
import argparse
import logging
import os

import datasets
import tensorflow as tf

from transformers import AutoTokenizer


_SCREAMING_SNAKE_CASE    :      Any          =			logging.getLogger(__name__)

def 				SCREAMING_SNAKE_CASE     (      ) ->       Optional[int]:

				"""simple docstring"""







				A__  :	List[Any]         =							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.'''							,						)

				A__  :	List[str]         =							parser.parse_args()
				return args

def 				SCREAMING_SNAKE_CASE     (      __UpperCamelCase :						Any       ) ->       Any:

				"""simple docstring"""







				def fn(__UpperCamelCase :						List[str]       ):
								return tokenizer(examples['''text''']       )

				return fn

def 				SCREAMING_SNAKE_CASE     (      __UpperCamelCase :						Optional[Any]       ) ->       List[str]:

				"""simple docstring"""







				A__  :	Optional[int]         =							[]
				for i in range(len(tokenized_data['''input_ids''']       )       ):
								A__  :	List[str]         =							{
								    "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__  :	Dict         =							tf.train.Features(feature=__UpperCamelCase       )
								A__  :	int         =							tf.train.Example(features=__UpperCamelCase       )
								A__  :	Tuple         =							example.SerializeToString()
								records.append(__UpperCamelCase       )
				return records

def 				SCREAMING_SNAKE_CASE     (      __UpperCamelCase :						Optional[Any]       ) ->       Dict:

				"""simple docstring"""







				A__  :	Dict         =							datasets.load_dataset(args.dataset_name							,						args.dataset_config							,						split=args.split       )

				if args.limit is not None:
								A__  :	Union[str, Any]         =							min(len(__UpperCamelCase       )							,						args.limit       )
								A__  :	List[str]         =							dataset.select(range(__UpperCamelCase       )       )
								print(F"Limiting the dataset to {args.limit} entries."       )

				A__  :	List[str]         =							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__  :	List[str]         =							os.path.join(args.output_dir							,						args.split       )
								if not os.path.exists(__UpperCamelCase       ):
												os.makedirs(__UpperCamelCase       )
				else:
								A__  :	int         =							os.path.join(args.output_dir							,						args.split       )

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

				A__  :	List[str]         =							dataset_tokenized.map(__UpperCamelCase							,						batched=__UpperCamelCase							,						batch_size=10_00							,						num_proc=4       )

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

								with tf.io.TFRecordWriter(__UpperCamelCase       ) as out_file:
												for i in range(len(__UpperCamelCase       )       ):
																A__  :	Tuple         =							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__":
					_SCREAMING_SNAKE_CASE    :      Optional[Any]          =			parse_args()
					main(args)







def 				SCREAMING_SNAKE_CASE     (      __UpperCamelCase :						int       ) ->       List[Any]:

				"""simple docstring"""







				A__  :	Optional[Any]         =							0
				A__  :	Optional[Any]         =							len(__UpperCamelCase       )

				for i in range(n - 1       ):
								for j in range(i + 1							,						__UpperCamelCase       ):
												if arr[i] > arr[j]:
																num_inversions += 1

				return num_inversions

def 				SCREAMING_SNAKE_CASE     (      __UpperCamelCase :						int       ) ->       Tuple:

				"""simple docstring"""







				if len(__UpperCamelCase       ) <= 1:
								return arr, 0
				A__  :	Optional[int]         =							len(__UpperCamelCase       ) // 2
				A__  :	List[str]         =							arr[0:mid]
				A__  :	Union[str, Any]         =							arr[mid:]

				A__       ,							A__  :	List[Any]         =							count_inversions_recursive(__UpperCamelCase       )
				A__       ,							A__  :	int         =							count_inversions_recursive(__UpperCamelCase       )
				A__       ,							A__  :	Dict         =							_count_cross_inversions(__UpperCamelCase							,						__UpperCamelCase       )

				A__  :	Any         =							inversion_p + inversions_q + cross_inversions
				return c, num_inversions

def 				SCREAMING_SNAKE_CASE     (      __UpperCamelCase :						List[str]							,						__UpperCamelCase :						List[Any]       ) ->       Dict:

				"""simple docstring"""







				A__  :	str         =							[]
				A__  :	Tuple         =							0
				while i < len(__UpperCamelCase       ) and j < len(__UpperCamelCase       ):
								if p[i] > q[j]:
												# if P[1] > Q[j], then P[k] > Q[k] for all  i < k <= len(P)
												# These are all inversions. The claim emerges from the
												# property that P is sorted.
												num_inversion += len(__UpperCamelCase       ) - i
												r.append(q[j]       )
												j += 1
								else:
												r.append(p[i]       )
												i += 1

				if i < len(__UpperCamelCase       ):
								r.extend(p[i:]       )
				else:
								r.extend(q[j:]       )

				return r, num_inversion

def 				SCREAMING_SNAKE_CASE     (      ) ->       Tuple:

				"""simple docstring"""







				A__  :	List[str]         =							[10, 2, 1, 5, 5, 2, 11]

				# this arr has 8 inversions:
				# (10, 2), (10, 1), (10, 5), (10, 5), (10, 2), (2, 1), (5, 2), (5, 2)

				A__  :	int         =							count_inversions_bf(__UpperCamelCase       )
				A__       ,							A__  :	int         =							count_inversions_recursive(__UpperCamelCase       )

				assert num_inversions_bf == num_inversions_recursive == 8

				print('''number of inversions = '''							,						__UpperCamelCase       )

				# testing an array with zero inversion (a sorted arr_1)

				arr_a.sort()
				A__  :	Optional[Any]         =							count_inversions_bf(__UpperCamelCase       )
				A__       ,							A__  :	Dict         =							count_inversions_recursive(__UpperCamelCase       )

				assert num_inversions_bf == num_inversions_recursive == 0
				print('''number of inversions = '''							,						__UpperCamelCase       )

				# an empty list should also have zero inversions
				A__  :	Union[str, Any]         =							[]
				A__  :	Union[str, Any]         =							count_inversions_bf(__UpperCamelCase       )
				A__       ,							A__  :	Any         =							count_inversions_recursive(__UpperCamelCase       )

				assert num_inversions_bf == num_inversions_recursive == 0
				print('''number of inversions = '''							,						__UpperCamelCase       )


if __name__ == "__main__":
					main()