"""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     lowercase__( __SCREAMING_SNAKE_CASE							:  Accelerator  ,    __SCREAMING_SNAKE_CASE							:  int = 16  ,    __SCREAMING_SNAKE_CASE							:  str = "bert-base-cased"   ):
     lowercase_			:       List[Any]									=			AutoTokenizer.from_pretrained(snake_case__   )
     lowercase_			:       Optional[Any]									=			load_dataset('glue'  ,    'mrpc'   )

     def tokenize_function(__SCREAMING_SNAKE_CASE							:  List[str]   ):
          # max_length=None => use the model max length (it's actually the default)
          lowercase_			:       Union[str, 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
     lowercase_			:       Any									=			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
     lowercase_			:       Dict									=			tokenized_datasets.rename_column('label'  ,    'labels'   )

     def collate_fn(__SCREAMING_SNAKE_CASE							:  int   ):
          # 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=1_28  ,    return_tensors='pt'   )
          return tokenizer.pad(snake_case__  ,    padding='longest'  ,    return_tensors='pt'   )

     # Instantiate dataloaders.
     lowercase_			:       Any									=			DataLoader(
         tokenized_datasets['train']  ,    shuffle=snake_case__  ,    collate_fn=snake_case__  ,    batch_size=snake_case__   )
     lowercase_			:       List[str]									=			DataLoader(
         tokenized_datasets['validation']  ,    shuffle=snake_case__  ,    collate_fn=snake_case__  ,    batch_size=snake_case__   )

     return train_dataloader, eval_dataloader



def     lowercase__( __SCREAMING_SNAKE_CASE							:  Dict  ,    __SCREAMING_SNAKE_CASE							:  List[Any]   ):
     # Initialize accelerator
     lowercase_			:       List[Any]									=			Accelerator()

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

     set_seed(snake_case__   )
     lowercase_							,						lowercase_			:       Dict									=			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)
     lowercase_			:       Tuple									=			AutoModelForSequenceClassification.from_pretrained(snake_case__  ,    return_dict=snake_case__   )

     # Instantiate optimizer
     lowercase_			:       Optional[Any]									=			(
         AdamW
         if accelerator.state.deepspeed_plugin is None
         or 'optimizer' not in accelerator.state.deepspeed_plugin.deepspeed_config
         else DummyOptim
     )
     lowercase_			:       Optional[Any]									=			optimizer_cls(params=model.parameters()  ,    lr=snake_case__   )

     if accelerator.state.deepspeed_plugin is not None:
          lowercase_			:       Optional[int]									=			accelerator.state.deepspeed_plugin.deepspeed_config[
              'gradient_accumulation_steps'
          ]
     else:
          lowercase_			:       Optional[Any]									=			1
     lowercase_			:       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
     ):
          lowercase_			:       Any									=			get_linear_schedule_with_warmup(
              optimizer=snake_case__  ,    num_warmup_steps=0  ,    num_training_steps=snake_case__  ,    )
     else:
          lowercase_			:       Dict									=			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.
     lowercase_							,						lowercase_							,						lowercase_							,						lowercase_							,						lowercase_			:       Dict									=			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
     lowercase_			:       str									=			0
     # We also need to keep track of the stating epoch so files are named properly
     lowercase_			:       List[Any]									=			0

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

          lowercase_			:       Tuple									=			metric.compute()
          # Use accelerator.print to print only on the main process.
          accelerator.print(F'''epoch {epoch}:'''  ,    snake_case__   )
          lowercase_			:       Tuple									=			eval_metric['accuracy']

          if best_performance < eval_metric["accuracy"]:
               lowercase_			:       int									=			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     lowercase__( ):
     lowercase_			:       List[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.'  ,    )
     lowercase_			:       Optional[int]									=			parser.parse_args()
     lowercase_			:       Dict									=			{'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 os
import unittest
from tempfile import TemporaryDirectory

import torch
import torch.nn as nn

from accelerate.utils import (
    OffloadedWeightsLoader,
    extract_submodules_state_dict,
    load_offloaded_weight,
    offload_state_dict,
    offload_weight,
)







class  UpperCamelCase	(    nn.Module						):
   def __init__(    self    )			->   Any:


        '''simple docstring'''


        super().__init__()
        lowercase_			:       Dict									=			nn.Linear(3  ,4    )
        lowercase_			:       Union[str, Any]									=			nn.BatchNormad(4    )
        lowercase_			:       Union[str, Any]									=			nn.Linear(4  ,5    )







   def     _UpperCAmelCase     (    self  ,__UpperCamelCase    )			->   int:


        '''simple docstring'''


        return self.lineara(self.batchnorm(self.lineara(__UpperCamelCase    )    )    )







class  UpperCamelCase	(    unittest.TestCase						):
   def     _UpperCAmelCase     (    self    )			->   Dict:


        '''simple docstring'''


        lowercase_			:       int									=			ModelForTest()
        with TemporaryDirectory() as tmp_dir:
             offload_state_dict(__UpperCamelCase  ,model.state_dict()    )
             lowercase_			:       str									=			os.path.join(__UpperCamelCase  ,'index.json'    )
             self.assertTrue(os.path.isfile(__UpperCamelCase    )    )
             # TODO: add tests on what is inside the index

             for key in ["linear1.weight", "linear1.bias", "linear2.weight", "linear2.bias"]:
                  lowercase_			:       int									=			os.path.join(__UpperCamelCase  ,f'''{key}.dat'''    )
                  self.assertTrue(os.path.isfile(__UpperCamelCase    )    )
                  # TODO: add tests on the fact weights are properly loaded
   def     _UpperCAmelCase     (    self    )			->   Tuple:


        '''simple docstring'''


        lowercase_			:       List[str]									=			[torch.floataa, torch.floataa, torch.bfloataa]

        for dtype in dtypes:
             lowercase_			:       Tuple									=			torch.randn(2  ,3  ,dtype=__UpperCamelCase    )
             with TemporaryDirectory() as tmp_dir:
                  lowercase_			:       List[str]									=			offload_weight(__UpperCamelCase  ,'weight'  ,__UpperCamelCase  ,{}    )
                  lowercase_			:       str									=			os.path.join(__UpperCamelCase  ,'weight.dat'    )
                  self.assertTrue(os.path.isfile(__UpperCamelCase    )    )
                  self.assertDictEqual(__UpperCamelCase  ,{'weight': {'shape': [2, 3], 'dtype': str(__UpperCamelCase    ).split('.'    )[1]}}    )

                  lowercase_			:       Optional[Any]									=			load_offloaded_weight(__UpperCamelCase  ,index['weight']    )
                  self.assertTrue(torch.equal(__UpperCamelCase  ,__UpperCamelCase    )    )
   def     _UpperCAmelCase     (    self    )			->   List[Any]:


        '''simple docstring'''


        lowercase_			:       List[str]									=			ModelForTest()
        lowercase_			:       Optional[int]									=			model.state_dict()
        lowercase_			:       int									=			{k: v for k, v in state_dict.items() if 'linear2' not in k}
        lowercase_			:       Optional[Any]									=			{k: v for k, v in state_dict.items() if 'linear2' in k}

        with TemporaryDirectory() as tmp_dir:
             offload_state_dict(__UpperCamelCase  ,__UpperCamelCase    )
             lowercase_			:       Tuple									=			OffloadedWeightsLoader(state_dict=__UpperCamelCase  ,save_folder=__UpperCamelCase    )

             # Every key is there with the right value
             self.assertEqual(sorted(__UpperCamelCase    )  ,sorted(state_dict.keys()    )    )
             for key, param in state_dict.items():
                  self.assertTrue(torch.allclose(__UpperCamelCase  ,weight_map[key]    )    )

        lowercase_			:       int									=			{k: v for k, v in state_dict.items() if 'weight' in k}
        lowercase_			:       int									=			{k: v for k, v in state_dict.items() if 'weight' not in k}

        with TemporaryDirectory() as tmp_dir:
             offload_state_dict(__UpperCamelCase  ,__UpperCamelCase    )
             lowercase_			:       int									=			OffloadedWeightsLoader(state_dict=__UpperCamelCase  ,save_folder=__UpperCamelCase    )

             # Every key is there with the right value
             self.assertEqual(sorted(__UpperCamelCase    )  ,sorted(state_dict.keys()    )    )
             for key, param in state_dict.items():
                  self.assertTrue(torch.allclose(__UpperCamelCase  ,weight_map[key]    )    )

        with TemporaryDirectory() as tmp_dir:
             offload_state_dict(__UpperCamelCase  ,__UpperCamelCase    )
             # Duplicates are removed
             lowercase_			:       Tuple									=			OffloadedWeightsLoader(state_dict=__UpperCamelCase  ,save_folder=__UpperCamelCase    )

             # Every key is there with the right value
             self.assertEqual(sorted(__UpperCamelCase    )  ,sorted(state_dict.keys()    )    )
             for key, param in state_dict.items():
                  self.assertTrue(torch.allclose(__UpperCamelCase  ,weight_map[key]    )    )







   def     _UpperCAmelCase     (    self    )			->   Union[str, Any]:


        '''simple docstring'''


        lowercase_			:       Optional[int]									=			{'a.1': 0, 'a.10': 1, 'a.2': 2}
        lowercase_			:       int									=			extract_submodules_state_dict(__UpperCamelCase  ,['a.1', 'a.2']    )
        self.assertDictEqual(__UpperCamelCase  ,{'a.1': 0, 'a.2': 2}    )

        lowercase_			:       Union[str, Any]									=			{'a.1.a': 0, 'a.10.a': 1, 'a.2.a': 2}
        lowercase_			:       Optional[int]									=			extract_submodules_state_dict(__UpperCamelCase  ,['a.1', 'a.2']    )
        self.assertDictEqual(__UpperCamelCase  ,{'a.1.a': 0, 'a.2.a': 2}    )

