



'''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


snake_case_					:      Tuple    			=    'scheduler_config.json'






class 				lowercase__      (				lowercase  ):
	lowercase__    							=     1
	lowercase__    							=     2
	lowercase__    							=     3
	lowercase__    							=     4
	lowercase__    							=     5






@dataclass
class 				lowercase__      (				lowercase  ):
	lowercase__    							=     42






class 				lowercase__      :
	lowercase__    							=     SCHEDULER_CONFIG_NAME
	lowercase__    							=     ["""dtype"""]
	lowercase__    							=     []
	lowercase__    							=     True





	@classmethod
	def       UpperCamelCase_    (					cls   :							Optional[int]					,lowerCamelCase__   :							Dict[str, Any] = None					,lowerCamelCase__   :							Optional[str] = None					,lowerCamelCase__   :							Union[str, Any]=False					,**lowerCamelCase__   :							Optional[Any]					,):







							'''simple docstring'''
							_UpperCamelCase							,     _UpperCamelCase   :						int												=  cls.load_config(
							    pretrained_model_name_or_path=lowerCamelCase__					,subfolder=lowerCamelCase__					,return_unused_kwargs=lowerCamelCase__					,**lowerCamelCase__					,)
							_UpperCamelCase							,     _UpperCamelCase   :						Any												=  cls.from_config(lowerCamelCase__					,return_unused_kwargs=lowerCamelCase__					,**lowerCamelCase__					)

							if hasattr(lowerCamelCase__					,'create_state'					) and getattr(lowerCamelCase__					,'has_state'					,lowerCamelCase__					):
													_UpperCamelCase   :						int												=  scheduler.create_state()

							if return_unused_kwargs:
													return scheduler, state, unused_kwargs

							return scheduler, state





	def       UpperCamelCase_    (					self   :							Tuple					,lowerCamelCase__   :							Union[str, os.PathLike]					,lowerCamelCase__   :							bool = False					,**lowerCamelCase__   :							List[Any]					):







							'''simple docstring'''
							self.save_config(save_directory=lowerCamelCase__					,push_to_hub=lowerCamelCase__					,**lowerCamelCase__					)





	@property
	def       UpperCamelCase_    (					self   :							List[Any]					):







							'''simple docstring'''
							return self._get_compatibles()





	@classmethod
	def       UpperCamelCase_    (					cls   :							Dict					):







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






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






def A__       (	UpperCAmelCase_    ,    UpperCAmelCase_=0.999    ,    UpperCAmelCase_=jnp.floataa				):

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

						_UpperCamelCase   :						int												=  []
						for i in range(UpperCAmelCase_				):
												_UpperCamelCase   :						List[Any]												=  i / num_diffusion_timesteps
												_UpperCamelCase   :						Dict												=  (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 				lowercase__      :
	lowercase__    							=     42
	lowercase__    							=     42
	lowercase__    							=     42





	@classmethod
	def       UpperCamelCase_    (					cls   :							str					,lowerCamelCase__   :							List[str]					):







							'''simple docstring'''
							_UpperCamelCase   :						Union[str, Any]												=  scheduler.config

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

							_UpperCamelCase   :						str												=  1.0 - betas

							_UpperCamelCase   :						Dict												=  jnp.cumprod(lowerCamelCase__					,axis=0					)

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






def A__       (	UpperCAmelCase_    ,    UpperCAmelCase_    ,    UpperCAmelCase_    ,    UpperCAmelCase_				):
						_UpperCamelCase   :						List[Any]												=  state.alphas_cumprod

						_UpperCamelCase   :						Optional[int]												=  alphas_cumprod[timesteps] ** 0.5
						_UpperCamelCase   :						List[Any]												=  sqrt_alpha_prod.flatten()
						_UpperCamelCase   :						Any												=  broadcast_to_shape_from_left(UpperCAmelCase_    ,    original_samples.shape				)

						_UpperCamelCase   :						str												=  (1 - alphas_cumprod[timesteps]) ** 0.5
						_UpperCamelCase   :						Optional[int]												=  sqrt_one_minus_alpha_prod.flatten()
						_UpperCamelCase   :						Optional[Any]												=  broadcast_to_shape_from_left(UpperCAmelCase_    ,    original_samples.shape				)

						return sqrt_alpha_prod, sqrt_one_minus_alpha_prod






def A__       (	UpperCAmelCase_    ,    UpperCAmelCase_    ,    UpperCAmelCase_    ,    UpperCAmelCase_				):
						_UpperCamelCase							,     _UpperCamelCase   :						Dict												=  get_sqrt_alpha_prod(UpperCAmelCase_    ,    UpperCAmelCase_    ,    UpperCAmelCase_    ,    UpperCAmelCase_				)
						_UpperCamelCase   :						Tuple												=  sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
						return noisy_samples






def A__       (	UpperCAmelCase_    ,    UpperCAmelCase_    ,    UpperCAmelCase_    ,    UpperCAmelCase_				):
						_UpperCamelCase							,     _UpperCamelCase   :						List[str]												=  get_sqrt_alpha_prod(UpperCAmelCase_    ,    UpperCAmelCase_    ,    UpperCAmelCase_    ,    UpperCAmelCase_				)
						_UpperCamelCase   :						Optional[Any]												=  sqrt_alpha_prod * noise - sqrt_one_minus_alpha_prod * sample
						return velocity







'''simple docstring'''
def A__       (	UpperCAmelCase_    ,    UpperCAmelCase_				):
						return int((input_a, input_a).count(0				) != 0				)






def A__       (	):
						assert nand_gate(0    ,    0				) == 1
						assert nand_gate(0    ,    1				) == 1
						assert nand_gate(1    ,    0				) == 1
						assert nand_gate(1    ,    1				) == 0


if __name__ == "__main__":
					print(nand_gate(0, 0))
					print(nand_gate(0, 1))
					print(nand_gate(1, 0))
					print(nand_gate(1, 1))


