"""simple docstring"""






from typing import Dict, List, Optional

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


__lowerCAmelCase       :			Tuple   	=logging.get_logger(__name__)


__lowerCAmelCase       :			List[Any]   	={
    """nielsr/canine-s""": 2_0_4_8,
}

# Unicode defines 1,114,112 total “codepoints”
__lowerCAmelCase       :			Tuple   	=1_1_1_4_1_1_2

# Below: Constants defining canonical codepoints for special, pseudo-characters.
# Copied from https://github.com/google-research/language/blob/master/language/canine/special_codepoints.py
__lowerCAmelCase       :			List[str]   	=0

__lowerCAmelCase       :			int   	=0xE000
__lowerCAmelCase       :			Dict   	=0xE001
__lowerCAmelCase       :			Optional[int]   	=0xE002
__lowerCAmelCase       :			str   	=0xE003
__lowerCAmelCase       :			Optional[int]   	=0xE004

# Maps special codepoints to human-readable names.
__lowerCAmelCase       :			Dict[int, str]   	={
    # Special symbols are represented using codepoints values that are valid,
    # but designated as "Private Use", meaning that they will never be assigned
    # characters by the Unicode Consortium, and are thus safe for use here.
    #
    # NOTE: Do *NOT* add any sort of [UNK_CHAR] here. They are explicitly
    # excluded and should fail with a hard error.
    CLS: "[CLS]",
    SEP: "[SEP]",
    BOS: "[BOS]",
    MASK: "[MASK]",
    PAD: "[PAD]",
    RESERVED: "[RESERVED]",
}

# Maps special codepoint human-readable names to their codepoint values.
__lowerCAmelCase       :			Dict[str, int]   	={name: codepoint for codepoint, name in SPECIAL_CODEPOINTS.items()}





class 					_A		(			lowerCAmelCase      ):
      snake_case__     :  Dict				    =      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES

      def __init__(		self						,			__lowerCAmelCase=chr(__lowerCAmelCase		)						,			__lowerCAmelCase=chr(__lowerCAmelCase		)						,			__lowerCAmelCase=chr(__lowerCAmelCase		)						,			__lowerCAmelCase=chr(__lowerCAmelCase		)						,			__lowerCAmelCase=chr(__lowerCAmelCase		)						,			__lowerCAmelCase=chr(__lowerCAmelCase		)						,			__lowerCAmelCase=False						,			__lowerCAmelCase=2048						,			**__lowerCAmelCase						,			):







            """simple docstring"""



            lowercase								=      AddedToken(__lowerCAmelCase						,			lstrip=__lowerCAmelCase						,			rstrip=__lowerCAmelCase		) if isinstance(__lowerCAmelCase						,			__lowerCAmelCase		) else bos_token
            lowercase								=      AddedToken(__lowerCAmelCase						,			lstrip=__lowerCAmelCase						,			rstrip=__lowerCAmelCase		) if isinstance(__lowerCAmelCase						,			__lowerCAmelCase		) else eos_token
            lowercase								=      AddedToken(__lowerCAmelCase						,			lstrip=__lowerCAmelCase						,			rstrip=__lowerCAmelCase		) if isinstance(__lowerCAmelCase						,			__lowerCAmelCase		) else sep_token
            lowercase								=      AddedToken(__lowerCAmelCase						,			lstrip=__lowerCAmelCase						,			rstrip=__lowerCAmelCase		) if isinstance(__lowerCAmelCase						,			__lowerCAmelCase		) else cls_token
            lowercase								=      AddedToken(__lowerCAmelCase						,			lstrip=__lowerCAmelCase						,			rstrip=__lowerCAmelCase		) if isinstance(__lowerCAmelCase						,			__lowerCAmelCase		) else pad_token

            # Mask token behave like a normal word, i.e. include the space before it
            lowercase								=      AddedToken(__lowerCAmelCase						,			lstrip=__lowerCAmelCase						,			rstrip=__lowerCAmelCase		) if isinstance(__lowerCAmelCase						,			__lowerCAmelCase		) else mask_token

            super().__init__(
                bos_token=__lowerCAmelCase						,			eos_token=__lowerCAmelCase						,			sep_token=__lowerCAmelCase						,			cls_token=__lowerCAmelCase						,			pad_token=__lowerCAmelCase						,			mask_token=__lowerCAmelCase						,			add_prefix_space=__lowerCAmelCase						,			model_max_length=__lowerCAmelCase						,			**__lowerCAmelCase						,			)

            # Creates a mapping for looking up the IDs of special symbols.
            lowercase								=      {}
            for codepoint, name in SPECIAL_CODEPOINTS.items():
                  lowercase								=      codepoint

            # Creates a mapping for looking up the string forms of special symbol IDs.
            lowercase								=      {
                codepoint: name for name, codepoint in self._special_codepoints.items()
            }

            lowercase								=      UNICODE_VOCAB_SIZE
            lowercase								=      len(self._special_codepoints		)

      @property
      def 							A__			(		self		):







            """simple docstring"""



            return self._unicode_vocab_size

      def 							A__			(		self						,			__lowerCAmelCase		):







            """simple docstring"""



            return list(__lowerCAmelCase		)

      def 							A__			(		self						,			__lowerCAmelCase		):







            """simple docstring"""



            try:
                  return ord(__lowerCAmelCase		)
            except TypeError:
                  raise ValueError(f'invalid token: \'{token}\''		)

      def 							A__			(		self						,			__lowerCAmelCase		):







            """simple docstring"""



            try:
                  if index in SPECIAL_CODEPOINTS:
                        return SPECIAL_CODEPOINTS[index]
                  return chr(__lowerCAmelCase		)
            except TypeError:
                  raise ValueError(f'invalid id: {index}'		)

      def 							A__			(		self						,			__lowerCAmelCase		):







            """simple docstring"""



            return "".join(__lowerCAmelCase		)

      def 							A__			(		self						,			__lowerCAmelCase						,			__lowerCAmelCase = None		):







            """simple docstring"""



            lowercase								=      [self.sep_token_id]
            lowercase								=      [self.cls_token_id]

            lowercase								=      cls + token_ids_a + sep
            if token_ids_a is not None:
                  result += token_ids_a + sep
            return result

      def 							A__			(		self						,			__lowerCAmelCase						,			__lowerCAmelCase = None						,			__lowerCAmelCase = False		):







            """simple docstring"""



            if already_has_special_tokens:
                  return super().get_special_tokens_mask(
                      token_ids_a=__lowerCAmelCase						,			token_ids_a=__lowerCAmelCase						,			already_has_special_tokens=__lowerCAmelCase		)

            lowercase								=      [1] + ([0] * len(__lowerCAmelCase		)) + [1]
            if token_ids_a is not None:
                  result += ([0] * len(__lowerCAmelCase		)) + [1]
            return result

      def 							A__			(		self						,			__lowerCAmelCase						,			__lowerCAmelCase = None		):







            """simple docstring"""



            lowercase								=      [self.sep_token_id]
            lowercase								=      [self.cls_token_id]

            lowercase								=      len(cls + token_ids_a + sep		) * [0]
            if token_ids_a is not None:
                  result += len(token_ids_a + sep		) * [1]
            return result







      def 							A__			(		self						,			__lowerCAmelCase						,			__lowerCAmelCase = None		):







            """simple docstring"""



            return ()




"""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__":
       __lowerCAmelCase       :			int   	=pd.read_csv("""sample_data.csv""", header=None)
       __lowerCAmelCase       :			Optional[Any]   	=df.shape[:1][0]
       # If you're using some other dataset input the target column
       __lowerCAmelCase       :			Optional[int]   	=df.iloc[:, 1:2]
       __lowerCAmelCase       :			List[str]   	=actual_data.values.reshape(len_data, 1)
       __lowerCAmelCase       :			int   	=MinMaxScaler().fit_transform(actual_data)
       __lowerCAmelCase       :			List[Any]   	=1_0
       __lowerCAmelCase       :			int   	=5
       __lowerCAmelCase       :			str   	=2_0
       __lowerCAmelCase       :			Union[str, Any]   	=len_data - periods * look_back
       __lowerCAmelCase       :			Dict   	=actual_data[:division]
       __lowerCAmelCase       :			List[str]   	=actual_data[division - look_back :]
       __lowerCAmelCase	,	__lowerCAmelCase       :			List[str]   	=[], []
       __lowerCAmelCase	,	__lowerCAmelCase       :			Optional[int]   	=[], []

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

       __lowerCAmelCase       :			Optional[Any]   	=Sequential()
       model.add(LSTM(1_2_8, input_shape=(look_back, 1), return_sequences=True))
       model.add(LSTM(6_4, input_shape=(1_2_8, 1)))
       model.add(Dense(forward_days))
       model.compile(loss="""mean_squared_error""", optimizer="""adam""")
       __lowerCAmelCase       :			Optional[int]   	=model.fit(
           x_train, y_train, epochs=1_5_0, verbose=1, shuffle=True, batch_size=4
       )
       __lowerCAmelCase       :			Dict   	=model.predict(x_test)



