
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   =			16
_A   =			32



def 						lowercase_      (       A__			,		A__ = 16			,		A__ = "bert-base-cased"		)							->							Any:
    """simple docstring"""

    snake_case								= AutoTokenizer.from_pretrained(_lowerCAmelCase		)
    snake_case								= load_dataset("glue"			,		"mrpc"		)

    def tokenize_function(A__		):
        # max_length=None => use the model max length (it's actually the default)
        snake_case								= 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
    snake_case								= datasets.map(
        _lowerCAmelCase			,		batched=_lowerCAmelCase			,		remove_columns=["idx", "sentence1", "sentence2"]			,		load_from_cache_file=_lowerCAmelCase		)

    # We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
    # transformers library
    snake_case								= 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.
        if accelerator.distributed_type == DistributedType.TPU:
            return tokenizer.pad(_lowerCAmelCase			,		padding="max_length"			,		max_length=128			,		return_tensors="pt"		)
        return tokenizer.pad(_lowerCAmelCase			,		padding="longest"			,		return_tensors="pt"		)

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

    return train_dataloader, eval_dataloader



def 						lowercase_      (       A__			,		A__		)							->							int:
    """simple docstring"""

    snake_case								= Accelerator()

    # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
    snake_case								= config["""lr"""]
    snake_case								= int(config["num_epochs"]		)
    snake_case								= int(config["seed"]		)
    snake_case								= int(config["batch_size"]		)
    snake_case								= args.model_name_or_path

    set_seed(_lowerCAmelCase		)
    snake_case								= get_dataloaders(_lowerCAmelCase			,		_lowerCAmelCase			,		_lowerCAmelCase		)

    # Instantiate the model (we build the model here so that the seed also control new weights initialization)
    snake_case								= AutoModelForSequenceClassification.from_pretrained(_lowerCAmelCase			,		return_dict=_lowerCAmelCase		)

    # Instantiate optimizer
    snake_case								= (
        AdamW
        if accelerator.state.deepspeed_plugin is None
        or """optimizer""" not in accelerator.state.deepspeed_plugin.deepspeed_config
        else DummyOptim
    )
    snake_case								= optimizer_cls(params=model.parameters()			,		lr=_lowerCAmelCase		)

    if accelerator.state.deepspeed_plugin is not None:
        snake_case								= accelerator.state.deepspeed_plugin.deepspeed_config[
            """gradient_accumulation_steps"""
        ]
    else:
        snake_case								= 1
    snake_case								= (len(_lowerCAmelCase		) * num_epochs) // gradient_accumulation_steps

    # Instantiate scheduler
    if (
        accelerator.state.deepspeed_plugin is None
        or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
    ):
        snake_case								= get_linear_schedule_with_warmup(
            optimizer=_lowerCAmelCase			,		num_warmup_steps=0			,		num_training_steps=_lowerCAmelCase			,		)
    else:
        snake_case								= DummyScheduler(_lowerCAmelCase			,		total_num_steps=_lowerCAmelCase			,		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.
    snake_case								= accelerator.prepare(
        _lowerCAmelCase			,		_lowerCAmelCase			,		_lowerCAmelCase			,		_lowerCAmelCase			,		_lowerCAmelCase		)

    # We need to keep track of how many total steps we have iterated over
    snake_case								= 0
    # We also need to keep track of the stating epoch so files are named properly
    snake_case								= 0

    # Now we train the model
    snake_case								= evaluate.load("glue"			,		"mrpc"		)
    snake_case								= 0
    snake_case								= {}
    for epoch in range(_lowerCAmelCase			,		_lowerCAmelCase		):
        model.train()
        for step, batch in enumerate(_lowerCAmelCase		):
            snake_case								= model(**_lowerCAmelCase		)
            snake_case								= outputs.loss
            snake_case								= loss / gradient_accumulation_steps
            accelerator.backward(_lowerCAmelCase		)
            if step % gradient_accumulation_steps == 0:
                optimizer.step()
                lr_scheduler.step()
                optimizer.zero_grad()

            overall_step += 1

        model.eval()
        snake_case								= 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		)
            with torch.no_grad():
                snake_case								= model(**_lowerCAmelCase		)
            snake_case								= outputs.logits.argmax(dim=-1		)
            # It is slightly faster to call this once, than multiple times
            snake_case								= accelerator.gather(
                (predictions, batch["labels"])		)  # If we are in a multiprocess environment, the last batch has duplicates
            if accelerator.use_distributed:
                if step == len(_lowerCAmelCase		) - 1:
                    snake_case								= predictions[: len(eval_dataloader.dataset		) - samples_seen]
                    snake_case								= references[: len(eval_dataloader.dataset		) - samples_seen]
                else:
                    samples_seen += references.shape[0]
            metric.add_batch(
                predictions=_lowerCAmelCase			,		references=_lowerCAmelCase			,		)

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

        if best_performance < eval_metric["accuracy"]:
            snake_case								= 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(_lowerCAmelCase			,		_lowerCAmelCase		)



def 						lowercase_      (       )							->							List[str]:
    """simple docstring"""

    snake_case								= argparse.ArgumentParser(description="Simple example of training script tracking peak GPU memory usage."		)
    parser.add_argument(
        "--model_name_or_path"			,		type=_lowerCAmelCase			,		default="bert-base-cased"			,		help="Path to pretrained model or model identifier from huggingface.co/models."			,		required=_lowerCAmelCase			,		)
    parser.add_argument(
        "--output_dir"			,		type=_lowerCAmelCase			,		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=_lowerCAmelCase			,		default=_lowerCAmelCase			,		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=_lowerCAmelCase			,		default=3			,		help="Number of train epochs."			,		)
    snake_case								= parser.parse_args()
    snake_case								= {"""lr""": 2e-5, """num_epochs""": args.num_epochs, """seed""": 42, """batch_size""": 16}
    training_function(_lowerCAmelCase			,		_lowerCAmelCase		)


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 					lowerCamelCase		(  A_  ):
     UpperCAmelCase__    :							str									=		"realm"





     def __init__(self				:   Optional[int]    ,   _A				:   Optional[Any]=3_0_5_2_2    ,   _A				:   Tuple=7_6_8    ,   _A				:   List[str]=1_2_8    ,   _A				:   Optional[Any]=1_2    ,   _A				:   Dict=1_2    ,   _A				:   Tuple=8    ,   _A				:   Dict=3_0_7_2    ,   _A				:   Union[str, Any]="gelu_new"    ,   _A				:   Any=0.1    ,   _A				:   int=0.1    ,   _A				:   Union[str, Any]=5_1_2    ,   _A				:   List[str]=2    ,   _A				:   Any=0.02    ,   _A				:   int=1E-12    ,   _A				:   Tuple=2_5_6    ,   _A				:   Optional[Any]=1_0    ,   _A				:   Any=1E-3    ,   _A				:   int=5    ,   _A				:   int=3_2_0    ,   _A				:   Dict=1_3_3_5_3_7_1_8    ,   _A				:   Any=5_0_0_0    ,   _A				:   Union[str, Any]=1    ,   _A				:   Dict=0    ,   _A				:   int=2    ,   **_A				:   Union[str, Any]    ,   )						->						Optional[Any]:
         super().__init__(pad_token_id=_A    ,   bos_token_id=_A    ,   eos_token_id=_A    ,   **_A       )

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

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

         # Retrieval config
         snake_case								= num_block_records
         snake_case								= searcher_beam_size

