




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_					    = logging.getLogger(__name__)

lowerCAmelCase_					    = 50  # max width of layer names
lowerCAmelCase_					    = 70  # max width of quantizer names



def      __lowerCAmelCase			(					UpperCamelCase      )     ->	Tuple:
  lowerCAmelCase__				:  Any		       =     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      __lowerCAmelCase			(					UpperCamelCase      )     ->	Dict:

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

  lowerCAmelCase__				:  Optional[int]		       =     QuantDescriptor(num_bits=args.aprec	,      calib_method=UpperCamelCase      )
  lowerCAmelCase__				:  Optional[int]		       =     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      __lowerCAmelCase			(					UpperCamelCase	,      UpperCamelCase	,      UpperCamelCase=False	,      UpperCamelCase=False      )     ->	List[Any]:
  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      __lowerCAmelCase			(					UpperCamelCase      )     ->	Dict:
  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      )     ->	Optional[int]:
  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      __lowerCAmelCase			(					UpperCamelCase	,      UpperCamelCase      )     ->	Optional[Any]:

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

    lowerCAmelCase__				:  List[str]		       =     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      __lowerCAmelCase			(					UpperCamelCase	,      UpperCamelCase      )     ->	int:

  for name, mod in model.named_modules():
    if name.endswith('''.output.dense'''      ) and not name.endswith('''attention.output.dense'''      ):
      lowerCAmelCase__				:  str		       =     mod._input_quantizer._amax.data.detach().item()
      mod._input_quantizer._amax.data.detach().clamp_(max=UpperCamelCase      )
      lowerCAmelCase__				:  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      )     ->	Optional[int]:

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



def      __lowerCAmelCase			(					UpperCamelCase      )     ->	Tuple:

  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)
      lowerCAmelCase__				:  str		       =     set() if mod._weight_quantizer.axis is None else set(mod._weight_quantizer.axis      )
      lowerCAmelCase__				:  Tuple		       =     set(range(len(mod.weight.size()      )      )      ) - axis_set
      lowerCAmelCase__				:  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()}"""      )
      lowerCAmelCase__				:  Optional[Any]		       =     amax



def      __lowerCAmelCase			(					UpperCamelCase	,      UpperCamelCase=25	,      UpperCamelCase=180	,      UpperCamelCase=None      )     ->	List[Any]:

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

  lowerCAmelCase__				:  List[str]		       =     0
  for name, mod in model.named_modules():
    if not hasattr(UpperCamelCase	,      '''weight'''      ):
      continue
    lowerCAmelCase__				:  Optional[Any]		       =     max(UpperCamelCase	,      len(UpperCamelCase      )      )

  for name, mod in model.named_modules():
    lowerCAmelCase__				:  int		       =     getattr(UpperCamelCase	,      '''_input_quantizer'''	,      UpperCamelCase      )
    lowerCAmelCase__				:  Union[str, 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
    lowerCAmelCase__				:  Union[str, Any]		       =     F"""Act:{input_q.extra_repr()}"""
    lowerCAmelCase__				:  Dict		       =     F"""Wgt:{weight_q.extra_repr()}"""
    lowerCAmelCase__				:  List[Any]		       =     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      __lowerCAmelCase			(					UpperCamelCase      )     ->	List[Any]:
  lowerCAmelCase__				:  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      __lowerCAmelCase			(					UpperCamelCase	,      UpperCamelCase	,      UpperCamelCase	,      UpperCamelCase	,      UpperCamelCase      )     ->	Union[str, Any]:
  lowerCAmelCase__				:  Optional[int]		       =     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      __lowerCAmelCase			(					UpperCamelCase	,      UpperCamelCase	,      UpperCamelCase="both"	,      **UpperCamelCase      )     ->	List[Any]:
  lowerCAmelCase__				:  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      __lowerCAmelCase			(					UpperCamelCase	,      UpperCamelCase	,      **UpperCamelCase      )     ->	str:

  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      ):
          lowerCAmelCase__				:  Any		       =     F"""Warning: changing {name:{name_width}}"""
          for k, v in kwargs.items():
            s += F""" {k}={v}"""
            setattr(UpperCamelCase	,      UpperCamelCase	,      UpperCamelCase      )
          logger.info(UpperCamelCase      )







lowerCAmelCase_					    = [
    [0, 16, 13, 0, 0, 0],
    [0, 0, 10, 12, 0, 0],
    [0, 4, 0, 0, 14, 0],
    [0, 0, 9, 0, 0, 20],
    [0, 0, 0, 7, 0, 4],
    [0, 0, 0, 0, 0, 0],
]



def      __lowerCAmelCase			(					UpperCamelCase	,      UpperCamelCase	,      UpperCamelCase	,      UpperCamelCase      )     ->	int:
  # Return True if there is node that has not iterated.
  lowerCAmelCase__				:  List[str]		       =     [False] * len(UpperCamelCase      )
  lowerCAmelCase__				:  int		       =     [s]
  lowerCAmelCase__				:  Dict		       =     True

  while queue:
    lowerCAmelCase__				:  Union[str, Any]		       =     queue.pop(0      )
    for ind in range(len(graph[u]      )      ):
      if visited[ind] is False and graph[u][ind] > 0:
        queue.append(UpperCamelCase      )
        lowerCAmelCase__				:  List[str]		       =     True
        lowerCAmelCase__				:  int		       =     u

  return visited[t]



def      __lowerCAmelCase			(					UpperCamelCase	,      UpperCamelCase	,      UpperCamelCase      )     ->	Optional[Any]:
  lowerCAmelCase__				:  Dict		       =     [-1] * (len(UpperCamelCase      ))
  lowerCAmelCase__				:  Dict		       =     0
  lowerCAmelCase__				:  List[str]		       =     []
  lowerCAmelCase__				:  Tuple		       =     [i[:] for i in graph]  # Record original cut, copy.
  while bfs(UpperCamelCase	,      UpperCamelCase	,      UpperCamelCase	,      UpperCamelCase      ):
    lowerCAmelCase__				:  Optional[Any]		       =     float('''Inf'''      )
    lowerCAmelCase__				:  Optional[Any]		       =     sink

    while s != source:
      # Find the minimum value in select path
      lowerCAmelCase__				:  Optional[Any]		       =     min(UpperCamelCase	,      graph[parent[s]][s]      )
      lowerCAmelCase__				:  Union[str, Any]		       =     parent[s]

    max_flow += path_flow
    lowerCAmelCase__				:  List[str]		       =     sink

    while v != source:
      lowerCAmelCase__				:  Union[str, Any]		       =     parent[v]
      graph[u][v] -= path_flow
      graph[v][u] += path_flow
      lowerCAmelCase__				:  Dict		       =     parent[v]

  for i in range(len(UpperCamelCase      )      ):
    for j in range(len(graph[0]      )      ):
      if graph[i][j] == 0 and temp[i][j] > 0:
        res.append((i, j)      )

  return res


if __name__ == "__main__":
     print(mincut(test_graph, source=0, sink=5))

