'''simple docstring'''




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__				(     _A:   list ,  _A:   list ,  _A:   list ,  _A:   list ,  _A:   list					)       ->      float:

		'''simple docstring'''


		lowerCAmelCase     =    np.array([[1, item, train_mtch[i]] for i, item in enumerate(_A					)]					)
		lowerCAmelCase     =    np.array(_A					)
		lowerCAmelCase     =    np.dot(np.dot(np.linalg.inv(np.dot(x.transpose() ,  _A					)					) ,  x.transpose()					) ,  _A					)
		return abs(beta[0] + test_dt[0] * beta[1] + test_mtch[0] + beta[2]					)






def 							snake_case__				(     _A:   list ,  _A:   list ,  _A:   list					)       ->      float:

		'''simple docstring'''


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






def 							snake_case__				(     _A:   list ,  _A:   list ,  _A:   list					)       ->      float:

		'''simple docstring'''


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






def 							snake_case__				(     _A:   list					)       ->      float:

		'''simple docstring'''


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






def 							snake_case__				(     _A:   list ,  _A:   float					)       ->      bool:

		'''simple docstring'''


		lowerCAmelCase     =    0
		lowerCAmelCase     =    0
		for i in list_vote:
				if i > actual_result:
						lowerCAmelCase     =    not_safe + 1
				else:
						if abs(abs(_A					) - abs(_A					)					) <= 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        =    [[1_8_2_3_1, 0.0, 1], [2_2_6_2_1, 1.0, 2], [1_5_6_7_5, 0.0, 3], [2_3_5_8_3, 1.0, 4]]
						__lowercase        =    pd.DataFrame(
						    data_input, columns=['''total_user''', '''total_even''', '''days''']
						)
						__lowercase        =    Normalizer().fit_transform(data_input_df.values)
						# split data
						__lowercase        =    normalize_df[:, 2].tolist()
						__lowercase        =    normalize_df[:, 0].tolist()
						__lowercase        =    normalize_df[:, 1].tolist()

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

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

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

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





'''simple docstring'''




from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import convert_to_rgb, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
						import PIL


__lowercase        =    logging.get_logger(__name__)






class 							a__(   lowerCAmelCase__      ):


			'''simple docstring'''






			UpperCAmelCase_     :	List[Any]						=			['''pixel_values''']



			def __init__(  self			,       __lowerCAmelCase = True			,       __lowerCAmelCase = None			,       __lowerCAmelCase = PILImageResampling.BICUBIC			,       __lowerCAmelCase = True			,       __lowerCAmelCase = 1 / 255			,       __lowerCAmelCase = True			,       __lowerCAmelCase = None			,       __lowerCAmelCase = None			,       __lowerCAmelCase = True			,       **__lowerCAmelCase			,       ):




					"""simple docstring"""


					super().__init__(**__lowerCAmelCase)
					lowerCAmelCase     =    size if size is not None else {"""height""": 384, """width""": 384}
					lowerCAmelCase     =    get_size_dict(__lowerCAmelCase			,       default_to_square=__lowerCAmelCase)

					lowerCAmelCase     =    do_resize
					lowerCAmelCase     =    size
					lowerCAmelCase     =    resample
					lowerCAmelCase     =    do_rescale
					lowerCAmelCase     =    rescale_factor
					lowerCAmelCase     =    do_normalize
					lowerCAmelCase     =    image_mean if image_mean is not None else OPENAI_CLIP_MEAN
					lowerCAmelCase     =    image_std if image_std is not None else OPENAI_CLIP_STD
					lowerCAmelCase     =    do_convert_rgb



			def 				a_			(  self			,       __lowerCAmelCase			,       __lowerCAmelCase			,       __lowerCAmelCase = PILImageResampling.BICUBIC			,       __lowerCAmelCase = None			,       **__lowerCAmelCase			,       ):




					"""simple docstring"""


					lowerCAmelCase     =    get_size_dict(__lowerCAmelCase			,       default_to_square=__lowerCAmelCase)
					if "height" not in size or "width" not in size:
							raise ValueError(f"The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}")
					lowerCAmelCase     =    (size["""height"""], size["""width"""])
					return resize(__lowerCAmelCase			,       size=__lowerCAmelCase			,       resample=__lowerCAmelCase			,       data_format=__lowerCAmelCase			,       **__lowerCAmelCase)



			def 				a_			(  self			,       __lowerCAmelCase			,       __lowerCAmelCase			,       __lowerCAmelCase = None			,       **__lowerCAmelCase			,       ):




					"""simple docstring"""


					return rescale(__lowerCAmelCase			,       scale=__lowerCAmelCase			,       data_format=__lowerCAmelCase			,       **__lowerCAmelCase)



			def 				a_			(  self			,       __lowerCAmelCase			,       __lowerCAmelCase			,       __lowerCAmelCase			,       __lowerCAmelCase = None			,       **__lowerCAmelCase			,       ):




					"""simple docstring"""


					return normalize(__lowerCAmelCase			,       mean=__lowerCAmelCase			,       std=__lowerCAmelCase			,       data_format=__lowerCAmelCase			,       **__lowerCAmelCase)







			def 				a_			(  self			,       __lowerCAmelCase			,       __lowerCAmelCase = None			,       __lowerCAmelCase = None			,       __lowerCAmelCase = None			,       __lowerCAmelCase = None			,       __lowerCAmelCase = None			,       __lowerCAmelCase = None			,       __lowerCAmelCase = None			,       __lowerCAmelCase = None			,       __lowerCAmelCase = None			,       __lowerCAmelCase = None			,       __lowerCAmelCase = ChannelDimension.FIRST			,       **__lowerCAmelCase			,       ):




					"""simple docstring"""


					lowerCAmelCase     =    do_resize if do_resize is not None else self.do_resize
					lowerCAmelCase     =    resample if resample is not None else self.resample
					lowerCAmelCase     =    do_rescale if do_rescale is not None else self.do_rescale
					lowerCAmelCase     =    rescale_factor if rescale_factor is not None else self.rescale_factor
					lowerCAmelCase     =    do_normalize if do_normalize is not None else self.do_normalize
					lowerCAmelCase     =    image_mean if image_mean is not None else self.image_mean
					lowerCAmelCase     =    image_std if image_std is not None else self.image_std
					lowerCAmelCase     =    do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

					lowerCAmelCase     =    size if size is not None else self.size
					lowerCAmelCase     =    get_size_dict(__lowerCAmelCase			,       default_to_square=__lowerCAmelCase)

					lowerCAmelCase     =    make_list_of_images(__lowerCAmelCase)

					if not valid_images(__lowerCAmelCase):
							raise ValueError(
							    """Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
							    """torch.Tensor, tf.Tensor or jax.ndarray.""")

					if do_resize and size is None or resample is None:
							raise ValueError("""Size and resample must be specified if do_resize is True.""")

					if do_rescale and rescale_factor is None:
							raise ValueError("""Rescale factor must be specified if do_rescale is True.""")

					if do_normalize and (image_mean is None or image_std is None):
							raise ValueError("""Image mean and std must be specified if do_normalize is True.""")

					# PIL RGBA images are converted to RGB
					if do_convert_rgb:
							lowerCAmelCase     =    [convert_to_rgb(__lowerCAmelCase) for image in images]

					# All transformations expect numpy arrays.
					lowerCAmelCase     =    [to_numpy_array(__lowerCAmelCase) for image in images]

					if do_resize:
							lowerCAmelCase     =    [self.resize(image=__lowerCAmelCase			,       size=__lowerCAmelCase			,       resample=__lowerCAmelCase) for image in images]

					if do_rescale:
							lowerCAmelCase     =    [self.rescale(image=__lowerCAmelCase			,       scale=__lowerCAmelCase) for image in images]

					if do_normalize:
							lowerCAmelCase     =    [self.normalize(image=__lowerCAmelCase			,       mean=__lowerCAmelCase			,       std=__lowerCAmelCase) for image in images]

					lowerCAmelCase     =    [to_channel_dimension_format(__lowerCAmelCase			,       __lowerCAmelCase) for image in images]

					lowerCAmelCase     =    BatchFeature(data={"""pixel_values""": images}			,       tensor_type=__lowerCAmelCase)

					return encoded_outputs




