
"""simple docstring"""




from importlib import import_module

from .logging import get_logger


lowerCAmelCase__				=					get_logger(__name__)





class 			snake_case  :

							def __init__(self							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_=None     ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_    			=			attrs or []
									if module is not None:
											for key in module.__dict__:
													if key in attrs or not key.startswith('''__'''     ):
															setattr(self							,							SCREAMING_SNAKE_CASE_							,							getattr(SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_     )     )
									SCREAMING_SNAKE_CASE_    			=			module._original_module if isinstance(SCREAMING_SNAKE_CASE_							,							_PatchedModuleObj     ) else module







class 			snake_case  :
							UpperCAmelCase__      =   []

							def __init__(self							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_=None     ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_    			=			obj
									SCREAMING_SNAKE_CASE_    			=			target
									SCREAMING_SNAKE_CASE_    			=			new
									SCREAMING_SNAKE_CASE_    			=			target.split('''.'''     )[0]
									SCREAMING_SNAKE_CASE_    			=			{}
									SCREAMING_SNAKE_CASE_    			=			attrs or []

							def __enter__(self     ):

									"""simple docstring"""







									*SCREAMING_SNAKE_CASE_			,SCREAMING_SNAKE_CASE_    			=			self.target.split('''.'''     )

									# Patch modules:
									# it's used to patch attributes of submodules like "os.path.join";
									# in this case we need to patch "os" and "os.path"

									for i in range(len(SCREAMING_SNAKE_CASE_     )     ):
											try:
													SCREAMING_SNAKE_CASE_    			=			import_module('''.'''.join(submodules[: i + 1]     )     )
											except ModuleNotFoundError:
													continue
											# We iterate over all the globals in self.obj in case we find "os" or "os.path"
											for attr in self.obj.__dir__():
													SCREAMING_SNAKE_CASE_    			=			getattr(self.obj							,							SCREAMING_SNAKE_CASE_     )
													# We don't check for the name of the global, but rather if its value *is* "os" or "os.path".
													# This allows to patch renamed modules like "from os import path as ospath".
													if obj_attr is submodule or (
													    (isinstance(SCREAMING_SNAKE_CASE_							,							_PatchedModuleObj     ) and obj_attr._original_module is submodule)
													):
															SCREAMING_SNAKE_CASE_    			=			obj_attr
															# patch at top level
															setattr(self.obj							,							SCREAMING_SNAKE_CASE_							,							_PatchedModuleObj(SCREAMING_SNAKE_CASE_							,							attrs=self.attrs     )     )
															SCREAMING_SNAKE_CASE_    			=			getattr(self.obj							,							SCREAMING_SNAKE_CASE_     )
															# construct lower levels patches
															for key in submodules[i + 1 :]:
																	setattr(SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_							,							_PatchedModuleObj(getattr(SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_     )							,							attrs=self.attrs     )     )
																	SCREAMING_SNAKE_CASE_    			=			getattr(SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_     )
															# finally set the target attribute
															setattr(SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_							,							self.new     )

        # Patch attribute itself:
        # it's used for builtins like "open",
        # and also to patch "os.path.join" we may also need to patch "join"
        # itself if it was imported as "from os.path import join".

									if submodules:  # if it's an attribute of a submodule like "os.path.join"
											try:
													SCREAMING_SNAKE_CASE_    			=			getattr(import_module('''.'''.join(SCREAMING_SNAKE_CASE_     )     )							,							SCREAMING_SNAKE_CASE_     )
											except (AttributeError, ModuleNotFoundError):
													return
											# We iterate over all the globals in self.obj in case we find "os.path.join"
											for attr in self.obj.__dir__():
													# We don't check for the name of the global, but rather if its value *is* "os.path.join".
													# This allows to patch renamed attributes like "from os.path import join as pjoin".
													if getattr(self.obj							,							SCREAMING_SNAKE_CASE_     ) is attr_value:
															SCREAMING_SNAKE_CASE_    			=			getattr(self.obj							,							SCREAMING_SNAKE_CASE_     )
															setattr(self.obj							,							SCREAMING_SNAKE_CASE_							,							self.new     )
									elif target_attr in globals()["__builtins__"]:  # if it'a s builtin like "open"
											SCREAMING_SNAKE_CASE_    			=			globals()['''__builtins__'''][target_attr]
											setattr(self.obj							,							SCREAMING_SNAKE_CASE_							,							self.new     )
									else:
											raise RuntimeError(f'Tried to patch attribute {target_attr} instead of a submodule.'     )

							def __exit__(self							,							*SCREAMING_SNAKE_CASE_     ):

									"""simple docstring"""







									for attr in list(self.original     ):
											setattr(self.obj							,							SCREAMING_SNAKE_CASE_							,							self.original.pop(SCREAMING_SNAKE_CASE_     )     )

							def _lowercase       (self     ):

									"""simple docstring"""







									self.__enter__()
									self._active_patches.append(self     )







							def _lowercase       (self     ):

									"""simple docstring"""







									try:
											self._active_patches.remove(self     )
									except ValueError:
											# If the patch hasn't been started this will fail
											return None

									return self.__exit__()

"""simple docstring"""




import copy

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


lowerCAmelCase__				=					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(__lowercase )
class 			snake_case  (				__lowercase ):
							UpperCAmelCase__      =   '''rag'''
							UpperCAmelCase__      =   True

							def __init__(self							,							SCREAMING_SNAKE_CASE_=None							,							SCREAMING_SNAKE_CASE_=True							,							SCREAMING_SNAKE_CASE_=None							,							SCREAMING_SNAKE_CASE_=None							,							SCREAMING_SNAKE_CASE_=None							,							SCREAMING_SNAKE_CASE_=None							,							SCREAMING_SNAKE_CASE_=None							,							SCREAMING_SNAKE_CASE_=" / "							,							SCREAMING_SNAKE_CASE_=" // "							,							SCREAMING_SNAKE_CASE_=5							,							SCREAMING_SNAKE_CASE_=3_00							,							SCREAMING_SNAKE_CASE_=7_68							,							SCREAMING_SNAKE_CASE_=8							,							SCREAMING_SNAKE_CASE_="wiki_dpr"							,							SCREAMING_SNAKE_CASE_="train"							,							SCREAMING_SNAKE_CASE_="compressed"							,							SCREAMING_SNAKE_CASE_=None							,							SCREAMING_SNAKE_CASE_=None							,							SCREAMING_SNAKE_CASE_=False							,							SCREAMING_SNAKE_CASE_=False							,							SCREAMING_SNAKE_CASE_=0.0							,							SCREAMING_SNAKE_CASE_=True							,							SCREAMING_SNAKE_CASE_=False							,							SCREAMING_SNAKE_CASE_=False							,							SCREAMING_SNAKE_CASE_=False							,							SCREAMING_SNAKE_CASE_=True							,							SCREAMING_SNAKE_CASE_=None							,							**SCREAMING_SNAKE_CASE_							,							):

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

									from ..auto.configuration_auto import AutoConfig

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

									SCREAMING_SNAKE_CASE_    			=			reduce_loss
									SCREAMING_SNAKE_CASE_    			=			label_smoothing
									SCREAMING_SNAKE_CASE_    			=			exclude_bos_score
									SCREAMING_SNAKE_CASE_    			=			do_marginalize

									SCREAMING_SNAKE_CASE_    			=			title_sep
									SCREAMING_SNAKE_CASE_    			=			doc_sep
									SCREAMING_SNAKE_CASE_    			=			n_docs
									SCREAMING_SNAKE_CASE_    			=			max_combined_length

									SCREAMING_SNAKE_CASE_    			=			dataset
									SCREAMING_SNAKE_CASE_    			=			dataset_split
									SCREAMING_SNAKE_CASE_    			=			index_name

									SCREAMING_SNAKE_CASE_    			=			retrieval_vector_size
									SCREAMING_SNAKE_CASE_    			=			retrieval_batch_size
									SCREAMING_SNAKE_CASE_    			=			passages_path
									SCREAMING_SNAKE_CASE_    			=			index_path
									SCREAMING_SNAKE_CASE_    			=			use_dummy_dataset

									SCREAMING_SNAKE_CASE_    			=			output_retrieved

									SCREAMING_SNAKE_CASE_    			=			do_deduplication

									SCREAMING_SNAKE_CASE_    			=			use_cache

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

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

									"""simple docstring"""







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







							def _lowercase       (self     ):

									"""simple docstring"""







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