





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_big_bird import BigBirdTokenizer
else:
     UpperCAmelCase	:	int		     =       None

UpperCAmelCase	:	List[str]		     =       logging.get_logger(__name__)
UpperCAmelCase	:	List[Any]		     =       {"""vocab_file""": """spiece.model""", """tokenizer_file""": """tokenizer.json"""}

UpperCAmelCase	:	Union[str, Any]		     =       {
    """vocab_file""": {
        """google/bigbird-roberta-base""": """https://huggingface.co/google/bigbird-roberta-base/resolve/main/spiece.model""",
        """google/bigbird-roberta-large""": (
            """https://huggingface.co/google/bigbird-roberta-large/resolve/main/spiece.model"""
        ),
        """google/bigbird-base-trivia-itc""": (
            """https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/spiece.model"""
        ),
    },
    """tokenizer_file""": {
        """google/bigbird-roberta-base""": (
            """https://huggingface.co/google/bigbird-roberta-base/resolve/main/tokenizer.json"""
        ),
        """google/bigbird-roberta-large""": (
            """https://huggingface.co/google/bigbird-roberta-large/resolve/main/tokenizer.json"""
        ),
        """google/bigbird-base-trivia-itc""": (
            """https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/tokenizer.json"""
        ),
    },
}

UpperCAmelCase	:	Dict		     =       {
    """google/bigbird-roberta-base""": 4096,
    """google/bigbird-roberta-large""": 4096,
    """google/bigbird-base-trivia-itc""": 4096,
}


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







class 				__lowerCAmelCase       (    UpperCamelCase__):
      _lowercase				:					Union[str, Any]						=			VOCAB_FILES_NAMES
      _lowercase				:					Any						=			PRETRAINED_VOCAB_FILES_MAP
      _lowercase				:					Dict						=			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      _lowercase				:					Tuple						=			BigBirdTokenizer
      _lowercase				:					Any						=			["""input_ids""", """attention_mask"""]
      _lowercase				:					List[int]						=			[]







      def __init__(    self		,      lowerCAmelCase__=None		,      lowerCAmelCase__=None		,      lowerCAmelCase__="<unk>"		,      lowerCAmelCase__="<s>"		,      lowerCAmelCase__="</s>"		,      lowerCAmelCase__="<pad>"		,      lowerCAmelCase__="[SEP]"		,      lowerCAmelCase__="[MASK]"		,      lowerCAmelCase__="[CLS]"		,      **lowerCAmelCase__		,      )   ->						int:



        '''simple docstring'''




        a__	:					List[Any]										=AddedToken(lowerCAmelCase__		,      lstrip=lowerCAmelCase__		,      rstrip=lowerCAmelCase__							) if isinstance(lowerCAmelCase__		,      lowerCAmelCase__							) else bos_token
        a__	:					Optional[Any]										=AddedToken(lowerCAmelCase__		,      lstrip=lowerCAmelCase__		,      rstrip=lowerCAmelCase__							) if isinstance(lowerCAmelCase__		,      lowerCAmelCase__							) else eos_token
        a__	:					Any										=AddedToken(lowerCAmelCase__		,      lstrip=lowerCAmelCase__		,      rstrip=lowerCAmelCase__							) if isinstance(lowerCAmelCase__		,      lowerCAmelCase__							) else unk_token
        a__	:					int										=AddedToken(lowerCAmelCase__		,      lstrip=lowerCAmelCase__		,      rstrip=lowerCAmelCase__							) if isinstance(lowerCAmelCase__		,      lowerCAmelCase__							) else pad_token
        a__	:					List[Any]										=AddedToken(lowerCAmelCase__		,      lstrip=lowerCAmelCase__		,      rstrip=lowerCAmelCase__							) if isinstance(lowerCAmelCase__		,      lowerCAmelCase__							) else cls_token
        a__	:					Optional[Any]										=AddedToken(lowerCAmelCase__		,      lstrip=lowerCAmelCase__		,      rstrip=lowerCAmelCase__							) if isinstance(lowerCAmelCase__		,      lowerCAmelCase__							) else sep_token

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

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

        a__	:					Tuple										=vocab_file
        a__	:					str										=False if not self.vocab_file else True







      def    _lowercase  (    self		,      lowerCAmelCase__		,      lowerCAmelCase__ = None							)   ->						List[int]:



        '''simple docstring'''




        a__	:					str										=[self.sep_token_id]
        a__	:					Union[str, Any]										=[self.cls_token_id]
        if token_ids_a is None:
          return cls + token_ids_a + sep
        return cls + token_ids_a + sep + token_ids_a + sep







      def    _lowercase  (    self		,      lowerCAmelCase__		,      lowerCAmelCase__ = None		,      lowerCAmelCase__ = False							)   ->						List[int]:



        '''simple docstring'''




        if already_has_special_tokens:
          if token_ids_a is not None:
            raise ValueError(
                "You should not supply a second sequence if the provided sequence of "
                "ids is already formatted with special tokens for the model."							)
          return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]

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







      def    _lowercase  (    self		,      lowerCAmelCase__		,      lowerCAmelCase__ = None							)   ->						List[int]:



        '''simple docstring'''




        a__	:					Union[str, Any]										=[self.sep_token_id]
        a__	:					List[Any]										=[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							) * [0] + len(token_ids_a + sep							) * [1]







      def    _lowercase  (    self		,      lowerCAmelCase__		,      lowerCAmelCase__ = 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(lowerCAmelCase__							):
          logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''							)
          return
        a__	:					Optional[int]										=os.path.join(
            lowerCAmelCase__		,      (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]							)

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

        return (out_vocab_file,)











'''simple docstring'''






from __future__ import annotations

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow


if is_tf_available():
   import numpy as np
   import tensorflow as tf

   from transformers import TFCamembertModel



@require_tf
@require_sentencepiece
@require_tokenizers
class 							lowercase       (  unittest.TestCase				):


 """simple docstring"""


 @slow
 def _snake_case (							self							)		->   Any:
        _UpperCAmelCase  :						Dict			     = TFCamembertModel.from_pretrained("""jplu/tf-camembert-base"""							)

        _UpperCAmelCase  :						Optional[int]			     = tf.convert_to_tensor(
            [[5, 121, 11, 660, 16, 730, 25_543, 110, 83, 6]]  ,dtype=tf.intaa  ,)  # J'aime le camembert !"

        _UpperCAmelCase  :						Dict			     = model(a_							)["""last_hidden_state"""]
        _UpperCAmelCase  :						Dict			     = tf.TensorShape((1, 10, 768)							)
        self.assertEqual(output.shape  ,a_							)
        # compare the actual values for a slice.
        _UpperCAmelCase  :						Tuple			     = tf.convert_to_tensor(
            [[[-0.0254, 0.0235, 0.1027], [0.0606, -0.1811, -0.0418], [-0.1561, -0.1127, 0.2687]]]  ,dtype=tf.floataa  ,)
        # camembert = torch.hub.load('pytorch/fairseq', 'camembert.v0')
        # camembert.eval()
        # expected_slice = roberta.model.forward(input_ids)[0][:, :3, :3].detach()

        self.assertTrue(np.allclose(output[:, :3, :3].numpy()  ,expected_slice.numpy()  ,atol=1E-4							)							)



