
"""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


_SCREAMING_SNAKE_CASE	       =      logging.getLogger(__name__)

_SCREAMING_SNAKE_CASE	       =      50  # max width of layer names
_SCREAMING_SNAKE_CASE	       =      70  # max width of quantizer names
def   __UpperCamelCase   (					SCREAMING_SNAKE_CASE )  ->     Tuple:







							"""simple docstring"""


							__snake_case     			=       parser.add_argument_group("quant_trainer arguments" )
							group.add_argument("--wprec"							,    type=SCREAMING_SNAKE_CASE							,    default=8							,    help="weight precision" )
							group.add_argument("--aprec"							,    type=SCREAMING_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=SCREAMING_SNAKE_CASE							,    nargs="+"							,    help="disable quantizers by keyword" )
							group.add_argument("--quant-disable-layer-module"							,    type=SCREAMING_SNAKE_CASE							,    help="disable quantizers by keyword under layer." )
							group.add_argument("--quant-enable-layer-module"							,    type=SCREAMING_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=SCREAMING_SNAKE_CASE							,    type=SCREAMING_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=SCREAMING_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   __UpperCamelCase   (					SCREAMING_SNAKE_CASE )  ->     Dict:







							"""simple docstring"""


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

							__snake_case     			=       QuantDescriptor(num_bits=args.aprec							,    calib_method=SCREAMING_SNAKE_CASE )
							__snake_case     			=       QuantDescriptor(num_bits=args.wprec							,    axis=(None if args.quant_per_tensor else (0,)) )
							quant_nn.QuantLinear.set_default_quant_desc_input(SCREAMING_SNAKE_CASE )
							quant_nn.QuantLinear.set_default_quant_desc_weight(SCREAMING_SNAKE_CASE )
def   __UpperCamelCase   (					SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE=False							,    SCREAMING_SNAKE_CASE=False )  ->     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(SCREAMING_SNAKE_CASE							,    ["embeddings"]							,    which="weight"							,    _disabled=SCREAMING_SNAKE_CASE )

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

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

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

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

														if args.recalibrate_weights:
																					recalibrate_weights(SCREAMING_SNAKE_CASE )

														if args.fuse_qkv:
																					fuse_qkv(SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE )

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

							# if args.local_rank in [-1, 0] and not calib:
							print_quant_summary(SCREAMING_SNAKE_CASE )
def   __UpperCamelCase   (					SCREAMING_SNAKE_CASE )  ->     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   (					SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE )  ->     Optional[Any]:







							"""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(SCREAMING_SNAKE_CASE )
def   __UpperCamelCase   (					SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE )  ->     int:







							"""simple docstring"""


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

														__snake_case     			=       max(SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE )
														qq._amax.fill_(SCREAMING_SNAKE_CASE )
														qk._amax.fill_(SCREAMING_SNAKE_CASE )
														qv._amax.fill_(SCREAMING_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   __UpperCamelCase   (					SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE )  ->     str:







							"""simple docstring"""


							for name, mod in model.named_modules():
														if name.endswith(".output.dense" ) and not name.endswith("attention.output.dense" ):
																					__snake_case     			=       mod._input_quantizer._amax.data.detach().item()
																					mod._input_quantizer._amax.data.detach().clamp_(max=SCREAMING_SNAKE_CASE )
																					__snake_case     			=       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   (					SCREAMING_SNAKE_CASE )  ->     Optional[int]:







							"""simple docstring"""


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







							"""simple docstring"""


							for name, mod in model.named_modules():
														if hasattr(SCREAMING_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)
																					__snake_case     			=       set() if mod._weight_quantizer.axis is None else set(mod._weight_quantizer.axis )
																					__snake_case     			=       set(range(len(mod.weight.size() ) ) ) - axis_set
																					__snake_case     			=       pytorch_quantization.utils.reduce_amax(mod.weight							,    axis=SCREAMING_SNAKE_CASE							,    keepdims=SCREAMING_SNAKE_CASE ).detach()
																					logger.info(F'''RECALIB: {name:{name_width}} {mod._weight_quantizer._amax.flatten()} -> {amax.flatten()}''' )
																					__snake_case     			=       amax
def   __UpperCamelCase   (					SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE=25							,    SCREAMING_SNAKE_CASE=1_80							,    SCREAMING_SNAKE_CASE=None )  ->     List[Any]:







							"""simple docstring"""


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

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

							for name, mod in model.named_modules():
														__snake_case     			=       getattr(SCREAMING_SNAKE_CASE							,    "_input_quantizer"							,    SCREAMING_SNAKE_CASE )
														__snake_case     			=       getattr(SCREAMING_SNAKE_CASE							,    "_weight_quantizer"							,    SCREAMING_SNAKE_CASE )
														if not hasattr(SCREAMING_SNAKE_CASE							,    "weight" ):
																					continue
														if type(SCREAMING_SNAKE_CASE ) in ignore:
																					continue
														if [True for s in ignore if type(SCREAMING_SNAKE_CASE ) is str and s in name]:
																					continue
														__snake_case     			=       F'''Act:{input_q.extra_repr()}'''
														__snake_case     			=       F'''Wgt:{weight_q.extra_repr()}'''
														__snake_case     			=       F'''{name:{name_width}} {act_str} {wgt_str}'''
														if len(SCREAMING_SNAKE_CASE ) <= line_width:
																					logger.info(SCREAMING_SNAKE_CASE )
														else:
																					logger.info(F'''{name:{name_width}} {act_str}''' )
																					logger.info(F'''{"  ":{name_width}} {wgt_str}''' )
def   __UpperCamelCase   (					SCREAMING_SNAKE_CASE )  ->     int:







							"""simple docstring"""


							__snake_case     			=       0
							for name, mod in model.named_modules():
														if isinstance(SCREAMING_SNAKE_CASE							,    pytorch_quantization.nn.TensorQuantizer ):
																					print(F'''{name:80} {mod}''' )
																					count += 1
							print(F'''{count} TensorQuantizers found in model''' )
def   __UpperCamelCase   (					SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE )  ->     int:







							"""simple docstring"""


							__snake_case     			=       getattr(SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE )
							if quantizer_mod is not None:
														assert hasattr(SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE )
														setattr(SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE )
							else:
														logger.warning(F'''{name} has no {quantizer}''' )
def   __UpperCamelCase   (					SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE="both"							,    **SCREAMING_SNAKE_CASE )  ->     int:







							"""simple docstring"""


							__snake_case     			=       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(SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE							,    "_input_quantizer"							,    SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE )
														if which in ["weight", "both"]:
																					set_quantizer(SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE							,    "_weight_quantizer"							,    SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE )
							logger.info(SCREAMING_SNAKE_CASE )



def   __UpperCamelCase   (					SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE							,    **SCREAMING_SNAKE_CASE )  ->     Optional[Any]:







							"""simple docstring"""


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


"""simple docstring"""







def   __UpperCamelCase   (					SCREAMING_SNAKE_CASE )  ->     list:







							"""simple docstring"""


							__snake_case     			=       len(SCREAMING_SNAKE_CASE )
							for i in range(1							,    SCREAMING_SNAKE_CASE ):
														__snake_case     			=       collection[i]
														__snake_case     			=       0
														__snake_case     			=       i - 1

														while low <= high:
																					__snake_case     			=       (low + high) // 2
																					if val < collection[mid]:
																												__snake_case     			=       mid - 1
																					else:
																												__snake_case     			=       mid + 1
														for j in range(SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE							,    -1 ):
																					__snake_case     			=       collection[j - 1]
														__snake_case     			=       val
							return collection


if __name__ == "__main__":
		_SCREAMING_SNAKE_CASE	       =      input("""Enter numbers separated by a comma:\n""").strip()
		_SCREAMING_SNAKE_CASE	       =      [int(item) for item in user_input.split(""",""")]
		print(binary_insertion_sort(unsorted))
