
"""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


SCREAMING_SNAKE_CASE      =     16
SCREAMING_SNAKE_CASE      =     32



def 	lowerCamelCase__			(							UpperCAmelCase_		,      UpperCAmelCase_ = 16		,      UpperCAmelCase_ = "bert-base-cased"				)->							Tuple:







       """simple docstring"""
       UpperCamelCase					 =		AutoTokenizer.from_pretrained(UpperCAmelCase_				)
       UpperCamelCase					 =		load_dataset("glue"		,      "mrpc"				)

       def tokenize_function(UpperCAmelCase_				):
              # max_length=None => use the model max length (it's actually the default)
              UpperCamelCase					 =		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
       UpperCamelCase					 =		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
       UpperCamelCase					 =		tokenized_datasets.rename_column("label"		,      "labels"				)

       def collate_fn(UpperCAmelCase_				):
              # 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=1_28		,      return_tensors="pt"				)
              return tokenizer.pad(UpperCAmelCase_		,      padding="longest"		,      return_tensors="pt"				)

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

       return train_dataloader, eval_dataloader



def 	lowerCamelCase__			(							UpperCAmelCase_		,      UpperCAmelCase_				)->							str:







       """simple docstring"""
       # Initialize accelerator
       UpperCamelCase					 =		Accelerator()

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

       set_seed(UpperCAmelCase_				)
       UpperCamelCase ,       UpperCamelCase					 =		get_dataloaders(UpperCAmelCase_		,      UpperCAmelCase_		,      UpperCAmelCase_				)

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

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

       if accelerator.state.deepspeed_plugin is not None:
              UpperCamelCase					 =		accelerator.state.deepspeed_plugin.deepspeed_config[
                  "gradient_accumulation_steps"
              ]
       else:
              UpperCamelCase					 =		1
       UpperCamelCase					 =		(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
       ):
              UpperCamelCase					 =		get_linear_schedule_with_warmup(
                  optimizer=UpperCAmelCase_		,      num_warmup_steps=0		,      num_training_steps=UpperCAmelCase_		,      )
       else:
              UpperCamelCase					 =		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.
       UpperCamelCase ,       UpperCamelCase ,       UpperCamelCase ,       UpperCamelCase ,       UpperCamelCase					 =		accelerator.prepare(
           UpperCAmelCase_		,      UpperCAmelCase_		,      UpperCAmelCase_		,      UpperCAmelCase_		,      UpperCAmelCase_				)

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

       # Now we train the model
       UpperCamelCase					 =		evaluate.load("glue"		,      "mrpc"				)
       UpperCamelCase					 =		0
       UpperCamelCase					 =		{}
       for epoch in range(UpperCAmelCase_		,      UpperCAmelCase_				):
              model.train()
              for step, batch in enumerate(UpperCAmelCase_				):
                     UpperCamelCase					 =		model(**UpperCAmelCase_				)
                     UpperCamelCase					 =		outputs.loss
                     UpperCamelCase					 =		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()
              UpperCamelCase					 =		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():
                            UpperCamelCase					 =		model(**UpperCAmelCase_				)
                     UpperCamelCase					 =		outputs.logits.argmax(dim=-1				)
                     # It is slightly faster to call this once, than multiple times
                     UpperCamelCase ,       UpperCamelCase					 =		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:
                                   UpperCamelCase					 =		predictions[: len(eval_dataloader.dataset				) - samples_seen]
                                   UpperCamelCase					 =		references[: len(eval_dataloader.dataset				) - samples_seen]
                            else:
                                   samples_seen += references.shape[0]
                     metric.add_batch(
                         predictions=UpperCAmelCase_		,      references=UpperCAmelCase_		,      )

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

              if best_performance < eval_metric["accuracy"]:
                     UpperCamelCase					 =		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 	lowerCamelCase__			(							)->							Optional[int]:







       """simple docstring"""
       UpperCamelCase					 =		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."		,      )
       UpperCamelCase					 =		parser.parse_args()
       UpperCamelCase					 =		{"lr": 2E-5, "num_epochs": args.num_epochs, "seed": 42, "batch_size": 16}
       training_function(UpperCAmelCase_		,      UpperCAmelCase_				)


if __name__ == "__main__":
      main()







"""simple docstring"""


import unittest

from transformers import BarthezTokenizer, BarthezTokenizerFast, BatchEncoding
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow

from ...test_tokenization_common import TokenizerTesterMixin



@require_tokenizers
@require_sentencepiece
@slow  # see https://github.com/huggingface/transformers/issues/11457
class   __a  (      _lowerCAmelCase    ,     unittest.TestCase	):
       UpperCamelCase_		:					Any   =			BarthezTokenizer
       UpperCamelCase_		:					Tuple   =			BarthezTokenizerFast
       UpperCamelCase_		:					str   =			True
       UpperCamelCase_		:					Dict   =			True






       def 	_SCREAMING_SNAKE_CASE       (  self :							List[Any]			)->  int:







              """simple docstring"""
              super().setUp()

              UpperCamelCase					 =		BarthezTokenizerFast.from_pretrained("moussaKam/mbarthez"			)
              tokenizer.save_pretrained(self.tmpdirname			)
              tokenizer.save_pretrained(self.tmpdirname       ,      legacy_format=UpperCAmelCase_			)
              UpperCamelCase					 =		tokenizer






       def 	_SCREAMING_SNAKE_CASE       (  self :							Dict			)->  Optional[Any]:







              """simple docstring"""
              UpperCamelCase					 =		"<pad>"
              UpperCamelCase					 =		1

              self.assertEqual(self.get_tokenizer()._convert_token_to_id(UpperCAmelCase_			)       ,      UpperCAmelCase_			)
              self.assertEqual(self.get_tokenizer()._convert_id_to_token(UpperCAmelCase_			)       ,      UpperCAmelCase_			)






       def 	_SCREAMING_SNAKE_CASE       (  self :							Optional[int]			)->  int:







              """simple docstring"""
              UpperCamelCase					 =		list(self.get_tokenizer().get_vocab().keys()			)

              self.assertEqual(vocab_keys[0]       ,      "<s>"			)
              self.assertEqual(vocab_keys[1]       ,      "<pad>"			)
              self.assertEqual(vocab_keys[-1]       ,      "<mask>"			)
              self.assertEqual(len(UpperCAmelCase_			)       ,      101_122			)






       def 	_SCREAMING_SNAKE_CASE       (  self :							List[Any]			)->  List[str]:







              """simple docstring"""
              self.assertEqual(self.get_tokenizer().vocab_size       ,      101_122			)






       @require_torch
       def 	_SCREAMING_SNAKE_CASE       (  self :							Union[str, Any]			)->  int:







              """simple docstring"""
              UpperCamelCase					 =		["A long paragraph for summarization.", "Another paragraph for summarization."]
              UpperCamelCase					 =		[0, 57, 3_018, 70_307, 91, 2]

              UpperCamelCase					 =		self.tokenizer(
                  UpperCAmelCase_       ,      max_length=len(UpperCAmelCase_			)       ,      padding=UpperCAmelCase_       ,      truncation=UpperCAmelCase_       ,      return_tensors="pt"			)
              self.assertIsInstance(UpperCAmelCase_       ,      UpperCAmelCase_			)

              self.assertEqual((2, 6)       ,      batch.input_ids.shape			)
              self.assertEqual((2, 6)       ,      batch.attention_mask.shape			)
              UpperCamelCase					 =		batch.input_ids.tolist()[0]
              self.assertListEqual(UpperCAmelCase_       ,      UpperCAmelCase_			)






       def 	_SCREAMING_SNAKE_CASE       (  self :							Optional[int]			)->  Optional[int]:







              """simple docstring"""
              if not self.test_rust_tokenizer:
                     return

              UpperCamelCase					 =		self.get_tokenizer()
              UpperCamelCase					 =		self.get_rust_tokenizer()

              UpperCamelCase					 =		"I was born in 92000, and this is falsé."

              UpperCamelCase					 =		tokenizer.tokenize(UpperCAmelCase_			)
              UpperCamelCase					 =		rust_tokenizer.tokenize(UpperCAmelCase_			)
              self.assertListEqual(UpperCAmelCase_       ,      UpperCAmelCase_			)

              UpperCamelCase					 =		tokenizer.encode(UpperCAmelCase_       ,      add_special_tokens=UpperCAmelCase_			)
              UpperCamelCase					 =		rust_tokenizer.encode(UpperCAmelCase_       ,      add_special_tokens=UpperCAmelCase_			)
              self.assertListEqual(UpperCAmelCase_       ,      UpperCAmelCase_			)

              UpperCamelCase					 =		self.get_rust_tokenizer()
              UpperCamelCase					 =		tokenizer.encode(UpperCAmelCase_			)
              UpperCamelCase					 =		rust_tokenizer.encode(UpperCAmelCase_			)
              self.assertListEqual(UpperCAmelCase_       ,      UpperCAmelCase_			)






       @slow
       def 	_SCREAMING_SNAKE_CASE       (  self :							int			)->  Union[str, Any]:







              """simple docstring"""
              # fmt: off
              UpperCamelCase					 =		{"input_ids": [[0, 490, 14_328, 4_507, 354, 47, 43_669, 95, 25, 78_117, 20_215, 19_779, 190, 22, 400, 4, 35_343, 80_310, 603, 86, 24_937, 105, 33_438, 94_762, 196, 39_642, 7, 15, 15_933, 173, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 10_534, 87, 25, 66, 3_358, 196, 55_289, 8, 82_961, 81, 2_204, 75_203, 7, 15, 763, 12_956, 216, 178, 14_328, 9_595, 1_377, 69_693, 7, 448, 71_021, 196, 18_106, 1_437, 13_974, 108, 9_083, 4, 49_315, 7, 39, 86, 1_326, 2_793, 46_333, 4, 448, 196, 74_588, 7, 49_315, 7, 39, 21, 822, 38_470, 74, 21, 66_723, 62_480, 8, 22_050, 5, 2]], "attention_mask": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}  # noqa: E501
              # fmt: on

              # moussaKam/mbarthez is a french model. So we also use french texts.
              UpperCamelCase					 =		[
                  "Le transformeur est un modèle d'apprentissage profond introduit en 2017, "
                  "utilisé principalement dans le domaine du traitement automatique des langues (TAL).",
                  "À l'instar des réseaux de neurones récurrents (RNN), les transformeurs sont conçus "
                  "pour gérer des données séquentielles, telles que le langage naturel, pour des tâches "
                  "telles que la traduction et la synthèse de texte.",
              ]

              self.tokenizer_integration_test_util(
                  expected_encoding=UpperCAmelCase_       ,      model_name="moussaKam/mbarthez"       ,      revision="c2e4ecbca5e3cd2c37fe1ac285ca4fbdf1366fb6"       ,      sequences=UpperCAmelCase_       ,      )





