

# 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"""
  SCREAMING_SNAKE_CASE__					:			Optional[int]					    =   None
  SCREAMING_SNAKE_CASE__					:			Tuple					    =   None
  SCREAMING_SNAKE_CASE__					:			Optional[Any]					    =   None



  @classmethod
  def   _UpperCamelCase     (						cls     :					int						)       ->			Optional[int]:
      return cls()







@dataclass
class  UpperCamelCase_      (	_snake_case      ):


  """simple docstring"""
  SCREAMING_SNAKE_CASE__					:			List[Any]					    =   42
  SCREAMING_SNAKE_CASE__					:			List[str]					    =   42
  SCREAMING_SNAKE_CASE__					:			Any					    =   None







class  UpperCamelCase_      (	_snake_case		,  _snake_case      ):


  """simple docstring"""



  @property
  def   _UpperCamelCase     (						self     :					Tuple						)       ->			Optional[Any]:
      return True



  @register_to_config
  def __init__(						self     :					Any  ,	A_     :					str = 2000  ,	A_     :					Optional[Any] = 0.15  ,	A_     :					int = 0.01  ,	A_     :					Optional[Any] = 1_348.0  ,	A_     :					List[str] = 1E-5  ,	A_     :					Optional[Any] = 1  ,	)       ->			int:
      pass



  def   _UpperCamelCase     (						self     :					str						)       ->			List[Any]:
      SCREAMING_SNAKE_CASE__         =    ScoreSdeVeSchedulerState.create()
      return self.set_sigmas(
          lowerCamelCase_  ,	self.config.num_train_timesteps  ,	self.config.sigma_min  ,	self.config.sigma_max  ,	self.config.sampling_eps  ,	)



  def   _UpperCamelCase     (						self     :					List[Any]  ,	A_     :					List[str]  ,	A_     :					str  ,	A_     :					List[str] = ()  ,	A_     :					Optional[Any] = None						)       ->			Union[str, Any]:
      SCREAMING_SNAKE_CASE__         =    sampling_eps if sampling_eps is not None else self.config.sampling_eps

      SCREAMING_SNAKE_CASE__         =    jnp.linspace(1  ,	lowerCamelCase_  ,	lowerCamelCase_						)
      return state.replace(timesteps=lowerCamelCase_						)



  def   _UpperCamelCase     (						self     :					Optional[Any]  ,	A_     :					Optional[int]  ,	A_     :					Dict  ,	A_     :					Tuple = None  ,	A_     :					Tuple = None  ,	A_     :					int = None  ,	)       ->			Optional[int]:
      SCREAMING_SNAKE_CASE__         =    sigma_min if sigma_min is not None else self.config.sigma_min
      SCREAMING_SNAKE_CASE__         =    sigma_max if sigma_max is not None else self.config.sigma_max
      SCREAMING_SNAKE_CASE__         =    sampling_eps if sampling_eps is not None else self.config.sampling_eps
      if state.timesteps is None:
          SCREAMING_SNAKE_CASE__         =    self.set_timesteps(lowerCamelCase_  ,	lowerCamelCase_  ,	lowerCamelCase_						)

      SCREAMING_SNAKE_CASE__         =    jnp.exp(jnp.linspace(jnp.log(lowerCamelCase_						)  ,	jnp.log(lowerCamelCase_						)  ,	lowerCamelCase_						)						)
      SCREAMING_SNAKE_CASE__         =    jnp.array([sigma_min * (sigma_max / sigma_min) ** t for t in state.timesteps]						)

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



  def   _UpperCamelCase     (						self     :					Dict  ,	A_     :					List[str]  ,	A_     :					Tuple  ,	A_     :					List[Any]						)       ->			int:
      return jnp.where(timesteps == 0  ,	jnp.zeros_like(lowerCamelCase_						)  ,	state.discrete_sigmas[timesteps - 1]						)



  def   _UpperCamelCase     (						self     :					Any  ,	A_     :					int  ,	A_     :					Tuple  ,	A_     :					Dict  ,	A_     :					str  ,	A_     :					Dict  ,	A_     :					Dict = True  ,	)       ->			Any:

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

      SCREAMING_SNAKE_CASE__         =    timestep * jnp.ones(
          sample.shape[0]  ,	)
      SCREAMING_SNAKE_CASE__         =    (timestep * (len(state.timesteps						) - 1)).long()

      SCREAMING_SNAKE_CASE__         =    state.discrete_sigmas[timesteps]
      SCREAMING_SNAKE_CASE__         =    self.get_adjacent_sigma(lowerCamelCase_  ,	lowerCamelCase_  ,	lowerCamelCase_						)
      SCREAMING_SNAKE_CASE__         =    jnp.zeros_like(lowerCamelCase_						)
      SCREAMING_SNAKE_CASE__         =    (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
      SCREAMING_SNAKE_CASE__         =    diffusion.flatten()
      SCREAMING_SNAKE_CASE__         =    broadcast_to_shape_from_left(lowerCamelCase_  ,	sample.shape						)
      SCREAMING_SNAKE_CASE__         =    drift - diffusion**2 * model_output

      #  equation 6: sample noise for the diffusion term of
      SCREAMING_SNAKE_CASE__         =    random.split(lowerCamelCase_  ,	num=1						)
      SCREAMING_SNAKE_CASE__         =    random.normal(key=lowerCamelCase_  ,	shape=sample.shape						)
      SCREAMING_SNAKE_CASE__         =    sample - drift  # subtract because `dt` is a small negative timestep
      # TODO is the variable diffusion the correct scaling term for the noise?
      SCREAMING_SNAKE_CASE__         =    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=lowerCamelCase_  ,	prev_sample_mean=lowerCamelCase_  ,	state=lowerCamelCase_						)



  def   _UpperCamelCase     (						self     :					Dict  ,	A_     :					int  ,	A_     :					List[str]  ,	A_     :					str  ,	A_     :					List[str]  ,	A_     :					Tuple = True  ,	)       ->			Any:

      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
      SCREAMING_SNAKE_CASE__         =    random.split(lowerCamelCase_  ,	num=1						)
      SCREAMING_SNAKE_CASE__         =    random.normal(key=lowerCamelCase_  ,	shape=sample.shape						)

      # compute step size from the model_output, the noise, and the snr
      SCREAMING_SNAKE_CASE__         =    jnp.linalg.norm(lowerCamelCase_						)
      SCREAMING_SNAKE_CASE__         =    jnp.linalg.norm(lowerCamelCase_						)
      SCREAMING_SNAKE_CASE__         =    (self.config.snr * noise_norm / grad_norm) ** 2 * 2
      SCREAMING_SNAKE_CASE__         =    step_size * jnp.ones(sample.shape[0]						)

      # compute corrected sample: model_output term and noise term
      SCREAMING_SNAKE_CASE__         =    step_size.flatten()
      SCREAMING_SNAKE_CASE__         =    broadcast_to_shape_from_left(lowerCamelCase_  ,	sample.shape						)
      SCREAMING_SNAKE_CASE__         =    sample + step_size * model_output
      SCREAMING_SNAKE_CASE__         =    prev_sample_mean + ((step_size * 2) ** 0.5) * noise

      if not return_dict:
          return (prev_sample, state)

      return FlaxSdeVeOutput(prev_sample=lowerCamelCase_  ,	state=lowerCamelCase_						)







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







import platform
from argparse import ArgumentParser

import huggingface_hub
import pandas
import pyarrow

from datasets import __version__ as version
from datasets.commands import BaseDatasetsCLICommand






def        a( A			:       Dict			)						->			Optional[int]:
					"""simple docstring"""





					return EnvironmentCommand()







class       _lowercase				(  lowerCAmelCase      ):





	"""simple docstring"""




	@staticmethod
	def 	UpperCamelCase_			(lowerCamelCase_						):
						"""simple docstring"""

						a   	=  parser.add_parser("env"						,			help="Print relevant system environment info."						)
						download_parser.set_defaults(func=lowerCamelCase_						)




	def 	UpperCamelCase_			(self						):
						"""simple docstring"""

						a   	=  {
						    "`datasets` version": version,
						    "Platform": platform.platform(),
						    "Python version": platform.python_version(),
						    "Huggingface_hub version": huggingface_hub.__version__,
						    "PyArrow version": pyarrow.__version__,
						    "Pandas version": pandas.__version__,
						}

						print("\nCopy-and-paste the text below in your GitHub issue.\n"						)
						print(self.format_dict(lowerCamelCase_						)						)

						return info




	@staticmethod
	def 	UpperCamelCase_			(lowerCamelCase_						):
						"""simple docstring"""

						return "\n".join([F'''- {prop}: {val}''' for prop, val in d.items()]						) + "\n"
