"""Comprehensive patches for all failed operations in OmniGPU."""

import torch
import torch.nn as nn
import torch.nn.functional as F
from typing import Optional, Union, Tuple, List
import logging

logger = logging.getLogger("omnigpu.comprehensive_patches")

def patch_all_failed_operations():
    """Patch all operations that failed in testing."""
    
    patches_applied = 0
    
    # ========== TENSOR CREATION OPERATIONS ==========
    
    # 1. Eye (identity matrix)
    original_eye = torch.eye
    def mps_eye(n, m=None, *, out=None, dtype=None, layout=None, device=None, pin_memory=False, requires_grad=False):
        if device and ('mps' in str(device) or 'cuda' in str(device)):
            # Create on CPU first
            if m is None:
                result = original_eye(n, out=None, dtype=dtype, layout=layout, 
                                    device='cpu', pin_memory=False, requires_grad=requires_grad)
            else:
                result = original_eye(n, m, out=None, dtype=dtype, layout=layout,
                                    device='cpu', pin_memory=False, requires_grad=requires_grad)
            # Move to MPS
            result = result.to('mps' if torch.backends.mps.is_available() else 'cpu')
            if out is not None:
                out.copy_(result)
                return out
            return result
        return original_eye(n, m, out=out, dtype=dtype, layout=layout, 
                          device=device, pin_memory=pin_memory, requires_grad=requires_grad)
    torch.eye = mps_eye
    patches_applied += 1
    
    # 2. Arange
    original_arange = torch.arange
    def mps_arange(*args, **kwargs):
        device = kwargs.get('device', None)
        if device and 'mps' in str(device):
            kwargs['device'] = 'cpu'
            result = original_arange(*args, **kwargs)
            return result.to('mps')
        return original_arange(*args, **kwargs)
    torch.arange = mps_arange
    patches_applied += 1
    
    # 3. Linspace
    original_linspace = torch.linspace
    def mps_linspace(start, end, steps, **kwargs):
        device = kwargs.get('device', None)
        if device and 'mps' in str(device):
            kwargs['device'] = 'cpu'
            result = original_linspace(start, end, steps, **kwargs)
            return result.to('mps')
        return original_linspace(start, end, steps, **kwargs)
    torch.linspace = mps_linspace
    patches_applied += 1
    
    # 4. Full
    original_full = torch.full
    def mps_full(size, fill_value, **kwargs):
        device = kwargs.get('device', None)
        if device and 'mps' in str(device):
            kwargs['device'] = 'cpu'
            result = original_full(size, fill_value, **kwargs)
            return result.to('mps')
        return original_full(size, fill_value, **kwargs)
    torch.full = mps_full
    patches_applied += 1
    
    # 5. Empty
    original_empty = torch.empty
    def mps_empty(*args, **kwargs):
        device = kwargs.get('device', None)
        if device and 'mps' in str(device):
            # Empty tensors can be created directly on MPS
            try:
                return original_empty(*args, **kwargs)
            except:
                kwargs['device'] = 'cpu'
                result = original_empty(*args, **kwargs)
                return result.to('mps')
        return original_empty(*args, **kwargs)
    torch.empty = mps_empty
    patches_applied += 1
    
    # 6. Randint
    original_randint = torch.randint
    def mps_randint(*args, **kwargs):
        device = kwargs.get('device', None)
        if device and 'mps' in str(device):
            kwargs['device'] = 'cpu'
            result = original_randint(*args, **kwargs)
            return result.to('mps')
        return original_randint(*args, **kwargs)
    torch.randint = mps_randint
    patches_applied += 1
    
    # ========== ADVANCED INDEXING OPERATIONS ==========
    
    # 7. Gather
    original_gather = torch.gather
    def mps_gather(input, dim, index):
        if input.device.type == 'mps':
            # CPU fallback for gather
            cpu_input = input.cpu()
            cpu_index = index.cpu()
            cpu_result = original_gather(cpu_input, dim, cpu_index)
            return cpu_result.to('mps')
        return original_gather(input, dim, index)
    torch.gather = mps_gather
    patches_applied += 1
    
    # 8. Scatter
    original_scatter = torch.scatter
    def mps_scatter(input, dim, index, src, reduce=None):
        if input.device.type == 'mps':
            # CPU fallback for scatter
            cpu_input = input.cpu()
            cpu_index = index.cpu()
            cpu_src = src.cpu() if isinstance(src, torch.Tensor) else src
            if reduce:
                cpu_result = original_scatter(cpu_input, dim, cpu_index, cpu_src, reduce=reduce)
            else:
                cpu_result = original_scatter(cpu_input, dim, cpu_index, cpu_src)
            return cpu_result.to('mps')
        return original_scatter(input, dim, index, src, reduce=reduce) if reduce else original_scatter(input, dim, index, src)
    torch.scatter = mps_scatter
    patches_applied += 1
    
    # ========== LINEAR ALGEBRA OPERATIONS ==========
    
    # 9. Matrix-vector multiply (mv)
    original_mv = torch.mv
    def mps_mv(input, vec):
        if input.device.type == 'mps':
            try:
                return original_mv(input, vec)
            except:
                # CPU fallback
                return original_mv(input.cpu(), vec.cpu()).to('mps')
        return original_mv(input, vec)
    torch.mv = mps_mv
    patches_applied += 1
    
    # 10. Dot product
    original_dot = torch.dot
    def mps_dot(input, other):
        if input.device.type == 'mps':
            try:
                return original_dot(input, other)
            except:
                # CPU fallback
                return original_dot(input.cpu(), other.cpu()).to('mps')
        return original_dot(input, other)
    torch.dot = mps_dot
    patches_applied += 1
    
    # 11. Matrix multiply (mm)
    original_mm = torch.mm
    def mps_mm(input, mat2):
        if input.device.type == 'mps':
            try:
                return original_mm(input, mat2)
            except:
                # Use matmul as fallback
                return torch.matmul(input, mat2)
        return original_mm(input, mat2)
    torch.mm = mps_mm
    patches_applied += 1
    
    # 12. Batch matrix multiply (bmm)
    original_bmm = torch.bmm
    def mps_bmm(input, mat2):
        if input.device.type == 'mps':
            try:
                return original_bmm(input, mat2)
            except:
                # CPU fallback
                return original_bmm(input.cpu(), mat2.cpu()).to('mps')
        return original_bmm(input, mat2)
    torch.bmm = mps_bmm
    patches_applied += 1
    
    # 13. Determinant
    original_det = torch.det
    def mps_det(input):
        if input.device.type == 'mps':
            # CPU fallback for determinant
            return original_det(input.cpu()).to('mps')
        return original_det(input)
    torch.det = mps_det
    patches_applied += 1
    
    # 14. Matrix inverse
    original_inverse = torch.inverse
    def mps_inverse(input):
        if input.device.type == 'mps':
            # CPU fallback for inverse
            return original_inverse(input.cpu()).to('mps')
        return original_inverse(input)
    torch.inverse = mps_inverse
    patches_applied += 1
    
    # 15. SVD
    original_svd = torch.svd
    def mps_svd(input, some=True, compute_uv=True):
        if input.device.type == 'mps':
            # CPU fallback for SVD
            cpu_result = original_svd(input.cpu(), some=some, compute_uv=compute_uv)
            if compute_uv:
                return tuple(t.to('mps') for t in cpu_result)
            return cpu_result.to('mps')
        return original_svd(input, some=some, compute_uv=compute_uv)
    torch.svd = mps_svd
    patches_applied += 1
    
    # 16. QR decomposition
    original_qr = torch.qr
    def mps_qr(input, some=True):
        if input.device.type == 'mps':
            # CPU fallback for QR
            q, r = original_qr(input.cpu(), some=some)
            return q.to('mps'), r.to('mps')
        return original_qr(input, some=some)
    torch.qr = mps_qr
    patches_applied += 1
    
    # 17. Cholesky decomposition
    original_cholesky = torch.cholesky
    def mps_cholesky(input, upper=False):
        if input.device.type == 'mps':
            # CPU fallback for Cholesky
            return original_cholesky(input.cpu(), upper=upper).to('mps')
        return original_cholesky(input, upper=upper)
    torch.cholesky = mps_cholesky
    patches_applied += 1
    
    # ========== NEURAL NETWORK LAYERS ==========
    
    # 18. Conv1d
    original_conv1d = F.conv1d
    def mps_conv1d(input, weight, bias=None, stride=1, padding=0, dilation=1, groups=1):
        if input.device.type == 'mps':
            try:
                return original_conv1d(input, weight, bias, stride, padding, dilation, groups)
            except:
                # CPU fallback
                cpu_input = input.cpu()
                cpu_weight = weight.cpu()
                cpu_bias = bias.cpu() if bias is not None else None
                result = original_conv1d(cpu_input, cpu_weight, cpu_bias, stride, padding, dilation, groups)
                return result.to('mps')
        return original_conv1d(input, weight, bias, stride, padding, dilation, groups)
    F.conv1d = mps_conv1d
    patches_applied += 1
    
    # 19. Embedding
    original_embedding = F.embedding
    def mps_embedding(input, weight, padding_idx=None, max_norm=None, 
                     norm_type=2., scale_grad_by_freq=False, sparse=False):
        if weight.device.type == 'mps':
            try:
                return original_embedding(input, weight, padding_idx, max_norm, 
                                        norm_type, scale_grad_by_freq, sparse)
            except:
                # CPU fallback
                cpu_input = input.cpu() if input.is_cuda or input.device.type == 'mps' else input
                cpu_weight = weight.cpu()
                result = original_embedding(cpu_input, cpu_weight, padding_idx, 
                                          max_norm, norm_type, scale_grad_by_freq, sparse)
                return result.to('mps')
        return original_embedding(input, weight, padding_idx, max_norm, 
                                norm_type, scale_grad_by_freq, sparse)
    F.embedding = mps_embedding
    patches_applied += 1
    
    # 20. GELU activation
    original_gelu = F.gelu
    def mps_gelu(input, approximate='none'):
        if input.device.type == 'mps':
            try:
                return original_gelu(input, approximate=approximate)
            except:
                # Manual implementation as fallback
                if approximate == 'tanh':
                    # 0.5 * x * (1 + tanh(sqrt(2/pi) * (x + 0.044715 * x^3)))
                    import math
                    return 0.5 * input * (1 + torch.tanh(math.sqrt(2 / math.pi) * 
                                                        (input + 0.044715 * torch.pow(input, 3))))
                else:
                    # 0.5 * x * (1 + erf(x / sqrt(2)))
                    return 0.5 * input * (1 + torch.erf(input / torch.sqrt(torch.tensor(2.0))))
        return original_gelu(input, approximate=approximate)
    F.gelu = mps_gelu
    patches_applied += 1
    
    # 21. LSTM
    original_lstm = nn.LSTM
    
    class PatchedLSTM(original_lstm):
        def forward(self, input, hx=None):
            if input.device.type == 'mps':
                # Move everything to CPU for computation
                cpu_module = original_lstm(self.input_size, self.hidden_size, 
                                         self.num_layers, self.bias, self.batch_first,
                                         self.dropout, self.bidirectional).to('cpu')
                # Copy weights
                cpu_module.load_state_dict({k: v.cpu() for k, v in self.state_dict().items()})
                
                cpu_input = input.cpu()
                if hx is not None:
                    if isinstance(hx, tuple):
                        cpu_hx = (hx[0].cpu(), hx[1].cpu())
                    else:
                        cpu_hx = hx.cpu()
                else:
                    cpu_hx = None
                
                output, (hn, cn) = cpu_module(cpu_input, cpu_hx)
                return output.to('mps'), (hn.to('mps'), cn.to('mps'))
            return super().forward(input, hx)
    
    nn.LSTM = PatchedLSTM
    patches_applied += 1
    
    # 22. GRU
    original_gru = nn.GRU
    
    class PatchedGRU(original_gru):
        def forward(self, input, hx=None):
            if input.device.type == 'mps':
                # Move everything to CPU for computation
                cpu_module = original_gru(self.input_size, self.hidden_size,
                                        self.num_layers, self.bias, self.batch_first,
                                        self.dropout, self.bidirectional).to('cpu')
                # Copy weights
                cpu_module.load_state_dict({k: v.cpu() for k, v in self.state_dict().items()})
                
                cpu_input = input.cpu()
                cpu_hx = hx.cpu() if hx is not None else None
                
                output, hn = cpu_module(cpu_input, cpu_hx)
                return output.to('mps'), hn.to('mps')
            return super().forward(input, hx)
    
    nn.GRU = PatchedGRU
    patches_applied += 1
    
    # 23. Softmax (ensure it works on MPS)
    original_softmax = F.softmax
    def mps_softmax(input, dim=None, dtype=None, **kwargs):
        # Filter out internal kwargs like _stacklevel
        if '_stacklevel' in kwargs:
            kwargs.pop('_stacklevel')
            
        if input.device.type == 'mps':
            try:
                return original_softmax(input, dim=dim, dtype=dtype)
            except:
                # CPU fallback
                cpu_result = original_softmax(input.cpu(), dim=dim, dtype=dtype)
                return cpu_result.to('mps')
        return original_softmax(input, dim=dim, dtype=dtype, **kwargs)
    F.softmax = mps_softmax
    patches_applied += 1
    
    # ========== REDUCTION OPERATIONS WITH STABILITY ==========
    
    # 24. Prod (product)
    original_prod = torch.prod
    def mps_prod(input, dim=None, keepdim=False, dtype=None):
        if input.device.type == 'mps':
            try:
                return original_prod(input, dim=dim, keepdim=keepdim, dtype=dtype)
            except:
                # CPU fallback
                cpu_result = original_prod(input.cpu(), dim=dim, keepdim=keepdim, dtype=dtype)
                return cpu_result.to('mps')
        return original_prod(input, dim=dim, keepdim=keepdim, dtype=dtype)
    torch.prod = mps_prod
    patches_applied += 1
    
    logger.info(f"✅ Applied {patches_applied} comprehensive patches for failed operations")
    return patches_applied