
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


_UpperCamelCase:					List[Any]     =logging.getLogger(__name__)



def 	_a  (					__SCREAMING_SNAKE_CASE					:  Optional[Any]    ):






  """simple docstring"""




  _lowerCAmelCase						=		p * torch.log(__SCREAMING_SNAKE_CASE    )
  _lowerCAmelCase						=		0
  return -plogp.sum(dim=-1    )



def 	_a  (					__SCREAMING_SNAKE_CASE					:  Dict    ):






  """simple docstring"""




  logger.info('lv, h >\t' + '\t'.join(f'''{x + 1}''' for x in range(len(__SCREAMING_SNAKE_CASE    )    )    )    )
  for row in range(len(__SCREAMING_SNAKE_CASE    )    ):
    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 	_a  (					__SCREAMING_SNAKE_CASE					:  str		,				__SCREAMING_SNAKE_CASE					:  int		,				__SCREAMING_SNAKE_CASE					:  List[str]		,				__SCREAMING_SNAKE_CASE					:  List[str]=True		,				__SCREAMING_SNAKE_CASE					:  Tuple=True		,				__SCREAMING_SNAKE_CASE					:  Dict=None		,				__SCREAMING_SNAKE_CASE					:  int=False    ):






  """simple docstring"""




  _lowerCAmelCase	,       _lowerCAmelCase						=		model.config.num_hidden_layers, model.config.num_attention_heads
  _lowerCAmelCase						=		torch.zeros(__SCREAMING_SNAKE_CASE		,				__SCREAMING_SNAKE_CASE    ).to(args.device    )
  _lowerCAmelCase						=		torch.zeros(__SCREAMING_SNAKE_CASE		,				__SCREAMING_SNAKE_CASE    ).to(args.device    )

  if head_mask is None:
    _lowerCAmelCase						=		torch.ones(__SCREAMING_SNAKE_CASE		,				__SCREAMING_SNAKE_CASE    ).to(args.device    )

  head_mask.requires_grad_(requires_grad=__SCREAMING_SNAKE_CASE    )
  # 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(__SCREAMING_SNAKE_CASE		,				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(**__SCREAMING_SNAKE_CASE		,				head_mask=__SCREAMING_SNAKE_CASE    )
    _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(__SCREAMING_SNAKE_CASE    ):
        _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(__SCREAMING_SNAKE_CASE		,				logits.detach().cpu().numpy()		,				axis=0    )
      _lowerCAmelCase						=		np.append(__SCREAMING_SNAKE_CASE		,				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(__SCREAMING_SNAKE_CASE		,				__SCREAMING_SNAKE_CASE    ).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(__SCREAMING_SNAKE_CASE    )
  logger.info('Head importance scores'    )
  print_ad_tensor(__SCREAMING_SNAKE_CASE    )
  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(__SCREAMING_SNAKE_CASE    )
  print_ad_tensor(__SCREAMING_SNAKE_CASE    )

  return attn_entropy, head_importance, preds, labels



def 	_a  (					__SCREAMING_SNAKE_CASE					:  int		,				__SCREAMING_SNAKE_CASE					:  str		,				__SCREAMING_SNAKE_CASE					:  Any    ):






  """simple docstring"""




  _lowerCAmelCase	,       _lowerCAmelCase	,       _lowerCAmelCase	,       _lowerCAmelCase						=		compute_heads_importance(__SCREAMING_SNAKE_CASE		,				__SCREAMING_SNAKE_CASE		,				__SCREAMING_SNAKE_CASE		,				compute_entropy=__SCREAMING_SNAKE_CASE    )
  _lowerCAmelCase						=		np.argmax(__SCREAMING_SNAKE_CASE		,				axis=1    ) if args.output_mode == 'classification' else np.squeeze(__SCREAMING_SNAKE_CASE    )
  _lowerCAmelCase						=		glue_compute_metrics(args.task_name		,				__SCREAMING_SNAKE_CASE		,				__SCREAMING_SNAKE_CASE    )[args.metric_name]
  logger.info('Pruning: original score: %f, threshold: %f'		,				__SCREAMING_SNAKE_CASE		,				original_score * args.masking_threshold    )

  _lowerCAmelCase						=		torch.ones_like(__SCREAMING_SNAKE_CASE    )
  _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(__SCREAMING_SNAKE_CASE    ) <= 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(__SCREAMING_SNAKE_CASE    )
    _lowerCAmelCase						=		new_head_mask.clone().detach()
    print_ad_tensor(__SCREAMING_SNAKE_CASE    )

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

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

  return head_mask



def 	_a  (					__SCREAMING_SNAKE_CASE					:  int		,				__SCREAMING_SNAKE_CASE					:  int		,				__SCREAMING_SNAKE_CASE					:  Tuple		,				__SCREAMING_SNAKE_CASE					:  Union[str, Any]    ):






  """simple docstring"""




  _lowerCAmelCase						=		datetime.now()
  _lowerCAmelCase	,       _lowerCAmelCase	,       _lowerCAmelCase	,       _lowerCAmelCase						=		compute_heads_importance(
      __SCREAMING_SNAKE_CASE		,				__SCREAMING_SNAKE_CASE		,				__SCREAMING_SNAKE_CASE		,				compute_entropy=__SCREAMING_SNAKE_CASE		,				compute_importance=__SCREAMING_SNAKE_CASE		,				head_mask=__SCREAMING_SNAKE_CASE    )
  _lowerCAmelCase						=		np.argmax(__SCREAMING_SNAKE_CASE		,				axis=1    ) if args.output_mode == 'classification' else np.squeeze(__SCREAMING_SNAKE_CASE    )
  _lowerCAmelCase						=		glue_compute_metrics(args.task_name		,				__SCREAMING_SNAKE_CASE		,				__SCREAMING_SNAKE_CASE    )[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(__SCREAMING_SNAKE_CASE    )    )
  }

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

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

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



def 	_a  (					):






  """simple docstring"""




  _lowerCAmelCase						=		argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      '--data_dir'		,				default=__SCREAMING_SNAKE_CASE		,				type=__SCREAMING_SNAKE_CASE		,				required=__SCREAMING_SNAKE_CASE		,				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=__SCREAMING_SNAKE_CASE		,				type=__SCREAMING_SNAKE_CASE		,				required=__SCREAMING_SNAKE_CASE		,				help='Path to pretrained model or model identifier from huggingface.co/models'		,				)
  parser.add_argument(
      '--task_name'		,				default=__SCREAMING_SNAKE_CASE		,				type=__SCREAMING_SNAKE_CASE		,				required=__SCREAMING_SNAKE_CASE		,				help='The name of the task to train selected in the list: ' + ', '.join(glue_processors.keys()    )		,				)
  parser.add_argument(
      '--output_dir'		,				default=__SCREAMING_SNAKE_CASE		,				type=__SCREAMING_SNAKE_CASE		,				required=__SCREAMING_SNAKE_CASE		,				help='The output directory where the model predictions and checkpoints will be written.'		,				)

  # Other parameters
  parser.add_argument(
      '--config_name'		,				default=''		,				type=__SCREAMING_SNAKE_CASE		,				help='Pretrained config name or path if not the same as model_name_or_path'		,				)
  parser.add_argument(
      '--tokenizer_name'		,				default=''		,				type=__SCREAMING_SNAKE_CASE		,				help='Pretrained tokenizer name or path if not the same as model_name_or_path'		,				)
  parser.add_argument(
      '--cache_dir'		,				default=__SCREAMING_SNAKE_CASE		,				type=__SCREAMING_SNAKE_CASE		,				help='Where do you want to store the pre-trained models downloaded from huggingface.co'		,				)
  parser.add_argument(
      '--data_subset'		,				type=__SCREAMING_SNAKE_CASE		,				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=__SCREAMING_SNAKE_CASE		,				help='masking threshold in term of metrics (stop masking when metric < threshold * original metric value).'		,				)
  parser.add_argument(
      '--masking_amount'		,				default=0.1		,				type=__SCREAMING_SNAKE_CASE		,				help='Amount to heads to masking at each masking step.'    )
  parser.add_argument('--metric_name'		,				default='acc'		,				type=__SCREAMING_SNAKE_CASE		,				help='Metric to use for head masking.'    )

  parser.add_argument(
      '--max_seq_length'		,				default=128		,				type=__SCREAMING_SNAKE_CASE		,				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=__SCREAMING_SNAKE_CASE		,				help='Batch size.'    )

  parser.add_argument('--seed'		,				type=__SCREAMING_SNAKE_CASE		,				default=42    )
  parser.add_argument('--local_rank'		,				type=__SCREAMING_SNAKE_CASE		,				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=__SCREAMING_SNAKE_CASE		,				default=''		,				help='Can be used for distant debugging.'    )
  parser.add_argument('--server_port'		,				type=__SCREAMING_SNAKE_CASE		,				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=__SCREAMING_SNAKE_CASE    )
    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(__SCREAMING_SNAKE_CASE    )

  # 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=__SCREAMING_SNAKE_CASE		,				finetuning_task=args.task_name		,				output_attentions=__SCREAMING_SNAKE_CASE		,				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=__SCREAMING_SNAKE_CASE		,				cache_dir=args.cache_dir		,				)

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

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

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

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

  # 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(__SCREAMING_SNAKE_CASE		,				__SCREAMING_SNAKE_CASE		,				__SCREAMING_SNAKE_CASE    )
    prune_heads(__SCREAMING_SNAKE_CASE		,				__SCREAMING_SNAKE_CASE		,				__SCREAMING_SNAKE_CASE		,				__SCREAMING_SNAKE_CASE    )


if __name__ == "__main__":
       main()


import argparse
import os

import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing the checkpointing capability,
# and builds off the `nlp_example.py` script.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
#   - single CPU or single GPU
#   - multi GPUS (using PyTorch distributed mode)
#   - (multi) TPUs
#   - fp16 (mixed-precision) or fp32 (normal precision)
#
# To help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################

_UpperCamelCase:					Dict     =16
_UpperCamelCase:					Optional[int]     =32



def 	_a  (					__SCREAMING_SNAKE_CASE					:  Accelerator		,				__SCREAMING_SNAKE_CASE					:  int = 16    ):






  """simple docstring"""




  _lowerCAmelCase						=		AutoTokenizer.from_pretrained('bert-base-cased'    )
  _lowerCAmelCase						=		load_dataset('glue'		,				'mrpc'    )

  def tokenize_function(__SCREAMING_SNAKE_CASE					:  str    ):
    # max_length=None => use the model max length (it's actually the default)
    _lowerCAmelCase						=		tokenizer(examples['sentence1']		,				examples['sentence2']		,				truncation=__SCREAMING_SNAKE_CASE		,				max_length=__SCREAMING_SNAKE_CASE    )
    return outputs

  # Apply the method we just defined to all the examples in all the splits of the dataset
  # starting with the main process first:
  with accelerator.main_process_first():
    _lowerCAmelCase						=		datasets.map(
        __SCREAMING_SNAKE_CASE		,				batched=__SCREAMING_SNAKE_CASE		,				remove_columns=['idx', 'sentence1', 'sentence2']		,				)

  # We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
  # transformers library
  _lowerCAmelCase						=		tokenized_datasets.rename_column('label'		,				'labels'    )

  def collate_fn(__SCREAMING_SNAKE_CASE					:  Any    ):
    # On TPU it's best to pad everything to the same length or training will be very slow.
    _lowerCAmelCase						=		128 if accelerator.distributed_type == DistributedType.TPU else None
    # When using mixed precision we want round multiples of 8/16
    if accelerator.mixed_precision == "fp8":
      _lowerCAmelCase						=		16
    elif accelerator.mixed_precision != "no":
      _lowerCAmelCase						=		8
    else:
      _lowerCAmelCase						=		None

    return tokenizer.pad(
        __SCREAMING_SNAKE_CASE		,				padding='longest'		,				max_length=__SCREAMING_SNAKE_CASE		,				pad_to_multiple_of=__SCREAMING_SNAKE_CASE		,				return_tensors='pt'		,				)

  # Instantiate dataloaders.
  _lowerCAmelCase						=		DataLoader(
      tokenized_datasets['train']		,				shuffle=__SCREAMING_SNAKE_CASE		,				collate_fn=__SCREAMING_SNAKE_CASE		,				batch_size=__SCREAMING_SNAKE_CASE    )
  _lowerCAmelCase						=		DataLoader(
      tokenized_datasets['validation']		,				shuffle=__SCREAMING_SNAKE_CASE		,				collate_fn=__SCREAMING_SNAKE_CASE		,				batch_size=__SCREAMING_SNAKE_CASE    )

  return train_dataloader, eval_dataloader


# For testing only
if os.environ.get('TESTING_MOCKED_DATALOADERS', None) == "1":
       from accelerate.test_utils.training import mocked_dataloaders

       _UpperCamelCase:					int     =mocked_dataloaders  # noqa: F811



def 	_a  (					__SCREAMING_SNAKE_CASE					:  Dict		,				__SCREAMING_SNAKE_CASE					:  int    ):






  """simple docstring"""




  if os.environ.get('TESTING_MOCKED_DATALOADERS'		,				__SCREAMING_SNAKE_CASE    ) == "1":
    _lowerCAmelCase						=		2
  # Initialize accelerator
  _lowerCAmelCase						=		Accelerator(cpu=args.cpu		,				mixed_precision=args.mixed_precision    )
  # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
  _lowerCAmelCase						=		config['lr']
  _lowerCAmelCase						=		int(config['num_epochs']    )
  _lowerCAmelCase						=		int(config['seed']    )
  _lowerCAmelCase						=		int(config['batch_size']    )

  # New Code #
  # Parse out whether we are saving every epoch or after a certain number of batches
  if hasattr(args.checkpointing_steps		,				'isdigit'    ):
    if args.checkpointing_steps == "epoch":
      _lowerCAmelCase						=		args.checkpointing_steps
    elif args.checkpointing_steps.isdigit():
      _lowerCAmelCase						=		int(args.checkpointing_steps    )
    else:
      raise ValueError(
          f'''Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed.'''    )
  else:
    _lowerCAmelCase						=		None

  set_seed(__SCREAMING_SNAKE_CASE    )

  _lowerCAmelCase	,       _lowerCAmelCase						=		get_dataloaders(__SCREAMING_SNAKE_CASE		,				__SCREAMING_SNAKE_CASE    )
  _lowerCAmelCase						=		evaluate.load('glue'		,				'mrpc'    )

  # If the batch size is too big we use gradient accumulation
  _lowerCAmelCase						=		1
  if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
    _lowerCAmelCase						=		batch_size // MAX_GPU_BATCH_SIZE
    _lowerCAmelCase						=		MAX_GPU_BATCH_SIZE

  # Instantiate the model (we build the model here so that the seed also control new weights initialization)
  _lowerCAmelCase						=		AutoModelForSequenceClassification.from_pretrained('bert-base-cased'		,				return_dict=__SCREAMING_SNAKE_CASE    )

  # We could avoid this line since the accelerator is set with `device_placement=True` (default value).
  # Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
  # creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
  _lowerCAmelCase						=		model.to(accelerator.device    )

  # Instantiate optimizer
  _lowerCAmelCase						=		AdamW(params=model.parameters()		,				lr=__SCREAMING_SNAKE_CASE    )

  # Instantiate scheduler
  _lowerCAmelCase						=		get_linear_schedule_with_warmup(
      optimizer=__SCREAMING_SNAKE_CASE		,				num_warmup_steps=100		,				num_training_steps=(len(__SCREAMING_SNAKE_CASE    ) * num_epochs) // gradient_accumulation_steps		,				)

  # Prepare everything
  # There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
  # prepare method.
  _lowerCAmelCase	,       _lowerCAmelCase	,       _lowerCAmelCase	,       _lowerCAmelCase	,       _lowerCAmelCase						=		accelerator.prepare(
      __SCREAMING_SNAKE_CASE		,				__SCREAMING_SNAKE_CASE		,				__SCREAMING_SNAKE_CASE		,				__SCREAMING_SNAKE_CASE		,				__SCREAMING_SNAKE_CASE    )

  # New Code #
  # We need to keep track of how many total steps we have iterated over
  _lowerCAmelCase						=		0
  # We also need to keep track of the stating epoch so files are named properly
  _lowerCAmelCase						=		0

  # We need to load the checkpoint back in before training here with `load_state`
  # The total number of epochs is adjusted based on where the state is being loaded from,
  # as we assume continuation of the same training script
  if args.resume_from_checkpoint:
    if args.resume_from_checkpoint is not None or args.resume_from_checkpoint != "":
      accelerator.print(f'''Resumed from checkpoint: {args.resume_from_checkpoint}'''    )
      accelerator.load_state(args.resume_from_checkpoint    )
      _lowerCAmelCase						=		os.path.basename(args.resume_from_checkpoint    )
    else:
      # Get the most recent checkpoint
      _lowerCAmelCase						=		[f.name for f in os.scandir(os.getcwd()    ) if f.is_dir()]
      dirs.sort(key=os.path.getctime    )
      _lowerCAmelCase						=		dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
    # Extract `epoch_{i}` or `step_{i}`
    _lowerCAmelCase						=		os.path.splitext(__SCREAMING_SNAKE_CASE    )[0]

    if "epoch" in training_difference:
      _lowerCAmelCase						=		int(training_difference.replace('epoch_'		,				''    )    ) + 1
      _lowerCAmelCase						=		None
    else:
      _lowerCAmelCase						=		int(training_difference.replace('step_'		,				''    )    )
      _lowerCAmelCase						=		resume_step // len(__SCREAMING_SNAKE_CASE    )
      resume_step -= starting_epoch * len(__SCREAMING_SNAKE_CASE    )

    # Now we train the model
  for epoch in range(__SCREAMING_SNAKE_CASE		,				__SCREAMING_SNAKE_CASE    ):
    model.train()
    # New Code #
    if args.resume_from_checkpoint and epoch == starting_epoch and resume_step is not None:
      # We need to skip steps until we reach the resumed step
      _lowerCAmelCase						=		accelerator.skip_first_batches(__SCREAMING_SNAKE_CASE		,				__SCREAMING_SNAKE_CASE    )
      overall_step += resume_step
    else:
      # After the first iteration though, we need to go back to the original dataloader
      _lowerCAmelCase						=		train_dataloader
    for step, batch in enumerate(__SCREAMING_SNAKE_CASE    ):
      # We could avoid this line since we set the accelerator with `device_placement=True`.
      batch.to(accelerator.device    )
      _lowerCAmelCase						=		model(**__SCREAMING_SNAKE_CASE    )
      _lowerCAmelCase						=		outputs.loss
      _lowerCAmelCase						=		loss / gradient_accumulation_steps
      accelerator.backward(__SCREAMING_SNAKE_CASE    )
      if step % gradient_accumulation_steps == 0:
        optimizer.step()
        lr_scheduler.step()
        optimizer.zero_grad()
      # New Code #
      overall_step += 1

      # New Code #
      # We save the model, optimizer, lr_scheduler, and seed states by calling `save_state`
      # These are saved to folders named `step_{overall_step}`
      # Will contain files: "pytorch_model.bin", "optimizer.bin", "scheduler.bin", and "random_states.pkl"
      # If mixed precision was used, will also save a "scalar.bin" file
      if isinstance(__SCREAMING_SNAKE_CASE		,				__SCREAMING_SNAKE_CASE    ):
        _lowerCAmelCase						=		f'''step_{overall_step}'''
        if overall_step % checkpointing_steps == 0:
          if args.output_dir is not None:
            _lowerCAmelCase						=		os.path.join(args.output_dir		,				__SCREAMING_SNAKE_CASE    )
          accelerator.save_state(__SCREAMING_SNAKE_CASE    )

    model.eval()
    for step, batch in enumerate(__SCREAMING_SNAKE_CASE    ):
      # We could avoid this line since we set the accelerator with `device_placement=True` (the default).
      batch.to(accelerator.device    )
      with torch.no_grad():
        _lowerCAmelCase						=		model(**__SCREAMING_SNAKE_CASE    )
      _lowerCAmelCase						=		outputs.logits.argmax(dim=-1    )
      _lowerCAmelCase	,       _lowerCAmelCase						=		accelerator.gather_for_metrics((predictions, batch['labels'])    )
      metric.add_batch(
          predictions=__SCREAMING_SNAKE_CASE		,				references=__SCREAMING_SNAKE_CASE		,				)

    _lowerCAmelCase						=		metric.compute()
    # Use accelerator.print to print only on the main process.
    accelerator.print(f'''epoch {epoch}:'''		,				__SCREAMING_SNAKE_CASE    )

    # New Code #
    # We save the model, optimizer, lr_scheduler, and seed states by calling `save_state`
    # These are saved to folders named `epoch_{epoch}`
    # Will contain files: "pytorch_model.bin", "optimizer.bin", "scheduler.bin", and "random_states.pkl"
    # If mixed precision was used, will also save a "scalar.bin" file
    if checkpointing_steps == "epoch":
      _lowerCAmelCase						=		f'''epoch_{epoch}'''
      if args.output_dir is not None:
        _lowerCAmelCase						=		os.path.join(args.output_dir		,				__SCREAMING_SNAKE_CASE    )
      accelerator.save_state(__SCREAMING_SNAKE_CASE    )



def 	_a  (					):






  """simple docstring"""




  _lowerCAmelCase						=		argparse.ArgumentParser(description='Simple example of training script.'    )
  parser.add_argument(
      '--mixed_precision'		,				type=__SCREAMING_SNAKE_CASE		,				default=__SCREAMING_SNAKE_CASE		,				choices=['no', 'fp16', 'bf16', 'fp8']		,				help='Whether to use mixed precision. Choose'
      'between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10.'
      'and an Nvidia Ampere GPU.'		,				)
  parser.add_argument('--cpu'		,				action='store_true'		,				help='If passed, will train on the CPU.'    )
  parser.add_argument(
      '--checkpointing_steps'		,				type=__SCREAMING_SNAKE_CASE		,				default=__SCREAMING_SNAKE_CASE		,				help='Whether the various states should be saved at the end of every n steps, or \'epoch\' for each epoch.'		,				)
  parser.add_argument(
      '--output_dir'		,				type=__SCREAMING_SNAKE_CASE		,				default='.'		,				help='Optional save directory where all checkpoint folders will be stored. Default is the current working directory.'		,				)
  parser.add_argument(
      '--resume_from_checkpoint'		,				type=__SCREAMING_SNAKE_CASE		,				default=__SCREAMING_SNAKE_CASE		,				help='If the training should continue from a checkpoint folder.'		,				)
  _lowerCAmelCase						=		parser.parse_args()
  _lowerCAmelCase						=		{'lr': 2E-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
  training_function(__SCREAMING_SNAKE_CASE		,				__SCREAMING_SNAKE_CASE    )


if __name__ == "__main__":
       main()
