code: import torch
  import torch.nn as nn 
  import argparse
  import numpy as np
  from torch.utils.data import DataLoader , Dataset
  import pandas as pd 
  from tqdm import tqdm 
  from transformers import ( 
      BertTokenizer,
      AdamW ,  
      get_linear_schedule_with_warmup  ,
      T5Tokenizer,
      T5ForConditionalGeneration)
  
  
  device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
  
  
  def parse_args():
  
      parser = argparse.ArgumentParser(description="Pretrained Machine Translation French to Wolof")
      
      parser.add_argument(
          "--train_file", type=str, default=None, help="A csv  file containing the training data."
      )
   
  
      parser.add_argument(
          "--max_source_length",
          type=int,
          default=150,
          help="The maximum total input sequence length after "
          "tokenization.Sequences longer than this will be truncated, sequences shorter will be padded.",
      )
      parser.add_argument(
          "--max_target_length",
          type=int,
          default=150,
          help="The maximum total sequence length for target text after "
          "tokenization. Sequences longer than this will be truncated, sequences shorter will be padded."
           
      )
  
      parser.add_argument(
          "--number_epochs",
          type=int,
          default=3,
          help="Total number of training steps to perform the model .",
      ) 
  
      parser.add_argument(
          "--learning_rate",
          type=float,
          default=3e-5,
          help="Initial learning rate (after the potential warmup period) to use.",
      )
  
      parser.add_argument(
          "--epsilone",
          type=float,
          default=1e-8,
          help="Initial learning rate (after the potential warmup period) to use.",
      ) 
  
      parser.add_argument(
          "--train_batch_size",
          type=int,
          default=1,
          help="Batch size (per device) for the training dataloader.",
      )
  
      parser.add_argument(
          "--model_name",
          type=str,
          default="t5-base",
          help="Pretrained model name.",
      )
  
      parser.add_argument(
          "--task_prefix",
          type=str,
          default="translate French to Wolof: ",
          help="The task prefix for the translation.",
      )
   
   
      args = parser.parse_args()
  
   
      if args.train_file is not None:
          extension = args.train_file.split(".")[-1]
          assert extension in ["csv", "json"], "`train_file` should be a csv or a json file."
     
   
      return args
   
   
  
  
  class NMTDataset(Dataset):
      """
      Dataset Class 
      
      """
      def __init__(self, frenchs, wolofs , tokenizer , max_len_source , max_len_target):
          """
          Parameters :
          ------------
          frenchs : 
          wolofs  : 
          tokenizer:
          max_len_sourrce:
          max_len_target: 
          """
          self.frenchs = frenchs
          self.wolofs = wolofs
          self.tokenizer = tokenizer
          self.max_len_source = max_len_source
          self.max_len_target = max_len_target
      
      
      def __len__(self):
          return len(self.frenchs)
      
      def __getitem__(self, item):
          
          french = str(self.frenchs[item])
          wolof = str(self.wolofs[item])
  
          french_encoding = self.tokenizer(
                french,
                add_special_tokens=True,
                max_length=self.max_len_source,
                pad_to_max_length=True,
                return_attention_mask=True,
                return_tensors='pt')    
  
          labels = self.tokenizer(
                wolof,
                add_special_tokens=True,
                max_length=self.max_len_target,
                return_token_type_ids=True,
                pad_to_max_length=True,
                return_attention_mask=True
                ).input_ids
          
          
          
          labels = [
             [(label if label != self.tokenizer.pad_token_id else -100) for label in labels]]
  
          labels = torch.tensor(labels)
  
          return {
                    'input_ids': french_encoding['input_ids'].flatten(),
                    'attention_mask':french_encoding['attention_mask'].flatten(),
                    'labels': labels.flatten()
                  }
  
  
  
  
  
  def NMTDataloader(df , batch_size , tokenizer , max_len_source , max_len_target):
  
      dataset = NMTDataset(df.french.values , df.wolof.values , tokenizer , max_len_source , max_len_target)
      dataloader = DataLoader(dataset , batch_size , num_workers= 4)
      return dataloader
  
   
  
  def yield_optimizer(model):
      """
      Returns optimizer for specific parameters
      """
      param_optimizer = list(model.named_parameters())
      no_decay = ["bias", "LayerNorm.bias", "LayerNorm.weight"]
  
      optimizer_parameters = [
          {
              "params": [
                  p for n, p in param_optimizer if not any(nd in n for nd in no_decay)
              ],
              "weight_decay": 0.001,
          },
          {
              "params": [
                  p for n, p in param_optimizer if any(nd in n for nd in no_decay)
              ],
              "weight_decay": 0.0,
          },
      ]
      return AdamW(optimizer_parameters, lr=3e-5, eps=1e-8)
  
  
  def train_epoch (model , data_loader, optimizer , device , scheduler):
      model.train()
      losses = []
     
      for step , d in tqdm(enumerate(data_loader) , total=len(data_loader)):
          
          input_ids =d['input_ids'].to(device)
          attention_mask = d['attention_mask'].to(device)
          labels = d['labels'].to(device)
         
         
          outputs = model(input_ids=input_ids , attention_mask=attention_mask , labels=labels)
          loss =  outputs.loss
  
          losses.append(loss.item())
  
  
          loss.backward()
          optimizer.step()
          scheduler.step()
          optimizer.zero_grad()
          
          if (step + 1) % 10 == 0:
              
               
              print('Epoch: {} | loss: {} '.format(step+1, np.mean(losses)))
  
  
   
  
  def train():
  
      args = parse_args()
  
      tokenizer = T5Tokenizer.from_pretrained(args.model_name)
  
      model= T5ForConditionalGeneration.from_pretrained(args.model_name)
      model.to(device)
  
      df = pd.read_csv(args.train_file)
      df["french"]=df["french"].apply(lambda x:args.task_prefix +x)
  
  
  
      train_data_loader= NMTDataloader(df,args.train_batch_size , tokenizer , args.max_source_length , args.max_target_length) 
      
      nb_train_steps = int(len(train_data_loader) /args.train_batch_size * args.number_epochs)
      optimizer = yield_optimizer(model)
      scheduler = get_linear_schedule_with_warmup(
                                          optimizer,
                                          num_warmup_steps=0,
                                          num_training_steps=nb_train_steps)
      
      for epoch in range(args.number_epochs):
          
          print(f'Epoch {epoch + 1}')
  
          train_epoch(model,train_data_loader,optimizer,device,scheduler) 
          
       
      return model.save_pretrained("../model/bert2bert")
  
  
  if __name__ == '__main__':
      train()
apis: torch.utils.data.DataLoader pandas.read_csv torch.tensor torch.cuda.is_available numpy.mean