





'''simple docstring'''







import os
from collections import deque

import torch
from torch.utils.data import Dataset


class 					__a      (lowerCamelCase     ):

							def __init__(							self							:     List[Any]       ,     __magic_name__							:     Any=""       ,     __magic_name__							:     Tuple="train"   )					->       int:







													"""simple docstring"""
													assert os.path.isdir(__magic_name__   )

													UpperCAmelCase_			:      List[Any]     						=  []
													UpperCAmelCase_			:      Union[str, Any]     						=  os.listdir(__magic_name__   )
													for story_filename in story_filenames_list:
																			if "summary" in story_filename:
																									continue
																			UpperCAmelCase_			:      Dict     						=  os.path.join(__magic_name__       ,     __magic_name__   )
																			if not os.path.isfile(__magic_name__   ):
																									continue
																			self.documents.append(__magic_name__   )

							def __len__(							self							:     List[str]   )					->       Optional[Any]:







													"""simple docstring"""
													return len(self.documents   )



							def __getitem__(							self							:     Union[str, Any]       ,     __magic_name__							:     Dict   )					->       str:







													"""simple docstring"""
													UpperCAmelCase_			:      Optional[int]     						=  self.documents[idx]
													UpperCAmelCase_			:      str     						=  document_path.split('''/'''   )[-1]
													with open(__magic_name__       ,     encoding='''utf-8'''   ) as source:
																			UpperCAmelCase_			:      str     						=  source.read()
																			UpperCAmelCase_						,       UpperCAmelCase_			:      List[Any]     						=  process_story(__magic_name__   )
													return document_name, story_lines, summary_lines





def        lowerCamelCase_      (  SCREAMING_SNAKE_CASE__      :			List[Any]   ) ->	List[Any]:
						UpperCAmelCase_			:      Tuple     						=  list(filter(lambda SCREAMING_SNAKE_CASE__   : len(SCREAMING_SNAKE_CASE__   ) != 0,			[line.strip() for line in raw_story.split('''\n'''   )]   )   )

						# for some unknown reason some lines miss a period, add it
						UpperCAmelCase_			:      int     						=  [_add_missing_period(SCREAMING_SNAKE_CASE__   ) for line in nonempty_lines]

						# gather article lines
						UpperCAmelCase_			:      List[Any]     						=  []
						UpperCAmelCase_			:      Union[str, Any]     						=  deque(SCREAMING_SNAKE_CASE__   )
						while True:
												try:
																		UpperCAmelCase_			:      List[Any]     						=  lines.popleft()
																		if element.startswith('''@highlight'''   ):
																								break
																		story_lines.append(SCREAMING_SNAKE_CASE__   )
												except IndexError:
																		# if "@highlight" is absent from the file we pop
																		# all elements until there is None, raising an exception.
																		return story_lines, []

    # gather summary lines
						UpperCAmelCase_			:      Optional[int]     						=  list(filter(lambda SCREAMING_SNAKE_CASE__   : not t.startswith('''@highlight'''   ),			SCREAMING_SNAKE_CASE__   )   )

						return story_lines, summary_lines





def        lowerCamelCase_      (  SCREAMING_SNAKE_CASE__      :			Optional[Any]   ) ->	int:
						UpperCAmelCase_			:      Union[str, Any]     						=  ['''.''', '''!''', '''?''', '''...''', '''\'''', '''`''', '''"''', '''\u2019''', '''\u2019''', ''')''']
						if line.startswith('''@highlight'''   ):
												return line
						if line[-1] in END_TOKENS:
												return line
						return line + "."





def        lowerCamelCase_      (  SCREAMING_SNAKE_CASE__      :			Any,			SCREAMING_SNAKE_CASE__      :			Any,			SCREAMING_SNAKE_CASE__      :			List[Any]   ) ->	List[Any]:
						if len(SCREAMING_SNAKE_CASE__   ) > block_size:
												return sequence[:block_size]
						else:
												sequence.extend([pad_token_id] * (block_size - len(SCREAMING_SNAKE_CASE__   ))   )
												return sequence





def        lowerCamelCase_      (  SCREAMING_SNAKE_CASE__      :			int,			SCREAMING_SNAKE_CASE__      :			List[str]   ) ->	Optional[Any]:
						UpperCAmelCase_			:      Optional[Any]     						=  torch.ones_like(SCREAMING_SNAKE_CASE__   )
						UpperCAmelCase_			:      str     						=  sequence == pad_token_id
						UpperCAmelCase_			:      Tuple     						=  0
						return mask





def        lowerCamelCase_      (  SCREAMING_SNAKE_CASE__      :			Dict,			SCREAMING_SNAKE_CASE__      :			Dict,			SCREAMING_SNAKE_CASE__      :			str   ) ->	Dict:
						UpperCAmelCase_			:      Tuple     						=  [tokenizer.encode(SCREAMING_SNAKE_CASE__   ) for line in story_lines]
						UpperCAmelCase_			:      Any     						=  [token for sentence in story_lines_token_ids for token in sentence]
						UpperCAmelCase_			:      List[str]     						=  [tokenizer.encode(SCREAMING_SNAKE_CASE__   ) for line in summary_lines]
						UpperCAmelCase_			:      str     						=  [token for sentence in summary_lines_token_ids for token in sentence]

						return story_token_ids, summary_token_ids





def        lowerCamelCase_      (  SCREAMING_SNAKE_CASE__      :			int,			SCREAMING_SNAKE_CASE__      :			Union[str, Any]   ) ->	List[Any]:
						UpperCAmelCase_			:      Tuple     						=  []
						for sequence in batch:
												UpperCAmelCase_			:      Optional[int]     						=  -1
												UpperCAmelCase_			:      Tuple     						=  []
												for s in sequence:
																		if s == separator_token_id:
																								sentence_num += 1
																		embeddings.append(sentence_num % 2   )
												batch_embeddings.append(SCREAMING_SNAKE_CASE__   )
						return torch.tensor(SCREAMING_SNAKE_CASE__   )












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

					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])
					snake_case_     :       Any						=  np.array(train_x)
					snake_case_     :       Optional[Any]						=  np.array(test_x)
					snake_case_     :       Optional[Any]						=  np.array([list(i.ravel()) for i in train_y])
					snake_case_     :       List[str]						=  np.array([list(i.ravel()) for i in test_y])

					snake_case_     :       List[Any]						=  Sequential()
					model.add(LSTM(1_28, input_shape=(look_back, 1), return_sequences=True))
					model.add(LSTM(64, input_shape=(1_28, 1)))
					model.add(Dense(forward_days))
					model.compile(loss="mean_squared_error", optimizer="adam")
					snake_case_     :       Dict						=  model.fit(
					    x_train, y_train, epochs=1_50, verbose=1, shuffle=True, batch_size=4
					)
					snake_case_     :       Optional[Any]						=  model.predict(x_test)





