



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_						=       50  # max width of layer names
lowercase_						=       70  # max width of quantizer names



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

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

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



def __lowerCAmelCase	(       SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_=False  ,					SCREAMING_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(SCREAMING_SNAKE_CASE_  ,					["embeddings"]  ,					which="weight"  ,					_disabled=SCREAMING_SNAKE_CASE_					)

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

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

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

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

						if args.recalibrate_weights:
									recalibrate_weights(SCREAMING_SNAKE_CASE_					)

						if args.fuse_qkv:
									fuse_qkv(SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_					)

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

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



def __lowerCAmelCase	(       SCREAMING_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	(       SCREAMING_SNAKE_CASE_  ,					SCREAMING_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(SCREAMING_SNAKE_CASE_					)



def __lowerCAmelCase	(       SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_					):

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

						lowercase__   					=			max(SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_					)
						qq._amax.fill_(SCREAMING_SNAKE_CASE_					)
						qk._amax.fill_(SCREAMING_SNAKE_CASE_					)
						qv._amax.fill_(SCREAMING_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	(       SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_					):

			for name, mod in model.named_modules():
						if name.endswith(".output.dense"					) and not name.endswith("attention.output.dense"					):
									lowercase__   					=			mod._input_quantizer._amax.data.detach().item()
									mod._input_quantizer._amax.data.detach().clamp_(max=SCREAMING_SNAKE_CASE_					)
									lowercase__   					=			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	(       SCREAMING_SNAKE_CASE_					):

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



def __lowerCAmelCase	(       SCREAMING_SNAKE_CASE_					):

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



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

			if ignore is None:
						lowercase__   					=			[]
			elif not isinstance(SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_					):
						lowercase__   					=			[ignore]

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

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



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



def __lowerCAmelCase	(       SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_					):
			lowercase__   					=			getattr(SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_					)
			if quantizer_mod is not None:
						assert hasattr(SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_					)
						setattr(SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_					)
			else:
						logger.warning(f'''{name} has no {quantizer}'''					)



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




def __lowerCAmelCase	(       SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_  ,					**SCREAMING_SNAKE_CASE_					):

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









from graphs.minimum_spanning_tree_kruskal import kruskal



def __lowerCAmelCase	(       ):
			lowercase__   					=			9
			lowercase__   					=			[
			    [0, 1, 4],
			    [0, 7, 8],
			    [1, 2, 8],
			    [7, 8, 7],
			    [7, 6, 1],
			    [2, 8, 2],
			    [8, 6, 6],
			    [2, 3, 7],
			    [2, 5, 4],
			    [6, 5, 2],
			    [3, 5, 14],
			    [3, 4, 9],
			    [5, 4, 10],
			    [1, 7, 11],
			]

			lowercase__   					=			kruskal(SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_					)

			lowercase__   					=			[
			    [7, 6, 1],
			    [2, 8, 2],
			    [6, 5, 2],
			    [0, 1, 4],
			    [2, 5, 4],
			    [2, 3, 7],
			    [0, 7, 8],
			    [3, 4, 9],
			]

			assert sorted(SCREAMING_SNAKE_CASE_					) == sorted(SCREAMING_SNAKE_CASE_					)




