

"""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 			_snake_case     :
      __snake_case 			=     42
      __snake_case 			=     42

      # setable values
      __snake_case 			=     42
      __snake_case 			=     42
      __snake_case 			=     None







      @classmethod
      def        _UpperCamelCase    (cls     ,							UpperCAmelCase_     ,							UpperCAmelCase_     ,							UpperCAmelCase_     ,							UpperCAmelCase_     ,							)			->						str:
           return cls(
               common=UpperCAmelCase_     ,							final_alpha_cumprod=UpperCAmelCase_     ,							init_noise_sigma=UpperCAmelCase_     ,							timesteps=UpperCAmelCase_     ,							)



@dataclass
class 			_snake_case     (						UpperCamelCase       ):
      __snake_case 			=     42



class 			_snake_case     (						UpperCamelCase						,			UpperCamelCase       ):
      __snake_case 			=     [e.name for e in FlaxKarrasDiffusionSchedulers]

      __snake_case 			=     42







      @property
      def        _UpperCamelCase    (self							)			->						Dict:
           return True







      @register_to_config
      def __init__(self     ,							UpperCAmelCase_ = 1_0_0_0     ,							UpperCAmelCase_ = 0.0_0_0_1     ,							UpperCAmelCase_ = 0.0_2     ,							UpperCAmelCase_ = "linear"     ,							UpperCAmelCase_ = None     ,							UpperCAmelCase_ = True     ,							UpperCAmelCase_ = 0     ,							UpperCAmelCase_ = "epsilon"     ,							UpperCAmelCase_ = jnp.floataa     ,							)			->						Tuple:
           __lowercase												=			dtype







      def        _UpperCamelCase    (self     ,							UpperCAmelCase_ = None							)			->						DDIMSchedulerState:
           if common is None:
                __lowercase												=			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.
           __lowercase												=			(
               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
           __lowercase												=			jnp.array(1.0     ,							dtype=self.dtype							)

           __lowercase												=			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        _UpperCamelCase    (self     ,							UpperCAmelCase_     ,							UpperCAmelCase_     ,							UpperCAmelCase_ = None							)			->						jnp.ndarray:
           return sample







      def        _UpperCamelCase    (self     ,							UpperCAmelCase_     ,							UpperCAmelCase_     ,							UpperCAmelCase_ = ()							)			->						DDIMSchedulerState:
           __lowercase												=			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
           __lowercase												=			(jnp.arange(0     ,							UpperCAmelCase_							) * step_ratio).round()[::-1] + self.config.steps_offset

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







      def        _UpperCamelCase    (self     ,							UpperCAmelCase_     ,							UpperCAmelCase_     ,							UpperCAmelCase_							)			->						int:
           __lowercase												=			state.common.alphas_cumprod[timestep]
           __lowercase												=			jnp.where(
               prev_timestep >= 0     ,							state.common.alphas_cumprod[prev_timestep]     ,							state.final_alpha_cumprod							)
           __lowercase												=			1 - alpha_prod_t
           __lowercase												=			1 - alpha_prod_t_prev

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

           return variance







      def        _UpperCamelCase    (self     ,							UpperCAmelCase_     ,							UpperCAmelCase_     ,							UpperCAmelCase_     ,							UpperCAmelCase_     ,							UpperCAmelCase_ = 0.0     ,							UpperCAmelCase_ = 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)
           __lowercase												=			timestep - self.config.num_train_timesteps // state.num_inference_steps

           __lowercase												=			state.common.alphas_cumprod
           __lowercase												=			state.final_alpha_cumprod

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

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

           # 5. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
           __lowercase												=			(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
           __lowercase												=			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        _UpperCamelCase    (self     ,							UpperCAmelCase_     ,							UpperCAmelCase_     ,							UpperCAmelCase_     ,							UpperCAmelCase_     ,							)			->						jnp.ndarray:
           return add_noise_common(state.common     ,							UpperCAmelCase_     ,							UpperCAmelCase_     ,							UpperCAmelCase_							)







      def        _UpperCamelCase    (self     ,							UpperCAmelCase_     ,							UpperCAmelCase_     ,							UpperCAmelCase_     ,							UpperCAmelCase_     ,							)			->						jnp.ndarray:
           return get_velocity_common(state.common     ,							UpperCAmelCase_     ,							UpperCAmelCase_     ,							UpperCAmelCase_							)







      def __len__(self							)			->						List[Any]:
           return self.config.num_train_timesteps





"""simple docstring"""

import json
import logging
import os
import subprocess
from argparse import ArgumentParser


a__ :	List[str]						= logging.getLogger(__name__)





def 				__UpperCamelCase   (    )				->			Optional[Any]:




					"""simple docstring"""



					__lowercase												=			ArgumentParser()
					__lowercase						, __lowercase												=			parser.parse_known_args()
					for arg in unknown:
										if arg.startswith(('''-''', '''--''')			):
															parser.add_argument(arg.split('''='''			)[0]			)

					return parser.parse_args()





def 				__UpperCamelCase   (    )				->			int:




					"""simple docstring"""



					__lowercase												=			parse_args()
					__lowercase												=			88_88
					__lowercase												=			int(os.environ['''SM_NUM_GPUS''']			)
					__lowercase												=			json.loads(os.environ['''SM_HOSTS''']			)
					__lowercase												=			len(a__			)
					__lowercase												=			os.environ['''SM_CURRENT_HOST''']
					__lowercase												=			hosts.index(a__			)
					__lowercase												=			'''INFO'''

					if num_nodes > 1:
										__lowercase												=			f'''python -m torch.distributed.launch \
                --nnodes={num_nodes}  \
                --node_rank={rank}  \
                --nproc_per_node={num_gpus}  \
                --master_addr={hosts[0]}  \
                --master_port={port} \
                ./run_glue.py \
                {"".join(["nested fstring" for parameter,value in args.__dict__.items()]			)}'''
					else:
										__lowercase												=			f'''python -m torch.distributed.launch \
            --nproc_per_node={num_gpus}  \
            ./run_glue.py \
            {"".join(["nested fstring" for parameter,value in args.__dict__.items()]			)}'''
					try:
										subprocess.run(a__ ,	shell=a__			)
					except Exception as e:
										logger.info(a__			)


if __name__ == "__main__":
							main()


