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    SCREAMING_SNAKE_CASE_     (						snake_case__   ,  snake_case__   ,  snake_case__   ,  snake_case__   ,  snake_case__							)     ->	Optional[Any]:
							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    SCREAMING_SNAKE_CASE_     (						snake_case__   ,  snake_case__   ,  snake_case__							)     ->	Union[str, Any]:
							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=6_0_0   ,  method='''nm'''							)
							lowerCAmelCase			=		model_fit.predict(1   ,  len(__A							)   ,  exog=[test_match]							)
							return result[0]

def    SCREAMING_SNAKE_CASE_     (						snake_case__   ,  snake_case__   ,  snake_case__							)     ->	Union[str, Any]:
							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    SCREAMING_SNAKE_CASE_     (						snake_case__							)     ->	List[Any]:
							train_user.sort()
							lowerCAmelCase			=		np.percentile(__A   ,  2_5							)
							lowerCAmelCase			=		np.percentile(__A   ,  7_5							)
							lowerCAmelCase			=		qa - qa
							lowerCAmelCase			=		qa - (iqr * 0.1)
							return low_lim

def    SCREAMING_SNAKE_CASE_     (						snake_case__   ,  snake_case__							)     ->	Dict:
							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__			:				Dict		=	[[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__			:				List[str]		=	pd.DataFrame(
	    data_input, columns=['''total_user''', '''total_even''', '''days''']
	)
	lowercase__			:				Any		=	Normalizer().fit_transform(data_input_df.values)
	# split data
	lowercase__			:				Optional[Any]		=	normalize_df[:, 2].tolist()
	lowercase__			:				List[str]		=	normalize_df[:, 0].tolist()
	lowercase__			:				Union[str, Any]		=	normalize_df[:, 1].tolist()

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

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

	lowercase__			:				List[str]		=	total_date[len(total_date) - 1 :]
	lowercase__			:				int		=	total_user[len(total_user) - 1 :]
	lowercase__			:				Tuple		=	total_match[len(total_match) - 1 :]

	# voting system with forecasting
	lowercase__			:				int		=	[
	    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__			:				List[Any]		=	'''''' if data_safety_checker(res_vote, tst_user) else '''not '''
	print('''Today\'s data is {not_str}safe.''')








'''simple docstring'''





from collections import OrderedDict
from typing import Any, List, Mapping, Optional

from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging


lowercase_		       =  logging.get_logger(__name__)

lowercase_		       =  {
    "EleutherAI/gpt-j-6B": "https://huggingface.co/EleutherAI/gpt-j-6B/resolve/main/config.json",
    # See all GPT-J models at https://huggingface.co/models?filter=gpt_j
}






class __A			(  A						):



 '''simple docstring'''







 __lowerCamelCase    :       Any          =       'gptj'
 __lowerCamelCase    :       List[str]          =       {
     'max_position_embeddings': 'n_positions',
     'hidden_size': 'n_embd',
     'num_attention_heads': 'n_head',
     'num_hidden_layers': 'n_layer',
 }



 def __init__(self ,   A=50_400 ,   A=2_048 ,   A=4_096 ,   A=28 ,   A=16 ,   A=64 ,   A=None ,   A="gelu_new" ,   A=0.0 ,   A=0.0 ,   A=0.0 ,   A=1E-5 ,   A=0.02 ,   A=True ,   A=50_256 ,   A=50_256 ,   A=False ,   **A ,   )    ->     Tuple:


      """simple docstring"""



      _a      =  vocab_size
      _a      =  n_positions
      _a      =  n_embd
      _a      =  n_layer
      _a      =  n_head
      _a      =  n_inner
      _a      =  rotary_dim
      _a      =  activation_function
      _a      =  resid_pdrop
      _a      =  embd_pdrop
      _a      =  attn_pdrop
      _a      =  layer_norm_epsilon
      _a      =  initializer_range
      _a      =  use_cache

      _a      =  bos_token_id
      _a      =  eos_token_id

      super().__init__(
          bos_token_id=A ,   eos_token_id=A ,   tie_word_embeddings=A ,   **A       )






class __A			(  A						):



 '''simple docstring'''







 def __init__(self ,   A ,   A = "default" ,   A = None ,   A = False ,   )    ->     List[str]:


      """simple docstring"""



      super().__init__(A ,   task=A ,   patching_specs=A ,   use_past=A       )
      if not getattr(self._config ,   '''pad_token_id''' ,   A       ):
           # TODO: how to do that better?
           _a      =  0



 @property
 def 					a__  (self       )    ->     Mapping[str, Mapping[int, str]]:


      """simple docstring"""



      _a      =  OrderedDict({'''input_ids''': {0: '''batch''', 1: '''sequence'''}}       )
      if self.use_past:
           self.fill_with_past_key_values_(A ,   direction='''inputs'''       )
           _a      =  {0: '''batch''', 1: '''past_sequence + sequence'''}
      else:
           _a      =  {0: '''batch''', 1: '''sequence'''}

      return common_inputs



 @property
 def 					a__  (self       )    ->     int:


      """simple docstring"""



      return self._config.n_layer



 @property
 def 					a__  (self       )    ->     int:


      """simple docstring"""



      return self._config.n_head



 def 					a__  (self ,   A ,   A = -1 ,   A = -1 ,   A = False ,   A = None ,   )    ->     Mapping[str, Any]:


      """simple docstring"""



      _a      =  super(A ,   self       ).generate_dummy_inputs(
          A ,   batch_size=A ,   seq_length=A ,   is_pair=A ,   framework=A       )

      # We need to order the input in the way they appears in the forward()
      _a      =  OrderedDict({'''input_ids''': common_inputs['''input_ids''']}       )

      # Need to add the past_keys
      if self.use_past:
           if not is_torch_available():
                raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.'''       )
           else:
                import torch

                _a  ,    _a      =  common_inputs['''input_ids'''].shape
                # Not using the same length for past_key_values
                _a      =  seqlen + 2
                _a      =  (
                    batch,
                    self.num_attention_heads,
                    past_key_values_length,
                    self._config.hidden_size // self.num_attention_heads,
                )
                _a      =  [
                    (torch.zeros(A       ), torch.zeros(A       )) for _ in range(self.num_layers       )
                ]

      _a      =  common_inputs['''attention_mask''']
      if self.use_past:
           _a      =  ordered_inputs['''attention_mask'''].dtype
           _a      =  torch.cat(
               [ordered_inputs['''attention_mask'''], torch.ones(A ,   A ,   dtype=A       )] ,   dim=1       )

      return ordered_inputs



 @property
 def 					a__  (self       )    ->     int:


      """simple docstring"""



      return 13



