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							=       1_6
UpperCAmelCase							=       3_2






def 					SCREAMING_SNAKE_CASE_			(		lowerCAmelCase_: Accelerator ,	lowerCAmelCase_: int = 1_6     ):
      snake_case_     :			Tuple       =       AutoTokenizer.from_pretrained("bert-base-cased"     )
      snake_case_     :			Optional[int]       =       load_dataset("glue" ,	"mrpc"     )

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

      def collate_fn(lowerCAmelCase_: List[Any]     ):
            # On TPU it's best to pad everything to the same length or training will be very slow.
            snake_case_     :			int       =       1_2_8 if accelerator.distributed_type == DistributedType.TPU else None
            # When using mixed precision we want round multiples of 8/16
            if accelerator.mixed_precision == "fp8":
                  snake_case_     :			Any       =       1_6
            elif accelerator.mixed_precision != "no":
                  snake_case_     :			Optional[Any]       =       8
            else:
                  snake_case_     :			List[str]       =       None

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

      # Instantiate dataloaders.
      snake_case_     :			Dict       =       DataLoader(
          tokenized_datasets["train"] ,	shuffle=lowerCAmelCase_ ,	collate_fn=lowerCAmelCase_ ,	batch_size=lowerCAmelCase_     )
      snake_case_     :			Tuple       =       DataLoader(
          tokenized_datasets["validation"] ,	shuffle=lowerCAmelCase_ ,	collate_fn=lowerCAmelCase_ ,	batch_size=lowerCAmelCase_     )

      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							=       mocked_dataloaders  # noqa: F811






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

      set_seed(lowerCAmelCase_     )

      snake_case_  ,snake_case_     :			Optional[int]       =       get_dataloaders(lowerCAmelCase_ ,	lowerCAmelCase_     )
      snake_case_     :			Union[str, Any]       =       evaluate.load("glue" ,	"mrpc"     )

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

      # Instantiate the model (we build the model here so that the seed also control new weights initialization)
      snake_case_     :			int       =       AutoModelForSequenceClassification.from_pretrained("bert-base-cased" ,	return_dict=lowerCAmelCase_     )

      # 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).
      snake_case_     :			int       =       model.to(accelerator.device     )

      # Instantiate optimizer
      snake_case_     :			Optional[int]       =       AdamW(params=model.parameters() ,	lr=lowerCAmelCase_     )

      # Instantiate scheduler
      snake_case_     :			int       =       get_linear_schedule_with_warmup(
          optimizer=lowerCAmelCase_ ,	num_warmup_steps=1_0_0 ,	num_training_steps=(len(lowerCAmelCase_     ) * 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.
      snake_case_  ,snake_case_  ,snake_case_  ,snake_case_  ,snake_case_     :			str       =       accelerator.prepare(
          lowerCAmelCase_ ,	lowerCAmelCase_ ,	lowerCAmelCase_ ,	lowerCAmelCase_ ,	lowerCAmelCase_     )

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

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

    # Now we train the model
      for epoch in range(lowerCAmelCase_ ,	lowerCAmelCase_     ):
            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
                  snake_case_     :			int       =       accelerator.skip_first_batches(lowerCAmelCase_ ,	lowerCAmelCase_     )
                  overall_step += resume_step
            else:
                  # After the first iteration though, we need to go back to the original dataloader
                  snake_case_     :			str       =       train_dataloader
            for step, batch in enumerate(lowerCAmelCase_     ):
                  # We could avoid this line since we set the accelerator with `device_placement=True`.
                  batch.to(accelerator.device     )
                  snake_case_     :			int       =       model(**lowerCAmelCase_     )
                  snake_case_     :			Union[str, Any]       =       outputs.loss
                  snake_case_     :			str       =       loss / gradient_accumulation_steps
                  accelerator.backward(lowerCAmelCase_     )
                  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(lowerCAmelCase_ ,	lowerCAmelCase_     ):
                        snake_case_     :			Tuple       =       f"step_{overall_step}"
                        if overall_step % checkpointing_steps == 0:
                              if args.output_dir is not None:
                                    snake_case_     :			Any       =       os.path.join(args.output_dir ,	lowerCAmelCase_     )
                              accelerator.save_state(lowerCAmelCase_     )

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

            snake_case_     :			Optional[int]       =       metric.compute()
            # Use accelerator.print to print only on the main process.
            accelerator.print(f"epoch {epoch}:" ,	lowerCAmelCase_     )

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






def 					SCREAMING_SNAKE_CASE_			(		):
      snake_case_     :			Optional[int]       =       argparse.ArgumentParser(description="Simple example of training script."     )
      parser.add_argument(
          "--mixed_precision" ,	type=lowerCAmelCase_ ,	default=lowerCAmelCase_ ,	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=lowerCAmelCase_ ,	default=lowerCAmelCase_ ,	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=lowerCAmelCase_ ,	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=lowerCAmelCase_ ,	default=lowerCAmelCase_ ,	help="If the training should continue from a checkpoint folder." ,	)
      snake_case_     :			Dict       =       parser.parse_args()
      snake_case_     :			Dict       =       {"lr": 2e-5, "num_epochs": 3, "seed": 4_2, "batch_size": 1_6}
      training_function(lowerCAmelCase_ ,	lowerCAmelCase_     )


if __name__ == "__main__":
     main()




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


UpperCAmelCase							=       {
    "configuration_nystromformer": ["NYSTROMFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "NystromformerConfig"],
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCAmelCase							=       [
         "NYSTROMFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
         "NystromformerForMaskedLM",
         "NystromformerForMultipleChoice",
         "NystromformerForQuestionAnswering",
         "NystromformerForSequenceClassification",
         "NystromformerForTokenClassification",
         "NystromformerLayer",
         "NystromformerModel",
         "NystromformerPreTrainedModel",
     ]


if TYPE_CHECKING:
     from .configuration_nystromformer import NYSTROMFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, NystromformerConfig

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_nystromformer import (
              NYSTROMFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
              NystromformerForMaskedLM,
              NystromformerForMultipleChoice,
              NystromformerForQuestionAnswering,
              NystromformerForSequenceClassification,
              NystromformerForTokenClassification,
              NystromformerLayer,
              NystromformerModel,
              NystromformerPreTrainedModel,
          )


else:
     import sys

     UpperCAmelCase							=       _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



