


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

__UpperCamelCase							:							Union[str, Any]    							=					16
__UpperCamelCase							:							str    							=					32

def       _a	(					SCREAMING_SNAKE_CASE						:					Accelerator     ,			SCREAMING_SNAKE_CASE						:					int = 16  ):







 """simple docstring"""



 UpperCamelCase__       :     Tuple 							= AutoTokenizer.from_pretrained('''bert-base-cased'''  )
 UpperCamelCase__       :     Optional[int] 							= load_dataset('''glue'''     ,			'''mrpc'''  )

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

 def collate_fn(SCREAMING_SNAKE_CASE						:					Optional[Any]  ):
  # On TPU it's best to pad everything to the same length or training will be very slow.
  UpperCamelCase__       :     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":
   UpperCamelCase__       :     int 							= 16
  elif accelerator.mixed_precision != "no":
   UpperCamelCase__       :     Optional[Any] 							= 8
  else:
   UpperCamelCase__       :     Dict 							= 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__       :     Dict 							= DataLoader(
     tokenized_datasets['''train''']     ,			shuffle=SCREAMING_SNAKE_CASE     ,			collate_fn=SCREAMING_SNAKE_CASE     ,			batch_size=SCREAMING_SNAKE_CASE  )
 UpperCamelCase__       :     List[str] 							= 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

       __UpperCamelCase							:							str    							=					mocked_dataloaders  # noqa: F811

def       _a	(					SCREAMING_SNAKE_CASE						:					List[Any]     ,			SCREAMING_SNAKE_CASE						:					Optional[int]  ):







 """simple docstring"""



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

 set_seed(SCREAMING_SNAKE_CASE  )

 UpperCamelCase__							,					UpperCamelCase__       :     Union[str, Any] 							= get_dataloaders(SCREAMING_SNAKE_CASE     ,			SCREAMING_SNAKE_CASE  )
 UpperCamelCase__       :     List[Any] 							= evaluate.load('''glue'''     ,			'''mrpc'''  )

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

 # Instantiate the model (we build the model here so that the seed also control new weights initialization)
 UpperCamelCase__       :     Dict 							= 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__       :     Union[str, Any] 							= model.to(accelerator.device  )

 # Instantiate optimizer
 UpperCamelCase__       :     List[Any] 							= AdamW(params=model.parameters()     ,			lr=SCREAMING_SNAKE_CASE  )

 # Instantiate scheduler
 UpperCamelCase__       :     List[str] 							= 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__       :     int 							= accelerator.prepare(
     SCREAMING_SNAKE_CASE     ,			SCREAMING_SNAKE_CASE     ,			SCREAMING_SNAKE_CASE     ,			SCREAMING_SNAKE_CASE     ,			SCREAMING_SNAKE_CASE  )

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

  if "epoch" in training_difference:
   UpperCamelCase__       :     Optional[int] 							= int(training_difference.replace('''epoch_'''     ,			''''''  )  ) + 1
   UpperCamelCase__       :     str 							= None
  else:
   UpperCamelCase__       :     Dict 							= int(training_difference.replace('''step_'''     ,			''''''  )  )
   UpperCamelCase__       :     Any 							= resume_step // len(SCREAMING_SNAKE_CASE  )
   resume_step -= starting_epoch * len(SCREAMING_SNAKE_CASE  )

    # Now we train the model
 for epoch in range(SCREAMING_SNAKE_CASE     ,			SCREAMING_SNAKE_CASE  ):
  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
   UpperCamelCase__       :     str 							= accelerator.skip_first_batches(SCREAMING_SNAKE_CASE     ,			SCREAMING_SNAKE_CASE  )
   overall_step += resume_step
  else:
   # After the first iteration though, we need to go back to the original dataloader
   UpperCamelCase__       :     List[str] 							= train_dataloader
  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__       :     Optional[int] 							= model(**SCREAMING_SNAKE_CASE  )
   UpperCamelCase__       :     str 							= outputs.loss
   UpperCamelCase__       :     Optional[int] 							= loss / gradient_accumulation_steps
   accelerator.backward(SCREAMING_SNAKE_CASE  )
   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(SCREAMING_SNAKE_CASE     ,			SCREAMING_SNAKE_CASE  ):
    UpperCamelCase__       :     Union[str, Any] 							= F"step_{overall_step}"
    if overall_step % checkpointing_steps == 0:
     if args.output_dir is not None:
      UpperCamelCase__       :     Union[str, Any] 							= os.path.join(args.output_dir     ,			SCREAMING_SNAKE_CASE  )
     accelerator.save_state(SCREAMING_SNAKE_CASE  )

  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__       :     List[Any] 							= model(**SCREAMING_SNAKE_CASE  )
   UpperCamelCase__       :     Dict 							= outputs.logits.argmax(dim=-1  )
   UpperCamelCase__							,					UpperCamelCase__       :     List[str] 							= accelerator.gather_for_metrics((predictions, batch['''labels'''])  )
   metric.add_batch(
       predictions=SCREAMING_SNAKE_CASE     ,			references=SCREAMING_SNAKE_CASE     ,			)

  UpperCamelCase__       :     List[Any] 							= metric.compute()
  # Use accelerator.print to print only on the main process.
  accelerator.print(F"epoch {epoch}:"     ,			SCREAMING_SNAKE_CASE  )

  # 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":
   UpperCamelCase__       :     Optional[Any] 							= F"epoch_{epoch}"
   if args.output_dir is not None:
    UpperCamelCase__       :     Union[str, Any] 							= os.path.join(args.output_dir     ,			SCREAMING_SNAKE_CASE  )
   accelerator.save_state(SCREAMING_SNAKE_CASE  )

def       _a	(					):







 """simple docstring"""



 UpperCamelCase__       :     Union[str, Any] 							= 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(
     '''--checkpointing_steps'''     ,			type=SCREAMING_SNAKE_CASE     ,			default=SCREAMING_SNAKE_CASE     ,			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=SCREAMING_SNAKE_CASE     ,			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=SCREAMING_SNAKE_CASE     ,			default=SCREAMING_SNAKE_CASE     ,			help='''If the training should continue from a checkpoint folder.'''     ,			)
 UpperCamelCase__       :     Any 							= parser.parse_args()
 UpperCamelCase__       :     Tuple 							= {'''lr''': 2E-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
 training_function(SCREAMING_SNAKE_CASE     ,			SCREAMING_SNAKE_CASE  )


if __name__ == "__main__":
       main()






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__UpperCamelCase							:							Dict    							=					{
    "configuration_dpr": ["DPR_PRETRAINED_CONFIG_ARCHIVE_MAP", "DPRConfig"],
    "tokenization_dpr": [
        "DPRContextEncoderTokenizer",
        "DPRQuestionEncoderTokenizer",
        "DPRReaderOutput",
        "DPRReaderTokenizer",
    ],
}


try:
       if not is_tokenizers_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __UpperCamelCase							:							Any    							=					[
           "DPRContextEncoderTokenizerFast",
           "DPRQuestionEncoderTokenizerFast",
           "DPRReaderTokenizerFast",
       ]

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __UpperCamelCase							:							str    							=					[
           "DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST",
           "DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST",
           "DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST",
           "DPRContextEncoder",
           "DPRPretrainedContextEncoder",
           "DPRPreTrainedModel",
           "DPRPretrainedQuestionEncoder",
           "DPRPretrainedReader",
           "DPRQuestionEncoder",
           "DPRReader",
       ]

try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __UpperCamelCase							:							int    							=					[
           "TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST",
           "TF_DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST",
           "TF_DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST",
           "TFDPRContextEncoder",
           "TFDPRPretrainedContextEncoder",
           "TFDPRPretrainedQuestionEncoder",
           "TFDPRPretrainedReader",
           "TFDPRQuestionEncoder",
           "TFDPRReader",
       ]


if TYPE_CHECKING:
       from .configuration_dpr import DPR_PRETRAINED_CONFIG_ARCHIVE_MAP, DPRConfig
       from .tokenization_dpr import (
           DPRContextEncoderTokenizer,
           DPRQuestionEncoderTokenizer,
           DPRReaderOutput,
           DPRReaderTokenizer,
       )

       try:
              if not is_tokenizers_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_dpr_fast import (
                  DPRContextEncoderTokenizerFast,
                  DPRQuestionEncoderTokenizerFast,
                  DPRReaderTokenizerFast,
              )

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_dpr import (
                  DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
                  DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
                  DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
                  DPRContextEncoder,
                  DPRPretrainedContextEncoder,
                  DPRPreTrainedModel,
                  DPRPretrainedQuestionEncoder,
                  DPRPretrainedReader,
                  DPRQuestionEncoder,
                  DPRReader,
              )

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_dpr import (
                  TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
                  TF_DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
                  TF_DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
                  TFDPRContextEncoder,
                  TFDPRPretrainedContextEncoder,
                  TFDPRPretrainedQuestionEncoder,
                  TFDPRPretrainedReader,
                  TFDPRQuestionEncoder,
                  TFDPRReader,
              )

else:
       import sys

       __UpperCamelCase							:							Dict    							=					_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)


