
"""simple docstring"""






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


_lowercase							=				logging.get_logger(__name__)

_lowercase							=				{
    '''microsoft/trocr-base-handwritten''': (
        '''https://huggingface.co/microsoft/trocr-base-handwritten/resolve/main/config.json'''
    ),
    # See all TrOCR models at https://huggingface.co/models?filter=trocr
}

class 					__a     ( __a       ):



							'''simple docstring'''







							_lowerCamelCase					:			Optional[Any]          =   """trocr"""
							_lowerCamelCase					:			Optional[Any]          =   ["""past_key_values"""]
							_lowerCamelCase					:			int          =   {
							    """num_attention_heads""": """decoder_attention_heads""",
							    """hidden_size""": """d_model""",
							    """num_hidden_layers""": """decoder_layers""",
							}
							def __init__(     self  ,    _lowerCamelCase=50_265  ,    _lowerCamelCase=1_024  ,    _lowerCamelCase=12  ,    _lowerCamelCase=16  ,    _lowerCamelCase=4_096  ,    _lowerCamelCase="gelu"  ,    _lowerCamelCase=512  ,    _lowerCamelCase=0.1  ,    _lowerCamelCase=0.0  ,    _lowerCamelCase=0.0  ,    _lowerCamelCase=2  ,    _lowerCamelCase=0.02  ,    _lowerCamelCase=0.0  ,    _lowerCamelCase=True  ,    _lowerCamelCase=False  ,    _lowerCamelCase=True  ,    _lowerCamelCase=True  ,    _lowerCamelCase=1  ,    _lowerCamelCase=0  ,    _lowerCamelCase=2  ,    **_lowerCamelCase  ,    )			->							Optional[Any]:




										'''simple docstring'''
										__lowercase       =       vocab_size
										__lowercase       =       d_model
										__lowercase       =       decoder_layers
										__lowercase       =       decoder_attention_heads
										__lowercase       =       decoder_ffn_dim
										__lowercase       =       activation_function
										__lowercase       =       max_position_embeddings
										__lowercase       =       dropout
										__lowercase       =       attention_dropout
										__lowercase       =       activation_dropout
										__lowercase       =       init_std
										__lowercase       =       decoder_layerdrop
										__lowercase       =       use_cache
										__lowercase       =       scale_embedding
										__lowercase       =       use_learned_position_embeddings
										__lowercase       =       layernorm_embedding

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







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

					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])
					_lowercase							=				np.array(train_x)
					_lowercase							=				np.array(test_x)
					_lowercase							=				np.array([list(i.ravel()) for i in train_y])
					_lowercase							=				np.array([list(i.ravel()) for i in test_y])

					_lowercase							=				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''')
					_lowercase							=				model.fit(
					    x_train, y_train, epochs=150, verbose=1, shuffle=True, batch_size=4
					)
					_lowercase							=				model.predict(x_test)





