





'''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 	_A  (					A__   ):






 """simple docstring"""



 __lowercase													=				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 	_A  (					A__   ):






 """simple docstring"""



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

 __lowercase													=				QuantDescriptor(num_bits=args.aprec  ,       calib_method=A__   )
 __lowercase													=				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 	_A  (					A__  ,       A__  ,       A__=False  ,       A__=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(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 	_A  (					A__   ):






 """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  (					A__  ,       A__   ):






 """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(A__   )


def 	_A  (					A__  ,       A__   ):






 """simple docstring"""



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

  __lowercase													=				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 	_A  (					A__  ,       A__   ):






 """simple docstring"""



 for name, mod in model.named_modules():
  if name.endswith('''.output.dense'''   ) and not name.endswith('''attention.output.dense'''   ):
   __lowercase													=				mod._input_quantizer._amax.data.detach().item()
   mod._input_quantizer._amax.data.detach().clamp_(max=A__   )
   __lowercase													=				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  (					A__   ):






 """simple docstring"""



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


def 	_A  (					A__   ):






 """simple docstring"""



 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)
   __lowercase													=				set() if mod._weight_quantizer.axis is None else set(mod._weight_quantizer.axis   )
   __lowercase													=				set(range(len(mod.weight.size()   )   )   ) - axis_set
   __lowercase													=				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()}"   )
   __lowercase													=				amax


def 	_A  (					A__  ,       A__=25  ,       A__=180  ,       A__=None   ):






 """simple docstring"""



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

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

 for name, mod in model.named_modules():
  __lowercase													=				getattr(A__  ,       '''_input_quantizer'''  ,       A__   )
  __lowercase													=				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
  __lowercase													=				F"Act:{input_q.extra_repr()}"
  __lowercase													=				F"Wgt:{weight_q.extra_repr()}"
  __lowercase													=				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 	_A  (					A__   ):






 """simple docstring"""



 __lowercase													=				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 	_A  (					A__  ,       A__  ,       A__  ,       A__  ,       A__   ):






 """simple docstring"""



 __lowercase													=				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 	_A  (					A__  ,       A__  ,       A__="both"  ,       **A__   ):






 """simple docstring"""



 __lowercase													=				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 	_A  (					A__  ,       A__  ,       **A__   ):






 """simple docstring"""



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




'''simple docstring'''






import os
from shutil import copyfile
from typing import List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


lowerCAmelCase__      		=			logging.get_logger(__name__)

lowerCAmelCase__      		=			{'''vocab_file''': '''sentencepiece.model'''}

lowerCAmelCase__      		=			{
    '''vocab_file''': {
        '''google/rembert''': '''https://huggingface.co/google/rembert/resolve/main/sentencepiece.model''',
    },
}

lowerCAmelCase__      		=			{
    '''google/rembert''': 256,
}






class     lowercase_			(lowerCamelCase__   ):




  """simple docstring"""




  SCREAMING_SNAKE_CASE					:						Tuple           =    VOCAB_FILES_NAMES
  SCREAMING_SNAKE_CASE					:						Optional[int]           =    PRETRAINED_VOCAB_FILES_MAP
  SCREAMING_SNAKE_CASE					:						Union[str, Any]           =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES






  def __init__(   self     :  str      ,lowercase__     :  Optional[Any]      ,lowercase__     :  List[str]=False      ,lowercase__     :  Dict=True      ,lowercase__     :  List[str]=True      ,lowercase__     :  Dict="[CLS]"      ,lowercase__     :  Union[str, Any]="[SEP]"      ,lowercase__     :  List[str]="[UNK]"      ,lowercase__     :  int="[SEP]"      ,lowercase__     :  List[str]="[PAD]"      ,lowercase__     :  Optional[int]="[CLS]"      ,lowercase__     :  List[Any]="[MASK]"      ,**lowercase__     :  int      ,):
   super().__init__(
       do_lower_case=lowercase__      ,remove_space=lowercase__      ,keep_accents=lowercase__      ,bos_token=lowercase__      ,eos_token=lowercase__      ,unk_token=lowercase__      ,sep_token=lowercase__      ,pad_token=lowercase__      ,cls_token=lowercase__      ,mask_token=lowercase__      ,**lowercase__      ,)

   __lowercase													=				do_lower_case
   __lowercase													=				remove_space
   __lowercase													=				keep_accents
   __lowercase													=				vocab_file

   __lowercase													=				spm.SentencePieceProcessor()
   self.sp_model.Load(lowercase__    )






  @property
  def       SCREAMING_SNAKE_CASE   (   self     :  str    ):
   return len(self.sp_model    )






  def       SCREAMING_SNAKE_CASE   (   self     :  str    ):
   __lowercase													=				{self.convert_ids_to_tokens(lowercase__    ): i for i in range(self.vocab_size    )}
   vocab.update(self.added_tokens_encoder    )
   return vocab






  def __getstate__(   self     :  List[Any]    ):
   __lowercase													=				self.__dict__.copy()
   __lowercase													=				None
   return state






  def __setstate__(   self     :  str      ,lowercase__     :  Optional[int]    ):
   __lowercase													=				d
   __lowercase													=				spm.SentencePieceProcessor()
   self.sp_model.Load(self.vocab_file    )






  def       SCREAMING_SNAKE_CASE   (   self     :  List[str]      ,lowercase__     :  List[str]      ,lowercase__     :  List[Any]=False    ):
   __lowercase													=				self.sp_model.EncodeAsPieces(lowercase__    )
   return pieces






  def       SCREAMING_SNAKE_CASE   (   self     :  Tuple      ,lowercase__     :  List[Any]    ):
   return self.sp_model.PieceToId(lowercase__    )






  def       SCREAMING_SNAKE_CASE   (   self     :  Tuple      ,lowercase__     :  str    ):
   return self.sp_model.IdToPiece(lowercase__    )






  def       SCREAMING_SNAKE_CASE   (   self     :  Any      ,lowercase__     :  Tuple    ):
   __lowercase													=				self.sp_model.decode_pieces(lowercase__    )
   return out_string






  def       SCREAMING_SNAKE_CASE   (   self     :  Optional[int]      ,lowercase__     :  List[int]      ,lowercase__     :  Optional[List[int]] = None    ):
   __lowercase													=				[self.sep_token_id]
   __lowercase													=				[self.cls_token_id]
   if token_ids_a is None:
    return cls + token_ids_a + sep
   return cls + token_ids_a + sep + token_ids_a + sep






  def       SCREAMING_SNAKE_CASE   (   self     :  List[Any]      ,lowercase__     :  List[int]      ,lowercase__     :  Optional[List[int]] = None      ,lowercase__     :  bool = False    ):

   if already_has_special_tokens:
    if token_ids_a is not None:
     raise ValueError(
         '''You should not supply a second sequence if the provided sequence of '''
         '''ids is already formatted with special tokens for the model.'''    )
    return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]

   if token_ids_a is not None:
    return [1] + ([0] * len(lowercase__    )) + [1] + ([0] * len(lowercase__    )) + [1]
   return [1] + ([0] * len(lowercase__    )) + [1]






  def       SCREAMING_SNAKE_CASE   (   self     :  Dict      ,lowercase__     :  List[int]      ,lowercase__     :  Optional[List[int]] = None    ):
   __lowercase													=				[self.sep_token_id]
   __lowercase													=				[self.cls_token_id]

   if token_ids_a is None:
    return len(cls + token_ids_a + sep    ) * [0]
   return len(cls + token_ids_a + sep    ) * [0] + len(token_ids_a + sep    ) * [1]






  def       SCREAMING_SNAKE_CASE   (   self     :  Optional[int]      ,lowercase__     :  str      ,lowercase__     :  Optional[str] = None    ):
   if not os.path.isdir(lowercase__    ):
    logger.error('''Vocabulary path ({}) should be a directory'''.format(lowercase__    )    )
    return
   __lowercase													=				os.path.join(
       lowercase__      ,(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']    )

   if os.path.abspath(self.vocab_file    ) != os.path.abspath(lowercase__    ):
    copyfile(self.vocab_file      ,lowercase__    )

   return (out_vocab_file,)

