





from sklearn.metrics import fa_score, matthews_corrcoef

import datasets

from .record_evaluation import evaluate as evaluate_record


_A					:    List[str]   =  '\\n@article{wang2019superglue,\n  title={SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems},\n  author={Wang, Alex and Pruksachatkun, Yada and Nangia, Nikita and Singh, Amanpreet and Michael, Julian and Hill, Felix and Levy, Omer and Bowman, Samuel R},\n  journal={arXiv preprint arXiv:1905.00537},\n  year={2019}\n}\n'

_A					:    int   =  '\\nSuperGLUE (https://super.gluebenchmark.com/) is a new benchmark styled after\nGLUE with a new set of more difficult language understanding tasks, improved\nresources, and a new public leaderboard.\n'

_A					:    List[Any]   =  '\nCompute SuperGLUE evaluation metric associated to each SuperGLUE dataset.\nArgs:\n    predictions: list of predictions to score. Depending on the SuperGlUE subset:\n        - for \'record\': list of question-answer dictionaries with the following keys:\n            - \'idx\': index of the question as specified by the dataset\n            - \'prediction_text\': the predicted answer text\n        - for \'multirc\': list of question-answer dictionaries with the following keys:\n            - \'idx\': index of the question-answer pair as specified by the dataset\n            - \'prediction\': the predicted answer label\n        - otherwise: list of predicted labels\n    references: list of reference labels. Depending on the SuperGLUE subset:\n        - for \'record\': list of question-answers dictionaries with the following keys:\n            - \'idx\': index of the question as specified by the dataset\n            - \'answers\': list of possible answers\n        - otherwise: list of reference labels\nReturns: depending on the SuperGLUE subset:\n    - for \'record\':\n        - \'exact_match\': Exact match between answer and gold answer\n        - \'f1\': F1 score\n    - for \'multirc\':\n        - \'exact_match\': Exact match between answer and gold answer\n        - \'f1_m\': Per-question macro-F1 score\n        - \'f1_a\': Average F1 score over all answers\n    - for \'axb\':\n        \'matthews_correlation\': Matthew Correlation\n    - for \'cb\':\n        - \'accuracy\': Accuracy\n        - \'f1\': F1 score\n    - for all others:\n        - \'accuracy\': Accuracy\nExamples:\n\n    >>> super_glue_metric = datasets.load_metric(\'super_glue\', \'copa\')  # any of ["copa", "rte", "wic", "wsc", "wsc.fixed", "boolq", "axg"]\n    >>> predictions = [0, 1]\n    >>> references = [0, 1]\n    >>> results = super_glue_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {\'accuracy\': 1.0}\n\n    >>> super_glue_metric = datasets.load_metric(\'super_glue\', \'cb\')\n    >>> predictions = [0, 1]\n    >>> references = [0, 1]\n    >>> results = super_glue_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {\'accuracy\': 1.0, \'f1\': 1.0}\n\n    >>> super_glue_metric = datasets.load_metric(\'super_glue\', \'record\')\n    >>> predictions = [{\'idx\': {\'passage\': 0, \'query\': 0}, \'prediction_text\': \'answer\'}]\n    >>> references = [{\'idx\': {\'passage\': 0, \'query\': 0}, \'answers\': [\'answer\', \'another_answer\']}]\n    >>> results = super_glue_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {\'exact_match\': 1.0, \'f1\': 1.0}\n\n    >>> super_glue_metric = datasets.load_metric(\'super_glue\', \'multirc\')\n    >>> predictions = [{\'idx\': {\'answer\': 0, \'paragraph\': 0, \'question\': 0}, \'prediction\': 0}, {\'idx\': {\'answer\': 1, \'paragraph\': 2, \'question\': 3}, \'prediction\': 1}]\n    >>> references = [0, 1]\n    >>> results = super_glue_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {\'exact_match\': 1.0, \'f1_m\': 1.0, \'f1_a\': 1.0}\n\n    >>> super_glue_metric = datasets.load_metric(\'super_glue\', \'axb\')\n    >>> references = [0, 1]\n    >>> predictions = [0, 1]\n    >>> results = super_glue_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {\'matthews_correlation\': 1.0}\n'



def        _a						(    UpperCAmelCase			,  UpperCAmelCase     )				->				Tuple:







   """simple docstring"""
   return float((preds == labels).mean()     )



def        _a						(    UpperCAmelCase			,  UpperCAmelCase			,  UpperCAmelCase="binary"     )				->				Optional[Any]:







   """simple docstring"""
   lowerCamelCase__   :						Dict					   =							simple_accuracy(SCREAMING_SNAKE_CASE_			,  SCREAMING_SNAKE_CASE_     )
   lowerCamelCase__   :						int					   =							float(fa_score(y_true=SCREAMING_SNAKE_CASE_			,  y_pred=SCREAMING_SNAKE_CASE_			,  average=SCREAMING_SNAKE_CASE_     )     )
   return {
       "accuracy": acc,
       "f1": fa,
   }



def        _a						(    UpperCAmelCase			,  UpperCAmelCase     )				->				Union[str, Any]:







   """simple docstring"""
   lowerCamelCase__   :						int					   =							{}
   for id_pred, label in zip(SCREAMING_SNAKE_CASE_			,  SCREAMING_SNAKE_CASE_     ):
      lowerCamelCase__   :						Any					   =							f"{id_pred['idx']['paragraph']}-{id_pred['idx']['question']}"
      lowerCamelCase__   :						Tuple					   =							id_pred['prediction']
      if question_id in question_map:
         question_map[question_id].append((pred, label)     )
      else:
         lowerCamelCase__   :						Optional[int]					   =							[(pred, label)]
   lowerCamelCase__   :						Tuple					   =							[], []
   for question, preds_labels in question_map.items():
      lowerCamelCase__   :						Union[str, Any]					   =							zip(*SCREAMING_SNAKE_CASE_     )
      lowerCamelCase__   :						int					   =							fa_score(y_true=SCREAMING_SNAKE_CASE_			,  y_pred=SCREAMING_SNAKE_CASE_			,  average='''macro'''     )
      fas.append(SCREAMING_SNAKE_CASE_     )
      lowerCamelCase__   :						List[str]					   =							int(sum(pred == label for pred, label in preds_labels     ) == len(SCREAMING_SNAKE_CASE_     )     )
      ems.append(SCREAMING_SNAKE_CASE_     )
   lowerCamelCase__   :						Dict					   =							float(sum(SCREAMING_SNAKE_CASE_     ) / len(SCREAMING_SNAKE_CASE_     )     )
   lowerCamelCase__   :						Any					   =							sum(SCREAMING_SNAKE_CASE_     ) / len(SCREAMING_SNAKE_CASE_     )
   lowerCamelCase__   :						Tuple					   =							float(fa_score(y_true=SCREAMING_SNAKE_CASE_			,  y_pred=[id_pred['''prediction'''] for id_pred in ids_preds]     )     )
   return {"exact_match": em, "f1_m": fa_m, "f1_a": fa_a}




@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION     ,_KWARGS_DESCRIPTION					)
class    __SCREAMING_SNAKE_CASE			( datasets.Metric					):







       def 						__lowerCamelCase    (      self     :	int			)   ->Any:
          if self.config_name not in [
              "boolq",
              "cb",
              "copa",
              "multirc",
              "record",
              "rte",
              "wic",
              "wsc",
              "wsc.fixed",
              "axb",
              "axg",
          ]:
             raise KeyError(
                 '''You should supply a configuration name selected in '''
                 '''["boolq", "cb", "copa", "multirc", "record", "rte", "wic", "wsc", "wsc.fixed", "axb", "axg",]'''			)
          return datasets.MetricInfo(
              description=_DESCRIPTION		,					citation=_CITATION		,					inputs_description=_KWARGS_DESCRIPTION		,					features=datasets.Features(self._get_feature_types()			)		,					codebase_urls=[]		,					reference_urls=[]		,					format='''numpy''' if not self.config_name == '''record''' and not self.config_name == '''multirc''' else None		,					)







       def 						__lowerCamelCase    (      self     :	Dict			)   ->Union[str, Any]:
          if self.config_name == "record":
             return {
                 "predictions": {
                     "idx": {
                         "passage": datasets.Value('''int64'''			),
                         "query": datasets.Value('''int64'''			),
                     },
                     "prediction_text": datasets.Value('''string'''			),
                 },
                 "references": {
                     "idx": {
                         "passage": datasets.Value('''int64'''			),
                         "query": datasets.Value('''int64'''			),
                     },
                     "answers": datasets.Sequence(datasets.Value('''string'''			)			),
                 },
             }
          elif self.config_name == "multirc":
             return {
                 "predictions": {
                     "idx": {
                         "answer": datasets.Value('''int64'''			),
                         "paragraph": datasets.Value('''int64'''			),
                         "question": datasets.Value('''int64'''			),
                     },
                     "prediction": datasets.Value('''int64'''			),
                 },
                 "references": datasets.Value('''int64'''			),
             }
          else:
             return {
                 "predictions": datasets.Value('''int64'''			),
                 "references": datasets.Value('''int64'''			),
             }







       def 						__lowerCamelCase    (      self     :	Any		,					A     :	int		,					A     :	Dict			)   ->List[str]:
          if self.config_name == "axb":
             return {"matthews_correlation": matthews_corrcoef(A		,					A			)}
          elif self.config_name == "cb":
             return acc_and_fa(A		,					A		,					fa_avg='''macro'''			)
          elif self.config_name == "record":
             lowerCamelCase__   :						Dict					   =							[
                 {
                     'qas': [
                         {'id': ref['idx']['query'], 'answers': [{'text': ans} for ans in ref['answers']]}
                         for ref in references
                     ]
                 }
             ]
             lowerCamelCase__   :						List[Any]					   =							{pred['idx']['query']: pred['prediction_text'] for pred in predictions}
             return evaluate_record(A		,					A			)[0]
          elif self.config_name == "multirc":
             return evaluate_multirc(A		,					A			)
          elif self.config_name in ["copa", "rte", "wic", "wsc", "wsc.fixed", "boolq", "axg"]:
             return {"accuracy": simple_accuracy(A		,					A			)}
          else:
             raise KeyError(
                 '''You should supply a configuration name selected in '''
                 '''["boolq", "cb", "copa", "multirc", "record", "rte", "wic", "wsc", "wsc.fixed", "axb", "axg",]'''			)






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


snake_case_			  = logging.getLogger(__name__)

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

def 	snake_case__			(    SCREAMING_SNAKE_CASE_		:    Tuple			):



     '''simple docstring'''





     lowercase__    :   str      =       parser.add_argument_group('quant_trainer arguments'			)
     group.add_argument('--wprec'	,     type=SCREAMING_SNAKE_CASE_	,     default=8	,     help='weight precision'			)
     group.add_argument('--aprec'	,     type=SCREAMING_SNAKE_CASE_	,     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=SCREAMING_SNAKE_CASE_	,     nargs='+'	,     help='disable quantizers by keyword'			)
     group.add_argument('--quant-disable-layer-module'	,     type=SCREAMING_SNAKE_CASE_	,     help='disable quantizers by keyword under layer.'			)
     group.add_argument('--quant-enable-layer-module'	,     type=SCREAMING_SNAKE_CASE_	,     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=SCREAMING_SNAKE_CASE_	,     type=SCREAMING_SNAKE_CASE_	,     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=SCREAMING_SNAKE_CASE_	,     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 	snake_case__			(    SCREAMING_SNAKE_CASE_		:    List[Any]			):



     '''simple docstring'''





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

     lowercase__    :   Any      =       QuantDescriptor(num_bits=args.aprec	,     calib_method=SCREAMING_SNAKE_CASE_			)
     lowercase__    :   List[Any]      =       QuantDescriptor(num_bits=args.wprec	,     axis=(None if args.quant_per_tensor else (0,))			)
     quant_nn.QuantLinear.set_default_quant_desc_input(SCREAMING_SNAKE_CASE_			)
     quant_nn.QuantLinear.set_default_quant_desc_weight(SCREAMING_SNAKE_CASE_			)

def 	snake_case__			(    SCREAMING_SNAKE_CASE_		:    Optional[Any]	,     SCREAMING_SNAKE_CASE_		:    List[str]	,     SCREAMING_SNAKE_CASE_		:    Union[str, Any]=False	,     SCREAMING_SNAKE_CASE_		:    Any=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(SCREAMING_SNAKE_CASE_	,     ['embeddings']	,     which='weight'	,     _disabled=SCREAMING_SNAKE_CASE_			)

          if args.quant_disable:
               set_quantizer_by_name(SCREAMING_SNAKE_CASE_	,     ['']	,     _disabled=SCREAMING_SNAKE_CASE_			)

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

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

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

          if args.recalibrate_weights:
               recalibrate_weights(SCREAMING_SNAKE_CASE_			)

          if args.fuse_qkv:
               fuse_qkv(SCREAMING_SNAKE_CASE_	,     SCREAMING_SNAKE_CASE_			)

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

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

def 	snake_case__			(    SCREAMING_SNAKE_CASE_		:    Dict			):



     '''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 	snake_case__			(    SCREAMING_SNAKE_CASE_		:    Optional[Any]	,     SCREAMING_SNAKE_CASE_		:    Union[str, Any]			):



     '''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(SCREAMING_SNAKE_CASE_			)

def 	snake_case__			(    SCREAMING_SNAKE_CASE_		:    Any	,     SCREAMING_SNAKE_CASE_		:    Optional[Any]			):



     '''simple docstring'''





     def fusea(SCREAMING_SNAKE_CASE_		:    Any	,     SCREAMING_SNAKE_CASE_		:    str	,     SCREAMING_SNAKE_CASE_		:    List[str]			):
          for mod in [qq, qk, qv]:
               if not hasattr(SCREAMING_SNAKE_CASE_	,     '_amax'			):
                    print('          WARNING: NO AMAX BUFFER'			)
                    return
          lowercase__    :   List[Any]      =       qq._amax.detach().item()
          lowercase__    :   Optional[int]      =       qk._amax.detach().item()
          lowercase__    :   List[str]      =       qv._amax.detach().item()

          lowercase__    :   Tuple      =       max(SCREAMING_SNAKE_CASE_	,     SCREAMING_SNAKE_CASE_	,     SCREAMING_SNAKE_CASE_			)
          qq._amax.fill_(SCREAMING_SNAKE_CASE_			)
          qk._amax.fill_(SCREAMING_SNAKE_CASE_			)
          qv._amax.fill_(SCREAMING_SNAKE_CASE_			)
          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 	snake_case__			(    SCREAMING_SNAKE_CASE_		:    List[Any]	,     SCREAMING_SNAKE_CASE_		:    Optional[int]			):



     '''simple docstring'''





     for name, mod in model.named_modules():
          if name.endswith('.output.dense'			) and not name.endswith('attention.output.dense'			):
               lowercase__    :   Any      =       mod._input_quantizer._amax.data.detach().item()
               mod._input_quantizer._amax.data.detach().clamp_(max=SCREAMING_SNAKE_CASE_			)
               lowercase__    :   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 	snake_case__			(    SCREAMING_SNAKE_CASE_		:    Optional[int]			):



     '''simple docstring'''





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

def 	snake_case__			(    SCREAMING_SNAKE_CASE_		:    Union[str, Any]			):



     '''simple docstring'''





     for name, mod in model.named_modules():
          if hasattr(SCREAMING_SNAKE_CASE_	,     '_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__    :   Dict      =       set() if mod._weight_quantizer.axis is None else set(mod._weight_quantizer.axis			)
               lowercase__    :   Union[str, Any]      =       set(range(len(mod.weight.size()			)			)			) - axis_set
               lowercase__    :   str      =       pytorch_quantization.utils.reduce_amax(mod.weight	,     axis=SCREAMING_SNAKE_CASE_	,     keepdims=SCREAMING_SNAKE_CASE_			).detach()
               logger.info(f"""RECALIB: {name:{name_width}} {mod._weight_quantizer._amax.flatten()} -> {amax.flatten()}"""			)
               lowercase__    :   Union[str, Any]      =       amax

def 	snake_case__			(    SCREAMING_SNAKE_CASE_		:    Dict	,     SCREAMING_SNAKE_CASE_		:    Optional[Any]=25	,     SCREAMING_SNAKE_CASE_		:    Any=180	,     SCREAMING_SNAKE_CASE_		:    Optional[int]=None			):



     '''simple docstring'''





     if ignore is None:
          lowercase__    :   Tuple      =       []
     elif not isinstance(SCREAMING_SNAKE_CASE_	,     SCREAMING_SNAKE_CASE_			):
          lowercase__    :   str      =       [ignore]

     lowercase__    :   Optional[Any]      =       0
     for name, mod in model.named_modules():
          if not hasattr(SCREAMING_SNAKE_CASE_	,     'weight'			):
               continue
          lowercase__    :   Optional[Any]      =       max(SCREAMING_SNAKE_CASE_	,     len(SCREAMING_SNAKE_CASE_			)			)

     for name, mod in model.named_modules():
          lowercase__    :   Dict      =       getattr(SCREAMING_SNAKE_CASE_	,     '_input_quantizer'	,     SCREAMING_SNAKE_CASE_			)
          lowercase__    :   Optional[Any]      =       getattr(SCREAMING_SNAKE_CASE_	,     '_weight_quantizer'	,     SCREAMING_SNAKE_CASE_			)
          if not hasattr(SCREAMING_SNAKE_CASE_	,     'weight'			):
               continue
          if type(SCREAMING_SNAKE_CASE_			) in ignore:
               continue
          if [True for s in ignore if type(SCREAMING_SNAKE_CASE_			) is str and s in name]:
               continue
          lowercase__    :   Optional[int]      =       f"""Act:{input_q.extra_repr()}"""
          lowercase__    :   Dict      =       f"""Wgt:{weight_q.extra_repr()}"""
          lowercase__    :   Tuple      =       f"""{name:{name_width}} {act_str} {wgt_str}"""
          if len(SCREAMING_SNAKE_CASE_			) <= line_width:
               logger.info(SCREAMING_SNAKE_CASE_			)
          else:
               logger.info(f"""{name:{name_width}} {act_str}"""			)
               logger.info(f"""{"  ":{name_width}} {wgt_str}"""			)

def 	snake_case__			(    SCREAMING_SNAKE_CASE_		:    int			):



     '''simple docstring'''





     lowercase__    :   List[str]      =       0
     for name, mod in model.named_modules():
          if isinstance(SCREAMING_SNAKE_CASE_	,     pytorch_quantization.nn.TensorQuantizer			):
               print(f"""{name:80} {mod}"""			)
               count += 1
     print(f"""{count} TensorQuantizers found in model"""			)

def 	snake_case__			(    SCREAMING_SNAKE_CASE_		:    Optional[Any]	,     SCREAMING_SNAKE_CASE_		:    int	,     SCREAMING_SNAKE_CASE_		:    str	,     SCREAMING_SNAKE_CASE_		:    List[str]	,     SCREAMING_SNAKE_CASE_		:    Optional[int]			):



     '''simple docstring'''





     lowercase__    :   Dict      =       getattr(SCREAMING_SNAKE_CASE_	,     SCREAMING_SNAKE_CASE_	,     SCREAMING_SNAKE_CASE_			)
     if quantizer_mod is not None:
          assert hasattr(SCREAMING_SNAKE_CASE_	,     SCREAMING_SNAKE_CASE_			)
          setattr(SCREAMING_SNAKE_CASE_	,     SCREAMING_SNAKE_CASE_	,     SCREAMING_SNAKE_CASE_			)
     else:
          logger.warning(f"""{name} has no {quantizer}"""			)

def 	snake_case__			(    SCREAMING_SNAKE_CASE_		:    List[Any]	,     SCREAMING_SNAKE_CASE_		:    Optional[int]	,     SCREAMING_SNAKE_CASE_		:    List[Any]="both"	,     **SCREAMING_SNAKE_CASE_		:    Tuple			):



     '''simple docstring'''





     lowercase__    :   Optional[int]      =       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(SCREAMING_SNAKE_CASE_	,     SCREAMING_SNAKE_CASE_	,     '_input_quantizer'	,     SCREAMING_SNAKE_CASE_	,     SCREAMING_SNAKE_CASE_			)
          if which in ["weight", "both"]:
               set_quantizer(SCREAMING_SNAKE_CASE_	,     SCREAMING_SNAKE_CASE_	,     '_weight_quantizer'	,     SCREAMING_SNAKE_CASE_	,     SCREAMING_SNAKE_CASE_			)
     logger.info(SCREAMING_SNAKE_CASE_			)

def 	snake_case__			(    SCREAMING_SNAKE_CASE_		:    Optional[Any]	,     SCREAMING_SNAKE_CASE_		:    Dict	,     **SCREAMING_SNAKE_CASE_		:    List[str]			):



     '''simple docstring'''





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

