


snake_case__				:	str					       =					[
    'Audio',
    'Array2D',
    'Array3D',
    'Array4D',
    'Array5D',
    'ClassLabel',
    'Features',
    'Sequence',
    'Value',
    'Image',
    'Translation',
    'TranslationVariableLanguages',
]
from .audio import Audio
from .features import ArrayaD, ArrayaD, ArrayaD, ArrayaD, ClassLabel, Features, Sequence, Value
from .image import Image
from .translation import Translation, TranslationVariableLanguages









import argparse
import logging
import os

import datasets
import tensorflow as tf

from transformers import AutoTokenizer


snake_case__				:	Dict					       =					logging.getLogger(__name__)


def    __lowerCamelCase   (      )     ->    Any:
   lowerCamelCase_						:   str		= 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=1000						,						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=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=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."""						,						)

   lowerCamelCase_						:   Optional[int]		= parser.parse_args()
   return args


def    __lowerCamelCase   (      A__						: Tuple					)     ->    str:
   def fn(A__						: Dict					):
      return tokenizer(examples["""text"""]					)

   return fn


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


def    __lowerCamelCase   (      A__						: Tuple					)     ->    Union[str, Any]:
   lowerCamelCase_						:   int		= datasets.load_dataset(args.dataset_name						,						args.dataset_config						,						split=args.split					)

   if args.limit is not None:
      lowerCamelCase_						:   str		= min(len(A__					)						,						args.limit					)
      lowerCamelCase_						:   Dict		= dataset.select(range(A__					)					)
      print(f'''Limiting the dataset to {args.limit} entries.'''					)

   lowerCamelCase_						:   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					)
      lowerCamelCase_						:   Optional[int]		= os.path.join(args.output_dir						,						args.split					)
      if not os.path.exists(A__					):
         os.makedirs(A__					)
   else:
      lowerCamelCase_						:   Any		= os.path.join(args.output_dir						,						args.split					)

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

   lowerCamelCase_						:   Optional[int]		= dataset_tokenized.map(A__						,						batched=A__						,						batch_size=1000						,						num_proc=4					)

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

      with tf.io.TFRecordWriter(A__					) as out_file:
         for i in range(len(A__					)					):
            lowerCamelCase_						:   Dict		= 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__":
      snake_case__				:	str					       =					parse_args()
      main(args)





