'''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: Union[str, Any]          =   16
__a: List[Any]          =   32
def 			__UpperCamelCase       (   UpperCAmelCase		,     UpperCAmelCase = 16		,     UpperCAmelCase = "bert-base-cased"     ):
 lowercase__				:    Optional[Any]  =					AutoTokenizer.from_pretrained(UpperCAmelCase     )
 lowercase__				:    List[Any]  =					load_dataset('''glue'''		,     '''mrpc'''     )

 def tokenize_function(UpperCAmelCase     ):
  # max_length=None => use the model max length (it's actually the default)
  lowercase__				:    Optional[int]  =					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__				:    Optional[Any]  =					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__				:    Optional[int]  =					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=128		,     return_tensors='''pt'''     )
  return tokenizer.pad(UpperCAmelCase		,     padding='''longest'''		,     return_tensors='''pt'''     )

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

 return train_dataloader, eval_dataloader
def 			__UpperCamelCase       (   UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase     ):
 model.eval()
 lowercase__				:    Union[str, Any]  =					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__				:    Any  =					model(**UpperCAmelCase     )
  lowercase__				:    Tuple  =					outputs.logits.argmax(dim=-1     )
  # It is slightly faster to call this once, than multiple times
  lowercase__ ,							lowercase__				:    Optional[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(UpperCAmelCase     ) - 1:
    lowercase__				:    Any  =					predictions[: len(eval_dataloader.dataset     ) - samples_seen]
    lowercase__				:    Tuple  =					references[: len(eval_dataloader.dataset     ) - samples_seen]
   else:
    samples_seen += references.shape[0]
  metric.add_batch(
      predictions=UpperCAmelCase		,     references=UpperCAmelCase		,     )

 lowercase__				:    Union[str, Any]  =					metric.compute()
 return eval_metric["accuracy"]
def 			__UpperCamelCase       (   UpperCAmelCase		,     UpperCAmelCase     ):
 # Initialize accelerator
 lowercase__				:    Dict  =					Accelerator()

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

 set_seed(UpperCAmelCase     )
 lowercase__ ,							lowercase__				:    Any  =					get_dataloaders(UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase     )

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

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

 if accelerator.state.deepspeed_plugin is not None:
  lowercase__				:    Union[str, Any]  =					accelerator.state.deepspeed_plugin.deepspeed_config[
      '''gradient_accumulation_steps'''
  ]
 else:
  lowercase__				:    Tuple  =					1
 lowercase__				:    Optional[Any]  =					(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__				:    str  =					get_linear_schedule_with_warmup(
      optimizer=UpperCAmelCase		,     num_warmup_steps=0		,     num_training_steps=UpperCAmelCase		,     )
 else:
  lowercase__				:    Optional[int]  =					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__				:    Union[str, Any]  =					accelerator.prepare(
     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase     )

 # We need to keep track of how many total steps we have iterated over
 lowercase__				:    Tuple  =					0
 # We also need to keep track of the stating epoch so files are named properly
 lowercase__				:    int  =					0
 lowercase__				:    Optional[int]  =					evaluate.load('''glue'''		,     '''mrpc'''     )
 lowercase__				:    Dict  =					num_epochs

 if args.partial_train_epoch is not None:
  lowercase__				:    Tuple  =					args.partial_train_epoch

 if args.resume_from_checkpoint:
  accelerator.load_state(args.resume_from_checkpoint     )
  lowercase__				:    List[Any]  =					args.resume_from_checkpoint.split('''epoch_'''     )[1]
  lowercase__				:    Any  =					''''''
  for char in epoch_string:
   if char.isdigit():
    state_epoch_num += char
   else:
    break
  lowercase__				:    List[Any]  =					int(UpperCAmelCase     ) + 1
  lowercase__				:    Optional[Any]  =					evaluation_loop(UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase     )
  accelerator.print('''resumed checkpoint performance:'''		,     UpperCAmelCase     )
  accelerator.print('''resumed checkpoint\'s scheduler\'s lr:'''		,     lr_scheduler.get_lr()[0]     )
  accelerator.print('''resumed optimizers\'s lr:'''		,     optimizer.param_groups[0]['''lr''']     )
  with open(os.path.join(args.output_dir		,     F"""state_{starting_epoch-1}.json"""     )		,     '''r'''     ) as f:
   lowercase__				:    Optional[int]  =					json.load(UpperCAmelCase     )
   assert resumed_state["accuracy"] == accuracy, "Accuracy mismatch, loading from checkpoint failed"
   assert (
       resumed_state["lr"] == lr_scheduler.get_lr()[0]
   ), "Scheduler learning rate mismatch, loading from checkpoint failed"
   assert (
       resumed_state["optimizer_lr"] == optimizer.param_groups[0]["lr"]
   ), "Optimizer learning rate mismatch, loading from checkpoint failed"
   assert resumed_state["epoch"] == starting_epoch - 1, "Epoch mismatch, loading from checkpoint failed"
   return

    # Now we train the model
 lowercase__				:    Optional[int]  =					{}
 for epoch in range(UpperCAmelCase		,     UpperCAmelCase     ):
  model.train()
  for step, batch in enumerate(UpperCAmelCase     ):
   lowercase__				:    str  =					model(**UpperCAmelCase     )
   lowercase__				:    Optional[Any]  =					outputs.loss
   lowercase__				:    Union[str, Any]  =					loss / gradient_accumulation_steps
   accelerator.backward(UpperCAmelCase     )
   if step % gradient_accumulation_steps == 0:
    optimizer.step()
    lr_scheduler.step()
    optimizer.zero_grad()

   overall_step += 1
  lowercase__				:    Optional[Any]  =					F"""epoch_{epoch}"""
  lowercase__				:    Tuple  =					os.path.join(args.output_dir		,     UpperCAmelCase     )
  accelerator.save_state(UpperCAmelCase     )
  lowercase__				:    Union[str, Any]  =					evaluation_loop(UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase     )
  lowercase__				:    List[str]  =					accuracy
  lowercase__				:    Any  =					lr_scheduler.get_lr()[0]
  lowercase__				:    Union[str, Any]  =					optimizer.param_groups[0]['''lr''']
  lowercase__				:    str  =					epoch
  lowercase__				:    Any  =					overall_step
  accelerator.print(F"""epoch {epoch}:"""		,     UpperCAmelCase     )

  accelerator.wait_for_everyone()
  if accelerator.is_main_process:
   with open(os.path.join(args.output_dir		,     F"""state_{epoch}.json"""     )		,     '''w'''     ) as f:
    json.dump(UpperCAmelCase		,     UpperCAmelCase     )
def 			__UpperCamelCase       (   ):
 lowercase__				:    Tuple  =					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(
     '''--resume_from_checkpoint'''		,     type=UpperCAmelCase		,     default=UpperCAmelCase		,     help='''If the training should continue from a checkpoint folder.'''		,     )
 parser.add_argument(
     '''--partial_train_epoch'''		,     type=UpperCAmelCase		,     default=UpperCAmelCase		,     help='''If passed, the training will stop after this number of epochs.'''		,     )
 parser.add_argument(
     '''--num_epochs'''		,     type=UpperCAmelCase		,     default=2		,     help='''Number of train epochs.'''		,     )
 lowercase__				:    List[str]  =					parser.parse_args()
 lowercase__				:    Tuple  =					{'''lr''': 2E-5, '''num_epochs''': args.num_epochs, '''seed''': 42, '''batch_size''': 16}

 training_function(UpperCAmelCase		,     UpperCAmelCase     )


if __name__ == "__main__":
  main()


'''simple docstring'''

import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging


__a: List[str]          =   logging.get_logger(__name__)



class 			UpperCAmelCase  (						a__					):





       '''simple docstring'''



       SCREAMING_SNAKE_CASE							=    "encoder-decoder"
       SCREAMING_SNAKE_CASE							=    True

       def __init__(						self	,   **__lowerCAmelCase )  ->  int:
        super().__init__(**__lowerCAmelCase )
        assert (
            "encoder" in kwargs and "decoder" in kwargs
        ), "Config has to be initialized with encoder and decoder config"
        lowercase__				:    Optional[int]  =					kwargs.pop('''encoder''' )
        lowercase__				:    Union[str, Any]  =					encoder_config.pop('''model_type''' )
        lowercase__				:    Any  =					kwargs.pop('''decoder''' )
        lowercase__				:    Any  =					decoder_config.pop('''model_type''' )

        from ..auto.configuration_auto import AutoConfig

        lowercase__				:    Union[str, Any]  =					AutoConfig.for_model(__lowerCAmelCase	,   **__lowerCAmelCase )
        lowercase__				:    Optional[Any]  =					AutoConfig.for_model(__lowerCAmelCase	,   **__lowerCAmelCase )
        lowercase__				:    Tuple  =					True

       @classmethod
       def _lowerCAmelCase(						cls	,   __lowerCAmelCase	,   __lowerCAmelCase	,   **__lowerCAmelCase )  ->  PretrainedConfig:
        logger.info('''Set `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config''' )
        lowercase__				:    Union[str, Any]  =					True
        lowercase__				:    Any  =					True

        return cls(encoder=encoder_config.to_dict()	,   decoder=decoder_config.to_dict()	,   **__lowerCAmelCase )


       def _lowerCAmelCase(						self )  ->  Any:
        lowercase__				:    Any  =					copy.deepcopy(self.__dict__ )
        lowercase__				:    Optional[Any]  =					self.encoder.to_dict()
        lowercase__				:    Tuple  =					self.decoder.to_dict()
        lowercase__				:    Dict  =					self.__class__.model_type
        return output

