

'''simple docstring'''




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


_lowerCAmelCase            =		"""scheduler_config.json"""






class     _SCREAMING_SNAKE_CASE       (       UpperCAmelCase__      ):
 __SCREAMING_SNAKE_CASE	:Any     			=	1
 __SCREAMING_SNAKE_CASE	:Optional[int]     			=	2
 __SCREAMING_SNAKE_CASE	:List[Any]     			=	3
 __SCREAMING_SNAKE_CASE	:Dict     			=	4
 __SCREAMING_SNAKE_CASE	:Optional[Any]     			=	5






@dataclass
class     _SCREAMING_SNAKE_CASE       (       UpperCAmelCase__      ):
 __SCREAMING_SNAKE_CASE	:str     			=	42






class     _SCREAMING_SNAKE_CASE       :
 __SCREAMING_SNAKE_CASE	:Union[str, Any]     			=	SCHEDULER_CONFIG_NAME
 __SCREAMING_SNAKE_CASE	:Dict     			=	["""dtype"""]
 __SCREAMING_SNAKE_CASE	:Dict     			=	[]
 __SCREAMING_SNAKE_CASE	:List[str]     			=	True



 @classmethod
 def 					snake_case__    (       cls						:       List[Any]					, a__						:       Dict[str, Any] = None					, a__						:       Optional[str] = None					, a__						:       int=False					, **a__						:       Union[str, Any]					, ):
      __magic_name__  ,    __magic_name__		  =							cls.load_config(
          pretrained_model_name_or_path=UpperCAmelCase__					, subfolder=UpperCAmelCase__					, return_unused_kwargs=UpperCAmelCase__					, **UpperCAmelCase__					, )
      __magic_name__  ,    __magic_name__		  =							cls.from_config(UpperCAmelCase__					, return_unused_kwargs=UpperCAmelCase__					, **UpperCAmelCase__     )

      if hasattr(UpperCAmelCase__					, '''create_state'''     ) and getattr(UpperCAmelCase__					, '''has_state'''					, UpperCAmelCase__     ):
           __magic_name__		  =							scheduler.create_state()

      if return_unused_kwargs:
           return scheduler, state, unused_kwargs

      return scheduler, state



 def 					snake_case__    (       self						:       List[Any]					, a__						:       Union[str, os.PathLike]					, a__						:       bool = False					, **a__						:       Optional[Any]     ):
      self.save_config(save_directory=UpperCAmelCase__					, push_to_hub=UpperCAmelCase__					, **UpperCAmelCase__     )



 @property
 def 					snake_case__    (       self						:       Optional[int]     ):
      return self._get_compatibles()



 @classmethod
 def 					snake_case__    (       cls						:       int     ):
      __magic_name__		  =							list(set([cls.__name__] + cls._compatibles     )     )
      __magic_name__		  =							importlib.import_module(__name__.split('''.'''     )[0]     )
      __magic_name__		  =							[
          getattr(UpperCAmelCase__					, UpperCAmelCase__     ) for c in compatible_classes_str if hasattr(UpperCAmelCase__					, UpperCAmelCase__     )
      ]
      return compatible_classes




def 		UpperCamelCase			(					a						,		a					) ->      jnp.ndarray:






     '''simple docstring'''
     assert len(lowercase_					) >= x.ndim
     return jnp.broadcast_to(x.reshape(x.shape + (1,) * (len(lowercase_					) - x.ndim)					)						,		lowercase_					)




def 		UpperCamelCase			(					a						,		a=0.9_99						,		a=jnp.floataa					) ->      jnp.ndarray:






     '''simple docstring'''

     def alpha_bar(a					):
          return math.cos((time_step + 0.0_08) / 1.0_08 * math.pi / 2					) ** 2

     __magic_name__		  =							[]
     for i in range(lowercase_					):
          __magic_name__		  =							i / num_diffusion_timesteps
          __magic_name__		  =							(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     _SCREAMING_SNAKE_CASE       :
 __SCREAMING_SNAKE_CASE	:List[Any]     			=	42
 __SCREAMING_SNAKE_CASE	:int     			=	42
 __SCREAMING_SNAKE_CASE	:Any     			=	42



 @classmethod
 def 					snake_case__    (       cls						:       Union[str, Any]					, a__						:       List[str]     ):
      __magic_name__		  =							scheduler.config

      if config.trained_betas is not None:
           __magic_name__		  =							jnp.asarray(config.trained_betas					, dtype=scheduler.dtype     )
      elif config.beta_schedule == "linear":
           __magic_name__		  =							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.
           __magic_name__		  =							(
               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
           __magic_name__		  =							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__}'''     )

      __magic_name__		  =							1.0 - betas

      __magic_name__		  =							jnp.cumprod(UpperCAmelCase__					, axis=0     )

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




def 		UpperCamelCase			(					a						,		a						,		a						,		a					) ->      List[str]:






     '''simple docstring'''
     __magic_name__		  =							state.alphas_cumprod

     __magic_name__		  =							alphas_cumprod[timesteps] ** 0.5
     __magic_name__		  =							sqrt_alpha_prod.flatten()
     __magic_name__		  =							broadcast_to_shape_from_left(lowercase_						,		original_samples.shape					)

     __magic_name__		  =							(1 - alphas_cumprod[timesteps]) ** 0.5
     __magic_name__		  =							sqrt_one_minus_alpha_prod.flatten()
     __magic_name__		  =							broadcast_to_shape_from_left(lowercase_						,		original_samples.shape					)

     return sqrt_alpha_prod, sqrt_one_minus_alpha_prod




def 		UpperCamelCase			(					a						,		a						,		a						,		a					) ->      List[str]:






     '''simple docstring'''
     __magic_name__  ,    __magic_name__		  =							get_sqrt_alpha_prod(lowercase_						,		lowercase_						,		lowercase_						,		lowercase_					)
     __magic_name__		  =							sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
     return noisy_samples




def 		UpperCamelCase			(					a						,		a						,		a						,		a					) ->      List[str]:






     '''simple docstring'''
     __magic_name__  ,    __magic_name__		  =							get_sqrt_alpha_prod(lowercase_						,		lowercase_						,		lowercase_						,		lowercase_					)
     __magic_name__		  =							sqrt_alpha_prod * noise - sqrt_one_minus_alpha_prod * sample
     return velocity












import unittest

from transformers import JukeboxTokenizer
from transformers.testing_utils import require_torch


class 							UpperCamelCase_       (					unittest.TestCase	):

    '''simple docstring'''






    UpperCAmelCase__				    = JukeboxTokenizer
    UpperCAmelCase__				    = {
        '''artist''': '''Zac Brown Band''',
        '''genres''': '''Country''',
        '''lyrics''': '''I met a traveller from an antique land,
        Who said "Two vast and trunkless legs of stone
        Stand in the desert. . . . Near them, on the sand,
        Half sunk a shattered visage lies, whose frown,
        And wrinkled lip, and sneer of cold command,
        Tell that its sculptor well those passions read
        Which yet survive, stamped on these lifeless things,
        The hand that mocked them, and the heart that fed;
        And on the pedestal, these words appear:
        My name is Ozymandias, King of Kings;
        Look on my Works, ye Mighty, and despair!
        Nothing beside remains. Round the decay
        Of that colossal Wreck, boundless and bare
        The lone and level sands stretch far away
        ''',
    }


    @require_torch
    def 			SCREAMING_SNAKE_CASE				(     self :      Dict)		->int:





           '''simple docstring'''

           import torch

           A__ 					=       JukeboxTokenizer.from_pretrained('''openai/jukebox-1b-lyrics''')
           A__ 					=       tokenizer(**self.metas)['''input_ids''']
           # fmt: off
           A__ 					=       [
               torch.tensor([[
                   0, 0, 0, 7_169, 507, 9, 76, 39, 31, 46, 76, 27,
                   76, 46, 44, 27, 48, 31, 38, 38, 31, 44, 76, 32,
                   44, 41, 39, 76, 27, 40, 76, 27, 40, 46, 35, 43,
                   47, 31, 76, 38, 27, 40, 30, 64, 78, 76, 76, 76,
                   76, 76, 76, 76, 76, 23, 34, 41, 76, 45, 27, 35,
                   30, 76, 71, 20, 49, 41, 76, 48, 27, 45, 46, 76,
                   27, 40, 30, 76, 46, 44, 47, 40, 37, 38, 31, 45,
                   45, 76, 38, 31, 33, 45, 76, 41, 32, 76, 45, 46,
                   41, 40, 31, 78, 76, 76, 76, 76, 76, 76, 76, 76,
                   19, 46, 27, 40, 30, 76, 35, 40, 76, 46, 34, 31,
                   76, 30, 31, 45, 31, 44, 46, 63, 76, 63, 76, 63,
                   76, 63, 76, 14, 31, 27, 44, 76, 46, 34, 31, 39,
                   64, 76, 41, 40, 76, 46, 34, 31, 76, 45, 27, 40,
                   30, 64, 78, 76, 76, 76, 76, 76, 76, 76, 76, 8,
                   27, 38, 32, 76, 45, 47, 40, 37, 76, 27, 76, 45,
                   34, 27, 46, 46, 31, 44, 31, 30, 76, 48, 35, 45,
                   27, 33, 31, 76, 38, 35, 31, 45, 64, 76, 49, 34,
                   41, 45, 31, 76, 32, 44, 41, 49, 40, 64, 78, 76,
                   76, 76, 76, 76, 76, 76, 76, 1, 40, 30, 76, 49,
                   44, 35, 40, 37, 38, 31, 30, 76, 38, 35, 42, 64,
                   76, 27, 40, 30, 76, 45, 40, 31, 31, 44, 76, 41,
                   32, 76, 29, 41, 38, 30, 76, 29, 41, 39, 39, 27,
                   40, 30, 64, 78, 76, 76, 76, 76, 76, 76, 76, 76,
                   20, 31, 38, 38, 76, 46, 34, 27, 46, 76, 35, 46,
                   45, 76, 45, 29, 47, 38, 42, 46, 41, 44, 76, 49,
                   31, 38, 38, 76, 46, 34, 41, 45, 31, 76, 42, 27,
                   45, 45, 35, 41, 40, 45, 76, 44, 31, 27, 30, 78,
                   76, 76, 76, 76, 76, 76, 76, 76, 23, 34, 35, 29,
                   34, 76, 51, 31, 46, 76, 45, 47, 44, 48, 35, 48,
                   31, 64, 76, 45, 46, 27, 39, 42, 31, 30, 76, 41,
                   40, 76, 46, 34, 31, 45, 31, 76, 38, 35, 32, 31,
                   38, 31, 45, 45, 76, 46, 34, 35, 40, 33, 45, 64,
                   78, 76, 76, 76, 76, 76, 76, 76, 76, 20, 34, 31,
                   76, 34, 27, 40, 30, 76, 46, 34, 27, 46, 76, 39,
                   41, 29, 37, 31, 30, 76, 46, 34, 31, 39, 64, 76,
                   27, 40, 30, 76, 46, 34, 31, 76, 34, 31, 27, 44,
                   46, 76, 46, 34, 27, 46, 76, 32, 31, 30, 66, 78,
                   76, 76, 76, 76, 76, 76, 76, 76, 1, 40, 30, 76,
                   41, 40, 76, 46, 34, 31, 76, 42, 31, 30, 31, 45,
                   46, 27, 38, 64, 76, 46, 34, 31, 45, 31, 76, 49,
                   41, 44, 30, 45, 76, 27, 42, 42, 31, 27, 44, 65,
                   78, 76, 76, 76, 76, 76, 76, 76, 76, 13, 51, 76,
                   40, 27, 39, 31, 76, 35, 45, 76, 15, 52, 51, 39,
                   27, 40, 30, 35, 27, 45, 64, 76, 11, 35, 40, 33,
                   76, 41, 32, 76, 11, 35, 40, 33, 45, 66, 78, 76,
                   76, 76, 76, 76, 76, 76, 76, 12, 41, 41, 37, 76,
                   41, 40, 76, 39, 51, 76, 23, 41, 44, 37, 45, 64,
                   76, 51, 31, 76, 13, 35, 33, 34, 46, 51, 64, 76,
                   27, 40, 30, 76, 30, 31, 45, 42, 27, 35, 44, 67,
                   78, 76, 76, 76, 76, 76, 76, 76, 76, 14, 41, 46,
                   34, 35, 40, 33, 76, 28, 31, 45, 35, 30, 31, 76,
                   44, 31, 39, 27, 35, 40, 45, 63, 76, 18, 41, 47,
                   40, 30, 76, 46, 34, 31, 76, 30, 31, 29, 27, 51,
                   78, 76, 76, 76, 76, 76, 76, 76, 76, 15, 32, 76,
                   46, 34, 27, 46, 76, 29, 41, 38, 41, 45, 45, 27,
                   38, 76, 23, 44, 31, 29, 37, 64, 76, 28, 41, 47,
                   40, 30, 38, 31, 45, 45, 76, 27, 40, 30, 76, 28,
                   27, 44, 31, 78, 76, 76, 76, 76, 76, 76, 76, 76,
                   20, 34, 31, 76, 38, 41, 40, 31, 76, 27, 40, 30,
                   76, 38, 31, 48, 31, 38, 76, 45, 27, 40, 30, 45,
                   76, 45, 46, 44, 31, 46, 29, 34, 76, 32, 27, 44,
                   76, 27, 49, 27, 51, 78, 76, 76, 76, 76, 76, 76,
                   76, 76]]),
               torch.tensor([[0, 0, 0, 1_069, 11]]),
               torch.tensor([[0, 0, 0, 1_069, 11]]),
           ]
           # fmt: on
           self.assertTrue(torch.allclose(tokens[0]   ,					EXPECTED_OUTPUT[0]))
           self.assertTrue(torch.allclose(tokens[1]   ,					EXPECTED_OUTPUT[1]))
           self.assertTrue(torch.allclose(tokens[2]   ,					EXPECTED_OUTPUT[2]))





    @require_torch
    def 			SCREAMING_SNAKE_CASE				(     self :      List[str])		->Optional[int]:





           '''simple docstring'''

           import torch

           A__ 					=       JukeboxTokenizer.from_pretrained('''openai/jukebox-5b-lyrics''')
           A__ 					=       tokenizer(**self.metas)['''input_ids''']
           # fmt: off
           A__ 					=       [
               torch.tensor([[
                   0, 0, 0, 1_069, 11, -1, -1, -1, -1, 9, 77, 39,
                   31, 46, 77, 27, 77, 46, 44, 27, 48, 31, 38, 38,
                   31, 44, 77, 32, 44, 41, 39, 77, 27, 40, 77, 27,
                   40, 46, 35, 43, 47, 31, 77, 38, 27, 40, 30, 64,
                   79, 77, 77, 77, 77, 77, 77, 77, 77, 23, 34, 41,
                   77, 45, 27, 35, 30, 77, 72, 20, 49, 41, 77, 48,
                   27, 45, 46, 77, 27, 40, 30, 77, 46, 44, 47, 40,
                   37, 38, 31, 45, 45, 77, 38, 31, 33, 45, 77, 41,
                   32, 77, 45, 46, 41, 40, 31, 79, 77, 77, 77, 77,
                   77, 77, 77, 77, 19, 46, 27, 40, 30, 77, 35, 40,
                   77, 46, 34, 31, 77, 30, 31, 45, 31, 44, 46, 63,
                   77, 63, 77, 63, 77, 63, 77, 14, 31, 27, 44, 77,
                   46, 34, 31, 39, 64, 77, 41, 40, 77, 46, 34, 31,
                   77, 45, 27, 40, 30, 64, 79, 77, 77, 77, 77, 77,
                   77, 77, 77, 8, 27, 38, 32, 77, 45, 47, 40, 37,
                   77, 27, 77, 45, 34, 27, 46, 46, 31, 44, 31, 30,
                   77, 48, 35, 45, 27, 33, 31, 77, 38, 35, 31, 45,
                   64, 77, 49, 34, 41, 45, 31, 77, 32, 44, 41, 49,
                   40, 64, 79, 77, 77, 77, 77, 77, 77, 77, 77, 1,
                   40, 30, 77, 49, 44, 35, 40, 37, 38, 31, 30, 77,
                   38, 35, 42, 64, 77, 27, 40, 30, 77, 45, 40, 31,
                   31, 44, 77, 41, 32, 77, 29, 41, 38, 30, 77, 29,
                   41, 39, 39, 27, 40, 30, 64, 79, 77, 77, 77, 77,
                   77, 77, 77, 77, 20, 31, 38, 38, 77, 46, 34, 27,
                   46, 77, 35, 46, 45, 77, 45, 29, 47, 38, 42, 46,
                   41, 44, 77, 49, 31, 38, 38, 77, 46, 34, 41, 45,
                   31, 77, 42, 27, 45, 45, 35, 41, 40, 45, 77, 44,
                   31, 27, 30, 79, 77, 77, 77, 77, 77, 77, 77, 77,
                   23, 34, 35, 29, 34, 77, 51, 31, 46, 77, 45, 47,
                   44, 48, 35, 48, 31, 64, 77, 45, 46, 27, 39, 42,
                   31, 30, 77, 41, 40, 77, 46, 34, 31, 45, 31, 77,
                   38, 35, 32, 31, 38, 31, 45, 45, 77, 46, 34, 35,
                   40, 33, 45, 64, 79, 77, 77, 77, 77, 77, 77, 77,
                   77, 20, 34, 31, 77, 34, 27, 40, 30, 77, 46, 34,
                   27, 46, 77, 39, 41, 29, 37, 31, 30, 77, 46, 34,
                   31, 39, 64, 77, 27, 40, 30, 77, 46, 34, 31, 77,
                   34, 31, 27, 44, 46, 77, 46, 34, 27, 46, 77, 32,
                   31, 30, 66, 79, 77, 77, 77, 77, 77, 77, 77, 77,
                   1, 40, 30, 77, 41, 40, 77, 46, 34, 31, 77, 42,
                   31, 30, 31, 45, 46, 27, 38, 64, 77, 46, 34, 31,
                   45, 31, 77, 49, 41, 44, 30, 45, 77, 27, 42, 42,
                   31, 27, 44, 65, 79, 77, 77, 77, 77, 77, 77, 77,
                   77, 13, 51, 77, 40, 27, 39, 31, 77, 35, 45, 77,
                   15, 52, 51, 39, 27, 40, 30, 35, 27, 45, 64, 77,
                   11, 35, 40, 33, 77, 41, 32, 77, 11, 35, 40, 33,
                   45, 66, 79, 77, 77, 77, 77, 77, 77, 77, 77, 12,
                   41, 41, 37, 77, 41, 40, 77, 39, 51, 77, 23, 41,
                   44, 37, 45, 64, 77, 51, 31, 77, 13, 35, 33, 34,
                   46, 51, 64, 77, 27, 40, 30, 77, 30, 31, 45, 42,
                   27, 35, 44, 67, 79, 77, 77, 77, 77, 77, 77, 77,
                   77, 14, 41, 46, 34, 35, 40, 33, 77, 28, 31, 45,
                   35, 30, 31, 77, 44, 31, 39, 27, 35, 40, 45, 63,
                   77, 18, 41, 47, 40, 30, 77, 46, 34, 31, 77, 30,
                   31, 29, 27, 51, 79, 77, 77, 77, 77, 77, 77, 77,
                   77, 15, 32, 77, 46, 34, 27, 46, 77, 29, 41, 38,
                   41, 45, 45, 27, 38, 77, 23, 44, 31, 29, 37, 64,
                   77, 28, 41, 47, 40, 30, 38, 31, 45, 45, 77, 27,
                   40, 30, 77, 28, 27, 44, 31, 79, 77, 77, 77, 77,
                   77, 77, 77, 77, 20, 34, 31, 77, 38, 41, 40, 31,
                   77, 27, 40, 30, 77, 38, 31, 48, 31, 38, 77, 45,
                   27, 40, 30, 45, 77, 45, 46, 44, 31, 46, 29, 34,
                   77, 32, 27, 44, 77, 27, 49, 27, 51, 79, 77, 77,
                   77, 77, 77, 77, 77, 77]]),
               torch.tensor([[0, 0, 0, 1_069, 11, -1, -1, -1, -1]]),
               torch.tensor([[0, 0, 0, 1_069, 11, -1, -1, -1, -1]]),
           ]
           # fmt: on
           self.assertTrue(torch.allclose(tokens[0]   ,					EXPECTED_OUTPUT[0]))
           self.assertTrue(torch.allclose(tokens[1]   ,					EXPECTED_OUTPUT[1]))
           self.assertTrue(torch.allclose(tokens[2]   ,					EXPECTED_OUTPUT[2]))


