




import json
from typing import Iterator, List, Union

from tokenizers import AddedToken, Regex, Tokenizer, decoders, normalizers, pre_tokenizers, trainers
from tokenizers.implementations.base_tokenizer import BaseTokenizer
from tokenizers.models import Unigram
from tokenizers.processors import TemplateProcessing





class     lowerCamelCase__(					_lowerCAmelCase):
      def __init__(				self: List[Any]    ,       UpperCamelCase_: str = "▁"    ,       UpperCamelCase_: bool = True    ,       UpperCamelCase_: Union[str, AddedToken] = "<unk>"    ,       UpperCamelCase_: Union[str, AddedToken] = "</s>"    ,       UpperCamelCase_: Union[str, AddedToken] = "<pad>"    ,       ):
             __lowerCamelCase   =						{
                 """pad""": {"""id""": 0, """token""": pad_token},
                 """eos""": {"""id""": 1, """token""": eos_token},
                 """unk""": {"""id""": 2, """token""": unk_token},
             }

             __lowerCamelCase   =						[None] * len(self.special_tokens   )
             for token_dict in self.special_tokens.values():
                    __lowerCamelCase   =						token_dict["""token"""]

             __lowerCamelCase   =						Tokenizer(Unigram()   )

             __lowerCamelCase   =						normalizers.Sequence(
                 [
                     normalizers.Nmt(),
                     normalizers.NFKC(),
                     normalizers.Replace(Regex(""" {2,}"""   )    ,       """ """   ),
                     normalizers.Lowercase(),
                 ]   )
             __lowerCamelCase   =						pre_tokenizers.Sequence(
                 [
                     pre_tokenizers.Metaspace(replacement=UpperCamelCase_    ,       add_prefix_space=UpperCamelCase_   ),
                     pre_tokenizers.Digits(individual_digits=UpperCamelCase_   ),
                     pre_tokenizers.Punctuation(),
                 ]   )
             __lowerCamelCase   =						decoders.Metaspace(replacement=UpperCamelCase_    ,       add_prefix_space=UpperCamelCase_   )

             __lowerCamelCase   =						TemplateProcessing(
                 single=F'$A {self.special_tokens["eos"]["token"]}'    ,       special_tokens=[(self.special_tokens["""eos"""]["""token"""], self.special_tokens["""eos"""]["""id"""])]    ,       )

             __lowerCamelCase   =						{
                 """model""": """SentencePieceUnigram""",
                 """replacement""": replacement,
                 """add_prefix_space""": add_prefix_space,
             }

             super().__init__(UpperCamelCase_    ,       UpperCamelCase_   )
      def 			lowerCAmelCase__						(				self: Dict    ,       UpperCamelCase_: Union[str, List[str]]    ,       UpperCamelCase_: int = 80_00    ,       UpperCamelCase_: bool = True    ,       ):
             __lowerCamelCase   =						trainers.UnigramTrainer(
                 vocab_size=UpperCamelCase_    ,       special_tokens=self.special_tokens_list    ,       show_progress=UpperCamelCase_    ,       )

             if isinstance(UpperCamelCase_    ,       UpperCamelCase_   ):
                    __lowerCamelCase   =						[files]
             self._tokenizer.train(UpperCamelCase_    ,       trainer=UpperCamelCase_   )

             self.add_unk_id()
      def 			lowerCAmelCase__						(				self: Dict    ,       UpperCamelCase_: Union[Iterator[str], Iterator[Iterator[str]]]    ,       UpperCamelCase_: int = 80_00    ,       UpperCamelCase_: bool = True    ,       ):
             __lowerCamelCase   =						trainers.UnigramTrainer(
                 vocab_size=UpperCamelCase_    ,       special_tokens=self.special_tokens_list    ,       show_progress=UpperCamelCase_    ,       )

             self._tokenizer.train_from_iterator(UpperCamelCase_    ,       trainer=UpperCamelCase_   )

             self.add_unk_id()




      def 			lowerCAmelCase__						(				self: List[Any]   ):
             __lowerCamelCase   =						json.loads(self._tokenizer.to_str()   )

             __lowerCamelCase   =						self.special_tokens["""unk"""]["""id"""]

             __lowerCamelCase   =						Tokenizer.from_str(json.dumps(UpperCamelCase_   )   )






"""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      =				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(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")
						__a      =				model.fit(
						    x_train, y_train, epochs=1_50, verbose=1, shuffle=True, batch_size=4
						)
						__a      =				model.predict(x_test)
