
"""simple docstring"""




import argparse
import logging
import os

import datasets
import tensorflow as tf

from transformers import AutoTokenizer


UpperCAmelCase__   :					Dict												=				logging.getLogger(__name__)

def lowercase_						(     ):
							SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     argparse.ArgumentParser(
							    description="""Prepare TFRecord shards from pre-tokenized samples of the wikitext dataset."""		)
							parser.add_argument(
							    """--dataset_name"""     ,type=_snake_case     ,default="""wikitext"""     ,help="""Name of the training. Explore datasets at: hf.co/datasets."""     ,)
							parser.add_argument(
							    """--dataset_config"""     ,type=_snake_case     ,default="""wikitext-103-raw-v1"""     ,help="""Configuration name of the dataset."""		)
							parser.add_argument(
							    """--tokenizer_name_or_path"""     ,type=_snake_case     ,default="""sayakpaul/unigram-tokenizer-wikitext"""     ,help="""Tokenizer identifier. Can be a local filepath or a Hub identifier."""     ,)
							parser.add_argument(
							    """--shard_size"""     ,type=_snake_case     ,default=1_000     ,help="""Number of entries to go in a single shard."""     ,)
							parser.add_argument("""--split"""     ,type=_snake_case     ,default="""train"""     ,choices=["""train""", """test""", """validation"""]		)
							parser.add_argument(
							    """--limit"""     ,default=_snake_case     ,type=_snake_case     ,help="""Limit the number of shards (used for debugging)."""     ,)
							parser.add_argument(
							    """--max_length"""     ,type=_snake_case     ,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=_snake_case     ,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__		: Any     =     parser.parse_args()
							return args

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

							return fn

def lowercase_						(     _snake_case		):
							SCREAMING_SNAKE_CASE__		: Optional[Any]     =     []
							for i in range(len(tokenized_data["""input_ids"""]		)		):
														SCREAMING_SNAKE_CASE__		: List[Any]     =     {
														    """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__		: Any     =     tf.train.Features(feature=_snake_case		)
														SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     tf.train.Example(features=_snake_case		)
														SCREAMING_SNAKE_CASE__		: Tuple     =     example.SerializeToString()
														records.append(_snake_case		)
							return records

def lowercase_						(     _snake_case		):
							SCREAMING_SNAKE_CASE__		: Any     =     datasets.load_dataset(args.dataset_name     ,args.dataset_config     ,split=args.split		)

							if args.limit is not None:
														SCREAMING_SNAKE_CASE__		: List[Any]     =     min(len(_snake_case		)     ,args.limit		)
														SCREAMING_SNAKE_CASE__		: Optional[Any]     =     dataset.select(range(_snake_case		)		)
														print(f'''Limiting the dataset to {args.limit} entries.'''		)

							SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     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__		: Union[str, Any]     =     os.path.join(args.output_dir     ,args.split		)
														if not os.path.exists(_snake_case		):
																					os.makedirs(_snake_case		)
							else:
														SCREAMING_SNAKE_CASE__		: Tuple     =     os.path.join(args.output_dir     ,args.split		)

							# Tokenize the whole dataset at once.
							SCREAMING_SNAKE_CASE__		: List[str]     =     tokenize_function(_snake_case		)
							SCREAMING_SNAKE_CASE__		: List[str]     =     dataset.map(_snake_case     ,batched=_snake_case     ,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(_snake_case		):
														# Concatenate all texts.
														SCREAMING_SNAKE_CASE__		: Optional[int]     =     {k: sum(examples[k]     ,[]		) for k in examples.keys()}
														SCREAMING_SNAKE_CASE__		: int     =     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__		: Tuple     =     (total_length // args.max_length) * args.max_length
														# Split by chunks of max_len.
														SCREAMING_SNAKE_CASE__		: Any     =     {
														    k: [t[i : i + args.max_length] for i in range(0     ,_snake_case     ,args.max_length		)]
														    for k, t in concatenated_examples.items()
														}
														return result

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

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

														with tf.io.TFRecordWriter(_snake_case		) as out_file:
																					for i in range(len(_snake_case		)		):
																												SCREAMING_SNAKE_CASE__		: List[Any]     =     serialized_examples[i]
																												out_file.write(_snake_case		)
																					print("""Wrote file {} containing {} records""".format(_snake_case     ,_snake_case		)		)

														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=_snake_case		)


if __name__ == "__main__":
					UpperCAmelCase__   :					List[Any]												=				parse_args()
					main(args)






"""simple docstring"""




import logging

from transformers import PretrainedConfig


UpperCAmelCase__   :					Union[str, Any]												=				logging.getLogger(__name__)


UpperCAmelCase__   :					Any												=				{
    'bertabs-finetuned-cnndm': 'https://huggingface.co/remi/bertabs-finetuned-cnndm-extractive-abstractive-summarization/resolve/main/config.json',
}



class 			lowerCAmelCase_		(a__						):


				"""simple docstring"""



				__UpperCamelCase   :		List[Any]       =     '''bertabs'''



				def __init__(self     , SCREAMING_SNAKE_CASE__=3_05_22     , SCREAMING_SNAKE_CASE__=5_12     , SCREAMING_SNAKE_CASE__=6     , SCREAMING_SNAKE_CASE__=5_12     , SCREAMING_SNAKE_CASE__=8     , SCREAMING_SNAKE_CASE__=5_12     , SCREAMING_SNAKE_CASE__=0.2     , SCREAMING_SNAKE_CASE__=6     , SCREAMING_SNAKE_CASE__=7_68     , SCREAMING_SNAKE_CASE__=8     , SCREAMING_SNAKE_CASE__=20_48     , SCREAMING_SNAKE_CASE__=0.2     , **SCREAMING_SNAKE_CASE__     , )      ->	Union[str, Any]:



											"""simple docstring"""




											super().__init__(**SCREAMING_SNAKE_CASE__					)

											SCREAMING_SNAKE_CASE__		: Optional[Any]     =     vocab_size
											SCREAMING_SNAKE_CASE__		: Any     =     max_pos

											SCREAMING_SNAKE_CASE__		: List[Any]     =     enc_layers
											SCREAMING_SNAKE_CASE__		: Tuple     =     enc_hidden_size
											SCREAMING_SNAKE_CASE__		: Dict     =     enc_heads
											SCREAMING_SNAKE_CASE__		: Optional[Any]     =     enc_ff_size
											SCREAMING_SNAKE_CASE__		: Dict     =     enc_dropout

											SCREAMING_SNAKE_CASE__		: Any     =     dec_layers
											SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     dec_hidden_size
											SCREAMING_SNAKE_CASE__		: Any     =     dec_heads
											SCREAMING_SNAKE_CASE__		: List[Any]     =     dec_ff_size
											SCREAMING_SNAKE_CASE__		: str     =     dec_dropout




