
"""simple docstring"""
# DISCLAIMER: This code is strongly influenced by https://github.com/pesser/pytorch_diffusion
# and https://github.com/hojonathanho/diffusion

from dataclasses import dataclass
from typing import Optional, Tuple, Union

import flax
import jax.numpy as jnp

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import (
    CommonSchedulerState,
    FlaxKarrasDiffusionSchedulers,
    FlaxSchedulerMixin,
    FlaxSchedulerOutput,
    add_noise_common,
    get_velocity_common,
)




@flax.struct.dataclass
class  _snake_case	:
							SCREAMING_SNAKE_CASE		  =    42
							SCREAMING_SNAKE_CASE		  =    42

							# setable values
							SCREAMING_SNAKE_CASE		  =    42
							SCREAMING_SNAKE_CASE		  =    42
							SCREAMING_SNAKE_CASE		  =    None






							@classmethod
							def 				__lowercase	(       cls    :      List[str]    ,			snake_case__    :      CommonSchedulerState    ,			snake_case__    :      jnp.ndarray    ,			snake_case__    :      jnp.ndarray    ,			snake_case__    :      jnp.ndarray    ,			):






												"""simple docstring"""



												return cls(
												    common=__lowerCamelCase    ,			final_alpha_cumprod=__lowerCamelCase    ,			init_noise_sigma=__lowerCamelCase    ,			timesteps=__lowerCamelCase    ,			)






@dataclass
class  _snake_case	(    _a							):
							SCREAMING_SNAKE_CASE		  =    42






class  _snake_case	(    _a			,					_a							):
							SCREAMING_SNAKE_CASE		  =    [e.name for e in FlaxKarrasDiffusionSchedulers]

							SCREAMING_SNAKE_CASE		  =    42






							@property
							def 				__lowercase	(       self    :      Optional[int]     ):






												"""simple docstring"""



												return True






							@register_to_config
							def __init__(       self    :      List[str]    ,			snake_case__    :      int = 1_0_0_0    ,			snake_case__    :      float = 0.0_001    ,			snake_case__    :      float = 0.02    ,			snake_case__    :      str = "linear"    ,			snake_case__    :      Optional[jnp.ndarray] = None    ,			snake_case__    :      bool = True    ,			snake_case__    :      int = 0    ,			snake_case__    :      str = "epsilon"    ,			snake_case__    :      jnp.dtype = jnp.floataa    ,			):






												"""simple docstring"""



												__a:    str		     =       dtype






							def 				__lowercase	(       self    :      Tuple    ,			snake_case__    :      Optional[CommonSchedulerState] = None     ):






												"""simple docstring"""



												if common is None:
																	__a:    Union[str, Any]		     =       CommonSchedulerState.create(self     )

												# At every step in ddim, we are looking into the previous alphas_cumprod
												# For the final step, there is no previous alphas_cumprod because we are already at 0
												# `set_alpha_to_one` decides whether we set this parameter simply to one or
												# whether we use the final alpha of the "non-previous" one.
												__a:    Optional[Any]		     =       (
												    jnp.array(1.0    ,			dtype=self.dtype     ) if self.config.set_alpha_to_one else common.alphas_cumprod[0]
												)

												# standard deviation of the initial noise distribution
												__a:    Dict		     =       jnp.array(1.0    ,			dtype=self.dtype     )

												__a:    Optional[Any]		     =       jnp.arange(0    ,			self.config.num_train_timesteps     ).round()[::-1]

												return DDIMSchedulerState.create(
												    common=__lowerCamelCase    ,			final_alpha_cumprod=__lowerCamelCase    ,			init_noise_sigma=__lowerCamelCase    ,			timesteps=__lowerCamelCase    ,			)






							def 				__lowercase	(       self    :      int    ,			snake_case__    :      DDIMSchedulerState    ,			snake_case__    :      jnp.ndarray    ,			snake_case__    :      Optional[int] = None     ):






												"""simple docstring"""



												return sample






							def 				__lowercase	(       self    :      List[str]    ,			snake_case__    :      DDIMSchedulerState    ,			snake_case__    :      int    ,			snake_case__    :      Tuple = ()     ):






												"""simple docstring"""



												__a:    Optional[int]		     =       self.config.num_train_timesteps // num_inference_steps
												# creates integer timesteps by multiplying by ratio
												# rounding to avoid issues when num_inference_step is power of 3
												__a:    Optional[int]		     =       (jnp.arange(0    ,			__lowerCamelCase     ) * step_ratio).round()[::-1] + self.config.steps_offset

												return state.replace(
												    num_inference_steps=__lowerCamelCase    ,			timesteps=__lowerCamelCase    ,			)






							def 				__lowercase	(       self    :      Dict    ,			snake_case__    :      DDIMSchedulerState    ,			snake_case__    :      Union[str, Any]    ,			snake_case__    :      Optional[int]     ):






												"""simple docstring"""



												__a:    str		     =       state.common.alphas_cumprod[timestep]
												__a:    Union[str, Any]		     =       jnp.where(
												    prev_timestep >= 0    ,			state.common.alphas_cumprod[prev_timestep]    ,			state.final_alpha_cumprod     )
												__a:    Dict		     =       1 - alpha_prod_t
												__a:    List[str]		     =       1 - alpha_prod_t_prev

												__a:    Any		     =       (beta_prod_t_prev / beta_prod_t) * (1 - alpha_prod_t / alpha_prod_t_prev)

												return variance






							def 				__lowercase	(       self    :      Any    ,			snake_case__    :      DDIMSchedulerState    ,			snake_case__    :      jnp.ndarray    ,			snake_case__    :      int    ,			snake_case__    :      jnp.ndarray    ,			snake_case__    :      float = 0.0    ,			snake_case__    :      bool = True    ,			):






												"""simple docstring"""



												if state.num_inference_steps is None:
																	raise ValueError(
																	    """Number of inference steps is 'None', you need to run 'set_timesteps' after creating the scheduler"""     )

												# See formulas (12) and (16) of DDIM paper https://arxiv.org/pdf/2010.02502.pdf
												# Ideally, read DDIM paper in-detail understanding

												# Notation (<variable name> -> <name in paper>
												# - pred_noise_t -> e_theta(x_t, t)
												# - pred_original_sample -> f_theta(x_t, t) or x_0
												# - std_dev_t -> sigma_t
												# - eta -> η
												# - pred_sample_direction -> "direction pointing to x_t"
												# - pred_prev_sample -> "x_t-1"

												# 1. get previous step value (=t-1)
												__a:    Any		     =       timestep - self.config.num_train_timesteps // state.num_inference_steps

												__a:    str		     =       state.common.alphas_cumprod
												__a:    List[str]		     =       state.final_alpha_cumprod

												# 2. compute alphas, betas
												__a:    Tuple		     =       alphas_cumprod[timestep]
												__a:    List[str]		     =       jnp.where(prev_timestep >= 0    ,			alphas_cumprod[prev_timestep]    ,			__lowerCamelCase     )

												__a:    List[str]		     =       1 - alpha_prod_t

												# 3. compute predicted original sample from predicted noise also called
												# "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
												if self.config.prediction_type == "epsilon":
																	__a:    int		     =       (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
																	__a:    int		     =       model_output
												elif self.config.prediction_type == "sample":
																	__a:    Optional[int]		     =       model_output
																	__a:    Union[str, Any]		     =       (sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5
												elif self.config.prediction_type == "v_prediction":
																	__a:    Optional[int]		     =       (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
																	__a:    Any		     =       (alpha_prod_t**0.5) * model_output + (beta_prod_t**0.5) * sample
												else:
																	raise ValueError(
																	    f"""prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample`, or"""
																	    """ `v_prediction`"""     )

												# 4. compute variance: "sigma_t(η)" -> see formula (16)
												# σ_t = sqrt((1 − α_t−1)/(1 − α_t)) * sqrt(1 − α_t/α_t−1)
												__a:    Optional[Any]		     =       self._get_variance(__lowerCamelCase    ,			__lowerCamelCase    ,			__lowerCamelCase     )
												__a:    Optional[Any]		     =       eta * variance ** 0.5

												# 5. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
												__a:    Tuple		     =       (1 - alpha_prod_t_prev - std_dev_t**2) ** 0.5 * pred_epsilon

												# 6. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
												__a:    Optional[Any]		     =       alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction

												if not return_dict:
																	return (prev_sample, state)

												return FlaxDDIMSchedulerOutput(prev_sample=__lowerCamelCase    ,			state=__lowerCamelCase     )






							def 				__lowercase	(       self    :      List[str]    ,			snake_case__    :      DDIMSchedulerState    ,			snake_case__    :      jnp.ndarray    ,			snake_case__    :      jnp.ndarray    ,			snake_case__    :      jnp.ndarray    ,			):






												"""simple docstring"""



												return add_noise_common(state.common    ,			__lowerCamelCase    ,			__lowerCamelCase    ,			__lowerCamelCase     )






							def 				__lowercase	(       self    :      List[Any]    ,			snake_case__    :      DDIMSchedulerState    ,			snake_case__    :      jnp.ndarray    ,			snake_case__    :      jnp.ndarray    ,			snake_case__    :      jnp.ndarray    ,			):






												"""simple docstring"""



												return get_velocity_common(state.common    ,			__lowerCamelCase    ,			__lowerCamelCase    ,			__lowerCamelCase     )






							def __len__(       self    :      Union[str, Any]     ):






												"""simple docstring"""



												return self.config.num_train_timesteps








from typing import TYPE_CHECKING

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


UpperCAmelCase_					:		int           = {
    '''configuration_unispeech_sat''': ['''UNISPEECH_SAT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''UniSpeechSatConfig'''],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	UpperCAmelCase_					:		Tuple           = [
	    '''UNISPEECH_SAT_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''UniSpeechSatForAudioFrameClassification''',
	    '''UniSpeechSatForCTC''',
	    '''UniSpeechSatForPreTraining''',
	    '''UniSpeechSatForSequenceClassification''',
	    '''UniSpeechSatForXVector''',
	    '''UniSpeechSatModel''',
	    '''UniSpeechSatPreTrainedModel''',
	]

if TYPE_CHECKING:
	from .configuration_unispeech_sat import UNISPEECH_SAT_PRETRAINED_CONFIG_ARCHIVE_MAP, UniSpeechSatConfig

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_unispeech_sat import (
		    UNISPEECH_SAT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    UniSpeechSatForAudioFrameClassification,
		    UniSpeechSatForCTC,
		    UniSpeechSatForPreTraining,
		    UniSpeechSatForSequenceClassification,
		    UniSpeechSatForXVector,
		    UniSpeechSatModel,
		    UniSpeechSatPreTrainedModel,
		)

else:
	import sys

	UpperCAmelCase_					:		str           = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
