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:	Union[str, Any]       			=		None


_lowercase:	List[Any]       			=		logging.get_logger(__name__)

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

_lowercase:	Dict       			=		{
    '''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:	int       			=		{
    '''camembert-base''': 5_1_2,
}

_lowercase:	Tuple       			=		'''▁'''



class        lowerCamelCase__				(   UpperCAmelCase				):
			UpperCamelCase__    							=VOCAB_FILES_NAMES
			UpperCamelCase__    							=PRETRAINED_VOCAB_FILES_MAP
			UpperCamelCase__    							=PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			UpperCamelCase__    							=["input_ids", "attention_mask"]
			UpperCamelCase__    							=CamembertTokenizer





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

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

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





			def   SCREAMING_SNAKE_CASE__     (       self	:	Any  ,  lowercase__	:	List[int]  ,  lowercase__	:	Optional[List[int]] = None						):

										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   SCREAMING_SNAKE_CASE__     (       self	:	Tuple  ,  lowercase__	:	List[int]  ,  lowercase__	:	Optional[List[int]] = None						):
										_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   SCREAMING_SNAKE_CASE__     (       self	:	str  ,  lowercase__	:	str  ,  lowercase__	:	Optional[str] = None						):
										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(lowercase__						):
																	logger.error(f'Vocabulary path ({save_directory}) should be a directory'						)
																	return
										_lowerCAmelCase							=							os.path.join(
										    lowercase__  ,  (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']						)

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

										return (out_vocab_file,)







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


_lowercase:	Optional[Any]       			=		{
    '''configuration_rag''': ['''RagConfig'''],
    '''retrieval_rag''': ['''RagRetriever'''],
    '''tokenization_rag''': ['''RagTokenizer'''],
}

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_lowercase:	Optional[Any]       			=		[
				    '''RagModel''',
				    '''RagPreTrainedModel''',
				    '''RagSequenceForGeneration''',
				    '''RagTokenForGeneration''',
				]

try:
				if not is_tf_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_lowercase:	Optional[Any]       			=		[
				    '''TFRagModel''',
				    '''TFRagPreTrainedModel''',
				    '''TFRagSequenceForGeneration''',
				    '''TFRagTokenForGeneration''',
				]


if TYPE_CHECKING:
				from .configuration_rag import RagConfig
				from .retrieval_rag import RagRetriever
				from .tokenization_rag import RagTokenizer

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_rag import RagModel, RagPreTrainedModel, RagSequenceForGeneration, RagTokenForGeneration

				try:
								if not is_tf_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_tf_rag import (
								    TFRagModel,
								    TFRagPreTrainedModel,
								    TFRagSequenceForGeneration,
								    TFRagTokenForGeneration,
								)

else:
				import sys

				_lowercase:	Optional[int]       			=		_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)






