





import os
from typing import Optional

import fsspec
from fsspec.archive import AbstractArchiveFileSystem
from fsspec.utils import DEFAULT_BLOCK_SIZE



class 					__lowerCAmelCase       (			_a     ):
			lowerCamelCase_     :     int       			=      ''''''
			lowerCamelCase_     :     str       			=      (
			    None  # protocol passed in prefix to the url. ex: "gzip", for gzip://file.txt::http://foo.bar/file.txt.gz
			)
			lowerCamelCase_     :     str       			=      None  # compression type in fsspec. ex: "gzip"
			lowerCamelCase_     :     str       			=      None  # extension of the filename to strip. ex: "".gz" to get file.txt from file.txt.gz






			def __init__(self					,							__magic_name__ = ""					,							__magic_name__ = None					,							__magic_name__ = None					,							**__magic_name__    )     ->   Any:


							'''simple docstring'''



							super().__init__(self					,							**__magic_name__    )
							# always open as "rb" since fsspec can then use the TextIOWrapper to make it work for "r" mode
							snake_case_						:    Union[str, Any]				=  fsspec.open(
							    __magic_name__					,							mode='''rb'''					,							protocol=__magic_name__					,							compression=self.compression					,							client_kwargs={
							        '''requote_redirect_url''': False,  # see https://github.com/huggingface/datasets/pull/5459
							        '''trust_env''': True,  # Enable reading proxy env variables.
							        **(target_options or {}).pop('''client_kwargs'''					,							{}    ),  # To avoid issues if it was already passed.
							    }					,							**(target_options or {})					,							)
							snake_case_						:    Tuple				=  os.path.basename(self.file.path.split('''::'''    )[0]    )
							snake_case_						:    Optional[Any]				=  (
							    self.compressed_name[: self.compressed_name.rindex('''.'''    )]
							    if '''.''' in self.compressed_name
							    else self.compressed_name
							)
							snake_case_						:    Dict				=  None






			@classmethod
			def 			lowerCamelCase  (cls					,							__magic_name__    )     ->   Optional[int]:


							'''simple docstring'''



							return super()._strip_protocol(__magic_name__    ).lstrip('''/'''    )






			def 			lowerCamelCase  (self    )     ->   Union[str, Any]:


							'''simple docstring'''



							if self.dir_cache is None:
											snake_case_						:    Optional[int]				=  {**self.file.fs.info(self.file.path    ), '''name''': self.uncompressed_name}
											snake_case_						:    List[str]				=  {f['''name''']: f}






			def 			lowerCamelCase  (self					,							__magic_name__    )     ->   Optional[Any]:


							'''simple docstring'''



							return self.file.open().read()






			def 			lowerCamelCase  (self					,							__magic_name__					,							__magic_name__ = "rb"					,							__magic_name__=None					,							__magic_name__=True					,							__magic_name__=None					,							**__magic_name__					,							)     ->   int:


							'''simple docstring'''



							snake_case_						:    Union[str, Any]				=  self._strip_protocol(__magic_name__    )
							if mode != "rb":
											raise ValueError(F'''Tried to read with mode {mode} on file {self.file.path} opened with mode \'rb\''''    )
							return self.file.open()




class 					__lowerCAmelCase       (			_a     ):
			lowerCamelCase_     :     Union[str, Any]       			=      '''bz2'''
			lowerCamelCase_     :     Any       			=      '''bz2'''
			lowerCamelCase_     :     int       			=      '''.bz2'''




class 					__lowerCAmelCase       (			_a     ):
			lowerCamelCase_     :     Union[str, Any]       			=      '''gzip'''
			lowerCamelCase_     :     Dict       			=      '''gzip'''
			lowerCamelCase_     :     int       			=      '''.gz'''




class 					__lowerCAmelCase       (			_a     ):
			lowerCamelCase_     :     Any       			=      '''lz4'''
			lowerCamelCase_     :     Any       			=      '''lz4'''
			lowerCamelCase_     :     Optional[Any]       			=      '''.lz4'''




class 					__lowerCAmelCase       (			_a     ):
			lowerCamelCase_     :     Tuple       			=      '''xz'''
			lowerCamelCase_     :     Any       			=      '''xz'''
			lowerCamelCase_     :     int       			=      '''.xz'''




class 					__lowerCAmelCase       (			_a     ):
			lowerCamelCase_     :     Union[str, Any]       			=      '''zstd'''
			lowerCamelCase_     :     Tuple       			=      '''zstd'''
			lowerCamelCase_     :     Any       			=      '''.zst'''






			def __init__(self					,							__magic_name__					,							__magic_name__ = "rb"					,							__magic_name__ = None					,							__magic_name__ = None					,							__magic_name__ = DEFAULT_BLOCK_SIZE					,							**__magic_name__					,							)     ->   Tuple:


							'''simple docstring'''



							super().__init__(
							    fo=__magic_name__					,							mode=__magic_name__					,							target_protocol=__magic_name__					,							target_options=__magic_name__					,							block_size=__magic_name__					,							**__magic_name__					,							)
							# We need to wrap the zstd decompressor to avoid this error in fsspec==2021.7.0 and zstandard==0.15.2:
							#
							# File "/Users/user/.virtualenvs/hf-datasets/lib/python3.7/site-packages/fsspec/core.py", line 145, in open
							#     out.close = close
							# AttributeError: 'zstd.ZstdDecompressionReader' object attribute 'close' is read-only
							#
							# see https://github.com/intake/filesystem_spec/issues/725
							snake_case_						:    Dict				=  self.file.__enter__



							class 					__lowerCAmelCase       :






										def __init__(self					,							__magic_name__    )     ->   List[Any]:


														'''simple docstring'''



														snake_case_						:    str				=  file_






										def __enter__(self    )     ->   List[Any]:


														'''simple docstring'''



														self._file.__enter__()
														return self






										def __exit__(self					,							*__magic_name__					,							**__magic_name__    )     ->   int:


														'''simple docstring'''



														self._file.__exit__(*__magic_name__					,							**__magic_name__    )






										def __iter__(self    )     ->   Optional[int]:


														'''simple docstring'''



														return iter(self._file    )






										def 			lowerCamelCase  (self    )     ->   Union[str, Any]:


														'''simple docstring'''



														return next(self._file    )






										def __getattr__(self					,							__magic_name__    )     ->   str:


														'''simple docstring'''



														return getattr(self._file					,							__magic_name__    )

							def fixed_enter(*__magic_name__					,							**__magic_name__    ):
											return WrappedFile(_enter(*__magic_name__					,							**__magic_name__    )    )

							snake_case_						:    Tuple				=  fixed_enter







import argparse
import logging
import os

import datasets
import tensorflow as tf

from transformers import AutoTokenizer


lowerCAmelCase_    =				logging.getLogger(__name__)






def      lowerCamelCase_						(				)		->						Optional[Any]:






				"""simple docstring"""


				snake_case_						:    List[str]				=  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=1_000 ,				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=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=_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.''' ,				)

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






def      lowerCamelCase_						(				_UpperCamelCase )		->						Tuple:






				"""simple docstring"""


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

				return fn






def      lowerCamelCase_						(				_UpperCamelCase )		->						Union[str, Any]:






				"""simple docstring"""


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






def      lowerCamelCase_						(				_UpperCamelCase )		->						Optional[int]:






				"""simple docstring"""


				snake_case_						:    int				=  datasets.load_dataset(args.dataset_name ,				args.dataset_config ,				split=args.split )

				if args.limit is not None:
								snake_case_						:    Union[str, Any]				=  min(len(_UpperCamelCase ) ,				args.limit )
								snake_case_						:    int				=  dataset.select(range(_UpperCamelCase ) )
								print(f'''Limiting the dataset to {args.limit} entries.''' )

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

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

				snake_case_						:    int				=  dataset_tokenized.map(_UpperCamelCase ,				batched=_UpperCamelCase ,				batch_size=1_000 ,				num_proc=4 )

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

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