




"""simple docstring"""







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

import flax
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 							snake_case_ :







   """simple docstring"""




   A_										=					42
   A_										=					42

   # setable values
   A_										=					42
   A_										=					42
   A_										=					None







   @classmethod
   def      UpperCAmelCase__ (		cls					,				lowerCamelCase_					,				lowerCamelCase_					,				lowerCamelCase_					,				lowerCamelCase_					,				)							->      Any:
       return cls(
           common=UpperCamelCase__					,				final_alpha_cumprod=UpperCamelCase__					,				init_noise_sigma=UpperCamelCase__					,				timesteps=UpperCamelCase__					,				)

@dataclass
class 							snake_case_ (				_SCREAMING_SNAKE_CASE ):







   """simple docstring"""




   A_										=					42

class 							snake_case_ (				_SCREAMING_SNAKE_CASE  ,   _SCREAMING_SNAKE_CASE ):







   """simple docstring"""




   A_										=					[e.name for e in FlaxKarrasDiffusionSchedulers]

   A_										=					42







   @property
   def      UpperCAmelCase__ (		self)							->      List[Any]:
       return True







   @register_to_config
   def __init__(		self					,				lowerCamelCase_ = 1_0_0_0					,				lowerCamelCase_ = 0.0001					,				lowerCamelCase_ = 0.02					,				lowerCamelCase_ = "linear"					,				lowerCamelCase_ = None					,				lowerCamelCase_ = True					,				lowerCamelCase_ = 0					,				lowerCamelCase_ = "epsilon"					,				lowerCamelCase_ = jnp.floataa					,				)							->      Dict:
       UpperCamelCase										=  dtype







   def      UpperCAmelCase__ (		self					,				lowerCamelCase_ = None)							->      List[str]:
       if common is None:
           UpperCamelCase										=  CommonSchedulerState.create(self)

       # At every step in ddim, we are looking into the previous alphas_cumprod
       # For the final step, there is no previous alphas_cumprod because we are already at 0
       # `set_alpha_to_one` decides whether we set this parameter simply to one or
       # whether we use the final alpha of the "non-previous" one.
       UpperCamelCase										=  (
           jnp.array(1.0					,				dtype=self.dtype) if self.config.set_alpha_to_one else common.alphas_cumprod[0]
       )

       # standard deviation of the initial noise distribution
       UpperCamelCase										=  jnp.array(1.0					,				dtype=self.dtype)

       UpperCamelCase										=  jnp.arange(0					,				self.config.num_train_timesteps).round()[::-1]

       return DDIMSchedulerState.create(
           common=UpperCamelCase__					,				final_alpha_cumprod=UpperCamelCase__					,				init_noise_sigma=UpperCamelCase__					,				timesteps=UpperCamelCase__					,				)







   def      UpperCAmelCase__ (		self					,				lowerCamelCase_					,				lowerCamelCase_					,				lowerCamelCase_ = None)							->      Any:
       return sample







   def      UpperCAmelCase__ (		self					,				lowerCamelCase_					,				lowerCamelCase_					,				lowerCamelCase_ = ())							->      Dict:
       UpperCamelCase										=  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
       UpperCamelCase										=  (jnp.arange(0					,				UpperCamelCase__) * step_ratio).round()[::-1] + self.config.steps_offset

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







   def      UpperCAmelCase__ (		self					,				lowerCamelCase_					,				lowerCamelCase_					,				lowerCamelCase_)							->      Optional[int]:
       UpperCamelCase										=  state.common.alphas_cumprod[timestep]
       UpperCamelCase										=  jnp.where(
           prev_timestep >= 0					,				state.common.alphas_cumprod[prev_timestep]					,				state.final_alpha_cumprod)
       UpperCamelCase										=  1 - alpha_prod_t
       UpperCamelCase										=  1 - alpha_prod_t_prev

       UpperCamelCase										=  (beta_prod_t_prev / beta_prod_t) * (1 - alpha_prod_t / alpha_prod_t_prev)

       return variance







   def      UpperCAmelCase__ (		self					,				lowerCamelCase_					,				lowerCamelCase_					,				lowerCamelCase_					,				lowerCamelCase_					,				lowerCamelCase_ = 0.0					,				lowerCamelCase_ = True					,				)							->      Optional[int]:
       if state.num_inference_steps is None:
           raise ValueError(
               '''Number of inference steps is \'None\', you need to run \'set_timesteps\' after creating the scheduler''')

       # See formulas (12) and (16) of DDIM paper https://arxiv.org/pdf/2010.02502.pdf
       # Ideally, read DDIM paper in-detail understanding

       # Notation (<variable name> -> <name in paper>
       # - pred_noise_t -> e_theta(x_t, t)
       # - pred_original_sample -> f_theta(x_t, t) or x_0
       # - std_dev_t -> sigma_t
       # - eta -> η
       # - pred_sample_direction -> "direction pointing to x_t"
       # - pred_prev_sample -> "x_t-1"

       # 1. get previous step value (=t-1)
       UpperCamelCase										=  timestep - self.config.num_train_timesteps // state.num_inference_steps

       UpperCamelCase										=  state.common.alphas_cumprod
       UpperCamelCase										=  state.final_alpha_cumprod

       # 2. compute alphas, betas
       UpperCamelCase										=  alphas_cumprod[timestep]
       UpperCamelCase										=  jnp.where(prev_timestep >= 0					,				alphas_cumprod[prev_timestep]					,				UpperCamelCase__)

       UpperCamelCase										=  1 - alpha_prod_t

       # 3. compute predicted original sample from predicted noise also called
       # "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
       if self.config.prediction_type == "epsilon":
           UpperCamelCase										=  (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
           UpperCamelCase										=  model_output
       elif self.config.prediction_type == "sample":
           UpperCamelCase										=  model_output
           UpperCamelCase										=  (sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5
       elif self.config.prediction_type == "v_prediction":
           UpperCamelCase										=  (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
           UpperCamelCase										=  (alpha_prod_t**0.5) * model_output + (beta_prod_t**0.5) * sample
       else:
           raise ValueError(
               F'prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample`, or'
               ''' `v_prediction`''')

       # 4. compute variance: "sigma_t(η)" -> see formula (16)
       # σ_t = sqrt((1 − α_t−1)/(1 − α_t)) * sqrt(1 − α_t/α_t−1)
       UpperCamelCase										=  self._get_variance(UpperCamelCase__					,				UpperCamelCase__					,				UpperCamelCase__)
       UpperCamelCase										=  eta * variance ** 0.5

       # 5. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
       UpperCamelCase										=  (1 - alpha_prod_t_prev - std_dev_t**2) ** 0.5 * pred_epsilon

       # 6. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
       UpperCamelCase										=  alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction

       if not return_dict:
           return (prev_sample, state)

       return FlaxDDIMSchedulerOutput(prev_sample=UpperCamelCase__					,				state=UpperCamelCase__)







   def      UpperCAmelCase__ (		self					,				lowerCamelCase_					,				lowerCamelCase_					,				lowerCamelCase_					,				lowerCamelCase_					,				)							->      Any:
       return add_noise_common(state.common					,				UpperCamelCase__					,				UpperCamelCase__					,				UpperCamelCase__)







   def      UpperCAmelCase__ (		self					,				lowerCamelCase_					,				lowerCamelCase_					,				lowerCamelCase_					,				lowerCamelCase_					,				)							->      List[Any]:
       return get_velocity_common(state.common					,				UpperCamelCase__					,				UpperCamelCase__					,				UpperCamelCase__)







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





"""simple docstring"""







import os
from string import ascii_letters

SCREAMING_SNAKE_CASE_      =					ascii_letters + ' \t\n'





def     __snake_case   (							):


    """simple docstring"""







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


SCREAMING_SNAKE_CASE_      =					load_dictionary()





def     __snake_case   (							_lowercase						):


    """simple docstring"""







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





def     __snake_case   (							_lowercase						):


    """simple docstring"""







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





def     __snake_case   (							_lowercase ,_lowercase = 20 ,_lowercase = 85						):


    """simple docstring"""







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


if __name__ == "__main__":
  import doctest

  doctest.testmod()