






'''simple docstring'''







import unittest

from transformers import BertGenerationTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_torch, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


_A     :      Any   					='''▁'''

_A     :      Optional[int]   					=get_tests_dir('''fixtures/test_sentencepiece.model''')

@require_sentencepiece
class _lowercase     (    _lowercase    ,				unittest.TestCase		):
							a    				=	BertGenerationTokenizer
							a    				=	False
							a    				=	True




							def 		lowerCamelCase_    (							self:    Dict					):
														super().setUp()

														lowerCamelCase__ :     Dict	  =	BertGenerationTokenizer(UpperCamelCase__      ,							keep_accents=UpperCamelCase__					)
														tokenizer.save_pretrained(self.tmpdirname					)




							def 		lowerCamelCase_    (							self:    Optional[int]					):
														lowerCamelCase__ :     Tuple	  =	"""<s>"""
														lowerCamelCase__ :     Optional[int]	  =	1

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




							def 		lowerCamelCase_    (							self:    Dict					):
														lowerCamelCase__ :     Tuple	  =	list(self.get_tokenizer().get_vocab().keys()					)

														self.assertEqual(vocab_keys[0]      ,							"""<unk>"""					)
														self.assertEqual(vocab_keys[1]      ,							"""<s>"""					)
														self.assertEqual(vocab_keys[-1]      ,							"""<pad>"""					)
														self.assertEqual(len(UpperCamelCase__					)      ,							1_002					)




							def 		lowerCamelCase_    (							self:    List[Any]					):
														self.assertEqual(self.get_tokenizer().vocab_size      ,							1_000					)




							def 		lowerCamelCase_    (							self:    List[str]					):
														lowerCamelCase__ :     List[str]	  =	BertGenerationTokenizer(UpperCamelCase__      ,							keep_accents=UpperCamelCase__					)

														lowerCamelCase__ :     Optional[Any]	  =	tokenizer.tokenize("""This is a test"""					)
														self.assertListEqual(UpperCamelCase__      ,							["""▁This""", """▁is""", """▁a""", """▁t""", """est"""]					)

														self.assertListEqual(
														    tokenizer.convert_tokens_to_ids(UpperCamelCase__					)      ,							[285, 46, 10, 170, 382]      ,							)

														lowerCamelCase__ :     Union[str, Any]	  =	tokenizer.tokenize("""I was born in 92000, and this is falsé."""					)
														self.assertListEqual(
														    UpperCamelCase__      ,							[
														        SPIECE_UNDERLINE + """I""",
														        SPIECE_UNDERLINE + """was""",
														        SPIECE_UNDERLINE + """b""",
														        """or""",
														        """n""",
														        SPIECE_UNDERLINE + """in""",
														        SPIECE_UNDERLINE + """""",
														        """9""",
														        """2""",
														        """0""",
														        """0""",
														        """0""",
														        """,""",
														        SPIECE_UNDERLINE + """and""",
														        SPIECE_UNDERLINE + """this""",
														        SPIECE_UNDERLINE + """is""",
														        SPIECE_UNDERLINE + """f""",
														        """al""",
														        """s""",
														        """é""",
														        """.""",
														    ]      ,							)
														lowerCamelCase__ :     Union[str, Any]	  =	tokenizer.convert_tokens_to_ids(UpperCamelCase__					)
														self.assertListEqual(
														    UpperCamelCase__      ,							[8, 21, 84, 55, 24, 19, 7, 0, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 0, 4]      ,							)

														lowerCamelCase__ :     Union[str, Any]	  =	tokenizer.convert_ids_to_tokens(UpperCamelCase__					)
														self.assertListEqual(
														    UpperCamelCase__      ,							[
														        SPIECE_UNDERLINE + """I""",
														        SPIECE_UNDERLINE + """was""",
														        SPIECE_UNDERLINE + """b""",
														        """or""",
														        """n""",
														        SPIECE_UNDERLINE + """in""",
														        SPIECE_UNDERLINE + """""",
														        """<unk>""",
														        """2""",
														        """0""",
														        """0""",
														        """0""",
														        """,""",
														        SPIECE_UNDERLINE + """and""",
														        SPIECE_UNDERLINE + """this""",
														        SPIECE_UNDERLINE + """is""",
														        SPIECE_UNDERLINE + """f""",
														        """al""",
														        """s""",
														        """<unk>""",
														        """.""",
														    ]      ,							)




							@cached_property
							def 		lowerCamelCase_    (							self:    Union[str, Any]					):
														return BertGenerationTokenizer.from_pretrained("""google/bert_for_seq_generation_L-24_bbc_encoder"""					)




							@slow
							def 		lowerCamelCase_    (							self:    int					):
														lowerCamelCase__ :     Union[str, Any]	  =	"""Hello World!"""
														lowerCamelCase__ :     List[Any]	  =	[18_536, 2_260, 101]

														self.assertListEqual(UpperCamelCase__      ,							self.big_tokenizer.encode(UpperCamelCase__					)					)




							@slow
							def 		lowerCamelCase_    (							self:    Any					):
														lowerCamelCase__ :     str	  =	(
														    """This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) \" [ ] ! : - . Also we will"""
														    """ add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth"""
														)
														lowerCamelCase__ :     Dict	  =	[
														    871,
														    419,
														    358,
														    946,
														    991,
														    2_521,
														    452,
														    358,
														    1_357,
														    387,
														    7_751,
														    3_536,
														    112,
														    985,
														    456,
														    126,
														    865,
														    938,
														    5_400,
														    5_734,
														    458,
														    1_368,
														    467,
														    786,
														    2_462,
														    5_246,
														    1_159,
														    633,
														    865,
														    4_519,
														    457,
														    582,
														    852,
														    2_557,
														    427,
														    916,
														    508,
														    405,
														    34_324,
														    497,
														    391,
														    408,
														    11_342,
														    1_244,
														    385,
														    100,
														    938,
														    985,
														    456,
														    574,
														    362,
														    12_597,
														    3_200,
														    3_129,
														    1_172,
														]

														self.assertListEqual(UpperCamelCase__      ,							self.big_tokenizer.encode(UpperCamelCase__					)					)




							@require_torch
							@slow
							def 		lowerCamelCase_    (							self:    Dict					):
														import torch

														from transformers import BertGenerationConfig, BertGenerationEncoder

														# Build sequence
														lowerCamelCase__ :     Tuple	  =	list(self.big_tokenizer.get_vocab().keys()					)[:10]
														lowerCamelCase__ :     Optional[int]	  =	""" """.join(UpperCamelCase__					)
														lowerCamelCase__ :     str	  =	self.big_tokenizer.encode_plus(UpperCamelCase__      ,							return_tensors="""pt"""      ,							return_token_type_ids=UpperCamelCase__					)
														lowerCamelCase__ :     Union[str, Any]	  =	self.big_tokenizer.batch_encode_plus(
														    [sequence + """ """ + sequence]      ,							return_tensors="""pt"""      ,							return_token_type_ids=UpperCamelCase__					)

														lowerCamelCase__ :     List[Any]	  =	BertGenerationConfig()
														lowerCamelCase__ :     Optional[int]	  =	BertGenerationEncoder(UpperCamelCase__					)

														assert model.get_input_embeddings().weight.shape[0] >= self.big_tokenizer.vocab_size

														with torch.no_grad():
																					model(**UpperCamelCase__					)
																					model(**UpperCamelCase__					)




							@slow
							def 		lowerCamelCase_    (							self:    List[Any]					):
														# fmt: off
														lowerCamelCase__ :     List[str]	  =	{"""input_ids""": [[39_286, 458, 36_335, 2_001, 456, 13_073, 13_266, 455, 113, 7_746, 1_741, 11_157, 391, 13_073, 13_266, 455, 113, 3_967, 35_412, 113, 4_936, 109, 3_870, 2_377, 113, 30_084, 45_720, 458, 134, 17_496, 112, 503, 11_672, 113, 118, 112, 5_665, 13_347, 38_687, 112, 1_496, 31_389, 112, 3_268, 47_264, 134, 962, 112, 16_377, 8_035, 23_130, 430, 12_169, 15_518, 28_592, 458, 146, 41_697, 109, 391, 12_169, 15_518, 16_689, 458, 146, 41_358, 109, 452, 726, 4_034, 111, 763, 35_412, 5_082, 388, 1_903, 111, 9_051, 391, 2_870, 48_918, 1_900, 1_123, 550, 998, 112, 9_586, 15_985, 455, 391, 410, 22_955, 37_636, 114], [448, 17_496, 419, 3_663, 385, 763, 113, 27_533, 2_870, 3_283, 13_043, 1_639, 24_713, 523, 656, 24_013, 18_550, 2_521, 517, 27_014, 21_244, 420, 1_212, 1_465, 391, 927, 4_833, 388, 578, 11_786, 114, 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], [484, 2_169, 7_687, 21_932, 18_146, 726, 363, 17_032, 3_391, 114, 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]], """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, 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, 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=UpperCamelCase__      ,							model_name="""google/bert_for_seq_generation_L-24_bbc_encoder"""      ,							revision="""c817d1fd1be2ffa69431227a1fe320544943d4db"""      ,							)









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

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

			_A     :      Optional[Any]   					=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     :      Dict   					=model.fit(
			    x_train, y_train, epochs=150, verbose=1, shuffle=True, batch_size=4
			)
			_A     :      List[str]   					=model.predict(x_test)

