





"""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


a_			=      """scheduler_config.json"""






class 		__snake_case (		SCREAMING_SNAKE_CASE__					):





 """simple docstring"""


 _lowerCamelCase				=					1
 _lowerCamelCase				=					2
 _lowerCamelCase				=					3
 _lowerCamelCase				=					4
 _lowerCamelCase				=					5






@dataclass
class 		__snake_case (		SCREAMING_SNAKE_CASE__					):





 """simple docstring"""


 _lowerCamelCase				=					42






class 		__snake_case :





 """simple docstring"""


 _lowerCamelCase				=					SCHEDULER_CONFIG_NAME
 _lowerCamelCase				=					["""dtype"""]
 _lowerCamelCase				=					[]
 _lowerCamelCase				=					True

 @classmethod
 def  UpperCamelCase__(   cls ,						__lowerCamelCase = None ,						__lowerCamelCase = None ,						__lowerCamelCase=False ,						**__lowerCamelCase ,						):



     '''simple docstring'''




     __A			,	__A   :						Tuple									=					cls.load_config(
         pretrained_model_name_or_path=__lowerCamelCase ,						subfolder=__lowerCamelCase ,						return_unused_kwargs=__lowerCamelCase ,						**__lowerCamelCase ,						)
     __A			,	__A   :						Union[str, 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  UpperCamelCase__(   self ,						__lowerCamelCase ,						__lowerCamelCase = False ,						**__lowerCamelCase  ):



     '''simple docstring'''




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

 @property
 def  UpperCamelCase__(   self  ):



     '''simple docstring'''




     return self._get_compatibles()







 @classmethod
 def  UpperCamelCase__(   cls  ):



     '''simple docstring'''




     __A   :						Optional[Any]									=					list(set([cls.__name__] + cls._compatibles  )  )
     __A   :						Any									=					importlib.import_module(__name__.split('''.'''  )[0]  )
     __A   :						Optional[Any]									=					[
         getattr(__lowerCamelCase ,						__lowerCamelCase  ) for c in compatible_classes_str if hasattr(__lowerCamelCase ,						__lowerCamelCase  )
     ]
     return compatible_classes







def 		__lowercase					(       snake_case_  :  jnp.ndarray    ,snake_case_  :  Tuple[int]						)       ->jnp.ndarray:



    '''simple docstring'''







    assert len(snake_case_						) >= x.ndim
    return jnp.broadcast_to(x.reshape(x.shape + (1,) * (len(snake_case_						) - x.ndim)						)    ,snake_case_						)







def 		__lowercase					(       snake_case_  :  int    ,snake_case_  :  Dict=0.999    ,snake_case_  :  int=jnp.floataa						)       ->jnp.ndarray:



    '''simple docstring'''







    def alpha_bar(snake_case_  :  Optional[Any]						):
        return math.cos((time_step + 0.008) / 1.008 * math.pi / 2						) ** 2

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






@flax.struct.dataclass
class 		__snake_case :





 """simple docstring"""


 _lowerCamelCase				=					42
 _lowerCamelCase				=					42
 _lowerCamelCase				=					42

 @classmethod
 def  UpperCamelCase__(   cls ,						__lowerCamelCase  ):



     '''simple docstring'''




     __A   :						int									=					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   :						Tuple									=					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   :						Tuple									=					(
             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   :						Union[str, 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   :						Tuple									=					1.0 - betas

     __A   :						Dict									=					jnp.cumprod(__lowerCamelCase ,						axis=0  )

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







def 		__lowercase					(       snake_case_  :  CommonSchedulerState    ,snake_case_  :  jnp.ndarray    ,snake_case_  :  jnp.ndarray    ,snake_case_  :  jnp.ndarray						)       ->Dict:



    '''simple docstring'''







    __A   :						str									=					state.alphas_cumprod

    __A   :						Tuple									=					alphas_cumprod[timesteps] ** 0.5
    __A   :						Union[str, Any]									=					sqrt_alpha_prod.flatten()
    __A   :						Dict									=					broadcast_to_shape_from_left(snake_case_    ,original_samples.shape						)

    __A   :						Dict									=					(1 - alphas_cumprod[timesteps]) ** 0.5
    __A   :						Union[str, Any]									=					sqrt_one_minus_alpha_prod.flatten()
    __A   :						Dict									=					broadcast_to_shape_from_left(snake_case_    ,original_samples.shape						)

    return sqrt_alpha_prod, sqrt_one_minus_alpha_prod







def 		__lowercase					(       snake_case_  :  CommonSchedulerState    ,snake_case_  :  jnp.ndarray    ,snake_case_  :  jnp.ndarray    ,snake_case_  :  jnp.ndarray						)       ->Tuple:



    '''simple docstring'''







    __A			,	__A   :						Union[str, Any]									=					get_sqrt_alpha_prod(snake_case_    ,snake_case_    ,snake_case_    ,snake_case_						)
    __A   :						Any									=					sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
    return noisy_samples







def 		__lowercase					(       snake_case_  :  CommonSchedulerState    ,snake_case_  :  jnp.ndarray    ,snake_case_  :  jnp.ndarray    ,snake_case_  :  jnp.ndarray						)       ->int:



    '''simple docstring'''







    __A			,	__A   :						Tuple									=					get_sqrt_alpha_prod(snake_case_    ,snake_case_    ,snake_case_    ,snake_case_						)
    __A   :						Tuple									=					sqrt_alpha_prod * noise - sqrt_one_minus_alpha_prod * sample
    return velocity











"""simple docstring"""



import math
from collections.abc import Iterator
from itertools import takewhile







def 		__lowercase					(       snake_case_  :  int						)       ->bool:



    '''simple docstring'''







    if 1 < number < 4:
        # 2 and 3 are primes
        return True
    elif number < 2 or number % 2 == 0 or number % 3 == 0:
        # Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
        return False

    # All primes number are in format of 6k +/- 1
    for i in range(5    ,int(math.sqrt(snake_case_						) + 1						)    ,6						):
        if number % i == 0 or number % (i + 2) == 0:
            return False
    return True







def 		__lowercase					(       )       ->Iterator[int]:



    '''simple docstring'''







    __A   :						int									=					2
    while True:
        if is_prime(snake_case_						):
            yield num
        num += 1







def 		__lowercase					(       snake_case_  :  int = 2000000						)       ->int:



    '''simple docstring'''







    return sum(takewhile(lambda snake_case_						: x < n    ,prime_generator()						)						)


if __name__ == "__main__":
       print(f'''{solution() = }''')




