






"""simple docstring"""






import argparse
import logging
import os

import datasets
import tensorflow as tf

from transformers import AutoTokenizer


SCREAMING_SNAKE_CASE						:						int					   =       logging.getLogger(__name__)



def  __lowerCamelCase					(					):
				A__							 =       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.'       ,)

				A__							 =       parser.parse_args()
				return args



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

				return fn



def  __lowerCamelCase					(					lowerCAmelCase__				):
				A__							 =       []
				for i in range(len(tokenized_data['input_ids']				)				):
								A__							 =       {
								    '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__							 =       tf.train.Features(feature=lowerCAmelCase__				)
								A__							 =       tf.train.Example(features=lowerCAmelCase__				)
								A__							 =       example.SerializeToString()
								records.append(lowerCAmelCase__				)
				return records



def  __lowerCamelCase					(					lowerCAmelCase__				):
				A__							 =       datasets.load_dataset(args.dataset_name       ,args.dataset_config       ,split=args.split				)

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

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

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

				A__							 =       dataset_tokenized.map(lowerCAmelCase__       ,batched=lowerCAmelCase__       ,batch_size=1000       ,num_proc=4				)

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

								with tf.io.TFRecordWriter(lowerCAmelCase__				) as out_file:
												for i in range(len(lowerCAmelCase__				)				):
																A__							 =       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__":
					SCREAMING_SNAKE_CASE						:						List[Any]					   =       parse_args()
					main(args)










"""simple docstring"""






from ..utils import DummyObject, requires_backends





class       snake_case_       (    metaclass=_lowerCamelCase       ):



	"""simple docstring"""

	SCREAMING_SNAKE_CASE_:							List[str]   			=					["""note_seq"""]


	def __init__(      self  ,					*__a  ,					**__a						):


					"""simple docstring"""

					requires_backends(self  ,					['note_seq']						)


	@classmethod
	def       _UpperCAmelCase						(      cls  ,					*__a  ,					**__a						):


					"""simple docstring"""

					requires_backends(cls  ,					['note_seq']						)






	@classmethod
	def       _UpperCAmelCase						(      cls  ,					*__a  ,					**__a						):


					"""simple docstring"""

					requires_backends(cls  ,					['note_seq']						)


