"""simple docstring"""
import importlib
import math
import os
from dataclasses import dataclass
from enum import Enum
from typing import Any, Dict, Optional, Tuple, Union

import flax
import jax.numpy as jnp

from ..utils import BaseOutput


lowercase__					       =	'scheduler_config.json'




class    __snake_case       (     __lowerCAmelCase				):
      a__        =		1
      a__        =		2
      a__        =		3
      a__        =		4
      a__        =		5







@dataclass
class    __snake_case       (     __lowerCAmelCase				):
      a__        =		42







class    __snake_case       :
      a__        =		SCHEDULER_CONFIG_NAME
      a__        =		["""dtype"""]
      a__        =		[]
      a__        =		True



      @classmethod
      def   lowerCamelCase_   (						cls    ,			lowercase = None    ,			lowercase = None    ,			lowercase=False    ,			**lowercase    ,			)	->			Dict:





        '''simple docstring'''





        a__							,    a__:     List[Any]										=	cls.load_config(
            pretrained_model_name_or_path=lowerCamelCase__    ,			subfolder=lowerCamelCase__    ,			return_unused_kwargs=lowerCamelCase__    ,			**lowerCamelCase__    ,			)
        a__							,    a__:     Optional[Any]										=	cls.from_config(lowerCamelCase__    ,			return_unused_kwargs=lowerCamelCase__    ,			**lowerCamelCase__)

        if hasattr(lowerCamelCase__    ,			'create_state') and getattr(lowerCamelCase__    ,			'has_state'    ,			lowerCamelCase__):
          a__:     Optional[int]										=	scheduler.create_state()

        if return_unused_kwargs:
          return scheduler, state, unused_kwargs

        return scheduler, state



      def   lowerCamelCase_   (						self    ,			lowercase    ,			lowercase = False    ,			**lowercase)	->			Tuple:





        '''simple docstring'''





        self.save_config(save_directory=lowerCamelCase__    ,			push_to_hub=lowerCamelCase__    ,			**lowerCamelCase__)



      @property
      def   lowerCamelCase_   (						self)	->			int:





        '''simple docstring'''





        return self._get_compatibles()



      @classmethod
      def   lowerCamelCase_   (						cls)	->			Union[str, Any]:





        '''simple docstring'''





        a__:     Tuple										=	list(set([cls.__name__] + cls._compatibles))
        a__:     Union[str, Any]										=	importlib.import_module(__name__.split('.')[0])
        a__:     Optional[int]										=	[
            getattr(lowerCamelCase__    ,			lowerCamelCase__) for c in compatible_classes_str if hasattr(lowerCamelCase__    ,			lowerCamelCase__)
        ]
        return compatible_classes







def 						__a   (	_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )	->jnp.ndarray:
  assert len(UpperCamelCase__   ) >= x.ndim
  return jnp.broadcast_to(x.reshape(x.shape + (1,) * (len(UpperCamelCase__   ) - x.ndim)   )		,     UpperCamelCase__   )







def 						__a   (	_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE=0.999		,     _SCREAMING_SNAKE_CASE=jnp.floataa   )	->jnp.ndarray:


  def alpha_bar(_SCREAMING_SNAKE_CASE   ):
    return math.cos((time_step + 0.008) / 1.008 * math.pi / 2   ) ** 2

  a__:     List[Any]										=	[]
  for i in range(UpperCamelCase__   ):
    a__:     int										=	i / num_diffusion_timesteps
    a__:     str										=	(i + 1) / num_diffusion_timesteps
    betas.append(min(1 - alpha_bar(UpperCamelCase__   ) / alpha_bar(UpperCamelCase__   )		,     UpperCamelCase__   )   )
  return jnp.array(UpperCamelCase__		,     dtype=UpperCamelCase__   )




@flax.struct.dataclass
class    __snake_case       :
      a__        =		42
      a__        =		42
      a__        =		42



      @classmethod
      def   lowerCamelCase_   (						cls    ,			lowercase)	->			str:





        '''simple docstring'''





        a__:     Optional[Any]										=	scheduler.config

        if config.trained_betas is not None:
          a__:     str										=	jnp.asarray(config.trained_betas    ,			dtype=scheduler.dtype)
        elif config.beta_schedule == "linear":
          a__:     str										=	jnp.linspace(config.beta_start    ,			config.beta_end    ,			config.num_train_timesteps    ,			dtype=scheduler.dtype)
        elif config.beta_schedule == "scaled_linear":
          # this schedule is very specific to the latent diffusion model.
          a__:     Dict										=	(
              jnp.linspace(
                  config.beta_start**0.5    ,			config.beta_end**0.5    ,			config.num_train_timesteps    ,			dtype=scheduler.dtype)
              ** 2
          )
        elif config.beta_schedule == "squaredcos_cap_v2":
          # Glide cosine schedule
          a__:     List[Any]										=	betas_for_alpha_bar(config.num_train_timesteps    ,			dtype=scheduler.dtype)
        else:
          raise NotImplementedError(
              f'beta_schedule {config.beta_schedule} is not implemented for scheduler {scheduler.__class__.__name__}')

        a__:     int										=	1.0 - betas

        a__:     Any										=	jnp.cumprod(lowerCamelCase__    ,			axis=0)

        return cls(
            alphas=lowerCamelCase__    ,			betas=lowerCamelCase__    ,			alphas_cumprod=lowerCamelCase__    ,			)







def 						__a   (	_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )	->int:
  a__:     Dict										=	state.alphas_cumprod

  a__:     str										=	alphas_cumprod[timesteps] ** 0.5
  a__:     str										=	sqrt_alpha_prod.flatten()
  a__:     Dict										=	broadcast_to_shape_from_left(UpperCamelCase__		,     original_samples.shape   )

  a__:     Any										=	(1 - alphas_cumprod[timesteps]) ** 0.5
  a__:     Union[str, Any]										=	sqrt_one_minus_alpha_prod.flatten()
  a__:     List[str]										=	broadcast_to_shape_from_left(UpperCamelCase__		,     original_samples.shape   )

  return sqrt_alpha_prod, sqrt_one_minus_alpha_prod







def 						__a   (	_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )	->Optional[int]:
  a__							,    a__:     str										=	get_sqrt_alpha_prod(UpperCamelCase__		,     UpperCamelCase__		,     UpperCamelCase__		,     UpperCamelCase__   )
  a__:     Optional[int]										=	sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
  return noisy_samples







def 						__a   (	_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )	->List[str]:
  a__							,    a__:     Tuple										=	get_sqrt_alpha_prod(UpperCamelCase__		,     UpperCamelCase__		,     UpperCamelCase__		,     UpperCamelCase__   )
  a__:     Dict										=	sqrt_alpha_prod * noise - sqrt_one_minus_alpha_prod * sample
  return velocity








import string

# frequency taken from https://en.wikipedia.org/wiki/Letter_frequency
__A        =    {
    "E": 1_2.7_0,
    "T": 9.0_6,
    "A": 8.1_7,
    "O": 7.5_1,
    "I": 6.9_7,
    "N": 6.7_5,
    "S": 6.3_3,
    "H": 6.0_9,
    "R": 5.9_9,
    "D": 4.2_5,
    "L": 4.0_3,
    "C": 2.7_8,
    "U": 2.7_6,
    "M": 2.4_1,
    "W": 2.3_6,
    "F": 2.2_3,
    "G": 2.0_2,
    "Y": 1.9_7,
    "P": 1.9_3,
    "B": 1.2_9,
    "V": 0.9_8,
    "K": 0.7_7,
    "J": 0.1_5,
    "X": 0.1_5,
    "Q": 0.1_0,
    "Z": 0.0_7,
}
__A        =    "ETAOINSHRDLCUMWFGYPBVKJXQZ"
__A        =    "ABCDEFGHIJKLMNOPQRSTUVWXYZ"







def 			lowerCamelCase_		(       UpperCamelCase__				:      str      )     ->       dict[str, int]:





				"""simple docstring"""


				__lowerCamelCase		       =    {letter: 0 for letter in string.ascii_uppercase}
				for letter in message.upper():
								if letter in LETTERS:
												letter_count[letter] += 1

				return letter_count







def 			lowerCamelCase_		(       UpperCamelCase__				:      tuple      )     ->       str:





				"""simple docstring"""


				return x[0]







def 			lowerCamelCase_		(       UpperCamelCase__				:      str      )     ->       str:





				"""simple docstring"""


				__lowerCamelCase		       =    get_letter_count(UpperCamelCase__      )
				__lowerCamelCase		       =    {
				    freq: [] for letter, freq in letter_to_freq.items()
				}
				for letter in LETTERS:
								freq_to_letter[letter_to_freq[letter]].append(UpperCamelCase__      )

				__lowerCamelCase		       =    {}

				for freq in freq_to_letter:
								freq_to_letter[freq].sort(key=ETAOIN.find ,     reverse=UpperCamelCase__      )
								__lowerCamelCase		       =    ''.join(freq_to_letter[freq]      )

				__lowerCamelCase		       =    list(freq_to_letter_str.items()      )
				freq_pairs.sort(key=UpperCamelCase__ ,     reverse=UpperCamelCase__      )

				__lowerCamelCase		       =    [freq_pair[1] for freq_pair in freq_pairs]

				return "".join(UpperCamelCase__      )







def 			lowerCamelCase_		(       UpperCamelCase__				:      str      )     ->       int:





				"""simple docstring"""


				__lowerCamelCase		       =    get_frequency_order(UpperCamelCase__      )
				__lowerCamelCase		       =    0
				for common_letter in ETAOIN[:6]:
								if common_letter in freq_order[:6]:
												match_score += 1

				for uncommon_letter in ETAOIN[-6:]:
								if uncommon_letter in freq_order[-6:]:
												match_score += 1

				return match_score


if __name__ == "__main__":
					import doctest

					doctest.testmod()


