




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


lowerCamelCase				:		Any     =   logging.getLogger(__name__)

lowerCamelCase				:		List[str]     =   50  # max width of layer names
lowerCamelCase				:		int     =   70  # max width of quantizer names

def 			_SCREAMING_SNAKE_CASE			(							lowercase	:       Optional[int]     ):


      '''simple docstring'''



      lowerCamelCase_		     =    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 			_SCREAMING_SNAKE_CASE			(							lowercase	:       List[Any]     ):


      '''simple docstring'''



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

      lowerCamelCase_		     =    QuantDescriptor(num_bits=args.aprec	,							calib_method=lowercase     )
      lowerCamelCase_		     =    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 			_SCREAMING_SNAKE_CASE			(							lowercase	:       Optional[int]	,							lowercase	:       Tuple	,							lowercase	:       List[str]=False	,							lowercase	:       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(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 			_SCREAMING_SNAKE_CASE			(							lowercase	:       int     ):


      '''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 			_SCREAMING_SNAKE_CASE			(							lowercase	:       List[Any]	,							lowercase	:       List[str]     ):


      '''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 			_SCREAMING_SNAKE_CASE			(							lowercase	:       str	,							lowercase	:       Optional[int]     ):


      '''simple docstring'''



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

            lowerCamelCase_		     =    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 			_SCREAMING_SNAKE_CASE			(							lowercase	:       Any	,							lowercase	:       str     ):


      '''simple docstring'''



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

def 			_SCREAMING_SNAKE_CASE			(							lowercase	:       Optional[int]     ):


      '''simple docstring'''



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

def 			_SCREAMING_SNAKE_CASE			(							lowercase	:       List[Any]     ):


      '''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)
                  lowerCamelCase_		     =    set() if mod._weight_quantizer.axis is None else set(mod._weight_quantizer.axis     )
                  lowerCamelCase_		     =    set(range(len(mod.weight.size()     )     )     ) - axis_set
                  lowerCamelCase_		     =    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()}"""     )
                  lowerCamelCase_		     =    amax

def 			_SCREAMING_SNAKE_CASE			(							lowercase	:       List[Any]	,							lowercase	:       List[Any]=25	,							lowercase	:       List[str]=1_80	,							lowercase	:       Optional[int]=None     ):


      '''simple docstring'''



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

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

      for name, mod in model.named_modules():
            lowerCamelCase_		     =    getattr(lowercase	,							'_input_quantizer'	,							lowercase     )
            lowerCamelCase_		     =    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
            lowerCamelCase_		     =    f"""Act:{input_q.extra_repr()}"""
            lowerCamelCase_		     =    f"""Wgt:{weight_q.extra_repr()}"""
            lowerCamelCase_		     =    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 			_SCREAMING_SNAKE_CASE			(							lowercase	:       Tuple     ):


      '''simple docstring'''



      lowerCamelCase_		     =    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 			_SCREAMING_SNAKE_CASE			(							lowercase	:       int	,							lowercase	:       Tuple	,							lowercase	:       Optional[Any]	,							lowercase	:       Optional[Any]	,							lowercase	:       Optional[int]     ):


      '''simple docstring'''



      lowerCamelCase_		     =    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 			_SCREAMING_SNAKE_CASE			(							lowercase	:       Dict	,							lowercase	:       Union[str, Any]	,							lowercase	:       Any="both"	,							**lowercase	:       Any     ):


      '''simple docstring'''



      lowerCamelCase_		     =    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 			_SCREAMING_SNAKE_CASE			(							lowercase	:       Tuple	,							lowercase	:       int	,							**lowercase	:       List[Any]     ):


      '''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     ):
                              lowerCamelCase_		     =    f"""Warning: changing {name:{name_width}}"""
                              for k, v in kwargs.items():
                                    s += f""" {k}={v}"""
                                    setattr(lowercase	,							lowercase	,							lowercase     )
                              logger.info(lowercase     )









def 			_SCREAMING_SNAKE_CASE			(							lowercase	:       list     ):


      '''simple docstring'''



      for i in range(len(lowercase     ) - 1	,							0	,							-1     ):
            lowerCamelCase_		     =    False

            for j in range(lowercase	,							0	,							-1     ):
                  if unsorted[j] < unsorted[j - 1]:
                        lowerCamelCase_     ,				lowerCamelCase_		     =    unsorted[j - 1], unsorted[j]
                        lowerCamelCase_		     =    True

            for j in range(lowercase     ):
                  if unsorted[j] > unsorted[j + 1]:
                        lowerCamelCase_     ,				lowerCamelCase_		     =    unsorted[j + 1], unsorted[j]
                        lowerCamelCase_		     =    True

            if not swapped:
                  break
      return unsorted


if __name__ == "__main__":
 import doctest

 doctest.testmod()
 lowerCamelCase				:		Any     =   input("Enter numbers separated by a comma:\n").strip()
 lowerCamelCase				:		Dict     =   [int(item) for item in user_input.split(",")]
 print(F"""{cocktail_shaker_sort(unsorted) = }""")



