'''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__     :	Any				=			logging.getLogger(__name__)

UpperCamelCase__     :	Optional[int]				=			50  # max width of layer names
UpperCamelCase__     :	List[Any]				=			70  # max width of quantizer names







def __UpperCamelCase(   _A					:				str    ):
   '''simple docstring'''
   UpperCAmelCase__							:				Union[str, Any]   				=  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__							:				str   				=  '''histogram'''
   elif args.calibrator == "mse":
      UpperCAmelCase__							:				List[str]   				=  '''histogram'''
   else:
      raise ValueError(F'''Invalid calibrator {args.calibrator}'''    )

   UpperCAmelCase__							:				str   				=  QuantDescriptor(num_bits=args.aprec  ,			calib_method=_A    )
   UpperCAmelCase__							:				List[Any]   				=  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					:				Dict  ,			_A					:				List[Any]  ,			_A					:				Tuple=False  ,			_A					:				int=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					:				int    ):
   '''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					:				Optional[Any]  ,			_A					:				Dict    ):
   '''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					:				str  ,			_A					:				List[Any]    ):
   '''simple docstring'''

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

      UpperCAmelCase__							:				List[Any]   				=  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					:				Optional[Any]  ,			_A					:				Optional[int]    ):
   '''simple docstring'''

   for name, mod in model.named_modules():
      if name.endswith('''.output.dense'''    ) and not name.endswith('''attention.output.dense'''    ):
         UpperCAmelCase__							:				Tuple   				=  mod._input_quantizer._amax.data.detach().item()
         mod._input_quantizer._amax.data.detach().clamp_(max=_A    )
         UpperCAmelCase__							:				Any   				=  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					:				Any    ):
   '''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__							:				Optional[int]   				=  mod._weight_quantizer._amax.detach()
         UpperCAmelCase__							:				Optional[Any]   				=  torch.ones(_A  ,			dtype=amax.dtype  ,			device=amax.device    ) * amax
         print(F'''expanding {name} {amax} -> {mod._weight_quantizer._amax}'''    )







def __UpperCamelCase(   _A					:				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__							:				Tuple   				=  set() if mod._weight_quantizer.axis is None else set(mod._weight_quantizer.axis    )
         UpperCAmelCase__							:				Any   				=  set(range(len(mod.weight.size()    )    )    ) - axis_set
         UpperCAmelCase__							:				Optional[Any]   				=  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__							:				List[Any]   				=  amax







def __UpperCamelCase(   _A					:				List[str]  ,			_A					:				str=25  ,			_A					:				Any=1_80  ,			_A					:				int=None    ):
   '''simple docstring'''

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

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

   for name, mod in model.named_modules():
      UpperCAmelCase__							:				str   				=  getattr(_A  ,			'''_input_quantizer'''  ,			_A    )
      UpperCAmelCase__							:				Tuple   				=  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__							:				List[Any]   				=  F'''Act:{input_q.extra_repr()}'''
      UpperCAmelCase__							:				Tuple   				=  F'''Wgt:{weight_q.extra_repr()}'''
      UpperCAmelCase__							:				Union[str, Any]   				=  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					:				List[str]    ):
   '''simple docstring'''
   UpperCAmelCase__							:				Tuple   				=  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					:				List[str]  ,			_A					:				Union[str, Any]  ,			_A					:				str  ,			_A					:				Optional[int]  ,			_A					:				List[str]    ):
   '''simple docstring'''
   UpperCAmelCase__							:				Tuple   				=  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					:				Any  ,			_A					:				Any  ,			_A					:				Union[str, Any]="both"  ,			**_A					:				Optional[Any]    ):
   '''simple docstring'''
   UpperCAmelCase__							:				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(_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					:				List[str]  ,			_A					:				Optional[Any]  ,			**_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__							:				int   				=  F'''Warning: changing {name:{name_width}}'''
               for k, v in kwargs.items():
                  s += F''' {k}={v}'''
                  setattr(_A  ,			_A  ,			_A    )
               logger.info(_A    )





'''simple docstring'''

import unittest

import numpy as np







def __UpperCamelCase(   _A					:				np.ndarray  ,			_A					:				np.ndarray  ,			_A					:				np.ndarray  ,			_A					:				np.ndarray | None = None  ,			):
   '''simple docstring'''
   UpperCAmelCase__							:				Any   				=  np.shape(_A    )
   UpperCAmelCase__							:				List[Any]   				=  np.shape(_A    )
   UpperCAmelCase__							:				Tuple   				=  np.shape(_A    )

   if shape_a[0] != shape_b[0]:
      UpperCAmelCase__							:				Any   				=  (
          '''Expected the same number of rows for A and B. '''
          F'''Instead found A of size {shape_a} and B of size {shape_b}'''
      )
      raise ValueError(_A    )

   if shape_b[1] != shape_c[1]:
      UpperCAmelCase__							:				List[str]   				=  (
          '''Expected the same number of columns for B and C. '''
          F'''Instead found B of size {shape_b} and C of size {shape_c}'''
      )
      raise ValueError(_A    )

   UpperCAmelCase__							:				Optional[Any]   				=  pseudo_inv
   if a_inv is None:
      try:
         UpperCAmelCase__							:				str   				=  np.linalg.inv(_A    )
      except np.linalg.LinAlgError:
         raise ValueError(
             '''Input matrix A is not invertible. Cannot compute Schur complement.'''    )

   return mat_c - mat_b.T @ a_inv @ mat_b


class 				_lowercase	(      unittest.TestCase   ):
      '''simple docstring'''


      def 		lowerCAmelCase__					(				self							)			->			None:



         '''simple docstring'''



         UpperCAmelCase__							:				int   				=  np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]							)
         UpperCAmelCase__							:				List[Any]   				=  np.array([[0, 3], [3, 0], [2, 3]]							)
         UpperCAmelCase__							:				Optional[Any]   				=  np.array([[2, 1], [6, 3]]							)

         UpperCAmelCase__							:				Tuple   				=  schur_complement(lowerCamelCase_					,lowerCamelCase_					,lowerCamelCase_							)

         UpperCAmelCase__							:				Tuple   				=  np.block([[a, b], [b.T, c]]							)

         UpperCAmelCase__							:				int   				=  np.linalg.det(lowerCamelCase_							)
         UpperCAmelCase__							:				Optional[Any]   				=  np.linalg.det(lowerCamelCase_							)
         UpperCAmelCase__							:				Union[str, Any]   				=  np.linalg.det(lowerCamelCase_							)

         self.assertAlmostEqual(lowerCamelCase_					,det_a * det_s							)




      def 		lowerCAmelCase__					(				self							)			->			None:



         '''simple docstring'''



         UpperCAmelCase__							:				Optional[Any]   				=  np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]							)
         UpperCAmelCase__							:				Tuple   				=  np.array([[0, 3], [3, 0], [2, 3]]							)
         UpperCAmelCase__							:				Tuple   				=  np.array([[2, 1], [6, 3]]							)

         with self.assertRaises(lowerCamelCase_							):
            schur_complement(lowerCamelCase_					,lowerCamelCase_					,lowerCamelCase_							)




      def 		lowerCAmelCase__					(				self							)			->			None:



         '''simple docstring'''



         UpperCAmelCase__							:				Dict   				=  np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]							)
         UpperCAmelCase__							:				str   				=  np.array([[0, 3], [3, 0], [2, 3]]							)
         UpperCAmelCase__							:				Any   				=  np.array([[2, 1, 3], [6, 3, 5]]							)

         with self.assertRaises(lowerCamelCase_							):
            schur_complement(lowerCamelCase_					,lowerCamelCase_					,lowerCamelCase_							)


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  unittest.main()




