


'''simple docstring'''
import argparse
import logging
import os

import datasets
import tensorflow as tf

from transformers import AutoTokenizer


lowerCAmelCase   : Any             =				logging.getLogger(__name__)




def 	_A  (       )  ->				Union[str, Any]:
	lowercase						:		Optional[Any]   					=		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=1_0_0_0							,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_1_2							,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."							,)

	lowercase						:		List[Any]   					=		parser.parse_args()
	return args




def 	_A  (       A							)  ->				List[Any]:
	def fn(A							):
		return tokenizer(examples["text"]							)

	return fn




def 	_A  (       A							)  ->				List[str]:
	lowercase						:		List[Any]   					=		[]
	for i in range(len(tokenized_data["input_ids"]							)							):
		lowercase						:		Union[str, 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]							)							),
		}
		lowercase						:		Tuple   					=		tf.train.Features(feature=A							)
		lowercase						:		Optional[int]   					=		tf.train.Example(features=A							)
		lowercase						:		int   					=		example.SerializeToString()
		records.append(A							)
	return records




def 	_A  (       A							)  ->				Union[str, Any]:
	lowercase						:		Optional[Any]   					=		datasets.load_dataset(args.dataset_name							,args.dataset_config							,split=args.split							)

	if args.limit is not None:
		lowercase						:		Optional[Any]   					=		min(len(A							)							,args.limit							)
		lowercase						:		List[str]   					=		dataset.select(range(A							)							)
		print(F'''Limiting the dataset to {args.limit} entries.'''							)

	lowercase						:		Dict   					=		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							)
		lowercase						:		Union[str, Any]   					=		os.path.join(args.output_dir							,args.split							)
		if not os.path.exists(A							):
			os.makedirs(A							)
	else:
		lowercase						:		List[str]   					=		os.path.join(args.output_dir							,args.split							)

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

	lowercase						:		Union[str, Any]   					=		dataset_tokenized.map(A							,batched=A							,batch_size=1_0_0_0							,num_proc=4							)

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

		with tf.io.TFRecordWriter(A							) as out_file:
			for i in range(len(A							)							):
				lowercase						:		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   : str             =				parse_args()
					main(args)






'''simple docstring'''
lowerCAmelCase   : List[str]             =				2_5_6
# Modulus to hash a string
lowerCAmelCase   : Tuple             =				1_0_0_0_0_0_3




def 	_A  (       A							,A							)  ->				bool:
	lowercase						:		List[Any]   					=		len(A							)
	lowercase						:		List[Any]   					=		len(A							)
	if p_len > t_len:
		return False

	lowercase						:		List[str]   					=		0
	lowercase						:		Dict   					=		0
	lowercase						:		Any   					=		1

	# Calculating the hash of pattern and substring of text
	for i in range(A							):
		lowercase						:		Optional[int]   					=		(ord(pattern[i]							) + p_hash * alphabet_size) % modulus
		lowercase						:		Any   					=		(ord(text[i]							) + text_hash * alphabet_size) % modulus
		if i == p_len - 1:
			continue
		lowercase						:		List[str]   					=		(modulus_power * alphabet_size) % modulus

	for i in range(0							,t_len - p_len + 1							):
		if text_hash == p_hash and text[i : i + p_len] == pattern:
			return True
		if i == t_len - p_len:
			continue
		# Calculate the https://en.wikipedia.org/wiki/Rolling_hash
		lowercase						:		Any   					=		(
		    (text_hash - ord(text[i]							) * modulus_power) * alphabet_size
		    + ord(text[i + p_len]							)
		) % modulus
	return False




def 	_A  (       )  ->				None:
	lowercase						:		Dict   					=		"abc1abc12"
	lowercase						:		Union[str, Any]   					=		"alskfjaldsabc1abc1abc12k23adsfabcabc"
	lowercase						:		Any   					=		"alskfjaldsk23adsfabcabc"
	assert rabin_karp(A							,A							) and not rabin_karp(A							,A							)

	# Test 2)
	lowercase						:		str   					=		"ABABX"
	lowercase						:		Union[str, Any]   					=		"ABABZABABYABABX"
	assert rabin_karp(A							,A							)

	# Test 3)
	lowercase						:		str   					=		"AAAB"
	lowercase						:		List[str]   					=		"ABAAAAAB"
	assert rabin_karp(A							,A							)

	# Test 4)
	lowercase						:		List[str]   					=		"abcdabcy"
	lowercase						:		str   					=		"abcxabcdabxabcdabcdabcy"
	assert rabin_karp(A							,A							)

	# Test 5)
	lowercase						:		int   					=		"Lü"
	lowercase						:		Optional[Any]   					=		"Lüsai"
	assert rabin_karp(A							,A							)
	lowercase						:		Tuple   					=		"Lue"
	assert not rabin_karp(A							,A							)
	print("Success."							)


if __name__ == "__main__":
					test_rabin_karp()


