code: 
  
  import unittest
  
  from transformers.testing_utils import is_torch_available, require_torch, tooslow
  
  from ...generation.test_utils import torch_device
  from ...test_configuration_common import ConfigTester
  from ...test_modeling_common import ModelTesterMixin, ids_tensor
  from ...test_pipeline_mixin import PipelineTesterMixin
  
  
  if is_torch_available():
  					import torch
  
  					from transformers import (
  					    CpmAntConfig,
  					    CpmAntForCausalLM,
  					    CpmAntModel,
  					    CpmAntTokenizer,
  					)
  
  @require_torch
  class __UpperCamelCase    :
  
  
  
  
  
  
  					'''simple docstring'''
  
  
  
  					def __init__(							self							,			lowerCamelCase__							,			lowerCamelCase__=2							,			lowerCamelCase__=8							,			lowerCamelCase__=True							,			lowerCamelCase__=False							,			lowerCamelCase__=False							,			lowerCamelCase__=False							,			lowerCamelCase__=False							,			lowerCamelCase__=9_9							,			lowerCamelCase__=3_2							,			lowerCamelCase__=3							,			lowerCamelCase__=4							,			lowerCamelCase__=3_7							,			lowerCamelCase__=3_2							,			lowerCamelCase__=1_2_8							,			lowerCamelCase__=8							,			lowerCamelCase__=8							,			lowerCamelCase__=8							,			lowerCamelCase__=1.0							,			lowerCamelCase__=True							,			):
  						UpperCAmelCase__:  Optional[Any]		       =   parent
  						UpperCAmelCase__:  int		       =   batch_size
  						UpperCAmelCase__:  Optional[Any]		       =   seq_length
  						UpperCAmelCase__:  List[str]		       =   is_training
  						UpperCAmelCase__:  str		       =   use_token_type_ids
  						UpperCAmelCase__:  List[Any]		       =   use_input_mask
  						UpperCAmelCase__:  Dict		       =   use_labels
  						UpperCAmelCase__:  Optional[int]		       =   use_mc_token_ids
  						UpperCAmelCase__:  Union[str, Any]		       =   vocab_size
  						UpperCAmelCase__:  Dict		       =   hidden_size
  						UpperCAmelCase__:  List[str]		       =   num_hidden_layers
  						UpperCAmelCase__:  Tuple		       =   num_attention_heads
  						UpperCAmelCase__:  List[Any]		       =   intermediate_size
  						UpperCAmelCase__:  Union[str, Any]		       =   num_buckets
  						UpperCAmelCase__:  str		       =   max_distance
  						UpperCAmelCase__:  Union[str, Any]		       =   prompt_length
  						UpperCAmelCase__:  Tuple		       =   prompt_types
  						UpperCAmelCase__:  int		       =   segment_types
  						UpperCAmelCase__:  Any		       =   init_std
  						UpperCAmelCase__:  List[str]		       =   return_dict
  
  
  
  
  
  					def       _UpperCAmelCase						(							self	):
  						UpperCAmelCase__:  int		       =   {}
  						UpperCAmelCase__:  Optional[int]		       =   ids_tensor([self.batch_size, self.seq_length]							,			self.vocab_size	).type(torch.intaa	)
  						UpperCAmelCase__:  List[Any]		       =   False
  
  						UpperCAmelCase__:  List[str]		       =   self.get_config()
  
  						return (config, input_ids)
  
  
  
  
  
  					def       _UpperCAmelCase						(							self	):
  						return CpmAntConfig(
  						    vocab_size=self.vocab_size							,			hidden_size=self.hidden_size							,			num_hidden_layers=self.num_hidden_layers							,			num_attention_heads=self.num_attention_heads							,			dim_ff=self.intermediate_size							,			position_bias_num_buckets=self.num_buckets							,			position_bias_max_distance=self.max_distance							,			prompt_types=self.prompt_types							,			prompt_length=self.prompt_length							,			segment_types=self.segment_types							,			use_cache=lowerCamelCase__							,			init_std=self.init_std							,			return_dict=self.return_dict							,			)
  
  
  
  
  
  					def       _UpperCAmelCase						(							self							,			lowerCamelCase__							,			lowerCamelCase__							,			*lowerCamelCase__	):
  						UpperCAmelCase__:  Optional[Any]		       =   CpmAntModel(config=lowerCamelCase__	)
  						model.to(lowerCamelCase__	)
  						model.eval()
  
  						UpperCAmelCase__:  List[str]		       =   model(**lowerCamelCase__	).last_hidden_state
  
  						self.parent.assertEqual(hidden_states.shape							,			(self.batch_size, self.seq_length, config.hidden_size)	)
  
  
  
  
  
  					def       _UpperCAmelCase						(							self							,			lowerCamelCase__							,			lowerCamelCase__							,			*lowerCamelCase__	):
  						UpperCAmelCase__:  Tuple		       =   CpmAntForCausalLM(lowerCamelCase__	)
  						model.to(lowerCamelCase__	)
  						UpperCAmelCase__:  int		       =   input_ids["input_ids"].to(lowerCamelCase__	)
  						model.eval()
  
  						UpperCAmelCase__:  str		       =   model(**lowerCamelCase__	)
  						self.parent.assertEqual(
  						    model_output.logits.shape							,			(self.batch_size, self.seq_length, config.vocab_size + config.prompt_types * config.prompt_length)							,			)
  
  
  
  
  
  					def       _UpperCAmelCase						(							self	):
  						UpperCAmelCase__       ,	UpperCAmelCase__:  Any		       =   self.prepare_config_and_inputs()
  						return config, inputs_dict
  
  @require_torch
  class __UpperCamelCase    (							_a				,_a				,unittest.TestCase		):
  
  
  
  
  
  
  					'''simple docstring'''
  
  
  
  					__magic_name__     			=      (CpmAntModel, CpmAntForCausalLM) if is_torch_available() else ()
  					__magic_name__     			=      (
  					    {"feature-extraction": CpmAntModel, "text-generation": CpmAntForCausalLM} if is_torch_available() else {}
  					)
  
  					__magic_name__     			=      False
  					__magic_name__     			=      False
  					__magic_name__     			=      False
  					__magic_name__     			=      False
  					__magic_name__     			=      False
  
  
  
  
  
  					def       _UpperCAmelCase						(							self	):
  						UpperCAmelCase__:  Tuple		       =   CpmAntModelTester(self	)
  						UpperCAmelCase__:  Dict		       =   ConfigTester(self							,			config_class=lowerCamelCase__	)
  
  
  
  
  
  					def       _UpperCAmelCase						(							self	):
  						self.config_tester.create_and_test_config_common_properties()
  						self.config_tester.create_and_test_config_to_json_string()
  						self.config_tester.create_and_test_config_to_json_file()
  						self.config_tester.create_and_test_config_from_and_save_pretrained()
  						self.config_tester.check_config_can_be_init_without_params()
  						self.config_tester.check_config_arguments_init()
  
  
  
  
  
  					def       _UpperCAmelCase						(							self	):
  						unittest.skip("CPMAnt doesn't support input_embeds."	)(self.test_inputs_embeds	)
  
  
  
  
  
  					def       _UpperCAmelCase						(							self	):
  						unittest.skip(
  						    "CPMAnt doesn't support retain grad in hidden_states or attentions, because prompt management will peel off the output.hidden_states from graph.\
                   So is attentions. We strongly recommand you use loss to tune model."	)(self.test_retain_grad_hidden_states_attentions	)
  
  
  
  
  
  					def       _UpperCAmelCase						(							self	):
  						UpperCAmelCase__       ,	UpperCAmelCase__:  Optional[int]		       =   self.model_tester.prepare_config_and_inputs()
  						self.model_tester.create_and_check_cpmant_model(lowerCamelCase__							,			lowerCamelCase__	)
  
  
  
  
  
  					def       _UpperCAmelCase						(							self	):
  						UpperCAmelCase__       ,	UpperCAmelCase__:  List[Any]		       =   self.model_tester.prepare_config_and_inputs()
  						self.model_tester.create_and_check_lm_head_model(lowerCamelCase__							,			lowerCamelCase__	)
  
  @require_torch
  class __UpperCamelCase    (							unittest.TestCase		):
  
  
  
  
  
  
  					'''simple docstring'''
  
  
  
  					@tooslow
  					def       _UpperCAmelCase						(							self	):
  						UpperCAmelCase__:  Optional[Any]		       =   "今天天气真好！"
  						UpperCAmelCase__:  Optional[int]		       =   "openbmb/cpm-ant-10b"
  						UpperCAmelCase__:  List[Any]		       =   CpmAntModel.from_pretrained(lowerCamelCase__	)
  						UpperCAmelCase__:  Any		       =   CpmAntTokenizer.from_pretrained(lowerCamelCase__	)
  						UpperCAmelCase__:  Union[str, Any]		       =   tokenizer(lowerCamelCase__							,			return_tensors="pt"	)
  						UpperCAmelCase__:  List[Any]		       =   model(**lowerCamelCase__	).last_hidden_state
  
  						UpperCAmelCase__:  Optional[int]		       =   torch.tensor(
  						    [[[6.1_708, 5.9_244, 1.0_835], [6.5_207, 6.2_893, -11.3_324], [-1.0_107, -0.0_576, -5.9_577]]]							,			)
  						self.assertTrue(torch.allclose(hidden_states[:, :3, :3]							,			lowerCamelCase__							,			atol=1e-2	)	)
  
  @require_torch
  class __UpperCamelCase    (							unittest.TestCase		):
  
  
  
  
  
  
  					'''simple docstring'''
  
  
  
  					@tooslow
  					def       _UpperCAmelCase						(							self	):
  						UpperCAmelCase__:  List[str]		       =   "今天天气真好！"
  						UpperCAmelCase__:  Any		       =   "openbmb/cpm-ant-10b"
  						UpperCAmelCase__:  List[str]		       =   CpmAntForCausalLM.from_pretrained(lowerCamelCase__	)
  						UpperCAmelCase__:  Tuple		       =   CpmAntTokenizer.from_pretrained(lowerCamelCase__	)
  						UpperCAmelCase__:  int		       =   tokenizer(lowerCamelCase__							,			return_tensors="pt"	)
  						UpperCAmelCase__:  str		       =   model(**lowerCamelCase__	).logits
  
  						UpperCAmelCase__:  int		       =   torch.tensor(
  						    [[[-6.4_267, -6.4_083, -6.3_958], [-5.8_802, -5.9_447, -5.7_811], [-5.3_896, -5.4_820, -5.4_295]]]							,			)
  						self.assertTrue(torch.allclose(hidden_states[:, :3, :3]							,			lowerCamelCase__							,			atol=1e-2	)	)
  
  
  
  
  
  					@tooslow
  					def       _UpperCAmelCase						(							self	):
  						UpperCAmelCase__:  Any		       =   "openbmb/cpm-ant-10b"
  						UpperCAmelCase__:  Tuple		       =   CpmAntForCausalLM.from_pretrained(lowerCamelCase__	)
  						UpperCAmelCase__:  str		       =   CpmAntTokenizer.from_pretrained(lowerCamelCase__	)
  						UpperCAmelCase__:  int		       =   "今天天气不错，"
  						UpperCAmelCase__:  Tuple		       =   "今天天气不错，阳光明媚，我和妈妈一起去超市买东西。\n在超市里，我看到了一个很好玩的玩具，它的名字叫“机器人”。它有一个圆圆的脑袋，两只圆圆的眼睛，还有一个圆圆的"
  						UpperCAmelCase__:  Dict		       =   tokenizer(lowerCamelCase__							,			return_tensors="pt"	)
  						UpperCAmelCase__:  Tuple		       =   model.generate(**lowerCamelCase__	)
  						UpperCAmelCase__:  Union[str, Any]		       =   tokenizer.batch_decode(lowerCamelCase__	)
  						self.assertEqual(lowerCamelCase__							,			lowerCamelCase__	)
  
  
  
  
  
  					@tooslow
  					def       _UpperCAmelCase						(							self	):
  						UpperCAmelCase__:  Any		       =   "openbmb/cpm-ant-10b"
  						UpperCAmelCase__:  Union[str, Any]		       =   CpmAntForCausalLM.from_pretrained(lowerCamelCase__	)
  						UpperCAmelCase__:  List[str]		       =   CpmAntTokenizer.from_pretrained(lowerCamelCase__	)
  						UpperCAmelCase__:  Optional[int]		       =   ["今天天气不错，", "新年快乐，万事如意！"]
  						UpperCAmelCase__:  List[str]		       =   [
  						    "今天天气不错，阳光明媚，我和妈妈一起去超市买东西。\n在超市里，我看到了一个很好玩的玩具，它的名字叫“机器人”。它有一个圆圆的脑袋，两只圆圆的眼睛，还有一个圆圆的",
  						    "新年快乐，万事如意！在这辞旧迎新的美好时刻，我谨代表《农村新技术》杂志社全体同仁，向一直以来关心、支持《农村新技术》杂志发展的各级领导、各界朋友和广大读者致以最诚挚的",
  						]
  						UpperCAmelCase__:  List[Any]		       =   tokenizer(lowerCamelCase__							,			return_tensors="pt"							,			padding=lowerCamelCase__	)
  						UpperCAmelCase__:  List[Any]		       =   model.generate(**lowerCamelCase__	)
  						UpperCAmelCase__:  int		       =   tokenizer.batch_decode(lowerCamelCase__	)
  						self.assertEqual(lowerCamelCase__							,			lowerCamelCase__	)
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()