





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    __UpperCAmelCase(      lowercase_ ,    lowercase_ ,    lowercase_ ,    lowercase_ ,    lowercase_      ):
							_lowerCamelCase							:     Optional[int]	=		np.array([[1, item, train_mtch[i]] for i, item in enumerate(UpperCAmelCase__      )]      )
							_lowerCamelCase							:     int	=		np.array(UpperCAmelCase__      )
							_lowerCamelCase							:     Any	=		np.dot(np.dot(np.linalg.inv(np.dot(x.transpose() ,    UpperCAmelCase__      )      ) ,    x.transpose()      ) ,    UpperCAmelCase__      )
							return abs(beta[0] + test_dt[0] * beta[1] + test_mtch[0] + beta[2]      )




def    __UpperCAmelCase(      lowercase_ ,    lowercase_ ,    lowercase_      ):
							_lowerCamelCase							:     Any	=		(1, 2, 1)
							_lowerCamelCase							:     List[Any]	=		(1, 1, 0, 7)
							_lowerCamelCase							:     Tuple	=		SARIMAX(
							    UpperCAmelCase__ ,    exog=UpperCAmelCase__ ,    order=UpperCAmelCase__ ,    seasonal_order=UpperCAmelCase__      )
							_lowerCamelCase							:     str	=		model.fit(disp=UpperCAmelCase__ ,    maxiter=6_00 ,    method='''nm'''      )
							_lowerCamelCase							:     Optional[int]	=		model_fit.predict(1 ,    len(UpperCAmelCase__      ) ,    exog=[test_match]      )
							return result[0]




def    __UpperCAmelCase(      lowercase_ ,    lowercase_ ,    lowercase_      ):
							_lowerCamelCase							:     Optional[int]	=		SVR(kernel='''rbf''' ,    C=1 ,    gamma=0.1 ,    epsilon=0.1      )
							regressor.fit(UpperCAmelCase__ ,    UpperCAmelCase__      )
							_lowerCamelCase							:     Union[str, Any]	=		regressor.predict(UpperCAmelCase__      )
							return y_pred[0]




def    __UpperCAmelCase(      lowercase_      ):
							train_user.sort()
							_lowerCamelCase							:     Tuple	=		np.percentile(UpperCAmelCase__ ,    25      )
							_lowerCamelCase							:     Dict	=		np.percentile(UpperCAmelCase__ ,    75      )
							_lowerCamelCase							:     Optional[int]	=		qa - qa
							_lowerCamelCase							:     Any	=		qa - (iqr * 0.1)
							return low_lim




def    __UpperCAmelCase(      lowercase_ ,    lowercase_      ):
							_lowerCamelCase							:     List[str]	=		0
							_lowerCamelCase							:     Dict	=		0
							for i in list_vote:
														if i > actual_result:
																					_lowerCamelCase							:     Any	=		not_safe + 1
														else:
																					if abs(abs(UpperCAmelCase__      ) - abs(UpperCAmelCase__      )      ) <= 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			       =      [[1_8231, 0.0, 1], [2_2621, 1.0, 2], [1_5675, 0.0, 3], [2_3583, 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.')








import importlib.util
import os
import platform
from argparse import ArgumentParser

import huggingface_hub

from .. import __version__ as version
from ..utils import (
    is_accelerate_available,
    is_flax_available,
    is_safetensors_available,
    is_tf_available,
    is_torch_available,
)
from . import BaseTransformersCLICommand




def    __UpperCAmelCase(      lowercase_      ):
       return EnvironmentCommand()




def    __UpperCAmelCase(      lowercase_      ):
       return EnvironmentCommand(args.accelerate_config_file      )







class 			__A       (      lowerCamelCase__  ):




      """simple docstring"""







      @staticmethod
      def 				__snake_case			(		a__):



             """simple docstring"""





             _lowerCamelCase							:     List[Any]	=		parser.add_parser('''env''')
             download_parser.set_defaults(func=a__)
             download_parser.add_argument(
                 '''--accelerate-config_file'''      ,							default=a__      ,							help='''The accelerate config file to use for the default values in the launching script.'''      ,							)
             download_parser.set_defaults(func=a__)


      def __init__(		self      ,							a__      ,							*a__):



             """simple docstring"""





             _lowerCamelCase							:     str	=		accelerate_config_file


      def 				__snake_case			(		self):



             """simple docstring"""





             _lowerCamelCase							:     Union[str, Any]	=		'''not installed'''
             if is_safetensors_available():
                    import safetensors

                    _lowerCamelCase							:     Optional[Any]	=		safetensors.__version__
             elif importlib.util.find_spec('''safetensors''') is not None:
                    import safetensors

                    _lowerCamelCase							:     Optional[int]	=		F"""{safetensors.__version__} but is ignored because of PyTorch version too old."""

             _lowerCamelCase							:     Union[str, Any]	=		'''not installed'''
             _lowerCamelCase							:     Any	=		'''not found'''
             if is_accelerate_available():
                    import accelerate
                    from accelerate.commands.config import default_config_file, load_config_from_file

                    _lowerCamelCase							:     Optional[int]	=		accelerate.__version__
                    # Get the default from the config file.
                    if self._accelerate_config_file is not None or os.path.isfile(a__):
                           _lowerCamelCase							:     Optional[int]	=		load_config_from_file(self._accelerate_config_file).to_dict()

                    _lowerCamelCase							:     str	=		(
                        '''\n'''.join([F"""\t- {prop}: {val}""" for prop, val in accelerate_config.items()])
                        if isinstance(a__      ,							a__)
                        else F"""\t{accelerate_config}"""
                    )

             _lowerCamelCase							:     List[Any]	=		'''not installed'''
             _lowerCamelCase							:     Tuple	=		'''NA'''
             if is_torch_available():
                    import torch

                    _lowerCamelCase							:     int	=		torch.__version__
                    _lowerCamelCase							:     List[str]	=		torch.cuda.is_available()

             _lowerCamelCase							:     str	=		'''not installed'''
             _lowerCamelCase							:     Union[str, Any]	=		'''NA'''
             if is_tf_available():
                    import tensorflow as tf

                    _lowerCamelCase							:     List[str]	=		tf.__version__
                    try:
                           # deprecated in v2.1
                           _lowerCamelCase							:     Optional[int]	=		tf.test.is_gpu_available()
                    except AttributeError:
                           # returns list of devices, convert to bool
                           _lowerCamelCase							:     Optional[int]	=		bool(tf.config.list_physical_devices('''GPU'''))

             _lowerCamelCase							:     str	=		'''not installed'''
             _lowerCamelCase							:     List[Any]	=		'''not installed'''
             _lowerCamelCase							:     List[Any]	=		'''not installed'''
             _lowerCamelCase							:     Optional[int]	=		'''NA'''
             if is_flax_available():
                    import flax
                    import jax
                    import jaxlib

                    _lowerCamelCase							:     Any	=		flax.__version__
                    _lowerCamelCase							:     str	=		jax.__version__
                    _lowerCamelCase							:     Any	=		jaxlib.__version__
                    _lowerCamelCase							:     int	=		jax.lib.xla_bridge.get_backend().platform

             _lowerCamelCase							:     int	=		{
                 '''`transformers` version''': version,
                 '''Platform''': platform.platform(),
                 '''Python version''': platform.python_version(),
                 '''Huggingface_hub version''': huggingface_hub.__version__,
                 '''Safetensors version''': F"""{safetensors_version}""",
                 '''Accelerate version''': F"""{accelerate_version}""",
                 '''Accelerate config''': F"""{accelerate_config_str}""",
                 '''PyTorch version (GPU?)''': F"""{pt_version} ({pt_cuda_available})""",
                 '''Tensorflow version (GPU?)''': F"""{tf_version} ({tf_cuda_available})""",
                 '''Flax version (CPU?/GPU?/TPU?)''': F"""{flax_version} ({jax_backend})""",
                 '''Jax version''': F"""{jax_version}""",
                 '''JaxLib version''': F"""{jaxlib_version}""",
                 '''Using GPU in script?''': '''<fill in>''',
                 '''Using distributed or parallel set-up in script?''': '''<fill in>''',
             }

             print('''\nCopy-and-paste the text below in your GitHub issue and FILL OUT the two last points.\n''')
             print(self.format_dict(a__))

             return info



      @staticmethod
      def 				__snake_case			(		a__):



             """simple docstring"""





             return "\n".join([F"""- {prop}: {val}""" for prop, val in d.items()]) + "\n"

