



import numpy as np
import pandas as pd
from sklearn.preprocessing import Normalizer
from sklearn.svm import SVR
from statsmodels.tsa.statespace.sarimax import SARIMAX







def    _snake_case (       __snake_case					,  __snake_case					,  __snake_case					,  __snake_case					,  __snake_case    )     ->     float:






      '''simple docstring'''







      UpperCAmelCase_  :	List[str]				      =		np.array([[1, item, train_mtch[i]] for i, item in enumerate(__snake_case    )]    )
      UpperCAmelCase_  :	Union[str, Any]				      =		np.array(__snake_case    )
      UpperCAmelCase_  :	Union[str, Any]				      =		np.dot(np.dot(np.linalg.inv(np.dot(x.transpose()					,  __snake_case    )    )					,  x.transpose()    )					,  __snake_case    )
      return abs(beta[0] + test_dt[0] * beta[1] + test_mtch[0] + beta[2]    )







def    _snake_case (       __snake_case					,  __snake_case					,  __snake_case    )     ->     float:






      '''simple docstring'''







      UpperCAmelCase_  :	List[str]				      =		(1, 2, 1)
      UpperCAmelCase_  :	Union[str, Any]				      =		(1, 1, 0, 7)
      UpperCAmelCase_  :	str				      =		SARIMAX(
          __snake_case					,  exog=__snake_case					,  order=__snake_case					,  seasonal_order=__snake_case    )
      UpperCAmelCase_  :	Dict				      =		model.fit(disp=__snake_case					,  maxiter=6_0_0					,  method="nm"    )
      UpperCAmelCase_  :	Optional[int]				      =		model_fit.predict(1					,  len(__snake_case    )					,  exog=[test_match]    )
      return result[0]







def    _snake_case (       __snake_case					,  __snake_case					,  __snake_case    )     ->     float:






      '''simple docstring'''







      UpperCAmelCase_  :	Tuple				      =		SVR(kernel="rbf"					,  C=1					,  gamma=0.1					,  epsilon=0.1    )
      regressor.fit(__snake_case					,  __snake_case    )
      UpperCAmelCase_  :	Any				      =		regressor.predict(__snake_case    )
      return y_pred[0]







def    _snake_case (       __snake_case    )     ->     float:






      '''simple docstring'''







      train_user.sort()
      UpperCAmelCase_  :	List[Any]				      =		np.percentile(__snake_case					,  2_5    )
      UpperCAmelCase_  :	str				      =		np.percentile(__snake_case					,  7_5    )
      UpperCAmelCase_  :	Dict				      =		qa - qa
      UpperCAmelCase_  :	Tuple				      =		qa - (iqr * 0.1)
      return low_lim







def    _snake_case (       __snake_case					,  __snake_case    )     ->     bool:






      '''simple docstring'''







      UpperCAmelCase_  :	int				      =		0
      UpperCAmelCase_  :	List[str]				      =		0
      for i in list_vote:
            if i > actual_result:
                  UpperCAmelCase_  :	Tuple				      =		not_safe + 1
            else:
                  if abs(abs(__snake_case    ) - abs(__snake_case    )    ) <= 0.1:
                        safe += 1
                  else:
                        not_safe += 1
      return safe > not_safe


if __name__ == "__main__":
   # data_input_df = pd.read_csv("ex_data.csv", header=None)
   __lowerCamelCase         =			[[18231, 0.0, 1], [22621, 1.0, 2], [15675, 0.0, 3], [23583, 1.0, 4]]
   __lowerCamelCase         =			pd.DataFrame(
       data_input, columns=['''total_user''', '''total_even''', '''days''']
   )
   __lowerCamelCase         =			Normalizer().fit_transform(data_input_df.values)
   # split data
   __lowerCamelCase         =			normalize_df[:, 2].tolist()
   __lowerCamelCase         =			normalize_df[:, 0].tolist()
   __lowerCamelCase         =			normalize_df[:, 1].tolist()

   # for svr (input variable = total date and total match)
   __lowerCamelCase         =			normalize_df[:, [1, 2]].tolist()
   __lowerCamelCase         =			x[: len(x) - 1]
   __lowerCamelCase         =			x[len(x) - 1 :]

   # for linear regression & sarimax
   __lowerCamelCase         =			total_date[: len(total_date) - 1]
   __lowerCamelCase         =			total_user[: len(total_user) - 1]
   __lowerCamelCase         =			total_match[: len(total_match) - 1]

   __lowerCamelCase         =			total_date[len(total_date) - 1 :]
   __lowerCamelCase         =			total_user[len(total_user) - 1 :]
   __lowerCamelCase         =			total_match[len(total_match) - 1 :]

   # voting system with forecasting
   __lowerCamelCase         =			[
       linear_regression_prediction(
           trn_date, trn_user, trn_match, tst_date, tst_match
       ),
       sarimax_predictor(trn_user, trn_match, tst_match),
       support_vector_regressor(x_train, x_test, trn_user),
   ]

   # check the safety of today's data
   __lowerCamelCase         =			'''''' if data_safety_checker(res_vote, tst_user) else '''not '''
   print('''Today\'s data is {not_str}safe.''')





# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch

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

import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin, SchedulerOutput


@dataclass
class   snake_case_ (lowercase__  ):




       """simple docstring"""






       _lowerCamelCase			    =				42
       _lowerCamelCase			    =				42


class   snake_case_ (lowercase__ , lowercase__  ):




       """simple docstring"""






       _lowerCamelCase			    =				1



       @register_to_config
       def __init__(     self     ,lowercase = 2000     ,lowercase = 0.15     ,lowercase = 0.01     ,lowercase = 1348.0     ,lowercase = 1E-5     ,lowercase = 1     ,):

             """simple docstring"""





             UpperCAmelCase_  :	Optional[int]				      =		sigma_max

             # setable values
             UpperCAmelCase_  :	Optional[int]				      =		None

             self.set_sigmas(lowercase     ,lowercase     ,lowercase     ,lowercase)



       def  A_   (     self     ,lowercase     ,lowercase = None):

             """simple docstring"""





             return sample



       def  A_   (     self     ,lowercase     ,lowercase = None     ,lowercase = None):

             """simple docstring"""





             UpperCAmelCase_  :	int				      =		sampling_eps if sampling_eps is not None else self.config.sampling_eps

             UpperCAmelCase_  :	List[Any]				      =		torch.linspace(1     ,lowercase     ,lowercase     ,device=lowercase)



       def  A_   (     self     ,lowercase     ,lowercase = None     ,lowercase = None     ,lowercase = None):

             """simple docstring"""





             UpperCAmelCase_  :	Any				      =		sigma_min if sigma_min is not None else self.config.sigma_min
             UpperCAmelCase_  :	int				      =		sigma_max if sigma_max is not None else self.config.sigma_max
             UpperCAmelCase_  :	Union[str, Any]				      =		sampling_eps if sampling_eps is not None else self.config.sampling_eps
             if self.timesteps is None:
                   self.set_timesteps(lowercase     ,lowercase)

             UpperCAmelCase_  :	Union[str, Any]				      =		sigma_min * (sigma_max / sigma_min) ** (self.timesteps / sampling_eps)
             UpperCAmelCase_  :	Optional[int]				      =		torch.exp(torch.linspace(math.log(lowercase)     ,math.log(lowercase)     ,lowercase))
             UpperCAmelCase_  :	Any				      =		torch.tensor([sigma_min * (sigma_max / sigma_min) ** t for t in self.timesteps])



       def  A_   (     self     ,lowercase     ,lowercase):

             """simple docstring"""





             return torch.where(
                 timesteps == 0     ,torch.zeros_like(t.to(timesteps.device))     ,self.discrete_sigmas[timesteps - 1].to(timesteps.device)     ,)



       def  A_   (     self     ,lowercase     ,lowercase     ,lowercase     ,lowercase = None     ,lowercase = True     ,):

             """simple docstring"""





             if self.timesteps is None:
                   raise ValueError(
                       "`self.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler")

             UpperCAmelCase_  :	Optional[int]				      =		timestep * torch.ones(
                 sample.shape[0]     ,device=sample.device)  # torch.repeat_interleave(timestep, sample.shape[0])
             UpperCAmelCase_  :	Tuple				      =		(timestep * (len(self.timesteps) - 1)).long()

             # mps requires indices to be in the same device, so we use cpu as is the default with cuda
             UpperCAmelCase_  :	Optional[int]				      =		timesteps.to(self.discrete_sigmas.device)

             UpperCAmelCase_  :	Optional[Any]				      =		self.discrete_sigmas[timesteps].to(sample.device)
             UpperCAmelCase_  :	Optional[Any]				      =		self.get_adjacent_sigma(lowercase     ,lowercase).to(sample.device)
             UpperCAmelCase_  :	Any				      =		torch.zeros_like(lowercase)
             UpperCAmelCase_  :	Dict				      =		(sigma**2 - adjacent_sigma**2) ** 0.5

             # equation 6 in the paper: the model_output modeled by the network is grad_x log pt(x)
             # also equation 47 shows the analog from SDE models to ancestral sampling methods
             UpperCAmelCase_  :	Dict				      =		diffusion.flatten()
             while len(diffusion.shape) < len(sample.shape):
                   UpperCAmelCase_  :	List[str]				      =		diffusion.unsqueeze(-1)
             UpperCAmelCase_  :	List[Any]				      =		drift - diffusion**2 * model_output

             #  equation 6: sample noise for the diffusion term of
             UpperCAmelCase_  :	Union[str, Any]				      =		randn_tensor(
                 sample.shape     ,layout=sample.layout     ,generator=lowercase     ,device=sample.device     ,dtype=sample.dtype)
             UpperCAmelCase_  :	Any				      =		sample - drift  # subtract because `dt` is a small negative timestep
             # TODO is the variable diffusion the correct scaling term for the noise?
             UpperCAmelCase_  :	Tuple				      =		prev_sample_mean + diffusion * noise  # add impact of diffusion field g

             if not return_dict:
                   return (prev_sample, prev_sample_mean)

             return SdeVeOutput(prev_sample=lowercase     ,prev_sample_mean=lowercase)



       def  A_   (     self     ,lowercase     ,lowercase     ,lowercase = None     ,lowercase = True     ,):

             """simple docstring"""





             if self.timesteps is None:
                   raise ValueError(
                       "`self.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler")

             # For small batch sizes, the paper "suggest replacing norm(z) with sqrt(d), where d is the dim. of z"
             # sample noise for correction
             UpperCAmelCase_  :	int				      =		randn_tensor(sample.shape     ,layout=sample.layout     ,generator=lowercase).to(sample.device)

             # compute step size from the model_output, the noise, and the snr
             UpperCAmelCase_  :	Union[str, Any]				      =		torch.norm(model_output.reshape(model_output.shape[0]     ,-1)     ,dim=-1).mean()
             UpperCAmelCase_  :	Optional[Any]				      =		torch.norm(noise.reshape(noise.shape[0]     ,-1)     ,dim=-1).mean()
             UpperCAmelCase_  :	List[Any]				      =		(self.config.snr * noise_norm / grad_norm) ** 2 * 2
             UpperCAmelCase_  :	Optional[Any]				      =		step_size * torch.ones(sample.shape[0]).to(sample.device)
             # self.repeat_scalar(step_size, sample.shape[0])

             # compute corrected sample: model_output term and noise term
             UpperCAmelCase_  :	Any				      =		step_size.flatten()
             while len(step_size.shape) < len(sample.shape):
                   UpperCAmelCase_  :	Tuple				      =		step_size.unsqueeze(-1)
             UpperCAmelCase_  :	Dict				      =		sample + step_size * model_output
             UpperCAmelCase_  :	int				      =		prev_sample_mean + ((step_size * 2) ** 0.5) * noise

             if not return_dict:
                   return (prev_sample,)

             return SchedulerOutput(prev_sample=lowercase)



       def  A_   (     self     ,lowercase     ,lowercase     ,lowercase     ,):

             """simple docstring"""





             UpperCAmelCase_  :	Any				      =		timesteps.to(original_samples.device)
             UpperCAmelCase_  :	List[str]				      =		self.discrete_sigmas.to(original_samples.device)[timesteps]
             UpperCAmelCase_  :	Tuple				      =		(
                 noise * sigmas[:, None, None, None]
                 if noise is not None
                 else torch.randn_like(lowercase) * sigmas[:, None, None, None]
             )
             UpperCAmelCase_  :	Tuple				      =		noise + original_samples
             return noisy_samples



       def __len__(     self):

             """simple docstring"""





             return self.config.num_train_timesteps
