'''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
from accelerate.local_sgd import LocalSGD


########################################################################
# This is a fully working simple example to use Accelerate
# with LocalSGD, which is a method to synchronize model
# parameters every K batches. It is different, but complementary
# to gradient accumulation.
#
# 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
#
########################################################################


SCREAMING_SNAKE_CASE_:     str  =16
SCREAMING_SNAKE_CASE_:     Dict  =32





def 	lowerCAmelCase_				(					snake_case_       : Accelerator					,    snake_case_       : int = 16			)    ->      Union[str, Any]:


     '''simple docstring'''




     UpperCAmelCase_           =   AutoTokenizer.from_pretrained("bert-base-cased"			)
     UpperCAmelCase_           =   load_dataset("glue"					,    "mrpc"			)

     def tokenize_function(snake_case_       : Tuple			):
          # 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(snake_case_       : Optional[Any]			):
          # 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_			)
     UpperCAmelCase_           =   DataLoader(
         tokenized_datasets["validation"]					,    shuffle=snake_case_					,    collate_fn=snake_case_					,    batch_size=snake_case_			)

     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

    SCREAMING_SNAKE_CASE_:     List[Any]  =mocked_dataloaders  # noqa: F811





def 	lowerCAmelCase_				(					snake_case_       : Union[str, Any]					,    snake_case_       : int			)    ->      Dict:


     '''simple docstring'''




     if os.environ.get("TESTING_MOCKED_DATALOADERS"					,    snake_case_			) == "1":
          UpperCAmelCase_           =   2
     # New Code #
     UpperCAmelCase_           =   int(args.gradient_accumulation_steps			)
     UpperCAmelCase_           =   int(args.local_sgd_steps			)
     # Initialize accelerator
     UpperCAmelCase_           =   Accelerator(
         cpu=args.cpu					,    mixed_precision=args.mixed_precision					,    gradient_accumulation_steps=snake_case_			)
     if accelerator.distributed_type not in [DistributedType.NO, DistributedType.MULTI_CPU, DistributedType.MULTI_GPU]:
          raise NotImplementedError("LocalSGD is supported only for CPUs and GPUs (no DeepSpeed or MegatronLM)"			)
     # 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"			)

     set_seed(snake_case_			)
     UpperCAmelCase_    , 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)					,    )

     # 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_    , UpperCAmelCase_    , UpperCAmelCase_    , UpperCAmelCase_    , 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()
          with LocalSGD(
              accelerator=snake_case_					,    model=snake_case_					,    local_sgd_steps=snake_case_					,    enabled=local_sgd_steps is not None			) as local_sgd:
               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			)
                    # New code #
                    # We use the new `accumulate` context manager to perform gradient accumulation
                    # We also currently do not support TPUs nor advise it as bugs were found on the XLA side when running our tests.
                    with accelerator.accumulate(snake_case_			):
                         UpperCAmelCase_           =   model(**snake_case_			)
                         UpperCAmelCase_           =   output.loss
                         accelerator.backward(snake_case_			)
                         optimizer.step()
                         lr_scheduler.step()
                         optimizer.zero_grad()
                         # LocalSGD-specific line
                         local_sgd.step()

          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_    , 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 	lowerCAmelCase_				(					)    ->      Any:


     '''simple docstring'''




     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."					,    )
     # New Code #
     parser.add_argument(
         "--gradient_accumulation_steps"					,    type=snake_case_					,    default=1					,    help="The number of minibatches to be ran before gradients are accumulated."					,    )
     parser.add_argument(
         "--local_sgd_steps"					,    type=snake_case_					,    default=8					,    help="Number of local SGD steps or None to disable local SGD"			)
     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()





'''simple docstring'''





import os
import shutil
import tempfile
from unittest import TestCase
from unittest.mock import patch

import numpy as np
from datasets import Dataset

from transformers.models.realm.configuration_realm import RealmConfig
from transformers.models.realm.retrieval_realm import _REALM_BLOCK_RECORDS_FILENAME, RealmRetriever
from transformers.models.realm.tokenization_realm import VOCAB_FILES_NAMES, RealmTokenizer




class        __A      ( UpperCamelCase__	):



    def 					_lowercase						(self    :    Optional[Any] ):
         UpperCAmelCase_           =   tempfile.mkdtemp()
         UpperCAmelCase_           =   5

         # Realm tok
         UpperCAmelCase_           =   [
             "[UNK]",
             "[CLS]",
             "[SEP]",
             "[PAD]",
             "[MASK]",
             "test",
             "question",
             "this",
             "is",
             "the",
             "first",
             "second",
             "third",
             "fourth",
             "fifth",
             "record",
             "want",
             "##want",
             "##ed",
             "wa",
             "un",
             "runn",
             "##ing",
             ",",
             "low",
             "lowest",
         ]
         UpperCAmelCase_           =   os.path.join(self.tmpdirname   ,			"realm_tokenizer" )
         os.makedirs(__a   ,			exist_ok=__a )
         UpperCAmelCase_           =   os.path.join(__a   ,			VOCAB_FILES_NAMES["vocab_file"] )
         with open(self.vocab_file   ,			"w"   ,			encoding="utf-8" ) as vocab_writer:
              vocab_writer.write("".join([x + "\n" for x in vocab_tokens] ) )

         UpperCAmelCase_           =   os.path.join(self.tmpdirname   ,			"realm_block_records" )
         os.makedirs(__a   ,			exist_ok=__a )



    def 					_lowercase						(self    :    Optional[Any] ):
         return RealmTokenizer.from_pretrained(os.path.join(self.tmpdirname   ,			"realm_tokenizer" ) )



    def 					_lowercase						(self    :    Any ):
         shutil.rmtree(self.tmpdirname )



    def 					_lowercase						(self    :    List[Any] ):
         UpperCAmelCase_           =   RealmConfig(num_block_records=self.num_block_records )
         return config



    def 					_lowercase						(self    :    List[str] ):
         UpperCAmelCase_           =   Dataset.from_dict(
             {
                 "id": ["0", "1"],
                 "question": ["foo", "bar"],
                 "answers": [["Foo", "Bar"], ["Bar"]],
             } )
         return dataset



    def 					_lowercase						(self    :    Any ):
         UpperCAmelCase_           =   np.array(
             [
                 B"This is the first record",
                 B"This is the second record",
                 B"This is the third record",
                 B"This is the fourth record",
                 B"This is the fifth record",
                 B"This is a longer longer longer record",
             ]   ,			dtype=__a   ,			)
         return block_records



    def 					_lowercase						(self    :    Union[str, Any] ):
         UpperCAmelCase_           =   RealmRetriever(
             block_records=self.get_dummy_block_records()   ,			tokenizer=self.get_tokenizer()   ,			)
         return retriever



    def 					_lowercase						(self    :    int ):
         UpperCAmelCase_           =   self.get_config()
         UpperCAmelCase_           =   self.get_dummy_retriever()
         UpperCAmelCase_           =   retriever.tokenizer

         UpperCAmelCase_           =   np.array([0, 3]   ,			dtype="long" )
         UpperCAmelCase_           =   tokenizer(["Test question"] ).input_ids
         UpperCAmelCase_           =   tokenizer(
             ["the fourth"]   ,			add_special_tokens=__a   ,			return_token_type_ids=__a   ,			return_attention_mask=__a   ,			).input_ids
         UpperCAmelCase_           =   config.reader_seq_len

         UpperCAmelCase_    , UpperCAmelCase_    , UpperCAmelCase_    , UpperCAmelCase_           =   retriever(
             __a   ,			__a   ,			answer_ids=__a   ,			max_length=__a   ,			return_tensors="np" )

         self.assertEqual(len(__a )   ,			2 )
         self.assertEqual(len(__a )   ,			2 )
         self.assertEqual(len(__a )   ,			2 )
         self.assertEqual(concat_inputs.input_ids.shape   ,			(2, 10) )
         self.assertEqual(concat_inputs.attention_mask.shape   ,			(2, 10) )
         self.assertEqual(concat_inputs.token_type_ids.shape   ,			(2, 10) )
         self.assertEqual(concat_inputs.special_tokens_mask.shape   ,			(2, 10) )
         self.assertEqual(
             tokenizer.convert_ids_to_tokens(concat_inputs.input_ids[0] )   ,			["[CLS]", "test", "question", "[SEP]", "this", "is", "the", "first", "record", "[SEP]"]   ,			)
         self.assertEqual(
             tokenizer.convert_ids_to_tokens(concat_inputs.input_ids[1] )   ,			["[CLS]", "test", "question", "[SEP]", "this", "is", "the", "fourth", "record", "[SEP]"]   ,			)



    def 					_lowercase						(self    :    List[Any] ):
         UpperCAmelCase_           =   self.get_config()
         UpperCAmelCase_           =   self.get_dummy_retriever()
         UpperCAmelCase_           =   retriever.tokenizer

         UpperCAmelCase_           =   np.array([0, 3, 5]   ,			dtype="long" )
         UpperCAmelCase_           =   tokenizer(["Test question"] ).input_ids
         UpperCAmelCase_           =   tokenizer(
             ["the fourth", "longer longer"]   ,			add_special_tokens=__a   ,			return_token_type_ids=__a   ,			return_attention_mask=__a   ,			).input_ids
         UpperCAmelCase_           =   config.reader_seq_len

         UpperCAmelCase_    , UpperCAmelCase_    , UpperCAmelCase_    , UpperCAmelCase_           =   retriever(
             __a   ,			__a   ,			answer_ids=__a   ,			max_length=__a   ,			return_tensors="np" )

         self.assertEqual([False, True, True]   ,			__a )
         self.assertEqual([[-1, -1, -1], [6, -1, -1], [6, 7, 8]]   ,			__a )
         self.assertEqual([[-1, -1, -1], [7, -1, -1], [7, 8, 9]]   ,			__a )






    def 					_lowercase						(self    :    Optional[Any] ):
         UpperCAmelCase_           =   self.get_dummy_retriever()
         retriever.save_pretrained(os.path.join(self.tmpdirname   ,			"realm_block_records" ) )

         # Test local path
         UpperCAmelCase_           =   retriever.from_pretrained(os.path.join(self.tmpdirname   ,			"realm_block_records" ) )
         self.assertEqual(retriever.block_records[0]   ,			B"This is the first record" )

         # Test mocked remote path
         with patch("transformers.models.realm.retrieval_realm.hf_hub_download" ) as mock_hf_hub_download:
              UpperCAmelCase_           =   os.path.join(
                  os.path.join(self.tmpdirname   ,			"realm_block_records" )   ,			_REALM_BLOCK_RECORDS_FILENAME )
              UpperCAmelCase_           =   RealmRetriever.from_pretrained("google/realm-cc-news-pretrained-openqa" )

         self.assertEqual(retriever.block_records[0]   ,			B"This is the first record" )




