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


UpperCAmelCase     						=			16
UpperCAmelCase     						=			32


def SCREAMING_SNAKE_CASE			(       lowercase_:		Optional[int]						,  lowercase_:		Optional[Any]			):
							# Initialize accelerator
							if args.with_tracking:
														_UpperCamelCase       :    str					  =					Accelerator(
														    cpu=args.cpu						,  mixed_precision=args.mixed_precision						,  log_with='all'						,  project_dir=args.project_dir			)
							else:
														_UpperCamelCase       :    Union[str, Any]					  =					Accelerator(cpu=args.cpu						,  mixed_precision=args.mixed_precision			)

							if hasattr(args.checkpointing_steps						,  'isdigit'			):
														if args.checkpointing_steps == "epoch":
																					_UpperCamelCase       :    Union[str, Any]					  =					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       :    Union[str, Any]					  =					None
							# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
							_UpperCamelCase       :    Any					  =					config['lr']
							_UpperCamelCase       :    Optional[Any]					  =					int(config['num_epochs']			)
							_UpperCamelCase       :    Any					  =					int(config['seed']			)
							_UpperCamelCase       :    List[Any]					  =					int(config['batch_size']			)

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

							_UpperCamelCase       :    str					  =					AutoTokenizer.from_pretrained('bert-base-cased'			)
							_UpperCamelCase       :    Tuple					  =					load_dataset('glue'						,  'mrpc'			)
							_UpperCamelCase       :    Tuple					  =					evaluate.load('glue'						,  'mrpc'			)

							def tokenize_function(lowercase_:		Optional[Any]			):
														# max_length=None => use the model max length (it's actually the default)
														_UpperCamelCase       :    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():
														_UpperCamelCase       :    Dict					  =					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
							_UpperCamelCase       :    List[Any]					  =					tokenized_datasets.rename_column('label'						,  'labels'			)

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

							def collate_fn(lowercase_:		str			):
														# 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       :    Dict					  =					16
														elif accelerator.mixed_precision != "no":
																					_UpperCamelCase       :    List[str]					  =					8
														else:
																					_UpperCamelCase       :    str					  =					None

														return tokenizer.pad(
														    _lowerCAmelCase						,  padding='longest'						,  max_length=_lowerCAmelCase						,  pad_to_multiple_of=_lowerCAmelCase						,  return_tensors='pt'						,  )

							# Instantiate dataloaders.
							_UpperCamelCase       :    List[str]					  =					DataLoader(
							    tokenized_datasets['train']						,  shuffle=_lowerCAmelCase						,  collate_fn=_lowerCAmelCase						,  batch_size=_lowerCAmelCase			)
							_UpperCamelCase       :    str					  =					DataLoader(
							    tokenized_datasets['validation']						,  shuffle=_lowerCAmelCase						,  collate_fn=_lowerCAmelCase						,  batch_size=_lowerCAmelCase			)

							set_seed(_lowerCAmelCase			)

							# Instantiate the model (we build the model here so that the seed also control new weights initialization)
							_UpperCamelCase       :    List[str]					  =					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).
							_UpperCamelCase       :    List[Any]					  =					model.to(accelerator.device			)

							# Instantiate optimizer
							_UpperCamelCase       :    Dict					  =					AdamW(params=model.parameters()						,  lr=_lowerCAmelCase			)

							# Instantiate scheduler
							_UpperCamelCase       :    List[Any]					  =					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.
							_UpperCamelCase  , _UpperCamelCase  , _UpperCamelCase  , _UpperCamelCase  , _UpperCamelCase       :    List[Any]					  =					accelerator.prepare(
							    _lowerCAmelCase						,  _lowerCAmelCase						,  _lowerCAmelCase						,  _lowerCAmelCase						,  _lowerCAmelCase			)

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

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

    # Now we train the model
							for epoch in range(_lowerCAmelCase						,  _lowerCAmelCase			):
														model.train()
														if args.with_tracking:
																					_UpperCamelCase       :    List[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
																					_UpperCamelCase       :    Dict					  =					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
																					_UpperCamelCase       :    List[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			)
																					_UpperCamelCase       :    List[str]					  =					model(**_lowerCAmelCase			)
																					_UpperCamelCase       :    str					  =					outputs.loss
																					_UpperCamelCase       :    Tuple					  =					loss / gradient_accumulation_steps
																					# We keep track of the loss at each epoch
																					if args.with_tracking:
																												total_loss += loss.detach().float()
																					accelerator.backward(_lowerCAmelCase			)
																					if step % gradient_accumulation_steps == 0:
																												optimizer.step()
																												lr_scheduler.step()
																												optimizer.zero_grad()

																					overall_step += 1

																					if isinstance(_lowerCAmelCase						,  _lowerCAmelCase			):
																												_UpperCamelCase       :    Optional[Any]					  =					F"step_{overall_step}"
																												if overall_step % checkpointing_steps == 0:
																																			if args.output_dir is not None:
																																										_UpperCamelCase       :    Tuple					  =					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`.
																					batch.to(accelerator.device			)
																					with torch.no_grad():
																												_UpperCamelCase       :    int					  =					model(**_lowerCAmelCase			)
																					_UpperCamelCase       :    List[Any]					  =					outputs.logits.argmax(dim=-1			)
																					_UpperCamelCase  , _UpperCamelCase       :    List[Any]					  =					accelerator.gather_for_metrics((predictions, batch['labels'])			)
																					metric.add_batch(
																					    predictions=_lowerCAmelCase						,  references=_lowerCAmelCase						,  )

														_UpperCamelCase       :    Optional[int]					  =					metric.compute()
														# Use accelerator.print to print only on the main process.
														accelerator.print(F"epoch {epoch}:"						,  _lowerCAmelCase			)
														if args.with_tracking:
																					accelerator.log(
																					    {
																					        'accuracy': eval_metric['accuracy'],
																					        'f1': eval_metric['f1'],
																					        'train_loss': total_loss.item() / len(_lowerCAmelCase			),
																					        'epoch': epoch,
																					    }						,  step=_lowerCAmelCase						,  )

														if checkpointing_steps == "epoch":
																					_UpperCamelCase       :    int					  =					F"epoch_{epoch}"
																					if args.output_dir is not None:
																												_UpperCamelCase       :    Optional[Any]					  =					os.path.join(args.output_dir						,  _lowerCAmelCase			)
																					accelerator.save_state(_lowerCAmelCase			)

							if args.with_tracking:
														accelerator.end_training()


def SCREAMING_SNAKE_CASE			(       ):
							_UpperCamelCase       :    List[str]					  =					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(
							    '--resume_from_checkpoint'						,  type=_lowerCAmelCase						,  default=_lowerCAmelCase						,  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=_lowerCAmelCase						,  default='.'						,  help='Optional save directory where all checkpoint folders will be stored. Default is the current working directory.'						,  )
							parser.add_argument(
							    '--project_dir'						,  type=_lowerCAmelCase						,  default='logs'						,  help='Location on where to store experiment tracking logs` and relevent project information'						,  )
							_UpperCamelCase       :    Any					  =					parser.parse_args()
							_UpperCamelCase       :    Any					  =					{'lr': 2e-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
							training_function(_lowerCAmelCase						,  _lowerCAmelCase			)


if __name__ == "__main__":
	main()




from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_speech_available,
    is_torch_available,
)


UpperCAmelCase     						=			{
    '''configuration_speech_to_text_2''': ['''SPEECH_TO_TEXT_2_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''Speech2Text2Config'''],
    '''processing_speech_to_text_2''': ['''Speech2Text2Processor'''],
    '''tokenization_speech_to_text_2''': ['''Speech2Text2Tokenizer'''],
}


try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 UpperCAmelCase     						=			[
     '''SPEECH_TO_TEXT_2_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''Speech2Text2ForCausalLM''',
     '''Speech2Text2PreTrainedModel''',
 ]


if TYPE_CHECKING:
 from .configuration_speech_to_text_a import SPEECH_TO_TEXT_2_PRETRAINED_CONFIG_ARCHIVE_MAP, SpeechaTextaConfig
 from .processing_speech_to_text_a import SpeechaTextaProcessor
 from .tokenization_speech_to_text_a import SpeechaTextaTokenizer

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_speech_to_text_a import (
      SPEECH_TO_TEXT_2_PRETRAINED_MODEL_ARCHIVE_LIST,
      SpeechaTextaForCausalLM,
      SpeechaTextaPreTrainedModel,
  )

else:
 import sys

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



