


'''simple docstring'''




import os
import sys
from contextlib import contextmanager


# Windows only
if os.name == "nt":
				import ctypes
				import msvcrt  # noqa



				class  lowerCAmelCase				(				ctypes.Structure							):
					# _fields is a specific attr expected by ctypes
					_lowerCamelCase   :					int			      = [("""size""", ctypes.c_int), ("""visible""", ctypes.c_byte)]

def 			__UpperCamelCase    (    )    ->      Dict:




	"""simple docstring"""





	if os.name == "nt":
		lowerCAmelCase					:    int      							= CursorInfo()
		lowerCAmelCase					:    Optional[int]      							= ctypes.windll.kernelaa.GetStdHandle(-11				)
		ctypes.windll.kernelaa.GetConsoleCursorInfo(_A					,   ctypes.byref(_A				)				)
		lowerCAmelCase					:    List[str]      							= False
		ctypes.windll.kernelaa.SetConsoleCursorInfo(_A					,   ctypes.byref(_A				)				)
	elif os.name == "posix":
		sys.stdout.write('\033[?25l'				)
		sys.stdout.flush()

def 			__UpperCamelCase    (    )    ->      str:




	"""simple docstring"""





	if os.name == "nt":
		lowerCAmelCase					:    List[str]      							= CursorInfo()
		lowerCAmelCase					:    int      							= ctypes.windll.kernelaa.GetStdHandle(-11				)
		ctypes.windll.kernelaa.GetConsoleCursorInfo(_A					,   ctypes.byref(_A				)				)
		lowerCAmelCase					:    Dict      							= True
		ctypes.windll.kernelaa.SetConsoleCursorInfo(_A					,   ctypes.byref(_A				)				)
	elif os.name == "posix":
		sys.stdout.write('\033[?25h'				)
		sys.stdout.flush()






@contextmanager
def 			__UpperCamelCase    (    )    ->      Any:




	"""simple docstring"""





	try:
		hide_cursor()
		yield
	finally:
		show_cursor()




'''simple docstring'''




import argparse
import logging
import os

import datasets
import tensorflow as tf

from transformers import AutoTokenizer


_lowerCAmelCase  :		List[Any]		     =	logging.getLogger(__name__)

def 			__UpperCamelCase    (    )    ->      Any:




	"""simple docstring"""





	lowerCAmelCase					:    str      							= argparse.ArgumentParser(
	    description='Prepare TFRecord shards from pre-tokenized samples of the wikitext dataset.'				)
	parser.add_argument(
	    '--dataset_name'					,   type=_A					,   default='wikitext'					,   help='Name of the training. Explore datasets at: hf.co/datasets.'					,   )
	parser.add_argument(
	    '--dataset_config'					,   type=_A					,   default='wikitext-103-raw-v1'					,   help='Configuration name of the dataset.'				)
	parser.add_argument(
	    '--tokenizer_name_or_path'					,   type=_A					,   default='sayakpaul/unigram-tokenizer-wikitext'					,   help='Tokenizer identifier. Can be a local filepath or a Hub identifier.'					,   )
	parser.add_argument(
	    '--shard_size'					,   type=_A					,   default=10_00					,   help='Number of entries to go in a single shard.'					,   )
	parser.add_argument('--split'					,   type=_A					,   default='train'					,   choices=['train', 'test', 'validation']				)
	parser.add_argument(
	    '--limit'					,   default=_A					,   type=_A					,   help='Limit the number of shards (used for debugging).'					,   )
	parser.add_argument(
	    '--max_length'					,   type=_A					,   default=5_12					,   help='Maximum sequence length. For training on TPUs, it helps to have a maximum'
	    ' sequence length that is a multiple of 8.'					,   )
	parser.add_argument(
	    '--output_dir'					,   default='tf-tpu'					,   type=_A					,   help='Output directory where the TFRecord shards will be saved. If the'
	    ' path is appended with `gs://` (\'gs://tf-tpu\', for example) then the TFRecord'
	    ' shards will be directly saved to a Google Cloud Storage bucket.'					,   )

	lowerCAmelCase					:    Any      							= parser.parse_args()
	return args

def 			__UpperCamelCase    (    _A    :				Optional[int]				)    ->      int:




	"""simple docstring"""





	def fn(_A    :				Tuple				):
		return tokenizer(examples['text']				)

	return fn

def 			__UpperCamelCase    (    _A    :				int				)    ->      int:




	"""simple docstring"""





	lowerCAmelCase					:    Tuple      							= []
	for i in range(len(tokenized_data['input_ids']				)				):
		lowerCAmelCase					:    Optional[Any]      							= {
		    'input_ids': tf.train.Feature(intaa_list=tf.train.IntaaList(value=tokenized_data['input_ids'][i]				)				),
		    'attention_mask': tf.train.Feature(
		        intaa_list=tf.train.IntaaList(value=tokenized_data['attention_mask'][i]				)				),
		}
		lowerCAmelCase					:    Any      							= tf.train.Features(feature=_A				)
		lowerCAmelCase					:    List[str]      							= tf.train.Example(features=_A				)
		lowerCAmelCase					:    Tuple      							= example.SerializeToString()
		records.append(_A				)
	return records

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




	"""simple docstring"""





	lowerCAmelCase					:    Union[str, Any]      							= datasets.load_dataset(args.dataset_name					,   args.dataset_config					,   split=args.split				)

	if args.limit is not None:
		lowerCAmelCase					:    Optional[Any]      							= min(len(_A				)					,   args.limit				)
		lowerCAmelCase					:    Dict      							= dataset.select(range(_A				)				)
		print(F"Limiting the dataset to {args.limit} entries."				)

	lowerCAmelCase					:    str      							= AutoTokenizer.from_pretrained(args.tokenizer_name_or_path				)

	# Handle output directory creation.
	# For serializing into a Google Cloud Storage Bucket, one needs to first
	# create a bucket.
	if "gs" not in args.output_dir:
		if not os.path.exists(args.output_dir				):
			os.makedirs(args.output_dir				)
		lowerCAmelCase					:    Any      							= os.path.join(args.output_dir					,   args.split				)
		if not os.path.exists(_A				):
			os.makedirs(_A				)
	else:
		lowerCAmelCase					:    List[Any]      							= os.path.join(args.output_dir					,   args.split				)

	# Tokenize the whole dataset at once.
	lowerCAmelCase					:    Any      							= tokenize_function(_A				)
	lowerCAmelCase					:    Optional[int]      							= dataset.map(_A					,   batched=_A					,   num_proc=4					,   remove_columns=['text']				)

	# We need to concatenate all our texts together, and then split the result
	# into chunks of a fixed size, which we will call block_size. To do this, we
	# will use the map method again, with the option batched=True. When we use batched=True,
	# the function we pass to map() will be passed multiple inputs at once, allowing us
	# to group them into more or fewer examples than we had in the input.
	# This allows us to create our new fixed-length samples. The advantage of this
	# method is that we don't lose a whole lot of content from the dataset compared to the
	# case where we simply tokenize with a pre-defined max_length.

	def group_texts(_A    :				str				):
		# Concatenate all texts.
		lowerCAmelCase					:    Optional[int]      							= {k: sum(examples[k]					,   []				) for k in examples.keys()}
		lowerCAmelCase					:    str      							= len(concatenated_examples[list(examples.keys()				)[0]]				)
		# We drop the small remainder, though you could add padding instead if the model supports it
		# In this, as in all things, we advise you to follow your heart 🫀
		lowerCAmelCase					:    List[Any]      							= (total_length // args.max_length) * args.max_length
		# Split by chunks of max_len.
		lowerCAmelCase					:    str      							= {
		    k: [t[i : i + args.max_length] for i in range(0					,   _A					,   args.max_length				)]
		    for k, t in concatenated_examples.items()
		}
		return result

	lowerCAmelCase					:    List[Any]      							= dataset_tokenized.map(_A					,   batched=_A					,   batch_size=10_00					,   num_proc=4				)

	lowerCAmelCase					:    Union[str, Any]      							= 0
	lowerCAmelCase					:    Tuple      							= 0
	for shard in range(0					,   len(_A				)					,   args.shard_size				):
		lowerCAmelCase					:    Optional[Any]      							= grouped_dataset[shard : shard + args.shard_size]
		lowerCAmelCase					:    List[str]      							= len(dataset_snapshot['input_ids']				)
		lowerCAmelCase					:    Union[str, Any]      							= os.path.join(_A					,   F"dataset-{shard_count}-{records_containing}.tfrecord"				)
		lowerCAmelCase					:    List[Any]      							= get_serialized_examples(_A				)

		with tf.io.TFRecordWriter(_A				) as out_file:
			for i in range(len(_A				)				):
				lowerCAmelCase					:    Union[str, Any]      							= serialized_examples[i]
				out_file.write(_A				)
			print('Wrote file {} containing {} records'.format(_A					,   _A				)				)

		shard_count += 1
		total_records += records_containing

	with open(F"split-{args.split}-records-count.txt"					,   'w'				) as f:
		print(F"Total {args.split} records: {total_records}"					,   file=_A				)


if __name__ == "__main__":
				_lowerCAmelCase  :		List[Any]		     =	parse_args()
				main(args)
