




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 how to properly calculate the metrics on the
# validation dataset when in a distributed system, 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
#
########################################################################


__a          =			16
__a          =			32







def lowerCamelCase__       (      _lowercase			,						_lowercase = 16				):

     '''simple docstring'''


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

     def tokenize_function(_lowercase				):
          # max_length=None => use the model max length (it's actually the default)
          UpperCAmelCase_       =      tokenizer(examples['''sentence1''']			,						examples['''sentence2''']			,						truncation=__lowerCAmelCase			,						max_length=__lowerCAmelCase				)
          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():
          UpperCAmelCase_       =      datasets.map(
              __lowerCAmelCase			,						batched=__lowerCAmelCase			,						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
     UpperCAmelCase_       =      tokenized_datasets.rename_column('''label'''			,						'''labels'''				)

     def collate_fn(_lowercase				):
          # On TPU it's best to pad everything to the same length or training will be very slow.
          UpperCAmelCase_       =      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":
               UpperCAmelCase_       =      16
          elif accelerator.mixed_precision != "no":
               UpperCAmelCase_       =      8
          else:
               UpperCAmelCase_       =      None

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

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

     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

     __a          =			mocked_dataloaders  # noqa: F811







def lowerCamelCase__       (      _lowercase			,						_lowercase				):

     '''simple docstring'''


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

     UpperCAmelCase_       =      evaluate.load('''glue'''			,						'''mrpc'''				)

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

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

     # 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).
     UpperCAmelCase_       =      model.to(accelerator.device				)

     # Instantiate optimizer
     UpperCAmelCase_       =      AdamW(params=model.parameters()			,						lr=__lowerCAmelCase				)

     # Instantiate scheduler
     UpperCAmelCase_       =      get_linear_schedule_with_warmup(
         optimizer=__lowerCAmelCase			,						num_warmup_steps=100			,						num_training_steps=(len(__lowerCAmelCase				) * 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.
     UpperCAmelCase_       =      accelerator.prepare(
         __lowerCAmelCase			,						__lowerCAmelCase			,						__lowerCAmelCase			,						__lowerCAmelCase			,						__lowerCAmelCase				)

     # Now we train the model
     for epoch in range(__lowerCAmelCase				):
          model.train()
          for step, batch in enumerate(__lowerCAmelCase				):
               # We could avoid this line since we set the accelerator with `device_placement=True`.
               batch.to(accelerator.device				)
               UpperCAmelCase_       =      model(**__lowerCAmelCase				)
               UpperCAmelCase_       =      outputs.loss
               UpperCAmelCase_       =      loss / gradient_accumulation_steps
               accelerator.backward(__lowerCAmelCase				)
               if step % gradient_accumulation_steps == 0:
                    optimizer.step()
                    lr_scheduler.step()
                    optimizer.zero_grad()

          model.eval()
          UpperCAmelCase_       =      0
          for step, batch in enumerate(__lowerCAmelCase				):
               # We could avoid this line since we set the accelerator with `device_placement=True`.
               batch.to(accelerator.device				)
               with torch.no_grad():
                    UpperCAmelCase_       =      model(**__lowerCAmelCase				)
               UpperCAmelCase_       =      outputs.logits.argmax(dim=-1				)
               UpperCAmelCase_       =      accelerator.gather((predictions, batch['''labels'''])				)
               # New Code #
               # First we check if it's a distributed system
               if accelerator.use_distributed:
                    # Then see if we're on the last batch of our eval dataloader
                    if step == len(__lowerCAmelCase				) - 1:
                         # Last batch needs to be truncated on distributed systems as it contains additional samples
                         UpperCAmelCase_       =      predictions[: len(eval_dataloader.dataset				) - samples_seen]
                         UpperCAmelCase_       =      references[: len(eval_dataloader.dataset				) - samples_seen]
                    else:
                         # Otherwise we add the number of samples seen
                         samples_seen += references.shape[0]
            # All of this can be avoided if you use `Accelerator.gather_for_metrics` instead of `Accelerator.gather`:
            # accelerator.gather_for_metrics((predictions, batch["labels"]))
               metric.add_batch(
                   predictions=__lowerCAmelCase			,						references=__lowerCAmelCase			,						)

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







def lowerCamelCase__       (      ):

     '''simple docstring'''


     UpperCAmelCase_       =      argparse.ArgumentParser(description='''Simple example of training script.'''				)
     parser.add_argument(
         '''--mixed_precision'''			,						type=__lowerCAmelCase			,						default=__lowerCAmelCase			,						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.'''				)
     UpperCAmelCase_       =      parser.parse_args()
     UpperCAmelCase_       =      {'''lr''': 2E-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
     training_function(__lowerCAmelCase			,						__lowerCAmelCase				)


if __name__ == "__main__":
     main()





import argparse
import logging
import sys
from unittest.mock import patch

import run_glue_deebert

from transformers.testing_utils import TestCasePlus, get_gpu_count, require_torch_non_multi_gpu, slow


logging.basicConfig(level=logging.DEBUG)

__a          =			logging.getLogger()







def lowerCamelCase__       (      ):

     '''simple docstring'''


     UpperCAmelCase_      :							List[str]       =      argparse.ArgumentParser()
     parser.add_argument('''-f'''				)
     UpperCAmelCase_      :							Dict       =      parser.parse_args()
     return args.f



class   __a(					_a       ):






    """simple docstring"""





    def 							a__				(				self  )			->		None:
         UpperCAmelCase_      :							int       =      logging.StreamHandler(sys.stdout  )
         logger.addHandler(_SCREAMING_SNAKE_CASE  )





    def 							a__				(				self       ,_SCREAMING_SNAKE_CASE  )			->		str:
         UpperCAmelCase_      :							int       =      get_gpu_count()

         if n_gpu > 1:
              pass
              # XXX: doesn't quite work with n_gpu > 1 https://github.com/huggingface/transformers/issues/10560
              # script = f"{self.examples_dir_str}/research_projects/deebert/run_glue_deebert.py"
              # distributed_args = f"-m torch.distributed.launch --nproc_per_node={n_gpu} {script}".split()
              # cmd = [sys.executable] + distributed_args + args
              # execute_subprocess_async(cmd, env=self.get_env())
              # XXX: test the results - need to save them first into .json file
         else:
              args.insert(0       ,'''run_glue_deebert.py'''  )
              with patch.object(_SCREAMING_SNAKE_CASE       ,'''argv'''       ,_SCREAMING_SNAKE_CASE  ):
                   UpperCAmelCase_      :							List[str]       =      run_glue_deebert.main()
                   for value in result.values():
                        self.assertGreaterEqual(_SCREAMING_SNAKE_CASE       ,0.6_66  )





    @slow
    @require_torch_non_multi_gpu
    def 							a__				(				self  )			->		List[str]:
         UpperCAmelCase_      :							List[Any]       =      '''
            --model_type roberta
            --model_name_or_path roberta-base
            --task_name MRPC
            --do_train
            --do_eval
            --do_lower_case
            --data_dir ./tests/fixtures/tests_samples/MRPC/
            --max_seq_length 128
            --per_gpu_eval_batch_size=1
            --per_gpu_train_batch_size=8
            --learning_rate 2e-4
            --num_train_epochs 3
            --overwrite_output_dir
            --seed 42
            --output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage
            --plot_data_dir ./examples/deebert/results/
            --save_steps 0
            --overwrite_cache
            --eval_after_first_stage
            '''.split()
         self.run_and_check(_SCREAMING_SNAKE_CASE  )

         UpperCAmelCase_      :							Union[str, Any]       =      '''
            --model_type roberta
            --model_name_or_path ./examples/deebert/saved_models/roberta-base/MRPC/two_stage
            --task_name MRPC
            --do_eval
            --do_lower_case
            --data_dir ./tests/fixtures/tests_samples/MRPC/
            --output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage
            --plot_data_dir ./examples/deebert/results/
            --max_seq_length 128
            --eval_each_highway
            --eval_highway
            --overwrite_cache
            --per_gpu_eval_batch_size=1
            '''.split()
         self.run_and_check(_SCREAMING_SNAKE_CASE  )

         UpperCAmelCase_      :							Any       =      '''
            --model_type roberta
            --model_name_or_path ./examples/deebert/saved_models/roberta-base/MRPC/two_stage
            --task_name MRPC
            --do_eval
            --do_lower_case
            --data_dir ./tests/fixtures/tests_samples/MRPC/
            --output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage
            --plot_data_dir ./examples/deebert/results/
            --max_seq_length 128
            --early_exit_entropy 0.1
            --eval_highway
            --overwrite_cache
            --per_gpu_eval_batch_size=1
            '''.split()
         self.run_and_check(_SCREAMING_SNAKE_CASE  )