






"""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					:				Dict					    =						logging.getLogger(__name__)

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







def      lowerCamelCase__       (							_lowerCamelCase       :		List[Any] )      ->				int:
  lowerCamelCase_         =						parser.add_argument_group('quant_trainer arguments' )
  group.add_argument('--wprec'		,				type=_lowerCamelCase		,				default=8		,				help='weight precision' )
  group.add_argument('--aprec'		,				type=_lowerCamelCase		,				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=_lowerCamelCase		,				nargs='+'		,				help='disable quantizers by keyword' )
  group.add_argument('--quant-disable-layer-module'		,				type=_lowerCamelCase		,				help='disable quantizers by keyword under layer.' )
  group.add_argument('--quant-enable-layer-module'		,				type=_lowerCamelCase		,				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=_lowerCamelCase		,				type=_lowerCamelCase		,				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=_lowerCamelCase		,				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__       (							_lowerCamelCase       :		Tuple )      ->				Optional[int]:

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

  lowerCamelCase_         =						QuantDescriptor(num_bits=args.aprec		,				calib_method=_lowerCamelCase )
  lowerCamelCase_         =						QuantDescriptor(num_bits=args.wprec		,				axis=(None if args.quant_per_tensor else (0,)) )
  quant_nn.QuantLinear.set_default_quant_desc_input(_lowerCamelCase )
  quant_nn.QuantLinear.set_default_quant_desc_weight(_lowerCamelCase )







def      lowerCamelCase__       (							_lowerCamelCase       :		Tuple		,				_lowerCamelCase       :		Optional[Any]		,				_lowerCamelCase       :		List[Any]=False		,				_lowerCamelCase       :		List[Any]=False )      ->				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(_lowerCamelCase		,				['embeddings']		,				which='weight'		,				_disabled=_lowerCamelCase )

    if args.quant_disable:
      set_quantizer_by_name(_lowerCamelCase		,				['']		,				_disabled=_lowerCamelCase )

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

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

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

    if args.recalibrate_weights:
      recalibrate_weights(_lowerCamelCase )

    if args.fuse_qkv:
      fuse_qkv(_lowerCamelCase		,				_lowerCamelCase )

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

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







def      lowerCamelCase__       (							_lowerCamelCase       :		Tuple )      ->				List[Any]:
  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__       (							_lowerCamelCase       :		Any		,				_lowerCamelCase       :		str )      ->				Optional[Any]:
  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(_lowerCamelCase )







def      lowerCamelCase__       (							_lowerCamelCase       :		str		,				_lowerCamelCase       :		Tuple )      ->				str:

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

    lowerCamelCase_         =						max(_lowerCamelCase		,				_lowerCamelCase		,				_lowerCamelCase )
    qq._amax.fill_(_lowerCamelCase )
    qk._amax.fill_(_lowerCamelCase )
    qv._amax.fill_(_lowerCamelCase )
    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__       (							_lowerCamelCase       :		int		,				_lowerCamelCase       :		Optional[Any] )      ->				Optional[int]:

  for name, mod in model.named_modules():
    if name.endswith('.output.dense' ) and not name.endswith('attention.output.dense' ):
      lowerCamelCase_         =						mod._input_quantizer._amax.data.detach().item()
      mod._input_quantizer._amax.data.detach().clamp_(max=_lowerCamelCase )
      lowerCamelCase_         =						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__       (							_lowerCamelCase       :		List[str] )      ->				List[str]:

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







def      lowerCamelCase__       (							_lowerCamelCase       :		List[str] )      ->				Optional[int]:

  for name, mod in model.named_modules():
    if hasattr(_lowerCamelCase		,				'_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)
      lowerCamelCase_         =						set() if mod._weight_quantizer.axis is None else set(mod._weight_quantizer.axis )
      lowerCamelCase_         =						set(range(len(mod.weight.size() ) ) ) - axis_set
      lowerCamelCase_         =						pytorch_quantization.utils.reduce_amax(mod.weight		,				axis=_lowerCamelCase		,				keepdims=_lowerCamelCase ).detach()
      logger.info(F'''RECALIB: {name:{name_width}} {mod._weight_quantizer._amax.flatten()} -> {amax.flatten()}''' )
      lowerCamelCase_         =						amax







def      lowerCamelCase__       (							_lowerCamelCase       :		int		,				_lowerCamelCase       :		str=25		,				_lowerCamelCase       :		Any=180		,				_lowerCamelCase       :		str=None )      ->				int:

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

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

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







def      lowerCamelCase__       (							_lowerCamelCase       :		int )      ->				Optional[int]:
  lowerCamelCase_         =						0
  for name, mod in model.named_modules():
    if isinstance(_lowerCamelCase		,				pytorch_quantization.nn.TensorQuantizer ):
      print(F'''{name:80} {mod}''' )
      count += 1
  print(F'''{count} TensorQuantizers found in model''' )







def      lowerCamelCase__       (							_lowerCamelCase       :		Optional[Any]		,				_lowerCamelCase       :		Optional[int]		,				_lowerCamelCase       :		Optional[Any]		,				_lowerCamelCase       :		Dict		,				_lowerCamelCase       :		Optional[int] )      ->				List[str]:
  lowerCamelCase_         =						getattr(_lowerCamelCase		,				_lowerCamelCase		,				_lowerCamelCase )
  if quantizer_mod is not None:
    assert hasattr(_lowerCamelCase		,				_lowerCamelCase )
    setattr(_lowerCamelCase		,				_lowerCamelCase		,				_lowerCamelCase )
  else:
    logger.warning(F'''{name} has no {quantizer}''' )







def      lowerCamelCase__       (							_lowerCamelCase       :		List[Any]		,				_lowerCamelCase       :		Any		,				_lowerCamelCase       :		Tuple="both"		,				**_lowerCamelCase       :		str )      ->				int:
  lowerCamelCase_         =						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(_lowerCamelCase		,				_lowerCamelCase		,				'_input_quantizer'		,				_lowerCamelCase		,				_lowerCamelCase )
    if which in ["weight", "both"]:
      set_quantizer(_lowerCamelCase		,				_lowerCamelCase		,				'_weight_quantizer'		,				_lowerCamelCase		,				_lowerCamelCase )
  logger.info(_lowerCamelCase )







def      lowerCamelCase__       (							_lowerCamelCase       :		List[Any]		,				_lowerCamelCase       :		List[str]		,				**_lowerCamelCase       :		Optional[int] )      ->				Optional[Any]:

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












"""simple docstring"""





import json
import os
from collections import Counter

import torch
import torchvision
import torchvision.transforms as transforms
from PIL import Image
from torch import nn
from torch.utils.data import Dataset


_SCREAMING_SNAKE_CASE					:				List[Any]					    =						{1: (1, 1), 2: (2, 1), 3: (3, 1), 4: (2, 2), 5: (5, 1), 6: (3, 2), 7: (7, 1), 8: (4, 2), 9: (3, 3)}






class      a						(					nn.Module		):






    def __init__(       self     :							int       ,						__SCREAMING_SNAKE_CASE     :							Tuple					)     ->		List[Any]:
      super().__init__()
      lowerCamelCase_         =						torchvision.models.resnetaaa(pretrained=__SCREAMING_SNAKE_CASE					)
      lowerCamelCase_         =						list(model.children()					)[:-2]
      lowerCamelCase_         =						nn.Sequential(*__SCREAMING_SNAKE_CASE					)
      lowerCamelCase_         =						nn.AdaptiveAvgPoolad(POOLING_BREAKDOWN[args.num_image_embeds]					)






    def    UpperCamelCase       (       self     :							List[Any]       ,						__SCREAMING_SNAKE_CASE     :							Any					)     ->		Any:
      # Bx3x224x224 -> Bx2048x7x7 -> Bx2048xN -> BxNx2048
      lowerCamelCase_         =						self.pool(self.model(__SCREAMING_SNAKE_CASE					)					)
      lowerCamelCase_         =						torch.flatten(__SCREAMING_SNAKE_CASE       ,						start_dim=2					)
      lowerCamelCase_         =						out.transpose(1       ,						2					).contiguous()
      return out  # BxNx2048






class      a						(					__snake_case		):






    def __init__(       self     :							Dict       ,						__SCREAMING_SNAKE_CASE     :							Dict       ,						__SCREAMING_SNAKE_CASE     :							List[str]       ,						__SCREAMING_SNAKE_CASE     :							Tuple       ,						__SCREAMING_SNAKE_CASE     :							List[str]       ,						__SCREAMING_SNAKE_CASE     :							List[Any]					)     ->		Optional[Any]:
      lowerCamelCase_         =						[json.loads(__SCREAMING_SNAKE_CASE					) for l in open(__SCREAMING_SNAKE_CASE					)]
      lowerCamelCase_         =						os.path.dirname(__SCREAMING_SNAKE_CASE					)
      lowerCamelCase_         =						tokenizer
      lowerCamelCase_         =						labels
      lowerCamelCase_         =						len(__SCREAMING_SNAKE_CASE					)
      lowerCamelCase_         =						max_seq_length

      lowerCamelCase_         =						transforms






    def __len__(       self     :							Any					)     ->		Any:
      return len(self.data					)






    def __getitem__(       self     :							Optional[Any]       ,						__SCREAMING_SNAKE_CASE     :							List[str]					)     ->		List[str]:
      lowerCamelCase_         =						torch.LongTensor(self.tokenizer.encode(self.data[index]['text']       ,						add_special_tokens=__SCREAMING_SNAKE_CASE					)					)
      lowerCamelCase_     ,	lowerCamelCase_     ,	lowerCamelCase_         =						sentence[0], sentence[1:-1], sentence[-1]
      lowerCamelCase_         =						sentence[: self.max_seq_length]

      lowerCamelCase_         =						torch.zeros(self.n_classes					)
      lowerCamelCase_         =						1

      lowerCamelCase_         =						Image.open(os.path.join(self.data_dir       ,						self.data[index]['img']					)					).convert('RGB'					)
      lowerCamelCase_         =						self.transforms(__SCREAMING_SNAKE_CASE					)

      return {
          "image_start_token": start_token,
          "image_end_token": end_token,
          "sentence": sentence,
          "image": image,
          "label": label,
      }






    def    UpperCamelCase       (       self     :							Dict					)     ->		Dict:
      lowerCamelCase_         =						Counter()
      for row in self.data:
        label_freqs.update(row['label']					)
      return label_freqs







def      lowerCamelCase__       (							_lowerCamelCase       :		Union[str, Any] )      ->				str:
  lowerCamelCase_         =						[len(row['sentence'] ) for row in batch]
  lowerCamelCase_     ,	lowerCamelCase_         =						len(_lowerCamelCase ), max(_lowerCamelCase )

  lowerCamelCase_         =						torch.zeros(_lowerCamelCase		,				_lowerCamelCase		,				dtype=torch.long )
  lowerCamelCase_         =						torch.zeros(_lowerCamelCase		,				_lowerCamelCase		,				dtype=torch.long )

  for i_batch, (input_row, length) in enumerate(zip(_lowerCamelCase		,				_lowerCamelCase ) ):
    lowerCamelCase_         =						input_row['sentence']
    lowerCamelCase_         =						1

  lowerCamelCase_         =						torch.stack([row['image'] for row in batch] )
  lowerCamelCase_         =						torch.stack([row['label'] for row in batch] )
  lowerCamelCase_         =						torch.stack([row['image_start_token'] for row in batch] )
  lowerCamelCase_         =						torch.stack([row['image_end_token'] for row in batch] )

  return text_tensor, mask_tensor, img_tensor, img_start_token, img_end_token, tgt_tensor







def      lowerCamelCase__       (							)      ->				List[str]:
  return [
      "Crime",
      "Drama",
      "Thriller",
      "Action",
      "Comedy",
      "Romance",
      "Documentary",
      "Short",
      "Mystery",
      "History",
      "Family",
      "Adventure",
      "Fantasy",
      "Sci-Fi",
      "Western",
      "Horror",
      "Sport",
      "War",
      "Music",
      "Musical",
      "Animation",
      "Biography",
      "Film-Noir",
  ]







def      lowerCamelCase__       (							)      ->				Union[str, Any]:
  return transforms.Compose(
      [
          transforms.Resize(256 ),
          transforms.CenterCrop(224 ),
          transforms.ToTensor(),
          transforms.Normalize(
              mean=[0.46_77_70_44, 0.44_53_14_29, 0.40_66_10_17]		,				std=[0.12_22_19_94, 0.12_14_58_35, 0.14_38_04_69]		,				),
      ] )




