# 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 os

from ..utils import is_compressed_tensors_available, is_torch_available, logging
from ..utils.quantization_config import CompressedTensorsConfig
from .base import HfQuantizer


if is_torch_available():
    import torch

logger = logging.get_logger(__name__)


class CompressedTensorsHfQuantizer(HfQuantizer):
    """
    Quantizer for the compressed_tensors package.  Loads and restores models to
    quantized state with compressed_tensors
    """

    requires_calibration = True
    required_packages = ["compressed_tensors"]

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

        if not is_compressed_tensors_available():
            raise ImportError(
                "Using `compressed_tensors` quantized models requires the compressed-tensors library: "
                "`pip install compressed-tensors`"
            )

        from compressed_tensors.compressors import ModelCompressor

        self.compressor = ModelCompressor.from_compression_config(quantization_config)
        self.run_compressed = quantization_config.run_compressed
        self.quantization_config = quantization_config

    def validate_environment(self, *args, **kwargs):
        if not is_compressed_tensors_available():
            raise ImportError(
                "Using `compressed_tensors` quantized models requires the compressed-tensors library: "
                "`pip install compressed-tensors`"
            )
        if not is_torch_available():
            # torch already should be installed as part of compressed tensors
            raise ImportError("torch is required for using compressed-tensors quantization")

    def update_torch_dtype(self, torch_dtype: "torch.dtype") -> "torch.dtype":
        if torch_dtype is None:
            logger.info("Loading model using torch.float16 for compressed-tensors quantization")
            torch_dtype = torch.float16
        elif torch_dtype != torch.float16:
            logger.info(
                "We suggest you to set `torch_dtype=torch.float16` for better efficiency with compressed_tensors."
            )
        return torch_dtype

    def _process_model_before_weight_loading(self, model, **kwargs):
        from compressed_tensors.quantization import apply_quantization_config

        ct_quantization_config = self.compressor.quantization_config

        if self.run_compressed and self.is_quantization_compressed:
            apply_quantization_config(model, ct_quantization_config, run_compressed=True)
        elif not self.is_quantization_compressed:
            apply_quantization_config(model, ct_quantization_config)

    def _process_model_after_weight_loading(self, model, **kwargs):
        """Decompress loaded model if necessary - need for qat"""

        if (self.is_quantization_compressed and not self.run_compressed) or self.is_sparsification_compressed:
            config = kwargs.get("config", None)
            cache_path = config._name_or_path

            if not os.path.exists(cache_path):
                from my_transformers.utils import cached_file

                config_file_path = cached_file(cache_path, "config.json")
                cache_path = os.path.sep.join(config_file_path.split(os.path.sep)[:-1])

            if self.is_quantization_compressed and not self.run_compressed:
                from compressed_tensors.quantization import QuantizationStatus

                self.compressor.quantization_config.quantization_status = QuantizationStatus.FROZEN
            self.compressor.decompress(model_path=cache_path, model=model)

    @property
    def is_quantization_compressed(self):
        from compressed_tensors.quantization import QuantizationStatus

        return (
            self.quantization_config.quantization_config is not None
            and self.quantization_config.quantization_config.quantization_status == QuantizationStatus.COMPRESSED
        )

    @property
    def is_sparsification_compressed(self):
        from compressed_tensors.config.base import CompressionFormat

        return (
            self.quantization_config.sparsity_config is not None
            and self.quantization_config.sparsity_config.format != CompressionFormat.dense.value
        )

    @property
    def is_trainable(self):
        return True

    def is_qat_trainable(self) -> bool:
        """Loaded Models can carry out quantization aware training"""
        # models need to be decompressed carry out qat
        return not self.run_compressed or not self.is_quantization_compressed

    def is_serializable(self, safe_serialization=None) -> bool:
        """Models quantized using compressed tensors can be saved to disk"""
        return True
