





import argparse
import logging
import os

import datasets
import tensorflow as tf

from transformers import AutoTokenizer


UpperCAmelCase__						:			Any									=logging.getLogger(__name__)

def      _lowercase					(						)						->  Tuple:
				lowerCamelCase	 =argparse.ArgumentParser(
				    description="""Prepare TFRecord shards from pre-tokenized samples of the wikitext dataset."""				)
				parser.add_argument(
				    """--dataset_name"""			,      type=UpperCamelCase__			,      default="""wikitext"""			,      help="""Name of the training. Explore datasets at: hf.co/datasets."""			,      )
				parser.add_argument(
				    """--dataset_config"""			,      type=UpperCamelCase__			,      default="""wikitext-103-raw-v1"""			,      help="""Configuration name of the dataset."""				)
				parser.add_argument(
				    """--tokenizer_name_or_path"""			,      type=UpperCamelCase__			,      default="""sayakpaul/unigram-tokenizer-wikitext"""			,      help="""Tokenizer identifier. Can be a local filepath or a Hub identifier."""			,      )
				parser.add_argument(
				    """--shard_size"""			,      type=UpperCamelCase__			,      default=10_00			,      help="""Number of entries to go in a single shard."""			,      )
				parser.add_argument("""--split"""			,      type=UpperCamelCase__			,      default="""train"""			,      choices=["""train""", """test""", """validation"""]				)
				parser.add_argument(
				    """--limit"""			,      default=UpperCamelCase__			,      type=UpperCamelCase__			,      help="""Limit the number of shards (used for debugging)."""			,      )
				parser.add_argument(
				    """--max_length"""			,      type=UpperCamelCase__			,      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=UpperCamelCase__			,      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	 =parser.parse_args()
				return args

def      _lowercase					(						_UpperCAmelCase				)						->  Optional[Any]:
				def fn(_UpperCAmelCase				):
								return tokenizer(examples["""text"""]				)

				return fn

def      _lowercase					(						_UpperCAmelCase				)						->  str:
				lowerCamelCase	 =[]
				for i in range(len(tokenized_data["""input_ids"""]				)				):
								lowerCamelCase	 ={
								    """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	 =tf.train.Features(feature=UpperCamelCase__				)
								lowerCamelCase	 =tf.train.Example(features=UpperCamelCase__				)
								lowerCamelCase	 =example.SerializeToString()
								records.append(UpperCamelCase__				)
				return records

def      _lowercase					(						_UpperCAmelCase				)						->  Dict:
				lowerCamelCase	 =datasets.load_dataset(args.dataset_name			,      args.dataset_config			,      split=args.split				)

				if args.limit is not None:
								lowerCamelCase	 =min(len(UpperCamelCase__				)			,      args.limit				)
								lowerCamelCase	 =dataset.select(range(UpperCamelCase__				)				)
								print(F"""Limiting the dataset to {args.limit} entries."""				)

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

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

				lowerCamelCase	 =dataset_tokenized.map(UpperCamelCase__			,      batched=UpperCamelCase__			,      batch_size=10_00			,      num_proc=4				)

				lowerCamelCase	 =0
				lowerCamelCase	 =0
				for shard in range(0			,      len(UpperCamelCase__				)			,      args.shard_size				):
								lowerCamelCase	 =grouped_dataset[shard : shard + args.shard_size]
								lowerCamelCase	 =len(dataset_snapshot["""input_ids"""]				)
								lowerCamelCase	 =os.path.join(UpperCamelCase__			,      F"""dataset-{shard_count}-{records_containing}.tfrecord"""				)
								lowerCamelCase	 =get_serialized_examples(UpperCamelCase__				)

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

								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=UpperCamelCase__				)


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












import fire
from torch.utils.data import DataLoader
from tqdm import tqdm

from transformers import AutoTokenizer
from utils import SeqaSeqDataset, pickle_save





def      _lowercase					(						_UpperCAmelCase			,      _UpperCAmelCase			,      _UpperCAmelCase=10_24			,      _UpperCAmelCase=10_24			,      _UpperCAmelCase=False			,      **_UpperCAmelCase				)						->  Optional[Any]:
				lowerCamelCase	 =AutoTokenizer.from_pretrained(_UpperCAmelCase				)
				lowerCamelCase	 =SeqaSeqDataset(_UpperCAmelCase			,      _UpperCAmelCase			,      _UpperCAmelCase			,      _UpperCAmelCase			,      type_path="""train"""			,      **_UpperCAmelCase				)
				lowerCamelCase	 =tok.pad_token_id

				def get_lens(_UpperCAmelCase				):
								lowerCamelCase	 =tqdm(
								    DataLoader(_UpperCAmelCase			,      batch_size=5_12			,      num_workers=8			,      shuffle=_UpperCAmelCase			,      collate_fn=ds.collate_fn				)			,      desc=str(ds.len_file				)			,      )
								lowerCamelCase	 =[]
								for batch in dl:
												lowerCamelCase	 =batch["""input_ids"""].ne(_UpperCAmelCase				).sum(1				).tolist()
												lowerCamelCase	 =batch["""labels"""].ne(_UpperCAmelCase				).sum(1				).tolist()
												if consider_target:
																for src, tgt in zip(_UpperCAmelCase			,      _UpperCAmelCase				):
																				max_lens.append(max(_UpperCAmelCase			,      _UpperCAmelCase				)				)
												else:
																max_lens.extend(_UpperCAmelCase				)
								return max_lens

				lowerCamelCase	 =get_lens(_UpperCAmelCase				)
				lowerCamelCase	 =SeqaSeqDataset(_UpperCAmelCase			,      _UpperCAmelCase			,      _UpperCAmelCase			,      _UpperCAmelCase			,      type_path="""val"""			,      **_UpperCAmelCase				)
				lowerCamelCase	 =get_lens(_UpperCAmelCase				)
				pickle_save(_UpperCAmelCase			,      train_ds.len_file				)
				pickle_save(_UpperCAmelCase			,      val_ds.len_file				)


if __name__ == "__main__":
							fire.Fire(save_len_file)





