from dataclasses import dataclass
from typing import Optional, Tuple, Union

import flax
import jax.numpy as jnp
from scipy import integrate

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import (
    CommonSchedulerState,
    FlaxKarrasDiffusionSchedulers,
    FlaxSchedulerMixin,
    FlaxSchedulerOutput,
    broadcast_to_shape_from_left,
)







@flax.struct.dataclass
class __snake_case							:
   lowerCAmelCase_								=				42

   # setable values
   lowerCAmelCase_								=				42
   lowerCAmelCase_								=				42
   lowerCAmelCase_								=				42
   lowerCAmelCase_								=				None

   # running values
   lowerCAmelCase_								=				None




   @classmethod
   def       __a		(       cls     :							List[str]			,      _lowercase     :							CommonSchedulerState			,      _lowercase     :							jnp.ndarray			,      _lowercase     :							jnp.ndarray			,      _lowercase     :							jnp.ndarray ):



     """simple docstring"""






     return cls(common=_lowercase			,      init_noise_sigma=_lowercase			,      timesteps=_lowercase			,      sigmas=_lowercase )







@dataclass
class __snake_case							(		lowerCamelCase_						):
   lowerCAmelCase_								=				42







class __snake_case							(		lowerCamelCase_  ,						lowerCamelCase_						):
   lowerCAmelCase_								=				[e.name for e in FlaxKarrasDiffusionSchedulers]

   lowerCAmelCase_								=				42




   @property
   def       __a		(       self     :							str ):



     """simple docstring"""






     return True




   @register_to_config
   def __init__(       self     :							Optional[Any]			,      _lowercase     :							int = 10_00			,      _lowercase     :							float = 0.00_01			,      _lowercase     :							float = 0.02			,      _lowercase     :							str = "linear"			,      _lowercase     :							Optional[jnp.ndarray] = None			,      _lowercase     :							str = "epsilon"			,      _lowercase     :							jnp.dtype = jnp.floataa			,      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__     				= dtype




   def       __a		(       self     :							Dict			,      _lowercase     :							Optional[CommonSchedulerState] = None ):



     """simple docstring"""






     if common is None:
       SCREAMING_SNAKE_CASE__     				= CommonSchedulerState.create(self )

     SCREAMING_SNAKE_CASE__     				= jnp.arange(0			,      self.config.num_train_timesteps ).round()[::-1]
     SCREAMING_SNAKE_CASE__     				= ((1 - common.alphas_cumprod) / common.alphas_cumprod) ** 0.5

     # standard deviation of the initial noise distribution
     SCREAMING_SNAKE_CASE__     				= sigmas.max()

     return LMSDiscreteSchedulerState.create(
         common=_lowercase			,      init_noise_sigma=_lowercase			,      timesteps=_lowercase			,      sigmas=_lowercase			,      )




   def       __a		(       self     :							Optional[int]			,      _lowercase     :							LMSDiscreteSchedulerState			,      _lowercase     :							jnp.ndarray			,      _lowercase     :							int ):



     """simple docstring"""






     ((SCREAMING_SNAKE_CASE__)				,	)     				= jnp.where(state.timesteps == timestep			,      size=1 )
     SCREAMING_SNAKE_CASE__     				= step_index[0]

     SCREAMING_SNAKE_CASE__     				= state.sigmas[step_index]
     SCREAMING_SNAKE_CASE__     				= sample / ((sigma**2 + 1) ** 0.5)
     return sample




   def       __a		(       self     :							Any			,      _lowercase     :							LMSDiscreteSchedulerState			,      _lowercase     :							Dict			,      _lowercase     :							Optional[Any]			,      _lowercase     :							List[Any] ):



     """simple docstring"""






     def lms_derivative(_lowercase     :							List[str] ):
       SCREAMING_SNAKE_CASE__     				= 1.0
       for k in range(_lowercase ):
         if current_order == k:
           continue
         prod *= (tau - state.sigmas[t - k]) / (state.sigmas[t - current_order] - state.sigmas[t - k])
       return prod

     SCREAMING_SNAKE_CASE__     				= integrate.quad(_lowercase			,      state.sigmas[t]			,      state.sigmas[t + 1]			,      epsrel=1E-4 )[0]

     return integrated_coeff




   def       __a		(       self     :							Any			,      _lowercase     :							LMSDiscreteSchedulerState			,      _lowercase     :							int			,      _lowercase     :							Tuple = () ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__     				= jnp.linspace(self.config.num_train_timesteps - 1			,      0			,      _lowercase			,      dtype=self.dtype )

     SCREAMING_SNAKE_CASE__     				= jnp.floor(_lowercase ).astype(jnp.intaa )
     SCREAMING_SNAKE_CASE__     				= jnp.ceil(_lowercase ).astype(jnp.intaa )

     SCREAMING_SNAKE_CASE__     				= jnp.mod(_lowercase			,      1.0 )

     SCREAMING_SNAKE_CASE__     				= ((1 - state.common.alphas_cumprod) / state.common.alphas_cumprod) ** 0.5
     SCREAMING_SNAKE_CASE__     				= (1 - frac) * sigmas[low_idx] + frac * sigmas[high_idx]
     SCREAMING_SNAKE_CASE__     				= jnp.concatenate([sigmas, jnp.array([0.0]			,      dtype=self.dtype )] )

     SCREAMING_SNAKE_CASE__     				= timesteps.astype(jnp.intaa )

     # initial running values
     SCREAMING_SNAKE_CASE__     				= jnp.zeros((0,) + shape			,      dtype=self.dtype )

     return state.replace(
         timesteps=_lowercase			,      sigmas=_lowercase			,      num_inference_steps=_lowercase			,      derivatives=_lowercase			,      )




   def       __a		(       self     :							Any			,      _lowercase     :							LMSDiscreteSchedulerState			,      _lowercase     :							jnp.ndarray			,      _lowercase     :							int			,      _lowercase     :							jnp.ndarray			,      _lowercase     :							int = 4			,      _lowercase     :							bool = True			,      ):



     """simple docstring"""






     if state.num_inference_steps is None:
       raise ValueError(
           """Number of inference steps is 'None', you need to run 'set_timesteps' after creating the scheduler""" )

     SCREAMING_SNAKE_CASE__     				= state.sigmas[timestep]

     # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
     if self.config.prediction_type == "epsilon":
       SCREAMING_SNAKE_CASE__     				= sample - sigma * model_output
     elif self.config.prediction_type == "v_prediction":
       # * c_out + input * c_skip
       SCREAMING_SNAKE_CASE__     				= model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
     else:
       raise ValueError(
           f"""prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`""" )

     # 2. Convert to an ODE derivative
     SCREAMING_SNAKE_CASE__     				= (sample - pred_original_sample) / sigma
     SCREAMING_SNAKE_CASE__     				= state.replace(derivatives=jnp.append(state.derivatives			,      _lowercase ) )
     if len(state.derivatives ) > order:
       SCREAMING_SNAKE_CASE__     				= state.replace(derivatives=jnp.delete(state.derivatives			,      0 ) )

     # 3. Compute linear multistep coefficients
     SCREAMING_SNAKE_CASE__     				= min(timestep + 1			,      _lowercase )
     SCREAMING_SNAKE_CASE__     				= [self.get_lms_coefficient(_lowercase			,      _lowercase			,      _lowercase			,      _lowercase ) for curr_order in range(_lowercase )]

     # 4. Compute previous sample based on the derivatives path
     SCREAMING_SNAKE_CASE__     				= sample + sum(
         coeff * derivative for coeff, derivative in zip(_lowercase			,      reversed(state.derivatives ) ) )

     if not return_dict:
       return (prev_sample, state)

     return FlaxLMSSchedulerOutput(prev_sample=_lowercase			,      state=_lowercase )




   def       __a		(       self     :							Union[str, Any]			,      _lowercase     :							LMSDiscreteSchedulerState			,      _lowercase     :							jnp.ndarray			,      _lowercase     :							jnp.ndarray			,      _lowercase     :							jnp.ndarray			,      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__     				= state.sigmas[timesteps].flatten()
     SCREAMING_SNAKE_CASE__     				= broadcast_to_shape_from_left(_lowercase			,      noise.shape )

     SCREAMING_SNAKE_CASE__     				= original_samples + noise * sigma

     return noisy_samples




   def __len__(       self     :							Union[str, Any] ):



     """simple docstring"""






     return self.config.num_train_timesteps

from hashlib import mda, shaaaa

__lowerCamelCase     :					Dict				  =						(shaaaa, mda)







class __snake_case							:




   def __init__(       self     :							str			,      _lowercase     :							int = 8 ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__     				= 0B0
     SCREAMING_SNAKE_CASE__     				= size




   def       __a		(       self     :							List[str]			,      _lowercase     :							str ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__     				= self.hash_(_lowercase )
     self.bitarray |= h




   def       __a		(       self     :							int			,      _lowercase     :							str ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__     				= self.hash_(_lowercase )
     return (h & self.bitarray) == h




   def __contains__(       self     :							Union[str, Any]			,      _lowercase     :							str ):



     """simple docstring"""






     return self.exists(_lowercase )




   def       __a		(       self     :							Dict			,      _lowercase     :							int ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__     				= bin(_lowercase )[2:]
     return res.zfill(self.size )




   @property
   def       __a		(       self     :							Tuple ):



     """simple docstring"""






     return self.format_bin(self.bitarray )




   def       __a		(       self     :							Optional[int]			,      _lowercase     :							str ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__     				= 0B0
     for func in HASH_FUNCTIONS:
       SCREAMING_SNAKE_CASE__     				= (
           int.from_bytes(func(value.encode() ).digest()			,      """little""" ) % self.size
       )
       res |= 2**position
     return res




   def       __a		(       self     :							str			,      _lowercase     :							str ):



     """simple docstring"""






     return self.format_bin(self.hash_(_lowercase ) )




   @property
   def       __a		(       self     :							Union[str, Any] ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__     				= bin(self.bitarray ).count("""1""" )
     return (n_ones / self.size) ** len(_lowercase )
