





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 experiment tracking 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
#
########################################################################

lowercase    : Any    	=  16
lowercase    : List[str]    	=  32


def     SCREAMING_SNAKE_CASE			(      lowerCAmelCase    ,       lowerCAmelCase = 16					):
       _UpperCamelCase              =						AutoTokenizer.from_pretrained('''bert-base-cased'''					)
       _UpperCamelCase              =						load_dataset('''glue'''    ,       '''mrpc'''					)

       def tokenize_function(lowerCAmelCase					):
              # max_length=None => use the model max length (it's actually the default)
              _UpperCamelCase              =						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():
              _UpperCamelCase              =						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
       _UpperCamelCase              =						tokenized_datasets.rename_column('''label'''    ,       '''labels'''					)

       def collate_fn(lowerCAmelCase					):
              # 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(
                  _SCREAMING_SNAKE_CASE    ,       padding='''longest'''    ,       max_length=_SCREAMING_SNAKE_CASE    ,       pad_to_multiple_of=_SCREAMING_SNAKE_CASE    ,       return_tensors='''pt'''    ,       )

       # Instantiate dataloaders.
       _UpperCamelCase              =						DataLoader(
           tokenized_datasets['''train''']    ,       shuffle=_SCREAMING_SNAKE_CASE    ,       collate_fn=_SCREAMING_SNAKE_CASE    ,       batch_size=_SCREAMING_SNAKE_CASE					)
       _UpperCamelCase              =						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

       lowercase    : Tuple    	=  mocked_dataloaders  # noqa: F811


def     SCREAMING_SNAKE_CASE			(      lowerCAmelCase    ,       lowerCAmelCase					):
       # For testing only
       if os.environ.get('''TESTING_MOCKED_DATALOADERS'''    ,       _SCREAMING_SNAKE_CASE					) == "1":
              _UpperCamelCase              =						2
       # Initialize Accelerator

       # New Code #
       # We pass in "all" to `log_with` to grab all available trackers in the environment
       # Note: If using a custom `Tracker` class, should be passed in here such as:
       # >>> log_with = ["all", MyCustomTrackerClassInstance()]
       if args.with_tracking:
              _UpperCamelCase              =						Accelerator(
                  cpu=args.cpu    ,       mixed_precision=args.mixed_precision    ,       log_with='''all'''    ,       project_dir=args.project_dir					)
       else:
              _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''']					)
       set_seed(_SCREAMING_SNAKE_CASE					)

       _UpperCamelCase       ,		_UpperCamelCase              =						get_dataloaders(_SCREAMING_SNAKE_CASE    ,       _SCREAMING_SNAKE_CASE					)
       _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

       # 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=_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).
       _UpperCamelCase              =						model.to(accelerator.device					)

       # Instantiate optimizer
       _UpperCamelCase              =						AdamW(params=model.parameters()    ,       lr=_SCREAMING_SNAKE_CASE					)

       # Instantiate scheduler
       _UpperCamelCase              =						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.
       _UpperCamelCase       ,		_UpperCamelCase       ,		_UpperCamelCase       ,		_UpperCamelCase       ,		_UpperCamelCase              =						accelerator.prepare(
           _SCREAMING_SNAKE_CASE    ,       _SCREAMING_SNAKE_CASE    ,       _SCREAMING_SNAKE_CASE    ,       _SCREAMING_SNAKE_CASE    ,       _SCREAMING_SNAKE_CASE					)

       # New Code #
       # We need to initialize the trackers we use. Overall configurations can also be stored
       if args.with_tracking:
              _UpperCamelCase              =						os.path.split(_SCREAMING_SNAKE_CASE					)[-1].split('''.'''					)[0]
              accelerator.init_trackers(_SCREAMING_SNAKE_CASE    ,       _SCREAMING_SNAKE_CASE					)

       # Now we train the model
       for epoch in range(_SCREAMING_SNAKE_CASE					):
              model.train()
              # New Code #
              # For our tracking example, we will log the total loss of each epoch
              if args.with_tracking:
                     _UpperCamelCase              =						0
              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					)
                     _UpperCamelCase              =						model(**_SCREAMING_SNAKE_CASE					)
                     _UpperCamelCase              =						outputs.loss
                     # New Code #
                     if args.with_tracking:
                            total_loss += loss.detach().float()
                     _UpperCamelCase              =						loss / gradient_accumulation_steps
                     accelerator.backward(_SCREAMING_SNAKE_CASE					)
                     if step % gradient_accumulation_steps == 0:
                            optimizer.step()
                            lr_scheduler.step()
                            optimizer.zero_grad()

              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():
                            _UpperCamelCase              =						model(**_SCREAMING_SNAKE_CASE					)
                     _UpperCamelCase              =						outputs.logits.argmax(dim=-1					)
                     _UpperCamelCase       ,		_UpperCamelCase              =						accelerator.gather_for_metrics((predictions, batch['''labels'''])					)
                     metric.add_batch(
                         predictions=_SCREAMING_SNAKE_CASE    ,       references=_SCREAMING_SNAKE_CASE    ,       )

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

              # New Code #
              # To actually log, we call `Accelerator.log`
              # The values passed can be of `str`, `int`, `float` or `dict` of `str` to `float`/`int`
              if args.with_tracking:
                     accelerator.log(
                         {
                             '''accuracy''': eval_metric['''accuracy'''],
                             '''f1''': eval_metric['''f1'''],
                             '''train_loss''': total_loss.item() / len(_SCREAMING_SNAKE_CASE					),
                             '''epoch''': epoch,
                         }    ,       step=_SCREAMING_SNAKE_CASE    ,       )

    # New Code #
    # When a run is finished, you should call `accelerator.end_training()`
    # to close all of the open trackers
       if args.with_tracking:
              accelerator.end_training()


def     SCREAMING_SNAKE_CASE			(      ):
       _UpperCamelCase              =						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(
           '''--with_tracking'''    ,       action='''store_true'''    ,       help='''Whether to load in all available experiment trackers from the environment and use them for logging.'''    ,       )
       parser.add_argument(
           '''--project_dir'''    ,       type=_SCREAMING_SNAKE_CASE    ,       default='''logs'''    ,       help='''Location on where to store experiment tracking logs` and relevent project information'''    ,       )
       _UpperCamelCase              =						parser.parse_args()
       _UpperCamelCase              =						{'''lr''': 2E-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
       training_function(_SCREAMING_SNAKE_CASE    ,       _SCREAMING_SNAKE_CASE					)


if __name__ == "__main__":
       main()













from __future__ import annotations

import math


def     SCREAMING_SNAKE_CASE			(      lowerCAmelCase					):

       if 1 < number < 4:
              # 2 and 3 are primes
              return True
       elif number < 2 or number % 2 == 0 or number % 3 == 0:
              # Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
              return False

       # All primes number are in format of 6k +/- 1
       for i in range(5    ,       int(math.sqrt(lowerCAmelCase					) + 1					)    ,       6					):
              if number % i == 0 or number % (i + 2) == 0:
                     return False
       return True


lowercase    : Tuple    	=  [num for num in range(3, 10_0001, 2) if not is_prime(num)]


def     SCREAMING_SNAKE_CASE			(      lowerCAmelCase					):
       if not isinstance(lowerCAmelCase    ,       lowerCAmelCase					):
              raise ValueError('''n must be an integer'''					)
       if n <= 0:
              raise ValueError('''n must be >= 0'''					)

       _UpperCamelCase              =						[]
       for num in range(len(lowerCAmelCase					)					):
              _UpperCamelCase              =						0
              while 2 * i * i <= odd_composites[num]:
                     _UpperCamelCase              =						odd_composites[num] - 2 * i * i
                     if is_prime(lowerCAmelCase					):
                            break
                     i += 1
              else:
                     list_nums.append(odd_composites[num]					)
                     if len(lowerCAmelCase					) == n:
                            return list_nums

       return []


def     SCREAMING_SNAKE_CASE			(      ):
       return compute_nums(1					)[0]


if __name__ == "__main__":
       print(F'''{solution() = }''')






