


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 	A_ (	__UpperCamelCase   ):




      '''simple docstring'''


      __snake_case 	=       1
      __snake_case 	=       2
      __snake_case 	=       3
      __snake_case 	=       4
      __snake_case 	=       5



@dataclass
class 	A_ (	__UpperCamelCase   ):




      '''simple docstring'''


      __snake_case 	=       42



class 	A_ :




      '''simple docstring'''


      __snake_case 	=       SCHEDULER_CONFIG_NAME
      __snake_case 	=       ["""dtype"""]
      __snake_case 	=       []
      __snake_case 	=       True




      @classmethod
      def 					_snake_case     (    cls:	Union[str, Any]						,				a:	Dict[str, Any] = None						,				a:	Optional[str] = None						,				a:	Optional[Any]=False						,				**a:	str						,				):
       __lowerCamelCase ,					__lowerCamelCase		:							Tuple		 =						cls.load_config(
           pretrained_model_name_or_path=a						,				subfolder=a						,				return_unused_kwargs=a						,				**a						,				)
       __lowerCamelCase ,					__lowerCamelCase		:							str		 =						cls.from_config(a						,				return_unused_kwargs=a						,				**a    )

       if hasattr(a						,				'create_state'    ) and getattr(a						,				'has_state'						,				a    ):
        __lowerCamelCase		:							int		 =						scheduler.create_state()

       if return_unused_kwargs:
        return scheduler, state, unused_kwargs

       return scheduler, state




      def 					_snake_case     (    self:	Any						,				a:	Union[str, os.PathLike]						,				a:	bool = False						,				**a:	Optional[int]    ):
       self.save_config(save_directory=a						,				push_to_hub=a						,				**a    )




      @property
      def 					_snake_case     (    self:	Dict    ):
       return self._get_compatibles()




      @classmethod
      def 					_snake_case     (    cls:	Union[str, Any]    ):
       __lowerCamelCase		:							Optional[int]		 =						list(set([cls.__name__] + cls._compatibles    )    )
       __lowerCamelCase		:							Any		 =						importlib.import_module(__name__.split('.'    )[0]    )
       __lowerCamelCase		:							List[Any]		 =						[
           getattr(a						,				a    ) for c in compatible_classes_str if hasattr(a						,				a    )
       ]
       return compatible_classes



def 				UpperCamelCase__					(  SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__						):
 assert len(SCREAMING_SNAKE_CASE__						) >= x.ndim
 return jnp.broadcast_to(x.reshape(x.shape + (1,) * (len(SCREAMING_SNAKE_CASE__						) - x.ndim)						)      ,	SCREAMING_SNAKE_CASE__						)
def 				UpperCamelCase__					(  SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__=0.999      ,	SCREAMING_SNAKE_CASE__=jnp.floataa						):

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

 __lowerCamelCase		:							Union[str, Any]		 =						[]
 for i in range(SCREAMING_SNAKE_CASE__						):
  __lowerCamelCase		:							Any		 =						i / num_diffusion_timesteps
  __lowerCamelCase		:							Union[str, Any]		 =						(i + 1) / num_diffusion_timesteps
  betas.append(min(1 - alpha_bar(SCREAMING_SNAKE_CASE__						) / alpha_bar(SCREAMING_SNAKE_CASE__						)      ,	SCREAMING_SNAKE_CASE__						)						)
 return jnp.array(SCREAMING_SNAKE_CASE__      ,	dtype=SCREAMING_SNAKE_CASE__						)


@flax.struct.dataclass
class 	A_ :




      '''simple docstring'''


      __snake_case 	=       42
      __snake_case 	=       42
      __snake_case 	=       42




      @classmethod
      def 					_snake_case     (    cls:	Dict						,				a:	Dict    ):
       __lowerCamelCase		:							Any		 =						scheduler.config

       if config.trained_betas is not None:
        __lowerCamelCase		:							Optional[int]		 =						jnp.asarray(config.trained_betas						,				dtype=scheduler.dtype    )
       elif config.beta_schedule == "linear":
        __lowerCamelCase		:							Any		 =						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.
        __lowerCamelCase		:							Optional[Any]		 =						(
            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
        __lowerCamelCase		:							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__}'    )

       __lowerCamelCase		:							Any		 =						1.0 - betas

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

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



def 				UpperCamelCase__					(  SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__						):
 __lowerCamelCase		:							Optional[Any]		 =						state.alphas_cumprod

 __lowerCamelCase		:							int		 =						alphas_cumprod[timesteps] ** 0.5
 __lowerCamelCase		:							Any		 =						sqrt_alpha_prod.flatten()
 __lowerCamelCase		:							str		 =						broadcast_to_shape_from_left(SCREAMING_SNAKE_CASE__      ,	original_samples.shape						)

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

 return sqrt_alpha_prod, sqrt_one_minus_alpha_prod
def 				UpperCamelCase__					(  SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__						):
 __lowerCamelCase ,					__lowerCamelCase		:							Tuple		 =						get_sqrt_alpha_prod(SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__						)
 __lowerCamelCase		:							str		 =						sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
 return noisy_samples



def 				UpperCamelCase__					(  SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__						):
 __lowerCamelCase ,					__lowerCamelCase		:							List[str]		 =						get_sqrt_alpha_prod(SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__						)
 __lowerCamelCase		:							Any		 =						sqrt_alpha_prod * noise - sqrt_one_minus_alpha_prod * sample
 return velocity





from maths.is_square_free import is_square_free
from maths.prime_factors import prime_factors
def 				UpperCamelCase__					(  SCREAMING_SNAKE_CASE__						):
 __lowerCamelCase		:							Dict		 =						prime_factors(SCREAMING_SNAKE_CASE__						)
 if is_square_free(SCREAMING_SNAKE_CASE__						):
  return -1 if len(SCREAMING_SNAKE_CASE__						) % 2 else 1
 return 0


if __name__ == "__main__":
       import doctest

       doctest.testmod()

