
import argparse
import logging
import os

import datasets
import tensorflow as tf

from transformers import AutoTokenizer


_UpperCAmelCase      :				Optional[Any]          = logging.getLogger(__name__)


def    A (      )     ->						Dict:
		'''simple docstring'''

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

		UpperCamelCase						=  parser.parse_args()
		return args


def    A (      lowercase				)     ->						Tuple:
		'''simple docstring'''

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

		return fn


def    A (      lowercase				)     ->						Dict:
		'''simple docstring'''

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


def    A (      lowercase				)     ->						List[Any]:
		'''simple docstring'''

		UpperCamelCase						=  datasets.load_dataset(args.dataset_name					, args.dataset_config					, split=args.split				)

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

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

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

		UpperCamelCase						=  dataset_tokenized.map(lowercase					, batched=lowercase					, batch_size=1_000					, num_proc=4				)

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

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

				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=lowercase				)


if __name__ == "__main__":
					_UpperCAmelCase      :				Any          = parse_args()
					main(args)


import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
					from .tokenization_camembert import CamembertTokenizer
else:
					_UpperCAmelCase      :				List[str]          = None


_UpperCAmelCase      :				Any          = logging.get_logger(__name__)

_UpperCAmelCase      :				Tuple          = {"vocab_file": "sentencepiece.bpe.model", "tokenizer_file": "tokenizer.json"}

_UpperCAmelCase      :				List[str]          = {
    "vocab_file": {
        "camembert-base": "https://huggingface.co/camembert-base/resolve/main/sentencepiece.bpe.model",
    },
    "tokenizer_file": {
        "camembert-base": "https://huggingface.co/camembert-base/resolve/main/tokenizer.json",
    },
}

_UpperCAmelCase      :				Optional[int]          = {
    "camembert-base": 512,
}

_UpperCAmelCase      :				Union[str, Any]          = "▁"


class        lowercase      (	_SCREAMING_SNAKE_CASE     ):
		__lowercase : str			  =       VOCAB_FILES_NAMES
		__lowercase : Optional[Any]			  =       PRETRAINED_VOCAB_FILES_MAP
		__lowercase : List[str]			  =       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		__lowercase : List[str]			  =       ["input_ids", "attention_mask"]
		__lowercase : Tuple			  =       CamembertTokenizer



		def __init__(				self       ,   A_=None       ,   A_=None       ,   A_="<s>"       ,   A_="</s>"       ,   A_="</s>"       ,   A_="<s>"       ,   A_="<unk>"       ,   A_="<pad>"       ,   A_="<mask>"       ,   A_=["<s>NOTUSED", "</s>NOTUSED"]       ,   **A_       ,   )						->      List[Any]:
				"""simple docstring"""
				# Mask token behave like a normal word, i.e. include the space before it
				UpperCamelCase						=  AddedToken(A_       ,   lstrip=A_       ,   rstrip=A_ ) if isinstance(A_       ,   A_ ) else mask_token

				super().__init__(
				    A_       ,   tokenizer_file=A_       ,   bos_token=A_       ,   eos_token=A_       ,   sep_token=A_       ,   cls_token=A_       ,   unk_token=A_       ,   pad_token=A_       ,   mask_token=A_       ,   additional_special_tokens=A_       ,   **A_       ,   )

				UpperCamelCase						=  vocab_file
				UpperCamelCase						=  False if not self.vocab_file else True



		def __UpperCamelCase				(				self       ,   A_       ,   A_ = None )						->      List[int]:
				"""simple docstring"""

				if token_ids_a is None:
						return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
				UpperCamelCase						=  [self.cls_token_id]
				UpperCamelCase						=  [self.sep_token_id]
				return cls + token_ids_a + sep + sep + token_ids_a + sep



		def __UpperCamelCase				(				self       ,   A_       ,   A_ = None )						->      List[int]:
				"""simple docstring"""
				UpperCamelCase						=  [self.sep_token_id]
				UpperCamelCase						=  [self.cls_token_id]

				if token_ids_a is None:
						return len(cls + token_ids_a + sep ) * [0]
				return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0]



		def __UpperCamelCase				(				self       ,   A_       ,   A_ = None )						->      Tuple[str]:
				"""simple docstring"""
				if not self.can_save_slow_tokenizer:
						raise ValueError(
						    'Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '
						    'tokenizer.' )

				if not os.path.isdir(A_ ):
						logger.error(F'''Vocabulary path ({save_directory}) should be a directory''' )
						return
				UpperCamelCase						=  os.path.join(
				    A_       ,   (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'] )

				if os.path.abspath(self.vocab_file ) != os.path.abspath(A_ ):
						copyfile(self.vocab_file       ,   A_ )

				return (out_vocab_file,)
