

import argparse
import logging
import os

import datasets
import tensorflow as tf

from transformers import AutoTokenizer


lowerCAmelCase_       	=	logging.getLogger(__name__)


def   __SCREAMING_SNAKE_CASE					():
						snake_case_   =	argparse.ArgumentParser(
						    description='''Prepare TFRecord shards from pre-tokenized samples of the wikitext dataset.'''       )
						parser.add_argument(
						    '''--dataset_name'''  ,		type=SCREAMING_SNAKE_CASE__  ,		default='''wikitext'''  ,		help='''Name of the training. Explore datasets at: hf.co/datasets.'''  ,		)
						parser.add_argument(
						    '''--dataset_config'''  ,		type=SCREAMING_SNAKE_CASE__  ,		default='''wikitext-103-raw-v1'''  ,		help='''Configuration name of the dataset.'''       )
						parser.add_argument(
						    '''--tokenizer_name_or_path'''  ,		type=SCREAMING_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=SCREAMING_SNAKE_CASE__  ,		default=1000  ,		help='''Number of entries to go in a single shard.'''  ,		)
						parser.add_argument('''--split'''  ,		type=SCREAMING_SNAKE_CASE__  ,		default='''train'''  ,		choices=['''train''', '''test''', '''validation''']       )
						parser.add_argument(
						    '''--limit'''  ,		default=SCREAMING_SNAKE_CASE__  ,		type=SCREAMING_SNAKE_CASE__  ,		help='''Limit the number of shards (used for debugging).'''  ,		)
						parser.add_argument(
						    '''--max_length'''  ,		type=SCREAMING_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=SCREAMING_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.'''  ,		)

						snake_case_   =	parser.parse_args()
						return args


def   __SCREAMING_SNAKE_CASE					(SCREAMING_SNAKE_CASE__       ):
						def fn(SCREAMING_SNAKE_CASE__       ):
												return tokenizer(examples['''text''']       )

						return fn


def   __SCREAMING_SNAKE_CASE					(SCREAMING_SNAKE_CASE__       ):
						snake_case_   =	[]
						for i in range(len(tokenized_data['''input_ids''']       )       ):
												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]       )       ),
												}
												snake_case_   =	tf.train.Features(feature=SCREAMING_SNAKE_CASE__       )
												snake_case_   =	tf.train.Example(features=SCREAMING_SNAKE_CASE__       )
												snake_case_   =	example.SerializeToString()
												records.append(SCREAMING_SNAKE_CASE__       )
						return records


def   __SCREAMING_SNAKE_CASE					(SCREAMING_SNAKE_CASE__       ):
						snake_case_   =	datasets.load_dataset(args.dataset_name  ,		args.dataset_config  ,		split=args.split       )

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

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

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

						snake_case_   =	dataset_tokenized.map(SCREAMING_SNAKE_CASE__  ,		batched=SCREAMING_SNAKE_CASE__  ,		batch_size=1000  ,		num_proc=4       )

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

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


if __name__ == "__main__":
					lowerCAmelCase_       	=	parse_args()
					main(args)


from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCAmelCase_       	=	logging.get_logger(__name__)

lowerCAmelCase_       	=	{
    '''sayakpaul/vit-msn-base''': '''https://huggingface.co/sayakpaul/vit-msn-base/resolve/main/config.json''',
    # See all ViT MSN models at https://huggingface.co/models?filter=vit_msn
}





class 	snake_case_   (		__A					):







			'''simple docstring'''

			SCREAMING_SNAKE_CASE     :   List[Any]									=				"vit_msn"







			def __init__(     self :				Dict							,   _UpperCamelCase :				Optional[int]=7_6_8							,   _UpperCamelCase :				Optional[Any]=1_2							,   _UpperCamelCase :				Union[str, Any]=1_2							,   _UpperCamelCase :				str=3_0_7_2							,   _UpperCamelCase :				Tuple="gelu"							,   _UpperCamelCase :				Dict=0.0							,   _UpperCamelCase :				Dict=0.0							,   _UpperCamelCase :				List[str]=0.02							,   _UpperCamelCase :				List[Any]=1e-06							,   _UpperCamelCase :				Any=2_2_4							,   _UpperCamelCase :				Optional[Any]=1_6							,   _UpperCamelCase :				Any=3							,   _UpperCamelCase :				str=True							,   **_UpperCamelCase :				Any							,   )      ->int:
									super().__init__(**_UpperCamelCase							)

									snake_case_   =	hidden_size
									snake_case_   =	num_hidden_layers
									snake_case_   =	num_attention_heads
									snake_case_   =	intermediate_size
									snake_case_   =	hidden_act
									snake_case_   =	hidden_dropout_prob
									snake_case_   =	attention_probs_dropout_prob
									snake_case_   =	initializer_range
									snake_case_   =	layer_norm_eps
									snake_case_   =	image_size
									snake_case_   =	patch_size
									snake_case_   =	num_channels
									snake_case_   =	qkv_bias