

"""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       =							16
_a       =							32




def   _A  (  UpperCamelCase_     :       Accelerator,			UpperCamelCase_     :       int = 16,			UpperCamelCase_     :       str = "bert-base-cased")	->						List[str]:
   '''simple docstring'''


   __lowercase								=						AutoTokenizer.from_pretrained(UpperCamelCase_)
   __lowercase								=						load_dataset("glue",			"mrpc")

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

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

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

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

   # Instantiate dataloaders.
   __lowercase								=						DataLoader(
       tokenized_datasets["train"],			shuffle=UpperCamelCase_,			collate_fn=UpperCamelCase_,			batch_size=UpperCamelCase_)
   __lowercase								=						DataLoader(
       tokenized_datasets["validation"],			shuffle=UpperCamelCase_,			collate_fn=UpperCamelCase_,			batch_size=UpperCamelCase_)

   return train_dataloader, eval_dataloader




def   _A  (  UpperCamelCase_     :       Tuple,			UpperCamelCase_     :       Tuple)	->						Tuple:
   '''simple docstring'''


   __lowercase								=						Accelerator()

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

   set_seed(UpperCamelCase_)
   __lowercase		,__lowercase								=						get_dataloaders(UpperCamelCase_,			UpperCamelCase_,			UpperCamelCase_)

   # Instantiate the model (we build the model here so that the seed also control new weights initialization)
   __lowercase								=						AutoModelForSequenceClassification.from_pretrained(UpperCamelCase_,			return_dict=UpperCamelCase_)

   # Instantiate optimizer
   __lowercase								=						(
       AdamW
       if accelerator.state.deepspeed_plugin is None
       or "optimizer" not in accelerator.state.deepspeed_plugin.deepspeed_config
       else DummyOptim
   )
   __lowercase								=						optimizer_cls(params=model.parameters(),			lr=UpperCamelCase_)

   if accelerator.state.deepspeed_plugin is not None:
      __lowercase								=						accelerator.state.deepspeed_plugin.deepspeed_config[
          "gradient_accumulation_steps"
      ]
   else:
      __lowercase								=						1
   __lowercase								=						(len(UpperCamelCase_) * 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								=						get_linear_schedule_with_warmup(
          optimizer=UpperCamelCase_,			num_warmup_steps=0,			num_training_steps=UpperCamelCase_,			)
   else:
      __lowercase								=						DummyScheduler(UpperCamelCase_,			total_num_steps=UpperCamelCase_,			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								=						accelerator.prepare(
       UpperCamelCase_,			UpperCamelCase_,			UpperCamelCase_,			UpperCamelCase_,			UpperCamelCase_)

   # We need to keep track of how many total steps we have iterated over
   __lowercase								=						0
   # We also need to keep track of the stating epoch so files are named properly
   __lowercase								=						0

   # Now we train the model
   __lowercase								=						evaluate.load("glue",			"mrpc")
   __lowercase								=						0
   __lowercase								=						{}
   for epoch in range(UpperCamelCase_,			UpperCamelCase_):
      model.train()
      for step, batch in enumerate(UpperCamelCase_):
         __lowercase								=						model(**UpperCamelCase_)
         __lowercase								=						outputs.loss
         __lowercase								=						loss / gradient_accumulation_steps
         accelerator.backward(UpperCamelCase_)
         if step % gradient_accumulation_steps == 0:
            optimizer.step()
            lr_scheduler.step()
            optimizer.zero_grad()

         overall_step += 1

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

      __lowercase								=						metric.compute()
      # Use accelerator.print to print only on the main process.
      accelerator.print(F"""epoch {epoch}:""",			UpperCamelCase_)
      __lowercase								=						eval_metric["accuracy"]

      if best_performance < eval_metric["accuracy"]:
         __lowercase								=						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(UpperCamelCase_,			UpperCamelCase_)




def   _A  (  )	->						List[str]:
   '''simple docstring'''


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


if __name__ == "__main__":
   main()










from __future__ import annotations

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow


if is_tf_available():
  import numpy as np
  import tensorflow as tf

  from transformers import TFCamembertModel







@require_tf
@require_sentencepiece
@require_tokenizers
class 						lowercase							(   unittest.TestCase		):

     @slow
     def 					a__	(  self				)						->							Any:
           _A							:     Tuple			=						TFCamembertModel.from_pretrained("""jplu/tf-camembert-base"""				)

           _A							:     List[Any]			=						tf.convert_to_tensor(
               [[5, 121, 11, 660, 16, 730, 2_5543, 110, 83, 6]]		,     dtype=tf.intaa		,     )  # J'aime le camembert !"

           _A							:     List[str]			=						model(_a				)["""last_hidden_state"""]
           _A							:     Union[str, Any]			=						tf.TensorShape((1, 10, 768)				)
           self.assertEqual(output.shape		,     _a				)
           # compare the actual values for a slice.
           _A							:     List[Any]			=						tf.convert_to_tensor(
               [[[-0.0254, 0.0235, 0.1027], [0.0606, -0.1811, -0.0418], [-0.1561, -0.1127, 0.2687]]]		,     dtype=tf.floataa		,     )
           # camembert = torch.hub.load('pytorch/fairseq', 'camembert.v0')
           # camembert.eval()
           # expected_slice = roberta.model.forward(input_ids)[0][:, :3, :3].detach()

           self.assertTrue(np.allclose(output[:, :3, :3].numpy()		,     expected_slice.numpy()		,     atol=1e-4				)				)

