



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    :		Any		      =    logging.getLogger(__name__)

_lowerCamelCase    :		Optional[int]		      =    50  # max width of layer names
_lowerCamelCase    :		Tuple		      =    70  # max width of quantizer names

def        __a			(   __lowerCAmelCase   )     ->     List[Any]:
					SCREAMING_SNAKE_CASE							:     Optional[int]						=				parser.add_argument_group('quant_trainer arguments'   )
					group.add_argument('--wprec'      ,	type=__lowerCAmelCase      ,	default=8      ,	help='weight precision'   )
					group.add_argument('--aprec'      ,	type=__lowerCAmelCase      ,	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=__lowerCAmelCase      ,	nargs='+'      ,	help='disable quantizers by keyword'   )
					group.add_argument('--quant-disable-layer-module'      ,	type=__lowerCAmelCase      ,	help='disable quantizers by keyword under layer.'   )
					group.add_argument('--quant-enable-layer-module'      ,	type=__lowerCAmelCase      ,	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=__lowerCAmelCase      ,	type=__lowerCAmelCase      ,	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=__lowerCAmelCase      ,	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			(   __lowerCAmelCase   )     ->     Optional[Any]:

					if args.calibrator == "max":
										SCREAMING_SNAKE_CASE							:     Dict						=				'max'
					elif args.calibrator == "percentile":
										if args.percentile is None:
															raise ValueError('Specify --percentile when using percentile calibrator'   )
										SCREAMING_SNAKE_CASE							:     List[Any]						=				'histogram'
					elif args.calibrator == "mse":
										SCREAMING_SNAKE_CASE							:     Tuple						=				'histogram'
					else:
										raise ValueError(F'''Invalid calibrator {args.calibrator}'''   )

					SCREAMING_SNAKE_CASE							:     Optional[int]						=				QuantDescriptor(num_bits=args.aprec      ,	calib_method=__lowerCAmelCase   )
					SCREAMING_SNAKE_CASE							:     Tuple						=				QuantDescriptor(num_bits=args.wprec      ,	axis=(None if args.quant_per_tensor else (0,))   )
					quant_nn.QuantLinear.set_default_quant_desc_input(__lowerCAmelCase   )
					quant_nn.QuantLinear.set_default_quant_desc_weight(__lowerCAmelCase   )

def        __a			(   __lowerCAmelCase      ,	__lowerCAmelCase      ,	__lowerCAmelCase=False      ,	__lowerCAmelCase=False   )     ->     List[str]:
					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(__lowerCAmelCase      ,	['embeddings']      ,	which='weight'      ,	_disabled=__lowerCAmelCase   )

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

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

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

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

										if args.recalibrate_weights:
															recalibrate_weights(__lowerCAmelCase   )

										if args.fuse_qkv:
															fuse_qkv(__lowerCAmelCase      ,	__lowerCAmelCase   )

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

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

def        __a			(   __lowerCAmelCase   )     ->     List[str]:
					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			(   __lowerCAmelCase      ,	__lowerCAmelCase   )     ->     Optional[Any]:
					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(__lowerCAmelCase   )

def        __a			(   __lowerCAmelCase      ,	__lowerCAmelCase   )     ->     Optional[Any]:

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

										SCREAMING_SNAKE_CASE							:     Dict						=				max(__lowerCAmelCase      ,	__lowerCAmelCase      ,	__lowerCAmelCase   )
										qq._amax.fill_(__lowerCAmelCase   )
										qk._amax.fill_(__lowerCAmelCase   )
										qv._amax.fill_(__lowerCAmelCase   )
										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			(   __lowerCAmelCase      ,	__lowerCAmelCase   )     ->     Optional[int]:

					for name, mod in model.named_modules():
										if name.endswith('.output.dense'   ) and not name.endswith('attention.output.dense'   ):
															SCREAMING_SNAKE_CASE							:     Dict						=				mod._input_quantizer._amax.data.detach().item()
															mod._input_quantizer._amax.data.detach().clamp_(max=__lowerCAmelCase   )
															SCREAMING_SNAKE_CASE							:     Union[str, 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        __a			(   __lowerCAmelCase   )     ->     Union[str, Any]:

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

def        __a			(   __lowerCAmelCase   )     ->     Dict:

					for name, mod in model.named_modules():
										if hasattr(__lowerCAmelCase      ,	'_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)
															SCREAMING_SNAKE_CASE							:     Union[str, Any]						=				set() if mod._weight_quantizer.axis is None else set(mod._weight_quantizer.axis   )
															SCREAMING_SNAKE_CASE							:     str						=				set(range(len(mod.weight.size()   )   )   ) - axis_set
															SCREAMING_SNAKE_CASE							:     List[str]						=				pytorch_quantization.utils.reduce_amax(mod.weight      ,	axis=__lowerCAmelCase      ,	keepdims=__lowerCAmelCase   ).detach()
															logger.info(F'''RECALIB: {name:{name_width}} {mod._weight_quantizer._amax.flatten()} -> {amax.flatten()}'''   )
															SCREAMING_SNAKE_CASE							:     Any						=				amax

def        __a			(   __lowerCAmelCase      ,	__lowerCAmelCase=25      ,	__lowerCAmelCase=180      ,	__lowerCAmelCase=None   )     ->     List[Any]:

					if ignore is None:
										SCREAMING_SNAKE_CASE							:     List[str]						=				[]
					elif not isinstance(__lowerCAmelCase      ,	__lowerCAmelCase   ):
										SCREAMING_SNAKE_CASE							:     int						=				[ignore]

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

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

def        __a			(   __lowerCAmelCase   )     ->     Dict:
					SCREAMING_SNAKE_CASE							:     str						=				0
					for name, mod in model.named_modules():
										if isinstance(__lowerCAmelCase      ,	pytorch_quantization.nn.TensorQuantizer   ):
															print(F'''{name:80} {mod}'''   )
															count += 1
					print(F'''{count} TensorQuantizers found in model'''   )

def        __a			(   __lowerCAmelCase      ,	__lowerCAmelCase      ,	__lowerCAmelCase      ,	__lowerCAmelCase      ,	__lowerCAmelCase   )     ->     Union[str, Any]:
					SCREAMING_SNAKE_CASE							:     Union[str, Any]						=				getattr(__lowerCAmelCase      ,	__lowerCAmelCase      ,	__lowerCAmelCase   )
					if quantizer_mod is not None:
										assert hasattr(__lowerCAmelCase      ,	__lowerCAmelCase   )
										setattr(__lowerCAmelCase      ,	__lowerCAmelCase      ,	__lowerCAmelCase   )
					else:
										logger.warning(F'''{name} has no {quantizer}'''   )

def        __a			(   __lowerCAmelCase      ,	__lowerCAmelCase      ,	__lowerCAmelCase="both"      ,	**__lowerCAmelCase   )     ->     Any:
					SCREAMING_SNAKE_CASE							:     Tuple						=				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(__lowerCAmelCase      ,	__lowerCAmelCase      ,	'_input_quantizer'      ,	__lowerCAmelCase      ,	__lowerCAmelCase   )
										if which in ["weight", "both"]:
															set_quantizer(__lowerCAmelCase      ,	__lowerCAmelCase      ,	'_weight_quantizer'      ,	__lowerCAmelCase      ,	__lowerCAmelCase   )
					logger.info(__lowerCAmelCase   )



def        __a			(   __lowerCAmelCase      ,	__lowerCAmelCase      ,	**__lowerCAmelCase   )     ->     Optional[int]:

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




def        __a			(   __lowerCAmelCase      ,	__lowerCAmelCase = 0   )     ->     list:
					SCREAMING_SNAKE_CASE							:     int						=				length or len(__lowerCAmelCase   )
					SCREAMING_SNAKE_CASE							:     Union[str, Any]						=				False
					for i in range(length - 1   ):
										if list_data[i] > list_data[i + 1]:
															SCREAMING_SNAKE_CASE					,  SCREAMING_SNAKE_CASE							:     List[Any]						=				list_data[i + 1], list_data[i]
															SCREAMING_SNAKE_CASE							:     Any						=				True

					return list_data if not swapped else bubble_sort(__lowerCAmelCase      ,	length - 1   )


if __name__ == "__main__":
			import doctest

			doctest.testmod()