


"""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


__lowerCamelCase      :Optional[Any]	    =		'scheduler_config.json'


class 			A__	(						__lowercase):


     """simple docstring"""
     snake_case__	:					List[Any]													=1
     snake_case__	:					Tuple													=2
     snake_case__	:					Dict													=3
     snake_case__	:					Tuple													=4
     snake_case__	:					Tuple													=5


@dataclass
class 			A__	(						__lowercase):


     """simple docstring"""
     snake_case__	:					jnp.ndarray


class 			A__	:


     """simple docstring"""
     snake_case__	:					Tuple													=SCHEDULER_CONFIG_NAME
     snake_case__	:					Union[str, Any]													=['''dtype''']
     snake_case__	:					Union[str, Any]													=[]
     snake_case__	:					Any													=True

     @classmethod
     def 		a__						(	cls:						Union[str, Any]     ,		__a:						Dict[str, Any] = None     ,		__a:						Optional[str] = None     ,		__a:						Dict=False     ,		**__a:						Any     ,		)->    Optional[int]:
           lowerCamelCase					:					Optional[Any]        =	cls.load_config(
               pretrained_model_name_or_path=__a     ,		subfolder=__a     ,		return_unused_kwargs=__a     ,		**__a     ,		)
           lowerCamelCase					:					List[Any]        =	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 		a__						(	self:						List[Any]     ,		__a:						Union[str, os.PathLike]     ,		__a:						bool = False     ,		**__a:						int	)->    Optional[Any]:
           self.save_config(save_directory=__a     ,		push_to_hub=__a     ,		**__a	)

     @property
     def 		a__						(	self:						str	)->    int:
           return self._get_compatibles()







     @classmethod
     def 		a__						(	cls:						int	)->    Union[str, Any]:
           lowerCamelCase					:					List[Any]        =	list(set([cls.__name__] + cls._compatibles	)	)
           lowerCamelCase					:					int        =	importlib.import_module(__name__.split("""."""	)[0]	)
           lowerCamelCase					:					List[str]        =	[
               getattr(__a     ,		__a	) for c in compatible_classes_str if hasattr(__a     ,		__a	)
           ]
           return compatible_classes




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




def   snake_case		(     UpperCamelCase__       :						int			,				UpperCamelCase__       :						str=0.9_9_9			,				UpperCamelCase__       :						Dict=jnp.floataa    )   ->      jnp.ndarray:

      def alpha_bar(UpperCamelCase__       :						List[Any]    ):
            return math.cos((time_step + 0.0_0_8) / 1.0_0_8 * math.pi / 2    ) ** 2

      lowerCamelCase					:					Optional[Any]        =	[]
      for i in range(UpperCamelCase__    ):
            lowerCamelCase					:					Tuple        =	i / num_diffusion_timesteps
            lowerCamelCase					:					str        =	(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 			A__	:


     """simple docstring"""
     snake_case__	:					jnp.ndarray
     snake_case__	:					jnp.ndarray
     snake_case__	:					jnp.ndarray

     @classmethod
     def 		a__						(	cls:						Optional[Any]     ,		__a:						List[str]	)->    Optional[Any]:
           lowerCamelCase					:					Optional[int]        =	scheduler.config

           if config.trained_betas is not None:
                 lowerCamelCase					:					List[str]        =	jnp.asarray(config.trained_betas     ,		dtype=scheduler.dtype	)
           elif config.beta_schedule == "linear":
                 lowerCamelCase					:					str        =	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					:					int        =	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   snake_case		(     UpperCamelCase__       :						CommonSchedulerState			,				UpperCamelCase__       :						jnp.ndarray			,				UpperCamelCase__       :						jnp.ndarray			,				UpperCamelCase__       :						jnp.ndarray    )   ->      List[str]:
      lowerCamelCase					:					str        =	state.alphas_cumprod

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

      lowerCamelCase					:					Tuple        =	(1 - alphas_cumprod[timesteps]) ** 0.5
      lowerCamelCase					:					Optional[int]        =	sqrt_one_minus_alpha_prod.flatten()
      lowerCamelCase					:					Any        =	broadcast_to_shape_from_left(UpperCamelCase__			,				original_samples.shape    )

      return sqrt_alpha_prod, sqrt_one_minus_alpha_prod




def   snake_case		(     UpperCamelCase__       :						CommonSchedulerState			,				UpperCamelCase__       :						jnp.ndarray			,				UpperCamelCase__       :						jnp.ndarray			,				UpperCamelCase__       :						jnp.ndarray    )   ->      Tuple:
      lowerCamelCase					:					int        =	get_sqrt_alpha_prod(UpperCamelCase__			,				UpperCamelCase__			,				UpperCamelCase__			,				UpperCamelCase__    )
      lowerCamelCase					:					Tuple        =	sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
      return noisy_samples






def   snake_case		(     UpperCamelCase__       :						CommonSchedulerState			,				UpperCamelCase__       :						jnp.ndarray			,				UpperCamelCase__       :						jnp.ndarray			,				UpperCamelCase__       :						jnp.ndarray    )   ->      List[Any]:
      lowerCamelCase					:					Optional[Any]        =	get_sqrt_alpha_prod(UpperCamelCase__			,				UpperCamelCase__			,				UpperCamelCase__			,				UpperCamelCase__    )
      lowerCamelCase					:					Optional[int]        =	sqrt_alpha_prod * noise - sqrt_one_minus_alpha_prod * sample
      return velocity










"""simple docstring"""


from __future__ import annotations

import math




def   snake_case		(     UpperCamelCase__       :						float			,				UpperCamelCase__       :						int    )   ->      float:
      lowerCamelCase					:					Dict        =	u
      for i in range(1			,				UpperCamelCase__    ):
            lowerCamelCase					:					List[str]        =	temp * (u - i)
      return temp




def   snake_case		(     )   ->      None:
      lowerCamelCase					:					List[Any]        =	int(input("""enter the numbers of values: """    )    )
      lowerCamelCase					:					list[list[float]]        =	[]
      for _ in range(UpperCamelCase__    ):
            y.append([]    )
      for i in range(UpperCamelCase__    ):
            for j in range(UpperCamelCase__    ):
                  y[i].append(UpperCamelCase__    )
                  lowerCamelCase					:					Union[str, Any]        =	0

      print("""enter the values of parameters in a list: """    )
      lowerCamelCase					:					Any        =	list(map(UpperCamelCase__			,				input().split()    )    )

      print("""enter the values of corresponding parameters: """    )
      for i in range(UpperCamelCase__    ):
            lowerCamelCase					:					int        =	float(input()    )

      lowerCamelCase					:					Dict        =	int(input("""enter the value to interpolate: """    )    )
      lowerCamelCase					:					List[Any]        =	(value - x[0]) / (x[1] - x[0])

      # for calculating forward difference table

      for i in range(1			,				UpperCamelCase__    ):
            for j in range(n - i    ):
                  lowerCamelCase					:					str        =	y[j + 1][i - 1] - y[j][i - 1]

      lowerCamelCase					:					Any        =	y[0][0]
      for i in range(1			,				UpperCamelCase__    ):
            summ += (ucal(UpperCamelCase__			,				UpperCamelCase__    ) * y[0][i]) / math.factorial(UpperCamelCase__    )

      print(F'the value at {value} is {summ}'    )


if __name__ == "__main__":
    main()




