
import argparse
import logging
import os

import datasets
import tensorflow as tf

from transformers import AutoTokenizer


_lowerCamelCase	:    Tuple          =      logging.getLogger(__name__)






def 	a_		(	)			->  Any:
			_snake_case          =      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.'	, )

			_snake_case          =      parser.parse_args()
			return args






def 	a_		(	__lowercase       :     Dict			)			->  Union[str, Any]:
			def fn(__lowercase       :     Tuple			):
						return tokenizer(examples['text']			)

			return fn






def 	a_		(	__lowercase       :     List[Any]			)			->  Optional[Any]:
			_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=UpperCamelCase__			)
						_snake_case          =      tf.train.Example(features=UpperCamelCase__			)
						_snake_case          =      example.SerializeToString()
						records.append(UpperCamelCase__			)
			return records






def 	a_		(	__lowercase       :     Any			)			->  Optional[int]:
			_snake_case          =      datasets.load_dataset(args.dataset_name	, args.dataset_config	, split=args.split			)

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

			# Tokenize the whole dataset at once.
			_snake_case          =      tokenize_function(UpperCamelCase__			)
			_snake_case          =      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(__lowercase       :     Dict			):
						# 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	, UpperCamelCase__	, args.max_length			)]
						    for k, t in concatenated_examples.items()
						}
						return result

			_snake_case          =      dataset_tokenized.map(UpperCamelCase__	, batched=UpperCamelCase__	, batch_size=1_000	, num_proc=4			)

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

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

import itertools
import json
import os
import unittest

from transformers import AddedToken, RobertaTokenizer, RobertaTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin




@require_tokenizers
class        SCREAMING_SNAKE_CASE__					(     UpperCAmelCase							,unittest.TestCase ):





	'''simple docstring'''



	_UpperCAmelCase	:		Tuple          = RobertaTokenizer
	_UpperCAmelCase	:		Dict          = RobertaTokenizerFast
	_UpperCAmelCase	:		List[Any]          = True
	_UpperCAmelCase	:		Any          = {"cls_token": "<s>"}



	def 			A							(  self     :   Dict     ):


				'''simple docstring'''


				super().setUp()

				# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
				_snake_case          =      [
				    'l',
				    'o',
				    'w',
				    'e',
				    'r',
				    's',
				    't',
				    'i',
				    'd',
				    'n',
				    '\u0120',
				    '\u0120l',
				    '\u0120n',
				    '\u0120lo',
				    '\u0120low',
				    'er',
				    '\u0120lowest',
				    '\u0120newer',
				    '\u0120wider',
				    '<unk>',
				]
				_snake_case          =      dict(zip(lowercase      ,  range(len(lowercase     )     )     )     )
				_snake_case          =      ['#version: 0.2', '\u0120 l', '\u0120l o', '\u0120lo w', 'e r', '']
				_snake_case          =      {'unk_token': '<unk>'}

				_snake_case          =      os.path.join(self.tmpdirname      ,  VOCAB_FILES_NAMES['vocab_file']     )
				_snake_case          =      os.path.join(self.tmpdirname      ,  VOCAB_FILES_NAMES['merges_file']     )
				with open(self.vocab_file      ,  'w'      ,  encoding='utf-8'     ) as fp:
							fp.write(json.dumps(lowercase     ) + '\n'     )
				with open(self.merges_file      ,  'w'      ,  encoding='utf-8'     ) as fp:
							fp.write('\n'.join(lowercase     )     )



	def 			A							(  self     :   List[str]      ,  **lowercase     :   List[str]     ):


				'''simple docstring'''


				kwargs.update(self.special_tokens_map     )
				return self.tokenizer_class.from_pretrained(self.tmpdirname      ,  **lowercase     )



	def 			A							(  self     :   List[str]      ,  **lowercase     :   int     ):


				'''simple docstring'''


				kwargs.update(self.special_tokens_map     )
				return RobertaTokenizerFast.from_pretrained(self.tmpdirname      ,  **lowercase     )



	def 			A							(  self     :   Optional[Any]      ,  lowercase     :   List[str]     ):


				'''simple docstring'''


				_snake_case          =      'lower newer'
				_snake_case          =      'lower newer'
				return input_text, output_text



	def 			A							(  self     :   str     ):


				'''simple docstring'''


				_snake_case          =      self.tokenizer_class(self.vocab_file      ,  self.merges_file      ,  **self.special_tokens_map     )
				_snake_case          =      'lower newer'
				_snake_case          =      ['l', 'o', 'w', 'er', '\u0120', 'n', 'e', 'w', 'er']
				_snake_case          =      tokenizer.tokenize(lowercase     )  # , add_prefix_space=True)
				self.assertListEqual(lowercase      ,  lowercase     )

				_snake_case          =      tokens + [tokenizer.unk_token]
				_snake_case          =      [0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
				self.assertListEqual(tokenizer.convert_tokens_to_ids(lowercase     )      ,  lowercase     )



	def 			A							(  self     :   List[str]     ):


				'''simple docstring'''


				_snake_case          =      self.get_tokenizer()

				self.assertListEqual(tokenizer.encode('Hello world!'      ,  add_special_tokens=lowercase     )      ,  [0, 31_414, 232, 328, 2]     )
				self.assertListEqual(
				    tokenizer.encode('Hello world! cécé herlolip 418'      ,  add_special_tokens=lowercase     )      ,  [0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2]      ,  )



	@slow
	def 			A							(  self     :   Optional[int]     ):


				'''simple docstring'''


				_snake_case          =      self.tokenizer_class.from_pretrained('roberta-base'     )

				_snake_case          =      tokenizer.encode('sequence builders'      ,  add_special_tokens=lowercase     )
				_snake_case          =      tokenizer.encode('multi-sequence build'      ,  add_special_tokens=lowercase     )

				_snake_case          =      tokenizer.encode(
				    'sequence builders'      ,  add_special_tokens=lowercase      ,  add_prefix_space=lowercase     )
				_snake_case          =      tokenizer.encode(
				    'sequence builders'      ,  'multi-sequence build'      ,  add_special_tokens=lowercase      ,  add_prefix_space=lowercase     )

				_snake_case          =      tokenizer.build_inputs_with_special_tokens(lowercase     )
				_snake_case          =      tokenizer.build_inputs_with_special_tokens(lowercase      ,  lowercase     )

				assert encoded_sentence == encoded_text_from_decode
				assert encoded_pair == encoded_pair_from_decode



	def 			A							(  self     :   int     ):


				'''simple docstring'''


				_snake_case          =      self.get_tokenizer()

				_snake_case          =      'Encode this sequence.'
				_snake_case          =      tokenizer.byte_encoder[' '.encode('utf-8'     )[0]]

				# Testing encoder arguments
				_snake_case          =      tokenizer.encode(lowercase      ,  add_special_tokens=lowercase      ,  add_prefix_space=lowercase     )
				_snake_case          =      tokenizer.convert_ids_to_tokens(encoded[0]     )[0]
				self.assertNotEqual(lowercase      ,  lowercase     )

				_snake_case          =      tokenizer.encode(lowercase      ,  add_special_tokens=lowercase      ,  add_prefix_space=lowercase     )
				_snake_case          =      tokenizer.convert_ids_to_tokens(encoded[0]     )[0]
				self.assertEqual(lowercase      ,  lowercase     )

				tokenizer.add_special_tokens({'bos_token': '<s>'}     )
				_snake_case          =      tokenizer.encode(lowercase      ,  add_special_tokens=lowercase     )
				_snake_case          =      tokenizer.convert_ids_to_tokens(encoded[1]     )[0]
				self.assertNotEqual(lowercase      ,  lowercase     )

				# Testing spaces after special tokens
				_snake_case          =      '<mask>'
				tokenizer.add_special_tokens(
				    {'mask_token': AddedToken(lowercase      ,  lstrip=lowercase      ,  rstrip=lowercase     )}     )  # mask token has a left space
				_snake_case          =      tokenizer.convert_tokens_to_ids(lowercase     )

				_snake_case          =      'Encode <mask> sequence'
				_snake_case          =      'Encode <mask>sequence'

				_snake_case          =      tokenizer.encode(lowercase     )
				_snake_case          =      encoded.index(lowercase     )
				_snake_case          =      tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]     )[0]
				self.assertEqual(lowercase      ,  lowercase     )

				_snake_case          =      tokenizer.encode(lowercase     )
				_snake_case          =      encoded.index(lowercase     )
				_snake_case          =      tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]     )[0]
				self.assertNotEqual(lowercase      ,  lowercase     )



	def 			A							(  self     :   List[str]     ):


				'''simple docstring'''


				pass



	def 			A							(  self     :   List[str]     ):


				'''simple docstring'''


				for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
							with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})'''     ):
										_snake_case          =      self.rust_tokenizer_class.from_pretrained(lowercase      ,  **lowercase     )
										_snake_case          =      self.tokenizer_class.from_pretrained(lowercase      ,  **lowercase     )
										_snake_case          =      'A, <mask> AllenNLP sentence.'
										_snake_case          =      tokenizer_r.encode_plus(lowercase      ,  add_special_tokens=lowercase      ,  return_token_type_ids=lowercase     )
										_snake_case          =      tokenizer_p.encode_plus(lowercase      ,  add_special_tokens=lowercase      ,  return_token_type_ids=lowercase     )

										# token_type_ids should put 0 everywhere
										self.assertEqual(sum(tokens_r['token_type_ids']     )      ,  sum(tokens_p['token_type_ids']     )     )

										# attention_mask should put 1 everywhere, so sum over length should be 1
										self.assertEqual(
										    sum(tokens_r['attention_mask']     ) / len(tokens_r['attention_mask']     )      ,  sum(tokens_p['attention_mask']     ) / len(tokens_p['attention_mask']     )      ,  )

										_snake_case          =      tokenizer_r.convert_ids_to_tokens(tokens_r['input_ids']     )
										_snake_case          =      tokenizer_p.convert_ids_to_tokens(tokens_p['input_ids']     )

										# Rust correctly handles the space before the mask while python doesnt
										self.assertSequenceEqual(tokens_p['input_ids']      ,  [0, 250, 6, 50_264, 3_823, 487, 21_992, 3_645, 4, 2]     )
										self.assertSequenceEqual(tokens_r['input_ids']      ,  [0, 250, 6, 50_264, 3_823, 487, 21_992, 3_645, 4, 2]     )

										self.assertSequenceEqual(
										    lowercase      ,  ['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>']     )
										self.assertSequenceEqual(
										    lowercase      ,  ['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>']     )



	def 			A							(  self     :   str     ):


				'''simple docstring'''


				for trim_offsets, add_prefix_space in itertools.product([True, False]      ,  repeat=2     ):
							_snake_case          =      self.rust_tokenizer_class.from_pretrained(
							    self.tmpdirname      ,  use_fast=lowercase      ,  add_prefix_space=lowercase      ,  trim_offsets=lowercase     )

							_snake_case          =      json.loads(tokenizer_r.backend_tokenizer.pre_tokenizer.__getstate__()     )
							_snake_case          =      json.loads(tokenizer_r.backend_tokenizer.post_processor.__getstate__()     )

							self.assertEqual(pre_tokenizer_state['add_prefix_space']      ,  lowercase     )

							self.assertEqual(post_processor_state['add_prefix_space']      ,  lowercase     )
							self.assertEqual(post_processor_state['trim_offsets']      ,  lowercase     )





	def 			A							(  self     :   Union[str, Any]     ):


				'''simple docstring'''


				for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
							with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})'''     ):
										_snake_case          =      'hello'  # `hello` is a token in the vocabulary of `pretrained_name`
										_snake_case          =      f'''{text_of_1_token} {text_of_1_token}'''

										_snake_case          =      self.rust_tokenizer_class.from_pretrained(
										    lowercase      ,  use_fast=lowercase      ,  add_prefix_space=lowercase      ,  trim_offsets=lowercase     )
										_snake_case          =      tokenizer_r(lowercase      ,  return_offsets_mapping=lowercase      ,  add_special_tokens=lowercase     )
										self.assertEqual(encoding.offset_mapping[0]      ,  (0, len(lowercase     ))     )
										self.assertEqual(
										    encoding.offset_mapping[1]      ,  (len(lowercase     ) + 1, len(lowercase     ) + 1 + len(lowercase     ))      ,  )

										_snake_case          =      self.rust_tokenizer_class.from_pretrained(
										    lowercase      ,  use_fast=lowercase      ,  add_prefix_space=lowercase      ,  trim_offsets=lowercase     )
										_snake_case          =      tokenizer_r(lowercase      ,  return_offsets_mapping=lowercase      ,  add_special_tokens=lowercase     )
										self.assertEqual(encoding.offset_mapping[0]      ,  (0, len(lowercase     ))     )
										self.assertEqual(
										    encoding.offset_mapping[1]      ,  (len(lowercase     ) + 1, len(lowercase     ) + 1 + len(lowercase     ))      ,  )

										_snake_case          =      self.rust_tokenizer_class.from_pretrained(
										    lowercase      ,  use_fast=lowercase      ,  add_prefix_space=lowercase      ,  trim_offsets=lowercase     )
										_snake_case          =      tokenizer_r(lowercase      ,  return_offsets_mapping=lowercase      ,  add_special_tokens=lowercase     )
										self.assertEqual(encoding.offset_mapping[0]      ,  (0, len(lowercase     ))     )
										self.assertEqual(
										    encoding.offset_mapping[1]      ,  (len(lowercase     ), len(lowercase     ) + 1 + len(lowercase     ))      ,  )

										_snake_case          =      self.rust_tokenizer_class.from_pretrained(
										    lowercase      ,  use_fast=lowercase      ,  add_prefix_space=lowercase      ,  trim_offsets=lowercase     )
										_snake_case          =      tokenizer_r(lowercase      ,  return_offsets_mapping=lowercase      ,  add_special_tokens=lowercase     )
										self.assertEqual(encoding.offset_mapping[0]      ,  (0, len(lowercase     ))     )
										self.assertEqual(
										    encoding.offset_mapping[1]      ,  (len(lowercase     ), len(lowercase     ) + 1 + len(lowercase     ))      ,  )

										_snake_case          =      f''' {text}'''

										# tokenizer_r = self.rust_tokenizer_class.from_pretrained(
										#     pretrained_name, use_fast=True, add_prefix_space=True, trim_offsets=True
										# )
										# encoding = tokenizer_r(text, return_offsets_mapping=True, add_special_tokens=False)
										# self.assertEqual(encoding.offset_mapping[0], (1, 1 + len(text_of_1_token)))
										# self.assertEqual(
										#     encoding.offset_mapping[1],
										#     (1 + len(text_of_1_token) + 1, 1 + len(text_of_1_token) + 1 + len(text_of_1_token)),
										# )

										_snake_case          =      self.rust_tokenizer_class.from_pretrained(
										    lowercase      ,  use_fast=lowercase      ,  add_prefix_space=lowercase      ,  trim_offsets=lowercase     )
										_snake_case          =      tokenizer_r(lowercase      ,  return_offsets_mapping=lowercase      ,  add_special_tokens=lowercase     )
										self.assertEqual(encoding.offset_mapping[0]      ,  (1, 1 + len(lowercase     ))     )
										self.assertEqual(
										    encoding.offset_mapping[1]      ,  (1 + len(lowercase     ) + 1, 1 + len(lowercase     ) + 1 + len(lowercase     ))      ,  )

										_snake_case          =      self.rust_tokenizer_class.from_pretrained(
										    lowercase      ,  use_fast=lowercase      ,  add_prefix_space=lowercase      ,  trim_offsets=lowercase     )
										_snake_case          =      tokenizer_r(lowercase      ,  return_offsets_mapping=lowercase      ,  add_special_tokens=lowercase     )
										self.assertEqual(encoding.offset_mapping[0]      ,  (0, 1 + len(lowercase     ))     )
										self.assertEqual(
										    encoding.offset_mapping[1]      ,  (1 + len(lowercase     ), 1 + len(lowercase     ) + 1 + len(lowercase     ))      ,  )

										_snake_case          =      self.rust_tokenizer_class.from_pretrained(
										    lowercase      ,  use_fast=lowercase      ,  add_prefix_space=lowercase      ,  trim_offsets=lowercase     )
										_snake_case          =      tokenizer_r(lowercase      ,  return_offsets_mapping=lowercase      ,  add_special_tokens=lowercase     )
										self.assertEqual(encoding.offset_mapping[0]      ,  (0, 1 + len(lowercase     ))     )
										self.assertEqual(
										    encoding.offset_mapping[1]      ,  (1 + len(lowercase     ), 1 + len(lowercase     ) + 1 + len(lowercase     ))      ,  )