





from __future__ import annotations

class  __A       :


				def __init__(						self				,	UpperCAmelCase_							):
								lowerCamelCase			=TypeError(
								    """Matrices must be formed from a list of zero or more lists containing at """
								    """least one and the same number of values, each of which must be of type """
								    """int or float."""							)
								if len(_A							) != 0:
												lowerCamelCase			=len(rows[0]							)
												if cols == 0:
																raise error
												for row in rows:
																if len(_A							) != cols:
																				raise error
																for value in row:
																				if not isinstance(_A				,	(int, float)							):
																								raise error
												lowerCamelCase			=rows
								else:
												lowerCamelCase			=[]


				def _snake_case    (						self							):
								return [[row[i] for row in self.rows] for i in range(len(self.rows[0]							)							)]


				@property
				def _snake_case    (						self							):
								return len(self.rows							)


				@property
				def _snake_case    (						self							):
								return len(self.rows[0]							)


				@property
				def _snake_case    (						self							):
								return (self.num_rows, self.num_columns)


				@property
				def _snake_case    (						self							):
								return self.order[0] == self.order[1]


				def _snake_case    (						self							):
								lowerCamelCase			=[
								    [0 if column_num != row_num else 1 for column_num in range(self.num_rows							)]
								    for row_num in range(self.num_rows							)
								]
								return Matrix(_A							)


				def _snake_case    (						self							):



								if not self.is_square:
												return 0
								if self.order == (0, 0):
												return 1
								if self.order == (1, 1):
												return int(self.rows[0][0]							)
								if self.order == (2, 2):
												return int(
												    (self.rows[0][0] * self.rows[1][1])
												    - (self.rows[0][1] * self.rows[1][0])							)
								else:
												return sum(
												    self.rows[0][column] * self.cofactors().rows[0][column]
												    for column in range(self.num_columns							)							)


				def _snake_case    (						self							):
								return bool(self.determinant()							)


				def _snake_case    (						self				,	UpperCAmelCase_				,	UpperCAmelCase_							):
								lowerCamelCase			=[
								    [
								        self.rows[other_row][other_column]
								        for other_column in range(self.num_columns							)
								        if other_column != column
								    ]
								    for other_row in range(self.num_rows							)
								    if other_row != row
								]
								return Matrix(_A							).determinant()


				def _snake_case    (						self				,	UpperCAmelCase_				,	UpperCAmelCase_							):



								if (row + column) % 2 == 0:
												return self.get_minor(_A				,	_A							)
								return -1 * self.get_minor(_A				,	_A							)


				def _snake_case    (						self							):
								return Matrix(
								    [
								        [self.get_minor(_A				,	_A							) for column in range(self.num_columns							)]
								        for row in range(self.num_rows							)
								    ]							)


				def _snake_case    (						self							):
								return Matrix(
								    [
								        [
								            self.minors().rows[row][column]
								            if (row + column) % 2 == 0
								            else self.minors().rows[row][column] * -1
								            for column in range(self.minors().num_columns							)
								        ]
								        for row in range(self.minors().num_rows							)
								    ]							)


				def _snake_case    (						self							):
								lowerCamelCase			=[
								    [self.cofactors().rows[column][row] for column in range(self.num_columns							)]
								    for row in range(self.num_rows							)
								]
								return Matrix(_A							)


				def _snake_case    (						self							):
								lowerCamelCase			=self.determinant()
								if not determinant:
												raise TypeError("""Only matrices with a non-zero determinant have an inverse"""							)
								return self.adjugate() * (1 / determinant)


				def __repr__(						self							):
								return str(self.rows							)


				def __str__(						self							):



								if self.num_rows == 0:
												return "[]"
								if self.num_rows == 1:
												return "[[" + ". ".join(str(self.rows[0]							)							) + "]]"
								return (
								    "["
								    + "\n ".join(
								        [
								            """[""" + """. """.join([str(_A							) for value in row]							) + """.]"""
								            for row in self.rows
								        ]							)
								    + "]"
								)


				def _snake_case    (						self				,	UpperCAmelCase_				,	UpperCAmelCase_ = None							):
								lowerCamelCase			=TypeError("""Row must be a list containing all ints and/or floats"""							)
								if not isinstance(_A				,	_A							):
												raise type_error
								for value in row:
												if not isinstance(_A				,	(int, float)							):
																raise type_error
								if len(_A							) != self.num_columns:
												raise ValueError(
												    """Row must be equal in length to the other rows in the matrix"""							)
								if position is None:
												self.rows.append(_A							)
								else:
												lowerCamelCase			=self.rows[0:position] + [row] + self.rows[position:]


				def _snake_case    (						self				,	UpperCAmelCase_				,	UpperCAmelCase_ = None							):
								lowerCamelCase			=TypeError(
								    """Column must be a list containing all ints and/or floats"""							)
								if not isinstance(_A				,	_A							):
												raise type_error
								for value in column:
												if not isinstance(_A				,	(int, float)							):
																raise type_error
								if len(_A							) != self.num_rows:
												raise ValueError(
												    """Column must be equal in length to the other columns in the matrix"""							)
								if position is None:
												lowerCamelCase			=[self.rows[i] + [column[i]] for i in range(self.num_rows							)]
								else:
												lowerCamelCase			=[
												    self.rows[i][0:position] + [column[i]] + self.rows[i][position:]
												    for i in range(self.num_rows							)
												]


				def __eq__(						self				,	UpperCAmelCase_							):



								if not isinstance(_A				,	_A							):
												return NotImplemented
								return self.rows == other.rows


				def __ne__(						self				,	UpperCAmelCase_							):
								return not self == other


				def __neg__(						self							):
								return self * -1


				def __add__(						self				,	UpperCAmelCase_							):



								if self.order != other.order:
												raise ValueError("""Addition requires matrices of the same order"""							)
								return Matrix(
								    [
								        [self.rows[i][j] + other.rows[i][j] for j in range(self.num_columns							)]
								        for i in range(self.num_rows							)
								    ]							)


				def __sub__(						self				,	UpperCAmelCase_							):



								if self.order != other.order:
												raise ValueError("""Subtraction requires matrices of the same order"""							)
								return Matrix(
								    [
								        [self.rows[i][j] - other.rows[i][j] for j in range(self.num_columns							)]
								        for i in range(self.num_rows							)
								    ]							)


				def __mul__(						self				,	UpperCAmelCase_							):



								if isinstance(_A				,	(int, float)							):
												return Matrix(
												    [[int(element * other							) for element in row] for row in self.rows]							)
								elif isinstance(_A				,	_A							):
												if self.num_columns != other.num_rows:
																raise ValueError(
																    """The number of columns in the first matrix must """
																    """be equal to the number of rows in the second"""							)
												return Matrix(
												    [
												        [Matrix.dot_product(_A				,	_A							) for column in other.columns()]
												        for row in self.rows
												    ]							)
								else:
												raise TypeError(
												    """A Matrix can only be multiplied by an int, float, or another matrix"""							)


				def __pow__(						self				,	UpperCAmelCase_							):



								if not isinstance(_A				,	_A							):
												raise TypeError("""A Matrix can only be raised to the power of an int"""							)
								if not self.is_square:
												raise ValueError("""Only square matrices can be raised to a power"""							)
								if other == 0:
												return self.identity()
								if other < 0:
												if self.is_invertable():
																return self.inverse() ** (-other)
												raise ValueError(
												    """Only invertable matrices can be raised to a negative power"""							)
								lowerCamelCase			=self
								for _ in range(other - 1							):
												result *= self
								return result


				@classmethod
				def _snake_case    (						cls				,	UpperCAmelCase_				,	UpperCAmelCase_							):
								return sum(row[i] * column[i] for i in range(len(_A							)							)							)


if __name__ == "__main__":
				import doctest

				doctest.testmod()












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[int]   			=logging.getLogger(__name__)

UpperCAmelCase__  :     Tuple   			=50  # max width of layer names
UpperCAmelCase__  :     List[str]   			=70  # max width of quantizer names





def        _lowercase   (  _UpperCAmelCase			)  ->					List[str]:
				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        _lowercase   (  _UpperCAmelCase			)  ->					Dict:

				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        _lowercase   (  _UpperCAmelCase     , _UpperCAmelCase     , _UpperCAmelCase=False     , _UpperCAmelCase=False			)  ->					int:
				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        _lowercase   (  _UpperCAmelCase			)  ->					Optional[Any]:
				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        _lowercase   (  _UpperCAmelCase     , _UpperCAmelCase			)  ->					Optional[int]:
				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        _lowercase   (  _UpperCAmelCase     , _UpperCAmelCase			)  ->					Tuple:

				def fusea(_UpperCAmelCase     , _UpperCAmelCase     , _UpperCAmelCase			):
								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        _lowercase   (  _UpperCAmelCase     , _UpperCAmelCase			)  ->					int:

				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        _lowercase   (  _UpperCAmelCase			)  ->					Dict:

				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        _lowercase   (  _UpperCAmelCase			)  ->					List[str]:

				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        _lowercase   (  _UpperCAmelCase     , _UpperCAmelCase=25     , _UpperCAmelCase=1_80     , _UpperCAmelCase=None			)  ->					Dict:

				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        _lowercase   (  _UpperCAmelCase			)  ->					Dict:
				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        _lowercase   (  _UpperCAmelCase     , _UpperCAmelCase     , _UpperCAmelCase     , _UpperCAmelCase     , _UpperCAmelCase			)  ->					Union[str, Any]:
				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        _lowercase   (  _UpperCAmelCase     , _UpperCAmelCase     , _UpperCAmelCase="both"     , **_UpperCAmelCase			)  ->					List[str]:
				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        _lowercase   (  _UpperCAmelCase     , _UpperCAmelCase     , **_UpperCAmelCase			)  ->					int:

				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			)





