


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   							=						5_0  # max width of layer names
__snake_case   							=						7_0  # max width of quantizer names






def 		_A		(		_lowercase )					-> Union[str, Any]:


     """simple docstring"""


     __UpperCamelCase =			parser.add_argument_group('quant_trainer arguments' )
     group.add_argument('--wprec'					,  type=_lowercase					,  default=8					,  help='weight precision' )
     group.add_argument('--aprec'					,  type=_lowercase					,  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=_lowercase					,  nargs='+'					,  help='disable quantizers by keyword' )
     group.add_argument('--quant-disable-layer-module'					,  type=_lowercase					,  help='disable quantizers by keyword under layer.' )
     group.add_argument('--quant-enable-layer-module'					,  type=_lowercase					,  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=_lowercase					,  type=_lowercase					,  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=_lowercase					,  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		(		_lowercase )					-> str:


     """simple docstring"""


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

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






def 		_A		(		_lowercase					,  _lowercase					,  _lowercase=False					,  _lowercase=False )					-> Tuple:


     """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(_lowercase					,  ['embeddings']					,  which='weight'					,  _disabled=_lowercase )

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

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

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

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

          if args.recalibrate_weights:
               recalibrate_weights(_lowercase )

          if args.fuse_qkv:
               fuse_qkv(_lowercase					,  _lowercase )

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

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






def 		_A		(		_lowercase )					-> 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 		_A		(		_lowercase					,  _lowercase )					-> 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(_lowercase )






def 		_A		(		_lowercase					,  _lowercase )					-> Union[str, Any]:


     """simple docstring"""


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

          __UpperCamelCase =			max(_lowercase					,  _lowercase					,  _lowercase )
          qq._amax.fill_(_lowercase )
          qk._amax.fill_(_lowercase )
          qv._amax.fill_(_lowercase )
          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		(		_lowercase					,  _lowercase )					-> Any:


     """simple docstring"""


     for name, mod in model.named_modules():
          if name.endswith('.output.dense' ) and not name.endswith('attention.output.dense' ):
               __UpperCamelCase =			mod._input_quantizer._amax.data.detach().item()
               mod._input_quantizer._amax.data.detach().clamp_(max=_lowercase )
               __UpperCamelCase =			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		(		_lowercase )					-> Any:


     """simple docstring"""


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






def 		_A		(		_lowercase )					-> str:


     """simple docstring"""


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






def 		_A		(		_lowercase					,  _lowercase=25					,  _lowercase=1_80					,  _lowercase=None )					-> Dict:


     """simple docstring"""


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

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

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






def 		_A		(		_lowercase )					-> int:


     """simple docstring"""


     __UpperCamelCase =			0
     for name, mod in model.named_modules():
          if isinstance(_lowercase					,  pytorch_quantization.nn.TensorQuantizer ):
               print(f'''{name:80} {mod}''' )
               count += 1
     print(f'''{count} TensorQuantizers found in model''' )






def 		_A		(		_lowercase					,  _lowercase					,  _lowercase					,  _lowercase					,  _lowercase )					-> Tuple:


     """simple docstring"""


     __UpperCamelCase =			getattr(_lowercase					,  _lowercase					,  _lowercase )
     if quantizer_mod is not None:
          assert hasattr(_lowercase					,  _lowercase )
          setattr(_lowercase					,  _lowercase					,  _lowercase )
     else:
          logger.warning(f'''{name} has no {quantizer}''' )






def 		_A		(		_lowercase					,  _lowercase					,  _lowercase="both"					,  **_lowercase )					-> List[Any]:


     """simple docstring"""


     __UpperCamelCase =			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(_lowercase					,  _lowercase					,  '_input_quantizer'					,  _lowercase					,  _lowercase )
          if which in ["weight", "both"]:
               set_quantizer(_lowercase					,  _lowercase					,  '_weight_quantizer'					,  _lowercase					,  _lowercase )
     logger.info(_lowercase )






def 		_A		(		_lowercase					,  _lowercase					,  **_lowercase )					-> str:


     """simple docstring"""


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




def 							SCREAMING_SNAKE_CASE_	(					UpperCAmelCase_	:     int | float | str				)			->						tuple[int, int]:
       try:
              SCREAMING_SNAKE_CASE_							:				int		=float(UpperCAmelCase_				)
       except ValueError:
              raise ValueError('''Please enter a valid number'''				)
       SCREAMING_SNAKE_CASE_							:				Any		=decimal - int(UpperCAmelCase_				)
       if fractional_part == 0:
              return int(UpperCAmelCase_				), 1
       else:
              SCREAMING_SNAKE_CASE_							:				Any		=len(str(UpperCAmelCase_				).split('''.'''				)[1]				)
              SCREAMING_SNAKE_CASE_							:				str		=int(decimal * (1_0**number_of_frac_digits)				)
              SCREAMING_SNAKE_CASE_							:				Any		=1_0**number_of_frac_digits
              SCREAMING_SNAKE_CASE_	,     SCREAMING_SNAKE_CASE_							:				Tuple		=denominator, numerator
              while True:
                     SCREAMING_SNAKE_CASE_							:				Any		=dividend % divisor
                     if remainder == 0:
                            break
                     SCREAMING_SNAKE_CASE_	,     SCREAMING_SNAKE_CASE_							:				Optional[int]		=divisor, remainder
              SCREAMING_SNAKE_CASE_	,     SCREAMING_SNAKE_CASE_							:				Optional[int]		=numerator / divisor, denominator / divisor
              return int(UpperCAmelCase_				), int(UpperCAmelCase_				)


if __name__ == "__main__":
 print(F"{decimal_to_fraction(2) = }")
 print(F"{decimal_to_fraction(89.0) = }")
 print(F"{decimal_to_fraction('67') = }")
 print(F"{decimal_to_fraction('45.0') = }")
 print(F"{decimal_to_fraction(1.5) = }")
 print(F"{decimal_to_fraction('6.25') = }")
 print(F"{decimal_to_fraction('78td') = }")
