






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


lowerCamelCase			:					Dict         =	logging.getLogger(__name__)

lowerCamelCase			:					List[Any]         =	50  # max width of layer names
lowerCamelCase			:					List[str]         =	70  # max width of quantizer names





def       __lowerCAmelCase      (						__snake_case       ):
     __lowerCAmelCase								=					parser.add_argument_group("quant_trainer arguments"       )
     group.add_argument("--wprec"					, type=__snake_case					, default=8					, help="weight precision"       )
     group.add_argument("--aprec"					, type=__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=__snake_case					, nargs="+"					, help="disable quantizers by keyword"       )
     group.add_argument("--quant-disable-layer-module"					, type=__snake_case					, help="disable quantizers by keyword under layer."       )
     group.add_argument("--quant-enable-layer-module"					, type=__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=__snake_case					, type=__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=__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       __lowerCAmelCase      (						__snake_case       ):

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

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





def       __lowerCAmelCase      (						__snake_case					, __snake_case					, __snake_case=False					, __snake_case=False       ):
     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(__snake_case					, ["embeddings"]					, which="weight"					, _disabled=__snake_case       )

          if args.quant_disable:
               set_quantizer_by_name(__snake_case					, [""]					, _disabled=__snake_case       )

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

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

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

          if args.recalibrate_weights:
               recalibrate_weights(__snake_case       )

          if args.fuse_qkv:
               fuse_qkv(__snake_case					, __snake_case       )

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

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





def       __lowerCAmelCase      (						__snake_case       ):
     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       __lowerCAmelCase      (						__snake_case					, __snake_case       ):
     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(__snake_case       )





def       __lowerCAmelCase      (						__snake_case					, __snake_case       ):

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

          __lowerCAmelCase								=					max(__snake_case					, __snake_case					, __snake_case       )
          qq._amax.fill_(__snake_case       )
          qk._amax.fill_(__snake_case       )
          qv._amax.fill_(__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       __lowerCAmelCase      (						__snake_case					, __snake_case       ):

     for name, mod in model.named_modules():
          if name.endswith(".output.dense"       ) and not name.endswith("attention.output.dense"       ):
               __lowerCAmelCase								=					mod._input_quantizer._amax.data.detach().item()
               mod._input_quantizer._amax.data.detach().clamp_(max=__snake_case       )
               __lowerCAmelCase								=					mod._input_quantizer._amax.data.detach().item()
               logger.info(F"""CLIP_GELU: {name:{name_width}} amax: {amax_init:5.2f} -> {amax:5.2f}"""       )





def       __lowerCAmelCase      (						__snake_case       ):

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





def       __lowerCAmelCase      (						__snake_case       ):

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





def       __lowerCAmelCase      (						__snake_case					, __snake_case=25					, __snake_case=180					, __snake_case=None       ):

     if ignore is None:
          __lowerCAmelCase								=					[]
     elif not isinstance(__snake_case					, __snake_case       ):
          __lowerCAmelCase								=					[ignore]

     __lowerCAmelCase								=					0
     for name, mod in model.named_modules():
          if not hasattr(__snake_case					, "weight"       ):
               continue
          __lowerCAmelCase								=					max(__snake_case					, len(__snake_case       )       )

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





def       __lowerCAmelCase      (						__snake_case       ):
     __lowerCAmelCase								=					0
     for name, mod in model.named_modules():
          if isinstance(__snake_case					, pytorch_quantization.nn.TensorQuantizer       ):
               print(F"""{name:80} {mod}"""       )
               count += 1
     print(F"""{count} TensorQuantizers found in model"""       )





def       __lowerCAmelCase      (						__snake_case					, __snake_case					, __snake_case					, __snake_case					, __snake_case       ):
     __lowerCAmelCase								=					getattr(__snake_case					, __snake_case					, __snake_case       )
     if quantizer_mod is not None:
          assert hasattr(__snake_case					, __snake_case       )
          setattr(__snake_case					, __snake_case					, __snake_case       )
     else:
          logger.warning(F"""{name} has no {quantizer}"""       )





def       __lowerCAmelCase      (						__snake_case					, __snake_case					, __snake_case="both"					, **__snake_case       ):
     __lowerCAmelCase								=					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(__snake_case					, __snake_case					, "_input_quantizer"					, __snake_case					, __snake_case       )
          if which in ["weight", "both"]:
               set_quantizer(__snake_case					, __snake_case					, "_weight_quantizer"					, __snake_case					, __snake_case       )
     logger.info(__snake_case       )





def       __lowerCAmelCase      (						__snake_case					, __snake_case					, **__snake_case       ):

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














import random



class 							_UpperCamelCase			:


      @staticmethod
      def  __UpperCAmelCase		(	__UpperCamelCase	)->	tuple[list[int], list[int]]:
           __lowerCAmelCase								=					[ord(__UpperCamelCase	) for i in text]
           __lowerCAmelCase								=					[]
           __lowerCAmelCase								=					[]
           for i in plain:
                __lowerCAmelCase								=					random.randint(1					,						3_0_0	)
                __lowerCAmelCase								=					(i + k) * k
                cipher.append(__UpperCamelCase	)
                key.append(__UpperCamelCase	)
           return cipher, key


      @staticmethod
      def  __UpperCAmelCase		(	__UpperCamelCase					,						__UpperCamelCase	)->	str:
           __lowerCAmelCase								=					[]
           for i in range(len(__UpperCamelCase	)	):
                __lowerCAmelCase								=					int((cipher[i] - (key[i]) ** 2) / key[i]	)
                plain.append(chr(__UpperCamelCase	)	)
           return "".join(__UpperCamelCase	)


if __name__ == "__main__":
 lowerCamelCase      ,					lowerCamelCase			:					List[Any]         =	Onepad().encrypt('''Hello''')
 print(c, k)
 print(Onepad().decrypt(c, k))






