






"""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


UpperCAmelCase_   : Optional[Any]   			= logging.getLogger(__name__)

UpperCAmelCase_   : Any   			= 50  # max width of layer names
UpperCAmelCase_   : List[Any]   			= 70  # max width of quantizer names



def 					_A			(__a							) ->       Optional[Any]:
    """simple docstring"""






    SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		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 					_A			(__a							) ->       List[str]:
    """simple docstring"""






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

    SCREAMING_SNAKE_CASE_      :  List[str]    						=		QuantDescriptor(num_bits=args.aprec    ,				calib_method=__a							)
    SCREAMING_SNAKE_CASE_      :  Dict    						=		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 					_A			(__a    ,				__a    ,				__a=False    ,				__a=False							) ->       Optional[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(__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 					_A			(__a							) ->       int:
    """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			(__a    ,				__a							) ->       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(__a							)



def 					_A			(__a    ,				__a							) ->       Optional[int]:
    """simple docstring"""






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

        SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		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 					_A			(__a    ,				__a							) ->       List[str]:
    """simple docstring"""






    for name, mod in model.named_modules():
        if name.endswith('''.output.dense'''							) and not name.endswith('''attention.output.dense'''							):
            SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		mod._input_quantizer._amax.data.detach().item()
            mod._input_quantizer._amax.data.detach().clamp_(max=__a							)
            SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		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			(__a							) ->       Dict:
    """simple docstring"""






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



def 					_A			(__a							) ->       List[Any]:
    """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)
            SCREAMING_SNAKE_CASE_      :  int    						=		set() if mod._weight_quantizer.axis is None else set(mod._weight_quantizer.axis							)
            SCREAMING_SNAKE_CASE_      :  Tuple    						=		set(range(len(mod.weight.size()							)							)							) - axis_set
            SCREAMING_SNAKE_CASE_      :  Any    						=		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()}'							)
            SCREAMING_SNAKE_CASE_      :  Dict    						=		amax



def 					_A			(__a    ,				__a=25    ,				__a=1_80    ,				__a=None							) ->       List[str]:
    """simple docstring"""






    if ignore is None:
        SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		[]
    elif not isinstance(__a    ,				__a							):
        SCREAMING_SNAKE_CASE_      :  int    						=		[ignore]

    SCREAMING_SNAKE_CASE_      :  List[Any]    						=		0
    for name, mod in model.named_modules():
        if not hasattr(__a    ,				'''weight'''							):
            continue
        SCREAMING_SNAKE_CASE_      :  List[str]    						=		max(__a    ,				len(__a							)							)

    for name, mod in model.named_modules():
        SCREAMING_SNAKE_CASE_      :  int    						=		getattr(__a    ,				'''_input_quantizer'''    ,				__a							)
        SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		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
        SCREAMING_SNAKE_CASE_      :  List[str]    						=		f'Act:{input_q.extra_repr()}'
        SCREAMING_SNAKE_CASE_      :  Tuple    						=		f'Wgt:{weight_q.extra_repr()}'
        SCREAMING_SNAKE_CASE_      :  List[Any]    						=		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 					_A			(__a							) ->       List[Any]:
    """simple docstring"""






    SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		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 					_A			(__a    ,				__a    ,				__a    ,				__a    ,				__a							) ->       Tuple:
    """simple docstring"""






    SCREAMING_SNAKE_CASE_      :  Any    						=		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 					_A			(__a    ,				__a    ,				__a="both"    ,				**__a							) ->       Tuple:
    """simple docstring"""






    SCREAMING_SNAKE_CASE_      :  Optional[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(__a    ,				__a    ,				'''_input_quantizer'''    ,				__a    ,				__a							)
        if which in ["weight", "both"]:
            set_quantizer(__a    ,				__a    ,				'''_weight_quantizer'''    ,				__a    ,				__a							)
    logger.info(__a							)





def 					_A			(__a    ,				__a    ,				**__a							) ->       List[str]:
    """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							):
                    SCREAMING_SNAKE_CASE_      :  Dict    						=		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 . import (
    albert,
    align,
    altclip,
    audio_spectrogram_transformer,
    auto,
    autoformer,
    bark,
    bart,
    barthez,
    bartpho,
    beit,
    bert,
    bert_generation,
    bert_japanese,
    bertweet,
    big_bird,
    bigbird_pegasus,
    biogpt,
    bit,
    blenderbot,
    blenderbot_small,
    blip,
    blip_a,
    bloom,
    bridgetower,
    byta,
    camembert,
    canine,
    chinese_clip,
    clap,
    clip,
    clipseg,
    codegen,
    conditional_detr,
    convbert,
    convnext,
    convnextva,
    cpm,
    cpmant,
    ctrl,
    cvt,
    dataavec,
    deberta,
    deberta_va,
    decision_transformer,
    deformable_detr,
    deit,
    deprecated,
    deta,
    detr,
    dialogpt,
    dinat,
    distilbert,
    dit,
    donut,
    dpr,
    dpt,
    efficientformer,
    efficientnet,
    electra,
    encodec,
    encoder_decoder,
    ernie,
    ernie_m,
    esm,
    falcon,
    flaubert,
    flava,
    fnet,
    focalnet,
    fsmt,
    funnel,
    git,
    glpn,
    gpta,
    gpt_bigcode,
    gpt_neo,
    gpt_neox,
    gpt_neox_japanese,
    gpt_swa,
    gptj,
    gptsan_japanese,
    graphormer,
    groupvit,
    herbert,
    hubert,
    ibert,
    imagegpt,
    informer,
    instructblip,
    jukebox,
    layoutlm,
    layoutlmva,
    layoutlmva,
    layoutxlm,
    led,
    levit,
    lilt,
    llama,
    longformer,
    longta,
    luke,
    lxmert,
    mam_aaa,
    marian,
    markuplm,
    maskaformer,
    maskformer,
    mbart,
    mbartaa,
    mega,
    megatron_bert,
    megatron_gpta,
    mgp_str,
    mluke,
    mobilebert,
    mobilenet_va,
    mobilenet_va,
    mobilevit,
    mobilevitva,
    mpnet,
    mra,
    mta,
    musicgen,
    mvp,
    nat,
    nezha,
    nllb,
    nllb_moe,
    nystromformer,
    oneformer,
    open_llama,
    openai,
    opt,
    owlvit,
    pegasus,
    pegasus_x,
    perceiver,
    phobert,
    pixastruct,
    plbart,
    poolformer,
    prophetnet,
    qdqbert,
    rag,
    realm,
    reformer,
    regnet,
    rembert,
    resnet,
    roberta,
    roberta_prelayernorm,
    roc_bert,
    roformer,
    rwkv,
    sam,
    segformer,
    sew,
    sew_d,
    speech_encoder_decoder,
    speech_to_text,
    speech_to_text_a,
    speechta,
    splinter,
    squeezebert,
    swiftformer,
    swin,
    swinasr,
    swinva,
    switch_transformers,
    ta,
    table_transformer,
    tapas,
    time_series_transformer,
    timesformer,
    timm_backbone,
    transfo_xl,
    trocr,
    tvlt,
    umta,
    unispeech,
    unispeech_sat,
    upernet,
    videomae,
    vilt,
    vision_encoder_decoder,
    vision_text_dual_encoder,
    visual_bert,
    vit,
    vit_hybrid,
    vit_mae,
    vit_msn,
    vivit,
    wavaveca,
    wavaveca_conformer,
    wavaveca_phoneme,
    wavaveca_with_lm,
    wavlm,
    whisper,
    x_clip,
    xglm,
    xlm,
    xlm_prophetnet,
    xlm_roberta,
    xlm_roberta_xl,
    xlnet,
    xmod,
    yolos,
    yoso,
)


