"""simple docstring"""


import copy

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


__UpperCamelCase									=			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__   ):
       SCREAMING_SNAKE_CASE_			=      "rag"
       SCREAMING_SNAKE_CASE_			=      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__,							)      ->      Dict:
            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"
            snake_case_					       =					kwargs.pop('question_encoder')
            snake_case_					       =					question_encoder_config.pop('model_type')
            snake_case_					       =					kwargs.pop('generator')
            snake_case_					       =					decoder_config.pop('model_type')

            from ..auto.configuration_auto import AutoConfig

            snake_case_					       =					AutoConfig.for_model(lowerCAmelCase__,							**lowerCAmelCase__)
            snake_case_					       =					AutoConfig.for_model(lowerCAmelCase__,							**lowerCAmelCase__)

            snake_case_					       =					reduce_loss
            snake_case_					       =					label_smoothing
            snake_case_					       =					exclude_bos_score
            snake_case_					       =					do_marginalize

            snake_case_					       =					title_sep
            snake_case_					       =					doc_sep
            snake_case_					       =					n_docs
            snake_case_					       =					max_combined_length

            snake_case_					       =					dataset
            snake_case_					       =					dataset_split
            snake_case_					       =					index_name

            snake_case_					       =					retrieval_vector_size
            snake_case_					       =					retrieval_batch_size
            snake_case_					       =					passages_path
            snake_case_					       =					index_path
            snake_case_					       =					use_dummy_dataset

            snake_case_					       =					output_retrieved

            snake_case_					       =					do_deduplication

            snake_case_					       =					use_cache

            if self.forced_eos_token_id is None:
                 snake_case_					       =					getattr(self.generator,							'forced_eos_token_id',							lowerCAmelCase__)







       @classmethod
       def 		a_						( cls,							lowerCAmelCase__,							lowerCAmelCase__,							**lowerCAmelCase__)      ->      PretrainedConfig:
            return cls(question_encoder=question_encoder_config.to_dict(),							generator=generator_config.to_dict(),							**lowerCAmelCase__)







       def 		a_						( self)      ->      int:
            snake_case_					       =					copy.deepcopy(self.__dict__)
            snake_case_					       =					self.question_encoder.to_dict()
            snake_case_					       =					self.generator.to_dict()
            snake_case_					       =					self.__class__.model_type
            return output




from typing import TYPE_CHECKING

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


lowerCAmelCase_       	=	{'''configuration_dpt''': ['''DPT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''DPTConfig''']}

try:
					if not is_vision_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowerCAmelCase_       	=	['''DPTFeatureExtractor''']
					lowerCAmelCase_       	=	['''DPTImageProcessor''']

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowerCAmelCase_       	=	[
					    '''DPT_PRETRAINED_MODEL_ARCHIVE_LIST''',
					    '''DPTForDepthEstimation''',
					    '''DPTForSemanticSegmentation''',
					    '''DPTModel''',
					    '''DPTPreTrainedModel''',
					]


if TYPE_CHECKING:
					from .configuration_dpt import DPT_PRETRAINED_CONFIG_ARCHIVE_MAP, DPTConfig

					try:
										if not is_vision_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .feature_extraction_dpt import DPTFeatureExtractor
										from .image_processing_dpt import DPTImageProcessor

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_dpt import (
										    DPT_PRETRAINED_MODEL_ARCHIVE_LIST,
										    DPTForDepthEstimation,
										    DPTForSemanticSegmentation,
										    DPTModel,
										    DPTPreTrainedModel,
										)


else:
					import sys

					lowerCAmelCase_       	=	_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)