




"""simple docstring"""







import copy

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


A    =			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(UpperCamelCase     )
class      UpperCAmelCase__		(      UpperCamelCase     ):
		lowerCAmelCase_       :	Union[str, Any] 							=     'rag'
		lowerCAmelCase_       :	Optional[int] 							=     True



		def __init__( self  :    List[Any]      ,							snake_case  :    List[str]=None      ,							snake_case  :    Dict=True      ,							snake_case  :    Any=None      ,							snake_case  :    List[Any]=None      ,							snake_case  :    Union[str, Any]=None      ,							snake_case  :    str=None      ,							snake_case  :    Tuple=None      ,							snake_case  :    List[Any]=" / "      ,							snake_case  :    int=" // "      ,							snake_case  :    str=5      ,							snake_case  :    List[Any]=300      ,							snake_case  :    Dict=768      ,							snake_case  :    int=8      ,							snake_case  :    str="wiki_dpr"      ,							snake_case  :    List[Any]="train"      ,							snake_case  :    str="compressed"      ,							snake_case  :    Tuple=None      ,							snake_case  :    Union[str, Any]=None      ,							snake_case  :    Optional[int]=False      ,							snake_case  :    Dict=False      ,							snake_case  :    List[Any]=0.0      ,							snake_case  :    int=True      ,							snake_case  :    Optional[int]=False      ,							snake_case  :    str=False      ,							snake_case  :    str=False      ,							snake_case  :    List[str]=True      ,							snake_case  :    Union[str, Any]=None      ,							**snake_case  :    List[Any]      ,							)			->						List[Any]:






									'''simple docstring'''

									super().__init__(
									    bos_token_id=snake_case      ,							pad_token_id=snake_case      ,							eos_token_id=snake_case      ,							decoder_start_token_id=snake_case      ,							forced_eos_token_id=snake_case      ,							is_encoder_decoder=snake_case      ,							prefix=snake_case      ,							vocab_size=snake_case      ,							**snake_case      ,							)
									assert (
									    "question_encoder" in kwargs and "generator" in kwargs
									), "Config has to be initialized with question_encoder and generator config"
									A         =					kwargs.pop('question_encoder'				)
									A         =					question_encoder_config.pop('model_type'				)
									A         =					kwargs.pop('generator'				)
									A         =					decoder_config.pop('model_type'				)

									from ..auto.configuration_auto import AutoConfig

									A         =					AutoConfig.for_model(snake_case      ,							**snake_case				)
									A         =					AutoConfig.for_model(snake_case      ,							**snake_case				)

									A         =					reduce_loss
									A         =					label_smoothing
									A         =					exclude_bos_score
									A         =					do_marginalize

									A         =					title_sep
									A         =					doc_sep
									A         =					n_docs
									A         =					max_combined_length

									A         =					dataset
									A         =					dataset_split
									A         =					index_name

									A         =					retrieval_vector_size
									A         =					retrieval_batch_size
									A         =					passages_path
									A         =					index_path
									A         =					use_dummy_dataset

									A         =					output_retrieved

									A         =					do_deduplication

									A         =					use_cache

									if self.forced_eos_token_id is None:
																A         =					getattr(self.generator      ,							'forced_eos_token_id'      ,							snake_case				)



		@classmethod
		def 					A_		( cls  :    Optional[int]      ,							snake_case  :    PretrainedConfig      ,							snake_case  :    PretrainedConfig      ,							**snake_case  :    Any				)			->						PretrainedConfig:






									'''simple docstring'''

									return cls(question_encoder=question_encoder_config.to_dict()      ,							generator=generator_config.to_dict()      ,							**snake_case				)



		def 					A_		( self  :    Dict				)			->						str:






									'''simple docstring'''

									A         =					copy.deepcopy(self.__dict__				)
									A         =					self.question_encoder.to_dict()
									A         =					self.generator.to_dict()
									A         =					self.__class__.model_type
									return output








"""simple docstring"""







# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


A    =			{'configuration_mra': ['MRA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MraConfig']}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							A    =			[
							    'MRA_PRETRAINED_MODEL_ARCHIVE_LIST',
							    'MraForMaskedLM',
							    'MraForMultipleChoice',
							    'MraForQuestionAnswering',
							    'MraForSequenceClassification',
							    'MraForTokenClassification',
							    'MraLayer',
							    'MraModel',
							    'MraPreTrainedModel',
							]


if TYPE_CHECKING:
							from .configuration_mra import MRA_PRETRAINED_CONFIG_ARCHIVE_MAP, MraConfig

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_mra import (
														    MRA_PRETRAINED_MODEL_ARCHIVE_LIST,
														    MraForMaskedLM,
														    MraForMultipleChoice,
														    MraForQuestionAnswering,
														    MraForSequenceClassification,
														    MraForTokenClassification,
														    MraLayer,
														    MraModel,
														    MraPreTrainedModel,
														)
else:
							import sys

							A    =			_LazyModule(__name__, globals()['__file__'], _import_structure)



