






'''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


lowerCamelCase_												=					logging.getLogger(__name__)

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

def 					__lowercase						(			__lowercase       )       ->						int:

      '''simple docstring'''


      _A			     =	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 					__lowercase						(			__lowercase       )       ->						Tuple:

      '''simple docstring'''


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

      _A			     =	QuantDescriptor(num_bits=args.aprec   ,  calib_method=__lowercase       )
      _A			     =	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 					__lowercase						(			__lowercase   ,  __lowercase   ,  __lowercase=False   ,  __lowercase=False       )       ->						Dict:

      '''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 					__lowercase						(			__lowercase       )       ->						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 					__lowercase						(			__lowercase   ,  __lowercase       )       ->						Optional[Any]:

      '''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 					__lowercase						(			__lowercase   ,  __lowercase       )       ->						Union[str, Any]:

      '''simple docstring'''


      def fusea(__lowercase   ,  __lowercase   ,  __lowercase       ):
            for mod in [qq, qk, qv]:
                  if not hasattr(__lowercase   ,  "_amax"       ):
                        print("          WARNING: NO AMAX BUFFER"       )
                        return
            _A			     =	qq._amax.detach().item()
            _A			     =	qk._amax.detach().item()
            _A			     =	qv._amax.detach().item()

            _A			     =	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 					__lowercase						(			__lowercase   ,  __lowercase       )       ->						Optional[Any]:

      '''simple docstring'''


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

def 					__lowercase						(			__lowercase       )       ->						int:

      '''simple docstring'''


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

def 					__lowercase						(			__lowercase       )       ->						Union[str, 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)
                  _A			     =	set() if mod._weight_quantizer.axis is None else set(mod._weight_quantizer.axis       )
                  _A			     =	set(range(len(mod.weight.size()       )       )       ) - axis_set
                  _A			     =	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()}'''       )
                  _A			     =	amax

def 					__lowercase						(			__lowercase   ,  __lowercase=25   ,  __lowercase=180   ,  __lowercase=None       )       ->						int:

      '''simple docstring'''


      if ignore is None:
            _A			     =	[]
      elif not isinstance(__lowercase   ,  __lowercase       ):
            _A			     =	[ignore]

      _A			     =	0
      for name, mod in model.named_modules():
            if not hasattr(__lowercase   ,  "weight"       ):
                  continue
            _A			     =	max(__lowercase   ,  len(__lowercase       )       )

      for name, mod in model.named_modules():
            _A			     =	getattr(__lowercase   ,  "_input_quantizer"   ,  __lowercase       )
            _A			     =	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
            _A			     =	F'''Act:{input_q.extra_repr()}'''
            _A			     =	F'''Wgt:{weight_q.extra_repr()}'''
            _A			     =	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 					__lowercase						(			__lowercase       )       ->						List[str]:

      '''simple docstring'''


      _A			     =	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 					__lowercase						(			__lowercase   ,  __lowercase   ,  __lowercase   ,  __lowercase   ,  __lowercase       )       ->						Optional[int]:

      '''simple docstring'''


      _A			     =	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 					__lowercase						(			__lowercase   ,  __lowercase   ,  __lowercase="both"   ,  **__lowercase       )       ->						str:

      '''simple docstring'''


      _A			     =	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 					__lowercase						(			__lowercase   ,  __lowercase   ,  **__lowercase       )       ->						Optional[int]:

      '''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       ):
                              _A			     =	F'''Warning: changing {name:{name_width}}'''
                              for k, v in kwargs.items():
                                    s += F''' {k}={v}'''
                                    setattr(__lowercase   ,  __lowercase   ,  __lowercase       )
                              logger.info(__lowercase       )






"""simple docstring"""






from __future__ import annotations

from collections.abc import Callable

__lowerCAmelCase    :       str					     =list[list[float | int]]


def 			UpperCAmelCase__       (				lowerCAmelCase__     :Matrix					,		lowerCAmelCase__     :Matrix		) ->    Matrix:
      '''simple docstring'''


      lowercase														=	len(lowerCAmelCase__		)
      lowercase														=	[[0 for _ in range(size + 1		)] for _ in range(lowerCAmelCase__		)]
      lowercase														=	42
      lowercase														=	42
      lowercase														=	42
      lowercase														=	42
      lowercase														=	42
      lowercase														=	42

      for row in range(lowerCAmelCase__		):
            for col in range(lowerCAmelCase__		):
                  lowercase														=	matrix[row][col]

            lowercase														=	vector[row][0]

      lowercase														=	0
      lowercase														=	0
      while row < size and col < size:
            # pivoting
            lowercase														=	max((abs(augmented[rowa][col]		), rowa) for rowa in range(lowerCAmelCase__					,		lowerCAmelCase__		)		)[
                1
            ]
            if augmented[pivot_row][col] == 0:
                  col += 1
                  continue
            else:
                  lowercase							,							lowercase														=	augmented[pivot_row], augmented[row]

            for rowa in range(row + 1					,		lowerCAmelCase__		):
                  lowercase														=	augmented[rowa][col] / augmented[row][col]
                  lowercase														=	0
                  for cola in range(col + 1					,		size + 1		):
                        augmented[rowa][cola] -= augmented[row][cola] * ratio

            row += 1
            col += 1

      # back substitution
      for col in range(1					,		lowerCAmelCase__		):
            for row in range(lowerCAmelCase__		):
                  lowercase														=	augmented[row][col] / augmented[col][col]
                  for cola in range(lowerCAmelCase__					,		size + 1		):
                        augmented[row][cola] -= augmented[col][cola] * ratio

    # round to get rid of numbers like 2.000000000000004
      return [
          [round(augmented[row][size] / augmented[row][row]					,		1_0		)] for row in range(lowerCAmelCase__		)
      ]


def 			UpperCAmelCase__       (				lowerCAmelCase__     :list[int]		) ->    Callable[[int], int]:
      '''simple docstring'''


      lowercase														=	len(lowerCAmelCase__		)
      lowercase														=	[[0 for _ in range(lowerCAmelCase__		)] for _ in range(lowerCAmelCase__		)]
      lowercase														=	[[0] for _ in range(lowerCAmelCase__		)]
      lowercase														=	42
      lowercase														=	42
      lowercase														=	42
      lowercase														=	42

      for x_val, y_val in enumerate(lowerCAmelCase__		):
            for col in range(lowerCAmelCase__		):
                  lowercase														=	(x_val + 1) ** (size - col - 1)
            lowercase														=	y_val

      lowercase														=	solve(lowerCAmelCase__					,		lowerCAmelCase__		)

      def interpolated_func(lowerCAmelCase__     :int		) -> int:
            return sum(
                round(coeffs[x_val][0]		) * (var ** (size - x_val - 1))
                for x_val in range(lowerCAmelCase__		)		)

      return interpolated_func


def 			UpperCAmelCase__       (				lowerCAmelCase__     :int		) ->    int:
      '''simple docstring'''


      return (
          1
          - variable
          + variable**2
          - variable**3
          + variable**4
          - variable**5
          + variable**6
          - variable**7
          + variable**8
          - variable**9
          + variable**1_0
      )


def 			UpperCAmelCase__       (				lowerCAmelCase__     :Callable[[int], int] = question_function					,		lowerCAmelCase__     :int = 1_0		) ->    int:
      '''simple docstring'''


      lowercase														=	[func(lowerCAmelCase__		) for x_val in range(1					,		order + 1		)]

      lowercase														=	[
          interpolate(data_points[:max_coeff]		) for max_coeff in range(1					,		order + 1		)
      ]

      lowercase														=	0
      lowercase														=	42
      lowercase														=	42

      for poly in polynomials:
            lowercase														=	1
            while func(lowerCAmelCase__		) == poly(lowerCAmelCase__		):
                  x_val += 1

            ret += poly(lowerCAmelCase__		)

      return ret


if __name__ == "__main__":
       print(F"""{solution() = }""")



