



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


a_   :Any        =      16
a_   :Any        =      32
def        lowercase_							(A : Accelerator		,					A : int = 1_6		,					A : str = "bert-base-cased"      ):
       snake_case__							:	Tuple   	=	AutoTokenizer.from_pretrained(A      )
       snake_case__							:	Union[str, Any]   	=	load_dataset('glue'		,					'mrpc'      )

       def tokenize_function(A : List[Any]      ):
              # max_length=None => use the model max length (it's actually the default)
              snake_case__							:	Union[str, Any]   	=	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
       snake_case__							:	List[str]   	=	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
       snake_case__							:	Tuple   	=	tokenized_datasets.rename_column('label'		,					'labels'      )

       def collate_fn(A : Dict      ):
              # 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=1_2_8		,					return_tensors='pt'      )
              return tokenizer.pad(A		,					padding='longest'		,					return_tensors='pt'      )

       # Instantiate dataloaders.
       snake_case__							:	List[str]   	=	DataLoader(
           tokenized_datasets['train']		,					shuffle=A		,					collate_fn=A		,					batch_size=A      )
       snake_case__							:	Optional[Any]   	=	DataLoader(
           tokenized_datasets['validation']		,					shuffle=A		,					collate_fn=A		,					batch_size=A      )

       return train_dataloader, eval_dataloader
def        lowercase_							(A : Dict		,					A : List[str]      ):
       # Initialize accelerator
       snake_case__							:	Tuple   	=	Accelerator()

       # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
       snake_case__							:	Tuple   	=	config['lr']
       snake_case__							:	List[Any]   	=	int(config['num_epochs']      )
       snake_case__							:	Optional[int]   	=	int(config['seed']      )
       snake_case__							:	Any   	=	int(config['batch_size']      )
       snake_case__							:	List[str]   	=	args.model_name_or_path

       set_seed(A      )
       snake_case__	,				snake_case__							:	Optional[Any]   	=	get_dataloaders(A		,					A		,					A      )

       # Instantiate the model (we build the model here so that the seed also control new weights initialization)
       snake_case__							:	Any   	=	AutoModelForSequenceClassification.from_pretrained(A		,					return_dict=A      )

       # Instantiate optimizer
       snake_case__							:	List[Any]   	=	(
           AdamW
           if accelerator.state.deepspeed_plugin is None
           or 'optimizer' not in accelerator.state.deepspeed_plugin.deepspeed_config
           else DummyOptim
       )
       snake_case__							:	Optional[int]   	=	optimizer_cls(params=model.parameters()		,					lr=A      )

       if accelerator.state.deepspeed_plugin is not None:
              snake_case__							:	Any   	=	accelerator.state.deepspeed_plugin.deepspeed_config[
                  'gradient_accumulation_steps'
              ]
       else:
              snake_case__							:	int   	=	1
       snake_case__							:	Any   	=	(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
       ):
              snake_case__							:	str   	=	get_linear_schedule_with_warmup(
                  optimizer=A		,					num_warmup_steps=0		,					num_training_steps=A		,					)
       else:
              snake_case__							:	List[str]   	=	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.
       snake_case__	,				snake_case__	,				snake_case__	,				snake_case__	,				snake_case__							:	Dict   	=	accelerator.prepare(
           A		,					A		,					A		,					A		,					A      )

       # We need to keep track of how many total steps we have iterated over
       snake_case__							:	List[str]   	=	0
       # We also need to keep track of the stating epoch so files are named properly
       snake_case__							:	Union[str, Any]   	=	0

       # Now we train the model
       snake_case__							:	Any   	=	evaluate.load('glue'		,					'mrpc'      )
       snake_case__							:	List[str]   	=	0
       snake_case__							:	List[str]   	=	{}
       for epoch in range(A		,					A      ):
              model.train()
              for step, batch in enumerate(A      ):
                     snake_case__							:	str   	=	model(**A      )
                     snake_case__							:	Dict   	=	outputs.loss
                     snake_case__							:	Dict   	=	loss / gradient_accumulation_steps
                     accelerator.backward(A      )
                     if step % gradient_accumulation_steps == 0:
                            optimizer.step()
                            lr_scheduler.step()
                            optimizer.zero_grad()

                     overall_step += 1

              model.eval()
              snake_case__							:	List[Any]   	=	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():
                            snake_case__							:	Union[str, Any]   	=	model(**A      )
                     snake_case__							:	Tuple   	=	outputs.logits.argmax(dim=-1      )
                     # It is slightly faster to call this once, than multiple times
                     snake_case__	,				snake_case__							:	int   	=	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:
                                   snake_case__							:	int   	=	predictions[: len(eval_dataloader.dataset      ) - samples_seen]
                                   snake_case__							:	List[Any]   	=	references[: len(eval_dataloader.dataset      ) - samples_seen]
                            else:
                                   samples_seen += references.shape[0]
                     metric.add_batch(
                         predictions=A		,					references=A		,					)

              snake_case__							:	Optional[Any]   	=	metric.compute()
              # Use accelerator.print to print only on the main process.
              accelerator.print(F'''epoch {epoch}:'''		,					A      )
              snake_case__							:	Optional[Any]   	=	eval_metric['accuracy']

              if best_performance < eval_metric["accuracy"]:
                     snake_case__							:	List[Any]   	=	eval_metric['accuracy']

       if args.performance_lower_bound is not None:
              assert (
                  args.performance_lower_bound <= best_performance
              ), F'''Best performance metric {best_performance} is lower than the lower bound {args.performance_lower_bound}'''

       accelerator.wait_for_everyone()
       if accelerator.is_main_process:
              with open(os.path.join(args.output_dir		,					'all_results.json'      )		,					'w'      ) as f:
                     json.dump(A		,					A      )
def        lowercase_							():
       snake_case__							:	List[Any]   	=	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(
           '--performance_lower_bound'		,					type=A		,					default=A		,					help='Optional lower bound for the performance metric. If set, the training will throw error when the performance metric drops below this value.'		,					)
       parser.add_argument(
           '--num_epochs'		,					type=A		,					default=3		,					help='Number of train epochs.'		,					)
       snake_case__							:	str   	=	parser.parse_args()
       snake_case__							:	Dict   	=	{'lr': 2e-5, 'num_epochs': args.num_epochs, 'seed': 4_2, 'batch_size': 1_6}
       training_function(A		,					A      )


if __name__ == "__main__":
 main()











from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


a_   :Optional[Any]        =      {"configuration_reformer": ["REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "ReformerConfig"]}

try:
 if not is_sentencepiece_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a_   :str        =      ["ReformerTokenizer"]

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a_   :int        =      ["ReformerTokenizerFast"]

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a_   :List[str]        =      [
     "REFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
     "ReformerAttention",
     "ReformerForMaskedLM",
     "ReformerForQuestionAnswering",
     "ReformerForSequenceClassification",
     "ReformerLayer",
     "ReformerModel",
     "ReformerModelWithLMHead",
     "ReformerPreTrainedModel",
 ]


if TYPE_CHECKING:
 from .configuration_reformer import REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, ReformerConfig

 try:
  if not is_sentencepiece_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_reformer import ReformerTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_reformer_fast import ReformerTokenizerFast

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_reformer import (
      REFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
      ReformerAttention,
      ReformerForMaskedLM,
      ReformerForQuestionAnswering,
      ReformerForSequenceClassification,
      ReformerLayer,
      ReformerModel,
      ReformerModelWithLMHead,
      ReformerPreTrainedModel,
  )

else:
 import sys

 a_   :Optional[Any]        =      _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)






