




"""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							:    int  							=							logging.getLogger(__name__)

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



def 					a__				(       snake_case__       )		->      List[str]:
			lowerCamelCase       =      parser.add_argument_group("""quant_trainer arguments"""       )
			group.add_argument("""--wprec""" ,				type=snake_case__ ,				default=8 ,				help="""weight precision"""       )
			group.add_argument("""--aprec""" ,				type=snake_case__ ,				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=snake_case__ ,				nargs="""+""" ,				help="""disable quantizers by keyword"""       )
			group.add_argument("""--quant-disable-layer-module""" ,				type=snake_case__ ,				help="""disable quantizers by keyword under layer."""       )
			group.add_argument("""--quant-enable-layer-module""" ,				type=snake_case__ ,				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=snake_case__ ,				type=snake_case__ ,				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=snake_case__ ,				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 					a__				(       snake_case__       )		->      Any:

			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=snake_case__       )
			lowerCamelCase       =      QuantDescriptor(num_bits=args.wprec ,				axis=(None if args.quant_per_tensor else (0,))       )
			quant_nn.QuantLinear.set_default_quant_desc_input(snake_case__       )
			quant_nn.QuantLinear.set_default_quant_desc_weight(snake_case__       )



def 					a__				(       snake_case__ ,				snake_case__ ,				snake_case__=False ,				snake_case__=False       )		->      List[str]:
			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(snake_case__ ,				["""embeddings"""] ,				which="""weight""" ,				_disabled=snake_case__       )

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

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

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

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

						if args.recalibrate_weights:
									recalibrate_weights(snake_case__       )

						if args.fuse_qkv:
									fuse_qkv(snake_case__ ,				snake_case__       )

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

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



def 					a__				(       snake_case__       )		->      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 					a__				(       snake_case__ ,				snake_case__       )		->      Tuple:
			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(snake_case__       )



def 					a__				(       snake_case__ ,				snake_case__       )		->      str:

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

						lowerCamelCase       =      max(snake_case__ ,				snake_case__ ,				snake_case__       )
						qq._amax.fill_(snake_case__       )
						qk._amax.fill_(snake_case__       )
						qv._amax.fill_(snake_case__       )
						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 					a__				(       snake_case__ ,				snake_case__       )		->      List[str]:

			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=snake_case__       )
									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 					a__				(       snake_case__       )		->      Optional[Any]:

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



def 					a__				(       snake_case__       )		->      Tuple:

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



def 					a__				(       snake_case__ ,				snake_case__=25 ,				snake_case__=1_80 ,				snake_case__=None       )		->      Optional[Any]:

			if ignore is None:
						lowerCamelCase       =      []
			elif not isinstance(snake_case__ ,				snake_case__       ):
						lowerCamelCase       =      [ignore]

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

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



def 					a__				(       snake_case__       )		->      List[Any]:
			lowerCamelCase       =      0
			for name, mod in model.named_modules():
						if isinstance(snake_case__ ,				pytorch_quantization.nn.TensorQuantizer       ):
									print(F'{name:80} {mod}'       )
									count += 1
			print(F'{count} TensorQuantizers found in model'       )



def 					a__				(       snake_case__ ,				snake_case__ ,				snake_case__ ,				snake_case__ ,				snake_case__       )		->      Optional[int]:
			lowerCamelCase       =      getattr(snake_case__ ,				snake_case__ ,				snake_case__       )
			if quantizer_mod is not None:
						assert hasattr(snake_case__ ,				snake_case__       )
						setattr(snake_case__ ,				snake_case__ ,				snake_case__       )
			else:
						logger.warning(F'{name} has no {quantizer}'       )



def 					a__				(       snake_case__ ,				snake_case__ ,				snake_case__="both" ,				**snake_case__       )		->      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(snake_case__ ,				snake_case__ ,				"""_input_quantizer""" ,				snake_case__ ,				snake_case__       )
						if which in ["weight", "both"]:
									set_quantizer(snake_case__ ,				snake_case__ ,				"""_weight_quantizer""" ,				snake_case__ ,				snake_case__       )
			logger.info(snake_case__       )



def 					a__				(       snake_case__ ,				snake_case__ ,				**snake_case__       )		->      Any:

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









"""simple docstring"""




from __future__ import annotations

lowerCAmelCase							:    str  							=							[-10, -5, 0, 5, 5.1, 11, 13, 21, 3, 4, -21, -10, -5, -1, 0]
lowerCAmelCase							:    Dict  							=							[-5, 0, 5, 5.1, 11, 13, 21, -1, 4, -1, -10, -5, -1, 0, -1]



def 					a__				(       snake_case__       )		->      list[float]:
			lowerCamelCase       =      []
			lowerCamelCase       =      len(snake_case__       )

			for i in range(snake_case__       ):
						lowerCamelCase       =      -1
						for j in range(i + 1 ,				snake_case__       ):
									if arr[i] < arr[j]:
												lowerCamelCase       =      arr[j]
												break
						result.append(snake_case__       )
			return result



def 					a__				(       snake_case__       )		->      list[float]:
			lowerCamelCase       =      []
			for i, outer in enumerate(snake_case__       ):
						lowerCamelCase       =      -1
						for inner in arr[i + 1 :]:
									if outer < inner:
												lowerCamelCase       =      inner
												break
						result.append(snake_case__       )
			return result



def 					a__				(       snake_case__       )		->      list[float]:
			lowerCamelCase       =      len(snake_case__       )
			lowerCamelCase       =      []
			lowerCamelCase       =      [-1] * arr_size

			for index in reversed(range(snake_case__       )       ):
						if stack:
									while stack[-1] <= arr[index]:
												stack.pop()
												if not stack:
															break
						if stack:
									lowerCamelCase       =      stack[-1]
						stack.append(arr[index]       )
			return result


if __name__ == "__main__":
					from doctest import testmod
					from timeit import timeit

					testmod()
					print(next_greatest_element_slow(arr))
					print(next_greatest_element_fast(arr))
					print(next_greatest_element(arr))

					lowerCAmelCase							:    Dict  							=							(
					    """from __main__ import arr, next_greatest_element_slow, """
					    """next_greatest_element_fast, next_greatest_element"""
					)
					print(
					    """next_greatest_element_slow():""",
					    timeit("""next_greatest_element_slow(arr)""", setup=setup),
					)
					print(
					    """next_greatest_element_fast():""",
					    timeit("""next_greatest_element_fast(arr)""", setup=setup),
					)
					print(
					    """     next_greatest_element():""",
					    timeit("""next_greatest_element(arr)""", setup=setup),
					)



