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      :			str							       =		logging.getLogger(__name__)

__lowerCamelCase      :			Dict							       =		50  # max width of layer names
__lowerCamelCase      :			Union[str, Any]							       =		70  # max width of quantizer names



def      __SCREAMING_SNAKE_CASE  ( __UpperCamelCase   :	Optional[int]     ):







  """simple docstring"""





  SCREAMING_SNAKE_CASE__      							=				parser.add_argument_group("""quant_trainer arguments"""     )
  group.add_argument("""--wprec"""			,					type=_lowerCAmelCase			,					default=8			,					help="""weight precision"""     )
  group.add_argument("""--aprec"""			,					type=_lowerCAmelCase			,					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=_lowerCAmelCase			,					nargs="""+"""			,					help="""disable quantizers by keyword"""     )
  group.add_argument("""--quant-disable-layer-module"""			,					type=_lowerCAmelCase			,					help="""disable quantizers by keyword under layer."""     )
  group.add_argument("""--quant-enable-layer-module"""			,					type=_lowerCAmelCase			,					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=_lowerCAmelCase			,					type=_lowerCAmelCase			,					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=_lowerCAmelCase			,					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  ( __UpperCamelCase   :	List[Any]     ):







  """simple docstring"""





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

  SCREAMING_SNAKE_CASE__      							=				QuantDescriptor(num_bits=args.aprec			,					calib_method=_lowerCAmelCase     )
  SCREAMING_SNAKE_CASE__      							=				QuantDescriptor(num_bits=args.wprec			,					axis=(None if args.quant_per_tensor else (0,))     )
  quant_nn.QuantLinear.set_default_quant_desc_input(_lowerCAmelCase     )
  quant_nn.QuantLinear.set_default_quant_desc_weight(_lowerCAmelCase     )



def      __SCREAMING_SNAKE_CASE  ( __UpperCamelCase   :	Optional[int]			,					__UpperCamelCase   :	List[Any]			,					__UpperCamelCase   :	str=False			,					__UpperCamelCase   :	Tuple=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(_lowerCAmelCase			,					["""embeddings"""]			,					which="""weight"""			,					_disabled=_lowerCAmelCase     )

    if args.quant_disable:
      set_quantizer_by_name(_lowerCAmelCase			,					[""""""]			,					_disabled=_lowerCAmelCase     )

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

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

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

    if args.recalibrate_weights:
      recalibrate_weights(_lowerCAmelCase     )

    if args.fuse_qkv:
      fuse_qkv(_lowerCAmelCase			,					_lowerCAmelCase     )

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

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



def      __SCREAMING_SNAKE_CASE  ( __UpperCamelCase   :	str     ):







  """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  ( __UpperCamelCase   :	List[Any]			,					__UpperCamelCase   :	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(_lowerCAmelCase     )



def      __SCREAMING_SNAKE_CASE  ( __UpperCamelCase   :	Any			,					__UpperCamelCase   :	str     ):







  """simple docstring"""





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

    SCREAMING_SNAKE_CASE__      							=				max(_lowerCAmelCase			,					_lowerCAmelCase			,					_lowerCAmelCase     )
    qq._amax.fill_(_lowerCAmelCase     )
    qk._amax.fill_(_lowerCAmelCase     )
    qv._amax.fill_(_lowerCAmelCase     )
    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  ( __UpperCamelCase   :	Union[str, Any]			,					__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__      							=				mod._input_quantizer._amax.data.detach().item()
      mod._input_quantizer._amax.data.detach().clamp_(max=_lowerCAmelCase     )
      SCREAMING_SNAKE_CASE__      							=				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  ( __UpperCamelCase   :	Any     ):







  """simple docstring"""





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



def      __SCREAMING_SNAKE_CASE  ( __UpperCamelCase   :	Optional[Any]     ):







  """simple docstring"""





  for name, mod in model.named_modules():
    if hasattr(_lowerCAmelCase			,					"""_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__      							=				set() if mod._weight_quantizer.axis is None else set(mod._weight_quantizer.axis     )
      SCREAMING_SNAKE_CASE__      							=				set(range(len(mod.weight.size()     )     )     ) - axis_set
      SCREAMING_SNAKE_CASE__      							=				pytorch_quantization.utils.reduce_amax(mod.weight			,					axis=_lowerCAmelCase			,					keepdims=_lowerCAmelCase     ).detach()
      logger.info(f"""RECALIB: {name:{name_width}} {mod._weight_quantizer._amax.flatten()} -> {amax.flatten()}"""     )
      SCREAMING_SNAKE_CASE__      							=				amax



def      __SCREAMING_SNAKE_CASE  ( __UpperCamelCase   :	Optional[Any]			,					__UpperCamelCase   :	List[str]=25			,					__UpperCamelCase   :	Dict=1_80			,					__UpperCamelCase   :	List[str]=None     ):







  """simple docstring"""





  if ignore is None:
    SCREAMING_SNAKE_CASE__      							=				[]
  elif not isinstance(_lowerCAmelCase			,					_lowerCAmelCase     ):
    SCREAMING_SNAKE_CASE__      							=				[ignore]

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

  for name, mod in model.named_modules():
    SCREAMING_SNAKE_CASE__      							=				getattr(_lowerCAmelCase			,					"""_input_quantizer"""			,					_lowerCAmelCase     )
    SCREAMING_SNAKE_CASE__      							=				getattr(_lowerCAmelCase			,					"""_weight_quantizer"""			,					_lowerCAmelCase     )
    if not hasattr(_lowerCAmelCase			,					"""weight"""     ):
      continue
    if type(_lowerCAmelCase     ) in ignore:
      continue
    if [True for s in ignore if type(_lowerCAmelCase     ) is str and s in name]:
      continue
    SCREAMING_SNAKE_CASE__      							=				f"""Act:{input_q.extra_repr()}"""
    SCREAMING_SNAKE_CASE__      							=				f"""Wgt:{weight_q.extra_repr()}"""
    SCREAMING_SNAKE_CASE__      							=				f"""{name:{name_width}} {act_str} {wgt_str}"""
    if len(_lowerCAmelCase     ) <= line_width:
      logger.info(_lowerCAmelCase     )
    else:
      logger.info(f"""{name:{name_width}} {act_str}"""     )
      logger.info(f"""{'  ':{name_width}} {wgt_str}"""     )



def      __SCREAMING_SNAKE_CASE  ( __UpperCamelCase   :	List[Any]     ):







  """simple docstring"""





  SCREAMING_SNAKE_CASE__      							=				0
  for name, mod in model.named_modules():
    if isinstance(_lowerCAmelCase			,					pytorch_quantization.nn.TensorQuantizer     ):
      print(f"""{name:80} {mod}"""     )
      count += 1
  print(f"""{count} TensorQuantizers found in model"""     )



def      __SCREAMING_SNAKE_CASE  ( __UpperCamelCase   :	Optional[Any]			,					__UpperCamelCase   :	Tuple			,					__UpperCamelCase   :	Optional[Any]			,					__UpperCamelCase   :	List[str]			,					__UpperCamelCase   :	Tuple     ):







  """simple docstring"""





  SCREAMING_SNAKE_CASE__      							=				getattr(_lowerCAmelCase			,					_lowerCAmelCase			,					_lowerCAmelCase     )
  if quantizer_mod is not None:
    assert hasattr(_lowerCAmelCase			,					_lowerCAmelCase     )
    setattr(_lowerCAmelCase			,					_lowerCAmelCase			,					_lowerCAmelCase     )
  else:
    logger.warning(f"""{name} has no {quantizer}"""     )



def      __SCREAMING_SNAKE_CASE  ( __UpperCamelCase   :	Tuple			,					__UpperCamelCase   :	Union[str, Any]			,					__UpperCamelCase   :	Dict="both"			,					**__UpperCamelCase   :	Union[str, Any]     ):







  """simple docstring"""





  SCREAMING_SNAKE_CASE__      							=				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(_lowerCAmelCase			,					_lowerCAmelCase			,					"""_input_quantizer"""			,					_lowerCAmelCase			,					_lowerCAmelCase     )
    if which in ["weight", "both"]:
      set_quantizer(_lowerCAmelCase			,					_lowerCAmelCase			,					"""_weight_quantizer"""			,					_lowerCAmelCase			,					_lowerCAmelCase     )
  logger.info(_lowerCAmelCase     )






def      __SCREAMING_SNAKE_CASE  ( __UpperCamelCase   :	str			,					__UpperCamelCase   :	str			,					**__UpperCamelCase   :	Union[str, Any]     ):







  """simple docstring"""





  for name, mod in model.named_modules():
    if hasattr(_lowerCAmelCase			,					"""_input_quantizer"""     ) or hasattr(_lowerCAmelCase			,					"""_weight_quantizer"""     ):
      for n in names:
        if re.search(_lowerCAmelCase			,					_lowerCAmelCase     ):
          set_quantizers(_lowerCAmelCase			,					_lowerCAmelCase			,					**_lowerCAmelCase     )
    elif name.endswith("""_quantizer"""     ):
      for n in names:
        if re.search(_lowerCAmelCase			,					_lowerCAmelCase     ):
          SCREAMING_SNAKE_CASE__      							=				f"""Warning: changing {name:{name_width}}"""
          for k, v in kwargs.items():
            s += f""" {k}={v}"""
            setattr(_lowerCAmelCase			,					_lowerCAmelCase			,					_lowerCAmelCase     )
          logger.info(_lowerCAmelCase     )

from __future__ import annotations

__lowerCamelCase      :			Tuple							       =		list[list[int]]

# assigning initial values to the grid
__lowerCamelCase      :			Matrix							       =		[
    [3, 0, 6, 5, 0, 8, 4, 0, 0],
    [5, 2, 0, 0, 0, 0, 0, 0, 0],
    [0, 8, 7, 0, 0, 0, 0, 3, 1],
    [0, 0, 3, 0, 1, 0, 0, 8, 0],
    [9, 0, 0, 8, 6, 3, 0, 0, 5],
    [0, 5, 0, 0, 9, 0, 6, 0, 0],
    [1, 3, 0, 0, 0, 0, 2, 5, 0],
    [0, 0, 0, 0, 0, 0, 0, 7, 4],
    [0, 0, 5, 2, 0, 6, 3, 0, 0],
]

# a grid with no solution
__lowerCamelCase      :			Matrix							       =		[
    [5, 0, 6, 5, 0, 8, 4, 0, 3],
    [5, 2, 0, 0, 0, 0, 0, 0, 2],
    [1, 8, 7, 0, 0, 0, 0, 3, 1],
    [0, 0, 3, 0, 1, 0, 0, 8, 0],
    [9, 0, 0, 8, 6, 3, 0, 0, 5],
    [0, 5, 0, 0, 9, 0, 6, 0, 0],
    [1, 3, 0, 0, 0, 0, 2, 5, 0],
    [0, 0, 0, 0, 0, 0, 0, 7, 4],
    [0, 0, 5, 2, 0, 6, 3, 0, 0],
]



def      __SCREAMING_SNAKE_CASE  ( __UpperCamelCase   :	Matrix			,					__UpperCamelCase   :	int			,					__UpperCamelCase   :	int			,					__UpperCamelCase   :	int     )							-> bool:







  """simple docstring"""





  for i in range(9     ):
    if grid[row][i] == n or grid[i][column] == n:
      return False

  for i in range(3     ):
    for j in range(3     ):
      if grid[(row - row % 3) + i][(column - column % 3) + j] == n:
        return False

  return True



def      __SCREAMING_SNAKE_CASE  ( __UpperCamelCase   :	Matrix     )							-> tuple[int, int] | None:







  """simple docstring"""





  for i in range(9     ):
    for j in range(9     ):
      if grid[i][j] == 0:
        return i, j
  return None



def      __SCREAMING_SNAKE_CASE  ( __UpperCamelCase   :	Matrix     )							-> Matrix | None:







  """simple docstring"""





  if location := find_empty_location(__UpperCamelCase     ):
    SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__      							=				location
  else:
    # If the location is ``None``, then the grid is solved.
    return grid

  for digit in range(1			,					10     ):
    if is_safe(__UpperCamelCase			,					__UpperCamelCase			,					__UpperCamelCase			,					__UpperCamelCase     ):
      SCREAMING_SNAKE_CASE__      							=				digit

      if sudoku(__UpperCamelCase     ) is not None:
        return grid

      SCREAMING_SNAKE_CASE__      							=				0

  return None



def      __SCREAMING_SNAKE_CASE  ( __UpperCamelCase   :	Matrix     )							-> None:







  """simple docstring"""





  for row in grid:
    for cell in row:
      print(__UpperCamelCase			,					end=""" """     )
    print()


if __name__ == "__main__":
   # make a copy of grid so that you can compare with the unmodified grid
   for example_grid in (initial_grid, no_solution):
      print('''\nExample grid:\n''' + '''=''' * 20)
      print_solution(example_grid)
      print('''\nExample grid solution:''')
      __lowerCamelCase      :			str							       =		sudoku(example_grid)
      if solution is not None:
         print_solution(solution)
      else:
         print('''Cannot find a solution.''')
