






import argparse
import logging
import os
from datetime import datetime

import numpy as np
import torch
from torch import nn
from torch.utils.data import DataLoader, SequentialSampler, Subset
from torch.utils.data.distributed import DistributedSampler
from tqdm import tqdm

import transformers
from transformers import (
    AutoConfig,
    AutoModelForSequenceClassification,
    AutoTokenizer,
    GlueDataset,
    default_data_collator,
    glue_compute_metrics,
    glue_output_modes,
    glue_processors,
    set_seed,
)
from transformers.trainer_utils import is_main_process


_SCREAMING_SNAKE_CASE :    Union[str, Any]    =						logging.getLogger(__name__)







def 					lowerCamelCase__	(							_lowerCamelCase	:		int			)				->    Tuple:
  lowerCamelCase_   					=						p * torch.log(_lowerCamelCase			)
  lowerCamelCase_   					=						0
  return -plogp.sum(dim=-1			)







def 					lowerCamelCase__	(							_lowerCamelCase	:		Any			)				->    Union[str, Any]:
  logger.info('lv, h >\t' + '\t'.join(F'''{x + 1}''' for x in range(len(_lowerCamelCase			)			)			)			)
  for row in range(len(_lowerCamelCase			)			):
    if tensor.dtype != torch.long:
      logger.info(F'''layer {row + 1}:\t''' + '\t'.join(F'''{x:.5f}''' for x in tensor[row].cpu().data			)			)
    else:
      logger.info(F'''layer {row + 1}:\t''' + '\t'.join(F'''{x:d}''' for x in tensor[row].cpu().data			)			)







def 					lowerCamelCase__	(							_lowerCamelCase	:		Dict		,    _lowerCamelCase	:		int		,    _lowerCamelCase	:		List[str]		,    _lowerCamelCase	:		Tuple=True		,    _lowerCamelCase	:		Any=True		,    _lowerCamelCase	:		Dict=None		,    _lowerCamelCase	:		str=False			)				->    Tuple:
  lowerCamelCase_					,     lowerCamelCase_   					=						model.config.num_hidden_layers, model.config.num_attention_heads
  lowerCamelCase_   					=						torch.zeros(_lowerCamelCase		,    _lowerCamelCase			).to(args.device			)
  lowerCamelCase_   					=						torch.zeros(_lowerCamelCase		,    _lowerCamelCase			).to(args.device			)

  if head_mask is None:
    lowerCamelCase_   					=						torch.ones(_lowerCamelCase		,    _lowerCamelCase			).to(args.device			)

  head_mask.requires_grad_(requires_grad=_lowerCamelCase			)
  # If actually pruned attention multi-head, set head mask to None to avoid shape mismatch
  if actually_pruned:
    lowerCamelCase_   					=						None

  lowerCamelCase_   					=						None
  lowerCamelCase_   					=						None
  lowerCamelCase_   					=						0.0

  for step, inputs in enumerate(tqdm(_lowerCamelCase		,    desc='Iteration'		,    disable=args.local_rank not in [-1, 0]			)			):
    for k, v in inputs.items():
      lowerCamelCase_   					=						v.to(args.device			)

    # Do a forward pass (not with torch.no_grad() since we need gradients for importance score - see below)
    lowerCamelCase_   					=						model(**_lowerCamelCase		,    head_mask=_lowerCamelCase			)
    lowerCamelCase_					,     lowerCamelCase_					,     lowerCamelCase_   					=						(
        outputs[0],
        outputs[1],
        outputs[-1],
    )  # Loss and logits are the first, attention the last
    loss.backward()  # Backpropagate to populate the gradients in the head mask

    if compute_entropy:
      for layer, attn in enumerate(_lowerCamelCase			):
        lowerCamelCase_   					=						entropy(attn.detach()			) * inputs['attention_mask'].float().unsqueeze(1			)
        attn_entropy[layer] += masked_entropy.sum(-1			).sum(0			).detach()

    if compute_importance:
      head_importance += head_mask.grad.abs().detach()

    # Also store our logits/labels if we want to compute metrics afterwards
    if preds is None:
      lowerCamelCase_   					=						logits.detach().cpu().numpy()
      lowerCamelCase_   					=						inputs['labels'].detach().cpu().numpy()
    else:
      lowerCamelCase_   					=						np.append(_lowerCamelCase		,    logits.detach().cpu().numpy()		,    axis=0			)
      lowerCamelCase_   					=						np.append(_lowerCamelCase		,    inputs['labels'].detach().cpu().numpy()		,    axis=0			)

    tot_tokens += inputs["attention_mask"].float().detach().sum().data

  # Normalize
  attn_entropy /= tot_tokens
  head_importance /= tot_tokens
  # Layerwise importance normalization
  if not args.dont_normalize_importance_by_layer:
    lowerCamelCase_   					=						2
    lowerCamelCase_   					=						torch.pow(torch.pow(_lowerCamelCase		,    _lowerCamelCase			).sum(-1			)		,    1 / exponent			)
    head_importance /= norm_by_layer.unsqueeze(-1			) + 1e-20

  if not args.dont_normalize_global_importance:
    lowerCamelCase_   					=						(head_importance - head_importance.min()) / (head_importance.max() - head_importance.min())

  # Print/save matrices
  np.save(os.path.join(args.output_dir		,    'attn_entropy.npy'			)		,    attn_entropy.detach().cpu().numpy()			)
  np.save(os.path.join(args.output_dir		,    'head_importance.npy'			)		,    head_importance.detach().cpu().numpy()			)

  logger.info('Attention entropies'			)
  print_ad_tensor(_lowerCamelCase			)
  logger.info('Head importance scores'			)
  print_ad_tensor(_lowerCamelCase			)
  logger.info('Head ranked by importance scores'			)
  lowerCamelCase_   					=						torch.zeros(head_importance.numel()		,    dtype=torch.long		,    device=args.device			)
  lowerCamelCase_   					=						torch.arange(
      head_importance.numel()		,    device=args.device			)
  lowerCamelCase_   					=						head_ranks.view_as(_lowerCamelCase			)
  print_ad_tensor(_lowerCamelCase			)

  return attn_entropy, head_importance, preds, labels







def 					lowerCamelCase__	(							_lowerCamelCase	:		Dict		,    _lowerCamelCase	:		Optional[Any]		,    _lowerCamelCase	:		Optional[int]			)				->    Tuple:
  lowerCamelCase_					,     lowerCamelCase_					,     lowerCamelCase_					,     lowerCamelCase_   					=						compute_heads_importance(_lowerCamelCase		,    _lowerCamelCase		,    _lowerCamelCase		,    compute_entropy=_lowerCamelCase			)
  lowerCamelCase_   					=						np.argmax(_lowerCamelCase		,    axis=1			) if args.output_mode == 'classification' else np.squeeze(_lowerCamelCase			)
  lowerCamelCase_   					=						glue_compute_metrics(args.task_name		,    _lowerCamelCase		,    _lowerCamelCase			)[args.metric_name]
  logger.info('Pruning: original score: %f, threshold: %f'		,    _lowerCamelCase		,    original_score * args.masking_threshold			)

  lowerCamelCase_   					=						torch.ones_like(_lowerCamelCase			)
  lowerCamelCase_   					=						max(1		,    int(new_head_mask.numel() * args.masking_amount			)			)

  lowerCamelCase_   					=						original_score
  while current_score >= original_score * args.masking_threshold:
    lowerCamelCase_   					=						new_head_mask.clone()  # save current head mask
    # heads from least important to most - keep only not-masked heads
    lowerCamelCase_   					=						float('Inf'			)
    lowerCamelCase_   					=						head_importance.view(-1			).sort()[1]

    if len(_lowerCamelCase			) <= num_to_mask:
      break

    # mask heads
    lowerCamelCase_   					=						current_heads_to_mask[:num_to_mask]
    logger.info('Heads to mask: %s'		,    str(current_heads_to_mask.tolist()			)			)
    lowerCamelCase_   					=						new_head_mask.view(-1			)
    lowerCamelCase_   					=						0.0
    lowerCamelCase_   					=						new_head_mask.view_as(_lowerCamelCase			)
    lowerCamelCase_   					=						new_head_mask.clone().detach()
    print_ad_tensor(_lowerCamelCase			)

    # Compute metric and head importance again
    lowerCamelCase_					,     lowerCamelCase_					,     lowerCamelCase_					,     lowerCamelCase_   					=						compute_heads_importance(
        _lowerCamelCase		,    _lowerCamelCase		,    _lowerCamelCase		,    compute_entropy=_lowerCamelCase		,    head_mask=_lowerCamelCase			)
    lowerCamelCase_   					=						np.argmax(_lowerCamelCase		,    axis=1			) if args.output_mode == 'classification' else np.squeeze(_lowerCamelCase			)
    lowerCamelCase_   					=						glue_compute_metrics(args.task_name		,    _lowerCamelCase		,    _lowerCamelCase			)[args.metric_name]
    logger.info(
        'Masking: current score: %f, remaining heads %d (%.1f percents)'		,    _lowerCamelCase		,    new_head_mask.sum()		,    new_head_mask.sum() / new_head_mask.numel() * 100		,    )

  logger.info('Final head mask'			)
  print_ad_tensor(_lowerCamelCase			)
  np.save(os.path.join(args.output_dir		,    'head_mask.npy'			)		,    head_mask.detach().cpu().numpy()			)

  return head_mask







def 					lowerCamelCase__	(							_lowerCamelCase	:		Union[str, Any]		,    _lowerCamelCase	:		Tuple		,    _lowerCamelCase	:		Union[str, Any]		,    _lowerCamelCase	:		List[Any]			)				->    int:
  lowerCamelCase_   					=						datetime.now()
  lowerCamelCase_					,     lowerCamelCase_					,     lowerCamelCase_					,     lowerCamelCase_   					=						compute_heads_importance(
      _lowerCamelCase		,    _lowerCamelCase		,    _lowerCamelCase		,    compute_entropy=_lowerCamelCase		,    compute_importance=_lowerCamelCase		,    head_mask=_lowerCamelCase			)
  lowerCamelCase_   					=						np.argmax(_lowerCamelCase		,    axis=1			) if args.output_mode == 'classification' else np.squeeze(_lowerCamelCase			)
  lowerCamelCase_   					=						glue_compute_metrics(args.task_name		,    _lowerCamelCase		,    _lowerCamelCase			)[args.metric_name]
  lowerCamelCase_   					=						datetime.now() - before_time

  lowerCamelCase_   					=						sum(p.numel() for p in model.parameters()			)
  lowerCamelCase_   					=						{
      layer: (1 - head_mask[layer].long()).nonzero().squeeze().tolist() for layer in range(len(_lowerCamelCase			)			)
  }

  assert sum(len(_lowerCamelCase			) for h in heads_to_prune.values()			) == (1 - head_mask.long()).sum().item()
  model.prune_heads(_lowerCamelCase			)
  lowerCamelCase_   					=						sum(p.numel() for p in model.parameters()			)

  lowerCamelCase_   					=						datetime.now()
  lowerCamelCase_					,     lowerCamelCase_					,     lowerCamelCase_					,     lowerCamelCase_   					=						compute_heads_importance(
      _lowerCamelCase		,    _lowerCamelCase		,    _lowerCamelCase		,    compute_entropy=_lowerCamelCase		,    compute_importance=_lowerCamelCase		,    head_mask=_lowerCamelCase		,    actually_pruned=_lowerCamelCase		,    )
  lowerCamelCase_   					=						np.argmax(_lowerCamelCase		,    axis=1			) if args.output_mode == 'classification' else np.squeeze(_lowerCamelCase			)
  lowerCamelCase_   					=						glue_compute_metrics(args.task_name		,    _lowerCamelCase		,    _lowerCamelCase			)[args.metric_name]
  lowerCamelCase_   					=						datetime.now() - before_time

  logger.info(
      'Pruning: original num of params: %.2e, after pruning %.2e (%.1f percents)'		,    _lowerCamelCase		,    _lowerCamelCase		,    pruned_num_params / original_num_params * 100		,    )
  logger.info('Pruning: score with masking: %f score with pruning: %f'		,    _lowerCamelCase		,    _lowerCamelCase			)
  logger.info('Pruning: speed ratio (new timing / original timing): %f percents'		,    original_time / new_time * 100			)







def 					lowerCamelCase__	(							)				->    Optional[int]:
  lowerCamelCase_   					=						argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      '--data_dir'		,    default=_lowerCamelCase		,    type=_lowerCamelCase		,    required=_lowerCamelCase		,    help='The input data dir. Should contain the .tsv files (or other data files) for the task.'		,    )
  parser.add_argument(
      '--model_name_or_path'		,    default=_lowerCamelCase		,    type=_lowerCamelCase		,    required=_lowerCamelCase		,    help='Path to pretrained model or model identifier from huggingface.co/models'		,    )
  parser.add_argument(
      '--task_name'		,    default=_lowerCamelCase		,    type=_lowerCamelCase		,    required=_lowerCamelCase		,    help='The name of the task to train selected in the list: ' + ', '.join(glue_processors.keys()			)		,    )
  parser.add_argument(
      '--output_dir'		,    default=_lowerCamelCase		,    type=_lowerCamelCase		,    required=_lowerCamelCase		,    help='The output directory where the model predictions and checkpoints will be written.'		,    )

  # Other parameters
  parser.add_argument(
      '--config_name'		,    default=''		,    type=_lowerCamelCase		,    help='Pretrained config name or path if not the same as model_name_or_path'		,    )
  parser.add_argument(
      '--tokenizer_name'		,    default=''		,    type=_lowerCamelCase		,    help='Pretrained tokenizer name or path if not the same as model_name_or_path'		,    )
  parser.add_argument(
      '--cache_dir'		,    default=_lowerCamelCase		,    type=_lowerCamelCase		,    help='Where do you want to store the pre-trained models downloaded from huggingface.co'		,    )
  parser.add_argument(
      '--data_subset'		,    type=_lowerCamelCase		,    default=-1		,    help='If > 0: limit the data to a subset of data_subset instances.'			)
  parser.add_argument(
      '--overwrite_output_dir'		,    action='store_true'		,    help='Whether to overwrite data in output directory'			)
  parser.add_argument(
      '--overwrite_cache'		,    action='store_true'		,    help='Overwrite the cached training and evaluation sets'			)

  parser.add_argument(
      '--dont_normalize_importance_by_layer'		,    action='store_true'		,    help='Don\'t normalize importance score by layers'			)
  parser.add_argument(
      '--dont_normalize_global_importance'		,    action='store_true'		,    help='Don\'t normalize all importance scores between 0 and 1'		,    )

  parser.add_argument(
      '--try_masking'		,    action='store_true'		,    help='Whether to try to mask head until a threshold of accuracy.'			)
  parser.add_argument(
      '--masking_threshold'		,    default=0.9		,    type=_lowerCamelCase		,    help='masking threshold in term of metrics (stop masking when metric < threshold * original metric value).'		,    )
  parser.add_argument(
      '--masking_amount'		,    default=0.1		,    type=_lowerCamelCase		,    help='Amount to heads to masking at each masking step.'			)
  parser.add_argument('--metric_name'		,    default='acc'		,    type=_lowerCamelCase		,    help='Metric to use for head masking.'			)

  parser.add_argument(
      '--max_seq_length'		,    default=128		,    type=_lowerCamelCase		,    help=(
          'The maximum total input sequence length after WordPiece tokenization. \n'
          'Sequences longer than this will be truncated, sequences shorter padded.'
      )		,    )
  parser.add_argument('--batch_size'		,    default=1		,    type=_lowerCamelCase		,    help='Batch size.'			)

  parser.add_argument('--seed'		,    type=_lowerCamelCase		,    default=42			)
  parser.add_argument('--local_rank'		,    type=_lowerCamelCase		,    default=-1		,    help='local_rank for distributed training on gpus'			)
  parser.add_argument('--no_cuda'		,    action='store_true'		,    help='Whether not to use CUDA when available'			)
  parser.add_argument('--server_ip'		,    type=_lowerCamelCase		,    default=''		,    help='Can be used for distant debugging.'			)
  parser.add_argument('--server_port'		,    type=_lowerCamelCase		,    default=''		,    help='Can be used for distant debugging.'			)
  lowerCamelCase_   					=						parser.parse_args()

  if args.server_ip and args.server_port:
    # Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script
    import ptvsd

    print('Waiting for debugger attach'			)
    ptvsd.enable_attach(address=(args.server_ip, args.server_port)		,    redirect_output=_lowerCamelCase			)
    ptvsd.wait_for_attach()

  # Setup devices and distributed training
  if args.local_rank == -1 or args.no_cuda:
    lowerCamelCase_   					=						torch.device('cuda' if torch.cuda.is_available() and not args.no_cuda else 'cpu'			)
    lowerCamelCase_   					=						0 if args.no_cuda else torch.cuda.device_count()
  else:
    torch.cuda.set_device(args.local_rank			)
    lowerCamelCase_   					=						torch.device('cuda'		,    args.local_rank			)
    lowerCamelCase_   					=						1
    torch.distributed.init_process_group(backend='nccl'			)  # Initializes the distributed backend

  # Setup logging
  logging.basicConfig(level=logging.INFO if args.local_rank in [-1, 0] else logging.WARN			)
  logger.info('device: {} n_gpu: {}, distributed: {}'.format(args.device		,    args.n_gpu		,    bool(args.local_rank != -1			)			)			)
  # Set the verbosity to info of the Transformers logger (on main process only):
  if is_main_process(args.local_rank			):
    transformers.utils.logging.set_verbosity_info()
    transformers.utils.logging.enable_default_handler()
    transformers.utils.logging.enable_explicit_format()

  # Set seeds
  set_seed(args.seed			)

  # Prepare GLUE task
  lowerCamelCase_   					=						args.task_name.lower()
  if args.task_name not in glue_processors:
    raise ValueError('Task not found: %s' % (args.task_name)			)
  lowerCamelCase_   					=						glue_processors[args.task_name]()
  lowerCamelCase_   					=						glue_output_modes[args.task_name]
  lowerCamelCase_   					=						processor.get_labels()
  lowerCamelCase_   					=						len(_lowerCamelCase			)

  # Load pretrained model and tokenizer
  #
  # Distributed training:
  # The .from_pretrained methods guarantee that only one local process can concurrently
  # download model & vocab.

  lowerCamelCase_   					=						AutoConfig.from_pretrained(
      args.config_name if args.config_name else args.model_name_or_path		,    num_labels=_lowerCamelCase		,    finetuning_task=args.task_name		,    output_attentions=_lowerCamelCase		,    cache_dir=args.cache_dir		,    )
  lowerCamelCase_   					=						AutoTokenizer.from_pretrained(
      args.tokenizer_name if args.tokenizer_name else args.model_name_or_path		,    cache_dir=args.cache_dir		,    )
  lowerCamelCase_   					=						AutoModelForSequenceClassification.from_pretrained(
      args.model_name_or_path		,    from_tf=bool('.ckpt' in args.model_name_or_path			)		,    config=_lowerCamelCase		,    cache_dir=args.cache_dir		,    )

  # Distributed and parallel training
  model.to(args.device			)
  if args.local_rank != -1:
    lowerCamelCase_   					=						nn.parallel.DistributedDataParallel(
        _lowerCamelCase		,    device_ids=[args.local_rank]		,    output_device=args.local_rank		,    find_unused_parameters=_lowerCamelCase			)
  elif args.n_gpu > 1:
    lowerCamelCase_   					=						nn.DataParallel(_lowerCamelCase			)

  # Print/save training arguments
  os.makedirs(args.output_dir		,    exist_ok=_lowerCamelCase			)
  torch.save(_lowerCamelCase		,    os.path.join(args.output_dir		,    'run_args.bin'			)			)
  logger.info('Training/evaluation parameters %s'		,    _lowerCamelCase			)

  # Prepare dataset for the GLUE task
  lowerCamelCase_   					=						GlueDataset(_lowerCamelCase		,    tokenizer=_lowerCamelCase		,    mode='dev'			)
  if args.data_subset > 0:
    lowerCamelCase_   					=						Subset(_lowerCamelCase		,    list(range(min(args.data_subset		,    len(_lowerCamelCase			)			)			)			)			)
  lowerCamelCase_   					=						SequentialSampler(_lowerCamelCase			) if args.local_rank == -1 else DistributedSampler(_lowerCamelCase			)
  lowerCamelCase_   					=						DataLoader(
      _lowerCamelCase		,    sampler=_lowerCamelCase		,    batch_size=args.batch_size		,    collate_fn=_lowerCamelCase			)

  # Compute head entropy and importance score
  compute_heads_importance(_lowerCamelCase		,    _lowerCamelCase		,    _lowerCamelCase			)

  # Try head masking (set heads to zero until the score goes under a threshole)
  # and head pruning (remove masked heads and see the effect on the network)
  if args.try_masking and args.masking_threshold > 0.0 and args.masking_threshold < 1.0:
    lowerCamelCase_   					=						mask_heads(_lowerCamelCase		,    _lowerCamelCase		,    _lowerCamelCase			)
    prune_heads(_lowerCamelCase		,    _lowerCamelCase		,    _lowerCamelCase		,    _lowerCamelCase			)


if __name__ == "__main__":
  main()












"""simple docstring"""





import enum
import inspect
from typing import Iterable, List, Optional, Tuple, Union






class 					a						( enum.Enum		):
    SCREAMING_SNAKE_CASE						:					str						=			"""timm"""
    SCREAMING_SNAKE_CASE						:					List[Any]						=			"""transformers"""







def 					lowerCamelCase__	(							_lowerCamelCase	:		Optional[Iterable[str]]		,    _lowerCamelCase	:		Optional[Iterable[int]]		,    _lowerCamelCase	:		Optional[Iterable[str]]			)				->    List[Any]:
  if stage_names is None:
    raise ValueError('Stage_names must be set for transformers backbones'			)

  if out_features is not None:
    if not isinstance(_lowerCamelCase		,    (list,)			):
      raise ValueError(F'''out_features must be a list {type(_lowerCamelCase			)}'''			)
    if any(feat not in stage_names for feat in out_features			):
      raise ValueError(F'''out_features must be a subset of stage_names: {stage_names} got {out_features}'''			)

  if out_indices is not None:
    if not isinstance(_lowerCamelCase		,    (list, tuple)			):
      raise ValueError(F'''out_indices must be a list or tuple, got {type(_lowerCamelCase			)}'''			)
    if any(idx >= len(_lowerCamelCase			) for idx in out_indices			):
      raise ValueError('out_indices must be valid indices for stage_names {stage_names}, got {out_indices}'			)

  if out_features is not None and out_indices is not None:
    if len(_lowerCamelCase			) != len(_lowerCamelCase			):
      raise ValueError('out_features and out_indices should have the same length if both are set'			)
    if out_features != [stage_names[idx] for idx in out_indices]:
      raise ValueError('out_features and out_indices should correspond to the same stages if both are set'			)







def 					lowerCamelCase__	(							_lowerCamelCase	:		Optional[List[str]]		,    _lowerCamelCase	:		Optional[Union[List[int], Tuple[int]]]		,    _lowerCamelCase	:		List[str]		,    )				->    Union[str, Any]:
  if out_indices is None and out_features is None:
    lowerCamelCase_   					=						[len(_lowerCamelCase			) - 1]
    lowerCamelCase_   					=						[stage_names[-1]]
  elif out_indices is None and out_features is not None:
    lowerCamelCase_   					=						[stage_names.index(_lowerCamelCase			) for layer in out_features]
  elif out_features is None and out_indices is not None:
    lowerCamelCase_   					=						[stage_names[idx] for idx in out_indices]
  return out_features, out_indices







def 					lowerCamelCase__	(							_lowerCamelCase	:		Optional[List[str]]		,    _lowerCamelCase	:		Optional[Union[List[int], Tuple[int]]]		,    _lowerCamelCase	:		List[str]		,    )				->    Tuple[List[str], List[int]]:
  verify_out_features_out_indices(out_features=_lowerCamelCase		,    out_indices=_lowerCamelCase		,    stage_names=_lowerCamelCase			)
  lowerCamelCase_					,     lowerCamelCase_   					=						_align_output_features_output_indices(
      out_features=_lowerCamelCase		,    out_indices=_lowerCamelCase		,    stage_names=_lowerCamelCase			)
  # Verify that the aligned out_features and out_indices are valid
  verify_out_features_out_indices(out_features=_lowerCamelCase		,    out_indices=_lowerCamelCase		,    stage_names=_lowerCamelCase			)
  return output_features, output_indices






class 					a						:
    SCREAMING_SNAKE_CASE						:					Optional[BackboneType]						=			None






    def        UpperCamelCase	(	self					:							Optional[Any]	,						__SCREAMING_SNAKE_CASE					:							List[str] )					->		None:
      if getattr(self	,						'_backbone'	,						__SCREAMING_SNAKE_CASE ) is None:
        raise ValueError('self._backbone must be set before calling _init_timm_backbone' )

      # These will diagree with the defaults for the transformers models e.g. for resnet50
      # the transformer model has out_features = ['stem', 'stage1', 'stage2', 'stage3', 'stage4']
      # the timm model has out_features = ['act', 'layer1', 'layer2', 'layer3', 'layer4']
      lowerCamelCase_   					=						[stage['module'] for stage in self._backbone.feature_info.info]
      lowerCamelCase_   					=						[stage['num_chs'] for stage in self._backbone.feature_info.info]
      lowerCamelCase_   					=						self._backbone.feature_info.out_indices
      lowerCamelCase_   					=						self._backbone.feature_info.module_name()

      # We verify the out indices and out features are valid
      verify_out_features_out_indices(
          out_features=__SCREAMING_SNAKE_CASE	,						out_indices=__SCREAMING_SNAKE_CASE	,						stage_names=self.stage_names )
      lowerCamelCase_					,     lowerCamelCase_   					=						out_features, out_indices






    def        UpperCamelCase	(	self					:							Tuple	,						__SCREAMING_SNAKE_CASE					:							Any )					->		None:
      lowerCamelCase_   					=						getattr(__SCREAMING_SNAKE_CASE	,						'stage_names' )
      lowerCamelCase_   					=						getattr(__SCREAMING_SNAKE_CASE	,						'out_features'	,						__SCREAMING_SNAKE_CASE )
      lowerCamelCase_   					=						getattr(__SCREAMING_SNAKE_CASE	,						'out_indices'	,						__SCREAMING_SNAKE_CASE )

      lowerCamelCase_   					=						stage_names
      lowerCamelCase_					,     lowerCamelCase_   					=						get_aligned_output_features_output_indices(
          out_features=__SCREAMING_SNAKE_CASE	,						out_indices=__SCREAMING_SNAKE_CASE	,						stage_names=__SCREAMING_SNAKE_CASE )
      # Number of channels for each stage. This is set in the transformer backbone model init
      lowerCamelCase_   					=						None






    def        UpperCamelCase	(	self					:							Any	,						__SCREAMING_SNAKE_CASE					:							Dict )					->		None:
      lowerCamelCase_   					=						config

      lowerCamelCase_   					=						getattr(__SCREAMING_SNAKE_CASE	,						'use_timm_backbone'	,						__SCREAMING_SNAKE_CASE )
      lowerCamelCase_   					=						BackboneType.TIMM if self.use_timm_backbone else BackboneType.TRANSFORMERS

      if self.backbone_type == BackboneType.TIMM:
        self._init_timm_backbone(__SCREAMING_SNAKE_CASE )
      elif self.backbone_type == BackboneType.TRANSFORMERS:
        self._init_transformers_backbone(__SCREAMING_SNAKE_CASE )
      else:
        raise ValueError(F'''backbone_type {self.backbone_type} not supported.''' )






    @property
    def        UpperCamelCase	(	self					:							Dict )					->		Any:
      return self._out_features






    @out_features.setter
    def        UpperCamelCase	(	self					:							Optional[Any]	,						__SCREAMING_SNAKE_CASE					:							List[str] )					->		List[Any]:
      lowerCamelCase_					,     lowerCamelCase_   					=						get_aligned_output_features_output_indices(
          out_features=__SCREAMING_SNAKE_CASE	,						out_indices=__SCREAMING_SNAKE_CASE	,						stage_names=self.stage_names )






    @property
    def        UpperCamelCase	(	self					:							List[Any] )					->		Dict:
      return self._out_indices






    @out_indices.setter
    def        UpperCamelCase	(	self					:							List[str]	,						__SCREAMING_SNAKE_CASE					:							Union[Tuple[int], List[int]] )					->		List[str]:
      lowerCamelCase_					,     lowerCamelCase_   					=						get_aligned_output_features_output_indices(
          out_features=__SCREAMING_SNAKE_CASE	,						out_indices=__SCREAMING_SNAKE_CASE	,						stage_names=self.stage_names )






    @property
    def        UpperCamelCase	(	self					:							Tuple )					->		Tuple:
      # the current backbones will output the number of channels for each stage
      # even if that stage is not in the out_features list.
      return {stage: self.num_features[i] for i, stage in enumerate(self.stage_names )}






    @property
    def        UpperCamelCase	(	self					:							Dict )					->		List[Any]:
      return [self.out_feature_channels[name] for name in self.out_features]






    def        UpperCamelCase	(	self					:							Optional[int]	,						*__SCREAMING_SNAKE_CASE					:							Any	,						**__SCREAMING_SNAKE_CASE					:							str )					->		Tuple:
      lowerCamelCase_   					=						dict(inspect.signature(self.forward ).parameters )
      lowerCamelCase_   					=						{k: v for k, v in kwargs.items() if k in signature}
      return self(*__SCREAMING_SNAKE_CASE	,						**__SCREAMING_SNAKE_CASE )






    def        UpperCamelCase	(	self					:							Optional[int]	,						__SCREAMING_SNAKE_CASE					:							Tuple	,						__SCREAMING_SNAKE_CASE					:							Optional[bool] = None	,						__SCREAMING_SNAKE_CASE					:							Optional[bool] = None	,						__SCREAMING_SNAKE_CASE					:							Optional[bool] = None	,						)					->		int:
      raise NotImplementedError('This method should be implemented by the derived class.' )






    def        UpperCamelCase	(	self					:							Union[str, Any] )					->		List[str]:
      lowerCamelCase_   					=						super().to_dict()
      lowerCamelCase_   					=						output.pop('_out_features' )
      lowerCamelCase_   					=						output.pop('_out_indices' )
      return output






class 					a						:






    @property
    def        UpperCamelCase	(	self					:							int )					->		Optional[Any]:
      return self._out_features






    @out_features.setter
    def        UpperCamelCase	(	self					:							List[str]	,						__SCREAMING_SNAKE_CASE					:							List[str] )					->		List[str]:
      lowerCamelCase_					,     lowerCamelCase_   					=						get_aligned_output_features_output_indices(
          out_features=__SCREAMING_SNAKE_CASE	,						out_indices=__SCREAMING_SNAKE_CASE	,						stage_names=self.stage_names )






    @property
    def        UpperCamelCase	(	self					:							Union[str, Any] )					->		Tuple:
      return self._out_indices






    @out_indices.setter
    def        UpperCamelCase	(	self					:							List[Any]	,						__SCREAMING_SNAKE_CASE					:							Union[Tuple[int], List[int]] )					->		str:
      lowerCamelCase_					,     lowerCamelCase_   					=						get_aligned_output_features_output_indices(
          out_features=__SCREAMING_SNAKE_CASE	,						out_indices=__SCREAMING_SNAKE_CASE	,						stage_names=self.stage_names )






    def        UpperCamelCase	(	self					:							int )					->		str:
      lowerCamelCase_   					=						super().to_dict()
      lowerCamelCase_   					=						output.pop('_out_features' )
      lowerCamelCase_   					=						output.pop('_out_indices' )
      return output




