

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:
  lowercase_									=   None


lowercase_									=   logging.get_logger(__name__)

lowercase_									=   {'vocab_file': 'sentencepiece.bpe.model', 'tokenizer_file': 'tokenizer.json'}

lowercase_									=   {
    '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',
    },
}

lowercase_									=   {
    'camembert-base': 5_1_2,
}

lowercase_									=   '▁'





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




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






        _lowercase	       =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							,   sep_token=lowerCAmelCase							,   cls_token=lowerCAmelCase							,   unk_token=lowerCAmelCase							,   pad_token=lowerCAmelCase							,   mask_token=lowerCAmelCase							,   additional_special_tokens=lowerCAmelCase							,   **lowerCAmelCase							,   )

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




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






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




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






        _lowercase	       =[self.sep_token_id]
        _lowercase	       =[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							,   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
        _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					):
              copyfile(self.vocab_file							,   lowerCAmelCase					)

        return (out_vocab_file,)





import torch
import torch.nn as nn
from transformers.modeling_utils import ModuleUtilsMixin
from transformers.models.ta.modeling_ta import TaBlock, TaConfig, TaLayerNorm

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin





class  __lowerCAmelCase     (					SCREAMING_SNAKE_CASE			,			SCREAMING_SNAKE_CASE			,			SCREAMING_SNAKE_CASE							):




  @register_to_config
  def __init__(				self							,   lowerCAmelCase							,   lowerCAmelCase							,   lowerCAmelCase							,   lowerCAmelCase							,   lowerCAmelCase							,   lowerCAmelCase							,   lowerCAmelCase							,   lowerCAmelCase							,   lowerCAmelCase							,   lowerCAmelCase = False							,   )  -> Tuple:
        '''simple docstring'''






        super().__init__()

        _lowercase	       =nn.Embedding(lowerCAmelCase							,   lowerCAmelCase					)

        _lowercase	       =nn.Embedding(lowerCAmelCase							,   lowerCAmelCase					)
        _lowercase	       =False

        _lowercase	       =nn.Dropout(p=lowerCAmelCase					)

        _lowercase	       =TaConfig(
            vocab_size=lowerCAmelCase							,   d_model=lowerCAmelCase							,   num_heads=lowerCAmelCase							,   d_kv=lowerCAmelCase							,   d_ff=lowerCAmelCase							,   dropout_rate=lowerCAmelCase							,   feed_forward_proj=lowerCAmelCase							,   is_decoder=lowerCAmelCase							,   is_encoder_decoder=lowerCAmelCase							,   )

        _lowercase	       =nn.ModuleList()
        for lyr_num in range(lowerCAmelCase					):
              _lowercase	       =TaBlock(lowerCAmelCase					)
              self.encoders.append(lowerCAmelCase					)

        _lowercase	       =TaLayerNorm(lowerCAmelCase					)
        _lowercase	       =nn.Dropout(p=lowerCAmelCase					)




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






        _lowercase	       =self.token_embedder(lowerCAmelCase					)

        _lowercase	       =encoder_input_tokens.shape[1]
        _lowercase	       =torch.arange(lowerCAmelCase							,   device=encoder_input_tokens.device					)
        x += self.position_encoding(lowerCAmelCase					)

        _lowercase	       =self.dropout_pre(lowerCAmelCase					)

        # inverted the attention mask
        _lowercase	       =encoder_input_tokens.size()
        _lowercase	       =self.get_extended_attention_mask(lowerCAmelCase							,   lowerCAmelCase					)

        for lyr in self.encoders:
              _lowercase	       =lyr(lowerCAmelCase							,   lowerCAmelCase					)[0]
        _lowercase	       =self.layer_norm(lowerCAmelCase					)

        return self.dropout_post(lowerCAmelCase					), encoder_inputs_mask


