import argparse
import logging
import os

import datasets
import tensorflow as tf

from transformers import AutoTokenizer


UpperCamelCase__    							= logging.getLogger(__name__)




def        lowerCAmelCase_			(					)					->    Tuple:







      '''simple docstring'''


      UpperCAmelCase__								=  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.",  )

      UpperCAmelCase__								=  parser.parse_args()
      return args




def        lowerCAmelCase_			(					__A							)					->    Tuple:







      '''simple docstring'''


      def fn(__A							):
            return tokenizer(examples["text"]							)

      return fn




def        lowerCAmelCase_			(					__A							)					->    Optional[int]:







      '''simple docstring'''


      UpperCAmelCase__								=  []
      for i in range(len(tokenized_data["input_ids"]							)							):
            UpperCAmelCase__								=  {
                "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__								=  tf.train.Features(feature=__UpperCamelCase							)
            UpperCAmelCase__								=  tf.train.Example(features=__UpperCamelCase							)
            UpperCAmelCase__								=  example.SerializeToString()
            records.append(__UpperCamelCase							)
      return records




def        lowerCAmelCase_			(					__A							)					->    Any:







      '''simple docstring'''


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

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

      UpperCAmelCase__								=  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__								=  os.path.join(args.output_dir,  args.split							)
            if not os.path.exists(__UpperCamelCase							):
                  os.makedirs(__UpperCamelCase							)
      else:
            UpperCAmelCase__								=  os.path.join(args.output_dir,  args.split							)

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

      UpperCAmelCase__								=  dataset_tokenized.map(__UpperCamelCase,  batched=__UpperCamelCase,  batch_size=1_000,  num_proc=4							)

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

            with tf.io.TFRecordWriter(__UpperCamelCase							) as out_file:
                  for i in range(len(__UpperCamelCase							)							):
                        UpperCAmelCase__								=  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__":
       UpperCamelCase__    							= parse_args()
       main(args)




def        lowerCAmelCase_			(					__A							)					->    str:







      '''simple docstring'''


      return " ".join(input_str.split()[::-1]							)


if __name__ == "__main__":
      import doctest

      doctest.testmod()



