"""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')

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



def     a_		(   lowerCamelCase   ,     lowerCamelCase   ,     lowerCamelCase=None			):
				if seed is not None:  # useful for obtaining consistent results
								np.random.seed(lowercase__			)
				UpperCAmelCase__			 =				data.shape[0]  # number of data points

				# Pick K indices from range [0, N).
				UpperCAmelCase__			 =				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.
				UpperCAmelCase__			 =				data[rand_indices, :]

				return centroids



def     a_		(   lowerCamelCase   ,     lowerCamelCase			):
				return pairwise_distances(lowercase__   ,     lowercase__   ,     metric='euclidean'			)



def     a_		(   lowerCamelCase   ,     lowerCamelCase			):
				UpperCAmelCase__			 =				centroid_pairwise_dist(lowercase__   ,     lowercase__			)

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

				return cluster_assignment



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

				return new_centroids



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

				return heterogeneity



def     a_		(   lowerCamelCase   ,     lowerCamelCase			):
				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': 1_6}			)
				plt.show()



def     a_		(   lowerCamelCase   ,     lowerCamelCase   ,     lowerCamelCase   ,     lowerCamelCase=5_0_0   ,     lowerCamelCase=None   ,     lowerCamelCase=False			):
				UpperCAmelCase__			 =				initial_centroids[:]
				UpperCAmelCase__			 =				None

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

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

								# 2. Compute a new centroid for each of the k clusters, averaging all data
								#    points assigned to that cluster.
								UpperCAmelCase__			 =				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:
												UpperCAmelCase__			 =				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__			 =				compute_heterogeneity(lowercase__   ,     lowercase__   ,     lowercase__   ,     lowercase__			)
												record_heterogeneity.append(lowercase__			)

								UpperCAmelCase__			 =				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__    :    Union[str, Any]			=      ds.load_iris()
						lowerCAmelCase__    :    List[Any]			=      3
						lowerCAmelCase__    :    Optional[Any]			=      []
						lowerCAmelCase__    :    Optional[int]			=      get_initial_centroids(dataset['data'], k, seed=0)
						lowerCAmelCase__    :    Dict			=      kmeans(
						    dataset['data'],
						    k,
						    initial_centroids,
						    maxiter=400,
						    record_heterogeneity=heterogeneity,
						    verbose=True,
						)
						plot_heterogeneity(heterogeneity, k)



def     a_		(   lowerCamelCase   ,     lowerCamelCase   ,     lowerCamelCase=None			):
				# Fill missing values with given rules
				if fill_missing_report:
								UpperCAmelCase__			 =				df.fillna(value=lowercase__			)
				UpperCAmelCase__			 =				1
				UpperCAmelCase__			 =				df.select_dtypes(np.number			).columns
				UpperCAmelCase__			 =				(
				    df.groupby(['Cluster']			)[  # construct report dataframe
				        numeric_cols
				    ]  # group by cluster number
				    .agg(
				        [
				            ('sum', np.sum),
				            ('mean_with_zeros', lambda lowerCamelCase			: np.mean(np.nan_to_num(lowercase__			)			)),
				            ('mean_without_zeros', lambda lowerCamelCase			: x.replace(0   ,     np.NaN			).mean()),
				            (
				                'mean_25-75',
				                lambda lowerCamelCase			: np.mean(
				                    np.nan_to_num(
				                        sorted(lowercase__			)[
				                            round(len(lowercase__			) * 2_5 / 1_0_0			) : round(len(lowercase__			) * 7_5 / 1_0_0			)
				                        ]			)			),
				            ),
				            ('mean_with_na', np.mean),
				            ('min', lambda lowerCamelCase			: x.min()),
				            ('5%', lambda lowerCamelCase			: x.quantile(0.05			)),
				            ('25%', lambda lowerCamelCase			: x.quantile(0.25			)),
				            ('50%', lambda lowerCamelCase			: x.quantile(0.50			)),
				            ('75%', lambda lowerCamelCase			: x.quantile(0.75			)),
				            ('95%', lambda lowerCamelCase			: x.quantile(0.95			)),
				            ('max', lambda lowerCamelCase			: x.max()),
				            ('count', lambda lowerCamelCase			: x.count()),
				            ('stdev', lambda lowerCamelCase			: x.std()),
				            ('mode', lambda lowerCamelCase			: x.mode()[0]),
				            ('median', lambda lowerCamelCase			: x.median()),
				            ('# > 0', lambda lowerCamelCase			: (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)
				UpperCAmelCase__			 =				report[
				    (report['Features'] == 'dummy') & (report['Type'] == 'count')
				].copy()  # avoid SettingWithCopyWarning
				UpperCAmelCase__			 =				(
				    'ClusterSize'  # rename created cluster df to match report column names
				)
				UpperCAmelCase__			 =				'# of Customers'
				UpperCAmelCase__			 =				pd.DataFrame(
				    clustersize.iloc[:, 2:].values
				    / clustersize.iloc[:, 2:].values.sum()			)
				UpperCAmelCase__			 =				'% of Customers'  # rename created cluster df to match report column names
				UpperCAmelCase__			 =				'ClusterProportion'
				UpperCAmelCase__			 =				clusterproportion.columns.tolist()
				UpperCAmelCase__			 =				cols[-2:] + cols[:-2]
				UpperCAmelCase__			 =				clusterproportion[cols]  # rearrange columns to match report
				UpperCAmelCase__			 =				report.columns
				UpperCAmelCase__			 =				pd.DataFrame(
				    abs(
				        report[report['Type'] == 'count'].iloc[:, 2:].values
				        - clustersize.iloc[:, 2:].values			)			)  # generating df with count of nan values
				UpperCAmelCase__			 =				0
				UpperCAmelCase__			 =				'# of nan'
				UpperCAmelCase__			 =				report[
				    report['Type'] == 'count'
				].Features.tolist()  # filling values in order to match report
				UpperCAmelCase__			 =				a.columns.tolist()
				UpperCAmelCase__			 =				cols[-2:] + cols[:-2]
				UpperCAmelCase__			 =				a[cols]  # rearrange columns to match report
				UpperCAmelCase__			 =				report.columns  # rename columns to match report
				UpperCAmelCase__			 =				report.drop(
				    report[report.Type == 'count'].index			)  # drop count values except cluster size
				UpperCAmelCase__			 =				pd.concat(
				    [report, a, clustersize, clusterproportion]   ,     axis=0			)  # concat report with clustert size and nan values
				UpperCAmelCase__			 =				report['Features'].isin(lowercase__			)
				UpperCAmelCase__			 =				report.columns.tolist()
				UpperCAmelCase__			 =				cols[0:2] + cols[-1:] + cols[2:-1]
				UpperCAmelCase__			 =				report[cols]
				UpperCAmelCase__			 =				{
				    '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__			 =				(
				    report.assign(
				        Sortera=lambda lowerCamelCase			: x.Type.map(lowercase__			)   ,     Sortera=lambda lowerCamelCase			: list(reversed(range(len(lowercase__			)			)			)			)   ,     )
				    .sort_values(['Sorter1', 'Mark', 'Sorter2']   ,     ascending=lowercase__			)
				    .drop(['Sorter1', 'Sorter2']   ,     axis=1			)
				)
				UpperCAmelCase__			 =				''
				UpperCAmelCase__			 =				report.reset_index()
				UpperCAmelCase__			 =				report.drop(columns=['index']			)
				return report


if __name__ == "__main__":
						import doctest

						doctest.testmod()






"""simple docstring"""




import unittest

import torch

from diffusers import UNetaDModel
from diffusers.utils import floats_tensor, slow, torch_device

from .test_modeling_common import ModelTesterMixin, UNetTesterMixin





class      __UpperCamelCase					(  _A	,						_A	,						unittest.TestCase		):
		SCREAMING_SNAKE_CASE														=       UNetaDModel
		SCREAMING_SNAKE_CASE														=       "sample"


		@property
		def    SCREAMING_SNAKE_CASE__  (self					:						Tuple):
							A     =   4
							A     =   1_4
							A     =   1_6

							A     =   floats_tensor((batch_size, num_features, seq_len)).to(__SCREAMING_SNAKE_CASE)
							A     =   torch.tensor([1_0] * batch_size).to(__SCREAMING_SNAKE_CASE)

							return {"sample": noise, "timestep": time_step}


		@property
		def    SCREAMING_SNAKE_CASE__  (self					:						Optional[int]):
							return (4, 1_4, 1_6)


		@property
		def    SCREAMING_SNAKE_CASE__  (self					:						int):
							return (4, 1_4, 1_6)


		def    SCREAMING_SNAKE_CASE__  (self					:						List[Any]):
							pass


		def    SCREAMING_SNAKE_CASE__  (self					:						str):
							pass


		@unittest.skipIf(torch_device == "mps"	,	"mish op not supported in MPS")
		def    SCREAMING_SNAKE_CASE__  (self					:						Tuple):
							super().test_determinism()


		@unittest.skipIf(torch_device == "mps"	,	"mish op not supported in MPS")
		def    SCREAMING_SNAKE_CASE__  (self					:						Any):
							super().test_outputs_equivalence()


		@unittest.skipIf(torch_device == "mps"	,	"mish op not supported in MPS")
		def    SCREAMING_SNAKE_CASE__  (self					:						Any):
							super().test_from_save_pretrained()


		@unittest.skipIf(torch_device == "mps"	,	"mish op not supported in MPS")
		def    SCREAMING_SNAKE_CASE__  (self					:						Any):
							super().test_from_save_pretrained_variant()


		@unittest.skipIf(torch_device == "mps"	,	"mish op not supported in MPS")
		def    SCREAMING_SNAKE_CASE__  (self					:						Any):
							super().test_model_from_pretrained()


		@unittest.skipIf(torch_device == "mps"	,	"mish op not supported in MPS")
		def    SCREAMING_SNAKE_CASE__  (self					:						List[Any]):
							super().test_output()


		def    SCREAMING_SNAKE_CASE__  (self					:						Union[str, Any]):
							A     =   {
							    "block_out_channels": (3_2, 6_4, 1_2_8, 2_5_6),
							    "in_channels": 1_4,
							    "out_channels": 1_4,
							    "time_embedding_type": "positional",
							    "use_timestep_embedding": True,
							    "flip_sin_to_cos": False,
							    "freq_shift": 1.0,
							    "out_block_type": "OutConv1DBlock",
							    "mid_block_type": "MidResTemporalBlock1D",
							    "down_block_types": ("DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D"),
							    "up_block_types": ("UpResnetBlock1D", "UpResnetBlock1D", "UpResnetBlock1D"),
							    "act_fn": "mish",
							}
							A     =   self.dummy_input
							return init_dict, inputs_dict


		@unittest.skipIf(torch_device == "mps"	,	"mish op not supported in MPS")
		def    SCREAMING_SNAKE_CASE__  (self					:						Optional[Any]):
							A     ,       A     =   UNetaDModel.from_pretrained(
							    "bglick13/hopper-medium-v2-value-function-hor32"	,	output_loading_info=__SCREAMING_SNAKE_CASE	,	subfolder="unet")
							self.assertIsNotNone(__SCREAMING_SNAKE_CASE)
							self.assertEqual(len(loading_info["missing_keys"])	,	0)

							model.to(__SCREAMING_SNAKE_CASE)
							A     =   model(**self.dummy_input)

							assert image is not None, "Make sure output is not None"


		@unittest.skipIf(torch_device == "mps"	,	"mish op not supported in MPS")
		def    SCREAMING_SNAKE_CASE__  (self					:						Optional[Any]):
							A     =   UNetaDModel.from_pretrained("bglick13/hopper-medium-v2-value-function-hor32"	,	subfolder="unet")
							torch.manual_seed(0)
							if torch.cuda.is_available():
												torch.cuda.manual_seed_all(0)

							A     =   model.config.in_channels
							A     =   1_6
							A     =   torch.randn((1, seq_len, num_features)).permute(
							    0	,	2	,	1)  # match original, we can update values and remove
							A     =   torch.full((num_features,)	,	0)

							with torch.no_grad():
												A     =   model(__SCREAMING_SNAKE_CASE	,	__SCREAMING_SNAKE_CASE).sample.permute(0	,	2	,	1)

							A     =   output[0, -3:, -3:].flatten()
							# fmt: off
							A     =   torch.tensor([-2.1_3_7_1_7_2, 1.1_4_2_6_0_1_6, 0.3_6_8_8_6_8_7, -0.7_6_6_9_2_2, 0.7_3_0_3_1_4_6, 0.1_1_0_3_8_8_6_4, -0.4_7_6_0_6_3_3, 0.1_3_2_7_0_1_7_2, 0.0_2_5_9_1_3_4_8])
							# fmt: on
							self.assertTrue(torch.allclose(__SCREAMING_SNAKE_CASE	,	__SCREAMING_SNAKE_CASE	,	rtol=1E-3))


		def    SCREAMING_SNAKE_CASE__  (self					:						Union[str, Any]):
							# Not implemented yet for this UNet
							pass


		@slow
		def    SCREAMING_SNAKE_CASE__  (self					:						Dict):
							A     =   "harmonai/maestro-150k"
							A     =   UNetaDModel.from_pretrained(__SCREAMING_SNAKE_CASE	,	subfolder="unet")
							model.to(__SCREAMING_SNAKE_CASE)

							A     =   6_5_5_3_6
							A     =   torch.sin(torch.arange(__SCREAMING_SNAKE_CASE)[None, None, :].repeat(1	,	2	,	1)).to(__SCREAMING_SNAKE_CASE)
							A     =   torch.tensor([1]).to(__SCREAMING_SNAKE_CASE)

							with torch.no_grad():
												A     =   model(__SCREAMING_SNAKE_CASE	,	__SCREAMING_SNAKE_CASE).sample

							A     =   output.abs().sum()
							A     =   output.abs().max()

							assert (output_sum - 2_2_4.0_8_9_6).abs() < 0.5
							assert (output_max - 0.0_6_0_7).abs() < 4E-4





class      __UpperCamelCase					(  _A	,						_A	,						unittest.TestCase		):
		SCREAMING_SNAKE_CASE														=       UNetaDModel
		SCREAMING_SNAKE_CASE														=       "sample"


		@property
		def    SCREAMING_SNAKE_CASE__  (self					:						Any):
							A     =   4
							A     =   1_4
							A     =   1_6

							A     =   floats_tensor((batch_size, num_features, seq_len)).to(__SCREAMING_SNAKE_CASE)
							A     =   torch.tensor([1_0] * batch_size).to(__SCREAMING_SNAKE_CASE)

							return {"sample": noise, "timestep": time_step}


		@property
		def    SCREAMING_SNAKE_CASE__  (self					:						int):
							return (4, 1_4, 1_6)


		@property
		def    SCREAMING_SNAKE_CASE__  (self					:						Tuple):
							return (4, 1_4, 1)


		@unittest.skipIf(torch_device == "mps"	,	"mish op not supported in MPS")
		def    SCREAMING_SNAKE_CASE__  (self					:						Optional[Any]):
							super().test_determinism()


		@unittest.skipIf(torch_device == "mps"	,	"mish op not supported in MPS")
		def    SCREAMING_SNAKE_CASE__  (self					:						str):
							super().test_outputs_equivalence()


		@unittest.skipIf(torch_device == "mps"	,	"mish op not supported in MPS")
		def    SCREAMING_SNAKE_CASE__  (self					:						Dict):
							super().test_from_save_pretrained()


		@unittest.skipIf(torch_device == "mps"	,	"mish op not supported in MPS")
		def    SCREAMING_SNAKE_CASE__  (self					:						Dict):
							super().test_from_save_pretrained_variant()


		@unittest.skipIf(torch_device == "mps"	,	"mish op not supported in MPS")
		def    SCREAMING_SNAKE_CASE__  (self					:						List[Any]):
							super().test_model_from_pretrained()


		@unittest.skipIf(torch_device == "mps"	,	"mish op not supported in MPS")
		def    SCREAMING_SNAKE_CASE__  (self					:						Any):
							# UNetRL is a value-function is different output shape
							A     ,       A     =   self.prepare_init_args_and_inputs_for_common()
							A     =   self.model_class(**__SCREAMING_SNAKE_CASE)
							model.to(__SCREAMING_SNAKE_CASE)
							model.eval()

							with torch.no_grad():
												A     =   model(**__SCREAMING_SNAKE_CASE)

												if isinstance(__SCREAMING_SNAKE_CASE	,	__SCREAMING_SNAKE_CASE):
																	A     =   output.sample

							self.assertIsNotNone(__SCREAMING_SNAKE_CASE)
							A     =   torch.Size((inputs_dict["sample"].shape[0], 1))
							self.assertEqual(output.shape	,	__SCREAMING_SNAKE_CASE	,	"Input and output shapes do not match")


		def    SCREAMING_SNAKE_CASE__  (self					:						Union[str, Any]):
							pass


		def    SCREAMING_SNAKE_CASE__  (self					:						Optional[int]):
							pass


		def    SCREAMING_SNAKE_CASE__  (self					:						Any):
							A     =   {
							    "in_channels": 1_4,
							    "out_channels": 1_4,
							    "down_block_types": ["DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D"],
							    "up_block_types": [],
							    "out_block_type": "ValueFunction",
							    "mid_block_type": "ValueFunctionMidBlock1D",
							    "block_out_channels": [3_2, 6_4, 1_2_8, 2_5_6],
							    "layers_per_block": 1,
							    "downsample_each_block": True,
							    "use_timestep_embedding": True,
							    "freq_shift": 1.0,
							    "flip_sin_to_cos": False,
							    "time_embedding_type": "positional",
							    "act_fn": "mish",
							}
							A     =   self.dummy_input
							return init_dict, inputs_dict


		@unittest.skipIf(torch_device == "mps"	,	"mish op not supported in MPS")
		def    SCREAMING_SNAKE_CASE__  (self					:						int):
							A     ,       A     =   UNetaDModel.from_pretrained(
							    "bglick13/hopper-medium-v2-value-function-hor32"	,	output_loading_info=__SCREAMING_SNAKE_CASE	,	subfolder="value_function")
							self.assertIsNotNone(__SCREAMING_SNAKE_CASE)
							self.assertEqual(len(vf_loading_info["missing_keys"])	,	0)

							value_function.to(__SCREAMING_SNAKE_CASE)
							A     =   value_function(**self.dummy_input)

							assert image is not None, "Make sure output is not None"


		@unittest.skipIf(torch_device == "mps"	,	"mish op not supported in MPS")
		def    SCREAMING_SNAKE_CASE__  (self					:						Union[str, Any]):
							A     ,       A     =   UNetaDModel.from_pretrained(
							    "bglick13/hopper-medium-v2-value-function-hor32"	,	output_loading_info=__SCREAMING_SNAKE_CASE	,	subfolder="value_function")
							torch.manual_seed(0)
							if torch.cuda.is_available():
												torch.cuda.manual_seed_all(0)

							A     =   value_function.config.in_channels
							A     =   1_4
							A     =   torch.randn((1, seq_len, num_features)).permute(
							    0	,	2	,	1)  # match original, we can update values and remove
							A     =   torch.full((num_features,)	,	0)

							with torch.no_grad():
												A     =   value_function(__SCREAMING_SNAKE_CASE	,	__SCREAMING_SNAKE_CASE).sample

							# fmt: off
							A     =   torch.tensor([1_6_5.2_5] * seq_len)
							# fmt: on
							self.assertTrue(torch.allclose(__SCREAMING_SNAKE_CASE	,	__SCREAMING_SNAKE_CASE	,	rtol=1E-3))


		def    SCREAMING_SNAKE_CASE__  (self					:						Optional[Any]):
							# Not implemented yet for this UNet
							pass





