





'''simple docstring'''



import json
import os
from typing import Optional, Tuple

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


__magic_name__   :						Optional[int]    =			logging.get_logger(__name__)

__magic_name__   :						List[str]    =			{"""vocab_file""": """vocab.json"""}

__magic_name__   :						Union[str, Any]    =			{
    """vocab_file""": {
        """mgp-str""": """https://huggingface.co/alibaba-damo/mgp-str-base/blob/main/vocab.json""",
    }
}

__magic_name__   :						Optional[Any]    =			{"""mgp-str""": 27}


class 					__SCREAMING_SNAKE_CASE	(						_UpperCAmelCase			):



     '''simple docstring'''






     UpperCAmelCase__       :						Tuple      =   VOCAB_FILES_NAMES
     UpperCAmelCase__       :						Optional[Any]      =   PRETRAINED_VOCAB_FILES_MAP
     UpperCAmelCase__       :						Optional[int]      =   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES

     def __init__(					self						,   lowerCamelCase						,   lowerCamelCase="[GO]"						,   lowerCamelCase="[GO]"						,   lowerCamelCase="[s]"						,   lowerCamelCase="[GO]"						,   **lowerCamelCase	):
       super().__init__(
           unk_token=lowercase__						,   bos_token=lowercase__						,   eos_token=lowercase__						,   pad_token=lowercase__						,   **lowercase__						,   )

       with open(lowercase__						,   encoding="utf-8"	) as vocab_handle:
         _snake_case     		=				json.load(lowercase__	)
       _snake_case     		=				{v: k for k, v in self.vocab.items()}

     @property
     def 	UpperCamelCase(					self	):
       return len(self.vocab	)

     def 	UpperCamelCase(					self	):
       return dict(self.vocab						,   **self.added_tokens_encoder	)

     def 	UpperCamelCase(					self						,   lowerCamelCase	):
       _snake_case     		=				[]
       for s in text:
         char_tokens.extend(lowercase__	)
       return char_tokens

     def 	UpperCamelCase(					self						,   lowerCamelCase	):
       return self.vocab.get(lowercase__						,   self.vocab.get(self.unk_token	)	)

     def 	UpperCamelCase(					self						,   lowerCamelCase	):
       return self.decoder.get(lowercase__	)






     def 	UpperCamelCase(					self						,   lowerCamelCase						,   lowerCamelCase = None	):
       if not os.path.isdir(lowercase__	):
         logger.error("Vocabulary path ({}) should be a directory".format(lowercase__	)	)
         return
       _snake_case     		=				os.path.join(
           lowercase__						,   (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]	)

       with open(lowercase__						,   "w"						,   encoding="utf-8"	) as f:
         f.write(json.dumps(self.vocab						,   indent=2						,   sort_keys=lowercase__						,   ensure_ascii=lowercase__	) + "\n"	)

       return (vocab_file,)









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

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

 __magic_name__   :						Tuple    =			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""")
 __magic_name__   :						int    =			model.fit(
     x_train, y_train, epochs=150, verbose=1, shuffle=True, batch_size=4
 )
 __magic_name__   :						int    =			model.predict(x_test)


