



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

A_		:     Optional[Any]        =     16
A_		:     str        =     32


def 	UpperCAmelCase__      (      UpperCAmelCase__ :Accelerator ,					UpperCAmelCase__ :int = 16					):






 '''simple docstring'''

 a						=		AutoTokenizer.from_pretrained("bert-base-cased"					)
 a						=		load_dataset("glue" ,					"mrpc"					)

 def tokenize_function(UpperCAmelCase__ :str					):
  # 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__ :List[str]					):
  # On TPU it's best to pad everything to the same length or training will be very slow.
  a						=		1_28 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

     A_		:     str        =     mocked_dataloaders  # noqa: F811


def 	UpperCAmelCase__      (      UpperCAmelCase__ :Tuple ,					UpperCAmelCase__ :int					):






 '''simple docstring'''

 if os.environ.get("TESTING_MOCKED_DATALOADERS" ,					UpperCAmelCase__					) == "1":
  a						=		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:
  a						=		Accelerator(
      cpu=args.cpu ,					mixed_precision=args.mixed_precision ,					log_with="all" ,					project_dir=args.project_dir					)
 else:
  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"]					)
 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=1_00 ,					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 initialize the trackers we use. Overall configurations can also be stored
 if args.with_tracking:
  a						=		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:
   a						=		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					)
   a						=		model(**UpperCAmelCase__					)
   a						=		outputs.loss
   # New Code #
   if args.with_tracking:
    total_loss += loss.detach().float()
   a						=		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():
    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 #
  # 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 	UpperCAmelCase__      (      ):






 '''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(
     "--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" ,					)
 a						=		parser.parse_args()
 a						=		{"lr": 2E-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
 training_function(UpperCAmelCase__ ,					UpperCAmelCase__					)


if __name__ == "__main__":
     main()




import os
import tempfile
import unittest

from transformers.models.marian.convert_marian_tatoeba_to_pytorch import DEFAULT_REPO, TatoebaConverter
from transformers.testing_utils import slow
from transformers.utils import cached_property



@unittest.skipUnless(os.path.exists(UpperCAmelCase__	),   '''Tatoeba directory does not exist.'''	)
class 		_lowercase		(       unittest.TestCase	):

   @cached_property
   def 		A							(      self			:       List[str]							)       ->				int:


    """simple docstring"""






    a						=		tempfile.mkdtemp()
    return TatoebaConverter(save_dir=__lowerCAmelCase							)

   @slow
   def 		A							(      self			:       Optional[int]							)       ->				List[str]:


    """simple docstring"""






    self.resolver.convert_models(["heb-eng"]							)




   @slow
   def 		A							(      self			:       Dict							)       ->				Any:


    """simple docstring"""






    a		,  a						=		self.resolver.write_model_card("opus-mt-he-en"							,   dry_run=__lowerCAmelCase							)
    assert mmeta["long_pair"] == "heb-eng"

