


'''simple docstring'''






import logging
import re

import pytorch_quantization
import pytorch_quantization.nn as quant_nn
import torch
from pytorch_quantization import calib
from pytorch_quantization.tensor_quant import QuantDescriptor


__A   :    List[Any]											= logging.getLogger(__name__)

__A   :    Union[str, Any]											= 50  # max width of layer names
__A   :    str											= 70  # max width of quantizer names







def 							UpperCamelCase_     (       A__   :  Any   ):
  '''simple docstring'''





  lowerCAmelCase_     : Any     =  parser.add_argument_group("""quant_trainer arguments"""   )
  group.add_argument("""--wprec"""				,       type=A__				,       default=8				,       help="""weight precision"""   )
  group.add_argument("""--aprec"""				,       type=A__				,       default=8				,       help="""activation precision"""   )
  group.add_argument("""--quant-per-tensor"""				,       action="""store_true"""				,       help="""per tensor weight scaling"""   )
  group.add_argument("""--quant-disable"""				,       action="""store_true"""				,       help="""disable all quantizers"""   )
  group.add_argument("""--quant-disable-embeddings"""				,       action="""store_true"""				,       help="""disable all embeddings quantizers"""   )
  group.add_argument("""--quant-disable-keyword"""				,       type=A__				,       nargs="""+"""				,       help="""disable quantizers by keyword"""   )
  group.add_argument("""--quant-disable-layer-module"""				,       type=A__				,       help="""disable quantizers by keyword under layer."""   )
  group.add_argument("""--quant-enable-layer-module"""				,       type=A__				,       help="""enable quantizers by keyword under layer"""   )
  group.add_argument("""--calibrator"""				,       default="""max"""				,       help="""which quantization range calibrator to use"""   )
  group.add_argument("""--percentile"""				,       default=A__				,       type=A__				,       help="""percentile for PercentileCalibrator"""   )
  group.add_argument("""--fuse-qkv"""				,       action="""store_true"""				,       help="""use the same scale factor for qkv"""   )
  group.add_argument("""--clip-gelu"""				,       metavar="""N"""				,       type=A__				,       help="""clip gelu output maximum value to N"""   )
  group.add_argument(
      """--recalibrate-weights"""				,       action="""store_true"""				,       help=(
          """recalibrate weight amaxes by taking the max of the weights."""
          """ amaxes will be computed with the current quantization granularity (axis)."""
      )				,       )







def 							UpperCamelCase_     (       A__   :  Optional[Any]   ):
  '''simple docstring'''





  if args.calibrator == "max":
    lowerCAmelCase_     : Optional[int]     =  """max"""
  elif args.calibrator == "percentile":
    if args.percentile is None:
      raise ValueError("""Specify --percentile when using percentile calibrator"""   )
    lowerCAmelCase_     : Union[str, Any]     =  """histogram"""
  elif args.calibrator == "mse":
    lowerCAmelCase_     : str     =  """histogram"""
  else:
    raise ValueError(f'Invalid calibrator {args.calibrator}'   )

  lowerCAmelCase_     : Union[str, Any]     =  QuantDescriptor(num_bits=args.aprec				,       calib_method=A__   )
  lowerCAmelCase_     : Optional[int]     =  QuantDescriptor(num_bits=args.wprec				,       axis=(None if args.quant_per_tensor else (0,))   )
  quant_nn.QuantLinear.set_default_quant_desc_input(A__   )
  quant_nn.QuantLinear.set_default_quant_desc_weight(A__   )







def 							UpperCamelCase_     (       A__   :  int				,       A__   :  Any				,       A__   :  Optional[int]=False				,       A__   :  List[str]=False   ):
  '''simple docstring'''





  logger.info("""Configuring Model for Quantization"""   )
  logger.info(f'using quantization package {pytorch_quantization.__file__}'   )

  if not calib:
    if args.quant_disable_embeddings:
      set_quantizer_by_name(A__				,       ["""embeddings"""]				,       which="""weight"""				,       _disabled=A__   )

    if args.quant_disable:
      set_quantizer_by_name(A__				,       [""""""]				,       _disabled=A__   )

    if args.quant_disable_keyword:
      set_quantizer_by_name(A__				,       args.quant_disable_keyword				,       _disabled=A__   )

    if args.quant_disable_layer_module:
      set_quantizer_by_name(A__				,       [R"""layer.\d+.""" + args.quant_disable_layer_module]				,       _disabled=A__   )

    if args.quant_enable_layer_module:
      set_quantizer_by_name(A__				,       [R"""layer.\d+.""" + args.quant_enable_layer_module]				,       _disabled=A__   )

    if args.recalibrate_weights:
      recalibrate_weights(A__   )

    if args.fuse_qkv:
      fuse_qkv(A__				,       A__   )

  if args.clip_gelu:
    clip_gelu(A__				,       args.clip_gelu   )

  # if args.local_rank in [-1, 0] and not calib:
  print_quant_summary(A__   )







def 							UpperCamelCase_     (       A__   :  str   ):
  '''simple docstring'''





  logger.info("""Enabling Calibration"""   )
  for name, module in model.named_modules():
    if name.endswith("""_quantizer"""   ):
      if module._calibrator is not None:
        module.disable_quant()
        module.enable_calib()
      else:
        module.disable()
      logger.info(f'{name:80}: {module}'   )







def 							UpperCamelCase_     (       A__   :  Tuple				,       A__   :  Tuple   ):
  '''simple docstring'''





  logger.info("""Loading calibrated amax"""   )
  for name, module in model.named_modules():
    if name.endswith("""_quantizer"""   ):
      if module._calibrator is not None:
        if isinstance(module._calibrator				,       calib.MaxCalibrator   ):
          module.load_calib_amax()
        else:
          module.load_calib_amax("""percentile"""				,       percentile=args.percentile   )
        module.enable_quant()
        module.disable_calib()
      else:
        module.enable()
  model.cuda()
  print_quant_summary(A__   )







def 							UpperCamelCase_     (       A__   :  List[Any]				,       A__   :  Union[str, Any]   ):
  '''simple docstring'''





  def fusea(A__   :  str				,       A__   :  Dict				,       A__   :  Tuple   ):
    for mod in [qq, qk, qv]:
      if not hasattr(A__				,       """_amax"""   ):
        print("""          WARNING: NO AMAX BUFFER"""   )
        return
    lowerCAmelCase_     : Optional[int]     =  qq._amax.detach().item()
    lowerCAmelCase_     : Optional[Any]     =  qk._amax.detach().item()
    lowerCAmelCase_     : List[Any]     =  qv._amax.detach().item()

    lowerCAmelCase_     : Tuple     =  max(A__				,       A__				,       A__   )
    qq._amax.fill_(A__   )
    qk._amax.fill_(A__   )
    qv._amax.fill_(A__   )
    logger.info(f'          q={q:5.2f} k={k:5.2f} v={v:5.2f} -> {amax:5.2f}'   )

  for name, mod in model.named_modules():
    if name.endswith(""".attention.self"""   ):
      logger.info(f'FUSE_QKV: {name:{name_width}}'   )
      fusea(mod.matmul_q_input_quantizer				,       mod.matmul_k_input_quantizer				,       mod.matmul_v_input_quantizer   )
      if args.quant_per_tensor:
        fusea(mod.query._weight_quantizer				,       mod.key._weight_quantizer				,       mod.value._weight_quantizer   )







def 							UpperCamelCase_     (       A__   :  Tuple				,       A__   :  Optional[int]   ):
  '''simple docstring'''





  for name, mod in model.named_modules():
    if name.endswith(""".output.dense"""   ) and not name.endswith("""attention.output.dense"""   ):
      lowerCAmelCase_     : List[Any]     =  mod._input_quantizer._amax.data.detach().item()
      mod._input_quantizer._amax.data.detach().clamp_(max=A__   )
      lowerCAmelCase_     : List[str]     =  mod._input_quantizer._amax.data.detach().item()
      logger.info(f'CLIP_GELU: {name:{name_width}} amax: {amax_init:5.2f} -> {amax:5.2f}'   )







def 							UpperCamelCase_     (       A__   :  Optional[Any]   ):
  '''simple docstring'''





  for name, mod in model.named_modules():
    if hasattr(A__				,       """_weight_quantizer"""   ) and mod._weight_quantizer.axis is not None:
      lowerCAmelCase_     : Optional[int]     =  mod.weight.shape[0]
      lowerCAmelCase_     : Dict     =  mod._weight_quantizer._amax.detach()
      lowerCAmelCase_     : str     =  torch.ones(A__				,       dtype=amax.dtype				,       device=amax.device   ) * amax
      print(f'expanding {name} {amax} -> {mod._weight_quantizer._amax}'   )







def 							UpperCamelCase_     (       A__   :  List[Any]   ):
  '''simple docstring'''





  for name, mod in model.named_modules():
    if hasattr(A__				,       """_weight_quantizer"""   ):
      if not hasattr(mod.weight_quantizer				,       """_amax"""   ):
        print("""RECALIB: {name:{name_width}} WARNING: NO AMAX BUFFER"""   )
        continue

      # determine which axes to reduce across
      # e.g. a 4D tensor quantized per axis 0 should reduce over (1,2,3)
      lowerCAmelCase_     : int     =  set() if mod._weight_quantizer.axis is None else set(mod._weight_quantizer.axis   )
      lowerCAmelCase_     : Optional[Any]     =  set(range(len(mod.weight.size()   )   )   ) - axis_set
      lowerCAmelCase_     : str     =  pytorch_quantization.utils.reduce_amax(mod.weight				,       axis=A__				,       keepdims=A__   ).detach()
      logger.info(f'RECALIB: {name:{name_width}} {mod._weight_quantizer._amax.flatten()} -> {amax.flatten()}'   )
      lowerCAmelCase_     : Optional[int]     =  amax







def 							UpperCamelCase_     (       A__   :  Optional[int]				,       A__   :  Dict=25				,       A__   :  Optional[Any]=1_80				,       A__   :  Dict=None   ):
  '''simple docstring'''





  if ignore is None:
    lowerCAmelCase_     : List[str]     =  []
  elif not isinstance(A__				,       A__   ):
    lowerCAmelCase_     : Tuple     =  [ignore]

  lowerCAmelCase_     : Optional[int]     =  0
  for name, mod in model.named_modules():
    if not hasattr(A__				,       """weight"""   ):
      continue
    lowerCAmelCase_     : Dict     =  max(A__				,       len(A__   )   )

  for name, mod in model.named_modules():
    lowerCAmelCase_     : int     =  getattr(A__				,       """_input_quantizer"""				,       A__   )
    lowerCAmelCase_     : Union[str, Any]     =  getattr(A__				,       """_weight_quantizer"""				,       A__   )
    if not hasattr(A__				,       """weight"""   ):
      continue
    if type(A__   ) in ignore:
      continue
    if [True for s in ignore if type(A__   ) is str and s in name]:
      continue
    lowerCAmelCase_     : Union[str, Any]     =  f'Act:{input_q.extra_repr()}'
    lowerCAmelCase_     : Dict     =  f'Wgt:{weight_q.extra_repr()}'
    lowerCAmelCase_     : str     =  f'{name:{name_width}} {act_str} {wgt_str}'
    if len(A__   ) <= line_width:
      logger.info(A__   )
    else:
      logger.info(f'{name:{name_width}} {act_str}'   )
      logger.info(f'{"  ":{name_width}} {wgt_str}'   )







def 							UpperCamelCase_     (       A__   :  str   ):
  '''simple docstring'''





  lowerCAmelCase_     : Tuple     =  0
  for name, mod in model.named_modules():
    if isinstance(A__				,       pytorch_quantization.nn.TensorQuantizer   ):
      print(f'{name:80} {mod}'   )
      count += 1
  print(f'{count} TensorQuantizers found in model'   )







def 							UpperCamelCase_     (       A__   :  Dict				,       A__   :  Optional[int]				,       A__   :  str				,       A__   :  Optional[Any]				,       A__   :  str   ):
  '''simple docstring'''





  lowerCAmelCase_     : int     =  getattr(A__				,       A__				,       A__   )
  if quantizer_mod is not None:
    assert hasattr(A__				,       A__   )
    setattr(A__				,       A__				,       A__   )
  else:
    logger.warning(f'{name} has no {quantizer}'   )







def 							UpperCamelCase_     (       A__   :  Union[str, Any]				,       A__   :  Any				,       A__   :  List[str]="both"				,       **A__   :  Union[str, Any]   ):
  '''simple docstring'''





  lowerCAmelCase_     : str     =  f'Warning: changing {which} quantizers of {name:{qname_width}}'
  for k, v in kwargs.items():
    s += f' {k}={v}'
    if which in ["input", "both"]:
      set_quantizer(A__				,       A__				,       """_input_quantizer"""				,       A__				,       A__   )
    if which in ["weight", "both"]:
      set_quantizer(A__				,       A__				,       """_weight_quantizer"""				,       A__				,       A__   )
  logger.info(A__   )







def 							UpperCamelCase_     (       A__   :  List[str]				,       A__   :  Union[str, Any]				,       **A__   :  Optional[Any]   ):
  '''simple docstring'''





  for name, mod in model.named_modules():
    if hasattr(A__				,       """_input_quantizer"""   ) or hasattr(A__				,       """_weight_quantizer"""   ):
      for n in names:
        if re.search(A__				,       A__   ):
          set_quantizers(A__				,       A__				,       **A__   )
    elif name.endswith("""_quantizer"""   ):
      for n in names:
        if re.search(A__				,       A__   ):
          lowerCAmelCase_     : Any     =  f'Warning: changing {name:{name_width}}'
          for k, v in kwargs.items():
            s += f' {k}={v}'
            setattr(A__				,       A__				,       A__   )
          logger.info(A__   )







'''simple docstring'''






from ...utils import is_torch_available, is_transformers_available


if is_transformers_available() and is_torch_available():
  from .pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings, VQDiffusionPipeline



