




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,
# specifically showcasing the experiment tracking capability,
# and builds off the `nlp_example.py` script.
#
# 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 help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# 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
#
########################################################################

lowerCamelCase    :		List[str]      =       16
lowerCamelCase    :		Union[str, Any]      =       32

def       _SCREAMING_SNAKE_CASE      (							lowercase     :	Accelerator     ,							lowercase     :	int = 16					):


      '''simple docstring'''



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

      def tokenize_function(lowercase     :	Any					):
            # max_length=None => use the model max length (it's actually the default)
            lowerCamelCase_							=   tokenizer(examples['sentence1']     ,							examples['sentence2']     ,							truncation=a__     ,							max_length=a__					)
            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(
                a__     ,							batched=a__     ,							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(lowercase     :	List[Any]					):
            # 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(
                a__     ,							padding='longest'     ,							max_length=a__     ,							pad_to_multiple_of=a__     ,							return_tensors='pt'     ,							)

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

      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

 lowerCamelCase    :		Any      =       mocked_dataloaders  # noqa: F811

def       _SCREAMING_SNAKE_CASE      (							lowercase     :	List[Any]     ,							lowercase     :	Optional[Any]					):


      '''simple docstring'''



      if os.environ.get('TESTING_MOCKED_DATALOADERS'     ,							a__					) == "1":
            lowerCamelCase_							=   2
      # Initialize Accelerator

      # New Code #
      # We pass in "all" to `log_with` to grab all available trackers in the environment
      # Note: If using a custom `Tracker` class, should be passed in here such as:
      # >>> log_with = ["all", MyCustomTrackerClassInstance()]
      if args.with_tracking:
            lowerCamelCase_							=   Accelerator(
                cpu=args.cpu     ,							mixed_precision=args.mixed_precision     ,							log_with='all'     ,							project_dir=args.project_dir					)
      else:
            lowerCamelCase_							=   Accelerator(cpu=args.cpu     ,							mixed_precision=args.mixed_precision					)
      # 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']					)
      set_seed(a__					)

      lowerCamelCase_					,    lowerCamelCase_							=   get_dataloaders(a__     ,							a__					)
      lowerCamelCase_							=   evaluate.load('glue'     ,							'mrpc'					)

      # If the batch size is too big we use gradient accumulation
      lowerCamelCase_							=   1
      if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
            lowerCamelCase_							=   batch_size // MAX_GPU_BATCH_SIZE
            lowerCamelCase_							=   MAX_GPU_BATCH_SIZE

      # 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=a__					)

      # 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=a__					)

      # Instantiate scheduler
      lowerCamelCase_							=   get_linear_schedule_with_warmup(
          optimizer=a__     ,							num_warmup_steps=1_00     ,							num_training_steps=(len(a__					) * num_epochs) // gradient_accumulation_steps     ,							)

      # 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(
          a__     ,							a__     ,							a__     ,							a__     ,							a__					)

      # New Code #
      # We need to initialize the trackers we use. Overall configurations can also be stored
      if args.with_tracking:
            lowerCamelCase_							=   os.path.split(a__					)[-1].split('.'					)[0]
            accelerator.init_trackers(a__     ,							a__					)

      # Now we train the model
      for epoch in range(a__					):
            model.train()
            # New Code #
            # For our tracking example, we will log the total loss of each epoch
            if args.with_tracking:
                  lowerCamelCase_							=   0
            for step, batch in enumerate(a__					):
                  # We could avoid this line since we set the accelerator with `device_placement=True`.
                  batch.to(accelerator.device					)
                  lowerCamelCase_							=   model(**a__					)
                  lowerCamelCase_							=   outputs.loss
                  # New Code #
                  if args.with_tracking:
                        total_loss += loss.detach().float()
                  lowerCamelCase_							=   loss / gradient_accumulation_steps
                  accelerator.backward(a__					)
                  if step % gradient_accumulation_steps == 0:
                        optimizer.step()
                        lr_scheduler.step()
                        optimizer.zero_grad()

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

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

            # New Code #
            # To actually log, we call `Accelerator.log`
            # The values passed can be of `str`, `int`, `float` or `dict` of `str` to `float`/`int`
            if args.with_tracking:
                  accelerator.log(
                      {
                          'accuracy': eval_metric['accuracy'],
                          'f1': eval_metric['f1'],
                          'train_loss': total_loss.item() / len(a__					),
                          'epoch': epoch,
                      }     ,							step=a__     ,							)

    # New Code #
    # When a run is finished, you should call `accelerator.end_training()`
    # to close all of the open trackers
      if args.with_tracking:
            accelerator.end_training()

def       _SCREAMING_SNAKE_CASE      (							):


      '''simple docstring'''



      lowerCamelCase_							=   argparse.ArgumentParser(description='Simple example of training script.'					)
      parser.add_argument(
          '--mixed_precision'     ,							type=a__     ,							default=a__     ,							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.'     ,							)
      parser.add_argument('--cpu'     ,							action='store_true'     ,							help='If passed, will train on the CPU.'					)
      parser.add_argument(
          '--with_tracking'     ,							action='store_true'     ,							help='Whether to load in all available experiment trackers from the environment and use them for logging.'     ,							)
      parser.add_argument(
          '--project_dir'     ,							type=a__     ,							default='logs'     ,							help='Location on where to store experiment tracking logs` and relevent project information'     ,							)
      lowerCamelCase_							=   parser.parse_args()
      lowerCamelCase_							=   {'lr': 2e-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
      training_function(a__     ,							a__					)


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()
lowerCamelCase    :		Tuple			   =       logging.get_logger(__name__)

lowerCamelCase    :		Union[str, Any]			   =       "Hello world! cécé herlolip"

def       _SCREAMING_SNAKE_CASE      (							lowercase     :	str     ,							lowercase     :	str     ,							lowercase     :	bool					):


      '''simple docstring'''



      lowerCamelCase_							=   FairseqRobertaModel.from_pretrained(lowercase					)
      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_14     ,							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:'     ,							lowercase					)

      lowerCamelCase_							=   XLMRobertaXLForSequenceClassification(lowercase					) if classification_head else XLMRobertaXLForMaskedLM(lowercase					)
      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(lowercase					).unsqueeze(0					)  # batch of size 1

      lowerCamelCase_							=   model(lowercase					)[0]
      if classification_head:
            lowerCamelCase_							=   roberta.model.classification_heads['mnli'](roberta.extract_features(lowercase					)					)
      else:
            lowerCamelCase_							=   roberta.model(lowercase					)[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(lowercase     ,							lowercase     ,							atol=1e-3					)
      print('Do both models output the same tensors?'     ,							'🔥' if success else '💩'					)
      if not success:
            raise Exception('Something went wRoNg'					)

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


if __name__ == "__main__":
 lowerCamelCase    :		Optional[Any]			   =       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."
 )
 lowerCamelCase    :		List[Any]			   =       parser.parse_args()
 convert_xlm_roberta_xl_checkpoint_to_pytorch(
     args.roberta_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
 )



