


'''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				:  Optional[int]    		=      16
lowerCamelCase				:  Dict    		=      32

def _SCREAMING_SNAKE_CASE (A    ,					A = 16    ,					A = "bert-base-cased"			)			->			Optional[int]:






       """simple docstring"""







       lowercase__      =					AutoTokenizer.from_pretrained(A			)
       lowercase__      =					load_dataset('''glue'''    ,					'''mrpc'''			)

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

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

       # 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(A			):
              # 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(A    ,					padding='''max_length'''    ,					max_length=128    ,					return_tensors='''pt'''			)
              return tokenizer.pad(A    ,					padding='''longest'''    ,					return_tensors='''pt'''			)

       # Instantiate dataloaders.
       lowercase__      =					DataLoader(
           tokenized_datasets['''train''']    ,					shuffle=A    ,					collate_fn=A    ,					batch_size=A			)
       lowercase__      =					DataLoader(
           tokenized_datasets['''validation''']    ,					shuffle=A    ,					collate_fn=A    ,					batch_size=A			)

       return train_dataloader, eval_dataloader

def _SCREAMING_SNAKE_CASE (A    ,					A			)			->			Dict:






       """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(A			)
       lowercase__     ,lowercase__      =					get_dataloaders(A    ,					A    ,					A			)

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

       # 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=A			)

       if accelerator.state.deepspeed_plugin is not None:
              lowercase__      =					accelerator.state.deepspeed_plugin.deepspeed_config[
                  '''gradient_accumulation_steps'''
              ]
       else:
              lowercase__      =					1
       lowercase__      =					(len(A			) * 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=A    ,					num_warmup_steps=0    ,					num_training_steps=A    ,					)
       else:
              lowercase__      =					DummyScheduler(A    ,					total_num_steps=A    ,					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(
           A    ,					A    ,					A    ,					A    ,					A			)

       # 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(A    ,					A			):
              model.train()
              for step, batch in enumerate(A			):
                     lowercase__      =					model(**A			)
                     lowercase__      =					outputs.loss
                     lowercase__      =					loss / gradient_accumulation_steps
                     accelerator.backward(A			)
                     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(A			):
                     # We could avoid this line since we set the accelerator with `device_placement=True`.
                     batch.to(accelerator.device			)
                     with torch.no_grad():
                            lowercase__      =					model(**A			)
                     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(A			) - 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=A    ,					references=A    ,					)

              lowercase__      =					metric.compute()
              # Use accelerator.print to print only on the main process.
              accelerator.print(f"epoch {epoch}:"    ,					A			)
              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(A    ,					A			)

def _SCREAMING_SNAKE_CASE ()			->			Optional[Any]:






       """simple docstring"""







       lowercase__      =					argparse.ArgumentParser(description='''Simple example of training script tracking peak GPU memory usage.'''			)
       parser.add_argument(
           '''--model_name_or_path'''    ,					type=A    ,					default='''bert-base-cased'''    ,					help='''Path to pretrained model or model identifier from huggingface.co/models.'''    ,					required=A    ,					)
       parser.add_argument(
           '''--output_dir'''    ,					type=A    ,					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=A    ,					default=A    ,					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=A    ,					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(A    ,					A			)


if __name__ == "__main__":
 main()








'''simple docstring'''

import unittest

from transformers import RoFormerTokenizer, RoFormerTokenizerFast
from transformers.testing_utils import require_rjieba, require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin
@require_rjieba
@require_tokenizers
class     __lowerCAmelCase		(lowercase_    ,	unittest.TestCase	):





      '''simple docstring'''






      lowerCAmelCase__   :	str       =				RoFormerTokenizer
      lowerCAmelCase__   :	Dict       =				RoFormerTokenizerFast
      lowerCAmelCase__   :	List[str]       =				True
      lowerCAmelCase__   :	List[str]       =				True



      def  UpperCamelCase__						(self       :       Dict					):

             '''simple docstring'''







             super().setUp()



      def  UpperCamelCase__						(self       :       List[str]  ,		**UpperCamelCase       :       Dict					):

             '''simple docstring'''







             return self.tokenizer_class.from_pretrained('''junnyu/roformer_chinese_base'''  ,		**UpperCamelCase					)



      def  UpperCamelCase__						(self       :       List[str]  ,		**UpperCamelCase       :       int					):

             '''simple docstring'''







             return self.rust_tokenizer_class.from_pretrained('''junnyu/roformer_chinese_base'''  ,		**UpperCamelCase					)



      def  UpperCamelCase__						(self       :       List[str]					):

             '''simple docstring'''







             lowercase__      =					'''永和服装饰品有限公司,今天天气非常好'''
             lowercase__      =					'''永和 服装 饰品 有限公司 , 今 天 天 气 非常 好'''
             return input_text, output_text



      def  UpperCamelCase__						(self       :       int					):

             '''simple docstring'''







             lowercase__      =					self.get_tokenizer()
             lowercase__     ,lowercase__      =					self.get_chinese_input_output_texts()
             lowercase__      =					tokenizer.tokenize(UpperCamelCase					)

             self.assertListEqual(UpperCamelCase  ,		output_text.split()					)

             lowercase__      =					tokens + [tokenizer.unk_token]
             lowercase__      =					[22943, 21332, 34431, 45904, 117, 306, 1231, 1231, 2653, 33994, 1266, 100]
             self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCamelCase					)  ,		UpperCamelCase					)



      def  UpperCamelCase__						(self       :       Union[str, Any]					):

             '''simple docstring'''







             lowercase__      =					self.get_rust_tokenizer()
             lowercase__     ,lowercase__      =					self.get_chinese_input_output_texts()
             lowercase__      =					tokenizer.tokenize(UpperCamelCase					)
             self.assertListEqual(UpperCamelCase  ,		output_text.split()					)
             lowercase__      =					tokens + [tokenizer.unk_token]
             lowercase__      =					[22943, 21332, 34431, 45904, 117, 306, 1231, 1231, 2653, 33994, 1266, 100]
             self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCamelCase					)  ,		UpperCamelCase					)



      def  UpperCamelCase__						(self       :       str					):

             '''simple docstring'''







             pass



      def  UpperCamelCase__						(self       :       Any					):

             '''simple docstring'''







             pass






      def  UpperCamelCase__						(self       :       Union[str, Any]					):

             '''simple docstring'''







             pass




