




'''simple docstring'''







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
#
########################################################################

lowerCAmelCase_    	=							1_6
lowerCAmelCase_    	=							3_2







def 	_A			(   UpperCAmelCase			,UpperCAmelCase = 16	):






   '''simple docstring'''







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

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

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

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

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

   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

    lowerCAmelCase_    	=							mocked_dataloaders  # noqa: F811







def 	_A			(   UpperCAmelCase			,UpperCAmelCase	):






   '''simple docstring'''







   if os.environ.get('TESTING_MOCKED_DATALOADERS'			,UpperCAmelCase	) == "1":
      A__         =    2
   # Initialize accelerator
   A__         =    Accelerator(cpu=args.cpu			,mixed_precision=args.mixed_precision	)
   # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
   A__         =    config['lr']
   A__         =    int(config['num_epochs']	)
   A__         =    int(config['seed']	)
   A__         =    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":
         A__         =    args.checkpointing_steps
      elif args.checkpointing_steps.isdigit():
         A__         =    int(args.checkpointing_steps	)
      else:
         raise ValueError(
             F"""Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed."""	)
   else:
      A__         =    None

   set_seed(UpperCAmelCase	)

   A__     , A__         =    get_dataloaders(UpperCAmelCase			,UpperCAmelCase	)
   A__         =    evaluate.load('glue'			,'mrpc'	)

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

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

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

   # Instantiate optimizer
   A__         =    AdamW(params=model.parameters()			,lr=UpperCAmelCase	)

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

   # New Code #
   # We need to keep track of how many total steps we have iterated over
   A__         =    0
   # We also need to keep track of the stating epoch so files are named properly
   A__         =    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	)
         A__         =    os.path.basename(args.resume_from_checkpoint	)
      else:
         # Get the most recent checkpoint
         A__         =    [f.name for f in os.scandir(os.getcwd()	) if f.is_dir()]
         dirs.sort(key=os.path.getctime	)
         A__         =    dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
      # Extract `epoch_{i}` or `step_{i}`
      A__         =    os.path.splitext(UpperCAmelCase	)[0]

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

    # Now we train the model
   for epoch in range(UpperCAmelCase			,UpperCAmelCase	):
      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
         A__         =    accelerator.skip_first_batches(UpperCAmelCase			,UpperCAmelCase	)
         overall_step += resume_step
      else:
         # After the first iteration though, we need to go back to the original dataloader
         A__         =    train_dataloader
      for step, batch in enumerate(UpperCAmelCase	):
         # We could avoid this line since we set the accelerator with `device_placement=True`.
         batch.to(accelerator.device	)
         A__         =    model(**UpperCAmelCase	)
         A__         =    outputs.loss
         A__         =    loss / gradient_accumulation_steps
         accelerator.backward(UpperCAmelCase	)
         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(UpperCAmelCase			,UpperCAmelCase	):
            A__         =    F"""step_{overall_step}"""
            if overall_step % checkpointing_steps == 0:
               if args.output_dir is not None:
                  A__         =    os.path.join(args.output_dir			,UpperCAmelCase	)
               accelerator.save_state(UpperCAmelCase	)

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

      A__         =    metric.compute()
      # Use accelerator.print to print only on the main process.
      accelerator.print(F"""epoch {epoch}:"""			,UpperCAmelCase	)

      # 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":
         A__         =    F"""epoch_{epoch}"""
         if args.output_dir is not None:
            A__         =    os.path.join(args.output_dir			,UpperCAmelCase	)
         accelerator.save_state(UpperCAmelCase	)







def 	_A			(   ):






   '''simple docstring'''







   A__         =    argparse.ArgumentParser(description='Simple example of training script.'	)
   parser.add_argument(
       '--mixed_precision'			,type=UpperCAmelCase			,default=UpperCAmelCase			,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=UpperCAmelCase			,default=UpperCAmelCase			,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=UpperCAmelCase			,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=UpperCAmelCase			,default=UpperCAmelCase			,help='If the training should continue from a checkpoint folder.'			,)
   A__         =    parser.parse_args()
   A__         =    {'lr': 2e-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
   training_function(UpperCAmelCase			,UpperCAmelCase	)


if __name__ == "__main__":
    main()








'''simple docstring'''







import hashlib
import importlib.util
import json
import os
import pathlib
from types import ModuleType

import pytest
import requests

lowerCAmelCase_    	=							pathlib.Path.cwd().joinpath('''project_euler''')
lowerCAmelCase_    	=							pathlib.Path.cwd().joinpath(
    '''scripts''', '''project_euler_answers.json'''
)

with open(PROJECT_EULER_ANSWERS_PATH) as file_handle:
    lowerCAmelCase_    	=							json.load(file_handle)







def 	_A			(   UpperCAmelCase	):






   '''simple docstring'''







   A__         =    importlib.util.spec_from_file_location(file_path.name			,str(UpperCAmelCase	)	)
   A__         =    importlib.util.module_from_spec(UpperCAmelCase	)  # type: ignore
   spec.loader.exec_module(UpperCAmelCase	)  # type: ignore
   return module







def 	_A			(   ):






   '''simple docstring'''







   A__         =    []
   for problem_dir_path in PROJECT_EULER_DIR_PATH.iterdir():
      if problem_dir_path.is_file() or problem_dir_path.name.startswith('_'	):
         continue
      for file_path in problem_dir_path.iterdir():
         if file_path.suffix != ".py" or file_path.name.startswith(('_', 'test')	):
            continue
         solution_file_paths.append(UpperCAmelCase	)
   return solution_file_paths







def 	_A			(   ):






   '''simple docstring'''







   with open(os.environ['GITHUB_EVENT_PATH']	) as file:
      A__         =    json.load(UpperCAmelCase	)
   return event["pull_request"]["url"] + "/files"







def 	_A			(   ):






   '''simple docstring'''







   A__         =    []
   A__         =    {
       'Accept': 'application/vnd.github.v3+json',
       'Authorization': 'token ' + os.environ['GITHUB_TOKEN'],
   }
   A__         =    requests.get(get_files_url()			,headers=UpperCAmelCase	).json()
   for file in files:
      A__         =    pathlib.Path.cwd().joinpath(file['filename']	)
      if (
          filepath.suffix != ".py"
          or filepath.name.startswith(('_', 'test')	)
          or not filepath.name.startswith('sol'	)
      ):
         continue
      solution_file_paths.append(UpperCAmelCase	)
   return solution_file_paths







def 	_A			(   ):






   '''simple docstring'''







   if os.environ.get('CI'	) and os.environ.get('GITHUB_EVENT_NAME'	) == "pull_request":
      # Return only if there are any, otherwise default to all solutions
      if filepaths := added_solution_file_path():
         return filepaths
   return all_solution_file_paths()







@pytest.mark.parametrize(
    'solution_path'			,collect_solution_file_paths()			,ids=lambda UpperCAmelCase	: F"""{path.parent.name}/{path.name}"""			,)
def 	_A			(   UpperCAmelCase	):






   '''simple docstring'''







   A__         =    solution_path.parent.name[8:].zfill(3	)
   A__         =    PROBLEM_ANSWERS[problem_number]
   A__         =    convert_path_to_module(UpperCAmelCase	)
   A__         =    str(solution_module.solution()	)  # type: ignore
   A__         =    hashlib.shaaaa(answer.encode()	).hexdigest()
   assert (
       answer == expected
   ), F"""Expected solution to {problem_number} to have hash {expected}, got {answer}"""


