




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 ):
				__magic_name__						:						str										=np.array([[1, item, train_mtch[i]] for i, item in enumerate(lowerCamelCase )] )
				__magic_name__						:						str										=np.array(lowerCamelCase )
				__magic_name__						:						Union[str, Any]										=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 ):
				__magic_name__						:						int										=(1, 2, 1)
				__magic_name__						:						Tuple										=(1, 1, 0, 7)
				__magic_name__						:						int										=SARIMAX(
				    lowerCamelCase						,  exog=lowerCamelCase						,  order=lowerCamelCase						,  seasonal_order=lowerCamelCase )
				__magic_name__						:						Optional[Any]										=model.fit(disp=lowerCamelCase						,  maxiter=600						,  method="""nm""" )
				__magic_name__						:						Any										=model_fit.predict(1						,  len(lowerCamelCase )						,  exog=[test_match] )
				return result[0]




def 		lowerCAmelCase_ (						lowerCamelCase						,  lowerCamelCase						,  lowerCamelCase ):
				__magic_name__						:						str										=SVR(kernel="""rbf"""						,  C=1						,  gamma=0.1						,  epsilon=0.1 )
				regressor.fit(lowerCamelCase						,  lowerCamelCase )
				__magic_name__						:						List[str]										=regressor.predict(lowerCamelCase )
				return y_pred[0]




def 		lowerCAmelCase_ (						lowerCamelCase ):
				train_user.sort()
				__magic_name__						:						List[Any]										=np.percentile(lowerCamelCase						,  25 )
				__magic_name__						:						Dict										=np.percentile(lowerCamelCase						,  75 )
				__magic_name__						:						int										=qa - qa
				__magic_name__						:						List[Any]										=qa - (iqr * 0.1)
				return low_lim




def 		lowerCAmelCase_ (						lowerCamelCase						,  lowerCamelCase ):
				__magic_name__						:						str										=0
				__magic_name__						:						Any										=0
				for i in list_vote:
								if i > actual_result:
												__magic_name__						:						List[str]										=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)
							UpperCAmelCase_		:					Tuple          =  [[18231, 0.0, 1], [22621, 1.0, 2], [15675, 0.0, 3], [23583, 1.0, 4]]
							UpperCAmelCase_		:					str          =  pd.DataFrame(
							    data_input, columns=["total_user", "total_even", "days"]
							)
							UpperCAmelCase_		:					Tuple          =  Normalizer().fit_transform(data_input_df.values)
							# split data
							UpperCAmelCase_		:					Dict          =  normalize_df[:, 2].tolist()
							UpperCAmelCase_		:					List[str]          =  normalize_df[:, 0].tolist()
							UpperCAmelCase_		:					List[Any]          =  normalize_df[:, 1].tolist()

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

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

							UpperCAmelCase_		:					int          =  total_date[len(total_date) - 1 :]
							UpperCAmelCase_		:					Dict          =  total_user[len(total_user) - 1 :]
							UpperCAmelCase_		:					Optional[int]          =  total_match[len(total_match) - 1 :]

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










import warnings
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


UpperCAmelCase_		:					Any          =  logging.get_logger(__name__)

UpperCAmelCase_		:					List[str]          =  {
    "nvidia/segformer-b0-finetuned-ade-512-512": (
        "https://huggingface.co/nvidia/segformer-b0-finetuned-ade-512-512/resolve/main/config.json"
    ),
    # See all SegFormer models at https://huggingface.co/models?filter=segformer
}




class 		__A   (      UpperCamelCase__      ):
						UpperCamelCase      =       """segformer"""

						def __init__(		self :List[str]     ,			__snake_case :str=3     ,			__snake_case :Optional[Any]=4     ,			__snake_case :List[Any]=[2, 2, 2, 2]     ,			__snake_case :Dict=[8, 4, 2, 1]     ,			__snake_case :Optional[int]=[32, 64, 1_60, 2_56]     ,			__snake_case :Union[str, Any]=[7, 3, 3, 3]     ,			__snake_case :Optional[Any]=[4, 2, 2, 2]     ,			__snake_case :Tuple=[1, 2, 5, 8]     ,			__snake_case :List[Any]=[4, 4, 4, 4]     ,			__snake_case :Optional[Any]="gelu"     ,			__snake_case :Tuple=0.0     ,			__snake_case :Dict=0.0     ,			__snake_case :Optional[int]=0.1     ,			__snake_case :Optional[int]=0.02     ,			__snake_case :Tuple=0.1     ,			__snake_case :Union[str, Any]=1E-6     ,			__snake_case :int=2_56     ,			__snake_case :Optional[int]=2_55     ,			**__snake_case :Dict     ,			):
										'''simple docstring'''

										super().__init__(**__snake_case			)

										if "reshape_last_stage" in kwargs and kwargs["reshape_last_stage"] is False:
														warnings.warn(
														    """Reshape_last_stage is set to False in this config. This argument is deprecated and will soon be"""
														    """ removed, as the behaviour will default to that of reshape_last_stage = True."""     ,			__snake_case     ,			)

										__magic_name__						:						Dict										=num_channels
										__magic_name__						:						str										=num_encoder_blocks
										__magic_name__						:						List[Any]										=depths
										__magic_name__						:						Optional[Any]										=sr_ratios
										__magic_name__						:						List[str]										=hidden_sizes
										__magic_name__						:						List[str]										=patch_sizes
										__magic_name__						:						Any										=strides
										__magic_name__						:						Optional[Any]										=mlp_ratios
										__magic_name__						:						str										=num_attention_heads
										__magic_name__						:						int										=hidden_act
										__magic_name__						:						List[Any]										=hidden_dropout_prob
										__magic_name__						:						Optional[Any]										=attention_probs_dropout_prob
										__magic_name__						:						Optional[Any]										=classifier_dropout_prob
										__magic_name__						:						List[str]										=initializer_range
										__magic_name__						:						List[str]										=drop_path_rate
										__magic_name__						:						List[Any]										=layer_norm_eps
										__magic_name__						:						List[str]										=decoder_hidden_size
										__magic_name__						:						Union[str, Any]										=kwargs.get("""reshape_last_stage"""     ,			__snake_case			)
										__magic_name__						:						Dict										=semantic_loss_ignore_index




class 		__A   (      UpperCamelCase__      ):
						UpperCamelCase      =       version.parse("""1.11"""      )

						@property
						def      A__    (		self :List[str]			):
										'''simple docstring'''

										return OrderedDict(
										    [
										        ("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
										    ]			)

						@property
						def      A__    (		self :Any			):
										'''simple docstring'''

										return 1E-4


						@property
						def      A__    (		self :int			):
										'''simple docstring'''

										return 12




