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

snake_case :		Tuple							      =  50  # max width of layer names
snake_case :		Tuple							      =  70  # max width of quantizer names






def   lowerCAmelCase_						(					_snake_case      :  Optional[Any]   )				->		str:







	'''simple docstring'''


	__magic_name__		:		str	   =     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   lowerCAmelCase_						(					_snake_case      :  Optional[Any]   )				->		Any:







	'''simple docstring'''


	if args.calibrator == "max":
		__magic_name__		:		Optional[int]	   =     "max"
	elif args.calibrator == "percentile":
		if args.percentile is None:
			raise ValueError("Specify --percentile when using percentile calibrator"   )
		__magic_name__		:		Optional[int]	   =     "histogram"
	elif args.calibrator == "mse":
		__magic_name__		:		Any	   =     "histogram"
	else:
		raise ValueError(F'''Invalid calibrator {args.calibrator}'''   )

	__magic_name__		:		int	   =     QuantDescriptor(num_bits=args.aprec      ,   calib_method=A__   )
	__magic_name__		:		str	   =     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   lowerCAmelCase_						(					_snake_case      :  Any      ,   _snake_case      :  int      ,   _snake_case      :  int=False      ,   _snake_case      :  Optional[int]=False   )				->		List[str]:







	'''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   lowerCAmelCase_						(					_snake_case      :  Tuple   )				->		List[Any]:







	'''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   lowerCAmelCase_						(					_snake_case      :  Any      ,   _snake_case      :  Union[str, Any]   )				->		int:







	'''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   lowerCAmelCase_						(					_snake_case      :  List[Any]      ,   _snake_case      :  Union[str, Any]   )				->		Any:







	'''simple docstring'''


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

		__magic_name__		:		Tuple	   =     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   lowerCAmelCase_						(					_snake_case      :  Optional[Any]      ,   _snake_case      :  Optional[int]   )				->		Any:







	'''simple docstring'''


	for name, mod in model.named_modules():
		if name.endswith(".output.dense"   ) and not name.endswith("attention.output.dense"   ):
			__magic_name__		:		Optional[int]	   =     mod._input_quantizer._amax.data.detach().item()
			mod._input_quantizer._amax.data.detach().clamp_(max=A__   )
			__magic_name__		:		Optional[Any]	   =     mod._input_quantizer._amax.data.detach().item()
			logger.info(F'''CLIP_GELU: {name:{name_width}} amax: {amax_init:5.2f} -> {amax:5.2f}'''   )






def   lowerCAmelCase_						(					_snake_case      :  int   )				->		int:







	'''simple docstring'''


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






def   lowerCAmelCase_						(					_snake_case      :  Union[str, Any]   )				->		Optional[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)
			__magic_name__		:		Optional[Any]	   =     set() if mod._weight_quantizer.axis is None else set(mod._weight_quantizer.axis   )
			__magic_name__		:		Optional[int]	   =     set(range(len(mod.weight.size()   )   )   ) - axis_set
			__magic_name__		:		Dict	   =     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()}'''   )
			__magic_name__		:		Dict	   =     amax






def   lowerCAmelCase_						(					_snake_case      :  List[Any]      ,   _snake_case      :  Optional[Any]=25      ,   _snake_case      :  Dict=180      ,   _snake_case      :  int=None   )				->		int:







	'''simple docstring'''


	if ignore is None:
		__magic_name__		:		Union[str, Any]	   =     []
	elif not isinstance(A__      ,   A__   ):
		__magic_name__		:		Any	   =     [ignore]

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

	for name, mod in model.named_modules():
		__magic_name__		:		str	   =     getattr(A__      ,   "_input_quantizer"      ,   A__   )
		__magic_name__		:		str	   =     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
		__magic_name__		:		Optional[Any]	   =     F'''Act:{input_q.extra_repr()}'''
		__magic_name__		:		Optional[int]	   =     F'''Wgt:{weight_q.extra_repr()}'''
		__magic_name__		:		int	   =     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   lowerCAmelCase_						(					_snake_case      :  List[str]   )				->		Union[str, Any]:







	'''simple docstring'''


	__magic_name__		:		Optional[Any]	   =     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   lowerCAmelCase_						(					_snake_case      :  Optional[int]      ,   _snake_case      :  Any      ,   _snake_case      :  Optional[int]      ,   _snake_case      :  Tuple      ,   _snake_case      :  Optional[int]   )				->		List[Any]:







	'''simple docstring'''


	__magic_name__		:		Optional[int]	   =     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   lowerCAmelCase_						(					_snake_case      :  Optional[Any]      ,   _snake_case      :  Tuple      ,   _snake_case      :  Tuple="both"      ,   **_snake_case      :  Any   )				->		List[str]:







	'''simple docstring'''


	__magic_name__		:		Tuple	   =     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   lowerCAmelCase_						(					_snake_case      :  Any      ,   _snake_case      :  List[Any]      ,   **_snake_case      :  List[Any]   )				->		List[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__   ):
					__magic_name__		:		int	   =     F'''Warning: changing {name:{name_width}}'''
					for k, v in kwargs.items():
						s += F''' {k}={v}'''
						setattr(A__      ,   A__      ,   A__   )
					logger.info(A__   )



'''simple docstring'''






import colorsys

from PIL import Image  # type: ignore


def 		_A     (    A__     ,				A__     ,				A__      ):






 """simple docstring"""



 __lowercase							  = x
 __lowercase							  = y
 for step in range(A__      ):  # noqa: B007
  __lowercase							  = a * a - b * b + x
  __lowercase							  = 2 * a * b + y
  __lowercase							  = a_new

  # divergence happens for all complex number with an absolute value
  # greater than 4
  if a * a + b * b > 4:
   break
 return step / (max_step - 1)


def 		_A     (    A__      ):






 """simple docstring"""



 if distance == 1:
  return (0, 0, 0)
 else:
  return (255, 255, 255)


def 		_A     (    A__      ):






 """simple docstring"""



 if distance == 1:
  return (0, 0, 0)
 else:
  return tuple(round(i * 255      ) for i in colorsys.hsv_to_rgb(A__     ,				1     ,				1      )      )


def 		_A     (    A__ = 800     ,				A__ = 600     ,				A__ = -0.6     ,				A__ = 0     ,				A__ = 3.2     ,				A__ = 50     ,				A__ = True     ,				):






 """simple docstring"""



 __lowercase							  = Image.new('''RGB'''     ,				(image_width, image_height)      )
 __lowercase							  = img.load()

 # loop through the image-coordinates
 for image_x in range(A__      ):
  for image_y in range(A__      ):
   # determine the figure-coordinates based on the image-coordinates
   __lowercase							  = figure_width / image_width * image_height
   __lowercase							  = figure_center_x + (image_x / image_width - 0.5) * figure_width
   __lowercase							  = figure_center_y + (image_y / image_height - 0.5) * figure_height

   __lowercase							  = get_distance(A__     ,				A__     ,				A__      )

   # color the corresponding pixel based on the selected coloring-function
   if use_distance_color_coding:
    __lowercase							  = get_color_coded_rgb(A__      )
   else:
    __lowercase							  = get_black_and_white_rgb(A__      )

 return img


if __name__ == "__main__":
 import doctest

 doctest.testmod()

 # colored version, full figure
 lowerCAmelCase__											=	get_image()

 # uncomment for colored version, different section, zoomed in
 # img = get_image(figure_center_x = -0.6, figure_center_y = -0.4,
 # figure_width = 0.8)

 # uncomment for black and white version, full figure
 # img = get_image(use_distance_color_coding = False)

 # uncomment to save the image
 # img.save("mandelbrot.png")

 img.show()

