



"""simple docstring"""






import sys
from typing import Tuple

import numpy as np
import torch
from PIL import Image
from torch import nn

from transformers.image_utils import PILImageResampling
from utils import img_tensorize






class 			__snake_case			:




  def __init__(					self	:		Tuple		,   __lowerCAmelCase	:		Optional[int]		,   __lowerCAmelCase	:		Optional[int]=sys.maxsize						):






         """simple docstring"""






         _lowerCamelCase		:							Tuple            =					'''bilinear'''
         _lowerCamelCase		:							Union[str, Any]            =					max_size
         _lowerCamelCase		:							Optional[Any]            =					short_edge_length




  def __call__(					self	:		Optional[Any]		,   __lowerCAmelCase	:		List[str]						):






         """simple docstring"""






         _lowerCamelCase		:							Any            =					[]
         for img in imgs:
                _lowerCamelCase     ,       _lowerCamelCase		:							Optional[Any]            =					img.shape[:2]
                # later: provide list and randomly choose index for resize
                _lowerCamelCase		:							Optional[int]            =					np.random.randint(self.short_edge_length[0]		,   self.short_edge_length[1] + 1						)
                if size == 0:
                       return img
                _lowerCamelCase		:							str            =					size * 1.0 / min(__lowerCAmelCase		,   __lowerCAmelCase						)
                if h < w:
                       _lowerCamelCase     ,       _lowerCamelCase		:							Optional[Any]            =					size, scale * w
                else:
                       _lowerCamelCase     ,       _lowerCamelCase		:							List[str]            =					scale * h, size
                if max(__lowerCAmelCase		,   __lowerCAmelCase						) > self.max_size:
                       _lowerCamelCase		:							Optional[int]            =					self.max_size * 1.0 / max(__lowerCAmelCase		,   __lowerCAmelCase						)
                       _lowerCamelCase		:							int            =					newh * scale
                       _lowerCamelCase		:							Tuple            =					neww * scale
                _lowerCamelCase		:							Union[str, Any]            =					int(neww + 0.5						)
                _lowerCamelCase		:							List[str]            =					int(newh + 0.5						)

                if img.dtype == np.uinta:
                       _lowerCamelCase		:							int            =					Image.fromarray(__lowerCAmelCase						)
                       _lowerCamelCase		:							int            =					pil_image.resize((neww, newh)		,   PILImageResampling.BILINEAR						)
                       _lowerCamelCase		:							List[str]            =					np.asarray(__lowerCAmelCase						)
                else:
                       _lowerCamelCase		:							List[str]            =					img.permute(2		,   0		,   1						).unsqueeze(0						)  # 3, 0, 1)  # hw(c) -> nchw
                       _lowerCamelCase		:							Any            =					nn.functional.interpolate(
                           __lowerCAmelCase		,   (newh, neww)		,   mode=self.interp_method		,   align_corners=__lowerCAmelCase						).squeeze(0						)
                img_augs.append(__lowerCAmelCase						)

         return img_augs






class 			__snake_case			:




  def __init__(					self	:		Union[str, Any]		,   __lowerCAmelCase	:		List[str]						):






         """simple docstring"""






         _lowerCamelCase		:							str            =					ResizeShortestEdge([cfg.INPUT.MIN_SIZE_TEST, cfg.INPUT.MIN_SIZE_TEST]		,   cfg.INPUT.MAX_SIZE_TEST						)
         _lowerCamelCase		:							Optional[int]            =					cfg.INPUT.FORMAT
         _lowerCamelCase		:							str            =					cfg.SIZE_DIVISIBILITY
         _lowerCamelCase		:							int            =					cfg.PAD_VALUE
         _lowerCamelCase		:							List[Any]            =					cfg.INPUT.MAX_SIZE_TEST
         _lowerCamelCase		:							Union[str, Any]            =					cfg.MODEL.DEVICE
         _lowerCamelCase		:							Tuple            =					torch.tensor(cfg.MODEL.PIXEL_STD						).to(self.device						).view(len(cfg.MODEL.PIXEL_STD						)		,   1		,   1						)
         _lowerCamelCase		:							Tuple            =					torch.tensor(cfg.MODEL.PIXEL_MEAN						).to(self.device						).view(len(cfg.MODEL.PIXEL_STD						)		,   1		,   1						)
         _lowerCamelCase		:							int            =					lambda __lowerCAmelCase						: (x - self.pixel_mean) / self.pixel_std




  def 		SCREAMING_SNAKE_CASE							(					self	:		Optional[Any]		,   __lowerCAmelCase	:		Dict						):






         """simple docstring"""






         _lowerCamelCase		:							List[Any]            =					tuple(max(__lowerCAmelCase						) for s in zip(*[img.shape for img in images]						)						)
         _lowerCamelCase		:							Any            =					[im.shape[-2:] for im in images]
         _lowerCamelCase		:							int            =					[
             nn.functional.pad(
                 __lowerCAmelCase		,   [0, max_size[-1] - size[1], 0, max_size[-2] - size[0]]		,   value=self.pad_value		,   )
             for size, im in zip(__lowerCAmelCase		,   __lowerCAmelCase						)
         ]

         return torch.stack(__lowerCAmelCase						), torch.tensor(__lowerCAmelCase						)




  def __call__(					self	:		Any		,   __lowerCAmelCase	:		List[str]		,   __lowerCAmelCase	:		Any=False						):






         """simple docstring"""






         with torch.no_grad():
                if not isinstance(__lowerCAmelCase		,   __lowerCAmelCase						):
                       _lowerCamelCase		:							List[Any]            =					[images]
                if single_image:
                       assert len(__lowerCAmelCase						) == 1
                for i in range(len(__lowerCAmelCase						)						):
                       if isinstance(images[i]		,   torch.Tensor						):
                              images.insert(__lowerCAmelCase		,   images.pop(__lowerCAmelCase						).to(self.device						).float()						)
                       elif not isinstance(images[i]		,   torch.Tensor						):
                              images.insert(
                                  __lowerCAmelCase		,   torch.as_tensor(img_tensorize(images.pop(__lowerCAmelCase						)		,   input_format=self.input_format						)						)
                                  .to(self.device						)
                                  .float()		,   )
            # resize smallest edge
                _lowerCamelCase		:							Dict            =					torch.tensor([im.shape[:2] for im in images]						)
                _lowerCamelCase		:							str            =					self.aug(__lowerCAmelCase						)
                # transpose images and convert to torch tensors
                # images = [torch.as_tensor(i.astype("float32")).permute(2, 0, 1).to(self.device) for i in images]
                # now normalize before pad to avoid useless arithmetic
                _lowerCamelCase		:							Optional[int]            =					[self.normalizer(__lowerCAmelCase						) for x in images]
                # now pad them to do the following operations
                _lowerCamelCase     ,       _lowerCamelCase		:							Dict            =					self.pad(__lowerCAmelCase						)
                # Normalize

                if self.size_divisibility > 0:
                       raise NotImplementedError()
                # pad
                _lowerCamelCase		:							Union[str, Any]            =					torch.true_divide(__lowerCAmelCase		,   __lowerCAmelCase						)
                if single_image:
                       return images[0], sizes[0], scales_yx[0]
                else:
                       return images, sizes, scales_yx






def 			snake_case_    ( A_   :						Optional[int],							A_   :						Any		):


       '''simple docstring'''





       boxes[:, 0::2] *= scale_yx[:, 1]
       boxes[:, 1::2] *= scale_yx[:, 0]
       return boxes







def 			snake_case_    ( A_   :						str,							A_   :						Tuple[int, int]		):


       '''simple docstring'''





       assert torch.isfinite(A_		).all(), "Box tensor contains infinite or NaN!"
       _lowerCamelCase     ,       _lowerCamelCase		:							Union[str, Any]            =					box_size
       tensor[:, 0].clamp_(min=0,							max=A_		)
       tensor[:, 1].clamp_(min=0,							max=A_		)
       tensor[:, 2].clamp_(min=0,							max=A_		)
       tensor[:, 3].clamp_(min=0,							max=A_		)



"""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)



