






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:
	__UpperCAmelCase							:   Any							=  None


__UpperCAmelCase							:   int							=  logging.get_logger(__name__)

__UpperCAmelCase							:   List[str]							=  {"vocab_file": "sentencepiece.bpe.model", "tokenizer_file": "tokenizer.json"}

__UpperCAmelCase							:   str							=  {
    "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",
    },
}

__UpperCAmelCase							:   Optional[int]							=  {
    "camembert-base": 5_1_2,
}

__UpperCAmelCase							:   int							=  "▁"


class 					_snake_case						(			_A ):
	_A          =   VOCAB_FILES_NAMES
	_A          =   PRETRAINED_VOCAB_FILES_MAP
	_A          =   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	_A          =   ['input_ids', 'attention_mask']
	_A          =   CamembertTokenizer






	def __init__(		self					,UpperCamelCase=None					,UpperCamelCase=None					,UpperCamelCase="<s>"					,UpperCamelCase="</s>"					,UpperCamelCase="</s>"					,UpperCamelCase="<s>"					,UpperCamelCase="<unk>"					,UpperCamelCase="<pad>"					,UpperCamelCase="<mask>"					,UpperCamelCase=["<s>NOTUSED", "</s>NOTUSED"]					,**UpperCamelCase					,)						->    int:
					# Mask token behave like a normal word, i.e. include the space before it
					snake_case__      :List[str]         =   AddedToken(UpperCamelCase					,lstrip=UpperCamelCase					,rstrip=UpperCamelCase       ) if isinstance(UpperCamelCase					,UpperCamelCase       ) else mask_token

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

					snake_case__      :int         =   vocab_file
					snake_case__      :Dict         =   False if not self.vocab_file else True






	def    lowerCAmelCase_							(		self					,UpperCamelCase					,UpperCamelCase = None       )						->    List[int]:

					if token_ids_a is None:
									return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
					snake_case__      :Optional[int]         =   [self.cls_token_id]
					snake_case__      :Tuple         =   [self.sep_token_id]
					return cls + token_ids_a + sep + sep + token_ids_a + sep






	def    lowerCAmelCase_							(		self					,UpperCamelCase					,UpperCamelCase = None       )						->    List[int]:
					snake_case__      :str         =   [self.sep_token_id]
					snake_case__      :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 + sep + token_ids_a + sep       ) * [0]






	def    lowerCAmelCase_							(		self					,UpperCamelCase					,UpperCamelCase = None       )						->    Tuple[str]:
					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(UpperCamelCase       ):
									logger.error(f'Vocabulary path ({save_directory}) should be a directory'       )
									return
					snake_case__      :Dict         =   os.path.join(
					    UpperCamelCase					,(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]       )

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

					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 					_snake_case						(			_A       ,    _A       ,    _A ):






	@register_to_config
	def __init__(		self					,UpperCamelCase					,UpperCamelCase					,UpperCamelCase					,UpperCamelCase					,UpperCamelCase					,UpperCamelCase					,UpperCamelCase					,UpperCamelCase					,UpperCamelCase					,UpperCamelCase = False					,)						->    int:
					super().__init__()

					snake_case__      :Union[str, Any]         =   nn.Embedding(UpperCamelCase					,UpperCamelCase       )

					snake_case__      :int         =   nn.Embedding(UpperCamelCase					,UpperCamelCase       )
					snake_case__      :Any         =   False

					snake_case__      :List[Any]         =   nn.Dropout(p=UpperCamelCase       )

					snake_case__      :Tuple         =   TaConfig(
					    vocab_size=UpperCamelCase					,d_model=UpperCamelCase					,num_heads=UpperCamelCase					,d_kv=UpperCamelCase					,d_ff=UpperCamelCase					,dropout_rate=UpperCamelCase					,feed_forward_proj=UpperCamelCase					,is_decoder=UpperCamelCase					,is_encoder_decoder=UpperCamelCase					,)

					snake_case__      :List[str]         =   nn.ModuleList()
					for lyr_num in range(UpperCamelCase       ):
									snake_case__      :List[Any]         =   TaBlock(UpperCamelCase       )
									self.encoders.append(UpperCamelCase       )

					snake_case__      :Optional[Any]         =   TaLayerNorm(UpperCamelCase       )
					snake_case__      :Any         =   nn.Dropout(p=UpperCamelCase       )






	def    lowerCAmelCase_							(		self					,UpperCamelCase					,UpperCamelCase       )						->    int:
					snake_case__      :str         =   self.token_embedder(UpperCamelCase       )

					snake_case__      :int         =   encoder_input_tokens.shape[1]
					snake_case__      :List[Any]         =   torch.arange(UpperCamelCase					,device=encoder_input_tokens.device       )
					x += self.position_encoding(UpperCamelCase       )

					snake_case__      :Optional[int]         =   self.dropout_pre(UpperCamelCase       )

					# inverted the attention mask
					snake_case__      :Optional[Any]         =   encoder_input_tokens.size()
					snake_case__      :Dict         =   self.get_extended_attention_mask(UpperCamelCase					,UpperCamelCase       )

					for lyr in self.encoders:
									snake_case__      :str         =   lyr(UpperCamelCase					,UpperCamelCase       )[0]
					snake_case__      :List[Any]         =   self.layer_norm(UpperCamelCase       )

					return self.dropout_post(UpperCamelCase       ), encoder_inputs_mask