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

import torch

from ..utils import BaseOutput


_snake_case      = '''scheduler_config.json'''
class       _lowerCAmelCase  (		__magic_name__							):

 """simple docstring"""
 SCREAMING_SNAKE_CASE_				:					Dict       						=1
 SCREAMING_SNAKE_CASE_				:					Tuple       						=2
 SCREAMING_SNAKE_CASE_				:					Union[str, Any]       						=3
 SCREAMING_SNAKE_CASE_				:					Any       						=4
 SCREAMING_SNAKE_CASE_				:					Tuple       						=5
 SCREAMING_SNAKE_CASE_				:					Tuple       						=6
 SCREAMING_SNAKE_CASE_				:					List[str]       						=7
 SCREAMING_SNAKE_CASE_				:					str       						=8
 SCREAMING_SNAKE_CASE_				:					Union[str, Any]       						=9
 SCREAMING_SNAKE_CASE_				:					Optional[int]       						=10
 SCREAMING_SNAKE_CASE_				:					Dict       						=11
 SCREAMING_SNAKE_CASE_				:					Optional[Any]       						=12
 SCREAMING_SNAKE_CASE_				:					str       						=13
 SCREAMING_SNAKE_CASE_				:					Union[str, Any]       						=14




@dataclass
class       _lowerCAmelCase  (		__magic_name__							):

 """simple docstring"""
 SCREAMING_SNAKE_CASE_				:					torch.FloatTensor




class       _lowerCAmelCase  :

 """simple docstring"""
 SCREAMING_SNAKE_CASE_				:					Union[str, Any]       						=SCHEDULER_CONFIG_NAME
 SCREAMING_SNAKE_CASE_				:					str       						=[]
 SCREAMING_SNAKE_CASE_				:					Optional[Any]       						=True

 @classmethod
 def      __lowerCAmelCase  (						cls			:			List[str]					, SCREAMING_SNAKE_CASE__			:			Dict[str, Any] = None					, SCREAMING_SNAKE_CASE__			:			Optional[str] = None					, SCREAMING_SNAKE_CASE__			:			List[Any]=False					, **SCREAMING_SNAKE_CASE__			:			List[Any]					, ):






      """simple docstring"""



      UpperCamelCase    ,		UpperCamelCase    ,		UpperCamelCase						    =					cls.load_config(
          pretrained_model_name_or_path=SCREAMING_SNAKE_CASE__					, subfolder=SCREAMING_SNAKE_CASE__					, return_unused_kwargs=SCREAMING_SNAKE_CASE__					, return_commit_hash=SCREAMING_SNAKE_CASE__					, **SCREAMING_SNAKE_CASE__					, )
      return cls.from_config(SCREAMING_SNAKE_CASE__					, return_unused_kwargs=SCREAMING_SNAKE_CASE__					, **SCREAMING_SNAKE_CASE__   )

 def      __lowerCAmelCase  (						self			:			str					, SCREAMING_SNAKE_CASE__			:			Union[str, os.PathLike]					, SCREAMING_SNAKE_CASE__			:			bool = False					, **SCREAMING_SNAKE_CASE__			:			Any   ):






      """simple docstring"""



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

 @property
 def      __lowerCAmelCase  (						self			:			List[Any]   ):






      """simple docstring"""



      return self._get_compatibles()






 @classmethod
 def      __lowerCAmelCase  (						cls			:			List[Any]   ):






      """simple docstring"""



      UpperCamelCase						    =					list(set([cls.__name__] + cls._compatibles   )   )
      UpperCamelCase						    =					importlib.import_module(__name__.split('.'   )[0]   )
      UpperCamelCase						    =					[
          getattr(SCREAMING_SNAKE_CASE__					, SCREAMING_SNAKE_CASE__   ) for c in compatible_classes_str if hasattr(SCREAMING_SNAKE_CASE__					, SCREAMING_SNAKE_CASE__   )
      ]
      return compatible_classes




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       _lowerCAmelCase  :

 """simple docstring"""
 # setable values
 SCREAMING_SNAKE_CASE_				:					Optional[int]       						=None
 SCREAMING_SNAKE_CASE_				:					Optional[jnp.ndarray]       						=None
 SCREAMING_SNAKE_CASE_				:					Optional[jnp.ndarray]       						=None  # sigma(t_i)

 @classmethod
 def      __lowerCAmelCase  (						cls			:			Optional[Any]   ):






      """simple docstring"""



      return cls()




@dataclass
class       _lowerCAmelCase  (		__magic_name__							):

 """simple docstring"""
 SCREAMING_SNAKE_CASE_				:					jnp.ndarray
 SCREAMING_SNAKE_CASE_				:					jnp.ndarray
 SCREAMING_SNAKE_CASE_				:					KarrasVeSchedulerState




class       _lowerCAmelCase  (		__magic_name__			,	__magic_name__							):

 """simple docstring"""

 @property
 def      __lowerCAmelCase  (						self			:			Any   ):






      """simple docstring"""



      return True

 @register_to_config
 def __init__(						self			:			Dict					, SCREAMING_SNAKE_CASE__			:			float = 0.02					, SCREAMING_SNAKE_CASE__			:			float = 1_00					, SCREAMING_SNAKE_CASE__			:			float = 1.007					, SCREAMING_SNAKE_CASE__			:			float = 80					, SCREAMING_SNAKE_CASE__			:			float = 0.05					, SCREAMING_SNAKE_CASE__			:			float = 50					, ):






      """simple docstring"""



      pass

 def      __lowerCAmelCase  (						self			:			int   ):






      """simple docstring"""



      return KarrasVeSchedulerState.create()

 def      __lowerCAmelCase  (						self			:			Union[str, Any]					, SCREAMING_SNAKE_CASE__			:			KarrasVeSchedulerState					, SCREAMING_SNAKE_CASE__			:			int					, SCREAMING_SNAKE_CASE__			:			Tuple = ()   ):






      """simple docstring"""



      UpperCamelCase						    =					jnp.arange(0					, SCREAMING_SNAKE_CASE__   )[::-1].copy()
      UpperCamelCase						    =					[
          (
              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=SCREAMING_SNAKE_CASE__					, schedule=jnp.array(SCREAMING_SNAKE_CASE__					, dtype=jnp.floataa   )					, timesteps=SCREAMING_SNAKE_CASE__					, )

 def      __lowerCAmelCase  (						self			:			List[Any]					, SCREAMING_SNAKE_CASE__			:			KarrasVeSchedulerState					, SCREAMING_SNAKE_CASE__			:			jnp.ndarray					, SCREAMING_SNAKE_CASE__			:			float					, SCREAMING_SNAKE_CASE__			:			random.KeyArray					, ):






      """simple docstring"""



      if self.config.s_min <= sigma <= self.config.s_max:
           UpperCamelCase						    =					min(self.config.s_churn / state.num_inference_steps					, 2**0.5 - 1   )
      else:
           UpperCamelCase						    =					0

      # sample eps ~ N(0, S_noise^2 * I)
      UpperCamelCase						    =					random.split(SCREAMING_SNAKE_CASE__					, num=1   )
      UpperCamelCase						    =					self.config.s_noise * random.normal(key=SCREAMING_SNAKE_CASE__					, shape=sample.shape   )
      UpperCamelCase						    =					sigma + gamma * sigma
      UpperCamelCase						    =					sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)

      return sample_hat, sigma_hat

 def      __lowerCAmelCase  (						self			:			Optional[int]					, SCREAMING_SNAKE_CASE__			:			KarrasVeSchedulerState					, SCREAMING_SNAKE_CASE__			:			jnp.ndarray					, SCREAMING_SNAKE_CASE__			:			float					, SCREAMING_SNAKE_CASE__			:			float					, SCREAMING_SNAKE_CASE__			:			jnp.ndarray					, SCREAMING_SNAKE_CASE__			:			bool = True					, ):






      """simple docstring"""



      UpperCamelCase						    =					sample_hat + sigma_hat * model_output
      UpperCamelCase						    =					(sample_hat - pred_original_sample) / sigma_hat
      UpperCamelCase						    =					sample_hat + (sigma_prev - sigma_hat) * derivative

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

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

 def      __lowerCAmelCase  (						self			:			Tuple					, SCREAMING_SNAKE_CASE__			:			KarrasVeSchedulerState					, SCREAMING_SNAKE_CASE__			:			jnp.ndarray					, SCREAMING_SNAKE_CASE__			:			float					, SCREAMING_SNAKE_CASE__			:			float					, SCREAMING_SNAKE_CASE__			:			jnp.ndarray					, SCREAMING_SNAKE_CASE__			:			jnp.ndarray					, SCREAMING_SNAKE_CASE__			:			jnp.ndarray					, SCREAMING_SNAKE_CASE__			:			bool = True					, ):






      """simple docstring"""



      UpperCamelCase						    =					sample_prev + sigma_prev * model_output
      UpperCamelCase						    =					(sample_prev - pred_original_sample) / sigma_prev
      UpperCamelCase						    =					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=SCREAMING_SNAKE_CASE__					, derivative=SCREAMING_SNAKE_CASE__					, state=SCREAMING_SNAKE_CASE__   )






 def      __lowerCAmelCase  (						self			:			Any					, SCREAMING_SNAKE_CASE__			:			KarrasVeSchedulerState					, SCREAMING_SNAKE_CASE__			:			Any					, SCREAMING_SNAKE_CASE__			:			str					, SCREAMING_SNAKE_CASE__			:			int   ):






      """simple docstring"""



      raise NotImplementedError()


