



"""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,
# 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    :Dict     =					16
_lowerCAmelCase    :Optional[int]     =					32





def  lowerCamelCase_						(UpperCamelCase__		:		Accelerator     ,       UpperCamelCase__		:		int = 16							):
    _UpperCAmelCase							:  List[str]											=    AutoTokenizer.from_pretrained('''bert-base-cased'''							)
    _UpperCAmelCase							:  int											=    load_dataset('''glue'''     ,       '''mrpc'''							)

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

    def collate_fn(UpperCamelCase__		:		int							):
        # On TPU it's best to pad everything to the same length or training will be very slow.
        _UpperCAmelCase							:  Union[str, Any]											=    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":
            _UpperCAmelCase							:  Dict											=    16
        elif accelerator.mixed_precision != "no":
            _UpperCAmelCase							:  Optional[int]											=    8
        else:
            _UpperCAmelCase							:  int											=    None

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

    # Instantiate dataloaders.
    _UpperCAmelCase							:  str											=    DataLoader(
        tokenized_datasets['''train''']     ,       shuffle=UpperCamelCase__     ,       collate_fn=UpperCamelCase__     ,       batch_size=UpperCamelCase__							)
    _UpperCAmelCase							:  str											=    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

  _lowerCAmelCase    :int     =					mocked_dataloaders  # noqa: F811





def  lowerCamelCase_						(UpperCamelCase__		:		List[Any]     ,       UpperCamelCase__		:		str							):
    # For testing only
    if os.environ.get('''TESTING_MOCKED_DATALOADERS'''     ,       UpperCamelCase__							) == "1":
        _UpperCAmelCase							:  Dict											=    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:
        _UpperCAmelCase							:  str											=    Accelerator(
            cpu=args.cpu     ,       mixed_precision=args.mixed_precision     ,       log_with='''all'''     ,       project_dir=args.project_dir							)
    else:
        _UpperCAmelCase							:  List[str]											=    Accelerator(cpu=args.cpu     ,       mixed_precision=args.mixed_precision							)
    # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
    _UpperCAmelCase							:  List[str]											=    config['''lr''']
    _UpperCAmelCase							:  Optional[int]											=    int(config['''num_epochs''']							)
    _UpperCAmelCase							:  List[str]											=    int(config['''seed''']							)
    _UpperCAmelCase							:  Any											=    int(config['''batch_size''']							)
    set_seed(UpperCamelCase__							)

    _UpperCAmelCase							,				_UpperCAmelCase							:  int											=    get_dataloaders(UpperCamelCase__     ,       UpperCamelCase__							)
    _UpperCAmelCase							:  int											=    evaluate.load('''glue'''     ,       '''mrpc'''							)

    # If the batch size is too big we use gradient accumulation
    _UpperCAmelCase							:  int											=    1
    if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
        _UpperCAmelCase							:  str											=    batch_size // MAX_GPU_BATCH_SIZE
        _UpperCAmelCase							:  Union[str, Any]											=    MAX_GPU_BATCH_SIZE

    # Instantiate the model (we build the model here so that the seed also control new weights initialization)
    _UpperCAmelCase							:  Dict											=    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).
    _UpperCAmelCase							:  str											=    model.to(accelerator.device							)

    # Instantiate optimizer
    _UpperCAmelCase							:  Optional[Any]											=    AdamW(params=model.parameters()     ,       lr=UpperCamelCase__							)

    # Instantiate scheduler
    _UpperCAmelCase							:  Dict											=    get_linear_schedule_with_warmup(
        optimizer=UpperCamelCase__     ,       num_warmup_steps=100     ,       num_training_steps=(len(UpperCamelCase__							) * 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.
    _UpperCAmelCase							,				_UpperCAmelCase							,				_UpperCAmelCase							,				_UpperCAmelCase							,				_UpperCAmelCase							:  Dict											=    accelerator.prepare(
        UpperCamelCase__     ,       UpperCamelCase__     ,       UpperCamelCase__     ,       UpperCamelCase__     ,       UpperCamelCase__							)

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

    # Now we train the model
    for epoch in range(UpperCamelCase__							):
        model.train()
        # New Code #
        # For our tracking example, we will log the total loss of each epoch
        if args.with_tracking:
            _UpperCAmelCase							:  Optional[int]											=    0
        for step, batch in enumerate(UpperCamelCase__							):
            # We could avoid this line since we set the accelerator with `device_placement=True`.
            batch.to(accelerator.device							)
            _UpperCAmelCase							:  List[str]											=    model(**UpperCamelCase__							)
            _UpperCAmelCase							:  List[Any]											=    outputs.loss
            # New Code #
            if args.with_tracking:
                total_loss += loss.detach().float()
            _UpperCAmelCase							:  List[str]											=    loss / gradient_accumulation_steps
            accelerator.backward(UpperCamelCase__							)
            if step % gradient_accumulation_steps == 0:
                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` (the default).
            batch.to(accelerator.device							)
            with torch.no_grad():
                _UpperCAmelCase							:  Optional[Any]											=    model(**UpperCamelCase__							)
            _UpperCAmelCase							:  int											=    outputs.logits.argmax(dim=-1							)
            _UpperCAmelCase							,				_UpperCAmelCase							:  List[Any]											=    accelerator.gather_for_metrics((predictions, batch['''labels'''])							)
            metric.add_batch(
                predictions=UpperCamelCase__     ,       references=UpperCamelCase__     ,       )

        _UpperCAmelCase							:  Dict											=    metric.compute()
        # Use accelerator.print to print only on the main process.
        accelerator.print(F'epoch {epoch}:'     ,       UpperCamelCase__							)

        # 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(UpperCamelCase__							),
                    '''epoch''': epoch,
                }     ,       step=UpperCamelCase__     ,       )

    # 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  lowerCamelCase_						():
    _UpperCAmelCase							:  Union[str, Any]											=    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.'''     ,       )
    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=UpperCamelCase__     ,       default='''logs'''     ,       help='''Location on where to store experiment tracking logs` and relevent project information'''     ,       )
    _UpperCAmelCase							:  Tuple											=    parser.parse_args()
    _UpperCAmelCase							:  Optional[int]											=    {'''lr''': 2E-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
    training_function(UpperCamelCase__     ,       UpperCamelCase__							)


if __name__ == "__main__":
  main()







"""simple docstring"""
import argparse
import json
import os

import torch

from transformers import LukeConfig, LukeModel, LukeTokenizer, RobertaTokenizer
from transformers.tokenization_utils_base import AddedToken





@torch.no_grad()
def  lowerCamelCase_						(UpperCamelCase__		:		Optional[Any]     ,       UpperCamelCase__		:		Union[str, Any]     ,       UpperCamelCase__		:		int     ,       UpperCamelCase__		:		Dict     ,       UpperCamelCase__		:		Optional[Any]							):
    # Load configuration defined in the metadata file
    with open(UpperCamelCase__							) as metadata_file:
        _UpperCAmelCase							:  Dict											=    json.load(UpperCamelCase__							)
    _UpperCAmelCase							:  List[Any]											=    LukeConfig(use_entity_aware_attention=UpperCamelCase__     ,       **metadata['''model_config''']							)

    # Load in the weights from the checkpoint_path
    _UpperCAmelCase							:  List[Any]											=    torch.load(UpperCamelCase__     ,       map_location='''cpu'''							)

    # Load the entity vocab file
    _UpperCAmelCase							:  Optional[int]											=    load_entity_vocab(UpperCamelCase__							)

    _UpperCAmelCase							:  Optional[int]											=    RobertaTokenizer.from_pretrained(metadata['''model_config''']['''bert_model_name''']							)

    # Add special tokens to the token vocabulary for downstream tasks
    _UpperCAmelCase							:  int											=    AddedToken('''<ent>'''     ,       lstrip=UpperCamelCase__     ,       rstrip=UpperCamelCase__							)
    _UpperCAmelCase							:  Optional[Any]											=    AddedToken('''<ent2>'''     ,       lstrip=UpperCamelCase__     ,       rstrip=UpperCamelCase__							)
    tokenizer.add_special_tokens({'''additional_special_tokens''': [entity_token_a, entity_token_a]}							)
    config.vocab_size += 2

    print(F'Saving tokenizer to {pytorch_dump_folder_path}'							)
    tokenizer.save_pretrained(UpperCamelCase__							)
    with open(os.path.join(UpperCamelCase__     ,       LukeTokenizer.vocab_files_names['''entity_vocab_file''']							)     ,       '''w'''							) as f:
        json.dump(UpperCamelCase__     ,       UpperCamelCase__							)

    _UpperCAmelCase							:  Any											=    LukeTokenizer.from_pretrained(UpperCamelCase__							)

    # Initialize the embeddings of the special tokens
    _UpperCAmelCase							:  str											=    state_dict['''embeddings.word_embeddings.weight''']
    _UpperCAmelCase							:  Dict											=    word_emb[tokenizer.convert_tokens_to_ids(['''@''']							)[0]].unsqueeze(0							)
    _UpperCAmelCase							:  Union[str, Any]											=    word_emb[tokenizer.convert_tokens_to_ids(['''#''']							)[0]].unsqueeze(0							)
    _UpperCAmelCase							:  Tuple											=    torch.cat([word_emb, ent_emb, enta_emb]							)

    # Initialize the query layers of the entity-aware self-attention mechanism
    for layer_index in range(config.num_hidden_layers							):
        for matrix_name in ["query.weight", "query.bias"]:
            _UpperCAmelCase							:  List[Any]											=    F'encoder.layer.{layer_index}.attention.self.'
            _UpperCAmelCase							:  Optional[Any]											=    state_dict[prefix + matrix_name]
            _UpperCAmelCase							:  Tuple											=    state_dict[prefix + matrix_name]
            _UpperCAmelCase							:  str											=    state_dict[prefix + matrix_name]

    # Initialize the embedding of the [MASK2] entity using that of the [MASK] entity for downstream tasks
    _UpperCAmelCase							:  Any											=    state_dict['''entity_embeddings.entity_embeddings.weight''']
    _UpperCAmelCase							:  Dict											=    entity_emb[entity_vocab['''[MASK]''']]

    _UpperCAmelCase							:  Optional[int]											=    LukeModel(config=UpperCamelCase__							).eval()

    _UpperCAmelCase							,				_UpperCAmelCase							:  int											=    model.load_state_dict(UpperCamelCase__     ,       strict=UpperCamelCase__							)
    if not (len(UpperCamelCase__							) == 1 and missing_keys[0] == "embeddings.position_ids"):
        raise ValueError(F'Missing keys {", ".join(UpperCamelCase__							)}. Expected only missing embeddings.position_ids'							)
    if not (all(key.startswith('''entity_predictions'''							) or key.startswith('''lm_head'''							) for key in unexpected_keys							)):
        raise ValueError(
            '''Unexpected keys'''
            F' {", ".join([key for key in unexpected_keys if not (key.startswith("entity_predictions"							) or key.startswith("lm_head"							))]							)}'							)

    # Check outputs
    _UpperCAmelCase							:  Optional[int]											=    LukeTokenizer.from_pretrained(UpperCamelCase__     ,       task='''entity_classification'''							)

    _UpperCAmelCase							:  List[str]											=    (
        '''Top seed Ana Ivanovic said on Thursday she could hardly believe her luck as a fortuitous netcord helped the'''
        ''' new world number one avoid a humiliating second- round exit at Wimbledon .'''
    )
    _UpperCAmelCase							:  Dict											=    (39, 42)
    _UpperCAmelCase							:  Any											=    tokenizer(UpperCamelCase__     ,       entity_spans=[span]     ,       add_prefix_space=UpperCamelCase__     ,       return_tensors='''pt'''							)

    _UpperCAmelCase							:  List[Any]											=    model(**UpperCamelCase__							)

    # Verify word hidden states
    if model_size == "large":
        _UpperCAmelCase							:  str											=    torch.Size((1, 42, 1024)							)
        _UpperCAmelCase							:  Union[str, Any]											=    torch.tensor(
            [[0.0133, 0.0865, 0.0095], [0.3093, -0.2576, -0.7418], [-0.1720, -0.2117, -0.2869]]							)
    else:  # base
        _UpperCAmelCase							:  Optional[Any]											=    torch.Size((1, 42, 768)							)
        _UpperCAmelCase							:  str											=    torch.tensor([[0.0037, 0.1368, -0.0091], [0.1099, 0.3329, -0.1095], [0.0765, 0.5335, 0.1179]]							)

    if not (outputs.last_hidden_state.shape == expected_shape):
        raise ValueError(
            F'Outputs.last_hidden_state.shape is {outputs.last_hidden_state.shape}, Expected shape is {expected_shape}'							)
    if not torch.allclose(outputs.last_hidden_state[0, :3, :3]     ,       UpperCamelCase__     ,       atol=1E-4							):
        raise ValueError

    # Verify entity hidden states
    if model_size == "large":
        _UpperCAmelCase							:  int											=    torch.Size((1, 1, 1024)							)
        _UpperCAmelCase							:  str											=    torch.tensor([[0.0466, -0.0106, -0.0179]]							)
    else:  # base
        _UpperCAmelCase							:  List[str]											=    torch.Size((1, 1, 768)							)
        _UpperCAmelCase							:  List[Any]											=    torch.tensor([[0.1457, 0.1044, 0.0174]]							)

    if not (outputs.entity_last_hidden_state.shape != expected_shape):
        raise ValueError(
            F'Outputs.entity_last_hidden_state.shape is {outputs.entity_last_hidden_state.shape}, Expected shape is'
            F' {expected_shape}'							)
    if not torch.allclose(outputs.entity_last_hidden_state[0, :3, :3]     ,       UpperCamelCase__     ,       atol=1E-4							):
        raise ValueError

    # Finally, save our PyTorch model and tokenizer
    print('''Saving PyTorch model to {}'''.format(UpperCamelCase__							)							)
    model.save_pretrained(UpperCamelCase__							)





def  lowerCamelCase_						(UpperCamelCase__		:		Union[str, Any]							):
    _UpperCAmelCase							:  Any											=    {}
    with open(UpperCamelCase__     ,       '''r'''     ,       encoding='''utf-8'''							) as f:
        for index, line in enumerate(UpperCamelCase__							):
            _UpperCAmelCase							,				_UpperCAmelCase							:  Any											=    line.rstrip().split('''\t'''							)
            _UpperCAmelCase							:  Tuple											=    index

    return entity_vocab


if __name__ == "__main__":
  _lowerCAmelCase    :List[Any]     =					argparse.ArgumentParser()
  # Required parameters
  parser.add_argument('--checkpoint_path', type=str, help='Path to a pytorch_model.bin file.')
  parser.add_argument(
      '--metadata_path', default=None, type=str, help='Path to a metadata.json file, defining the configuration.'
  )
  parser.add_argument(
      '--entity_vocab_path',
      default=None,
      type=str,
      help='Path to an entity_vocab.tsv file, containing the entity vocabulary.',
  )
  parser.add_argument(
      '--pytorch_dump_folder_path', default=None, type=str, help='Path to where to dump the output PyTorch model.'
  )
  parser.add_argument(
      '--model_size', default='base', type=str, choices=['base', 'large'], help='Size of the model to be converted.'
  )
  _lowerCAmelCase    :Any     =					parser.parse_args()
  convert_luke_checkpoint(
      args.checkpoint_path,
      args.metadata_path,
      args.entity_vocab_path,
      args.pytorch_dump_folder_path,
      args.model_size,
  )


