"""simple docstring"""


import sys
import warnings
from os.path import abspath, dirname, join


# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip install -e .[dev]' when switching between checkouts and running tests.
lowerCAmelCase_:					int     =					abspath(join(dirname(dirname(__file__)), "src"))
sys.path.insert(1, git_repo_path)

# silence FutureWarning warnings in tests since often we can't act on them until
# they become normal warnings - i.e. the tests still need to test the current functionality
warnings.simplefilter(action="ignore", category=FutureWarning)







def 					__a   (		A      ):






     '''simple docstring'''



     from diffusers.utils.testing_utils import pytest_addoption_shared

     pytest_addoption_shared(A      )







def 					__a   (		A      ):






     '''simple docstring'''



     from diffusers.utils.testing_utils import pytest_terminal_summary_main

     lowercase__				 =							terminalreporter.config.getoption("--make-reports"      )
     if make_reports:
          pytest_terminal_summary_main(A    ,				id=A      )





"""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_:					List[str]     =					1_6
lowerCAmelCase_:					Optional[Any]     =					3_2







def 					__a   (		A    ,				A = 16    ,				A = "bert-base-cased"      ):






     '''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=1_28    ,				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 					__a   (		A    ,				A      ):






     '''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 					__a   (		):






     '''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()




