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# 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.
from typing import TYPE_CHECKING

from .base import HfQuantizer


if TYPE_CHECKING:
    from ..modeling_utils import PreTrainedModel

from ..utils import is_auto_round_available, is_torch_available, logging
from ..utils.quantization_config import QuantizationConfigMixin


if is_torch_available():
    import torch

logger = logging.get_logger(__name__)


class AutoRoundQuantizer(HfQuantizer):
    """
    Quantizer of the AutoRound method. (https://huggingface.co/papers/2309.05516)
    """

    # AutoRound requires data calibration - we support only inference
    requires_calibration = True
    required_packages = ["auto_round"]

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

    def validate_environment(self, *args, **kwargs):
        self.device_map = kwargs.get("device_map")
        if not is_auto_round_available():
            raise ImportError(
                "Loading an AutoRound quantized model requires auto-round library (`pip install 'auto-round>=0.5'`)"
            )

    def update_dtype(self, dtype: "torch.dtype") -> "torch.dtype":
        if dtype is None:
            dtype = torch.bfloat16
            logger.info("Loading the model in `torch.bfloat16`. To overwrite it, set `dtype` manually.")
        return dtype

    def _process_model_before_weight_loading(self, model: "PreTrainedModel", **kwargs):
        if model.__class__.main_input_name != "input_ids":
            logger.warning("AutoRound offers only limited support for models that are not strictly text-based.")
        from auto_round.inference.convert_model import convert_hf_model, infer_target_device

        if self.pre_quantized:
            target_device = infer_target_device(self.device_map)
            model, used_backends = convert_hf_model(model, target_device)
            self.used_backends = used_backends

    def _process_model_after_weight_loading(self, model: "PreTrainedModel", **kwargs):
        if self.pre_quantized:
            from auto_round.inference.convert_model import post_init

            post_init(model, self.used_backends)
        else:
            raise ValueError("AutoRound only sports pre-quantized models.")

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

    def is_serializable(self, safe_serialization=None):
        ## for gptq/awq models, the quantization config will be changed
        return True

	# 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.
	from typing import TYPE_CHECKING

	from .base import HfQuantizer


	if TYPE_CHECKING:
	from ..modeling_utils import PreTrainedModel

	from ..utils import is_auto_round_available, is_torch_available, logging
	from ..utils.quantization_config import QuantizationConfigMixin


	if is_torch_available():
	import torch

	logger = logging.get_logger(__name__)


	class AutoRoundQuantizer(HfQuantizer):
	"""
	Quantizer of the AutoRound method. (https://huggingface.co/papers/2309.05516)
	"""

	# AutoRound requires data calibration - we support only inference
	requires_calibration = True
	required_packages = ["auto_round"]

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

	def validate_environment(self, args, *kwargs):
	self.device_map = kwargs.get("device_map")
	if not is_auto_round_available():
	raise ImportError(
	"Loading an AutoRound quantized model requires auto-round library (`pip install 'auto-round>=0.5'`)"
	)

	def update_dtype(self, dtype: "torch.dtype") -> "torch.dtype":
	if dtype is None:
	dtype = torch.bfloat16
	logger.info("Loading the model in `torch.bfloat16`. To overwrite it, set `dtype` manually.")
	return dtype

	def _process_model_before_weight_loading(self, model: "PreTrainedModel", **kwargs):
	if model.__class__.main_input_name != "input_ids":
	logger.warning("AutoRound offers only limited support for models that are not strictly text-based.")
	from auto_round.inference.convert_model import convert_hf_model, infer_target_device

	if self.pre_quantized:
	target_device = infer_target_device(self.device_map)
	model, used_backends = convert_hf_model(model, target_device)
	self.used_backends = used_backends

	def _process_model_after_weight_loading(self, model: "PreTrainedModel", **kwargs):
	if self.pre_quantized:
	from auto_round.inference.convert_model import post_init

	post_init(model, self.used_backends)
	else:
	raise ValueError("AutoRound only sports pre-quantized models.")

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

	def is_serializable(self, safe_serialization=None):
	## for gptq/awq models, the quantization config will be changed
	return True