
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')

A  :			Optional[Any]     				=				'K-MEANS-CLUST/ '


def 					SCREAMING_SNAKE_CASE    (    a__		,					a__		,					a__=None						) ->						Dict:
				if seed is not None:  # useful for obtaining consistent results
								np.random.seed(lowercase__						)
				__a    							=					data.shape[0]  # number of data points

				# Pick K indices from range [0, N).
				__a    							=					np.random.randint(0		,					lowercase__		,					lowercase__						)

				# 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.
				__a    							=					data[rand_indices, :]

				return centroids


def 					SCREAMING_SNAKE_CASE    (    a__		,					a__						) ->						List[Any]:
				return pairwise_distances(lowercase__		,					lowercase__		,					metric='''euclidean'''						)


def 					SCREAMING_SNAKE_CASE    (    a__		,					a__						) ->						Any:
				# Compute distances between each data point and the set of centroids:
				# Fill in the blank (RHS only)
				__a    							=					centroid_pairwise_dist(lowercase__		,					lowercase__						)

				# Compute cluster assignments for each data point:
				# Fill in the blank (RHS only)
				__a    							=					np.argmin(lowercase__		,					axis=1						)

				return cluster_assignment


def 					SCREAMING_SNAKE_CASE    (    a__		,					a__		,					a__						) ->						int:
				__a    							=					[]
				for i in range(lowercase__						):
								# Select all data points that belong to cluster i. Fill in the blank (RHS only)
								__a    							=					data[cluster_assignment == i]
								# Compute the mean of the data points. Fill in the blank (RHS only)
								__a    							=					member_data_points.mean(axis=0						)
								new_centroids.append(lowercase__						)
				__a    							=					np.array(lowercase__						)

				return new_centroids


def 					SCREAMING_SNAKE_CASE    (    a__		,					a__		,					a__		,					a__						) ->						str:
				__a    							=					0.0
				for i in range(lowercase__						):
								# Select all data points that belong to cluster i. Fill in the blank (RHS only)
								__a    							=					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)
												__a    							=					pairwise_distances(
												    lowercase__		,					[centroids[i]]		,					metric='''euclidean'''						)
												__a    							=					distances**2
												heterogeneity += np.sum(lowercase__						)

				return heterogeneity


def 					SCREAMING_SNAKE_CASE    (    a__		,					a__						) ->						Tuple:
				plt.figure(figsize=(7, 4)						)
				plt.plot(lowercase__		,					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 					SCREAMING_SNAKE_CASE    (    a__		,					a__		,					a__		,					a__=500		,					a__=None		,					a__=False						) ->						Union[str, Any]:
				__a    							=					initial_centroids[:]
				__a    							=					None

				for itr in range(lowercase__						):
								if verbose:
												print(lowercase__		,					end=''''''						)

								# 1. Make cluster assignments using nearest centroids
								__a    							=					assign_clusters(lowercase__		,					lowercase__						)

								# 2. Compute a new centroid for each of the k clusters, averaging all data
								#    points assigned to that cluster.
								__a    							=					revise_centroids(lowercase__		,					lowercase__		,					lowercase__						)

								# 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:
												__a    							=					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
												__a    							=					compute_heterogeneity(lowercase__		,					lowercase__		,					lowercase__		,					lowercase__						)
												record_heterogeneity.append(lowercase__						)

								__a    							=					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

					A  :			str     				=				ds.load_iris()
					A  :			Optional[Any]     				=				3
					A  :			Tuple     				=				[]
					A  :			Any     				=				get_initial_centroids(dataset['data'], k, seed=0)
					A  :			Any     				=				kmeans(
					    dataset['data'],
					    k,
					    initial_centroids,
					    maxiter=4_0_0,
					    record_heterogeneity=heterogeneity,
					    verbose=True,
					)
					plot_heterogeneity(heterogeneity, k)


def 					SCREAMING_SNAKE_CASE    (    a__		,					a__		,					a__=None						) ->						int:
				# Fill missing values with given rules
				if fill_missing_report:
								__a    							=					df.fillna(value=lowercase__						)
				__a    							=					1
				__a    							=					df.select_dtypes(np.number						).columns
				__a    							=					(
				    df.groupby(['''Cluster''']						)[  # construct report dataframe
				        numeric_cols
				    ]  # group by cluster number
				    .agg(
				        [
				            ('''sum''', np.sum),
				            ('''mean_with_zeros''', lambda a__						: np.mean(np.nan_to_num(lowercase__						)						)),
				            ('''mean_without_zeros''', lambda a__						: x.replace(0		,					np.NaN						).mean()),
				            (
				                '''mean_25-75''',
				                lambda a__						: np.mean(
				                    np.nan_to_num(
				                        sorted(lowercase__						)[
				                            round(len(lowercase__						) * 25 / 100						) : round(len(lowercase__						) * 75 / 100						)
				                        ]						)						),
				            ),
				            ('''mean_with_na''', np.mean),
				            ('''min''', lambda a__						: x.min()),
				            ('''5%''', lambda a__						: x.quantile(0.05						)),
				            ('''25%''', lambda a__						: x.quantile(0.25						)),
				            ('''50%''', lambda a__						: x.quantile(0.50						)),
				            ('''75%''', lambda a__						: x.quantile(0.75						)),
				            ('''95%''', lambda a__						: x.quantile(0.95						)),
				            ('''max''', lambda a__						: x.max()),
				            ('''count''', lambda a__						: x.count()),
				            ('''stdev''', lambda a__						: x.std()),
				            ('''mode''', lambda a__						: x.mode()[0]),
				            ('''median''', lambda a__						: x.median()),
				            ('''# > 0''', lambda a__						: (x > 0).sum()),
				        ]						)
				    .T.reset_index()
				    .rename(index=lowercase__		,					columns={'''level_0''': '''Features''', '''level_1''': '''Type'''}						)
				)  # rename columns
				# calculate the size of cluster(count of clientID's)
				__a    							=					report[
				    (report['''Features'''] == '''dummy''') & (report['''Type'''] == '''count''')
				].copy()  # avoid SettingWithCopyWarning
				__a    							=					(
				    '''ClusterSize'''  # rename created cluster df to match report column names
				)
				__a    							=					'''# of Customers'''
				__a    							=					pd.DataFrame(
				    clustersize.iloc[:, 2:].values
				    / clustersize.iloc[:, 2:].values.sum()						)
				__a    							=					'''% of Customers'''  # rename created cluster df to match report column names
				__a    							=					'''ClusterProportion'''
				__a    							=					clusterproportion.columns.tolist()
				__a    							=					cols[-2:] + cols[:-2]
				__a    							=					clusterproportion[cols]  # rearrange columns to match report
				__a    							=					report.columns
				__a    							=					pd.DataFrame(
				    abs(
				        report[report['''Type'''] == '''count'''].iloc[:, 2:].values
				        - clustersize.iloc[:, 2:].values						)						)  # generating df with count of nan values
				__a    							=					0
				__a    							=					'''# of nan'''
				__a    							=					report[
				    report['''Type'''] == '''count'''
				].Features.tolist()  # filling values in order to match report
				__a    							=					a.columns.tolist()
				__a    							=					cols[-2:] + cols[:-2]
				__a    							=					a[cols]  # rearrange columns to match report
				__a    							=					report.columns  # rename columns to match report
				__a    							=					report.drop(
				    report[report.Type == '''count'''].index						)  # drop count values except cluster size
				__a    							=					pd.concat(
				    [report, a, clustersize, clusterproportion]		,					axis=0						)  # concat report with clustert size and nan values
				__a    							=					report['''Features'''].isin(lowercase__						)
				__a    							=					report.columns.tolist()
				__a    							=					cols[0:2] + cols[-1:] + cols[2:-1]
				__a    							=					report[cols]
				__a    							=					{
				    '''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,
				}
				__a    							=					(
				    report.assign(
				        Sortera=lambda a__						: x.Type.map(lowercase__						)		,					Sortera=lambda a__						: list(reversed(range(len(lowercase__						)						)						)						)		,					)
				    .sort_values(['''Sorter1''', '''Mark''', '''Sorter2''']		,					ascending=lowercase__						)
				    .drop(['''Sorter1''', '''Sorter2''']		,					axis=1						)
				)
				__a    							=					''''''
				__a    							=					report.reset_index()
				__a    							=					report.drop(columns=['''index''']						)
				return report


if __name__ == "__main__":
					import doctest

					doctest.testmod()

import argparse

import torch

from transformers import MraConfig, MraForMaskedLM


def 					__lowerCAmelCase    (    a__						) ->						Any:
				if "model" in orig_key:
								__a    							=					orig_key.replace('''model.'''		,					''''''						)
				if "norm1" in orig_key:
								__a    							=					orig_key.replace('''norm1'''		,					'''attention.output.LayerNorm'''						)
				if "norm2" in orig_key:
								__a    							=					orig_key.replace('''norm2'''		,					'''output.LayerNorm'''						)
				if "norm" in orig_key:
								__a    							=					orig_key.replace('''norm'''		,					'''LayerNorm'''						)
				if "transformer" in orig_key:
								__a    							=					orig_key.split('''.'''						)[0].split('''_'''						)[-1]
								__a    							=					orig_key.replace(F"""transformer_{layer_num}"""		,					F"""encoder.layer.{layer_num}"""						)
				if "mha.attn" in orig_key:
								__a    							=					orig_key.replace('''mha.attn'''		,					'''attention.self'''						)
				if "mha" in orig_key:
								__a    							=					orig_key.replace('''mha'''		,					'''attention'''						)
				if "W_q" in orig_key:
								__a    							=					orig_key.replace('''W_q'''		,					'''self.query'''						)
				if "W_k" in orig_key:
								__a    							=					orig_key.replace('''W_k'''		,					'''self.key'''						)
				if "W_v" in orig_key:
								__a    							=					orig_key.replace('''W_v'''		,					'''self.value'''						)
				if "ff.0" in orig_key:
								__a    							=					orig_key.replace('''ff.0'''		,					'''intermediate.dense'''						)
				if "ff.2" in orig_key:
								__a    							=					orig_key.replace('''ff.2'''		,					'''output.dense'''						)
				if "ff" in orig_key:
								__a    							=					orig_key.replace('''ff'''		,					'''output.dense'''						)
				if "mlm_class" in orig_key:
								__a    							=					orig_key.replace('''mlm.mlm_class'''		,					'''cls.predictions.decoder'''						)
				if "mlm" in orig_key:
								__a    							=					orig_key.replace('''mlm'''		,					'''cls.predictions.transform'''						)
				if "backbone.backbone.encoders" in orig_key:
								__a    							=					orig_key.replace('''backbone.backbone.encoders'''		,					'''encoder.layer'''						)
				if "cls" not in orig_key:
								__a    							=					'''mra.''' + orig_key

				return orig_key


def 					__lowerCAmelCase    (    a__		,					a__						) ->						Any:
				for key in orig_state_dict.copy().keys():
								__a    							=					orig_state_dict.pop(a__						)

								if ("pooler" in key) or ("sen_class" in key):
												continue
								else:
												__a    							=					val

				__a    							=					orig_state_dict['''cls.predictions.decoder.bias''']
				__a    							=					torch.arange(a__						).expand((1, -1)						) + 2

				return orig_state_dict


def 					__lowerCAmelCase    (    a__		,					a__		,					a__						) ->						List[Any]:
				__a    							=					torch.load(a__		,					map_location='''cpu'''						)['''model_state_dict''']
				__a    							=					MraConfig.from_json_file(a__						)
				__a    							=					MraForMaskedLM(a__						)

				__a    							=					convert_checkpoint_helper(config.max_position_embeddings		,					a__						)

				print(model.load_state_dict(a__						)						)
				model.eval()
				model.save_pretrained(a__						)

				print(F"""Checkpoint successfuly converted. Model saved at {pytorch_dump_path}"""						)


if __name__ == "__main__":
					A  :			Any     				=				argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    '--pytorch_model_path', default=None, type=str, required=True, help='Path to Mra pytorch checkpoint.'
					)
					parser.add_argument(
					    '--config_file',
					    default=None,
					    type=str,
					    required=True,
					    help='The json file for Mra model config.',
					)
					parser.add_argument(
					    '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
					)
					A  :			Tuple     				=				parser.parse_args()
					convert_mra_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)