"""simple docstring"""






import copy
import os
from typing import Union

from ...configuration_utils import PretrainedConfig
from ...models.auto.modeling_auto import MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
from ...utils import logging
from ..auto import CONFIG_MAPPING


__lowerCAmelCase       :			Dict   	=logging.get_logger(__name__)

__lowerCAmelCase       :			Optional[int]   	={
    """Salesforce/instruct-blip-flan-t5""": """https://huggingface.co/Salesforce/instruct-blip-flan-t5/resolve/main/config.json""",
}





class 					_A		(			__a      ):
      snake_case__     :  Union[str, Any]				    =      """instructblip_vision_model"""

      def __init__(		self						,			__lowerCAmelCase=1408						,			__lowerCAmelCase=6144						,			__lowerCAmelCase=39						,			__lowerCAmelCase=16						,			__lowerCAmelCase=224						,			__lowerCAmelCase=14						,			__lowerCAmelCase="gelu"						,			__lowerCAmelCase=1E-6						,			__lowerCAmelCase=0.0						,			__lowerCAmelCase=1E-10						,			__lowerCAmelCase=True						,			**__lowerCAmelCase						,			):







            """simple docstring"""



            super().__init__(**_lowerCamelCase		)

            lowercase								=      hidden_size
            lowercase								=      intermediate_size
            lowercase								=      num_hidden_layers
            lowercase								=      num_attention_heads
            lowercase								=      patch_size
            lowercase								=      image_size
            lowercase								=      initializer_range
            lowercase								=      attention_dropout
            lowercase								=      layer_norm_eps
            lowercase								=      hidden_act
            lowercase								=      qkv_bias







      @classmethod
      def 							A__			(		cls						,			__lowerCAmelCase						,			**__lowerCAmelCase		):







            """simple docstring"""



            cls._set_token_in_kwargs(_lowerCamelCase		)

            lowercase				,				lowercase								=      cls.get_config_dict(_lowerCamelCase						,			**_lowerCamelCase		)

            # get the vision config dict if we are loading from InstructBlipConfig
            if config_dict.get("""model_type"""		) == "instructblip":
                  lowercase								=      config_dict["""vision_config"""]

            if "model_type" in config_dict and hasattr(cls						,			"""model_type"""		) and config_dict["model_type"] != cls.model_type:
                  logger.warning(
                      f'You are using a model of type {config_dict["model_type"]} to instantiate a model of type '
                      f'{cls.model_type}. This is not supported for all configurations of models and can yield errors.'		)

            return cls.from_dict(_lowerCamelCase						,			**_lowerCamelCase		)






class 					_A		(			__a      ):
      snake_case__     :  Any				    =      """instructblip_qformer"""

      def __init__(		self						,			__lowerCAmelCase=3_0522						,			__lowerCAmelCase=768						,			__lowerCAmelCase=12						,			__lowerCAmelCase=12						,			__lowerCAmelCase=3072						,			__lowerCAmelCase="gelu"						,			__lowerCAmelCase=0.1						,			__lowerCAmelCase=0.1						,			__lowerCAmelCase=512						,			__lowerCAmelCase=0.0_2						,			__lowerCAmelCase=1E-12						,			__lowerCAmelCase=0						,			__lowerCAmelCase="absolute"						,			__lowerCAmelCase=2						,			__lowerCAmelCase=1408						,			**__lowerCAmelCase						,			):







            """simple docstring"""



            super().__init__(pad_token_id=_lowerCamelCase						,			**_lowerCamelCase		)

            lowercase								=      vocab_size
            lowercase								=      hidden_size
            lowercase								=      num_hidden_layers
            lowercase								=      num_attention_heads
            lowercase								=      hidden_act
            lowercase								=      intermediate_size
            lowercase								=      hidden_dropout_prob
            lowercase								=      attention_probs_dropout_prob
            lowercase								=      max_position_embeddings
            lowercase								=      initializer_range
            lowercase								=      layer_norm_eps
            lowercase								=      position_embedding_type
            lowercase								=      cross_attention_frequency
            lowercase								=      encoder_hidden_size







      @classmethod
      def 							A__			(		cls						,			__lowerCAmelCase						,			**__lowerCAmelCase		):







            """simple docstring"""



            cls._set_token_in_kwargs(_lowerCamelCase		)

            lowercase				,				lowercase								=      cls.get_config_dict(_lowerCamelCase						,			**_lowerCamelCase		)

            # get the qformer config dict if we are loading from InstructBlipConfig
            if config_dict.get("""model_type"""		) == "instructblip":
                  lowercase								=      config_dict["""qformer_config"""]

            if "model_type" in config_dict and hasattr(cls						,			"""model_type"""		) and config_dict["model_type"] != cls.model_type:
                  logger.warning(
                      f'You are using a model of type {config_dict["model_type"]} to instantiate a model of type '
                      f'{cls.model_type}. This is not supported for all configurations of models and can yield errors.'		)

            return cls.from_dict(_lowerCamelCase						,			**_lowerCamelCase		)






class 					_A		(			__a      ):
      snake_case__     :  Optional[int]				    =      """instructblip"""
      snake_case__     :  Dict				    =      True

      def __init__(		self						,			__lowerCAmelCase=None						,			__lowerCAmelCase=None						,			__lowerCAmelCase=None						,			__lowerCAmelCase=32						,			**__lowerCAmelCase		):







            """simple docstring"""



            super().__init__(**_lowerCamelCase		)

            if vision_config is None:
                  lowercase								=      {}
                  logger.info("""vision_config is None. initializing the InstructBlipVisionConfig with default values."""		)

            if qformer_config is None:
                  lowercase								=      {}
                  logger.info("""qformer_config is None. Initializing the InstructBlipQFormerConfig with default values."""		)

            if text_config is None:
                  lowercase								=      {}
                  logger.info("""text_config is None. Initializing the text config with default values (`OPTConfig`)."""		)

            lowercase								=      InstructBlipVisionConfig(**_lowerCamelCase		)
            lowercase								=      InstructBlipQFormerConfig(**_lowerCamelCase		)
            lowercase								=      text_config["""model_type"""] if """model_type""" in text_config else """opt"""
            lowercase								=      CONFIG_MAPPING[text_model_type](**_lowerCamelCase		)

            lowercase								=      self.text_config.tie_word_embeddings
            lowercase								=      self.text_config.is_encoder_decoder

            lowercase								=      num_query_tokens
            lowercase								=      self.vision_config.hidden_size
            lowercase								=      self.text_config.model_type in MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
            lowercase								=      1.0
            lowercase								=      0.0_2

      @classmethod
      def 							A__			(		cls						,			__lowerCAmelCase						,			__lowerCAmelCase						,			__lowerCAmelCase						,			**__lowerCAmelCase						,			):







            """simple docstring"""



            return cls(
                vision_config=vision_config.to_dict()						,			qformer_config=qformer_config.to_dict()						,			text_config=text_config.to_dict()						,			**_lowerCamelCase						,			)







      def 							A__			(		self		):







            """simple docstring"""



            lowercase								=      copy.deepcopy(self.__dict__		)
            lowercase								=      self.vision_config.to_dict()
            lowercase								=      self.qformer_config.to_dict()
            lowercase								=      self.text_config.to_dict()
            lowercase								=      self.__class__.model_type
            return output





"""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__			(		__magic_name__			,   __magic_name__			,   __magic_name__			,   __magic_name__			,   __magic_name__      )						->float:
			__lowercase       =       np.array([[1, item, train_mtch[i]] for i, item in enumerate(__magic_name__      )]      )
			__lowercase       =       np.array(__magic_name__      )
			__lowercase       =       np.dot(np.dot(np.linalg.inv(np.dot(x.transpose()			,   __magic_name__      )      )			,   x.transpose()      )			,   __magic_name__      )
			return abs(beta[0] + test_dt[0] * beta[1] + test_mtch[0] + beta[2]      )



def      lowerCAmelCase__			(		__magic_name__			,   __magic_name__			,   __magic_name__      )						->float:
			__lowercase       =       (1, 2, 1)
			__lowercase       =       (1, 1, 0, 7)
			__lowercase       =       SARIMAX(
			    __magic_name__			,   exog=__magic_name__			,   order=__magic_name__			,   seasonal_order=__magic_name__      )
			__lowercase       =       model.fit(disp=__magic_name__			,   maxiter=6_0_0			,   method="nm"      )
			__lowercase       =       model_fit.predict(1			,   len(__magic_name__      )			,   exog=[test_match]      )
			return result[0]



def      lowerCAmelCase__			(		__magic_name__			,   __magic_name__			,   __magic_name__      )						->float:
			__lowercase       =       SVR(kernel="rbf"			,   C=1			,   gamma=0.1			,   epsilon=0.1      )
			regressor.fit(__magic_name__			,   __magic_name__      )
			__lowercase       =       regressor.predict(__magic_name__      )
			return y_pred[0]



def      lowerCAmelCase__			(		__magic_name__      )						->float:
			train_user.sort()
			__lowercase       =       np.percentile(__magic_name__			,   2_5      )
			__lowercase       =       np.percentile(__magic_name__			,   7_5      )
			__lowercase       =       qa - qa
			__lowercase       =       qa - (iqr * 0.1)
			return low_lim



def      lowerCAmelCase__			(		__magic_name__			,   __magic_name__      )						->bool:
			__lowercase       =       0
			__lowercase       =       0
			for i in list_vote:
						if i > actual_result:
									__lowercase       =       not_safe + 1
						else:
									if abs(abs(__magic_name__      ) - abs(__magic_name__      )      ) <= 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							=				[[18_231, 0.0, 1], [22_621, 1.0, 2], [15_675, 0.0, 3], [23_583, 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.''')





