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# Copyright 2024 The HuggingFace 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.

from collections import defaultdict
from typing import TYPE_CHECKING

from ..integrations import prepare_for_hqq_linear
from ..utils import is_hqq_available, is_torch_available, logging
from .base import HfQuantizer
from .quantizers_utils import get_module_from_name


if TYPE_CHECKING:
    from ..modeling_utils import PreTrainedModel


if is_torch_available():
    import torch

if is_hqq_available():
    from hqq.core.quantize import HQQLinear

    # This is a compatibility hack. HQQ-quantized linear layers do not have a `weight` attribute,
    # but some models attempt to access `weight.dtype` during the forward pass. To prevent runtime errors,
    # we patch HQQLinear with a dummy `weight` property that returns an empty tensor with the correct dtype and device.
    @property
    def weight(self):
        return torch.empty(0, dtype=self.compute_dtype, device=self.device)

    HQQLinear.weight = weight

logger = logging.get_logger(__name__)


class HqqHfQuantizer(HfQuantizer):
    """
    HQQ quantizer base HF class.
    nn.Linear modules are first tagged with quant_config in _process_model_before_weight_loading().
    """

    use_keep_in_fp32_modules = False
    requires_parameters_quantization = True
    requires_calibration = False
    required_packages = ["hqq"]

    def __init__(self, quantization_config, **kwargs):
        if not is_hqq_available():
            raise ImportError(
                "A valid HQQ version (>=0.2.1) is not available. Please follow the instructions to install it: `https://github.com/mobiusml/hqq/`."
            )
        super().__init__(quantization_config, **kwargs)
        self.dtype = None
        self.using_multi_gpu = False
        # Keys that are serialized specifically by hqq
        self.hqq_keys = HQQLinear(None, None).state_dict_keys() - {"bias"}

        if kwargs.get("from_tf", False) or kwargs.get("from_flax", False):
            raise ValueError(
                "Converting weights from tf/flax weights is currently not supported, please make"
                " sure the weights are in PyTorch format."
            )

        if self.dtype is None:
            if "dtype" in kwargs:
                self.dtype = kwargs["dtype"]
            else:
                self.dtype = torch.float32
                logger.info("Setting dtype to torch.float32 as the default value since it was not specified.")

        device_map = kwargs.get("device_map")
        if isinstance(device_map, dict):
            if "cpu" in device_map.values() or "disk" in device_map.values():
                raise ValueError(
                    "You are attempting to use an HQQ model with a device_map that contains a CPU or disk device."
                    " This is not supported. Please remove the CPU or disk device from the device_map."
                )
            else:
                self.using_multi_gpu = len(set(device_map.values())) > 1

    def update_missing_keys(
        self, model: "PreTrainedModel", missing_keys: list[str], prefix: str, **kwargs
    ) -> list[str]:
        if self.pre_quantized:
            return [key for key in missing_keys if ("weight" not in key)]
        else:
            return missing_keys

    # Adds missing keys for HQQLinear modules that are loaded but the model with initialized with torch.nn.Linear
    def update_expected_keys(
        self, model: "PreTrainedModel", expected_keys: list[str], loaded_keys: list[str]
    ) -> list[str]:
        if not self.pre_quantized:
            return expected_keys

        # Collects all quantizable (linear) layers
        def _find_hqq_quantizable_layers(model, layers):
            for name, module in model.named_children():
                if isinstance(module, (torch.nn.Linear)):
                    layers.add(module.name)
                _find_hqq_quantizable_layers(module, layers)

        new_keys = set(expected_keys)

        # Name modules
        for name, module in model.named_modules():
            module.name = name

        # valid modules are Linear layers that have HQQLinear state_dict. We ignore skip_modules and any layers with Linear state_dict() params
        _valid_modules = set()
        _find_hqq_quantizable_layers(model, _valid_modules)

        # Remove skipped modules
        _skipped_modules = set()
        for _module in _valid_modules:
            for _skip_module in model.config.quantization_config["skip_modules"]:
                if _skip_module in _module:
                    _skipped_modules.add(_module)
        _valid_modules -= _skipped_modules

        # Append new expected layers based on _ref_keys
        _ref_keys = HQQLinear(
            linear_layer=None,
            quant_config=None,
            compute_dtype=torch.float16,
            device="cpu",
            del_orig=False,
        ).state_dict_keys() - {"bias"}

        # Clean-up
        _rm_keys = set()
        for key in new_keys:
            if any(_module in key for _module in _valid_modules):
                _rm_keys.add(key)
        new_keys -= _rm_keys
        # At this point, new_keys contains all the keys of the layers that are NOT HQQLinear or torch.nn.Linear

        # Re-populate Linear/HQQLinear
        for _module in _valid_modules:
            if _module + ".weight" in loaded_keys:
                new_keys.add(_module + ".weight")
            else:
                new_keys.update({_module + "." + _ref_key for _ref_key in _ref_keys})
            if _module + ".bias" in loaded_keys:
                new_keys.add(_module + ".bias")

        return list(new_keys)

    def param_needs_quantization(self, model: "PreTrainedModel", param_name: str, **kwargs) -> bool:
        module, _ = get_module_from_name(model, param_name)
        # Since we do not prepare the modules in advance, we need every param of the Linear layer to go through
        # `create_quantized_param`, even when `self.is_quantized == True`
        return isinstance(module, torch.nn.Linear)

    def create_quantized_param(
        self,
        model: "PreTrainedModel",
        param_value: "torch.Tensor",
        param_name: str,
        target_device: "torch.device",
        **kwargs,
    ):
        module, tensor_name = get_module_from_name(model, param_name)
        module_name = param_name.rsplit(".", 1)[0]
        parent_module, node = get_module_from_name(model, module_name)

        quant_config = model.config.quantization_config["quant_config"]
        skip_modules = model.config.quantization_config["skip_modules"]

        # In this case we do not quantize this layer (it's explicitly skipped) -> simply load param
        if any(skip_module in module.name for skip_module in skip_modules):
            module.load_state_dict(
                {tensor_name: param_value.to(device=target_device, dtype=self.dtype)}, strict=False, assign=True
            )
            return

        # We need this hack as the model is not pre-prepared as an empty skeleton on meta device
        if self.pre_quantized:
            # Save them for later
            if not hasattr(self, "hqq_params"):
                self.hqq_params = defaultdict(dict)
            self.hqq_params[module_name].update({tensor_name: param_value})
            hqq_params = self.hqq_params[module_name]

            # If they are all present and saved, make it a HQQLinear layer! (we cannot do it param after param because
            # hqq does not support it...)
            if all(k in hqq_params for k in self.hqq_keys) and ("bias" in hqq_params or module.bias is None):
                hqq_layer = HQQLinear(
                    linear_layer=None,
                    quant_config=None,
                    compute_dtype=self.dtype,
                    device=target_device,
                    del_orig=False,
                )
                hqq_layer.load_state_dict(hqq_params)

                if hqq_layer.bias is not None and isinstance(hqq_layer.bias, torch.Tensor):
                    hqq_layer.bias = torch.nn.Parameter(hqq_layer.bias)
                if self.using_multi_gpu:
                    hqq_layer = self._patch_layer_for_multigpu(hqq_layer)

                setattr(parent_module, node, hqq_layer)
                del self.hqq_params[module_name], module
            return

        # Load param in the module (without caring about device or dtype, it will be changed later)
        module.load_state_dict({tensor_name: param_value}, strict=False, assign=True)

        # If both the weight and bias have already been loaded, time to quantize!
        module_is_ready = module.weight.device.type != "meta" and (
            module.bias is None or module.bias.device.type != "meta"
        )

        if module_is_ready:
            module_tag = ".".join(module.name.split(".")[-2:])
            if "weight_quant_params" in quant_config:
                module_quant_config = quant_config
            elif module_tag in quant_config:
                module_quant_config = quant_config[module_tag]

            hqq_layer = HQQLinear(
                module,
                quant_config=module_quant_config,
                compute_dtype=self.dtype,
                device=target_device,
                del_orig=True,
            )

            if hqq_layer.bias is not None and isinstance(hqq_layer.bias, torch.Tensor):
                hqq_layer.bias = torch.nn.Parameter(hqq_layer.bias)

            if self.using_multi_gpu:
                hqq_layer = self._patch_layer_for_multigpu(hqq_layer)

            setattr(parent_module, node, hqq_layer)

    def _patch_layer_for_multigpu(self, hqq_layer):
        def forward_with_device(self, x):
            out = torch.matmul(x.to(self.device), self.dequantize().t())
            if self.bias is not None:
                out += self.bias
            return out

        hqq_layer.forward = lambda x: forward_with_device(hqq_layer, x)
        return hqq_layer

    def _process_model_before_weight_loading(
        self,
        model: "PreTrainedModel",
        **kwargs,
    ):
        # Add the corresponding quant_config to each valid module. This allows us to do the actual nn.Linear -> HQQLinear conversion in create_quantized_param().
        # prepare_for_hqq_linear() also sets the right quantization config inside the model (model.config.quantization_config) and the layers (hqq_layer.quant_config)
        model = prepare_for_hqq_linear(model, quantization_config=self.quantization_config)

    def _process_model_after_weight_loading(self, model: "PreTrainedModel", **kwargs):
        model.is_hqq_quantized = True
        model.is_hqq_serializable = self.is_serializable()
        return model

    def is_serializable(self, safe_serialization=None):
        return True

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

	# Copyright 2024 The HuggingFace 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.

	from collections import defaultdict
	from typing import TYPE_CHECKING

	from ..integrations import prepare_for_hqq_linear
	from ..utils import is_hqq_available, is_torch_available, logging
	from .base import HfQuantizer
	from .quantizers_utils import get_module_from_name


	if TYPE_CHECKING:
	from ..modeling_utils import PreTrainedModel


	if is_torch_available():
	import torch

	if is_hqq_available():
	from hqq.core.quantize import HQQLinear

	# This is a compatibility hack. HQQ-quantized linear layers do not have a `weight` attribute,
	# but some models attempt to access `weight.dtype` during the forward pass. To prevent runtime errors,
	# we patch HQQLinear with a dummy `weight` property that returns an empty tensor with the correct dtype and device.
	@property
	def weight(self):
	return torch.empty(0, dtype=self.compute_dtype, device=self.device)

	HQQLinear.weight = weight

	logger = logging.get_logger(__name__)


	class HqqHfQuantizer(HfQuantizer):
	"""
	HQQ quantizer base HF class.
	nn.Linear modules are first tagged with quant_config in _process_model_before_weight_loading().
	"""

	use_keep_in_fp32_modules = False
	requires_parameters_quantization = True
	requires_calibration = False
	required_packages = ["hqq"]

	def __init__(self, quantization_config, **kwargs):
	if not is_hqq_available():
	raise ImportError(
	"A valid HQQ version (>=0.2.1) is not available. Please follow the instructions to install it: `https://github.com/mobiusml/hqq/`."
	)
	super().__init__(quantization_config, **kwargs)
	self.dtype = None
	self.using_multi_gpu = False
	# Keys that are serialized specifically by hqq
	self.hqq_keys = HQQLinear(None, None).state_dict_keys() - {"bias"}

	if kwargs.get("from_tf", False) or kwargs.get("from_flax", False):
	raise ValueError(
	"Converting weights from tf/flax weights is currently not supported, please make"
	" sure the weights are in PyTorch format."
	)

	if self.dtype is None:
	if "dtype" in kwargs:
	self.dtype = kwargs["dtype"]
	else:
	self.dtype = torch.float32
	logger.info("Setting dtype to torch.float32 as the default value since it was not specified.")

	device_map = kwargs.get("device_map")
	if isinstance(device_map, dict):
	if "cpu" in device_map.values() or "disk" in device_map.values():
	raise ValueError(
	"You are attempting to use an HQQ model with a device_map that contains a CPU or disk device."
	" This is not supported. Please remove the CPU or disk device from the device_map."
	)
	else:
	self.using_multi_gpu = len(set(device_map.values())) > 1

	def update_missing_keys(
	self, model: "PreTrainedModel", missing_keys: list[str], prefix: str, **kwargs
	) -> list[str]:
	if self.pre_quantized:
	return [key for key in missing_keys if ("weight" not in key)]
	else:
	return missing_keys

	# Adds missing keys for HQQLinear modules that are loaded but the model with initialized with torch.nn.Linear
	def update_expected_keys(
	self, model: "PreTrainedModel", expected_keys: list[str], loaded_keys: list[str]
	) -> list[str]:
	if not self.pre_quantized:
	return expected_keys

	# Collects all quantizable (linear) layers
	def _find_hqq_quantizable_layers(model, layers):
	for name, module in model.named_children():
	if isinstance(module, (torch.nn.Linear)):
	layers.add(module.name)
	_find_hqq_quantizable_layers(module, layers)

	new_keys = set(expected_keys)

	# Name modules
	for name, module in model.named_modules():
	module.name = name

	# valid modules are Linear layers that have HQQLinear state_dict. We ignore skip_modules and any layers with Linear state_dict() params
	_valid_modules = set()
	_find_hqq_quantizable_layers(model, _valid_modules)

	# Remove skipped modules
	_skipped_modules = set()
	for _module in _valid_modules:
	for _skip_module in model.config.quantization_config["skip_modules"]:
	if _skip_module in _module:
	_skipped_modules.add(_module)
	_valid_modules -= _skipped_modules

	# Append new expected layers based on _ref_keys
	_ref_keys = HQQLinear(
	linear_layer=None,
	quant_config=None,
	compute_dtype=torch.float16,
	device="cpu",
	del_orig=False,
	).state_dict_keys() - {"bias"}

	# Clean-up
	_rm_keys = set()
	for key in new_keys:
	if any(_module in key for _module in _valid_modules):
	_rm_keys.add(key)
	new_keys -= _rm_keys
	# At this point, new_keys contains all the keys of the layers that are NOT HQQLinear or torch.nn.Linear

	# Re-populate Linear/HQQLinear
	for _module in _valid_modules:
	if _module + ".weight" in loaded_keys:
	new_keys.add(_module + ".weight")
	else:
	new_keys.update({_module + "." + _ref_key for _ref_key in _ref_keys})
	if _module + ".bias" in loaded_keys:
	new_keys.add(_module + ".bias")

	return list(new_keys)

	def param_needs_quantization(self, model: "PreTrainedModel", param_name: str, **kwargs) -> bool:
	module, _ = get_module_from_name(model, param_name)
	# Since we do not prepare the modules in advance, we need every param of the Linear layer to go through
	# `create_quantized_param`, even when `self.is_quantized == True`
	return isinstance(module, torch.nn.Linear)

	def create_quantized_param(
	self,
	model: "PreTrainedModel",
	param_value: "torch.Tensor",
	param_name: str,
	target_device: "torch.device",
	**kwargs,
	):
	module, tensor_name = get_module_from_name(model, param_name)
	module_name = param_name.rsplit(".", 1)[0]
	parent_module, node = get_module_from_name(model, module_name)

	quant_config = model.config.quantization_config["quant_config"]
	skip_modules = model.config.quantization_config["skip_modules"]

	# In this case we do not quantize this layer (it's explicitly skipped) -> simply load param
	if any(skip_module in module.name for skip_module in skip_modules):
	module.load_state_dict(
	{tensor_name: param_value.to(device=target_device, dtype=self.dtype)}, strict=False, assign=True
	)
	return

	# We need this hack as the model is not pre-prepared as an empty skeleton on meta device
	if self.pre_quantized:
	# Save them for later
	if not hasattr(self, "hqq_params"):
	self.hqq_params = defaultdict(dict)
	self.hqq_params[module_name].update({tensor_name: param_value})
	hqq_params = self.hqq_params[module_name]

	# If they are all present and saved, make it a HQQLinear layer! (we cannot do it param after param because
	# hqq does not support it...)
	if all(k in hqq_params for k in self.hqq_keys) and ("bias" in hqq_params or module.bias is None):
	hqq_layer = HQQLinear(
	linear_layer=None,
	quant_config=None,
	compute_dtype=self.dtype,
	device=target_device,
	del_orig=False,
	)
	hqq_layer.load_state_dict(hqq_params)

	if hqq_layer.bias is not None and isinstance(hqq_layer.bias, torch.Tensor):
	hqq_layer.bias = torch.nn.Parameter(hqq_layer.bias)
	if self.using_multi_gpu:
	hqq_layer = self._patch_layer_for_multigpu(hqq_layer)

	setattr(parent_module, node, hqq_layer)
	del self.hqq_params[module_name], module
	return

	# Load param in the module (without caring about device or dtype, it will be changed later)
	module.load_state_dict({tensor_name: param_value}, strict=False, assign=True)

	# If both the weight and bias have already been loaded, time to quantize!
	module_is_ready = module.weight.device.type != "meta" and (
	module.bias is None or module.bias.device.type != "meta"
	)

	if module_is_ready:
	module_tag = ".".join(module.name.split(".")[-2:])
	if "weight_quant_params" in quant_config:
	module_quant_config = quant_config
	elif module_tag in quant_config:
	module_quant_config = quant_config[module_tag]

	hqq_layer = HQQLinear(
	module,
	quant_config=module_quant_config,
	compute_dtype=self.dtype,
	device=target_device,
	del_orig=True,
	)

	if hqq_layer.bias is not None and isinstance(hqq_layer.bias, torch.Tensor):
	hqq_layer.bias = torch.nn.Parameter(hqq_layer.bias)

	if self.using_multi_gpu:
	hqq_layer = self._patch_layer_for_multigpu(hqq_layer)

	setattr(parent_module, node, hqq_layer)

	def _patch_layer_for_multigpu(self, hqq_layer):
	def forward_with_device(self, x):
	out = torch.matmul(x.to(self.device), self.dequantize().t())
	if self.bias is not None:
	out += self.bias
	return out

	hqq_layer.forward = lambda x: forward_with_device(hqq_layer, x)
	return hqq_layer

	def _process_model_before_weight_loading(
	self,
	model: "PreTrainedModel",
	**kwargs,
	):
	# Add the corresponding quant_config to each valid module. This allows us to do the actual nn.Linear -> HQQLinear conversion in create_quantized_param().
	# prepare_for_hqq_linear() also sets the right quantization config inside the model (model.config.quantization_config) and the layers (hqq_layer.quant_config)
	model = prepare_for_hqq_linear(model, quantization_config=self.quantization_config)

	def _process_model_after_weight_loading(self, model: "PreTrainedModel", **kwargs):
	model.is_hqq_quantized = True
	model.is_hqq_serializable = self.is_serializable()
	return model

	def is_serializable(self, safe_serialization=None):
	return True

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