





"""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


__lowerCamelCase      :Tuple	    =		16
__lowerCamelCase      :Union[str, Any]	    =		32




def   snake_case		(     UpperCamelCase__       :						List[str]			,				UpperCamelCase__       :						Dict = 16			,				UpperCamelCase__       :						str = "bert-base-cased"    )   ->      List[Any]:
      lowerCamelCase					:					Dict        =	AutoTokenizer.from_pretrained(UpperCamelCase__    )
      lowerCamelCase					:					Optional[int]        =	load_dataset("""glue"""			,				"""mrpc"""    )

      def tokenize_function(UpperCamelCase__       :						str    ):
            # max_length=None => use the model max length (it's actually the default)
            lowerCamelCase					:					List[str]        =	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
      lowerCamelCase					:					List[str]        =	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
      lowerCamelCase					:					Optional[Any]        =	tokenized_datasets.rename_column("""label"""			,				"""labels"""    )

      def collate_fn(UpperCamelCase__       :						List[str]    ):
            # 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.
      lowerCamelCase					:					Any        =	DataLoader(
          tokenized_datasets["""train"""]			,				shuffle=UpperCamelCase__			,				collate_fn=UpperCamelCase__			,				batch_size=UpperCamelCase__    )
      lowerCamelCase					:					int        =	DataLoader(
          tokenized_datasets["""validation"""]			,				shuffle=UpperCamelCase__			,				collate_fn=UpperCamelCase__			,				batch_size=UpperCamelCase__    )

      return train_dataloader, eval_dataloader




def   snake_case		(     UpperCamelCase__       :						Optional[int]			,				UpperCamelCase__       :						List[str]    )   ->      Optional[Any]:
      # Initialize accelerator
      lowerCamelCase					:					List[Any]        =	Accelerator()

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

      set_seed(UpperCamelCase__    )
      lowerCamelCase					:					List[str]        =	get_dataloaders(UpperCamelCase__			,				UpperCamelCase__			,				UpperCamelCase__    )

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

      # Instantiate optimizer
      lowerCamelCase					:					Tuple        =	(
          AdamW
          if accelerator.state.deepspeed_plugin is None
          or """optimizer""" not in accelerator.state.deepspeed_plugin.deepspeed_config
          else DummyOptim
      )
      lowerCamelCase					:					List[str]        =	optimizer_cls(params=model.parameters()			,				lr=UpperCamelCase__    )

      if accelerator.state.deepspeed_plugin is not None:
            lowerCamelCase					:					Any        =	accelerator.state.deepspeed_plugin.deepspeed_config[
                """gradient_accumulation_steps"""
            ]
      else:
            lowerCamelCase					:					Any        =	1
      lowerCamelCase					:					str        =	(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
      ):
            lowerCamelCase					:					List[Any]        =	get_linear_schedule_with_warmup(
                optimizer=UpperCamelCase__			,				num_warmup_steps=0			,				num_training_steps=UpperCamelCase__			,				)
      else:
            lowerCamelCase					:					Dict        =	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.
      lowerCamelCase					:					Dict        =	accelerator.prepare(
          UpperCamelCase__			,				UpperCamelCase__			,				UpperCamelCase__			,				UpperCamelCase__			,				UpperCamelCase__    )

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

      # Now we train the model
      lowerCamelCase					:					str        =	evaluate.load("""glue"""			,				"""mrpc"""    )
      lowerCamelCase					:					int        =	0
      lowerCamelCase					:					Tuple        =	{}
      for epoch in range(UpperCamelCase__			,				UpperCamelCase__    ):
            model.train()
            for step, batch in enumerate(UpperCamelCase__    ):
                  lowerCamelCase					:					List[str]        =	model(**UpperCamelCase__    )
                  lowerCamelCase					:					int        =	outputs.loss
                  lowerCamelCase					:					List[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

            model.eval()
            lowerCamelCase					:					List[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():
                        lowerCamelCase					:					Optional[int]        =	model(**UpperCamelCase__    )
                  lowerCamelCase					:					Dict        =	outputs.logits.argmax(dim=-1    )
                  # It is slightly faster to call this once, than multiple times
                  lowerCamelCase					:					str        =	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:
                              lowerCamelCase					:					int        =	predictions[: len(eval_dataloader.dataset    ) - samples_seen]
                              lowerCamelCase					:					Union[str, Any]        =	references[: len(eval_dataloader.dataset    ) - samples_seen]
                        else:
                              samples_seen += references.shape[0]
                  metric.add_batch(
                      predictions=UpperCamelCase__			,				references=UpperCamelCase__			,				)

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

            if best_performance < eval_metric["accuracy"]:
                  lowerCamelCase					:					Dict        =	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(UpperCamelCase__			,				UpperCamelCase__    )




def   snake_case		(     )   ->      Optional[int]:
      lowerCamelCase					:					int        =	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(
          """--performance_lower_bound"""			,				type=UpperCamelCase__			,				default=UpperCamelCase__			,				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=UpperCamelCase__			,				default=3			,				help="""Number of train epochs."""			,				)
      lowerCamelCase					:					Optional[int]        =	parser.parse_args()
      lowerCamelCase					:					Any        =	{"""lr""": 2E-5, """num_epochs""": args.num_epochs, """seed""": 42, """batch_size""": 16}
      training_function(UpperCamelCase__			,				UpperCamelCase__    )


if __name__ == "__main__":
    main()








"""simple docstring"""


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


__lowerCamelCase      :Optional[int]	    =		logging.get_logger(__name__)

__lowerCamelCase      :List[str]	    =		{
    '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__	(						__lowercase):


     """simple docstring"""
     snake_case__	:					Optional[Any]													='''realm'''

     def __init__(	self:						Union[str, Any]     ,		__a:						List[Any]=30_522     ,		__a:						List[Any]=768     ,		__a:						List[Any]=128     ,		__a:						Union[str, Any]=12     ,		__a:						Union[str, Any]=12     ,		__a:						Optional[Any]=8     ,		__a:						Dict=3_072     ,		__a:						List[Any]="gelu_new"     ,		__a:						List[Any]=0.1     ,		__a:						Tuple=0.1     ,		__a:						Optional[Any]=512     ,		__a:						Optional[int]=2     ,		__a:						str=0.02     ,		__a:						int=1e-1_2     ,		__a:						Optional[Any]=256     ,		__a:						Any=10     ,		__a:						Dict=1e-3     ,		__a:						Optional[Any]=5     ,		__a:						Dict=320     ,		__a:						Tuple=13_353_718     ,		__a:						List[Any]=5_000     ,		__a:						Dict=1     ,		__a:						int=0     ,		__a:						Dict=2     ,		**__a:						List[str]     ,		)->    Any:
           super().__init__(pad_token_id=__a     ,		bos_token_id=__a     ,		eos_token_id=__a     ,		**__a	)

           # Common config
           lowerCamelCase					:					Optional[Any]        =	vocab_size
           lowerCamelCase					:					str        =	max_position_embeddings
           lowerCamelCase					:					Dict        =	hidden_size
           lowerCamelCase					:					Dict        =	retriever_proj_size
           lowerCamelCase					:					Optional[Any]        =	num_hidden_layers
           lowerCamelCase					:					List[str]        =	num_attention_heads
           lowerCamelCase					:					Tuple        =	num_candidates
           lowerCamelCase					:					int        =	intermediate_size
           lowerCamelCase					:					Dict        =	hidden_act
           lowerCamelCase					:					List[str]        =	hidden_dropout_prob
           lowerCamelCase					:					Dict        =	attention_probs_dropout_prob
           lowerCamelCase					:					Optional[int]        =	initializer_range
           lowerCamelCase					:					Dict        =	type_vocab_size
           lowerCamelCase					:					Optional[Any]        =	layer_norm_eps

           # Reader config
           lowerCamelCase					:					List[str]        =	span_hidden_size
           lowerCamelCase					:					Dict        =	max_span_width
           lowerCamelCase					:					Optional[Any]        =	reader_layer_norm_eps
           lowerCamelCase					:					Optional[int]        =	reader_beam_size
           lowerCamelCase					:					List[Any]        =	reader_seq_len

           # Retrieval config
           lowerCamelCase					:					int        =	num_block_records
           lowerCamelCase					:					Dict        =	searcher_beam_size




