
"""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


SCREAMING_SNAKE_CASE_					     =	'scheduler_config.json'
class       lowerCAmelCase_		(				_lowercase ):



							'''simple docstring'''





							_snake_case						      =		1
							_snake_case						      =		2
							_snake_case						      =		3
							_snake_case						      =		4
							_snake_case						      =		5
@dataclass
class       lowerCAmelCase_		(				_lowercase ):



							'''simple docstring'''





							_snake_case						      =		42
class       lowerCAmelCase_		:



							'''simple docstring'''





							_snake_case						      =		SCHEDULER_CONFIG_NAME
							_snake_case						      =		['dtype']
							_snake_case						      =		[]
							_snake_case						      =		True




							@classmethod
							def    A__						( cls    ,				snake_case_ = None    ,				snake_case_ = None    ,				snake_case_=False    ,				**snake_case_    ,				)       ->  List[Any]:
													__lowerCAmelCase   ,     __lowerCAmelCase     =  cls.load_config(
													    pretrained_model_name_or_path=__SCREAMING_SNAKE_CASE    ,				subfolder=__SCREAMING_SNAKE_CASE    ,				return_unused_kwargs=__SCREAMING_SNAKE_CASE    ,				**__SCREAMING_SNAKE_CASE    ,				)
													__lowerCAmelCase   ,     __lowerCAmelCase     =  cls.from_config(__SCREAMING_SNAKE_CASE    ,				return_unused_kwargs=__SCREAMING_SNAKE_CASE    ,				**__SCREAMING_SNAKE_CASE				)

													if hasattr(__SCREAMING_SNAKE_CASE    ,				"""create_state"""				) and getattr(__SCREAMING_SNAKE_CASE    ,				"""has_state"""    ,				__SCREAMING_SNAKE_CASE				):
																			__lowerCAmelCase     =  scheduler.create_state()

													if return_unused_kwargs:
																			return scheduler, state, unused_kwargs

													return scheduler, state




							def    A__						( self    ,				snake_case_    ,				snake_case_ = False    ,				**snake_case_				)       ->  Union[str, Any]:
													self.save_config(save_directory=__SCREAMING_SNAKE_CASE    ,				push_to_hub=__SCREAMING_SNAKE_CASE    ,				**__SCREAMING_SNAKE_CASE				)




							@property
							def    A__						( self				)       ->  Tuple:
													return self._get_compatibles()




							@classmethod
							def    A__						( cls				)       ->  List[str]:
													__lowerCAmelCase     =  list(set([cls.__name__] + cls._compatibles				)				)
													__lowerCAmelCase     =  importlib.import_module(__name__.split("""."""				)[0]				)
													__lowerCAmelCase     =  [
													    getattr(__SCREAMING_SNAKE_CASE    ,				__SCREAMING_SNAKE_CASE				) for c in compatible_classes_str if hasattr(__SCREAMING_SNAKE_CASE    ,				__SCREAMING_SNAKE_CASE				)
													]
													return compatible_classes

def  lowercase     (_lowerCAmelCase ,    _lowerCAmelCase						):
						assert len(_lowerCAmelCase						) >= x.ndim
						return jnp.broadcast_to(x.reshape(x.shape + (1,) * (len(_lowerCAmelCase						) - x.ndim)						) ,    _lowerCAmelCase						)

def  lowercase     (_lowerCAmelCase ,    _lowerCAmelCase=0.999 ,    _lowerCAmelCase=jnp.floataa						):

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

						__lowerCAmelCase     =  []
						for i in range(_lowerCAmelCase						):
												__lowerCAmelCase     =  i / num_diffusion_timesteps
												__lowerCAmelCase     =  (i + 1) / num_diffusion_timesteps
												betas.append(min(1 - alpha_bar(_lowerCAmelCase						) / alpha_bar(_lowerCAmelCase						) ,    _lowerCAmelCase						)						)
						return jnp.array(_lowerCAmelCase ,    dtype=_lowerCAmelCase						)
@flax.struct.dataclass
class       lowerCAmelCase_		:



							'''simple docstring'''





							_snake_case						      =		42
							_snake_case						      =		42
							_snake_case						      =		42




							@classmethod
							def    A__						( cls    ,				snake_case_				)       ->  Dict:
													__lowerCAmelCase     =  scheduler.config

													if config.trained_betas is not None:
																			__lowerCAmelCase     =  jnp.asarray(config.trained_betas    ,				dtype=scheduler.dtype				)
													elif config.beta_schedule == "linear":
																			__lowerCAmelCase     =  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     =  (
																			    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     =  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     =  1.0 - betas

													__lowerCAmelCase     =  jnp.cumprod(__SCREAMING_SNAKE_CASE    ,				axis=0				)

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

def  lowercase     (_lowerCAmelCase ,    _lowerCAmelCase ,    _lowerCAmelCase ,    _lowerCAmelCase						):
						__lowerCAmelCase     =  state.alphas_cumprod

						__lowerCAmelCase     =  alphas_cumprod[timesteps] ** 0.5
						__lowerCAmelCase     =  sqrt_alpha_prod.flatten()
						__lowerCAmelCase     =  broadcast_to_shape_from_left(_lowerCAmelCase ,    original_samples.shape						)

						__lowerCAmelCase     =  (1 - alphas_cumprod[timesteps]) ** 0.5
						__lowerCAmelCase     =  sqrt_one_minus_alpha_prod.flatten()
						__lowerCAmelCase     =  broadcast_to_shape_from_left(_lowerCAmelCase ,    original_samples.shape						)

						return sqrt_alpha_prod, sqrt_one_minus_alpha_prod

def  lowercase     (_lowerCAmelCase ,    _lowerCAmelCase ,    _lowerCAmelCase ,    _lowerCAmelCase						):
						__lowerCAmelCase   ,     __lowerCAmelCase     =  get_sqrt_alpha_prod(_lowerCAmelCase ,    _lowerCAmelCase ,    _lowerCAmelCase ,    _lowerCAmelCase						)
						__lowerCAmelCase     =  sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
						return noisy_samples



def  lowercase     (_lowerCAmelCase ,    _lowerCAmelCase ,    _lowerCAmelCase ,    _lowerCAmelCase						):
						__lowerCAmelCase   ,     __lowerCAmelCase     =  get_sqrt_alpha_prod(_lowerCAmelCase ,    _lowerCAmelCase ,    _lowerCAmelCase ,    _lowerCAmelCase						)
						__lowerCAmelCase     =  sqrt_alpha_prod * noise - sqrt_one_minus_alpha_prod * sample
						return velocity


"""simple docstring"""




def     __magic_name__		(					UpperCamelCase     :		int							,		UpperCamelCase     :		list[int]							,		UpperCamelCase     :		int     )     ->						int:

    def count_of_possible_combinations(UpperCamelCase     :		int     ) -> int:
        if target < 0:
            return 0
        if target == 0:
            return 1
        return sum(count_of_possible_combinations(target - item     ) for item in array     )

    return count_of_possible_combinations(UpperCamelCase     )



def     __magic_name__		(					UpperCamelCase     :		int							,		UpperCamelCase     :		list[int]							,		UpperCamelCase     :		int     )     ->						int:

    def count_of_possible_combinations_with_dp_array(
        UpperCamelCase     :		int							,		UpperCamelCase     :		list[int]     ) -> int:
        if target < 0:
            return 0
        if target == 0:
            return 1
        if dp_array[target] != -1:
            return dp_array[target]
        a__		=			sum(
            count_of_possible_combinations_with_dp_array(target - item							,		UpperCamelCase     )
            for item in array     )
        a__		=			answer
        return answer

    a__		=			[-1] * (target + 1)
    return count_of_possible_combinations_with_dp_array(UpperCamelCase							,		UpperCamelCase     )



def     __magic_name__		(					UpperCamelCase     :		int							,		UpperCamelCase     :		list[int]							,		UpperCamelCase     :		int     )     ->						int:
    a__		=			[0] * (target + 1)
    a__		=			1

    for i in range(1							,		target + 1     ):
        for j in range(UpperCamelCase     ):
            if i - array[j] >= 0:
                dp_array[i] += dp_array[i - array[j]]

    return dp_array[target]


if __name__ == "__main__":
    import doctest

    doctest.testmod()
    a		:       Optional[Any]     						=       3
    a		:       List[str]     						=       5
    a		:       Dict     						=       [1, 2, 5]
    print(combination_sum_iv(n, array, target))


