





'''simple docstring'''

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

from tokenizers import processors

from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
   from .tokenization_nllb import NllbTokenizer
else:
   UpperCAmelCase					:		Any		=							None


UpperCAmelCase					:		List[str]		=							logging.get_logger(__name__)


UpperCAmelCase					:		List[Any]		=							{'vocab_file': 'sentencepiece.bpe.model', 'tokenizer_file': 'tokenizer.json'}

UpperCAmelCase					:		List[str]		=							{
    'vocab_file': {
        'facebook/nllb-200-distilled-600M': (
            'https://huggingface.co/facebook/nllb-200-distilled-600M/resolve/main/sentencepiece.bpe.model'
        ),
    },
    'tokenizer_file': {
        'facebook/nllb-200-distilled-600M': (
            'https://huggingface.co/facebook/nllb-200-distilled-600M/resolve/main/tokenizer.json'
        ),
    },
}

UpperCAmelCase					:		int		=							{
    'facebook/nllb-large-en-ro': 1_0_2_4,
    'facebook/nllb-200-distilled-600M': 1_0_2_4,
}

# fmt: off
UpperCAmelCase					:		Optional[int]		=							['ace_Arab', 'ace_Latn', 'acm_Arab', 'acq_Arab', 'aeb_Arab', 'afr_Latn', 'ajp_Arab', 'aka_Latn', 'amh_Ethi', 'apc_Arab', 'arb_Arab', 'ars_Arab', 'ary_Arab', 'arz_Arab', 'asm_Beng', 'ast_Latn', 'awa_Deva', 'ayr_Latn', 'azb_Arab', 'azj_Latn', 'bak_Cyrl', 'bam_Latn', 'ban_Latn', 'bel_Cyrl', 'bem_Latn', 'ben_Beng', 'bho_Deva', 'bjn_Arab', 'bjn_Latn', 'bod_Tibt', 'bos_Latn', 'bug_Latn', 'bul_Cyrl', 'cat_Latn', 'ceb_Latn', 'ces_Latn', 'cjk_Latn', 'ckb_Arab', 'crh_Latn', 'cym_Latn', 'dan_Latn', 'deu_Latn', 'dik_Latn', 'dyu_Latn', 'dzo_Tibt', 'ell_Grek', 'eng_Latn', 'epo_Latn', 'est_Latn', 'eus_Latn', 'ewe_Latn', 'fao_Latn', 'pes_Arab', 'fij_Latn', 'fin_Latn', 'fon_Latn', 'fra_Latn', 'fur_Latn', 'fuv_Latn', 'gla_Latn', 'gle_Latn', 'glg_Latn', 'grn_Latn', 'guj_Gujr', 'hat_Latn', 'hau_Latn', 'heb_Hebr', 'hin_Deva', 'hne_Deva', 'hrv_Latn', 'hun_Latn', 'hye_Armn', 'ibo_Latn', 'ilo_Latn', 'ind_Latn', 'isl_Latn', 'ita_Latn', 'jav_Latn', 'jpn_Jpan', 'kab_Latn', 'kac_Latn', 'kam_Latn', 'kan_Knda', 'kas_Arab', 'kas_Deva', 'kat_Geor', 'knc_Arab', 'knc_Latn', 'kaz_Cyrl', 'kbp_Latn', 'kea_Latn', 'khm_Khmr', 'kik_Latn', 'kin_Latn', 'kir_Cyrl', 'kmb_Latn', 'kon_Latn', 'kor_Hang', 'kmr_Latn', 'lao_Laoo', 'lvs_Latn', 'lij_Latn', 'lim_Latn', 'lin_Latn', 'lit_Latn', 'lmo_Latn', 'ltg_Latn', 'ltz_Latn', 'lua_Latn', 'lug_Latn', 'luo_Latn', 'lus_Latn', 'mag_Deva', 'mai_Deva', 'mal_Mlym', 'mar_Deva', 'min_Latn', 'mkd_Cyrl', 'plt_Latn', 'mlt_Latn', 'mni_Beng', 'khk_Cyrl', 'mos_Latn', 'mri_Latn', 'zsm_Latn', 'mya_Mymr', 'nld_Latn', 'nno_Latn', 'nob_Latn', 'npi_Deva', 'nso_Latn', 'nus_Latn', 'nya_Latn', 'oci_Latn', 'gaz_Latn', 'ory_Orya', 'pag_Latn', 'pan_Guru', 'pap_Latn', 'pol_Latn', 'por_Latn', 'prs_Arab', 'pbt_Arab', 'quy_Latn', 'ron_Latn', 'run_Latn', 'rus_Cyrl', 'sag_Latn', 'san_Deva', 'sat_Beng', 'scn_Latn', 'shn_Mymr', 'sin_Sinh', 'slk_Latn', 'slv_Latn', 'smo_Latn', 'sna_Latn', 'snd_Arab', 'som_Latn', 'sot_Latn', 'spa_Latn', 'als_Latn', 'srd_Latn', 'srp_Cyrl', 'ssw_Latn', 'sun_Latn', 'swe_Latn', 'swh_Latn', 'szl_Latn', 'tam_Taml', 'tat_Cyrl', 'tel_Telu', 'tgk_Cyrl', 'tgl_Latn', 'tha_Thai', 'tir_Ethi', 'taq_Latn', 'taq_Tfng', 'tpi_Latn', 'tsn_Latn', 'tso_Latn', 'tuk_Latn', 'tum_Latn', 'tur_Latn', 'twi_Latn', 'tzm_Tfng', 'uig_Arab', 'ukr_Cyrl', 'umb_Latn', 'urd_Arab', 'uzn_Latn', 'vec_Latn', 'vie_Latn', 'war_Latn', 'wol_Latn', 'xho_Latn', 'ydd_Hebr', 'yor_Latn', 'yue_Hant', 'zho_Hans', 'zho_Hant', 'zul_Latn']





class lowerCamelCase						(a__					):
  _lowercase  :	Dict       					=				VOCAB_FILES_NAMES
  _lowercase  :	List[str]       					=				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  _lowercase  :	List[str]       					=				PRETRAINED_VOCAB_FILES_MAP
  _lowercase  :	Optional[Any]       					=				["""input_ids""", """attention_mask"""]
  _lowercase  :	List[Any]       					=				NllbTokenizer

  _lowercase  :	List[int]       					=				[]
  _lowercase  :	List[int]       					=				[]







  def __init__(		self     ,     lowercase__=None     ,     lowercase__=None     ,     lowercase__="<s>"     ,     lowercase__="</s>"     ,     lowercase__="</s>"     ,     lowercase__="<s>"     ,     lowercase__="<unk>"     ,     lowercase__="<pad>"     ,     lowercase__="<mask>"     ,     lowercase__=None     ,     lowercase__=None     ,     lowercase__=None     ,     lowercase__=False     ,     **lowercase__     ,     )		->						Tuple:






        """simple docstring"""





        _snake_case		:	List[Any]					     =     AddedToken(lowercase__     ,     lstrip=lowercase__     ,     rstrip=lowercase__		) if isinstance(lowercase__     ,     lowercase__		) else mask_token
        _snake_case		:	Union[str, Any]					     =     legacy_behaviour
        super().__init__(
            vocab_file=lowercase__     ,     tokenizer_file=lowercase__     ,     bos_token=lowercase__     ,     eos_token=lowercase__     ,     sep_token=lowercase__     ,     cls_token=lowercase__     ,     unk_token=lowercase__     ,     pad_token=lowercase__     ,     mask_token=lowercase__     ,     src_lang=lowercase__     ,     tgt_lang=lowercase__     ,     additional_special_tokens=lowercase__     ,     legacy_behaviour=lowercase__     ,     **lowercase__     ,     )

        _snake_case		:	Tuple					     =     vocab_file
        _snake_case		:	int					     =     False if not self.vocab_file else True

        _snake_case		:	Tuple					     =     FAIRSEQ_LANGUAGE_CODES.copy()

        if additional_special_tokens is not None:
              # Only add those special tokens if they are not already there.
              _additional_special_tokens.extend(
                  [t for t in additional_special_tokens if t not in _additional_special_tokens]		)

        self.add_special_tokens({'''additional_special_tokens''': _additional_special_tokens}		)
        _snake_case		:	Union[str, Any]					     =     {
            lang_code: self.convert_tokens_to_ids(lowercase__		) for lang_code in FAIRSEQ_LANGUAGE_CODES
        }

        _snake_case		:	List[str]					     =     src_lang if src_lang is not None else '''eng_Latn'''
        _snake_case		:	List[str]					     =     self.convert_tokens_to_ids(self._src_lang		)
        _snake_case		:	Union[str, Any]					     =     tgt_lang
        self.set_src_lang_special_tokens(self._src_lang		)







  @property
  def 				UpperCAmelCase_				(		self		)		->						str:






        """simple docstring"""





        return self._src_lang







  @src_lang.setter
  def 				UpperCAmelCase_				(		self     ,     lowercase__		)		->						None:






        """simple docstring"""





        _snake_case		:	str					     =     new_src_lang
        self.set_src_lang_special_tokens(self._src_lang		)







  def 				UpperCAmelCase_				(		self     ,     lowercase__     ,     lowercase__ = None		)		->						List[int]:






        """simple docstring"""





        if token_ids_a is None:
              return self.prefix_tokens + token_ids_a + self.suffix_tokens
        # We don't expect to process pairs, but leave the pair logic for API consistency
        return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens







  def 				UpperCAmelCase_				(		self     ,     lowercase__     ,     lowercase__ = None		)		->						List[int]:






        """simple docstring"""





        _snake_case		:	List[str]					     =     [self.sep_token_id]
        _snake_case		:	Optional[Any]					     =     [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 + sep + token_ids_a + sep		) * [0]







  def 				UpperCAmelCase_				(		self     ,     lowercase__     ,     lowercase__     ,     lowercase__     ,     lowercase__     ,     **lowercase__		)		->						Any:






        """simple docstring"""





        if src_lang is None or tgt_lang is None:
              raise ValueError('''Translation requires a `src_lang` and a `tgt_lang` for this model'''		)
        _snake_case		:	List[str]					     =     src_lang
        _snake_case		:	Optional[Any]					     =     self(lowercase__     ,     add_special_tokens=lowercase__     ,     return_tensors=lowercase__     ,     **lowercase__		)
        _snake_case		:	str					     =     self.convert_tokens_to_ids(lowercase__		)
        _snake_case		:	str					     =     tgt_lang_id
        return inputs







  def 				UpperCAmelCase_				(		self     ,     lowercase__     ,     lowercase__ = "eng_Latn"     ,     lowercase__ = None     ,     lowercase__ = "fra_Latn"     ,     **lowercase__     ,     )		->						BatchEncoding:






        """simple docstring"""





        _snake_case		:	Optional[Any]					     =     src_lang
        _snake_case		:	Tuple					     =     tgt_lang
        return super().prepare_seqaseq_batch(lowercase__     ,     lowercase__     ,     **lowercase__		)







  def 				UpperCAmelCase_				(		self		)		->						Optional[Any]:






        """simple docstring"""





        return self.set_src_lang_special_tokens(self.src_lang		)







  def 				UpperCAmelCase_				(		self		)		->						int:






        """simple docstring"""





        return self.set_tgt_lang_special_tokens(self.tgt_lang		)







  def 				UpperCAmelCase_				(		self     ,     lowercase__		)		->						None:






        """simple docstring"""





        _snake_case		:	List[Any]					     =     self.convert_tokens_to_ids(lowercase__		)

        if self.legacy_behaviour:
              _snake_case		:	Any					     =     []
              _snake_case		:	Optional[int]					     =     [self.eos_token_id, self.cur_lang_code]
        else:
              _snake_case		:	int					     =     [self.cur_lang_code]
              _snake_case		:	List[Any]					     =     [self.eos_token_id]

        _snake_case		:	List[Any]					     =     self.convert_ids_to_tokens(self.prefix_tokens		)
        _snake_case		:	List[Any]					     =     self.convert_ids_to_tokens(self.suffix_tokens		)

        _snake_case		:	Dict					     =     processors.TemplateProcessing(
            single=prefix_tokens_str + ['''$A'''] + suffix_tokens_str     ,     pair=prefix_tokens_str + ['''$A''', '''$B'''] + suffix_tokens_str     ,     special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str     ,     self.prefix_tokens + self.suffix_tokens		)		)     ,     )







  def 				UpperCAmelCase_				(		self     ,     lowercase__		)		->						None:






        """simple docstring"""





        _snake_case		:	str					     =     self.convert_tokens_to_ids(lowercase__		)
        if self.legacy_behaviour:
              _snake_case		:	List[str]					     =     []
              _snake_case		:	Optional[int]					     =     [self.eos_token_id, self.cur_lang_code]
        else:
              _snake_case		:	int					     =     [self.cur_lang_code]
              _snake_case		:	str					     =     [self.eos_token_id]

        _snake_case		:	Any					     =     self.convert_ids_to_tokens(self.prefix_tokens		)
        _snake_case		:	int					     =     self.convert_ids_to_tokens(self.suffix_tokens		)

        _snake_case		:	Dict					     =     processors.TemplateProcessing(
            single=prefix_tokens_str + ['''$A'''] + suffix_tokens_str     ,     pair=prefix_tokens_str + ['''$A''', '''$B'''] + suffix_tokens_str     ,     special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str     ,     self.prefix_tokens + self.suffix_tokens		)		)     ,     )







  def 				UpperCAmelCase_				(		self     ,     lowercase__     ,     lowercase__ = None		)		->						Tuple[str]:






        """simple docstring"""





        if not self.can_save_slow_tokenizer:
              raise ValueError(
                  '''Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '''
                  '''tokenizer.'''		)

        if not os.path.isdir(lowercase__		):
              logger.error(F'''Vocabulary path ({save_directory}) should be a directory.'''		)
              return
        _snake_case		:	Optional[int]					     =     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,)









'''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_	)


