






'''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_					= logging.getLogger(__name__)

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





def  _a(  UpperCamelCase__				:						Dict							):




      '''simple docstring'''






      SCREAMING_SNAKE_CASE__     :				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  _a(  UpperCamelCase__				:						List[str]							):




      '''simple docstring'''






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

      SCREAMING_SNAKE_CASE__     :				Optional[Any]									=QuantDescriptor(num_bits=args.aprec,	calib_method=UpperCamelCase__							)
      SCREAMING_SNAKE_CASE__     :				Tuple									=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  _a(  UpperCamelCase__				:						List[str],	UpperCamelCase__				:						int,	UpperCamelCase__				:						List[str]=False,	UpperCamelCase__				:						Optional[int]=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(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  _a(  UpperCamelCase__				:						Tuple							):




      '''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  _a(  UpperCamelCase__				:						Any,	UpperCamelCase__				:						int							):




      '''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(UpperCamelCase__							)





def  _a(  UpperCamelCase__				:						List[str],	UpperCamelCase__				:						Optional[int]							):




      '''simple docstring'''






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

            SCREAMING_SNAKE_CASE__     :				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  _a(  UpperCamelCase__				:						List[str],	UpperCamelCase__				:						Dict							):




      '''simple docstring'''






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




      '''simple docstring'''






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





def  _a(  UpperCamelCase__				:						Tuple							):




      '''simple docstring'''






      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)
                  SCREAMING_SNAKE_CASE__     :				Dict									=set() if mod._weight_quantizer.axis is None else set(mod._weight_quantizer.axis							)
                  SCREAMING_SNAKE_CASE__     :				List[Any]									=set(range(len(mod.weight.size()							)							)							) - axis_set
                  SCREAMING_SNAKE_CASE__     :				int									=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()}"							)
                  SCREAMING_SNAKE_CASE__     :				str									=amax





def  _a(  UpperCamelCase__				:						Any,	UpperCamelCase__				:						Optional[int]=2_5,	UpperCamelCase__				:						Dict=1_8_0,	UpperCamelCase__				:						Union[str, Any]=None							):




      '''simple docstring'''






      if ignore is None:
            SCREAMING_SNAKE_CASE__     :				str									=[]
      elif not isinstance(UpperCamelCase__,	UpperCamelCase__							):
            SCREAMING_SNAKE_CASE__     :				Dict									=[ignore]

      SCREAMING_SNAKE_CASE__     :				Tuple									=0
      for name, mod in model.named_modules():
            if not hasattr(UpperCamelCase__,	'''weight'''							):
                  continue
            SCREAMING_SNAKE_CASE__     :				Tuple									=max(UpperCamelCase__,	len(UpperCamelCase__							)							)

      for name, mod in model.named_modules():
            SCREAMING_SNAKE_CASE__     :				Optional[Any]									=getattr(UpperCamelCase__,	'''_input_quantizer''',	UpperCamelCase__							)
            SCREAMING_SNAKE_CASE__     :				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
            SCREAMING_SNAKE_CASE__     :				Any									=f"Act:{input_q.extra_repr()}"
            SCREAMING_SNAKE_CASE__     :				List[str]									=f"Wgt:{weight_q.extra_repr()}"
            SCREAMING_SNAKE_CASE__     :				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  _a(  UpperCamelCase__				:						Tuple							):




      '''simple docstring'''






      SCREAMING_SNAKE_CASE__     :				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  _a(  UpperCamelCase__				:						Union[str, Any],	UpperCamelCase__				:						int,	UpperCamelCase__				:						Optional[Any],	UpperCamelCase__				:						List[str],	UpperCamelCase__				:						Optional[int]							):




      '''simple docstring'''






      SCREAMING_SNAKE_CASE__     :				List[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  _a(  UpperCamelCase__				:						Dict,	UpperCamelCase__				:						Optional[Any],	UpperCamelCase__				:						int="both",	**UpperCamelCase__				:						Union[str, Any]							):




      '''simple docstring'''






      SCREAMING_SNAKE_CASE__     :				Optional[int]									=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  _a(  UpperCamelCase__				:						Union[str, Any],	UpperCamelCase__				:						Any,	**UpperCamelCase__				:						Union[str, Any]							):




      '''simple docstring'''






      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__							):
                              SCREAMING_SNAKE_CASE__     :				Dict									=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'''
class      __SCREAMING_SNAKE_CASE      :




 def __init__(      self	:							List[Any]					,      __lowercase	:							int      )	->						None:
       SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=size
       SCREAMING_SNAKE_CASE__     :				List[Any]									=[0] * size
       SCREAMING_SNAKE_CASE__     :				str									=[0] * size




 @staticmethod
 def      __magic_name__	(      __lowercase	:							int      )	->						int:
       return index | (index + 1)




 @staticmethod
 def      __magic_name__	(      __lowercase	:							int      )	->						int:
       return (index & (index + 1)) - 1




 def      __magic_name__	(      self	:							Dict					,      __lowercase	:							int					,      __lowercase	:							int      )	->						None:
       SCREAMING_SNAKE_CASE__     :				List[str]									=value
       while index < self.size:
             SCREAMING_SNAKE_CASE__     :				Any									=self.get_prev(__lowercase      ) + 1
             if current_left_border == index:
                   SCREAMING_SNAKE_CASE__     :				List[str]									=value
             else:
                   SCREAMING_SNAKE_CASE__     :				str									=max(__lowercase					,      __lowercase					,      __lowercase      )
             SCREAMING_SNAKE_CASE__     :				Optional[Any]									=self.get_next(__lowercase      )




 def      __magic_name__	(      self	:							Optional[int]					,      __lowercase	:							int					,      __lowercase	:							int      )	->						int:
       right -= 1  # Because of right is exclusive
       SCREAMING_SNAKE_CASE__     :				str									=0
       while left <= right:
             SCREAMING_SNAKE_CASE__     :				Optional[Any]									=self.get_prev(__lowercase      )
             if left <= current_left:
                   SCREAMING_SNAKE_CASE__     :				List[Any]									=max(__lowercase					,      self.tree[right]      )
                   SCREAMING_SNAKE_CASE__     :				Any									=current_left
             else:
                   SCREAMING_SNAKE_CASE__     :				Optional[Any]									=max(__lowercase					,      self.arr[right]      )
                   right -= 1
       return result


if __name__ == "__main__":
  import doctest

  doctest.testmod()