



import warnings

from .generation import FlaxGenerationMixin






class 	lowercase		(				snake_case__):






	"""simple docstring"""


	warnings.warn(
	    "Importing `FlaxGenerationMixin` from `src/transformers/generation_flax_utils.py` is deprecated and will "
	    "be removed in Transformers v5. Import as `from transformers import FlaxGenerationMixin` instead."				,					snake_case__				,					)









# 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 	lowercase		:






	"""simple docstring"""


	a__   :      CommonSchedulerState
	a__   :      jnp.ndarray

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




	@classmethod
	def 						_SCREAMING_SNAKE_CASE	(			cls			:		Tuple		,						__UpperCAmelCase			:		CommonSchedulerState		,						__UpperCAmelCase			:		jnp.ndarray		,						__UpperCAmelCase			:		jnp.ndarray		,						__UpperCAmelCase			:		jnp.ndarray		,						)    ->				int:
							return cls(
							    common=__UpperCAmelCase		,						final_alpha_cumprod=__UpperCAmelCase		,						init_noise_sigma=__UpperCAmelCase		,						timesteps=__UpperCAmelCase		,						)






@dataclass
class 	lowercase		(				snake_case__):






	"""simple docstring"""


	a__   :      DDIMSchedulerState






class 	lowercase		(				snake_case__				,					snake_case__):






	"""simple docstring"""


	a__   :      Dict	     =							[e.name for e in FlaxKarrasDiffusionSchedulers]

	a__   :      jnp.dtype




	@property
	def 						_SCREAMING_SNAKE_CASE	(			self			:		Dict  )    ->				List[str]:
							return True




	@register_to_config
	def __init__(			self			:		List[Any]		,						__UpperCAmelCase			:		int = 1_000		,						__UpperCAmelCase			:		float = 0.0_001		,						__UpperCAmelCase			:		float = 0.02		,						__UpperCAmelCase			:		str = "linear"		,						__UpperCAmelCase			:		Optional[jnp.ndarray] = None		,						__UpperCAmelCase			:		bool = True		,						__UpperCAmelCase			:		int = 0		,						__UpperCAmelCase			:		str = "epsilon"		,						__UpperCAmelCase			:		jnp.dtype = jnp.floataa		,						)    ->				Union[str, Any]:
							UpperCAmelCase_=     dtype




	def 						_SCREAMING_SNAKE_CASE	(			self			:		Dict		,						__UpperCAmelCase			:		Optional[CommonSchedulerState] = None  )    ->				DDIMSchedulerState:
							if common is None:
													UpperCAmelCase_=     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.
							UpperCAmelCase_=     (
							    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
							UpperCAmelCase_=     jnp.array(1.0		,						dtype=self.dtype  )

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

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




	def 						_SCREAMING_SNAKE_CASE	(			self			:		Union[str, Any]		,						__UpperCAmelCase			:		DDIMSchedulerState		,						__UpperCAmelCase			:		jnp.ndarray		,						__UpperCAmelCase			:		Optional[int] = None  )    ->				jnp.ndarray:
							return sample




	def 						_SCREAMING_SNAKE_CASE	(			self			:		Optional[int]		,						__UpperCAmelCase			:		DDIMSchedulerState		,						__UpperCAmelCase			:		int		,						__UpperCAmelCase			:		Tuple = ()  )    ->				DDIMSchedulerState:
							UpperCAmelCase_=     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
							UpperCAmelCase_=     (jnp.arange(0		,						__UpperCAmelCase  ) * step_ratio).round()[::-1] + self.config.steps_offset

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




	def 						_SCREAMING_SNAKE_CASE	(			self			:		Optional[Any]		,						__UpperCAmelCase			:		DDIMSchedulerState		,						__UpperCAmelCase			:		Optional[int]		,						__UpperCAmelCase			:		Tuple  )    ->				int:
							UpperCAmelCase_=     state.common.alphas_cumprod[timestep]
							UpperCAmelCase_=     jnp.where(
							    prev_timestep >= 0		,						state.common.alphas_cumprod[prev_timestep]		,						state.final_alpha_cumprod  )
							UpperCAmelCase_=     1 - alpha_prod_t
							UpperCAmelCase_=     1 - alpha_prod_t_prev

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

							return variance




	def 						_SCREAMING_SNAKE_CASE	(			self			:		List[str]		,						__UpperCAmelCase			:		DDIMSchedulerState		,						__UpperCAmelCase			:		jnp.ndarray		,						__UpperCAmelCase			:		int		,						__UpperCAmelCase			:		jnp.ndarray		,						__UpperCAmelCase			:		float = 0.0		,						__UpperCAmelCase			:		bool = True		,						)    ->				Union[FlaxDDIMSchedulerOutput, Tuple]:
							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)
							UpperCAmelCase_=     timestep - self.config.num_train_timesteps // state.num_inference_steps

							UpperCAmelCase_=     state.common.alphas_cumprod
							UpperCAmelCase_=     state.final_alpha_cumprod

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

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

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

							if not return_dict:
													return (prev_sample, state)

							return FlaxDDIMSchedulerOutput(prev_sample=__UpperCAmelCase		,						state=__UpperCAmelCase  )




	def 						_SCREAMING_SNAKE_CASE	(			self			:		Optional[Any]		,						__UpperCAmelCase			:		DDIMSchedulerState		,						__UpperCAmelCase			:		jnp.ndarray		,						__UpperCAmelCase			:		jnp.ndarray		,						__UpperCAmelCase			:		jnp.ndarray		,						)    ->				jnp.ndarray:
							return add_noise_common(state.common		,						__UpperCAmelCase		,						__UpperCAmelCase		,						__UpperCAmelCase  )




	def 						_SCREAMING_SNAKE_CASE	(			self			:		List[Any]		,						__UpperCAmelCase			:		DDIMSchedulerState		,						__UpperCAmelCase			:		jnp.ndarray		,						__UpperCAmelCase			:		jnp.ndarray		,						__UpperCAmelCase			:		jnp.ndarray		,						)    ->				jnp.ndarray:
							return get_velocity_common(state.common		,						__UpperCAmelCase		,						__UpperCAmelCase		,						__UpperCAmelCase  )




	def __len__(			self			:		Union[str, Any]  )    ->				Optional[int]:
							return self.config.num_train_timesteps




