
import os

from apache_beam.io.filesystems import FileSystems
from apache_beam.pipeline import Pipeline

from .logging import get_logger


__SCREAMING_SNAKE_CASE     :				Union[str, Any]             =       2 << 20  # 2mb
__SCREAMING_SNAKE_CASE     :				List[Any]             =       get_logger(__name__)


class 						__magic_name__				(  snake_case      ):





		def  _A							(							self      :		List[Any]			):
							lowerCAmelCase						:   Any							=       self._options.get_all_options().get('''runner'''			)
							return runner in [None, "DirectRunner", "PortableRunner"]



def       UpperCAmelCase__							(    __magic_name__  :       Any							,					__magic_name__  :       List[Any]							,					__magic_name__  :       Tuple=False					):


					'''simple docstring'''

					lowerCAmelCase						:   List[Any]							=       FileSystems
					if fs.exists(__magic_name__					):
										if force_upload:
															logger.info(f'''Remote path already exist: {remote_file_path}. Overwriting it as force_upload=True.'''					)
										else:
															logger.info(f'''Remote path already exist: {remote_file_path}. Skipping it as force_upload=False.'''					)
															return
					with fs.create(__magic_name__					) as remote_file:
										with open(__magic_name__							,					'''rb'''					) as local_file:
															lowerCAmelCase						:   str							=       local_file.read(__magic_name__					)
															while chunk:
																				remote_file.write(__magic_name__					)
																				lowerCAmelCase						:   List[Any]							=       local_file.read(__magic_name__					)
def       UpperCAmelCase__							(    __magic_name__  :       Dict							,					__magic_name__  :       List[Any]							,					__magic_name__  :       Union[str, Any]=False					):


					'''simple docstring'''

					lowerCAmelCase						:   List[str]							=       FileSystems
					if os.path.exists(__magic_name__					):
										if force_download:
															logger.info(f'''Local path already exist: {remote_file_path}. Overwriting it as force_upload=True.'''					)
										else:
															logger.info(f'''Local path already exist: {remote_file_path}. Skipping it as force_upload=False.'''					)
															return
					with fs.open(__magic_name__					) as remote_file:
										with open(__magic_name__							,					'''wb'''					) as local_file:
															lowerCAmelCase						:   Any							=       remote_file.read(__magic_name__					)
															while chunk:
																				local_file.write(__magic_name__					)
																				lowerCAmelCase						:   List[str]							=       remote_file.read(__magic_name__					)


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)
