'''simple docstring'''




import copy

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


__lowercase          =  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 				a__(					lowerCAmelCase__	):


			'''simple docstring'''






			UpperCAmelCase_			:     str      =       '''rag'''
			UpperCAmelCase_			:     str      =       True



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




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

					from ..auto.configuration_auto import AutoConfig

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

					lowerCAmelCase   					=  reduce_loss
					lowerCAmelCase   					=  label_smoothing
					lowerCAmelCase   					=  exclude_bos_score
					lowerCAmelCase   					=  do_marginalize

					lowerCAmelCase   					=  title_sep
					lowerCAmelCase   					=  doc_sep
					lowerCAmelCase   					=  n_docs
					lowerCAmelCase   					=  max_combined_length

					lowerCAmelCase   					=  dataset
					lowerCAmelCase   					=  dataset_split
					lowerCAmelCase   					=  index_name

					lowerCAmelCase   					=  retrieval_vector_size
					lowerCAmelCase   					=  retrieval_batch_size
					lowerCAmelCase   					=  passages_path
					lowerCAmelCase   					=  index_path
					lowerCAmelCase   					=  use_dummy_dataset

					lowerCAmelCase   					=  output_retrieved

					lowerCAmelCase   					=  do_deduplication

					lowerCAmelCase   					=  use_cache

					if self.forced_eos_token_id is None:
							lowerCAmelCase   					=  getattr(self.generator       ,    """forced_eos_token_id"""       ,    __lowerCAmelCase)



			@classmethod
			def 							a_       (   cls       ,    __lowerCAmelCase       ,    __lowerCAmelCase       ,    **__lowerCAmelCase):




					"""simple docstring"""


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







			def 							a_       (   self):




					"""simple docstring"""


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





'''simple docstring'''




from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


__lowercase          =  {
    '''configuration_resnet''': ['''RESNET_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ResNetConfig''', '''ResNetOnnxConfig''']
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__lowercase          =  [
						    '''RESNET_PRETRAINED_MODEL_ARCHIVE_LIST''',
						    '''ResNetForImageClassification''',
						    '''ResNetModel''',
						    '''ResNetPreTrainedModel''',
						    '''ResNetBackbone''',
						]

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__lowercase          =  [
						    '''TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST''',
						    '''TFResNetForImageClassification''',
						    '''TFResNetModel''',
						    '''TFResNetPreTrainedModel''',
						]

try:
						if not is_flax_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__lowercase          =  [
						    '''FlaxResNetForImageClassification''',
						    '''FlaxResNetModel''',
						    '''FlaxResNetPreTrainedModel''',
						]

if TYPE_CHECKING:
						from .configuration_resnet import RESNET_PRETRAINED_CONFIG_ARCHIVE_MAP, ResNetConfig, ResNetOnnxConfig

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_resnet import (
												    RESNET_PRETRAINED_MODEL_ARCHIVE_LIST,
												    ResNetBackbone,
												    ResNetForImageClassification,
												    ResNetModel,
												    ResNetPreTrainedModel,
												)

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_resnet import (
												    TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST,
												    TFResNetForImageClassification,
												    TFResNetModel,
												    TFResNetPreTrainedModel,
												)

						try:
												if not is_flax_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_flax_resnet import FlaxResNetForImageClassification, FlaxResNetModel, FlaxResNetPreTrainedModel


else:
						import sys

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




