




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  __lowerCamelCase       :




    """simple docstring"""

    snake_case__					=      4_2
    snake_case__					=      4_2

    # setable values
    snake_case__					=      4_2
    snake_case__					=      4_2
    snake_case__					=      None


    @classmethod
    def    a      (       cls     :     str   ,	SCREAMING_SNAKE_CASE__     :     Any   ,	SCREAMING_SNAKE_CASE__     :     str   ,	SCREAMING_SNAKE_CASE__     :     Tuple   ,	SCREAMING_SNAKE_CASE__     :     List[Any]   ,	)      ->       List[Any]:
         return cls(
             common=snake_case__   ,	final_alpha_cumprod=snake_case__   ,	init_noise_sigma=snake_case__   ,	timesteps=snake_case__   ,	)






@dataclass
class  __lowerCamelCase       (    SCREAMING_SNAKE_CASE__    ):




    """simple docstring"""

    snake_case__					=      4_2






class  __lowerCamelCase       (    SCREAMING_SNAKE_CASE__	,				SCREAMING_SNAKE_CASE__    ):




    """simple docstring"""

    snake_case__					=      [e.name for e in FlaxKarrasDiffusionSchedulers]

    snake_case__					=      4_2


    @property
    def    a      (       self     :     Any   )      ->       Optional[int]:
         return True


    @register_to_config
    def __init__(       self     :     Optional[int]   ,	SCREAMING_SNAKE_CASE__     :     List[Any] = 1_000   ,	SCREAMING_SNAKE_CASE__     :     Dict = 0.0_001   ,	SCREAMING_SNAKE_CASE__     :     int = 0.02   ,	SCREAMING_SNAKE_CASE__     :     Optional[int] = "linear"   ,	SCREAMING_SNAKE_CASE__     :     Optional[Any] = None   ,	SCREAMING_SNAKE_CASE__     :     Union[str, Any] = True   ,	SCREAMING_SNAKE_CASE__     :     int = 0   ,	SCREAMING_SNAKE_CASE__     :     Tuple = "epsilon"   ,	SCREAMING_SNAKE_CASE__     :     Optional[Any] = jnp.floataa   ,	)      ->       Optional[Any]:
         lowerCAmelCase__		= dtype


    def    a      (       self     :     Dict   ,	SCREAMING_SNAKE_CASE__     :     Optional[int] = None   )      ->       List[str]:
         if common is None:
              lowerCAmelCase__		= 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__		= (
             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__		= jnp.array(1.0   ,	dtype=self.dtype   )

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

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


    def    a      (       self     :     int   ,	SCREAMING_SNAKE_CASE__     :     Union[str, Any]   ,	SCREAMING_SNAKE_CASE__     :     List[str]   ,	SCREAMING_SNAKE_CASE__     :     Tuple = None   )      ->       List[Any]:
         return sample


    def    a      (       self     :     Any   ,	SCREAMING_SNAKE_CASE__     :     int   ,	SCREAMING_SNAKE_CASE__     :     Tuple   ,	SCREAMING_SNAKE_CASE__     :     Tuple = ()   )      ->       str:
         lowerCAmelCase__		= 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__		= (jnp.arange(0   ,	snake_case__   ) * step_ratio).round()[::-1] + self.config.steps_offset

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


    def    a      (       self     :     List[str]   ,	SCREAMING_SNAKE_CASE__     :     Optional[Any]   ,	SCREAMING_SNAKE_CASE__     :     Optional[int]   ,	SCREAMING_SNAKE_CASE__     :     int   )      ->       List[str]:
         lowerCAmelCase__		= state.common.alphas_cumprod[timestep]
         lowerCAmelCase__		= jnp.where(
             prev_timestep >= 0   ,	state.common.alphas_cumprod[prev_timestep]   ,	state.final_alpha_cumprod   )
         lowerCAmelCase__		= 1 - alpha_prod_t
         lowerCAmelCase__		= 1 - alpha_prod_t_prev

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

         return variance


    def    a      (       self     :     Optional[int]   ,	SCREAMING_SNAKE_CASE__     :     Union[str, Any]   ,	SCREAMING_SNAKE_CASE__     :     List[Any]   ,	SCREAMING_SNAKE_CASE__     :     List[Any]   ,	SCREAMING_SNAKE_CASE__     :     Dict   ,	SCREAMING_SNAKE_CASE__     :     Optional[int] = 0.0   ,	SCREAMING_SNAKE_CASE__     :     Union[str, Any] = True   ,	)      ->       int:
         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__		= timestep - self.config.num_train_timesteps // state.num_inference_steps

         lowerCAmelCase__		= state.common.alphas_cumprod
         lowerCAmelCase__		= state.final_alpha_cumprod

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

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

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

         if not return_dict:
              return (prev_sample, state)

         return FlaxDDIMSchedulerOutput(prev_sample=snake_case__   ,	state=snake_case__   )


    def    a      (       self     :     List[Any]   ,	SCREAMING_SNAKE_CASE__     :     str   ,	SCREAMING_SNAKE_CASE__     :     Any   ,	SCREAMING_SNAKE_CASE__     :     Optional[int]   ,	SCREAMING_SNAKE_CASE__     :     int   ,	)      ->       Optional[Any]:
         return add_noise_common(state.common   ,	snake_case__   ,	snake_case__   ,	snake_case__   )


    def    a      (       self     :     int   ,	SCREAMING_SNAKE_CASE__     :     Optional[Any]   ,	SCREAMING_SNAKE_CASE__     :     str   ,	SCREAMING_SNAKE_CASE__     :     Optional[int]   ,	SCREAMING_SNAKE_CASE__     :     List[str]   ,	)      ->       Any:
         return get_velocity_common(state.common   ,	snake_case__   ,	snake_case__   ,	snake_case__   )


    def __len__(       self     :     Any   )      ->       str:
         return self.config.num_train_timesteps








"""simple docstring"""







import importlib.metadata
import subprocess
import sys





def 						lowercase__(A = False						)				->List[str]:




						"""simple docstring"""



						lowercase__				:      Any=       False
						if "IPython" in sys.modules:
												lowercase__				:      Tuple=       "google.colab" in str(sys.modules["IPython"].get_ipython()						)

						if in_colab:
												if upgrade:
																		lowercase__				:      Optional[int]=       ["pip", "install", "-U", "torch"]
																		subprocess.run(A					,		check=A						)
												# get the current version of torch
												lowercase__				:      Tuple=       importlib.metadata.version("torch"						)
												lowercase__				:      Dict=       torch_version[: torch_version.rindex("."						)]
												lowercase__				:      Dict=       f'''https://storage.googleapis.com/tpu-pytorch/wheels/colab/torch_xla-{torch_version_trunc}-cp37-cp37m-linux_x86_64.whl'''
												lowercase__				:      Union[str, Any]=       ["pip", "install", xla_wheel]
												subprocess.run(A					,		check=A						)
						else:
												raise RuntimeError("`install_xla` utility works only on google colab."						)






