





'''simple docstring'''

from collections import Counter
from pathlib import Path
from typing import Optional, Tuple

import yaml





class lowerCamelCase						(yaml.SafeLoader					):







  def 				UpperCAmelCase_				(		self     ,     lowercase__		)		->						Any:






        """simple docstring"""





        _snake_case		:	int					     =     [self.constructed_objects[key_node] for key_node, _ in node.value]
        _snake_case		:	str					     =     [tuple(lowercase__		) if isinstance(lowercase__     ,     lowercase__		) else key for key in keys]
        _snake_case		:	str					     =     Counter(lowercase__		)
        _snake_case		:	List[str]					     =     [key for key in counter if counter[key] > 1]
        if duplicate_keys:
              raise TypeError(F'''Got duplicate yaml keys: {duplicate_keys}'''		)







  def 				UpperCAmelCase_				(		self     ,     lowercase__     ,     lowercase__=False		)		->						Tuple:






        """simple docstring"""





        _snake_case		:	Tuple					     =     super().construct_mapping(lowercase__     ,     deep=lowercase__		)
        self._check_no_duplicates_on_constructed_node(lowercase__		)
        return mapping




def    _a    (							lowerCAmelCase_	):





      """simple docstring"""



      _snake_case		:	int					     =     list(readme_content.splitlines()	)
      if full_content and full_content[0] == "---" and "---" in full_content[1:]:
            _snake_case		:	int					     =     full_content[1:].index('''---'''	) + 1
            _snake_case		:	Optional[Any]					     =     '''\n'''.join(full_content[1:sep_idx]	)
            return yamlblock, "\n".join(full_content[sep_idx + 1 :]	)

      return None, "\n".join(lowerCAmelCase_	)





class lowerCamelCase						(a__					):
  # class attributes
  _lowercase  :	Tuple       					=				{"""train_eval_index"""}  # train-eval-index in the YAML metadata







  @classmethod
  def 				UpperCAmelCase_				(		cls     ,     lowercase__		)		->						"DatasetMetadata":






        """simple docstring"""





        with open(lowercase__     ,     encoding='''utf-8'''		) as readme_file:
              _snake_case     ,							_snake_case		:	Tuple					     =     _split_yaml_from_readme(readme_file.read()		)
        if yaml_string is not None:
              return cls.from_yaml_string(lowercase__		)
        else:
              return cls()







  def 				UpperCAmelCase_				(		self     ,     lowercase__		)		->						Dict:






        """simple docstring"""





        if path.exists():
              with open(lowercase__     ,     encoding='''utf-8'''		) as readme_file:
                    _snake_case		:	Any					     =     readme_file.read()
        else:
              _snake_case		:	int					     =     None
        _snake_case		:	Optional[int]					     =     self._to_readme(lowercase__		)
        with open(lowercase__     ,     '''w'''     ,     encoding='''utf-8'''		) as readme_file:
              readme_file.write(lowercase__		)







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






        """simple docstring"""





        if readme_content is not None:
              _snake_case     ,							_snake_case		:	Any					     =     _split_yaml_from_readme(lowercase__		)
              _snake_case		:	Tuple					     =     '''---\n''' + self.to_yaml_string() + '''---\n''' + content
        else:
              _snake_case		:	int					     =     '''---\n''' + self.to_yaml_string() + '''---\n'''
        return full_content







  @classmethod
  def 				UpperCAmelCase_				(		cls     ,     lowercase__		)		->						"DatasetMetadata":






        """simple docstring"""





        _snake_case		:	Optional[int]					     =     yaml.load(lowercase__     ,     Loader=_NoDuplicateSafeLoader		) or {}

        # Convert the YAML keys to DatasetMetadata fields
        _snake_case		:	Optional[Any]					     =     {
            (key.replace('''-'''     ,     '''_'''		) if key.replace('''-'''     ,     '''_'''		) in cls._FIELDS_WITH_DASHES else key): value
            for key, value in metadata_dict.items()
        }
        return cls(**lowercase__		)







  def 				UpperCAmelCase_				(		self		)		->						str:






        """simple docstring"""





        return yaml.safe_dump(
            {
                (key.replace('''_'''     ,     '''-'''		) if key in self._FIELDS_WITH_DASHES else key): value
                for key, value in self.items()
            }     ,     sort_keys=lowercase__     ,     allow_unicode=lowercase__     ,     encoding='''utf-8'''     ,     ).decode('''utf-8'''		)



UpperCAmelCase					:		Optional[Any]		=							{
    'image-classification': [],
    'translation': [],
    'image-segmentation': [],
    'fill-mask': [],
    'automatic-speech-recognition': [],
    'token-classification': [],
    'sentence-similarity': [],
    'audio-classification': [],
    'question-answering': [],
    'summarization': [],
    'zero-shot-classification': [],
    'table-to-text': [],
    'feature-extraction': [],
    'other': [],
    'multiple-choice': [],
    'text-classification': [],
    'text-to-image': [],
    'text2text-generation': [],
    'zero-shot-image-classification': [],
    'tabular-classification': [],
    'tabular-regression': [],
    'image-to-image': [],
    'tabular-to-text': [],
    'unconditional-image-generation': [],
    'text-retrieval': [],
    'text-to-speech': [],
    'object-detection': [],
    'audio-to-audio': [],
    'text-generation': [],
    'conversational': [],
    'table-question-answering': [],
    'visual-question-answering': [],
    'image-to-text': [],
    'reinforcement-learning': [],
    'voice-activity-detection': [],
    'time-series-forecasting': [],
    'document-question-answering': [],
}


if __name__ == "__main__":
   from argparse import ArgumentParser

   UpperCAmelCase					:		Optional[Any]		=							ArgumentParser(usage='Validate the yaml metadata block of a README.md file.')
   ap.add_argument('readme_filepath')
   UpperCAmelCase					:		Optional[int]		=							ap.parse_args()

   UpperCAmelCase					:		int		=							Path(args.readme_filepath)
   UpperCAmelCase					:		Optional[int]		=							DatasetMetadata.from_readme(readme_filepath)
   print(dataset_metadata)
   dataset_metadata.to_readme(readme_filepath)









'''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_	)


