



"""simple docstring"""






import json
from typing import List, Optional, Tuple

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_mvp import MvpTokenizer


_lowercase			:			str										=						logging.get_logger(__name__)


_lowercase			:			Optional[int]										=						{"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}

# See all MVP models at https://huggingface.co/models?filter=mvp
_lowercase			:			Optional[int]										=						{
    "vocab_file": {
        "RUCAIBox/mvp": "https://huggingface.co/RUCAIBox/mvp/resolve/main/vocab.json",
    },
    "added_tokens.json": {
        "RUCAIBox/mvp": "https://huggingface.co/RUCAIBox/mvp/resolve/main/added_tokens.json",
    },
    "merges_file": {
        "RUCAIBox/mvp": "https://huggingface.co/RUCAIBox/mvp/resolve/main/merges.txt",
    },
    "tokenizer_file": {
        "RUCAIBox/mvp": "https://huggingface.co/RUCAIBox/mvp/resolve/main/tokenizer.json",
    },
}

_lowercase			:			str										=						{
    "RUCAIBox/mvp": 1_0_2_4,
}

class 	__SCREAMING_SNAKE_CASE     (				lowerCAmelCase_       ):


					'''simple docstring'''





					_a             =							VOCAB_FILES_NAMES
					_a             =							PRETRAINED_VOCAB_FILES_MAP
					_a             =							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					_a             =							['input_ids', 'attention_mask']
					_a             =							MvpTokenizer



					def __init__(	self							:  Optional[Any],  lowerCamelCase							:  Any=None,  lowerCamelCase							:  List[str]=None,  lowerCamelCase							:  int=None,  lowerCamelCase							:  int="replace",  lowerCamelCase							:  List[Any]="<s>",  lowerCamelCase							:  Optional[Any]="</s>",  lowerCamelCase							:  Dict="</s>",  lowerCamelCase							:  Union[str, Any]="<s>",  lowerCamelCase							:  int="<unk>",  lowerCamelCase							:  List[str]="<pad>",  lowerCamelCase							:  List[str]="<mask>",  lowerCamelCase							:  Union[str, Any]=False,  lowerCamelCase							:  Dict=True,  **lowerCamelCase							:  Optional[Any],  )-> Optional[Any]:
								super().__init__(
								    lowerCamelCase,  lowerCamelCase,  tokenizer_file=lowerCamelCase,  errors=lowerCamelCase,  bos_token=lowerCamelCase,  eos_token=lowerCamelCase,  sep_token=lowerCamelCase,  cls_token=lowerCamelCase,  unk_token=lowerCamelCase,  pad_token=lowerCamelCase,  mask_token=lowerCamelCase,  add_prefix_space=lowerCamelCase,  trim_offsets=lowerCamelCase,  **lowerCamelCase,  )

								lowerCamelCase__		:		List[Any]		     =json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()					)
								if pre_tok_state.get('''add_prefix_space''',  lowerCamelCase					) != add_prefix_space:
											lowerCamelCase__		:		Optional[Any]		     =getattr(lowerCamelCase,  pre_tok_state.pop('''type'''					)					)
											lowerCamelCase__		:		str		     =add_prefix_space
											lowerCamelCase__		:		List[Any]		     =pre_tok_class(**lowerCamelCase					)

								lowerCamelCase__		:		str		     =add_prefix_space

								# the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
								lowerCamelCase__		:		int		     ='''post_processor'''
								lowerCamelCase__		:		Optional[Any]		     =getattr(self.backend_tokenizer,  lowerCamelCase,  lowerCamelCase					)
								if tokenizer_component_instance:
											lowerCamelCase__		:		int		     =json.loads(tokenizer_component_instance.__getstate__()					)

											# The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
											if "sep" in state:
														lowerCamelCase__		:		Optional[int]		     =tuple(state['''sep''']					)
											if "cls" in state:
														lowerCamelCase__		:		str		     =tuple(state['''cls''']					)

											lowerCamelCase__		:		List[Any]		     =False

											if state.get('''add_prefix_space''',  lowerCamelCase					) != add_prefix_space:
														lowerCamelCase__		:		Optional[Any]		     =add_prefix_space
														lowerCamelCase__		:		Dict		     =True

											if state.get('''trim_offsets''',  lowerCamelCase					) != trim_offsets:
														lowerCamelCase__		:		List[str]		     =trim_offsets
														lowerCamelCase__		:		List[Any]		     =True

											if changes_to_apply:
														lowerCamelCase__		:		Tuple		     =getattr(lowerCamelCase,  state.pop('''type'''					)					)
														lowerCamelCase__		:		Optional[Any]		     =component_class(**lowerCamelCase					)
														setattr(self.backend_tokenizer,  lowerCamelCase,  lowerCamelCase					)



					@property
					def 		snake_case	(	self							:  Tuple					)-> str:
								if self._mask_token is None:
											if self.verbose:
														logger.error('''Using mask_token, but it is not set yet.'''					)
											return None
								return str(self._mask_token					)



					@mask_token.setter
					def 		snake_case	(	self							:  Tuple,  lowerCamelCase							:  List[Any]					)-> List[str]:
								lowerCamelCase__		:		Dict		     =AddedToken(lowerCamelCase,  lstrip=lowerCamelCase,  rstrip=lowerCamelCase					) if isinstance(lowerCamelCase,  lowerCamelCase					) else value
								lowerCamelCase__		:		Union[str, Any]		     =value



					def 		snake_case	(	self							:  Tuple,  *lowerCamelCase							:  Optional[Any],  **lowerCamelCase							:  int					)-> BatchEncoding:
								lowerCamelCase__		:		Any		     =kwargs.get('''is_split_into_words''',  lowerCamelCase					)

								if is_split_into_words and not self.add_prefix_space:
											raise ValueError(
											    F'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
											    '''to use it with pretokenized inputs.'''					)

								return super()._batch_encode_plus(*lowerCamelCase,  **lowerCamelCase					)



					def 		snake_case	(	self							:  Any,  *lowerCamelCase							:  str,  **lowerCamelCase							:  Dict					)-> BatchEncoding:
								lowerCamelCase__		:		Dict		     =kwargs.get('''is_split_into_words''',  lowerCamelCase					)

								if is_split_into_words and not self.add_prefix_space:
											raise ValueError(
											    F'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
											    '''to use it with pretokenized inputs.'''					)

								return super()._encode_plus(*lowerCamelCase,  **lowerCamelCase					)



					def 		snake_case	(	self							:  List[str],  lowerCamelCase							:  str,  lowerCamelCase							:  Optional[str] = None					)-> Tuple[str]:
								lowerCamelCase__		:		str		     =self._tokenizer.model.save(lowerCamelCase,  name=lowerCamelCase					)
								return tuple(lowerCamelCase					)



					def 		snake_case	(	self							:  Optional[int],  lowerCamelCase							:  List[Any],  lowerCamelCase							:  List[str]=None					)-> int:
								lowerCamelCase__		:		Optional[Any]		     =[self.bos_token_id] + token_ids_a + [self.eos_token_id]
								if token_ids_a is None:
											return output

								return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]




					def 		snake_case	(	self							:  List[Any],  lowerCamelCase							:  List[int],  lowerCamelCase							:  Optional[List[int]] = None					)-> List[int]:
								lowerCamelCase__		:		Optional[Any]		     =[self.sep_token_id]
								lowerCamelCase__		:		int		     =[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]









"""simple docstring"""






import copy

from ...configuration_utils import PretrainedConfig
from ...utils import add_start_docstrings


_lowercase			:			Tuple										=						r"\n    [`RagConfig`] stores the configuration of a *RagModel*. Configuration objects inherit from [`PretrainedConfig`] and\n    can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information.\n\n    Args:\n        title_sep (`str`, *optional*, defaults to  `\" / \"`):\n            Separator inserted between the title and the text of the retrieved document when calling [`RagRetriever`].\n        doc_sep (`str`, *optional*, defaults to  `\" // \"`):\n            Separator inserted between the text of the retrieved document and the original input when calling\n            [`RagRetriever`].\n        n_docs (`int`, *optional*, defaults to 5):\n            Number of documents to retrieve.\n        max_combined_length (`int`, *optional*, defaults to 300):\n            Max length of contextualized input returned by [`~RagRetriever.__call__`].\n        retrieval_vector_size (`int`, *optional*, defaults to 768):\n            Dimensionality of the document embeddings indexed by [`RagRetriever`].\n        retrieval_batch_size (`int`, *optional*, defaults to 8):\n            Retrieval batch size, defined as the number of queries issues concurrently to the faiss index encapsulated\n            [`RagRetriever`].\n        dataset (`str`, *optional*, defaults to `\"wiki_dpr\"`):\n            A dataset identifier of the indexed dataset in HuggingFace Datasets (list all available datasets and ids\n            using `datasets.list_datasets()`).\n        dataset_split (`str`, *optional*, defaults to `\"train\"`)\n            Which split of the `dataset` to load.\n        index_name (`str`, *optional*, defaults to `\"compressed\"`)\n            The index name of the index associated with the `dataset`. One can choose between `\"legacy\"`, `\"exact\"` and\n            `\"compressed\"`.\n        index_path (`str`, *optional*)\n            The path to the serialized faiss index on disk.\n        passages_path (`str`, *optional*):\n            A path to text passages compatible with the faiss index. Required if using\n            [`~models.rag.retrieval_rag.LegacyIndex`]\n        use_dummy_dataset (`bool`, *optional*, defaults to `False`)\n            Whether to load a \"dummy\" variant of the dataset specified by `dataset`.\n        label_smoothing (`float`, *optional*, defaults to 0.0):\n            Only relevant if `return_loss` is set to `True`. Controls the `epsilon` parameter value for label smoothing\n            in the loss calculation. If set to 0, no label smoothing is performed.\n        do_marginalize (`bool`, *optional*, defaults to `False`):\n            If `True`, the logits are marginalized over all documents by making use of\n            `torch.nn.functional.log_softmax`.\n        reduce_loss (`bool`, *optional*, defaults to `False`):\n            Whether or not to reduce the NLL loss using the `torch.Tensor.sum` operation.\n        do_deduplication (`bool`, *optional*, defaults to `True`):\n            Whether or not to deduplicate the generations from different context documents for a given input. Has to be\n            set to `False` if used while training with distributed backend.\n        exclude_bos_score (`bool`, *optional*, defaults to `False`):\n            Whether or not to disregard the BOS token when computing the loss.\n        output_retrieved(`bool`, *optional*, defaults to `False`):\n            If set to `True`, `retrieved_doc_embeds`, `retrieved_doc_ids`, `context_input_ids` and\n            `context_attention_mask` are returned. See returned tensors for more detail.\n        use_cache (`bool`, *optional*, defaults to `True`):\n            Whether or not the model should return the last key/values attentions (not used by all models).\n        forced_eos_token_id (`int`, *optional*):\n            The id of the token to force as the last generated token when `max_length` is reached. Usually set to\n            `eos_token_id`.\n"

@add_start_docstrings(lowerCAmelCase_       )
class 	__SCREAMING_SNAKE_CASE     (				lowerCAmelCase_       ):


					'''simple docstring'''





					_a             =							'rag'
					_a             =							True



					def __init__(	self							:  Optional[int],  lowerCamelCase							:  Union[str, Any]=None,  lowerCamelCase							:  Union[str, Any]=True,  lowerCamelCase							:  Dict=None,  lowerCamelCase							:  Optional[Any]=None,  lowerCamelCase							:  Any=None,  lowerCamelCase							:  List[Any]=None,  lowerCamelCase							:  Optional[Any]=None,  lowerCamelCase							:  Any=" / ",  lowerCamelCase							:  Union[str, Any]=" // ",  lowerCamelCase							:  List[Any]=5,  lowerCamelCase							:  int=300,  lowerCamelCase							:  Optional[Any]=768,  lowerCamelCase							:  Optional[Any]=8,  lowerCamelCase							:  Tuple="wiki_dpr",  lowerCamelCase							:  Tuple="train",  lowerCamelCase							:  Union[str, Any]="compressed",  lowerCamelCase							:  List[str]=None,  lowerCamelCase							:  Any=None,  lowerCamelCase							:  Union[str, Any]=False,  lowerCamelCase							:  Dict=False,  lowerCamelCase							:  Optional[int]=0.0,  lowerCamelCase							:  Any=True,  lowerCamelCase							:  Dict=False,  lowerCamelCase							:  Tuple=False,  lowerCamelCase							:  List[str]=False,  lowerCamelCase							:  List[Any]=True,  lowerCamelCase							:  Optional[Any]=None,  **lowerCamelCase							:  str,  )-> List[Any]:
								super().__init__(
								    bos_token_id=lowerCamelCase,  pad_token_id=lowerCamelCase,  eos_token_id=lowerCamelCase,  decoder_start_token_id=lowerCamelCase,  forced_eos_token_id=lowerCamelCase,  is_encoder_decoder=lowerCamelCase,  prefix=lowerCamelCase,  vocab_size=lowerCamelCase,  **lowerCamelCase,  )
								assert (
								    "question_encoder" in kwargs and "generator" in kwargs
								), "Config has to be initialized with question_encoder and generator config"
								lowerCamelCase__		:		Tuple		     =kwargs.pop('''question_encoder'''					)
								lowerCamelCase__		:		int		     =question_encoder_config.pop('''model_type'''					)
								lowerCamelCase__		:		Dict		     =kwargs.pop('''generator'''					)
								lowerCamelCase__		:		Tuple		     =decoder_config.pop('''model_type'''					)

								from ..auto.configuration_auto import AutoConfig

								lowerCamelCase__		:		Tuple		     =AutoConfig.for_model(lowerCamelCase,  **lowerCamelCase					)
								lowerCamelCase__		:		Dict		     =AutoConfig.for_model(lowerCamelCase,  **lowerCamelCase					)

								lowerCamelCase__		:		Any		     =reduce_loss
								lowerCamelCase__		:		Tuple		     =label_smoothing
								lowerCamelCase__		:		List[str]		     =exclude_bos_score
								lowerCamelCase__		:		Dict		     =do_marginalize

								lowerCamelCase__		:		Union[str, Any]		     =title_sep
								lowerCamelCase__		:		Dict		     =doc_sep
								lowerCamelCase__		:		List[Any]		     =n_docs
								lowerCamelCase__		:		List[str]		     =max_combined_length

								lowerCamelCase__		:		List[Any]		     =dataset
								lowerCamelCase__		:		int		     =dataset_split
								lowerCamelCase__		:		List[Any]		     =index_name

								lowerCamelCase__		:		int		     =retrieval_vector_size
								lowerCamelCase__		:		Dict		     =retrieval_batch_size
								lowerCamelCase__		:		str		     =passages_path
								lowerCamelCase__		:		Any		     =index_path
								lowerCamelCase__		:		List[Any]		     =use_dummy_dataset

								lowerCamelCase__		:		Optional[int]		     =output_retrieved

								lowerCamelCase__		:		List[str]		     =do_deduplication

								lowerCamelCase__		:		Tuple		     =use_cache

								if self.forced_eos_token_id is None:
											lowerCamelCase__		:		int		     =getattr(self.generator,  '''forced_eos_token_id''',  lowerCamelCase					)



					@classmethod
					def 		snake_case	(	cls							:  List[str],  lowerCamelCase							:  PretrainedConfig,  lowerCamelCase							:  PretrainedConfig,  **lowerCamelCase							:  int					)-> PretrainedConfig:
								return cls(question_encoder=question_encoder_config.to_dict(),  generator=generator_config.to_dict(),  **lowerCamelCase					)




					def 		snake_case	(	self							:  Optional[int]					)-> int:
								lowerCamelCase__		:		Union[str, Any]		     =copy.deepcopy(self.__dict__					)
								lowerCamelCase__		:		Optional[int]		     =self.question_encoder.to_dict()
								lowerCamelCase__		:		Tuple		     =self.generator.to_dict()
								lowerCamelCase__		:		Optional[Any]		     =self.__class__.model_type
								return output




