





from dataclasses import dataclass
from typing import Optional, Tuple, Union

import flax
import jax.numpy as jnp
from jax import random

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .scheduling_utils_flax import FlaxSchedulerMixin







@flax.struct.dataclass
class    __snake_case	:





       '''simple docstring'''







       lowerCAmelCase__						=					None
       lowerCAmelCase__						=					None
       lowerCAmelCase__						=					None  # sigma(t_i)




       @classmethod
       def 							UpperCAmelCase__			(  cls			:   Tuple		):
         return cls()







@dataclass
class    __snake_case	(							__lowerCamelCase      ):





       '''simple docstring'''







       lowerCAmelCase__						=					42
       lowerCAmelCase__						=					42
       lowerCAmelCase__						=					42







class    __snake_case	(							__lowerCamelCase       ,      __lowerCamelCase      ):





       '''simple docstring'''







       @property
       def 							UpperCAmelCase__			(  self			:   Dict		):
         return True




       @register_to_config
       def __init__(  self			:   Optional[int]							,    A			:   float = 0.02							,    A			:   float = 100							,    A			:   float = 1.007							,    A			:   float = 80							,    A			:   float = 0.05							,    A			:   float = 50							,    ):
         pass




       def 							UpperCAmelCase__			(  self			:   Any		):
         return KarrasVeSchedulerState.create()




       def 							UpperCAmelCase__			(  self			:   List[str]							,    A			:   KarrasVeSchedulerState							,    A			:   int							,    A			:   Tuple = ()		):
         __snake_case:						Any			  =						jnp.arange(0							,    A		)[::-1].copy()
         __snake_case:						Optional[int]			  =						[
             (
                 self.config.sigma_max**2
                 * (self.config.sigma_min**2 / self.config.sigma_max**2) ** (i / (num_inference_steps - 1))
             )
             for i in timesteps
         ]

         return state.replace(
             num_inference_steps=A							,    schedule=jnp.array(A							,    dtype=jnp.floataa		)							,    timesteps=A							,    )




       def 							UpperCAmelCase__			(  self			:   str							,    A			:   KarrasVeSchedulerState							,    A			:   jnp.ndarray							,    A			:   float							,    A			:   random.KeyArray							,    ):
         if self.config.s_min <= sigma <= self.config.s_max:
           __snake_case:						List[Any]			  =						min(self.config.s_churn / state.num_inference_steps							,    2**0.5 - 1		)
         else:
           __snake_case:						Any			  =						0

         # sample eps ~ N(0, S_noise^2 * I)
         __snake_case:						List[Any]			  =						random.split(A							,    num=1		)
         __snake_case:						Optional[int]			  =						self.config.s_noise * random.normal(key=A							,    shape=sample.shape		)
         __snake_case:						Any			  =						sigma + gamma * sigma
         __snake_case:						Optional[int]			  =						sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)

         return sample_hat, sigma_hat




       def 							UpperCAmelCase__			(  self			:   str							,    A			:   KarrasVeSchedulerState							,    A			:   jnp.ndarray							,    A			:   float							,    A			:   float							,    A			:   jnp.ndarray							,    A			:   bool = True							,    ):
         __snake_case:						Tuple			  =						sample_hat + sigma_hat * model_output
         __snake_case:						str			  =						(sample_hat - pred_original_sample) / sigma_hat
         __snake_case:						Optional[int]			  =						sample_hat + (sigma_prev - sigma_hat) * derivative

         if not return_dict:
           return (sample_prev, derivative, state)

         return FlaxKarrasVeOutput(prev_sample=A							,    derivative=A							,    state=A		)




       def 							UpperCAmelCase__			(  self			:   Any							,    A			:   KarrasVeSchedulerState							,    A			:   jnp.ndarray							,    A			:   float							,    A			:   float							,    A			:   jnp.ndarray							,    A			:   jnp.ndarray							,    A			:   jnp.ndarray							,    A			:   bool = True							,    ):
         __snake_case:						Optional[int]			  =						sample_prev + sigma_prev * model_output
         __snake_case:						int			  =						(sample_prev - pred_original_sample) / sigma_prev
         __snake_case:						Tuple			  =						sample_hat + (sigma_prev - sigma_hat) * (0.5 * derivative + 0.5 * derivative_corr)

         if not return_dict:
           return (sample_prev, derivative, state)

         return FlaxKarrasVeOutput(prev_sample=A							,    derivative=A							,    state=A		)






       def 							UpperCAmelCase__			(  self			:   str							,    A			:   KarrasVeSchedulerState							,    A			:   Any							,    A			:   Any							,    A			:   Any		):
         raise NotImplementedError()













from __future__ import annotations

from decimal import Decimal

from numpy import array







def       A__							(   SCREAMING_SNAKE_CASE__)       ->   list[list[float]]:
  __snake_case:						Any			  =						Decimal

  # Check if the provided matrix has 2 rows and 2 columns
  # since this implementation only works for 2x2 matrices
  if len(SCREAMING_SNAKE_CASE__) == 2 and len(matrix[0]) == 2 and len(matrix[1]) == 2:
    # Calculate the determinant of the matrix
    __snake_case:						Tuple			  =						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
    __snake_case:						Optional[int]			  =						[[0.0, 0.0], [0.0, 0.0]]
    __snake_case  ,       __snake_case:						Optional[Any]			  =						matrix[1][1], matrix[0][0]
    __snake_case  ,       __snake_case:						Union[str, Any]			  =						-matrix[1][0], -matrix[0][1]

    # Calculate the inverse of the matrix
    return [
        [(float(d(SCREAMING_SNAKE_CASE__)) / determinant) or 0.0 for n in row] for row in swapped_matrix
    ]
  elif (
      len(SCREAMING_SNAKE_CASE__) == 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
    __snake_case:						Any			  =						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
    __snake_case:						Tuple			  =						[
        [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)],
    ]
    __snake_case:						Dict			  =						(d(matrix[1][1]) * d(matrix[2][2])) - (
        d(matrix[1][2]) * d(matrix[2][1])
    )
    __snake_case:						Tuple			  =						-(
        (d(matrix[1][0]) * d(matrix[2][2])) - (d(matrix[1][2]) * d(matrix[2][0]))
    )
    __snake_case:						Optional[int]			  =						(d(matrix[1][0]) * d(matrix[2][1])) - (
        d(matrix[1][1]) * d(matrix[2][0])
    )
    __snake_case:						Union[str, Any]			  =						-(
        (d(matrix[0][1]) * d(matrix[2][2])) - (d(matrix[0][2]) * d(matrix[2][1]))
    )
    __snake_case:						str			  =						(d(matrix[0][0]) * d(matrix[2][2])) - (
        d(matrix[0][2]) * d(matrix[2][0])
    )
    __snake_case:						List[Any]			  =						-(
        (d(matrix[0][0]) * d(matrix[2][1])) - (d(matrix[0][1]) * d(matrix[2][0]))
    )
    __snake_case:						Optional[Any]			  =						(d(matrix[0][1]) * d(matrix[1][2])) - (
        d(matrix[0][2]) * d(matrix[1][1])
    )
    __snake_case:						List[str]			  =						-(
        (d(matrix[0][0]) * d(matrix[1][2])) - (d(matrix[0][2]) * d(matrix[1][0]))
    )
    __snake_case:						Optional[int]			  =						(d(matrix[0][0]) * d(matrix[1][1])) - (
        d(matrix[0][1]) * d(matrix[1][0])
    )

    # Transpose the cofactor matrix (Adjoint matrix)
    __snake_case:						List[Any]			  =						array(SCREAMING_SNAKE_CASE__)
    for i in range(3):
      for j in range(3):
        __snake_case:						Tuple			  =						cofactor_matrix[j][i]

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

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






