


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    		=       1_0
						__UpperCamelCase    		=       5
						__UpperCamelCase    		=       2_0
						__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(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')
						__UpperCamelCase    		=       model.fit(
						    x_train, y_train, epochs=1_5_0, verbose=1, shuffle=True, batch_size=4
						)
						__UpperCamelCase    		=       model.predict(x_test)







from collections import OrderedDict
from typing import Mapping

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


__UpperCamelCase    		=       logging.get_logger(__name__)

__UpperCamelCase    		=       {
    'facebook/data2vec-text-base': 'https://huggingface.co/data2vec/resolve/main/config.json',
}







class        lowerCamelCase__	(							UpperCAmelCase      ):






			"""simple docstring"""






			_UpperCamelCase		:					int    =		'data2vec-text'




			def __init__(							self      ,						snake_case=30522      ,						snake_case=768      ,						snake_case=12      ,						snake_case=12      ,						snake_case=3072      ,						snake_case="gelu"      ,						snake_case=0.1      ,						snake_case=0.1      ,						snake_case=512      ,						snake_case=2      ,						snake_case=0.02      ,						snake_case=1E-1_2      ,						snake_case=1      ,						snake_case=0      ,						snake_case=2      ,						snake_case="absolute"      ,						snake_case=True      ,						snake_case=None      ,						**snake_case      ,						):

						'''simple docstring'''


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

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







class        lowerCamelCase__	(							UpperCAmelCase      ):






			"""simple docstring"""






			@property
			def 				snake_case__				(							self     ):

						'''simple docstring'''


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


