



"""simple docstring"""







import os
import re
import sys
import traceback
import warnings
from pathlib import Path
from typing import Dict, Optional, Union
from uuid import uuida

from huggingface_hub import HfFolder, ModelCard, ModelCardData, hf_hub_download, whoami
from huggingface_hub.file_download import REGEX_COMMIT_HASH
from huggingface_hub.utils import (
    EntryNotFoundError,
    RepositoryNotFoundError,
    RevisionNotFoundError,
    is_jinja_available,
)
from packaging import version
from requests import HTTPError

from .. import __version__
from .constants import (
    DEPRECATED_REVISION_ARGS,
    DIFFUSERS_CACHE,
    HUGGINGFACE_CO_RESOLVE_ENDPOINT,
    SAFETENSORS_WEIGHTS_NAME,
    WEIGHTS_NAME,
)
from .import_utils import (
    ENV_VARS_TRUE_VALUES,
    _flax_version,
    _jax_version,
    _onnxruntime_version,
    _torch_version,
    is_flax_available,
    is_onnx_available,
    is_torch_available,
)
from .logging import get_logger


lowercase__		:    Any				   =     get_logger(__name__)


lowercase__		:    Optional[Any]				   =     Path(__file__).parent / """model_card_template.md"""
lowercase__		:    List[Any]				   =     uuida().hex
lowercase__		:    Dict				   =     os.getenv("""HF_HUB_OFFLINE""", """""").upper() in ENV_VARS_TRUE_VALUES
lowercase__		:    int				   =     os.getenv("""DISABLE_TELEMETRY""", """""").upper() in ENV_VARS_TRUE_VALUES
lowercase__		:    List[Any]				   =     HUGGINGFACE_CO_RESOLVE_ENDPOINT + """/api/telemetry/"""

def     UpperCamelCase_   (   lowerCAmelCase__				:    Union[Dict, str, None] = None						)						->       str:

		"""simple docstring"""




		lowerCAmelCase_       :    str								=			f"diffusers/{__version__}; python/{sys.version.split()[0]}; session_id/{SESSION_ID}"
		if DISABLE_TELEMETRY or HF_HUB_OFFLINE:
				return ua + "; telemetry/off"
		if is_torch_available():
				ua += f"; torch/{_torch_version}"
		if is_flax_available():
				ua += f"; jax/{_jax_version}"
				ua += f"; flax/{_flax_version}"
		if is_onnx_available():
				ua += f"; onnxruntime/{_onnxruntime_version}"
		# CI will set this value to True
		if os.environ.get('DIFFUSERS_IS_CI'   ,       ''						).upper() in ENV_VARS_TRUE_VALUES:
				ua += "; is_ci/true"
		if isinstance(lowerCAmelCase__   ,       lowerCAmelCase__						):
				ua += "; " + "; ".join(f"{k}/{v}" for k, v in user_agent.items()						)
		elif isinstance(lowerCAmelCase__   ,       lowerCAmelCase__						):
				ua += "; " + user_agent
		return ua

def     UpperCamelCase_   (   lowerCAmelCase__				:    str   ,       lowerCAmelCase__				:    Optional[str] = None   ,       lowerCAmelCase__				:    Optional[str] = None						)						->       Any:

		"""simple docstring"""




		if token is None:
				lowerCAmelCase_       :    Union[str, Any]								=			HfFolder.get_token()
		if organization is None:
				lowerCAmelCase_       :    List[str]								=			whoami(lowerCAmelCase__						)['name']
				return f"{username}/{model_id}"
		else:
				return f"{organization}/{model_id}"

def     UpperCamelCase_   (   lowerCAmelCase__				:    Optional[Any]   ,       lowerCAmelCase__				:    Optional[Any]						)						->       Optional[Any]:

		"""simple docstring"""




		if not is_jinja_available():
				raise ValueError(
				    'Modelcard rendering is based on Jinja templates.'
				    ' Please make sure to have `jinja` installed before using `create_model_card`.'
				    ' To install it, please run `pip install Jinja2`.'						)

		if hasattr(lowerCAmelCase__   ,       'local_rank'						) and args.local_rank not in [-1, 0]:
				return

		lowerCAmelCase_       :    Optional[int]								=			args.hub_token if hasattr(lowerCAmelCase__   ,       'hub_token'						) else None
		lowerCAmelCase_       :    Optional[Any]								=			get_full_repo_name(lowerCAmelCase__   ,       token=lowerCAmelCase__						)

		lowerCAmelCase_       :    Any								=			ModelCard.from_template(
		    card_data=ModelCardData(  # Card metadata object that will be converted to YAML block
		        language='en'   ,       license='apache-2.0'   ,       library_name='diffusers'   ,       tags=[]   ,       datasets=args.dataset_name   ,       metrics=[]   ,       )   ,       template_path=lowerCAmelCase__   ,       model_name=lowerCAmelCase__   ,       repo_name=lowerCAmelCase__   ,       dataset_name=args.dataset_name if hasattr(lowerCAmelCase__   ,       'dataset_name'						) else None   ,       learning_rate=args.learning_rate   ,       train_batch_size=args.train_batch_size   ,       eval_batch_size=args.eval_batch_size   ,       gradient_accumulation_steps=(
		        args.gradient_accumulation_steps if hasattr(lowerCAmelCase__   ,       'gradient_accumulation_steps'						) else None
		    )   ,       adam_betaa=args.adam_betaa if hasattr(lowerCAmelCase__   ,       'adam_beta1'						) else None   ,       adam_betaa=args.adam_betaa if hasattr(lowerCAmelCase__   ,       'adam_beta2'						) else None   ,       adam_weight_decay=args.adam_weight_decay if hasattr(lowerCAmelCase__   ,       'adam_weight_decay'						) else None   ,       adam_epsilon=args.adam_epsilon if hasattr(lowerCAmelCase__   ,       'adam_epsilon'						) else None   ,       lr_scheduler=args.lr_scheduler if hasattr(lowerCAmelCase__   ,       'lr_scheduler'						) else None   ,       lr_warmup_steps=args.lr_warmup_steps if hasattr(lowerCAmelCase__   ,       'lr_warmup_steps'						) else None   ,       ema_inv_gamma=args.ema_inv_gamma if hasattr(lowerCAmelCase__   ,       'ema_inv_gamma'						) else None   ,       ema_power=args.ema_power if hasattr(lowerCAmelCase__   ,       'ema_power'						) else None   ,       ema_max_decay=args.ema_max_decay if hasattr(lowerCAmelCase__   ,       'ema_max_decay'						) else None   ,       mixed_precision=args.mixed_precision   ,       )

		lowerCAmelCase_       :    Optional[Any]								=			os.path.join(args.output_dir   ,       'README.md'						)
		model_card.save(lowerCAmelCase__						)

def     UpperCamelCase_   (   lowerCAmelCase__				:    Optional[str]   ,       lowerCAmelCase__				:    Optional[str] = None						)						->       int:

		"""simple docstring"""




		if resolved_file is None or commit_hash is not None:
				return commit_hash
		lowerCAmelCase_       :    Any								=			str(Path(lowerCAmelCase__						).as_posix()						)
		lowerCAmelCase_       :    Union[str, Any]								=			re.search(R'snapshots/([^/]+)/'   ,       lowerCAmelCase__						)
		if search is None:
				return None
		lowerCAmelCase_       :    Tuple								=			search.groups()[0]
		return commit_hash if REGEX_COMMIT_HASH.match(lowerCAmelCase__						) else None


# Old default cache path, potentially to be migrated.
# This logic was more or less taken from `transformers`, with the following differences:
# - Diffusers doesn't use custom environment variables to specify the cache path.
# - There is no need to migrate the cache format, just move the files to the new location.
lowercase__		:    Optional[int]				   =     os.path.expanduser(
    os.getenv("""HF_HOME""", os.path.join(os.getenv("""XDG_CACHE_HOME""", """~/.cache"""), """huggingface"""))
)
lowercase__		:    Tuple				   =     os.path.join(hf_cache_home, """diffusers""")

def     UpperCamelCase_   (   lowerCAmelCase__				:    Optional[str] = None   ,       lowerCAmelCase__				:    Optional[str] = None						)						->       None:

		"""simple docstring"""




		if new_cache_dir is None:
				lowerCAmelCase_       :    Union[str, Any]								=			DIFFUSERS_CACHE
		if old_cache_dir is None:
				lowerCAmelCase_       :    List[str]								=			old_diffusers_cache

		lowerCAmelCase_       :    List[str]								=			Path(lowerCAmelCase__						).expanduser()
		lowerCAmelCase_       :    Tuple								=			Path(lowerCAmelCase__						).expanduser()
		for old_blob_path in old_cache_dir.glob('**/blobs/*'						):
				if old_blob_path.is_file() and not old_blob_path.is_symlink():
						lowerCAmelCase_       :    Union[str, Any]								=			new_cache_dir / old_blob_path.relative_to(lowerCAmelCase__						)
						new_blob_path.parent.mkdir(parents=lowerCAmelCase__   ,       exist_ok=lowerCAmelCase__						)
						os.replace(lowerCAmelCase__   ,       lowerCAmelCase__						)
						try:
								os.symlink(lowerCAmelCase__   ,       lowerCAmelCase__						)
						except OSError:
								logger.warning(
								    'Could not create symlink between old cache and new cache. If you use an older version of diffusers again, files will be re-downloaded.'						)
		# At this point, old_cache_dir contains symlinks to the new cache (it can still be used).


lowercase__		:    Tuple				   =     os.path.join(DIFFUSERS_CACHE, """version_diffusers_cache.txt""")
if not os.path.isfile(cache_version_file):
					lowercase__		:    str				   =     0
else:
					with open(cache_version_file) as f:
										try:
															lowercase__		:    Dict				   =     int(f.read())
										except ValueError:
															lowercase__		:    Optional[Any]				   =     0

if cache_version < 1:
					lowercase__		:    Optional[int]				   =     os.path.isdir(old_diffusers_cache) and len(os.listdir(old_diffusers_cache)) > 0
					if old_cache_is_not_empty:
										logger.warning(
										    """The cache for model files in Diffusers v0.14.0 has moved to a new location. Moving your """
										    """existing cached models. This is a one-time operation, you can interrupt it or run it """
										    """later by calling `diffusers.utils.hub_utils.move_cache()`."""
										)
										try:
															move_cache()
										except Exception as e:
															lowercase__		:    List[Any]				   =     """\n""".join(traceback.format_tb(e.__traceback__))
															logger.error(
															    f'There was a problem when trying to move your cache:\n\n{trace}\n{e.__class__.__name__}: {e}\n\nPlease '
															    """file an issue at https://github.com/huggingface/diffusers/issues/new/choose, copy paste this whole """
															    """message and we will do our best to help."""
															)

if cache_version < 1:
					try:
										os.makedirs(DIFFUSERS_CACHE, exist_ok=True)
										with open(cache_version_file, """w""") as f:
															f.write("""1""")
					except Exception:
										logger.warning(
										    f'There was a problem when trying to write in your cache folder ({DIFFUSERS_CACHE}). Please, ensure '
										    """the directory exists and can be written to."""
										)

def     UpperCamelCase_   (   lowerCAmelCase__				:    str   ,       lowerCAmelCase__				:    Optional[str] = None						)						->       str:

		"""simple docstring"""




		if variant is not None:
				lowerCAmelCase_       :    Dict								=			weights_name.split('.'						)
				lowerCAmelCase_       :    Union[str, Any]								=			splits[:-1] + [variant] + splits[-1:]
				lowerCAmelCase_       :    Union[str, Any]								=			'.'.join(lowerCAmelCase__						)

		return weights_name

def     UpperCamelCase_   (   lowerCAmelCase__				:    Union[str, Any]   ,       *,
    lowerCAmelCase__				:    Any   ,       lowerCAmelCase__				:    Any   ,       lowerCAmelCase__				:    Dict   ,       lowerCAmelCase__				:    Union[str, Any]   ,       lowerCAmelCase__				:    List[Any]   ,       lowerCAmelCase__				:    List[Any]   ,       lowerCAmelCase__				:    Optional[Any]   ,       lowerCAmelCase__				:    Dict   ,       lowerCAmelCase__				:    Tuple   ,       lowerCAmelCase__				:    Optional[int]   ,       lowerCAmelCase__				:    Optional[Any]=None   ,       )						->       Optional[Any]:

		"""simple docstring"""




		lowerCAmelCase_       :    Any								=			str(lowerCAmelCase__						)
		if os.path.isfile(lowerCAmelCase__						):
				return pretrained_model_name_or_path
		elif os.path.isdir(lowerCAmelCase__						):
				if os.path.isfile(os.path.join(lowerCAmelCase__   ,       lowerCAmelCase__						)						):
						# Load from a PyTorch checkpoint
						lowerCAmelCase_       :    Optional[int]								=			os.path.join(lowerCAmelCase__   ,       lowerCAmelCase__						)
						return model_file
				elif subfolder is not None and os.path.isfile(
				    os.path.join(lowerCAmelCase__   ,       lowerCAmelCase__   ,       lowerCAmelCase__						)						):
						lowerCAmelCase_       :    List[Any]								=			os.path.join(lowerCAmelCase__   ,       lowerCAmelCase__   ,       lowerCAmelCase__						)
						return model_file
				else:
						raise EnvironmentError(
						    f"Error no file named {weights_name} found in directory {pretrained_model_name_or_path}."						)
		else:
				# 1. First check if deprecated way of loading from branches is used
				if (
				    revision in DEPRECATED_REVISION_ARGS
				    and (weights_name == WEIGHTS_NAME or weights_name == SAFETENSORS_WEIGHTS_NAME)
				    and version.parse(version.parse(lowerCAmelCase__						).base_version						) >= version.parse('0.20.0'						)
				):
						try:
								lowerCAmelCase_       :    Any								=			hf_hub_download(
								    lowerCAmelCase__   ,       filename=_add_variant(lowerCAmelCase__   ,       lowerCAmelCase__						)   ,       cache_dir=lowerCAmelCase__   ,       force_download=lowerCAmelCase__   ,       proxies=lowerCAmelCase__   ,       resume_download=lowerCAmelCase__   ,       local_files_only=lowerCAmelCase__   ,       use_auth_token=lowerCAmelCase__   ,       user_agent=lowerCAmelCase__   ,       subfolder=lowerCAmelCase__   ,       revision=revision or commit_hash   ,       )
								warnings.warn(
								    f"Loading the variant {revision} from {pretrained_model_name_or_path} via `revision='{revision}'` is deprecated. Loading instead from `revision='main'` with `variant={revision}`. Loading model variants via `revision='{revision}'` will be removed in diffusers v1. Please use `variant='{revision}'` instead."   ,       lowerCAmelCase__   ,       )
								return model_file
						except:  # noqa: E722
								warnings.warn(
								    f"You are loading the variant {revision} from {pretrained_model_name_or_path} via `revision='{revision}'`. This behavior is deprecated and will be removed in diffusers v1. One should use `variant='{revision}'` instead. However, it appears that {pretrained_model_name_or_path} currently does not have a {_add_variant(lowerCAmelCase__   ,       lowerCAmelCase__						)} file in the 'main' branch of {pretrained_model_name_or_path}. \n The Diffusers team and community would be very grateful if you could open an issue: https://github.com/huggingface/diffusers/issues/new with the title '{pretrained_model_name_or_path} is missing {_add_variant(lowerCAmelCase__   ,       lowerCAmelCase__						)}' so that the correct variant file can be added."   ,       lowerCAmelCase__   ,       )
				try:
						# 2. Load model file as usual
						lowerCAmelCase_       :    int								=			hf_hub_download(
						    lowerCAmelCase__   ,       filename=lowerCAmelCase__   ,       cache_dir=lowerCAmelCase__   ,       force_download=lowerCAmelCase__   ,       proxies=lowerCAmelCase__   ,       resume_download=lowerCAmelCase__   ,       local_files_only=lowerCAmelCase__   ,       use_auth_token=lowerCAmelCase__   ,       user_agent=lowerCAmelCase__   ,       subfolder=lowerCAmelCase__   ,       revision=revision or commit_hash   ,       )
						return model_file

				except RepositoryNotFoundError:
						raise EnvironmentError(
						    f"{pretrained_model_name_or_path} is not a local folder and is not a valid model identifier "
						    'listed on \'https://huggingface.co/models\'\nIf this is a private repository, make sure to pass a '
						    'token having permission to this repo with `use_auth_token` or log in with `huggingface-cli '
						    'login`.'						)
				except RevisionNotFoundError:
						raise EnvironmentError(
						    f"{revision} is not a valid git identifier (branch name, tag name or commit id) that exists for "
						    'this model name. Check the model page at '
						    f"'https://huggingface.co/{pretrained_model_name_or_path}' for available revisions."						)
				except EntryNotFoundError:
						raise EnvironmentError(
						    f"{pretrained_model_name_or_path} does not appear to have a file named {weights_name}."						)
				except HTTPError as err:
						raise EnvironmentError(
						    f"There was a specific connection error when trying to load {pretrained_model_name_or_path}:\n{err}"						)
				except ValueError:
						raise EnvironmentError(
						    f"We couldn't connect to '{HUGGINGFACE_CO_RESOLVE_ENDPOINT}' to load this model, couldn't find it"
						    f" in the cached files and it looks like {pretrained_model_name_or_path} is not the path to a"
						    f" directory containing a file named {weights_name} or"
						    ' \nCheckout your internet connection or see how to run the library in'
						    ' offline mode at \'https://huggingface.co/docs/diffusers/installation#offline-mode\'.'						)
				except EnvironmentError:
						raise EnvironmentError(
						    f"Can't load the model for '{pretrained_model_name_or_path}'. If you were trying to load it from "
						    '\'https://huggingface.co/models\', make sure you don\'t have a local directory with the same name. '
						    f"Otherwise, make sure '{pretrained_model_name_or_path}' is the correct path to a directory "
						    f"containing a file named {weights_name}"						)










"""simple docstring"""







import argparse
import os

# New Code #
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 import find_executable_batch_size


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to ensure out-of-memory errors never
# interrupt training, 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)
#
# 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
#
########################################################################


lowercase__		:    Optional[int]				   =     1_6
lowercase__		:    List[str]				   =     3_2

def     UpperCamelCase_   (   lowerCAmelCase__				:    Accelerator   ,       lowerCAmelCase__				:    int = 16						)						->       Dict:

  """simple docstring"""




  lowerCAmelCase_       :    Union[str, Any]								=			AutoTokenizer.from_pretrained('bert-base-cased'						)
  lowerCAmelCase_       :    Union[str, Any]								=			load_dataset('glue'   ,       'mrpc'						)

  def tokenize_function(lowerCAmelCase__				:    Tuple						):
    # max_length=None => use the model max length (it's actually the default)
    lowerCAmelCase_       :    Dict								=			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_       :    Dict								=			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_       :    int								=			tokenized_datasets.rename_column('label'   ,       'labels'						)

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

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

  # Instantiate dataloaders.
  lowerCAmelCase_       :    List[Any]								=			DataLoader(
      tokenized_datasets['train']   ,       shuffle=lowerCAmelCase__   ,       collate_fn=lowerCAmelCase__   ,       batch_size=lowerCAmelCase__						)
  lowerCAmelCase_       :    Tuple								=			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

     lowercase__		:    int				   =     mocked_dataloaders  # noqa: F811

def     UpperCamelCase_   (   lowerCAmelCase__				:    Optional[int]   ,       lowerCAmelCase__				:    Optional[Any]						)						->       Optional[int]:

  """simple docstring"""




  if os.environ.get('TESTING_MOCKED_DATALOADERS'   ,       lowerCAmelCase__						) == "1":
    lowerCAmelCase_       :    Optional[int]								=			2
  # Initialize accelerator
  lowerCAmelCase_       :    str								=			Accelerator(cpu=args.cpu   ,       mixed_precision=args.mixed_precision						)
  # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
  lowerCAmelCase_       :    Optional[int]								=			config['lr']
  lowerCAmelCase_       :    Tuple								=			int(config['num_epochs']						)
  lowerCAmelCase_       :    int								=			int(config['seed']						)
  lowerCAmelCase_       :    str								=			int(config['batch_size']						)

  lowerCAmelCase_       :    str								=			evaluate.load('glue'   ,       'mrpc'						)

  # New Code #
  # We now can define an inner training loop function. It should take a batch size as the only parameter,
  # and build the dataloaders in there.
  # It also gets our decorator
  @find_executable_batch_size(starting_batch_size=lowerCAmelCase__						)
  def inner_training_loop(lowerCAmelCase__				:    Optional[int]						):
    # And now just move everything below under this function
    # We need to bring in the Accelerator object from earlier
    nonlocal accelerator
    # And reset all of its attributes that could hold onto any memory:
    accelerator.free_memory()

    # Then we can declare the model, optimizer, and everything else:
    set_seed(lowerCAmelCase__						)

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

    # Instantiate optimizer
    lowerCAmelCase_       :    List[Any]								=			AdamW(params=model.parameters()   ,       lr=lowerCAmelCase__						)
    lowerCAmelCase_						,lowerCAmelCase_       :    Any								=			get_dataloaders(lowerCAmelCase__   ,       lowerCAmelCase__						)

    # Instantiate scheduler
    lowerCAmelCase_       :    Dict								=			get_linear_schedule_with_warmup(
        optimizer=lowerCAmelCase__   ,       num_warmup_steps=100   ,       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_						,lowerCAmelCase_						,lowerCAmelCase_						,lowerCAmelCase_						,lowerCAmelCase_       :    List[Any]								=			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						)
        lowerCAmelCase_       :    List[str]								=			model(**lowerCAmelCase__						)
        lowerCAmelCase_       :    Optional[Any]								=			outputs.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_       :    Union[str, Any]								=			model(**lowerCAmelCase__						)
        lowerCAmelCase_       :    List[Any]								=			outputs.logits.argmax(dim=-1						)
        lowerCAmelCase_						,lowerCAmelCase_       :    Optional[Any]								=			accelerator.gather_for_metrics((predictions, batch['labels'])						)
        metric.add_batch(
            predictions=lowerCAmelCase__   ,       references=lowerCAmelCase__   ,       )

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

    # New Code #
    # And call it at the end with no arguments
    # Note: You could also refactor this outside of your training loop function
  inner_training_loop()

def     UpperCamelCase_   (   )						->       Optional[Any]:

  """simple docstring"""




  lowerCAmelCase_       :    int								=			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.'   ,       )
  parser.add_argument('--cpu'   ,       action='store_true'   ,       help='If passed, will train on the CPU.'						)
  lowerCAmelCase_       :    str								=			parser.parse_args()
  lowerCAmelCase_       :    Union[str, Any]								=			{'lr': 2e-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
  training_function(lowerCAmelCase__   ,       lowerCAmelCase__						)


if __name__ == "__main__":
     main()





