


"""simple docstring"""




import argparse

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


########################################################################
# This is a fully working simple example to use Accelerate
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
#   - single CPU or single GPU
#   - multi GPUS (using PyTorch distributed mode)
#   - (multi) TPUs
#   - fp16 (mixed-precision) or fp32 (normal precision)
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################


_A			=						16
_A			=						32

def 						_UpperCAmelCase    (      SCREAMING_SNAKE_CASE__      : Any      ,					SCREAMING_SNAKE_CASE__      : str = 16    ):
     __UpperCamelCase   							=AutoTokenizer.from_pretrained('bert-base-cased'    )
     __UpperCamelCase   							=load_dataset('glue'      ,					'mrpc'    )

     def tokenize_function(SCREAMING_SNAKE_CASE__      : str    ):
          # max_length=None => use the model max length (it's actually the default)
          __UpperCamelCase   							=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
     # starting with the main process first:
     with accelerator.main_process_first():
          __UpperCamelCase   							=datasets.map(
              __snake_case      ,					batched=__snake_case      ,					remove_columns=['idx', 'sentence1', 'sentence2']      ,					)

     # 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(SCREAMING_SNAKE_CASE__      : Tuple    ):
          # On TPU it's best to pad everything to the same length or training will be very slow.
          __UpperCamelCase   							=1_28 if accelerator.distributed_type == DistributedType.TPU else None
          # When using mixed precision we want round multiples of 8/16
          if accelerator.mixed_precision == "fp8":
               __UpperCamelCase   							=16
          elif accelerator.mixed_precision != "no":
               __UpperCamelCase   							=8
          else:
               __UpperCamelCase   							=None

          return tokenizer.pad(
              __snake_case      ,					padding='longest'      ,					max_length=__snake_case      ,					pad_to_multiple_of=__snake_case      ,					return_tensors='pt'      ,					)

     # Instantiate dataloaders.
     __UpperCamelCase   							=DataLoader(
         tokenized_datasets['train']      ,					shuffle=__snake_case      ,					collate_fn=__snake_case      ,					batch_size=__snake_case      ,					drop_last=__snake_case    )
     __UpperCamelCase   							=DataLoader(
         tokenized_datasets['validation']      ,					shuffle=__snake_case      ,					collate_fn=__snake_case      ,					batch_size=__snake_case      ,					drop_last=(accelerator.mixed_precision == 'fp8')      ,					)

     return train_dataloader, eval_dataloader

def 						_UpperCAmelCase    (      SCREAMING_SNAKE_CASE__      : Tuple      ,					SCREAMING_SNAKE_CASE__      : Any    ):
     # Initialize accelerator
     __UpperCamelCase   							=Accelerator(cpu=args.cpu      ,					mixed_precision=args.mixed_precision    )
     # 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   							=evaluate.load('glue'      ,					'mrpc'    )

     # If the batch size is too big we use gradient accumulation
     __UpperCamelCase   							=1
     if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
          __UpperCamelCase   							=batch_size // MAX_GPU_BATCH_SIZE
          __UpperCamelCase   							=MAX_GPU_BATCH_SIZE

     set_seed(__snake_case    )
     __UpperCamelCase   							=get_dataloaders(__snake_case      ,					__snake_case    )
     # Instantiate the model (we build the model here so that the seed also control new weights initialization)
     __UpperCamelCase   							=AutoModelForSequenceClassification.from_pretrained('bert-base-cased'      ,					return_dict=__snake_case    )

     # We could avoid this line since the accelerator is set with `device_placement=True` (default value).
     # Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
     # creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
     __UpperCamelCase   							=model.to(accelerator.device    )
     # Instantiate optimizer
     __UpperCamelCase   							=AdamW(params=model.parameters()      ,					lr=__snake_case    )

     # Instantiate scheduler
     __UpperCamelCase   							=get_linear_schedule_with_warmup(
         optimizer=__snake_case      ,					num_warmup_steps=1_00      ,					num_training_steps=(len(__snake_case    ) * num_epochs) // gradient_accumulation_steps      ,					)

     # 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   							=accelerator.prepare(
         __snake_case      ,					__snake_case      ,					__snake_case      ,					__snake_case      ,					__snake_case    )

     # Now we train the model
     for epoch in range(__snake_case    ):
          model.train()
          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    )
               __UpperCamelCase   							=model(**__snake_case    )
               __UpperCamelCase   							=outputs.loss
               __UpperCamelCase   							=loss / gradient_accumulation_steps
               accelerator.backward(__snake_case    )
               if step % gradient_accumulation_steps == 0:
                    optimizer.step()
                    lr_scheduler.step()
                    optimizer.zero_grad()

          model.eval()
          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():
                    __UpperCamelCase   							=model(**__snake_case    )
               __UpperCamelCase   							=outputs.logits.argmax(dim=-1    )
               __UpperCamelCase   							=accelerator.gather_for_metrics((predictions, batch['labels'])    )
               metric.add_batch(
                   predictions=__snake_case      ,					references=__snake_case      ,					)

          __UpperCamelCase   							=metric.compute()
          # Use accelerator.print to print only on the main process.
          accelerator.print(F'epoch {epoch}:'      ,					__snake_case    )

def 						_UpperCAmelCase    (      ):
     __UpperCamelCase   							=argparse.ArgumentParser(description='Simple example of training script.'    )
     parser.add_argument(
         '--mixed_precision'      ,					type=__snake_case      ,					default=__snake_case      ,					choices=['no', 'fp16', 'bf16', 'fp8']      ,					help='Whether to use mixed precision. Choose'
         'between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10.'
         'and an Nvidia Ampere GPU.'      ,					)
     parser.add_argument('--cpu'      ,					action='store_true'      ,					help='If passed, will train on the CPU.'    )
     __UpperCamelCase   							=parser.parse_args()
     __UpperCamelCase   							={"lr": 2E-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
     training_function(__snake_case      ,					__snake_case    )


if __name__ == "__main__":
   main()









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


_A			=						logging.get_logger(__name__)

_A			=						{
    'google/realm-cc-news-pretrained-embedder': (
        'https://huggingface.co/google/realm-cc-news-pretrained-embedder/resolve/main/config.json'
    ),
    'google/realm-cc-news-pretrained-encoder': (
        'https://huggingface.co/google/realm-cc-news-pretrained-encoder/resolve/main/config.json'
    ),
    'google/realm-cc-news-pretrained-scorer': (
        'https://huggingface.co/google/realm-cc-news-pretrained-scorer/resolve/main/config.json'
    ),
    'google/realm-cc-news-pretrained-openqa': (
        'https://huggingface.co/google/realm-cc-news-pretrained-openqa/aresolve/main/config.json'
    ),
    'google/realm-orqa-nq-openqa': 'https://huggingface.co/google/realm-orqa-nq-openqa/resolve/main/config.json',
    'google/realm-orqa-nq-reader': 'https://huggingface.co/google/realm-orqa-nq-reader/resolve/main/config.json',
    'google/realm-orqa-wq-openqa': 'https://huggingface.co/google/realm-orqa-wq-openqa/resolve/main/config.json',
    'google/realm-orqa-wq-reader': 'https://huggingface.co/google/realm-orqa-wq-reader/resolve/main/config.json',
    # See all REALM models at https://huggingface.co/models?filter=realm
}






class    UpperCAmelCase__	( A_		):



    """simple docstring"""






    UpperCAmelCase__						:		str              =  "realm"






    def __init__(      self ,       A_=30522 ,       A_=768 ,       A_=128 ,       A_=12 ,       A_=12 ,       A_=8 ,       A_=3072 ,       A_="gelu_new" ,       A_=0.1 ,       A_=0.1 ,       A_=512 ,       A_=2 ,       A_=0.02 ,       A_=1E-12 ,       A_=256 ,       A_=10 ,       A_=1E-3 ,       A_=5 ,       A_=320 ,       A_=13353718 ,       A_=5000 ,       A_=1 ,       A_=0 ,       A_=2 ,       **A_ ,       )  ->	Dict:
         super().__init__(pad_token_id=A_ ,       bos_token_id=A_ ,       eos_token_id=A_ ,       **A_   )

         # Common config
         __UpperCamelCase   							=vocab_size
         __UpperCamelCase   							=max_position_embeddings
         __UpperCamelCase   							=hidden_size
         __UpperCamelCase   							=retriever_proj_size
         __UpperCamelCase   							=num_hidden_layers
         __UpperCamelCase   							=num_attention_heads
         __UpperCamelCase   							=num_candidates
         __UpperCamelCase   							=intermediate_size
         __UpperCamelCase   							=hidden_act
         __UpperCamelCase   							=hidden_dropout_prob
         __UpperCamelCase   							=attention_probs_dropout_prob
         __UpperCamelCase   							=initializer_range
         __UpperCamelCase   							=type_vocab_size
         __UpperCamelCase   							=layer_norm_eps

         # Reader config
         __UpperCamelCase   							=span_hidden_size
         __UpperCamelCase   							=max_span_width
         __UpperCamelCase   							=reader_layer_norm_eps
         __UpperCamelCase   							=reader_beam_size
         __UpperCamelCase   							=reader_seq_len

         # Retrieval config
         __UpperCamelCase   							=num_block_records
         __UpperCamelCase   							=searcher_beam_size





