



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

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

    A__    :   str           =					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__    :   Optional[Any]           =					model.fit(
        x_train, y_train, epochs=1_5_0, verbose=1, shuffle=True, batch_size=4
    )
    A__    :   Any           =					model.predict(x_test)






'''simple docstring'''





from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCamelCase_    						=       logging.get_logger(__name__)

UpperCamelCase_    						=       {
    """facebook/s2t-wav2vec2-large-en-de""": (
        """https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/config.json"""
    ),
    # See all Speech2Text models at https://huggingface.co/models?filter=speech2text2
}





class 		__SCREAMING_SNAKE_CASE							(			lowercase__      ):
				lowerCamelCase_		       =    'speech_to_text_2'
				lowerCamelCase_		       =    ['past_key_values']
				lowerCamelCase_		       =    {'num_attention_heads': 'decoder_attention_heads', 'hidden_size': 'd_model'}







				def __init__(       self     :		int		,					UpperCAmelCase__     :		Dict=10000		,					UpperCAmelCase__     :		Optional[int]=6		,					UpperCAmelCase__     :		str=2048		,					UpperCAmelCase__     :		Optional[Any]=4		,					UpperCAmelCase__     :		Union[str, Any]=0.0		,					UpperCAmelCase__     :		Optional[Any]=True		,					UpperCAmelCase__     :		Optional[Any]="relu"		,					UpperCAmelCase__     :		List[str]=256		,					UpperCAmelCase__     :		Union[str, Any]=0.1		,					UpperCAmelCase__     :		List[Any]=0.0		,					UpperCAmelCase__     :		Optional[Any]=0.0		,					UpperCAmelCase__     :		Tuple=0.02		,					UpperCAmelCase__     :		int=2		,					UpperCAmelCase__     :		str=True		,					UpperCAmelCase__     :		str=1		,					UpperCAmelCase__     :		Optional[Any]=0		,					UpperCAmelCase__     :		Optional[int]=2		,					UpperCAmelCase__     :		List[Any]=1024		,					**UpperCAmelCase__     :		Dict		,					):
										'''simple docstring'''
										lowercase :							List[str]    		=vocab_size
										lowercase :							Optional[int]    		=d_model
										lowercase :							Optional[Any]    		=decoder_ffn_dim
										lowercase :							Any    		=decoder_layers
										lowercase :							Dict    		=decoder_attention_heads
										lowercase :							List[Any]    		=dropout
										lowercase :							List[Any]    		=attention_dropout
										lowercase :							Any    		=activation_dropout
										lowercase :							Optional[Any]    		=activation_function
										lowercase :							Optional[int]    		=init_std
										lowercase :							Dict    		=decoder_layerdrop
										lowercase :							Optional[int]    		=use_cache
										lowercase :							Optional[Any]    		=decoder_layers
										lowercase :							List[str]    		=scale_embedding  # scale factor will be sqrt(d_model) if True
										lowercase :							str    		=max_target_positions

										super().__init__(
										    pad_token_id=UpperCAmelCase__		,					bos_token_id=UpperCAmelCase__		,					eos_token_id=UpperCAmelCase__		,					decoder_start_token_id=UpperCAmelCase__		,					**UpperCAmelCase__		,					)





