


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
#
# 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)
#
# This example also demonstrates the checkpointing and sharding capabilities
#
# 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					=					16
_snake_case					=					32






def 		lowercase_(  SCREAMING_SNAKE_CASE_				,						SCREAMING_SNAKE_CASE_     ):


  '''simple docstring'''

  if args.with_tracking:
    lowerCamelCase			: List[str]   =    Accelerator(
        cpu=args.cpu				,						mixed_precision=args.mixed_precision				,						log_with="all"				,						project_dir=args.project_dir     )
  else:
    lowerCamelCase			: Tuple   =    Accelerator(cpu=args.cpu				,						mixed_precision=args.mixed_precision     )

  if hasattr(args.checkpointing_steps				,						"isdigit"     ):
    if args.checkpointing_steps == "epoch":
      lowerCamelCase			: Union[str, Any]   =    args.checkpointing_steps
    elif args.checkpointing_steps.isdigit():
      lowerCamelCase			: Dict   =    int(args.checkpointing_steps     )
    else:
      raise ValueError(
          f"""Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed."""     )
  else:
    lowerCamelCase			: int   =    None
  # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
  lowerCamelCase			: Union[str, Any]   =    config["lr"]
  lowerCamelCase			: Any   =    int(config["num_epochs"]     )
  lowerCamelCase			: Tuple   =    int(config["seed"]     )
  lowerCamelCase			: Any   =    int(config["batch_size"]     )

  # We need to initialize the trackers we use, and also store our configuration
  if args.with_tracking:
    lowerCamelCase			: List[Any]   =    os.path.split(__A     )[-1].split("."     )[0]
    accelerator.init_trackers(__A				,						__A     )

  lowerCamelCase			: Optional[Any]   =    AutoTokenizer.from_pretrained("bert-base-cased"     )
  lowerCamelCase			: Optional[int]   =    load_dataset("glue"				,						"mrpc"     )
  lowerCamelCase			: Union[str, Any]   =    evaluate.load("glue"				,						"mrpc"     )

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

  # 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			: Any   =    batch_size // MAX_GPU_BATCH_SIZE
    lowerCamelCase			: str   =    MAX_GPU_BATCH_SIZE

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

    return tokenizer.pad(
        __A				,						padding="longest"				,						max_length=__A				,						pad_to_multiple_of=__A				,						return_tensors="pt"				,						)

  # Instantiate dataloaders.
  lowerCamelCase			: List[Any]   =    DataLoader(
      tokenized_datasets["train"]				,						shuffle=__A				,						collate_fn=__A				,						batch_size=__A     )
  lowerCamelCase			: Optional[Any]   =    DataLoader(
      tokenized_datasets["validation"]				,						shuffle=__A				,						collate_fn=__A				,						batch_size=__A     )

  set_seed(__A     )

  # Instantiate the model (we build the model here so that the seed also control new weights initialization)
  lowerCamelCase			: List[str]   =    AutoModelForSequenceClassification.from_pretrained("bert-base-cased"				,						return_dict=__A     )

  # 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			: Tuple   =    model.to(accelerator.device     )

  # Instantiate optimizer
  lowerCamelCase			: str   =    AdamW(params=model.parameters()				,						lr=__A     )

  # Instantiate scheduler
  lowerCamelCase			: Optional[Any]   =    get_linear_schedule_with_warmup(
      optimizer=__A				,						num_warmup_steps=100				,						num_training_steps=(len(__A     ) * 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(
      __A				,						__A				,						__A				,						__A				,						__A     )

  # We need to keep track of how many total steps we have iterated over
  lowerCamelCase			: Tuple   =    0
  # We also need to keep track of the stating epoch so files are named properly
  lowerCamelCase			: Optional[int]   =    0

  # Potentially load in the weights and states from a previous save
  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     )
      lowerCamelCase			: List[str]   =    os.path.basename(args.resume_from_checkpoint     )
    else:
      # Get the most recent checkpoint
      lowerCamelCase			: int   =    [f.name for f in os.scandir(os.getcwd()     ) if f.is_dir()]
      dirs.sort(key=os.path.getctime     )
      lowerCamelCase			: Tuple   =    dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
    # Extract `epoch_{i}` or `step_{i}`
    lowerCamelCase			: Optional[int]   =    os.path.splitext(__A     )[0]

    if "epoch" in training_difference:
      lowerCamelCase			: str   =    int(training_difference.replace("epoch_"				,						""     )     ) + 1
      lowerCamelCase			: Union[str, Any]   =    None
    else:
      lowerCamelCase			: List[str]   =    int(training_difference.replace("step_"				,						""     )     )
      lowerCamelCase			: str   =    resume_step // len(__A     )
      resume_step -= starting_epoch * len(__A     )

    # Now we train the model
  for epoch in range(__A				,						__A     ):
    model.train()
    if args.with_tracking:
      lowerCamelCase			: Any   =    0
    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
      lowerCamelCase			: List[str]   =    accelerator.skip_first_batches(__A				,						__A     )
      overall_step += resume_step
    else:
      # After the first iteration though, we need to go back to the original dataloader
      lowerCamelCase			: str   =    train_dataloader
    for step, batch in enumerate(__A     ):
      # We could avoid this line since we set the accelerator with `device_placement=True`.
      batch.to(accelerator.device     )
      lowerCamelCase			: str   =    model(**__A     )
      lowerCamelCase			: Dict   =    outputs.loss
      lowerCamelCase			: List[str]   =    loss / gradient_accumulation_steps
      # We keep track of the loss at each epoch
      if args.with_tracking:
        total_loss += loss.detach().float()
      accelerator.backward(__A     )
      if step % gradient_accumulation_steps == 0:
        optimizer.step()
        lr_scheduler.step()
        optimizer.zero_grad()

      overall_step += 1

      if isinstance(__A				,						__A     ):
        lowerCamelCase			: List[Any]   =    f"""step_{overall_step}"""
        if overall_step % checkpointing_steps == 0:
          if args.output_dir is not None:
            lowerCamelCase			: Union[str, Any]   =    os.path.join(args.output_dir				,						__A     )
          accelerator.save_state(__A     )

    model.eval()
    for step, batch in enumerate(__A     ):
      # We could avoid this line since we set the accelerator with `device_placement=True`.
      batch.to(accelerator.device     )
      with torch.no_grad():
        lowerCamelCase			: Tuple   =    model(**__A     )
      lowerCamelCase			: List[str]   =    outputs.logits.argmax(dim=-1     )
      lowerCamelCase   ,     lowerCamelCase			: Optional[int]   =    accelerator.gather_for_metrics((predictions, batch["labels"])     )
      metric.add_batch(
          predictions=__A				,						references=__A				,						)

    lowerCamelCase			: List[Any]   =    metric.compute()
    # Use accelerator.print to print only on the main process.
    accelerator.print(f"""epoch {epoch}:"""				,						__A     )
    if args.with_tracking:
      accelerator.log(
          {
              "accuracy": eval_metric["accuracy"],
              "f1": eval_metric["f1"],
              "train_loss": total_loss.item() / len(__A     ),
              "epoch": epoch,
          }				,						step=__A				,						)

    if checkpointing_steps == "epoch":
      lowerCamelCase			: List[Any]   =    f"""epoch_{epoch}"""
      if args.output_dir is not None:
        lowerCamelCase			: List[Any]   =    os.path.join(args.output_dir				,						__A     )
      accelerator.save_state(__A     )

  if args.with_tracking:
    accelerator.end_training()






def 		lowercase_(  ):


  '''simple docstring'''

  lowerCamelCase			: Dict   =    argparse.ArgumentParser(description="Simple example of training script."     )
  parser.add_argument(
      "--mixed_precision"				,						type=__A				,						default=__A				,						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=__A				,						default=__A				,						help="Whether the various states should be saved at the end of every n steps, or \'epoch\' for each epoch."				,						)
  parser.add_argument(
      "--resume_from_checkpoint"				,						type=__A				,						default=__A				,						help="If the training should continue from a checkpoint folder."				,						)
  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(
      "--output_dir"				,						type=__A				,						default="."				,						help="Optional save directory where all checkpoint folders will be stored. Default is the current working directory."				,						)
  parser.add_argument(
      "--project_dir"				,						type=__A				,						default="logs"				,						help="Location on where to store experiment tracking logs` and relevent project information"				,						)
  lowerCamelCase			: Optional[Any]   =    parser.parse_args()
  lowerCamelCase			: Optional[Any]   =    {"lr": 2E-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
  training_function(__A				,						__A     )


if __name__ == "__main__":
   main()







import unittest
from pathlib import Path

from transformers import is_vision_available, load_tool
from transformers.testing_utils import get_tests_dir

from .test_tools_common import ToolTesterMixin


if is_vision_available():
   from PIL import Image
class      UpperCAmelCase_ ( unittest.TestCase		,			UpperCamelCase   ):

   '''simple docstring'''




   def _snake_case   (		self			):






     """simple docstring"""





     lowerCamelCase			: List[Any]   =    load_tool("image-segmentation"			)
     self.tool.setup()
     lowerCamelCase			: Optional[Any]   =    load_tool("image-segmentation"						,    remote=__A			)






   def _snake_case   (		self			):






     """simple docstring"""





     lowerCamelCase			: List[str]   =    Image.open(Path(get_tests_dir("fixtures/tests_samples/COCO"			)			) / "000000039769.png"			)
     lowerCamelCase			: Dict   =    self.tool(__A						,    "cat"			)
     self.assertTrue(isinstance(__A						,    Image.Image			)			)






   def _snake_case   (		self			):






     """simple docstring"""





     lowerCamelCase			: int   =    Image.open(Path(get_tests_dir("fixtures/tests_samples/COCO"			)			) / "000000039769.png"			)
     lowerCamelCase			: int   =    self.remote_tool(__A						,    "cat"			)
     self.assertTrue(isinstance(__A						,    Image.Image			)			)






   def _snake_case   (		self			):






     """simple docstring"""





     lowerCamelCase			: Optional[int]   =    Image.open(Path(get_tests_dir("fixtures/tests_samples/COCO"			)			) / "000000039769.png"			)
     lowerCamelCase			: Optional[Any]   =    self.tool(image=__A						,    label="cat"			)
     self.assertTrue(isinstance(__A						,    Image.Image			)			)






   def _snake_case   (		self			):






     """simple docstring"""





     lowerCamelCase			: str   =    Image.open(Path(get_tests_dir("fixtures/tests_samples/COCO"			)			) / "000000039769.png"			)
     lowerCamelCase			: Dict   =    self.remote_tool(image=__A						,    label="cat"			)
     self.assertTrue(isinstance(__A						,    Image.Image			)			)



