


# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch

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

import flax
import jax.numpy as jnp
from jax import random

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import FlaxSchedulerMixin, FlaxSchedulerOutput, broadcast_to_shape_from_left



@flax.struct.dataclass
class  UpperCAmelCase_						:
       '''simple docstring'''
       # setable values
       __UpperCamelCase   :  Optional[jnp.ndarray]				=       None
       __UpperCamelCase   :  Optional[jnp.ndarray]				=       None
       __UpperCamelCase   :  Optional[jnp.ndarray]				=       None






       @classmethod
       def 			_lowercase (  cls    ):




              """simple docstring"""




              return cls()




@dataclass
class  UpperCAmelCase_						(				_a):
       '''simple docstring'''
       __UpperCamelCase   :  ScoreSdeVeSchedulerState
       __UpperCamelCase   :  jnp.ndarray
       __UpperCamelCase   :  Optional[jnp.ndarray]				=       None




class  UpperCAmelCase_						(				_a,  _a):
       '''simple docstring'''






       @property
       def 			_lowercase (  self    ):




              """simple docstring"""




              return True






       @register_to_config
       def __init__(  self			,       __SCREAMING_SNAKE_CASE = 2_000			,       __SCREAMING_SNAKE_CASE = 0.15			,       __SCREAMING_SNAKE_CASE = 0.01			,       __SCREAMING_SNAKE_CASE = 1_348.0			,       __SCREAMING_SNAKE_CASE = 1e-5			,       __SCREAMING_SNAKE_CASE = 1			,       ):




              """simple docstring"""




              pass






       def 			_lowercase (  self    ):




              """simple docstring"""




              UpperCamelCase     :						str 						=		ScoreSdeVeSchedulerState.create()
              return self.set_sigmas(
                  __SCREAMING_SNAKE_CASE			,       self.config.num_train_timesteps			,       self.config.sigma_min			,       self.config.sigma_max			,       self.config.sampling_eps			,       )






       def 			_lowercase (  self			,       __SCREAMING_SNAKE_CASE			,       __SCREAMING_SNAKE_CASE			,       __SCREAMING_SNAKE_CASE = ()			,       __SCREAMING_SNAKE_CASE = None    ):




              """simple docstring"""




              UpperCamelCase     :						List[str] 						=		sampling_eps if sampling_eps is not None else self.config.sampling_eps

              UpperCamelCase     :						Tuple 						=		jnp.linspace(1			,       __SCREAMING_SNAKE_CASE			,       __SCREAMING_SNAKE_CASE    )
              return state.replace(timesteps=__SCREAMING_SNAKE_CASE    )






       def 			_lowercase (  self			,       __SCREAMING_SNAKE_CASE			,       __SCREAMING_SNAKE_CASE			,       __SCREAMING_SNAKE_CASE = None			,       __SCREAMING_SNAKE_CASE = None			,       __SCREAMING_SNAKE_CASE = None			,       ):




              """simple docstring"""




              UpperCamelCase     :						Union[str, Any] 						=		sigma_min if sigma_min is not None else self.config.sigma_min
              UpperCamelCase     :						Tuple 						=		sigma_max if sigma_max is not None else self.config.sigma_max
              UpperCamelCase     :						Optional[int] 						=		sampling_eps if sampling_eps is not None else self.config.sampling_eps
              if state.timesteps is None:
                     UpperCamelCase     :						int 						=		self.set_timesteps(__SCREAMING_SNAKE_CASE			,       __SCREAMING_SNAKE_CASE			,       __SCREAMING_SNAKE_CASE    )

              UpperCamelCase     :						Optional[Any] 						=		jnp.exp(jnp.linspace(jnp.log(__SCREAMING_SNAKE_CASE    )			,       jnp.log(__SCREAMING_SNAKE_CASE    )			,       __SCREAMING_SNAKE_CASE    )    )
              UpperCamelCase     :						Optional[Any] 						=		jnp.array([sigma_min * (sigma_max / sigma_min) ** t for t in state.timesteps]    )

              return state.replace(discrete_sigmas=__SCREAMING_SNAKE_CASE			,       sigmas=__SCREAMING_SNAKE_CASE    )






       def 			_lowercase (  self			,       __SCREAMING_SNAKE_CASE			,       __SCREAMING_SNAKE_CASE			,       __SCREAMING_SNAKE_CASE    ):




              """simple docstring"""




              return jnp.where(timesteps == 0			,       jnp.zeros_like(__SCREAMING_SNAKE_CASE    )			,       state.discrete_sigmas[timesteps - 1]    )






       def 			_lowercase (  self			,       __SCREAMING_SNAKE_CASE			,       __SCREAMING_SNAKE_CASE			,       __SCREAMING_SNAKE_CASE			,       __SCREAMING_SNAKE_CASE			,       __SCREAMING_SNAKE_CASE			,       __SCREAMING_SNAKE_CASE = True			,       ):




              """simple docstring"""




              if state.timesteps is None:
                     raise ValueError(
                         '''`state.timesteps` is not set, you need to run \'set_timesteps\' after creating the scheduler'''    )

              UpperCamelCase     :						str 						=		timestep * jnp.ones(
                  sample.shape[0]			,       )
              UpperCamelCase     :						List[Any] 						=		(timestep * (len(state.timesteps    ) - 1)).long()

              UpperCamelCase     :						List[Any] 						=		state.discrete_sigmas[timesteps]
              UpperCamelCase     :						int 						=		self.get_adjacent_sigma(__SCREAMING_SNAKE_CASE			,       __SCREAMING_SNAKE_CASE			,       __SCREAMING_SNAKE_CASE    )
              UpperCamelCase     :						List[Any] 						=		jnp.zeros_like(__SCREAMING_SNAKE_CASE    )
              UpperCamelCase     :						int 						=		(sigma**2 - adjacent_sigma**2) ** 0.5

              # equation 6 in the paper: the model_output modeled by the network is grad_x log pt(x)
              # also equation 47 shows the analog from SDE models to ancestral sampling methods
              UpperCamelCase     :						Optional[Any] 						=		diffusion.flatten()
              UpperCamelCase     :						str 						=		broadcast_to_shape_from_left(__SCREAMING_SNAKE_CASE			,       sample.shape    )
              UpperCamelCase     :						Optional[int] 						=		drift - diffusion**2 * model_output

              #  equation 6: sample noise for the diffusion term of
              UpperCamelCase     :						Any 						=		random.split(__SCREAMING_SNAKE_CASE			,       num=1    )
              UpperCamelCase     :						Optional[int] 						=		random.normal(key=__SCREAMING_SNAKE_CASE			,       shape=sample.shape    )
              UpperCamelCase     :						int 						=		sample - drift  # subtract because `dt` is a small negative timestep
              # TODO is the variable diffusion the correct scaling term for the noise?
              UpperCamelCase     :						Tuple 						=		prev_sample_mean + diffusion * noise  # add impact of diffusion field g

              if not return_dict:
                     return (prev_sample, prev_sample_mean, state)

              return FlaxSdeVeOutput(prev_sample=__SCREAMING_SNAKE_CASE			,       prev_sample_mean=__SCREAMING_SNAKE_CASE			,       state=__SCREAMING_SNAKE_CASE    )






       def 			_lowercase (  self			,       __SCREAMING_SNAKE_CASE			,       __SCREAMING_SNAKE_CASE			,       __SCREAMING_SNAKE_CASE			,       __SCREAMING_SNAKE_CASE			,       __SCREAMING_SNAKE_CASE = True			,       ):




              """simple docstring"""




              if state.timesteps is None:
                     raise ValueError(
                         '''`state.timesteps` is not set, you need to run \'set_timesteps\' after creating the scheduler'''    )

              # For small batch sizes, the paper "suggest replacing norm(z) with sqrt(d), where d is the dim. of z"
              # sample noise for correction
              UpperCamelCase     :						Optional[int] 						=		random.split(__SCREAMING_SNAKE_CASE			,       num=1    )
              UpperCamelCase     :						Optional[int] 						=		random.normal(key=__SCREAMING_SNAKE_CASE			,       shape=sample.shape    )

              # compute step size from the model_output, the noise, and the snr
              UpperCamelCase     :						List[Any] 						=		jnp.linalg.norm(__SCREAMING_SNAKE_CASE    )
              UpperCamelCase     :						int 						=		jnp.linalg.norm(__SCREAMING_SNAKE_CASE    )
              UpperCamelCase     :						Tuple 						=		(self.config.snr * noise_norm / grad_norm) ** 2 * 2
              UpperCamelCase     :						Tuple 						=		step_size * jnp.ones(sample.shape[0]    )

              # compute corrected sample: model_output term and noise term
              UpperCamelCase     :						Tuple 						=		step_size.flatten()
              UpperCamelCase     :						str 						=		broadcast_to_shape_from_left(__SCREAMING_SNAKE_CASE			,       sample.shape    )
              UpperCamelCase     :						Optional[Any] 						=		sample + step_size * model_output
              UpperCamelCase     :						Tuple 						=		prev_sample_mean + ((step_size * 2) ** 0.5) * noise

              if not return_dict:
                     return (prev_sample, state)

              return FlaxSdeVeOutput(prev_sample=__SCREAMING_SNAKE_CASE			,       state=__SCREAMING_SNAKE_CASE    )






       def __len__(  self    ):




              """simple docstring"""




              return self.config.num_train_timesteps





import os

from apache_beam.io.filesystems import FileSystems
from apache_beam.pipeline import Pipeline

from .logging import get_logger


__UpperCAmelCase			:			Optional[Any]			      =					2 << 20  # 2mb
__UpperCAmelCase			:			List[Any]			      =					get_logger(__name__)



class  UpperCAmelCase_						(				_a):
       '''simple docstring'''






       def 			_lowercase (  self    ):




              """simple docstring"""




              UpperCamelCase     :						Optional[Any] 						=		self._options.get_all_options().get('''runner'''    )
              return runner in [None, "DirectRunner", "PortableRunner"]




def   a						(				SCREAMING_SNAKE_CASE_   :		str				,      SCREAMING_SNAKE_CASE_   :		int				,      SCREAMING_SNAKE_CASE_   :		Optional[Any]=False							):






       """simple docstring"""





       UpperCamelCase     :						int 						=		FileSystems
       if fs.exists(SCREAMING_SNAKE_CASE_							):
              if force_upload:
                     logger.info(F"""Remote path already exist: {remote_file_path}. Overwriting it as force_upload=True."""							)
              else:
                     logger.info(F"""Remote path already exist: {remote_file_path}. Skipping it as force_upload=False."""							)
                     return
       with fs.create(SCREAMING_SNAKE_CASE_							) as remote_file:
              with open(SCREAMING_SNAKE_CASE_				,      '''rb'''							) as local_file:
                     UpperCamelCase     :						List[str] 						=		local_file.read(SCREAMING_SNAKE_CASE_							)
                     while chunk:
                            remote_file.write(SCREAMING_SNAKE_CASE_							)
                            UpperCamelCase     :						Any 						=		local_file.read(SCREAMING_SNAKE_CASE_							)


def   a						(				SCREAMING_SNAKE_CASE_   :		str				,      SCREAMING_SNAKE_CASE_   :		Optional[Any]				,      SCREAMING_SNAKE_CASE_   :		List[str]=False							):






       """simple docstring"""





       UpperCamelCase     :						List[Any] 						=		FileSystems
       if os.path.exists(SCREAMING_SNAKE_CASE_							):
              if force_download:
                     logger.info(F"""Local path already exist: {remote_file_path}. Overwriting it as force_upload=True."""							)
              else:
                     logger.info(F"""Local path already exist: {remote_file_path}. Skipping it as force_upload=False."""							)
                     return
       with fs.open(SCREAMING_SNAKE_CASE_							) as remote_file:
              with open(SCREAMING_SNAKE_CASE_				,      '''wb'''							) as local_file:
                     UpperCamelCase     :						List[str] 						=		remote_file.read(SCREAMING_SNAKE_CASE_							)
                     while chunk:
                            local_file.write(SCREAMING_SNAKE_CASE_							)
                            UpperCamelCase     :						str 						=		remote_file.read(SCREAMING_SNAKE_CASE_							)

