






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


a_							=    logging.getLogger(__name__)

a_							=    50  # max width of layer names
a_							=    70  # max width of quantizer names


def        _a  (     UpperCamelCase_					:				str   ) ->   List[str]:
						"""simple docstring"""







						lowerCAmelCase__       =						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        _a  (     UpperCamelCase_					:				int   ) ->   List[Any]:
						"""simple docstring"""







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

						lowerCAmelCase__       =						QuantDescriptor(num_bits=args.aprec    ,   calib_method=UpperCamelCase_   )
						lowerCAmelCase__       =						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        _a  (     UpperCamelCase_					:				Union[str, Any]    ,   UpperCamelCase_					:				List[Any]    ,   UpperCamelCase_					:				Any=False    ,   UpperCamelCase_					:				List[Any]=False   ) ->   Any:
						"""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(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        _a  (     UpperCamelCase_					:				Union[str, Any]   ) ->   Tuple:
						"""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        _a  (     UpperCamelCase_					:				Dict    ,   UpperCamelCase_					:				Tuple   ) ->   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(UpperCamelCase_   )


def        _a  (     UpperCamelCase_					:				Optional[Any]    ,   UpperCamelCase_					:				int   ) ->   int:
						"""simple docstring"""







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

												lowerCAmelCase__       =						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        _a  (     UpperCamelCase_					:				Any    ,   UpperCamelCase_					:				List[Any]   ) ->   Optional[int]:
						"""simple docstring"""







						for name, mod in model.named_modules():
												if name.endswith(".output.dense"   ) and not name.endswith("attention.output.dense"   ):
																		lowerCAmelCase__       =						mod._input_quantizer._amax.data.detach().item()
																		mod._input_quantizer._amax.data.detach().clamp_(max=UpperCamelCase_   )
																		lowerCAmelCase__       =						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  (     UpperCamelCase_					:				str   ) ->   int:
						"""simple docstring"""







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


def        _a  (     UpperCamelCase_					:				Any   ) ->   Optional[int]:
						"""simple docstring"""







						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)
																		lowerCAmelCase__       =						set() if mod._weight_quantizer.axis is None else set(mod._weight_quantizer.axis   )
																		lowerCAmelCase__       =						set(range(len(mod.weight.size()   )   )   ) - axis_set
																		lowerCAmelCase__       =						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()}"   )
																		lowerCAmelCase__       =						amax


def        _a  (     UpperCamelCase_					:				str    ,   UpperCamelCase_					:				Optional[Any]=25    ,   UpperCamelCase_					:				str=180    ,   UpperCamelCase_					:				Optional[int]=None   ) ->   Tuple:
						"""simple docstring"""







						if ignore is None:
												lowerCAmelCase__       =						[]
						elif not isinstance(UpperCamelCase_    ,   UpperCamelCase_   ):
												lowerCAmelCase__       =						[ignore]

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

						for name, mod in model.named_modules():
												lowerCAmelCase__       =						getattr(UpperCamelCase_    ,   "_input_quantizer"    ,   UpperCamelCase_   )
												lowerCAmelCase__       =						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
												lowerCAmelCase__       =						F"Act:{input_q.extra_repr()}"
												lowerCAmelCase__       =						F"Wgt:{weight_q.extra_repr()}"
												lowerCAmelCase__       =						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        _a  (     UpperCamelCase_					:				str   ) ->   Dict:
						"""simple docstring"""







						lowerCAmelCase__       =						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        _a  (     UpperCamelCase_					:				List[str]    ,   UpperCamelCase_					:				Union[str, Any]    ,   UpperCamelCase_					:				Union[str, Any]    ,   UpperCamelCase_					:				Optional[Any]    ,   UpperCamelCase_					:				int   ) ->   Tuple:
						"""simple docstring"""







						lowerCAmelCase__       =						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        _a  (     UpperCamelCase_					:				List[Any]    ,   UpperCamelCase_					:				int    ,   UpperCamelCase_					:				Union[str, Any]="both"    ,   **UpperCamelCase_					:				Dict   ) ->   Optional[int]:
						"""simple docstring"""







						lowerCAmelCase__       =						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        _a  (     UpperCamelCase_					:				str    ,   UpperCamelCase_					:				List[Any]    ,   **UpperCamelCase_					:				Any   ) ->   Any:
						"""simple docstring"""







						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_   ):
																														lowerCAmelCase__       =						F"Warning: changing {name:{name_width}}"
																														for k, v in kwargs.items():
																																				s += F" {k}={v}"
																																				setattr(UpperCamelCase_    ,   UpperCamelCase_    ,   UpperCamelCase_   )
																														logger.info(UpperCamelCase_   )














import argparse

from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection

from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline


if __name__ == "__main__":
	a_							=    argparse.ArgumentParser()

	parser.add_argument('''--dump_path''', default=None, type=str, required=True, help='''Path to the output model.''')

	parser.add_argument(
	    '''--txt2img_unclip''',
	    default='''kakaobrain/karlo-v1-alpha''',
	    type=str,
	    required=False,
	    help='''The pretrained txt2img unclip.''',
	)

	a_							=    parser.parse_args()

	a_							=    UnCLIPPipeline.from_pretrained(args.txtaimg_unclip)

	a_							=    CLIPImageProcessor()
	a_							=    CLIPVisionModelWithProjection.from_pretrained('''openai/clip-vit-large-patch14''')

	a_							=    UnCLIPImageVariationPipeline(
	    decoder=txtaimg.decoder,
	    text_encoder=txtaimg.text_encoder,
	    tokenizer=txtaimg.tokenizer,
	    text_proj=txtaimg.text_proj,
	    feature_extractor=feature_extractor,
	    image_encoder=image_encoder,
	    super_res_first=txtaimg.super_res_first,
	    super_res_last=txtaimg.super_res_last,
	    decoder_scheduler=txtaimg.decoder_scheduler,
	    super_res_scheduler=txtaimg.super_res_scheduler,
	)

	imgaimg.save_pretrained(args.dump_path)






