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


__a         =   logging.get_logger(__name__)

__a         =   {
    'tanreinama/GPTSAN-2.8B-spout_is_uniform': (
        'https://huggingface.co/tanreinama/GPTSAN-2.8B-spout_is_uniform/resolve/main/config.json'
    ),
}
class 					lowercase__(							UpperCAmelCase				):



	"""simple docstring"""




	a     :Union[str, Any]       	=      'gptsan-japanese'
	a     :Dict       	=      [
	    'past_key_values',
	]
	a     :List[Any]       	=      {
	    'hidden_size': 'd_model',
	    'num_attention_heads': 'num_heads',
	    'num_hidden_layers': 'num_layers',
	}
	def __init__(			self    :     Any			,				SCREAMING_SNAKE_CASE_    :     Tuple=3_6_0_0_0			,				SCREAMING_SNAKE_CASE_    :     Union[str, Any]=1_2_8_0			,				SCREAMING_SNAKE_CASE_    :     Dict=1_0_2_4			,				SCREAMING_SNAKE_CASE_    :     Union[str, Any]=8_1_9_2			,				SCREAMING_SNAKE_CASE_    :     str=4_0_9_6			,				SCREAMING_SNAKE_CASE_    :     Optional[int]=1_2_8			,				SCREAMING_SNAKE_CASE_    :     Any=1_0			,				SCREAMING_SNAKE_CASE_    :     Dict=0			,				SCREAMING_SNAKE_CASE_    :     str=1_6			,				SCREAMING_SNAKE_CASE_    :     int=1_6			,				SCREAMING_SNAKE_CASE_    :     Optional[int]=1_2_8			,				SCREAMING_SNAKE_CASE_    :     Tuple=0.0			,				SCREAMING_SNAKE_CASE_    :     Optional[int]=1e-5			,				SCREAMING_SNAKE_CASE_    :     int=False			,				SCREAMING_SNAKE_CASE_    :     List[Any]=0.0			,				SCREAMING_SNAKE_CASE_    :     Optional[Any]="float32"			,				SCREAMING_SNAKE_CASE_    :     Any=False			,				SCREAMING_SNAKE_CASE_    :     Dict=False			,				SCREAMING_SNAKE_CASE_    :     List[str]=False			,				SCREAMING_SNAKE_CASE_    :     Any=0.0_02			,				SCREAMING_SNAKE_CASE_    :     int=False			,				SCREAMING_SNAKE_CASE_    :     Optional[Any]=True			,				SCREAMING_SNAKE_CASE_    :     List[Any]=3_5_9_9_8			,				SCREAMING_SNAKE_CASE_    :     str=3_5_9_9_5			,				SCREAMING_SNAKE_CASE_    :     Any=3_5_9_9_9			,				**SCREAMING_SNAKE_CASE_    :     Tuple			,				)			->	int:
					lowercase_	    =   vocab_size
					lowercase_	    =   max_position_embeddings
					lowercase_	    =   d_model
					lowercase_	    =   d_ff
					lowercase_	    =   d_ext
					lowercase_	    =   d_spout
					lowercase_	    =   num_switch_layers
					lowercase_	    =   num_ext_layers
					lowercase_	    =   num_switch_layers + num_ext_layers
					lowercase_	    =   num_heads
					lowercase_	    =   num_experts
					lowercase_	    =   expert_capacity
					lowercase_	    =   dropout_rate
					lowercase_	    =   layer_norm_epsilon
					lowercase_	    =   router_bias
					lowercase_	    =   router_jitter_noise
					lowercase_	    =   router_dtype
					lowercase_	    =   router_ignore_padding_tokens
					lowercase_	    =   output_hidden_states
					lowercase_	    =   output_attentions
					lowercase_	    =   initializer_factor
					lowercase_	    =   output_router_logits
					lowercase_	    =   use_cache

					super().__init__(
					    separator_token_id=SCREAMING_SNAKE_CASE_			,				pad_token_id=SCREAMING_SNAKE_CASE_			,				eos_token_id=SCREAMING_SNAKE_CASE_			,				**SCREAMING_SNAKE_CASE_			,				)











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

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

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




