






from dataclasses import dataclass
from typing import Optional, Tuple, Union

import flax
import jax
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 				_UpperCamelCase      :
      snake_case_	=				42

      # setable values
      snake_case_	=				42
      snake_case_	=				42
      snake_case_	=				None


      @classmethod
      def 			__UpperCAmelCase		(						cls       ,    __UpperCamelCase       ,    __UpperCamelCase       ,    __UpperCamelCase						)->						List[Any]:
           return cls(common=__lowercase       ,    init_noise_sigma=__lowercase       ,    timesteps=__lowercase						)



@dataclass
class 				_UpperCamelCase      (snake_case_      ):
      snake_case_	=				42



class 				_UpperCamelCase      (snake_case_				,		snake_case_      ):
      snake_case_	=				[e.name for e in FlaxKarrasDiffusionSchedulers]

      snake_case_	=				42


      @property
      def 			__UpperCAmelCase		(						self						)->						Tuple:
           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 = "fixed_small"       ,    __UpperCamelCase = True       ,    __UpperCamelCase = "epsilon"       ,    __UpperCamelCase = jnp.floataa       ,    )->						Tuple:
           __lowerCAmelCase             =       dtype


      def 			__UpperCAmelCase		(						self       ,    __UpperCamelCase = None						)->						Dict:







           if common is None:
                __lowerCAmelCase             =       CommonSchedulerState.create(self						)

           # 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 DDPMSchedulerState.create(
               common=__lowercase       ,    init_noise_sigma=__lowercase       ,    timesteps=__lowercase       ,    )


      def 			__UpperCAmelCase		(						self       ,    __UpperCamelCase       ,    __UpperCamelCase       ,    __UpperCamelCase = None						)->						str:
           return sample


      def 			__UpperCAmelCase		(						self       ,    __UpperCamelCase       ,    __UpperCamelCase       ,    __UpperCamelCase = ()						)->						Union[str, Any]:
           __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       ,    __lowercase						) * step_ratio).round()[::-1]

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


      def 			__UpperCAmelCase		(						self       ,    __UpperCamelCase       ,    __UpperCamelCase       ,    __UpperCamelCase=None       ,    __UpperCamelCase=None						)->						Union[str, Any]:
           __lowerCAmelCase             =       state.common.alphas_cumprod[t]
           __lowerCAmelCase             =       jnp.where(t > 0       ,    state.common.alphas_cumprod[t - 1]       ,    jnp.array(1.0       ,    dtype=self.dtype						)						)

           # For t > 0, compute predicted variance βt (see formula (6) and (7) from https://arxiv.org/pdf/2006.11239.pdf)
           # and sample from it to get previous sample
           # x_{t-1} ~ N(pred_prev_sample, variance) == add variance to pred_sample
           __lowerCAmelCase             =       (1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * state.common.betas[t]

           if variance_type is None:
                __lowerCAmelCase             =       self.config.variance_type

           # hacks - were probably added for training stability
           if variance_type == "fixed_small":
                __lowerCAmelCase             =       jnp.clip(__lowercase       ,    a_min=1e-20						)
           # for rl-diffuser https://arxiv.org/abs/2205.09991
           elif variance_type == "fixed_small_log":
                __lowerCAmelCase             =       jnp.log(jnp.clip(__lowercase       ,    a_min=1e-20						)						)
           elif variance_type == "fixed_large":
                __lowerCAmelCase             =       state.common.betas[t]
           elif variance_type == "fixed_large_log":
                # Glide max_log
                __lowerCAmelCase             =       jnp.log(state.common.betas[t]						)
           elif variance_type == "learned":
                return predicted_variance
           elif variance_type == "learned_range":
                __lowerCAmelCase             =       variance
                __lowerCAmelCase             =       state.common.betas[t]
                __lowerCAmelCase             =       (predicted_variance + 1) / 2
                __lowerCAmelCase             =       frac * max_log + (1 - frac) * min_log

           return variance


      def 			__UpperCAmelCase		(						self       ,    __UpperCamelCase       ,    __UpperCamelCase       ,    __UpperCamelCase       ,    __UpperCamelCase       ,    __UpperCamelCase = None       ,    __UpperCamelCase = True       ,    )->						Union[str, Any]:
           __lowerCAmelCase             =       timestep

           if key is None:
                __lowerCAmelCase             =       jax.random.PRNGKey(0						)

           if model_output.shape[1] == sample.shape[1] * 2 and self.config.variance_type in ["learned", "learned_range"]:
                __lowerCAmelCase             =       jnp.split(__lowercase       ,    sample.shape[1]       ,    axis=1						)
           else:
                __lowerCAmelCase             =       None

           # 1. compute alphas, betas
           __lowerCAmelCase             =       state.common.alphas_cumprod[t]
           __lowerCAmelCase             =       jnp.where(t > 0       ,    state.common.alphas_cumprod[t - 1]       ,    jnp.array(1.0       ,    dtype=self.dtype						)						)
           __lowerCAmelCase             =       1 - alpha_prod_t
           __lowerCAmelCase             =       1 - alpha_prod_t_prev

           # 2. compute predicted original sample from predicted noise also called
           # "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
           if self.config.prediction_type == "epsilon":
                __lowerCAmelCase             =       (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
           elif self.config.prediction_type == "sample":
                __lowerCAmelCase             =       model_output
           elif self.config.prediction_type == "v_prediction":
                __lowerCAmelCase             =       (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
           else:
                raise ValueError(
                    F"""prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample` """
                    " for the FlaxDDPMScheduler."						)

           # 3. Clip "predicted x_0"
           if self.config.clip_sample:
                __lowerCAmelCase             =       jnp.clip(__lowercase       ,    -1       ,    1						)

           # 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
           # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
           __lowerCAmelCase             =       (alpha_prod_t_prev ** 0.5 * state.common.betas[t]) / beta_prod_t
           __lowerCAmelCase             =       state.common.alphas[t] ** 0.5 * beta_prod_t_prev / beta_prod_t

           # 5. Compute predicted previous sample µ_t
           # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
           __lowerCAmelCase             =       pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

           # 6. Add noise
           def random_variance():
                __lowerCAmelCase             =       jax.random.split(__lowercase       ,    num=1						)
                __lowerCAmelCase             =       jax.random.normal(__lowercase       ,    shape=model_output.shape       ,    dtype=self.dtype						)
                return (self._get_variance(__lowercase       ,    __lowercase       ,    predicted_variance=__lowercase						) ** 0.5) * noise

           __lowerCAmelCase             =       jnp.where(t > 0       ,    random_variance()       ,    jnp.zeros(model_output.shape       ,    dtype=self.dtype						)						)

           __lowerCAmelCase             =       pred_prev_sample + variance

           if not return_dict:
                return (pred_prev_sample, state)

           return FlaxDDPMSchedulerOutput(prev_sample=__lowercase       ,    state=__lowercase						)


      def 			__UpperCAmelCase		(						self       ,    __UpperCamelCase       ,    __UpperCamelCase       ,    __UpperCamelCase       ,    __UpperCamelCase       ,    )->						int:
           return add_noise_common(state.common       ,    __lowercase       ,    __lowercase       ,    __lowercase						)


      def 			__UpperCAmelCase		(						self       ,    __UpperCamelCase       ,    __UpperCamelCase       ,    __UpperCamelCase       ,    __UpperCamelCase       ,    )->						int:
           return get_velocity_common(state.common       ,    __lowercase       ,    __lowercase       ,    __lowercase						)


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










'''simple docstring'''
import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path

import torch_xla.distributed.xla_multiprocessing as xmp


def       _lowercase							(					):
 __A	:		List[Any]	  =      ArgumentParser(
     description=(
         'PyTorch TPU distributed training launch helper utility that will spawn up multiple distributed processes'
     )      )

 # Optional arguments for the launch helper
 parser.add_argument('--num_cores',					type=UpperCamelCase__,					default=1,					help='Number of TPU cores to use (1 or 8).'      )

 # positional
 parser.add_argument(
     'training_script',					type=UpperCamelCase__,					help=(
         'The full path to the single TPU training '
         'program/script to be launched in parallel, '
         'followed by all the arguments for the '
         'training script'
     ),					)

 # rest from the training program
 parser.add_argument('training_script_args',					nargs=UpperCamelCase__      )

 return parser.parse_args()


def       _lowercase							(					):
 __A	:		str	  =      parse_args()

 # Import training_script as a module.
 __A	:		Tuple	  =      Path(args.training_script      )
 sys.path.append(str(script_fpath.parent.resolve()      )      )
 __A	:		str	  =      script_fpath.stem
 __A	:		Dict	  =      importlib.import_module(UpperCamelCase__      )

 # Patch sys.argv
 __A	:		Optional[Any]	  =      [args.training_script] + args.training_script_args + ['--tpu_num_cores', str(args.num_cores      )]

 xmp.spawn(mod._mp_fn,					args=(),					nprocs=args.num_cores      )


if __name__ == "__main__":
 main()






