



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__     :   List[Any]     = logging.getLogger(__name__)

UpperCAmelCase__     :   Optional[Any]     = 50  # max width of layer names
UpperCAmelCase__     :   int     = 70  # max width of quantizer names





def 							A			(  _UpperCAmelCase       :     Tuple					)       ->				Union[str, Any]:







			'''simple docstring'''
			_UpperCAmelCase	 =     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 							A			(  _UpperCAmelCase       :     Tuple					)       ->				Optional[Any]:







			'''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=_UpperCAmelCase					)
			_UpperCAmelCase	 =     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 							A			(  _UpperCAmelCase       :     str							,     _UpperCAmelCase       :     List[Any]							,     _UpperCAmelCase       :     Optional[int]=False							,     _UpperCAmelCase       :     List[Any]=False					)       ->				Optional[int]:







			'''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(_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 							A			(  _UpperCAmelCase       :     int					)       ->				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 							A			(  _UpperCAmelCase       :     int							,     _UpperCAmelCase       :     Dict					)       ->				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(_UpperCAmelCase					)





def 							A			(  _UpperCAmelCase       :     Any							,     _UpperCAmelCase       :     Any					)       ->				List[Any]:







			'''simple docstring'''

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

						_UpperCAmelCase	 =     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 							A			(  _UpperCAmelCase       :     int							,     _UpperCAmelCase       :     Union[str, Any]					)       ->				str:







			'''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=_UpperCAmelCase					)
									_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 							A			(  _UpperCAmelCase       :     str					)       ->				Union[str, Any]:







			'''simple docstring'''

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





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







			'''simple docstring'''

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





def 							A			(  _UpperCAmelCase       :     int							,     _UpperCAmelCase       :     Union[str, Any]=25							,     _UpperCAmelCase       :     int=180							,     _UpperCAmelCase       :     int=None					)       ->				Tuple:







			'''simple docstring'''

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

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

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





def 							A			(  _UpperCAmelCase       :     Optional[Any]					)       ->				Any:







			'''simple docstring'''
			_UpperCAmelCase	 =     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 							A			(  _UpperCAmelCase       :     Dict							,     _UpperCAmelCase       :     List[Any]							,     _UpperCAmelCase       :     Optional[Any]							,     _UpperCAmelCase       :     Any							,     _UpperCAmelCase       :     str					)       ->				List[str]:







			'''simple docstring'''
			_UpperCAmelCase	 =     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 							A			(  _UpperCAmelCase       :     Dict							,     _UpperCAmelCase       :     Optional[int]							,     _UpperCAmelCase       :     Optional[Any]="both"							,     **_UpperCAmelCase       :     Union[str, Any]					)       ->				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(_UpperCAmelCase							,     _UpperCAmelCase							,     '_input_quantizer'							,     _UpperCAmelCase							,     _UpperCAmelCase					)
						if which in ["weight", "both"]:
									set_quantizer(_UpperCAmelCase							,     _UpperCAmelCase							,     '_weight_quantizer'							,     _UpperCAmelCase							,     _UpperCAmelCase					)
			logger.info(_UpperCAmelCase					)





def 							A			(  _UpperCAmelCase       :     str							,     _UpperCAmelCase       :     int							,     **_UpperCAmelCase       :     Optional[Any]					)       ->				Optional[int]:







			'''simple docstring'''

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





from sympy import diff, lambdify, symbols
from sympy.functions import *  # noqa: F403





def 							A			(  _UpperCAmelCase       :     str							,     _UpperCAmelCase       :     complex							,     _UpperCAmelCase       :     str = "x"							,     _UpperCAmelCase       :     float = 10**-10							,     _UpperCAmelCase       :     int = 1							,     )       ->				complex:







			'''simple docstring'''
			_UpperCAmelCase	 =     symbols(_UpperCAmelCase					)
			_UpperCAmelCase	 =     lambdify(_UpperCAmelCase							,     _UpperCAmelCase					)
			_UpperCAmelCase	 =     lambdify(_UpperCAmelCase							,     diff(_UpperCAmelCase							,     _UpperCAmelCase					)					)

			_UpperCAmelCase	 =     starting_point

			while True:
						if diff_function(_UpperCAmelCase					) != 0:
									_UpperCAmelCase	 =     prev_guess - multiplicity * func(_UpperCAmelCase					) / diff_function(
									    _UpperCAmelCase					)
						else:
									raise ZeroDivisionError('Could not find root'					) from None

						# Precision is checked by comparing the difference of consecutive guesses
						if abs(next_guess - prev_guess					) < precision:
									return next_guess

						_UpperCAmelCase	 =     next_guess


# Let's Execute
if __name__ == "__main__":
							# Find root of trigonometric function
							# Find value of pi
							print(f"""The root of sin(x) = 0 is {newton_raphson("sin(x)", 2)}""")

							# Find root of polynomial
							# Find fourth Root of 5
							print(f"""The root of x**4 - 5 = 0 is {newton_raphson("x**4 -5", 0.4 +5J)}""")

							# Find value of e
							print(
							    "The root of log(y) - 1 = 0 is ",
							    f"""{newton_raphson("log(y) - 1", 2, variable="y")}""",
							)

							# Exponential Roots
							print(
							    "The root of exp(x) - 1 = 0 is",
							    f"""{newton_raphson("exp(x) - 1", 10, precision=0.005)}""",
							)

							# Find root of cos(x)
							print(f"""The root of cos(x) = 0 is {newton_raphson("cos(x)", 0)}""")
