# mypy: disable-error-code="name-defined,no-redef"
from __future__ import annotations
import logging
import typing as t

from openllm_core._typing_compat import overload
from openllm_core.utils import LazyLoader
from openllm_core.utils import is_autogptq_available
from openllm_core.utils import is_bitsandbytes_available
from openllm_core.utils import is_transformers_supports_kbit
from openllm_core.utils import pkg

if t.TYPE_CHECKING:
  from openllm_core._typing_compat import DictStrAny

  from ._llm import LLM

autogptq, torch, transformers = LazyLoader('autogptq', globals(),
                                           'auto_gptq'), LazyLoader('torch', globals(), 'torch'), LazyLoader('transformers', globals(), 'transformers')

logger = logging.getLogger(__name__)

QuantiseMode = t.Literal['int8', 'int4', 'gptq']

@overload
def infer_quantisation_config(cls: type[LLM[t.Any, t.Any]], quantise: t.Literal['int8', 'int4'],
                              **attrs: t.Any) -> tuple[transformers.BitsAndBytesConfig, DictStrAny]:
  ...

@overload
def infer_quantisation_config(cls: type[LLM[t.Any, t.Any]], quantise: t.Literal['gptq'],
                              **attrs: t.Any) -> tuple[autogptq.BaseQuantizeConfig, DictStrAny]:
  ...

def infer_quantisation_config(cls: type[LLM[t.Any, t.Any]], quantise: QuantiseMode,
                              **attrs: t.Any) -> tuple[transformers.BitsAndBytesConfig | autogptq.BaseQuantizeConfig, DictStrAny]:
  # 8 bit configuration
  int8_threshold = attrs.pop('llm_int8_threshhold', 6.0)
  int8_enable_fp32_cpu_offload = attrs.pop('llm_int8_enable_fp32_cpu_offload', False)
  int8_skip_modules: list[str] | None = attrs.pop('llm_int8_skip_modules', None)
  int8_has_fp16_weight = attrs.pop('llm_int8_has_fp16_weight', False)

  autogptq_attrs: DictStrAny = {
      'bits': attrs.pop('gptq_bits', 4),
      'group_size': attrs.pop('gptq_group_size', -1),
      'damp_percent': attrs.pop('gptq_damp_percent', 0.01),
      'desc_act': attrs.pop('gptq_desc_act', True),
      'sym': attrs.pop('gptq_sym', True),
      'true_sequential': attrs.pop('gptq_true_sequential', True),
  }

  def create_int8_config(int8_skip_modules: list[str] | None) -> transformers.BitsAndBytesConfig:
    if int8_skip_modules is None: int8_skip_modules = []
    if 'lm_head' not in int8_skip_modules and cls.config_class.__openllm_model_type__ == 'causal_lm':
      logger.debug("Skipping 'lm_head' for quantization for %s", cls.__name__)
      int8_skip_modules.append('lm_head')
    return transformers.BitsAndBytesConfig(load_in_8bit=True,
                                           llm_int8_enable_fp32_cpu_offload=int8_enable_fp32_cpu_offload,
                                           llm_int8_threshhold=int8_threshold,
                                           llm_int8_skip_modules=int8_skip_modules,
                                           llm_int8_has_fp16_weight=int8_has_fp16_weight,
                                           )

  # 4 bit configuration
  int4_compute_dtype = attrs.pop('bnb_4bit_compute_dtype', torch.bfloat16)
  int4_quant_type = attrs.pop('bnb_4bit_quant_type', 'nf4')
  int4_use_double_quant = attrs.pop('bnb_4bit_use_double_quant', True)

  # NOTE: Quantization setup
  # quantize is a openllm.LLM feature, where we can quantize the model
  # with bitsandbytes or quantization aware training.
  if not is_bitsandbytes_available():
    raise RuntimeError("Quantization requires bitsandbytes to be installed. Make sure to install OpenLLM with 'pip install \"openllm[fine-tune]\"'")
  if quantise == 'int8': quantisation_config = create_int8_config(int8_skip_modules)
  elif quantise == 'int4':
    if is_transformers_supports_kbit():
      quantisation_config = transformers.BitsAndBytesConfig(load_in_4bit=True,
                                                            bnb_4bit_compute_dtype=int4_compute_dtype,
                                                            bnb_4bit_quant_type=int4_quant_type,
                                                            bnb_4bit_use_double_quant=int4_use_double_quant)
    else:
      logger.warning(
          "'quantize' is set to int4, while the current transformers version %s does not support k-bit quantization. k-bit quantization is supported since transformers 4.30, therefore make sure to install the latest version of transformers either via PyPI or from git source: 'pip install git+https://github.com/huggingface/transformers'. Fallback to int8 quantisation.",
          pkg.pkg_version_info('transformers'))
      quantisation_config = create_int8_config(int8_skip_modules)
  elif quantise == 'gptq':
    if not is_autogptq_available():
      logger.warning(
          "'quantize=\"gptq\"' requires 'auto-gptq' to be installed (not available with local environment). Make sure to have 'auto-gptq' available locally: 'pip install \"openllm[gptq]\"'. OpenLLM will fallback to int8 with bitsandbytes."
      )
      quantisation_config = create_int8_config(int8_skip_modules)
    else:
      quantisation_config = autogptq.BaseQuantizeConfig(**autogptq_attrs)
  else:
    raise ValueError(f"'quantize' must be one of ['int8', 'int4', 'gptq'], got {quantise} instead.")
  return quantisation_config, attrs