

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


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

A_	:			List[Any]  	=      50  # max width of layer names
A_	:			Dict  	=      70  # max width of quantizer names

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






 if args.calibrator == "max":
  A__							:   Dict					       =		"max"
 elif args.calibrator == "percentile":
  if args.percentile is None:
   raise ValueError("""Specify --percentile when using percentile calibrator""" )
  A__							:   List[str]					       =		"histogram"
 elif args.calibrator == "mse":
  A__							:   List[str]					       =		"histogram"
 else:
  raise ValueError(f"""Invalid calibrator {args.calibrator}""" )

 A__							:   Optional[int]					       =		QuantDescriptor(num_bits=args.aprec   ,				calib_method=_lowercase )
 A__							:   Optional[int]					       =		QuantDescriptor(num_bits=args.wprec   ,				axis=(None if args.quant_per_tensor else (0,)) )
 quant_nn.QuantLinear.set_default_quant_desc_input(_lowercase )
 quant_nn.QuantLinear.set_default_quant_desc_weight(_lowercase )

def 						UpperCamelCase (lowercase_:			Optional[int]   ,				lowercase_:			List[Any]   ,				lowercase_:			Tuple=False   ,				lowercase_:			Tuple=False )					->				Optional[int]:
 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(_lowercase   ,				["""embeddings"""]   ,				which="""weight"""   ,				_disabled=_lowercase )

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

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

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

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

  if args.recalibrate_weights:
   recalibrate_weights(_lowercase )

  if args.fuse_qkv:
   fuse_qkv(_lowercase   ,				_lowercase )

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

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

def 						UpperCamelCase (lowercase_:			Optional[Any] )					->				Optional[int]:
 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 (lowercase_:			Any   ,				lowercase_:			int )					->				Dict:
 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(_lowercase )

def 						UpperCamelCase (lowercase_:			Any   ,				lowercase_:			Optional[Any] )					->				Union[str, Any]:






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

  A__							:   Any					       =		max(_lowercase   ,				_lowercase   ,				_lowercase )
  qq._amax.fill_(_lowercase )
  qk._amax.fill_(_lowercase )
  qv._amax.fill_(_lowercase )
  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 (lowercase_:			int   ,				lowercase_:			Union[str, Any] )					->				Union[str, Any]:






 for name, mod in model.named_modules():
  if name.endswith(""".output.dense""" ) and not name.endswith("""attention.output.dense""" ):
   A__							:   Dict					       =		mod._input_quantizer._amax.data.detach().item()
   mod._input_quantizer._amax.data.detach().clamp_(max=_lowercase )
   A__							:   List[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 						UpperCamelCase (lowercase_:			str )					->				Optional[int]:






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

def 						UpperCamelCase (lowercase_:			Union[str, Any] )					->				Tuple:






 for name, mod in model.named_modules():
  if hasattr(_lowercase   ,				"""_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__							:   Optional[Any]					       =		set() if mod._weight_quantizer.axis is None else set(mod._weight_quantizer.axis )
   A__							:   Union[str, Any]					       =		set(range(len(mod.weight.size() ) ) ) - axis_set
   A__							:   str					       =		pytorch_quantization.utils.reduce_amax(mod.weight   ,				axis=_lowercase   ,				keepdims=_lowercase ).detach()
   logger.info(f"""RECALIB: {name:{name_width}} {mod._weight_quantizer._amax.flatten()} -> {amax.flatten()}""" )
   A__							:   Dict					       =		amax

def 						UpperCamelCase (lowercase_:			Dict   ,				lowercase_:			Tuple=25   ,				lowercase_:			List[str]=180   ,				lowercase_:			List[str]=None )					->				Optional[int]:






 if ignore is None:
  A__							:   Optional[Any]					       =		[]
 elif not isinstance(_lowercase   ,				_lowercase ):
  A__							:   int					       =		[ignore]

 A__							:   int					       =		0
 for name, mod in model.named_modules():
  if not hasattr(_lowercase   ,				"""weight""" ):
   continue
  A__							:   Optional[Any]					       =		max(_lowercase   ,				len(_lowercase ) )

 for name, mod in model.named_modules():
  A__							:   Any					       =		getattr(_lowercase   ,				"""_input_quantizer"""   ,				_lowercase )
  A__							:   Union[str, Any]					       =		getattr(_lowercase   ,				"""_weight_quantizer"""   ,				_lowercase )
  if not hasattr(_lowercase   ,				"""weight""" ):
   continue
  if type(_lowercase ) in ignore:
   continue
  if [True for s in ignore if type(_lowercase ) is str and s in name]:
   continue
  A__							:   List[str]					       =		f"""Act:{input_q.extra_repr()}"""
  A__							:   Optional[int]					       =		f"""Wgt:{weight_q.extra_repr()}"""
  A__							:   Tuple					       =		f"""{name:{name_width}} {act_str} {wgt_str}"""
  if len(_lowercase ) <= line_width:
   logger.info(_lowercase )
  else:
   logger.info(f"""{name:{name_width}} {act_str}""" )
   logger.info(f"""{'  ':{name_width}} {wgt_str}""" )

def 						UpperCamelCase (lowercase_:			Optional[int] )					->				List[Any]:
 A__							:   int					       =		0
 for name, mod in model.named_modules():
  if isinstance(_lowercase   ,				pytorch_quantization.nn.TensorQuantizer ):
   print(f"""{name:80} {mod}""" )
   count += 1
 print(f"""{count} TensorQuantizers found in model""" )

def 						UpperCamelCase (lowercase_:			Tuple   ,				lowercase_:			Dict   ,				lowercase_:			List[str]   ,				lowercase_:			List[Any]   ,				lowercase_:			Dict )					->				Dict:
 A__							:   Optional[int]					       =		getattr(_lowercase   ,				_lowercase   ,				_lowercase )
 if quantizer_mod is not None:
  assert hasattr(_lowercase   ,				_lowercase )
  setattr(_lowercase   ,				_lowercase   ,				_lowercase )
 else:
  logger.warning(f"""{name} has no {quantizer}""" )

def 						UpperCamelCase (lowercase_:			Optional[Any]   ,				lowercase_:			Tuple   ,				lowercase_:			Tuple="both"   ,				**lowercase_:			Tuple )					->				Optional[Any]:
 A__							:   int					       =		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(_lowercase   ,				_lowercase   ,				"""_input_quantizer"""   ,				_lowercase   ,				_lowercase )
  if which in ["weight", "both"]:
   set_quantizer(_lowercase   ,				_lowercase   ,				"""_weight_quantizer"""   ,				_lowercase   ,				_lowercase )
 logger.info(_lowercase )


def 						UpperCamelCase (lowercase_:			Optional[int]   ,				lowercase_:			List[str]   ,				**lowercase_:			Tuple )					->				Tuple:






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






from typing import TYPE_CHECKING

from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable


A_	:			List[Any]  	=      {'configuration_gpt_neox': ['GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GPTNeoXConfig']}

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	A_	:			Optional[Any]  	=      ['GPTNeoXTokenizerFast']

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	A_	:			Optional[int]  	=      [
	    'GPT_NEOX_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'GPTNeoXForCausalLM',
	    'GPTNeoXForQuestionAnswering',
	    'GPTNeoXForSequenceClassification',
	    'GPTNeoXForTokenClassification',
	    'GPTNeoXLayer',
	    'GPTNeoXModel',
	    'GPTNeoXPreTrainedModel',
	]


if TYPE_CHECKING:
	from .configuration_gpt_neox import GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoXConfig

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_gpt_neox_fast import GPTNeoXTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_gpt_neox import (
		    GPT_NEOX_PRETRAINED_MODEL_ARCHIVE_LIST,
		    GPTNeoXForCausalLM,
		    GPTNeoXForQuestionAnswering,
		    GPTNeoXForSequenceClassification,
		    GPTNeoXForTokenClassification,
		    GPTNeoXLayer,
		    GPTNeoXModel,
		    GPTNeoXPreTrainedModel,
		)


else:
	import sys

	A_	:			Any  	=      _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



