
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


__magic_name__            =  logging.getLogger(__name__)

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





def 	SCREAMING_SNAKE_CASE__    (      __lowerCAmelCase							):
    snake_case__						=				parser.add_argument_group("quant_trainer arguments"							)
    group.add_argument("--wprec"    ,		type=__UpperCamelCase    ,		default=8    ,		help="weight precision"							)
    group.add_argument("--aprec"    ,		type=__UpperCamelCase    ,		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=__UpperCamelCase    ,		nargs="+"    ,		help="disable quantizers by keyword"							)
    group.add_argument("--quant-disable-layer-module"    ,		type=__UpperCamelCase    ,		help="disable quantizers by keyword under layer."							)
    group.add_argument("--quant-enable-layer-module"    ,		type=__UpperCamelCase    ,		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=__UpperCamelCase    ,		type=__UpperCamelCase    ,		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=__UpperCamelCase    ,		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__    (      __lowerCAmelCase							):






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

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





def 	SCREAMING_SNAKE_CASE__    (      __lowerCAmelCase    ,		__lowerCAmelCase    ,		__lowerCAmelCase=False    ,		__lowerCAmelCase=False							):
    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(__UpperCamelCase    ,		["embeddings"]    ,		which="weight"    ,		_disabled=__UpperCamelCase							)

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

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

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

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

        if args.recalibrate_weights:
            recalibrate_weights(__UpperCamelCase							)

        if args.fuse_qkv:
            fuse_qkv(__UpperCamelCase    ,		__UpperCamelCase							)

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

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





def 	SCREAMING_SNAKE_CASE__    (      __lowerCAmelCase							):
    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__    (      __lowerCAmelCase    ,		__lowerCAmelCase							):
    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(__UpperCamelCase							)





def 	SCREAMING_SNAKE_CASE__    (      __lowerCAmelCase    ,		__lowerCAmelCase							):






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

        snake_case__						=				max(__UpperCamelCase    ,		__UpperCamelCase    ,		__UpperCamelCase							)
        qq._amax.fill_(__UpperCamelCase							)
        qk._amax.fill_(__UpperCamelCase							)
        qv._amax.fill_(__UpperCamelCase							)
        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__    (      __lowerCAmelCase    ,		__lowerCAmelCase							):






    for name, mod in model.named_modules():
        if name.endswith(".output.dense"							) and not name.endswith("attention.output.dense"							):
            snake_case__						=				mod._input_quantizer._amax.data.detach().item()
            mod._input_quantizer._amax.data.detach().clamp_(max=__UpperCamelCase							)
            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__    (      __lowerCAmelCase							):






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





def 	SCREAMING_SNAKE_CASE__    (      __lowerCAmelCase							):






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





def 	SCREAMING_SNAKE_CASE__    (      __lowerCAmelCase    ,		__lowerCAmelCase=25    ,		__lowerCAmelCase=180    ,		__lowerCAmelCase=None							):






    if ignore is None:
        snake_case__						=				[]
    elif not isinstance(__UpperCamelCase    ,		__UpperCamelCase							):
        snake_case__						=				[ignore]

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

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





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





def 	SCREAMING_SNAKE_CASE__    (      __lowerCAmelCase    ,		__lowerCAmelCase    ,		__lowerCAmelCase    ,		__lowerCAmelCase    ,		__lowerCAmelCase							):
    snake_case__						=				getattr(__UpperCamelCase    ,		__UpperCamelCase    ,		__UpperCamelCase							)
    if quantizer_mod is not None:
        assert hasattr(__UpperCamelCase    ,		__UpperCamelCase							)
        setattr(__UpperCamelCase    ,		__UpperCamelCase    ,		__UpperCamelCase							)
    else:
        logger.warning(F"""{name} has no {quantizer}"""							)





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





def 	SCREAMING_SNAKE_CASE__    (      __lowerCAmelCase    ,		__lowerCAmelCase    ,		**__lowerCAmelCase							):






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





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


__magic_name__            =  logging.get_logger(__name__)

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

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

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




class 					_SCREAMING_SNAKE_CASE  (      __UpperCamelCase ):
      _A						:		Optional[Any]    	=     VOCAB_FILES_NAMES
      _A						:		Optional[int]    	=     PRETRAINED_VOCAB_FILES_MAP
      _A						:		Any    	=     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      def __init__(			self		,	lowerCamelCase		,	lowerCamelCase=False		,	lowerCamelCase=True		,	lowerCamelCase=True		,	lowerCamelCase="[CLS]"		,	lowerCamelCase="[SEP]"		,	lowerCamelCase="[UNK]"		,	lowerCamelCase="[SEP]"		,	lowerCamelCase="[PAD]"		,	lowerCamelCase="[CLS]"		,	lowerCamelCase="[MASK]"		,	**lowerCamelCase		,	):
          super().__init__(
              do_lower_case=lowerCamelCase		,	remove_space=lowerCamelCase		,	keep_accents=lowerCamelCase		,	bos_token=lowerCamelCase		,	eos_token=lowerCamelCase		,	unk_token=lowerCamelCase		,	sep_token=lowerCamelCase		,	pad_token=lowerCamelCase		,	cls_token=lowerCamelCase		,	mask_token=lowerCamelCase		,	**lowerCamelCase		,	)

          snake_case__						=				do_lower_case
          snake_case__						=				remove_space
          snake_case__						=				keep_accents
          snake_case__						=				vocab_file

          snake_case__						=				spm.SentencePieceProcessor()
          self.sp_model.Load(lowerCamelCase )
      @property
      def A_			(			self ):
          return len(self.sp_model )
      def A_			(			self ):
          snake_case__						=				{self.convert_ids_to_tokens(lowerCamelCase ): i for i in range(self.vocab_size )}
          vocab.update(self.added_tokens_encoder )
          return vocab
      def __getstate__(			self ):
          snake_case__						=				self.__dict__.copy()
          snake_case__						=				None
          return state
      def __setstate__(			self		,	lowerCamelCase ):
          snake_case__						=				d
          snake_case__						=				spm.SentencePieceProcessor()
          self.sp_model.Load(self.vocab_file )
      def A_			(			self		,	lowerCamelCase		,	lowerCamelCase=False ):
          snake_case__						=				self.sp_model.EncodeAsPieces(lowerCamelCase )
          return pieces
      def A_			(			self		,	lowerCamelCase ):
          return self.sp_model.PieceToId(lowerCamelCase )
      def A_			(			self		,	lowerCamelCase ):
          return self.sp_model.IdToPiece(lowerCamelCase )
      def A_			(			self		,	lowerCamelCase ):
          snake_case__						=				self.sp_model.decode_pieces(lowerCamelCase )
          return out_string
      def A_			(			self		,	lowerCamelCase		,	lowerCamelCase = None ):
          snake_case__						=				[self.sep_token_id]
          snake_case__						=				[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 A_			(			self		,	lowerCamelCase		,	lowerCamelCase = None		,	lowerCamelCase = 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(lowerCamelCase )) + [1] + ([0] * len(lowerCamelCase )) + [1]
          return [1] + ([0] * len(lowerCamelCase )) + [1]
      def A_			(			self		,	lowerCamelCase		,	lowerCamelCase = None ):
          snake_case__						=				[self.sep_token_id]
          snake_case__						=				[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 A_			(			self		,	lowerCamelCase		,	lowerCamelCase = None ):
          if not os.path.isdir(lowerCamelCase ):
              logger.error("Vocabulary path ({}) should be a directory".format(lowerCamelCase ) )
              return
          snake_case__						=				os.path.join(
              lowerCamelCase		,	(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )

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

          return (out_vocab_file,)



