



import argparse
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


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing the experiment tracking capability,
# and builds off the `nlp_example.py` script.
#
# 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 help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# 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
#
########################################################################

lowercase_	       =			16
lowercase_	       =			32






def 					__lowerCAmelCase		(  __lowerCamelCase	:    Accelerator				,   __lowerCamelCase	:    int = 16	)  ->						str:
     __lowerCAmelCase	   =AutoTokenizer.from_pretrained("""bert-base-cased"""	)
     __lowerCAmelCase	   =load_dataset("""glue"""				,   """mrpc"""	)

     def tokenize_function(__lowerCamelCase	:    List[str]	):
          # max_length=None => use the model max length (it's actually the default)
          __lowerCAmelCase	   =tokenizer(examples["""sentence1"""]				,   examples["""sentence2"""]				,   truncation=__lowerCamelCase				,   max_length=__lowerCamelCase	)
          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():
          __lowerCAmelCase	   =datasets.map(
              __lowerCamelCase				,   batched=__lowerCamelCase				,   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
     __lowerCAmelCase	   =tokenized_datasets.rename_column("""label"""				,   """labels"""	)

     def collate_fn(__lowerCamelCase	:    Any	):
          # On TPU it's best to pad everything to the same length or training will be very slow.
          __lowerCAmelCase	   =128 if accelerator.distributed_type == DistributedType.TPU else None
          # When using mixed precision we want round multiples of 8/16
          if accelerator.mixed_precision == "fp8":
               __lowerCAmelCase	   =16
          elif accelerator.mixed_precision != "no":
               __lowerCAmelCase	   =8
          else:
               __lowerCAmelCase	   =None

          return tokenizer.pad(
              __lowerCamelCase				,   padding="""longest"""				,   max_length=__lowerCamelCase				,   pad_to_multiple_of=__lowerCamelCase				,   return_tensors="""pt"""				,   )

     # Instantiate dataloaders.
     __lowerCAmelCase	   =DataLoader(
         tokenized_datasets["""train"""]				,   shuffle=__lowerCamelCase				,   collate_fn=__lowerCamelCase				,   batch_size=__lowerCamelCase	)
     __lowerCAmelCase	   =DataLoader(
         tokenized_datasets["""validation"""]				,   shuffle=__lowerCamelCase				,   collate_fn=__lowerCamelCase				,   batch_size=__lowerCamelCase	)

     return train_dataloader, eval_dataloader


# For testing only
if os.environ.get('''TESTING_MOCKED_DATALOADERS''', None) == "1":
    from accelerate.test_utils.training import mocked_dataloaders

    lowercase_	       =			mocked_dataloaders  # noqa: F811






def 					__lowerCAmelCase		(  __lowerCamelCase	:    int				,   __lowerCamelCase	:    Any	)  ->						Dict:
     # For testing only
     if os.environ.get("""TESTING_MOCKED_DATALOADERS"""				,   __lowerCamelCase	) == "1":
          __lowerCAmelCase	   =2
     # Initialize Accelerator

     # New Code #
     # We pass in "all" to `log_with` to grab all available trackers in the environment
     # Note: If using a custom `Tracker` class, should be passed in here such as:
     # >>> log_with = ["all", MyCustomTrackerClassInstance()]
     if args.with_tracking:
          __lowerCAmelCase	   =Accelerator(
              cpu=args.cpu				,   mixed_precision=args.mixed_precision				,   log_with="""all"""				,   project_dir=args.project_dir	)
     else:
          __lowerCAmelCase	   =Accelerator(cpu=args.cpu				,   mixed_precision=args.mixed_precision	)
     # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
     __lowerCAmelCase	   =config["""lr"""]
     __lowerCAmelCase	   =int(config["""num_epochs"""]	)
     __lowerCAmelCase	   =int(config["""seed"""]	)
     __lowerCAmelCase	   =int(config["""batch_size"""]	)
     set_seed(__lowerCamelCase	)

     __lowerCAmelCase   ,	__lowerCAmelCase	   =get_dataloaders(__lowerCamelCase				,   __lowerCamelCase	)
     __lowerCAmelCase	   =evaluate.load("""glue"""				,   """mrpc"""	)

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

     # Instantiate the model (we build the model here so that the seed also control new weights initialization)
     __lowerCAmelCase	   =AutoModelForSequenceClassification.from_pretrained("""bert-base-cased"""				,   return_dict=__lowerCamelCase	)

     # 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).
     __lowerCAmelCase	   =model.to(accelerator.device	)

     # Instantiate optimizer
     __lowerCAmelCase	   =AdamW(params=model.parameters()				,   lr=__lowerCamelCase	)

     # Instantiate scheduler
     __lowerCAmelCase	   =get_linear_schedule_with_warmup(
         optimizer=__lowerCamelCase				,   num_warmup_steps=100				,   num_training_steps=(len(__lowerCamelCase	) * 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.
     __lowerCAmelCase   ,	__lowerCAmelCase   ,	__lowerCAmelCase   ,	__lowerCAmelCase   ,	__lowerCAmelCase	   =accelerator.prepare(
         __lowerCamelCase				,   __lowerCamelCase				,   __lowerCamelCase				,   __lowerCamelCase				,   __lowerCamelCase	)

     # New Code #
     # We need to initialize the trackers we use. Overall configurations can also be stored
     if args.with_tracking:
          __lowerCAmelCase	   =os.path.split(__lowerCamelCase	)[-1].split("""."""	)[0]
          accelerator.init_trackers(__lowerCamelCase				,   __lowerCamelCase	)

     # Now we train the model
     for epoch in range(__lowerCamelCase	):
          model.train()
          # New Code #
          # For our tracking example, we will log the total loss of each epoch
          if args.with_tracking:
               __lowerCAmelCase	   =0
          for step, batch in enumerate(__lowerCamelCase	):
               # We could avoid this line since we set the accelerator with `device_placement=True`.
               batch.to(accelerator.device	)
               __lowerCAmelCase	   =model(**__lowerCamelCase	)
               __lowerCAmelCase	   =outputs.loss
               # New Code #
               if args.with_tracking:
                    total_loss += loss.detach().float()
               __lowerCAmelCase	   =loss / gradient_accumulation_steps
               accelerator.backward(__lowerCamelCase	)
               if step % gradient_accumulation_steps == 0:
                    optimizer.step()
                    lr_scheduler.step()
                    optimizer.zero_grad()

          model.eval()
          for step, batch in enumerate(__lowerCamelCase	):
               # We could avoid this line since we set the accelerator with `device_placement=True` (the default).
               batch.to(accelerator.device	)
               with torch.no_grad():
                    __lowerCAmelCase	   =model(**__lowerCamelCase	)
               __lowerCAmelCase	   =outputs.logits.argmax(dim=-1	)
               __lowerCAmelCase   ,	__lowerCAmelCase	   =accelerator.gather_for_metrics((predictions, batch["""labels"""])	)
               metric.add_batch(
                   predictions=__lowerCamelCase				,   references=__lowerCamelCase				,   )

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

          # New Code #
          # To actually log, we call `Accelerator.log`
          # The values passed can be of `str`, `int`, `float` or `dict` of `str` to `float`/`int`
          if args.with_tracking:
               accelerator.log(
                   {
                       """accuracy""": eval_metric["""accuracy"""],
                       """f1""": eval_metric["""f1"""],
                       """train_loss""": total_loss.item() / len(__lowerCamelCase	),
                       """epoch""": epoch,
                   }				,   step=__lowerCamelCase				,   )

    # New Code #
    # When a run is finished, you should call `accelerator.end_training()`
    # to close all of the open trackers
     if args.with_tracking:
          accelerator.end_training()






def 					__lowerCAmelCase		(  )  ->						List[Any]:
     __lowerCAmelCase	   =argparse.ArgumentParser(description="""Simple example of training script."""	)
     parser.add_argument(
         """--mixed_precision"""				,   type=__lowerCamelCase				,   default=__lowerCamelCase				,   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."""	)
     parser.add_argument(
         """--with_tracking"""				,   action="""store_true"""				,   help="""Whether to load in all available experiment trackers from the environment and use them for logging."""				,   )
     parser.add_argument(
         """--project_dir"""				,   type=__lowerCamelCase				,   default="""logs"""				,   help="""Location on where to store experiment tracking logs` and relevent project information"""				,   )
     __lowerCAmelCase	   =parser.parse_args()
     __lowerCAmelCase	   ={"""lr""": 2E-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
     training_function(__lowerCamelCase				,   __lowerCamelCase	)


if __name__ == "__main__":
    main()









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


lowercase_	       =			logging.get_logger(__name__)

lowercase_	       =			{
    '''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 						__a (     SCREAMING_SNAKE_CASE				):
	SCREAMING_SNAKE_CASE       =       "realm"



	def __init__(						self							:    Tuple				,	snake_case_							:    Tuple=3_05_22				,	snake_case_							:    List[Any]=7_68				,	snake_case_							:    Any=1_28				,	snake_case_							:    Union[str, Any]=12				,	snake_case_							:    List[str]=12				,	snake_case_							:    Any=8				,	snake_case_							:    Any=30_72				,	snake_case_							:    int="gelu_new"				,	snake_case_							:    Optional[int]=0.1				,	snake_case_							:    Tuple=0.1				,	snake_case_							:    List[Any]=5_12				,	snake_case_							:    List[Any]=2				,	snake_case_							:    Any=0.0_2				,	snake_case_							:    List[str]=1e-12				,	snake_case_							:    Optional[Any]=2_56				,	snake_case_							:    Dict=10				,	snake_case_							:    str=1e-3				,	snake_case_							:    List[str]=5				,	snake_case_							:    Optional[Any]=3_20				,	snake_case_							:    Optional[Any]=13_35_37_18				,	snake_case_							:    Tuple=50_00				,	snake_case_							:    Union[str, Any]=1				,	snake_case_							:    Union[str, Any]=0				,	snake_case_							:    List[str]=2				,	**snake_case_							:    List[str]				,	)->							str:
						super().__init__(pad_token_id=snake_case_				,	bos_token_id=snake_case_				,	eos_token_id=snake_case_				,	**snake_case_)

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

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

						# Retrieval config
						__lowerCAmelCase	   =num_block_records
						__lowerCAmelCase	   =searcher_beam_size




