import torch
import torch.nn as nn
import torch.multiprocessing as mp

class Kala_torch:
    def __init__(self):
        pass

    # Tensor operations
    def is_tensor(self, tensor):
        # Check if the given object is a tensor
        return torch.is_tensor(tensor)

    def is_storage(self, obj):
        # Check if the given object is a storage object
        return torch.is_storage(obj)

    def is_memory_format(self, memory_format):
        # Check if the input is a valid memory format
        return isinstance(memory_format, torch.memory_format)

    def is_floating_point_tensor(self, tensor):
        # Check if the tensor is of floating point type
        return torch.is_floating_point(tensor)

    def is_complex(self, input_complex):
        # Check if the input is a complex tensor
        return torch.is_complex(input_complex)

    def is_conj(self, input_conj):
        # Compute the conjugate of the input tensor
        return torch.conj(input_conj)

    def is_nonzero(self, input_tensor):
        # Check if the input tensor has a nonzero value
        return torch.is_nonzero(input_tensor)

    # Default settings
    def set_default_dtype(self, input_dtype):
        # Set the default data type for tensors
        torch.set_default_dtype(input_dtype)

    def get_default_dtype(self):
        # Get the current default data type for tensors
        return torch.get_default_dtype()

    def set_default_tensor_type(self, tensor_type):
        # Set the default tensor type
        torch.set_default_tensor_type(tensor_type)

    # Tensor creation
    def tensor(self, tensor):
        # Create a tensor with the default data type
        return torch.tensor(tensor, dtype=torch.get_default_dtype())

    def zeros(self, *size, dtype=None, requires_grad=False):
        # Create a tensor filled with zeros
        return torch.zeros(*size, dtype=dtype, requires_grad=requires_grad)

    def ones(self, *size, dtype=None, requires_grad=False):
        # Create a tensor filled with ones
        return torch.ones(*size, dtype=dtype, requires_grad=requires_grad)

    def arange(self, start, end, step=1, dtype=None):
        # Create a 1D tensor with values from start to end with a given step
        return torch.arange(start, end, step, dtype=dtype)

    # Print options
    def set_printoptions(self, threshold=None, linewidth=None):
        # Configure print options for tensors
        torch.set_printoptions(threshold=threshold, linewidth=linewidth)

    # Autograd operations
    def backward(self, tensor, gradient=None, retain_graph=None, create_graph=False):
        # Perform backpropagation on the tensor
        tensor.backward(gradient=gradient, retain_graph=retain_graph, create_graph=create_graph)

    def grad(self, outputs, inputs, grad_outputs=None, retain_graph=None, create_graph=False, allow_unused=False):
        # Compute gradients of outputs with respect to inputs
        return torch.autograd.grad(outputs, inputs, grad_outputs=grad_outputs, retain_graph=retain_graph, create_graph=create_graph, allow_unused=allow_unused)

    def no_grad(self):
        # Context manager to disable gradient calculations
        return torch.no_grad()

    def enable_grad(self):
        # Context manager to enable gradient calculations
        return torch.enable_grad()

    # TorchScript utilities
    def script(self, func):
        # Convert a function to TorchScript using scripting
        return torch.jit.script(func)

    def trace(self, func, example_inputs):
        # Convert a function to TorchScript using tracing
        return torch.jit.trace(func, example_inputs)

    def save_script_module(self, module, path):
        # Save a TorchScript module to the specified path
        module.save(path)

    def load_script_module(self, path):
        # Load a TorchScript module from the specified path
        return torch.jit.load(path)

    # nn Modules
    def linear(self, in_features, out_features, bias=True):
        # Create a linear layer
        return nn.Linear(in_features, out_features, bias=bias)

    def conv2d(self, in_channels, out_channels, kernel_size, stride=1, padding=0):
        # Create a 2D convolutional layer
        return nn.Conv2d(in_channels, out_channels, kernel_size, stride=stride, padding=padding)

    def relu(self):
        # Create a ReLU activation function
        return nn.ReLU()

    def batch_norm(self, num_features):
        # Create a 1D batch normalization layer
        return nn.BatchNorm1d(num_features)

    def lstm(self, input_size, hidden_size, num_layers=1, bias=True, batch_first=False, dropout=0, bidirectional=False):
        # Create an LSTM layer
        return nn.LSTM(input_size, hidden_size, num_layers=num_layers, bias=bias, batch_first=batch_first, dropout=dropout, bidirectional=bidirectional)

    def transformer(self, d_model, nhead, num_encoder_layers=6, num_decoder_layers=6, dim_feedforward=2048, dropout=0.1):
        # Create a Transformer model
        return nn.Transformer(d_model=d_model, nhead=nhead, num_encoder_layers=num_encoder_layers, num_decoder_layers=num_decoder_layers, dim_feedforward=dim_feedforward, dropout=dropout)

    def cross_entropy_loss(self):
        # Create a cross-entropy loss function
        return nn.CrossEntropyLoss()

    # Multiprocessing utilities
    def get_all_sharing_strategies(self):
        # Get all available sharing strategies for tensors
        return mp.get_all_sharing_strategies()

    def get_sharing_strategy(self):
        # Get the current sharing strategy for tensors
        return mp.get_sharing_strategy()

    def set_sharing_strategy(self, strategy):
        # Set the sharing strategy for tensors
        mp.set_sharing_strategy(strategy)

    def spawn_processes(self, fn, args=(), nprocs=1, join=True, daemon=False):
        # Spawn multiple processes to execute a function
        return mp.spawn(fn, args=args, nprocs=nprocs, join=join, daemon=daemon)

    def share_memory(self, tensor):
        # Move a tensor to shared memory
        tensor.share_memory_()

    # Data utilities
    def data_loader(self, dataset, batch_size=1, shuffle=False, num_workers=0, pin_memory=False):
        # Create a DataLoader for a dataset
        return torch.utils.data.DataLoader(dataset, batch_size=batch_size, shuffle=shuffle, num_workers=num_workers, pin_memory=pin_memory)

    def random_split(self, dataset, lengths):
        # Randomly split a dataset into multiple subsets
        return torch.utils.data.random_split(dataset, lengths)

    class CustomFunction(torch.autograd.Function):
        @staticmethod
        def forward(ctx, *args):
            # Define the forward pass for the custom function
            raise NotImplementedError("Forward method must be implemented.")

        @staticmethod
        def backward(ctx, *grad_outputs):
            # Define the backward pass for the custom function
            raise NotImplementedError("Backward method must be implemented.")
