



import argparse
import logging
import os
import sys
import time

import tensorflow as tf
from datasets import load_dataset
from tqdm import tqdm

from transformers import AutoTokenizer, TFAutoModelForSequenceClassification
from transformers.utils import is_sagemaker_dp_enabled


if os.environ.get('''SDP_ENABLED''') or is_sagemaker_dp_enabled():
     A_		:  Dict										=  True
     A_		:  Union[str, Any]										=  '''p3dn.24xlarge'''
     import smdistributed.dataparallel.tensorflow as sdp
else:
     A_		:  Optional[int]										=  False


def 						UpperCAmelCase__   (   UpperCAmelCase__			:int			,       UpperCAmelCase__			:List[Any]			,       UpperCAmelCase__			:Optional[int]			,       UpperCAmelCase__			:Union[str, Any]			,       UpperCAmelCase__			:Any			,       UpperCAmelCase__			:List[Any]			,       UpperCAmelCase__			:Optional[int]=None       ):






 '''simple docstring'''

 a							=		tqdm(UpperCAmelCase__       )
 for i, batch in enumerate(UpperCAmelCase__       ):
  with tf.GradientTape() as tape:
   a		,	a							=		batch
   a							=		model(UpperCAmelCase__       )
   a							=		loss(UpperCAmelCase__			,       outputs.logits       )

  if SDP_ENABLED:
   a							=		sdp.DistributedGradientTape(UpperCAmelCase__			,       sparse_as_dense=UpperCAmelCase__       )

  a							=		tape.gradient(UpperCAmelCase__			,       model.trainable_variables       )
  opt.apply_gradients(zip(UpperCAmelCase__			,       model.trainable_variables       )       )

  pbar.set_description(F"""Loss: {loss_value:.4f}"""       )

  if SDP_ENABLED and i == 0:
   sdp.broadcast_variables(model.variables			,       root_rank=0       )
   sdp.broadcast_variables(opt.variables()			,       root_rank=0       )

  if max_steps and i >= max_steps:
   break

 a							=		{"loss": loss_value.numpy()}
 return train_results


def 						UpperCAmelCase__   (   UpperCAmelCase__			:Optional[int]			,       UpperCAmelCase__			:int			,       UpperCAmelCase__			:List[str]       ):






 '''simple docstring'''

 a		,	a							=		load_dataset("imdb"			,       split=["train", "test"]       )

 # Preprocess train dataset
 a							=		train_dataset.map(
     lambda UpperCAmelCase__       : tokenizer(e["text"]			,       truncation=UpperCAmelCase__			,       padding="max_length"       )			,       batched=UpperCAmelCase__       )
 train_dataset.set_format(type="tensorflow"			,       columns=["input_ids", "attention_mask", "label"]       )

 a							=		{
     x: train_dataset[x].to_tensor(default_value=0			,       shape=[None, tokenizer.model_max_length]       )
     for x in ["input_ids", "attention_mask"]
 }
 a							=		tf.data.Dataset.from_tensor_slices((train_features, train_dataset["label"])       )

 # Preprocess test dataset
 a							=		test_dataset.map(
     lambda UpperCAmelCase__       : tokenizer(e["text"]			,       truncation=UpperCAmelCase__			,       padding="max_length"       )			,       batched=UpperCAmelCase__       )
 test_dataset.set_format(type="tensorflow"			,       columns=["input_ids", "attention_mask", "label"]       )

 a							=		{
     x: test_dataset[x].to_tensor(default_value=0			,       shape=[None, tokenizer.model_max_length]       )
     for x in ["input_ids", "attention_mask"]
 }
 a							=		tf.data.Dataset.from_tensor_slices((test_features, test_dataset["label"])       )

 if SDP_ENABLED:
  a							=		tf_train_dataset.shard(sdp.size()			,       sdp.rank()       )
  a							=		tf_test_dataset.shard(sdp.size()			,       sdp.rank()       )
 a							=		tf_train_dataset.batch(UpperCAmelCase__			,       drop_remainder=UpperCAmelCase__       )
 a							=		tf_test_dataset.batch(UpperCAmelCase__			,       drop_remainder=UpperCAmelCase__       )

 return tf_train_dataset, tf_test_dataset


if __name__ == "__main__":
     A_		:  List[str]										=  argparse.ArgumentParser()

     # Hyperparameters sent by the client are passed as command-line arguments to the script.
     parser.add_argument('''--epochs''', type=int, default=3)
     parser.add_argument('''--per_device_train_batch_size''', type=int, default=16)
     parser.add_argument('''--per_device_eval_batch_size''', type=int, default=8)
     parser.add_argument('''--model_name_or_path''', type=str)
     parser.add_argument('''--learning_rate''', type=str, default=5E-5)
     parser.add_argument('''--do_train''', type=bool, default=True)
     parser.add_argument('''--do_eval''', type=bool, default=True)
     parser.add_argument('''--output_dir''', type=str)
     parser.add_argument('''--max_steps''', type=int, default=None)

     # Data, model, and output directories
     parser.add_argument('''--output_data_dir''', type=str, default=os.environ['''SM_OUTPUT_DATA_DIR'''])
     parser.add_argument('''--model_dir''', type=str, default=os.environ['''SM_MODEL_DIR'''])
     parser.add_argument('''--n_gpus''', type=str, default=os.environ['''SM_NUM_GPUS'''])

     A_			,	A_		:  Dict										=  parser.parse_known_args()

     # Set up logging
     A_		:  Any										=  logging.getLogger(__name__)

     logging.basicConfig(
         level=logging.getLevelName('''INFO'''),
         handlers=[logging.StreamHandler(sys.stdout)],
         format='''%(asctime)s - %(name)s - %(levelname)s - %(message)s''',
     )

     if SDP_ENABLED:
          sdp.init()

          A_		:  int										=  tf.config.experimental.list_physical_devices('''GPU''')
          for gpu in gpus:
               tf.config.experimental.set_memory_growth(gpu, True)
          if gpus:
               tf.config.experimental.set_visible_devices(gpus[sdp.local_rank()], '''GPU''')

    # Load model and tokenizer
     A_		:  Optional[int]										=  TFAutoModelForSequenceClassification.from_pretrained(args.model_name_or_path)
     A_		:  List[Any]										=  AutoTokenizer.from_pretrained(args.model_name_or_path)

     # get datasets
     A_			,	A_		:  Tuple										=  get_datasets(
         tokenizer=tokenizer,
         train_batch_size=args.per_device_train_batch_size,
         eval_batch_size=args.per_device_eval_batch_size,
     )

     # fine optimizer and loss
     A_		:  Any										=  tf.keras.optimizers.Adam(learning_rate=args.learning_rate)
     A_		:  Dict										=  tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
     A_		:  int										=  [tf.keras.metrics.SparseCategoricalAccuracy()]
     model.compile(optimizer=optimizer, loss=loss, metrics=metrics)

     # Training
     if args.do_train:
          # train_results = model.fit(tf_train_dataset, epochs=args.epochs, batch_size=args.train_batch_size)
          A_		:  int										=  time.time()
          A_		:  Dict										=  fit(
              model,
              loss,
              optimizer,
              tf_train_dataset,
              args.epochs,
              args.per_device_train_batch_size,
              max_steps=args.max_steps,
          )
          A_		:  Tuple										=  time.time() - start_train_time
          logger.info('''*** Train ***''')
          logger.info(F"""train_runtime = {end_train_time}""")

          A_		:  Dict										=  os.path.join(args.output_dir, '''train_results.txt''')

          if not SDP_ENABLED or sdp.rank() == 0:
               with open(output_eval_file, '''w''') as writer:
                    logger.info('''***** Train results *****''')
                    logger.info(train_results)
                    for key, value in train_results.items():
                         logger.info(F"""  {key} = {value}""")
                         writer.write(F"""{key} = {value}\n""")

    # Evaluation
     if args.do_eval and (not SDP_ENABLED or sdp.rank() == 0):
          A_		:  List[str]										=  model.evaluate(tf_test_dataset, batch_size=args.per_device_eval_batch_size, return_dict=True)
          logger.info('''*** Evaluate ***''')

          A_		:  List[str]										=  os.path.join(args.output_dir, '''eval_results.txt''')

          with open(output_eval_file, '''w''') as writer:
               logger.info('''***** Eval results *****''')
               logger.info(result)
               for key, value in result.items():
                    logger.info(F"""  {key} = {value}""")
                    writer.write(F"""{key} = {value}\n""")

    # Save result
     if SDP_ENABLED:
          if sdp.rank() == 0:
               model.save_pretrained(args.output_dir)
               tokenizer.save_pretrained(args.output_dir)
     else:
          model.save_pretrained(args.output_dir)
          tokenizer.save_pretrained(args.output_dir)


import importlib
import importlib.metadata
import os
import warnings
from distutils.util import strtobool
from functools import lru_cache

import torch
from packaging import version
from packaging.version import parse

from .environment import parse_flag_from_env
from .versions import compare_versions, is_torch_version


try:
    import torch_xla.core.xla_model as xm  # noqa: F401

    _lowerCAmelCase        =       True
except ImportError:
    _lowerCAmelCase        =       False


# Cache this result has it's a C FFI call which can be pretty time-consuming
_lowerCAmelCase        =       torch.distributed.is_available()


def       a__      (					a							)		->	Optional[Any]:
       # Check we're not importing a "pkg_name" directory somewhere but the actual library by trying to grab the version
       A_							:       str	      =				importlib.util.find_spec(a							) is not None
       if package_exists:
              try:
                     A_							:       str	      =				importlib.metadata.metadata(a							)
                     return True
              except importlib.metadata.PackageNotFoundError:
                     return False


def       a__      (					)		->	bool:
       return _torch_distributed_available


def       a__      (					)		->	int:
       try:
              pass
       except ImportError:
              print(
                  '''Intel(R) oneCCL Bindings for PyTorch* is required to run DDP on Intel(R) GPUs, but it is not'''
                  ''' detected. If you see \"ValueError: Invalid backend: \'ccl\'\" error, please install Intel(R) oneCCL'''
                  ''' Bindings for PyTorch*.'''							)
       return (
           importlib.util.find_spec('''torch_ccl'''							) is not None
           or importlib.util.find_spec('''oneccl_bindings_for_pytorch'''							) is not None
       )


def       a__      (					)		->	List[str]:
       return importlib.metadata.version('''oneccl_bind_pt'''							)


def       a__      (					)		->	List[str]:
       return _is_package_available('''transformer_engine'''							)


@lru_cache
def       a__      (					a=True							)		->	Union[str, Any]:
       # Due to bugs on the amp series GPUs, we disable torch-xla on them
       if torch.cuda.is_available():
              return False
       if _tpu_available and check_device:
              try:
                     # Will raise a RuntimeError if no XLA configuration is found
                     A_							:       Tuple	      =				xm.xla_device()
                     return True
              except RuntimeError:
                     return False
       return _tpu_available


def       a__      (					)		->	Dict:
       return _is_package_available('''deepspeed'''							)


def       a__      (					a=False							)		->	Optional[int]:
       if is_tpu_available():
              return not ignore_tpu
       if torch.cuda.is_available():
              return torch.cuda.is_bfaa_supported()
       if is_npu_available():
              return False
       return True


def       a__      (					)		->	Optional[Any]:
       A_							:       Optional[int]	      =				_is_package_available('''bitsandbytes'''							)
       if package_exists:
              A_							:       Any	      =				version.parse(importlib.metadata.version('''bitsandbytes'''							)							)
              return compare_versions(a  ,   '''>='''  ,   '''0.39.0'''							)
       return False


def       a__      (					)		->	Any:
       A_							:       List[str]	      =				_is_package_available('''bitsandbytes'''							)
       if package_exists:
              A_							:       Tuple	      =				version.parse(importlib.metadata.version('''bitsandbytes'''							)							)
              return compare_versions(a  ,   '''>='''  ,   '''0.37.2'''							)
       return False


def       a__      (					)		->	List[str]:
       return _is_package_available('''bitsandbytes'''							)


def       a__      (					)		->	List[str]:
       if strtobool(os.environ.get('''ACCELERATE_USE_MEGATRON_LM'''  ,   '''False'''							)							) == 1:
              A_							:       str	      =				importlib.util.find_spec('''megatron'''							) is not None
              if package_exists:
                     try:
                            A_							:       Dict	      =				parse(importlib.metadata.version('''megatron-lm'''							)							)
                            return compare_versions(a  ,   '''>='''  ,   '''2.2.0'''							)
                     except Exception as e:
                            warnings.warn(f"""Parse Megatron version failed. Exception:{e}"""							)
                            return False


def       a__      (					)		->	Tuple:
       return _is_package_available('''safetensors'''							)


def       a__      (					)		->	Tuple:
       return _is_package_available('''transformers'''							)


def       a__      (					)		->	int:
       return _is_package_available('''datasets'''							)


def       a__      (					)		->	Optional[int]:
       return _is_package_available('''aim'''							)


def       a__      (					)		->	Optional[Any]:
       return _is_package_available('''tensorboard'''							) or _is_package_available('''tensorboardX'''							)


def       a__      (					)		->	Optional[int]:
       return _is_package_available('''wandb'''							)


def       a__      (					)		->	Tuple:
       return _is_package_available('''comet_ml'''							)


def       a__      (					)		->	Tuple:
       return _is_package_available('''boto3'''							)


def       a__      (					)		->	Tuple:
       if _is_package_available('''rich'''							):
              if "ACCELERATE_DISABLE_RICH" in os.environ:
                     warnings.warn(
                         '''`ACCELERATE_DISABLE_RICH` is deprecated and will be removed in v0.22.0 and deactivated by default. Please use `ACCELERATE_ENABLE_RICH` if you wish to use `rich`.'''							)
                     return not parse_flag_from_env('''ACCELERATE_DISABLE_RICH'''  ,   a							)
              return parse_flag_from_env('''ACCELERATE_ENABLE_RICH'''  ,   a							)
       return False


def       a__      (					)		->	Any:
       return _is_package_available('''sagemaker'''							)


def       a__      (					)		->	Union[str, Any]:
       return _is_package_available('''tqdm'''							)


def       a__      (					)		->	List[str]:
       return _is_package_available('''mlflow'''							)


def       a__      (					)		->	Tuple:
       return is_torch_version('''>='''  ,   '''1.12'''							) and torch.backends.mps.is_available() and torch.backends.mps.is_built()


def       a__      (					)		->	Tuple:
       def get_major_and_minor_from_version(a							):
              return str(version.parse(a							).major							) + "." + str(version.parse(a							).minor							)

       A_							:       Tuple	      =				importlib.metadata.version('''torch'''							)
       if importlib.util.find_spec('''intel_extension_for_pytorch'''							) is None:
              return False
       A_							:       Optional[int]	      =				'''N/A'''
       try:
              A_							:       List[Any]	      =				importlib.metadata.version('''intel_extension_for_pytorch'''							)
       except importlib.metadata.PackageNotFoundError:
              return False
       A_							:       Tuple	      =				get_major_and_minor_from_version(_torch_version							)
       A_							:       int	      =				get_major_and_minor_from_version(_ipex_version							)
       if torch_major_and_minor != ipex_major_and_minor:
              warnings.warn(
                  f"""Intel Extension for PyTorch {ipex_major_and_minor} needs to work with PyTorch {ipex_major_and_minor}.*,"""
                  f""" but PyTorch {_torch_version} is found. Please switch to the matching version and run again."""							)
              return False
       return True


@lru_cache
def       a__      (					a=False							)		->	Optional[int]:
       if importlib.util.find_spec('''torch'''							) is None or importlib.util.find_spec('''torch_npu'''							) is None:
              return False

       import torch
       import torch_npu  # noqa: F401

       if check_device:
              try:
                     # Will raise a RuntimeError if no NPU is found
                     A_							:       Dict	      =				torch.npu.device_count()
                     return torch.npu.is_available()
              except RuntimeError:
                     return False
       return hasattr(a  ,   '''npu'''							) and torch.npu.is_available()


@lru_cache
def       a__      (					a=False							)		->	Optional[Any]:
       if not parse_flag_from_env('''ACCELERATE_USE_XPU'''  ,   default=a							):
              return False
       if is_ipex_available():
              import torch

              if is_torch_version('''<='''  ,   '''1.12'''							):
                     return False
       else:
              return False

       import intel_extension_for_pytorch  # noqa: F401

       if check_device:
              try:
                     # Will raise a RuntimeError if no XPU  is found
                     A_							:       Tuple	      =				torch.xpu.device_count()
                     return torch.xpu.is_available()
              except RuntimeError:
                     return False
       return hasattr(a  ,   '''xpu'''							) and torch.xpu.is_available()


