
from __future__ import annotations

from decimal import Decimal

from numpy import array


def    A (      lowercase				)     ->						str:
		'''simple docstring'''

		UpperCamelCase						=  Decimal

		# Check if the provided matrix has 2 rows and 2 columns
		# since this implementation only works for 2x2 matrices
		if len(_lowerCamelCase				) == 2 and len(matrix[0]				) == 2 and len(matrix[1]				) == 2:
				# Calculate the determinant of the matrix
				UpperCamelCase						=  float(
				    d(matrix[0][0]				) * d(matrix[1][1]				) - d(matrix[1][0]				) * d(matrix[0][1]				)				)
				if determinant == 0:
						raise ValueError('This matrix has no inverse.'				)

				# Creates a copy of the matrix with swapped positions of the elements
				UpperCamelCase						=  [[0.0, 0.0], [0.0, 0.0]]
				UpperCamelCase						=  matrix[1][1], matrix[0][0]
				UpperCamelCase						=  -matrix[1][0], -matrix[0][1]

				# Calculate the inverse of the matrix
				return [
				    [(float(d(_lowerCamelCase				)				) / determinant) or 0.0 for n in row] for row in swapped_matrix
				]
		elif (
		    len(_lowerCamelCase				) == 3
		    and len(matrix[0]				) == 3
		    and len(matrix[1]				) == 3
		    and len(matrix[2]				) == 3
		):
				# Calculate the determinant of the matrix using Sarrus rule
				UpperCamelCase						=  float(
				    (
				        (d(matrix[0][0]				) * d(matrix[1][1]				) * d(matrix[2][2]				))
				        + (d(matrix[0][1]				) * d(matrix[1][2]				) * d(matrix[2][0]				))
				        + (d(matrix[0][2]				) * d(matrix[1][0]				) * d(matrix[2][1]				))
				    )
				    - (
				        (d(matrix[0][2]				) * d(matrix[1][1]				) * d(matrix[2][0]				))
				        + (d(matrix[0][1]				) * d(matrix[1][0]				) * d(matrix[2][2]				))
				        + (d(matrix[0][0]				) * d(matrix[1][2]				) * d(matrix[2][1]				))
				    )				)
				if determinant == 0:
						raise ValueError('This matrix has no inverse.'				)

				# Creating cofactor matrix
				UpperCamelCase						=  [
				    [d(0.0				), d(0.0				), d(0.0				)],
				    [d(0.0				), d(0.0				), d(0.0				)],
				    [d(0.0				), d(0.0				), d(0.0				)],
				]
				UpperCamelCase						=  (d(matrix[1][1]				) * d(matrix[2][2]				)) - (
				    d(matrix[1][2]				) * d(matrix[2][1]				)
				)
				UpperCamelCase						=  -(
				    (d(matrix[1][0]				) * d(matrix[2][2]				)) - (d(matrix[1][2]				) * d(matrix[2][0]				))
				)
				UpperCamelCase						=  (d(matrix[1][0]				) * d(matrix[2][1]				)) - (
				    d(matrix[1][1]				) * d(matrix[2][0]				)
				)
				UpperCamelCase						=  -(
				    (d(matrix[0][1]				) * d(matrix[2][2]				)) - (d(matrix[0][2]				) * d(matrix[2][1]				))
				)
				UpperCamelCase						=  (d(matrix[0][0]				) * d(matrix[2][2]				)) - (
				    d(matrix[0][2]				) * d(matrix[2][0]				)
				)
				UpperCamelCase						=  -(
				    (d(matrix[0][0]				) * d(matrix[2][1]				)) - (d(matrix[0][1]				) * d(matrix[2][0]				))
				)
				UpperCamelCase						=  (d(matrix[0][1]				) * d(matrix[1][2]				)) - (
				    d(matrix[0][2]				) * d(matrix[1][1]				)
				)
				UpperCamelCase						=  -(
				    (d(matrix[0][0]				) * d(matrix[1][2]				)) - (d(matrix[0][2]				) * d(matrix[1][0]				))
				)
				UpperCamelCase						=  (d(matrix[0][0]				) * d(matrix[1][1]				)) - (
				    d(matrix[0][1]				) * d(matrix[1][0]				)
				)

				# Transpose the cofactor matrix (Adjoint matrix)
				UpperCamelCase						=  array(_lowerCamelCase				)
				for i in range(3				):
						for j in range(3				):
								UpperCamelCase						=  cofactor_matrix[j][i]

        # Inverse of the matrix using the formula (1/determinant) * adjoint matrix
				UpperCamelCase						=  array(_lowerCamelCase				)
				for i in range(3				):
						for j in range(3				):
								inverse_matrix[i][j] /= d(_lowerCamelCase				)

        # Calculate the inverse of the matrix
				return [[float(d(_lowerCamelCase				)				) or 0.0 for n in row] for row in inverse_matrix]
		raise ValueError('Please provide a matrix of size 2x2 or 3x3.'				)


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
