





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							(     _lowerCamelCase      :  list			,   _lowerCamelCase      :  list			,   _lowerCamelCase      :  list			,   _lowerCamelCase      :  list			,   _lowerCamelCase      :  list     ):





						'''simple docstring'''


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







def       UpperCAmelCase							(     _lowerCamelCase      :  list			,   _lowerCamelCase      :  list			,   _lowerCamelCase      :  list     ):





						'''simple docstring'''


						SCREAMING_SNAKE_CASE__     :				int   =					(1, 2, 1)
						SCREAMING_SNAKE_CASE__     :				Optional[int]   =					(1, 1, 0, 7)
						SCREAMING_SNAKE_CASE__     :				Optional[int]   =					SARIMAX(
						    _lowerCamelCase			,   exog=_lowerCamelCase			,   order=_lowerCamelCase			,   seasonal_order=_lowerCamelCase     )
						SCREAMING_SNAKE_CASE__     :				int   =					model.fit(disp=_lowerCamelCase			,   maxiter=600			,   method="nm"     )
						SCREAMING_SNAKE_CASE__     :				List[Any]   =					model_fit.predict(1			,   len(_lowerCamelCase     )			,   exog=[test_match]     )
						return result[0]







def       UpperCAmelCase							(     _lowerCamelCase      :  list			,   _lowerCamelCase      :  list			,   _lowerCamelCase      :  list     ):





						'''simple docstring'''


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







def       UpperCAmelCase							(     _lowerCamelCase      :  list     ):





						'''simple docstring'''


						train_user.sort()
						SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					np.percentile(_lowerCamelCase			,   25     )
						SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					np.percentile(_lowerCamelCase			,   75     )
						SCREAMING_SNAKE_CASE__     :				Dict   =					qa - qa
						SCREAMING_SNAKE_CASE__     :				List[Any]   =					qa - (iqr * 0.1)
						return low_lim







def       UpperCAmelCase							(     _lowerCamelCase      :  list			,   _lowerCamelCase      :  float     ):





						'''simple docstring'''


						SCREAMING_SNAKE_CASE__     :				Tuple   =					0
						SCREAMING_SNAKE_CASE__     :				str   =					0
						for i in list_vote:
												if i > actual_result:
																		SCREAMING_SNAKE_CASE__     :				Any   =					not_safe + 1
												else:
																		if abs(abs(_lowerCamelCase     ) - abs(_lowerCamelCase     )     ) <= 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)
				__lowercase       :str       =  [[18_231, 0.0, 1], [22_621, 1.0, 2], [15_675, 0.0, 3], [23_583, 1.0, 4]]
				__lowercase       :Any       =  pd.DataFrame(
				    data_input, columns=["total_user", "total_even", "days"]
				)
				__lowercase       :str       =  Normalizer().fit_transform(data_input_df.values)
				# split data
				__lowercase       :Tuple       =  normalize_df[:, 2].tolist()
				__lowercase       :List[Any]       =  normalize_df[:, 0].tolist()
				__lowercase       :str       =  normalize_df[:, 1].tolist()

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

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

				__lowercase       :Optional[Any]       =  total_date[len(total_date) - 1 :]
				__lowercase       :Optional[Any]       =  total_user[len(total_user) - 1 :]
				__lowercase       :Optional[Any]       =  total_match[len(total_match) - 1 :]

				# voting system with forecasting
				__lowercase       :Optional[Any]       =  [
				    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
				__lowercase       :Optional[Any]       =  "" if data_safety_checker(res_vote, tst_user) else "not "
				print("Today's data is {not_str}safe.")






import unittest
from typing import Dict, List, Optional, Union

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 BridgeTowerImageProcessor



class    _a     (				unittest.TestCase							):






							"""simple docstring"""

							def __init__(      self				:     Optional[int]							,	a				:     Any							,	a				:     bool = True							,	a				:     Dict[str, int] = None							,	a				:     int = 32							,	a				:     bool = True							,	a				:     Union[int, float] = 1 / 2_55							,	a				:     bool = True							,	a				:     bool = True							,	a				:     Optional[Union[float, List[float]]] = [0.4814_5466, 0.457_8275, 0.4082_1073]							,	a				:     Optional[Union[float, List[float]]] = [0.2686_2954, 0.2613_0258, 0.2757_7711]							,	a				:     bool = True							,	a				:     Any=7							,	a				:     str=30							,	a				:     Dict=4_00							,	a				:     Optional[int]=3							,	)	->int:
													SCREAMING_SNAKE_CASE__     :				int   =					parent
													SCREAMING_SNAKE_CASE__     :				Dict   =					do_resize
													SCREAMING_SNAKE_CASE__     :				List[str]   =					size if size is not None else {"shortest_edge": 2_88}
													SCREAMING_SNAKE_CASE__     :				List[Any]   =					size_divisor
													SCREAMING_SNAKE_CASE__     :				List[Any]   =					do_rescale
													SCREAMING_SNAKE_CASE__     :				Tuple   =					rescale_factor
													SCREAMING_SNAKE_CASE__     :				Optional[int]   =					do_normalize
													SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					do_center_crop
													SCREAMING_SNAKE_CASE__     :				Optional[int]   =					image_mean
													SCREAMING_SNAKE_CASE__     :				Dict   =					image_std
													SCREAMING_SNAKE_CASE__     :				List[str]   =					do_pad
													SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					batch_size
													SCREAMING_SNAKE_CASE__     :				int   =					num_channels
													SCREAMING_SNAKE_CASE__     :				Optional[int]   =					min_resolution
													SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					max_resolution

							def      A_  (      self				:     List[str]    )	->Tuple:
													return {
													    "image_mean": self.image_mean,
													    "image_std": self.image_std,
													    "do_normalize": self.do_normalize,
													    "do_resize": self.do_resize,
													    "size": self.size,
													    "size_divisor": self.size_divisor,
													}






							def      A_  (      self				:     int							,	a				:     Optional[int]							,	a				:     Union[str, Any]=False    )	->Optional[Any]:
													if not batched:
																			SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					self.size["shortest_edge"]
																			SCREAMING_SNAKE_CASE__     :				Dict   =					image_inputs[0]
																			if isinstance(a							,	Image.Image    ):
																									SCREAMING_SNAKE_CASE__,							SCREAMING_SNAKE_CASE__     :				Dict   =					image.size
																			else:
																									SCREAMING_SNAKE_CASE__,							SCREAMING_SNAKE_CASE__     :				List[str]   =					image.shape[1], image.shape[2]
																			SCREAMING_SNAKE_CASE__     :				Any   =					size / min(a							,	a    )
																			if h < w:
																									SCREAMING_SNAKE_CASE__,							SCREAMING_SNAKE_CASE__     :				Optional[int]   =					size, scale * w
																			else:
																									SCREAMING_SNAKE_CASE__,							SCREAMING_SNAKE_CASE__     :				List[str]   =					scale * h, size

																			SCREAMING_SNAKE_CASE__     :				List[Any]   =					int((13_33 / 8_00) * size    )
																			if max(a							,	a    ) > max_size:
																									SCREAMING_SNAKE_CASE__     :				List[Any]   =					max_size / max(a							,	a    )
																									SCREAMING_SNAKE_CASE__     :				int   =					newh * scale
																									SCREAMING_SNAKE_CASE__     :				Optional[int]   =					neww * scale

																			SCREAMING_SNAKE_CASE__,							SCREAMING_SNAKE_CASE__     :				Any   =					int(newh + 0.5    ), int(neww + 0.5    )
																			SCREAMING_SNAKE_CASE__,							SCREAMING_SNAKE_CASE__     :				Any   =					(
																			    newh // self.size_divisor * self.size_divisor,
																			    neww // self.size_divisor * self.size_divisor,
																			)

													else:
																			SCREAMING_SNAKE_CASE__     :				List[Any]   =					[]
																			for image in image_inputs:
																									SCREAMING_SNAKE_CASE__,							SCREAMING_SNAKE_CASE__     :				List[Any]   =					self.get_expected_values([image]    )
																									expected_values.append((expected_height, expected_width)    )
																			SCREAMING_SNAKE_CASE__     :				Tuple   =					max(a							,	key=lambda a    : item[0]    )[0]
																			SCREAMING_SNAKE_CASE__     :				Tuple   =					max(a							,	key=lambda a    : item[1]    )[1]

													return expected_height, expected_width







@require_torch
@require_vision
class    _a     (				lowercase__					,							unittest.TestCase							):






							"""simple docstring"""

							snake_case_							 =						BridgeTowerImageProcessor if is_vision_available() else None

							def      A_  (      self				:     List[Any]    )	->Union[str, Any]:
													SCREAMING_SNAKE_CASE__     :				Any   =					BridgeTowerImageProcessingTester(self    )

							@property
							def      A_  (      self				:     Optional[int]    )	->Optional[Any]:
													return self.image_processor_tester.prepare_image_processor_dict()

							def      A_  (      self				:     Tuple    )	->str:
													SCREAMING_SNAKE_CASE__     :				int   =					self.image_processing_class(**self.image_processor_dict    )
													self.assertTrue(hasattr(a							,	"image_mean"    )    )
													self.assertTrue(hasattr(a							,	"image_std"    )    )
													self.assertTrue(hasattr(a							,	"do_normalize"    )    )
													self.assertTrue(hasattr(a							,	"do_resize"    )    )
													self.assertTrue(hasattr(a							,	"size"    )    )
													self.assertTrue(hasattr(a							,	"size_divisor"    )    )

							def      A_  (      self				:     List[Any]    )	->List[Any]:
													pass

							def      A_  (      self				:     Tuple    )	->Optional[Any]:
													# Initialize image processor
													SCREAMING_SNAKE_CASE__     :				Dict   =					self.image_processing_class(**self.image_processor_dict    )
													# create random PIL images
													SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					prepare_image_inputs(self.image_processor_tester							,	equal_resolution=a    )
													for image in image_inputs:
																			self.assertIsInstance(a							,	Image.Image    )

													# Test not batched input
													SCREAMING_SNAKE_CASE__     :				List[Any]   =					image_processing(image_inputs[0]							,	return_tensors="pt"    ).pixel_values

													SCREAMING_SNAKE_CASE__,							SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					self.image_processor_tester.get_expected_values(a    )
													self.assertEqual(
													    encoded_images.shape							,	(1, self.image_processor_tester.num_channels, expected_height, expected_width)							,	)

													# Test batched
													SCREAMING_SNAKE_CASE__     :				int   =					image_processing(a							,	return_tensors="pt"    ).pixel_values

													SCREAMING_SNAKE_CASE__,							SCREAMING_SNAKE_CASE__     :				Any   =					self.image_processor_tester.get_expected_values(a							,	batched=a    )
													self.assertEqual(
													    encoded_images.shape							,	(
													        self.image_processor_tester.batch_size,
													        self.image_processor_tester.num_channels,
													        expected_height,
													        expected_width,
													    )							,	)

							def      A_  (      self				:     Optional[int]    )	->Any:
													# Initialize image processor
													SCREAMING_SNAKE_CASE__     :				str   =					self.image_processing_class(**self.image_processor_dict    )
													# create random numpy tensors
													SCREAMING_SNAKE_CASE__     :				str   =					prepare_image_inputs(self.image_processor_tester							,	equal_resolution=a							,	numpify=a    )
													for image in image_inputs:
																			self.assertIsInstance(a							,	np.ndarray    )

													# Test not batched input
													SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					image_processing(image_inputs[0]							,	return_tensors="pt"    ).pixel_values

													SCREAMING_SNAKE_CASE__,							SCREAMING_SNAKE_CASE__     :				List[Any]   =					self.image_processor_tester.get_expected_values(a    )
													self.assertEqual(
													    encoded_images.shape							,	(1, self.image_processor_tester.num_channels, expected_height, expected_width)							,	)

													# Test batched
													SCREAMING_SNAKE_CASE__     :				Tuple   =					image_processing(a							,	return_tensors="pt"    ).pixel_values

													SCREAMING_SNAKE_CASE__,							SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					self.image_processor_tester.get_expected_values(a							,	batched=a    )
													self.assertEqual(
													    encoded_images.shape							,	(
													        self.image_processor_tester.batch_size,
													        self.image_processor_tester.num_channels,
													        expected_height,
													        expected_width,
													    )							,	)






							def      A_  (      self				:     str    )	->Optional[int]:
													# Initialize image processor
													SCREAMING_SNAKE_CASE__     :				Dict   =					self.image_processing_class(**self.image_processor_dict    )
													# create random PyTorch tensors
													SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					prepare_image_inputs(self.image_processor_tester							,	equal_resolution=a							,	torchify=a    )
													for image in image_inputs:
																			self.assertIsInstance(a							,	torch.Tensor    )

													# Test not batched input
													SCREAMING_SNAKE_CASE__     :				Tuple   =					image_processing(image_inputs[0]							,	return_tensors="pt"    ).pixel_values

													SCREAMING_SNAKE_CASE__,							SCREAMING_SNAKE_CASE__     :				Tuple   =					self.image_processor_tester.get_expected_values(a    )
													self.assertEqual(
													    encoded_images.shape							,	(1, self.image_processor_tester.num_channels, expected_height, expected_width)							,	)

													# Test batched
													SCREAMING_SNAKE_CASE__     :				Any   =					image_processing(a							,	return_tensors="pt"    ).pixel_values

													SCREAMING_SNAKE_CASE__,							SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					self.image_processor_tester.get_expected_values(a							,	batched=a    )
													self.assertEqual(
													    encoded_images.shape							,	(
													        self.image_processor_tester.batch_size,
													        self.image_processor_tester.num_channels,
													        expected_height,
													        expected_width,
													    )							,	)