

import argparse
import ast
import logging
import os
import sys

import pandas as pd
import torch
from tqdm import tqdm

from transformers import BartForConditionalGeneration, RagRetriever, RagSequenceForGeneration, RagTokenForGeneration
from transformers import logging as transformers_logging


sys.path.append(os.path.join(os.getcwd()))  # noqa: E402 # isort:skip
from utils_rag import exact_match_score, fa_score  # noqa: E402 # isort:skip


_lowerCAmelCase     :			Tuple    =logging.getLogger(__name__)
logging.basicConfig(level=logging.INFO)

transformers_logging.set_verbosity_info()



def  _A  (			SCREAMING_SNAKE_CASE			):
	if "token" in model_name_or_path:
		return "rag_token"
	if "sequence" in model_name_or_path:
		return "rag_sequence"
	if "bart" in model_name_or_path:
		return "bart"
	return None



def  _A  (			SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE			):
	return max(metric_fn(__snake_case  ,__snake_case			) for gt in ground_truths			)



def  _A  (			SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE			):
	UpperCAmelCase__:  Tuple		       =   [line.strip() for line in open(__snake_case  ,"r"			).readlines()]
	UpperCAmelCase__:  Any		       =   []

	if args.gold_data_mode == "qa":
		UpperCAmelCase__:  int		       =   pd.read_csv(__snake_case  ,sep="\t"  ,header=__snake_case			)
		for answer_list in data[1]:
			UpperCAmelCase__:  List[str]		       =   ast.literal_eval(__snake_case			)
			answers.append(__snake_case			)
	else:
		UpperCAmelCase__:  int		       =   [line.strip() for line in open(__snake_case  ,"r"			).readlines()]
		UpperCAmelCase__:  Any		       =   [[reference] for reference in references]

	UpperCAmelCase__:  Optional[int]		       =   0
	for prediction, ground_truths in zip(__snake_case  ,__snake_case			):
		total += 1
		em += metric_max_over_ground_truths(__snake_case  ,__snake_case  ,__snake_case			)
		fa += metric_max_over_ground_truths(__snake_case  ,__snake_case  ,__snake_case			)

	UpperCAmelCase__:  int		       =   1_0_0.0 * em / total
	UpperCAmelCase__:  Optional[Any]		       =   1_0_0.0 * fa / total

	logger.info(f"F1: {fa:.2f}"			)
	logger.info(f"EM: {em:.2f}"			)



def  _A  (			SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE			):
	UpperCAmelCase__:  Dict		       =   args.k
	UpperCAmelCase__:  str		       =   [line.strip() for line in open(__snake_case  ,"r"			).readlines()]
	UpperCAmelCase__:  Dict		       =   [line.strip() for line in open(__snake_case  ,"r"			).readlines()]

	UpperCAmelCase__:  List[str]		       =   0
	for hypo, reference in zip(__snake_case  ,__snake_case			):
		UpperCAmelCase__:  Union[str, Any]		       =   set(hypo.split("\t"			)[:k]			)
		UpperCAmelCase__:  Optional[int]		       =   set(reference.split("\t"			)			)
		total += 1
		em += len(hypo_provenance & ref_provenance			) / k

	UpperCAmelCase__:  Optional[int]		       =   1_0_0.0 * em / total
	logger.info(f"Precision@{k}: {em: .2f}"			)



def  _A  (			SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE			):
	def strip_title(SCREAMING_SNAKE_CASE			):
		if title.startswith("\""			):
			UpperCAmelCase__:  List[str]		       =   title[1:]
		if title.endswith("\""			):
			UpperCAmelCase__:  Any		       =   title[:-1]
		return title

	UpperCAmelCase__:  Optional[Any]		       =   rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
	    __snake_case  ,return_tensors="pt"  ,padding=__snake_case  ,truncation=__snake_case  ,)["input_ids"].to(args.device			)

	UpperCAmelCase__:  Dict		       =   rag_model.rag.question_encoder(__snake_case			)
	UpperCAmelCase__:  Optional[Any]		       =   question_enc_outputs[0]

	UpperCAmelCase__:  str		       =   rag_model.retriever(
	    __snake_case  ,question_enc_pool_output.cpu().detach().to(torch.floataa			).numpy()  ,prefix=rag_model.rag.generator.config.prefix  ,n_docs=rag_model.config.n_docs  ,return_tensors="pt"  ,)
	UpperCAmelCase__:  Any		       =   rag_model.retriever.index.get_doc_dicts(result.doc_ids			)
	UpperCAmelCase__:  Optional[Any]		       =   []
	for docs in all_docs:
		UpperCAmelCase__:  List[Any]		       =   [strip_title(__snake_case			) for title in docs["title"]]
		provenance_strings.append("\t".join(__snake_case			)			)
	return provenance_strings



def  _A  (			SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE			):
	with torch.no_grad():
		UpperCAmelCase__:  Any		       =   rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
		    __snake_case  ,return_tensors="pt"  ,padding=__snake_case  ,truncation=__snake_case			)

		UpperCAmelCase__:  List[Any]		       =   inputs_dict.input_ids.to(args.device			)
		UpperCAmelCase__:  Any		       =   inputs_dict.attention_mask.to(args.device			)
		UpperCAmelCase__:  List[str]		       =   rag_model.generate(  # rag_model overwrites generate
		    __snake_case  ,attention_mask=__snake_case  ,num_beams=args.num_beams  ,min_length=args.min_length  ,max_length=args.max_length  ,early_stopping=__snake_case  ,num_return_sequences=1  ,bad_words_ids=[[0, 0]]  ,)
		UpperCAmelCase__:  Tuple		       =   rag_model.retriever.generator_tokenizer.batch_decode(__snake_case  ,skip_special_tokens=__snake_case			)

		if args.print_predictions:
			for q, a in zip(__snake_case  ,__snake_case			):
				logger.info("Q: {} - A: {}".format(__snake_case  ,__snake_case			)			)

		return answers



def  _A  (			):
	UpperCAmelCase__:  Any		       =   argparse.ArgumentParser()
	parser.add_argument(
	    "--model_type"  ,choices=["rag_sequence", "rag_token", "bart"]  ,type=__snake_case  ,help=(
	        "RAG model type: rag_sequence, rag_token or bart, if none specified, the type is inferred from the"
	        " model_name_or_path"
	    )  ,)
	parser.add_argument(
	    "--index_name"  ,default=__snake_case  ,choices=["exact", "compressed", "legacy"]  ,type=__snake_case  ,help="RAG model retriever type"  ,)
	parser.add_argument(
	    "--index_path"  ,default=__snake_case  ,type=__snake_case  ,help="Path to the retrieval index"  ,)
	parser.add_argument("--n_docs"  ,default=5  ,type=__snake_case  ,help="Number of retrieved docs"			)
	parser.add_argument(
	    "--model_name_or_path"  ,default=__snake_case  ,type=__snake_case  ,required=__snake_case  ,help="Path to pretrained checkpoints or model identifier from huggingface.co/models"  ,)
	parser.add_argument(
	    "--eval_mode"  ,choices=["e2e", "retrieval"]  ,default="e2e"  ,type=__snake_case  ,help=(
	        "Evaluation mode, e2e calculates exact match and F1 of the downstream task, retrieval calculates"
	        " precision@k."
	    )  ,)
	parser.add_argument("--k"  ,default=1  ,type=__snake_case  ,help="k for the precision@k calculation"			)
	parser.add_argument(
	    "--evaluation_set"  ,default=__snake_case  ,type=__snake_case  ,required=__snake_case  ,help="Path to a file containing evaluation samples"  ,)
	parser.add_argument(
	    "--gold_data_path"  ,default=__snake_case  ,type=__snake_case  ,required=__snake_case  ,help="Path to a tab-separated file with gold samples"  ,)
	parser.add_argument(
	    "--gold_data_mode"  ,default="qa"  ,type=__snake_case  ,choices=["qa", "ans"]  ,help=(
	        "Format of the gold data file"
	        "qa - a single line in the following format: question [tab] answer_list"
	        "ans - a single line of the gold file contains the expected answer string"
	    )  ,)
	parser.add_argument(
	    "--predictions_path"  ,type=__snake_case  ,default="predictions.txt"  ,help="Name of the predictions file, to be stored in the checkpoints directory"  ,)
	parser.add_argument(
	    "--eval_all_checkpoints"  ,action="store_true"  ,help="Evaluate all checkpoints starting with the same prefix as model_name ending and ending with step number"  ,)
	parser.add_argument(
	    "--eval_batch_size"  ,default=8  ,type=__snake_case  ,help="Batch size per GPU/CPU for evaluation."  ,)
	parser.add_argument(
	    "--recalculate"  ,help="Recalculate predictions even if the prediction file exists"  ,action="store_true"  ,)
	parser.add_argument(
	    "--num_beams"  ,default=4  ,type=__snake_case  ,help="Number of beams to be used when generating answers"  ,)
	parser.add_argument("--min_length"  ,default=1  ,type=__snake_case  ,help="Min length of the generated answers"			)
	parser.add_argument("--max_length"  ,default=5_0  ,type=__snake_case  ,help="Max length of the generated answers"			)

	parser.add_argument(
	    "--print_predictions"  ,action="store_true"  ,help="If True, prints predictions while evaluating."  ,)
	parser.add_argument(
	    "--print_docs"  ,action="store_true"  ,help="If True, prints docs retried while generating."  ,)
	UpperCAmelCase__:  Dict		       =   parser.parse_args()
	UpperCAmelCase__:  List[str]		       =   torch.device("cuda" if torch.cuda.is_available() else "cpu"			)
	return args



def  _A  (			SCREAMING_SNAKE_CASE			):
	UpperCAmelCase__:  List[Any]		       =   {}
	if args.model_type is None:
		UpperCAmelCase__:  List[str]		       =   infer_model_type(args.model_name_or_path			)
		assert args.model_type is not None
	if args.model_type.startswith("rag"			):
		UpperCAmelCase__:  List[Any]		       =   RagTokenForGeneration if args.model_type == "rag_token" else RagSequenceForGeneration
		UpperCAmelCase__:  List[Any]		       =   args.n_docs
		if args.index_name is not None:
			UpperCAmelCase__:  Any		       =   args.index_name
		if args.index_path is not None:
			UpperCAmelCase__:  List[Any]		       =   args.index_path
	else:
		UpperCAmelCase__:  Dict		       =   BartForConditionalGeneration

	UpperCAmelCase__:  int		       =   (
	    [f.path for f in os.scandir(args.model_name_or_path			) if f.is_dir()]
	    if args.eval_all_checkpoints
	    else [args.model_name_or_path]
	)

	logger.info("Evaluate the following checkpoints: %s"  ,__snake_case			)

	UpperCAmelCase__:  Optional[Any]		       =   get_scores if args.eval_mode == "e2e" else get_precision_at_k
	UpperCAmelCase__:  int		       =   evaluate_batch_eae if args.eval_mode == "e2e" else evaluate_batch_retrieval

	for checkpoint in checkpoints:
		if os.path.exists(args.predictions_path			) and (not args.recalculate):
			logger.info("Calculating metrics based on an existing predictions file: {}".format(args.predictions_path			)			)
			score_fn(__snake_case  ,args.predictions_path  ,args.gold_data_path			)
			continue

		logger.info("***** Running evaluation for {} *****".format(__snake_case			)			)
		logger.info("  Batch size = %d"  ,args.eval_batch_size			)
		logger.info("  Predictions will be stored under {}".format(args.predictions_path			)			)

		if args.model_type.startswith("rag"			):
			UpperCAmelCase__:  Any		       =   RagRetriever.from_pretrained(__snake_case  ,**__snake_case			)
			UpperCAmelCase__:  List[str]		       =   model_class.from_pretrained(__snake_case  ,retriever=__snake_case  ,**__snake_case			)
			model.retriever.init_retrieval()
		else:
			UpperCAmelCase__:  int		       =   model_class.from_pretrained(__snake_case  ,**__snake_case			)
		model.to(args.device			)

		with open(args.evaluation_set  ,"r"			) as eval_file, open(args.predictions_path  ,"w"			) as preds_file:
			UpperCAmelCase__:  Dict		       =   []
			for line in tqdm(__snake_case			):
				questions.append(line.strip()			)
				if len(__snake_case			) == args.eval_batch_size:
					UpperCAmelCase__:  Tuple		       =   evaluate_batch_fn(__snake_case  ,__snake_case  ,__snake_case			)
					preds_file.write("\n".join(__snake_case			) + "\n"			)
					preds_file.flush()
					UpperCAmelCase__:  Optional[int]		       =   []
			if len(__snake_case			) > 0:
				UpperCAmelCase__:  Any		       =   evaluate_batch_fn(__snake_case  ,__snake_case  ,__snake_case			)
				preds_file.write("\n".join(__snake_case			)			)
				preds_file.flush()

			score_fn(__snake_case  ,args.predictions_path  ,args.gold_data_path			)


if __name__ == "__main__":
						_lowerCAmelCase     :			List[str]    =get_args()
						main(args)


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()