





'''simple docstring'''

import html

from ...feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from ...utils import is_bsa_available, logging, requires_backends


if is_bsa_available():
   import bsa
   from bsa import BeautifulSoup


UpperCAmelCase					:		Optional[int]		=							logging.get_logger(__name__)





class lowerCamelCase						(a__					):







  def __init__(		self     ,     **lowercase__		)		->						Union[str, Any]:






        """simple docstring"""





        requires_backends(self     ,     ['''bs4''']		)
        super().__init__(**lowercase__		)







  def 				UpperCAmelCase_				(		self     ,     lowercase__		)		->						Dict:






        """simple docstring"""





        _snake_case		:	Optional[int]					     =     []
        _snake_case		:	List[str]					     =     []
        _snake_case		:	List[Any]					     =     element if element.name else element.parent
        for parent in child.parents:  # type: bs4.element.Tag
              _snake_case		:	List[Any]					     =     parent.find_all(child.name     ,     recursive=lowercase__		)
              xpath_tags.append(child.name		)
              xpath_subscripts.append(
                  0 if 1 == len(lowercase__		) else next(i for i, s in enumerate(lowercase__     ,     1		) if s is child		)		)
              _snake_case		:	Any					     =     parent
        xpath_tags.reverse()
        xpath_subscripts.reverse()
        return xpath_tags, xpath_subscripts







  def 				UpperCAmelCase_				(		self     ,     lowercase__		)		->						Optional[int]:






        """simple docstring"""





        _snake_case		:	Optional[int]					     =     BeautifulSoup(lowercase__     ,     '''html.parser'''		)

        _snake_case		:	List[str]					     =     []
        _snake_case		:	Tuple					     =     []
        _snake_case		:	Any					     =     []

        for element in html_code.descendants:
              if type(lowercase__		) == bsa.element.NavigableString:
                    if type(element.parent		) != bsa.element.Tag:
                          continue

                    _snake_case		:	str					     =     html.unescape(lowercase__		).strip()
                    if not text_in_this_tag:
                          continue

                    all_doc_strings.append(lowercase__		)

                    _snake_case		:	List[str]					     =     self.xpath_soup(lowercase__		)
                    stringaxtag_seq.append(lowercase__		)
                    stringaxsubs_seq.append(lowercase__		)

        if len(lowercase__		) != len(lowercase__		):
              raise ValueError('''Number of doc strings and xtags does not correspond'''		)
        if len(lowercase__		) != len(lowercase__		):
              raise ValueError('''Number of doc strings and xsubs does not correspond'''		)

        return all_doc_strings, stringaxtag_seq, stringaxsubs_seq







  def 				UpperCAmelCase_				(		self     ,     lowercase__     ,     lowercase__		)		->						Union[str, Any]:






        """simple docstring"""





        _snake_case		:	Tuple					     =     ''''''
        for tagname, subs in zip(lowercase__     ,     lowercase__		):
              xpath += F'''/{tagname}'''
              if subs != 0:
                    xpath += F'''[{subs}]'''
        return xpath







  def __call__(		self     ,     lowercase__		)		->						BatchFeature:






        """simple docstring"""





        _snake_case		:	Dict					     =     False

        # Check that strings has a valid type
        if isinstance(lowercase__     ,     lowercase__		):
              _snake_case		:	str					     =     True
        elif isinstance(lowercase__     ,     (list, tuple)		):
              if len(lowercase__		) == 0 or isinstance(html_strings[0]     ,     lowercase__		):
                    _snake_case		:	Optional[Any]					     =     True

        if not valid_strings:
              raise ValueError(
                  '''HTML strings must of type `str`, `List[str]` (batch of examples), '''
                  F'''but is of type {type(lowercase__		)}.'''		)

        _snake_case		:	Any					     =     bool(isinstance(lowercase__     ,     (list, tuple)		) and (isinstance(html_strings[0]     ,     lowercase__		))		)

        if not is_batched:
              _snake_case		:	Optional[Any]					     =     [html_strings]

        # Get nodes + xpaths
        _snake_case		:	Any					     =     []
        _snake_case		:	str					     =     []
        for html_string in html_strings:
              _snake_case		:	Union[str, Any]					     =     self.get_three_from_single(lowercase__		)
              nodes.append(lowercase__		)
              _snake_case		:	Union[str, Any]					     =     []
              for node, tag_list, sub_list in zip(lowercase__     ,     lowercase__     ,     lowercase__		):
                    _snake_case		:	Union[str, Any]					     =     self.construct_xpath(lowercase__     ,     lowercase__		)
                    xpath_strings.append(lowercase__		)
              xpaths.append(lowercase__		)

        # return as Dict
        _snake_case		:	Optional[int]					     =     {'''nodes''': nodes, '''xpaths''': xpaths}
        _snake_case		:	Tuple					     =     BatchFeature(data=lowercase__     ,     tensor_type=lowercase__		)

        return encoded_inputs









'''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


UpperCAmelCase					:		str		=							logging.getLogger(__name__)

UpperCAmelCase					:		Dict		=							5_0  # max width of layer names
UpperCAmelCase					:		Union[str, Any]		=							7_0  # max width of quantizer names




def    _a    (							lowerCAmelCase_	):





      """simple docstring"""



      _snake_case		:	Dict					     =     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    _a    (							lowerCAmelCase_	):





      """simple docstring"""



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

      _snake_case		:	Tuple					     =     QuantDescriptor(num_bits=args.aprec			,      calib_method=lowerCAmelCase_	)
      _snake_case		:	str					     =     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    _a    (							lowerCAmelCase_			,      lowerCAmelCase_			,      lowerCAmelCase_=False			,      lowerCAmelCase_=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    _a    (							lowerCAmelCase_	):





      """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    _a    (							lowerCAmelCase_			,      lowerCAmelCase_	):





      """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    _a    (							lowerCAmelCase_			,      lowerCAmelCase_	):





      """simple docstring"""



      def fusea(lowerCAmelCase_			,      lowerCAmelCase_			,      lowerCAmelCase_	):
            for mod in [qq, qk, qv]:
                  if not hasattr(lowerCAmelCase_			,      '''_amax'''	):
                        print('''          WARNING: NO AMAX BUFFER'''	)
                        return
            _snake_case		:	Tuple					     =     qq._amax.detach().item()
            _snake_case		:	Tuple					     =     qk._amax.detach().item()
            _snake_case		:	List[Any]					     =     qv._amax.detach().item()

            _snake_case		:	List[str]					     =     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    _a    (							lowerCAmelCase_			,      lowerCAmelCase_	):





      """simple docstring"""



      for name, mod in model.named_modules():
            if name.endswith('''.output.dense'''	) and not name.endswith('''attention.output.dense'''	):
                  _snake_case		:	List[Any]					     =     mod._input_quantizer._amax.data.detach().item()
                  mod._input_quantizer._amax.data.detach().clamp_(max=lowerCAmelCase_	)
                  _snake_case		:	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    _a    (							lowerCAmelCase_	):





      """simple docstring"""



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




def    _a    (							lowerCAmelCase_	):





      """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)
                  _snake_case		:	int					     =     set() if mod._weight_quantizer.axis is None else set(mod._weight_quantizer.axis	)
                  _snake_case		:	Dict					     =     set(range(len(mod.weight.size()	)	)	) - axis_set
                  _snake_case		:	Optional[int]					     =     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()}'''	)
                  _snake_case		:	Tuple					     =     amax




def    _a    (							lowerCAmelCase_			,      lowerCAmelCase_=25			,      lowerCAmelCase_=180			,      lowerCAmelCase_=None	):





      """simple docstring"""



      if ignore is None:
            _snake_case		:	Dict					     =     []
      elif not isinstance(lowerCAmelCase_			,      lowerCAmelCase_	):
            _snake_case		:	Optional[int]					     =     [ignore]

      _snake_case		:	str					     =     0
      for name, mod in model.named_modules():
            if not hasattr(lowerCAmelCase_			,      '''weight'''	):
                  continue
            _snake_case		:	Optional[int]					     =     max(lowerCAmelCase_			,      len(lowerCAmelCase_	)	)

      for name, mod in model.named_modules():
            _snake_case		:	Optional[Any]					     =     getattr(lowerCAmelCase_			,      '''_input_quantizer'''			,      lowerCAmelCase_	)
            _snake_case		:	Tuple					     =     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
            _snake_case		:	Optional[int]					     =     f'''Act:{input_q.extra_repr()}'''
            _snake_case		:	Any					     =     f'''Wgt:{weight_q.extra_repr()}'''
            _snake_case		:	Optional[int]					     =     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    _a    (							lowerCAmelCase_	):





      """simple docstring"""



      _snake_case		:	str					     =     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    _a    (							lowerCAmelCase_			,      lowerCAmelCase_			,      lowerCAmelCase_			,      lowerCAmelCase_			,      lowerCAmelCase_	):





      """simple docstring"""



      _snake_case		:	Optional[Any]					     =     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    _a    (							lowerCAmelCase_			,      lowerCAmelCase_			,      lowerCAmelCase_="both"			,      **lowerCAmelCase_	):





      """simple docstring"""



      _snake_case		:	Optional[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(lowerCAmelCase_			,      lowerCAmelCase_			,      '''_input_quantizer'''			,      lowerCAmelCase_			,      lowerCAmelCase_	)
            if which in ["weight", "both"]:
                  set_quantizer(lowerCAmelCase_			,      lowerCAmelCase_			,      '''_weight_quantizer'''			,      lowerCAmelCase_			,      lowerCAmelCase_	)
      logger.info(lowerCAmelCase_	)




def    _a    (							lowerCAmelCase_			,      lowerCAmelCase_			,      **lowerCAmelCase_	):





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


