

'''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    __UpperCAmelCase	(				SCREAMING_SNAKE_CASE__:	list,					SCREAMING_SNAKE_CASE__:	list,					SCREAMING_SNAKE_CASE__:	list,					SCREAMING_SNAKE_CASE__:	list,					SCREAMING_SNAKE_CASE__:	list    )  ->			Optional[int]:

   """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    __UpperCAmelCase	(				SCREAMING_SNAKE_CASE__:	list,					SCREAMING_SNAKE_CASE__:	list,					SCREAMING_SNAKE_CASE__:	list    )  ->			Tuple:

   """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    __UpperCAmelCase	(				SCREAMING_SNAKE_CASE__:	list,					SCREAMING_SNAKE_CASE__:	list,					SCREAMING_SNAKE_CASE__:	list    )  ->			List[str]:

   """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    __UpperCAmelCase	(				SCREAMING_SNAKE_CASE__:	list    )  ->			Dict:

   """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    __UpperCAmelCase	(				SCREAMING_SNAKE_CASE__:	list,					SCREAMING_SNAKE_CASE__:	float    )  ->			List[Any]:

   """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)
 __UpperCamelCase      :       Optional[int]      			=      [[18231, 0.0, 1], [22621, 1.0, 2], [15675, 0.0, 3], [23583, 1.0, 4]]
 __UpperCamelCase      :       Any      			=      pd.DataFrame(
     data_input, columns=["""total_user""", """total_even""", """days"""]
 )
 __UpperCamelCase      :       Tuple      			=      Normalizer().fit_transform(data_input_df.values)
 # split data
 __UpperCamelCase      :       str      			=      normalize_df[:, 2].tolist()
 __UpperCamelCase      :       Union[str, Any]      			=      normalize_df[:, 0].tolist()
 __UpperCamelCase      :       Tuple      			=      normalize_df[:, 1].tolist()

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

 # for linear regression & sarimax
 __UpperCamelCase      :       Any      			=      total_date[: len(total_date) - 1]
 __UpperCamelCase      :       Optional[Any]      			=      total_user[: len(total_user) - 1]
 __UpperCamelCase      :       int      			=      total_match[: len(total_match) - 1]

 __UpperCamelCase      :       List[Any]      			=      total_date[len(total_date) - 1 :]
 __UpperCamelCase      :       str      			=      total_user[len(total_user) - 1 :]
 __UpperCamelCase      :       List[str]      			=      total_match[len(total_match) - 1 :]

 # voting system with forecasting
 __UpperCamelCase      :       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
 __UpperCamelCase      :       Dict      			=      '''''' 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 transformers.utils import is_vision_available
from transformers.utils.generic import TensorType

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    is_valid_image,
    to_numpy_array,
    valid_images,
)
from ...utils import logging


if is_vision_available():
 import PIL

__UpperCamelCase      :       Dict      			=      logging.get_logger(__name__)



def    __UpperCAmelCase	(				SCREAMING_SNAKE_CASE__:	str    )  ->			List[List[ImageInput]]:

   """simple docstring"""
   if isinstance(SCREAMING_SNAKE_CASE__,					(list, tuple)    ) and isinstance(videos[0],					(list, tuple)    ) and is_valid_image(videos[0][0]    ):
      return videos

   elif isinstance(SCREAMING_SNAKE_CASE__,					(list, tuple)    ) and is_valid_image(videos[0]    ):
      return [videos]

   elif is_valid_image(SCREAMING_SNAKE_CASE__    ):
      return [[videos]]

   raise ValueError(f"""Could not make batched video from {videos}"""    )



class 							__SCREAMING_SNAKE_CASE       (							_lowerCAmelCase     ):
      __a  					=["pixel_values"]




      def __init__(  self ,   lowerCamelCase = True ,   lowerCamelCase = None ,   lowerCamelCase = PILImageResampling.BILINEAR ,   lowerCamelCase = True ,   lowerCamelCase = None ,   lowerCamelCase = True ,   lowerCamelCase = 1 / 255 ,   lowerCamelCase = True ,   lowerCamelCase = True ,   lowerCamelCase = None ,   lowerCamelCase = None ,   **lowerCamelCase ,   )							->None:



         '''simple docstring'''
         super().__init__(**lowerCamelCase      )
         __a				     =    size if size is not None else {'shortest_edge': 256}
         __a				     =    get_size_dict(lowerCamelCase ,   default_to_square=lowerCamelCase      )
         __a				     =    crop_size if crop_size is not None else {'height': 224, 'width': 224}
         __a				     =    get_size_dict(lowerCamelCase ,   param_name='crop_size'      )

         __a				     =    do_resize
         __a				     =    size
         __a				     =    do_center_crop
         __a				     =    crop_size
         __a				     =    resample
         __a				     =    do_rescale
         __a				     =    rescale_factor
         __a				     =    offset
         __a				     =    do_normalize
         __a				     =    image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
         __a				     =    image_std if image_std is not None else IMAGENET_STANDARD_STD




      def  __UpperCamelCase							(  self ,   lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase = PILImageResampling.BILINEAR ,   lowerCamelCase = None ,   **lowerCamelCase ,   )							->np.ndarray:



         '''simple docstring'''
         __a				     =    get_size_dict(lowerCamelCase ,   default_to_square=lowerCamelCase      )
         if "shortest_edge" in size:
            __a				     =    get_resize_output_image_size(lowerCamelCase ,   size['shortest_edge'] ,   default_to_square=lowerCamelCase      )
         elif "height" in size and "width" in size:
            __a				     =    (size['height'], size['width'])
         else:
            raise ValueError(F"""Size must have 'height' and 'width' or 'shortest_edge' as keys. Got {size.keys()}"""      )
         return resize(lowerCamelCase ,   size=lowerCamelCase ,   resample=lowerCamelCase ,   data_format=lowerCamelCase ,   **lowerCamelCase      )




      def  __UpperCamelCase							(  self ,   lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase = None ,   **lowerCamelCase ,   )							->np.ndarray:



         '''simple docstring'''
         __a				     =    get_size_dict(lowerCamelCase      )
         if "height" not in size or "width" not in size:
            raise ValueError(F"""Size must have 'height' and 'width' as keys. Got {size.keys()}"""      )
         return center_crop(lowerCamelCase ,   size=(size['height'], size['width']) ,   data_format=lowerCamelCase ,   **lowerCamelCase      )




      def  __UpperCamelCase							(  self ,   lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase = True ,   lowerCamelCase = None ,   **lowerCamelCase ,   )							->Optional[Any]:



         '''simple docstring'''
         __a				     =    image.astype(np.floataa      )
         if offset:
            __a				     =    image - (scale / 2)
         return rescale(lowerCamelCase ,   scale=lowerCamelCase ,   data_format=lowerCamelCase ,   **lowerCamelCase      )




      def  __UpperCamelCase							(  self ,   lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase = None ,   **lowerCamelCase ,   )							->np.ndarray:



         '''simple docstring'''
         return normalize(lowerCamelCase ,   mean=lowerCamelCase ,   std=lowerCamelCase ,   data_format=lowerCamelCase ,   **lowerCamelCase      )




      def  __UpperCamelCase							(  self ,   lowerCamelCase ,   lowerCamelCase = None ,   lowerCamelCase = None ,   lowerCamelCase = None ,   lowerCamelCase = None ,   lowerCamelCase = None ,   lowerCamelCase = None ,   lowerCamelCase = None ,   lowerCamelCase = None ,   lowerCamelCase = None ,   lowerCamelCase = None ,   lowerCamelCase = None ,   lowerCamelCase = ChannelDimension.FIRST ,   )							->np.ndarray:



         '''simple docstring'''
         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_center_crop and crop_size is None:
            raise ValueError('Crop size must be specified if do_center_crop 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.'      )

         if offset and not do_rescale:
            raise ValueError('For offset, do_rescale must also be set to True.'      )

         # All transformations expect numpy arrays.
         __a				     =    to_numpy_array(lowerCamelCase      )

         if do_resize:
            __a				     =    self.resize(image=lowerCamelCase ,   size=lowerCamelCase ,   resample=lowerCamelCase      )

         if do_center_crop:
            __a				     =    self.center_crop(lowerCamelCase ,   size=lowerCamelCase      )

         if do_rescale:
            __a				     =    self.rescale(image=lowerCamelCase ,   scale=lowerCamelCase ,   offset=lowerCamelCase      )

         if do_normalize:
            __a				     =    self.normalize(image=lowerCamelCase ,   mean=lowerCamelCase ,   std=lowerCamelCase      )

         __a				     =    to_channel_dimension_format(lowerCamelCase ,   lowerCamelCase      )
         return image






      def  __UpperCamelCase							(  self ,   lowerCamelCase ,   lowerCamelCase = None ,   lowerCamelCase = None ,   lowerCamelCase = None ,   lowerCamelCase = None ,   lowerCamelCase = None ,   lowerCamelCase = None ,   lowerCamelCase = None ,   lowerCamelCase = None ,   lowerCamelCase = None ,   lowerCamelCase = None ,   lowerCamelCase = None ,   lowerCamelCase = None ,   lowerCamelCase = ChannelDimension.FIRST ,   **lowerCamelCase ,   )							->PIL.Image.Image:



         '''simple docstring'''
         __a				     =    do_resize if do_resize is not None else self.do_resize
         __a				     =    resample if resample is not None else self.resample
         __a				     =    do_center_crop if do_center_crop is not None else self.do_center_crop
         __a				     =    do_rescale if do_rescale is not None else self.do_rescale
         __a				     =    rescale_factor if rescale_factor is not None else self.rescale_factor
         __a				     =    offset if offset is not None else self.offset
         __a				     =    do_normalize if do_normalize is not None else self.do_normalize
         __a				     =    image_mean if image_mean is not None else self.image_mean
         __a				     =    image_std if image_std is not None else self.image_std

         __a				     =    size if size is not None else self.size
         __a				     =    get_size_dict(lowerCamelCase ,   default_to_square=lowerCamelCase      )
         __a				     =    crop_size if crop_size is not None else self.crop_size
         __a				     =    get_size_dict(lowerCamelCase ,   param_name='crop_size'      )

         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.'      )

         __a				     =    make_batched(lowerCamelCase      )

         __a				     =    [
             [
                 self._preprocess_image(
                     image=lowerCamelCase ,   do_resize=lowerCamelCase ,   size=lowerCamelCase ,   resample=lowerCamelCase ,   do_center_crop=lowerCamelCase ,   crop_size=lowerCamelCase ,   do_rescale=lowerCamelCase ,   rescale_factor=lowerCamelCase ,   offset=lowerCamelCase ,   do_normalize=lowerCamelCase ,   image_mean=lowerCamelCase ,   image_std=lowerCamelCase ,   data_format=lowerCamelCase ,   )
                 for img in video
             ]
             for video in videos
         ]

         __a				     =    {'pixel_values': videos}
         return BatchFeature(data=lowerCamelCase ,   tensor_type=lowerCamelCase      )