
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


_a      :			Dict					=							"""scheduler_config.json"""






class      _UpperCAmelCase				(					_snake_case):
    __lowercase							: Tuple  =					1
    __lowercase							: Any  =					2
    __lowercase							: Optional[int]  =					3
    __lowercase							: Optional[int]  =					4
    __lowercase							: int  =					5






@dataclass
class      _UpperCAmelCase				(					_snake_case):
    __lowercase							: jnp.ndarray






class      _UpperCAmelCase				:
    __lowercase							: Dict  =					SCHEDULER_CONFIG_NAME
    __lowercase							: Optional[int]  =					["""dtype"""]
    __lowercase							: List[Any]  =					[]
    __lowercase							: List[Any]  =					True
    @classmethod
    def 					lowerCamelCase__	(    cls						,    snake_case_ = None						,    snake_case_ = None						,    snake_case_=False						,    **snake_case_						,    ):
      _snake_case     :						Optional[int]						=   cls.load_config(
          pretrained_model_name_or_path=snake_case_						,    subfolder=snake_case_						,    return_unused_kwargs=snake_case_						,    **snake_case_						,    )
      _snake_case     :						Dict						=   cls.from_config(snake_case_						,    return_unused_kwargs=snake_case_						,    **snake_case_     )

      if hasattr(snake_case_						,    "create_state"     ) and getattr(snake_case_						,    "has_state"						,    snake_case_     ):
        _snake_case     :						Optional[Any]						=   scheduler.create_state()

      if return_unused_kwargs:
        return scheduler, state, unused_kwargs

      return scheduler, state
    def 					lowerCamelCase__	(    self						,    snake_case_						,    snake_case_ = False						,    **snake_case_     ):
      self.save_config(save_directory=snake_case_						,    push_to_hub=snake_case_						,    **snake_case_     )
    @property
    def 					lowerCamelCase__	(    self     ):
      return self._get_compatibles()


    @classmethod
    def 					lowerCamelCase__	(    cls     ):
      _snake_case     :						Union[str, Any]						=   list(set([cls.__name__] + cls._compatibles     )     )
      _snake_case     :						List[Any]						=   importlib.import_module(__name__.split("."     )[0]     )
      _snake_case     :						Optional[int]						=   [
          getattr(snake_case_						,    snake_case_     ) for c in compatible_classes_str if hasattr(snake_case_						,    snake_case_     )
      ]
      return compatible_classes


def 				a__   (   a   :				jnp.ndarray       ,     a   :				Tuple[int]		):

  """simple docstring"""







  assert len(a		) >= x.ndim
  return jnp.broadcast_to(x.reshape(x.shape + (1,) * (len(a		) - x.ndim)		)       ,     a		)
def 				a__   (   a   :				int       ,     a   :				str=0.999       ,     a   :				Optional[Any]=jnp.floataa		):

  """simple docstring"""







  def alpha_bar(a   :				Optional[int]		):
    return math.cos((time_step + 0.008) / 1.008 * math.pi / 2		) ** 2

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






@flax.struct.dataclass
class      _UpperCAmelCase				:
    __lowercase							: jnp.ndarray
    __lowercase							: jnp.ndarray
    __lowercase							: jnp.ndarray
    @classmethod
    def 					lowerCamelCase__	(    cls						,    snake_case_     ):
      _snake_case     :						int						=   scheduler.config

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

      _snake_case     :						List[str]						=   1.0 - betas

      _snake_case     :						List[Any]						=   jnp.cumprod(snake_case_						,    axis=0     )

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


def 				a__   (   a   :				CommonSchedulerState       ,     a   :				jnp.ndarray       ,     a   :				jnp.ndarray       ,     a   :				jnp.ndarray		):

  """simple docstring"""







  _snake_case     :						Optional[int]						=   state.alphas_cumprod

  _snake_case     :						Union[str, Any]						=   alphas_cumprod[timesteps] ** 0.5
  _snake_case     :						Tuple						=   sqrt_alpha_prod.flatten()
  _snake_case     :						Any						=   broadcast_to_shape_from_left(a       ,     original_samples.shape		)

  _snake_case     :						Any						=   (1 - alphas_cumprod[timesteps]) ** 0.5
  _snake_case     :						List[Any]						=   sqrt_one_minus_alpha_prod.flatten()
  _snake_case     :						List[str]						=   broadcast_to_shape_from_left(a       ,     original_samples.shape		)

  return sqrt_alpha_prod, sqrt_one_minus_alpha_prod
def 				a__   (   a   :				CommonSchedulerState       ,     a   :				jnp.ndarray       ,     a   :				jnp.ndarray       ,     a   :				jnp.ndarray		):

  """simple docstring"""







  _snake_case     :						Any						=   get_sqrt_alpha_prod(a       ,     a       ,     a       ,     a		)
  _snake_case     :						List[Any]						=   sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
  return noisy_samples



def 				a__   (   a   :				CommonSchedulerState       ,     a   :				jnp.ndarray       ,     a   :				jnp.ndarray       ,     a   :				jnp.ndarray		):

  """simple docstring"""







  _snake_case     :						int						=   get_sqrt_alpha_prod(a       ,     a       ,     a       ,     a		)
  _snake_case     :						Any						=   sqrt_alpha_prod * noise - sqrt_one_minus_alpha_prod * sample
  return velocity



"""simple docstring"""
from __future__ import annotations

from collections import namedtuple
def 				a__   (   a   :				float       ,     a   :				float       ,     a   :				float		):

  """simple docstring"""







  _snake_case     :						Optional[Any]						=   namedtuple("result"       ,     "name value"		)
  if (voltage, current, power).count(0		) != 1:
    raise ValueError("Only one argument must be 0"		)
  elif power < 0:
    raise ValueError(
        "Power cannot be negative in any electrical/electronics system"		)
  elif voltage == 0:
    return result("voltage"       ,     power / current		)
  elif current == 0:
    return result("current"       ,     power / voltage		)
  elif power == 0:
    return result("power"       ,     float(round(abs(voltage * current		)       ,     2		)		)		)
  else:
    raise ValueError("Exactly one argument must be 0"		)


if __name__ == "__main__":
    import doctest

    doctest.testmod()

