

"""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			=					50  # max width of layer names
__UpperCAmelCase			=					70  # max width of quantizer names
def 	lowerCAmelCase    (       __UpperCamelCase   ):
 '''simple docstring'''





 UpperCAmelCase__       :	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 	lowerCAmelCase    (       __UpperCamelCase   ):
 '''simple docstring'''





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

 UpperCAmelCase__       :	Union[str, Any]        =       QuantDescriptor(num_bits=args.aprec				,     calib_method=A__   )
 UpperCAmelCase__       :	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 	lowerCAmelCase    (       __UpperCamelCase				,     __UpperCamelCase				,     __UpperCamelCase=False				,     __UpperCamelCase=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 	lowerCAmelCase    (       __UpperCamelCase   ):
 '''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 	lowerCAmelCase    (       __UpperCamelCase				,     __UpperCamelCase   ):
 '''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 	lowerCAmelCase    (       __UpperCamelCase				,     __UpperCamelCase   ):
 '''simple docstring'''





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

  UpperCAmelCase__       :	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 	lowerCAmelCase    (       __UpperCamelCase				,     __UpperCamelCase   ):
 '''simple docstring'''





 for name, mod in model.named_modules():
  if name.endswith(""".output.dense"""   ) and not name.endswith("""attention.output.dense"""   ):
   UpperCAmelCase__       :	List[Any]        =       mod._input_quantizer._amax.data.detach().item()
   mod._input_quantizer._amax.data.detach().clamp_(max=A__   )
   UpperCAmelCase__       :	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 	lowerCAmelCase    (       __UpperCamelCase   ):
 '''simple docstring'''





 for name, mod in model.named_modules():
  if hasattr(A__				,     """_weight_quantizer"""   ) and mod._weight_quantizer.axis is not None:
   UpperCAmelCase__       :	Optional[int]        =       mod.weight.shape[0]
   UpperCAmelCase__       :	Dict        =       mod._weight_quantizer._amax.detach()
   UpperCAmelCase__       :	str        =       torch.ones(A__				,     dtype=amax.dtype				,     device=amax.device   ) * amax
   print(F"expanding {name} {amax} -> {mod._weight_quantizer._amax}"   )
def 	lowerCAmelCase    (       __UpperCamelCase   ):
 '''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)
   UpperCAmelCase__       :	int        =       set() if mod._weight_quantizer.axis is None else set(mod._weight_quantizer.axis   )
   UpperCAmelCase__       :	Optional[Any]        =       set(range(len(mod.weight.size()   )   )   ) - axis_set
   UpperCAmelCase__       :	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()}"   )
   UpperCAmelCase__       :	Optional[int]        =       amax
def 	lowerCAmelCase    (       __UpperCamelCase				,     __UpperCamelCase=25				,     __UpperCamelCase=180				,     __UpperCamelCase=None   ):
 '''simple docstring'''





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

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

 for name, mod in model.named_modules():
  UpperCAmelCase__       :	int        =       getattr(A__				,     """_input_quantizer"""				,     A__   )
  UpperCAmelCase__       :	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
  UpperCAmelCase__       :	Union[str, Any]        =       F"Act:{input_q.extra_repr()}"
  UpperCAmelCase__       :	Dict        =       F"Wgt:{weight_q.extra_repr()}"
  UpperCAmelCase__       :	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 	lowerCAmelCase    (       __UpperCamelCase   ):
 '''simple docstring'''





 UpperCAmelCase__       :	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 	lowerCAmelCase    (       __UpperCamelCase				,     __UpperCamelCase				,     __UpperCamelCase				,     __UpperCamelCase				,     __UpperCamelCase   ):
 '''simple docstring'''





 UpperCAmelCase__       :	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 	lowerCAmelCase    (       __UpperCamelCase				,     __UpperCamelCase				,     __UpperCamelCase="both"				,     **__UpperCamelCase   ):
 '''simple docstring'''





 UpperCAmelCase__       :	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 	lowerCAmelCase    (       __UpperCamelCase				,     __UpperCamelCase				,     **__UpperCamelCase   ):
 '''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__   ):
     UpperCAmelCase__       :	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 __future__ import annotations







def 							UpperCamelCase_     (       A__   :  list				,       A__   :  int				,       A__   :  int				,       A__   :  int   ):
  '''simple docstring'''





  lowerCAmelCase_     : int     =  []
  lowerCAmelCase_,				lowerCAmelCase_     : Any     =  input_list[low:mid], input_list[mid : high + 1]
  while left and right:
    result.append((left if left[0] <= right[0] else right).pop(0   )   )
  lowerCAmelCase_     : Dict     =  result + left + right
  return input_list







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





  if len(A__   ) <= 1:
    return input_list
  lowerCAmelCase_     : str     =  list(A__   )

  # iteration for two-way merging
  lowerCAmelCase_     : Dict     =  2
  while p <= len(A__   ):
    # getting low, high and middle value for merge-sort of single list
    for i in range(0				,       len(A__   )				,       A__   ):
      lowerCAmelCase_     : Tuple     =  i
      lowerCAmelCase_     : List[str]     =  i + p - 1
      lowerCAmelCase_     : Optional[Any]     =  (low + high + 1) // 2
      lowerCAmelCase_     : List[str]     =  merge(A__				,       A__				,       A__				,       A__   )
    # final merge of last two parts
    if p * 2 >= len(A__   ):
      lowerCAmelCase_     : Union[str, Any]     =  i
      lowerCAmelCase_     : Dict     =  merge(A__				,       0				,       A__				,       len(A__   ) - 1   )
      break
    p *= 2

  return input_list


if __name__ == "__main__":
  __A   :    int											= input("Enter numbers separated by a comma:\n").strip()
  if user_input == "":
    __A   :    str											= []
  else:
    __A   :    Optional[Any]											= [int(item.strip()) for item in user_input.split(",")]
  print(iter_merge_sort(unsorted))



