

'''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


__snake_case			:     List[Any]										=      logging.getLogger(__name__)

__snake_case			:     str										=      50  # max width of layer names
__snake_case			:     List[str]										=      70  # max width of quantizer names
def 						__lowerCamelCase	(       __snake_case					:		Union[str, Any]	)	->							Union[str, Any]:






						"""simple docstring"""





						A__					:       Dict     =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					:		Tuple	)	->							Optional[Any]:






						"""simple docstring"""





						if args.calibrator == "max":
												A__					:       List[Any]     ="""max"""
						elif args.calibrator == "percentile":
												if args.percentile is None:
																		raise ValueError("""Specify --percentile when using percentile calibrator"""	)
												A__					:       str     ="""histogram"""
						elif args.calibrator == "mse":
												A__					:       str     ="""histogram"""
						else:
												raise ValueError(f"Invalid calibrator {args.calibrator}"	)

						A__					:       Optional[int]     =QuantDescriptor(num_bits=args.aprec,		calib_method=__snake_case	)
						A__					:       Tuple     =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					:		Any,		__snake_case					:		List[str],		__snake_case					:		Union[str, Any]=False,		__snake_case					:		List[str]=False	)	->							List[str]:






						"""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(__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					:		List[str]	)	->							List[Any]:






						"""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 						__lowerCamelCase	(       __snake_case					:		List[str],		__snake_case					:		Optional[int]	)	->							Optional[int]:






						"""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(__snake_case	)
def 						__lowerCamelCase	(       __snake_case					:		str,		__snake_case					:		Optional[Any]	)	->							int:






						"""simple docstring"""





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

												A__					:       Any     =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					:		List[str],		__snake_case					:		List[Any]	)	->							int:






						"""simple docstring"""





						for name, mod in model.named_modules():
												if name.endswith(""".output.dense"""	) and not name.endswith("""attention.output.dense"""	):
																		A__					:       Optional[int]     =mod._input_quantizer._amax.data.detach().item()
																		mod._input_quantizer._amax.data.detach().clamp_(max=__snake_case	)
																		A__					:       List[str]     =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					:		str	)	->							Optional[int]:






						"""simple docstring"""





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






						"""simple docstring"""





						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)
																		A__					:       Optional[int]     =set() if mod._weight_quantizer.axis is None else set(mod._weight_quantizer.axis	)
																		A__					:       str     =set(range(len(mod.weight.size()	)	)	) - axis_set
																		A__					:       str     =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()}"	)
																		A__					:       Dict     =amax
def 						__lowerCamelCase	(       __snake_case					:		Dict,		__snake_case					:		Union[str, Any]=25,		__snake_case					:		List[str]=180,		__snake_case					:		Any=None	)	->							int:






						"""simple docstring"""





						if ignore is None:
												A__					:       Union[str, Any]     =[]
						elif not isinstance(__snake_case,		__snake_case	):
												A__					:       Optional[Any]     =[ignore]

						A__					:       int     =0
						for name, mod in model.named_modules():
												if not hasattr(__snake_case,		"""weight"""	):
																		continue
												A__					:       List[Any]     =max(__snake_case,		len(__snake_case	)	)

						for name, mod in model.named_modules():
												A__					:       Any     =getattr(__snake_case,		"""_input_quantizer""",		__snake_case	)
												A__					:       Dict     =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
												A__					:       Dict     =f"Act:{input_q.extra_repr()}"
												A__					:       Optional[int]     =f"Wgt:{weight_q.extra_repr()}"
												A__					:       List[str]     =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					:		int	)	->							Dict:






						"""simple docstring"""





						A__					:       Optional[Any]     =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					:		Optional[Any],		__snake_case					:		Optional[int],		__snake_case					:		Dict,		__snake_case					:		Optional[int],		__snake_case					:		str	)	->							Optional[Any]:






						"""simple docstring"""





						A__					:       int     =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					:		Optional[Any],		__snake_case					:		Any,		__snake_case					:		Tuple="both",		**__snake_case					:		str	)	->							Optional[Any]:






						"""simple docstring"""





						A__					:       List[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(__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					:		Any,		__snake_case					:		int,		**__snake_case					:		Optional[int]	)	->							Union[str, Any]:






						"""simple docstring"""





						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	):
																														A__					:       Union[str, Any]     =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	)




'''simple docstring'''



import math

__snake_case			:     List[Any]										=      10
__snake_case			:     Dict										=      7
__snake_case			:     str										=      BALLS_PER_COLOUR * NUM_COLOURS
def 						__lowerCamelCase	(       __snake_case					:		int = 20	)	->							str:






						"""simple docstring"""





						A__					:       Union[str, Any]     =math.comb(__snake_case,		__snake_case	)
						A__					:       str     =math.comb(NUM_BALLS - BALLS_PER_COLOUR,		__snake_case	)

						A__					:       Optional[int]     =NUM_COLOURS * (1 - missing_colour / total)

						return f"{result:.9f}"


if __name__ == "__main__":
		print(solution(20))

