






import logging
import os
import sys
from pathlib import Path
from unittest.mock import patch

from parameterized import parameterized
from run_eval import run_generate
from run_eval_search import run_search

from transformers.testing_utils import CaptureStdout, TestCasePlus, slow
from utils import ROUGE_KEYS


logging.basicConfig(level=logging.DEBUG)
__A					=	logging.getLogger()







def       lowercase__	(					A_:						Path  ,      A_:						list		)	->				Any:







       """simple docstring"""






       __UpperCAmelCase     				="""\n""".join(A_		)
       Path(A_		).open("""w"""		).writelines(A_		)


__A					=	"patrickvonplaten/t5-tiny-random"
__A					=	"sshleifer/bart-tiny-random"
__A					=	"sshleifer/tiny-mbart"

__A					=	logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
logging.disable(logging.CRITICAL)  # remove noisy download output from tracebacks





class 				_A			(     _UpperCamelCase				):
    """simple docstring"""

    def _a							(				self      :     Dict ,     __SCREAMING_SNAKE_CASE      :     Optional[Any]      )      ->							Any:
           __UpperCAmelCase     				=Path(self.get_auto_remove_tmp_dir()      ) / """utest_input.source"""
           __UpperCAmelCase     				=input_file_name.parent / """utest_output.txt"""
           assert not output_file_name.exists()
           __UpperCAmelCase     				=[""" New York (CNN)When Liana Barrientos was 23 years old, she got married in Westchester County."""]
           _dump_articles(__SCREAMING_SNAKE_CASE ,     __SCREAMING_SNAKE_CASE      )

           __UpperCAmelCase     				=str(Path(self.get_auto_remove_tmp_dir()      ) / """scores.json"""      )
           __UpperCAmelCase     				="""translation_en_to_de""" if model == T5_TINY else """summarization"""
           __UpperCAmelCase     				=f'''
            run_eval_search.py
            {model}
            {input_file_name}
            {output_file_name}
            --score_path {score_path}
            --task {task}
            --num_beams 2
            --length_penalty 2.0
            '''.split()

           with patch.object(__SCREAMING_SNAKE_CASE ,     """argv""" ,     __SCREAMING_SNAKE_CASE      ):
                  run_generate()
                  assert Path(__SCREAMING_SNAKE_CASE      ).exists()
                  # os.remove(Path(output_file_name))



    def _a							(				self      :     List[Any]      )      ->							Optional[int]:
           self.run_eval_tester(__SCREAMING_SNAKE_CASE      )



    @parameterized.expand([BART_TINY, MBART_TINY]      )
    @slow
    def _a							(				self      :     Optional[int] ,     __SCREAMING_SNAKE_CASE      :     Union[str, Any]      )      ->							Any:
           self.run_eval_tester(__SCREAMING_SNAKE_CASE      )



    @parameterized.expand([T5_TINY, MBART_TINY]      )
    @slow
    def _a							(				self      :     List[Any] ,     __SCREAMING_SNAKE_CASE      :     Any      )      ->							Any:
           __UpperCAmelCase     				=Path(self.get_auto_remove_tmp_dir()      ) / """utest_input.source"""
           __UpperCAmelCase     				=input_file_name.parent / """utest_output.txt"""
           assert not output_file_name.exists()

           __UpperCAmelCase     				={
               """en""": ["""Machine learning is great, isn't it?""", """I like to eat bananas""", """Tomorrow is another great day!"""],
               """de""": [
                   """Maschinelles Lernen ist großartig, oder?""",
                   """Ich esse gerne Bananen""",
                   """Morgen ist wieder ein toller Tag!""",
               ],
           }

           __UpperCAmelCase     				=Path(self.get_auto_remove_tmp_dir()      )
           __UpperCAmelCase     				=str(tmp_dir / """scores.json"""      )
           __UpperCAmelCase     				=str(tmp_dir / """val.target"""      )
           _dump_articles(__SCREAMING_SNAKE_CASE ,     text["""en"""]      )
           _dump_articles(__SCREAMING_SNAKE_CASE ,     text["""de"""]      )
           __UpperCAmelCase     				="""translation_en_to_de""" if model == T5_TINY else """summarization"""
           __UpperCAmelCase     				=f'''
            run_eval_search.py
            {model}
            {str(__SCREAMING_SNAKE_CASE      )}
            {str(__SCREAMING_SNAKE_CASE      )}
            --score_path {score_path}
            --reference_path {reference_path}
            --task {task}
            '''.split()
           testargs.extend(["""--search""", """num_beams=1:2 length_penalty=0.9:1.0"""]      )

           with patch.object(__SCREAMING_SNAKE_CASE ,     """argv""" ,     __SCREAMING_SNAKE_CASE      ):
                  with CaptureStdout() as cs:
                         run_search()
                  __UpperCAmelCase     				=[""" num_beams | length_penalty""", model, """Best score args"""]
                  __UpperCAmelCase     				=["""Info"""]
                  if "translation" in task:
                         expected_strings.append("""bleu"""      )
                  else:
                         expected_strings.extend(__SCREAMING_SNAKE_CASE      )
                  for w in expected_strings:
                         assert w in cs.out
                  for w in un_expected_strings:
                         assert w not in cs.out
                  assert Path(__SCREAMING_SNAKE_CASE      ).exists()
                  os.remove(Path(__SCREAMING_SNAKE_CASE      )      )










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)






