






"""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


_UpperCamelCase					      =  logging.getLogger(__name__)

_UpperCamelCase					      =  50  # max width of layer names
_UpperCamelCase					      =  70  # max width of quantizer names

def _a			(		_snake_case							):







      """simple docstring"""






      UpperCAmelCase          =	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							):







      """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=_snake_case							)
      UpperCAmelCase          =	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							):







      """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(_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							):







      """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			(		_snake_case				,	_snake_case							):







      """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(_snake_case							)

def _a			(		_snake_case				,	_snake_case							):







      """simple docstring"""






      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
            UpperCAmelCase          =	qq._amax.detach().item()
            UpperCAmelCase          =	qk._amax.detach().item()
            UpperCAmelCase          =	qv._amax.detach().item()

            UpperCAmelCase          =	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							):







      """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=_snake_case							)
                  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			(		_snake_case							):







      """simple docstring"""






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

def _a			(		_snake_case							):







      """simple docstring"""






      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)
                  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=_snake_case				,	keepdims=_snake_case							).detach()
                  logger.info(F'''RECALIB: {name:{name_width}} {mod._weight_quantizer._amax.flatten()} -> {amax.flatten()}'''							)
                  UpperCAmelCase          =	amax

def _a			(		_snake_case				,	_snake_case=25				,	_snake_case=180				,	_snake_case=None							):







      """simple docstring"""






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

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

      for name, mod in model.named_modules():
            UpperCAmelCase          =	getattr(_snake_case				,	"""_input_quantizer"""				,	_snake_case							)
            UpperCAmelCase          =	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
            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(_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							):







      """simple docstring"""






      UpperCAmelCase          =	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							):







      """simple docstring"""






      UpperCAmelCase          =	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							):







      """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(_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							):







      """simple docstring"""






      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							):
                              UpperCAmelCase          =	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

import json

import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent

_UpperCamelCase					      =  {"""UserAgent""": UserAgent().random}

def _a			(		_snake_case							):







      """simple docstring"""






      UpperCAmelCase          =	script.contents[0]
      UpperCAmelCase          =	json.loads(data[data.find("""{\"config\""""							) : -1]							)
      return info["entry_data"]["ProfilePage"][0]["graphql"]["user"]







class  lowerCamelCase__ :






    def __init__(			self     ,A ):
          UpperCAmelCase          =	F'''https://www.instagram.com/{username}/'''
          UpperCAmelCase          =	self.get_json()






    def     _UpperCamelCase							(			self ):
          UpperCAmelCase          =	requests.get(self.url     ,headers=A ).text
          UpperCAmelCase          =	BeautifulSoup(A     ,"""html.parser""" ).find_all("""script""" )
          try:
                return extract_user_profile(scripts[4] )
          except (json.decoder.JSONDecodeError, KeyError):
                return extract_user_profile(scripts[3] )






    def __repr__(			self ):
          return F'''{self.__class__.__name__}(\'{self.username}\')'''






    def __str__(			self ):
          return F'''{self.fullname} ({self.username}) is {self.biography}'''






    @property
    def     _UpperCamelCase							(			self ):
          return self.user_data["username"]






    @property
    def     _UpperCamelCase							(			self ):
          return self.user_data["full_name"]






    @property
    def     _UpperCamelCase							(			self ):
          return self.user_data["biography"]






    @property
    def     _UpperCamelCase							(			self ):
          return self.user_data["business_email"]






    @property
    def     _UpperCamelCase							(			self ):
          return self.user_data["external_url"]






    @property
    def     _UpperCamelCase							(			self ):
          return self.user_data["edge_followed_by"]["count"]






    @property
    def     _UpperCamelCase							(			self ):
          return self.user_data["edge_follow"]["count"]






    @property
    def     _UpperCamelCase							(			self ):
          return self.user_data["edge_owner_to_timeline_media"]["count"]






    @property
    def     _UpperCamelCase							(			self ):
          return self.user_data["profile_pic_url_hd"]






    @property
    def     _UpperCamelCase							(			self ):
          return self.user_data["is_verified"]






    @property
    def     _UpperCamelCase							(			self ):
          return self.user_data["is_private"]







def _a			(		_snake_case = "github"							):







      """simple docstring"""






      import os

      if os.environ.get("""CI"""							):
            return  # test failing on GitHub Actions
      UpperCAmelCase          =	InstagramUser(_snake_case							)
      assert instagram_user.user_data
      assert isinstance(instagram_user.user_data				,	_snake_case							)
      assert instagram_user.username == username
      if username != "github":
            return
      assert instagram_user.fullname == "GitHub"
      assert instagram_user.biography == "Built for developers."
      assert instagram_user.number_of_posts > 150
      assert instagram_user.number_of_followers > 12_0000
      assert instagram_user.number_of_followings > 15
      assert instagram_user.email == "support@github.com"
      assert instagram_user.website == "https://github.com/readme"
      assert instagram_user.profile_picture_url.startswith("""https://instagram."""							)
      assert instagram_user.is_verified is True
      assert instagram_user.is_private is False


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  _UpperCamelCase					      =  InstagramUser("""github""")
  print(instagram_user)
  print(F"""{instagram_user.number_of_posts = }""")
  print(F"""{instagram_user.number_of_followers = }""")
  print(F"""{instagram_user.number_of_followings = }""")
  print(F"""{instagram_user.email = }""")
  print(F"""{instagram_user.website = }""")
  print(F"""{instagram_user.profile_picture_url = }""")
  print(F"""{instagram_user.is_verified = }""")
  print(F"""{instagram_user.is_private = }""")



