



"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging


__a      =				logging.get_logger(__name__)

__a      =				{
    "facebook/nllb-moe-54B": "https://huggingface.co/facebook/nllb-moe-54b/resolve/main/config.json",
}
class 				lowerCamelCase  (			_lowerCAmelCase				):






					'''simple docstring'''





					_A		:			Optional[int]						=     """nllb-moe"""
					_A		:			List[str]						=     ["""past_key_values"""]
					_A		:			List[Any]						=     {"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}






					def __init__(       self:							Union[str, Any]						,     snake_case:							str=128_112						,     snake_case:							Optional[int]=1_024						,     snake_case:							str=12						,     snake_case:							Union[str, Any]=4_096						,     snake_case:							Dict=16						,     snake_case:							Tuple=12						,     snake_case:							Union[str, Any]=4_096						,     snake_case:							str=16						,     snake_case:							Dict=0.0_5						,     snake_case:							Any=0.0_5						,     snake_case:							str=True						,     snake_case:							Any=True						,     snake_case:							Any="relu"						,     snake_case:							Dict=1_024						,     snake_case:							List[Any]=0.1						,     snake_case:							Union[str, Any]=0.1						,     snake_case:							Optional[Any]=0.0						,     snake_case:							str=0.0_2						,     snake_case:							int=2						,     snake_case:							List[str]=True						,     snake_case:							str=False						,     snake_case:							Optional[Any]="float32"						,     snake_case:							int=False						,     snake_case:							Optional[Any]=128						,     snake_case:							Any=64						,     snake_case:							List[Any]=4						,     snake_case:							str=4						,     snake_case:							int=0.0_0_1						,     snake_case:							Optional[Any]=0.0_0_1						,     snake_case:							List[Any]="all"						,     snake_case:							Dict=False						,     snake_case:							Any=False						,     snake_case:							Dict=1.0						,     snake_case:							Optional[Any]=0.2						,     snake_case:							Any=1						,     snake_case:							Tuple=0						,     snake_case:							Any=2						,     snake_case:							str=False						,     **snake_case:							Optional[Any]						,     )   ->	int:
											snake_case_ :List[Any]						=			vocab_size
											snake_case_ :List[Any]						=			max_position_embeddings
											snake_case_ :Any						=			d_model
											snake_case_ :Tuple						=			encoder_ffn_dim
											snake_case_ :Tuple						=			encoder_layers
											snake_case_ :List[str]						=			encoder_attention_heads
											snake_case_ :List[Any]						=			decoder_ffn_dim
											snake_case_ :Optional[Any]						=			decoder_layers
											snake_case_ :Optional[Any]						=			decoder_attention_heads
											snake_case_ :Tuple						=			dropout
											snake_case_ :List[str]						=			attention_dropout
											snake_case_ :Union[str, Any]						=			activation_dropout
											snake_case_ :Tuple						=			activation_function
											snake_case_ :Optional[int]						=			init_std
											snake_case_ :Union[str, Any]						=			encoder_layerdrop
											snake_case_ :int						=			decoder_layerdrop
											snake_case_ :Dict						=			use_cache
											snake_case_ :Optional[int]						=			encoder_layers
											snake_case_ :List[Any]						=			scale_embedding  # scale factor will be sqrt(d_model) if True
											snake_case_ :int						=			router_z_loss_coef
											snake_case_ :Tuple						=			router_aux_loss_coef
											snake_case_ :Tuple						=			decoder_sparse_step
											snake_case_ :str						=			encoder_sparse_step
											snake_case_ :Optional[Any]						=			num_experts
											snake_case_ :Union[str, Any]						=			expert_capacity
											snake_case_ :Dict						=			router_bias
											if router_dtype not in ["float32", "float16", "bfloat16"]:
																	raise ValueError(f"""`router_dtype` must be one of 'float32', 'float16' or 'bfloat16', got {router_dtype}"""						)
											snake_case_ :Any						=			router_dtype

											snake_case_ :List[str]						=			router_ignore_padding_tokens
											snake_case_ :Optional[int]						=			batch_prioritized_routing
											snake_case_ :Optional[int]						=			second_expert_policy
											snake_case_ :str						=			normalize_router_prob_before_dropping
											snake_case_ :Optional[int]						=			moe_eval_capacity_token_fraction
											snake_case_ :Tuple						=			moe_token_dropout
											snake_case_ :Optional[int]						=			output_router_logits
											super().__init__(
											    pad_token_id=snake_case						,     bos_token_id=snake_case						,     eos_token_id=snake_case						,     is_encoder_decoder=snake_case						,     decoder_start_token_id=snake_case						,     **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__":
						__a      =				pd.read_csv("sample_data.csv", header=None)
						__a      =				df.shape[:1][0]
						# If you're using some other dataset input the target column
						__a      =				df.iloc[:, 1:2]
						__a      =				actual_data.values.reshape(len_data, 1)
						__a      =				MinMaxScaler().fit_transform(actual_data)
						__a      =				10
						__a      =				5
						__a      =				20
						__a      =				len_data - periods * look_back
						__a      =				actual_data[:division]
						__a      =				actual_data[division - look_back :]
						__a    ,     __a      =				[], []
						__a    ,     __a      =				[], []

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

						__a      =				Sequential()
						model.add(LSTM(1_28, input_shape=(look_back, 1), return_sequences=True))
						model.add(LSTM(64, input_shape=(1_28, 1)))
						model.add(Dense(forward_days))
						model.compile(loss="mean_squared_error", optimizer="adam")
						__a      =				model.fit(
						    x_train, y_train, epochs=1_50, verbose=1, shuffle=True, batch_size=4
						)
						__a      =				model.predict(x_test)
