


'''simple docstring'''



import copy

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


_lowercase    :    Optional[int]			     =  r"""
    [`RagConfig`] stores the configuration of a *RagModel*. Configuration objects inherit from [`PretrainedConfig`] and
    can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information.

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






@add_start_docstrings(lowerCAmelCase )
class  UpperCamelCase__(     lowerCAmelCase ):
				__magic_name__       :   List[Any]							= "rag"
				__magic_name__       :   Any							= True




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

										"""simple docstring"""




										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"
										UpperCAmelCase         =			kwargs.pop('''question_encoder'''   )
										UpperCAmelCase         =			question_encoder_config.pop('''model_type'''   )
										UpperCAmelCase         =			kwargs.pop('''generator'''   )
										UpperCAmelCase         =			decoder_config.pop('''model_type'''   )

										from ..auto.configuration_auto import AutoConfig

										UpperCAmelCase         =			AutoConfig.for_model(lowerCAmelCase   ,					**lowerCAmelCase   )
										UpperCAmelCase         =			AutoConfig.for_model(lowerCAmelCase   ,					**lowerCAmelCase   )

										UpperCAmelCase         =			reduce_loss
										UpperCAmelCase         =			label_smoothing
										UpperCAmelCase         =			exclude_bos_score
										UpperCAmelCase         =			do_marginalize

										UpperCAmelCase         =			title_sep
										UpperCAmelCase         =			doc_sep
										UpperCAmelCase         =			n_docs
										UpperCAmelCase         =			max_combined_length

										UpperCAmelCase         =			dataset
										UpperCAmelCase         =			dataset_split
										UpperCAmelCase         =			index_name

										UpperCAmelCase         =			retrieval_vector_size
										UpperCAmelCase         =			retrieval_batch_size
										UpperCAmelCase         =			passages_path
										UpperCAmelCase         =			index_path
										UpperCAmelCase         =			use_dummy_dataset

										UpperCAmelCase         =			output_retrieved

										UpperCAmelCase         =			do_deduplication

										UpperCAmelCase         =			use_cache

										if self.forced_eos_token_id is None:
																UpperCAmelCase         =			getattr(self.generator   ,					'''forced_eos_token_id'''   ,					lowerCAmelCase   )




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

										"""simple docstring"""




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





				def   a__(	self    :				List[str]   )->  Tuple:

										"""simple docstring"""




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









'''simple docstring'''



from typing import TYPE_CHECKING

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


_lowercase    :    Any			     =  {
    """configuration_maskformer""": ["""MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MaskFormerConfig"""],
    """configuration_maskformer_swin""": ["""MaskFormerSwinConfig"""],
}

try:
	if not is_vision_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_lowercase    :    Any			     =  ["""MaskFormerFeatureExtractor"""]
	_lowercase    :    Dict			     =  ["""MaskFormerImageProcessor"""]


try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_lowercase    :    Any			     =  [
	    """MASKFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """MaskFormerForInstanceSegmentation""",
	    """MaskFormerModel""",
	    """MaskFormerPreTrainedModel""",
	]
	_lowercase    :    List[Any]			     =  [
	    """MaskFormerSwinBackbone""",
	    """MaskFormerSwinModel""",
	    """MaskFormerSwinPreTrainedModel""",
	]

if TYPE_CHECKING:
	from .configuration_maskformer import MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, MaskFormerConfig
	from .configuration_maskformer_swin import MaskFormerSwinConfig

	try:
		if not is_vision_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .feature_extraction_maskformer import MaskFormerFeatureExtractor
		from .image_processing_maskformer import MaskFormerImageProcessor
	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_maskformer import (
		    MASKFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
		    MaskFormerForInstanceSegmentation,
		    MaskFormerModel,
		    MaskFormerPreTrainedModel,
		)
		from .modeling_maskformer_swin import (
		    MaskFormerSwinBackbone,
		    MaskFormerSwinModel,
		    MaskFormerSwinPreTrainedModel,
		)


else:
	import sys

	_lowercase    :    List[Any]			     =  _LazyModule(__name__, globals()["""__file__"""], _import_structure)





