


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


SCREAMING_SNAKE_CASE       :      Optional[Any]												= 16
SCREAMING_SNAKE_CASE       :      Union[str, Any]												= 32



def 				UpperCamelCase_(      lowerCamelCase_							, lowerCamelCase_ = 16							, lowerCamelCase_ = "bert-base-cased" )    ->						Optional[Any]:
    _lowercase       :							str      		=		AutoTokenizer.from_pretrained(lowerCamelCase_ )
    _lowercase       :							Dict      		=		load_dataset('glue'							, 'mrpc' )

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

    # We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
    # transformers library
    _lowercase       :							int      		=		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.
        if accelerator.distributed_type == DistributedType.TPU:
            return tokenizer.pad(lowerCamelCase_							, padding='max_length'							, max_length=128							, return_tensors='pt' )
        return tokenizer.pad(lowerCamelCase_							, padding='longest'							, return_tensors='pt' )

    # Instantiate dataloaders.
    _lowercase       :							Tuple      		=		DataLoader(
        tokenized_datasets['train']							, shuffle=lowerCamelCase_							, collate_fn=lowerCamelCase_							, batch_size=lowerCamelCase_ )
    _lowercase       :							str      		=		DataLoader(
        tokenized_datasets['validation']							, shuffle=lowerCamelCase_							, collate_fn=lowerCamelCase_							, batch_size=lowerCamelCase_ )

    return train_dataloader, eval_dataloader



def 				UpperCamelCase_(      lowerCamelCase_							, lowerCamelCase_ )    ->						Tuple:
    # Initialize accelerator
    _lowercase       :							str      		=		Accelerator()

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

    set_seed(lowerCamelCase_ )
    _lowercase		,				_lowercase       :							str      		=		get_dataloaders(lowerCamelCase_							, lowerCamelCase_							, lowerCamelCase_ )

    # Instantiate the model (we build the model here so that the seed also control new weights initialization)
    _lowercase       :							List[Any]      		=		AutoModelForSequenceClassification.from_pretrained(lowerCamelCase_							, return_dict=lowerCamelCase_ )

    # Instantiate optimizer
    _lowercase       :							Union[str, Any]      		=		(
        AdamW
        if accelerator.state.deepspeed_plugin is None
        or 'optimizer' not in accelerator.state.deepspeed_plugin.deepspeed_config
        else DummyOptim
    )
    _lowercase       :							int      		=		optimizer_cls(params=model.parameters()							, lr=lowerCamelCase_ )

    if accelerator.state.deepspeed_plugin is not None:
        _lowercase       :							Any      		=		accelerator.state.deepspeed_plugin.deepspeed_config[
            'gradient_accumulation_steps'
        ]
    else:
        _lowercase       :							Tuple      		=		1
    _lowercase       :							Union[str, Any]      		=		(len(lowerCamelCase_ ) * num_epochs) // gradient_accumulation_steps

    # Instantiate scheduler
    if (
        accelerator.state.deepspeed_plugin is None
        or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
    ):
        _lowercase       :							List[str]      		=		get_linear_schedule_with_warmup(
            optimizer=lowerCamelCase_							, num_warmup_steps=0							, num_training_steps=lowerCamelCase_							, )
    else:
        _lowercase       :							Dict      		=		DummyScheduler(lowerCamelCase_							, total_num_steps=lowerCamelCase_							, 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.
    _lowercase		,				_lowercase		,				_lowercase		,				_lowercase		,				_lowercase       :							Union[str, Any]      		=		accelerator.prepare(
        lowerCamelCase_							, lowerCamelCase_							, lowerCamelCase_							, lowerCamelCase_							, lowerCamelCase_ )

    # We need to keep track of how many total steps we have iterated over
    _lowercase       :							Any      		=		0
    # We also need to keep track of the stating epoch so files are named properly
    _lowercase       :							Union[str, Any]      		=		0

    # Now we train the model
    _lowercase       :							Tuple      		=		evaluate.load('glue'							, 'mrpc' )
    _lowercase       :							Any      		=		0
    _lowercase       :							Optional[Any]      		=		{}
    for epoch in range(lowerCamelCase_							, lowerCamelCase_ ):
        model.train()
        for step, batch in enumerate(lowerCamelCase_ ):
            _lowercase       :							List[str]      		=		model(**lowerCamelCase_ )
            _lowercase       :							Tuple      		=		outputs.loss
            _lowercase       :							Tuple      		=		loss / gradient_accumulation_steps
            accelerator.backward(lowerCamelCase_ )
            if step % gradient_accumulation_steps == 0:
                optimizer.step()
                lr_scheduler.step()
                optimizer.zero_grad()

            overall_step += 1

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

        _lowercase       :							Optional[Any]      		=		metric.compute()
        # Use accelerator.print to print only on the main process.
        accelerator.print(F'''epoch {epoch}:'''							, lowerCamelCase_ )
        _lowercase       :							List[str]      		=		eval_metric['accuracy']

        if best_performance < eval_metric["accuracy"]:
            _lowercase       :							Union[str, Any]      		=		eval_metric['accuracy']

    if args.performance_lower_bound is not None:
        assert (
            args.performance_lower_bound <= best_performance
        ), F'''Best performance metric {best_performance} is lower than the lower bound {args.performance_lower_bound}'''

    accelerator.wait_for_everyone()
    if accelerator.is_main_process:
        with open(os.path.join(args.output_dir							, 'all_results.json' )							, 'w' ) as f:
            json.dump(lowerCamelCase_							, lowerCamelCase_ )



def 				UpperCamelCase_(      )    ->						Any:
    _lowercase       :							Optional[Any]      		=		argparse.ArgumentParser(description='Simple example of training script tracking peak GPU memory usage.' )
    parser.add_argument(
        '--model_name_or_path'							, type=lowerCamelCase_							, default='bert-base-cased'							, help='Path to pretrained model or model identifier from huggingface.co/models.'							, required=lowerCamelCase_							, )
    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(
        '--performance_lower_bound'							, type=lowerCamelCase_							, default=lowerCamelCase_							, help='Optional lower bound for the performance metric. If set, the training will throw error when the performance metric drops below this value.'							, )
    parser.add_argument(
        '--num_epochs'							, type=lowerCamelCase_							, default=3							, help='Number of train epochs.'							, )
    _lowercase       :							List[str]      		=		parser.parse_args()
    _lowercase       :							List[str]      		=		{'lr': 2e-5, 'num_epochs': args.num_epochs, '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_tf_available,
    is_torch_available,
)


SCREAMING_SNAKE_CASE       :      List[str]												= {
    "configuration_speech_to_text": ["SPEECH_TO_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP", "Speech2TextConfig"],
    "processing_speech_to_text": ["Speech2TextProcessor"],
}

try:
 if not is_sentencepiece_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE       :      Union[str, Any]												= ["Speech2TextTokenizer"]

try:
 if not is_speech_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE       :      int												= ["Speech2TextFeatureExtractor"]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE       :      Optional[Any]												= [
     "TF_SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST",
     "TFSpeech2TextForConditionalGeneration",
     "TFSpeech2TextModel",
     "TFSpeech2TextPreTrainedModel",
 ]

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE       :      str												= [
     "SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST",
     "Speech2TextForConditionalGeneration",
     "Speech2TextModel",
     "Speech2TextPreTrainedModel",
 ]


if TYPE_CHECKING:
 from .configuration_speech_to_text import SPEECH_TO_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP, SpeechaTextConfig
 from .processing_speech_to_text import SpeechaTextProcessor

 try:
  if not is_sentencepiece_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_speech_to_text import SpeechaTextTokenizer

 try:
  if not is_speech_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .feature_extraction_speech_to_text import SpeechaTextFeatureExtractor

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_speech_to_text import (
      TF_SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFSpeechaTextForConditionalGeneration,
      TFSpeechaTextModel,
      TFSpeechaTextPreTrainedModel,
  )

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_speech_to_text import (
      SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
      SpeechaTextForConditionalGeneration,
      SpeechaTextModel,
      SpeechaTextPreTrainedModel,
  )

else:
 import sys

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


