



"""simple docstring"""


import argparse
import logging
import os

import datasets
import tensorflow as tf

from transformers import AutoTokenizer


__A   :							Optional[Any]										=   logging.getLogger(__name__)







def  _a	(      ):






				'''simple docstring'''



				_UpperCAmelCase					=    argparse.ArgumentParser(
				    description='''Prepare TFRecord shards from pre-tokenized samples of the wikitext dataset.'''				)
				parser.add_argument(
				    '''--dataset_name'''					,     type=_SCREAMING_SNAKE_CASE					,     default='''wikitext'''					,     help='''Name of the training. Explore datasets at: hf.co/datasets.'''					,     )
				parser.add_argument(
				    '''--dataset_config'''					,     type=_SCREAMING_SNAKE_CASE					,     default='''wikitext-103-raw-v1'''					,     help='''Configuration name of the dataset.'''				)
				parser.add_argument(
				    '''--tokenizer_name_or_path'''					,     type=_SCREAMING_SNAKE_CASE					,     default='''sayakpaul/unigram-tokenizer-wikitext'''					,     help='''Tokenizer identifier. Can be a local filepath or a Hub identifier.'''					,     )
				parser.add_argument(
				    '''--shard_size'''					,     type=_SCREAMING_SNAKE_CASE					,     default=1000					,     help='''Number of entries to go in a single shard.'''					,     )
				parser.add_argument('''--split'''					,     type=_SCREAMING_SNAKE_CASE					,     default='''train'''					,     choices=['''train''', '''test''', '''validation''']				)
				parser.add_argument(
				    '''--limit'''					,     default=_SCREAMING_SNAKE_CASE					,     type=_SCREAMING_SNAKE_CASE					,     help='''Limit the number of shards (used for debugging).'''					,     )
				parser.add_argument(
				    '''--max_length'''					,     type=_SCREAMING_SNAKE_CASE					,     default=512					,     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=_SCREAMING_SNAKE_CASE					,     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.'''					,     )

				_UpperCAmelCase					=    parser.parse_args()
				return args







def  _a	(      _SCREAMING_SNAKE_CASE   :						List[Any]				):






				'''simple docstring'''



				def fn(_SCREAMING_SNAKE_CASE   :						Optional[int]				):
								return tokenizer(examples['''text''']				)

				return fn







def  _a	(      _SCREAMING_SNAKE_CASE   :						Any				):






				'''simple docstring'''



				_UpperCAmelCase					=    []
				for i in range(len(tokenized_data['''input_ids''']				)				):
								_UpperCAmelCase					=    {
								    '''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]				)				),
								}
								_UpperCAmelCase					=    tf.train.Features(feature=_SCREAMING_SNAKE_CASE				)
								_UpperCAmelCase					=    tf.train.Example(features=_SCREAMING_SNAKE_CASE				)
								_UpperCAmelCase					=    example.SerializeToString()
								records.append(_SCREAMING_SNAKE_CASE				)
				return records







def  _a	(      _SCREAMING_SNAKE_CASE   :						Tuple				):






				'''simple docstring'''



				_UpperCAmelCase					=    datasets.load_dataset(args.dataset_name					,     args.dataset_config					,     split=args.split				)

				if args.limit is not None:
								_UpperCAmelCase					=    min(len(_SCREAMING_SNAKE_CASE				)					,     args.limit				)
								_UpperCAmelCase					=    dataset.select(range(_SCREAMING_SNAKE_CASE				)				)
								print(f'Limiting the dataset to {args.limit} entries.'				)

				_UpperCAmelCase					=    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				)
								_UpperCAmelCase					=    os.path.join(args.output_dir					,     args.split				)
								if not os.path.exists(_SCREAMING_SNAKE_CASE				):
												os.makedirs(_SCREAMING_SNAKE_CASE				)
				else:
								_UpperCAmelCase					=    os.path.join(args.output_dir					,     args.split				)

				# Tokenize the whole dataset at once.
				_UpperCAmelCase					=    tokenize_function(_SCREAMING_SNAKE_CASE				)
				_UpperCAmelCase					=    dataset.map(_SCREAMING_SNAKE_CASE					,     batched=_SCREAMING_SNAKE_CASE					,     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(_SCREAMING_SNAKE_CASE   :						List[str]				):
								# Concatenate all texts.
								_UpperCAmelCase					=    {k: sum(examples[k]					,     []				) for k in examples.keys()}
								_UpperCAmelCase					=    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 🫀
								_UpperCAmelCase					=    (total_length // args.max_length) * args.max_length
								# Split by chunks of max_len.
								_UpperCAmelCase					=    {
								    k: [t[i : i + args.max_length] for i in range(0					,     _SCREAMING_SNAKE_CASE					,     args.max_length				)]
								    for k, t in concatenated_examples.items()
								}
								return result

				_UpperCAmelCase					=    dataset_tokenized.map(_SCREAMING_SNAKE_CASE					,     batched=_SCREAMING_SNAKE_CASE					,     batch_size=1000					,     num_proc=4				)

				_UpperCAmelCase					=    0
				_UpperCAmelCase					=    0
				for shard in range(0					,     len(_SCREAMING_SNAKE_CASE				)					,     args.shard_size				):
								_UpperCAmelCase					=    grouped_dataset[shard : shard + args.shard_size]
								_UpperCAmelCase					=    len(dataset_snapshot['''input_ids''']				)
								_UpperCAmelCase					=    os.path.join(_SCREAMING_SNAKE_CASE					,     f'dataset-{shard_count}-{records_containing}.tfrecord'				)
								_UpperCAmelCase					=    get_serialized_examples(_SCREAMING_SNAKE_CASE				)

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

								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=_SCREAMING_SNAKE_CASE				)


if __name__ == "__main__":
					__A   :							Tuple										=   parse_args()
					main(args)








"""simple docstring"""


import inspect
import os
import unittest

import torch

import accelerate
from accelerate import debug_launcher
from accelerate.test_utils import (
    execute_subprocess_async,
    require_cpu,
    require_huggingface_suite,
    require_multi_gpu,
    require_single_gpu,
)
from accelerate.utils import patch_environment
@require_huggingface_suite
class 			_a (					unittest.TestCase):
			"""simple docstring"""



			def  lowercase__						(		self     :    Tuple   )->int:
							_UpperCAmelCase					=    inspect.getfile(accelerate.test_utils   )
							_UpperCAmelCase					=    os.path.sep.join(
							    mod_file.split(os.path.sep   )[:-1] + ['''scripts''', '''external_deps''', '''test_metrics.py''']   )

							from accelerate.test_utils.scripts.external_deps import test_metrics  # noqa: F401

							_UpperCAmelCase					=    test_metrics



			@require_cpu
			def  lowercase__						(		self     :    Any   )->int:
							debug_launcher(self.test_metrics.main				,   num_processes=1   )



			@require_cpu
			def  lowercase__						(		self     :    List[str]   )->List[str]:
							debug_launcher(self.test_metrics.main   )



			@require_single_gpu
			def  lowercase__						(		self     :    List[Any]   )->Dict:
							self.test_metrics.main()



			@require_multi_gpu
			def  lowercase__						(		self     :    str   )->int:
							print(F'Found {torch.cuda.device_count()} devices.'   )
							_UpperCAmelCase					=    ['''torchrun''', F'--nproc_per_node={torch.cuda.device_count()}', self.test_file_path]
							with patch_environment(omp_num_threads=1   ):
											execute_subprocess_async(__UpperCamelCase				,   env=os.environ.copy()   )



