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__  	=      "K-MEANS-CLUST/ "

def 	__lowerCamelCase							(				__a    : Any      ,							__a    : int      ,							__a    : int=None		)      ->       Union[str, Any]:
	if seed is not None:  # useful for obtaining consistent results
		np.random.seed(__a		)
	_lowercase   =data.shape[0]  # number of data points

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

	# 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.
	_lowercase   =data[rand_indices, :]

	return centroids

def 	__lowerCamelCase							(				__a    : List[Any]      ,							__a    : Optional[int]		)      ->       int:
	return pairwise_distances(__a      ,							__a      ,							metric="euclidean"		)

def 	__lowerCamelCase							(				__a    : List[str]      ,							__a    : str		)      ->       Any:
	# Compute distances between each data point and the set of centroids:
	# Fill in the blank (RHS only)
	_lowercase   =centroid_pairwise_dist(__a      ,							__a		)

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

	return cluster_assignment

def 	__lowerCamelCase							(				__a    : Union[str, Any]      ,							__a    : List[Any]      ,							__a    : Optional[int]		)      ->       Tuple:
	_lowercase   =[]
	for i in range(__a		):
		# Select all data points that belong to cluster i. Fill in the blank (RHS only)
		_lowercase   =data[cluster_assignment == i]
		# Compute the mean of the data points. Fill in the blank (RHS only)
		_lowercase   =member_data_points.mean(axis=0		)
		new_centroids.append(__a		)
	_lowercase   =np.array(__a		)

	return new_centroids

def 	__lowerCamelCase							(				__a    : Optional[int]      ,							__a    : Optional[Any]      ,							__a    : Optional[int]      ,							__a    : Union[str, Any]		)      ->       List[str]:
	_lowercase   =0.0
	for i in range(__a		):
		# Select all data points that belong to cluster i. Fill in the blank (RHS only)
		_lowercase   =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)
			_lowercase   =pairwise_distances(
			    __a      ,							[centroids[i]]      ,							metric="euclidean"		)
			_lowercase   =distances**2
			heterogeneity += np.sum(__a		)

	return heterogeneity

def 	__lowerCamelCase							(				__a    : List[Any]      ,							__a    : List[Any]		)      ->       str:
	plt.figure(figsize=(7, 4)		)
	plt.plot(__a      ,							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							(				__a    : Dict      ,							__a    : List[str]      ,							__a    : List[Any]      ,							__a    : Union[str, Any]=500      ,							__a    : List[str]=None      ,							__a    : List[str]=False		)      ->       Optional[Any]:
	_lowercase   =initial_centroids[:]
	_lowercase   =None

	for itr in range(__a		):
		if verbose:
			print(__a      ,							end=""		)

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

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

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

		_lowercase   =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__  	=      ds.load_iris()
						lowerCAmelCase__  	=      3
						lowerCAmelCase__  	=      []
						lowerCAmelCase__  	=      get_initial_centroids(dataset["data"], k, seed=0)
						lowerCAmelCase__	,					lowerCAmelCase__  	=      kmeans(
						    dataset["data"],
						    k,
						    initial_centroids,
						    maxiter=4_0_0,
						    record_heterogeneity=heterogeneity,
						    verbose=True,
						)
						plot_heterogeneity(heterogeneity, k)

def 	__lowerCamelCase							(				__a    : pd.DataFrame      ,							__a    : np.ndarray      ,							__a    : Union[str, Any]=None		)      ->       pd.DataFrame:
	# Fill missing values with given rules
	if fill_missing_report:
		_lowercase   =df.fillna(value=__a		)
	_lowercase   =1
	_lowercase   =df.select_dtypes(np.number		).columns
	_lowercase   =(
	    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(__a		)		)),
	            ("mean_without_zeros", lambda __a		: x.replace(0      ,							np.NaN		).mean()),
	            (
	                "mean_25-75",
	                lambda __a		: np.mean(
	                    np.nan_to_num(
	                        sorted(__a		)[
	                            round(len(__a		) * 25 / 100		) : round(len(__a		) * 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=__a      ,							columns={"level_0": "Features", "level_1": "Type"}		)
	)  # rename columns
	# calculate the size of cluster(count of clientID's)
	_lowercase   =report[
	    (report["Features"] == "dummy") & (report["Type"] == "count")
	].copy()  # avoid SettingWithCopyWarning
	_lowercase   =(
	    "ClusterSize"  # rename created cluster df to match report column names
	)
	_lowercase   ="# of Customers"
	_lowercase   =pd.DataFrame(
	    clustersize.iloc[:, 2:].values
	    / clustersize.iloc[:, 2:].values.sum()		)
	_lowercase   ="% of Customers"  # rename created cluster df to match report column names
	_lowercase   ="ClusterProportion"
	_lowercase   =clusterproportion.columns.tolist()
	_lowercase   =cols[-2:] + cols[:-2]
	_lowercase   =clusterproportion[cols]  # rearrange columns to match report
	_lowercase   =report.columns
	_lowercase   =pd.DataFrame(
	    abs(
	        report[report["Type"] == "count"].iloc[:, 2:].values
	        - clustersize.iloc[:, 2:].values		)		)  # generating df with count of nan values
	_lowercase   =0
	_lowercase   ="# of nan"
	_lowercase   =report[
	    report["Type"] == "count"
	].Features.tolist()  # filling values in order to match report
	_lowercase   =a.columns.tolist()
	_lowercase   =cols[-2:] + cols[:-2]
	_lowercase   =a[cols]  # rearrange columns to match report
	_lowercase   =report.columns  # rename columns to match report
	_lowercase   =report.drop(
	    report[report.Type == "count"].index		)  # drop count values except cluster size
	_lowercase   =pd.concat(
	    [report, a, clustersize, clusterproportion]      ,							axis=0		)  # concat report with clustert size and nan values
	_lowercase   =report["Features"].isin(__a		)
	_lowercase   =report.columns.tolist()
	_lowercase   =cols[0:2] + cols[-1:] + cols[2:-1]
	_lowercase   =report[cols]
	_lowercase   ={
	    "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,
	}
	_lowercase   =(
	    report.assign(
	        Sortera=lambda __a		: x.Type.map(__a		)      ,							Sortera=lambda __a		: list(reversed(range(len(__a		)		)		)		)      ,							)
	    .sort_values(["Sorter1", "Mark", "Sorter2"]      ,							ascending=__a		)
	    .drop(["Sorter1", "Sorter2"]      ,							axis=1		)
	)
	_lowercase   =""
	_lowercase   =report.reset_index()
	_lowercase   =report.drop(columns=["index"]		)
	return report


if __name__ == "__main__":
						import doctest

						doctest.testmod()





from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging


lowerCAmelCase__  	=      logging.get_logger(__name__)

lowerCAmelCase__  	=      {
    "google/long-t5-local-base": "https://huggingface.co/google/long-t5-local-base/blob/main/config.json",
    "google/long-t5-local-large": "https://huggingface.co/google/long-t5-local-large/blob/main/config.json",
    "google/long-t5-tglobal-base": "https://huggingface.co/google/long-t5-tglobal-base/blob/main/config.json",
    "google/long-t5-tglobal-large": "https://huggingface.co/google/long-t5-tglobal-large/blob/main/config.json",
}
class 	_a  (							lowerCamelCase_					):



	"""simple docstring"""



	__SCREAMING_SNAKE_CASE              =					'longt5'
	__SCREAMING_SNAKE_CASE              =					['past_key_values']
	__SCREAMING_SNAKE_CASE              =					{'hidden_size': 'd_model', 'num_attention_heads': 'num_heads', 'num_hidden_layers': 'num_layers'}


	def __init__(   self       ,  lowerCAmelCase_=32128       ,  lowerCAmelCase_=512       ,  lowerCAmelCase_=64       ,  lowerCAmelCase_=2048       ,  lowerCAmelCase_=6       ,  lowerCAmelCase_=None       ,  lowerCAmelCase_=8       ,  lowerCAmelCase_=127       ,  lowerCAmelCase_=16       ,  lowerCAmelCase_=32       ,  lowerCAmelCase_=128       ,  lowerCAmelCase_=0.1       ,  lowerCAmelCase_=1e-6       ,  lowerCAmelCase_=1.0       ,  lowerCAmelCase_="relu"       ,  lowerCAmelCase_=True       ,  lowerCAmelCase_="local"       ,  lowerCAmelCase_=True       ,  lowerCAmelCase_=0       ,  lowerCAmelCase_=1       ,  **lowerCAmelCase_       ,  ):
		_lowercase   =vocab_size
		_lowercase   =d_model
		_lowercase   =d_kv
		_lowercase   =d_ff
		_lowercase   =num_layers
		# default = symmetry
		_lowercase   =num_decoder_layers if num_decoder_layers is not None else self.num_layers
		_lowercase   =num_heads
		_lowercase   =local_radius
		_lowercase   =global_block_size
		_lowercase   =relative_attention_num_buckets
		_lowercase   =relative_attention_max_distance
		_lowercase   =dropout_rate
		_lowercase   =layer_norm_epsilon
		_lowercase   =initializer_factor
		_lowercase   =feed_forward_proj
		_lowercase   =encoder_attention_type
		_lowercase   =use_cache

		_lowercase   =self.feed_forward_proj.split("-"       )
		_lowercase   =act_info[-1]
		_lowercase   =act_info[0] == "gated"

		if len(lowerCAmelCase_       ) > 1 and act_info[0] != "gated" or len(lowerCAmelCase_       ) > 2:
			raise ValueError(
			    F'''`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer.'''
			    "Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. "
			    "'gated-gelu' or 'relu'"       )

		# for backwards compatibility
		if feed_forward_proj == "gated-gelu":
			_lowercase   ="gelu_new"

		super().__init__(
		    pad_token_id=lowerCAmelCase_       ,  eos_token_id=lowerCAmelCase_       ,  is_encoder_decoder=lowerCAmelCase_       ,  **lowerCAmelCase_       ,  )

class 	_a  (							lowerCamelCase_					):



	"""simple docstring"""



	@property
	def __lowerCAmelCase      (   self       ):
		_lowercase   ={
		    "input_ids": {0: "batch", 1: "encoder_sequence"},
		    "attention_mask": {0: "batch", 1: "encoder_sequence"},
		}
		if self.use_past:
			_lowercase   ="past_encoder_sequence + sequence"
			_lowercase   ={0: "batch"}
			_lowercase   ={0: "batch", 1: "past_decoder_sequence + sequence"}
		else:
			_lowercase   ={0: "batch", 1: "decoder_sequence"}
			_lowercase   ={0: "batch", 1: "decoder_sequence"}

		if self.use_past:
			self.fill_with_past_key_values_(lowerCAmelCase_       ,  direction="inputs"       )

		return common_inputs


	@property
	def __lowerCAmelCase      (   self       ):
		return 13




