



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
#
########################################################################


UpperCAmelCase_									=							1_6
UpperCAmelCase_									=							3_2
def 							UpperCAmelCase__		(				_SCREAMING_SNAKE_CASE   :	str     ,				_SCREAMING_SNAKE_CASE   :	str = 1_6				)->Tuple:
       _lowerCAmelCase 			=  AutoTokenizer.from_pretrained('''bert-base-cased'''				)
       _lowerCAmelCase 			=  load_dataset('''glue'''     ,				'''mrpc'''				)

       def tokenize_function(_SCREAMING_SNAKE_CASE   :	Any				):
              # max_length=None => use the model max length (it's actually the default)
              _lowerCAmelCase 			=  tokenizer(examples['''sentence1''']     ,				examples['''sentence2''']     ,				truncation=lowercase__     ,				max_length=lowercase__				)
              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(
                  lowercase__     ,				batched=lowercase__     ,				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(_SCREAMING_SNAKE_CASE   :	Tuple				):
              # On TPU it's best to pad everything to the same length or training will be very slow.
              _lowerCAmelCase 			=  1_2_8 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 			=  1_6
              elif accelerator.mixed_precision != "no":
                     _lowerCAmelCase 			=  8
              else:
                     _lowerCAmelCase 			=  None

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

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

       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

    UpperCAmelCase_									=							mocked_dataloaders  # noqa: F811
def 							UpperCAmelCase__		(				_SCREAMING_SNAKE_CASE   :	List[str]     ,				_SCREAMING_SNAKE_CASE   :	Optional[Any]				)->Dict:
       # For testing only
       if os.environ.get('''TESTING_MOCKED_DATALOADERS'''     ,				lowercase__				) == "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=lowercase__				)
       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(lowercase__				)
       _lowerCAmelCase			,   _lowerCAmelCase 			=  get_dataloaders(lowercase__     ,				lowercase__				)
       # 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=lowercase__				)

       # 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=lowercase__				)

       # Instantiate scheduler
       _lowerCAmelCase 			=  get_linear_schedule_with_warmup(
           optimizer=lowercase__     ,				num_warmup_steps=1_0_0     ,				num_training_steps=(len(lowercase__				) * 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			,   _lowerCAmelCase			,   _lowerCAmelCase			,   _lowerCAmelCase			,   _lowerCAmelCase 			=  accelerator.prepare(
           lowercase__     ,				lowercase__     ,				lowercase__     ,				lowercase__     ,				lowercase__				)

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

              model.eval()
              for step, batch in enumerate(lowercase__				):
                     # We could avoid this line since we set the accelerator with `device_placement=True`.
                     batch.to(accelerator.device				)
                     with torch.no_grad():
                            _lowerCAmelCase 			=  model(**lowercase__				)
                     _lowerCAmelCase 			=  outputs.logits.argmax(dim=-1				)
                     _lowerCAmelCase			,   _lowerCAmelCase 			=  accelerator.gather_for_metrics((predictions, batch['''labels'''])				)
                     metric.add_batch(
                         predictions=lowercase__     ,				references=lowercase__     ,				)

              _lowerCAmelCase 			=  metric.compute()
              # Use accelerator.print to print only on the main process.
              accelerator.print(f'''epoch {epoch}:'''     ,				lowercase__				)
def 							UpperCAmelCase__		(				)->str:
       _lowerCAmelCase 			=  argparse.ArgumentParser(description='''Simple example of training script.'''				)
       parser.add_argument(
           '''--mixed_precision'''     ,				type=lowercase__     ,				default=lowercase__     ,				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=lowercase__     ,				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''': 4_2, '''batch_size''': 1_6}
       training_function(lowercase__     ,				lowercase__				)


if __name__ == "__main__":
    main()




import argparse
import pathlib

import fairseq
import torch
from fairseq.models.roberta import RobertaModel as FairseqRobertaModel
from fairseq.modules import TransformerSentenceEncoderLayer
from packaging import version

from transformers import XLMRobertaConfig, XLMRobertaXLForMaskedLM, XLMRobertaXLForSequenceClassification
from transformers.models.bert.modeling_bert import (
    BertIntermediate,
    BertLayer,
    BertOutput,
    BertSelfAttention,
    BertSelfOutput,
)
from transformers.models.roberta.modeling_roberta import RobertaAttention
from transformers.utils import logging


if version.parse(fairseq.__version__) < version.parse("1.0.0a"):
    raise Exception("requires fairseq >= 1.0.0a")

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

UpperCAmelCase_									=							"Hello world! cécé herlolip"
def 							UpperCAmelCase__		(				_SCREAMING_SNAKE_CASE   :	str     ,				_SCREAMING_SNAKE_CASE   :	str     ,				_SCREAMING_SNAKE_CASE   :	bool				)->List[Any]:
      _lowerCAmelCase 			=  FairseqRobertaModel.from_pretrained(_SCREAMING_SNAKE_CASE				)
      roberta.eval()  # disable dropout
      _lowerCAmelCase 			=  roberta.model.encoder.sentence_encoder
      _lowerCAmelCase 			=  XLMRobertaConfig(
          vocab_size=roberta_sent_encoder.embed_tokens.num_embeddings     ,				hidden_size=roberta.cfg.model.encoder_embed_dim     ,				num_hidden_layers=roberta.cfg.model.encoder_layers     ,				num_attention_heads=roberta.cfg.model.encoder_attention_heads     ,				intermediate_size=roberta.cfg.model.encoder_ffn_embed_dim     ,				max_position_embeddings=5_1_4     ,				type_vocab_size=1     ,				layer_norm_eps=1e-5     ,				)
      if classification_head:
            _lowerCAmelCase 			=  roberta.model.classification_heads['''mnli'''].out_proj.weight.shape[0]

      print('''Our RoBERTa config:'''     ,				_SCREAMING_SNAKE_CASE				)

      _lowerCAmelCase 			=  XLMRobertaXLForSequenceClassification(_SCREAMING_SNAKE_CASE				) if classification_head else XLMRobertaXLForMaskedLM(_SCREAMING_SNAKE_CASE				)
      model.eval()

      # Now let's copy all the weights.
      # Embeddings
      _lowerCAmelCase 			=  roberta_sent_encoder.embed_tokens.weight
      _lowerCAmelCase 			=  roberta_sent_encoder.embed_positions.weight
      _lowerCAmelCase 			=  torch.zeros_like(
          model.roberta.embeddings.token_type_embeddings.weight				)  # just zero them out b/c RoBERTa doesn't use them.

      _lowerCAmelCase 			=  roberta_sent_encoder.layer_norm.weight
      _lowerCAmelCase 			=  roberta_sent_encoder.layer_norm.bias

      for i in range(config.num_hidden_layers				):
            # Encoder: start of layer
            _lowerCAmelCase 			=  model.roberta.encoder.layer[i]
            _lowerCAmelCase 			=  roberta_sent_encoder.layers[i]

            _lowerCAmelCase 			=  layer.attention
            _lowerCAmelCase 			=  roberta_layer.self_attn_layer_norm.weight
            _lowerCAmelCase 			=  roberta_layer.self_attn_layer_norm.bias

            # self attention
            _lowerCAmelCase 			=  layer.attention.self
            assert (
                roberta_layer.self_attn.k_proj.weight.data.shape
                == roberta_layer.self_attn.q_proj.weight.data.shape
                == roberta_layer.self_attn.v_proj.weight.data.shape
                == torch.Size((config.hidden_size, config.hidden_size)				)
            )

            _lowerCAmelCase 			=  roberta_layer.self_attn.q_proj.weight
            _lowerCAmelCase 			=  roberta_layer.self_attn.q_proj.bias
            _lowerCAmelCase 			=  roberta_layer.self_attn.k_proj.weight
            _lowerCAmelCase 			=  roberta_layer.self_attn.k_proj.bias
            _lowerCAmelCase 			=  roberta_layer.self_attn.v_proj.weight
            _lowerCAmelCase 			=  roberta_layer.self_attn.v_proj.bias

            # self-attention output
            _lowerCAmelCase 			=  layer.attention.output
            assert self_output.dense.weight.shape == roberta_layer.self_attn.out_proj.weight.shape
            _lowerCAmelCase 			=  roberta_layer.self_attn.out_proj.weight
            _lowerCAmelCase 			=  roberta_layer.self_attn.out_proj.bias

            # this one is final layer norm
            _lowerCAmelCase 			=  roberta_layer.final_layer_norm.weight
            _lowerCAmelCase 			=  roberta_layer.final_layer_norm.bias

            # intermediate
            _lowerCAmelCase 			=  layer.intermediate
            assert intermediate.dense.weight.shape == roberta_layer.fca.weight.shape
            _lowerCAmelCase 			=  roberta_layer.fca.weight
            _lowerCAmelCase 			=  roberta_layer.fca.bias

            # output
            _lowerCAmelCase 			=  layer.output
            assert bert_output.dense.weight.shape == roberta_layer.fca.weight.shape
            _lowerCAmelCase 			=  roberta_layer.fca.weight
            _lowerCAmelCase 			=  roberta_layer.fca.bias
            # end of layer

      if classification_head:
            _lowerCAmelCase 			=  roberta.model.classification_heads['''mnli'''].dense.weight
            _lowerCAmelCase 			=  roberta.model.classification_heads['''mnli'''].dense.bias
            _lowerCAmelCase 			=  roberta.model.classification_heads['''mnli'''].out_proj.weight
            _lowerCAmelCase 			=  roberta.model.classification_heads['''mnli'''].out_proj.bias
      else:
            # LM Head
            _lowerCAmelCase 			=  roberta.model.encoder.lm_head.dense.weight
            _lowerCAmelCase 			=  roberta.model.encoder.lm_head.dense.bias
            _lowerCAmelCase 			=  roberta.model.encoder.lm_head.layer_norm.weight
            _lowerCAmelCase 			=  roberta.model.encoder.lm_head.layer_norm.bias
            _lowerCAmelCase 			=  roberta.model.encoder.lm_head.weight
            _lowerCAmelCase 			=  roberta.model.encoder.lm_head.bias

      # Let's check that we get the same results.
      _lowerCAmelCase 			=  roberta.encode(_SCREAMING_SNAKE_CASE				).unsqueeze(0				)  # batch of size 1

      _lowerCAmelCase 			=  model(_SCREAMING_SNAKE_CASE				)[0]
      if classification_head:
            _lowerCAmelCase 			=  roberta.model.classification_heads['''mnli'''](roberta.extract_features(_SCREAMING_SNAKE_CASE				)				)
      else:
            _lowerCAmelCase 			=  roberta.model(_SCREAMING_SNAKE_CASE				)[0]
      print(our_output.shape     ,				their_output.shape				)
      _lowerCAmelCase 			=  torch.max(torch.abs(our_output - their_output				)				).item()
      print(f'''max_absolute_diff = {max_absolute_diff}'''				)  # ~ 1e-7
      _lowerCAmelCase 			=  torch.allclose(_SCREAMING_SNAKE_CASE     ,				_SCREAMING_SNAKE_CASE     ,				atol=1e-3				)
      print('''Do both models output the same tensors?'''     ,				'''🔥''' if success else '''💩'''				)
      if not success:
            raise Exception('''Something went wRoNg'''				)

      pathlib.Path(_SCREAMING_SNAKE_CASE				).mkdir(parents=_SCREAMING_SNAKE_CASE     ,				exist_ok=_SCREAMING_SNAKE_CASE				)
      print(f'''Saving model to {pytorch_dump_folder_path}'''				)
      model.save_pretrained(_SCREAMING_SNAKE_CASE				)


if __name__ == "__main__":
    UpperCAmelCase_									=							argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        "--roberta_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."
    )
    parser.add_argument(
        "--classification_head", action="store_true", help="Whether to convert a final classification head."
    )
    UpperCAmelCase_									=							parser.parse_args()
    convert_xlm_roberta_xl_checkpoint_to_pytorch(
        args.roberta_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
    )