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# coding=utf-8
# Copyright 2025 Advanced Micro Devices, Inc. and 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_quark_available, logging


logger = logging.get_logger(__name__)


CHECKPOINT_KEYS = {
    "weight_scale": "weight_quantizer.scale",
    "bias_scale": "bias_quantizer.scale",
    "input_scale": "input_quantizer.scale",
    "output_scale": "output_quantizer.scale",
    "weight_zero_point": "weight_quantizer.zero_point",
    "bias_zero_point": "bias_quantizer.zero_point",
    "input_zero_point": "input_quantizer.zero_point",
    "output_zero_point": "output_quantizer.zero_point",
}


class QuarkHfQuantizer(HfQuantizer):
    """
    Quark quantizer (https://quark.docs.amd.com/latest/).
    """

    requires_calibration = True  # On-the-fly quantization with quark is not supported for now.
    required_packages = ["quark"]

    # Checkpoints are expected to be already quantized when loading a quark model. However, as some keys from
    # the checkpoint might mismatch the model parameters keys, we use the `create_quantized_param` method
    # to load the checkpoints, remapping the keys.
    requires_parameters_quantization = True

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

        self.json_export_config = quantization_config.json_export_config

    def validate_environment(self, *args, **kwargs):
        if not is_quark_available():
            raise ImportError(
                "Loading a Quark quantized model requires the `quark` library but it was not found in the environment. Please refer to https://quark.docs.amd.com/latest/install.html."
            )

    def _process_model_before_weight_loading(self, model: "PreTrainedModel", **kwargs):
        from quark.torch.export.api import _map_to_quark

        _map_to_quark(
            model,
            self.quantization_config.quant_config,
            pack_method=self.json_export_config.pack_method,
            custom_mode=self.quantization_config.custom_mode,
        )

        return model

    def param_needs_quantization(self, model: "PreTrainedModel", param_name: str, **kwargs) -> bool:
        return True

    def create_quantized_param(self, model, param, param_name, param_device, **kwargs):
        from ..modeling_utils import _load_parameter_into_model

        postfix = param_name.split(".")[-1]

        if postfix in CHECKPOINT_KEYS:
            param_name = param_name.replace(postfix, CHECKPOINT_KEYS[postfix])

        _load_parameter_into_model(model, param_name, param.to(param_device))

    def _process_model_after_weight_loading(self, model: "PreTrainedModel", **kwargs):
        return model

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

    @property
    def is_trainable(self):
        return False

	# coding=utf-8
	# Copyright 2025 Advanced Micro Devices, Inc. and 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_quark_available, logging


	logger = logging.get_logger(__name__)


	CHECKPOINT_KEYS = {
	"weight_scale": "weight_quantizer.scale",
	"bias_scale": "bias_quantizer.scale",
	"input_scale": "input_quantizer.scale",
	"output_scale": "output_quantizer.scale",
	"weight_zero_point": "weight_quantizer.zero_point",
	"bias_zero_point": "bias_quantizer.zero_point",
	"input_zero_point": "input_quantizer.zero_point",
	"output_zero_point": "output_quantizer.zero_point",
	}


	class QuarkHfQuantizer(HfQuantizer):
	"""
	Quark quantizer (https://quark.docs.amd.com/latest/).
	"""

	requires_calibration = True # On-the-fly quantization with quark is not supported for now.
	required_packages = ["quark"]

	# Checkpoints are expected to be already quantized when loading a quark model. However, as some keys from
	# the checkpoint might mismatch the model parameters keys, we use the `create_quantized_param` method
	# to load the checkpoints, remapping the keys.
	requires_parameters_quantization = True

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

	self.json_export_config = quantization_config.json_export_config

	def validate_environment(self, args, *kwargs):
	if not is_quark_available():
	raise ImportError(
	"Loading a Quark quantized model requires the `quark` library but it was not found in the environment. Please refer to https://quark.docs.amd.com/latest/install.html."
	)

	def _process_model_before_weight_loading(self, model: "PreTrainedModel", **kwargs):
	from quark.torch.export.api import _map_to_quark

	_map_to_quark(
	model,
	self.quantization_config.quant_config,
	pack_method=self.json_export_config.pack_method,
	custom_mode=self.quantization_config.custom_mode,
	)

	return model

	def param_needs_quantization(self, model: "PreTrainedModel", param_name: str, **kwargs) -> bool:
	return True

	def create_quantized_param(self, model, param, param_name, param_device, **kwargs):
	from ..modeling_utils import _load_parameter_into_model

	postfix = param_name.split(".")[-1]

	if postfix in CHECKPOINT_KEYS:
	param_name = param_name.replace(postfix, CHECKPOINT_KEYS[postfix])

	_load_parameter_into_model(model, param_name, param.to(param_device))

	def _process_model_after_weight_loading(self, model: "PreTrainedModel", **kwargs):
	return model

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

	@property
	def is_trainable(self):
	return False