



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      lowerCAmelCase__  (							_SCREAMING_SNAKE_CASE				:	str ,   _SCREAMING_SNAKE_CASE				:	Any ,   _SCREAMING_SNAKE_CASE				:	Dict ,   _SCREAMING_SNAKE_CASE				:	List[Any] ,   _SCREAMING_SNAKE_CASE				:	Optional[Any]			):
				"""simple docstring"""







				__a        =						np.array([[1, item, train_mtch[i]] for i, item in enumerate(_UpperCamelCase			)]			)
				__a        =						np.array(_UpperCamelCase			)
				__a        =						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      lowerCAmelCase__  (							_SCREAMING_SNAKE_CASE				:	int ,   _SCREAMING_SNAKE_CASE				:	Tuple ,   _SCREAMING_SNAKE_CASE				:	Union[str, Any]			):
				"""simple docstring"""







				__a        =						(1, 2, 1)
				__a        =						(1, 1, 0, 7)
				__a        =						SARIMAX(
				    _UpperCamelCase ,   exog=_UpperCamelCase ,   order=_UpperCamelCase ,   seasonal_order=_UpperCamelCase			)
				__a        =						model.fit(disp=_UpperCamelCase ,   maxiter=600 ,   method="""nm"""			)
				__a        =						model_fit.predict(1 ,   len(_UpperCamelCase			) ,   exog=[test_match]			)
				return result[0]

def      lowerCAmelCase__  (							_SCREAMING_SNAKE_CASE				:	List[str] ,   _SCREAMING_SNAKE_CASE				:	Dict ,   _SCREAMING_SNAKE_CASE				:	Tuple			):
				"""simple docstring"""







				__a        =						SVR(kernel="""rbf""" ,   C=1 ,   gamma=0.1 ,   epsilon=0.1			)
				regressor.fit(_UpperCamelCase ,   _UpperCamelCase			)
				__a        =						regressor.predict(_UpperCamelCase			)
				return y_pred[0]

def      lowerCAmelCase__  (							_SCREAMING_SNAKE_CASE				:	Tuple			):
				"""simple docstring"""







				train_user.sort()
				__a        =						np.percentile(_UpperCamelCase ,   25			)
				__a        =						np.percentile(_UpperCamelCase ,   75			)
				__a        =						qa - qa
				__a        =						qa - (iqr * 0.1)
				return low_lim

def      lowerCAmelCase__  (							_SCREAMING_SNAKE_CASE				:	Any ,   _SCREAMING_SNAKE_CASE				:	Tuple			):
				"""simple docstring"""







				__a        =						0
				__a        =						0
				for i in list_vote:
								if i > actual_result:
												__a        =						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 unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
	import torch

if is_vision_available():
	from PIL import Image

	from transformers import GLPNImageProcessor



class 					__lowerCAmelCase       (			unittest.TestCase     ):






			def __init__(self					,							__magic_name__					,							__magic_name__=7					,							__magic_name__=3					,							__magic_name__=18					,							__magic_name__=30					,							__magic_name__=400					,							__magic_name__=True					,							__magic_name__=32					,							__magic_name__=True					,							)     ->   Dict:


							'''simple docstring'''



							snake_case_						:    Tuple				=  parent
							snake_case_						:    Union[str, Any]				=  batch_size
							snake_case_						:    Union[str, Any]				=  num_channels
							snake_case_						:    Optional[Any]				=  image_size
							snake_case_						:    int				=  min_resolution
							snake_case_						:    Any				=  max_resolution
							snake_case_						:    Tuple				=  do_resize
							snake_case_						:    str				=  size_divisor
							snake_case_						:    Optional[Any]				=  do_rescale






			def 			lowerCamelCase  (self    )     ->   Dict:


							'''simple docstring'''



							return {
							    "do_resize": self.do_resize,
							    "size_divisor": self.size_divisor,
							    "do_rescale": self.do_rescale,
							}




@require_torch
@require_vision
class 					__lowerCAmelCase       (			_a,     unittest.TestCase     ):
			lowerCamelCase_     :     Optional[Any]       			=      GLPNImageProcessor if is_vision_available() else None






			def 			lowerCamelCase  (self    )     ->   Optional[Any]:


							'''simple docstring'''



							snake_case_						:    str				=  GLPNImageProcessingTester(self    )






			@property
			def 			lowerCamelCase  (self    )     ->   Union[str, Any]:


							'''simple docstring'''



							return self.image_processor_tester.prepare_image_processor_dict()






			def 			lowerCamelCase  (self    )     ->   List[str]:


							'''simple docstring'''



							snake_case_						:    Optional[int]				=  self.image_processing_class(**self.image_processor_dict    )
							self.assertTrue(hasattr(__magic_name__					,							'''do_resize'''    )    )
							self.assertTrue(hasattr(__magic_name__					,							'''size_divisor'''    )    )
							self.assertTrue(hasattr(__magic_name__					,							'''resample'''    )    )
							self.assertTrue(hasattr(__magic_name__					,							'''do_rescale'''    )    )






			def 			lowerCamelCase  (self    )     ->   List[Any]:


							'''simple docstring'''



							pass






			def 			lowerCamelCase  (self    )     ->   int:


							'''simple docstring'''



							snake_case_						:    Optional[Any]				=  self.image_processing_class(**self.image_processor_dict    )
							# create random PIL images
							snake_case_						:    Union[str, Any]				=  prepare_image_inputs(self.image_processor_tester					,							equal_resolution=__magic_name__    )
							for image in image_inputs:
											self.assertIsInstance(__magic_name__					,							Image.Image    )

							# Test not batched input (GLPNImageProcessor doesn't support batching)
							snake_case_						:    Optional[int]				=  image_processing(image_inputs[0]					,							return_tensors='''pt'''    ).pixel_values
							self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0    )
							self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0    )






			def 			lowerCamelCase  (self    )     ->   Dict:


							'''simple docstring'''



							snake_case_						:    str				=  self.image_processing_class(**self.image_processor_dict    )
							# create random numpy tensors
							snake_case_						:    List[str]				=  prepare_image_inputs(self.image_processor_tester					,							equal_resolution=__magic_name__					,							numpify=__magic_name__    )
							for image in image_inputs:
											self.assertIsInstance(__magic_name__					,							np.ndarray    )

							# Test not batched input (GLPNImageProcessor doesn't support batching)
							snake_case_						:    Any				=  image_processing(image_inputs[0]					,							return_tensors='''pt'''    ).pixel_values
							self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0    )
							self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0    )






			def 			lowerCamelCase  (self    )     ->   Optional[Any]:


							'''simple docstring'''



							snake_case_						:    Dict				=  self.image_processing_class(**self.image_processor_dict    )
							# create random PyTorch tensors
							snake_case_						:    Optional[Any]				=  prepare_image_inputs(self.image_processor_tester					,							equal_resolution=__magic_name__					,							torchify=__magic_name__    )
							for image in image_inputs:
											self.assertIsInstance(__magic_name__					,							torch.Tensor    )

							# Test not batched input (GLPNImageProcessor doesn't support batching)
							snake_case_						:    Dict				=  image_processing(image_inputs[0]					,							return_tensors='''pt'''    ).pixel_values
							self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0    )
							self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0    )
