"""TODO(Wafer-316): CuTeDSL Analyzer - Interactive compiler analysis for CuTe kernels.

STATUS: INCOMPLETE - Feature marked as TODO throughout codebase
- Parsing functions are placeholder implementations
- Correlation graph building is simplified
- Layout/memory/pipeline extraction needs completion
- Entity ID assignment logic needs refinement

Provides analysis of MLIR, PTX, and SASS artifacts to understand kernel structure,
layouts, memory paths, and pipeline stages with correlation tracking across
compilation stages.
"""

from __future__ import annotations

import re
from dataclasses import dataclass, field
from typing import Any

from wafer_core.telemetry.decorators import with_telemetry


# =============================================================================
# Data Models
# =============================================================================


@dataclass(frozen=True)
class EntityId:
    """Stable ID for an entity across compilation stages.
    
    Attributes:
        stage: Compilation stage ('cute', 'mlir', 'ptx', 'sass')
        entity_type: Type of entity ('op', 'instruction', 'layout', 'partition')
        index: Unique index within the stage
        kernel_name: Optional kernel name for scoping
    """
    stage: str
    entity_type: str
    index: int
    kernel_name: str | None = None
    
    def to_string(self) -> str:
        """Convert to stable string ID."""
        parts = [self.stage, self.entity_type, str(self.index)]
        if self.kernel_name:
            parts.append(self.kernel_name)
        return ":".join(parts)
    
    @staticmethod
    def from_string(s: str) -> "EntityId":
        """Parse from stable string ID."""
        parts = s.split(":")
        assert len(parts) >= 3, f"Invalid EntityId string: {s}"
        return EntityId(
            stage=parts[0],
            entity_type=parts[1],
            index=int(parts[2]),
            kernel_name=parts[3] if len(parts) > 3 else None
        )


@dataclass(frozen=True)
class CorrelationEdge:
    """Edge in correlation graph linking entities across stages.
    
    Attributes:
        source: Source entity ID
        target: Target entity ID  
        confidence: Confidence score 0.0-1.0
        edge_type: Type of correlation ('exact', 'inferred', 'heuristic')
    """
    source: EntityId
    target: EntityId
    confidence: float
    edge_type: str = "inferred"


@dataclass(frozen=True)
class LayoutInfo:
    """Layout/shape information extracted from MLIR.
    
    Attributes:
        entity_id: Entity this layout belongs to
        shape: Tensor shape as tuple
        strides: Memory strides as tuple
        tile_shape: Tile shape if partitioned
        thread_mapping: Thread ID to element coordinates
        block_mapping: Block ID to tile coordinates
        contiguous: Whether layout is contiguous in memory
    """
    entity_id: EntityId
    shape: tuple[int, ...]
    strides: tuple[int, ...]
    tile_shape: tuple[int, ...] | None = None
    thread_mapping: dict[int, tuple[int, ...]] = field(default_factory=dict)
    block_mapping: dict[int, tuple[int, ...]] = field(default_factory=dict)
    contiguous: bool = True


@dataclass(frozen=True)
class MemoryPath:
    """Memory access path: global → shared → register.
    
    Attributes:
        entity_id: Entity this path belongs to
        source: Source memory space ('global', 'shared', 'constant')
        destination: Destination memory space ('shared', 'register')
        vectorized: Whether access is vectorized
        vector_width: Vector width in elements
        alignment: Memory alignment in bytes
        width_bytes: Total access width in bytes
        async_copy: Whether this is an async copy operation
    """
    entity_id: EntityId
    source: str
    destination: str
    vectorized: bool
    vector_width: int = 1
    alignment: int = 4
    width_bytes: int = 4
    async_copy: bool = False


@dataclass(frozen=True)
class PipelineStage:
    """Pipeline stage identification.
    
    Attributes:
        stage_type: Type of stage ('prologue', 'steady_state', 'epilogue')
        stage_index: Index within pipeline
        start_entity: First entity in this stage
        end_entity: Last entity in this stage
        async_ops: List of async operations in this stage
        iteration_count: Number of iterations (for steady state)
    """
    stage_type: str
    stage_index: int
    start_entity: EntityId
    end_entity: EntityId
    async_ops: tuple[EntityId, ...] = field(default_factory=tuple)
    iteration_count: int | None = None


@dataclass(frozen=True)
class MLIROp:
    """Parsed MLIR operation.
    
    Attributes:
        entity_id: Stable entity ID
        op_type: Operation type (e.g., 'cute.layout', 'cute.partition')
        result_name: SSA result name
        operands: List of operand names
        attributes: Operation attributes
        line_number: Source line number
    """
    entity_id: EntityId
    op_type: str
    result_name: str | None
    operands: list[str]
    attributes: dict[str, Any]
    line_number: int


@dataclass(frozen=True)
class PTXInstruction:
    """Parsed PTX instruction.
    
    Attributes:
        entity_id: Stable entity ID
        opcode: Instruction opcode (e.g., 'ld.global.u32')
        operands: List of operands
        predicate: Optional predicate register
        line_number: Source line number
        source_loc: Optional source location from .loc directive
    """
    entity_id: EntityId
    opcode: str
    operands: list[str]
    predicate: str | None
    line_number: int
    source_loc: tuple[int, int] | None = None  # (file_id, line)


@dataclass(frozen=True)
class SASSInstruction:
    """Parsed SASS instruction.
    
    Attributes:
        entity_id: Stable entity ID
        address: Instruction address
        opcode: Instruction opcode
        operands: List of operands
        control: Control information (scheduling)
        line_number: Line in disassembly
    """
    entity_id: EntityId
    address: str
    opcode: str
    operands: list[str]
    control: str | None
    line_number: int


@dataclass(frozen=True)
class ParsedMLIR:
    """Result of MLIR parsing."""
    ops: tuple[MLIROp, ...]
    kernels: tuple[str, ...]
    shapes: dict[str, tuple[int, ...]]
    layouts: dict[str, tuple[int, ...]]


@dataclass(frozen=True)
class ParsedPTX:
    """Result of PTX parsing."""
    instructions: tuple[PTXInstruction, ...]
    kernels: tuple[str, ...]
    memory_ops: tuple[PTXInstruction, ...]
    mma_ops: tuple[PTXInstruction, ...]
    registers: dict[str, str]  # register -> type


@dataclass(frozen=True)
class ParsedSASS:
    """Result of SASS parsing."""
    instructions: tuple[SASSInstruction, ...]
    kernels: tuple[str, ...]


@dataclass(frozen=True)
class AnalysisResult:
    """Complete analysis result.
    
    Attributes:
        kernel_name: Name of the kernel
        source_code: Optional CuTeDSL source code
        mlir_text: Raw MLIR text
        ptx_text: Raw PTX text
        sass_text: Raw SASS text
        parsed_mlir: Parsed MLIR data
        parsed_ptx: Parsed PTX data
        parsed_sass: Parsed SASS data
        correlation_graph: Edges linking entities across stages
        layouts: Extracted layout information
        memory_paths: Extracted memory paths
        pipeline_stages: Extracted pipeline stages
        entity_registry: Mapping from string ID to EntityId
    """
    kernel_name: str
    source_code: str | None
    mlir_text: str
    ptx_text: str
    sass_text: str
    parsed_mlir: ParsedMLIR
    parsed_ptx: ParsedPTX
    parsed_sass: ParsedSASS
    correlation_graph: tuple[CorrelationEdge, ...]
    layouts: tuple[LayoutInfo, ...]
    memory_paths: tuple[MemoryPath, ...]
    pipeline_stages: tuple[PipelineStage, ...]
    entity_registry: dict[str, EntityId]


@dataclass(frozen=True)
class DiffResult:
    """Result of diffing two kernel analyses."""
    mlir_added: tuple[EntityId, ...]
    mlir_removed: tuple[EntityId, ...]
    mlir_modified: tuple[tuple[EntityId, EntityId], ...]
    ptx_added: tuple[EntityId, ...]
    ptx_removed: tuple[EntityId, ...]
    ptx_modified: tuple[tuple[EntityId, EntityId], ...]
    sass_added: tuple[EntityId, ...]
    sass_removed: tuple[EntityId, ...]
    sass_modified: tuple[tuple[EntityId, EntityId], ...]
    layout_changes: tuple[tuple[LayoutInfo, LayoutInfo], ...]
    memory_path_changes: tuple[tuple[MemoryPath, MemoryPath], ...]
    pipeline_changes: tuple[tuple[PipelineStage, PipelineStage], ...]


# =============================================================================
# Parsers
# =============================================================================


# TODO(Wafer-316): Complete MLIR parsing - current implementation is simplified
@with_telemetry("parse_mlir")
def parse_mlir(mlir_text: str, kernel_name: str | None = None) -> ParsedMLIR:
    """Parse MLIR text and extract structured information.
    
    TODO: Implement full CuTe IR dialect parsing
    TODO: Extract all operation types and attributes
    TODO: Parse nested structures and control flow
    TODO: Handle multiple kernels in one module
    
    Args:
        mlir_text: MLIR/CuTe IR text
        kernel_name: Optional kernel name for scoping entity IDs
        
    Returns:
        ParsedMLIR with ops, kernels, shapes, and layouts
    """
    ops: list[MLIROp] = []
    kernels: list[str] = []
    shapes: dict[str, tuple[int, ...]] = {}
    layouts: dict[str, tuple[int, ...]] = {}
    
    lines = mlir_text.split("\n")
    op_index = 0
    
    # Patterns for MLIR parsing
    func_pattern = re.compile(r'func\.func\s+@(\w+)')
    op_pattern = re.compile(r'(%\w+)\s*=\s*(\w+(?:\.\w+)*)\s*(.*)$')
    shape_pattern = re.compile(r'shape\s*=\s*\[([^\]]+)\]')
    stride_pattern = re.compile(r'stride\s*=\s*\[([^\]]+)\]')
    tensor_pattern = re.compile(r'tensor<([^>]+)>')
    
    for line_num, line in enumerate(lines):
        line = line.strip()
        
        # Extract kernel/function names
        func_match = func_pattern.search(line)
        if func_match:
            kernels.append(func_match.group(1))
        
        # Extract operations
        op_match = op_pattern.match(line)
        if op_match:
            result_name = op_match.group(1)
            op_type = op_match.group(2)
            rest = op_match.group(3)
            
            # Extract attributes
            attrs: dict[str, Any] = {}
            
            shape_match = shape_pattern.search(rest)
            if shape_match:
                shape_str = shape_match.group(1)
                shape = tuple(int(x.strip()) for x in shape_str.split(",") if x.strip().isdigit())
                attrs["shape"] = shape
                shapes[result_name] = shape
            
            stride_match = stride_pattern.search(rest)
            if stride_match:
                stride_str = stride_match.group(1)
                stride = tuple(int(x.strip()) for x in stride_str.split(",") if x.strip().isdigit())
                attrs["stride"] = stride
                layouts[result_name] = stride
            
            # Extract tensor shapes
            tensor_match = tensor_pattern.search(rest)
            if tensor_match:
                tensor_spec = tensor_match.group(1)
                dims = re.findall(r'(\d+)', tensor_spec)
                if dims:
                    shape = tuple(int(d) for d in dims)
                    attrs["tensor_shape"] = shape
                    if result_name not in shapes:
                        shapes[result_name] = shape
            
            # Extract operands (simplified)
            operands = re.findall(r'%\w+', rest)
            
            entity_id = EntityId(
                stage="mlir",
                entity_type="op",
                index=op_index,
                kernel_name=kernel_name or (kernels[-1] if kernels else None)
            )
            
            ops.append(MLIROp(
                entity_id=entity_id,
                op_type=op_type,
                result_name=result_name,
                operands=operands,
                attributes=attrs,
                line_number=line_num
            ))
            op_index += 1
    
    return ParsedMLIR(
        ops=tuple(ops),
        kernels=tuple(kernels),
        shapes=shapes,
        layouts=layouts
    )


# TODO(Wafer-316): Complete PTX parsing - current implementation is simplified
@with_telemetry("parse_ptx")
def parse_ptx(ptx_text: str, kernel_name: str | None = None) -> ParsedPTX:
    """Parse PTX text and extract instructions.
    
    Args:
        ptx_text: PTX assembly text
        kernel_name: Optional kernel name for scoping entity IDs
        
    Returns:
        ParsedPTX with instructions, kernels, memory_ops, mma_ops
    """
    instructions: list[PTXInstruction] = []
    kernels: list[str] = []
    memory_ops: list[PTXInstruction] = []
    mma_ops: list[PTXInstruction] = []
    registers: dict[str, str] = {}
    
    lines = ptx_text.split("\n")
    inst_index = 0
    current_source_loc: tuple[int, int] | None = None
    
    # Patterns
    entry_pattern = re.compile(r'\.visible\s+\.entry\s+(\w+)')
    func_pattern = re.compile(r'\.func\s+(\w+)')
    loc_pattern = re.compile(r'\.loc\s+(\d+)\s+(\d+)')
    reg_pattern = re.compile(r'\.reg\s+\.(\w+)\s+(%\w+)')
    inst_pattern = re.compile(r'^\s*(@%?\w+)?\s*(\w+(?:\.\w+)*)\s+(.*);\s*$')
    
    for line_num, line in enumerate(lines):
        # Extract kernel names
        entry_match = entry_pattern.search(line)
        if entry_match:
            kernels.append(entry_match.group(1))
        
        func_match = func_pattern.search(line)
        if func_match:
            kernels.append(func_match.group(1))
        
        # Track source location
        loc_match = loc_pattern.search(line)
        if loc_match:
            current_source_loc = (int(loc_match.group(1)), int(loc_match.group(2)))
            continue
        
        # Extract register declarations
        reg_match = reg_pattern.search(line)
        if reg_match:
            reg_type = reg_match.group(1)
            reg_name = reg_match.group(2)
            registers[reg_name] = reg_type
            continue
        
        # Extract instructions
        inst_match = inst_pattern.match(line)
        if inst_match:
            predicate = inst_match.group(1)
            opcode = inst_match.group(2)
            operands_str = inst_match.group(3)
            
            # Skip directives
            if opcode.startswith("."):
                continue
            
            # Parse operands (simplified)
            operands = [op.strip() for op in operands_str.split(",") if op.strip()]
            
            entity_id = EntityId(
                stage="ptx",
                entity_type="instruction",
                index=inst_index,
                kernel_name=kernel_name or (kernels[-1] if kernels else None)
            )
            
            inst = PTXInstruction(
                entity_id=entity_id,
                opcode=opcode,
                operands=operands,
                predicate=predicate.strip() if predicate else None,
                line_number=line_num,
                source_loc=current_source_loc
            )
            
            instructions.append(inst)
            
            # Categorize
            if opcode.startswith("ld.") or opcode.startswith("st."):
                memory_ops.append(inst)
            elif opcode.startswith("mma.") or opcode.startswith("wmma."):
                mma_ops.append(inst)
            
            inst_index += 1
    
    return ParsedPTX(
        instructions=tuple(instructions),
        kernels=tuple(kernels),
        memory_ops=tuple(memory_ops),
        mma_ops=tuple(mma_ops),
        registers=registers
    )


# TODO(Wafer-316): Complete SASS parsing - current implementation is simplified
@with_telemetry("parse_sass")
def parse_sass(sass_text: str, kernel_name: str | None = None) -> ParsedSASS:
    """Parse SASS disassembly (nvdisasm output).
    
    Args:
        sass_text: SASS disassembly text
        kernel_name: Optional kernel name for scoping entity IDs
        
    Returns:
        ParsedSASS with instructions and kernels
    """
    instructions: list[SASSInstruction] = []
    kernels: list[str] = []
    
    lines = sass_text.split("\n")
    inst_index = 0
    
    # Patterns for nvdisasm output
    # Format: /*address*/ opcode operands ; /* control */
    inst_pattern = re.compile(r'/\*([0-9a-fA-F]+)\*/\s+(\w+(?:\.\w+)*)\s*([^;]*);?\s*(?:/\*(.+)\*/)?')
    func_pattern = re.compile(r'Function\s*:\s*(\w+)')
    section_pattern = re.compile(r'\.text\.(\w+)')
    
    for line_num, line in enumerate(lines):
        # Extract function/kernel names
        func_match = func_pattern.search(line)
        if func_match:
            kernels.append(func_match.group(1))
        
        section_match = section_pattern.search(line)
        if section_match:
            kernels.append(section_match.group(1))
        
        # Extract instructions
        inst_match = inst_pattern.match(line.strip())
        if inst_match:
            address = inst_match.group(1)
            opcode = inst_match.group(2)
            operands_str = inst_match.group(3) or ""
            control = inst_match.group(4)
            
            # Parse operands
            operands = [op.strip() for op in operands_str.split(",") if op.strip()]
            
            entity_id = EntityId(
                stage="sass",
                entity_type="instruction",
                index=inst_index,
                kernel_name=kernel_name or (kernels[-1] if kernels else None)
            )
            
            instructions.append(SASSInstruction(
                entity_id=entity_id,
                address=address,
                opcode=opcode,
                operands=operands,
                control=control.strip() if control else None,
                line_number=line_num
            ))
            
            inst_index += 1
    
    return ParsedSASS(
        instructions=tuple(instructions),
        kernels=tuple(kernels)
    )


# =============================================================================
# Correlation Graph Builder
# =============================================================================


@with_telemetry("build_correlation_graph")
# TODO(Wafer-316): Complete correlation graph building - current implementation is simplified
def build_correlation_graph(
    parsed_mlir: ParsedMLIR,
    parsed_ptx: ParsedPTX,
    parsed_sass: ParsedSASS
) -> tuple[CorrelationEdge, ...]:
    """Build correlation graph linking entities across stages.
    
    Strategy:
    1. Match MLIR ops to PTX instructions by:
       - Kernel name matching
       - Memory operation patterns
       - Shape/layout information
    2. Match PTX instructions to SASS by:
       - Instruction sequence
       - Opcode similarity
       - Memory addresses
    
    Args:
        parsed_mlir: Parsed MLIR data
        parsed_ptx: Parsed PTX data
        parsed_sass: Parsed SASS data
        
    Returns:
        Tuple of CorrelationEdge objects
    """
    edges: list[CorrelationEdge] = []
    
    # Build PTX instruction index by opcode category
    ptx_memory_ops = {inst.entity_id.index: inst for inst in parsed_ptx.memory_ops}
    ptx_mma_ops = {inst.entity_id.index: inst for inst in parsed_ptx.mma_ops}
    
    # Match MLIR ops to PTX instructions
    mlir_memory_ops = [op for op in parsed_mlir.ops if "copy" in op.op_type.lower() or "load" in op.op_type.lower() or "store" in op.op_type.lower()]
    mlir_compute_ops = [op for op in parsed_mlir.ops if "mma" in op.op_type.lower() or "gemm" in op.op_type.lower()]
    
    # Heuristic: Match memory ops sequentially
    ptx_mem_list = list(ptx_memory_ops.values())
    for i, mlir_op in enumerate(mlir_memory_ops):
        if i < len(ptx_mem_list):
            edges.append(CorrelationEdge(
                source=mlir_op.entity_id,
                target=ptx_mem_list[i].entity_id,
                confidence=0.7,
                edge_type="heuristic"
            ))
    
    # Heuristic: Match MMA ops
    ptx_mma_list = list(ptx_mma_ops.values())
    for i, mlir_op in enumerate(mlir_compute_ops):
        if i < len(ptx_mma_list):
            edges.append(CorrelationEdge(
                source=mlir_op.entity_id,
                target=ptx_mma_list[i].entity_id,
                confidence=0.8,
                edge_type="heuristic"
            ))
    
    # Match PTX to SASS by opcode similarity
    ptx_to_sass_map: dict[str, list[SASSInstruction]] = {}
    
    # Build SASS index by opcode prefix
    for sass_inst in parsed_sass.instructions:
        prefix = sass_inst.opcode.split(".")[0] if "." in sass_inst.opcode else sass_inst.opcode
        if prefix not in ptx_to_sass_map:
            ptx_to_sass_map[prefix] = []
        # Note: We need to handle the typing properly - using a separate list
    
    sass_by_category: dict[str, list[SASSInstruction]] = {
        "LDG": [],  # Global loads
        "STG": [],  # Global stores
        "LDS": [],  # Shared loads
        "STS": [],  # Shared stores
        "HMMA": [],  # Tensor core MMA
        "IMMA": [],  # Integer MMA
        "DMMA": [],  # Double MMA
    }
    
    for sass_inst in parsed_sass.instructions:
        for category in sass_by_category:
            if sass_inst.opcode.startswith(category):
                sass_by_category[category].append(sass_inst)
                break
    
    # Match PTX memory ops to SASS
    sass_loads = sass_by_category["LDG"] + sass_by_category["LDS"]
    sass_stores = sass_by_category["STG"] + sass_by_category["STS"]
    
    ptx_loads = [inst for inst in parsed_ptx.memory_ops if inst.opcode.startswith("ld.")]
    ptx_stores = [inst for inst in parsed_ptx.memory_ops if inst.opcode.startswith("st.")]
    
    for i, ptx_inst in enumerate(ptx_loads):
        if i < len(sass_loads):
            edges.append(CorrelationEdge(
                source=ptx_inst.entity_id,
                target=sass_loads[i].entity_id,
                confidence=0.6,
                edge_type="heuristic"
            ))
    
    for i, ptx_inst in enumerate(ptx_stores):
        if i < len(sass_stores):
            edges.append(CorrelationEdge(
                source=ptx_inst.entity_id,
                target=sass_stores[i].entity_id,
                confidence=0.6,
                edge_type="heuristic"
            ))
    
    # Match PTX MMA to SASS HMMA/IMMA
    sass_mma = sass_by_category["HMMA"] + sass_by_category["IMMA"] + sass_by_category["DMMA"]
    for i, ptx_inst in enumerate(parsed_ptx.mma_ops):
        if i < len(sass_mma):
            edges.append(CorrelationEdge(
                source=ptx_inst.entity_id,
                target=sass_mma[i].entity_id,
                confidence=0.8,
                edge_type="heuristic"
            ))
    
    return tuple(edges)


# =============================================================================
# Feature Extractors
# =============================================================================


@with_telemetry("extract_layouts")
# TODO(Wafer-316): Complete layout extraction - current implementation is simplified
def extract_layouts(
    parsed_mlir: ParsedMLIR,
    correlation_graph: tuple[CorrelationEdge, ...]
) -> tuple[LayoutInfo, ...]:
    """Extract layout information from MLIR.
    
    Args:
        parsed_mlir: Parsed MLIR data
        correlation_graph: Correlation edges
        
    Returns:
        Tuple of LayoutInfo objects
    """
    layouts: list[LayoutInfo] = []
    
    for op in parsed_mlir.ops:
        if "layout" in op.op_type.lower() or "partition" in op.op_type.lower():
            shape = op.attributes.get("shape", op.attributes.get("tensor_shape", ()))
            stride = op.attributes.get("stride", ())
            tile_shape = op.attributes.get("tile_shape")
            
            if shape:
                # Compute basic thread mapping (simplified)
                thread_mapping: dict[int, tuple[int, ...]] = {}
                if len(shape) >= 2:
                    # Simple row-major mapping for visualization
                    for tid in range(min(32, shape[0] * shape[1] if len(shape) >= 2 else shape[0])):
                        if len(shape) >= 2:
                            row = tid // shape[1] if shape[1] > 0 else 0
                            col = tid % shape[1] if shape[1] > 0 else tid
                            thread_mapping[tid] = (row, col)
                        else:
                            thread_mapping[tid] = (tid,)
                
                layouts.append(LayoutInfo(
                    entity_id=op.entity_id,
                    shape=tuple(shape) if isinstance(shape, (list, tuple)) else (shape,),
                    strides=tuple(stride) if isinstance(stride, (list, tuple)) else (stride,) if stride else (),
                    tile_shape=tuple(tile_shape) if tile_shape else None,
                    thread_mapping=thread_mapping,
                    block_mapping={},
                    contiguous=_is_contiguous(shape, stride) if stride else True
                ))
    
    return tuple(layouts)


def _is_contiguous(shape: tuple[int, ...] | list[int], strides: tuple[int, ...] | list[int]) -> bool:
    """Check if layout is contiguous (row-major)."""
    if not strides or not shape:
        return True
    
    expected_stride = 1
    for i in range(len(shape) - 1, -1, -1):
        if i < len(strides) and strides[i] != expected_stride:
            return False
        if i < len(shape):
            expected_stride *= shape[i]
    return True


@with_telemetry("extract_memory_paths")
# TODO(Wafer-316): Complete memory path extraction - current implementation is simplified
def extract_memory_paths(
    parsed_ptx: ParsedPTX,
    parsed_sass: ParsedSASS,
    correlation_graph: tuple[CorrelationEdge, ...]
) -> tuple[MemoryPath, ...]:
    """Extract memory access paths from PTX/SASS.
    
    Args:
        parsed_ptx: Parsed PTX data
        parsed_sass: Parsed SASS data
        correlation_graph: Correlation edges
        
    Returns:
        Tuple of MemoryPath objects
    """
    paths: list[MemoryPath] = []
    
    for inst in parsed_ptx.memory_ops:
        opcode_parts = inst.opcode.split(".")
        
        # Determine source/destination
        if inst.opcode.startswith("ld."):
            # Load: memory -> register
            if "global" in inst.opcode:
                source = "global"
            elif "shared" in inst.opcode:
                source = "shared"
            elif "const" in inst.opcode:
                source = "constant"
            else:
                source = "global"
            destination = "register"
        else:
            # Store: register -> memory
            source = "register"
            if "global" in inst.opcode:
                destination = "global"
            elif "shared" in inst.opcode:
                destination = "shared"
            else:
                destination = "global"
        
        # Determine if vectorized
        vectorized = False
        vector_width = 1
        width_bytes = 4
        
        for part in opcode_parts:
            if part.startswith("v"):
                try:
                    vector_width = int(part[1:])
                    vectorized = vector_width > 1
                except ValueError:
                    pass
            elif part in ("u8", "s8", "b8"):
                width_bytes = 1
            elif part in ("u16", "s16", "b16", "f16"):
                width_bytes = 2
            elif part in ("u32", "s32", "b32", "f32"):
                width_bytes = 4
            elif part in ("u64", "s64", "b64", "f64"):
                width_bytes = 8
            elif part == "b128":
                width_bytes = 16
        
        # Check for async
        async_copy = "async" in inst.opcode or "cp.async" in inst.opcode
        
        paths.append(MemoryPath(
            entity_id=inst.entity_id,
            source=source,
            destination=destination,
            vectorized=vectorized,
            vector_width=vector_width,
            alignment=width_bytes,  # Simplified
            width_bytes=width_bytes * vector_width,
            async_copy=async_copy
        ))
    
    return tuple(paths)


@with_telemetry("extract_pipeline_structure")
# TODO(Wafer-316): Complete pipeline structure extraction - current implementation is simplified
def extract_pipeline_structure(
    parsed_ptx: ParsedPTX,
    parsed_sass: ParsedSASS
) -> tuple[PipelineStage, ...]:
    """Identify pipeline structure from instruction sequences.
    
    Args:
        parsed_ptx: Parsed PTX data
        parsed_sass: Parsed SASS data
        
    Returns:
        Tuple of PipelineStage objects
    """
    stages: list[PipelineStage] = []
    
    if not parsed_ptx.instructions:
        return tuple()
    
    # Simple heuristic: Look for loop structures and async operations
    instructions = list(parsed_ptx.instructions)
    
    # Find async operations
    async_ops: list[EntityId] = []
    for inst in instructions:
        if "async" in inst.opcode or "cp.async" in inst.opcode:
            async_ops.append(inst.entity_id)
    
    # Simple 3-stage pipeline if we have async ops
    if async_ops and len(instructions) >= 3:
        n = len(instructions)
        prologue_end = min(n // 4, 10)
        epilogue_start = max(n * 3 // 4, n - 10)
        
        # Prologue
        stages.append(PipelineStage(
            stage_type="prologue",
            stage_index=0,
            start_entity=instructions[0].entity_id,
            end_entity=instructions[prologue_end].entity_id,
            async_ops=tuple(op for op in async_ops if op.index <= prologue_end),
            iteration_count=None
        ))
        
        # Steady state
        if prologue_end < epilogue_start:
            steady_async = tuple(op for op in async_ops if prologue_end < op.index < epilogue_start)
            stages.append(PipelineStage(
                stage_type="steady_state",
                stage_index=1,
                start_entity=instructions[prologue_end + 1].entity_id if prologue_end + 1 < n else instructions[prologue_end].entity_id,
                end_entity=instructions[epilogue_start - 1].entity_id if epilogue_start > 0 else instructions[0].entity_id,
                async_ops=steady_async,
                iteration_count=None  # Would need loop analysis
            ))
        
        # Epilogue
        stages.append(PipelineStage(
            stage_type="epilogue",
            stage_index=2,
            start_entity=instructions[epilogue_start].entity_id,
            end_entity=instructions[-1].entity_id,
            async_ops=tuple(op for op in async_ops if op.index >= epilogue_start),
            iteration_count=None
        ))
    elif len(instructions) > 0:
        # Single stage if no async ops
        stages.append(PipelineStage(
            stage_type="steady_state",
            stage_index=0,
            start_entity=instructions[0].entity_id,
            end_entity=instructions[-1].entity_id,
            async_ops=tuple(),
            iteration_count=None
        ))
    
    return tuple(stages)


# =============================================================================
# Main Analysis Function
# =============================================================================


def _build_entity_registry(
    parsed_mlir: ParsedMLIR,
    parsed_ptx: ParsedPTX,
    parsed_sass: ParsedSASS
) -> dict[str, EntityId]:
    """Build registry mapping string IDs to EntityId objects."""
    registry: dict[str, EntityId] = {}
    
    for op in parsed_mlir.ops:
        registry[op.entity_id.to_string()] = op.entity_id
    
    for inst in parsed_ptx.instructions:
        registry[inst.entity_id.to_string()] = inst.entity_id
    
    for inst in parsed_sass.instructions:
        registry[inst.entity_id.to_string()] = inst.entity_id
    
    return registry


# TODO(Wafer-316): Complete kernel analysis - integrate all parsing and extraction functions
@with_telemetry("analyze_kernel")
def analyze_kernel(
    mlir_text: str,
    ptx_text: str,
    sass_text: str,
    source_code: str | None = None,
    kernel_name: str | None = None
) -> AnalysisResult:
    """Main entry point for kernel analysis.
    
    Args:
        mlir_text: MLIR/CuTe IR text
        ptx_text: PTX assembly text
        sass_text: SASS disassembly text
        source_code: Optional CuTeDSL source code
        kernel_name: Optional kernel name
        
    Returns:
        Complete AnalysisResult with all extracted information
    """
    # 1. Parse all artifacts
    parsed_mlir = parse_mlir(mlir_text, kernel_name)
    parsed_ptx = parse_ptx(ptx_text, kernel_name)
    parsed_sass = parse_sass(sass_text, kernel_name)
    
    # Infer kernel name if not provided
    if not kernel_name:
        if parsed_mlir.kernels:
            kernel_name = parsed_mlir.kernels[0]
        elif parsed_ptx.kernels:
            kernel_name = parsed_ptx.kernels[0]
        elif parsed_sass.kernels:
            kernel_name = parsed_sass.kernels[0]
        else:
            kernel_name = "unknown"
    
    # 2. Build correlation graph
    correlation_graph = build_correlation_graph(parsed_mlir, parsed_ptx, parsed_sass)
    
    # 3. Extract features
    layouts = extract_layouts(parsed_mlir, correlation_graph)
    memory_paths = extract_memory_paths(parsed_ptx, parsed_sass, correlation_graph)
    pipeline_stages = extract_pipeline_structure(parsed_ptx, parsed_sass)
    
    # 4. Build entity registry
    entity_registry = _build_entity_registry(parsed_mlir, parsed_ptx, parsed_sass)
    
    # 5. Return result
    return AnalysisResult(
        kernel_name=kernel_name,
        source_code=source_code,
        mlir_text=mlir_text,
        ptx_text=ptx_text,
        sass_text=sass_text,
        parsed_mlir=parsed_mlir,
        parsed_ptx=parsed_ptx,
        parsed_sass=parsed_sass,
        correlation_graph=correlation_graph,
        layouts=layouts,
        memory_paths=memory_paths,
        pipeline_stages=pipeline_stages,
        entity_registry=entity_registry
    )


# =============================================================================
# Diff Function
# =============================================================================


@with_telemetry("diff_kernels")
# TODO(Wafer-316): Complete kernel diffing - current implementation is simplified
def diff_kernels(
    result1: AnalysisResult,
    result2: AnalysisResult
) -> DiffResult:
    """Compare two kernel analysis results.
    
    Args:
        result1: First analysis result
        result2: Second analysis result
        
    Returns:
        DiffResult with changes across all stages
    """
    # Compare MLIR ops
    mlir1_ops = {op.op_type + str(op.attributes): op.entity_id for op in result1.parsed_mlir.ops}
    mlir2_ops = {op.op_type + str(op.attributes): op.entity_id for op in result2.parsed_mlir.ops}
    
    mlir1_keys = set(mlir1_ops.keys())
    mlir2_keys = set(mlir2_ops.keys())
    
    mlir_added = tuple(mlir2_ops[k] for k in mlir2_keys - mlir1_keys)
    mlir_removed = tuple(mlir1_ops[k] for k in mlir1_keys - mlir2_keys)
    mlir_modified: tuple[tuple[EntityId, EntityId], ...] = tuple()  # Would need more sophisticated comparison
    
    # Compare PTX instructions
    ptx1_insts = {inst.opcode + str(inst.operands): inst.entity_id for inst in result1.parsed_ptx.instructions}
    ptx2_insts = {inst.opcode + str(inst.operands): inst.entity_id for inst in result2.parsed_ptx.instructions}
    
    ptx1_keys = set(ptx1_insts.keys())
    ptx2_keys = set(ptx2_insts.keys())
    
    ptx_added = tuple(ptx2_insts[k] for k in ptx2_keys - ptx1_keys)
    ptx_removed = tuple(ptx1_insts[k] for k in ptx1_keys - ptx2_keys)
    ptx_modified: tuple[tuple[EntityId, EntityId], ...] = tuple()
    
    # Compare SASS instructions
    sass1_insts = {inst.opcode + str(inst.operands): inst.entity_id for inst in result1.parsed_sass.instructions}
    sass2_insts = {inst.opcode + str(inst.operands): inst.entity_id for inst in result2.parsed_sass.instructions}
    
    sass1_keys = set(sass1_insts.keys())
    sass2_keys = set(sass2_insts.keys())
    
    sass_added = tuple(sass2_insts[k] for k in sass2_keys - sass1_keys)
    sass_removed = tuple(sass1_insts[k] for k in sass1_keys - sass2_keys)
    sass_modified: tuple[tuple[EntityId, EntityId], ...] = tuple()
    
    # Compare layouts
    layout_changes: list[tuple[LayoutInfo, LayoutInfo]] = []
    layouts1 = {l.entity_id.to_string(): l for l in result1.layouts}
    layouts2 = {l.entity_id.to_string(): l for l in result2.layouts}
    
    for key in set(layouts1.keys()) & set(layouts2.keys()):
        l1, l2 = layouts1[key], layouts2[key]
        if l1.shape != l2.shape or l1.strides != l2.strides:
            layout_changes.append((l1, l2))
    
    # Compare memory paths (simplified)
    memory_path_changes: list[tuple[MemoryPath, MemoryPath]] = []
    
    # Compare pipeline stages (simplified)
    pipeline_changes: list[tuple[PipelineStage, PipelineStage]] = []
    if len(result1.pipeline_stages) == len(result2.pipeline_stages):
        for s1, s2 in zip(result1.pipeline_stages, result2.pipeline_stages):
            if s1.stage_type != s2.stage_type or len(s1.async_ops) != len(s2.async_ops):
                pipeline_changes.append((s1, s2))
    
    return DiffResult(
        mlir_added=mlir_added,
        mlir_removed=mlir_removed,
        mlir_modified=mlir_modified,
        ptx_added=ptx_added,
        ptx_removed=ptx_removed,
        ptx_modified=ptx_modified,
        sass_added=sass_added,
        sass_removed=sass_removed,
        sass_modified=sass_modified,
        layout_changes=tuple(layout_changes),
        memory_path_changes=tuple(memory_path_changes),
        pipeline_changes=tuple(pipeline_changes)
    )


# =============================================================================
# Lineage Query Functions
# =============================================================================


# TODO(Wafer-316): Complete lineage tracking - current implementation is simplified
def get_lineage(
    analysis: AnalysisResult,
    entity_id: EntityId
) -> dict[str, list[EntityId]]:
    """Get related entities across all stages for a given entity.
    
    Args:
        analysis: Analysis result containing correlation graph
        entity_id: Entity to find lineage for
        
    Returns:
        Dict with keys 'upstream' and 'downstream' containing related entities
    """
    upstream: list[EntityId] = []
    downstream: list[EntityId] = []
    
    for edge in analysis.correlation_graph:
        if edge.source == entity_id:
            downstream.append(edge.target)
        elif edge.target == entity_id:
            upstream.append(edge.source)
    
    # Recursively find transitive relations
    visited_up = set()
    visited_down = set()
    
    def find_upstream(eid: EntityId) -> None:
        if eid.to_string() in visited_up:
            return
        visited_up.add(eid.to_string())
        for edge in analysis.correlation_graph:
            if edge.target == eid and edge.source not in upstream:
                upstream.append(edge.source)
                find_upstream(edge.source)
    
    def find_downstream(eid: EntityId) -> None:
        if eid.to_string() in visited_down:
            return
        visited_down.add(eid.to_string())
        for edge in analysis.correlation_graph:
            if edge.source == eid and edge.target not in downstream:
                downstream.append(edge.target)
                find_downstream(edge.target)
    
    for up in list(upstream):
        find_upstream(up)
    
    for down in list(downstream):
        find_downstream(down)
    
    return {
        "upstream": upstream,
        "downstream": downstream
    }

