


import copy

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


A	:		Any    =       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(a__							)
class 							A   (	a__							):
					'''simple docstring'''






					A__      =			"rag"
					A__      =			True

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




							"""simple docstring"""


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

							from ..auto.configuration_auto import AutoConfig

							lowercase__					      =		AutoConfig.for_model(SCREAMING_SNAKE_CASE_				, **SCREAMING_SNAKE_CASE_		)
							lowercase__					      =		AutoConfig.for_model(SCREAMING_SNAKE_CASE_				, **SCREAMING_SNAKE_CASE_		)

							lowercase__					      =		reduce_loss
							lowercase__					      =		label_smoothing
							lowercase__					      =		exclude_bos_score
							lowercase__					      =		do_marginalize

							lowercase__					      =		title_sep
							lowercase__					      =		doc_sep
							lowercase__					      =		n_docs
							lowercase__					      =		max_combined_length

							lowercase__					      =		dataset
							lowercase__					      =		dataset_split
							lowercase__					      =		index_name

							lowercase__					      =		retrieval_vector_size
							lowercase__					      =		retrieval_batch_size
							lowercase__					      =		passages_path
							lowercase__					      =		index_path
							lowercase__					      =		use_dummy_dataset

							lowercase__					      =		output_retrieved

							lowercase__					      =		do_deduplication

							lowercase__					      =		use_cache

							if self.forced_eos_token_id is None:
									lowercase__					      =		getattr(self.generator				, """forced_eos_token_id"""				, SCREAMING_SNAKE_CASE_		)

					@classmethod
					def   lowerCamelCase__					(cls   :						Any				, _UpperCAmelCase   :						Dict				, _UpperCAmelCase   :						str				, **_UpperCAmelCase   :						Any		)						->       PretrainedConfig:




							"""simple docstring"""


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


					def   lowerCamelCase__					(self   :						int		)						->       List[Any]:




							"""simple docstring"""


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









from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


A	:		Union[str, Any]    =       logging.get_logger(__name__)

A	:		int    =       {
    'andreasmadsen/efficient_mlm_m0.40': (
        'https://huggingface.co/andreasmadsen/efficient_mlm_m0.40/resolve/main/config.json'
    ),
}



class 							A   (	UpperCAmelCase__							):
					'''simple docstring'''






					A__      =			'''roberta-prelayernorm'''

					def __init__(self   :						Dict				, _UpperCAmelCase   :						List[Any]=5_0265				, _UpperCAmelCase   :						List[Any]=768				, _UpperCAmelCase   :						str=12				, _UpperCAmelCase   :						Any=12				, _UpperCAmelCase   :						Any=3072				, _UpperCAmelCase   :						int="gelu"				, _UpperCAmelCase   :						Tuple=0.1				, _UpperCAmelCase   :						Tuple=0.1				, _UpperCAmelCase   :						Any=512				, _UpperCAmelCase   :						Dict=2				, _UpperCAmelCase   :						int=0.02				, _UpperCAmelCase   :						List[Any]=1E-1_2				, _UpperCAmelCase   :						Tuple=1				, _UpperCAmelCase   :						int=0				, _UpperCAmelCase   :						int=2				, _UpperCAmelCase   :						Optional[Any]="absolute"				, _UpperCAmelCase   :						int=True				, _UpperCAmelCase   :						Optional[int]=None				, **_UpperCAmelCase   :						Any				, )						->       List[str]:




							"""simple docstring"""


							super().__init__(pad_token_id=_UpperCAmelCase				, bos_token_id=_UpperCAmelCase				, eos_token_id=_UpperCAmelCase				, **_UpperCAmelCase		)

							lowercase__					      =		vocab_size
							lowercase__					      =		hidden_size
							lowercase__					      =		num_hidden_layers
							lowercase__					      =		num_attention_heads
							lowercase__					      =		hidden_act
							lowercase__					      =		intermediate_size
							lowercase__					      =		hidden_dropout_prob
							lowercase__					      =		attention_probs_dropout_prob
							lowercase__					      =		max_position_embeddings
							lowercase__					      =		type_vocab_size
							lowercase__					      =		initializer_range
							lowercase__					      =		layer_norm_eps
							lowercase__					      =		position_embedding_type
							lowercase__					      =		use_cache
							lowercase__					      =		classifier_dropout






class 							A   (	UpperCAmelCase__							):
					'''simple docstring'''






					@property
					def   lowerCamelCase__					(self   :						Any		)						->       Mapping[str, Mapping[int, str]]:




							"""simple docstring"""


							if self.task == "multiple-choice":
									lowercase__					      =		{0: """batch""", 1: """choice""", 2: """sequence"""}
							else:
									lowercase__					      =		{0: """batch""", 1: """sequence"""}
							return OrderedDict(
							    [
							        ("""input_ids""", dynamic_axis),
							        ("""attention_mask""", dynamic_axis),
							    ]		)

