"""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


__lowercase		:		Dict														=							logging.getLogger(__name__)

__lowercase		:		Tuple														=							50  # max width of layer names
__lowercase		:		List[str]														=							70  # max width of quantizer names





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






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

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





def    SCREAMING_SNAKE_CASE       (     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(__UpperCamelCase,      ['''embeddings'''],      which='''weight''',      _disabled=__UpperCamelCase)

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

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

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

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

          if args.recalibrate_weights:
               recalibrate_weights(__UpperCamelCase)

          if args.fuse_qkv:
               fuse_qkv(__UpperCamelCase,      __UpperCamelCase)

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

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





def    SCREAMING_SNAKE_CASE       (     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    SCREAMING_SNAKE_CASE       (     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(__UpperCamelCase)





def    SCREAMING_SNAKE_CASE       (     snake_case,      snake_case):






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

          __snake_case				=							max(__UpperCamelCase,      __UpperCamelCase,      __UpperCamelCase)
          qq._amax.fill_(__UpperCamelCase)
          qk._amax.fill_(__UpperCamelCase)
          qv._amax.fill_(__UpperCamelCase)
          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    SCREAMING_SNAKE_CASE       (     snake_case,      snake_case):






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





def    SCREAMING_SNAKE_CASE       (     snake_case):






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





def    SCREAMING_SNAKE_CASE       (     snake_case):






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





def    SCREAMING_SNAKE_CASE       (     snake_case,      snake_case=25,      snake_case=1_80,      snake_case=None):






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

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

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





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





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





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





def    SCREAMING_SNAKE_CASE       (     snake_case,      snake_case,      **snake_case):






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



import math
import random
from typing import Any

from .hill_climbing import SearchProblem





def    SCREAMING_SNAKE_CASE       (     snake_case,      snake_case = True,      snake_case = math.inf,      snake_case = -math.inf,      snake_case = math.inf,      snake_case = -math.inf,      snake_case = False,      snake_case = 1_00,      snake_case = 0.01,      snake_case = 1,      ):
     __snake_case				=							False
     __snake_case				=							search_prob
     __snake_case				=							start_temperate
     __snake_case				=							[]
     __snake_case				=							0
     __snake_case				=							None

     while not search_end:
          __snake_case				=							current_state.score()
          if best_state is None or current_score > best_state.score():
               __snake_case				=							current_state
          scores.append(snake_case)
          iterations += 1
          __snake_case				=							None
          __snake_case				=							current_state.get_neighbors()
          while (
              next_state is None and neighbors
          ):  # till we do not find a neighbor that we can move to
               __snake_case				=							random.randint(0,      len(snake_case) - 1)  # picking a random neighbor
               __snake_case				=							neighbors.pop(snake_case)
               __snake_case				=							picked_neighbor.score() - current_score

               if (
                   picked_neighbor.x > max_x
                   or picked_neighbor.x < min_x
                   or picked_neighbor.y > max_y
                   or picked_neighbor.y < min_y
               ):
                    continue  # neighbor outside our bounds

               if not find_max:
                    __snake_case				=							change * -1  # in case we are finding minimum
               if change > 0:  # improves the solution
                    __snake_case				=							picked_neighbor
               else:
                    __snake_case				=							(math.e) ** (
                        change / current_temp
                    )  # probability generation function
                    if random.random() < probability:  # random number within probability
                         __snake_case				=							picked_neighbor
          __snake_case				=							current_temp - (current_temp * rate_of_decrease)

          if current_temp < threshold_temp or next_state is None:
               # temperature below threshold, or could not find a suitable neighbor
               __snake_case				=							True
          else:
               __snake_case				=							next_state

     if visualization:
          from matplotlib import pyplot as plt

          plt.plot(range(snake_case),      snake_case)
          plt.xlabel('''Iterations''')
          plt.ylabel('''Function values''')
          plt.show()
     return best_state


if __name__ == "__main__":





       def    SCREAMING_SNAKE_CASE       (     snake_case,      snake_case):
            return (x**2) + (y**2)

       # starting the problem with initial coordinates (12, 47)
       __lowercase		:		Dict														=							SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_fa)
       __lowercase		:		Union[str, Any]														=							simulated_annealing(
           prob, find_max=False, max_x=100, min_x=5, max_y=50, min_y=-5, visualization=True
       )
       print(
           "The minimum score for f(x, y) = x^2 + y^2 with the domain 100 > x > 5 "
           F"""and 50 > y > - 5 found via hill climbing: {local_min.score()}"""
       )

       # starting the problem with initial coordinates (12, 47)
       __lowercase		:		Any														=							SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_fa)
       __lowercase		:		int														=							simulated_annealing(
           prob, find_max=True, max_x=100, min_x=5, max_y=50, min_y=-5, visualization=True
       )
       print(
           "The maximum score for f(x, y) = x^2 + y^2 with the domain 100 > x > 5 "
           F"""and 50 > y > - 5 found via hill climbing: {local_min.score()}"""
       )





       def    SCREAMING_SNAKE_CASE       (     snake_case,      snake_case):
            return (3 * x**2) - (6 * y)

       __lowercase		:		List[str]														=							SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
       __lowercase		:		Dict														=							simulated_annealing(prob, find_max=False, visualization=True)
       print(
           "The minimum score for f(x, y) = 3*x^2 - 6*y found via hill climbing: "
           F"""{local_min.score()}"""
       )

       __lowercase		:		Any														=							SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
       __lowercase		:		Tuple														=							simulated_annealing(prob, find_max=True, visualization=True)




       print(
           "The maximum score for f(x, y) = 3*x^2 - 6*y found via hill climbing: "
           F"""{local_min.score()}"""
       )