
import argparse
import json
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
from accelerate.utils.deepspeed import DummyOptim, DummyScheduler


a					:	List[str]							=		1_6
a					:	Any							=		3_2


def  _UpperCamelCase	(						_A					,      _A = 1_6					,      _A = "bert-base-cased"      )    ->  Any:





    """simple docstring"""

    _UpperCAmelCase          =   AutoTokenizer.from_pretrained(_SCREAMING_SNAKE_CASE      )
    _UpperCAmelCase          =   load_dataset("""glue"""					,      """mrpc"""      )

    def tokenize_function(_A      ):
        # max_length=None => use the model max length (it's actually the default)
        _UpperCAmelCase          =   tokenizer(examples["""sentence1"""]					,      examples["""sentence2"""]					,      truncation=_SCREAMING_SNAKE_CASE					,      max_length=_SCREAMING_SNAKE_CASE      )
        return outputs

    # Apply the method we just defined to all the examples in all the splits of the dataset
    _UpperCAmelCase          =   datasets.map(
        _SCREAMING_SNAKE_CASE					,      batched=_SCREAMING_SNAKE_CASE					,      remove_columns=["""idx""", """sentence1""", """sentence2"""]					,      load_from_cache_file=_SCREAMING_SNAKE_CASE      )

    # We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
    # transformers library
    _UpperCAmelCase          =   tokenized_datasets.rename_column("""label"""					,      """labels"""      )

    def collate_fn(_A      ):
        # On TPU it's best to pad everything to the same length or training will be very slow.
        if accelerator.distributed_type == DistributedType.TPU:
            return tokenizer.pad(_SCREAMING_SNAKE_CASE					,      padding="""max_length"""					,      max_length=1_2_8					,      return_tensors="""pt"""      )
        return tokenizer.pad(_SCREAMING_SNAKE_CASE					,      padding="""longest"""					,      return_tensors="""pt"""      )

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

    return train_dataloader, eval_dataloader


def  _UpperCamelCase	(						_A					,      _A					,      _A					,      _A      )    ->  Dict:





    """simple docstring"""

    model.eval()
    _UpperCAmelCase          =   0
    for step, batch in enumerate(_SCREAMING_SNAKE_CASE      ):
        # We could avoid this line since we set the accelerator with `device_placement=True`.
        batch.to(accelerator.device      )
        with torch.no_grad():
            _UpperCAmelCase          =   model(**_SCREAMING_SNAKE_CASE      )
        _UpperCAmelCase          =   outputs.logits.argmax(dim=-1      )
        # It is slightly faster to call this once, than multiple times
        _UpperCAmelCase    ,_UpperCAmelCase          =   accelerator.gather(
            (predictions, batch["""labels"""])      )  # If we are in a multiprocess environment, the last batch has duplicates
        if accelerator.use_distributed:
            if step == len(_SCREAMING_SNAKE_CASE      ) - 1:
                _UpperCAmelCase          =   predictions[: len(eval_dataloader.dataset      ) - samples_seen]
                _UpperCAmelCase          =   references[: len(eval_dataloader.dataset      ) - samples_seen]
            else:
                samples_seen += references.shape[0]
        metric.add_batch(
            predictions=_SCREAMING_SNAKE_CASE					,      references=_SCREAMING_SNAKE_CASE					,      )

    _UpperCAmelCase          =   metric.compute()
    return eval_metric["accuracy"]


def  _UpperCamelCase	(						_A					,      _A      )    ->  Optional[Any]:





    """simple docstring"""

    _UpperCAmelCase          =   Accelerator()

    # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
    _UpperCAmelCase          =   config["""lr"""]
    _UpperCAmelCase          =   int(config["""num_epochs"""]      )
    _UpperCAmelCase          =   int(config["""seed"""]      )
    _UpperCAmelCase          =   int(config["""batch_size"""]      )
    _UpperCAmelCase          =   args.model_name_or_path

    set_seed(_SCREAMING_SNAKE_CASE      )
    _UpperCAmelCase    ,_UpperCAmelCase          =   get_dataloaders(_SCREAMING_SNAKE_CASE					,      _SCREAMING_SNAKE_CASE					,      _SCREAMING_SNAKE_CASE      )

    # Instantiate the model (we build the model here so that the seed also control new weights initialization)
    _UpperCAmelCase          =   AutoModelForSequenceClassification.from_pretrained(_SCREAMING_SNAKE_CASE					,      return_dict=_SCREAMING_SNAKE_CASE      )

    # Instantiate optimizer
    _UpperCAmelCase          =   (
        AdamW
        if accelerator.state.deepspeed_plugin is None
        or """optimizer""" not in accelerator.state.deepspeed_plugin.deepspeed_config
        else DummyOptim
    )
    _UpperCAmelCase          =   optimizer_cls(params=model.parameters()					,      lr=_SCREAMING_SNAKE_CASE      )

    if accelerator.state.deepspeed_plugin is not None:
        _UpperCAmelCase          =   accelerator.state.deepspeed_plugin.deepspeed_config[
            """gradient_accumulation_steps"""
        ]
    else:
        _UpperCAmelCase          =   1
    _UpperCAmelCase          =   (len(_SCREAMING_SNAKE_CASE      ) * num_epochs) // gradient_accumulation_steps

    # Instantiate scheduler
    if (
        accelerator.state.deepspeed_plugin is None
        or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
    ):
        _UpperCAmelCase          =   get_linear_schedule_with_warmup(
            optimizer=_SCREAMING_SNAKE_CASE					,      num_warmup_steps=0					,      num_training_steps=_SCREAMING_SNAKE_CASE					,      )
    else:
        _UpperCAmelCase          =   DummyScheduler(_SCREAMING_SNAKE_CASE					,      total_num_steps=_SCREAMING_SNAKE_CASE					,      warmup_num_steps=0      )

    # 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          =   accelerator.prepare(
        _SCREAMING_SNAKE_CASE					,      _SCREAMING_SNAKE_CASE					,      _SCREAMING_SNAKE_CASE					,      _SCREAMING_SNAKE_CASE					,      _SCREAMING_SNAKE_CASE      )

    # We need to keep track of how many total steps we have iterated over
    _UpperCAmelCase          =   0
    # We also need to keep track of the stating epoch so files are named properly
    _UpperCAmelCase          =   0
    _UpperCAmelCase          =   evaluate.load("""glue"""					,      """mrpc"""      )
    _UpperCAmelCase          =   num_epochs

    if args.partial_train_epoch is not None:
        _UpperCAmelCase          =   args.partial_train_epoch

    if args.resume_from_checkpoint:
        accelerator.load_state(args.resume_from_checkpoint      )
        _UpperCAmelCase          =   args.resume_from_checkpoint.split("""epoch_"""      )[1]
        _UpperCAmelCase          =   """"""
        for char in epoch_string:
            if char.isdigit():
                state_epoch_num += char
            else:
                break
        _UpperCAmelCase          =   int(_SCREAMING_SNAKE_CASE      ) + 1
        _UpperCAmelCase          =   evaluation_loop(_SCREAMING_SNAKE_CASE					,      _SCREAMING_SNAKE_CASE					,      _SCREAMING_SNAKE_CASE					,      _SCREAMING_SNAKE_CASE      )
        accelerator.print("""resumed checkpoint performance:"""					,      _SCREAMING_SNAKE_CASE      )
        accelerator.print("""resumed checkpoint\'s scheduler\'s lr:"""					,      lr_scheduler.get_lr()[0]      )
        accelerator.print("""resumed optimizers\'s lr:"""					,      optimizer.param_groups[0]["""lr"""]      )
        with open(os.path.join(args.output_dir					,      F"""state_{starting_epoch-1}.json"""      )					,      """r"""      ) as f:
            _UpperCAmelCase          =   json.load(_SCREAMING_SNAKE_CASE      )
            assert resumed_state["accuracy"] == accuracy, "Accuracy mismatch, loading from checkpoint failed"
            assert (
                resumed_state["lr"] == lr_scheduler.get_lr()[0]
            ), "Scheduler learning rate mismatch, loading from checkpoint failed"
            assert (
                resumed_state["optimizer_lr"] == optimizer.param_groups[0]["lr"]
            ), "Optimizer learning rate mismatch, loading from checkpoint failed"
            assert resumed_state["epoch"] == starting_epoch - 1, "Epoch mismatch, loading from checkpoint failed"
            return

    # Now we train the model
    _UpperCAmelCase          =   {}
    for epoch in range(_SCREAMING_SNAKE_CASE					,      _SCREAMING_SNAKE_CASE      ):
        model.train()
        for step, batch in enumerate(_SCREAMING_SNAKE_CASE      ):
            _UpperCAmelCase          =   model(**_SCREAMING_SNAKE_CASE      )
            _UpperCAmelCase          =   outputs.loss
            _UpperCAmelCase          =   loss / gradient_accumulation_steps
            accelerator.backward(_SCREAMING_SNAKE_CASE      )
            if step % gradient_accumulation_steps == 0:
                optimizer.step()
                lr_scheduler.step()
                optimizer.zero_grad()

            overall_step += 1
        _UpperCAmelCase          =   F"""epoch_{epoch}"""
        _UpperCAmelCase          =   os.path.join(args.output_dir					,      _SCREAMING_SNAKE_CASE      )
        accelerator.save_state(_SCREAMING_SNAKE_CASE      )
        _UpperCAmelCase          =   evaluation_loop(_SCREAMING_SNAKE_CASE					,      _SCREAMING_SNAKE_CASE					,      _SCREAMING_SNAKE_CASE					,      _SCREAMING_SNAKE_CASE      )
        _UpperCAmelCase          =   accuracy
        _UpperCAmelCase          =   lr_scheduler.get_lr()[0]
        _UpperCAmelCase          =   optimizer.param_groups[0]["""lr"""]
        _UpperCAmelCase          =   epoch
        _UpperCAmelCase          =   overall_step
        accelerator.print(F"""epoch {epoch}:"""					,      _SCREAMING_SNAKE_CASE      )

        accelerator.wait_for_everyone()
        if accelerator.is_main_process:
            with open(os.path.join(args.output_dir					,      F"""state_{epoch}.json"""      )					,      """w"""      ) as f:
                json.dump(_SCREAMING_SNAKE_CASE					,      _SCREAMING_SNAKE_CASE      )


def  _UpperCamelCase	(						)    ->  int:





    """simple docstring"""

    _UpperCAmelCase          =   argparse.ArgumentParser(description="""Simple example of training script tracking peak GPU memory usage."""      )
    parser.add_argument(
        """--model_name_or_path"""					,      type=_SCREAMING_SNAKE_CASE					,      default="""bert-base-cased"""					,      help="""Path to pretrained model or model identifier from huggingface.co/models."""					,      required=_SCREAMING_SNAKE_CASE					,      )
    parser.add_argument(
        """--output_dir"""					,      type=_SCREAMING_SNAKE_CASE					,      default="""."""					,      help="""Optional save directory where all checkpoint folders will be stored. Default is the current working directory."""					,      )
    parser.add_argument(
        """--resume_from_checkpoint"""					,      type=_SCREAMING_SNAKE_CASE					,      default=_SCREAMING_SNAKE_CASE					,      help="""If the training should continue from a checkpoint folder."""					,      )
    parser.add_argument(
        """--partial_train_epoch"""					,      type=_SCREAMING_SNAKE_CASE					,      default=_SCREAMING_SNAKE_CASE					,      help="""If passed, the training will stop after this number of epochs."""					,      )
    parser.add_argument(
        """--num_epochs"""					,      type=_SCREAMING_SNAKE_CASE					,      default=2					,      help="""Number of train epochs."""					,      )
    _UpperCAmelCase          =   parser.parse_args()
    _UpperCAmelCase          =   {"""lr""": 2e-5, """num_epochs""": args.num_epochs, """seed""": 4_2, """batch_size""": 1_6}

    training_function(_SCREAMING_SNAKE_CASE					,      _SCREAMING_SNAKE_CASE      )


if __name__ == "__main__":
 main()

"""simple docstring"""



import argparse
import os
import re

import packaging.version


a					:	str							=		'''examples/'''
a					:	List[str]							=		{
    '''examples''': (re.compile(r'''^check_min_version\("[^"]+"\)\s*$''', re.MULTILINE), '''check_min_version("VERSION")\n'''),
    '''init''': (re.compile(r'''^__version__\s+=\s+"([^"]+)"\s*$''', re.MULTILINE), '''__version__ = "VERSION"\n'''),
    '''setup''': (re.compile(r'''^(\s*)version\s*=\s*"[^"]+",''', re.MULTILINE), r'''\1version="VERSION",'''),
    '''doc''': (re.compile(r'''^(\s*)release\s*=\s*"[^"]+"$''', re.MULTILINE), '''release = "VERSION"\n'''),
}
a					:	Tuple							=		{
    '''init''': '''src/diffusers/__init__.py''',
    '''setup''': '''setup.py''',
}
a					:	List[str]							=		'''README.md'''


def  _UpperCamelCase	(						_A					,      _A					,      _A      )    ->  Dict:





    """simple docstring"""

    with open(_A					,      """r"""					,      encoding="""utf-8"""					,      newline="""\n"""      ) as f:
        _UpperCAmelCase          =   f.read()
    _UpperCAmelCase    ,_UpperCAmelCase          =   REPLACE_PATTERNS[pattern]
    _UpperCAmelCase          =   replace.replace("""VERSION"""					,      _A      )
    _UpperCAmelCase          =   re_pattern.sub(_A					,      _A      )
    with open(_A					,      """w"""					,      encoding="""utf-8"""					,      newline="""\n"""      ) as f:
        f.write(_A      )


def  _UpperCamelCase	(						_A      )    ->  Union[str, Any]:





    """simple docstring"""

    for folder, directories, fnames in os.walk(_A      ):
        # Removing some of the folders with non-actively maintained examples from the walk
        if "research_projects" in directories:
            directories.remove("""research_projects"""      )
        if "legacy" in directories:
            directories.remove("""legacy"""      )
        for fname in fnames:
            if fname.endswith(""".py"""      ):
                update_version_in_file(os.path.join(_A					,      _A      )					,      _A					,      pattern="""examples"""      )


def  _UpperCamelCase	(						_A					,      _A=False      )    ->  int:





    """simple docstring"""

    for pattern, fname in REPLACE_FILES.items():
        update_version_in_file(_A					,      _A					,      _A      )
    if not patch:
        update_version_in_examples(_A      )


def  _UpperCamelCase	(						)    ->  Any:





    """simple docstring"""

    _UpperCAmelCase          =   """🤗 Transformers currently provides the following architectures"""
    _UpperCAmelCase          =   """1. Want to contribute a new model?"""
    with open(_A					,      """r"""					,      encoding="""utf-8"""					,      newline="""\n"""      ) as f:
        _UpperCAmelCase          =   f.readlines()

    # Find the start of the list.
    _UpperCAmelCase          =   0
    while not lines[start_index].startswith(_start_prompt      ):
        start_index += 1
    start_index += 1

    _UpperCAmelCase          =   start_index
    # Update the lines in the model list.
    while not lines[index].startswith(_end_prompt      ):
        if lines[index].startswith("""1."""      ):
            _UpperCAmelCase          =   lines[index].replace(
                """https://huggingface.co/docs/diffusers/main/model_doc"""					,      """https://huggingface.co/docs/diffusers/model_doc"""					,      )
        index += 1

    with open(_A					,      """w"""					,      encoding="""utf-8"""					,      newline="""\n"""      ) as f:
        f.writelines(_A      )


def  _UpperCamelCase	(						)    ->  Optional[int]:





    """simple docstring"""

    with open(REPLACE_FILES["""init"""]					,      """r"""      ) as f:
        _UpperCAmelCase          =   f.read()
    _UpperCAmelCase          =   REPLACE_PATTERNS["""init"""][0].search(_A      ).groups()[0]
    return packaging.version.parse(_A      )


def  _UpperCamelCase	(						_A=False      )    ->  List[Any]:





    """simple docstring"""

    _UpperCAmelCase          =   get_version()
    if patch and default_version.is_devrelease:
        raise ValueError("""Can't create a patch version from the dev branch, checkout a released version!"""      )
    if default_version.is_devrelease:
        _UpperCAmelCase          =   default_version.base_version
    elif patch:
        _UpperCAmelCase          =   F"""{default_version.major}.{default_version.minor}.{default_version.micro + 1}"""
    else:
        _UpperCAmelCase          =   F"""{default_version.major}.{default_version.minor + 1}.0"""

    # Now let's ask nicely if that's the right one.
    _UpperCAmelCase          =   input(F"""Which version are you releasing? [{default_version}]"""      )
    if len(_A      ) == 0:
        _UpperCAmelCase          =   default_version

    print(F"""Updating version to {version}."""      )
    global_version_update(_A					,      patch=_A      )


def  _UpperCamelCase	(						)    ->  List[Any]:





    """simple docstring"""

    _UpperCAmelCase          =   get_version()
    _UpperCAmelCase          =   F"""{current_version.major}.{current_version.minor + 1}.0.dev0"""
    _UpperCAmelCase          =   current_version.base_version

    # Check with the user we got that right.
    _UpperCAmelCase          =   input(F"""Which version are we developing now? [{dev_version}]"""      )
    if len(_A      ) == 0:
        _UpperCAmelCase          =   dev_version

    print(F"""Updating version to {version}."""      )
    global_version_update(_A      )


#    print("Cleaning main README, don't forget to run `make fix-copies`.")
#    clean_main_ref_in_model_list()


if __name__ == "__main__":
 a					:	Dict							=		argparse.ArgumentParser()
 parser.add_argument('''--post_release''', action='''store_true''', help='''Whether this is pre or post release.''')
 parser.add_argument('''--patch''', action='''store_true''', help='''Whether or not this is a patch release.''')
 a					:	Tuple							=		parser.parse_args()
 if not args.post_release:
  pre_release_work(patch=args.patch)
 elif args.patch:
  print('''Nothing to do after a patch :-)''')
 else:
  post_release_work()