'''simple docstring'''

import numpy as np
import torch
from torch.utils.data import Dataset, IterableDataset

from ..utils.generic import ModelOutput


class 					_lowercase						(   lowerCAmelCase ):
      '''simple docstring'''


      def __init__(					self       ,lowerCamelCase_       ,lowerCamelCase_       ,lowerCamelCase_				)      ->      List[str]:



         '''simple docstring'''



         UpperCAmelCase__				:					Union[str, Any] 					=	dataset
         UpperCAmelCase__				:					Optional[int] 					=	process
         UpperCAmelCase__				:					str 					=	params




      def __len__(					self				)      ->      List[Any]:



         '''simple docstring'''



         return len(self.dataset				)




      def __getitem__(					self       ,lowerCamelCase_				)      ->      int:



         '''simple docstring'''



         UpperCAmelCase__				:					Tuple 					=	self.dataset[i]
         UpperCAmelCase__				:					Union[str, Any] 					=	self.process(lowerCamelCase_       ,**self.params				)
         return processed




class 					_lowercase						(   lowerCAmelCase ):
      '''simple docstring'''


      def __init__(					self       ,lowerCamelCase_       ,lowerCamelCase_       ,lowerCamelCase_       ,lowerCamelCase_=None				)      ->      Tuple:



         '''simple docstring'''



         UpperCAmelCase__				:					Optional[int] 					=	loader
         UpperCAmelCase__				:					Union[str, Any] 					=	infer
         UpperCAmelCase__				:					int 					=	params
         if loader_batch_size == 1:
            # Let's spare some time by deactivating altogether
            UpperCAmelCase__				:					str 					=	None
         UpperCAmelCase__				:					Dict 					=	loader_batch_size

         # Internal bookkeeping
         UpperCAmelCase__				:					Tuple 					=	None
         UpperCAmelCase__				:					Union[str, Any] 					=	None




      def __len__(					self				)      ->      int:



         '''simple docstring'''



         return len(self.loader				)




      def __iter__(					self				)      ->      List[Any]:



         '''simple docstring'''



         UpperCAmelCase__				:					List[str] 					=	iter(self.loader				)
         return self




      def 		lowerCAmelCase__       (					self				)      ->      int:



         '''simple docstring'''



         if isinstance(self._loader_batch_data       ,torch.Tensor				):
            # Batch data is simple tensor, just fetch the slice
            UpperCAmelCase__				:					Optional[Any] 					=	self._loader_batch_data[self._loader_batch_index]
         else:
            # Batch data is assumed to be BaseModelOutput (or dict)
            UpperCAmelCase__				:					Tuple 					=	{}
            for k, element in self._loader_batch_data.items():
               if isinstance(lowerCamelCase_       ,lowerCamelCase_				):
                  # Convert ModelOutput to tuple first
                  UpperCAmelCase__				:					str 					=	element.to_tuple()
                  if isinstance(element[0]       ,torch.Tensor				):
                     UpperCAmelCase__				:					Dict 					=	tuple(el[self._loader_batch_index].unsqueeze(0				) for el in element				)
                  elif isinstance(element[0]       ,np.ndarray				):
                     UpperCAmelCase__				:					List[Any] 					=	tuple(np.expand_dims(el[self._loader_batch_index]       ,0				) for el in element				)
                  continue
               if k in {"hidden_states", "past_key_values", "attentions"} and isinstance(lowerCamelCase_       ,lowerCamelCase_				):
                  # Those are stored as lists of tensors so need specific unbatching.
                  if isinstance(element[0]       ,torch.Tensor				):
                     UpperCAmelCase__				:					Tuple 					=	tuple(el[self._loader_batch_index].unsqueeze(0				) for el in element				)
                  elif isinstance(element[0]       ,np.ndarray				):
                     UpperCAmelCase__				:					str 					=	tuple(np.expand_dims(el[self._loader_batch_index]       ,0				) for el in element				)
                  continue
               if element is None:
                  # This can happen for optional data that get passed around
                  UpperCAmelCase__				:					Optional[int] 					=	None
               elif isinstance(element[self._loader_batch_index]       ,torch.Tensor				):
                  # Take correct batch data, but make it looked like batch_size=1
                  # For compatibility with other methods within transformers

                  UpperCAmelCase__				:					int 					=	element[self._loader_batch_index].unsqueeze(0				)
               elif isinstance(element[self._loader_batch_index]       ,np.ndarray				):
                  # Take correct batch data, but make it looked like batch_size=1
                  # For compatibility with other methods within transformers
                  UpperCAmelCase__				:					int 					=	np.expand_dims(element[self._loader_batch_index]       ,0				)
               else:
                  # This is typically a list, so no need to `unsqueeze`.
                  UpperCAmelCase__				:					List[Any] 					=	element[self._loader_batch_index]
            # Recreate the element by reusing the original class to make it look
            # batch_size=1
            UpperCAmelCase__				:					List[str] 					=	self._loader_batch_data.__class__(lowerCamelCase_				)
         self._loader_batch_index += 1
         return result




      def 		lowerCAmelCase__       (					self				)      ->      Dict:



         '''simple docstring'''



         if self._loader_batch_index is not None and self._loader_batch_index < self.loader_batch_size:
            # We are currently unrolling a batch so we just need to return
            # the current item within a batch
            return self.loader_batch_item()

         # We're out of items within a batch
         UpperCAmelCase__				:					Optional[int] 					=	next(self.iterator				)
         UpperCAmelCase__				:					Dict 					=	self.infer(lowerCamelCase_       ,**self.params				)
         # We now have a batch of "inferred things".
         if self.loader_batch_size is not None:
            # Try to infer the size of the batch
            if isinstance(lowerCamelCase_       ,torch.Tensor				):
               UpperCAmelCase__				:					List[str] 					=	processed
            else:
               UpperCAmelCase__				:					str 					=	list(processed.keys()				)[0]
               UpperCAmelCase__				:					Optional[int] 					=	processed[key]
            if isinstance(lowerCamelCase_       ,lowerCamelCase_				):
               UpperCAmelCase__				:					Optional[int] 					=	len(lowerCamelCase_				)
            else:
               UpperCAmelCase__				:					Optional[Any] 					=	first_tensor.shape[0]
            if 0 < observed_batch_size < self.loader_batch_size:
               # could be last batch so we can't unroll as many
               # elements.
               UpperCAmelCase__				:					int 					=	observed_batch_size
            # Setting internal index to unwrap the batch
            UpperCAmelCase__				:					Union[str, Any] 					=	processed
            UpperCAmelCase__				:					int 					=	0
            return self.loader_batch_item()
         else:
            # We're not unrolling batches
            return processed




class 					_lowercase						(   lowerCAmelCase ):
      '''simple docstring'''


      def __init__(					self       ,lowerCamelCase_       ,lowerCamelCase_       ,lowerCamelCase_       ,lowerCamelCase_=None				)      ->      Optional[int]:



         '''simple docstring'''



         super().__init__(lowerCamelCase_       ,lowerCamelCase_       ,lowerCamelCase_				)




      def __iter__(					self				)      ->      Dict:



         '''simple docstring'''



         UpperCAmelCase__				:					List[Any] 					=	iter(self.loader				)
         UpperCAmelCase__				:					Any 					=	None
         return self




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



         '''simple docstring'''



         if self.subiterator is None:
            UpperCAmelCase__				:					Any 					=	self.infer(next(self.iterator				)       ,**self.params				)
         try:
            # Try to return next item
            UpperCAmelCase__				:					Tuple 					=	next(self.subiterator				)
         except StopIteration:
            # When a preprocess iterator ends, we can start lookig at the next item
            # ChunkIterator will keep feeding until ALL elements of iterator
            # all have created their subiterator and have been iterating against.
            #
            # Another way to look at it, is we're basically flattening lists of lists
            # into a single list, but with generators
            UpperCAmelCase__				:					Dict 					=	self.infer(next(self.iterator				)       ,**self.params				)
            UpperCAmelCase__				:					List[Any] 					=	next(self.subiterator				)
         return processed




class 					_lowercase						(   lowerCAmelCase ):
      '''simple docstring'''


      def __iter__(					self				)      ->      Optional[int]:



         '''simple docstring'''



         UpperCAmelCase__				:					Any 					=	iter(self.loader				)
         return self




      def 		lowerCAmelCase__       (					self				)      ->      str:



         '''simple docstring'''



         UpperCAmelCase__				:					Optional[int] 					=	False
         UpperCAmelCase__				:					Tuple 					=	[]
         if self._loader_batch_index is not None and self._loader_batch_index < self.loader_batch_size:
            while self._loader_batch_index < self.loader_batch_size:
               UpperCAmelCase__				:					Union[str, Any] 					=	self.loader_batch_item()
               UpperCAmelCase__				:					List[Any] 					=	item.pop('''is_last'''				)
               accumulator.append(lowerCamelCase_				)
               if is_last:
                  return accumulator

         while not is_last:
            UpperCAmelCase__				:					Tuple 					=	self.infer(next(self.iterator				)       ,**self.params				)
            if self.loader_batch_size is not None:
               if isinstance(lowerCamelCase_       ,torch.Tensor				):
                  UpperCAmelCase__				:					str 					=	processed
               else:
                  UpperCAmelCase__				:					List[str] 					=	list(processed.keys()				)[0]
                  UpperCAmelCase__				:					int 					=	processed[key]
               if isinstance(lowerCamelCase_       ,lowerCamelCase_				):
                  UpperCAmelCase__				:					int 					=	len(lowerCamelCase_				)
               else:
                  UpperCAmelCase__				:					Union[str, Any] 					=	first_tensor.shape[0]
               if 0 < observed_batch_size < self.loader_batch_size:
                  # could be last batch so we can't unroll as many
                  # elements.
                  UpperCAmelCase__				:					Dict 					=	observed_batch_size
               UpperCAmelCase__				:					Dict 					=	processed
               UpperCAmelCase__				:					str 					=	0
               while self._loader_batch_index < self.loader_batch_size:
                  UpperCAmelCase__				:					Optional[int] 					=	self.loader_batch_item()
                  UpperCAmelCase__				:					Optional[Any] 					=	item.pop('''is_last'''				)
                  accumulator.append(lowerCamelCase_				)
                  if is_last:
                     return accumulator
            else:
               UpperCAmelCase__				:					str 					=	processed
               UpperCAmelCase__				:					Any 					=	item.pop('''is_last'''				)
               accumulator.append(lowerCamelCase_				)
         return accumulator




class 					_lowercase						(   lowerCAmelCase ):
      '''simple docstring'''


      def __init__(					self       ,lowerCamelCase_       ,lowerCamelCase_				)      ->      str:



         '''simple docstring'''



         UpperCAmelCase__				:					int 					=	dataset
         UpperCAmelCase__				:					int 					=	key




      def __len__(					self				)      ->      Tuple:



         '''simple docstring'''



         return len(self.dataset				)




      def __getitem__(					self       ,lowerCamelCase_				)      ->      Optional[int]:



         '''simple docstring'''



         return self.dataset[i][self.key]




class 					_lowercase						(   lowerCAmelCase ):
      '''simple docstring'''


      def __init__(					self       ,lowerCamelCase_       ,lowerCamelCase_       ,lowerCamelCase_				)      ->      Optional[int]:



         '''simple docstring'''



         UpperCAmelCase__				:					Dict 					=	dataset
         UpperCAmelCase__				:					Union[str, Any] 					=	keya
         UpperCAmelCase__				:					str 					=	keya




      def __len__(					self				)      ->      List[str]:



         '''simple docstring'''



         return len(self.dataset				)




      def __getitem__(					self       ,lowerCamelCase_				)      ->      Union[str, Any]:



         '''simple docstring'''



         return {"text": self.dataset[i][self.keya], "text_pair": self.dataset[i][self.keya]}





'''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__  :						Optional[Any]					=      logging.getLogger(__name__)

UpperCamelCase__  :						Dict					=      50  # max width of layer names
UpperCamelCase__  :						Any					=      70  # max width of quantizer names







def __UpperCamelCase( _A       :					Union[str, Any]     ):
   '''simple docstring'''
   UpperCAmelCase__				:					List[str] 					=	parser.add_argument_group('''quant_trainer arguments'''     )
   group.add_argument('''--wprec'''	,      type=_A	,      default=8	,      help='''weight precision'''     )
   group.add_argument('''--aprec'''	,      type=_A	,      default=8	,      help='''activation precision'''     )
   group.add_argument('''--quant-per-tensor'''	,      action='''store_true'''	,      help='''per tensor weight scaling'''     )
   group.add_argument('''--quant-disable'''	,      action='''store_true'''	,      help='''disable all quantizers'''     )
   group.add_argument('''--quant-disable-embeddings'''	,      action='''store_true'''	,      help='''disable all embeddings quantizers'''     )
   group.add_argument('''--quant-disable-keyword'''	,      type=_A	,      nargs='''+'''	,      help='''disable quantizers by keyword'''     )
   group.add_argument('''--quant-disable-layer-module'''	,      type=_A	,      help='''disable quantizers by keyword under layer.'''     )
   group.add_argument('''--quant-enable-layer-module'''	,      type=_A	,      help='''enable quantizers by keyword under layer'''     )
   group.add_argument('''--calibrator'''	,      default='''max'''	,      help='''which quantization range calibrator to use'''     )
   group.add_argument('''--percentile'''	,      default=_A	,      type=_A	,      help='''percentile for PercentileCalibrator'''     )
   group.add_argument('''--fuse-qkv'''	,      action='''store_true'''	,      help='''use the same scale factor for qkv'''     )
   group.add_argument('''--clip-gelu'''	,      metavar='''N'''	,      type=_A	,      help='''clip gelu output maximum value to N'''     )
   group.add_argument(
       '''--recalibrate-weights'''	,      action='''store_true'''	,      help=(
           '''recalibrate weight amaxes by taking the max of the weights.'''
           ''' amaxes will be computed with the current quantization granularity (axis).'''
       )	,      )







def __UpperCamelCase( _A       :					Tuple     ):
   '''simple docstring'''

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

   UpperCAmelCase__				:					Dict 					=	QuantDescriptor(num_bits=args.aprec	,      calib_method=_A     )
   UpperCAmelCase__				:					str 					=	QuantDescriptor(num_bits=args.wprec	,      axis=(None if args.quant_per_tensor else (0,))     )
   quant_nn.QuantLinear.set_default_quant_desc_input(_A     )
   quant_nn.QuantLinear.set_default_quant_desc_weight(_A     )







def __UpperCamelCase( _A       :					Any	,      _A       :					Any	,      _A       :					Any=False	,      _A       :					Optional[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(_A	,      ['''embeddings''']	,      which='''weight'''	,      _disabled=_A     )

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

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

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

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

      if args.recalibrate_weights:
         recalibrate_weights(_A     )

      if args.fuse_qkv:
         fuse_qkv(_A	,      _A     )

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

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







def __UpperCamelCase( _A       :					str     ):
   '''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 __UpperCamelCase( _A       :					Tuple	,      _A       :					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(_A     )







def __UpperCamelCase( _A       :					Dict	,      _A       :					Optional[int]     ):
   '''simple docstring'''

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

      UpperCAmelCase__				:					Dict 					=	max(_A	,      _A	,      _A     )
      qq._amax.fill_(_A     )
      qk._amax.fill_(_A     )
      qv._amax.fill_(_A     )
      logger.info(F'''          q={q:5.2f} k={k:5.2f} v={v:5.2f} -> {amax:5.2f}'''     )

   for name, mod in model.named_modules():
      if name.endswith('''.attention.self'''     ):
         logger.info(F'''FUSE_QKV: {name:{name_width}}'''     )
         fusea(mod.matmul_q_input_quantizer	,      mod.matmul_k_input_quantizer	,      mod.matmul_v_input_quantizer     )
         if args.quant_per_tensor:
            fusea(mod.query._weight_quantizer	,      mod.key._weight_quantizer	,      mod.value._weight_quantizer     )







def __UpperCamelCase( _A       :					Dict	,      _A       :					Any     ):
   '''simple docstring'''

   for name, mod in model.named_modules():
      if name.endswith('''.output.dense'''     ) and not name.endswith('''attention.output.dense'''     ):
         UpperCAmelCase__				:					Union[str, Any] 					=	mod._input_quantizer._amax.data.detach().item()
         mod._input_quantizer._amax.data.detach().clamp_(max=_A     )
         UpperCAmelCase__				:					Tuple 					=	mod._input_quantizer._amax.data.detach().item()
         logger.info(F'''CLIP_GELU: {name:{name_width}} amax: {amax_init:5.2f} -> {amax:5.2f}'''     )







def __UpperCamelCase( _A       :					str     ):
   '''simple docstring'''

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







def __UpperCamelCase( _A       :					List[str]     ):
   '''simple docstring'''

   for name, mod in model.named_modules():
      if hasattr(_A	,      '''_weight_quantizer'''     ):
         if not hasattr(mod.weight_quantizer	,      '''_amax'''     ):
            print('''RECALIB: {name:{name_width}} WARNING: NO AMAX BUFFER'''     )
            continue

         # determine which axes to reduce across
         # e.g. a 4D tensor quantized per axis 0 should reduce over (1,2,3)
         UpperCAmelCase__				:					Any 					=	set() if mod._weight_quantizer.axis is None else set(mod._weight_quantizer.axis     )
         UpperCAmelCase__				:					Optional[Any] 					=	set(range(len(mod.weight.size()     )     )     ) - axis_set
         UpperCAmelCase__				:					int 					=	pytorch_quantization.utils.reduce_amax(mod.weight	,      axis=_A	,      keepdims=_A     ).detach()
         logger.info(F'''RECALIB: {name:{name_width}} {mod._weight_quantizer._amax.flatten()} -> {amax.flatten()}'''     )
         UpperCAmelCase__				:					str 					=	amax







def __UpperCamelCase( _A       :					Dict	,      _A       :					Tuple=25	,      _A       :					Any=1_80	,      _A       :					Optional[int]=None     ):
   '''simple docstring'''

   if ignore is None:
      UpperCAmelCase__				:					Dict 					=	[]
   elif not isinstance(_A	,      _A     ):
      UpperCAmelCase__				:					int 					=	[ignore]

   UpperCAmelCase__				:					Optional[int] 					=	0
   for name, mod in model.named_modules():
      if not hasattr(_A	,      '''weight'''     ):
         continue
      UpperCAmelCase__				:					Dict 					=	max(_A	,      len(_A     )     )

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







def __UpperCamelCase( _A       :					Optional[Any]     ):
   '''simple docstring'''
   UpperCAmelCase__				:					str 					=	0
   for name, mod in model.named_modules():
      if isinstance(_A	,      pytorch_quantization.nn.TensorQuantizer     ):
         print(F'''{name:80} {mod}'''     )
         count += 1
   print(F'''{count} TensorQuantizers found in model'''     )







def __UpperCamelCase( _A       :					Dict	,      _A       :					Optional[Any]	,      _A       :					Union[str, Any]	,      _A       :					Union[str, Any]	,      _A       :					Dict     ):
   '''simple docstring'''
   UpperCAmelCase__				:					Any 					=	getattr(_A	,      _A	,      _A     )
   if quantizer_mod is not None:
      assert hasattr(_A	,      _A     )
      setattr(_A	,      _A	,      _A     )
   else:
      logger.warning(F'''{name} has no {quantizer}'''     )







def __UpperCamelCase( _A       :					str	,      _A       :					Any	,      _A       :					Optional[int]="both"	,      **_A       :					List[str]     ):
   '''simple docstring'''
   UpperCAmelCase__				:					Tuple 					=	F'''Warning: changing {which} quantizers of {name:{qname_width}}'''
   for k, v in kwargs.items():
      s += F''' {k}={v}'''
      if which in ["input", "both"]:
         set_quantizer(_A	,      _A	,      '''_input_quantizer'''	,      _A	,      _A     )
      if which in ["weight", "both"]:
         set_quantizer(_A	,      _A	,      '''_weight_quantizer'''	,      _A	,      _A     )
   logger.info(_A     )







def __UpperCamelCase( _A       :					Tuple	,      _A       :					List[str]	,      **_A       :					Optional[Any]     ):
   '''simple docstring'''

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




