





# 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      UpperCamelCase_				:
							lowerCamelCase_							=    42
							lowerCamelCase_							=    42

							# setable values
							lowerCamelCase_							=    42
							lowerCamelCase_							=    42
							lowerCamelCase_							=    None


							@classmethod
							def    _snake_case     (							cls			:str							, __A			:CommonSchedulerState							, __A			:jnp.ndarray							, __A			:jnp.ndarray							, __A			:jnp.ndarray							, )      ->      List[Any]:



									"""simple docstring"""



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




@dataclass
class      UpperCamelCase_				(   UpperCamelCase__			):
							lowerCamelCase_							=    42




class      UpperCamelCase_				(   UpperCamelCase__ ,     UpperCamelCase__			):
							lowerCamelCase_							=    [e.name for e in FlaxKarrasDiffusionSchedulers]

							lowerCamelCase_							=    42


							@property
							def    _snake_case     (							self			:Tuple						)      ->      Optional[Any]:



									"""simple docstring"""



									return True


							@register_to_config
							def __init__(							self			:int							, __A			:int = 1000							, __A			:float = 0.0_0_0_1							, __A			:float = 0.0_2							, __A			:str = "linear"							, __A			:Optional[jnp.ndarray] = None							, __A			:bool = True							, __A			:int = 0							, __A			:str = "epsilon"							, __A			:jnp.dtype = jnp.floataa							, )      ->      str:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=					dtype


							def    _snake_case     (							self			:str							, __A			:Optional[CommonSchedulerState] = None						)      ->      DDIMSchedulerState:



									"""simple docstring"""



									if common is None:
											SCREAMING_SNAKE_CASE__								=					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.
									SCREAMING_SNAKE_CASE__								=					(
									    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
									SCREAMING_SNAKE_CASE__								=					jnp.array(1.0							, dtype=self.dtype						)

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

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


							def    _snake_case     (							self			:Optional[Any]							, __A			:DDIMSchedulerState							, __A			:jnp.ndarray							, __A			:Optional[int] = None						)      ->      jnp.ndarray:



									"""simple docstring"""



									return sample


							def    _snake_case     (							self			:str							, __A			:DDIMSchedulerState							, __A			:int							, __A			:Tuple = ()						)      ->      DDIMSchedulerState:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=					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
									SCREAMING_SNAKE_CASE__								=					(jnp.arange(0							, __A						) * step_ratio).round()[::-1] + self.config.steps_offset

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


							def    _snake_case     (							self			:List[str]							, __A			:DDIMSchedulerState							, __A			:Any							, __A			:str						)      ->      int:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=					state.common.alphas_cumprod[timestep]
									SCREAMING_SNAKE_CASE__								=					jnp.where(
									    prev_timestep >= 0							, state.common.alphas_cumprod[prev_timestep]							, state.final_alpha_cumprod						)
									SCREAMING_SNAKE_CASE__								=					1 - alpha_prod_t
									SCREAMING_SNAKE_CASE__								=					1 - alpha_prod_t_prev

									SCREAMING_SNAKE_CASE__								=					(beta_prod_t_prev / beta_prod_t) * (1 - alpha_prod_t / alpha_prod_t_prev)

									return variance


							def    _snake_case     (							self			:List[str]							, __A			:DDIMSchedulerState							, __A			:jnp.ndarray							, __A			:int							, __A			:jnp.ndarray							, __A			:float = 0.0							, __A			:bool = True							, )      ->      Union[FlaxDDIMSchedulerOutput, Tuple]:



									"""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)
									SCREAMING_SNAKE_CASE__								=					timestep - self.config.num_train_timesteps // state.num_inference_steps

									SCREAMING_SNAKE_CASE__								=					state.common.alphas_cumprod
									SCREAMING_SNAKE_CASE__								=					state.final_alpha_cumprod

									# 2. compute alphas, betas
									SCREAMING_SNAKE_CASE__								=					alphas_cumprod[timestep]
									SCREAMING_SNAKE_CASE__								=					jnp.where(prev_timestep >= 0							, alphas_cumprod[prev_timestep]							, __A						)

									SCREAMING_SNAKE_CASE__								=					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":
											SCREAMING_SNAKE_CASE__								=					(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
											SCREAMING_SNAKE_CASE__								=					model_output
									elif self.config.prediction_type == "sample":
											SCREAMING_SNAKE_CASE__								=					model_output
											SCREAMING_SNAKE_CASE__								=					(sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5
									elif self.config.prediction_type == "v_prediction":
											SCREAMING_SNAKE_CASE__								=					(alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
											SCREAMING_SNAKE_CASE__								=					(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)
									SCREAMING_SNAKE_CASE__								=					self._get_variance(__A							, __A							, __A						)
									SCREAMING_SNAKE_CASE__								=					eta * variance ** 0.5

									# 5. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
									SCREAMING_SNAKE_CASE__								=					(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
									SCREAMING_SNAKE_CASE__								=					alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction

									if not return_dict:
											return (prev_sample, state)

									return FlaxDDIMSchedulerOutput(prev_sample=__A							, state=__A						)


							def    _snake_case     (							self			:str							, __A			:DDIMSchedulerState							, __A			:jnp.ndarray							, __A			:jnp.ndarray							, __A			:jnp.ndarray							, )      ->      jnp.ndarray:



									"""simple docstring"""



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


							def    _snake_case     (							self			:str							, __A			:DDIMSchedulerState							, __A			:jnp.ndarray							, __A			:jnp.ndarray							, __A			:jnp.ndarray							, )      ->      jnp.ndarray:



									"""simple docstring"""



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


							def __len__(							self			:Tuple						)      ->      str:



									"""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,
)


_lowerCamelCase       =	{
    'configuration_gptsan_japanese': ['GPTSAN_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GPTSanJapaneseConfig'],
    'tokenization_gptsan_japanese': ['GPTSanJapaneseTokenizer'],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_lowerCamelCase       =	[
	    'GPTSAN_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'GPTSanJapaneseForConditionalGeneration',
	    'GPTSanJapaneseModel',
	    'GPTSanJapanesePreTrainedModel',
	]
	_lowerCamelCase       =	[
	    'GPTSanJapaneseTokenizer',
	]


if TYPE_CHECKING:
	from .configuration_gptsan_japanese import GPTSAN_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTSanJapaneseConfig
	from .tokenization_gptsan_japanese import GPTSanJapaneseTokenizer

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_gptsan_japanese import (
		    GPTSAN_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST,
		    GPTSanJapaneseForConditionalGeneration,
		    GPTSanJapaneseModel,
		    GPTSanJapanesePreTrainedModel,
		)
		from .tokenization_gptsan_japanese import GPTSanJapaneseTokenizer


else:
	import sys

	_lowerCamelCase       =	_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)