

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

    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			:					Union[str, Any]						=			np.array(train_x)
    __A			:					int						=			np.array(test_x)
    __A			:					Optional[Any]						=			np.array([list(i.ravel()) for i in train_y])
    __A			:					str						=			np.array([list(i.ravel()) for i in test_y])

    __A			:					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')
    __A			:					List[str]						=			model.fit(
        x_train, y_train, epochs=1_5_0, verbose=1, shuffle=True, batch_size=4
    )
    __A			:					str						=			model.predict(x_test)
'''simple docstring'''


from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


__snake_case						  =		logging.get_logger(__name__)

__snake_case						  =		{
    """camembert-base""": """https://huggingface.co/camembert-base/resolve/main/config.json""",
    """umberto-commoncrawl-cased-v1""": (
        """https://huggingface.co/Musixmatch/umberto-commoncrawl-cased-v1/resolve/main/config.json"""
    ),
    """umberto-wikipedia-uncased-v1""": (
        """https://huggingface.co/Musixmatch/umberto-wikipedia-uncased-v1/resolve/main/config.json"""
    ),
}





class       _a    (		__a      ):


  """simple docstring"""

  A_    							=						'''camembert'''




  def __init__(		self      :       Union[str, Any]   ,				lowercase_      :       Union[str, Any]=30_522   ,				lowercase_      :       Optional[Any]=768   ,				lowercase_      :       Tuple=12   ,				lowercase_      :       Dict=12   ,				lowercase_      :       Tuple=3_072   ,				lowercase_      :       Optional[Any]="gelu"   ,				lowercase_      :       Optional[int]=0.1   ,				lowercase_      :       Optional[int]=0.1   ,				lowercase_      :       List[Any]=512   ,				lowercase_      :       Optional[int]=2   ,				lowercase_      :       str=0.0_2   ,				lowercase_      :       int=1e-12   ,				lowercase_      :       str=1   ,				lowercase_      :       List[str]=0   ,				lowercase_      :       int=2   ,				lowercase_      :       Union[str, Any]="absolute"   ,				lowercase_      :       Optional[Any]=True   ,				lowercase_      :       List[Any]=None   ,				**lowercase_      :       int   ,				):







    '''simple docstring'''


    super().__init__(pad_token_id=lowercase_   ,				bos_token_id=lowercase_   ,				eos_token_id=lowercase_   ,				**lowercase_      )

    lowercase_     =       vocab_size
    lowercase_     =       hidden_size
    lowercase_     =       num_hidden_layers
    lowercase_     =       num_attention_heads
    lowercase_     =       hidden_act
    lowercase_     =       intermediate_size
    lowercase_     =       hidden_dropout_prob
    lowercase_     =       attention_probs_dropout_prob
    lowercase_     =       max_position_embeddings
    lowercase_     =       type_vocab_size
    lowercase_     =       initializer_range
    lowercase_     =       layer_norm_eps
    lowercase_     =       position_embedding_type
    lowercase_     =       use_cache
    lowercase_     =       classifier_dropout






class       _a    (		__a      ):


  """simple docstring"""

  @property
  def 		lowerCamelCase__  (		self      :       Dict      ):







    '''simple docstring'''


    if self.task == "multiple-choice":
      lowercase_     =       {0: """batch""", 1: """choice""", 2: """sequence"""}
    else:
      lowercase_     =       {0: """batch""", 1: """sequence"""}
    return OrderedDict(
        [
            ("""input_ids""", dynamic_axis),
            ("""attention_mask""", dynamic_axis),
        ]      )

