"""
This module contains utility method for mobile model optimization and lint.
"""

import torch
from enum import Enum
from torch._C import _MobileOptimizerType as MobileOptimizerType
from typing import Optional, Set, List, AnyStr

class LintCode(Enum):
    BUNDLED_INPUT = 1
    REQUIRES_GRAD = 2
    DROPOUT = 3
    BATCHNORM = 4

def optimize_for_mobile(
        script_module: torch.jit.ScriptModule,
        optimization_blocklist: Optional[Set[MobileOptimizerType]] = None,
        preserved_methods: Optional[List[AnyStr]] = None,
        backend: str = 'CPU') -> torch.jit.RecursiveScriptModule:
    """
    Args:
        script_module: An instance of torch script module with type of ScriptModule.
        optimization_blocklist: A set with type of MobileOptimizerType. When set is not passed,
            optimization method will run all the optimizer pass; otherwise, optimizer
            method will run the optimization pass that is not included inside optimization_blocklist.
        preserved_methods: A list of methods that needed to be preserved when freeze_module pass is invoked
        backend: Device type to use for running the result model ('CPU'(default), 'Vulkan' or 'Metal').
    Returns:
        A new optimized torch script module
    """
    if not isinstance(script_module, torch.jit.ScriptModule):
        raise TypeError(
            f'Got {type(script_module)}, but ScriptModule is expected.')

    if optimization_blocklist is None:
        optimization_blocklist = set()

    if preserved_methods is None:
        preserved_methods = []

    # Convert potential byte arrays into strings (if there is any) to pass type checking
    # Here we use a new name as assigning it back to preserved_methods will invoke
    # mypy errors (i.e. List[AnyStr] = List[str])
    preserved_methods_str: List[str] = [str(method) for method in preserved_methods]

    bundled_inputs_attributes = _get_bundled_inputs_preserved_attributes(script_module, preserved_methods_str)
    if all(hasattr(script_module, method) for method in bundled_inputs_attributes):
        preserved_methods_str = list(set(preserved_methods_str + bundled_inputs_attributes))

    non_exist_methods = []
    for method in preserved_methods_str:
        if not hasattr(script_module, method):
            non_exist_methods.append(method)
    if non_exist_methods:
        raise AttributeError(
            f"The following methods to preserve do not exist in script_module: {', '.join(non_exist_methods)}")

    backend = backend.lower()
    if backend == 'cpu':
        optimized_cpp_module = torch._C._jit_pass_optimize_for_mobile(
            script_module._c,
            optimization_blocklist,
            preserved_methods_str)
    elif backend == 'vulkan':
        optimized_cpp_module = torch._C._jit_pass_vulkan_optimize_for_mobile(
            script_module._c,
            optimization_blocklist,
            preserved_methods_str)
    elif backend == 'metal':
        optimized_cpp_module = torch._C._jit_pass_metal_optimize_for_mobile(script_module._c, preserved_methods_str)
    else:
        raise TypeError("Unknown backend, must be one of 'CPU', 'Vulkan' or 'Metal'")

    return torch.jit._recursive.wrap_cpp_module(optimized_cpp_module)


def generate_mobile_module_lints(script_module: torch.jit.ScriptModule):
    """
    Args:
        script_module: An instance of torch script module with type of ScriptModule

    Returns:
        lint_map: A list of dictionary that contains modules lints
    """
    if not isinstance(script_module, torch.jit.ScriptModule):
        raise TypeError(
            f'Got {type(script_module)}, but ScriptModule is expected.')

    lint_list = []

    if not hasattr(script_module, "_generate_bundled_inputs_for_forward"):
        lint_list.append({"name": LintCode.BUNDLED_INPUT.name, "message": "No bundled input for forward, please add bundled inputs "
                          "before saving the module using torch.utils.bundled_inputs.augment_model_with_bundled_inputs."})

    for name, param in script_module.named_parameters():
        if param.requires_grad:
            lint_list.append({"name": LintCode.REQUIRES_GRAD.name, "message": f"Param {name} requires grad, "
                             "please set torch.no_grad() to reduce memory usage and improve computation speed during "
                              "inference phase."})

    op_names = torch.jit.export_opnames(script_module)
    for op_name in op_names:
        if "dropout" in op_name:
            lint_list.append({"name": LintCode.DROPOUT.name, "message": "Operator {} exists, remember to call eval() before "
                              "saving the module.and call torch.utils.mobile_optimizer.optimize_for_mobile to drop dropout "
                              "operator.".format(op_name)})
        if "batch_norm" in op_name:
            lint_list.append({"name": LintCode.BATCHNORM.name, "message": "Operator {} exists, remember to call eval() before "
                              "saving the module and call torch.utils.mobile_optimizer.optimize_for_mobile to drop batch_norm "
                              "operator.".format(op_name)})

    return lint_list

def _get_bundled_inputs_preserved_attributes(script_module: torch.jit.ScriptModule, preserved_methods: List[str]) -> List[str]:

    bundled_inputs_attributes = []
    # Has bundled inputs for forward
    if hasattr(script_module, 'get_all_bundled_inputs'):
        bundled_inputs_attributes.append('get_all_bundled_inputs')
        bundled_inputs_attributes.append('get_num_bundled_inputs')

    # Bundled inputs in module after the change that introduced bundled inputs for multiple functions
    if hasattr(script_module, 'get_bundled_inputs_functions_and_info'):
        bundled_inputs_attributes.append('get_bundled_inputs_functions_and_info')
        all_info = script_module.get_bundled_inputs_functions_and_info()
        for function_name in all_info:
            if function_name not in preserved_methods:
                bundled_inputs_attributes.append(function_name)
            bundled_inputs_attributes.append("get_all_bundled_inputs_for_" + function_name)
            bundled_inputs_attributes.append("_bundled_inputs_deflated_" + function_name)

    return bundled_inputs_attributes
