"""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


UpperCAmelCase__  		=						logging.getLogger(__name__)

UpperCAmelCase__  		=						5_0  # max width of layer names
UpperCAmelCase__  		=						7_0  # max width of quantizer names






def     __UpperCAmelCase					(			lowercase		):







  """simple docstring"""
  _UpperCAmelCase						 =      parser.add_argument_group("""quant_trainer arguments"""		)
  group.add_argument("""--wprec"""   ,type=lowercase   ,default=8   ,help="""weight precision"""		)
  group.add_argument("""--aprec"""   ,type=lowercase   ,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=lowercase   ,nargs="""+"""   ,help="""disable quantizers by keyword"""		)
  group.add_argument("""--quant-disable-layer-module"""   ,type=lowercase   ,help="""disable quantizers by keyword under layer."""		)
  group.add_argument("""--quant-enable-layer-module"""   ,type=lowercase   ,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=lowercase   ,type=lowercase   ,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=lowercase   ,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					(			lowercase		):







  """simple docstring"""

  if args.calibrator == "max":
    _UpperCAmelCase						 =      """max"""
  elif args.calibrator == "percentile":
    if args.percentile is None:
      raise ValueError("""Specify --percentile when using percentile calibrator"""		)
    _UpperCAmelCase						 =      """histogram"""
  elif args.calibrator == "mse":
    _UpperCAmelCase						 =      """histogram"""
  else:
    raise ValueError(f'''Invalid calibrator {args.calibrator}'''		)

  _UpperCAmelCase						 =      QuantDescriptor(num_bits=args.aprec   ,calib_method=lowercase		)
  _UpperCAmelCase						 =      QuantDescriptor(num_bits=args.wprec   ,axis=(None if args.quant_per_tensor else (0,))		)
  quant_nn.QuantLinear.set_default_quant_desc_input(lowercase		)
  quant_nn.QuantLinear.set_default_quant_desc_weight(lowercase		)






def     __UpperCAmelCase					(			lowercase   ,lowercase   ,lowercase=False   ,lowercase=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(lowercase   ,["""embeddings"""]   ,which="""weight"""   ,_disabled=lowercase		)

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

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

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

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

    if args.recalibrate_weights:
      recalibrate_weights(lowercase		)

    if args.fuse_qkv:
      fuse_qkv(lowercase   ,lowercase		)

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

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






def     __UpperCAmelCase					(			lowercase		):







  """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					(			lowercase   ,lowercase		):







  """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(lowercase		)






def     __UpperCAmelCase					(			lowercase   ,lowercase		):







  """simple docstring"""

  def fusea(lowercase   ,lowercase   ,lowercase		):
    for mod in [qq, qk, qv]:
      if not hasattr(lowercase   ,"""_amax"""		):
        print("""          WARNING: NO AMAX BUFFER"""		)
        return
    _UpperCAmelCase						 =      qq._amax.detach().item()
    _UpperCAmelCase						 =      qk._amax.detach().item()
    _UpperCAmelCase						 =      qv._amax.detach().item()

    _UpperCAmelCase						 =      max(lowercase   ,lowercase   ,lowercase		)
    qq._amax.fill_(lowercase		)
    qk._amax.fill_(lowercase		)
    qv._amax.fill_(lowercase		)
    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					(			lowercase   ,lowercase		):







  """simple docstring"""

  for name, mod in model.named_modules():
    if name.endswith(""".output.dense"""		) and not name.endswith("""attention.output.dense"""		):
      _UpperCAmelCase						 =      mod._input_quantizer._amax.data.detach().item()
      mod._input_quantizer._amax.data.detach().clamp_(max=lowercase		)
      _UpperCAmelCase						 =      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					(			lowercase		):







  """simple docstring"""

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






def     __UpperCAmelCase					(			lowercase		):







  """simple docstring"""

  for name, mod in model.named_modules():
    if hasattr(lowercase   ,"""_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)
      _UpperCAmelCase						 =      set() if mod._weight_quantizer.axis is None else set(mod._weight_quantizer.axis		)
      _UpperCAmelCase						 =      set(range(len(mod.weight.size()		)		)		) - axis_set
      _UpperCAmelCase						 =      pytorch_quantization.utils.reduce_amax(mod.weight   ,axis=lowercase   ,keepdims=lowercase		).detach()
      logger.info(f'''RECALIB: {name:{name_width}} {mod._weight_quantizer._amax.flatten()} -> {amax.flatten()}'''		)
      _UpperCAmelCase						 =      amax






def     __UpperCAmelCase					(			lowercase   ,lowercase=25   ,lowercase=1_80   ,lowercase=None		):







  """simple docstring"""

  if ignore is None:
    _UpperCAmelCase						 =      []
  elif not isinstance(lowercase   ,lowercase		):
    _UpperCAmelCase						 =      [ignore]

  _UpperCAmelCase						 =      0
  for name, mod in model.named_modules():
    if not hasattr(lowercase   ,"""weight"""		):
      continue
    _UpperCAmelCase						 =      max(lowercase   ,len(lowercase		)		)

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






def     __UpperCAmelCase					(			lowercase		):







  """simple docstring"""
  _UpperCAmelCase						 =      0
  for name, mod in model.named_modules():
    if isinstance(lowercase   ,pytorch_quantization.nn.TensorQuantizer		):
      print(f'''{name:80} {mod}'''		)
      count += 1
  print(f'''{count} TensorQuantizers found in model'''		)






def     __UpperCAmelCase					(			lowercase   ,lowercase   ,lowercase   ,lowercase   ,lowercase		):







  """simple docstring"""
  _UpperCAmelCase						 =      getattr(lowercase   ,lowercase   ,lowercase		)
  if quantizer_mod is not None:
    assert hasattr(lowercase   ,lowercase		)
    setattr(lowercase   ,lowercase   ,lowercase		)
  else:
    logger.warning(f'''{name} has no {quantizer}'''		)






def     __UpperCAmelCase					(			lowercase   ,lowercase   ,lowercase="both"   ,**lowercase		):







  """simple docstring"""
  _UpperCAmelCase						 =      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(lowercase   ,lowercase   ,"""_input_quantizer"""   ,lowercase   ,lowercase		)
    if which in ["weight", "both"]:
      set_quantizer(lowercase   ,lowercase   ,"""_weight_quantizer"""   ,lowercase   ,lowercase		)
  logger.info(lowercase		)






def     __UpperCAmelCase					(			lowercase   ,lowercase   ,**lowercase		):







  """simple docstring"""

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

"""simple docstring"""


from __future__ import annotations

from random import choice






def     __UpperCAmelCase					(			lowercase		):







  """simple docstring"""
  return choice(lowercase		)






def     __UpperCAmelCase					(			lowercase   ,lowercase		):







  """simple docstring"""
  _UpperCAmelCase						 =      random_pivot(lowercase		)

  # partition based on pivot
  # linear time
  _UpperCAmelCase						 =      [e for e in lst if e < pivot]
  _UpperCAmelCase						 =      [e for e in lst if e > pivot]

  # if we get lucky, pivot might be the element we want.
  # we can easily see this:
  # small (elements smaller than k)
  # + pivot (kth element)
  # + big (elements larger than k)
  if len(lowercase		) == k - 1:
    return pivot
  # pivot is in elements bigger than k
  elif len(lowercase		) < k - 1:
    return kth_number(lowercase   ,k - len(lowercase		) - 1		)
  # pivot is in elements smaller than k
  else:
    return kth_number(lowercase   ,lowercase		)


if __name__ == "__main__":
    import doctest

    doctest.testmod()
