     :							List[str]             =    model(__UpperCamelCase ).logits
  
  		# note: the logits below were obtained without center cropping
  		if checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_tiny_1k_224_ema.pth":
  				lowerCAmelCase_     :							Union[str, Any]             =    torch.tensor([-0.12_10, -0.66_05, 0.19_18] )
  		elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_small_1k_224_ema.pth":
  				lowerCAmelCase_     :							Optional[int]             =    torch.tensor([-0.44_73, -0.18_47, -0.63_65] )
  		elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_1k_224_ema.pth":
  				lowerCAmelCase_     :							Any             =    torch.tensor([0.45_25, 0.75_39, 0.03_08] )
  		elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_1k_384.pth":
  				lowerCAmelCase_     :							int             =    torch.tensor([0.35_61, 0.63_50, -0.03_84] )
  		elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_1k_224_ema.pth":
  				lowerCAmelCase_     :							Optional[Any]             =    torch.tensor([0.41_74, -0.09_89, 0.14_89] )
  		elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_1k_384.pth":
  				lowerCAmelCase_     :							Optional[int]             =    torch.tensor([0.25_13, -0.13_49, -0.16_13] )
  		elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_22k_224.pth":
  				lowerCAmelCase_     :							Optional[Any]             =    torch.tensor([1.29_80, 0.36_31, -0.11_98] )
  		elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_22k_224.pth":
  				lowerCAmelCase_     :							Union[str, Any]             =    torch.tensor([1.29_63, 0.12_27, 0.17_23] )
  		elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_xlarge_22k_224.pth":
  				lowerCAmelCase_     :							List[Any]             =    torch.tensor([1.79_56, 0.83_90, 0.28_20] )
  		elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_22k_1k_224.pth":
  				lowerCAmelCase_     :							Optional[Any]             =    torch.tensor([-0.28_22, -0.05_02, -0.08_78] )
  		elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_22k_1k_384.pth":
  				lowerCAmelCase_     :							int             =    torch.tensor([-0.56_72, -0.07_30, -0.43_48] )
  		elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_22k_1k_224.pth":
  				lowerCAmelCase_     :							Optional[Any]             =    torch.tensor([0.26_81, 0.23_65, 0.62_46] )
  		elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_22k_1k_384.pth":
  				lowerCAmelCase_     :							Optional[int]             =    torch.tensor([-0.26_42, 0.39_31, 0.51_16] )
  		elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_xlarge_22k_1k_224_ema.pth":
  				lowerCAmelCase_     :							Optional[int]             =    torch.tensor([-0.66_77, -0.18_73, -0.83_79] )
  		elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_xlarge_22k_1k_384_ema.pth":
  				lowerCAmelCase_     :							int             =    torch.tensor([-0.77_49, -0.29_67, -0.64_44] )
  		else:
  				raise ValueError(f'''Unknown URL: {checkpoint_url}''' )
  
  		assert torch.allclose(logits[0, :3]   , __UpperCamelCase   , atol=1e-3 )
  		assert logits.shape == expected_shape
  
  		Path(__UpperCamelCase ).mkdir(exist_ok=__UpperCamelCase )
  		print(f'''Saving model to {pytorch_dump_folder_path}''' )
  		model.save_pretrained(__UpperCamelCase )
  		print(f'''Saving image processor to {pytorch_dump_folder_path}''' )
  		image_processor.save_pretrained(__UpperCamelCase )
  
  		print("Pushing model to the hub..." )
  		lowerCAmelCase_     :							Dict             =    "convnext"
  		if "tiny" in checkpoint_url:
  				model_name += "-tiny"
  		elif "small" in checkpoint_url:
  				model_name += "-small"
  		elif "base" in checkpoint_url:
  				model_name += "-base"
  		elif "xlarge" in checkpoint_url:
  				model_name += "-xlarge"
  		elif "large" in checkpoint_url:
  				model_name += "-large"
  		if "224" in checkpoint_url:
  				model_name += "-224"
  		elif "384" in checkpoint_url:
  				model_name += "-384"
  		if "22k" in checkpoint_url and "1k" not in checkpoint_url:
  				model_name += "-22k"
  		if "22k" in checkpoint_url and "1k" in checkpoint_url:
  				model_name += "-22k-1k"
  
  		model.push_to_hub(
  		    repo_path_or_name=Path(__UpperCamelCase   , __UpperCamelCase )   , organization="nielsr"   , commit_message="Add model"   , )
  
  
  if __name__ == "__main__":
  					lowercase__      =				argparse.ArgumentParser()
  					# Required parameters
  					parser.add_argument(
  					    """--checkpoint_url""",
  					    default="""https://dl.fbaipublicfiles.com/convnext/convnext_tiny_1k_224_ema.pth""",
  					    type=str,
  					    help="""URL of the original ConvNeXT checkpoint you'd like to convert.""",
  					)
  					parser.add_argument(
  					    """--pytorch_dump_folder_path""",
  					    default=None,
  					    type=str,
  					    required=True,
  					    help="""Path to the output PyTorch model directory.""",
  					)
  
  					lowercase__      =				parser.parse_args()
  					convert_convnext_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)
  
style_context: 
  
  
  
  
  
  
  """simple docstring"""
  
  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""")
  
  lowercase__      =				"""K-MEANS-CLUST/ """
  
  def 	__lowerCamelCase			(						__UpperCamelCase   , __UpperCamelCase   , __UpperCamelCase=None )		->					Optional[Any]:
  
  		"""simple docstring"""
  
  
  
  
  
  
  
  		if seed is not None:  # useful for obtaining consistent results
  				np.random.seed(__UpperCamelCase )
  		lowerCAmelCase_     :							List[Any]             =    data.shape[0]  # number of data points
  
  		# Pick K indices from range [0, N).
  		lowerCAmelCase_     :							Optional[Any]             =    np.random.randint(0   , __UpperCamelCase   , __UpperCamelCase )
  
  		# 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.
  		lowerCAmelCase_     :							List[str]             =    data[rand_indices, :]
  
  		return centroids
  
  def 	__lowerCamelCase			(						__UpperCamelCase   , __UpperCamelCase )		->					Dict:
  
  		"""simple docstring"""
  
  
  
  
  
  
  
  		return pairwise_distances(__UpperCamelCase   , __UpperCamelCase   , metric="euclidean" )
  
  def 	__lowerCamelCase			(						__UpperCamelCase   , __UpperCamelCase )		->					List[str]:
  
  		"""simple docstring"""
  
  
  
  
  
  
  
  		lowerCAmelCase_     :							Optional[Any]             =    centroid_pairwise_dist(__UpperCamelCase   , __UpperCamelCase )
  
  		# Compute cluster assignments for each data point:
  		# Fill in the blank (RHS only)
  		lowerCAmelCase_     :							str             =    np.argmin(__UpperCamelCase   , axis=1 )
  
  		return cluster_assignment
  
  def 	__lowerCamelCase			(						__UpperCamelCase   , __UpperCamelCase   , __UpperCamelCase )		->					Dict:
  
  		"""simple docstring"""
  
  
  
  
  
  
  
  		lowerCAmelCase_     :							Tuple             =    []
  		for i in range(__UpperCamelCase ):
  				# Select all data points that belong to cluster i. Fill in the blank (RHS only)
  				lowerCAmelCase_     :							List[Any]             =    data[cluster_assignment == i]
  				# Compute the mean of the data points. Fill in the blank (RHS only)
  				lowerCAmelCase_     :							List[str]             =    member_data_points.mean(axis=0 )
  				new_centroids.append(__UpperCamelCase )
  		lowerCAmelCase_     :							Tuple             =    np.array(__UpperCamelCase )
  
  		return new_centroids
  
  def 	__lowerCamelCase			(						__UpperCamelCase   , __UpperCamelCase   , __UpperCamelCase   , __UpperCamelCase )		->					int:
  
  		"""simple docstring"""
  
  
  
  
  
  
  
  		lowerCAmelCase_     :							Union[str, Any]             =    0.0
  		for i in range(__UpperCamelCase ):
  				# Select all data points that belong to cluster i. Fill in the blank (RHS only)
  				lowerCAmelCase_     :							List[str]             =    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)
  						lowerCAmelCase_     :							int             =    pairwise_distances(
  						    __UpperCamelCase   , [centroids[i]]   , metric="euclidean" )
  						lowerCAmelCase_     :							Optional[int]             =    distances**2
  						heterogeneity += np.sum(__UpperCamelCase )
  
  		return heterogeneity
  
  def 	__lowerCamelCase			(						__UpperCamelCase   , __UpperCamelCase )		->					Dict:
  
  		"""simple docstring"""
  
  
  
  
  
  
  
  		plt.figure(figsize=(7, 4) )
  		plt.plot(__UpperCamelCase   , linewidth=4 )
  		plt.xlabel("# Iterations" )
  		plt.ylabel("Heterogeneity" )
  		plt.title(f'''Heterogeneity of clustering over time, K={k:d}''' )
  		plt.rcParams.update({"font.size": 16} )
  		plt.show()
  
  def 	__lowerCamelCase			(						__UpperCamelCase   , __UpperCamelCase   , __UpperCamelCase   , __UpperCamelCase=500   , __UpperCamelCase=None   , __UpperCamelCase=False )		->					List[Any]:
  
  		"""simple docstring"""
  
  
  
  
  
  
  
  		lowerCAmelCase_     :							Dict             =    initial_centroids[:]
  		lowerCAmelCase_     :							Optional[int]             =    None
  
  		for itr in range(__UpperCamelCase ):
  				if verbose:
  						print(__UpperCamelCase   , end="" )
  
  				# 1. Make cluster assignments using nearest centroids
  				lowerCAmelCase_     :							Optional[Any]             =    assign_clusters(__UpperCamelCase   , __UpperCamelCase )
  
  				# 2. Compute a new centroid for each of the k clusters, averaging all data
  				#    points assigned to that cluster.
  				lowerCAmelCase_     :							Tuple             =    revise_centroids(__UpperCamelCase   , __UpperCamelCase   , __UpperCamelCase )
  
  				# 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:
  						lowerCAmelCase_     :							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
  						lowerCAmelCase_     :							Tuple             =    compute_heterogeneity(__UpperCamelCase   , __UpperCamelCase   , __UpperCamelCase   , __UpperCamelCase )
  						record_heterogeneity.append(__UpperCamelCase )
  
  				lowerCAmelCase_     :							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
  
  					lowercase__      =				ds.load_iris()
  					lowercase__      =				3
  					lowercase__      =				[]
  					lowercase__      =				get_initial_centroids(dataset["""data"""], k, seed=0)
  					lowercase__ ,     lowercase__      =				kmeans(
  					    dataset["""data"""],
  					    k,
  					    initial_centroids,
  					    maxiter=400,
  					    record_heterogeneity=heterogeneity,
  					    verbose=True,
  					)
  					plot_heterogeneity(heterogeneity, k)
  
  def 	__lowerCamelCase			(						__UpperCamelCase   , __UpperCamelCase   , __UpperCamelCase=None )		->					pd.DataFrame:
  
  		"""simple docstring"""
  
  
  
  
  
  
  
  		if fill_missing_report:
  				lowerCAmelCase_     :							str             =    df.fillna(value=__UpperCamelCase )
  		lowerCAmelCase_     :							Optional[int]             =    1
  		lowerCAmelCase_     :							Optional[int]             =    df.select_dtypes(np.number ).columns
  		lowerCAmelCase_     :							Dict             =    (
  		    df.groupby(["Cluster"] )[  # construct report dataframe
  		        numeric_cols
  		    ]  # group by cluster number
  		    .agg(
  		        [
  		            ("sum", np.sum),
  		            ("mean_with_zeros", lambda __UpperCamelCase : np.mean(np.nan_to_num(__UpperCamelCase ) )),
  		            ("mean_without_zeros", lambda __UpperCamelCase : x.replace(0   , np.NaN ).mean()),
  		            (
  		                "mean_25-75",
  		                lambda __UpperCamelCase : np.mean(
  		                    np.nan_to_num(
  		                        sorted(__UpperCamelCase )[
  		                            round(len(__UpperCamelCase ) * 25 / 100 ) : round(len(__UpperCamelCase ) * 75 / 100 )
  		                        ] ) ),
  		            ),
  		            ("mean_with_na", np.mean),
  		            ("min", lambda __UpperCamelCase : x.min()),
  		            ("5%", lambda __UpperCamelCase : x.quantile(0.05 )),
  		            ("25%", lambda __UpperCamelCase : x.quantile(0.25 )),
  		            ("50%", lambda __UpperCamelCase : x.quantile(0.50 )),
  		            ("75%", lambda __UpperCamelCase : x.quantile(0.75 )),
  		            ("95%", lambda __UpperCamelCase : x.quantile(0.95 )),
  		            ("max", lambda __UpperCamelCase : x.max()),
  		            ("count", lambda __UpperCamelCase : x.count()),
  		            ("stdev", lambda __UpperCamelCase : x.std()),
  		            ("mode", lambda __UpperCamelCase : x.mode()[0]),
  		            ("median", lambda __UpperCamelCase : x.median()),
  		            ("# > 0", lambda __UpperCamelCase : (x > 0).sum()),
  		        ] )
  		    .T.reset_index()
  		    .rename(index=__UpperCamelCase   , columns={"level_0": "Features", "level_1": "Type"} )
  		)  # rename columns
  		# calculate the size of cluster(count of clientID's)
  		lowerCAmelCase_     :							Optional[Any]             =    report[
  		    (report["Features"] == "dummy") & (report["Type"] == "count")
  		].copy()  # avoid SettingWithCopyWarning
  		lowerCAmelCase_     :							List[Any]             =    (
  		    "ClusterSize"  # rename created cluster df to match report column names
  		)
  		lowerCAmelCase_     :							Dict             =    "# of Customers"
  		lowerCAmelCase_     :							List[str]             =    pd.DataFrame(
  		    clustersize.iloc[:, 2:].values
  		    / clustersize.iloc[:, 2:].values.sum() )
  		lowerCAmelCase_     :							Optional[Any]             =    "% of Customers"  # rename created cluster df to match report column names
  		lowerCAmelCase_     :							Dict             =    "ClusterProportion"
  		lowerCAmelCase_     :							Optional[Any]             =    clusterproportion.columns.tolist()
  		lowerCAmelCase_     :							Optional[Any]             =    cols[-2:] + cols[:-2]
  		lowerCAmelCase_     :							Dict             =    clusterproportion[cols]  # rearrange columns to match report
  		lowerCAmelCase_     :							Union[str, Any]             =    report.columns
  		lowerCAmelCase_     :							Dict             =    pd.DataFrame(
  		    abs(
  		        report[report["Type"] == "count"].iloc[:, 2:].values
  		        - clustersize.iloc[:, 2:].values ) )  # generating df with count of nan values
  		lowerCAmelCase_     :							Union[str, Any]             =    0
  		lowerCAmelCase_     :							Any             =    "# of nan"
  		lowerCAmelCase_     :							Dict             =    report[
  		    report["Type"] == "count"
  		].Features.tolist()  # filling values in order to match report
  		lowerCAmelCase_     :							Optional[Any]             =    a.columns.tolist()
  		lowerCAmelCase_     :							Optional[Any]             =    cols[-2:] + cols[:-2]
  		lowerCAmelCase_     :							str             =    a[cols]  # rearrange columns to match report
  		lowerCAmelCase_     :							Tuple             =    report.columns  # rename columns to match report
  		lowerCAmelCase_     :							List[str]             =    report.drop(
  		    report[report.Type == "count"].index )  # drop count values except cluster size
  		lowerCAmelCase_     :							Tuple             =    pd.concat(
  		    [report, a, clustersize, clusterproportion]   , axis=0 )  # concat report with clustert size and nan values
  		lowerCAmelCase_     :							List[Any]             =    report["Features"].isin(__UpperCamelCase )
  		lowerCAmelCase_     :							Tuple             =    report.columns.tolist()
  		lowerCAmelCase_     :							List[str]             =    cols[0:2] + cols[-1:] + cols[2:-1]
  		lowerCAmelCase_     :							List[str]             =    report[cols]
  		lowerCAmelCase_     :							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,
  		}
  		lowerCAmelCase_     :							List[str]             =    (
  		    report.assign(
  		        Sortera=lambda __UpperCamelCase : x.Type.map(__UpperCamelCase )   , Sortera=lambda __UpperCamelCase : list(reversed(range(len(__UpperCamelCase ) ) ) )   , )
  		    .sort_values(["Sorter1", "Mark", "Sorter2"]   , ascending=__UpperCamelCase )
  		    .drop(["Sorter1", "Sorter2"]   , axis=1 )
  		)
  		lowerCAmelCase_     :							str             =    ""
  		lowerCAmelCase_     :							Any             =    report.reset_index()
  		lowerCAmelCase_     :							Any             =    report.drop(columns=["index"] )
  		return report
  
  
  if __name__ == "__main__":
  					import doctest
  
  					doctest.testmod()
  