code: 
  
  """simple docstring"""
  
  
  
  
  # 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				:
  		# setable values
  		SCREAMING_SNAKE_CASE              =					None
  		SCREAMING_SNAKE_CASE              =					None
  		SCREAMING_SNAKE_CASE              =					None
  
  
  		@classmethod
  		def       SCREAMING_SNAKE_CASE__     (cls				:	int):
  							return cls()
  
  
  
  
  
  @dataclass
  class     __UpperCamelCase				(     _A		):
  		SCREAMING_SNAKE_CASE              =					42
  		SCREAMING_SNAKE_CASE              =					42
  		SCREAMING_SNAKE_CASE              =					None
  
  
  
  
  
  class     __UpperCamelCase				(     _A  ,	_A		):
  
  
  		@property
  		def       SCREAMING_SNAKE_CASE__     (self				:	Dict):
  							return True
  
  
  		@register_to_config
  		def __init__(self				:	Any  ,  __SCREAMING_SNAKE_CASE				:	int = 2_0_0_0  ,  __SCREAMING_SNAKE_CASE				:	float = 0.1_5  ,  __SCREAMING_SNAKE_CASE				:	float = 0.0_1  ,  __SCREAMING_SNAKE_CASE				:	float = 1_3_4_8.0  ,  __SCREAMING_SNAKE_CASE				:	float = 1E-5  ,  __SCREAMING_SNAKE_CASE				:	int = 1  ,  ):
  							pass
  
  
  		def       SCREAMING_SNAKE_CASE__     (self				:	Tuple):
  							A    =      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       SCREAMING_SNAKE_CASE__     (self				:	Any  ,  __SCREAMING_SNAKE_CASE				:	ScoreSdeVeSchedulerState  ,  __SCREAMING_SNAKE_CASE				:	int  ,  __SCREAMING_SNAKE_CASE				:	Tuple = ()  ,  __SCREAMING_SNAKE_CASE				:	float = None):
  							A    =      sampling_eps if sampling_eps is not None else self.config.sampling_eps
  
  							A    =      jnp.linspace(1  ,  __SCREAMING_SNAKE_CASE  ,  __SCREAMING_SNAKE_CASE)
  							return state.replace(timesteps=__SCREAMING_SNAKE_CASE)
  
  
  		def       SCREAMING_SNAKE_CASE__     (self				:	List[str]  ,  __SCREAMING_SNAKE_CASE				:	ScoreSdeVeSchedulerState  ,  __SCREAMING_SNAKE_CASE				:	int  ,  __SCREAMING_SNAKE_CASE				:	float = None  ,  __SCREAMING_SNAKE_CASE				:	float = None  ,  __SCREAMING_SNAKE_CASE				:	float = None  ,  ):
  							A    =      sigma_min if sigma_min is not None else self.config.sigma_min
  							A    =      sigma_max if sigma_max is not None else self.config.sigma_max
  							A    =      sampling_eps if sampling_eps is not None else self.config.sampling_eps
  							if state.timesteps is None:
  												A    =      self.set_timesteps(__SCREAMING_SNAKE_CASE  ,  __SCREAMING_SNAKE_CASE  ,  __SCREAMING_SNAKE_CASE)
  
  							A    =      jnp.exp(jnp.linspace(jnp.log(__SCREAMING_SNAKE_CASE)  ,  jnp.log(__SCREAMING_SNAKE_CASE)  ,  __SCREAMING_SNAKE_CASE))
  							A    =      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       SCREAMING_SNAKE_CASE__     (self				:	Optional[int]  ,  __SCREAMING_SNAKE_CASE				:	Union[str, Any]  ,  __SCREAMING_SNAKE_CASE				:	Tuple  ,  __SCREAMING_SNAKE_CASE				:	Dict):
  							return jnp.where(timesteps == 0  ,  jnp.zeros_like(__SCREAMING_SNAKE_CASE)  ,  state.discrete_sigmas[timesteps - 1])
  
  
  		def       SCREAMING_SNAKE_CASE__     (self				:	Optional[int]  ,  __SCREAMING_SNAKE_CASE				:	ScoreSdeVeSchedulerState  ,  __SCREAMING_SNAKE_CASE				:	jnp.ndarray  ,  __SCREAMING_SNAKE_CASE				:	int  ,  __SCREAMING_SNAKE_CASE				:	jnp.ndarray  ,  __SCREAMING_SNAKE_CASE				:	random.KeyArray  ,  __SCREAMING_SNAKE_CASE				:	bool = True  ,  ):
  							if state.timesteps is None:
  												raise ValueError(
  												    "`state.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler")
  
  							A    =      timestep * jnp.ones(
  							    sample.shape[0]  ,  )
  							A    =      (timestep * (len(state.timesteps) - 1)).long()
  
  							A    =      state.discrete_sigmas[timesteps]
  							A    =      self.get_adjacent_sigma(__SCREAMING_SNAKE_CASE  ,  __SCREAMING_SNAKE_CASE  ,  __SCREAMING_SNAKE_CASE)
  							A    =      jnp.zeros_like(__SCREAMING_SNAKE_CASE)
  							A    =      (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
  							A    =      diffusion.flatten()
  							A    =      broadcast_to_shape_from_left(__SCREAMING_SNAKE_CASE  ,  sample.shape)
  							A    =      drift - diffusion**2 * model_output
  
  							#  equation 6: sample noise for the diffusion term of
  							A    =      random.split(__SCREAMING_SNAKE_CASE  ,  num=1)
  							A    =      random.normal(key=__SCREAMING_SNAKE_CASE  ,  shape=sample.shape)
  							A    =      sample - drift  # subtract because `dt` is a small negative timestep
  							# TODO is the variable diffusion the correct scaling term for the noise?
  							A    =      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       SCREAMING_SNAKE_CASE__     (self				:	Optional[Any]  ,  __SCREAMING_SNAKE_CASE				:	ScoreSdeVeSchedulerState  ,  __SCREAMING_SNAKE_CASE				:	jnp.ndarray  ,  __SCREAMING_SNAKE_CASE				:	jnp.ndarray  ,  __SCREAMING_SNAKE_CASE				:	random.KeyArray  ,  __SCREAMING_SNAKE_CASE				:	bool = True  ,  ):
  							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
  							A    =      random.split(__SCREAMING_SNAKE_CASE  ,  num=1)
  							A    =      random.normal(key=__SCREAMING_SNAKE_CASE  ,  shape=sample.shape)
  
  							# compute step size from the model_output, the noise, and the snr
  							A    =      jnp.linalg.norm(__SCREAMING_SNAKE_CASE)
  							A    =      jnp.linalg.norm(__SCREAMING_SNAKE_CASE)
  							A    =      (self.config.snr * noise_norm / grad_norm) ** 2 * 2
  							A    =      step_size * jnp.ones(sample.shape[0])
  
  							# compute corrected sample: model_output term and noise term
  							A    =      step_size.flatten()
  							A    =      broadcast_to_shape_from_left(__SCREAMING_SNAKE_CASE  ,  sample.shape)
  							A    =      sample + step_size * model_output
  							A    =      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				:	Dict):
  							return self.config.num_train_timesteps
  
  
  
  
  
  
style_context: 
  
  """simple docstring"""
  
  
  
  
  import os
  
  from ...utils import logging
  from .utils import DataProcessor, InputExample
  
  
  __A  :					List[Any]												=	logging.get_logger(__name__)
  
  
  
  
  
  class     __UpperCamelCase				(     _A		):
  
  
  		def __init__(self				:	Dict  ,  __SCREAMING_SNAKE_CASE				:	int  ,  __SCREAMING_SNAKE_CASE				:	Any=None):
  							A    =      language
  							A    =      train_language
  
  
  		def       SCREAMING_SNAKE_CASE__     (self				:	Tuple  ,  __SCREAMING_SNAKE_CASE				:	Tuple):
  							A    =      self.language if self.train_language is None else self.train_language
  							A    =      self._read_tsv(os.path.join(__SCREAMING_SNAKE_CASE  ,  F"""XNLI-MT-1.0/multinli/multinli.train.{lg}.tsv"""))
  							A    =      []
  							for i, line in enumerate(__SCREAMING_SNAKE_CASE):
  												if i == 0:
  																	continue
  												A    =      F"""train-{i}"""
  												A    =      line[0]
  												A    =      line[1]
  												A    =      "contradiction" if line[2] == "contradictory" else line[2]
  												if not isinstance(__SCREAMING_SNAKE_CASE  ,  __SCREAMING_SNAKE_CASE):
  																	raise ValueError(F"""Training input {text_a} is not a string""")
  												if not isinstance(__SCREAMING_SNAKE_CASE  ,  __SCREAMING_SNAKE_CASE):
  																	raise ValueError(F"""Training input {text_b} is not a string""")
  												if not isinstance(__SCREAMING_SNAKE_CASE  ,  __SCREAMING_SNAKE_CASE):
  																	raise ValueError(F"""Training label {label} is not a string""")
  												examples.append(InputExample(guid=__SCREAMING_SNAKE_CASE  ,  text_a=__SCREAMING_SNAKE_CASE  ,  text_b=__SCREAMING_SNAKE_CASE  ,  label=__SCREAMING_SNAKE_CASE))
  							return examples
  
  
  		def       SCREAMING_SNAKE_CASE__     (self				:	List[str]  ,  __SCREAMING_SNAKE_CASE				:	Tuple):
  							A    =      self._read_tsv(os.path.join(__SCREAMING_SNAKE_CASE  ,  "XNLI-1.0/xnli.test.tsv"))
  							A    =      []
  							for i, line in enumerate(__SCREAMING_SNAKE_CASE):
  												if i == 0:
  																	continue
  												A    =      line[0]
  												if language != self.language:
  																	continue
  												A    =      F"""test-{i}"""
  												A    =      line[6]
  												A    =      line[7]
  												A    =      line[1]
  												if not isinstance(__SCREAMING_SNAKE_CASE  ,  __SCREAMING_SNAKE_CASE):
  																	raise ValueError(F"""Training input {text_a} is not a string""")
  												if not isinstance(__SCREAMING_SNAKE_CASE  ,  __SCREAMING_SNAKE_CASE):
  																	raise ValueError(F"""Training input {text_b} is not a string""")
  												if not isinstance(__SCREAMING_SNAKE_CASE  ,  __SCREAMING_SNAKE_CASE):
  																	raise ValueError(F"""Training label {label} is not a string""")
  												examples.append(InputExample(guid=__SCREAMING_SNAKE_CASE  ,  text_a=__SCREAMING_SNAKE_CASE  ,  text_b=__SCREAMING_SNAKE_CASE  ,  label=__SCREAMING_SNAKE_CASE))
  							return examples
  
  
  		def       SCREAMING_SNAKE_CASE__     (self				:	str):
  							return ["contradiction", "entailment", "neutral"]
  
  __A  :					Optional[int]												=	{
      'xnli': XnliProcessor,
  }
  
  __A  :					Union[str, Any]												=	{
      'xnli': 'classification',
  }
  
  __A  :					Tuple												=	{
      'xnli': 3,
  }
  
  
  
  
  
  