


"""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
# 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
#
########################################################################


__snake_case		=       16
__snake_case		=       32
def       A_   (   _lowerCAmelCase     :					Accelerator,     _lowerCAmelCase     :					int = 16       ):






     """simple docstring"""

     _a    	=				AutoTokenizer.from_pretrained('''bert-base-cased'''       )
     _a    	=				load_dataset('''glue''',     '''mrpc'''       )

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

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

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

     # Instantiate dataloaders.
     _a    	=				DataLoader(
         tokenized_datasets['''train'''],     shuffle=_UpperCamelCase,     collate_fn=_UpperCamelCase,     batch_size=_UpperCamelCase       )
     _a    	=				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

   __snake_case		=       mocked_dataloaders  # noqa: F811
def       A_   (   _lowerCAmelCase     :					Tuple,     _lowerCAmelCase     :					Optional[Any]       ):






     """simple docstring"""

     if os.environ.get('''TESTING_MOCKED_DATALOADERS''',     _UpperCamelCase       ) == "1":
          _a    	=				2
     # New Code #
     _a    	=				int(args.gradient_accumulation_steps       )
     # Initialize accelerator
     _a    	=				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
     _a    	=				config['''lr''']
     _a    	=				int(config['''num_epochs''']       )
     _a    	=				int(config['''seed''']       )
     _a    	=				int(config['''batch_size''']       )

     _a    	=				evaluate.load('''glue''',     '''mrpc'''       )

     set_seed(_UpperCamelCase       )
     _a	,      _a    	=				get_dataloaders(_UpperCamelCase,     _UpperCamelCase       )
     # Instantiate the model (we build the model here so that the seed also control new weights initialization)
     _a    	=				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).
     _a    	=				model.to(accelerator.device       )

     # Instantiate optimizer
     _a    	=				AdamW(params=model.parameters(),     lr=_UpperCamelCase       )

     # Instantiate scheduler
     _a    	=				get_linear_schedule_with_warmup(
         optimizer=_UpperCamelCase,     num_warmup_steps=1_00,     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.
     _a	,      _a	,      _a	,      _a	,      _a    	=				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       ):
                    _a    	=				model(**_UpperCamelCase       )
                    _a    	=				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():
                    _a    	=				model(**_UpperCamelCase       )
               _a    	=				outputs.logits.argmax(dim=-1       )
               _a	,      _a    	=				accelerator.gather_for_metrics((predictions, batch['''labels'''])       )
               metric.add_batch(
                   predictions=_UpperCamelCase,     references=_UpperCamelCase,     )

          _a    	=				metric.compute()
          # Use accelerator.print to print only on the main process.
          accelerator.print(f'epoch {epoch}:',     _UpperCamelCase       )
def       A_   (   ):






     """simple docstring"""

     _a    	=				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.'''       )
     _a    	=				parser.parse_args()
     _a    	=				{'''lr''': 2e-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
     training_function(_UpperCamelCase,     _UpperCamelCase       )


if __name__ == "__main__":
   main()



"""simple docstring"""






import argparse

import torch
from torch import nn

from transformers import MBartConfig, MBartForConditionalGeneration
def       A_   (   _lowerCAmelCase     :					Optional[Any]       ):






     """simple docstring"""

     _a    	=				[
         '''encoder.version''',
         '''decoder.version''',
         '''model.encoder.version''',
         '''model.decoder.version''',
         '''_float_tensor''',
         '''decoder.output_projection.weight''',
     ]
     for k in ignore_keys:
          state_dict.pop(_lowerCAmelCase,     _lowerCAmelCase       )
def       A_   (   _lowerCAmelCase     :					Dict       ):






     """simple docstring"""

     _a	,      _a    	=				emb.weight.shape
     _a    	=				nn.Linear(_lowerCAmelCase,     _lowerCAmelCase,     bias=_lowerCAmelCase       )
     _a    	=				emb.weight.data
     return lin_layer
def       A_   (   _lowerCAmelCase     :					Dict,     _lowerCAmelCase     :					Union[str, Any]="facebook/mbart-large-en-ro",     _lowerCAmelCase     :					Any=False,     _lowerCAmelCase     :					int=False       ):






     """simple docstring"""

     _a    	=				torch.load(_lowerCAmelCase,     map_location='''cpu'''       )['''model''']
     remove_ignore_keys_(_lowerCAmelCase       )
     _a    	=				state_dict['''encoder.embed_tokens.weight'''].shape[0]

     _a    	=				MBartConfig.from_pretrained(_lowerCAmelCase,     vocab_size=_lowerCAmelCase       )
     if mbart_aa and finetuned:
          _a    	=				'''relu'''

     _a    	=				state_dict['''decoder.embed_tokens.weight''']
     _a    	=				MBartForConditionalGeneration(_lowerCAmelCase       )
     model.model.load_state_dict(_lowerCAmelCase       )

     if finetuned:
          _a    	=				make_linear_from_emb(model.model.shared       )

     return model


if __name__ == "__main__":
   __snake_case		=       argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       '''fairseq_path''', type=str, help='''bart.large, bart.large.cnn or a path to a model.pt on local filesystem.'''
   )
   parser.add_argument('''pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
   parser.add_argument(
       '''--hf_config''',
       default='''facebook/mbart-large-cc25''',
       type=str,
       help='''Which huggingface architecture to use: mbart-large''',
   )
   parser.add_argument('''--mbart_50''', action='''store_true''', help='''whether the model is mMART-50 checkpoint''')
   parser.add_argument('''--finetuned''', action='''store_true''', help='''whether the model is a fine-tuned checkpoint''')
   __snake_case		=       parser.parse_args()
   __snake_case		=       convert_fairseq_mbart_checkpoint_from_disk(
       args.fairseq_path, hf_config_path=args.hf_config, finetuned=args.finetuned, mbart_aa=args.mbart_aa
   )
   model.save_pretrained(args.pytorch_dump_folder_path)