



"""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


_lowerCAmelCase    :Tuple     =					logging.getLogger(__name__)

_lowerCAmelCase    :List[str]     =					50  # max width of layer names
_lowerCAmelCase    :int     =					70  # max width of quantizer names





def  lowerCamelCase_						(UpperCamelCase__		:		str							):
    _UpperCAmelCase							:  Optional[Any]											=    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  lowerCamelCase_						(UpperCamelCase__		:		Optional[Any]							):

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

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





def  lowerCamelCase_						(UpperCamelCase__		:		List[Any]     ,       UpperCamelCase__		:		Dict							):

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

        _UpperCAmelCase							:  Any											=    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  lowerCamelCase_						(UpperCamelCase__		:		int     ,       UpperCamelCase__		:		Dict							):

    for name, mod in model.named_modules():
        if name.endswith('''.output.dense'''							) and not name.endswith('''attention.output.dense'''							):
            _UpperCAmelCase							:  int											=    mod._input_quantizer._amax.data.detach().item()
            mod._input_quantizer._amax.data.detach().clamp_(max=UpperCamelCase__							)
            _UpperCAmelCase							:  str											=    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__		:		Optional[Any]							):

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





def  lowerCamelCase_						(UpperCamelCase__		:		List[Any]							):

    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)
            _UpperCAmelCase							:  Union[str, Any]											=    set() if mod._weight_quantizer.axis is None else set(mod._weight_quantizer.axis							)
            _UpperCAmelCase							:  Dict											=    set(range(len(mod.weight.size()							)							)							) - axis_set
            _UpperCAmelCase							:  Optional[int]											=    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()}'							)
            _UpperCAmelCase							:  Union[str, Any]											=    amax





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

    if ignore is None:
        _UpperCAmelCase							:  Optional[int]											=    []
    elif not isinstance(UpperCamelCase__     ,       UpperCamelCase__							):
        _UpperCAmelCase							:  Union[str, Any]											=    [ignore]

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

    for name, mod in model.named_modules():
        _UpperCAmelCase							:  Any											=    getattr(UpperCamelCase__     ,       '''_input_quantizer'''     ,       UpperCamelCase__							)
        _UpperCAmelCase							:  int											=    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
        _UpperCAmelCase							:  str											=    F'Act:{input_q.extra_repr()}'
        _UpperCAmelCase							:  Optional[int]											=    F'Wgt:{weight_q.extra_repr()}'
        _UpperCAmelCase							:  List[Any]											=    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  lowerCamelCase_						(UpperCamelCase__		:		Dict							):
    _UpperCAmelCase							:  str											=    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  lowerCamelCase_						(UpperCamelCase__		:		str     ,       UpperCamelCase__		:		Optional[Any]     ,       UpperCamelCase__		:		Optional[int]     ,       UpperCamelCase__		:		str     ,       UpperCamelCase__		:		str							):
    _UpperCAmelCase							:  Tuple											=    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  lowerCamelCase_						(UpperCamelCase__		:		Dict     ,       UpperCamelCase__		:		Any     ,       UpperCamelCase__		:		List[str]="both"     ,       **UpperCamelCase__		:		List[Any]							):
    _UpperCAmelCase							:  List[str]											=    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  lowerCamelCase_						(UpperCamelCase__		:		Union[str, Any]     ,       UpperCamelCase__		:		List[str]     ,       **UpperCamelCase__		:		Tuple							):

    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__							):
                    _UpperCAmelCase							:  Optional[Any]											=    F'Warning: changing {name:{name_width}}'
                    for k, v in kwargs.items():
                        s += F' {k}={v}'
                        setattr(UpperCamelCase__     ,       UpperCamelCase__     ,       UpperCamelCase__							)
                    logger.info(UpperCamelCase__							)







"""simple docstring"""
import re

from flax.core.frozen_dict import freeze
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.experimental import PartitionSpec as P


# Sentinels
_lowerCAmelCase    :str     =					object()

# For specifying empty leaf dict `{}`
_lowerCAmelCase    :str     =					object()





def  lowerCamelCase_						(UpperCamelCase__		:		List[str]     ,       UpperCamelCase__		:		int							):
    _UpperCAmelCase							:  Dict											=    tuple((re.compile(x + '''$'''							) for x in qs)							)
    for i in range(len(UpperCamelCase__							) - len(UpperCamelCase__							) + 1							):
        _UpperCAmelCase							:  str											=    [x.match(UpperCamelCase__							) for x, y in zip(UpperCamelCase__     ,       ks[i:]							)]
        if matches and all(UpperCamelCase__							):
            return True
    return False





def  lowerCamelCase_						(UpperCamelCase__		:		List[str]							):
    def replace(UpperCamelCase__		:		List[str]     ,       UpperCamelCase__		:		Tuple							):
        for rule, replacement in rules:
            if _match(UpperCamelCase__     ,       UpperCamelCase__							):
                return replacement
        return val

    return replace





def  lowerCamelCase_						():
    return [
        # embeddings
        (("transformer", "wpe", "embedding"), P('''mp'''     ,       UpperCamelCase__							)),
        (("transformer", "wte", "embedding"), P('''mp'''     ,       UpperCamelCase__							)),
        # atention
        (("attention", "(q_proj|k_proj|v_proj)", "kernel"), P(UpperCamelCase__     ,       '''mp'''							)),
        (("attention", "out_proj", "kernel"), P('''mp'''     ,       UpperCamelCase__							)),
        (("attention", "out_proj", "bias"), None),
        # mlp
        (("mlp", "c_fc", "kernel"), P(UpperCamelCase__     ,       '''mp'''							)),
        (("mlp", "c_fc", "bias"), P('''mp'''							)),
        (("mlp", "c_proj", "kernel"), P('''mp'''     ,       UpperCamelCase__							)),
        (("mlp", "c_proj", "bias"), None),
        # layer norms
        ((r"ln_\d+", "bias"), None),
        ((r"\d+", r"ln_\d+", "scale"), None),
        (("ln_f", "bias"), None),
        (("ln_f", "scale"), None),
    ]





def  lowerCamelCase_						(UpperCamelCase__		:		str							):
    _UpperCAmelCase							:  List[str]											=    _get_partition_rules()
    _UpperCAmelCase							:  List[str]											=    _replacement_rules(UpperCamelCase__							)
    _UpperCAmelCase							:  List[Any]											=    {k: _unmatched for k in flatten_dict(UpperCamelCase__							)}
    _UpperCAmelCase							:  int											=    {k: replace(UpperCamelCase__     ,       UpperCamelCase__							) for k, v in initd.items()}
    assert _unmatched not in result.values(), "Incomplete partition spec."
    return freeze(unflatten_dict(UpperCamelCase__							)							)


