
"""simple docstring"""




from typing import Callable, Dict, Optional, Tuple

import torch
from torch import nn
from torch.distributions import (
    AffineTransform,
    Distribution,
    Independent,
    NegativeBinomial,
    Normal,
    StudentT,
    TransformedDistribution,
)


class       __magic_name__							(							SCREAMING_SNAKE_CASE__			):
       def __init__(  self			,					A_			,					A_=None			,					A_=None			,					A_=0							)  ->						Optional[int]:





         """simple docstring"""




         _lowercase:      Tuple   			=  1.0 if scale is None else scale
         _lowercase:      int   			=  0.0 if loc is None else loc

         super().__init__(__lowerCAmelCase			,					[AffineTransform(loc=self.loc			,					scale=self.scale			,					event_dim=__lowerCAmelCase							)]							)
       @property
       def 				lowercase_				(  self							)  ->						List[Any]:





         """simple docstring"""




         return self.base_dist.mean * self.scale + self.loc
       @property
       def 				lowercase_				(  self							)  ->						Optional[Any]:





         """simple docstring"""




         return self.base_dist.variance * self.scale**2



       @property
       def 				lowercase_				(  self							)  ->						Optional[Any]:





         """simple docstring"""




         return self.variance.sqrt()


class       __magic_name__							(							nn.Module			):
       def __init__(  self			,					A_			,					A_			,					A_			,					**A_							)  ->						Optional[int]:





         """simple docstring"""




         super().__init__(**__lowerCAmelCase							)
         _lowercase:      Optional[int]   			=  args_dim
         _lowercase:      List[str]   			=  nn.ModuleList([nn.Linear(__lowerCAmelCase			,					__lowerCAmelCase							) for dim in args_dim.values()]							)
         _lowercase:      Tuple   			=  domain_map



       def 				lowercase_				(  self			,					A_							)  ->						str:





         """simple docstring"""




         _lowercase:      Dict   			=  [proj(__lowerCAmelCase							) for proj in self.proj]

         return self.domain_map(*__lowerCAmelCase							)


class       __magic_name__							(							nn.Module			):
       def __init__(  self			,					A_							)  ->						Optional[int]:





         """simple docstring"""




         super().__init__()
         _lowercase:      int   			=  function



       def 				lowercase_				(  self			,					A_			,					*A_							)  ->						List[str]:





         """simple docstring"""




         return self.function(__lowerCAmelCase			,					*__lowerCAmelCase							)


class       __magic_name__							:
       UpperCamelCase_ =		42
       UpperCamelCase_ =		42
       UpperCamelCase_ =		42
       def __init__(  self			,					A_ = 1							)  ->						Optional[Any]:





         """simple docstring"""




         _lowercase:      str   			=  dim
         _lowercase:      str   			=  {k: dim * self.args_dim[k] for k in self.args_dim}
       def 				lowercase_				(  self			,					A_							)  ->						Tuple:





         """simple docstring"""




         if self.dim == 1:
           return self.distribution_class(*__lowerCAmelCase							)
         else:
           return Independent(self.distribution_class(*__lowerCAmelCase							)			,					1							)
       def 				lowercase_				(  self			,					A_			,					A_ = None			,					A_ = None			,					)  ->						Any:





         """simple docstring"""




         _lowercase:      int   			=  self._base_distribution(__lowerCAmelCase							)
         if loc is None and scale is None:
           return distr
         else:
           return AffineTransformed(__lowerCAmelCase			,					loc=__lowerCAmelCase			,					scale=__lowerCAmelCase			,					event_dim=self.event_dim							)
       @property
       def 				lowercase_				(  self							)  ->						Optional[int]:





         """simple docstring"""




         return () if self.dim == 1 else (self.dim,)
       @property
       def 				lowercase_				(  self							)  ->						List[str]:





         """simple docstring"""




         return len(self.event_shape							)
       @property
       def 				lowercase_				(  self							)  ->						int:





         """simple docstring"""




         return 0.0
       def 				lowercase_				(  self			,					A_							)  ->						Optional[Any]:





         """simple docstring"""




         return ParameterProjection(
             in_features=__lowerCAmelCase			,					args_dim=self.args_dim			,					domain_map=LambdaLayer(self.domain_map							)			,					)
       def 				lowercase_				(  self			,					*A_							)  ->						Any:





         """simple docstring"""




         raise NotImplementedError()



       @staticmethod
       def 				lowercase_				(  A_							)  ->						Dict:





         """simple docstring"""




         return (x + torch.sqrt(torch.square(__lowerCAmelCase							) + 4.0							)) / 2.0


class       __magic_name__							(							SCREAMING_SNAKE_CASE__			):
       UpperCamelCase_ =		{"df": 1, "loc": 1, "scale": 1}
       UpperCamelCase_ =		StudentT
       @classmethod
       def 				lowercase_				(  cls			,					A_			,					A_			,					A_							)  ->						Optional[Any]:





         """simple docstring"""




         _lowercase:      Dict   			=  cls.squareplus(__lowerCAmelCase							).clamp_min(torch.finfo(scale.dtype							).eps							)
         _lowercase:      Any   			=  2.0 + cls.squareplus(__lowerCAmelCase							)
         return df.squeeze(-1							), loc.squeeze(-1							), scale.squeeze(-1							)


class       __magic_name__							(							SCREAMING_SNAKE_CASE__			):
       UpperCamelCase_ =		{"loc": 1, "scale": 1}
       UpperCamelCase_ =		Normal
       @classmethod
       def 				lowercase_				(  cls			,					A_			,					A_							)  ->						Dict:





         """simple docstring"""




         _lowercase:      str   			=  cls.squareplus(__lowerCAmelCase							).clamp_min(torch.finfo(scale.dtype							).eps							)
         return loc.squeeze(-1							), scale.squeeze(-1							)


class       __magic_name__							(							SCREAMING_SNAKE_CASE__			):
       UpperCamelCase_ =		{"total_count": 1, "logits": 1}
       UpperCamelCase_ =		NegativeBinomial
       @classmethod
       def 				lowercase_				(  cls			,					A_			,					A_							)  ->						Any:





         """simple docstring"""




         _lowercase:      Dict   			=  cls.squareplus(__lowerCAmelCase							)
         return total_count.squeeze(-1							), logits.squeeze(-1							)
       def 				lowercase_				(  self			,					A_							)  ->						Any:





         """simple docstring"""




         _lowercase			,					_lowercase:      Any   			=  distr_args
         if self.dim == 1:
           return self.distribution_class(total_count=__lowerCAmelCase			,					logits=__lowerCAmelCase							)
         else:
           return Independent(self.distribution_class(total_count=__lowerCAmelCase			,					logits=__lowerCAmelCase							)			,					1							)



       def 				lowercase_				(  self			,					A_			,					A_ = None			,					A_ = None							)  ->						Union[str, Any]:





         """simple docstring"""




         _lowercase			,					_lowercase:      List[str]   			=  distr_args

         if scale is not None:
           # See scaling property of Gamma.
           logits += scale.log()

         return self._base_distribution((total_count, logits)							)

"""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__":
       __lowerCAmelCase       :			int   	=pd.read_csv("""sample_data.csv""", header=None)
       __lowerCAmelCase       :			Optional[Any]   	=df.shape[:1][0]
       # If you're using some other dataset input the target column
       __lowerCAmelCase       :			Optional[int]   	=df.iloc[:, 1:2]
       __lowerCAmelCase       :			List[str]   	=actual_data.values.reshape(len_data, 1)
       __lowerCAmelCase       :			int   	=MinMaxScaler().fit_transform(actual_data)
       __lowerCAmelCase       :			List[Any]   	=1_0
       __lowerCAmelCase       :			int   	=5
       __lowerCAmelCase       :			str   	=2_0
       __lowerCAmelCase       :			Union[str, Any]   	=len_data - periods * look_back
       __lowerCAmelCase       :			Dict   	=actual_data[:division]
       __lowerCAmelCase       :			List[str]   	=actual_data[division - look_back :]
       __lowerCAmelCase	,	__lowerCAmelCase       :			List[str]   	=[], []
       __lowerCAmelCase	,	__lowerCAmelCase       :			Optional[int]   	=[], []

       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])
       __lowerCAmelCase       :			int   	=np.array(train_x)
       __lowerCAmelCase       :			List[str]   	=np.array(test_x)
       __lowerCAmelCase       :			Dict   	=np.array([list(i.ravel()) for i in train_y])
       __lowerCAmelCase       :			str   	=np.array([list(i.ravel()) for i in test_y])

       __lowerCAmelCase       :			Optional[Any]   	=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""")
       __lowerCAmelCase       :			Optional[int]   	=model.fit(
           x_train, y_train, epochs=1_5_0, verbose=1, shuffle=True, batch_size=4
       )
       __lowerCAmelCase       :			Dict   	=model.predict(x_test)



