import json
import os
from functools import lru_cache
from typing import Dict, List, Optional, Tuple, Union

import regex as re

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...tokenization_utils_base import BatchEncoding, EncodedInput
from ...utils import PaddingStrategy, logging


UpperCAmelCase_           =				logging.get_logger(__name__)


UpperCAmelCase_           =				{'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt'''}

# See all LED models at https://huggingface.co/models?filter=LED
UpperCAmelCase_           =				{
    '''vocab_file''': {
        '''allenai/led-base-16384''': '''https://huggingface.co/allenai/led-base-16384/resolve/main/vocab.json''',
    },
    '''merges_file''': {
        '''allenai/led-base-16384''': '''https://huggingface.co/allenai/led-base-16384/resolve/main/merges.txt''',
    },
    '''tokenizer_file''': {
        '''allenai/led-base-16384''': '''https://huggingface.co/allenai/led-base-16384/resolve/main/tokenizer.json''',
    },
}

UpperCAmelCase_           =				{
    '''allenai/led-base-16384''': 1_6_3_8_4,
}




@lru_cache()
# Copied from transformers.models.bart.tokenization_bart.bytes_to_unicode
def    lowerCAmelCase_  (		)     ->					Tuple:







 '''simple docstring'''
 _UpperCamelCase:  Any    						=			(
     list(range(ord('''!'''		)	,       ord('''~'''		) + 1		)		) + list(range(ord('''¡'''		)	,       ord('''¬'''		) + 1		)		) + list(range(ord('''®'''		)	,       ord('''ÿ'''		) + 1		)		)
 )
 _UpperCamelCase:  int    						=			bs[:]
 _UpperCamelCase:  List[str]    						=			0
 for b in range(2**8		):
  if b not in bs:
   bs.append(lowercase		)
   cs.append(2**8 + n		)
   n += 1
 _UpperCamelCase:  Dict    						=			[chr(lowercase		) for n in cs]
 return dict(zip(lowercase	,       lowercase		)		)




def    lowerCAmelCase_  (		lowercase:				str		)     ->					List[Any]:







 '''simple docstring'''
 _UpperCamelCase:  Union[str, Any]    						=			set()
 _UpperCamelCase:  List[Any]    						=			word[0]
 for char in word[1:]:
  pairs.add((prev_char, char)		)
  _UpperCamelCase:  List[Any]    						=			char
 return pairs





class 			__magic_name__	(		__a     ):





   """simple docstring"""




   lowerCAmelCase   :     Optional[int]	      = VOCAB_FILES_NAMES
   lowerCAmelCase   :     Any	      = PRETRAINED_VOCAB_FILES_MAP
   lowerCAmelCase   :     str	      = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   lowerCAmelCase   :     Any	      = ['''input_ids''', '''attention_mask''']



   def __init__(						self       :	List[Any]		,	_lowercase       :	int		,	_lowercase       :	List[Any]		,	_lowercase       :	Union[str, Any]="replace"		,	_lowercase       :	int="<s>"		,	_lowercase       :	List[Any]="</s>"		,	_lowercase       :	List[Any]="</s>"		,	_lowercase       :	Any="<s>"		,	_lowercase       :	int="<unk>"		,	_lowercase       :	List[str]="<pad>"		,	_lowercase       :	str="<mask>"		,	_lowercase       :	Optional[Any]=False		,	**_lowercase       :	Optional[int]		,	):



    """simple docstring"""



    _UpperCamelCase:  int    						=			AddedToken(_lowercase		,	lstrip=_lowercase		,	rstrip=_lowercase				) if isinstance(_lowercase		,	_lowercase				) else bos_token
    _UpperCamelCase:  List[Any]    						=			AddedToken(_lowercase		,	lstrip=_lowercase		,	rstrip=_lowercase				) if isinstance(_lowercase		,	_lowercase				) else eos_token
    _UpperCamelCase:  Tuple    						=			AddedToken(_lowercase		,	lstrip=_lowercase		,	rstrip=_lowercase				) if isinstance(_lowercase		,	_lowercase				) else sep_token
    _UpperCamelCase:  Any    						=			AddedToken(_lowercase		,	lstrip=_lowercase		,	rstrip=_lowercase				) if isinstance(_lowercase		,	_lowercase				) else cls_token
    _UpperCamelCase:  Tuple    						=			AddedToken(_lowercase		,	lstrip=_lowercase		,	rstrip=_lowercase				) if isinstance(_lowercase		,	_lowercase				) else unk_token
    _UpperCamelCase:  List[str]    						=			AddedToken(_lowercase		,	lstrip=_lowercase		,	rstrip=_lowercase				) if isinstance(_lowercase		,	_lowercase				) else pad_token

    # Mask token behave like a normal word, i.e. include the space before it
    _UpperCamelCase:  Optional[Any]    						=			AddedToken(_lowercase		,	lstrip=_lowercase		,	rstrip=_lowercase				) if isinstance(_lowercase		,	_lowercase				) else mask_token

    super().__init__(
        errors=_lowercase		,	bos_token=_lowercase		,	eos_token=_lowercase		,	unk_token=_lowercase		,	sep_token=_lowercase		,	cls_token=_lowercase		,	pad_token=_lowercase		,	mask_token=_lowercase		,	add_prefix_space=_lowercase		,	**_lowercase		,	)

    with open(_lowercase		,	encoding='''utf-8'''				) as vocab_handle:
     _UpperCamelCase:  Any    						=			json.load(_lowercase				)
    _UpperCamelCase:  Dict    						=			{v: k for k, v in self.encoder.items()}
    _UpperCamelCase:  Dict    						=			errors  # how to handle errors in decoding
    _UpperCamelCase:  List[Any]    						=			bytes_to_unicode()
    _UpperCamelCase:  Union[str, Any]    						=			{v: k for k, v in self.byte_encoder.items()}
    with open(_lowercase		,	encoding='''utf-8'''				) as merges_handle:
     _UpperCamelCase:  List[Any]    						=			merges_handle.read().split('''\n'''				)[1:-1]
    _UpperCamelCase:  Optional[int]    						=			[tuple(merge.split()				) for merge in bpe_merges]
    _UpperCamelCase:  Optional[int]    						=			dict(zip(_lowercase		,	range(len(_lowercase				)				)				)				)
    _UpperCamelCase:  Optional[int]    						=			{}
    _UpperCamelCase:  List[str]    						=			add_prefix_space

    # Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
    _UpperCamelCase:  Optional[Any]    						=			re.compile(R'''\'s|\'t|\'re|\'ve|\'m|\'ll|\'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+'''				)



   @property
   # Copied from transformers.models.bart.tokenization_bart.BartTokenizer.vocab_size
   def 	lowerCAmelCase    (						self       :	Optional[Any]				):



    """simple docstring"""



    return len(self.encoder				)



   def 	lowerCAmelCase    (						self       :	int				):



    """simple docstring"""



    return dict(self.encoder		,	**self.added_tokens_encoder				)



   def 	lowerCAmelCase    (						self       :	str		,	_lowercase       :	Tuple				):



    """simple docstring"""



    if token in self.cache:
     return self.cache[token]
    _UpperCamelCase:  Union[str, Any]    						=			tuple(_lowercase				)
    _UpperCamelCase:  List[str]    						=			get_pairs(_lowercase				)

    if not pairs:
     return token

    while True:
     _UpperCamelCase:  str    						=			min(_lowercase		,	key=lambda _lowercase				: self.bpe_ranks.get(_lowercase		,	float('''inf'''				)				)				)
     if bigram not in self.bpe_ranks:
      break
     _UpperCamelCase						,				_UpperCamelCase:  Any    						=			bigram
     _UpperCamelCase:  Optional[Any]    						=			[]
     _UpperCamelCase:  Dict    						=			0
     while i < len(_lowercase				):
      try:
       _UpperCamelCase:  List[str]    						=			word.index(_lowercase		,	_lowercase				)
      except ValueError:
       new_word.extend(word[i:]				)
       break
      else:
       new_word.extend(word[i:j]				)
       _UpperCamelCase:  str    						=			j

      if word[i] == first and i < len(_lowercase				) - 1 and word[i + 1] == second:
       new_word.append(first + second				)
       i += 2
      else:
       new_word.append(word[i]				)
       i += 1
     _UpperCamelCase:  List[str]    						=			tuple(_lowercase				)
     _UpperCamelCase:  Tuple    						=			new_word
     if len(_lowercase				) == 1:
      break
     else:
      _UpperCamelCase:  Any    						=			get_pairs(_lowercase				)
    _UpperCamelCase:  Union[str, Any]    						=			''' '''.join(_lowercase				)
    _UpperCamelCase:  int    						=			word
    return word



   def 	lowerCAmelCase    (						self       :	Dict		,	_lowercase       :	Optional[int]				):



    """simple docstring"""



    _UpperCamelCase:  Any    						=			[]
    for token in re.findall(self.pat		,	_lowercase				):
     _UpperCamelCase:  Union[str, Any]    						=			''''''.join(
         self.byte_encoder[b] for b in token.encode('''utf-8'''				)				)  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
     bpe_tokens.extend(bpe_token for bpe_token in self.bpe(_lowercase				).split(''' '''				)				)
    return bpe_tokens



   def 	lowerCAmelCase    (						self       :	Union[str, Any]		,	_lowercase       :	Dict				):



    """simple docstring"""



    return self.encoder.get(_lowercase		,	self.encoder.get(self.unk_token				)				)



   def 	lowerCAmelCase    (						self       :	Union[str, Any]		,	_lowercase       :	Any				):



    """simple docstring"""



    return self.decoder.get(_lowercase				)



   def 	lowerCAmelCase    (						self       :	str		,	_lowercase       :	Union[str, Any]				):



    """simple docstring"""



    _UpperCamelCase:  List[Any]    						=			''''''.join(_lowercase				)
    _UpperCamelCase:  List[str]    						=			bytearray([self.byte_decoder[c] for c in text]				).decode('''utf-8'''		,	errors=self.errors				)
    return text



   def 	lowerCAmelCase    (						self       :	Dict		,	_lowercase       :	str		,	_lowercase       :	Optional[str] = None				):



    """simple docstring"""



    if not os.path.isdir(_lowercase				):
     logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""				)
     return
    _UpperCamelCase:  Union[str, Any]    						=			os.path.join(
        _lowercase		,	(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']				)
    _UpperCamelCase:  Dict    						=			os.path.join(
        _lowercase		,	(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''merges_file''']				)

    with open(_lowercase		,	'''w'''		,	encoding='''utf-8'''				) as f:
     f.write(json.dumps(self.encoder		,	indent=2		,	sort_keys=_lowercase		,	ensure_ascii=_lowercase				) + '''\n'''				)

    _UpperCamelCase:  Optional[int]    						=			0
    with open(_lowercase		,	'''w'''		,	encoding='''utf-8'''				) as writer:
     writer.write('''#version: 0.2\n'''				)
     for bpe_tokens, token_index in sorted(self.bpe_ranks.items()		,	key=lambda _lowercase				: kv[1]				):
      if index != token_index:
       logger.warning(
           f"""Saving vocabulary to {merge_file}: BPE merge indices are not consecutive."""
           ''' Please check that the tokenizer is not corrupted!'''				)
       _UpperCamelCase:  int    						=			token_index
      writer.write(''' '''.join(_lowercase				) + '''\n'''				)
      index += 1

    return vocab_file, merge_file



   def 	lowerCAmelCase    (						self       :	Dict		,	_lowercase       :	List[int]		,	_lowercase       :	Optional[List[int]] = None				):



    """simple docstring"""



    if token_ids_a is None:
     return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
    _UpperCamelCase:  int    						=			[self.cls_token_id]
    _UpperCamelCase:  List[Any]    						=			[self.sep_token_id]
    return cls + token_ids_a + sep + sep + token_ids_a + sep



   def 	lowerCAmelCase    (						self       :	Any		,	_lowercase       :	List[int]		,	_lowercase       :	Optional[List[int]] = None		,	_lowercase       :	bool = False				):



    """simple docstring"""



    if already_has_special_tokens:
     return super().get_special_tokens_mask(
         token_ids_a=_lowercase		,	token_ids_a=_lowercase		,	already_has_special_tokens=_lowercase				)

    if token_ids_a is None:
     return [1] + ([0] * len(_lowercase				)) + [1]
    return [1] + ([0] * len(_lowercase				)) + [1, 1] + ([0] * len(_lowercase				)) + [1]



   def 	lowerCAmelCase    (						self       :	Dict		,	_lowercase       :	List[int]		,	_lowercase       :	Optional[List[int]] = None				):



    """simple docstring"""



    _UpperCamelCase:  int    						=			[self.sep_token_id]
    _UpperCamelCase:  Dict    						=			[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 	lowerCAmelCase    (						self       :	str		,	_lowercase       :	Any		,	_lowercase       :	Dict=False		,	**_lowercase       :	Union[str, Any]				):



    """simple docstring"""



    _UpperCamelCase:  int    						=			kwargs.pop('''add_prefix_space'''		,	self.add_prefix_space				)
    if (is_split_into_words or add_prefix_space) and (len(_lowercase				) > 0 and not text[0].isspace()):
     _UpperCamelCase:  Any    						=			''' ''' + text
    return (text, kwargs)



   def 	lowerCAmelCase    (						self       :	List[Any]		,	_lowercase       :	Union[Dict[str, EncodedInput], BatchEncoding]		,	_lowercase       :	Optional[int] = None		,	_lowercase       :	PaddingStrategy = PaddingStrategy.DO_NOT_PAD		,	_lowercase       :	Optional[int] = None		,	_lowercase       :	Optional[bool] = None		,	):



    """simple docstring"""



    _UpperCamelCase:  List[str]    						=			super()._pad(
        encoded_inputs=_lowercase		,	max_length=_lowercase		,	padding_strategy=_lowercase		,	pad_to_multiple_of=_lowercase		,	return_attention_mask=_lowercase		,	)

    # Load from model defaults
    if return_attention_mask is None:
     _UpperCamelCase:  int    						=			'''attention_mask''' in self.model_input_names

    if return_attention_mask and "global_attention_mask" in encoded_inputs:
     _UpperCamelCase:  Optional[int]    						=			encoded_inputs[self.model_input_names[0]]
     # `global_attention_mask` need to have the same length as other (sequential) inputs.
     _UpperCamelCase:  Optional[int]    						=			len(encoded_inputs['''global_attention_mask''']				) != len(_lowercase				)

     if needs_to_be_padded:
      _UpperCamelCase:  Optional[int]    						=			len(_lowercase				) - len(encoded_inputs['''global_attention_mask''']				)

      if self.padding_side == "right":
       # Use `-1` since `0` in `global_attention_mask` means `local attention` instead of `not to attend`
       _UpperCamelCase:  List[str]    						=			(
           encoded_inputs['''global_attention_mask'''] + [-1] * difference
       )
      elif self.padding_side == "left":
       _UpperCamelCase:  Dict    						=			[-1] * difference + encoded_inputs[
           '''global_attention_mask'''
       ]
      else:
       raise ValueError('''Invalid padding strategy:''' + str(self.padding_side				)				)

    return encoded_inputs
import argparse
import logging
import os

import datasets
import tensorflow as tf

from transformers import AutoTokenizer


UpperCAmelCase_           =				logging.getLogger(__name__)




def    lowerCAmelCase_  (		)     ->					Optional[Any]:







 '''simple docstring'''
 _UpperCamelCase:  List[str]    						=			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:  str    						=			parser.parse_args()
 return args




def    lowerCAmelCase_  (		lowercase:				Optional[int]		)     ->					Optional[Any]:







 '''simple docstring'''
 def fn(lowercase:				List[Any]		):
  return tokenizer(examples['''text''']		)

 return fn




def    lowerCAmelCase_  (		lowercase:				List[str]		)     ->					Optional[Any]:







 '''simple docstring'''
 _UpperCamelCase:  int    						=			[]
 for i in range(len(tokenized_data['''input_ids''']		)		):
  _UpperCamelCase:  Tuple    						=			{
      '''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:  Optional[int]    						=			tf.train.Features(feature=lowercase		)
  _UpperCamelCase:  Optional[int]    						=			tf.train.Example(features=lowercase		)
  _UpperCamelCase:  List[Any]    						=			example.SerializeToString()
  records.append(lowercase		)
 return records




def    lowerCAmelCase_  (		lowercase:				List[str]		)     ->					Tuple:







 '''simple docstring'''
 _UpperCamelCase:  Tuple    						=			datasets.load_dataset(args.dataset_name	,       args.dataset_config	,       split=args.split		)

 if args.limit is not None:
  _UpperCamelCase:  str    						=			min(len(lowercase		)	,       args.limit		)
  _UpperCamelCase:  Optional[Any]    						=			dataset.select(range(lowercase		)		)
  print(F"""Limiting the dataset to {args.limit} entries."""		)

 _UpperCamelCase:  List[Any]    						=			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:  Any    						=			os.path.join(args.output_dir	,       args.split		)
  if not os.path.exists(lowercase		):
   os.makedirs(lowercase		)
 else:
  _UpperCamelCase:  str    						=			os.path.join(args.output_dir	,       args.split		)

 # Tokenize the whole dataset at once.
 _UpperCamelCase:  Union[str, Any]    						=			tokenize_function(lowercase		)
 _UpperCamelCase:  List[Any]    						=			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:				int		):
  # Concatenate all texts.
  _UpperCamelCase:  List[str]    						=			{k: sum(examples[k]	,       []		) for k in examples.keys()}
  _UpperCamelCase:  Any    						=			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:  Optional[int]    						=			(total_length // args.max_length) * args.max_length
  # Split by chunks of max_len.
  _UpperCamelCase:  Tuple    						=			{
      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:  Dict    						=			dataset_tokenized.map(lowercase	,       batched=lowercase	,       batch_size=1_000	,       num_proc=4		)

 _UpperCamelCase:  List[str]    						=			0
 _UpperCamelCase:  Tuple    						=			0
 for shard in range(0	,       len(lowercase		)	,       args.shard_size		):
  _UpperCamelCase:  List[str]    						=			grouped_dataset[shard : shard + args.shard_size]
  _UpperCamelCase:  Optional[Any]    						=			len(dataset_snapshot['''input_ids''']		)
  _UpperCamelCase:  str    						=			os.path.join(lowercase	,       F"""dataset-{shard_count}-{records_containing}.tfrecord"""		)
  _UpperCamelCase:  Any    						=			get_serialized_examples(lowercase		)

  with tf.io.TFRecordWriter(lowercase		) as out_file:
   for i in range(len(lowercase		)		):
    _UpperCamelCase:  List[Any]    						=			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_           =				parse_args()
  main(args)