


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__  :Optional[int]   			=							'''scheduler_config.json'''

class 		__a      (				__SCREAMING_SNAKE_CASE			):
		_a	:  str				=	1
		_a	:  Dict				=	2
		_a	:  int				=	3
		_a	:  Tuple				=	4
		_a	:  Union[str, Any]				=	5



@dataclass
class 		__a      (				__SCREAMING_SNAKE_CASE			):
		_a	:  jnp.ndarray



class 		__a      :
		_a	:  int				=	SCHEDULER_CONFIG_NAME
		_a	:  str				=	['dtype']
		_a	:  List[str]				=	[]
		_a	:  Any				=	True
		@classmethod
		def 	UpperCAmelCase__				(			cls			,    _SCREAMING_SNAKE_CASE = None			,    _SCREAMING_SNAKE_CASE = None			,    _SCREAMING_SNAKE_CASE=False			,    **_SCREAMING_SNAKE_CASE			,    ) ->       Dict:




									"""simple docstring"""





									_UpperCAmelCase		,       _UpperCAmelCase 		=			cls.load_config(
									    pretrained_model_name_or_path=_a			,    subfolder=_a			,    return_unused_kwargs=_a			,    **_a			,    )
									_UpperCAmelCase		,       _UpperCAmelCase 		=			cls.from_config(_a			,    return_unused_kwargs=_a			,    **_a						)

									if hasattr(_a			,    'create_state'						) and getattr(_a			,    'has_state'			,    _a						):
																_UpperCAmelCase 		=			scheduler.create_state()

									if return_unused_kwargs:
																return scheduler, state, unused_kwargs

									return scheduler, state
		def 	UpperCAmelCase__				(			self			,    _SCREAMING_SNAKE_CASE			,    _SCREAMING_SNAKE_CASE = False			,    **_SCREAMING_SNAKE_CASE						) ->       List[str]:




									"""simple docstring"""





									self.save_config(save_directory=_a			,    push_to_hub=_a			,    **_a						)
		@property
		def 	UpperCAmelCase__				(			self						) ->       List[Any]:




									"""simple docstring"""





									return self._get_compatibles()






		@classmethod
		def 	UpperCAmelCase__				(			cls						) ->       List[str]:




									"""simple docstring"""





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



def        lowerCAmelCase__ (			a__:		jnp.ndarray			,  a__:		Tuple[int]       )  ->		jnp.ndarray:


							'''simple docstring'''





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

def        lowerCAmelCase__ (			a__:		int			,  a__:		str=0.999			,  a__:		List[str]=jnp.floataa       )  ->		jnp.ndarray:


							'''simple docstring'''





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

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

@flax.struct.dataclass
class 		__a      :
		_a	:  jnp.ndarray
		_a	:  jnp.ndarray
		_a	:  jnp.ndarray
		@classmethod
		def 	UpperCAmelCase__				(			cls			,    _SCREAMING_SNAKE_CASE						) ->       Any:




									"""simple docstring"""





									_UpperCAmelCase 		=			scheduler.config

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

									_UpperCAmelCase 		=			1.0 - betas

									_UpperCAmelCase 		=			jnp.cumprod(_a			,    axis=0						)

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



def        lowerCAmelCase__ (			a__:		CommonSchedulerState			,  a__:		jnp.ndarray			,  a__:		jnp.ndarray			,  a__:		jnp.ndarray       )  ->		Optional[int]:


							'''simple docstring'''





							_UpperCAmelCase 		=			state.alphas_cumprod

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

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

							return sqrt_alpha_prod, sqrt_one_minus_alpha_prod

def        lowerCAmelCase__ (			a__:		CommonSchedulerState			,  a__:		jnp.ndarray			,  a__:		jnp.ndarray			,  a__:		jnp.ndarray       )  ->		Dict:


							'''simple docstring'''





							_UpperCAmelCase		,       _UpperCAmelCase 		=			get_sqrt_alpha_prod(lowerCAmelCase__			,  lowerCAmelCase__			,  lowerCAmelCase__			,  lowerCAmelCase__       )
							_UpperCAmelCase 		=			sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
							return noisy_samples







def        lowerCAmelCase__ (			a__:		CommonSchedulerState			,  a__:		jnp.ndarray			,  a__:		jnp.ndarray			,  a__:		jnp.ndarray       )  ->		List[Any]:


							'''simple docstring'''





							_UpperCAmelCase		,       _UpperCAmelCase 		=			get_sqrt_alpha_prod(lowerCAmelCase__			,  lowerCAmelCase__			,  lowerCAmelCase__			,  lowerCAmelCase__       )
							_UpperCAmelCase 		=			sqrt_alpha_prod * noise - sqrt_one_minus_alpha_prod * sample
							return velocity




"""simple docstring"""




from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


lowercase_			     =  {"configuration_unispeech": ["UNISPEECH_PRETRAINED_CONFIG_ARCHIVE_MAP", "UniSpeechConfig"]}

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				lowercase_			     =  [
				    "UNISPEECH_PRETRAINED_MODEL_ARCHIVE_LIST",
				    "UniSpeechForCTC",
				    "UniSpeechForPreTraining",
				    "UniSpeechForSequenceClassification",
				    "UniSpeechModel",
				    "UniSpeechPreTrainedModel",
				]

if TYPE_CHECKING:
				from .configuration_unispeech import UNISPEECH_PRETRAINED_CONFIG_ARCHIVE_MAP, UniSpeechConfig

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_unispeech import (
								    UNISPEECH_PRETRAINED_MODEL_ARCHIVE_LIST,
								    UniSpeechForCTC,
								    UniSpeechForPreTraining,
								    UniSpeechForSequenceClassification,
								    UniSpeechModel,
								    UniSpeechPreTrainedModel,
								)

else:
				import sys

				lowercase_			     =  _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)






