'''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:			Any	       =       logging.getLogger(__name__)

__a:			List[Any]	       =       50  # max width of layer names
__a:			List[Any]	       =       70  # max width of quantizer names
def       __UpperCamelCase	(       UpperCAmelCase					):
 lowercase__    :   Dict						= parser.add_argument_group('''quant_trainer arguments'''					)
 group.add_argument('''--wprec'''		,					type=UpperCAmelCase		,					default=8		,					help='''weight precision'''					)
 group.add_argument('''--aprec'''		,					type=UpperCAmelCase		,					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=UpperCAmelCase		,					nargs='''+'''		,					help='''disable quantizers by keyword'''					)
 group.add_argument('''--quant-disable-layer-module'''		,					type=UpperCAmelCase		,					help='''disable quantizers by keyword under layer.'''					)
 group.add_argument('''--quant-enable-layer-module'''		,					type=UpperCAmelCase		,					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=UpperCAmelCase		,					type=UpperCAmelCase		,					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=UpperCAmelCase		,					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	(       UpperCAmelCase					):

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

 lowercase__    :   List[Any]						= QuantDescriptor(num_bits=args.aprec		,					calib_method=UpperCAmelCase					)
 lowercase__    :   List[Any]						= QuantDescriptor(num_bits=args.wprec		,					axis=(None if args.quant_per_tensor else (0,))					)
 quant_nn.QuantLinear.set_default_quant_desc_input(UpperCAmelCase					)
 quant_nn.QuantLinear.set_default_quant_desc_weight(UpperCAmelCase					)
def       __UpperCamelCase	(       UpperCAmelCase		,					UpperCAmelCase		,					UpperCAmelCase=False		,					UpperCAmelCase=False					):
 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(UpperCAmelCase		,					['''embeddings''']		,					which='''weight'''		,					_disabled=UpperCAmelCase					)

  if args.quant_disable:
   set_quantizer_by_name(UpperCAmelCase		,					['''''']		,					_disabled=UpperCAmelCase					)

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

  if args.quant_disable_layer_module:
   set_quantizer_by_name(UpperCAmelCase		,					[r'''layer.\d+.''' + args.quant_disable_layer_module]		,					_disabled=UpperCAmelCase					)

  if args.quant_enable_layer_module:
   set_quantizer_by_name(UpperCAmelCase		,					[r'''layer.\d+.''' + args.quant_enable_layer_module]		,					_disabled=UpperCAmelCase					)

  if args.recalibrate_weights:
   recalibrate_weights(UpperCAmelCase					)

  if args.fuse_qkv:
   fuse_qkv(UpperCAmelCase		,					UpperCAmelCase					)

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

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

 def fusea(UpperCAmelCase		,					UpperCAmelCase		,					UpperCAmelCase					):
  for mod in [qq, qk, qv]:
   if not hasattr(UpperCAmelCase		,					'''_amax'''					):
    print('''          WARNING: NO AMAX BUFFER'''					)
    return
  lowercase__    :   List[Any]						= qq._amax.detach().item()
  lowercase__    :   List[str]						= qk._amax.detach().item()
  lowercase__    :   Tuple						= qv._amax.detach().item()

  lowercase__    :   Optional[int]						= max(UpperCAmelCase		,					UpperCAmelCase		,					UpperCAmelCase					)
  qq._amax.fill_(UpperCAmelCase					)
  qk._amax.fill_(UpperCAmelCase					)
  qv._amax.fill_(UpperCAmelCase					)
  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	(       UpperCAmelCase		,					UpperCAmelCase					):

 for name, mod in model.named_modules():
  if name.endswith('''.output.dense'''					) and not name.endswith('''attention.output.dense'''					):
   lowercase__    :   Tuple						= mod._input_quantizer._amax.data.detach().item()
   mod._input_quantizer._amax.data.detach().clamp_(max=UpperCAmelCase					)
   lowercase__    :   List[Any]						= 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	(       UpperCAmelCase					):

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

 for name, mod in model.named_modules():
  if hasattr(UpperCAmelCase		,					'''_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)
   lowercase__    :   Union[str, Any]						= set() if mod._weight_quantizer.axis is None else set(mod._weight_quantizer.axis					)
   lowercase__    :   List[str]						= set(range(len(mod.weight.size()					)					)					) - axis_set
   lowercase__    :   Optional[Any]						= pytorch_quantization.utils.reduce_amax(mod.weight		,					axis=UpperCAmelCase		,					keepdims=UpperCAmelCase					).detach()
   logger.info(F"""RECALIB: {name:{name_width}} {mod._weight_quantizer._amax.flatten()} -> {amax.flatten()}"""					)
   lowercase__    :   Union[str, Any]						= amax
def       __UpperCamelCase	(       UpperCAmelCase		,					UpperCAmelCase=25		,					UpperCAmelCase=180		,					UpperCAmelCase=None					):

 if ignore is None:
  lowercase__    :   Union[str, Any]						= []
 elif not isinstance(UpperCAmelCase		,					UpperCAmelCase					):
  lowercase__    :   Optional[int]						= [ignore]

 lowercase__    :   Optional[Any]						= 0
 for name, mod in model.named_modules():
  if not hasattr(UpperCAmelCase		,					'''weight'''					):
   continue
  lowercase__    :   int						= max(UpperCAmelCase		,					len(UpperCAmelCase					)					)

 for name, mod in model.named_modules():
  lowercase__    :   str						= getattr(UpperCAmelCase		,					'''_input_quantizer'''		,					UpperCAmelCase					)
  lowercase__    :   List[Any]						= getattr(UpperCAmelCase		,					'''_weight_quantizer'''		,					UpperCAmelCase					)
  if not hasattr(UpperCAmelCase		,					'''weight'''					):
   continue
  if type(UpperCAmelCase					) in ignore:
   continue
  if [True for s in ignore if type(UpperCAmelCase					) is str and s in name]:
   continue
  lowercase__    :   str						= F"""Act:{input_q.extra_repr()}"""
  lowercase__    :   Union[str, Any]						= F"""Wgt:{weight_q.extra_repr()}"""
  lowercase__    :   List[str]						= F"""{name:{name_width}} {act_str} {wgt_str}"""
  if len(UpperCAmelCase					) <= line_width:
   logger.info(UpperCAmelCase					)
  else:
   logger.info(F"""{name:{name_width}} {act_str}"""					)
   logger.info(F"""{'  ':{name_width}} {wgt_str}"""					)
def       __UpperCamelCase	(       UpperCAmelCase					):
 lowercase__    :   Optional[Any]						= 0
 for name, mod in model.named_modules():
  if isinstance(UpperCAmelCase		,					pytorch_quantization.nn.TensorQuantizer					):
   print(F"""{name:80} {mod}"""					)
   count += 1
 print(F"""{count} TensorQuantizers found in model"""					)
def       __UpperCamelCase	(       UpperCAmelCase		,					UpperCAmelCase		,					UpperCAmelCase		,					UpperCAmelCase		,					UpperCAmelCase					):
 lowercase__    :   str						= getattr(UpperCAmelCase		,					UpperCAmelCase		,					UpperCAmelCase					)
 if quantizer_mod is not None:
  assert hasattr(UpperCAmelCase		,					UpperCAmelCase					)
  setattr(UpperCAmelCase		,					UpperCAmelCase		,					UpperCAmelCase					)
 else:
  logger.warning(F"""{name} has no {quantizer}"""					)
def       __UpperCamelCase	(       UpperCAmelCase		,					UpperCAmelCase		,					UpperCAmelCase="both"		,					**UpperCAmelCase					):
 lowercase__    :   Optional[Any]						= 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(UpperCAmelCase		,					UpperCAmelCase		,					'''_input_quantizer'''		,					UpperCAmelCase		,					UpperCAmelCase					)
  if which in ["weight", "both"]:
   set_quantizer(UpperCAmelCase		,					UpperCAmelCase		,					'''_weight_quantizer'''		,					UpperCAmelCase		,					UpperCAmelCase					)
 logger.info(UpperCAmelCase					)





def       __UpperCamelCase	(       UpperCAmelCase		,					UpperCAmelCase		,					**UpperCAmelCase					):

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


'''simple docstring'''

def       __UpperCamelCase	(       UpperCAmelCase					):

 def merge(UpperCAmelCase		,					UpperCAmelCase					) -> list:

  def _merge():
   while left and right:
    yield (left if left[0] <= right[0] else right).pop(0					)
   yield from left
   yield from right

  return list(_merge()					)

 if len(UpperCAmelCase					) <= 1:
  return collection
 lowercase__    :   int						= len(UpperCAmelCase					) // 2
 return merge(merge_sort(collection[:mid]					)		,					merge_sort(collection[mid:]					)					)


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  __a:			str	       =       input("""Enter numbers separated by a comma:\n""").strip()
  __a:			Optional[int]	       =       [int(item) for item in user_input.split(""",""")]
  print(*merge_sort(unsorted), sep=""",""")

