import torch
import sys
import numpy as np
print('Python Version : '+sys.version)
print('Pytorch Version : '+torch.__version__)

empty_tensor = torch.empty(2,5) ## Please make a note that it intiates tensor with garbage values
print(empty_tensor, empty_tensor.dtype)
empty_tensor = torch.empty(2,5, dtype=torch.int32) ## One can provide data type same as that of numpy array creation
print(empty_tensor)

rand_tensor = torch.rand(3,3) ## Returns between [0,1)
print(rand_tensor, rand_tensor.dtype)
rand_tensor = torch.rand(3,3, dtype=torch.float64)
print(rand_tensor)
rand_tensor = torch.randn(2,4) ## Returns values between (-1,1)
print(rand_tensor)
rand_tensor = torch.randint(0,50, (2,4))
print(rand_tensor, rand_tensor.dtype)

zero_tensor = torch.zeros(2,4)
print(zero_tensor, zero_tensor.dtype)
zero_tensor = torch.zeros(2,4, dtype=torch.int32)
print(zero_tensor)

range_tensor = torch.arange(0,10,1)
print(range_tensor)
range_tensor = torch.arange(1,10,2)
print(range_tensor)

eye_tensor = torch.eye(3)
print(eye_tensor, eye_tensor.dtype)
eye_tensor = torch.eye(3,2)
print(eye_tensor, eye_tensor.dtype)
eye_tensor = torch.eye(3, dtype=torch.int32)
print(eye_tensor)

tensor = torch.Tensor([1,2,3,4])
print(tensor)

one_tensor = torch.ones(2,3)
print(one_tensor)

rand_tensor = torch.rand(2,3)
like_tensor = torch.rand_like(rand_tensor) ## *_like functions can take other tensor as input and outs new tensor of same size as that of input
print(like_tensor)

torch.from_numpy(np.eye(5))

like_tensor.shape, like_tensor.size(),

tensor.dim() ## Number of dimensions

tensor.requires_grad

tensor.device

tensor.diag() ## Creates diagonal tensor with elements of tensor

tensor.dtype

tensor = torch.rand(2,5)
print(tensor)
print(tensor.argmax()) ## Returns dimension as if applied on flat array
print(tensor.argmax(0))
print(tensor.argmax(1))

tensor.min(), tensor.max()

tensor.max(dim=1) ##Returns max lements at 1st dimension and location in that dimension as well for max element.

tensor.numpy()

tensor = tensor.unsqueeze(dim=0) ## Adds one dimension to specified index
print(tensor.shape)
tensor = tensor.squeeze(dim=0) ## Removes extra dimension at specified index
print(tensor.shape)

tensor.reshape(5,2), tensor.view(5,2)

tensor = torch.rand(2,4)
tensor + 5

torch.add(tensor, tensor)

tensor.add(5)

tensor.add(tensor) ## This one adds first argument to tensor and then multiply with second

out_tensor = torch.empty(2,4)
torch.add(tensor, 7, out=out_tensor) ## Stores addition results in another tensor provided to out parameter.
print(out_tensor)
out_tensor = torch.empty(2,4)
torch.add(tensor, tensor, out=out_tensor)
print(out_tensor)

summation = torch.sum(tensor)
print(summation, summation.item())

tensor * 10

tensor = torch.rand(2,4)
tensor.add_(10)
tensor

tensor = torch.rand(2,3)
print(tensor.t())
tensor = torch.mul(tensor, tensor)  ## Element wise matrix multiplication
print(tensor)
tensor = torch.matmul(tensor, tensor.t()) ## Matrix dot product
print(tensor)

tensor.mean(), tensor.std(), tensor.var()

tensor = torch.rand(2,4)
tensor.clamp(0.2,0.4) ## It's same as numpy's clip function.

device = 'cuda' if torch.cuda.is_available() else 'cpu'
device

torch.device(device)

tensor = torch.rand(2,4,device='cuda')
print(tensor.device)

tensor1 = torch.rand(2,5, device = torch.device(device))
print(tensor1.device)
tensor2 = torch.rand(2,5)
print(tensor2.device)

tensor3 = tensor2.to(device)
tensor3.device

tensor1 + tensor2

tensor4 = tensor1.cpu()
print(tensor4.device)