

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

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


lowercase_             =     logging.get_logger(__name__)

lowercase_             =     '▁'

lowercase_             =     {'vocab_file': 'sentencepiece.bpe.model'}

lowercase_             =     {
    'vocab_file': {
        'facebook/xglm-564M': 'https://huggingface.co/facebook/xglm-564M/resolve/main/sentencepiece.bpe.model',
    }
}

lowercase_             =     {
    'facebook/xglm-564M': 2_0_4_8,
}





class 				__lowerCAmelCase  (      SCREAMING_SNAKE_CASE							):
  _a		    =						VOCAB_FILES_NAMES
  _a		    =						PRETRAINED_VOCAB_FILES_MAP
  _a		    =						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  _a		    =						["""input_ids""", """attention_mask"""]




  def __init__(       self							,     lowerCAmelCase							,     lowerCAmelCase="<s>"							,     lowerCAmelCase="</s>"							,     lowerCAmelCase="</s>"							,     lowerCAmelCase="<s>"							,     lowerCAmelCase="<unk>"							,     lowerCAmelCase="<pad>"							,     lowerCAmelCase = None							,     **lowerCAmelCase							,     )						->				None:
        '''simple docstring'''






        _lowercase			 ={} if sp_model_kwargs is None else sp_model_kwargs

        # Compatibility with the original tokenizer
        _lowercase			 =7
        _lowercase			 =[F'''<madeupword{i}>''' for i in range(self.num_madeup_words      )]

        _lowercase			 =kwargs.get('additional_special_tokens'							,     []      )
        kwargs["additional_special_tokens"] += [
            word for word in madeup_words if word not in kwargs["additional_special_tokens"]
        ]

        super().__init__(
            bos_token=lowerCAmelCase							,     eos_token=lowerCAmelCase							,     unk_token=lowerCAmelCase							,     sep_token=lowerCAmelCase							,     cls_token=lowerCAmelCase							,     pad_token=lowerCAmelCase							,     sp_model_kwargs=self.sp_model_kwargs							,     **lowerCAmelCase							,     )

        _lowercase			 =spm.SentencePieceProcessor(**self.sp_model_kwargs      )
        self.sp_model.Load(str(lowerCAmelCase      )      )
        _lowercase			 =vocab_file

        # Original fairseq vocab and spm vocab must be "aligned":
        # Vocab    |    0    |    1    |   2    |    3    |  4  |  5  |  6  |   7   |   8   |  9
        # -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
        # fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
        # spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

        # The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
        _lowercase			 =1

        # Mimic fairseq token-to-id alignment for the first 4 token
        _lowercase			 ={'<s>': 0, '<pad>': 1, '</s>': 2, '<unk>': 3}

        _lowercase			 =len(self.sp_model      )
        _lowercase			 ={F'''<madeupword{i}>''': sp_size + i + self.fairseq_offset for i in range(self.num_madeup_words      )}
        self.fairseq_tokens_to_ids.update(lowerCAmelCase      )

        _lowercase			 ={v: k for k, v in self.fairseq_tokens_to_ids.items()}




  def __getstate__(       self      )						->				List[str]:
        '''simple docstring'''






        _lowercase			 =self.__dict__.copy()
        _lowercase			 =None
        _lowercase			 =self.sp_model.serialized_model_proto()
        return state




  def __setstate__(       self							,     lowerCAmelCase      )						->				List[str]:
        '''simple docstring'''






        _lowercase			 =d

        # for backward compatibility
        if not hasattr(self							,     'sp_model_kwargs'      ):
              _lowercase			 ={}

        _lowercase			 =spm.SentencePieceProcessor(**self.sp_model_kwargs      )
        self.sp_model.LoadFromSerializedProto(self.sp_model_proto      )




  def 						A__  (       self							,     lowerCAmelCase							,     lowerCAmelCase = None      )						->				List[int]:
        '''simple docstring'''






        if token_ids_a is None:
              return [self.sep_token_id] + token_ids_a
        _lowercase			 =[self.sep_token_id]
        return sep + token_ids_a + sep + sep + token_ids_a




  def 						A__  (       self							,     lowerCAmelCase							,     lowerCAmelCase = None							,     lowerCAmelCase = False      )						->				List[int]:
        '''simple docstring'''






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

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




  def 						A__  (       self							,     lowerCAmelCase							,     lowerCAmelCase = None      )						->				List[int]:
        '''simple docstring'''






        _lowercase			 =[self.sep_token_id]

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




  @property
  def 						A__  (       self      )						->				Optional[int]:
        '''simple docstring'''






        return len(self.sp_model      ) + self.fairseq_offset + self.num_madeup_words




  def 						A__  (       self      )						->				Union[str, Any]:
        '''simple docstring'''






        _lowercase			 ={self.convert_ids_to_tokens(lowerCAmelCase      ): i for i in range(self.vocab_size      )}
        vocab.update(self.added_tokens_encoder      )
        return vocab




  def 						A__  (       self							,     lowerCAmelCase      )						->				List[str]:
        '''simple docstring'''






        return self.sp_model.encode(lowerCAmelCase							,     out_type=lowerCAmelCase      )




  def 						A__  (       self							,     lowerCAmelCase      )						->				Any:
        '''simple docstring'''






        if token in self.fairseq_tokens_to_ids:
              return self.fairseq_tokens_to_ids[token]
        _lowercase			 =self.sp_model.PieceToId(lowerCAmelCase      )

        # Need to return unknown token if the SP model returned 0
        return spm_id + self.fairseq_offset if spm_id else self.unk_token_id




  def 						A__  (       self							,     lowerCAmelCase      )						->				Tuple:
        '''simple docstring'''






        if index in self.fairseq_ids_to_tokens:
              return self.fairseq_ids_to_tokens[index]
        return self.sp_model.IdToPiece(index - self.fairseq_offset      )




  def 						A__  (       self							,     lowerCAmelCase      )						->				Any:
        '''simple docstring'''






        _lowercase			 =''.join(lowerCAmelCase      ).replace(lowerCAmelCase							,     ' '      ).strip()
        return out_string




  def 						A__  (       self							,     lowerCAmelCase							,     lowerCAmelCase = None      )						->				Tuple[str]:
        '''simple docstring'''






        if not os.path.isdir(lowerCAmelCase      ):
              logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''      )
              return
        _lowercase			 =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      ) and os.path.isfile(self.vocab_file      ):
              copyfile(self.vocab_file							,     lowerCAmelCase      )
        elif not os.path.isfile(self.vocab_file      ):
              with open(lowerCAmelCase							,     'wb'      ) as fi:
                    _lowercase			 =self.sp_model.serialized_model_proto()
                    fi.write(lowerCAmelCase      )

        return (out_vocab_file,)





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 				__lowerCAmelCase  (      unittest.TestCase							):




  @slow
  def 						A__  (       self      )						->				str:
        '''simple docstring'''






        _lowercase			 =TFCamembertModel.from_pretrained('jplu/tf-camembert-base'      )

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

        _lowercase			 =model(lowerCAmelCase      )['last_hidden_state']
        _lowercase			 =tf.TensorShape((1, 10, 768)      )
        self.assertEqual(output.shape							,     lowerCAmelCase      )
        # compare the actual values for a slice.
        _lowercase			 =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      )      )


