






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

snake_case_   :	Any							      =16
snake_case_   :	Dict							      =32



def      UpperCAmelCase						( lowerCAmelCase__					,     lowerCAmelCase__ = 16	):


		'''simple docstring'''

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

		def tokenize_function(lowerCAmelCase__	):
				# max_length=None => use the model max length (it's actually the default)
				__A    =							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():
				__A    =							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
		__A    =							tokenized_datasets.rename_column("label"					,     "labels"	)

		def collate_fn(lowerCAmelCase__	):
				# On TPU it's best to pad everything to the same length or training will be very slow.
				__A    =							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":
						__A    =							16
				elif accelerator.mixed_precision != "no":
						__A    =							8
				else:
						__A    =							None

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

		# Instantiate dataloaders.
		__A    =							DataLoader(
		    tokenized_datasets["train"]					,     shuffle=lowerCAmelCase__					,     collate_fn=lowerCAmelCase__					,     batch_size=lowerCAmelCase__	)
		__A    =							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

					snake_case_   :	Any							      =mocked_dataloaders  # noqa: F811



def      UpperCAmelCase						( lowerCAmelCase__					,     lowerCAmelCase__	):


		'''simple docstring'''

		if os.environ.get("TESTING_MOCKED_DATALOADERS"					,     lowerCAmelCase__	) == "1":
				__A    =							2
		# Initialize accelerator
		__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"]	)

		# 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":
						__A    =							args.checkpointing_steps
				elif args.checkpointing_steps.isdigit():
						__A    =							int(args.checkpointing_steps	)
				else:
						raise ValueError(
						    F"""Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed."""	)
		else:
				__A    =							None

		set_seed(lowerCAmelCase__	)

		__A ,   __A    =							get_dataloaders(lowerCAmelCase__					,     lowerCAmelCase__	)
		__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=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).
		__A    =							model.to(accelerator.device	)

		# Instantiate optimizer
		__A    =							AdamW(params=model.parameters()					,     lr=lowerCAmelCase__	)

		# Instantiate scheduler
		__A    =							get_linear_schedule_with_warmup(
		    optimizer=lowerCAmelCase__					,     num_warmup_steps=100					,     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.
		__A ,   __A ,   __A ,   __A ,   __A    =							accelerator.prepare(
		    lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__	)

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

				if "epoch" in training_difference:
						__A    =							int(training_difference.replace("epoch_"					,     ""	)	) + 1
						__A    =							None
				else:
						__A    =							int(training_difference.replace("step_"					,     ""	)	)
						__A    =							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
						__A    =							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
						__A    =							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	)
						__A    =							model(**lowerCAmelCase__	)
						__A    =							outputs.loss
						__A    =							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__	):
								__A    =							F"""step_{overall_step}"""
								if overall_step % checkpointing_steps == 0:
										if args.output_dir is not None:
												__A    =							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():
								__A    =							model(**lowerCAmelCase__	)
						__A    =							outputs.logits.argmax(dim=-1	)
						__A ,   __A    =							accelerator.gather_for_metrics((predictions, batch["labels"])	)
						metric.add_batch(
						    predictions=lowerCAmelCase__					,     references=lowerCAmelCase__					,     )

				__A    =							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":
						__A    =							F"""epoch_{epoch}"""
						if args.output_dir is not None:
								__A    =							os.path.join(args.output_dir					,     lowerCAmelCase__	)
						accelerator.save_state(lowerCAmelCase__	)



def      UpperCAmelCase						( ):


		'''simple docstring'''

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


if __name__ == "__main__":
					main()







'''simple docstring'''






from typing import TYPE_CHECKING

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


lowercase						:   int      						=							{
    '''configuration_xlm_prophetnet''': ['''XLM_PROPHETNET_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''XLMProphetNetConfig'''],
}

try:
      if not is_sentencepiece_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase						:   Any      						=							['''XLMProphetNetTokenizer''']

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase						:   Tuple      						=							[
          '''XLM_PROPHETNET_PRETRAINED_MODEL_ARCHIVE_LIST''',
          '''XLMProphetNetDecoder''',
          '''XLMProphetNetEncoder''',
          '''XLMProphetNetForCausalLM''',
          '''XLMProphetNetForConditionalGeneration''',
          '''XLMProphetNetModel''',
          '''XLMProphetNetPreTrainedModel''',
      ]


if TYPE_CHECKING:
      from .configuration_xlm_prophetnet import XLM_PROPHETNET_PRETRAINED_CONFIG_ARCHIVE_MAP, XLMProphetNetConfig

      try:
            if not is_sentencepiece_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_xlm_prophetnet import XLMProphetNetTokenizer

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_xlm_prophetnet import (
                XLM_PROPHETNET_PRETRAINED_MODEL_ARCHIVE_LIST,
                XLMProphetNetDecoder,
                XLMProphetNetEncoder,
                XLMProphetNetForCausalLM,
                XLMProphetNetForConditionalGeneration,
                XLMProphetNetModel,
                XLMProphetNetPreTrainedModel,
            )

else:
      import sys

      lowercase						:   Optional[Any]      						=							_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




