
import warnings
from dataclasses import dataclass, field
from typing import Optional, Tuple

from .training_args import TrainingArguments
from .utils import cached_property, is_tf_available, logging, requires_backends


__SCREAMING_SNAKE_CASE     :				Optional[Any]             =       logging.get_logger(__name__)

if is_tf_available():
		import tensorflow as tf


@dataclass
class 						__magic_name__				(  snake_case      ):
		_lowerCAmelCase						=		"tf"
		_lowerCAmelCase						=		field(
		    default=snake_case,			metadata={"help": "Name of TPU"},			)

		_lowerCAmelCase						=		field(
		    default=snake_case,			metadata={"help": "Zone of TPU"},			)

		_lowerCAmelCase						=		field(
		    default=snake_case,			metadata={"help": "Name of Cloud TPU-enabled project"},			)

		_lowerCAmelCase						=		field(
		    default=1.0,			metadata={"help": "Power for the Polynomial decay LR scheduler."},			)

		_lowerCAmelCase						=		field(default=snake_case,			metadata={"help": "Whether to activate the XLA compilation or not"}      )





		@cached_property
		def  _A							(							self      :		List[str]			):
							requires_backends(self  ,					['''tf''']			)
							logger.info('''Tensorflow: setting up strategy'''			)

							lowerCAmelCase						:   List[Any]							=       tf.config.list_physical_devices('''GPU'''			)

							# Set to float16 at first
							if self.fpaa:
												tf.keras.mixed_precision.set_global_policy('''mixed_float16'''			)

							if self.no_cuda:
												lowerCAmelCase						:   Dict							=       tf.distribute.OneDeviceStrategy(device='''/cpu:0'''			)
							else:
												try:
																	if self.tpu_name:
																						lowerCAmelCase						:   Dict							=       tf.distribute.cluster_resolver.TPUClusterResolver(
																						    self.tpu_name  ,					zone=self.tpu_zone  ,					project=self.gcp_project			)
																	else:
																						lowerCAmelCase						:   Tuple							=       tf.distribute.cluster_resolver.TPUClusterResolver()
												except ValueError:
																	if self.tpu_name:
																						raise RuntimeError(f'''Couldn\'t connect to TPU {self.tpu_name}!'''			)
																	else:
																						lowerCAmelCase						:   Tuple							=       None

												if tpu:
																	# Set to bfloat16 in case of TPU
																	if self.fpaa:
																						tf.keras.mixed_precision.set_global_policy('''mixed_bfloat16'''			)

																	tf.config.experimental_connect_to_cluster(lowerCamelCase__			)
																	tf.tpu.experimental.initialize_tpu_system(lowerCamelCase__			)

																	lowerCAmelCase						:   List[str]							=       tf.distribute.TPUStrategy(lowerCamelCase__			)

												elif len(lowerCamelCase__			) == 0:
																	lowerCAmelCase						:   Union[str, Any]							=       tf.distribute.OneDeviceStrategy(device='''/cpu:0'''			)
												elif len(lowerCamelCase__			) == 1:
																	lowerCAmelCase						:   Union[str, Any]							=       tf.distribute.OneDeviceStrategy(device='''/gpu:0'''			)
												elif len(lowerCamelCase__			) > 1:
																	# If you only want to use a specific subset of GPUs use `CUDA_VISIBLE_DEVICES=0`
																	lowerCAmelCase						:   List[Any]							=       tf.distribute.MirroredStrategy()
												else:
																	raise ValueError('''Cannot find the proper strategy, please check your environment properties.'''			)

							return strategy





		@property
		def  _A							(							self      :		List[str]			):
							requires_backends(self  ,					['''tf''']			)
							return self._setup_strategy





		@property
		def  _A							(							self      :		str			):
							requires_backends(self  ,					['''tf''']			)
							return self._setup_strategy.num_replicas_in_sync





		@property
		def  _A							(							self      :		Dict			):
							return False  # TF Logging is handled by Keras not the Trainer





		@property
		def  _A							(							self      :		int			):
							if self.per_gpu_train_batch_size:
												logger.warning(
												    '''Using deprecated `--per_gpu_train_batch_size` argument which will be removed in a future '''
												    '''version. Using `--per_device_train_batch_size` is preferred.'''			)
							lowerCAmelCase						:   Any							=       self.per_gpu_train_batch_size or self.per_device_train_batch_size
							return per_device_batch_size * self.n_replicas





		@property
		def  _A							(							self      :		Tuple			):
							if self.per_gpu_eval_batch_size:
												logger.warning(
												    '''Using deprecated `--per_gpu_eval_batch_size` argument which will be removed in a future '''
												    '''version. Using `--per_device_eval_batch_size` is preferred.'''			)
							lowerCAmelCase						:   Any							=       self.per_gpu_eval_batch_size or self.per_device_eval_batch_size
							return per_device_batch_size * self.n_replicas





		@property
		def  _A							(							self      :		Optional[Any]			):
							requires_backends(self  ,					['''tf''']			)
							warnings.warn(
							    '''The n_gpu argument is deprecated and will be removed in a future version, use n_replicas instead.'''  ,					lowerCamelCase__  ,					)
							return self._setup_strategy.num_replicas_in_sync


from arguments import TokenizerTrainingArguments
from datasets import load_dataset
from tqdm import tqdm

from transformers import AutoTokenizer, HfArgumentParser
from transformers.models.gpta.tokenization_gpta import bytes_to_unicode
def       UpperCAmelCase__							(    __magic_name__  :       str=10					):


					'''simple docstring'''

					for _ in tqdm(range(0							,					args.n_examples							,					__magic_name__					)					):
										yield [next(__magic_name__					)[args.text_column] for _ in range(__magic_name__					)]


# Configuration
__SCREAMING_SNAKE_CASE     :				Dict             =       HfArgumentParser(TokenizerTrainingArguments)
__SCREAMING_SNAKE_CASE     :				str             =       parser.parse_args()

# Base tokenizer
__SCREAMING_SNAKE_CASE     :				Tuple             =       AutoTokenizer.from_pretrained(args.base_tokenizer)
__SCREAMING_SNAKE_CASE     :				List[str]             =       list(bytes_to_unicode().values())

# Load dataset
__SCREAMING_SNAKE_CASE     :				Union[str, Any]             =       load_dataset(args.dataset_name, split='train', streaming=True)
__SCREAMING_SNAKE_CASE     :				Any             =       iter(dataset)


# Training and saving
__SCREAMING_SNAKE_CASE     :				Optional[int]             =       tokenizer.train_new_from_iterator(
    batch_iterator(), vocab_size=args.vocab_size, initial_alphabet=base_vocab
)
new_tokenizer.save_pretrained(args.tokenizer_name, push_to_hub=args.push_to_hub)
