

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
# and perform 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
#
########################################################################


_a							=							16
_a							=							32




def   _A  (  UpperCamelCase_					:	Accelerator,      UpperCamelCase_					:	int = 16)     ->						Optional[Any]:
   '''simple docstring'''


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

   def tokenize_function(UpperCamelCase_					:	Optional[Any]):
      # max_length=None => use the model max length (it's actually the default)
      __lowercase								=						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
   # starting with the main process first:
   with accelerator.main_process_first():
      __lowercase								=						datasets.map(
          UpperCamelCase_,      batched=UpperCamelCase_,      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
   __lowercase								=						tokenized_datasets.rename_column("label",      "labels")

   def collate_fn(UpperCamelCase_					:	Tuple):
      # On TPU it's best to pad everything to the same length or training will be very slow.
      __lowercase								=						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":
         __lowercase								=						16
      elif accelerator.mixed_precision != "no":
         __lowercase								=						8
      else:
         __lowercase								=						None

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

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

   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

   _a							=							mocked_dataloaders  # noqa: F811




def   _A  (  UpperCamelCase_					:	Union[str, Any],      UpperCamelCase_					:	str)     ->						str:
   '''simple docstring'''


   if os.environ.get("TESTING_MOCKED_DATALOADERS",      UpperCamelCase_) == "1":
      __lowercase								=						2
   # New Code #
   __lowercase								=						int(args.gradient_accumulation_steps)
   # Initialize accelerator
   __lowercase								=						Accelerator(
       cpu=args.cpu,      mixed_precision=args.mixed_precision,      gradient_accumulation_steps=UpperCamelCase_)
   if accelerator.distributed_type == DistributedType.TPU and gradient_accumulation_steps > 1:
      raise NotImplementedError(
          "Gradient accumulation on TPUs is currently not supported. Pass `gradient_accumulation_steps=1`")
   # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
   __lowercase								=						config["lr"]
   __lowercase								=						int(config["num_epochs"])
   __lowercase								=						int(config["seed"])
   __lowercase								=						int(config["batch_size"])

   __lowercase								=						evaluate.load("glue",      "mrpc")

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

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

   # Instantiate optimizer
   __lowercase								=						AdamW(params=model.parameters(),      lr=UpperCamelCase_)

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

   # Now we train the model
   for epoch in range(UpperCamelCase_):
      model.train()
      for step, batch in enumerate(UpperCamelCase_):
         # 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(UpperCamelCase_):
            __lowercase								=						model(**UpperCamelCase_)
            __lowercase								=						output.loss
            accelerator.backward(UpperCamelCase_)
            optimizer.step()
            lr_scheduler.step()
            optimizer.zero_grad()

      model.eval()
      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():
            __lowercase								=						model(**UpperCamelCase_)
         __lowercase								=						outputs.logits.argmax(dim=-1)
         __lowercase		,__lowercase								=						accelerator.gather_for_metrics((predictions, batch["labels"]))
         metric.add_batch(
             predictions=UpperCamelCase_,      references=UpperCamelCase_,      )

      __lowercase								=						metric.compute()
      # Use accelerator.print to print only on the main process.
      accelerator.print(F"""epoch {epoch}:""",      UpperCamelCase_)




def   _A  (  )     ->						Optional[Any]:
   '''simple docstring'''


   __lowercase								=						argparse.ArgumentParser(description="Simple example of training script.")
   parser.add_argument(
       "--mixed_precision",      type=UpperCamelCase_,      default=UpperCamelCase_,      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=UpperCamelCase_,      default=1,      help="The number of minibatches to be ran before gradients are accumulated.",      )
   parser.add_argument("--cpu",      action="store_true",      help="If passed, will train on the CPU.")
   __lowercase								=						parser.parse_args()
   __lowercase								=						{"lr": 2E-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
   training_function(UpperCamelCase_,      UpperCamelCase_)


if __name__ == "__main__":
   main()







"""simple docstring"""





import os
from pathlib import Path

import numpy as np
import pytest
from pack_dataset import pack_data_dir
from parameterized import parameterized
from save_len_file import save_len_file
from torch.utils.data import DataLoader

from transformers import AutoTokenizer
from transformers.models.mbart.modeling_mbart import shift_tokens_right
from transformers.testing_utils import TestCasePlus, slow
from utils import FAIRSEQ_AVAILABLE, DistributedSortishSampler, LegacySeqaSeqDataset, SeqaSeqDataset


_a							=							'bert-base-cased'
_a							=							'google/pegasus-xsum'
_a							=							[' Sam ate lunch today.', 'Sams lunch ingredients.']
_a							=							['A very interesting story about what I ate for lunch.', 'Avocado, celery, turkey, coffee']
_a							=							'patrickvonplaten/t5-tiny-random'
_a							=							'sshleifer/bart-tiny-random'
_a							=							'sshleifer/tiny-mbart'
_a							=							'sshleifer/tiny-marian-en-de'




def   _A  (  UpperCamelCase_					:	Path,      UpperCamelCase_					:	list)     ->						Optional[Any]:
   '''simple docstring'''


   __lowercase								=						"\n".join(UpperCamelCase_)
   Path(UpperCamelCase_).open("w").writelines(UpperCamelCase_)




def   _A  (  UpperCamelCase_					:	Union[str, Any])     ->						int:
   '''simple docstring'''


   for split in ["train", "val", "test"]:
      _dump_articles(os.path.join(UpperCamelCase_,      F"""{split}.source"""),      UpperCamelCase_)
      _dump_articles(os.path.join(UpperCamelCase_,      F"""{split}.target"""),      UpperCamelCase_)
   return tmp_dir




class  _lowerCAmelCase  (     lowercase       ):







 """simple docstring"""







 @parameterized.expand(
     [
         MBART_TINY,
         MARIAN_TINY,
         T5_TINY,
         BART_TINY,
         PEGASUS_XSUM,
     ], )
 @slow
 def  _lowercase			(      self							:      List[Any], UpperCAmelCase__							:      Optional[Any]     ):
    __lowercase								=						AutoTokenizer.from_pretrained(UpperCAmelCase__     )
    __lowercase								=						make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()     )
    __lowercase								=						max(len(tokenizer.encode(UpperCAmelCase__     )     ) for a in ARTICLES     )
    __lowercase								=						max(len(tokenizer.encode(UpperCAmelCase__     )     ) for a in SUMMARIES     )
    __lowercase								=						4
    __lowercase								=						8
    assert max_len_target > max_src_len  # Will be truncated
    assert max_len_source > max_src_len  # Will be truncated
    __lowercase		,__lowercase								=						"ro_RO", "de_DE"  # ignored for all but mbart, but never causes error.
    __lowercase								=						SeqaSeqDataset(
        UpperCAmelCase__, data_dir=UpperCAmelCase__, type_path="train", max_source_length=UpperCAmelCase__, max_target_length=UpperCAmelCase__, src_lang=UpperCAmelCase__, tgt_lang=UpperCAmelCase__, )
    __lowercase								=						DataLoader(UpperCAmelCase__, batch_size=2, collate_fn=train_dataset.collate_fn     )
    for batch in dataloader:
       assert isinstance(UpperCAmelCase__, UpperCAmelCase__     )
       assert batch["attention_mask"].shape == batch["input_ids"].shape
       # show that articles were trimmed.
       assert batch["input_ids"].shape[1] == max_src_len
       # show that targets are the same len
       assert batch["labels"].shape[1] == max_tgt_len
       if tok_name != MBART_TINY:
          continue
       # check language codes in correct place
       __lowercase								=						shift_tokens_right(batch["labels"], tokenizer.pad_token_id     )
       assert batch["decoder_input_ids"][0, 0].item() == tokenizer.lang_code_to_id[tgt_lang]
       assert batch["decoder_input_ids"][0, -1].item() == tokenizer.eos_token_id
       assert batch["input_ids"][0, -2].item() == tokenizer.eos_token_id
       assert batch["input_ids"][0, -1].item() == tokenizer.lang_code_to_id[src_lang]

       break  # No need to test every batch


 @parameterized.expand([BART_TINY, BERT_BASE_CASED]     )
 def  _lowercase			(      self							:      Dict, UpperCAmelCase__							:      int     ):
    __lowercase								=						AutoTokenizer.from_pretrained(UpperCAmelCase__     )
    __lowercase								=						make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()     )
    __lowercase								=						max(len(tokenizer.encode(UpperCAmelCase__     )     ) for a in ARTICLES     )
    __lowercase								=						max(len(tokenizer.encode(UpperCAmelCase__     )     ) for a in SUMMARIES     )
    __lowercase								=						4
    __lowercase								=						LegacySeqaSeqDataset(
        UpperCAmelCase__, data_dir=UpperCAmelCase__, type_path="train", max_source_length=2_0, max_target_length=UpperCAmelCase__, )
    __lowercase								=						DataLoader(UpperCAmelCase__, batch_size=2, collate_fn=train_dataset.collate_fn     )
    for batch in dataloader:
       assert batch["attention_mask"].shape == batch["input_ids"].shape
       # show that articles were trimmed.
       assert batch["input_ids"].shape[1] == max_len_source
       assert 2_0 >= batch["input_ids"].shape[1]  # trimmed significantly
       # show that targets were truncated
       assert batch["labels"].shape[1] == trunc_target  # Truncated
       assert max_len_target > trunc_target  # Truncated
       break  # No need to test every batch


 def  _lowercase			(      self							:      str     ):
    __lowercase								=						AutoTokenizer.from_pretrained("facebook/mbart-large-cc25"     )

    __lowercase								=						Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()     )     )
    __lowercase								=						tmp_dir.joinpath("train.source"     ).open().readlines()
    __lowercase								=						Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()     )     )
    pack_data_dir(UpperCAmelCase__, UpperCAmelCase__, 1_2_8, UpperCAmelCase__     )
    __lowercase								=						{x.name for x in tmp_dir.iterdir()}
    __lowercase								=						{x.name for x in save_dir.iterdir()}
    __lowercase								=						save_dir.joinpath("train.source"     ).open().readlines()
    # orig: [' Sam ate lunch today.\n', 'Sams lunch ingredients.']
    # desired_packed: [' Sam ate lunch today.\n Sams lunch ingredients.']
    assert len(UpperCAmelCase__     ) < len(UpperCAmelCase__     )
    assert len(UpperCAmelCase__     ) == 1
    assert len(packed_examples[0]     ) == sum(len(UpperCAmelCase__     ) for x in orig_examples     )
    assert orig_paths == new_paths


 @pytest.mark.skipif(not FAIRSEQ_AVAILABLE, reason="This test requires fairseq"     )
 def  _lowercase			(      self							:      List[str]     ):
    if not FAIRSEQ_AVAILABLE:
       return
    __lowercase		,__lowercase		,__lowercase								=						self._get_dataset(max_len=6_4     )
    __lowercase								=						6_4
    __lowercase								=						ds.make_dynamic_sampler(UpperCAmelCase__, required_batch_size_multiple=UpperCAmelCase__     )
    __lowercase								=						[len(UpperCAmelCase__     ) for x in batch_sampler]
    assert len(set(UpperCAmelCase__     )     ) > 1  # it's not dynamic batch size if every batch is the same length
    assert sum(UpperCAmelCase__     ) == len(UpperCAmelCase__     )  # no dropped or added examples
    __lowercase								=						DataLoader(UpperCAmelCase__, batch_sampler=UpperCAmelCase__, collate_fn=ds.collate_fn, num_workers=2     )
    __lowercase								=						[]
    __lowercase								=						[]
    for batch in data_loader:
       __lowercase								=						batch["input_ids"].shape
       __lowercase								=						src_shape[0]
       assert bs % required_batch_size_multiple == 0 or bs < required_batch_size_multiple
       __lowercase								=						np.product(batch["input_ids"].shape     )
       num_src_per_batch.append(UpperCAmelCase__     )
       if num_src_tokens > (max_tokens * 1.1):
          failures.append(UpperCAmelCase__     )
    assert num_src_per_batch[0] == max(UpperCAmelCase__     )
    if failures:
       raise AssertionError(F"""too many tokens in {len(UpperCAmelCase__     )} batches"""     )


 def  _lowercase			(      self							:      Any     ):
    __lowercase		,__lowercase		,__lowercase								=						self._get_dataset(max_len=5_1_2     )
    __lowercase								=						2
    __lowercase								=						ds.make_sortish_sampler(UpperCAmelCase__, shuffle=UpperCAmelCase__     )

    __lowercase								=						DataLoader(UpperCAmelCase__, batch_size=UpperCAmelCase__, collate_fn=ds.collate_fn, num_workers=2     )
    __lowercase								=						DataLoader(UpperCAmelCase__, batch_size=UpperCAmelCase__, collate_fn=ds.collate_fn, num_workers=2, sampler=UpperCAmelCase__     )

    __lowercase								=						tokenizer.pad_token_id

    def count_pad_tokens(UpperCAmelCase__							:      Any, UpperCAmelCase__							:      Union[str, Any]="input_ids"     ):
       return [batch[k].eq(UpperCAmelCase__     ).sum().item() for batch in data_loader]

    assert sum(count_pad_tokens(UpperCAmelCase__, k="labels"     )     ) < sum(count_pad_tokens(UpperCAmelCase__, k="labels"     )     )
    assert sum(count_pad_tokens(UpperCAmelCase__     )     ) < sum(count_pad_tokens(UpperCAmelCase__     )     )
    assert len(UpperCAmelCase__     ) == len(UpperCAmelCase__     )


 def  _lowercase			(      self							:      List[Any], UpperCAmelCase__							:      int=1_0_0_0, UpperCAmelCase__							:      str=1_2_8     ):
    if os.getenv("USE_REAL_DATA", UpperCAmelCase__     ):
       __lowercase								=						"examples/seq2seq/wmt_en_ro"
       __lowercase								=						max_len * 2 * 6_4
       if not Path(UpperCAmelCase__     ).joinpath("train.len"     ).exists():
          save_len_file(UpperCAmelCase__, UpperCAmelCase__     )
    else:
       __lowercase								=						"examples/seq2seq/test_data/wmt_en_ro"
       __lowercase								=						max_len * 4
       save_len_file(UpperCAmelCase__, UpperCAmelCase__     )

    __lowercase								=						AutoTokenizer.from_pretrained(UpperCAmelCase__     )
    __lowercase								=						SeqaSeqDataset(
        UpperCAmelCase__, data_dir=UpperCAmelCase__, type_path="train", max_source_length=UpperCAmelCase__, max_target_length=UpperCAmelCase__, n_obs=UpperCAmelCase__, )
    return ds, max_tokens, tokenizer


 def  _lowercase			(      self							:      Union[str, Any]     ):
    __lowercase		,__lowercase		,__lowercase								=						self._get_dataset()
    __lowercase								=						set(DistributedSortishSampler(UpperCAmelCase__, 2_5_6, num_replicas=2, rank=0, add_extra_examples=UpperCAmelCase__     )     )
    __lowercase								=						set(DistributedSortishSampler(UpperCAmelCase__, 2_5_6, num_replicas=2, rank=1, add_extra_examples=UpperCAmelCase__     )     )
    assert idsa.intersection(UpperCAmelCase__     ) == set()



 @parameterized.expand(
     [
         MBART_TINY,
         MARIAN_TINY,
         T5_TINY,
         BART_TINY,
         PEGASUS_XSUM,
     ], )
 def  _lowercase			(      self							:      int, UpperCAmelCase__							:      Optional[Any]     ):
    __lowercase								=						AutoTokenizer.from_pretrained(UpperCAmelCase__, use_fast=UpperCAmelCase__     )
    if tok_name == MBART_TINY:
       __lowercase								=						SeqaSeqDataset(
           UpperCAmelCase__, data_dir=make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()     ), type_path="train", max_source_length=4, max_target_length=8, src_lang="EN", tgt_lang="FR", )
       __lowercase								=						train_dataset.dataset_kwargs
       assert "src_lang" in kwargs and "tgt_lang" in kwargs
    else:
       __lowercase								=						SeqaSeqDataset(
           UpperCAmelCase__, data_dir=make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()     ), type_path="train", max_source_length=4, max_target_length=8, )
       __lowercase								=						train_dataset.dataset_kwargs
       assert "add_prefix_space" not in kwargs if tok_name != BART_TINY else "add_prefix_space" in kwargs
       assert len(UpperCAmelCase__     ) == 1 if tok_name == BART_TINY else len(UpperCAmelCase__     ) == 0




