
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


SCREAMING_SNAKE_CASE							:Optional[int]										=    logging.getLogger(__name__)

SCREAMING_SNAKE_CASE							:Union[str, Any]										=    50  # max width of layer names
SCREAMING_SNAKE_CASE							:Optional[Any]										=    70  # max width of quantizer names


def 						UpperCAmelCase      (     a_						)		->     Optional[int]:







				"""simple docstring"""



				__A             =			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 						UpperCAmelCase      (     a_						)		->     Tuple:







				"""simple docstring"""



				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=a_						)
				__A             =			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 						UpperCAmelCase      (     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 						UpperCAmelCase      (     a_						)		->     Optional[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 						UpperCAmelCase      (     a_		,       a_						)		->     List[str]:







				"""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 						UpperCAmelCase      (     a_		,       a_						)		->     List[Any]:







				"""simple docstring"""



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

								__A             =			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 						UpperCAmelCase      (     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"						):
												__A             =			mod._input_quantizer._amax.data.detach().item()
												mod._input_quantizer._amax.data.detach().clamp_(max=a_						)
												__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 						UpperCAmelCase      (     a_						)		->     Dict:







				"""simple docstring"""



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


def 						UpperCAmelCase      (     a_						)		->     int:







				"""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)
												__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=a_		,       keepdims=a_						).detach()
												logger.info(F'''RECALIB: {name:{name_width}} {mod._weight_quantizer._amax.flatten()} -> {amax.flatten()}'''						)
												__A             =			amax


def 						UpperCAmelCase      (     a_		,       a_=2_5		,       a_=1_8_0		,       a_=None						)		->     List[Any]:







				"""simple docstring"""



				if ignore is None:
								__A             =			[]
				elif not isinstance(a_		,       a_						):
								__A             =			[ignore]

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

				for name, mod in model.named_modules():
								__A             =			getattr(a_		,       "_input_quantizer"		,       a_						)
								__A             =			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
								__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(a_						) <= line_width:
												logger.info(a_						)
								else:
												logger.info(F'''{name:{name_width}} {act_str}'''						)
												logger.info(F'''{'  ':{name_width}} {wgt_str}'''						)


def 						UpperCAmelCase      (     a_						)		->     Optional[int]:







				"""simple docstring"""



				__A             =			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 						UpperCAmelCase      (     a_		,       a_		,       a_		,       a_		,       a_						)		->     Optional[int]:







				"""simple docstring"""



				__A             =			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 						UpperCAmelCase      (     a_		,       a_		,       a_="both"		,       **a_						)		->     Dict:







				"""simple docstring"""



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


def 						UpperCAmelCase      (     a_		,       a_		,       **a_						)		->     Optional[Any]:







				"""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_						):
																				__A             =			F'''Warning: changing {name:{name_width}}'''
																				for k, v in kwargs.items():
																								s += F''' {k}={v}'''
																								setattr(a_		,       a_		,       a_						)
																				logger.info(a_						)


def 						UpperCAmelCase      (     )		->     list[list[int]]:







				"""simple docstring"""



				return [list(range(1_0_0_0 - i		,       -1_0_0_0 - i		,       -1						)						) for i in range(1_0_0_0						)]


SCREAMING_SNAKE_CASE							:List[str]										=    generate_large_matrix()
SCREAMING_SNAKE_CASE							:str										=    (
    [[4, 3, 2, -1], [3, 2, 1, -1], [1, 1, -1, -2], [-1, -1, -2, -3]],
    [[3, 2], [1, 0]],
    [[7, 7, 6]],
    [[7, 7, 6], [-1, -2, -3]],
    grid,
)


def 						UpperCAmelCase      (     a_						)		->     None:







				"""simple docstring"""



				assert all(row == sorted(a_		,       reverse=a_						) for row in grid						)
				assert all(list(a_						) == sorted(a_		,       reverse=a_						) for col in zip(*a_						)						)


def 						UpperCAmelCase      (     a_						)		->     int:







				"""simple docstring"""



				__A             =			0
				__A             =			len(a_						) - 1

				# Edge cases such as no values or all numbers are negative.
				if not array or array[0] < 0:
								return 0

				while right + 1 > left:
								__A             =			(left + right) // 2
								__A             =			array[mid]

								# Num must be negative and the index must be greater than or equal to 0.
								if num < 0 and array[mid - 1] >= 0:
												return mid

								if num >= 0:
												__A             =			mid + 1
								else:
												__A             =			mid - 1
    # No negative numbers so return the last index of the array + 1 which is the length.
				return len(a_						)


def 						UpperCAmelCase      (     a_						)		->     int:







				"""simple docstring"""



				__A             =			0
				__A             =			len(grid[0]						)

				for i in range(len(a_						)						):
								__A             =			find_negative_index(grid[i][:bound]						)
								total += bound
				return (len(a_						) * len(grid[0]						)) - total


def 						UpperCAmelCase      (     a_						)		->     int:







				"""simple docstring"""



				return len([number for row in grid for number in row if number < 0]						)


def 						UpperCAmelCase      (     a_						)		->     int:







				"""simple docstring"""



				__A             =			0
				for row in grid:
								for i, number in enumerate(a_						):
												if number < 0:
																total += len(a_						) - i
																break
				return total


def 						UpperCAmelCase      (     )		->     None:







				"""simple docstring"""



				from timeit import timeit

				print("Running benchmarks"						)
				__A             =			(
				    "from __main__ import count_negatives_binary_search, "
				    "count_negatives_brute_force, count_negatives_brute_force_with_break, grid"
				)
				for func in (
				    "count_negatives_binary_search",  # took 0.7727 seconds
				    "count_negatives_brute_force_with_break",  # took 4.6505 seconds
				    "count_negatives_brute_force",  # took 12.8160 seconds
				):
								__A             =			timeit(F'''{func}(grid=grid)'''		,       setup=a_		,       number=5_0_0						)
								print(F'''{func}() took {time:0.4f} seconds'''						)


if __name__ == "__main__":
					import doctest

					doctest.testmod()
					benchmark()
