



"""simple docstring"""







class       UpperCamelCase__   :




      """simple docstring"""




      def __init__(      self				:      Union[str, Any]    ,			SCREAMING_SNAKE_CASE_				:      str = ""    ,			SCREAMING_SNAKE_CASE_				:      bool = False       ):
        # Mapping from the first character of the prefix of the node
        lowerCAmelCase_   :				dict[str, RadixNode]								= {}

        # A node will be a leaf if the tree contains its word
        lowerCAmelCase_   :				str								= is_leaf

        lowerCAmelCase_   :				Optional[int]								= prefix



      def      SCREAMING_SNAKE_CASE__ (      self				:      List[Any]    ,			SCREAMING_SNAKE_CASE_				:      str       ):
        lowerCAmelCase_   :				Any								= 0
        for q, w in zip(self.prefix    ,			SCREAMING_SNAKE_CASE_       ):
          if q != w:
            break

          x += 1

        return self.prefix[:x], self.prefix[x:], word[x:]



      def      SCREAMING_SNAKE_CASE__ (      self				:      List[str]    ,			SCREAMING_SNAKE_CASE_				:      list[str]       ):
        for word in words:
          self.insert(SCREAMING_SNAKE_CASE_       )



      def      SCREAMING_SNAKE_CASE__ (      self				:      Optional[Any]    ,			SCREAMING_SNAKE_CASE_				:      str       ):
        # Case 1: If the word is the prefix of the node
        # Solution: We set the current node as leaf
        if self.prefix == word:
          lowerCAmelCase_   :				Union[str, Any]								= True

        # Case 2: The node has no edges that have a prefix to the word
        # Solution: We create an edge from the current node to a new one
        # containing the word
        elif word[0] not in self.nodes:
          lowerCAmelCase_   :				Union[str, Any]								= RadixNode(prefix=SCREAMING_SNAKE_CASE_    ,			is_leaf=SCREAMING_SNAKE_CASE_       )

        else:
          lowerCAmelCase_   :				Dict								= self.nodes[word[0]]
          lowerCAmelCase_						,lowerCAmelCase_						,lowerCAmelCase_   :				List[str]								= incoming_node.match(
              SCREAMING_SNAKE_CASE_       )

          # Case 3: The node prefix is equal to the matching
          # Solution: We insert remaining word on the next node
          if remaining_prefix == "":
            self.nodes[matching_string[0]].insert(SCREAMING_SNAKE_CASE_       )

          # Case 4: The word is greater equal to the matching
          # Solution: Create a node in between both nodes, change
          # prefixes and add the new node for the remaining word
          else:
            lowerCAmelCase_   :				str								= remaining_prefix

            lowerCAmelCase_   :				Optional[Any]								= self.nodes[matching_string[0]]
            lowerCAmelCase_   :				Union[str, Any]								= RadixNode(SCREAMING_SNAKE_CASE_    ,			SCREAMING_SNAKE_CASE_       )
            lowerCAmelCase_   :				Optional[Any]								= aux_node

            if remaining_word == "":
              lowerCAmelCase_   :				List[str]								= True
            else:
              self.nodes[matching_string[0]].insert(SCREAMING_SNAKE_CASE_       )



      def      SCREAMING_SNAKE_CASE__ (      self				:      Tuple    ,			SCREAMING_SNAKE_CASE_				:      str       ):
        lowerCAmelCase_   :				int								= self.nodes.get(word[0]    ,			SCREAMING_SNAKE_CASE_       )
        if not incoming_node:
          return False
        else:
          lowerCAmelCase_						,lowerCAmelCase_						,lowerCAmelCase_   :				List[str]								= incoming_node.match(
              SCREAMING_SNAKE_CASE_       )
          # If there is remaining prefix, the word can't be on the tree
          if remaining_prefix != "":
            return False
          # This applies when the word and the prefix are equal
          elif remaining_word == "":
            return incoming_node.is_leaf
          # We have word remaining so we check the next node
          else:
            return incoming_node.find(SCREAMING_SNAKE_CASE_       )



      def      SCREAMING_SNAKE_CASE__ (      self				:      Optional[int]    ,			SCREAMING_SNAKE_CASE_				:      str       ):
        lowerCAmelCase_   :				Tuple								= self.nodes.get(word[0]    ,			SCREAMING_SNAKE_CASE_       )
        if not incoming_node:
          return False
        else:
          lowerCAmelCase_						,lowerCAmelCase_						,lowerCAmelCase_   :				Dict								= incoming_node.match(
              SCREAMING_SNAKE_CASE_       )
          # If there is remaining prefix, the word can't be on the tree
          if remaining_prefix != "":
            return False
          # We have word remaining so we check the next node
          elif remaining_word != "":
            return incoming_node.delete(SCREAMING_SNAKE_CASE_       )
          else:
            # If it is not a leaf, we don't have to delete
            if not incoming_node.is_leaf:
              return False
            else:
              # We delete the nodes if no edges go from it
              if len(incoming_node.nodes       ) == 0:
                del self.nodes[word[0]]
                # We merge the current node with its only child
                if len(self.nodes       ) == 1 and not self.is_leaf:
                  lowerCAmelCase_   :				Optional[int]								= list(self.nodes.values()       )[0]
                  lowerCAmelCase_   :				Any								= merging_node.is_leaf
                  self.prefix += merging_node.prefix
                  lowerCAmelCase_   :				Optional[Any]								= merging_node.nodes
                    # If there is more than 1 edge, we just mark it as non-leaf
              elif len(incoming_node.nodes       ) > 1:
                lowerCAmelCase_   :				Optional[int]								= False
              # If there is 1 edge, we merge it with its child
              else:
                lowerCAmelCase_   :				str								= list(incoming_node.nodes.values()       )[0]
                lowerCAmelCase_   :				int								= merging_node.is_leaf
                incoming_node.prefix += merging_node.prefix
                lowerCAmelCase_   :				str								= merging_node.nodes

              return True





      def      SCREAMING_SNAKE_CASE__ (      self				:      Tuple    ,			SCREAMING_SNAKE_CASE_				:      int = 0       ):
        if self.prefix != "":
          print('-' * height    ,			self.prefix    ,			'  (leaf)' if self.is_leaf else ''       )

        for value in self.nodes.values():
          value.print_tree(height + 1       )




def 				UpperCamelCase_    (    )						->   bool:

  """simple docstring"""




  lowerCAmelCase_   :				int								= 'banana bananas bandana band apple all beast'.split()
  lowerCAmelCase_   :				List[str]								= RadixNode()
  root.insert_many(lowerCAmelCase__						)

  assert all(root.find(lowerCAmelCase__						) for word in words						)
  assert not root.find('bandanas'						)
  assert not root.find('apps'						)
  root.delete('all'						)
  assert not root.find('all'						)
  root.delete('banana'						)
  assert not root.find('banana'						)
  assert root.find('bananas'						)

  return True

def 				UpperCamelCase_    (    )						->   None:

  """simple docstring"""




  assert test_trie()

def 				UpperCamelCase_    (    )						->   None:

  """simple docstring"""




  lowerCAmelCase_   :				Any								= RadixNode()
  lowerCAmelCase_   :				List[Any]								= 'banana bananas bandanas bandana band apple all beast'.split()
  root.insert_many(lowerCAmelCase__						)

  print('Words:'    ,   lowerCAmelCase__						)
  print('Tree:'						)
  root.print_tree()


if __name__ == "__main__":
     main()










"""simple docstring"""







import argparse
import logging
import os

import datasets
import tensorflow as tf

from transformers import AutoTokenizer


lowercase__		:				List[Any]          =	logging.getLogger(__name__)

def 				UpperCamelCase_    (    )						->   Dict:

  """simple docstring"""




  lowerCAmelCase_   :				Tuple								= argparse.ArgumentParser(
      description='Prepare TFRecord shards from pre-tokenized samples of the wikitext dataset.'						)
  parser.add_argument(
      '--dataset_name'    ,   type=lowerCAmelCase__    ,   default='wikitext'    ,   help='Name of the training. Explore datasets at: hf.co/datasets.'    ,   )
  parser.add_argument(
      '--dataset_config'    ,   type=lowerCAmelCase__    ,   default='wikitext-103-raw-v1'    ,   help='Configuration name of the dataset.'						)
  parser.add_argument(
      '--tokenizer_name_or_path'    ,   type=lowerCAmelCase__    ,   default='sayakpaul/unigram-tokenizer-wikitext'    ,   help='Tokenizer identifier. Can be a local filepath or a Hub identifier.'    ,   )
  parser.add_argument(
      '--shard_size'    ,   type=lowerCAmelCase__    ,   default=1000    ,   help='Number of entries to go in a single shard.'    ,   )
  parser.add_argument('--split'    ,   type=lowerCAmelCase__    ,   default='train'    ,   choices=['train', 'test', 'validation']						)
  parser.add_argument(
      '--limit'    ,   default=lowerCAmelCase__    ,   type=lowerCAmelCase__    ,   help='Limit the number of shards (used for debugging).'    ,   )
  parser.add_argument(
      '--max_length'    ,   type=lowerCAmelCase__    ,   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=lowerCAmelCase__    ,   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_   :				List[Any]								= parser.parse_args()
  return args

def 				UpperCamelCase_    (    lowerCAmelCase__      :				int						)						->   Union[str, Any]:

  """simple docstring"""




  def fn(lowerCAmelCase__      :				Optional[Any]						):
    return tokenizer(examples['text']						)

  return fn

def 				UpperCamelCase_    (    lowerCAmelCase__      :				Tuple						)						->   Dict:

  """simple docstring"""




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

def 				UpperCamelCase_    (    lowerCAmelCase__      :				Optional[Any]						)						->   Optional[int]:

  """simple docstring"""




  lowerCAmelCase_   :				str								= datasets.load_dataset(args.dataset_name    ,   args.dataset_config    ,   split=args.split						)

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

  lowerCAmelCase_   :				List[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_   :				int								= os.path.join(args.output_dir    ,   args.split						)
    if not os.path.exists(lowerCAmelCase__						):
      os.makedirs(lowerCAmelCase__						)
  else:
    lowerCAmelCase_   :				Dict								= os.path.join(args.output_dir    ,   args.split						)

  # Tokenize the whole dataset at once.
  lowerCAmelCase_   :				Dict								= tokenize_function(lowerCAmelCase__						)
  lowerCAmelCase_   :				List[Any]								= dataset.map(lowerCAmelCase__    ,   batched=lowerCAmelCase__    ,   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(lowerCAmelCase__      :				List[Any]						):
    # Concatenate all texts.
    lowerCAmelCase_   :				int								= {k: sum(examples[k]    ,   []						) for k in examples.keys()}
    lowerCAmelCase_   :				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_   :				Optional[Any]								= {
        k: [t[i : i + args.max_length] for i in range(0    ,   lowerCAmelCase__    ,   args.max_length						)]
        for k, t in concatenated_examples.items()
    }
    return result

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

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

    with tf.io.TFRecordWriter(lowerCAmelCase__						) as out_file:
      for i in range(len(lowerCAmelCase__						)						):
        lowerCAmelCase_   :				Dict								= serialized_examples[i]
        out_file.write(lowerCAmelCase__						)
      print('Wrote file {} containing {} records'.format(lowerCAmelCase__    ,   lowerCAmelCase__						)						)

    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=lowerCAmelCase__						)


if __name__ == "__main__":
     lowercase__		:				int          =	parse_args()
     main(args)





