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									=				logging.getLogger(__name__)

lowercase									=				5_0  # max width of layer names
lowercase									=				7_0  # max width of quantizer names






def        lowerCamelCase_					(     UpperCamelCase__       :						List[str]			):







				'''simple docstring'''







				UpperCamelCase__					=				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        lowerCamelCase_					(     UpperCamelCase__       :						int			):







				'''simple docstring'''







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

				UpperCamelCase__					=				QuantDescriptor(num_bits=args.aprec,		calib_method=__a			)
				UpperCamelCase__					=				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        lowerCamelCase_					(     UpperCamelCase__       :						str,		UpperCamelCase__       :						Tuple,		UpperCamelCase__       :						Tuple=False,		UpperCamelCase__       :						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        lowerCamelCase_					(     UpperCamelCase__       :						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_					(     UpperCamelCase__       :						Optional[Any],		UpperCamelCase__       :						str			):







				'''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        lowerCamelCase_					(     UpperCamelCase__       :						Any,		UpperCamelCase__       :						Optional[Any]			):







				'''simple docstring'''







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

								UpperCamelCase__					=				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        lowerCamelCase_					(     UpperCamelCase__       :						Optional[Any],		UpperCamelCase__       :						List[str]			):







				'''simple docstring'''







				for name, mod in model.named_modules():
								if name.endswith('''.output.dense'''			) and not name.endswith('''attention.output.dense'''			):
												UpperCamelCase__					=				mod._input_quantizer._amax.data.detach().item()
												mod._input_quantizer._amax.data.detach().clamp_(max=__a			)
												UpperCamelCase__					=				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_					(     UpperCamelCase__       :						int			):







				'''simple docstring'''







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






def        lowerCamelCase_					(     UpperCamelCase__       :						int			):







				'''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__					=				set() if mod._weight_quantizer.axis is None else set(mod._weight_quantizer.axis			)
												UpperCamelCase__					=				set(range(len(mod.weight.size()			)			)			) - axis_set
												UpperCamelCase__					=				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__					=				amax






def        lowerCamelCase_					(     UpperCamelCase__       :						Optional[int],		UpperCamelCase__       :						Union[str, Any]=25,		UpperCamelCase__       :						str=180,		UpperCamelCase__       :						str=None			):







				'''simple docstring'''







				if ignore is None:
								UpperCamelCase__					=				[]
				elif not isinstance(__a,		__a			):
								UpperCamelCase__					=				[ignore]

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

				for name, mod in model.named_modules():
								UpperCamelCase__					=				getattr(__a,		'''_input_quantizer''',		__a			)
								UpperCamelCase__					=				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__					=				F"""Act:{input_q.extra_repr()}"""
								UpperCamelCase__					=				F"""Wgt:{weight_q.extra_repr()}"""
								UpperCamelCase__					=				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        lowerCamelCase_					(     UpperCamelCase__       :						str			):







				'''simple docstring'''







				UpperCamelCase__					=				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        lowerCamelCase_					(     UpperCamelCase__       :						List[Any],		UpperCamelCase__       :						Tuple,		UpperCamelCase__       :						List[Any],		UpperCamelCase__       :						List[str],		UpperCamelCase__       :						int			):







				'''simple docstring'''







				UpperCamelCase__					=				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        lowerCamelCase_					(     UpperCamelCase__       :						Optional[Any],		UpperCamelCase__       :						Dict,		UpperCamelCase__       :						List[str]="both",		**UpperCamelCase__       :						int			):







				'''simple docstring'''







				UpperCamelCase__					=				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        lowerCamelCase_					(     UpperCamelCase__       :						Any,		UpperCamelCase__       :						List[Any],		**UpperCamelCase__       :						Dict			):







				'''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__					=				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"""





from __future__ import annotations
def 				A_  (__a					, __a = None					, __a = None					):

					'''simple docstring'''





					if start is None:
										A_								=    0

					if end is None:
										A_								=    len(__a					) - 1

					if start >= end:
										return

					A_								=    (start + end) // 2

					slowsort(__a					, __a					, __a					)
					slowsort(__a					, mid + 1					, __a					)

					if sequence[end] < sequence[mid]:
										A_			,     A_								=    sequence[mid], sequence[end]

					slowsort(__a					, __a					, end - 1					)


if __name__ == "__main__":
		from doctest import testmod

		testmod()




