



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


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

__snake_case		:str   	=  50  # max width of layer names
__snake_case		:int   	=  70  # max width of quantizer names







def   __snake_case			(    _UpperCAmelCase       ):
    __a		 =       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   __snake_case			(    _UpperCAmelCase       ):

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

    __a		 =       QuantDescriptor(num_bits=args.aprec     ,				calib_method=_lowerCamelCase       )
    __a		 =       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   __snake_case			(    _UpperCAmelCase     ,				_UpperCAmelCase     ,				_UpperCAmelCase=False     ,				_UpperCAmelCase=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(_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   __snake_case			(    _UpperCAmelCase       ):
    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   __snake_case			(    _UpperCAmelCase     ,				_UpperCAmelCase       ):
    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   __snake_case			(    _UpperCAmelCase     ,				_UpperCAmelCase       ):

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

        __a		 =       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   __snake_case			(    _UpperCAmelCase     ,				_UpperCAmelCase       ):

    for name, mod in model.named_modules():
        if name.endswith('''.output.dense'''       ) and not name.endswith('''attention.output.dense'''       ):
            __a		 =       mod._input_quantizer._amax.data.detach().item()
            mod._input_quantizer._amax.data.detach().clamp_(max=_lowerCamelCase       )
            __a		 =       mod._input_quantizer._amax.data.detach().item()
            logger.info(f'CLIP_GELU: {name:{name_width}} amax: {amax_init:5.2f} -> {amax:5.2f}'       )







def   __snake_case			(    _UpperCAmelCase       ):

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







def   __snake_case			(    _UpperCAmelCase       ):

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







def   __snake_case			(    _UpperCAmelCase     ,				_UpperCAmelCase=25     ,				_UpperCAmelCase=180     ,				_UpperCAmelCase=None       ):

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

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

    for name, mod in model.named_modules():
        __a		 =       getattr(_lowerCamelCase     ,				'''_input_quantizer'''     ,				_lowerCamelCase       )
        __a		 =       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
        __a		 =       f'Act:{input_q.extra_repr()}'
        __a		 =       f'Wgt:{weight_q.extra_repr()}'
        __a		 =       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   __snake_case			(    _UpperCAmelCase       ):
    __a		 =       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   __snake_case			(    _UpperCAmelCase     ,				_UpperCAmelCase     ,				_UpperCAmelCase     ,				_UpperCAmelCase     ,				_UpperCAmelCase       ):
    __a		 =       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   __snake_case			(    _UpperCAmelCase     ,				_UpperCAmelCase     ,				_UpperCAmelCase="both"     ,				**_UpperCAmelCase       ):
    __a		 =       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   __snake_case			(    _UpperCAmelCase     ,				_UpperCAmelCase     ,				**_UpperCAmelCase       ):

    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       ):
                    __a		 =       f'Warning: changing {name:{name_width}}'
                    for k, v in kwargs.items():
                        s += f' {k}={v}'
                        setattr(_lowerCamelCase     ,				_lowerCamelCase     ,				_lowerCamelCase       )
                    logger.info(_lowerCamelCase       )










from __future__ import annotations

__snake_case		:Optional[Any]   	=  []





def   __snake_case			(    _UpperCAmelCase     ,				_UpperCAmelCase     ,				_UpperCAmelCase       ):
    for i in range(len(_UpperCAmelCase       )       ):
        if board[row][i] == 1:
            return False
    for i in range(len(_UpperCAmelCase       )       ):
        if board[i][column] == 1:
            return False
    for i, j in zip(range(_UpperCAmelCase     ,				-1     ,				-1       )     ,				range(_UpperCAmelCase     ,				-1     ,				-1       )       ):
        if board[i][j] == 1:
            return False
    for i, j in zip(range(_UpperCAmelCase     ,				-1     ,				-1       )     ,				range(_UpperCAmelCase     ,				len(_UpperCAmelCase       )       )       ):
        if board[i][j] == 1:
            return False
    return True





def   __snake_case			(    _UpperCAmelCase     ,				_UpperCAmelCase       ):
    if row >= len(_UpperCAmelCase       ):
        solution.append(_UpperCAmelCase       )
        printboard(_UpperCAmelCase       )
        print()
        return True
    for i in range(len(_UpperCAmelCase       )       ):
        if is_safe(_UpperCAmelCase     ,				_UpperCAmelCase     ,				_UpperCAmelCase       ):
            __a		 =       1
            solve(_UpperCAmelCase     ,				row + 1       )
            __a		 =       0
    return False





def   __snake_case			(    _UpperCAmelCase       ):
    for i in range(len(_UpperCAmelCase       )       ):
        for j in range(len(_UpperCAmelCase       )       ):
            if board[i][j] == 1:
                print('''Q'''     ,				end=''' '''       )
            else:
                print('''.'''     ,				end=''' '''       )
        print()


# n=int(input("The no. of queens"))
__snake_case		:Optional[Any]   	=  8
__snake_case		:Tuple   	=  [[0 for i in range(n)] for j in range(n)]
solve(board, 0)
print('''The total no. of solutions are :''', len(solution))





