




'''simple docstring'''


import argparse
import logging
import os

import datasets
import tensorflow as tf

from transformers import AutoTokenizer


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

						return fn
def 						lowerCamelCase__   (				a							):
						__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=a							)
												__snake_case						 =   tf.train.Example(features=a							)
												__snake_case						 =   example.SerializeToString()
												records.append(a							)
						return records
def 						lowerCamelCase__   (				a							):
						__snake_case						 =   datasets.load_dataset(args.dataset_name    ,	args.dataset_config    ,	split=args.split							)

						if args.limit is not None:
												__snake_case						 =   min(len(a							)    ,	args.limit							)
												__snake_case						 =   dataset.select(range(a							)							)
												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(a							):
																		os.makedirs(a							)
						else:
												__snake_case						 =   os.path.join(args.output_dir    ,	args.split							)

						# Tokenize the whole dataset at once.
						__snake_case						 =   tokenize_function(a							)
						__snake_case						 =   dataset.map(a    ,	batched=a    ,	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(a							):
												# 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    ,	a    ,	args.max_length							)]
												    for k, t in concatenated_examples.items()
												}
												return result

						__snake_case						 =   dataset_tokenized.map(a    ,	batched=a    ,	batch_size=1000    ,	num_proc=4							)

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

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

												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=a							)


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







'''simple docstring'''


from __future__ import annotations

import math
def 						lowerCamelCase__   (				a    ,	a							):
						__snake_case						 =   u
						for i in range(1    ,	a							):
												__snake_case						 =   temp * (u - i)
						return temp
def 						lowerCamelCase__   (				):
						__snake_case						 =   int(input('enter the numbers of values: '							)							)
						__snake_case						 =   []
						for _ in range(a							):
												y.append([]							)
						for i in range(a							):
												for j in range(a							):
																		y[i].append(a							)
																		__snake_case						 =   0

						print('enter the values of parameters in a list: '							)
						__snake_case						 =   list(map(a    ,	input().split()							)							)

						print('enter the values of corresponding parameters: '							)
						for i in range(a							):
												__snake_case						 =   float(input()							)

						__snake_case						 =   int(input('enter the value to interpolate: '							)							)
						__snake_case						 =   (value - x[0]) / (x[1] - x[0])

						# for calculating forward difference table

						for i in range(1    ,	a							):
												for j in range(n - i							):
																		__snake_case						 =   y[j + 1][i - 1] - y[j][i - 1]

						__snake_case						 =   y[0][0]
						for i in range(1    ,	a							):
												summ += (ucal(a    ,	a							) * y[0][i]) / math.factorial(a							)

						print(f'the value at {value} is {summ}'							)


if __name__ == "__main__":
	main()

