

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


_lowerCamelCase    :       Dict        =							'''scheduler_config.json'''





class  __UpperCAmelCase					(	lowerCamelCase__						):
   UpperCamelCase       					=					1
   UpperCamelCase       					=					2
   UpperCamelCase       					=					3
   UpperCamelCase       					=					4
   UpperCamelCase       					=					5






@dataclass
class  __UpperCAmelCase					(	lowerCamelCase__						):
   UpperCamelCase       					=					4_2






class  __UpperCAmelCase					:
   UpperCamelCase       					=					SCHEDULER_CONFIG_NAME
   UpperCamelCase       					=					["""dtype"""]
   UpperCamelCase       					=					[]
   UpperCamelCase       					=					True



   @classmethod
   def 	__magic_name__   (							cls							:					List[str],		__A							:					Dict[str, Any] = None,		__A							:					Optional[str] = None,		__A							:					Tuple=False,		**__A							:					str,		):
     UpperCAmelCase	:							Optional[int]						=			cls.load_config(
         pretrained_model_name_or_path=__A,		subfolder=__A,		return_unused_kwargs=__A,		**__A,		)
     UpperCAmelCase	:							List[str]						=			cls.from_config(__A,		return_unused_kwargs=__A,		**__A						)

     if hasattr(__A,		'''create_state'''						) and getattr(__A,		'''has_state''',		__A						):
       UpperCAmelCase	:							List[str]						=			scheduler.create_state()

     if return_unused_kwargs:
       return scheduler, state, unused_kwargs

     return scheduler, state



   def 	__magic_name__   (							self							:					Tuple,		__A							:					Union[str, os.PathLike],		__A							:					bool = False,		**__A							:					Optional[Any]						):
     self.save_config(save_directory=__A,		push_to_hub=__A,		**__A						)



   @property
   def 	__magic_name__   (							self							:					int						):
     return self._get_compatibles()





   @classmethod
   def 	__magic_name__   (							cls							:					int						):
     UpperCAmelCase	:							Optional[int]						=			list(set([cls.__name__] + cls._compatibles						)						)
     UpperCAmelCase	:							Optional[int]						=			importlib.import_module(__name__.split('''.'''						)[0]						)
     UpperCAmelCase	:							Dict						=			[
         getattr(__A,		__A						) for c in compatible_classes_str if hasattr(__A,		__A						)
     ]
     return compatible_classes






def       a__					(		UpperCAmelCase							:      jnp.ndarray    ,			UpperCAmelCase							:      Tuple[int]      )  ->						jnp.ndarray:
  assert len(_lowerCAmelCase      ) >= x.ndim
  return jnp.broadcast_to(x.reshape(x.shape + (1,) * (len(_lowerCAmelCase      ) - x.ndim)      )    ,			_lowerCAmelCase      )

def       a__					(		UpperCAmelCase							:      int    ,			UpperCAmelCase							:      Dict=0.999    ,			UpperCAmelCase							:      Optional[int]=jnp.floataa      )  ->						jnp.ndarray:

  def alpha_bar(UpperCAmelCase							:      List[str]      ):
    return math.cos((time_step + 0.008) / 1.008 * math.pi / 2      ) ** 2

  UpperCAmelCase	:							str						=			[]
  for i in range(_lowerCAmelCase      ):
    UpperCAmelCase	:							Any						=			i / num_diffusion_timesteps
    UpperCAmelCase	:							List[Any]						=			(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  __UpperCAmelCase					:
   UpperCamelCase       					=					4_2
   UpperCamelCase       					=					4_2
   UpperCamelCase       					=					4_2



   @classmethod
   def 	__magic_name__   (							cls							:					int,		__A							:					Dict						):
     UpperCAmelCase	:							List[str]						=			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	:							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.
       UpperCAmelCase	:							List[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
       UpperCAmelCase	:							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__}'''						)

     UpperCAmelCase	:							str						=			1.0 - betas

     UpperCAmelCase	:							int						=			jnp.cumprod(__A,		axis=0						)

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






def       a__					(		UpperCAmelCase							:      CommonSchedulerState    ,			UpperCAmelCase							:      jnp.ndarray    ,			UpperCAmelCase							:      jnp.ndarray    ,			UpperCAmelCase							:      jnp.ndarray      )  ->						str:
  UpperCAmelCase	:							str						=			state.alphas_cumprod

  UpperCAmelCase	:							Dict						=			alphas_cumprod[timesteps] ** 0.5
  UpperCAmelCase	:							int						=			sqrt_alpha_prod.flatten()
  UpperCAmelCase	:							Optional[Any]						=			broadcast_to_shape_from_left(_lowerCAmelCase    ,			original_samples.shape      )

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

  return sqrt_alpha_prod, sqrt_one_minus_alpha_prod

def       a__					(		UpperCAmelCase							:      CommonSchedulerState    ,			UpperCAmelCase							:      jnp.ndarray    ,			UpperCAmelCase							:      jnp.ndarray    ,			UpperCAmelCase							:      jnp.ndarray      )  ->						Optional[Any]:
  UpperCAmelCase	:							int						=			get_sqrt_alpha_prod(_lowerCAmelCase    ,			_lowerCAmelCase    ,			_lowerCAmelCase    ,			_lowerCAmelCase      )
  UpperCAmelCase	:							Dict						=			sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
  return noisy_samples






def       a__					(		UpperCAmelCase							:      CommonSchedulerState    ,			UpperCAmelCase							:      jnp.ndarray    ,			UpperCAmelCase							:      jnp.ndarray    ,			UpperCAmelCase							:      jnp.ndarray      )  ->						List[str]:
  UpperCAmelCase	:							Any						=			get_sqrt_alpha_prod(_lowerCAmelCase    ,			_lowerCAmelCase    ,			_lowerCAmelCase    ,			_lowerCAmelCase      )
  UpperCAmelCase	:							int						=			sqrt_alpha_prod * noise - sqrt_one_minus_alpha_prod * sample
  return velocity








from math import log

from scipy.constants import Boltzmann, physical_constants

_lowerCamelCase    :       Tuple        =							3_0_0  # TEMPERATURE (unit = K)

def       a__					(		UpperCAmelCase							:      float    ,			UpperCAmelCase							:      float    ,			UpperCAmelCase							:      float    ,			)  ->						float:

  if donor_conc <= 0:
    raise ValueError('''Donor concentration should be positive'''      )
  elif acceptor_conc <= 0:
    raise ValueError('''Acceptor concentration should be positive'''      )
  elif intrinsic_conc <= 0:
    raise ValueError('''Intrinsic concentration should be positive'''      )
  elif donor_conc <= intrinsic_conc:
    raise ValueError(
        '''Donor concentration should be greater than intrinsic concentration'''      )
  elif acceptor_conc <= intrinsic_conc:
    raise ValueError(
        '''Acceptor concentration should be greater than intrinsic concentration'''      )
  else:
    return (
        Boltzmann
        * T
        * log((donor_conc * acceptor_conc) / intrinsic_conc**2      )
        / physical_constants["electron volt"][0]
    )


if __name__ == "__main__":
     import doctest

     doctest.testmod()





