




import tempfile
import unittest
from pathlib import Path
from shutil import copyfile

from transformers import BatchEncoding, MarianTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, slow
from transformers.utils import is_sentencepiece_available, is_tf_available, is_torch_available


if is_sentencepiece_available():
	from transformers.models.marian.tokenization_marian import VOCAB_FILES_NAMES, save_json

from ...test_tokenization_common import TokenizerTesterMixin


a__		     =     get_tests_dir('''fixtures/test_sentencepiece.model''')

a__		     =     {'''target_lang''': '''fi''', '''source_lang''': '''en'''}
a__		     =     '''>>zh<<'''
a__		     =     '''Helsinki-NLP/'''

if is_torch_available():
	a__		     =     '''pt'''
elif is_tf_available():
	a__		     =     '''tf'''
else:
	a__		     =     '''jax'''


@require_sentencepiece
class    SCREAMING_SNAKE_CASE_				(       _UpperCamelCase	,  unittest.TestCase   ):



	"""simple docstring"""


	__magic_name__   :       Optional[Any]      	=		MarianTokenizer
	__magic_name__   :       Tuple      	=		False
	__magic_name__   :       int      	=		True






	def 					lowerCamelCase__     (					self		:						Any	)	->       Optional[int]:







						"""simple docstring"""







						super().setUp()
						__UpperCamelCase					:	Dict             =      ["""</s>""", """<unk>""", """▁This""", """▁is""", """▁a""", """▁t""", """est""", """\u0120""", """<pad>"""]
						__UpperCamelCase					:	Tuple             =      dict(zip(lowerCAmelCase					,     range(len(lowerCAmelCase	)	)	)	)
						__UpperCamelCase					:	Optional[Any]             =      Path(self.tmpdirname	)
						save_json(lowerCAmelCase					,     save_dir / VOCAB_FILES_NAMES["""vocab"""]	)
						save_json(lowerCAmelCase					,     save_dir / VOCAB_FILES_NAMES["""tokenizer_config_file"""]	)
						if not (save_dir / VOCAB_FILES_NAMES["source_spm"]).exists():
											copyfile(lowerCAmelCase					,     save_dir / VOCAB_FILES_NAMES["""source_spm"""]	)
											copyfile(lowerCAmelCase					,     save_dir / VOCAB_FILES_NAMES["""target_spm"""]	)

						__UpperCamelCase					:	Any             =      MarianTokenizer.from_pretrained(self.tmpdirname	)
						tokenizer.save_pretrained(self.tmpdirname	)






	def 					lowerCamelCase__     (					self		:						str					,     **lowerCAmelCase		:						Optional[Any]	)	->       MarianTokenizer:







						"""simple docstring"""







						return MarianTokenizer.from_pretrained(self.tmpdirname					,     **lowerCAmelCase	)






	def 					lowerCamelCase__     (					self		:						Union[str, Any]					,     lowerCAmelCase		:						Union[str, Any]	)	->       Optional[Any]:







						"""simple docstring"""







						return (
						    "This is a test",
						    "This is a test",
						)






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







						"""simple docstring"""







						__UpperCamelCase					:	str             =      """</s>"""
						__UpperCamelCase					:	str             =      0

						self.assertEqual(self.get_tokenizer()._convert_token_to_id(lowerCAmelCase	)					,     lowerCAmelCase	)
						self.assertEqual(self.get_tokenizer()._convert_id_to_token(lowerCAmelCase	)					,     lowerCAmelCase	)






	def 					lowerCamelCase__     (					self		:						Optional[int]	)	->       Dict:







						"""simple docstring"""







						__UpperCamelCase					:	List[Any]             =      list(self.get_tokenizer().get_vocab().keys()	)

						self.assertEqual(vocab_keys[0]					,     """</s>"""	)
						self.assertEqual(vocab_keys[1]					,     """<unk>"""	)
						self.assertEqual(vocab_keys[-1]					,     """<pad>"""	)
						self.assertEqual(len(lowerCAmelCase	)					,     9	)






	def 					lowerCamelCase__     (					self		:						Tuple	)	->       Optional[int]:







						"""simple docstring"""







						self.assertEqual(self.get_tokenizer().vocab_size					,     9	)






	def 					lowerCamelCase__     (					self		:						Union[str, Any]	)	->       List[Any]:







						"""simple docstring"""







						__UpperCamelCase					:	Optional[int]             =      MarianTokenizer.from_pretrained(F'''{ORG_NAME}opus-mt-en-de'''	)
						__UpperCamelCase					:	List[str]             =      en_de_tokenizer(["""I am a small frog"""]					,     return_tensors=lowerCAmelCase	)
						self.assertIsInstance(lowerCAmelCase					,     lowerCAmelCase	)
						__UpperCamelCase					:	Optional[int]             =      [38, 121, 14, 697, 38848, 0]
						self.assertListEqual(lowerCAmelCase					,     batch.input_ids[0]	)

						__UpperCamelCase					:	List[str]             =      tempfile.mkdtemp()
						en_de_tokenizer.save_pretrained(lowerCAmelCase	)
						__UpperCamelCase					:	Dict             =      [x.name for x in Path(lowerCAmelCase	).glob("""*"""	)]
						self.assertIn("""source.spm"""					,     lowerCAmelCase	)
						MarianTokenizer.from_pretrained(lowerCAmelCase	)






	def 					lowerCamelCase__     (					self		:						Any	)	->       Tuple:







						"""simple docstring"""







						__UpperCamelCase					:	List[str]             =      self.get_tokenizer()

						__UpperCamelCase					:	Dict             =      tok(
						    ["""I am a small frog""" * 1000, """I am a small frog"""]					,     padding=lowerCAmelCase					,     truncation=lowerCAmelCase					,     return_tensors=lowerCAmelCase	)
						self.assertIsInstance(lowerCAmelCase					,     lowerCAmelCase	)
						self.assertEqual(batch.input_ids.shape					,     (2, 512)	)






	def 					lowerCamelCase__     (					self		:						str	)	->       Any:







						"""simple docstring"""







						__UpperCamelCase					:	Optional[Any]             =      self.get_tokenizer()
						__UpperCamelCase					:	List[Any]             =      tok(["""I am a tiny frog""", """I am a small frog"""]					,     padding=lowerCAmelCase					,     return_tensors=lowerCAmelCase	)
						self.assertIsInstance(lowerCAmelCase					,     lowerCAmelCase	)
						self.assertEqual(batch_smaller.input_ids.shape					,     (2, 10)	)






	@slow
	def 					lowerCamelCase__     (					self		:						List[str]	)	->       int:







						"""simple docstring"""







						__UpperCamelCase					:	List[Any]             =      {"""input_ids""": [[43495, 462, 20, 42164, 1369, 52, 464, 132, 1703, 492, 13, 7491, 38999, 6, 8, 464, 132, 1703, 492, 13, 4669, 37867, 13, 7525, 27, 1593, 988, 13, 33972, 7029, 6, 20, 8251, 383, 2, 270, 5866, 3788, 2, 2353, 8251, 12338, 2, 13958, 387, 2, 3629, 6953, 188, 2900, 2, 13958, 8011, 11501, 23, 8460, 4073, 34009, 20, 435, 11439, 27, 8, 8460, 4073, 6004, 20, 9988, 375, 27, 33, 266, 1945, 1076, 1350, 37867, 3288, 5, 577, 1076, 4374, 8, 5082, 5, 26453, 257, 556, 403, 2, 242, 132, 383, 316, 492, 8, 10767, 6, 316, 304, 4239, 3, 0], [148, 15722, 19, 1839, 12, 1350, 13, 22327, 5082, 5418, 47567, 35938, 59, 318, 19552, 108, 2183, 54, 14976, 4835, 32, 547, 1114, 8, 315, 2417, 5, 92, 19088, 3, 0, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100], [36, 6395, 12570, 39147, 11597, 6, 266, 4, 45405, 7296, 3, 0, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100]], """attention_mask""": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
						# fmt: on

						self.tokenizer_integration_test_util(
						    expected_encoding=lowerCAmelCase					,     model_name="""Helsinki-NLP/opus-mt-en-de"""					,     revision="""1a8c2263da11e68e50938f97e10cd57820bd504c"""					,     decode_kwargs={"""use_source_tokenizer""": True}					,     )






	def 					lowerCamelCase__     (					self		:						Optional[Any]	)	->       Tuple:







						"""simple docstring"""







						__UpperCamelCase					:	int             =      MarianTokenizer.from_pretrained("""hf-internal-testing/test-marian-two-vocabs"""	)

						__UpperCamelCase					:	List[Any]             =      """Tämä on testi"""
						__UpperCamelCase					:	Dict             =      """This is a test"""

						__UpperCamelCase					:	List[str]             =      [76, 7, 2047, 2]
						__UpperCamelCase					:	Tuple             =      [69, 12, 11, 940, 2]

						__UpperCamelCase					:	List[str]             =      tokenizer(lowerCAmelCase	).input_ids
						self.assertListEqual(lowerCAmelCase					,     lowerCAmelCase	)

						__UpperCamelCase					:	List[str]             =      tokenizer(text_target=lowerCAmelCase	).input_ids
						self.assertListEqual(lowerCAmelCase					,     lowerCAmelCase	)

						__UpperCamelCase					:	Dict             =      tokenizer.decode(lowerCAmelCase					,     skip_special_tokens=lowerCAmelCase	)
						self.assertEqual(lowerCAmelCase					,     lowerCAmelCase	)












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)






