



'''simple docstring'''





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 __A      :



 '''simple docstring'''







 __lowerCamelCase   :	Optional[jnp.ndarray]										=	None
 __lowerCamelCase   :	Optional[jnp.ndarray]										=	None
 __lowerCamelCase   :	Optional[jnp.ndarray]										=	None



 @classmethod
 def  a__  (cls	)   ->					List[str]:


      """simple docstring"""



      return cls()






@dataclass
class __A      (  SCREAMING_SNAKE_CASE__						):



 '''simple docstring'''







 __lowerCamelCase   :	ScoreSdeVeSchedulerState
 __lowerCamelCase   :	jnp.ndarray
 __lowerCamelCase   :	Optional[jnp.ndarray]										=	None






class __A      (  SCREAMING_SNAKE_CASE__       ,       SCREAMING_SNAKE_CASE__						):



 '''simple docstring'''







 @property
 def  a__  (self	)   ->					Optional[Any]:


      """simple docstring"""



      return True



 @register_to_config
 def __init__(self			,       A = 2_000			,       A = 0.15			,       A = 0.01			,       A = 1348.0			,       A = 1E-5			,       A = 1			,       )   ->					Tuple:


      """simple docstring"""



      pass



 def  a__  (self	)   ->					int:


      """simple docstring"""



      _a     =  ScoreSdeVeSchedulerState.create()
      return self.set_sigmas(
          A			,       self.config.num_train_timesteps			,       self.config.sigma_min			,       self.config.sigma_max			,       self.config.sampling_eps			,       )



 def  a__  (self			,       A			,       A			,       A = ()			,       A = None	)   ->					Tuple:


      """simple docstring"""



      _a     =  sampling_eps if sampling_eps is not None else self.config.sampling_eps

      _a     =  jnp.linspace(1			,       A			,       A	)
      return state.replace(timesteps=A	)



 def  a__  (self			,       A			,       A			,       A = None			,       A = None			,       A = None			,       )   ->					Optional[Any]:


      """simple docstring"""



      _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(A			,       A			,       A	)

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

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



 def  a__  (self			,       A			,       A			,       A	)   ->					Union[str, Any]:


      """simple docstring"""



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



 def  a__  (self			,       A			,       A			,       A			,       A			,       A			,       A = True			,       )   ->					Optional[int]:


      """simple docstring"""



      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(A			,       A			,       A	)
      _a     =  jnp.zeros_like(A	)
      _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(A			,       sample.shape	)
      _a     =  drift - diffusion**2 * model_output

      #  equation 6: sample noise for the diffusion term of
      _a     =  random.split(A			,       num=1	)
      _a     =  random.normal(key=A			,       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=A			,       prev_sample_mean=A			,       state=A	)



 def  a__  (self			,       A			,       A			,       A			,       A			,       A = True			,       )   ->					Optional[int]:


      """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
      _a     =  random.split(A			,       num=1	)
      _a     =  random.normal(key=A			,       shape=sample.shape	)

      # compute step size from the model_output, the noise, and the snr
      _a     =  jnp.linalg.norm(A	)
      _a     =  jnp.linalg.norm(A	)
      _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(A			,       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=A			,       state=A	)



 def __len__(self	)   ->					List[str]:


      """simple docstring"""



      return self.config.num_train_timesteps








'''simple docstring'''





import os
from string import ascii_letters

lowercase_			=  ascii_letters + " \t\n"




def lowerCAmelCase  ():



     """simple docstring"""



     _a     =  os.path.split(os.path.realpath(__A))
     _a     =  {}
     with open(path[0] + '''/dictionary.txt''') as dictionary_file:
          for word in dictionary_file.read().split('''\n'''):
               _a     =  None
     return english_words


lowercase_			=  load_dictionary()




def lowerCAmelCase  (__A):



     """simple docstring"""



     _a     =  message.upper()
     _a     =  remove_non_letters(__A)
     _a     =  message.split()
     _a     =  len([word for word in possible_words if word in ENGLISH_WORDS])
     return float(__A) / len(__A)




def lowerCAmelCase  (__A):



     """simple docstring"""



     return "".join(symbol for symbol in message if symbol in LETTERS_AND_SPACE)




def lowerCAmelCase  (__A  ,     __A = 20  ,     __A = 85):



     """simple docstring"""



     _a     =  get_english_count(__A) * 100 >= word_percentage
     _a     =  len(remove_non_letters(__A))
     _a     =  (float(__A) / len(__A)) * 100
     _a     =  message_letters_percentage >= letter_percentage
     return words_match and letters_match


if __name__ == "__main__":
  import doctest

  doctest.testmod()



