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_							=      50  # max width of layer names
a_							=      70  # max width of quantizer names




def       __lowerCAmelCase      (						A_   : List[str]					)    ->  Union[str, Any]:
  __UpperCAmelCase						    =       parser.add_argument_group("quant_trainer arguments"					)
  group.add_argument("--wprec"						,	type=A_						,	default=8						,	help="weight precision"					)
  group.add_argument("--aprec"						,	type=A_						,	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=A_						,	nargs="+"						,	help="disable quantizers by keyword"					)
  group.add_argument("--quant-disable-layer-module"						,	type=A_						,	help="disable quantizers by keyword under layer."					)
  group.add_argument("--quant-enable-layer-module"						,	type=A_						,	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=A_						,	type=A_						,	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=A_						,	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      (						A_   : List[str]					)    ->  Union[str, Any]:

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

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




def       __lowerCAmelCase      (						A_   : Any						,	A_   : List[str]						,	A_   : Optional[Any]=False						,	A_   : Dict=False					)    ->  Optional[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(A_						,	["embeddings"]						,	which="weight"						,	_disabled=A_					)

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

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

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

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

    if args.recalibrate_weights:
      recalibrate_weights(A_					)

    if args.fuse_qkv:
      fuse_qkv(A_						,	A_					)

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

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




def       __lowerCAmelCase      (						A_   : Optional[int]					)    ->  List[Any]:
  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      (						A_   : int						,	A_   : Union[str, Any]					)    ->  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(A_					)




def       __lowerCAmelCase      (						A_   : Union[str, Any]						,	A_   : int					)    ->  List[Any]:

  def fusea(A_   : Dict						,	A_   : Union[str, Any]						,	A_   : str					):
    for mod in [qq, qk, qv]:
      if not hasattr(A_						,	"_amax"					):
        print("          WARNING: NO AMAX BUFFER"					)
        return
    __UpperCAmelCase						    =       qq._amax.detach().item()
    __UpperCAmelCase						    =       qk._amax.detach().item()
    __UpperCAmelCase						    =       qv._amax.detach().item()

    __UpperCAmelCase						    =       max(A_						,	A_						,	A_					)
    qq._amax.fill_(A_					)
    qk._amax.fill_(A_					)
    qv._amax.fill_(A_					)
    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      (						A_   : Union[str, Any]						,	A_   : Union[str, Any]					)    ->  Tuple:

  for name, mod in model.named_modules():
    if name.endswith(".output.dense"					) and not name.endswith("attention.output.dense"					):
      __UpperCAmelCase						    =       mod._input_quantizer._amax.data.detach().item()
      mod._input_quantizer._amax.data.detach().clamp_(max=A_					)
      __UpperCAmelCase						    =       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      (						A_   : Any					)    ->  List[Any]:

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




def       __lowerCAmelCase      (						A_   : List[str]					)    ->  List[str]:

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




def       __lowerCAmelCase      (						A_   : List[Any]						,	A_   : List[Any]=25						,	A_   : int=1_80						,	A_   : Dict=None					)    ->  Any:

  if ignore is None:
    __UpperCAmelCase						    =       []
  elif not isinstance(A_						,	A_					):
    __UpperCAmelCase						    =       [ignore]

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

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




def       __lowerCAmelCase      (						A_   : Dict					)    ->  Tuple:
  __UpperCAmelCase						    =       0
  for name, mod in model.named_modules():
    if isinstance(A_						,	pytorch_quantization.nn.TensorQuantizer					):
      print(F'''{name:80} {mod}'''					)
      count += 1
  print(F'''{count} TensorQuantizers found in model'''					)




def       __lowerCAmelCase      (						A_   : str						,	A_   : Any						,	A_   : int						,	A_   : Tuple						,	A_   : List[str]					)    ->  Optional[int]:
  __UpperCAmelCase						    =       getattr(A_						,	A_						,	A_					)
  if quantizer_mod is not None:
    assert hasattr(A_						,	A_					)
    setattr(A_						,	A_						,	A_					)
  else:
    logger.warning(F'''{name} has no {quantizer}'''					)




def       __lowerCAmelCase      (						A_   : Any						,	A_   : Optional[int]						,	A_   : Optional[int]="both"						,	**A_   : Optional[Any]					)    ->  Optional[Any]:
  __UpperCAmelCase						    =       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(A_						,	A_						,	"_input_quantizer"						,	A_						,	A_					)
    if which in ["weight", "both"]:
      set_quantizer(A_						,	A_						,	"_weight_quantizer"						,	A_						,	A_					)
  logger.info(A_					)





def       __lowerCAmelCase      (						A_   : Dict						,	A_   : Tuple						,	**A_   : Union[str, Any]					)    ->  List[Any]:

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




from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
    is_vision_available,
)


a_							=      {"""processing_layoutxlm""": ["""LayoutXLMProcessor"""]}

try:
       if not is_sentencepiece_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a_							=      ["""LayoutXLMTokenizer"""]

try:
       if not is_tokenizers_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a_							=      ["""LayoutXLMTokenizerFast"""]

if TYPE_CHECKING:
       from .processing_layoutxlm import LayoutXLMProcessor

       try:
              if not is_sentencepiece_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_layoutxlm import LayoutXLMTokenizer

       try:
              if not is_tokenizers_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_layoutxlm_fast import LayoutXLMTokenizerFast

else:
       import sys

       a_							=      _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



