



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

       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])
       __a  			=						np.array(train_x)
       __a  			=						np.array(test_x)
       __a  			=						np.array([list(i.ravel()) for i in train_y])
       __a  			=						np.array([list(i.ravel()) for i in test_y])

       __a  			=						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")
       __a  			=						model.fit(
           x_train, y_train, epochs=150, verbose=1, shuffle=True, batch_size=4
       )
       __a  			=						model.predict(x_test)





'''simple docstring'''





import importlib
import shutil
import threading
import warnings
from typing import List

import fsspec
import fsspec.asyn

from . import compression
from .hffilesystem import HfFileSystem


__a  			=						importlib.util.find_spec("s3fs") is not None

if _has_safs:
       from .safilesystem import SaFileSystem  # noqa: F401

__a  			=						[
    compression.BzaFileSystem,
    compression.GzipFileSystem,
    compression.LzaFileSystem,
    compression.XzFileSystem,
    compression.ZstdFileSystem,
]

# Register custom filesystems
for fs_class in COMPRESSION_FILESYSTEMS + [HfFileSystem]:
       if fs_class.protocol in fsspec.registry and fsspec.registry[fs_class.protocol] is not fs_class:
              warnings.warn(F"A filesystem protocol was already set for {fs_class.protocol} and will be overwritten.")
       fsspec.register_implementation(fs_class.protocol, fs_class, clobber=True)





def 		__snake_case(							_lowerCAmelCase						)						->					str:
      if "://" in dataset_path:
            snake_case__    :       Tuple       		= dataset_path.split("""://"""						)[1]
      return dataset_path





def 		__snake_case(							_lowerCAmelCase						)						->					bool:
      if fs is not None and fs.protocol != "file":
            return True
      else:
            return False





def 		__snake_case(							_lowerCAmelCase	,  _lowerCAmelCase	,  _lowerCAmelCase						)						->					Optional[int]:
      snake_case__    :       int       		= not is_remote_filesystem(_lowerCAmelCase						)
      if is_local:
            # LocalFileSystem.mv does copy + rm, it is more efficient to simply move a local directory
            shutil.move(fs._strip_protocol(_lowerCAmelCase						)	,  fs._strip_protocol(_lowerCAmelCase						)						)
      else:
            fs.mv(_lowerCAmelCase	,  _lowerCAmelCase	,  recursive=_lowerCAmelCase						)





def 		__snake_case(							)						->					None:
      if hasattr(fsspec.asyn	,  """reset_lock"""						):
            # for future fsspec>2022.05.0
            fsspec.asyn.reset_lock()
      else:
            snake_case__    :       Dict       		= None
            snake_case__    :       List[str]       		= None
            snake_case__    :       Union[str, Any]       		= threading.Lock()
