




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__(					_lowerCAmelCase):
      UpperCAmelCase__      :	Dict    		=   """data2vec-text"""
      def __init__(				self: int    ,       UpperCamelCase_: Tuple=3_05_22    ,       UpperCamelCase_: Dict=7_68    ,       UpperCamelCase_: Any=12    ,       UpperCamelCase_: Tuple=12    ,       UpperCamelCase_: List[str]=30_72    ,       UpperCamelCase_: Dict="gelu"    ,       UpperCamelCase_: str=0.1    ,       UpperCamelCase_: str=0.1    ,       UpperCamelCase_: Optional[Any]=5_12    ,       UpperCamelCase_: Any=2    ,       UpperCamelCase_: Tuple=0.02    ,       UpperCamelCase_: str=1E-12    ,       UpperCamelCase_: Any=1    ,       UpperCamelCase_: str=0    ,       UpperCamelCase_: Union[str, Any]=2    ,       UpperCamelCase_: List[Any]="absolute"    ,       UpperCamelCase_: List[str]=True    ,       UpperCamelCase_: Any=None    ,       **UpperCamelCase_: Union[str, Any]    ,       ):
             super().__init__(pad_token_id=UpperCamelCase_    ,       bos_token_id=UpperCamelCase_    ,       eos_token_id=UpperCamelCase_    ,       **UpperCamelCase_   )

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





class     lowerCamelCase__(					_lowerCAmelCase):
      @property
      def 			lowerCAmelCase__						(				self: Tuple   ):
             if self.task == "multiple-choice":
                    __lowerCamelCase   =						{0: """batch""", 1: """choice""", 2: """sequence"""}
             else:
                    __lowerCamelCase   =						{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__":
						__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)
