



'''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      __lowerCamelCase     (      lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_  )			->      float:
		_a     :      List[str]        =							np.array([[1, item, train_mtch[i]] for i, item in enumerate(lowerCAmelCase_  )]  )
		_a     :      Optional[int]        =							np.array(lowerCAmelCase_  )
		_a     :      Optional[int]        =							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      __lowerCamelCase     (      lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_  )			->      float:
		_a     :      Any        =							(1, 2, 1)
		_a     :      List[Any]        =							(1, 1, 0, 7)
		_a     :      int        =							SARIMAX(
		    lowerCAmelCase_						,			exog=lowerCAmelCase_						,			order=lowerCAmelCase_						,			seasonal_order=lowerCAmelCase_  )
		_a     :      Optional[int]        =							model.fit(disp=lowerCAmelCase_						,			maxiter=600						,			method='nm'  )
		_a     :      Union[str, Any]        =							model_fit.predict(1						,			len(lowerCAmelCase_  )						,			exog=[test_match]  )
		return result[0]



def      __lowerCamelCase     (      lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_  )			->      float:
		_a     :      Any        =							SVR(kernel='rbf'						,			C=1						,			gamma=0.1						,			epsilon=0.1  )
		regressor.fit(lowerCAmelCase_						,			lowerCAmelCase_  )
		_a     :      Optional[int]        =							regressor.predict(lowerCAmelCase_  )
		return y_pred[0]



def      __lowerCamelCase     (      lowerCAmelCase_  )			->      float:
		train_user.sort()
		_a     :      Optional[Any]        =							np.percentile(lowerCAmelCase_						,			25  )
		_a     :      Optional[int]        =							np.percentile(lowerCAmelCase_						,			75  )
		_a     :      str        =							qa - qa
		_a     :      Optional[Any]        =							qa - (iqr * 0.1)
		return low_lim



def      __lowerCamelCase     (      lowerCAmelCase_						,			lowerCAmelCase_  )			->      bool:
		_a     :      str        =							0
		_a     :      List[Any]        =							0
		for i in list_vote:
				if i > actual_result:
						_a     :      Optional[int]        =							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)
							__lowerCAmelCase									=   [[18_231, 0.0, 1], [22_621, 1.0, 2], [15_675, 0.0, 3], [23_583, 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.''')









'''simple docstring'''

import copy
from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


__lowerCAmelCase									=   logging.get_logger(__name__)

__lowerCAmelCase									=   {
    '''microsoft/conditional-detr-resnet-50''': (
        '''https://huggingface.co/microsoft/conditional-detr-resnet-50/resolve/main/config.json'''
    ),
}

class 	__magic_name__						(    _UpperCamelCase   ):
							lowerCAmelCase       :    Any   				=					'conditional_detr'
							lowerCAmelCase       :    List[str]   				=					['past_key_values']
							lowerCAmelCase       :    Optional[int]   				=					{
							    'hidden_size': 'd_model',
							    'num_attention_heads': 'encoder_attention_heads',
							}







							def __init__(							self					:       Optional[int]	,_UpperCAmelCase					:       Optional[int]=True	,_UpperCAmelCase					:       Union[str, Any]=None	,_UpperCAmelCase					:       List[Any]=3	,_UpperCAmelCase					:       List[Any]=300	,_UpperCAmelCase					:       Dict=6	,_UpperCAmelCase					:       List[str]=2048	,_UpperCAmelCase					:       Optional[int]=8	,_UpperCAmelCase					:       List[Any]=6	,_UpperCAmelCase					:       Optional[int]=2048	,_UpperCAmelCase					:       Dict=8	,_UpperCAmelCase					:       int=0.0	,_UpperCAmelCase					:       Optional[Any]=0.0	,_UpperCAmelCase					:       Optional[Any]=True	,_UpperCAmelCase					:       str="relu"	,_UpperCAmelCase					:       Tuple=256	,_UpperCAmelCase					:       Optional[int]=0.1	,_UpperCAmelCase					:       str=0.0	,_UpperCAmelCase					:       Optional[int]=0.0	,_UpperCAmelCase					:       Union[str, Any]=0.02	,_UpperCAmelCase					:       List[str]=1.0	,_UpperCAmelCase					:       Any=False	,_UpperCAmelCase					:       int="sine"	,_UpperCAmelCase					:       List[str]="resnet50"	,_UpperCAmelCase					:       Optional[int]=True	,_UpperCAmelCase					:       str=False	,_UpperCAmelCase					:       str=2	,_UpperCAmelCase					:       int=5	,_UpperCAmelCase					:       Optional[int]=2	,_UpperCAmelCase					:       str=1	,_UpperCAmelCase					:       Union[str, Any]=1	,_UpperCAmelCase					:       List[str]=2	,_UpperCAmelCase					:       Union[str, Any]=5	,_UpperCAmelCase					:       List[Any]=2	,_UpperCAmelCase					:       Optional[int]=0.25	,**_UpperCAmelCase					:       Tuple	,):
									if backbone_config is not None and use_timm_backbone:
											raise ValueError('You can\'t specify both `backbone_config` and `use_timm_backbone`.'		)

									if not use_timm_backbone:
											if backbone_config is None:
													logger.info('`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.'		)
													_a     :      Optional[Any]        =							CONFIG_MAPPING['resnet'](out_features=['stage4']		)
											elif isinstance(_UpperCAmelCase	,_UpperCAmelCase		):
													_a     :      str        =							backbone_config.get('model_type'		)
													_a     :      Union[str, Any]        =							CONFIG_MAPPING[backbone_model_type]
													_a     :      List[Any]        =							config_class.from_dict(_UpperCAmelCase		)

									_a     :      Tuple        =							use_timm_backbone
									_a     :      Union[str, Any]        =							backbone_config
									_a     :      List[Any]        =							num_channels
									_a     :      Union[str, Any]        =							num_queries
									_a     :      Optional[Any]        =							d_model
									_a     :      Tuple        =							encoder_ffn_dim
									_a     :      Dict        =							encoder_layers
									_a     :      List[str]        =							encoder_attention_heads
									_a     :      Union[str, Any]        =							decoder_ffn_dim
									_a     :      Optional[int]        =							decoder_layers
									_a     :      int        =							decoder_attention_heads
									_a     :      Optional[int]        =							dropout
									_a     :      Tuple        =							attention_dropout
									_a     :      List[Any]        =							activation_dropout
									_a     :      str        =							activation_function
									_a     :      Optional[Any]        =							init_std
									_a     :      Union[str, Any]        =							init_xavier_std
									_a     :      List[Any]        =							encoder_layerdrop
									_a     :      List[Any]        =							decoder_layerdrop
									_a     :      Dict        =							encoder_layers
									_a     :      List[Any]        =							auxiliary_loss
									_a     :      Optional[int]        =							position_embedding_type
									_a     :      List[Any]        =							backbone
									_a     :      Optional[int]        =							use_pretrained_backbone
									_a     :      Optional[int]        =							dilation
									# Hungarian matcher
									_a     :      Tuple        =							class_cost
									_a     :      str        =							bbox_cost
									_a     :      Any        =							giou_cost
									# Loss coefficients
									_a     :      Tuple        =							mask_loss_coefficient
									_a     :      Dict        =							dice_loss_coefficient
									_a     :      Tuple        =							cls_loss_coefficient
									_a     :      Any        =							bbox_loss_coefficient
									_a     :      Dict        =							giou_loss_coefficient
									_a     :      Union[str, Any]        =							focal_alpha
									super().__init__(is_encoder_decoder=_UpperCAmelCase	,**_UpperCAmelCase		)







							@property
							def 							__lowercase			(							self					:       Dict		):
									return self.encoder_attention_heads







							@property
							def 							__lowercase			(							self					:       str		):
									return self.d_model







							def 							__lowercase			(							self					:       int		):
									_a     :      List[str]        =							copy.deepcopy(self.__dict__		)
									if self.backbone_config is not None:
											_a     :      Dict        =							self.backbone_config.to_dict()
									_a     :      Union[str, Any]        =							self.__class__.model_type
									return output

class 	__magic_name__						(    _UpperCamelCase   ):
							lowerCAmelCase       :    str   				=					version.parse('1.11'   )







							@property
							def 							__lowercase			(							self					:       Dict		):
									return OrderedDict(
									    [
									        ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
									        ('pixel_mask', {0: 'batch'}),
									    ]		)







							@property
							def 							__lowercase			(							self					:       Any		):
									return 1E-5







							@property
							def 							__lowercase			(							self					:       List[Any]		):
									return 12




