# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import importlib
import re
import types
from typing import TYPE_CHECKING, Optional, Union

from packaging import version

from .base import HfQuantizer
from .quantizers_utils import get_module_from_name


if TYPE_CHECKING:
    from ..modeling_utils import PreTrainedModel

from typing import Any

from ..utils import is_torch_available, is_torchao_available, logging
from ..utils.quantization_config import TorchAoConfig


if is_torch_available():
    import torch
    import torch.nn as nn

logger = logging.get_logger(__name__)


def fuzzy_match_size(config_name: str) -> Optional[str]:
    """
    Extract the size digit from strings like "4weight", "8weight".
    Returns the digit as an integer if found, otherwise None.
    """
    config_name = config_name.lower()

    str_match = re.search(r"(\d)weight", config_name)

    if str_match:
        return str_match.group(1)

    return None


# Finds the parent of a node module named "name"
def find_parent(model, name):
    module_tree = name.split(".")[:-1]
    parent = model
    for m in module_tree:
        parent = parent._modules[m]
    return parent


def _quantization_type(weight):
    from torchao.dtypes import AffineQuantizedTensor
    from torchao.quantization.linear_activation_quantized_tensor import LinearActivationQuantizedTensor

    if isinstance(weight, AffineQuantizedTensor):
        return f"{weight.__class__.__name__}({weight._quantization_type()})"

    if isinstance(weight, LinearActivationQuantizedTensor):
        return f"{weight.__class__.__name__}(activation={weight.input_quant_func}, weight={_quantization_type(weight.original_weight_tensor)})"


def _linear_extra_repr(self):
    weight = _quantization_type(self.weight)
    if weight is None:
        return f"in_features={self.weight.shape[1]}, out_features={self.weight.shape[0]}, weight=None"
    else:
        return f"in_features={self.weight.shape[1]}, out_features={self.weight.shape[0]}, weight={weight}"


class TorchAoHfQuantizer(HfQuantizer):
    """
    Quantizer for torchao: https://github.com/pytorch/ao/
    """

    requires_parameters_quantization = True
    requires_calibration = False
    required_packages = ["torchao"]

    def __init__(self, quantization_config, **kwargs):
        super().__init__(quantization_config, **kwargs)

    def validate_environment(self, *args, **kwargs):
        if not is_torchao_available():
            raise ImportError("Loading an torchao quantized model requires torchao library (`pip install torchao`)")

        self.offload = False
        device_map = kwargs.get("device_map")
        if isinstance(device_map, dict):
            if ("disk" in device_map.values() or "cpu" in device_map.values()) and len(device_map) > 1:
                self.offload = True
                if self.pre_quantized and "disk" in device_map.values():
                    raise ValueError(
                        "You are attempting to perform disk offload with a pre-quantized torchao model "
                        "This is not supported yet . Please remove the disk device from the device_map."
                    )
        if self.pre_quantized:
            weights_only = kwargs.get("weights_only")
            if weights_only:
                torch_version = version.parse(importlib.metadata.version("torch"))
                if torch_version < version.parse("2.5.0"):
                    raise RuntimeError(
                        f"In order to use torchao pre-quantized model, you need to have torch>=2.5.0. However, the current version is {torch_version}."
                        f" You can also set with `weights_only=False` in `from_pretrained` if you don't want to update torch"
                    )

    def update_torch_dtype(self, torch_dtype):
        if self.quantization_config.quant_type == "int4_weight_only":
            if torch_dtype is not None and torch_dtype != torch.bfloat16:
                logger.warning_once(
                    f"Setting torch_dtype to {torch_dtype} for int4_weight_only quantization, but only bfloat16 is supported right now. Please set the torch_dtype to bfloat16."
                )
            if torch_dtype is None:
                logger.warning_once(
                    "Setting torch_dtype to torch.bfloat16 for int4_weight_only quantization since only bfloat16 is supported right now. Please set torch_dtype=torch.bfloat16 to remove this warning."
                )
                torch_dtype = torch.bfloat16
        if self.quantization_config.quant_type == "int8_dynamic_activation_int8_weight":
            if torch_dtype is None:
                logger.info(
                    "Setting torch_dtype to torch.float32 for int8_dynamic_activation_int8_weight quantization as no torch_dtype was specified in from_pretrained"
                )
                # we need to set the torch_dtype, otherwise we have dtype mismatch when performing the quantized linear op
                torch_dtype = torch.float32
        return torch_dtype

    def adjust_target_dtype(self, torch_dtype: "torch.dtype") -> "torch.dtype":
        if version.parse(importlib.metadata.version("accelerate")) > version.parse("0.19.0"):
            from accelerate.utils import CustomDtype

            # Import AOBaseConfig directly since we know we have the right version
            if self.quantization_config._get_ao_version() > version.Version("0.9.0"):
                from torchao.core.config import AOBaseConfig

                quant_type = self.quantization_config.quant_type
                if isinstance(quant_type, AOBaseConfig):
                    # Extract size digit using fuzzy match on the class name
                    config_name = quant_type.__class__.__name__
                    size_digit = fuzzy_match_size(config_name)

                    # Map the extracted digit to appropriate dtype
                    if size_digit == "4":
                        return CustomDtype.INT4
                    else:
                        # Default to int8
                        return torch.int8

            # Original mapping for non-AOBaseConfig types
            map_to_target_dtype = {
                "int4_weight_only": CustomDtype.INT4,
                "int8_weight_only": torch.int8,
                "int8_dynamic_activation_int8_weight": torch.int8,
                "autoquant": None,
            }
            return map_to_target_dtype[self.quantization_config.quant_type]
        else:
            raise ValueError(
                "You are using `device_map='auto'` on a torchao quantized model. To automatically compute"
                " the appropriate device map, you should upgrade your `accelerate` library with "
                "`pip install --upgrade accelerate`"
            )

    def adjust_max_memory(self, max_memory: dict[str, Union[int, str]]) -> dict[str, Union[int, str]]:
        # need more space for the quantization parameters (e.g. scale). Tested with int4 wo and group size = 128
        max_memory = {key: val * 0.9 for key, val in max_memory.items()}
        return max_memory

    def _process_model_before_weight_loading(
        self, model: "PreTrainedModel", keep_in_fp32_modules: Optional[list[str]] = None, **kwargs
    ):
        self.modules_to_not_convert = self.get_modules_to_not_convert(
            model, self.quantization_config.modules_to_not_convert, keep_in_fp32_modules
        )
        if self.quantization_config.include_input_output_embeddings:
            input_emb = model.get_input_embeddings()
            input_emb_names = [name for name, module in model.named_modules() if id(module) == id(input_emb)]
            output_emb = model.get_output_embeddings()
            output_emb_names = [name for name, module in model.named_modules() if id(module) == id(output_emb)]
            self.modules_to_not_convert = [
                x for x in self.modules_to_not_convert if x not in input_emb_names + output_emb_names
            ]
        return

    def check_quantized_param(
        self,
        model: "PreTrainedModel",
        param_value: "torch.Tensor",
        param_name: str,
        state_dict: dict[str, Any],
        **kwargs,
    ) -> bool:
        if self.quantization_config.quant_type == "autoquant":
            return False

        param_device = kwargs.pop("param_device", None)
        # check if the param_name is not in self.modules_to_not_convert
        if any((key + "." in param_name) or (key == param_name) for key in self.modules_to_not_convert):
            return False
        elif param_device == "cpu" and self.offload:
            # We don't quantize weights that we offload
            return False
        else:
            # we only quantize the weight of nn.Linear and nn.Embedding
            module, tensor_name = get_module_from_name(model, param_name)
            _QUANTIZABLE = [torch.nn.Linear]
            if self.quantization_config.include_input_output_embeddings:
                _QUANTIZABLE.append(torch.nn.Embedding)
            return isinstance(module, tuple(_QUANTIZABLE)) and (tensor_name == "weight")

    def create_quantized_param(
        self,
        model: "PreTrainedModel",
        param_value: "torch.Tensor",
        param_name: str,
        target_device: "torch.device",
        state_dict: dict[str, Any],
        unexpected_keys: list[str],
    ):
        """
        Each nn.Linear layer that needs to be quantized is processed here.
        First, we set the value the weight tensor, then we move it to the target device. Finally, we quantize the module.
        """
        if self.quantization_config.quant_type == "autoquant":
            return

        from torchao.quantization import quantize_

        module, tensor_name = get_module_from_name(model, param_name)
        if self.pre_quantized:
            module._parameters[tensor_name] = torch.nn.Parameter(
                param_value.to(device=target_device), requires_grad=param_value.requires_grad
            )
            if isinstance(module, nn.Linear):
                module.extra_repr = types.MethodType(_linear_extra_repr, module)
        else:
            assert isinstance(self.quantization_config, TorchAoConfig)
            module._parameters[tensor_name] = torch.nn.Parameter(
                param_value, requires_grad=param_value.requires_grad
            ).to(device=target_device)
            # if we are quantizing tied parameters, to avoid tying the quantized weights
            # the correct order to do it is
            # 1. load the weight to model
            # 2. run tie_weights to populate the weights
            # 3. quantize
            input_embed = model.get_input_embeddings()
            if self.quantization_config.untie_embedding_weights and id(module) == id(input_embed):
                model.tie_weights()
                setattr(model.config.get_text_config(decoder=True), "tie_word_embeddings", False)

            # handle ModuleFqnToConfig, introduced in torchao 0.12.0+
            if self.quantization_config._get_ao_version() >= version.Version("0.12.0"):
                from torchao.quantization import ModuleFqnToConfig

                config = self.quantization_config.get_apply_tensor_subclass()
                if isinstance(config, ModuleFqnToConfig):
                    module_fqn, _ = param_name.rsplit(".", 1)
                    c = None
                    if module_fqn in config.module_fqn_to_config:
                        c = config.module_fqn_to_config[module_fqn]
                    else:
                        c = config.module_fqn_to_config.get("_default", None)
                    if c is not None:
                        # filter_fn: not filtering out any modules
                        quantize_(module, c, filter_fn=lambda x, fqn: True)
                    return

            quantize_(module, self.quantization_config.get_apply_tensor_subclass())

    def _process_model_after_weight_loading(self, model, **kwargs):
        """No process required for torchao quantized model"""
        if self.quantization_config.quant_type == "autoquant":
            from torchao import autoquant
            from torchao.quantization import ALL_AUTOQUANT_CLASS_LIST

            model = torch.compile(model, mode="max-autotune")
            model = autoquant(
                model,
                qtensor_class_list=ALL_AUTOQUANT_CLASS_LIST,
                set_inductor_config=False,
                **self.quantization_config.quant_type_kwargs,
            )
            return model
        return

    def is_serializable(self, safe_serialization=None) -> bool:
        if safe_serialization:
            logger.warning(
                "torchao quantized model does not support safe serialization, please set `safe_serialization` to False"
            )
            return False
        _is_torchao_serializable = version.parse(importlib.metadata.version("huggingface_hub")) >= version.parse(
            "0.25.0"
        )
        if not _is_torchao_serializable:
            logger.warning("torchao quantized model is only serializable after huggingface_hub >= 0.25.0 ")
        if self.offload and self.quantization_config.modules_to_not_convert is None:
            logger.warning(
                "The model contains offloaded modules and these modules are not quantized. We don't recommend saving the model as we won't be able to reload them."
                "If you want to specify modules to not quantize, please specify modules_to_not_convert in the quantization_config."
            )
            return False
        return _is_torchao_serializable

    def get_cuda_warm_up_factor(self):
        """
        This factor is used in caching_allocator_warmup to determine how many bytes to pre-allocate for CUDA warmup.
        - A factor of 2 means we pre-allocate the full memory footprint of the model.
        - A factor of 4 means we pre-allocate half of that, and so on

        However, when using TorchAO, calculating memory usage with param.numel() * param.element_size() doesn't give the correct size for quantized weights (like int4 or int8)
        That's because TorchAO internally represents quantized tensors using subtensors and metadata, and the reported element_size() still corresponds to the torch_dtype
        not the actual bit-width of the quantized data.

        To correct for this:
        - Use a division factor of 8 for int4 weights
        - Use a division factor of 4 for int8 weights
        """
        if self.quantization_config._get_ao_version() > version.Version("0.9.0"):
            from torchao.core.config import AOBaseConfig

            quant_type = self.quantization_config.quant_type
            # For autoquant case, it will be treated in the string implementation below in map_to_target_dtype
            if isinstance(quant_type, AOBaseConfig):
                # Extract size digit using fuzzy match on the class name
                config_name = quant_type.__class__.__name__
                size_digit = fuzzy_match_size(config_name)

                if size_digit == "4":
                    return 8
                else:
                    return 4

        # Original mapping for non-AOBaseConfig types
        map_to_target_dtype = {
            "int4_weight_only": 8,
            "int8_weight_only": 4,
            "int8_dynamic_activation_int8_weight": 4,
            "autoquant": 4,
        }

        return map_to_target_dtype[self.quantization_config.quant_type]

    @property
    def is_trainable(self) -> bool:
        supported_quant_types_for_training = [
            "int8_weight_only",
            "int8_dynamic_activation_int8_weight",
        ]
        return self.quantization_config.quant_type in supported_quant_types_for_training

    @property
    def is_compileable(self) -> bool:
        return True
