"""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


_a  :  Optional[int]=				"scheduler_config.json"

class 	UpperCamelCase    (					lowercase		):
				UpperCAmelCase       :   Tuple					=	1
				UpperCAmelCase       :   Any					=	2
				UpperCAmelCase       :   Optional[Any]					=	3
				UpperCAmelCase       :   str					=	4
				UpperCAmelCase       :   Any					=	5







@dataclass
class 	UpperCamelCase    (					lowercase		):
				UpperCAmelCase       :   jnp.ndarray







class 	UpperCamelCase    :
				UpperCAmelCase       :   str					=	SCHEDULER_CONFIG_NAME
				UpperCAmelCase       :   Optional[Any]					=	["""dtype"""]
				UpperCAmelCase       :   Optional[Any]					=	[]
				UpperCAmelCase       :   Tuple					=	True



				@classmethod
				def       _lowercase  (cls       :   Optional[Any]			,						_A       :   Dict[str, Any] = None			,						_A       :   Optional[str] = None			,						_A       :   List[str]=False			,						**_A       :   List[Any]			,						)					->			int:
					__snake_case    :							List[str]   	=				cls.load_config(
					    pretrained_model_name_or_path=_A			,						subfolder=_A			,						return_unused_kwargs=_A			,						**_A			,						)
					__snake_case    :							Union[str, Any]   	=				cls.from_config(_A			,						return_unused_kwargs=_A			,						**_A)

					if hasattr(_A			,						'create_state') and getattr(_A			,						'has_state'			,						_A):
						__snake_case    :							List[Any]   	=				scheduler.create_state()

					if return_unused_kwargs:
						return scheduler, state, unused_kwargs

					return scheduler, state



				def       _lowercase  (self       :   Optional[int]			,						_A       :   Union[str, os.PathLike]			,						_A       :   bool = False			,						**_A       :   List[str])					->			Union[str, Any]:
					self.save_config(save_directory=_A			,						push_to_hub=_A			,						**_A)



				@property
				def       _lowercase  (self       :   Tuple)					->			Tuple:
					return self._get_compatibles()






				@classmethod
				def       _lowercase  (cls       :   Tuple)					->			Optional[int]:
					__snake_case    :							str   	=				list(set([cls.__name__] + cls._compatibles))
					__snake_case    :							List[str]   	=				importlib.import_module(__name__.split('.')[0])
					__snake_case    :							Dict   	=				[
					    getattr(_A			,						_A) for c in compatible_classes_str if hasattr(_A			,						_A)
					]
					return compatible_classes







def 						__UpperCAmelCase       (					UpperCAmelCase_					:       jnp.ndarray    ,							UpperCAmelCase_					:       Tuple[int]						)							->    jnp.ndarray:

	'''simple docstring'''





	assert len(UpperCAmelCase_						) >= x.ndim
	return jnp.broadcast_to(x.reshape(x.shape + (1,) * (len(UpperCAmelCase_						) - x.ndim)						)    ,							UpperCAmelCase_						)
def 						__UpperCAmelCase       (					UpperCAmelCase_					:       int    ,							UpperCAmelCase_					:       Optional[int]=0.999    ,							UpperCAmelCase_					:       Tuple=jnp.floataa						)							->    jnp.ndarray:

	'''simple docstring'''





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

	__snake_case    :							int   	=				[]
	for i in range(UpperCAmelCase_						):
		__snake_case    :							Optional[int]   	=				i / num_diffusion_timesteps
		__snake_case    :							Dict   	=				(i + 1) / num_diffusion_timesteps
		betas.append(min(1 - alpha_bar(UpperCAmelCase_						) / alpha_bar(UpperCAmelCase_						)    ,							UpperCAmelCase_						)						)
	return jnp.array(UpperCAmelCase_    ,							dtype=UpperCAmelCase_						)

@flax.struct.dataclass
class 	UpperCamelCase    :
				UpperCAmelCase       :   jnp.ndarray
				UpperCAmelCase       :   jnp.ndarray
				UpperCAmelCase       :   jnp.ndarray



				@classmethod
				def       _lowercase  (cls       :   str			,						_A       :   Tuple)					->			Optional[int]:
					__snake_case    :							Dict   	=				scheduler.config

					if config.trained_betas is not None:
						__snake_case    :							Tuple   	=				jnp.asarray(config.trained_betas			,						dtype=scheduler.dtype)
					elif config.beta_schedule == "linear":
						__snake_case    :							Tuple   	=				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    :							List[str]   	=				(
						    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[int]   	=				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    :							Any   	=				1.0 - betas

					__snake_case    :							str   	=				jnp.cumprod(_A			,						axis=0)

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







def 						__UpperCAmelCase       (					UpperCAmelCase_					:       CommonSchedulerState    ,							UpperCAmelCase_					:       jnp.ndarray    ,							UpperCAmelCase_					:       jnp.ndarray    ,							UpperCAmelCase_					:       jnp.ndarray						)							->    List[str]:

	'''simple docstring'''





	__snake_case    :							Any   	=				state.alphas_cumprod

	__snake_case    :							Tuple   	=				alphas_cumprod[timesteps] ** 0.5
	__snake_case    :							Optional[int]   	=				sqrt_alpha_prod.flatten()
	__snake_case    :							Union[str, Any]   	=				broadcast_to_shape_from_left(UpperCAmelCase_    ,							original_samples.shape						)

	__snake_case    :							Union[str, Any]   	=				(1 - alphas_cumprod[timesteps]) ** 0.5
	__snake_case    :							Dict   	=				sqrt_one_minus_alpha_prod.flatten()
	__snake_case    :							List[str]   	=				broadcast_to_shape_from_left(UpperCAmelCase_    ,							original_samples.shape						)

	return sqrt_alpha_prod, sqrt_one_minus_alpha_prod
def 						__UpperCAmelCase       (					UpperCAmelCase_					:       CommonSchedulerState    ,							UpperCAmelCase_					:       jnp.ndarray    ,							UpperCAmelCase_					:       jnp.ndarray    ,							UpperCAmelCase_					:       jnp.ndarray						)							->    List[Any]:

	'''simple docstring'''





	__snake_case    :							Any   	=				get_sqrt_alpha_prod(UpperCAmelCase_    ,							UpperCAmelCase_    ,							UpperCAmelCase_    ,							UpperCAmelCase_						)
	__snake_case    :							Dict   	=				sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
	return noisy_samples

def 						__UpperCAmelCase       (					UpperCAmelCase_					:       CommonSchedulerState    ,							UpperCAmelCase_					:       jnp.ndarray    ,							UpperCAmelCase_					:       jnp.ndarray    ,							UpperCAmelCase_					:       jnp.ndarray						)							->    Optional[int]:

	'''simple docstring'''





	__snake_case    :							Optional[Any]   	=				get_sqrt_alpha_prod(UpperCAmelCase_    ,							UpperCAmelCase_    ,							UpperCAmelCase_    ,							UpperCAmelCase_						)
	__snake_case    :							str   	=				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_torch_available


_a  :  Any=				{"configuration_speech_encoder_decoder": ["SpeechEncoderDecoderConfig"]}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_a  :  Union[str, Any]=				["SpeechEncoderDecoderModel"]

try:
	if not is_flax_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_a  :  Tuple=				["FlaxSpeechEncoderDecoderModel"]

if TYPE_CHECKING:
	from .configuration_speech_encoder_decoder import SpeechEncoderDecoderConfig

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_speech_encoder_decoder import SpeechEncoderDecoderModel

	try:
		if not is_flax_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_flax_speech_encoder_decoder import FlaxSpeechEncoderDecoderModel

else:
	import sys

	_a  :  str=				_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





