# Copyright 2023-present the HuggingFace Inc. team.
#
# 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 contextlib import contextmanager

import packaging.version
import torch
import transformers


@contextmanager
def gather_params_ctx(module: torch.nn.Module, modifier_rank: int = 0):
    """Call DeepSpeed GatheredParameters context manager if DeepSpeed is enabled, otherwise do nothing."""
    if packaging.version.parse(transformers.__version__) >= packaging.version.parse("4.33.0"):
        from transformers.integrations import is_deepspeed_zero3_enabled
    else:
        from transformers.deepspeed import is_deepspeed_zero3_enabled

    if not is_deepspeed_zero3_enabled():
        yield
        return

    import deepspeed

    params_to_gather = module.parameters()
    with deepspeed.zero.GatheredParameters(params_to_gather, modifier_rank=modifier_rank):
        yield
    return


def dequantize_bnb_weight(weight: torch.nn.Parameter, state=None):
    """
    Helper function to dequantize 4bit or 8bit bnb weights.

    If the weight is not a bnb quantized weight, it will be returned as is.
    """
    if not isinstance(weight, torch.nn.Parameter):
        raise TypeError(f"Input weight should be of type nn.Parameter, got {type(weight)} instead")

    cls_name = weight.__class__.__name__
    if cls_name not in ("Params4bit", "Int8Params"):
        return weight

    import bitsandbytes as bnb

    if cls_name == "Params4bit":
        return bnb.functional.dequantize_4bit(weight.data, weight.quant_state)

    if state.SCB is None:
        state.SCB = weight.SCB

    im = torch.eye(weight.data.shape[-1]).contiguous().half().to(weight.device)
    im, imt, SCim, SCimt, coo_tensorim = bnb.functional.double_quant(im)
    im, Sim = bnb.functional.transform(im, "col32")
    if state.CxB is None:
        state.CxB, state.SB = bnb.functional.transform(weight.data, to_order=state.formatB)
    out32, Sout32 = bnb.functional.igemmlt(im, state.CxB, Sim, state.SB)
    return bnb.functional.mm_dequant(out32, Sout32, SCim, state.SCB, bias=None).t()