import copy

import torch
from torch import nn

import torch.nn.functional as F
import torch.nn.intrinsic as nni
import torch.nn.intrinsic.quantized as nniq
import torch.nn.intrinsic.quantized.dynamic as nniqd
import torch.nn.intrinsic.qat as nniqat
import torch.nn.quantized as nnq
import torch.nn.quantized._reference as nnqr
import torch.nn.quantized.dynamic as nnqd
import torch.nn.qat as nnqat
import torch.nn.qat.dynamic as nnqatd

from typing import Optional, Union, Dict, Set, Callable, Any

import torch.ao.nn as ao_nn
from torch.ao.quantization.stubs import QuantStub, DeQuantStub
from torch.ao.quantization.fake_quantize import (
    default_affine_fixed_qparams_fake_quant,
    default_symmetric_fixed_qparams_fake_quant,
)
from torch.ao.quantization.utils import get_combined_dict

# Default map for swapping float module to reference quantized modules
DEFAULT_REFERENCE_STATIC_QUANT_MODULE_MAPPINGS : Dict[Callable, Any] = {
    nn.Linear: nnqr.Linear,
    nn.Conv1d: nnqr.Conv1d,
    nn.Conv2d: nnqr.Conv2d,
    nn.Conv3d: nnqr.Conv3d,
}

# Default map for swapping float module to quantized ones
DEFAULT_STATIC_QUANT_MODULE_MAPPINGS : Dict[Callable, Any] = {
    QuantStub: nnq.Quantize,
    DeQuantStub: nnq.DeQuantize,
    nn.BatchNorm2d: nnq.BatchNorm2d,
    nn.BatchNorm3d: nnq.BatchNorm3d,
    nn.Dropout: nnq.Dropout,
    nn.Conv1d: nnq.Conv1d,
    nn.Conv2d: nnq.Conv2d,
    nn.Conv3d: nnq.Conv3d,
    nn.ConvTranspose1d: nnq.ConvTranspose1d,
    nn.ConvTranspose2d: nnq.ConvTranspose2d,
    nn.ConvTranspose3d: nnq.ConvTranspose3d,
    nn.ELU: nnq.ELU,
    nn.Embedding: nnq.Embedding,
    nn.EmbeddingBag: nnq.EmbeddingBag,
    nn.GroupNorm: nnq.GroupNorm,
    nn.Hardswish: nnq.Hardswish,
    nn.InstanceNorm1d: nnq.InstanceNorm1d,
    nn.InstanceNorm2d: nnq.InstanceNorm2d,
    nn.InstanceNorm3d: nnq.InstanceNorm3d,
    nn.LayerNorm: nnq.LayerNorm,
    nn.LeakyReLU: nnq.LeakyReLU,
    nn.modules.linear.NonDynamicallyQuantizableLinear: nnq.Linear,
    nn.Linear: nnq.Linear,
    nn.ReLU6: nnq.ReLU6,
    nn.Dropout: nnq.Dropout,
    # Wrapper Modules:
    nnq.FloatFunctional: nnq.QFunctional,
    # Intrinsic modules:
    nni.BNReLU2d: nniq.BNReLU2d,
    nni.BNReLU3d: nniq.BNReLU3d,
    nni.ConvReLU1d: nniq.ConvReLU1d,
    nni.ConvReLU2d: nniq.ConvReLU2d,
    nni.ConvReLU3d: nniq.ConvReLU3d,
    nni.LinearReLU: nniq.LinearReLU,
    nniqat.ConvBn1d: nnq.Conv1d,
    nniqat.ConvBn2d: nnq.Conv2d,
    nniqat.ConvBn3d: nnq.Conv3d,
    nniqat.ConvBnReLU1d: nniq.ConvReLU1d,
    nniqat.ConvBnReLU2d: nniq.ConvReLU2d,
    nniqat.ConvBnReLU3d: nniq.ConvReLU3d,
    nniqat.ConvReLU2d: nniq.ConvReLU2d,
    nniqat.ConvReLU3d: nniq.ConvReLU3d,
    nniqat.LinearReLU: nniq.LinearReLU,
    # QAT modules:
    nnqat.Linear: nnq.Linear,
    nnqat.Conv2d: nnq.Conv2d,
    nnqat.Conv3d: nnq.Conv3d,
}

# Default map for swapping float module to qat modules
DEFAULT_QAT_MODULE_MAPPINGS : Dict[Callable, Any] = {
    nn.Conv2d: nnqat.Conv2d,
    nn.Conv3d: nnqat.Conv3d,
    nn.Linear: nnqat.Linear,
    nn.modules.linear.NonDynamicallyQuantizableLinear: nnqat.Linear,
    # Intrinsic modules:
    nni.ConvBn1d: nniqat.ConvBn1d,
    nni.ConvBn2d: nniqat.ConvBn2d,
    nni.ConvBn3d: nniqat.ConvBn3d,
    nni.ConvBnReLU1d: nniqat.ConvBnReLU1d,
    nni.ConvBnReLU2d: nniqat.ConvBnReLU2d,
    nni.ConvBnReLU3d: nniqat.ConvBnReLU3d,
    nni.ConvReLU2d: nniqat.ConvReLU2d,
    nni.ConvReLU3d: nniqat.ConvReLU3d,
    nni.LinearReLU: nniqat.LinearReLU,
}

# Default map for swapping dynamic modules
DEFAULT_DYNAMIC_QUANT_MODULE_MAPPINGS : Dict[Callable, Any] = {
    nn.GRUCell: nnqd.GRUCell,
    nn.Linear: nnqd.Linear,
    nnqatd.Linear: nnqd.Linear,
    nn.modules.linear.NonDynamicallyQuantizableLinear: nnqd.Linear,
    nn.LSTM: nnqd.LSTM,
    nn.GRU: nnqd.GRU,
    nn.LSTMCell: nnqd.LSTMCell,
    nn.RNNCell: nnqd.RNNCell,
    nni.LinearReLU: nniqd.LinearReLU,
    nn.EmbeddingBag: nnq.EmbeddingBag,
    nn.Embedding: nnq.Embedding,
    # Don't want to enable these by default because the numerical
    # accuracy is poor compared to other dynamic ops
    # nn.Conv1d: nnqd.Conv1d,
    # nn.Conv2d: nnqd.Conv2d,
    # nn.Conv3d: nnqd.Conv3d,
    # nn.ConvTranspose1d: nnqd.ConvTranspose1d,
    # nn.ConvTranspose2d: nnqd.ConvTranspose2d,
    # nn.ConvTranspose3d: nnqd.ConvTranspose3d,
}

# Allowlist for propagating the qconfig
_INCLUDE_QCONFIG_PROPAGATE_LIST : Set[Callable] = {
    nn.Sequential,
}

# Default mapping from floating point function or torch ops to quantized ops
# TODO: merge with default static mapping
DEFAULT_FLOAT_TO_QUANTIZED_OPERATOR_MAPPINGS : Dict[Union[Callable, str], Callable] = {
    F.elu: torch.ops.quantized.elu,
    F.hardswish: torch.ops.quantized.hardswish,
    F.instance_norm: torch.ops.quantized.instance_norm,
    F.layer_norm: torch.ops.quantized.layer_norm,
    F.leaky_relu: torch.ops.quantized.leaky_relu,
    F.dropout: torch.ops.quantized.dropout,
}

# mapping from module to output activation post process class
DEFAULT_MODULE_TO_ACT_POST_PROCESS : Dict[Callable, Callable] = {
    nn.Hardsigmoid: default_affine_fixed_qparams_fake_quant,
    nn.Sigmoid: default_affine_fixed_qparams_fake_quant,
    nn.Tanh: default_symmetric_fixed_qparams_fake_quant,
}

# Default map for swapping float module to static sparse quantized ones
DEFAULT_STATIC_SPARSE_QUANT_MODULE_MAPPINGS : Dict[Callable, Any] = {
    nn.Linear: ao_nn.sparse.quantized.Linear
}

# Default map for swapping float module to dynamic sparse quantized ones
DEFAULT_DYNAMIC_SPARSE_QUANT_MODULE_MAPPINGS : Dict[Callable, Any] = {
    nn.Linear: ao_nn.sparse.quantized.dynamic.Linear
}

def no_observer_set() -> Set[Any]:
    r"""These modules cannot have observers inserted by default."""
    no_observers = set([
        nn.quantizable.LSTM,
        nn.quantizable.MultiheadAttention
    ])
    return no_observers

def get_default_static_quant_module_mappings() -> Dict[Callable, Any]:
    ''' Get module mapping for post training static quantization
    '''
    return copy.deepcopy(DEFAULT_STATIC_QUANT_MODULE_MAPPINGS)

def get_embedding_static_quant_module_mappings() -> Dict[Callable, Any]:
    ''' Get module mapping, including mapping for embedding QAT
    '''
    mapping = copy.deepcopy(DEFAULT_STATIC_QUANT_MODULE_MAPPINGS)
    mapping[nnqat.EmbeddingBag] = nnq.EmbeddingBag
    mapping[nnqat.Embedding] = nnq.Embedding
    return mapping

def get_default_static_sparse_quant_module_mappings() -> Dict[Callable, Any]:
    ''' Get module mapping for post training static sparse quantization
    '''
    return copy.deepcopy(DEFAULT_STATIC_SPARSE_QUANT_MODULE_MAPPINGS)

def get_static_quant_module_class(
        float_module_class: Callable,
        additional_static_quant_mapping: Optional[Dict[Callable, Any]] = None,
        is_reference: bool = False) -> Any:
    r"""n Get the statically quantized module class corresponding to
    the floating point module class
    """
    if additional_static_quant_mapping is None:
        additional_static_quant_mapping = {}
    all_mappings = get_combined_dict(
        DEFAULT_REFERENCE_STATIC_QUANT_MODULE_MAPPINGS if is_reference
        else DEFAULT_STATIC_QUANT_MODULE_MAPPINGS, additional_static_quant_mapping)
    static_quant_module_class = all_mappings.get(float_module_class, None)
    assert static_quant_module_class is not None, \
        "Floating point module class {}".format(str(float_module_class)) + \
        " does not have a corresponding quantized module class"
    return copy.deepcopy(static_quant_module_class)

def get_dynamic_quant_module_class(
        float_module_class: Callable,
        additional_dynamic_quant_mapping: Optional[Dict[Callable, Any]] = None) -> Any:
    r"""n Get the dynamically quantized module class corresponding to
    the floating point module class
    """
    if additional_dynamic_quant_mapping is None:
        additional_dynamic_quant_mapping = {}
    all_mappings = get_combined_dict(DEFAULT_DYNAMIC_QUANT_MODULE_MAPPINGS, additional_dynamic_quant_mapping)
    dynamic_quant_module_class = all_mappings.get(float_module_class, None)
    assert dynamic_quant_module_class is not None, \
        "Floating point module class {}".format(str(float_module_class)) + \
        " does not have a corresponding quantized module class"
    return copy.deepcopy(dynamic_quant_module_class)

def get_default_qat_module_mappings() -> Dict[Callable, Any]:
    ''' Get default module mapping for quantization aware training
    '''
    return copy.deepcopy(DEFAULT_QAT_MODULE_MAPPINGS)

def get_embedding_qat_module_mappings() -> Dict[Callable, Any]:
    ''' Get module mapping for quantization aware training
        This is includes default values in addition to
        enabling qat for embeddings.
    '''
    mapping = copy.deepcopy(DEFAULT_QAT_MODULE_MAPPINGS)
    mapping[nn.EmbeddingBag] = nnqat.EmbeddingBag
    mapping[nn.Embedding] = nnqat.Embedding
    return mapping

def get_default_dynamic_quant_module_mappings() -> Dict[Callable, Any]:
    ''' Get module mapping for post training dynamic quantization
    '''
    return DEFAULT_DYNAMIC_QUANT_MODULE_MAPPINGS

def get_default_dynamic_sparse_quant_module_mappings() -> Dict[Callable, Any]:
    ''' Get module mapping for post training dynamic sparse quantization
    '''
    return DEFAULT_DYNAMIC_SPARSE_QUANT_MODULE_MAPPINGS

def get_default_qconfig_propagation_list() -> Set[Callable]:
    ''' Get the default list of module types that we'll attach qconfig
    attribute to in prepare
    '''
    QCONFIG_PROPAGATE_MODULE_CLASS_LIST = (
        (set(DEFAULT_STATIC_QUANT_MODULE_MAPPINGS.keys()) |
         set(DEFAULT_QAT_MODULE_MAPPINGS.keys()) |
         set(DEFAULT_DYNAMIC_QUANT_MODULE_MAPPINGS.keys()) |
         _INCLUDE_QCONFIG_PROPAGATE_LIST)
    )
    return copy.deepcopy(QCONFIG_PROPAGATE_MODULE_CLASS_LIST)

def get_default_compare_output_module_list() -> Set[Callable]:
    ''' Get list of module class types that we will record output
    in numeric suite
    '''
    NUMERIC_SUITE_COMPARE_MODEL_OUTPUT_MODULE_LIST = (
        set(DEFAULT_STATIC_QUANT_MODULE_MAPPINGS.values())
        | set(DEFAULT_QAT_MODULE_MAPPINGS.values())
        | set(DEFAULT_DYNAMIC_QUANT_MODULE_MAPPINGS.values())
        | set(DEFAULT_STATIC_QUANT_MODULE_MAPPINGS.keys())
        | set(DEFAULT_QAT_MODULE_MAPPINGS.keys())
        | set(DEFAULT_DYNAMIC_QUANT_MODULE_MAPPINGS.keys())
        | _INCLUDE_QCONFIG_PROPAGATE_LIST
    )
    return copy.deepcopy(NUMERIC_SUITE_COMPARE_MODEL_OUTPUT_MODULE_LIST)

def get_default_float_to_quantized_operator_mappings(
) -> Dict[Union[Callable, str], Callable]:
    return copy.deepcopy(DEFAULT_FLOAT_TO_QUANTIZED_OPERATOR_MAPPINGS)

# TODO: merge with get_static_quant_module_class
def get_quantized_operator(float_op: Union[Callable, str]) -> Callable:
    ''' Get the quantized operator corresponding to the float operator
    '''
    quantized_op = DEFAULT_FLOAT_TO_QUANTIZED_OPERATOR_MAPPINGS.get(float_op, None)
    assert quantized_op is not None, \
        'Operator {} does not have corresponding quantized op'.format(str(float_op))
    return quantized_op

def _get_special_act_post_process(module: torch.nn.Module) -> Optional[Callable]:
    r""" Get the special activation post process for `module`, this has
    higher priority than the activation post process in `qconfig`
    e.g.
    input: torch.nn.Sigmoid
    output: default_affine_fixed_qparam_fake_quant
    """
    return DEFAULT_MODULE_TO_ACT_POST_PROCESS.get(type(module), None)

def _has_special_act_post_process(module: torch.nn.Module) -> bool:
    return module.training and type(module) in DEFAULT_MODULE_TO_ACT_POST_PROCESS
