






"""simple docstring"""






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 how to properly calculate the metrics on the
# validation dataset when in a distributed system, 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
#
########################################################################


UpperCAmelCase_   : Any   			= 16
UpperCAmelCase_   : List[str]   			= 32



def 					_A			(__a    ,				__a = 16							) ->       Optional[int]:
    """simple docstring"""






    SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		AutoTokenizer.from_pretrained('''bert-base-cased'''							)
    SCREAMING_SNAKE_CASE_      :  str    						=		load_dataset('''glue'''    ,				'''mrpc'''							)

    def tokenize_function(__a							):
        # max_length=None => use the model max length (it's actually the default)
        SCREAMING_SNAKE_CASE_      :  Dict    						=		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
    # starting with the main process first:
    with accelerator.main_process_first():
        SCREAMING_SNAKE_CASE_      :  Dict    						=		datasets.map(
            __a    ,				batched=__a    ,				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
    SCREAMING_SNAKE_CASE_      :  Tuple    						=		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.
        SCREAMING_SNAKE_CASE_      :  Dict    						=		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":
            SCREAMING_SNAKE_CASE_      :  Tuple    						=		16
        elif accelerator.mixed_precision != "no":
            SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		8
        else:
            SCREAMING_SNAKE_CASE_      :  Any    						=		None

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

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

    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

  UpperCAmelCase_   : Union[str, Any]   			= mocked_dataloaders  # noqa: F811



def 					_A			(__a    ,				__a							) ->       Union[str, Any]:
    """simple docstring"""






    if os.environ.get('''TESTING_MOCKED_DATALOADERS'''    ,				__a							) == "1":
        SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		2
    # Initialize accelerator
    SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		Accelerator(cpu=args.cpu    ,				mixed_precision=args.mixed_precision							)
    # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
    SCREAMING_SNAKE_CASE_      :  str    						=		config['''lr''']
    SCREAMING_SNAKE_CASE_      :  Any    						=		int(config['''num_epochs''']							)
    SCREAMING_SNAKE_CASE_      :  str    						=		int(config['''seed''']							)
    SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		int(config['''batch_size''']							)

    SCREAMING_SNAKE_CASE_      :  List[str]    						=		evaluate.load('''glue'''    ,				'''mrpc'''							)

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

    set_seed(__a							)
    SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_      :  Tuple    						=		get_dataloaders(__a    ,				__a							)
    # Instantiate the model (we build the model here so that the seed also control new weights initialization)
    SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		AutoModelForSequenceClassification.from_pretrained('''bert-base-cased'''    ,				return_dict=__a							)

    # 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).
    SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		model.to(accelerator.device							)

    # Instantiate optimizer
    SCREAMING_SNAKE_CASE_      :  Tuple    						=		AdamW(params=model.parameters()    ,				lr=__a							)

    # Instantiate scheduler
    SCREAMING_SNAKE_CASE_      :  Dict    						=		get_linear_schedule_with_warmup(
        optimizer=__a    ,				num_warmup_steps=1_00    ,				num_training_steps=(len(__a							) * 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.
    SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		accelerator.prepare(
        __a    ,				__a    ,				__a    ,				__a    ,				__a							)

    # Now we train the model
    for epoch in range(__a							):
        model.train()
        for step, batch in enumerate(__a							):
            # We could avoid this line since we set the accelerator with `device_placement=True`.
            batch.to(accelerator.device							)
            SCREAMING_SNAKE_CASE_      :  List[str]    						=		model(**__a							)
            SCREAMING_SNAKE_CASE_      :  str    						=		outputs.loss
            SCREAMING_SNAKE_CASE_      :  int    						=		loss / gradient_accumulation_steps
            accelerator.backward(__a							)
            if step % gradient_accumulation_steps == 0:
                optimizer.step()
                lr_scheduler.step()
                optimizer.zero_grad()

        model.eval()
        SCREAMING_SNAKE_CASE_      :  Any    						=		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():
                SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		model(**__a							)
            SCREAMING_SNAKE_CASE_      :  Tuple    						=		outputs.logits.argmax(dim=-1							)
            SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_      :  List[Any]    						=		accelerator.gather((predictions, batch['''labels'''])							)
            # New Code #
            # First we check if it's a distributed system
            if accelerator.use_distributed:
                # Then see if we're on the last batch of our eval dataloader
                if step == len(__a							) - 1:
                    # Last batch needs to be truncated on distributed systems as it contains additional samples
                    SCREAMING_SNAKE_CASE_      :  Any    						=		predictions[: len(eval_dataloader.dataset							) - samples_seen]
                    SCREAMING_SNAKE_CASE_      :  int    						=		references[: len(eval_dataloader.dataset							) - samples_seen]
                else:
                    # Otherwise we add the number of samples seen
                    samples_seen += references.shape[0]
            # All of this can be avoided if you use `Accelerator.gather_for_metrics` instead of `Accelerator.gather`:
            # accelerator.gather_for_metrics((predictions, batch["labels"]))
            metric.add_batch(
                predictions=__a    ,				references=__a    ,				)

        SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		metric.compute()
        # Use accelerator.print to print only on the main process.
        accelerator.print(f'epoch {epoch}:'    ,				__a							)



def 					_A			() ->       Tuple:
    """simple docstring"""






    SCREAMING_SNAKE_CASE_      :  int    						=		argparse.ArgumentParser(description='''Simple example of training script.'''							)
    parser.add_argument(
        '''--mixed_precision'''    ,				type=__a    ,				default=__a    ,				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.'''							)
    SCREAMING_SNAKE_CASE_      :  int    						=		parser.parse_args()
    SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		{'''lr''': 2e-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
    training_function(__a    ,				__a							)


if __name__ == "__main__":
  main()







"""simple docstring"""







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


lowercase__		:				Tuple          =	logging.get_logger(__name__)

lowercase__		:				Any          =	{
    """facebook/dpr-ctx_encoder-single-nq-base""": (
        """https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/config.json"""
    ),
    """facebook/dpr-question_encoder-single-nq-base""": (
        """https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/config.json"""
    ),
    """facebook/dpr-reader-single-nq-base""": (
        """https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/config.json"""
    ),
    """facebook/dpr-ctx_encoder-multiset-base""": (
        """https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/config.json"""
    ),
    """facebook/dpr-question_encoder-multiset-base""": (
        """https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/config.json"""
    ),
    """facebook/dpr-reader-multiset-base""": (
        """https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/config.json"""
    ),
}







class       UpperCamelCase__   (	lowercase_  ):




      """simple docstring"""




      _SCREAMING_SNAKE_CASE  				=     """dpr"""



      def __init__(      self				:      List[Any]    ,			SCREAMING_SNAKE_CASE_				:      Tuple=3_0_5_2_2    ,			SCREAMING_SNAKE_CASE_				:      Any=7_6_8    ,			SCREAMING_SNAKE_CASE_				:      Dict=1_2    ,			SCREAMING_SNAKE_CASE_				:      List[str]=1_2    ,			SCREAMING_SNAKE_CASE_				:      int=3_0_7_2    ,			SCREAMING_SNAKE_CASE_				:      List[Any]="gelu"    ,			SCREAMING_SNAKE_CASE_				:      int=0.1    ,			SCREAMING_SNAKE_CASE_				:      int=0.1    ,			SCREAMING_SNAKE_CASE_				:      Union[str, Any]=5_1_2    ,			SCREAMING_SNAKE_CASE_				:      Optional[int]=2    ,			SCREAMING_SNAKE_CASE_				:      Optional[int]=0.02    ,			SCREAMING_SNAKE_CASE_				:      List[str]=1E-12    ,			SCREAMING_SNAKE_CASE_				:      List[Any]=0    ,			SCREAMING_SNAKE_CASE_				:      Optional[int]="absolute"    ,			SCREAMING_SNAKE_CASE_				:      int = 0    ,			**SCREAMING_SNAKE_CASE_				:      Optional[int]    ,			):
        super().__init__(pad_token_id=SCREAMING_SNAKE_CASE_    ,			**SCREAMING_SNAKE_CASE_       )

        lowerCAmelCase_   :				Optional[int]								= vocab_size
        lowerCAmelCase_   :				Tuple								= hidden_size
        lowerCAmelCase_   :				int								= num_hidden_layers
        lowerCAmelCase_   :				str								= num_attention_heads
        lowerCAmelCase_   :				Any								= hidden_act
        lowerCAmelCase_   :				List[str]								= intermediate_size
        lowerCAmelCase_   :				int								= hidden_dropout_prob
        lowerCAmelCase_   :				Union[str, Any]								= attention_probs_dropout_prob
        lowerCAmelCase_   :				Tuple								= max_position_embeddings
        lowerCAmelCase_   :				List[str]								= type_vocab_size
        lowerCAmelCase_   :				str								= initializer_range
        lowerCAmelCase_   :				str								= layer_norm_eps
        lowerCAmelCase_   :				List[str]								= projection_dim
        lowerCAmelCase_   :				List[str]								= position_embedding_type





