

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    				=     'scheduler_config.json'





class A__    ( A ):



 """simple docstring"""







 _lowercase     :					Optional[Any]      =						1
 _lowercase     :					List[str]      =						2
 _lowercase     :					Dict      =						3
 _lowercase     :					str      =						4
 _lowercase     :					str      =						5





@dataclass
class A__    ( A ):



 """simple docstring"""







 _lowercase     :					jnp.ndarray





class A__    :



 """simple docstring"""







 _lowercase     :					Union[str, Any]      =						SCHEDULER_CONFIG_NAME
 _lowercase     :					int      =						['''dtype''']
 _lowercase     :					int      =						[]
 _lowercase     :					Tuple      =						True

 @classmethod
 def    __magic_name__       (		cls :	int     ,						A_ :	Dict[str, Any] = None     ,						A_ :	Optional[str] = None     ,						A_ :	Optional[Any]=False     ,						**A_ :	Optional[int]     ,						):






      '''simple docstring'''




      _lowerCAmelCase   :					int        =    cls.load_config(
          pretrained_model_name_or_path=A_     ,						subfolder=A_     ,						return_unused_kwargs=A_     ,						**A_     ,						)
      _lowerCAmelCase   :					Optional[Any]        =    cls.from_config(A_     ,						return_unused_kwargs=A_     ,						**A_       )

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

      if return_unused_kwargs:
           return scheduler, state, unused_kwargs

      return scheduler, state

 def    __magic_name__       (		self :	int     ,						A_ :	Union[str, os.PathLike]     ,						A_ :	bool = False     ,						**A_ :	List[Any]       ):






      '''simple docstring'''




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

 @property
 def    __magic_name__       (		self :	int       ):






      '''simple docstring'''




      return self._get_compatibles()


 @classmethod
 def    __magic_name__       (		cls :	List[str]       ):






      '''simple docstring'''




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







def 	_snake_case		(    SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE				)       ->      jnp.ndarray:

     """simple docstring"""







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







def 	_snake_case		(    SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE=0.999		,							SCREAMING_SNAKE_CASE=jnp.floataa				)       ->      jnp.ndarray:

     """simple docstring"""







     def alpha_bar(SCREAMING_SNAKE_CASE				):
          return math.cos((time_step + 0.008) / 1.008 * math.pi / 2				) ** 2

     _lowerCAmelCase   :					int        =    []
     for i in range(SCREAMING_SNAKE_CASE				):
          _lowerCAmelCase   :					Union[str, Any]        =    i / num_diffusion_timesteps
          _lowerCAmelCase   :					Tuple        =    (i + 1) / num_diffusion_timesteps
          betas.append(min(1 - alpha_bar(SCREAMING_SNAKE_CASE				) / alpha_bar(SCREAMING_SNAKE_CASE				)		,							SCREAMING_SNAKE_CASE				)				)
     return jnp.array(SCREAMING_SNAKE_CASE		,							dtype=SCREAMING_SNAKE_CASE				)





@flax.struct.dataclass
class A__    :



 """simple docstring"""







 _lowercase     :					jnp.ndarray
 _lowercase     :					jnp.ndarray
 _lowercase     :					jnp.ndarray

 @classmethod
 def    __magic_name__       (		cls :	Optional[Any]     ,						A_ :	Dict       ):






      '''simple docstring'''




      _lowerCAmelCase   :					str        =    scheduler.config

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

      _lowerCAmelCase   :					Dict        =    1.0 - betas

      _lowerCAmelCase   :					Optional[Any]        =    jnp.cumprod(A_     ,						axis=0       )

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







def 	_snake_case		(    SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE				)       ->      Optional[Any]:

     """simple docstring"""







     _lowerCAmelCase   :					Optional[int]        =    state.alphas_cumprod

     _lowerCAmelCase   :					Tuple        =    alphas_cumprod[timesteps] ** 0.5
     _lowerCAmelCase   :					Dict        =    sqrt_alpha_prod.flatten()
     _lowerCAmelCase   :					Tuple        =    broadcast_to_shape_from_left(SCREAMING_SNAKE_CASE		,							original_samples.shape				)

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

     return sqrt_alpha_prod, sqrt_one_minus_alpha_prod







def 	_snake_case		(    SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE				)       ->      List[str]:

     """simple docstring"""







     _lowerCAmelCase   :					Union[str, Any]        =    get_sqrt_alpha_prod(SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE				)
     _lowerCAmelCase   :					Dict        =    sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
     return noisy_samples







def 	_snake_case		(    SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE				)       ->      Tuple:

     """simple docstring"""







     _lowerCAmelCase   :					Any        =    get_sqrt_alpha_prod(SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE				)
     _lowerCAmelCase   :					str        =    sqrt_alpha_prod * noise - sqrt_one_minus_alpha_prod * sample
     return velocity





from __future__ import annotations

from math import pi
from typing import Protocol

import matplotlib.pyplot as plt
import numpy as np





class A__    ( A ):



 """simple docstring"""







 def    __magic_name__       (		self :	List[Any]     ,						A_ :	float       ):






      '''simple docstring'''




      return 0.0







def 	_snake_case		(    SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE				)       ->      tuple[int | float, int | float]:

     """simple docstring"""







     _lowerCAmelCase   :					Union[str, Any]        =    min([-20, np.min(fft_results[1 : samplerate // 2 - 1]				)]				)
     _lowerCAmelCase   :					Optional[Any]        =    max([20, np.max(fft_results[1 : samplerate // 2 - 1]				)]				)
     return lowest, highest







def 	_snake_case		(    SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE				)       ->      None:

     """simple docstring"""







     _lowerCAmelCase   :					Any        =    512
     _lowerCAmelCase   :					Tuple        =    [1] + [0] * (size - 1)
     _lowerCAmelCase   :					Optional[Any]        =    [filter_type.process(SCREAMING_SNAKE_CASE				) for item in inputs]

     _lowerCAmelCase   :					Dict        =    [0] * (samplerate - size)  # zero-padding
     outputs += filler
     _lowerCAmelCase   :					Dict        =    np.abs(np.fft.fft(SCREAMING_SNAKE_CASE				)				)
     _lowerCAmelCase   :					str        =    20 * np.logaa(SCREAMING_SNAKE_CASE				)

     # Frequencies on log scale from 24 to nyquist frequency
     plt.xlim(24		,							samplerate / 2 - 1				)
     plt.xlabel("Frequency (Hz)"				)
     plt.xscale("log"				)

     # Display within reasonable bounds
     _lowerCAmelCase   :					Tuple        =    get_bounds(SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE				)
     plt.ylim(max([-80, bounds[0]]				)		,							min([80, bounds[1]]				)				)
     plt.ylabel("Gain (dB)"				)

     plt.plot(SCREAMING_SNAKE_CASE				)
     plt.show()







def 	_snake_case		(    SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE				)       ->      None:

     """simple docstring"""







     _lowerCAmelCase   :					Tuple        =    512
     _lowerCAmelCase   :					Tuple        =    [1] + [0] * (size - 1)
     _lowerCAmelCase   :					Dict        =    [filter_type.process(SCREAMING_SNAKE_CASE				) for item in inputs]

     _lowerCAmelCase   :					Tuple        =    [0] * (samplerate - size)  # zero-padding
     outputs += filler
     _lowerCAmelCase   :					Tuple        =    np.angle(np.fft.fft(SCREAMING_SNAKE_CASE				)				)

     # Frequencies on log scale from 24 to nyquist frequency
     plt.xlim(24		,							samplerate / 2 - 1				)
     plt.xlabel("Frequency (Hz)"				)
     plt.xscale("log"				)

     plt.ylim(-2 * pi		,							2 * pi				)
     plt.ylabel("Phase shift (Radians)"				)
     plt.plot(np.unwrap(SCREAMING_SNAKE_CASE		,							-2 * pi				)				)
     plt.show()






