



"""simple docstring"""

import copy

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


lowercase__			   =  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(__snake_case      )
class SCREAMING_SNAKE_CASE__					(		__snake_case      ):
						_lowerCAmelCase							=					"rag"
						_lowerCAmelCase							=					True



						def __init__(self						,     _lowercase=None						,     _lowercase=True						,     _lowercase=None						,     _lowercase=None						,     _lowercase=None						,     _lowercase=None						,     _lowercase=None						,     _lowercase=" / "						,     _lowercase=" // "						,     _lowercase=5						,     _lowercase=300						,     _lowercase=768						,     _lowercase=8						,     _lowercase="wiki_dpr"						,     _lowercase="train"						,     _lowercase="compressed"						,     _lowercase=None						,     _lowercase=None						,     _lowercase=False						,     _lowercase=False						,     _lowercase=0.0						,     _lowercase=True						,     _lowercase=False						,     _lowercase=False						,     _lowercase=False						,     _lowercase=True						,     _lowercase=None						,     **_lowercase						,     ):





													'''simple docstring'''

													super().__init__(
													    bos_token_id=_lowercase						,     pad_token_id=_lowercase						,     eos_token_id=_lowercase						,     decoder_start_token_id=_lowercase						,     forced_eos_token_id=_lowercase						,     is_encoder_decoder=_lowercase						,     prefix=_lowercase						,     vocab_size=_lowercase						,     **_lowercase						,     )
													assert (
													    "question_encoder" in kwargs and "generator" in kwargs
													), "Config has to be initialized with question_encoder and generator config"
													__a		:		Optional[int]									=       kwargs.pop("""question_encoder"""							)
													__a		:		List[str]									=       question_encoder_config.pop("""model_type"""							)
													__a		:		List[Any]									=       kwargs.pop("""generator"""							)
													__a		:		List[Any]									=       decoder_config.pop("""model_type"""							)

													from ..auto.configuration_auto import AutoConfig

													__a		:		Optional[Any]									=       AutoConfig.for_model(_lowercase						,     **_lowercase							)
													__a		:		Optional[Any]									=       AutoConfig.for_model(_lowercase						,     **_lowercase							)

													__a		:		Dict									=       reduce_loss
													__a		:		Tuple									=       label_smoothing
													__a		:		List[Any]									=       exclude_bos_score
													__a		:		Optional[int]									=       do_marginalize

													__a		:		int									=       title_sep
													__a		:		Any									=       doc_sep
													__a		:		str									=       n_docs
													__a		:		int									=       max_combined_length

													__a		:		List[str]									=       dataset
													__a		:		Optional[int]									=       dataset_split
													__a		:		Optional[Any]									=       index_name

													__a		:		Tuple									=       retrieval_vector_size
													__a		:		Dict									=       retrieval_batch_size
													__a		:		Optional[int]									=       passages_path
													__a		:		Optional[Any]									=       index_path
													__a		:		int									=       use_dummy_dataset

													__a		:		List[str]									=       output_retrieved

													__a		:		List[Any]									=       do_deduplication

													__a		:		int									=       use_cache

													if self.forced_eos_token_id is None:
																				__a		:		str									=       getattr(self.generator						,     """forced_eos_token_id"""						,     _lowercase							)



						@classmethod
						def     lowerCAmelCase__(cls						,     _lowercase						,     _lowercase						,     **_lowercase							):





													'''simple docstring'''

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





						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													__a		:		Union[str, Any]									=       copy.deepcopy(self.__dict__							)
													__a		:		List[str]									=       self.question_encoder.to_dict()
													__a		:		Any									=       self.generator.to_dict()
													__a		:		Optional[Any]									=       self.__class__.model_type
													return output






"""simple docstring"""

from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowercase__			   =  logging.get_logger(__name__)

lowercase__			   =  {
    "sayakpaul/vit-msn-base": "https://huggingface.co/sayakpaul/vit-msn-base/resolve/main/config.json",
    # See all ViT MSN models at https://huggingface.co/models?filter=vit_msn
}




class SCREAMING_SNAKE_CASE__					(		__snake_case      ):
						_lowerCAmelCase							=					"vit_msn"



						def __init__(self						,     _lowercase=768						,     _lowercase=12						,     _lowercase=12						,     _lowercase=3072						,     _lowercase="gelu"						,     _lowercase=0.0						,     _lowercase=0.0						,     _lowercase=0.02						,     _lowercase=1e-06						,     _lowercase=224						,     _lowercase=16						,     _lowercase=3						,     _lowercase=True						,     **_lowercase						,     ):





													'''simple docstring'''

													super().__init__(**_lowercase							)

													__a		:		int									=       hidden_size
													__a		:		str									=       num_hidden_layers
													__a		:		str									=       num_attention_heads
													__a		:		Optional[Any]									=       intermediate_size
													__a		:		Union[str, Any]									=       hidden_act
													__a		:		Tuple									=       hidden_dropout_prob
													__a		:		Any									=       attention_probs_dropout_prob
													__a		:		List[Any]									=       initializer_range
													__a		:		Any									=       layer_norm_eps
													__a		:		Dict									=       image_size
													__a		:		List[Any]									=       patch_size
													__a		:		Dict									=       num_channels
													__a		:		Optional[Any]									=       qkv_bias

