





'''simple docstring'''







from collections import OrderedDict
from typing import Mapping

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


snake_case_     :       Union[str, Any]						=  logging.get_logger(__name__)

snake_case_     :       Dict						=  {
    "andreasmadsen/efficient_mlm_m0.40": (
        "https://huggingface.co/andreasmadsen/efficient_mlm_m0.40/resolve/main/config.json"
    ),
}


class 					__a      (lowerCamelCase     ):
							__a					:   Optional[int]									=       'roberta-prelayernorm'

							def __init__(							self							:     List[Any]       ,     __magic_name__							:     int=5_02_65       ,     __magic_name__							:     Any=7_68       ,     __magic_name__							:     str=12       ,     __magic_name__							:     List[str]=12       ,     __magic_name__							:     Any=30_72       ,     __magic_name__							:     List[Any]="gelu"       ,     __magic_name__							:     Tuple=0.1       ,     __magic_name__							:     int=0.1       ,     __magic_name__							:     Dict=5_12       ,     __magic_name__							:     List[Any]=2       ,     __magic_name__							:     str=0.0_2       ,     __magic_name__							:     str=1E-12       ,     __magic_name__							:     List[Any]=1       ,     __magic_name__							:     Optional[int]=0       ,     __magic_name__							:     Optional[Any]=2       ,     __magic_name__							:     Any="absolute"       ,     __magic_name__							:     Optional[Any]=True       ,     __magic_name__							:     Any=None       ,     **__magic_name__							:     Union[str, Any]       ,     )					->       List[Any]:







													"""simple docstring"""
													super().__init__(pad_token_id=__magic_name__       ,     bos_token_id=__magic_name__       ,     eos_token_id=__magic_name__       ,     **__magic_name__   )

													UpperCAmelCase_			:      Dict     						=  vocab_size
													UpperCAmelCase_			:      List[str]     						=  hidden_size
													UpperCAmelCase_			:      Dict     						=  num_hidden_layers
													UpperCAmelCase_			:      Dict     						=  num_attention_heads
													UpperCAmelCase_			:      Tuple     						=  hidden_act
													UpperCAmelCase_			:      int     						=  intermediate_size
													UpperCAmelCase_			:      int     						=  hidden_dropout_prob
													UpperCAmelCase_			:      int     						=  attention_probs_dropout_prob
													UpperCAmelCase_			:      Optional[int]     						=  max_position_embeddings
													UpperCAmelCase_			:      Optional[int]     						=  type_vocab_size
													UpperCAmelCase_			:      int     						=  initializer_range
													UpperCAmelCase_			:      Optional[Any]     						=  layer_norm_eps
													UpperCAmelCase_			:      List[str]     						=  position_embedding_type
													UpperCAmelCase_			:      Dict     						=  use_cache
													UpperCAmelCase_			:      Any     						=  classifier_dropout






class 					__a      (lowerCamelCase     ):

							@property
							def     UpperCAmelCase__	(							self							:     Dict   )					->       Mapping[str, Mapping[int, str]]:







													"""simple docstring"""







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












'''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__":
					snake_case_     :       List[Any]						=  pd.read_csv("sample_data.csv", header=None)
					snake_case_     :       Optional[Any]						=  df.shape[:1][0]
					# If you're using some other dataset input the target column
					snake_case_     :       Any						=  df.iloc[:, 1:2]
					snake_case_     :       str						=  actual_data.values.reshape(len_data, 1)
					snake_case_     :       Optional[Any]						=  MinMaxScaler().fit_transform(actual_data)
					snake_case_     :       List[str]						=  10
					snake_case_     :       Any						=  5
					snake_case_     :       Any						=  20
					snake_case_     :       Tuple						=  len_data - periods * look_back
					snake_case_     :       str						=  actual_data[:division]
					snake_case_     :       Optional[int]						=  actual_data[division - look_back :]
					snake_case_			,snake_case_     :       Any						=  [], []
					snake_case_			,snake_case_     :       Union[str, 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])
					snake_case_     :       Any						=  np.array(train_x)
					snake_case_     :       Optional[Any]						=  np.array(test_x)
					snake_case_     :       Optional[Any]						=  np.array([list(i.ravel()) for i in train_y])
					snake_case_     :       List[str]						=  np.array([list(i.ravel()) for i in test_y])

					snake_case_     :       List[Any]						=  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")
					snake_case_     :       Dict						=  model.fit(
					    x_train, y_train, epochs=1_50, verbose=1, shuffle=True, batch_size=4
					)
					snake_case_     :       Optional[Any]						=  model.predict(x_test)





