





import argparse
import logging
import os

import datasets
import tensorflow as tf

from transformers import AutoTokenizer


snake_case_     =	logging.getLogger(__name__)
def 							_lowerCamelCase(  )		->  Optional[Any]:
    A       :       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.'''    ,       )

    A       :       Any							=						parser.parse_args()
    return args
def 							_lowerCamelCase(  UpperCamelCase__    : Any		)		->  Dict:
    def fn(UpperCamelCase__    : Tuple		):
        return tokenizer(examples['''text''']		)

    return fn
def 							_lowerCamelCase(  UpperCamelCase__    : Dict		)		->  Tuple:
    A       :       str							=						[]
    for i in range(len(tokenized_data['''input_ids''']		)		):
        A       :       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]		)		),
        }
        A       :       int							=						tf.train.Features(feature=UpperCamelCase__		)
        A       :       Any							=						tf.train.Example(features=UpperCamelCase__		)
        A       :       List[Any]							=						example.SerializeToString()
        records.append(UpperCamelCase__		)
    return records
def 							_lowerCamelCase(  UpperCamelCase__    : Dict		)		->  Tuple:
    A       :       Dict							=						datasets.load_dataset(args.dataset_name    ,       args.dataset_config    ,       split=args.split		)

    if args.limit is not None:
        A       :       int							=						min(len(UpperCamelCase__		)    ,       args.limit		)
        A       :       Optional[int]							=						dataset.select(range(UpperCamelCase__		)		)
        print(F'''Limiting the dataset to {args.limit} entries.'''		)

    A       :       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		)
        A       :       Dict							=						os.path.join(args.output_dir    ,       args.split		)
        if not os.path.exists(UpperCamelCase__		):
            os.makedirs(UpperCamelCase__		)
    else:
        A       :       Tuple							=						os.path.join(args.output_dir    ,       args.split		)

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

    A       :       Union[str, Any]							=						dataset_tokenized.map(UpperCamelCase__    ,       batched=UpperCamelCase__    ,       batch_size=1_000    ,       num_proc=4		)

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

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







'''simple docstring'''






def 							_lowerCamelCase(  UpperCamelCase__    : Optional[int]    ,       UpperCamelCase__    : str		)		->  Optional[int]:
    A       :       Optional[int]							=						0
    A       :       str							=						len(UpperCamelCase__		) - 1

    while left <= right:
        # avoid divided by 0 during interpolation
        if sorted_collection[left] == sorted_collection[right]:
            if sorted_collection[left] == item:
                return left
            else:
                return None

        A       :       Any							=						left + ((item - sorted_collection[left]) * (right - left)) // (
            sorted_collection[right] - sorted_collection[left]
        )

        # out of range check
        if point < 0 or point >= len(UpperCamelCase__		):
            return None

        A       :       Union[str, Any]							=						sorted_collection[point]
        if current_item == item:
            return point
        else:
            if point < left:
                A       :       Tuple							=						left
                A       :       Tuple							=						point
            elif point > right:
                A       :       Optional[Any]							=						right
                A       :       str							=						point
            else:
                if item < current_item:
                    A       :       Tuple							=						point - 1
                else:
                    A       :       Optional[Any]							=						point + 1
    return None
def 							_lowerCamelCase(  UpperCamelCase__    : Dict    ,       UpperCamelCase__    : str    ,       UpperCamelCase__    : Dict    ,       UpperCamelCase__    : List[str]		)		->  List[Any]:

    # avoid divided by 0 during interpolation
    if sorted_collection[left] == sorted_collection[right]:
        if sorted_collection[left] == item:
            return left
        else:
            return None

    A       :       Dict							=						left + ((item - sorted_collection[left]) * (right - left)) // (
        sorted_collection[right] - sorted_collection[left]
    )

    # out of range check
    if point < 0 or point >= len(UpperCamelCase__		):
        return None

    if sorted_collection[point] == item:
        return point
    elif point < left:
        return interpolation_search_by_recursion(UpperCamelCase__    ,       UpperCamelCase__    ,       UpperCamelCase__    ,       UpperCamelCase__		)
    elif point > right:
        return interpolation_search_by_recursion(UpperCamelCase__    ,       UpperCamelCase__    ,       UpperCamelCase__    ,       UpperCamelCase__		)
    else:
        if sorted_collection[point] > item:
            return interpolation_search_by_recursion(
                UpperCamelCase__    ,       UpperCamelCase__    ,       UpperCamelCase__    ,       point - 1		)
        else:
            return interpolation_search_by_recursion(
                UpperCamelCase__    ,       UpperCamelCase__    ,       point + 1    ,       UpperCamelCase__		)
def 							_lowerCamelCase(  UpperCamelCase__    : Optional[int]		)		->  List[Any]:
    if collection != sorted(UpperCamelCase__		):
        raise ValueError('''Collection must be ascending sorted'''		)
    return True


if __name__ == "__main__":
     import sys
     snake_case_     =	0
     if debug == 1:
          snake_case_     =	[10, 30, 40, 45, 50, 66, 77, 93]
          try:
               __assert_sorted(collection)
          except ValueError:
               sys.exit("""Sequence must be ascending sorted to apply interpolation search""")
          snake_case_     =	67

     snake_case_     =	interpolation_search(collection, target)
     if result is not None:
          print(f'''{target} found at positions: {result}''')
     else:
          print("""Not found""")
