




import os
import unittest

from transformers.models.phobert.tokenization_phobert import VOCAB_FILES_NAMES, PhobertTokenizer

from ...test_tokenization_common import TokenizerTesterMixin


class    SCREAMING_SNAKE_CASE_				(       _UpperCamelCase	,  unittest.TestCase   ):



	"""simple docstring"""


	__magic_name__   :       List[str]      	=		PhobertTokenizer
	__magic_name__   :       Dict      	=		False






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







						"""simple docstring"""







						super().setUp()

						# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
						__UpperCamelCase					:	Dict             =      ["""T@@""", """i""", """I""", """R@@""", """r""", """e@@"""]
						__UpperCamelCase					:	Union[str, Any]             =      dict(zip(lowerCAmelCase					,     range(len(lowerCAmelCase	)	)	)	)
						__UpperCamelCase					:	Any             =      ["""#version: 0.2""", """l à</w>"""]
						__UpperCamelCase					:	Any             =      {"""unk_token""": """<unk>"""}

						__UpperCamelCase					:	Any             =      os.path.join(self.tmpdirname					,     VOCAB_FILES_NAMES["""vocab_file"""]	)
						__UpperCamelCase					:	Tuple             =      os.path.join(self.tmpdirname					,     VOCAB_FILES_NAMES["""merges_file"""]	)

						with open(self.vocab_file					,     """w"""					,     encoding="""utf-8"""	) as fp:
											for token in vocab_tokens:
																fp.write(F'''{token} {vocab_tokens[token]}\n'''	)
						with open(self.merges_file					,     """w"""					,     encoding="""utf-8"""	) as fp:
											fp.write("""\n""".join(lowerCAmelCase	)	)






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







						"""simple docstring"""







						kwargs.update(self.special_tokens_map	)
						return PhobertTokenizer.from_pretrained(self.tmpdirname					,     **lowerCAmelCase	)






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







						"""simple docstring"""







						__UpperCamelCase					:	Optional[Any]             =      """Tôi là VinAI Research"""
						__UpperCamelCase					:	int             =      """T<unk> i <unk> <unk> <unk> <unk> <unk> <unk> I Re<unk> e<unk> <unk> <unk> <unk>"""
						return input_text, output_text






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







						"""simple docstring"""







						__UpperCamelCase					:	Tuple             =      PhobertTokenizer(self.vocab_file					,     self.merges_file					,     **self.special_tokens_map	)
						__UpperCamelCase					:	Dict             =      """Tôi là VinAI Research"""
						__UpperCamelCase					:	int             =      """T@@ ô@@ i l@@ à V@@ i@@ n@@ A@@ I R@@ e@@ s@@ e@@ a@@ r@@ c@@ h""".split()
						__UpperCamelCase					:	Optional[Any]             =      tokenizer.tokenize(lowerCAmelCase	)
						print(lowerCAmelCase	)
						self.assertListEqual(lowerCAmelCase					,     lowerCAmelCase	)

						__UpperCamelCase					:	List[Any]             =      tokens + [tokenizer.unk_token]

						__UpperCamelCase					:	Any             =      [4, 3, 5, 3, 3, 3, 3, 3, 3, 6, 7, 9, 3, 9, 3, 3, 3, 3, 3]
						self.assertListEqual(tokenizer.convert_tokens_to_ids(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)






