



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


_snake_case	=      "scheduler_config.json"



class 						UpperCAmelCase_					( _SCREAMING_SNAKE_CASE):
							lowerCamelCase__         =	1
							lowerCamelCase__         =	2
							lowerCamelCase__         =	3
							lowerCamelCase__         =	4
							lowerCamelCase__         =	5



@dataclass
class 						UpperCAmelCase_					( _SCREAMING_SNAKE_CASE):
							lowerCamelCase__         =	42



class 						UpperCAmelCase_					:
							lowerCamelCase__         =	SCHEDULER_CONFIG_NAME
							lowerCamelCase__         =	["dtype"]
							lowerCamelCase__         =	[]
							lowerCamelCase__         =	True







							@classmethod
							def      snake_case__   (     cls,    __a = None,    __a = None,    __a=False,    **__a,    ):

														'''simple docstring'''

														_lowerCAmelCase     ,					_lowerCAmelCase  :							List[str]				     =					cls.load_config(
														    pretrained_model_name_or_path=A_,    subfolder=A_,    return_unused_kwargs=A_,    **A_,    )
														_lowerCAmelCase     ,					_lowerCAmelCase  :							Dict				     =					cls.from_config(A_,    return_unused_kwargs=A_,    **A_)

														if hasattr(A_,    "create_state") and getattr(A_,    "has_state",    A_):
																					_lowerCAmelCase  :							int				     =					scheduler.create_state()

														if return_unused_kwargs:
																					return scheduler, state, unused_kwargs

														return scheduler, state







							def      snake_case__   (     self,    __a,    __a = False,    **__a):

														'''simple docstring'''

														self.save_config(save_directory=A_,    push_to_hub=A_,    **A_)







							@property
							def      snake_case__   (     self):

														'''simple docstring'''

														return self._get_compatibles()







							@classmethod
							def      snake_case__   (     cls):

														'''simple docstring'''

														_lowerCAmelCase  :							int				     =					list(set([cls.__name__] + cls._compatibles))
														_lowerCAmelCase  :							Optional[Any]				     =					importlib.import_module(__name__.split(".")[0])
														_lowerCAmelCase  :							int				     =					[
														    getattr(A_,    A_) for c in compatible_classes_str if hasattr(A_,    A_)
														]
														return compatible_classes






def        A       (      _lowerCamelCase       ,				_lowerCamelCase						):





							'''simple docstring'''




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






def        A       (      _lowerCamelCase       ,				_lowerCamelCase=0.9_99       ,				_lowerCamelCase=jnp.floataa						):





							'''simple docstring'''




							def alpha_bar(_lowerCamelCase						):
														return math.cos((time_step + 0.0_08) / 1.0_08 * math.pi / 2						) ** 2

							_lowerCAmelCase  :							Optional[Any]				     =					[]
							for i in range(_lowerCamelCase						):
														_lowerCAmelCase  :							List[Any]				     =					i / num_diffusion_timesteps
														_lowerCAmelCase  :							Optional[Any]				     =					(i + 1) / num_diffusion_timesteps
														betas.append(min(1 - alpha_bar(_lowerCamelCase						) / alpha_bar(_lowerCamelCase						)       ,				_lowerCamelCase						)						)
							return jnp.array(_lowerCamelCase       ,				dtype=_lowerCamelCase						)



@flax.struct.dataclass
class 						UpperCAmelCase_					:
							lowerCamelCase__         =	42
							lowerCamelCase__         =	42
							lowerCamelCase__         =	42







							@classmethod
							def      snake_case__   (     cls,    __a):

														'''simple docstring'''

														_lowerCAmelCase  :							List[Any]				     =					scheduler.config

														if config.trained_betas is not None:
																					_lowerCAmelCase  :							Union[str, Any]				     =					jnp.asarray(config.trained_betas,    dtype=scheduler.dtype)
														elif config.beta_schedule == "linear":
																					_lowerCAmelCase  :							List[Any]				     =					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.
																					_lowerCAmelCase  :							List[Any]				     =					(
																					    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
																					_lowerCAmelCase  :							List[str]				     =					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__}")

														_lowerCAmelCase  :							Union[str, Any]				     =					1.0 - betas

														_lowerCAmelCase  :							Dict				     =					jnp.cumprod(A_,    axis=0)

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






def        A       (      _lowerCamelCase       ,				_lowerCamelCase       ,				_lowerCamelCase       ,				_lowerCamelCase						):





							'''simple docstring'''




							_lowerCAmelCase  :							Optional[Any]				     =					state.alphas_cumprod

							_lowerCAmelCase  :							Any				     =					alphas_cumprod[timesteps] ** 0.5
							_lowerCAmelCase  :							List[Any]				     =					sqrt_alpha_prod.flatten()
							_lowerCAmelCase  :							Optional[Any]				     =					broadcast_to_shape_from_left(_lowerCamelCase       ,				original_samples.shape						)

							_lowerCAmelCase  :							Dict				     =					(1 - alphas_cumprod[timesteps]) ** 0.5
							_lowerCAmelCase  :							str				     =					sqrt_one_minus_alpha_prod.flatten()
							_lowerCAmelCase  :							Optional[Any]				     =					broadcast_to_shape_from_left(_lowerCamelCase       ,				original_samples.shape						)

							return sqrt_alpha_prod, sqrt_one_minus_alpha_prod






def        A       (      _lowerCamelCase       ,				_lowerCamelCase       ,				_lowerCamelCase       ,				_lowerCamelCase						):





							'''simple docstring'''




							_lowerCAmelCase     ,					_lowerCAmelCase  :							str				     =					get_sqrt_alpha_prod(_lowerCamelCase       ,				_lowerCamelCase       ,				_lowerCamelCase       ,				_lowerCamelCase						)
							_lowerCAmelCase  :							List[str]				     =					sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
							return noisy_samples






def        A       (      _lowerCamelCase       ,				_lowerCamelCase       ,				_lowerCamelCase       ,				_lowerCamelCase						):





							'''simple docstring'''




							_lowerCAmelCase     ,					_lowerCAmelCase  :							Any				     =					get_sqrt_alpha_prod(_lowerCamelCase       ,				_lowerCamelCase       ,				_lowerCamelCase       ,				_lowerCamelCase						)
							_lowerCAmelCase  :							List[Any]				     =					sqrt_alpha_prod * noise - sqrt_one_minus_alpha_prod * sample
							return velocity





import logging

import numpy as np
import pytest
from scipy.linalg import eigh

logging.basicConfig(level=logging.INFO, format="%(message)s")






def        A       (      _lowerCamelCase						):





							'''simple docstring'''




							return input_array.reshape((input_array.size, 1)						)






def        A       (      _lowerCamelCase       ,				_lowerCamelCase       ,				_lowerCamelCase						):





							'''simple docstring'''




							_lowerCAmelCase  :							Any				     =					np.nan
							for i in range(_lowerCamelCase						):
														_lowerCAmelCase  :							Tuple				     =					features[:, labels == i]
														_lowerCAmelCase  :							Dict				     =					data.mean(1						)
														# Centralize the data of class i
														_lowerCAmelCase  :							Union[str, Any]				     =					data - column_reshape(_lowerCamelCase						)
														if i > 0:
																					# If covariance_sum is not None
																					covariance_sum += np.dot(_lowerCamelCase       ,				centered_data.T						)
														else:
																					# If covariance_sum is np.nan (i.e. first loop)
																					_lowerCAmelCase  :							int				     =					np.dot(_lowerCamelCase       ,				centered_data.T						)

							return covariance_sum / features.shape[1]






def        A       (      _lowerCamelCase       ,				_lowerCamelCase       ,				_lowerCamelCase						):





							'''simple docstring'''




							_lowerCAmelCase  :							Optional[Any]				     =					features.mean(1						)
							_lowerCAmelCase  :							List[str]				     =					np.nan
							for i in range(_lowerCamelCase						):
														_lowerCAmelCase  :							str				     =					features[:, labels == i]
														_lowerCAmelCase  :							Optional[Any]				     =					data.shape[1]
														_lowerCAmelCase  :							Optional[Any]				     =					data.mean(1						)
														if i > 0:
																					# If covariance_sum is not None
																					covariance_sum += device_data * np.dot(
																					    column_reshape(_lowerCamelCase						) - column_reshape(_lowerCamelCase						)       ,				(column_reshape(_lowerCamelCase						) - column_reshape(_lowerCamelCase						)).T       ,				)
														else:
																					# If covariance_sum is np.nan (i.e. first loop)
																					_lowerCAmelCase  :							Optional[Any]				     =					device_data * np.dot(
																					    column_reshape(_lowerCamelCase						) - column_reshape(_lowerCamelCase						)       ,				(column_reshape(_lowerCamelCase						) - column_reshape(_lowerCamelCase						)).T       ,				)

							return covariance_sum / features.shape[1]






def        A       (      _lowerCamelCase       ,				_lowerCamelCase						):





							'''simple docstring'''




							if features.any():
														_lowerCAmelCase  :							List[Any]				     =					features.mean(1						)
														# Center the dataset
														_lowerCAmelCase  :							List[Any]				     =					features - np.reshape(_lowerCamelCase       ,				(data_mean.size, 1)						)
														_lowerCAmelCase  :							Optional[Any]				     =					np.dot(_lowerCamelCase       ,				centered_data.T						) / features.shape[1]
														_lowerCAmelCase     ,					_lowerCAmelCase  :							List[Any]				     =					np.linalg.eigh(_lowerCamelCase						)
														# Take all the columns in the reverse order (-1), and then takes only the first
														_lowerCAmelCase  :							Union[str, Any]				     =					eigenvectors[:, ::-1][:, 0:dimensions]
														# Project the database on the new space
														_lowerCAmelCase  :							List[Any]				     =					np.dot(filtered_eigenvectors.T       ,				_lowerCamelCase						)
														logging.info("Principal Component Analysis computed"						)

														return projected_data
							else:
														logging.basicConfig(level=logging.ERROR       ,				format="%(message)s"       ,				force=_lowerCamelCase						)
														logging.error("Dataset empty"						)
														raise AssertionError






def        A       (      _lowerCamelCase       ,				_lowerCamelCase       ,				_lowerCamelCase       ,				_lowerCamelCase						):





							'''simple docstring'''




							assert classes > dimensions

							# Check if features have been already loaded
							if features.any:
														_lowerCAmelCase     ,					_lowerCAmelCase  :							List[str]				     =					eigh(
														    covariance_between_classes(_lowerCamelCase       ,				_lowerCamelCase       ,				_lowerCamelCase						)       ,				covariance_within_classes(_lowerCamelCase       ,				_lowerCamelCase       ,				_lowerCamelCase						)       ,				)
														_lowerCAmelCase  :							List[str]				     =					eigenvectors[:, ::-1][:, :dimensions]
														_lowerCAmelCase     ,					_lowerCAmelCase     ,					_lowerCAmelCase  :							Any				     =					np.linalg.svd(_lowerCamelCase						)
														_lowerCAmelCase  :							Optional[Any]				     =					svd_matrix[:, 0:dimensions]
														_lowerCAmelCase  :							str				     =					np.dot(filtered_svd_matrix.T       ,				_lowerCamelCase						)
														logging.info("Linear Discriminant Analysis computed"						)

														return projected_data
							else:
														logging.basicConfig(level=logging.ERROR       ,				format="%(message)s"       ,				force=_lowerCamelCase						)
														logging.error("Dataset empty"						)
														raise AssertionError






def        A       (      ):





							'''simple docstring'''




							_lowerCAmelCase  :							Optional[int]				     =					np.array([[1, 2, 3, 4, 5], [2, 3, 4, 5, 6], [3, 4, 5, 6, 7]]						)
							_lowerCAmelCase  :							List[Any]				     =					np.array([0, 0, 0, 1, 1]						)
							_lowerCAmelCase  :							List[Any]				     =					2
							_lowerCAmelCase  :							Union[str, Any]				     =					2

							# Assert that the function raises an AssertionError if dimensions > classes
							with pytest.raises(_lowerCamelCase						) as error_info:
														_lowerCAmelCase  :							Union[str, Any]				     =					linear_discriminant_analysis(
														    _lowerCamelCase       ,				_lowerCamelCase       ,				_lowerCamelCase       ,				_lowerCamelCase						)
														if isinstance(_lowerCamelCase       ,				np.ndarray						):
																					raise AssertionError(
																					    "Did not raise AssertionError for dimensions > classes"						)
														assert error_info.type is AssertionError






def        A       (      ):





							'''simple docstring'''




							_lowerCAmelCase  :							Optional[Any]				     =					np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]						)
							_lowerCAmelCase  :							List[str]				     =					2
							_lowerCAmelCase  :							List[Any]				     =					np.array([[6.92_82_03_23, 8.66_02_54_04, 10.39_23_04_85], [3.0, 3.0, 3.0]]						)

							with pytest.raises(_lowerCamelCase						) as error_info:
														_lowerCAmelCase  :							Tuple				     =					principal_component_analysis(_lowerCamelCase       ,				_lowerCamelCase						)
														if not np.allclose(_lowerCamelCase       ,				_lowerCamelCase						):
																					raise AssertionError
														assert error_info.type is AssertionError


if __name__ == "__main__":
						import doctest

						doctest.testmod()
