


"""simple docstring"""







import numpy as np
import pandas as pd
from sklearn.preprocessing import MinMaxScaler
from tensorflow.keras.layers import LSTM, Dense
from tensorflow.keras.models import Sequential

if __name__ == "__main__":
     lowerCAmelCase__				    =       pd.read_csv('''sample_data.csv''', header=None)
     lowerCAmelCase__				    =       df.shape[:1][0]
     # If you're using some other dataset input the target column
     lowerCAmelCase__				    =       df.iloc[:, 1:2]
     lowerCAmelCase__				    =       actual_data.values.reshape(len_data, 1)
     lowerCAmelCase__				    =       MinMaxScaler().fit_transform(actual_data)
     lowerCAmelCase__				    =       10
     lowerCAmelCase__				    =       5
     lowerCAmelCase__				    =       20
     lowerCAmelCase__				    =       len_data - periods * look_back
     lowerCAmelCase__				    =       actual_data[:division]
     lowerCAmelCase__				    =       actual_data[division - look_back :]
     lowerCAmelCase__    ,lowerCAmelCase__				    =       [], []
     lowerCAmelCase__    ,lowerCAmelCase__				    =       [], []

     for i in range(0, len(train_data) - forward_days - look_back + 1):
          train_x.append(train_data[i : i + look_back])
          train_y.append(train_data[i + look_back : i + look_back + forward_days])
     for i in range(0, len(test_data) - forward_days - look_back + 1):
          test_x.append(test_data[i : i + look_back])
          test_y.append(test_data[i + look_back : i + look_back + forward_days])
     lowerCAmelCase__				    =       np.array(train_x)
     lowerCAmelCase__				    =       np.array(test_x)
     lowerCAmelCase__				    =       np.array([list(i.ravel()) for i in train_y])
     lowerCAmelCase__				    =       np.array([list(i.ravel()) for i in test_y])

     lowerCAmelCase__				    =       Sequential()
     model.add(LSTM(128, input_shape=(look_back, 1), return_sequences=True))
     model.add(LSTM(64, input_shape=(128, 1)))
     model.add(Dense(forward_days))
     model.compile(loss='''mean_squared_error''', optimizer='''adam''')
     lowerCAmelCase__				    =       model.fit(
         x_train, y_train, epochs=150, verbose=1, shuffle=True, batch_size=4
     )
     lowerCAmelCase__				    =       model.predict(x_test)









"""simple docstring"""







import multiprocessing
from typing import TYPE_CHECKING, Optional, Union

from .. import Dataset, Features, config
from ..formatting import query_table
from ..packaged_modules.sql.sql import Sql
from ..utils import logging
from .abc import AbstractDatasetInputStream


if TYPE_CHECKING:
     import sqlitea

     import sqlalchemy


class 				SCREAMING_SNAKE_CASE__    (		lowercase    ):
       """simple docstring"""


       def __init__(						self		,					snake_case__		,					snake_case__		,					snake_case__ = None		,					snake_case__ = None		,					snake_case__ = False		,					**snake_case__		,					):






         """simple docstring"""





         super().__init__(features=snake_case__		,					cache_dir=snake_case__		,					keep_in_memory=snake_case__		,					**snake_case__						)
         lowerCAmelCase					:					Dict								=							Sql(
             cache_dir=snake_case__		,					features=snake_case__		,					sql=snake_case__		,					con=snake_case__		,					**snake_case__		,					)







       def 	lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase					:					Tuple								=							None
         lowerCAmelCase					:					Optional[int]								=							None
         lowerCAmelCase					:					str								=							None
         lowerCAmelCase					:					int								=							None

         self.builder.download_and_prepare(
             download_config=snake_case__		,					download_mode=snake_case__		,					verification_mode=snake_case__		,					base_path=snake_case__		,					)

         # Build dataset for splits
         lowerCAmelCase					:					str								=							self.builder.as_dataset(
             split="train"		,					verification_mode=snake_case__		,					in_memory=self.keep_in_memory						)
         return dataset


class 				SCREAMING_SNAKE_CASE__    :
       """simple docstring"""


       def __init__(						self		,					snake_case__		,					snake_case__		,					snake_case__		,					snake_case__ = None		,					snake_case__ = None		,					**snake_case__		,					):






         """simple docstring"""





         if num_proc is not None and num_proc <= 0:
           raise ValueError(f"""num_proc {num_proc} must be an integer > 0."""						)

         lowerCAmelCase					:					Tuple								=							dataset
         lowerCAmelCase					:					Tuple								=							name
         lowerCAmelCase					:					List[str]								=							con
         lowerCAmelCase					:					List[Any]								=							batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
         lowerCAmelCase					:					Optional[int]								=							num_proc
         lowerCAmelCase					:					Optional[int]								=							to_sql_kwargs

       def 	lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase					:					List[str]								=							self.to_sql_kwargs.pop("sql"		,					snake_case__						)
         lowerCAmelCase					:					List[Any]								=							self.to_sql_kwargs.pop("con"		,					snake_case__						)
         lowerCAmelCase					:					Dict								=							self.to_sql_kwargs.pop("index"		,					snake_case__						)

         lowerCAmelCase					:					Dict								=							self._write(index=snake_case__		,					**self.to_sql_kwargs						)
         return written

       def 	lowercase__							(						self		,					snake_case__						):






         """simple docstring"""





         lowerCAmelCase						,  lowerCAmelCase						,  lowerCAmelCase					:					Optional[int]								=							args
         lowerCAmelCase					:					Optional[int]								=							{**to_sql_kwargs, "if_exists": "append"} if offset > 0 else to_sql_kwargs
         lowerCAmelCase					:					List[Any]								=							query_table(
             table=self.dataset.data		,					key=slice(snake_case__		,					offset + self.batch_size						)		,					indices=self.dataset._indices		,					)
         lowerCAmelCase					:					Tuple								=							batch.to_pandas()
         lowerCAmelCase					:					str								=							df.to_sql(self.name		,					self.con		,					index=snake_case__		,					**snake_case__						)
         return num_rows or len(snake_case__						)







       def 	lowercase__							(						self		,					snake_case__		,					**snake_case__						):






         """simple docstring"""





         lowerCAmelCase					:					Tuple								=							0

         if self.num_proc is None or self.num_proc == 1:
           for offset in logging.tqdm(
               range(0		,					len(self.dataset						)		,					self.batch_size						)		,					unit="ba"		,					disable=not logging.is_progress_bar_enabled()		,					desc="Creating SQL from Arrow format"		,					):
             written += self._batch_sql((offset, index, to_sql_kwargs)						)
         else:
           lowerCAmelCase						,  lowerCAmelCase					:					Dict								=							len(self.dataset						), self.batch_size
           with multiprocessing.Pool(self.num_proc						) as pool:
             for num_rows in logging.tqdm(
                 pool.imap(
                     self._batch_sql		,					[(offset, index, to_sql_kwargs) for offset in range(0		,					snake_case__		,					snake_case__						)]		,					)		,					total=(num_rows // batch_size) + 1 if num_rows % batch_size else num_rows // batch_size		,					unit="ba"		,					disable=not logging.is_progress_bar_enabled()		,					desc="Creating SQL from Arrow format"		,					):
               written += num_rows

         return written





