
from random import shuffle

import tensorflow as tf
from numpy import array


def    A (      lowercase					, lowercase				)     ->						Optional[Any]:
		'''simple docstring'''

		UpperCamelCase						=  int(lowercase				)
		assert noofclusters < len(lowercase				)

		# Find out the dimensionality
		UpperCamelCase						=  len(vectors[0]				)

		# Will help select random centroids from among the available vectors
		UpperCamelCase						=  list(range(len(lowercase				)				)				)
		shuffle(lowercase				)

		# GRAPH OF COMPUTATION
		# We initialize a new graph and set it as the default during each run
		# of this algorithm. This ensures that as this function is called
		# multiple times, the default graph doesn't keep getting crowded with
		# unused ops and Variables from previous function calls.

		UpperCamelCase						=  tf.Graph()

		with graph.as_default():
				# SESSION OF COMPUTATION

				UpperCamelCase						=  tf.Session()

				##CONSTRUCTING THE ELEMENTS OF COMPUTATION

				##First lets ensure we have a Variable vector for each centroid,
				##initialized to one of the vectors from the available data points
				UpperCamelCase						=  [
				    tf.Variable(vectors[vector_indices[i]]				) for i in range(lowercase				)
				]
				##These nodes will assign the centroid Variables the appropriate
				##values
				UpperCamelCase						=  tf.placeholder('float64'					, [dim]				)
				UpperCamelCase						=  []
				for centroid in centroids:
						cent_assigns.append(tf.assign(lowercase					, lowercase				)				)

				##Variables for cluster assignments of individual vectors(initialized
				##to 0 at first)
				UpperCamelCase						=  [tf.Variable(0				) for i in range(len(lowercase				)				)]
				##These nodes will assign an assignment Variable the appropriate
				##value
				UpperCamelCase						=  tf.placeholder('int32'				)
				UpperCamelCase						=  []
				for assignment in assignments:
						cluster_assigns.append(tf.assign(lowercase					, lowercase				)				)

				##Now lets construct the node that will compute the mean
				# The placeholder for the input
				UpperCamelCase						=  tf.placeholder('float'					, [None, dim]				)
				# The Node/op takes the input and computes a mean along the 0th
				# dimension, i.e. the list of input vectors
				UpperCamelCase						=  tf.reduce_mean(lowercase					, 0				)

				##Node for computing Euclidean distances
				# Placeholders for input
				UpperCamelCase						=  tf.placeholder('float'					, [dim]				)
				UpperCamelCase						=  tf.placeholder('float'					, [dim]				)
				UpperCamelCase						=  tf.sqrt(tf.reduce_sum(tf.pow(tf.sub(lowercase					, lowercase				)					, 2				)				)				)

				##This node will figure out which cluster to assign a vector to,
				##based on Euclidean distances of the vector from the centroids.
				# Placeholder for input
				UpperCamelCase						=  tf.placeholder('float'					, [noofclusters]				)
				UpperCamelCase						=  tf.argmin(lowercase					, 0				)

				##INITIALIZING STATE VARIABLES

				##This will help initialization of all Variables defined with respect
				##to the graph. The Variable-initializer should be defined after
				##all the Variables have been constructed, so that each of them
				##will be included in the initialization.
				UpperCamelCase						=  tf.initialize_all_variables()

				# Initialize all variables
				sess.run(lowercase				)

				##CLUSTERING ITERATIONS

				# Now perform the Expectation-Maximization steps of K-Means clustering
				# iterations. To keep things simple, we will only do a set number of
				# iterations, instead of using a Stopping Criterion.
				UpperCamelCase						=  100
				for _ in range(lowercase				):
						##EXPECTATION STEP
						##Based on the centroid locations till last iteration, compute
						##the _expected_ centroid assignments.
						# Iterate over each vector
						for vector_n in range(len(lowercase				)				):
								UpperCamelCase						=  vectors[vector_n]
								# Compute Euclidean distance between this vector and each
								# centroid. Remember that this list cannot be named
								#'centroid_distances', since that is the input to the
								# cluster assignment node.
								UpperCamelCase						=  [
								    sess.run(lowercase					, feed_dict={va: vect, va: sess.run(lowercase				)}				)
								    for centroid in centroids
								]
								# Now use the cluster assignment node, with the distances
								# as the input
								UpperCamelCase						=  sess.run(
								    lowercase					, feed_dict={centroid_distances: distances}				)
								# Now assign the value to the appropriate state variable
								sess.run(
								    cluster_assigns[vector_n]					, feed_dict={assignment_value: assignment}				)

						##MAXIMIZATION STEP
						# Based on the expected state computed from the Expectation Step,
						# compute the locations of the centroids so as to maximize the
						# overall objective of minimizing within-cluster Sum-of-Squares
						for cluster_n in range(lowercase				):
								# Collect all the vectors assigned to this cluster
								UpperCamelCase						=  [
								    vectors[i]
								    for i in range(len(lowercase				)				)
								    if sess.run(assignments[i]				) == cluster_n
								]
								# Compute new centroid location
								UpperCamelCase						=  sess.run(
								    lowercase					, feed_dict={mean_input: array(lowercase				)}				)
								# Assign value to appropriate variable
								sess.run(
								    cent_assigns[cluster_n]					, feed_dict={centroid_value: new_location}				)

        # Return centroids and assignments
				UpperCamelCase						=  sess.run(lowercase				)
				UpperCamelCase						=  sess.run(lowercase				)
				return centroids, assignments


import importlib
import math
import os
from dataclasses import dataclass
from enum import Enum
from typing import Any, Dict, Optional, Tuple, Union

import flax
import jax.numpy as jnp

from ..utils import BaseOutput


_UpperCAmelCase      :				str          = "scheduler_config.json"


class        lowercase      (	_SCREAMING_SNAKE_CASE     ):
		__lowercase : Tuple			  =       1
		__lowercase : int			  =       2
		__lowercase : List[Any]			  =       3
		__lowercase : str			  =       4
		__lowercase : Optional[Any]			  =       5






@dataclass
class        lowercase      (	_SCREAMING_SNAKE_CASE     ):
		__lowercase : jnp.ndarray






class        lowercase      :
		__lowercase : Union[str, Any]			  =       SCHEDULER_CONFIG_NAME
		__lowercase : Dict			  =       ["dtype"]
		__lowercase : List[Any]			  =       []
		__lowercase : Dict			  =       True



		@classmethod
		def __UpperCamelCase				(				cls       ,   A_ = None       ,   A_ = None       ,   A_=False       ,   **A_       ,   )						->      Optional[Any]:
				"""simple docstring"""
				UpperCamelCase			,						UpperCamelCase						=  cls.load_config(
				    pretrained_model_name_or_path=A_       ,   subfolder=A_       ,   return_unused_kwargs=A_       ,   **A_       ,   )
				UpperCamelCase			,						UpperCamelCase						=  cls.from_config(A_       ,   return_unused_kwargs=A_       ,   **A_ )

				if hasattr(A_       ,   'create_state' ) and getattr(A_       ,   'has_state'       ,   A_ ):
						UpperCamelCase						=  scheduler.create_state()

				if return_unused_kwargs:
						return scheduler, state, unused_kwargs

				return scheduler, state



		def __UpperCamelCase				(				self       ,   A_       ,   A_ = False       ,   **A_ )						->      str:
				"""simple docstring"""
				self.save_config(save_directory=A_       ,   push_to_hub=A_       ,   **A_ )



		@property
		def __UpperCamelCase				(				self )						->      int:
				"""simple docstring"""
				return self._get_compatibles()



		@classmethod
		def __UpperCamelCase				(				cls )						->      int:
				"""simple docstring"""
				UpperCamelCase						=  list(set([cls.__name__] + cls._compatibles ) )
				UpperCamelCase						=  importlib.import_module(__name__.split('.' )[0] )
				UpperCamelCase						=  [
				    getattr(A_       ,   A_ ) for c in compatible_classes_str if hasattr(A_       ,   A_ )
				]
				return compatible_classes






def    A (      lowercase					, lowercase				)     ->						jnp.ndarray:
		'''simple docstring'''

		assert len(lowercase				) >= x.ndim
		return jnp.broadcast_to(x.reshape(x.shape + (1,) * (len(lowercase				) - x.ndim)				)					, lowercase				)


def    A (      lowercase					, lowercase=0.9_9_9					, lowercase=jnp.floataa				)     ->						jnp.ndarray:
		'''simple docstring'''

		def alpha_bar(lowercase				):
				return math.cos((time_step + 0.0_0_8) / 1.0_0_8 * math.pi / 2				) ** 2

		UpperCamelCase						=  []
		for i in range(lowercase				):
				UpperCamelCase						=  i / num_diffusion_timesteps
				UpperCamelCase						=  (i + 1) / num_diffusion_timesteps
				betas.append(min(1 - alpha_bar(lowercase				) / alpha_bar(lowercase				)					, lowercase				)				)
		return jnp.array(lowercase					, dtype=lowercase				)


@flax.struct.dataclass
class        lowercase      :
		__lowercase : jnp.ndarray
		__lowercase : jnp.ndarray
		__lowercase : jnp.ndarray



		@classmethod
		def __UpperCamelCase				(				cls       ,   A_ )						->      Optional[int]:
				"""simple docstring"""
				UpperCamelCase						=  scheduler.config

				if config.trained_betas is not None:
						UpperCamelCase						=  jnp.asarray(config.trained_betas       ,   dtype=scheduler.dtype )
				elif config.beta_schedule == "linear":
						UpperCamelCase						=  jnp.linspace(config.beta_start       ,   config.beta_end       ,   config.num_train_timesteps       ,   dtype=scheduler.dtype )
				elif config.beta_schedule == "scaled_linear":
						# this schedule is very specific to the latent diffusion model.
						UpperCamelCase						=  (
						    jnp.linspace(
						        config.beta_start**0.5       ,   config.beta_end**0.5       ,   config.num_train_timesteps       ,   dtype=scheduler.dtype )
						    ** 2
						)
				elif config.beta_schedule == "squaredcos_cap_v2":
						# Glide cosine schedule
						UpperCamelCase						=  betas_for_alpha_bar(config.num_train_timesteps       ,   dtype=scheduler.dtype )
				else:
						raise NotImplementedError(
						    F'''beta_schedule {config.beta_schedule} is not implemented for scheduler {scheduler.__class__.__name__}''' )

				UpperCamelCase						=  1.0 - betas

				UpperCamelCase						=  jnp.cumprod(A_       ,   axis=0 )

				return cls(
				    alphas=A_       ,   betas=A_       ,   alphas_cumprod=A_       ,   )






def    A (      lowercase					, lowercase					, lowercase					, lowercase				)     ->						List[Any]:
		'''simple docstring'''

		UpperCamelCase						=  state.alphas_cumprod

		UpperCamelCase						=  alphas_cumprod[timesteps] ** 0.5
		UpperCamelCase						=  sqrt_alpha_prod.flatten()
		UpperCamelCase						=  broadcast_to_shape_from_left(lowercase					, original_samples.shape				)

		UpperCamelCase						=  (1 - alphas_cumprod[timesteps]) ** 0.5
		UpperCamelCase						=  sqrt_one_minus_alpha_prod.flatten()
		UpperCamelCase						=  broadcast_to_shape_from_left(lowercase					, original_samples.shape				)

		return sqrt_alpha_prod, sqrt_one_minus_alpha_prod


def    A (      lowercase					, lowercase					, lowercase					, lowercase				)     ->						Dict:
		'''simple docstring'''

		UpperCamelCase			,						UpperCamelCase						=  get_sqrt_alpha_prod(lowercase					, lowercase					, lowercase					, lowercase				)
		UpperCamelCase						=  sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
		return noisy_samples


def    A (      lowercase					, lowercase					, lowercase					, lowercase				)     ->						int:
		'''simple docstring'''

		UpperCamelCase			,						UpperCamelCase						=  get_sqrt_alpha_prod(lowercase					, lowercase					, lowercase					, lowercase				)
		UpperCamelCase						=  sqrt_alpha_prod * noise - sqrt_one_minus_alpha_prod * sample
		return velocity
