


"""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 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
#
########################################################################

snake_case__		:     Optional[Any]    				=				16
snake_case__		:     List[Any]    				=				32


def      _snake_case						(						_snake_case				:			Optional[Any] ,   _snake_case				:			str = 16			):
      lowerCAmelCase					:	str       =   AutoTokenizer.from_pretrained('''bert-base-cased'''			)
      lowerCAmelCase					:	Tuple       =   load_dataset('''glue''' ,   '''mrpc'''			)

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

      def collate_fn(_snake_case				:			Tuple			):
            # On TPU it's best to pad everything to the same length or training will be very slow.
            lowerCAmelCase					:	Optional[Any]       =   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":
                  lowerCAmelCase					:	int       =   16
            elif accelerator.mixed_precision != "no":
                  lowerCAmelCase					:	Optional[Any]       =   8
            else:
                  lowerCAmelCase					:	int       =   None

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

      # Instantiate dataloaders.
      lowerCAmelCase					:	Tuple       =   DataLoader(
          tokenized_datasets['''train'''] ,   shuffle=__UpperCAmelCase ,   collate_fn=__UpperCAmelCase ,   batch_size=__UpperCAmelCase			)
      lowerCAmelCase					:	Optional[Any]       =   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

 snake_case__		:     Optional[int]    				=				mocked_dataloaders  # noqa: F811


def      _snake_case						(						_snake_case				:			int ,   _snake_case				:			Tuple			):







      if os.environ.get('''TESTING_MOCKED_DATALOADERS''' ,   __UpperCAmelCase			) == "1":
            lowerCAmelCase					:	List[Any]       =   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:
            lowerCAmelCase					:	Any       =   Accelerator(
                cpu=args.cpu ,   mixed_precision=args.mixed_precision ,   log_with='''all''' ,   project_dir=args.project_dir			)
      else:
            lowerCAmelCase					:	int       =   Accelerator(cpu=args.cpu ,   mixed_precision=args.mixed_precision			)
      # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
      lowerCAmelCase					:	str       =   config['''lr''']
      lowerCAmelCase					:	List[Any]       =   int(config['''num_epochs''']			)
      lowerCAmelCase					:	Union[str, Any]       =   int(config['''seed''']			)
      lowerCAmelCase					:	List[Any]       =   int(config['''batch_size''']			)
      set_seed(__UpperCAmelCase			)

      lowerCAmelCase,      lowerCAmelCase					:	str       =   get_dataloaders(__UpperCAmelCase ,   __UpperCAmelCase			)
      lowerCAmelCase					:	Dict       =   evaluate.load('''glue''' ,   '''mrpc'''			)

      # If the batch size is too big we use gradient accumulation
      lowerCAmelCase					:	List[Any]       =   1
      if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
            lowerCAmelCase					:	int       =   batch_size // MAX_GPU_BATCH_SIZE
            lowerCAmelCase					:	Tuple       =   MAX_GPU_BATCH_SIZE

      # Instantiate the model (we build the model here so that the seed also control new weights initialization)
      lowerCAmelCase					:	str       =   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).
      lowerCAmelCase					:	Optional[Any]       =   model.to(accelerator.device			)

      # Instantiate optimizer
      lowerCAmelCase					:	Optional[Any]       =   AdamW(params=model.parameters() ,   lr=__UpperCAmelCase			)

      # Instantiate scheduler
      lowerCAmelCase					:	int       =   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.
      lowerCAmelCase,      lowerCAmelCase,      lowerCAmelCase,      lowerCAmelCase,      lowerCAmelCase					:	int       =   accelerator.prepare(
          __UpperCAmelCase ,   __UpperCAmelCase ,   __UpperCAmelCase ,   __UpperCAmelCase ,   __UpperCAmelCase			)

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

      # Now we train the model
      for epoch in range(__UpperCAmelCase			):
            model.train()
            # New Code #
            # For our tracking example, we will log the total loss of each epoch
            if args.with_tracking:
                  lowerCAmelCase					:	Union[str, Any]       =   0
            for step, batch in enumerate(__UpperCAmelCase			):
                  # We could avoid this line since we set the accelerator with `device_placement=True`.
                  batch.to(accelerator.device			)
                  lowerCAmelCase					:	Optional[Any]       =   model(**__UpperCAmelCase			)
                  lowerCAmelCase					:	Dict       =   outputs.loss
                  # New Code #
                  if args.with_tracking:
                        total_loss += loss.detach().float()
                  lowerCAmelCase					:	Optional[Any]       =   loss / gradient_accumulation_steps
                  accelerator.backward(__UpperCAmelCase			)
                  if step % gradient_accumulation_steps == 0:
                        optimizer.step()
                        lr_scheduler.step()
                        optimizer.zero_grad()

            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():
                        lowerCAmelCase					:	Union[str, Any]       =   model(**__UpperCAmelCase			)
                  lowerCAmelCase					:	Tuple       =   outputs.logits.argmax(dim=-1			)
                  lowerCAmelCase,      lowerCAmelCase					:	Optional[int]       =   accelerator.gather_for_metrics((predictions, batch['''labels'''])			)
                  metric.add_batch(
                      predictions=__UpperCAmelCase ,   references=__UpperCAmelCase ,   )

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

            # 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(__UpperCAmelCase			),
                          '''epoch''': epoch,
                      } ,   step=__UpperCAmelCase ,   )

    # 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      _snake_case						(						):
      lowerCAmelCase					:	List[str]       =   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(
          '''--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=__UpperCAmelCase ,   default='''logs''' ,   help='''Location on where to store experiment tracking logs` and relevent project information''' ,   )
      lowerCAmelCase					:	List[str]       =   parser.parse_args()
      lowerCAmelCase					:	str       =   {'''lr''': 2E-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
      training_function(__UpperCAmelCase ,   __UpperCAmelCase			)


if __name__ == "__main__":
 main()






"""simple docstring"""





# using dfs for finding eulerian path traversal


def      _snake_case						(						_snake_case				:			Optional[Any] ,   _snake_case				:			List[Any] ,   _snake_case				:			str ,   _snake_case				:			List[Any]=None			):
      lowerCAmelCase					:	Any       =   (path or []) + [u]
      for v in graph[u]:
            if visited_edge[u][v] is False:
                  lowerCAmelCase,      lowerCAmelCase					:	Union[str, Any]       =   True, True
                  lowerCAmelCase					:	int       =   dfs(_snake_case ,   _snake_case ,   _snake_case ,   _snake_case			)
      return path


def      _snake_case						(						_snake_case				:			Optional[int] ,   _snake_case				:			Dict			):
      lowerCAmelCase					:	Tuple       =   0
      lowerCAmelCase					:	Optional[Any]       =   -1
      for i in range(_snake_case			):
            if i not in graph.keys():
                  continue
            if len(graph[i]			) % 2 == 1:
                  odd_degree_nodes += 1
                  lowerCAmelCase					:	Optional[Any]       =   i
      if odd_degree_nodes == 0:
            return 1, odd_node
      if odd_degree_nodes == 2:
            return 2, odd_node
      return 3, odd_node


def      _snake_case						(						_snake_case				:			Tuple ,   _snake_case				:			List[Any]			):
      lowerCAmelCase					:	Any       =   [[False for _ in range(max_node + 1			)] for _ in range(max_node + 1			)]
      lowerCAmelCase,      lowerCAmelCase					:	Optional[int]       =   check_circuit_or_path(_snake_case ,   _snake_case			)
      if check == 3:
            print('''graph is not Eulerian'''			)
            print('''no path'''			)
            return
      lowerCAmelCase					:	Dict       =   1
      if check == 2:
            lowerCAmelCase					:	int       =   odd_node
            print('''graph has a Euler path'''			)
      if check == 1:
            print('''graph has a Euler cycle'''			)
      lowerCAmelCase					:	List[str]       =   dfs(_snake_case ,   _snake_case ,   _snake_case			)
      print(_snake_case			)


def      _snake_case						(						):
      lowerCAmelCase					:	Optional[Any]       =   {1: [2, 3, 4], 2: [1, 3], 3: [1, 2], 4: [1, 5], 5: [4]}
      lowerCAmelCase					:	Union[str, Any]       =   {1: [2, 3, 4, 5], 2: [1, 3], 3: [1, 2], 4: [1, 5], 5: [1, 4]}
      lowerCAmelCase					:	List[Any]       =   {1: [2, 3, 4], 2: [1, 3, 4], 3: [1, 2], 4: [1, 2, 5], 5: [4]}
      lowerCAmelCase					:	Optional[Any]       =   {1: [2, 3], 2: [1, 3], 3: [1, 2]}
      lowerCAmelCase					:	Any       =   {
          1: [],
          2: []
          # all degree is zero
      }
      lowerCAmelCase					:	List[str]       =   10
      check_euler(_snake_case ,   _snake_case			)
      check_euler(_snake_case ,   _snake_case			)
      check_euler(_snake_case ,   _snake_case			)
      check_euler(_snake_case ,   _snake_case			)
      check_euler(_snake_case ,   _snake_case			)


if __name__ == "__main__":
 main()


