


'''simple docstring'''

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


lowercase   :						Optional[int]								=			logging.get_logger(__name__)


lowercase   :						List[str]								=			{'vocab_file': 'vocab.json', 'merges_file': 'merges.txt'}

# See all LED models at https://huggingface.co/models?filter=LED
lowercase   :						Optional[Any]								=			{
    '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',
    },
}

lowercase   :						Any								=			{
    'allenai/led-base-16384': 1_6_3_8_4,
}
@lru_cache()
# Copied from transformers.models.bart.tokenization_bart.bytes_to_unicode
def      __a	(							)						->			Optional[Any]:
  lowerCAmelCase											=	(
      list(range(ord("!"       )						,       ord("~"       ) + 1       )       ) + list(range(ord("¡"       )						,       ord("¬"       ) + 1       )       ) + list(range(ord("®"       )						,       ord("ÿ"       ) + 1       )       )
  )
  lowerCAmelCase											=	bs[:]
  lowerCAmelCase											=	0
  for b in range(2**8       ):
    if b not in bs:
      bs.append(UpperCamelCase__       )
      cs.append(2**8 + n       )
      n += 1
  lowerCAmelCase											=	[chr(UpperCamelCase__       ) for n in cs]
  return dict(zip(UpperCamelCase__						,       UpperCamelCase__       )       )
def      __a	(							A__       )						->			Any:
  lowerCAmelCase											=	set()
  lowerCAmelCase											=	word[0]
  for char in word[1:]:
    pairs.add((prev_char, char)       )
    lowerCAmelCase											=	char
  return pairs


class      _lowerCAmelCase				(			_a			):





  """simple docstring"""



  lowerCAmelCase					   =			VOCAB_FILES_NAMES
  lowerCAmelCase					   =			PRETRAINED_VOCAB_FILES_MAP
  lowerCAmelCase					   =			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  lowerCAmelCase					   =			["""input_ids""", """attention_mask"""]
  def __init__(      self      :     Union[str, Any]      , SCREAMING_SNAKE_CASE      :     Any      , SCREAMING_SNAKE_CASE      :     Dict      , SCREAMING_SNAKE_CASE      :     str="replace"      , SCREAMING_SNAKE_CASE      :     Optional[Any]="<s>"      , SCREAMING_SNAKE_CASE      :     Optional[int]="</s>"      , SCREAMING_SNAKE_CASE      :     int="</s>"      , SCREAMING_SNAKE_CASE      :     Union[str, Any]="<s>"      , SCREAMING_SNAKE_CASE      :     Any="<unk>"      , SCREAMING_SNAKE_CASE      :     str="<pad>"      , SCREAMING_SNAKE_CASE      :     str="<mask>"      , SCREAMING_SNAKE_CASE      :     int=False      , **SCREAMING_SNAKE_CASE      :     Any      , )	->						Optional[Any]:
    """simple docstring"""

    lowerCAmelCase											=	AddedToken(A_      , lstrip=A_      , rstrip=A_				) if isinstance(A_      , A_				) else bos_token
    lowerCAmelCase											=	AddedToken(A_      , lstrip=A_      , rstrip=A_				) if isinstance(A_      , A_				) else eos_token
    lowerCAmelCase											=	AddedToken(A_      , lstrip=A_      , rstrip=A_				) if isinstance(A_      , A_				) else sep_token
    lowerCAmelCase											=	AddedToken(A_      , lstrip=A_      , rstrip=A_				) if isinstance(A_      , A_				) else cls_token
    lowerCAmelCase											=	AddedToken(A_      , lstrip=A_      , rstrip=A_				) if isinstance(A_      , A_				) else unk_token
    lowerCAmelCase											=	AddedToken(A_      , lstrip=A_      , rstrip=A_				) if isinstance(A_      , A_				) else pad_token

    # Mask token behave like a normal word, i.e. include the space before it
    lowerCAmelCase											=	AddedToken(A_      , lstrip=A_      , rstrip=A_				) if isinstance(A_      , A_				) else mask_token

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

    with open(A_      , encoding="utf-8"				) as vocab_handle:
      lowerCAmelCase											=	json.load(A_				)
    lowerCAmelCase											=	{v: k for k, v in self.encoder.items()}
    lowerCAmelCase											=	errors  # how to handle errors in decoding
    lowerCAmelCase											=	bytes_to_unicode()
    lowerCAmelCase											=	{v: k for k, v in self.byte_encoder.items()}
    with open(A_      , encoding="utf-8"				) as merges_handle:
      lowerCAmelCase											=	merges_handle.read().split("\n"				)[1:-1]
    lowerCAmelCase											=	[tuple(merge.split()				) for merge in bpe_merges]
    lowerCAmelCase											=	dict(zip(A_      , range(len(A_				)				)				)				)
    lowerCAmelCase											=	{}
    lowerCAmelCase											=	add_prefix_space

    # Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
    lowerCAmelCase											=	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 __A				(      self      :     List[str]				)	->						Tuple:
    """simple docstring"""

    return len(self.encoder				)
  def __A				(      self      :     List[Any]				)	->						List[Any]:
    """simple docstring"""

    return dict(self.encoder      , **self.added_tokens_encoder				)
  def __A				(      self      :     List[str]      , SCREAMING_SNAKE_CASE      :     Optional[int]				)	->						Any:
    """simple docstring"""

    if token in self.cache:
      return self.cache[token]
    lowerCAmelCase											=	tuple(A_				)
    lowerCAmelCase											=	get_pairs(A_				)

    if not pairs:
      return token

    while True:
      lowerCAmelCase											=	min(A_      , key=lambda SCREAMING_SNAKE_CASE				: self.bpe_ranks.get(A_      , float("inf"				)				)				)
      if bigram not in self.bpe_ranks:
        break
      lowerCAmelCase				,       lowerCAmelCase											=	bigram
      lowerCAmelCase											=	[]
      lowerCAmelCase											=	0
      while i < len(A_				):
        try:
          lowerCAmelCase											=	word.index(A_      , A_				)
        except ValueError:
          new_word.extend(word[i:]				)
          break
        else:
          new_word.extend(word[i:j]				)
          lowerCAmelCase											=	j

        if word[i] == first and i < len(A_				) - 1 and word[i + 1] == second:
          new_word.append(first + second				)
          i += 2
        else:
          new_word.append(word[i]				)
          i += 1
      lowerCAmelCase											=	tuple(A_				)
      lowerCAmelCase											=	new_word
      if len(A_				) == 1:
        break
      else:
        lowerCAmelCase											=	get_pairs(A_				)
    lowerCAmelCase											=	" ".join(A_				)
    lowerCAmelCase											=	word
    return word
  def __A				(      self      :     Any      , SCREAMING_SNAKE_CASE      :     List[str]				)	->						Tuple:
    """simple docstring"""

    lowerCAmelCase											=	[]
    for token in re.findall(self.pat      , A_				):
      lowerCAmelCase											=	"".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(A_				).split(" "				)				)
    return bpe_tokens
  def __A				(      self      :     Optional[int]      , SCREAMING_SNAKE_CASE      :     Union[str, Any]				)	->						Optional[int]:
    """simple docstring"""

    return self.encoder.get(A_      , self.encoder.get(self.unk_token				)				)
  def __A				(      self      :     int      , SCREAMING_SNAKE_CASE      :     Any				)	->						List[Any]:
    """simple docstring"""

    return self.decoder.get(A_				)
  def __A				(      self      :     List[Any]      , SCREAMING_SNAKE_CASE      :     Tuple				)	->						str:
    """simple docstring"""

    lowerCAmelCase											=	"".join(A_				)
    lowerCAmelCase											=	bytearray([self.byte_decoder[c] for c in text]				).decode("utf-8"      , errors=self.errors				)
    return text
  def __A				(      self      :     Optional[int]      , SCREAMING_SNAKE_CASE      :     str      , SCREAMING_SNAKE_CASE      :     Optional[str] = None				)	->						Union[str, Any]:
    """simple docstring"""

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

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

    lowerCAmelCase											=	0
    with open(A_      , "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 SCREAMING_SNAKE_CASE				: 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!"				)
          lowerCAmelCase											=	token_index
        writer.write(" ".join(A_				) + "\n"				)
        index += 1

    return vocab_file, merge_file
  def __A				(      self      :     Union[str, Any]      , SCREAMING_SNAKE_CASE      :     List[int]      , SCREAMING_SNAKE_CASE      :     Optional[List[int]] = None				)	->						Union[str, Any]:
    """simple docstring"""

    if token_ids_a is None:
      return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
    lowerCAmelCase											=	[self.cls_token_id]
    lowerCAmelCase											=	[self.sep_token_id]
    return cls + token_ids_a + sep + sep + token_ids_a + sep
  def __A				(      self      :     Tuple      , SCREAMING_SNAKE_CASE      :     List[int]      , SCREAMING_SNAKE_CASE      :     Optional[List[int]] = None      , SCREAMING_SNAKE_CASE      :     bool = False				)	->						Optional[Any]:
    """simple docstring"""

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

    if token_ids_a is None:
      return [1] + ([0] * len(A_				)) + [1]
    return [1] + ([0] * len(A_				)) + [1, 1] + ([0] * len(A_				)) + [1]
  def __A				(      self      :     Optional[int]      , SCREAMING_SNAKE_CASE      :     List[int]      , SCREAMING_SNAKE_CASE      :     Optional[List[int]] = None				)	->						int:
    """simple docstring"""

    lowerCAmelCase											=	[self.sep_token_id]
    lowerCAmelCase											=	[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 __A				(      self      :     Optional[Any]      , SCREAMING_SNAKE_CASE      :     List[str]      , SCREAMING_SNAKE_CASE      :     Optional[int]=False      , **SCREAMING_SNAKE_CASE      :     Union[str, Any]				)	->						Optional[int]:
    """simple docstring"""

    lowerCAmelCase											=	kwargs.pop("add_prefix_space"      , self.add_prefix_space				)
    if (is_split_into_words or add_prefix_space) and (len(A_				) > 0 and not text[0].isspace()):
      lowerCAmelCase											=	" " + text
    return (text, kwargs)


  def __A				(      self      :     Tuple      , SCREAMING_SNAKE_CASE      :     Union[Dict[str, EncodedInput], BatchEncoding]      , SCREAMING_SNAKE_CASE      :     Optional[int] = None      , SCREAMING_SNAKE_CASE      :     PaddingStrategy = PaddingStrategy.DO_NOT_PAD      , SCREAMING_SNAKE_CASE      :     Optional[int] = None      , SCREAMING_SNAKE_CASE      :     Optional[bool] = None      , )	->						Optional[int]:
    """simple docstring"""

    lowerCAmelCase											=	super()._pad(
        encoded_inputs=A_      , max_length=A_      , padding_strategy=A_      , pad_to_multiple_of=A_      , return_attention_mask=A_      , )

    # Load from model defaults
    if return_attention_mask is None:
      lowerCAmelCase											=	"attention_mask" in self.model_input_names

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

      if needs_to_be_padded:
        lowerCAmelCase											=	len(A_				) - 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`
          lowerCAmelCase											=	(
              encoded_inputs["global_attention_mask"] + [-1] * difference
          )
        elif self.padding_side == "left":
          lowerCAmelCase											=	[-1] * difference + encoded_inputs[
              "global_attention_mask"
          ]
        else:
          raise ValueError("Invalid padding strategy:" + str(self.padding_side				)				)

    return encoded_inputs






"""simple docstring"""

import unittest

from transformers import BarthezTokenizer, BarthezTokenizerFast, BatchEncoding
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow

from ...test_tokenization_common import TokenizerTesterMixin





@require_tokenizers
@require_sentencepiece
@slow  # see https://github.com/huggingface/transformers/issues/11457
class  lowerCamelCase__    (		_a      , unittest.TestCase     ):
      a			:  Union[str, Any]           =		BarthezTokenizer
      a			:  Any           =		BarthezTokenizerFast
      a			:  Tuple           =		True
      a			:  List[Any]           =		True
      def SCREAMING_SNAKE_CASE_						(		self						:						List[str]				):






       '''simple docstring'''


       super().setUp()

       __lowercase						=						BarthezTokenizerFast.from_pretrained("""moussaKam/mbarthez"""				)
       tokenizer.save_pretrained(self.tmpdirname				)
       tokenizer.save_pretrained(self.tmpdirname   ,							legacy_format=A_				)
       __lowercase						=						tokenizer
      def SCREAMING_SNAKE_CASE_						(		self						:						Tuple				):






       '''simple docstring'''


       __lowercase						=						"""<pad>"""
       __lowercase						=						1

       self.assertEqual(self.get_tokenizer()._convert_token_to_id(A_				)   ,							A_				)
       self.assertEqual(self.get_tokenizer()._convert_id_to_token(A_				)   ,							A_				)
      def SCREAMING_SNAKE_CASE_						(		self						:						List[str]				):






       '''simple docstring'''


       __lowercase						=						list(self.get_tokenizer().get_vocab().keys()				)

       self.assertEqual(vocab_keys[0]   ,							"""<s>"""				)
       self.assertEqual(vocab_keys[1]   ,							"""<pad>"""				)
       self.assertEqual(vocab_keys[-1]   ,							"""<mask>"""				)
       self.assertEqual(len(A_				)   ,							1_0_1_1_2_2				)
      def SCREAMING_SNAKE_CASE_						(		self						:						Union[str, Any]				):






       '''simple docstring'''


       self.assertEqual(self.get_tokenizer().vocab_size   ,							1_0_1_1_2_2				)
      @require_torch
      def SCREAMING_SNAKE_CASE_						(		self						:						str				):






       '''simple docstring'''


       __lowercase						=						["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
       __lowercase						=						[0, 5_7, 3_0_1_8, 7_0_3_0_7, 9_1, 2]

       __lowercase						=						self.tokenizer(
           A_   ,							max_length=len(A_				)   ,							padding=A_   ,							truncation=A_   ,							return_tensors="""pt"""				)
       self.assertIsInstance(A_   ,							A_				)

       self.assertEqual((2, 6)   ,							batch.input_ids.shape				)
       self.assertEqual((2, 6)   ,							batch.attention_mask.shape				)
       __lowercase						=						batch.input_ids.tolist()[0]
       self.assertListEqual(A_   ,							A_				)
      def SCREAMING_SNAKE_CASE_						(		self						:						int				):






       '''simple docstring'''


       if not self.test_rust_tokenizer:
        return

       __lowercase						=						self.get_tokenizer()
       __lowercase						=						self.get_rust_tokenizer()

       __lowercase						=						"""I was born in 92000, and this is falsé."""

       __lowercase						=						tokenizer.tokenize(A_				)
       __lowercase						=						rust_tokenizer.tokenize(A_				)
       self.assertListEqual(A_   ,							A_				)

       __lowercase						=						tokenizer.encode(A_   ,							add_special_tokens=A_				)
       __lowercase						=						rust_tokenizer.encode(A_   ,							add_special_tokens=A_				)
       self.assertListEqual(A_   ,							A_				)

       __lowercase						=						self.get_rust_tokenizer()
       __lowercase						=						tokenizer.encode(A_				)
       __lowercase						=						rust_tokenizer.encode(A_				)
       self.assertListEqual(A_   ,							A_				)






      @slow
      def SCREAMING_SNAKE_CASE_						(		self						:						Union[str, Any]				):






       '''simple docstring'''


       __lowercase						=						{"""input_ids""": [[0, 4_9_0, 1_4_3_2_8, 4_5_0_7, 3_5_4, 4_7, 4_3_6_6_9, 9_5, 2_5, 7_8_1_1_7, 2_0_2_1_5, 1_9_7_7_9, 1_9_0, 2_2, 4_0_0, 4, 3_5_3_4_3, 8_0_3_1_0, 6_0_3, 8_6, 2_4_9_3_7, 1_0_5, 3_3_4_3_8, 9_4_7_6_2, 1_9_6, 3_9_6_4_2, 7, 1_5, 1_5_9_3_3, 1_7_3, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 1_0_5_3_4, 8_7, 2_5, 6_6, 3_3_5_8, 1_9_6, 5_5_2_8_9, 8, 8_2_9_6_1, 8_1, 2_2_0_4, 7_5_2_0_3, 7, 1_5, 7_6_3, 1_2_9_5_6, 2_1_6, 1_7_8, 1_4_3_2_8, 9_5_9_5, 1_3_7_7, 6_9_6_9_3, 7, 4_4_8, 7_1_0_2_1, 1_9_6, 1_8_1_0_6, 1_4_3_7, 1_3_9_7_4, 1_0_8, 9_0_8_3, 4, 4_9_3_1_5, 7, 3_9, 8_6, 1_3_2_6, 2_7_9_3, 4_6_3_3_3, 4, 4_4_8, 1_9_6, 7_4_5_8_8, 7, 4_9_3_1_5, 7, 3_9, 2_1, 8_2_2, 3_8_4_7_0, 7_4, 2_1, 6_6_7_2_3, 6_2_4_8_0, 8, 2_2_0_5_0, 5, 2]], """attention_mask""": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}  # noqa: E501
       # fmt: on

       # moussaKam/mbarthez is a french model. So we also use french texts.
       __lowercase						=						[
           """Le transformeur est un modèle d'apprentissage profond introduit en 2017, """
           """utilisé principalement dans le domaine du traitement automatique des langues (TAL).""",
           """À l'instar des réseaux de neurones récurrents (RNN), les transformeurs sont conçus """
           """pour gérer des données séquentielles, telles que le langage naturel, pour des tâches """
           """telles que la traduction et la synthèse de texte.""",
       ]

       self.tokenizer_integration_test_util(
           expected_encoding=A_   ,							model_name="""moussaKam/mbarthez"""   ,							revision="""c2e4ecbca5e3cd2c37fe1ac285ca4fbdf1366fb6"""   ,							sequences=A_   ,							)
