




'''simple docstring'''

# Lint as: python3
import dataclasses
import re
from dataclasses import dataclass
from functools import total_ordering
from typing import Optional, Union


a       :						Tuple								=		re.compile(r'''^(?P<major>\d+)''' r'''\.(?P<minor>\d+)''' r'''\.(?P<patch>\d+)$''')



@total_ordering
@dataclass
class 		SCREAMING_SNAKE_CASE__  :
			__SCREAMING_SNAKE_CASE    =			42
			__SCREAMING_SNAKE_CASE    =			None
			__SCREAMING_SNAKE_CASE    =			None
			__SCREAMING_SNAKE_CASE    =			None
			__SCREAMING_SNAKE_CASE    =			None





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



						"""simple docstring"""




						__snake_case		,     __snake_case		,     __snake_case			=		_str_to_version_tuple(self.version_str							)





			def __repr__(      self						:    Tuple							):



						"""simple docstring"""




						return f'''{self.tuple[0]}.{self.tuple[1]}.{self.tuple[2]}'''





			@property
			def 							A (      self						:    List[Any]							):



						"""simple docstring"""




						return self.major, self.minor, self.patch





			def 							A (      self						:    Tuple      ,		a_						:    str							):



						"""simple docstring"""




						if isinstance(a_      ,		a_							):
									return Version(a_							)
						elif isinstance(a_      ,		a_							):
									return other
						raise TypeError(f'''{other} (type {type(a_							)}) cannot be compared to version.'''							)





			def __eq__(      self						:    str      ,		a_						:    Tuple							):



						"""simple docstring"""




						try:
									__snake_case			=		self._validate_operand(a_							)
						except (TypeError, ValueError):
									return False
						else:
									return self.tuple == other.tuple





			def __lt__(      self						:    Optional[Any]      ,		a_						:    Union[str, Any]							):



						"""simple docstring"""




						__snake_case			=		self._validate_operand(a_							)
						return self.tuple < other.tuple





			def __hash__(      self						:    Union[str, Any]							):



						"""simple docstring"""




						return hash(_version_tuple_to_str(self.tuple							)							)





			@classmethod
			def 							A (      cls						:    Optional[int]      ,		a_						:    Optional[Any]							):



						"""simple docstring"""




						__snake_case			=		{f.name for f in dataclasses.fields(cls							)}
						return cls(**{k: v for k, v in dic.items() if k in field_names}							)






			def 							A (      self						:    List[Any]							):



						"""simple docstring"""




						return self.version_str







def __UpperCAmelCase	(   _UpperCAmelCase		:   List[Any]						)      ->      List[Any]:
			__snake_case			=		_VERSION_REG.match(_UpperCAmelCase						)
			if not res:
						raise ValueError(F'''Invalid version \'{version_str}\'. Format should be x.y.z with {{x,y,z}} being digits.'''						)
			return tuple(int(_UpperCAmelCase						) for v in [res.group("major"						), res.group("minor"						), res.group("patch"						)]						)






def __UpperCAmelCase	(   _UpperCAmelCase		:   List[str]						)      ->      List[Any]:
			return ".".join(str(_UpperCAmelCase						) for v in version_tuple						)






import argparse
import logging
import os

import datasets
import tensorflow as tf

from transformers import AutoTokenizer


UpperCAmelCase_           =				logging.getLogger(__name__)




def    lowerCAmelCase_  (		)     ->					Optional[Any]:







 '''simple docstring'''
 _UpperCamelCase:  List[str]    						=			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=1_000	,       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.'''	,       )

 _UpperCamelCase:  str    						=			parser.parse_args()
 return args




def    lowerCAmelCase_  (		lowercase:				Optional[int]		)     ->					Optional[Any]:







 '''simple docstring'''
 def fn(lowercase:				List[Any]		):
  return tokenizer(examples['''text''']		)

 return fn




def    lowerCAmelCase_  (		lowercase:				List[str]		)     ->					Optional[Any]:







 '''simple docstring'''
 _UpperCamelCase:  int    						=			[]
 for i in range(len(tokenized_data['''input_ids''']		)		):
  _UpperCamelCase:  Tuple    						=			{
      '''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]		)		),
  }
  _UpperCamelCase:  Optional[int]    						=			tf.train.Features(feature=lowercase		)
  _UpperCamelCase:  Optional[int]    						=			tf.train.Example(features=lowercase		)
  _UpperCamelCase:  List[Any]    						=			example.SerializeToString()
  records.append(lowercase		)
 return records




def    lowerCAmelCase_  (		lowercase:				List[str]		)     ->					Tuple:







 '''simple docstring'''
 _UpperCamelCase:  Tuple    						=			datasets.load_dataset(args.dataset_name	,       args.dataset_config	,       split=args.split		)

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

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

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

 _UpperCamelCase:  Dict    						=			dataset_tokenized.map(lowercase	,       batched=lowercase	,       batch_size=1_000	,       num_proc=4		)

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

  with tf.io.TFRecordWriter(lowercase		) as out_file:
   for i in range(len(lowercase		)		):
    _UpperCamelCase:  List[Any]    						=			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__":
  UpperCAmelCase_           =				parse_args()
  main(args)