

'''simple docstring'''






import argparse
import json
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
from accelerate.utils.deepspeed import DummyOptim, DummyScheduler


lowerCAmelCase__											=	16
lowerCAmelCase__											=	32


def 		_A     (    A__     ,				A__ = 16     ,				A__ = "bert-base-cased"      ):






 """simple docstring"""



 __lowercase							  = AutoTokenizer.from_pretrained(A__      )
 __lowercase							  = load_dataset('''glue'''     ,				'''mrpc'''      )

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

 # Apply the method we just defined to all the examples in all the splits of the dataset
 __lowercase							  = datasets.map(
     A__     ,				batched=A__     ,				remove_columns=['''idx''', '''sentence1''', '''sentence2''']     ,				load_from_cache_file=A__      )

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

 def collate_fn(A__      ):
  # On TPU it's best to pad everything to the same length or training will be very slow.
  if accelerator.distributed_type == DistributedType.TPU:
   return tokenizer.pad(A__     ,				padding='''max_length'''     ,				max_length=128     ,				return_tensors='''pt'''      )
  return tokenizer.pad(A__     ,				padding='''longest'''     ,				return_tensors='''pt'''      )

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

 return train_dataloader, eval_dataloader


def 		_A     (    A__     ,				A__     ,				A__     ,				A__      ):






 """simple docstring"""



 model.eval()
 __lowercase							  = 0
 for step, batch in enumerate(A__      ):
  # We could avoid this line since we set the accelerator with `device_placement=True`.
  batch.to(accelerator.device      )
  with torch.no_grad():
   __lowercase							  = model(**A__      )
  __lowercase							  = outputs.logits.argmax(dim=-1      )
  # It is slightly faster to call this once, than multiple times
  __lowercase  ,	__lowercase							  = accelerator.gather(
      (predictions, batch['''labels'''])      )  # If we are in a multiprocess environment, the last batch has duplicates
  if accelerator.use_distributed:
   if step == len(A__      ) - 1:
    __lowercase							  = predictions[: len(eval_dataloader.dataset      ) - samples_seen]
    __lowercase							  = references[: len(eval_dataloader.dataset      ) - samples_seen]
   else:
    samples_seen += references.shape[0]
  metric.add_batch(
      predictions=A__     ,				references=A__     ,				)

 __lowercase							  = metric.compute()
 return eval_metric["accuracy"]


def 		_A     (    A__     ,				A__      ):






 """simple docstring"""



 __lowercase							  = Accelerator()

 # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
 __lowercase							  = config['''lr''']
 __lowercase							  = int(config['''num_epochs''']      )
 __lowercase							  = int(config['''seed''']      )
 __lowercase							  = int(config['''batch_size''']      )
 __lowercase							  = args.model_name_or_path

 set_seed(A__      )
 __lowercase  ,	__lowercase							  = get_dataloaders(A__     ,				A__     ,				A__      )

 # Instantiate the model (we build the model here so that the seed also control new weights initialization)
 __lowercase							  = AutoModelForSequenceClassification.from_pretrained(A__     ,				return_dict=A__      )

 # Instantiate optimizer
 __lowercase							  = (
     AdamW
     if accelerator.state.deepspeed_plugin is None
     or '''optimizer''' not in accelerator.state.deepspeed_plugin.deepspeed_config
     else DummyOptim
 )
 __lowercase							  = optimizer_cls(params=model.parameters()     ,				lr=A__      )

 if accelerator.state.deepspeed_plugin is not None:
  __lowercase							  = accelerator.state.deepspeed_plugin.deepspeed_config[
      '''gradient_accumulation_steps'''
  ]
 else:
  __lowercase							  = 1
 __lowercase							  = (len(A__      ) * num_epochs) // gradient_accumulation_steps

 # Instantiate scheduler
 if (
     accelerator.state.deepspeed_plugin is None
     or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
 ):
  __lowercase							  = get_linear_schedule_with_warmup(
      optimizer=A__     ,				num_warmup_steps=0     ,				num_training_steps=A__     ,				)
 else:
  __lowercase							  = DummyScheduler(A__     ,				total_num_steps=A__     ,				warmup_num_steps=0      )

 # 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.
 __lowercase  ,	__lowercase  ,	__lowercase  ,	__lowercase  ,	__lowercase							  = accelerator.prepare(
     A__     ,				A__     ,				A__     ,				A__     ,				A__      )

 # We need to keep track of how many total steps we have iterated over
 __lowercase							  = 0
 # We also need to keep track of the stating epoch so files are named properly
 __lowercase							  = 0
 __lowercase							  = evaluate.load('''glue'''     ,				'''mrpc'''      )
 __lowercase							  = num_epochs

 if args.partial_train_epoch is not None:
  __lowercase							  = args.partial_train_epoch

 if args.resume_from_checkpoint:
  accelerator.load_state(args.resume_from_checkpoint      )
  __lowercase							  = args.resume_from_checkpoint.split('''epoch_'''      )[1]
  __lowercase							  = ''''''
  for char in epoch_string:
   if char.isdigit():
    state_epoch_num += char
   else:
    break
  __lowercase							  = int(A__      ) + 1
  __lowercase							  = evaluation_loop(A__     ,				A__     ,				A__     ,				A__      )
  accelerator.print('''resumed checkpoint performance:'''     ,				A__      )
  accelerator.print('''resumed checkpoint\'s scheduler\'s lr:'''     ,				lr_scheduler.get_lr()[0]      )
  accelerator.print('''resumed optimizers\'s lr:'''     ,				optimizer.param_groups[0]['''lr''']      )
  with open(os.path.join(args.output_dir     ,				F"state_{starting_epoch-1}.json"      )     ,				'''r'''      ) as f:
   __lowercase							  = json.load(A__      )
   assert resumed_state["accuracy"] == accuracy, "Accuracy mismatch, loading from checkpoint failed"
   assert (
       resumed_state["lr"] == lr_scheduler.get_lr()[0]
   ), "Scheduler learning rate mismatch, loading from checkpoint failed"
   assert (
       resumed_state["optimizer_lr"] == optimizer.param_groups[0]["lr"]
   ), "Optimizer learning rate mismatch, loading from checkpoint failed"
   assert resumed_state["epoch"] == starting_epoch - 1, "Epoch mismatch, loading from checkpoint failed"
   return

    # Now we train the model
 __lowercase							  = {}
 for epoch in range(A__     ,				A__      ):
  model.train()
  for step, batch in enumerate(A__      ):
   __lowercase							  = model(**A__      )
   __lowercase							  = outputs.loss
   __lowercase							  = loss / gradient_accumulation_steps
   accelerator.backward(A__      )
   if step % gradient_accumulation_steps == 0:
    optimizer.step()
    lr_scheduler.step()
    optimizer.zero_grad()

   overall_step += 1
  __lowercase							  = F"epoch_{epoch}"
  __lowercase							  = os.path.join(args.output_dir     ,				A__      )
  accelerator.save_state(A__      )
  __lowercase							  = evaluation_loop(A__     ,				A__     ,				A__     ,				A__      )
  __lowercase							  = accuracy
  __lowercase							  = lr_scheduler.get_lr()[0]
  __lowercase							  = optimizer.param_groups[0]['''lr''']
  __lowercase							  = epoch
  __lowercase							  = overall_step
  accelerator.print(F"epoch {epoch}:"     ,				A__      )

  accelerator.wait_for_everyone()
  if accelerator.is_main_process:
   with open(os.path.join(args.output_dir     ,				F"state_{epoch}.json"      )     ,				'''w'''      ) as f:
    json.dump(A__     ,				A__      )


def 		_A     (    ):






 """simple docstring"""



 __lowercase							  = argparse.ArgumentParser(description='''Simple example of training script tracking peak GPU memory usage.'''      )
 parser.add_argument(
     '''--model_name_or_path'''     ,				type=A__     ,				default='''bert-base-cased'''     ,				help='''Path to pretrained model or model identifier from huggingface.co/models.'''     ,				required=A__     ,				)
 parser.add_argument(
     '''--output_dir'''     ,				type=A__     ,				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=A__     ,				default=A__     ,				help='''If the training should continue from a checkpoint folder.'''     ,				)
 parser.add_argument(
     '''--partial_train_epoch'''     ,				type=A__     ,				default=A__     ,				help='''If passed, the training will stop after this number of epochs.'''     ,				)
 parser.add_argument(
     '''--num_epochs'''     ,				type=A__     ,				default=2     ,				help='''Number of train epochs.'''     ,				)
 __lowercase							  = parser.parse_args()
 __lowercase							  = {'''lr''': 2e-5, '''num_epochs''': args.num_epochs, '''seed''': 42, '''batch_size''': 16}

 training_function(A__     ,				A__      )


if __name__ == "__main__":
 main()




'''simple docstring'''






import unicodedata
from dataclasses import dataclass
from typing import Optional, Union

import numpy as np

from transformers.data.data_collator import DataCollatorMixin
from transformers.file_utils import PaddingStrategy
from transformers.tokenization_utils_base import PreTrainedTokenizerBase


def 		_A     (    A__     ,				A__     ,				A__     ,				A__      ):






 """simple docstring"""



 if isinstance(A__     ,				A__      ):
  __lowercase							  = np.full((len(A__      ), sequence_length, 2)     ,				A__      )
 else:
  __lowercase							  = np.full((len(A__      ), sequence_length)     ,				A__      )

 for i, tensor in enumerate(A__      ):
  if padding_side == "right":
   if isinstance(A__     ,				A__      ):
    __lowercase							  = tensor[:sequence_length]
   else:
    __lowercase							  = tensor[:sequence_length]
  else:
   if isinstance(A__     ,				A__      ):
    __lowercase							  = tensor[:sequence_length]
   else:
    __lowercase							  = tensor[:sequence_length]

 return out_tensor.tolist()


def 		_A     (    A__      ):






 """simple docstring"""



 __lowercase							  = ord(A__      )
 if (cp >= 33 and cp <= 47) or (cp >= 58 and cp <= 64) or (cp >= 91 and cp <= 96) or (cp >= 123 and cp <= 126):
  return True
 __lowercase							  = unicodedata.category(A__      )
 if cat.startswith('''P'''      ):
  return True
 return False






@dataclass
class 			lowercase_	(lowerCamelCase__      ):




  """simple docstring"""




  SCREAMING_SNAKE_CASE    :  PreTrainedTokenizerBase
  SCREAMING_SNAKE_CASE    :  Union[bool, str, PaddingStrategy]							=			True
  SCREAMING_SNAKE_CASE    :  Optional[int]							=			None
  SCREAMING_SNAKE_CASE    :  Optional[int]							=			None
  SCREAMING_SNAKE_CASE    :  int							=			-1_0_0
  SCREAMING_SNAKE_CASE    :  str							=			"pt"






  def 					SCREAMING_SNAKE_CASE      (      self   :     List[str]					,lowercase__   :     List[str]			):
   import torch

   __lowercase							  = '''label''' if '''label''' in features[0].keys() else '''labels'''
   __lowercase							  = [feature[label_name] for feature in features] if label_name in features[0].keys() else None
   __lowercase							  = self.tokenizer.pad(
       lowercase__					,padding=self.padding					,max_length=self.max_length					,pad_to_multiple_of=self.pad_to_multiple_of					,return_tensors='''pt''' if labels is None else None					,)

   if labels is None:
    return batch

   __lowercase							  = torch.tensor(batch['''entity_ids''']			).shape[1]
   __lowercase							  = self.tokenizer.padding_side
   if padding_side == "right":
    __lowercase							  = [
        list(lowercase__			) + [self.label_pad_token_id] * (sequence_length - len(lowercase__			)) for label in labels
    ]
   else:
    __lowercase							  = [
        [self.label_pad_token_id] * (sequence_length - len(lowercase__			)) + list(lowercase__			) for label in labels
    ]

   __lowercase							  = [feature['''ner_tags'''] for feature in features]
   __lowercase							  = padding_tensor(lowercase__					,-1					,lowercase__					,lowercase__			)
   __lowercase							  = [feature['''original_entity_spans'''] for feature in features]
   __lowercase							  = padding_tensor(lowercase__					,(-1, -1)					,lowercase__					,lowercase__			)
   __lowercase							  = {k: torch.tensor(lowercase__					,dtype=torch.intaa			) for k, v in batch.items()}

   return batch

