





'''simple docstring'''







from __future__ import annotations

from typing import TypedDict
class 				__SCREAMING_SNAKE_CASE  (lowerCamelCase_      ):






				"""simple docstring"""







				__a            =42
				__a            =42






def  _lowerCamelCase				( lowercase						:      str						)	->	list[str]:
				if not isinstance(lowercase    ,							lowercase						):
								raise TypeError("The parameter s type must be str."						)

				return [s[i:] + s[:i] for i in range(len(lowercase						)						)]






def  _lowerCamelCase				( lowercase						:      str						)	->	BWTTransformDict:
				if not isinstance(lowercase    ,							lowercase						):
								raise TypeError("The parameter s type must be str."						)
				if not s:
								raise ValueError("The parameter s must not be empty."						)

				_a								= all_rotations(lowercase						)
				rotations.sort()  # sort the list of rotations in alphabetically order
				# make a string composed of the last char of each rotation
				_a								= {
				    "bwt_string": "".join([word[-1] for word in rotations]						),
				    "idx_original_string": rotations.index(lowercase						),
				}
				return response






def  _lowerCamelCase				( lowercase						:      str    ,							lowercase						:      int						)	->	str:
				if not isinstance(lowercase    ,							lowercase						):
								raise TypeError("The parameter bwt_string type must be str."						)
				if not bwt_string:
								raise ValueError("The parameter bwt_string must not be empty."						)
				try:
								_a								= int(lowercase						)
				except ValueError:
								raise TypeError(
								    "The parameter idx_original_string type must be int or passive"
								    " of cast to int."						)
				if idx_original_string < 0:
								raise ValueError("The parameter idx_original_string must not be lower than 0."						)
				if idx_original_string >= len(lowercase						):
								raise ValueError(
								    "The parameter idx_original_string must be lower than" " len(bwt_string)."						)

				_a								= [""] * len(lowercase						)
				for _ in range(len(lowercase						)						):
								for i in range(len(lowercase						)						):
												_a								= bwt_string[i] + ordered_rotations[i]
								ordered_rotations.sort()
				return ordered_rotations[idx_original_string]


if __name__ == "__main__":
					lowerCAmelCase_					:    str      	=			'Provide a string that I will generate its BWT transform: '
					lowerCAmelCase_					:    List[str]      	=			input(entry_msg).strip()
					lowerCAmelCase_					:    List[str]      	=			bwt_transform(s)
					print(
					    f"""Burrows Wheeler transform for string '{s}' results """
					    f"""in '{result['bwt_string']}'"""
					)
					lowerCAmelCase_					:    Optional[Any]      	=			reverse_bwt(result['bwt_string'], result['idx_original_string'])
					print(
					    f"""Reversing Burrows Wheeler transform for entry '{result['bwt_string']}' """
					    f"""we get original string '{original_string}'"""
					)

import argparse
import logging
import os

import datasets
import tensorflow as tf

from transformers import AutoTokenizer


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





def 				UpperCAmelCase						(    ):







				"""simple docstring"""



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





def 				UpperCAmelCase						(    lowercase							):







				"""simple docstring"""



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

				return fn





def 				UpperCAmelCase						(    lowercase							):







				"""simple docstring"""



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





def 				UpperCAmelCase						(    lowercase							):







				"""simple docstring"""



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

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

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

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

				__lowercase 			=							dataset_tokenized.map(lowercase							,			batched=lowercase							,			batch_size=1000							,			num_proc=4							)

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

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

								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=lowercase							)


if __name__ == "__main__":
		__a  :     Optional[Any]       =  parse_args()
		main(args)