
"""simple docstring"""







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 					A__			:
		A_		:			CommonSchedulerState
		A_		:			jnp.ndarray

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

		@classmethod
		def 				__lowerCamelCase				( cls			,						_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE			,						):
			return cls(
			    common=lowerCamelCase_			,						final_alpha_cumprod=lowerCamelCase_			,						init_noise_sigma=lowerCamelCase_			,						timesteps=lowerCamelCase_			,						)




@dataclass
class 					A__			(      A_):
		A_		:			DDIMSchedulerState




class 					A__			(      A_			,	A_):
		A_		:			Optional[int]							  =      [e.name for e in FlaxKarrasDiffusionSchedulers]

		A_		:			jnp.dtype

		@property
		def 				__lowerCamelCase				( self					):
			return True

		@register_to_config
		def __init__( self			,						_SCREAMING_SNAKE_CASE = 10_00			,						_SCREAMING_SNAKE_CASE = 0.0001			,						_SCREAMING_SNAKE_CASE = 0.02			,						_SCREAMING_SNAKE_CASE = "linear"			,						_SCREAMING_SNAKE_CASE = None			,						_SCREAMING_SNAKE_CASE = True			,						_SCREAMING_SNAKE_CASE = 0			,						_SCREAMING_SNAKE_CASE = "epsilon"			,						_SCREAMING_SNAKE_CASE = jnp.floataa			,						):
			__lowerCAmelCase     :		Union[str, Any]		     = dtype

		def 				__lowerCamelCase				( self			,						_SCREAMING_SNAKE_CASE = None					):
			if common is None:
				__lowerCAmelCase     :		List[str]		     = 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.
			__lowerCAmelCase     :		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
			__lowerCAmelCase     :		Union[str, Any]		     = jnp.array(1.0			,						dtype=self.dtype					)

			__lowerCAmelCase     :		List[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 				__lowerCamelCase				( self			,						_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE = None					):
			return sample

		def 				__lowerCamelCase				( self			,						_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE = ()					):
			__lowerCAmelCase     :		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
			__lowerCAmelCase     :		Optional[int]		     = (jnp.arange(0			,						lowerCamelCase_					) * step_ratio).round()[::-1] + self.config.steps_offset

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

		def 				__lowerCamelCase				( self			,						_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE					):
			__lowerCAmelCase     :		List[Any]		     = state.common.alphas_cumprod[timestep]
			__lowerCAmelCase     :		List[Any]		     = jnp.where(
			    prev_timestep >= 0			,						state.common.alphas_cumprod[prev_timestep]			,						state.final_alpha_cumprod					)
			__lowerCAmelCase     :		Any		     = 1 - alpha_prod_t
			__lowerCAmelCase     :		Dict		     = 1 - alpha_prod_t_prev

			__lowerCAmelCase     :		int		     = (beta_prod_t_prev / beta_prod_t) * (1 - alpha_prod_t / alpha_prod_t_prev)

			return variance

		def 				__lowerCamelCase				( self			,						_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE = 0.0			,						_SCREAMING_SNAKE_CASE = True			,						):
			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)
			__lowerCAmelCase     :		Any		     = timestep - self.config.num_train_timesteps // state.num_inference_steps

			__lowerCAmelCase     :		Any		     = state.common.alphas_cumprod
			__lowerCAmelCase     :		str		     = state.final_alpha_cumprod

			# 2. compute alphas, betas
			__lowerCAmelCase     :		Optional[int]		     = alphas_cumprod[timestep]
			__lowerCAmelCase     :		Optional[Any]		     = jnp.where(prev_timestep >= 0			,						alphas_cumprod[prev_timestep]			,						lowerCamelCase_					)

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

			# 5. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
			__lowerCAmelCase     :		Optional[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
			__lowerCAmelCase     :		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 				__lowerCamelCase				( self			,						_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE			,						):
			return add_noise_common(state.common			,						lowerCamelCase_			,						lowerCamelCase_			,						lowerCamelCase_					)

		def 				__lowerCamelCase				( self			,						_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE			,						):
			return get_velocity_common(state.common			,						lowerCamelCase_			,						lowerCamelCase_			,						lowerCamelCase_					)







		def __len__( self					):
			return self.config.num_train_timesteps
"""simple docstring"""


from ..utils import deprecate
from .attention_processor import (  # noqa: F401
    Attention,
    AttentionProcessor,
    AttnAddedKVProcessor,
    AttnProcessor2_0,
    LoRAAttnProcessor,
    LoRALinearLayer,
    LoRAXFormersAttnProcessor,
    SlicedAttnAddedKVProcessor,
    SlicedAttnProcessor,
    XFormersAttnProcessor,
)
from .attention_processor import AttnProcessor as AttnProcessorRename  # noqa: F401


deprecate(
    """cross_attention""",
    """0.20.0""",
    """Importing from cross_attention is deprecated. Please import from diffusers.models.attention_processor instead.""",
    standard_warn=False,
)


__A       			=      AttentionProcessor






class 					a					(				A_							):



  def __init__(		self   :    Optional[int]						,  *lowerCamelCase_   :    Union[str, Any]						,  **lowerCamelCase_   :    str     )      -> int:
      __a			      =						F"""{self.__class__.__name__} is deprecated and will be removed in `0.20.0`. Please use `from diffusers.models.attention_processor import {"".join(self.__class__.__name__.split("Cross"     )     )} instead."""
      deprecate("""cross_attention"""						,  """0.20.0"""						,  lowerCamelCase_						,  standard_warn=lowerCamelCase_     )
      super().__init__(*lowerCamelCase_						,  **lowerCamelCase_     )






class 					a					(				A_							):



  def __init__(		self   :    Optional[int]						,  *lowerCamelCase_   :    Dict						,  **lowerCamelCase_   :    Union[str, Any]     )      -> List[str]:
      __a			      =						F"""{self.__class__.__name__} is deprecated and will be removed in `0.20.0`. Please use `from diffusers.models.attention_processor import {"".join(self.__class__.__name__.split("Cross"     )     )} instead."""
      deprecate("""cross_attention"""						,  """0.20.0"""						,  lowerCamelCase_						,  standard_warn=lowerCamelCase_     )
      super().__init__(*lowerCamelCase_						,  **lowerCamelCase_     )






class 					a					(				A_							):



  def __init__(		self   :    Tuple						,  *lowerCamelCase_   :    Tuple						,  **lowerCamelCase_   :    List[str]     )      -> Optional[Any]:
      __a			      =						F"""{self.__class__.__name__} is deprecated and will be removed in `0.20.0`. Please use `from diffusers.models.attention_processor import {"".join(self.__class__.__name__.split("Cross"     )     )} instead."""
      deprecate("""cross_attention"""						,  """0.20.0"""						,  lowerCamelCase_						,  standard_warn=lowerCamelCase_     )
      super().__init__(*lowerCamelCase_						,  **lowerCamelCase_     )






class 					a					(				A_							):



  def __init__(		self   :    str						,  *lowerCamelCase_   :    Tuple						,  **lowerCamelCase_   :    Any     )      -> List[Any]:
      __a			      =						F"""{self.__class__.__name__} is deprecated and will be removed in `0.20.0`. Please use `from diffusers.models.attention_processor import {"".join(self.__class__.__name__.split("Cross"     )     )} instead."""
      deprecate("""cross_attention"""						,  """0.20.0"""						,  lowerCamelCase_						,  standard_warn=lowerCamelCase_     )
      super().__init__(*lowerCamelCase_						,  **lowerCamelCase_     )






class 					a					(				A_							):



  def __init__(		self   :    Union[str, Any]						,  *lowerCamelCase_   :    Optional[Any]						,  **lowerCamelCase_   :    Tuple     )      -> Union[str, Any]:
      __a			      =						F"""{self.__class__.__name__} is deprecated and will be removed in `0.20.0`. Please use `from diffusers.models.attention_processor import {"".join(self.__class__.__name__.split("Cross"     )     )} instead."""
      deprecate("""cross_attention"""						,  """0.20.0"""						,  lowerCamelCase_						,  standard_warn=lowerCamelCase_     )
      super().__init__(*lowerCamelCase_						,  **lowerCamelCase_     )






class 					a					(				A_							):



  def __init__(		self   :    List[str]						,  *lowerCamelCase_   :    Dict						,  **lowerCamelCase_   :    List[Any]     )      -> Any:
      __a			      =						F"""{self.__class__.__name__} is deprecated and will be removed in `0.20.0`. Please use `from diffusers.models.attention_processor import {"".join(self.__class__.__name__.split("Cross"     )     )} instead."""
      deprecate("""cross_attention"""						,  """0.20.0"""						,  lowerCamelCase_						,  standard_warn=lowerCamelCase_     )
      super().__init__(*lowerCamelCase_						,  **lowerCamelCase_     )






class 					a					(				A_							):



  def __init__(		self   :    Tuple						,  *lowerCamelCase_   :    List[str]						,  **lowerCamelCase_   :    Dict     )      -> Any:
      __a			      =						F"""{self.__class__.__name__} is deprecated and will be removed in `0.20.0`. Please use `from diffusers.models.attention_processor import {"".join(self.__class__.__name__.split("Cross"     )     )} instead."""
      deprecate("""cross_attention"""						,  """0.20.0"""						,  lowerCamelCase_						,  standard_warn=lowerCamelCase_     )
      super().__init__(*lowerCamelCase_						,  **lowerCamelCase_     )






class 					a					(				A_							):



  def __init__(		self   :    List[Any]						,  *lowerCamelCase_   :    Tuple						,  **lowerCamelCase_   :    Optional[Any]     )      -> List[Any]:
      __a			      =						F"""{self.__class__.__name__} is deprecated and will be removed in `0.20.0`. Please use `from diffusers.models.attention_processor import {"".join(self.__class__.__name__.split("Cross"     )     )} instead."""
      deprecate("""cross_attention"""						,  """0.20.0"""						,  lowerCamelCase_						,  standard_warn=lowerCamelCase_     )
      super().__init__(*lowerCamelCase_						,  **lowerCamelCase_     )
