import argparse
import logging
import os

import datasets
import tensorflow as tf

from transformers import AutoTokenizer


A     :  str					     =       logging.getLogger(__name__)


def      a__ (		):
      SCREAMING_SNAKE_CASE_          =  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_0_0_0     ,      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_1_2     ,      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."     ,      )

      SCREAMING_SNAKE_CASE_          =  parser.parse_args()
      return args


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

      return fn


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


def      a__ (		__UpperCamelCase				):
      SCREAMING_SNAKE_CASE_          =  datasets.load_dataset(args.dataset_name     ,      args.dataset_config     ,      split=args.split				)

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

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

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

      SCREAMING_SNAKE_CASE_          =  dataset_tokenized.map(__UpperCamelCase     ,      batched=__UpperCamelCase     ,      batch_size=1_0_0_0     ,      num_proc=4				)

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

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



from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


A     :  str					     =       {
    "configuration_rembert": ["REMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "RemBertConfig", "RemBertOnnxConfig"]
}

try:
      if not is_sentencepiece_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A     :  Tuple					     =       ["RemBertTokenizer"]

try:
      if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A     :  Tuple					     =       ["RemBertTokenizerFast"]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A     :  Tuple					     =       [
          "REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
          "RemBertForCausalLM",
          "RemBertForMaskedLM",
          "RemBertForMultipleChoice",
          "RemBertForQuestionAnswering",
          "RemBertForSequenceClassification",
          "RemBertForTokenClassification",
          "RemBertLayer",
          "RemBertModel",
          "RemBertPreTrainedModel",
          "load_tf_weights_in_rembert",
      ]


try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A     :  List[str]					     =       [
          "TF_REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
          "TFRemBertForCausalLM",
          "TFRemBertForMaskedLM",
          "TFRemBertForMultipleChoice",
          "TFRemBertForQuestionAnswering",
          "TFRemBertForSequenceClassification",
          "TFRemBertForTokenClassification",
          "TFRemBertLayer",
          "TFRemBertModel",
          "TFRemBertPreTrainedModel",
      ]


if TYPE_CHECKING:
      from .configuration_rembert import REMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, RemBertConfig, RemBertOnnxConfig

      try:
            if not is_sentencepiece_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_rembert import RemBertTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_rembert_fast import RemBertTokenizerFast

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_rembert import (
                REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                RemBertForCausalLM,
                RemBertForMaskedLM,
                RemBertForMultipleChoice,
                RemBertForQuestionAnswering,
                RemBertForSequenceClassification,
                RemBertForTokenClassification,
                RemBertLayer,
                RemBertModel,
                RemBertPreTrainedModel,
                load_tf_weights_in_rembert,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_rembert import (
                TF_REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                TFRemBertForCausalLM,
                TFRemBertForMaskedLM,
                TFRemBertForMultipleChoice,
                TFRemBertForQuestionAnswering,
                TFRemBertForSequenceClassification,
                TFRemBertForTokenClassification,
                TFRemBertLayer,
                TFRemBertModel,
                TFRemBertPreTrainedModel,
            )


else:
      import sys

      A     :  Tuple					     =       _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)


