





'''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
#
########################################################################


A__				:   List[Any]      =16
A__				:   Optional[int]      =32
def    UpperCamelCase__      (		lowerCAmelCase							,	lowerCAmelCase = 16  ):





      """simple docstring"""


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

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

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

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

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

      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__				:   Optional[Any]      =mocked_dataloaders  # noqa: F811
def    UpperCamelCase__      (		lowerCAmelCase							,	lowerCAmelCase  ):





      """simple docstring"""


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

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

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

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

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

      # Instantiate scheduler
      _lowerCAmelCase							     =							get_linear_schedule_with_warmup(
          optimizer=_lowerCAmelCase							,	num_warmup_steps=1_00							,	num_training_steps=(len(_lowerCAmelCase  ) * 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.
      _lowerCAmelCase							     =							accelerator.prepare(
          _lowerCAmelCase							,	_lowerCAmelCase							,	_lowerCAmelCase							,	_lowerCAmelCase							,	_lowerCAmelCase  )

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

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

            _lowerCAmelCase							     =							metric.compute()
            # Use accelerator.print to print only on the main process.
            accelerator.print(f"epoch {epoch}:"							,	_lowerCAmelCase  )
def    UpperCamelCase__      (		):





      """simple docstring"""


      _lowerCAmelCase							     =							argparse.ArgumentParser(description="""Simple example of training script."""  )
      parser.add_argument(
          """--mixed_precision"""							,	type=_lowerCAmelCase							,	default=_lowerCAmelCase							,	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=_lowerCAmelCase							,	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."""  )
      _lowerCAmelCase							     =							parser.parse_args()
      _lowerCAmelCase							     =							{'lr': 2e-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
      training_function(_lowerCAmelCase							,	_lowerCAmelCase  )


if __name__ == "__main__":
      main()











'''simple docstring'''





import argparse

from torch import nn

# transformers_old should correspond to branch `save_old_prophetnet_model_structure` here
# original prophetnet_checkpoints are saved under `patrickvonplaten/..._old` respectively
from transformers_old.modeling_prophetnet import (
    ProphetNetForConditionalGeneration as ProphetNetForConditionalGenerationOld,
)
from transformers_old.modeling_xlm_prophetnet import (
    XLMProphetNetForConditionalGeneration as XLMProphetNetForConditionalGenerationOld,
)

from transformers import ProphetNetForConditionalGeneration, XLMProphetNetForConditionalGeneration, logging


_UpperCamelCase											=						logging.get_logger(__name__)
logging.set_verbosity_info()

def        a_     (							_lowerCAmelCase       ,_lowerCAmelCase       )      ->      Optional[int]:
  if "xprophetnet" in prophetnet_checkpoint_path:
    __lowerCamelCase       :       Union[str, Any]           = XLMProphetNetForConditionalGenerationOld.from_pretrained(_lowerCAmelCase       )
    __lowerCamelCase       ,__lowerCamelCase       :       List[str]           = XLMProphetNetForConditionalGeneration.from_pretrained(
        _lowerCAmelCase       ,output_loading_info=_lowerCAmelCase       )
  else:
    __lowerCamelCase       :       Optional[int]           = ProphetNetForConditionalGenerationOld.from_pretrained(_lowerCAmelCase       )
    __lowerCamelCase       ,__lowerCamelCase       :       List[str]           = ProphetNetForConditionalGeneration.from_pretrained(
        _lowerCAmelCase       ,output_loading_info=_lowerCAmelCase       )

  __lowerCamelCase       :       Union[str, Any]           = ['key_proj', 'value_proj', 'query_proj']

  __lowerCamelCase       :       Optional[Any]           = {
      'self_attn': 'ngram_self_attn',
      'cross_attn': 'encoder_attn',
      'cross_attn_layer_norm': 'encoder_attn_layer_norm',
      'feed_forward_layer_norm': 'final_layer_norm',
      'feed_forward': '',
      'intermediate': 'fc1',
      'output': 'fc2',
      'key_proj': 'k_proj',
      'query_proj': 'q_proj',
      'value_proj': 'v_proj',
      'word_embeddings': 'embed_tokens',
      'embeddings_layer_norm': 'emb_layer_norm',
      'relative_pos_embeddings': 'relative_linear',
      'ngram_embeddings': 'ngram_input_embed',
      'position_embeddings': 'embed_positions',
  }

  for key in loading_info["missing_keys"]:
    __lowerCamelCase       :       Optional[int]           = key.split('.'       )

    if attributes[0] == "lm_head":
      __lowerCamelCase       :       Dict           = prophet
      __lowerCamelCase       :       List[Any]           = prophet_old
    else:
      __lowerCamelCase       :       Any           = prophet.prophetnet
      __lowerCamelCase       :       Any           = prophet_old.model

    __lowerCamelCase       :       Optional[Any]           = False
    for attribute in attributes:
      if attribute in mapping:
        __lowerCamelCase       :       Any           = mapping[attribute]
        if not hasattr(_lowerCAmelCase       ,_lowerCAmelCase       ) and len(_lowerCAmelCase       ) > 0:
          __lowerCamelCase       :       int           = attribute
      elif hasattr(_lowerCAmelCase       ,_lowerCAmelCase       ):
        __lowerCamelCase       :       Optional[int]           = attribute

      if attribute == "weight":
        assert old_model.weight.shape == model.weight.shape, "Shapes have to match!"
        __lowerCamelCase       :       List[Any]           = old_model.weight
        logger.info(F'{attribute} is initialized.'       )
        __lowerCamelCase       :       List[Any]           = True
        break
      elif attribute == "bias":
        assert old_model.bias.shape == model.bias.shape, "Shapes have to match!"
        __lowerCamelCase       :       List[Any]           = old_model.bias
        logger.info(F'{attribute} is initialized'       )
        __lowerCamelCase       :       Dict           = True
        break
      elif attribute in special_keys and hasattr(_lowerCAmelCase       ,'in_proj_weight'       ):
        __lowerCamelCase       :       Optional[Any]           = old_model.in_proj_weight.shape[0] // 3
        __lowerCamelCase       :       Optional[Any]           = getattr(_lowerCAmelCase       ,_lowerCAmelCase       )
        param.weight.shape == old_model.in_proj_weight[:embed_dim, :].shape, "Shapes have to match"
        param.bias.shape == old_model.in_proj_bias[:embed_dim].shape, "Shapes have to match"
        if attribute == "query_proj":
          __lowerCamelCase       :       Optional[int]           = nn.Parameter(old_model.in_proj_weight[:embed_dim, :]       )
          __lowerCamelCase       :       Dict           = nn.Parameter(old_model.in_proj_bias[:embed_dim]       )

        elif attribute == "key_proj":
          __lowerCamelCase       :       List[str]           = nn.Parameter(old_model.in_proj_weight[embed_dim : 2 * embed_dim, :]       )
          __lowerCamelCase       :       Dict           = nn.Parameter(old_model.in_proj_bias[embed_dim : 2 * embed_dim]       )
        elif attribute == "value_proj":
          __lowerCamelCase       :       str           = nn.Parameter(old_model.in_proj_weight[2 * embed_dim :, :]       )
          __lowerCamelCase       :       Optional[int]           = nn.Parameter(old_model.in_proj_bias[2 * embed_dim :]       )
        __lowerCamelCase       :       Optional[int]           = True
        break
      elif attribute == "position_embeddings":
        assert (
            model.position_embeddings.weight.shape[-1] == old_model.embed_positions.weight.shape[-1]
        ), "Hidden size has to match"
        assert model.position_embeddings.weight.shape[0] == 512, "We want 512 position_embeddings."
        __lowerCamelCase       :       Optional[int]           = nn.Parameter(old_model.embed_positions.weight[:512, :]       )
        __lowerCamelCase       :       Dict           = True
        break

      if attribute.isdigit():
        __lowerCamelCase       :       List[str]           = model[int(_lowerCAmelCase       )]
        __lowerCamelCase       :       Union[str, Any]           = old_model[int(_lowerCAmelCase       )]
      else:
        __lowerCamelCase       :       Union[str, Any]           = getattr(_lowerCAmelCase       ,_lowerCAmelCase       )

        if old_attribute == "":
          __lowerCamelCase       :       str           = old_model
        else:
          if not hasattr(_lowerCAmelCase       ,_lowerCAmelCase       ):
            raise ValueError(F'{old_model} does not have {old_attribute}'       )
          __lowerCamelCase       :       str           = getattr(_lowerCAmelCase       ,_lowerCAmelCase       )

    if not is_key_init:
      raise ValueError(F'{key} was not correctly initialized!'       )

  print(F'Saving model to {pytorch_dump_folder_path}'       )
  prophet.save_pretrained(_lowerCAmelCase       )


if __name__ == "__main__":
   _UpperCamelCase											=						argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       '--prophetnet_checkpoint_path', default=None, type=str, required=True, help='Path the official PyTorch dump.'
   )
   parser.add_argument(
       '--pytorch_dump_folder_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
   )
   _UpperCamelCase											=						parser.parse_args()
   convert_prophetnet_checkpoint_to_pytorch(args.prophetnet_checkpoint_path, args.pytorch_dump_folder_path)






