






"""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


_lowerCAmelCase	:	str				=      16
_lowerCAmelCase	:	int				=      32


def 	SCREAMING_SNAKE_CASE__       (  snake_case       : Accelerator			,					snake_case       : int = 16			,					snake_case       : str = "bert-base-cased"				)->		List[str]:






     '''simple docstring'''



     UpperCAmelCase__						:    Tuple							=							AutoTokenizer.from_pretrained(snake_case				)
     UpperCAmelCase__						:    List[Any]							=							load_dataset("glue"			,					"mrpc"				)

     def tokenize_function(snake_case       : Any				):
          # max_length=None => use the model max length (it's actually the default)
          UpperCAmelCase__						:    Any							=							tokenizer(examples["sentence1"]			,					examples["sentence2"]			,					truncation=snake_case			,					max_length=snake_case				)
          return outputs

     # Apply the method we just defined to all the examples in all the splits of the dataset
     UpperCAmelCase__						:    int							=							datasets.map(
         snake_case			,					batched=snake_case			,					remove_columns=["idx", "sentence1", "sentence2"]			,					load_from_cache_file=snake_case				)

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

     def collate_fn(snake_case       : Tuple				):
          # 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(snake_case			,					padding="max_length"			,					max_length=128			,					return_tensors="pt"				)
          return tokenizer.pad(snake_case			,					padding="longest"			,					return_tensors="pt"				)

     # Instantiate dataloaders.
     UpperCAmelCase__						:    int							=							DataLoader(
         tokenized_datasets["train"]			,					shuffle=snake_case			,					collate_fn=snake_case			,					batch_size=snake_case				)
     UpperCAmelCase__						:    Optional[int]							=							DataLoader(
         tokenized_datasets["validation"]			,					shuffle=snake_case			,					collate_fn=snake_case			,					batch_size=snake_case				)

     return train_dataloader, eval_dataloader


def 	SCREAMING_SNAKE_CASE__       (  snake_case       : Dict			,					snake_case       : str				)->		Optional[Any]:






     '''simple docstring'''



     UpperCAmelCase__						:    Tuple							=							Accelerator()

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

     set_seed(snake_case				)
     UpperCAmelCase__							,				UpperCAmelCase__						:    int							=							get_dataloaders(snake_case			,					snake_case			,					snake_case				)

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

     # Instantiate optimizer
     UpperCAmelCase__						:    List[str]							=							(
         AdamW
         if accelerator.state.deepspeed_plugin is None
         or "optimizer" not in accelerator.state.deepspeed_plugin.deepspeed_config
         else DummyOptim
     )
     UpperCAmelCase__						:    Tuple							=							optimizer_cls(params=model.parameters()			,					lr=snake_case				)

     if accelerator.state.deepspeed_plugin is not None:
          UpperCAmelCase__						:    List[str]							=							accelerator.state.deepspeed_plugin.deepspeed_config[
              "gradient_accumulation_steps"
          ]
     else:
          UpperCAmelCase__						:    List[str]							=							1
     UpperCAmelCase__						:    Optional[Any]							=							(len(snake_case				) * 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=snake_case			,					num_warmup_steps=0			,					num_training_steps=snake_case			,					)
     else:
          UpperCAmelCase__						:    Tuple							=							DummyScheduler(snake_case			,					total_num_steps=snake_case			,					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__						:    int							=							accelerator.prepare(
         snake_case			,					snake_case			,					snake_case			,					snake_case			,					snake_case				)

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

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

               overall_step += 1

          model.eval()
          UpperCAmelCase__						:    Optional[Any]							=							0
          for step, batch in enumerate(snake_case				):
               # We could avoid this line since we set the accelerator with `device_placement=True`.
               batch.to(accelerator.device				)
               with torch.no_grad():
                    UpperCAmelCase__						:    List[str]							=							model(**snake_case				)
               UpperCAmelCase__						:    List[Any]							=							outputs.logits.argmax(dim=-1				)
               # It is slightly faster to call this once, than multiple times
               UpperCAmelCase__							,				UpperCAmelCase__						:    Any							=							accelerator.gather(
                   (predictions, batch["labels"])				)  # If we are in a multiprocess environment, the last batch has duplicates
               if accelerator.use_distributed:
                    if step == len(snake_case				) - 1:
                         UpperCAmelCase__						:    List[Any]							=							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=snake_case			,					references=snake_case			,					)

          UpperCAmelCase__						:    str							=							metric.compute()
          # Use accelerator.print to print only on the main process.
          accelerator.print(f'epoch {epoch}:'			,					snake_case				)
          UpperCAmelCase__						:    int							=							eval_metric["accuracy"]

          if best_performance < eval_metric["accuracy"]:
               UpperCAmelCase__						:    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(snake_case			,					snake_case				)


def 	SCREAMING_SNAKE_CASE__       (  )->		Dict:






     '''simple docstring'''



     UpperCAmelCase__						:    Union[str, Any]							=							argparse.ArgumentParser(description="Simple example of training script tracking peak GPU memory usage."				)
     parser.add_argument(
         "--model_name_or_path"			,					type=snake_case			,					default="bert-base-cased"			,					help="Path to pretrained model or model identifier from huggingface.co/models."			,					required=snake_case			,					)
     parser.add_argument(
         "--output_dir"			,					type=snake_case			,					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=snake_case			,					default=snake_case			,					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=snake_case			,					default=3			,					help="Number of train epochs."			,					)
     UpperCAmelCase__						:    Optional[int]							=							parser.parse_args()
     UpperCAmelCase__						:    List[str]							=							{"lr": 2E-5, "num_epochs": args.num_epochs, "seed": 42, "batch_size": 16}
     training_function(snake_case			,					snake_case				)


if __name__ == "__main__":
 main()










"""simple docstring"""



import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow


if is_torch_available():
 import torch

 from transformers import XLMRobertaModel




@require_sentencepiece
@require_tokenizers
@require_torch
class 			lowerCAmelCase__			(    unittest.TestCase   ):
   @slow
   def       __a							(		self      :	Tuple							):


        '''simple docstring'''
        UpperCAmelCase__						:    List[str]							=							XLMRobertaModel.from_pretrained("xlm-roberta-base"							)
        UpperCAmelCase__						:    str							=							torch.tensor([[0, 5_8_1, 1_0_2_6_9, 8_3, 9_9_9_4_2, 1_3_6, 6_0_7_4_2, 2_3, 7_0, 8_0_5_8_3, 1_8_2_7_6, 2]]							)
        # The dog is cute and lives in the garden house

        UpperCAmelCase__						:    Tuple							=							torch.Size((1, 1_2, 7_6_8)							)  # batch_size, sequence_length, embedding_vector_dim
        UpperCAmelCase__						:    Optional[int]							=							torch.tensor(
            [[-0.0101, 0.1218, -0.0803, 0.0801, 0.1327, 0.0776, -0.1215, 0.2383, 0.3338, 0.3106, 0.0300, 0.0252]]							)
        #  xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')
        #  xlmr.eval()
        #  expected_output_values_last_dim = xlmr.extract_features(input_ids[0])[:, :, -1]
        with torch.no_grad():
             UpperCAmelCase__						:    int							=							model(snake_case__							)["last_hidden_state"].detach()
        self.assertEqual(output.shape							,			snake_case__							)
        # compare the actual values for a slice of last dim
        self.assertTrue(torch.allclose(output[:, :, -1]							,			snake_case__							,			atol=1e-3							)							)




   @slow
   def       __a							(		self      :	Optional[Any]							):


        '''simple docstring'''
        UpperCAmelCase__						:    Any							=							XLMRobertaModel.from_pretrained("xlm-roberta-large"							)
        UpperCAmelCase__						:    Tuple							=							torch.tensor([[0, 5_8_1, 1_0_2_6_9, 8_3, 9_9_9_4_2, 1_3_6, 6_0_7_4_2, 2_3, 7_0, 8_0_5_8_3, 1_8_2_7_6, 2]]							)
        # The dog is cute and lives in the garden house

        UpperCAmelCase__						:    Union[str, Any]							=							torch.Size((1, 1_2, 1_0_2_4)							)  # batch_size, sequence_length, embedding_vector_dim
        UpperCAmelCase__						:    Optional[Any]							=							torch.tensor(
            [[-0.0699, -0.0318, 0.0705, -0.1241, 0.0999, -0.0520, 0.1004, -0.1838, -0.4704, 0.1437, 0.0821, 0.0126]]							)
        #  xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.large')
        #  xlmr.eval()
        #  expected_output_values_last_dim = xlmr.extract_features(input_ids[0])[:, :, -1]
        with torch.no_grad():
             UpperCAmelCase__						:    Optional[Any]							=							model(snake_case__							)["last_hidden_state"].detach()
        self.assertEqual(output.shape							,			snake_case__							)
        # compare the actual values for a slice of last dim
        self.assertTrue(torch.allclose(output[:, :, -1]							,			snake_case__							,			atol=1e-3							)							)


