




'''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     __magic_name__       (				):






			'''simple docstring'''







			a					=    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=1000,						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=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=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.",						)

			a					=    parser.parse_args()
			return args




def     __magic_name__       (				A	:      List[str]     ):






			'''simple docstring'''







			def fn(A	:      Tuple     ):
						return tokenizer(examples["text"]     )

			return fn




def     __magic_name__       (				A	:      Any     ):






			'''simple docstring'''







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




def     __magic_name__       (				A	:      Union[str, Any]     ):






			'''simple docstring'''







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

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

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

			# Tokenize the whole dataset at once.
			a					=    tokenize_function(A     )
			a					=    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	:      List[Any]     ):
						# Concatenate all texts.
						a					=    {k: sum(examples[k],						[]     ) for k in examples.keys()}
						a					=    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 🫀
						a					=    (total_length // args.max_length) * args.max_length
						# Split by chunks of max_len.
						a					=    {
						    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

			a					=    dataset_tokenized.map(A,						batched=A,						batch_size=1000,						num_proc=4     )

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

						with tf.io.TFRecordWriter(A     ) as out_file:
									for i in range(len(A     )     ):
												a					=    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	:       Optional[int]     =	parse_args()
		main(args)












from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


__lowerCAmelCase	:       Any     =	{
    'configuration_roc_bert': ['ROC_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'RoCBertConfig'],
    'tokenization_roc_bert': ['RoCBertTokenizer'],
}

try:
		if not is_tokenizers_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		pass

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__lowerCAmelCase	:       Optional[Any]     =	[
		    'ROC_BERT_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'RoCBertForCausalLM',
		    'RoCBertForMaskedLM',
		    'RoCBertForMultipleChoice',
		    'RoCBertForPreTraining',
		    'RoCBertForQuestionAnswering',
		    'RoCBertForSequenceClassification',
		    'RoCBertForTokenClassification',
		    'RoCBertLayer',
		    'RoCBertModel',
		    'RoCBertPreTrainedModel',
		    'load_tf_weights_in_roc_bert',
		]

if TYPE_CHECKING:
		from .configuration_roc_bert import ROC_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, RoCBertConfig
		from .tokenization_roc_bert import RoCBertTokenizer

		try:
				if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				raise OptionalDependencyNotAvailable()

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_roc_bert import (
				    ROC_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    RoCBertForCausalLM,
				    RoCBertForMaskedLM,
				    RoCBertForMultipleChoice,
				    RoCBertForPreTraining,
				    RoCBertForQuestionAnswering,
				    RoCBertForSequenceClassification,
				    RoCBertForTokenClassification,
				    RoCBertLayer,
				    RoCBertModel,
				    RoCBertPreTrainedModel,
				    load_tf_weights_in_roc_bert,
				)


else:
		import sys

		__lowerCAmelCase	:       Union[str, Any]     =	_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)






