


"""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 			SCREAMING_SNAKE_CASE_  :




							'''simple docstring'''





							__magic_name__					:     CommonSchedulerState
							__magic_name__					:     jnp.ndarray

							# setable values
							__magic_name__					:     jnp.ndarray
							__magic_name__					:     jnp.ndarray
							__magic_name__					:     Optional[int]				  =  None




							@classmethod
							def        UpperCAmelCase (						cls	,					lowerCamelCase__	,					lowerCamelCase__	,					lowerCamelCase__	,					lowerCamelCase__	,					)   ->	Tuple:







								'''simple docstring'''




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


@dataclass
class 			SCREAMING_SNAKE_CASE_  (   _lowercase):




							'''simple docstring'''





							__magic_name__					:     DDIMSchedulerState


class 			SCREAMING_SNAKE_CASE_  (   _lowercase	,      _lowercase):




							'''simple docstring'''





							__magic_name__					:     Optional[int]				  =  [e.name for e in FlaxKarrasDiffusionSchedulers]

							__magic_name__					:     jnp.dtype




							@property
							def        UpperCAmelCase (						self)   ->	Tuple:







								'''simple docstring'''




								return True




							@register_to_config
							def __init__(						self	,					lowerCamelCase__ = 1_000	,					lowerCamelCase__ = 0.00_01	,					lowerCamelCase__ = 0.02	,					lowerCamelCase__ = "linear"	,					lowerCamelCase__ = None	,					lowerCamelCase__ = True	,					lowerCamelCase__ = 0	,					lowerCamelCase__ = "epsilon"	,					lowerCamelCase__ = jnp.floataa	,					)   ->	int:







								'''simple docstring'''




								snake_case__  :						Optional[int]		=      dtype




							def        UpperCAmelCase (						self	,					lowerCamelCase__ = None)   ->	DDIMSchedulerState:







								'''simple docstring'''




								if common is None:
									snake_case__  :						int		=      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.
								snake_case__  :						Tuple		=      (
								    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
								snake_case__  :						List[str]		=      jnp.array(1.0	,					dtype=self.dtype)

								snake_case__  :						Dict		=      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        UpperCAmelCase (						self	,					lowerCamelCase__	,					lowerCamelCase__	,					lowerCamelCase__ = None)   ->	jnp.ndarray:







								'''simple docstring'''




								return sample




							def        UpperCAmelCase (						self	,					lowerCamelCase__	,					lowerCamelCase__	,					lowerCamelCase__ = ())   ->	DDIMSchedulerState:







								'''simple docstring'''




								snake_case__  :						List[str]		=      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
								snake_case__  :						str		=      (jnp.arange(0	,					lowerCamelCase__) * step_ratio).round()[::-1] + self.config.steps_offset

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




							def        UpperCAmelCase (						self	,					lowerCamelCase__	,					lowerCamelCase__	,					lowerCamelCase__)   ->	Optional[int]:







								'''simple docstring'''




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

								snake_case__  :						Union[str, Any]		=      (beta_prod_t_prev / beta_prod_t) * (1 - alpha_prod_t / alpha_prod_t_prev)

								return variance




							def        UpperCAmelCase (						self	,					lowerCamelCase__	,					lowerCamelCase__	,					lowerCamelCase__	,					lowerCamelCase__	,					lowerCamelCase__ = 0.0	,					lowerCamelCase__ = 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)
								snake_case__  :						Optional[Any]		=      timestep - self.config.num_train_timesteps // state.num_inference_steps

								snake_case__  :						List[str]		=      state.common.alphas_cumprod
								snake_case__  :						Any		=      state.final_alpha_cumprod

								# 2. compute alphas, betas
								snake_case__  :						Optional[int]		=      alphas_cumprod[timestep]
								snake_case__  :						Dict		=      jnp.where(prev_timestep >= 0	,					alphas_cumprod[prev_timestep]	,					lowerCamelCase__)

								snake_case__  :						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":
									snake_case__  :						List[Any]		=      (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
									snake_case__  :						Union[str, Any]		=      model_output
								elif self.config.prediction_type == "sample":
									snake_case__  :						Dict		=      model_output
									snake_case__  :						Tuple		=      (sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5
								elif self.config.prediction_type == "v_prediction":
									snake_case__  :						str		=      (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
									snake_case__  :						Optional[int]		=      (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)
								snake_case__  :						List[str]		=      self._get_variance(lowerCamelCase__	,					lowerCamelCase__	,					lowerCamelCase__)
								snake_case__  :						Optional[int]		=      eta * variance ** 0.5

								# 5. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
								snake_case__  :						List[Any]		=      (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
								snake_case__  :						str		=      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        UpperCAmelCase (						self	,					lowerCamelCase__	,					lowerCamelCase__	,					lowerCamelCase__	,					lowerCamelCase__	,					)   ->	jnp.ndarray:







								'''simple docstring'''




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




							def        UpperCAmelCase (						self	,					lowerCamelCase__	,					lowerCamelCase__	,					lowerCamelCase__	,					lowerCamelCase__	,					)   ->	jnp.ndarray:







								'''simple docstring'''




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







							def __len__(						self)   ->	List[Any]:







								'''simple docstring'''




								return self.config.num_train_timesteps










from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCAmelCase :				List[Any]							={"""configuration_qdqbert""": ["""QDQBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """QDQBertConfig"""]}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	UpperCAmelCase :				Dict							=[
	    """QDQBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """QDQBertForMaskedLM""",
	    """QDQBertForMultipleChoice""",
	    """QDQBertForNextSentencePrediction""",
	    """QDQBertForQuestionAnswering""",
	    """QDQBertForSequenceClassification""",
	    """QDQBertForTokenClassification""",
	    """QDQBertLayer""",
	    """QDQBertLMHeadModel""",
	    """QDQBertModel""",
	    """QDQBertPreTrainedModel""",
	    """load_tf_weights_in_qdqbert""",
	]


if TYPE_CHECKING:
	from .configuration_qdqbert import QDQBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, QDQBertConfig

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_qdqbert import (
		    QDQBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    QDQBertForMaskedLM,
		    QDQBertForMultipleChoice,
		    QDQBertForNextSentencePrediction,
		    QDQBertForQuestionAnswering,
		    QDQBertForSequenceClassification,
		    QDQBertForTokenClassification,
		    QDQBertLayer,
		    QDQBertLMHeadModel,
		    QDQBertModel,
		    QDQBertPreTrainedModel,
		    load_tf_weights_in_qdqbert,
		)


else:
	import sys

	UpperCAmelCase :				Optional[Any]							=_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
