'''simple docstring'''
import argparse
import json
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
from accelerate.utils.deepspeed import DummyOptim, DummyScheduler


a=  1_6
a=  3_2







def  _UpperCamelCase (     _a	:	Accelerator				,     _a	:	int = 1_6				,     _a	:	str = "bert-base-cased"			):




    """simple docstring"""



    __UpperCamelCase	:	Dict		    =  AutoTokenizer.from_pretrained(_a			)
    __UpperCamelCase	:	Union[str, Any]		    =  load_dataset('glue'				,     'mrpc'			)

    def tokenize_function(_a	:	Optional[Any]			):
        # max_length=None => use the model max length (it's actually the default)
        __UpperCamelCase	:	Optional[Any]		    =  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
    __UpperCamelCase	:	Any		    =  datasets.map(
        _a				,     batched=_a				,     remove_columns=['idx', 'sentence1', 'sentence2']				,     load_from_cache_file=_a			)

    # We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
    # transformers library
    __UpperCamelCase	:	Any		    =  tokenized_datasets.rename_column('label'				,     'labels'			)

    def collate_fn(_a	:	Dict			):
        # On TPU it's best to pad everything to the same length or training will be very slow.
        if accelerator.distributed_type == DistributedType.TPU:
            return tokenizer.pad(_a				,     padding='max_length'				,     max_length=1_2_8				,     return_tensors='pt'			)
        return tokenizer.pad(_a				,     padding='longest'				,     return_tensors='pt'			)

    # Instantiate dataloaders.
    __UpperCamelCase	:	List[Any]		    =  DataLoader(
        tokenized_datasets['train']				,     shuffle=_a				,     collate_fn=_a				,     batch_size=_a			)
    __UpperCamelCase	:	Union[str, Any]		    =  DataLoader(
        tokenized_datasets['validation']				,     shuffle=_a				,     collate_fn=_a				,     batch_size=_a			)

    return train_dataloader, eval_dataloader







def  _UpperCamelCase (     _a	:	Any				,     _a	:	str				,     _a	:	Optional[int]				,     _a	:	Union[str, Any]			):




    """simple docstring"""



    model.eval()
    __UpperCamelCase	:	List[str]		    =  0
    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():
            __UpperCamelCase	:	str		    =  model(**_a			)
        __UpperCamelCase	:	Any		    =  outputs.logits.argmax(dim=-1			)
        # It is slightly faster to call this once, than multiple times
        __UpperCamelCase    ,					__UpperCamelCase	:	str		    =  accelerator.gather(
            (predictions, batch['labels'])			)  # If we are in a multiprocess environment, the last batch has duplicates
        if accelerator.use_distributed:
            if step == len(_a			) - 1:
                __UpperCamelCase	:	Dict		    =  predictions[: len(eval_dataloader.dataset			) - samples_seen]
                __UpperCamelCase	:	int		    =  references[: len(eval_dataloader.dataset			) - samples_seen]
            else:
                samples_seen += references.shape[0]
        metric.add_batch(
            predictions=_a				,     references=_a				,     )

    __UpperCamelCase	:	str		    =  metric.compute()
    return eval_metric["accuracy"]







def  _UpperCamelCase (     _a	:	Union[str, Any]				,     _a	:	int			):




    """simple docstring"""



    __UpperCamelCase	:	Union[str, Any]		    =  Accelerator()

    # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
    __UpperCamelCase	:	int		    =  config['lr']
    __UpperCamelCase	:	Dict		    =  int(config['num_epochs']			)
    __UpperCamelCase	:	Tuple		    =  int(config['seed']			)
    __UpperCamelCase	:	List[str]		    =  int(config['batch_size']			)
    __UpperCamelCase	:	str		    =  args.model_name_or_path

    set_seed(_a			)
    __UpperCamelCase    ,					__UpperCamelCase	:	Optional[Any]		    =  get_dataloaders(_a				,     _a				,     _a			)

    # Instantiate the model (we build the model here so that the seed also control new weights initialization)
    __UpperCamelCase	:	Any		    =  AutoModelForSequenceClassification.from_pretrained(_a				,     return_dict=_a			)

    # Instantiate optimizer
    __UpperCamelCase	:	Optional[Any]		    =  (
        AdamW
        if accelerator.state.deepspeed_plugin is None
        or 'optimizer' not in accelerator.state.deepspeed_plugin.deepspeed_config
        else DummyOptim
    )
    __UpperCamelCase	:	List[Any]		    =  optimizer_cls(params=model.parameters()				,     lr=_a			)

    if accelerator.state.deepspeed_plugin is not None:
        __UpperCamelCase	:	Optional[int]		    =  accelerator.state.deepspeed_plugin.deepspeed_config[
            'gradient_accumulation_steps'
        ]
    else:
        __UpperCamelCase	:	Tuple		    =  1
    __UpperCamelCase	:	Dict		    =  (len(_a			) * num_epochs) // gradient_accumulation_steps

    # Instantiate scheduler
    if (
        accelerator.state.deepspeed_plugin is None
        or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
    ):
        __UpperCamelCase	:	List[str]		    =  get_linear_schedule_with_warmup(
            optimizer=_a				,     num_warmup_steps=0				,     num_training_steps=_a				,     )
    else:
        __UpperCamelCase	:	int		    =  DummyScheduler(_a				,     total_num_steps=_a				,     warmup_num_steps=0			)

    # 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	:	Tuple		    =  accelerator.prepare(
        _a				,     _a				,     _a				,     _a				,     _a			)

    # We need to keep track of how many total steps we have iterated over
    __UpperCamelCase	:	Dict		    =  0
    # We also need to keep track of the stating epoch so files are named properly
    __UpperCamelCase	:	int		    =  0
    __UpperCamelCase	:	Tuple		    =  evaluate.load('glue'				,     'mrpc'			)
    __UpperCamelCase	:	Union[str, Any]		    =  num_epochs

    if args.partial_train_epoch is not None:
        __UpperCamelCase	:	Optional[Any]		    =  args.partial_train_epoch

    if args.resume_from_checkpoint:
        accelerator.load_state(args.resume_from_checkpoint			)
        __UpperCamelCase	:	str		    =  args.resume_from_checkpoint.split('epoch_'			)[1]
        __UpperCamelCase	:	Any		    =  ''
        for char in epoch_string:
            if char.isdigit():
                state_epoch_num += char
            else:
                break
        __UpperCamelCase	:	Any		    =  int(_a			) + 1
        __UpperCamelCase	:	Optional[Any]		    =  evaluation_loop(_a				,     _a				,     _a				,     _a			)
        accelerator.print('resumed checkpoint performance:'				,     _a			)
        accelerator.print('resumed checkpoint\'s scheduler\'s lr:'				,     lr_scheduler.get_lr()[0]			)
        accelerator.print('resumed optimizers\'s lr:'				,     optimizer.param_groups[0]['lr']			)
        with open(os.path.join(args.output_dir				,     f"""state_{starting_epoch-1}.json"""			)				,     'r'			) as f:
            __UpperCamelCase	:	str		    =  json.load(_a			)
            assert resumed_state["accuracy"] == accuracy, "Accuracy mismatch, loading from checkpoint failed"
            assert (
                resumed_state["lr"] == lr_scheduler.get_lr()[0]
            ), "Scheduler learning rate mismatch, loading from checkpoint failed"
            assert (
                resumed_state["optimizer_lr"] == optimizer.param_groups[0]["lr"]
            ), "Optimizer learning rate mismatch, loading from checkpoint failed"
            assert resumed_state["epoch"] == starting_epoch - 1, "Epoch mismatch, loading from checkpoint failed"
            return

    # Now we train the model
    __UpperCamelCase	:	int		    =  {}
    for epoch in range(_a				,     _a			):
        model.train()
        for step, batch in enumerate(_a			):
            __UpperCamelCase	:	Tuple		    =  model(**_a			)
            __UpperCamelCase	:	Any		    =  outputs.loss
            __UpperCamelCase	:	int		    =  loss / gradient_accumulation_steps
            accelerator.backward(_a			)
            if step % gradient_accumulation_steps == 0:
                optimizer.step()
                lr_scheduler.step()
                optimizer.zero_grad()

            overall_step += 1
        __UpperCamelCase	:	Optional[Any]		    =  f"""epoch_{epoch}"""
        __UpperCamelCase	:	Optional[Any]		    =  os.path.join(args.output_dir				,     _a			)
        accelerator.save_state(_a			)
        __UpperCamelCase	:	Optional[Any]		    =  evaluation_loop(_a				,     _a				,     _a				,     _a			)
        __UpperCamelCase	:	Union[str, Any]		    =  accuracy
        __UpperCamelCase	:	Tuple		    =  lr_scheduler.get_lr()[0]
        __UpperCamelCase	:	Any		    =  optimizer.param_groups[0]['lr']
        __UpperCamelCase	:	Tuple		    =  epoch
        __UpperCamelCase	:	Tuple		    =  overall_step
        accelerator.print(f"""epoch {epoch}:"""				,     _a			)

        accelerator.wait_for_everyone()
        if accelerator.is_main_process:
            with open(os.path.join(args.output_dir				,     f"""state_{epoch}.json"""			)				,     'w'			) as f:
                json.dump(_a				,     _a			)







def  _UpperCamelCase (     ):




    """simple docstring"""



    __UpperCamelCase	:	Optional[int]		    =  argparse.ArgumentParser(description='Simple example of training script tracking peak GPU memory usage.'			)
    parser.add_argument(
        '--model_name_or_path'				,     type=_a				,     default='bert-base-cased'				,     help='Path to pretrained model or model identifier from huggingface.co/models.'				,     required=_a				,     )
    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(
        '--resume_from_checkpoint'				,     type=_a				,     default=_a				,     help='If the training should continue from a checkpoint folder.'				,     )
    parser.add_argument(
        '--partial_train_epoch'				,     type=_a				,     default=_a				,     help='If passed, the training will stop after this number of epochs.'				,     )
    parser.add_argument(
        '--num_epochs'				,     type=_a				,     default=2				,     help='Number of train epochs.'				,     )
    __UpperCamelCase	:	Any		    =  parser.parse_args()
    __UpperCamelCase	:	Optional[int]		    =  {'lr': 2E-5, 'num_epochs': args.num_epochs, 'seed': 4_2, 'batch_size': 1_6}

    training_function(_a				,     _a			)


if __name__ == "__main__":
  main()




'''simple docstring'''
from ...processing_utils import ProcessorMixin






class 			__lowercase		(						_lowerCamelCase						):







     """simple docstring"""






     SCREAMING_SNAKE_CASE__		   =						'''SpeechT5FeatureExtractor'''
     SCREAMING_SNAKE_CASE__		   =						'''SpeechT5Tokenizer'''







     def __init__(		self     ,			_lowerCamelCase     ,			_lowerCamelCase    ):
         super().__init__(_lowerCamelCase     ,			_lowerCamelCase    )







     def __call__(		self     ,			*_lowerCamelCase     ,			**_lowerCamelCase    ):
         __UpperCamelCase	:	Union[str, Any]		    =  kwargs.pop('audio'     ,			_lowerCamelCase    )
         __UpperCamelCase	:	Tuple		    =  kwargs.pop('text'     ,			_lowerCamelCase    )
         __UpperCamelCase	:	Union[str, Any]		    =  kwargs.pop('text_target'     ,			_lowerCamelCase    )
         __UpperCamelCase	:	List[Any]		    =  kwargs.pop('audio_target'     ,			_lowerCamelCase    )
         __UpperCamelCase	:	Any		    =  kwargs.pop('sampling_rate'     ,			_lowerCamelCase    )

         if audio is not None and text is not None:
             raise ValueError(
                 'Cannot process both `audio` and `text` inputs. Did you mean `audio_target` or `text_target`?'    )
         if audio_target is not None and text_target is not None:
             raise ValueError(
                 'Cannot process both `audio_target` and `text_target` inputs. Did you mean `audio` or `text`?'    )
         if audio is None and audio_target is None and text is None and text_target is None:
             raise ValueError(
                 'You need to specify either an `audio`, `audio_target`, `text`, or `text_target` input to process.'    )

         if audio is not None:
             __UpperCamelCase	:	Dict		    =  self.feature_extractor(_lowerCamelCase     ,			*_lowerCamelCase     ,			sampling_rate=_lowerCamelCase     ,			**_lowerCamelCase    )
         elif text is not None:
             __UpperCamelCase	:	int		    =  self.tokenizer(_lowerCamelCase     ,			**_lowerCamelCase    )
         else:
             __UpperCamelCase	:	List[str]		    =  None

         if audio_target is not None:
             __UpperCamelCase	:	Dict		    =  self.feature_extractor(audio_target=_lowerCamelCase     ,			*_lowerCamelCase     ,			sampling_rate=_lowerCamelCase     ,			**_lowerCamelCase    )
             __UpperCamelCase	:	Optional[int]		    =  targets['input_values']
         elif text_target is not None:
             __UpperCamelCase	:	Any		    =  self.tokenizer(_lowerCamelCase     ,			**_lowerCamelCase    )
             __UpperCamelCase	:	Optional[int]		    =  targets['input_ids']
         else:
             __UpperCamelCase	:	List[str]		    =  None

         if inputs is None:
             return targets

         if targets is not None:
             __UpperCamelCase	:	List[Any]		    =  labels

             __UpperCamelCase	:	Any		    =  targets.get('attention_mask'    )
             if decoder_attention_mask is not None:
                 __UpperCamelCase	:	int		    =  decoder_attention_mask

         return inputs







     def lowerCAmelCase     (		self     ,			*_lowerCamelCase     ,			**_lowerCamelCase    ):
         __UpperCamelCase	:	Optional[Any]		    =  kwargs.pop('input_values'     ,			_lowerCamelCase    )
         __UpperCamelCase	:	Union[str, Any]		    =  kwargs.pop('input_ids'     ,			_lowerCamelCase    )
         __UpperCamelCase	:	List[Any]		    =  kwargs.pop('labels'     ,			_lowerCamelCase    )

         if input_values is not None and input_ids is not None:
             raise ValueError('Cannot process both `input_values` and `input_ids` inputs.'    )
         if input_values is None and input_ids is None and labels is None:
             raise ValueError(
                 'You need to specify either an `input_values`, `input_ids`, or `labels` input to be padded.'    )

         if input_values is not None:
             __UpperCamelCase	:	Optional[int]		    =  self.feature_extractor.pad(_lowerCamelCase     ,			*_lowerCamelCase     ,			**_lowerCamelCase    )
         elif input_ids is not None:
             __UpperCamelCase	:	int		    =  self.tokenizer.pad(_lowerCamelCase     ,			**_lowerCamelCase    )
         else:
             __UpperCamelCase	:	str		    =  None

         if labels is not None:
             if "input_ids" in labels or (isinstance(_lowerCamelCase     ,			_lowerCamelCase    ) and "input_ids" in labels[0]):
                 __UpperCamelCase	:	Dict		    =  self.tokenizer.pad(_lowerCamelCase     ,			**_lowerCamelCase    )
                 __UpperCamelCase	:	str		    =  targets['input_ids']
             else:
                 __UpperCamelCase	:	int		    =  self.feature_extractor.feature_size
                 __UpperCamelCase	:	Any		    =  self.feature_extractor.num_mel_bins
                 __UpperCamelCase	:	Dict		    =  self.feature_extractor.pad(_lowerCamelCase     ,			*_lowerCamelCase     ,			**_lowerCamelCase    )
                 __UpperCamelCase	:	List[str]		    =  feature_size_hack
                 __UpperCamelCase	:	Tuple		    =  targets['input_values']
         else:
             __UpperCamelCase	:	List[str]		    =  None

         if inputs is None:
             return targets

         if targets is not None:
             __UpperCamelCase	:	Optional[int]		    =  labels

             __UpperCamelCase	:	Union[str, Any]		    =  targets.get('attention_mask'    )
             if decoder_attention_mask is not None:
                 __UpperCamelCase	:	Optional[Any]		    =  decoder_attention_mask

         return inputs







     def lowerCAmelCase     (		self     ,			*_lowerCamelCase     ,			**_lowerCamelCase    ):
         return self.tokenizer.batch_decode(*_lowerCamelCase     ,			**_lowerCamelCase    )







     def lowerCAmelCase     (		self     ,			*_lowerCamelCase     ,			**_lowerCamelCase    ):
         return self.tokenizer.decode(*_lowerCamelCase     ,			**_lowerCamelCase    )



