
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					:	Union[str, Any]							=		logging.getLogger(__name__)

a					:	str							=		5_0  # max width of layer names
a					:	int							=		7_0  # max width of quantizer names


def  _UpperCamelCase	(						_A      )    ->  str:





    """simple docstring"""

    _UpperCAmelCase          =   parser.add_argument_group("""quant_trainer arguments"""      )
    group.add_argument("""--wprec"""					,      type=_lowerCamelCase					,      default=8					,      help="""weight precision"""      )
    group.add_argument("""--aprec"""					,      type=_lowerCamelCase					,      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=_lowerCamelCase					,      nargs="""+"""					,      help="""disable quantizers by keyword"""      )
    group.add_argument("""--quant-disable-layer-module"""					,      type=_lowerCamelCase					,      help="""disable quantizers by keyword under layer."""      )
    group.add_argument("""--quant-enable-layer-module"""					,      type=_lowerCamelCase					,      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=_lowerCamelCase					,      type=_lowerCamelCase					,      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=_lowerCamelCase					,      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  _UpperCamelCase	(						_A      )    ->  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=_lowerCamelCase      )
    _UpperCAmelCase          =   QuantDescriptor(num_bits=args.wprec					,      axis=(None if args.quant_per_tensor else (0,))      )
    quant_nn.QuantLinear.set_default_quant_desc_input(_lowerCamelCase      )
    quant_nn.QuantLinear.set_default_quant_desc_weight(_lowerCamelCase      )


def  _UpperCamelCase	(						_A					,      _A					,      _A=False					,      _A=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(_lowerCamelCase					,      ["""embeddings"""]					,      which="""weight"""					,      _disabled=_lowerCamelCase      )

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

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

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

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

        if args.recalibrate_weights:
            recalibrate_weights(_lowerCamelCase      )

        if args.fuse_qkv:
            fuse_qkv(_lowerCamelCase					,      _lowerCamelCase      )

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

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


def  _UpperCamelCase	(						_A      )    ->  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  _UpperCamelCase	(						_A					,      _A      )    ->  Union[str, Any]:





    """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(_lowerCamelCase      )


def  _UpperCamelCase	(						_A					,      _A      )    ->  Tuple:





    """simple docstring"""

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

        _UpperCAmelCase          =   max(_lowerCamelCase					,      _lowerCamelCase					,      _lowerCamelCase      )
        qq._amax.fill_(_lowerCamelCase      )
        qk._amax.fill_(_lowerCamelCase      )
        qv._amax.fill_(_lowerCamelCase      )
        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  _UpperCamelCase	(						_A					,      _A      )    ->  Tuple:





    """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=_lowerCamelCase      )
            _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  _UpperCamelCase	(						_A      )    ->  Tuple:





    """simple docstring"""

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


def  _UpperCamelCase	(						_A      )    ->  Dict:





    """simple docstring"""

    for name, mod in model.named_modules():
        if hasattr(_lowerCamelCase					,      """_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=_lowerCamelCase					,      keepdims=_lowerCamelCase      ).detach()
            logger.info(F"""RECALIB: {name:{name_width}} {mod._weight_quantizer._amax.flatten()} -> {amax.flatten()}"""      )
            _UpperCAmelCase          =   amax


def  _UpperCamelCase	(						_A					,      _A=2_5					,      _A=1_8_0					,      _A=None      )    ->  Dict:





    """simple docstring"""

    if ignore is None:
        _UpperCAmelCase          =   []
    elif not isinstance(_lowerCamelCase					,      _lowerCamelCase      ):
        _UpperCAmelCase          =   [ignore]

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

    for name, mod in model.named_modules():
        _UpperCAmelCase          =   getattr(_lowerCamelCase					,      """_input_quantizer"""					,      _lowerCamelCase      )
        _UpperCAmelCase          =   getattr(_lowerCamelCase					,      """_weight_quantizer"""					,      _lowerCamelCase      )
        if not hasattr(_lowerCamelCase					,      """weight"""      ):
            continue
        if type(_lowerCamelCase      ) in ignore:
            continue
        if [True for s in ignore if type(_lowerCamelCase      ) 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(_lowerCamelCase      ) <= line_width:
            logger.info(_lowerCamelCase      )
        else:
            logger.info(F"""{name:{name_width}} {act_str}"""      )
            logger.info(F"""{"  ":{name_width}} {wgt_str}"""      )


def  _UpperCamelCase	(						_A      )    ->  Dict:





    """simple docstring"""

    _UpperCAmelCase          =   0
    for name, mod in model.named_modules():
        if isinstance(_lowerCamelCase					,      pytorch_quantization.nn.TensorQuantizer      ):
            print(F"""{name:80} {mod}"""      )
            count += 1
    print(F"""{count} TensorQuantizers found in model"""      )


def  _UpperCamelCase	(						_A					,      _A					,      _A					,      _A					,      _A      )    ->  Optional[int]:





    """simple docstring"""

    _UpperCAmelCase          =   getattr(_lowerCamelCase					,      _lowerCamelCase					,      _lowerCamelCase      )
    if quantizer_mod is not None:
        assert hasattr(_lowerCamelCase					,      _lowerCamelCase      )
        setattr(_lowerCamelCase					,      _lowerCamelCase					,      _lowerCamelCase      )
    else:
        logger.warning(F"""{name} has no {quantizer}"""      )


def  _UpperCamelCase	(						_A					,      _A					,      _A="both"					,      **_A      )    ->  str:





    """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(_lowerCamelCase					,      _lowerCamelCase					,      """_input_quantizer"""					,      _lowerCamelCase					,      _lowerCamelCase      )
        if which in ["weight", "both"]:
            set_quantizer(_lowerCamelCase					,      _lowerCamelCase					,      """_weight_quantizer"""					,      _lowerCamelCase					,      _lowerCamelCase      )
    logger.info(_lowerCamelCase      )







def  _UpperCamelCase	(						_A					,      _A					,      **_A      )    ->  Optional[int]:





    """simple docstring"""

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

"""simple docstring"""



from collections.abc import Callable

import numpy as np


def  _UpperCamelCase	(						_A					,      _A					,      _A					,      _A					,      _A      )    ->  np.array:





    """simple docstring"""

    _UpperCAmelCase          =   int(np.ceil((x_end - xa) / step_size      )      )
    _UpperCAmelCase          =   np.zeros((n + 1,)      )
    _UpperCAmelCase          =   ya
    _UpperCAmelCase          =   xa

    for k in range(_A      ):
        _UpperCAmelCase          =   y[k] + step_size * ode_func(_A					,      y[k]      )
        _UpperCAmelCase          =   y[k] + (
            (step_size / 2) * (ode_func(_A					,      y[k]      ) + ode_func(x + step_size					,      _A      ))
        )
        x += step_size

    return y


if __name__ == "__main__":
 import doctest

 doctest.testmod()