
"""simple docstring"""
import copy

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


SCREAMING_SNAKE_CASE_					     =	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(A__ )
class       lowerCAmelCase_		(				A__ ):



							'''simple docstring'''





							_snake_case						      =		'''rag'''
							_snake_case						      =		True




							def __init__( self    ,				snake_case_=None    ,				snake_case_=True    ,				snake_case_=None    ,				snake_case_=None    ,				snake_case_=None    ,				snake_case_=None    ,				snake_case_=None    ,				snake_case_=" / "    ,				snake_case_=" // "    ,				snake_case_=5    ,				snake_case_=300    ,				snake_case_=768    ,				snake_case_=8    ,				snake_case_="wiki_dpr"    ,				snake_case_="train"    ,				snake_case_="compressed"    ,				snake_case_=None    ,				snake_case_=None    ,				snake_case_=False    ,				snake_case_=False    ,				snake_case_=0.0    ,				snake_case_=True    ,				snake_case_=False    ,				snake_case_=False    ,				snake_case_=False    ,				snake_case_=True    ,				snake_case_=None    ,				**snake_case_    ,				)       ->  int:
													super().__init__(
													    bos_token_id=snake_case_    ,				pad_token_id=snake_case_    ,				eos_token_id=snake_case_    ,				decoder_start_token_id=snake_case_    ,				forced_eos_token_id=snake_case_    ,				is_encoder_decoder=snake_case_    ,				prefix=snake_case_    ,				vocab_size=snake_case_    ,				**snake_case_    ,				)
													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(snake_case_    ,				**snake_case_				)
													__lowerCAmelCase     =  AutoConfig.for_model(snake_case_    ,				**snake_case_				)

													__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"""    ,				snake_case_				)




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




							def    A__						( self				)       ->  Union[str, Any]:
													__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"""
import os
import socket
from contextlib import contextmanager

import torch

from ..commands.config.default import write_basic_config  # noqa: F401
from ..state import PartialState
from .dataclasses import DistributedType
from .imports import is_deepspeed_available, is_tpu_available
from .transformer_engine import convert_model
from .versions import is_torch_version


if is_deepspeed_available():
			from deepspeed import DeepSpeedEngine

if is_tpu_available(check_device=False):
			import torch_xla.core.xla_model as xm

def  lowercase     (_lowerCAmelCase						):
						if is_torch_version("""<""" ,    """2.0.0"""						) or not hasattr(_lowerCAmelCase ,    """_dynamo"""						):
												return False
						return isinstance(_lowerCAmelCase ,    torch._dynamo.eval_frame.OptimizedModule						)

def  lowercase     (_lowerCAmelCase ,    _lowerCAmelCase = True						):
						__lowerCAmelCase     =  (torch.nn.parallel.DistributedDataParallel, torch.nn.DataParallel)

						__lowerCAmelCase     =  is_compiled_module(_lowerCAmelCase						)
						if is_compiled:
												__lowerCAmelCase     =  model
												__lowerCAmelCase     =  model._orig_mod

						if is_deepspeed_available():
												options += (DeepSpeedEngine,)

						while isinstance(_lowerCAmelCase ,    _lowerCAmelCase						):
												__lowerCAmelCase     =  model.module

						if not keep_fpaa_wrapper:
												__lowerCAmelCase     =  getattr(_lowerCAmelCase ,    """forward"""						)
												__lowerCAmelCase     =  model.__dict__.pop("""_original_forward""" ,    _lowerCAmelCase						)
												if original_forward is not None:
																		while hasattr(_lowerCAmelCase ,    """__wrapped__"""						):
																								__lowerCAmelCase     =  forward.__wrapped__
																								if forward == original_forward:
																														break
																		__lowerCAmelCase     =  forward
												if getattr(_lowerCAmelCase ,    """_converted_to_transformer_engine""" ,    _lowerCAmelCase						):
																		convert_model(_lowerCAmelCase ,    to_transformer_engine=_lowerCAmelCase						)

						if is_compiled:
												__lowerCAmelCase     =  model
												__lowerCAmelCase     =  compiled_model

						return model

def  lowercase     ():
						PartialState().wait_for_everyone()

def  lowercase     (_lowerCAmelCase ,    _lowerCAmelCase						):
						if PartialState().distributed_type == DistributedType.TPU:
												xm.save(_lowerCAmelCase ,    _lowerCAmelCase						)
						elif PartialState().local_process_index == 0:
												torch.save(_lowerCAmelCase ,    _lowerCAmelCase						)

@contextmanager
def  lowercase     (**_lowerCAmelCase						):
						for key, value in kwargs.items():
												__lowerCAmelCase     =  str(_lowerCAmelCase						)

						yield

						for key in kwargs:
												if key.upper() in os.environ:
																		del os.environ[key.upper()]

def  lowercase     (_lowerCAmelCase						):
						if not hasattr(_lowerCAmelCase ,    """__qualname__"""						) and not hasattr(_lowerCAmelCase ,    """__name__"""						):
												__lowerCAmelCase     =  getattr(_lowerCAmelCase ,    """__class__""" ,    _lowerCAmelCase						)
						if hasattr(_lowerCAmelCase ,    """__qualname__"""						):
												return obj.__qualname__
						if hasattr(_lowerCAmelCase ,    """__name__"""						):
												return obj.__name__
						return str(_lowerCAmelCase						)

def  lowercase     (_lowerCAmelCase ,    _lowerCAmelCase						):
						for key, value in source.items():
												if isinstance(_lowerCAmelCase ,    _lowerCAmelCase						):
																		__lowerCAmelCase     =  destination.setdefault(_lowerCAmelCase ,    {}						)
																		merge_dicts(_lowerCAmelCase ,    _lowerCAmelCase						)
												else:
																		__lowerCAmelCase     =  value

						return destination



def  lowercase     (_lowerCAmelCase = None						):
						if port is None:
												__lowerCAmelCase     =  2_9500
						with socket.socket(socket.AF_INET ,    socket.SOCK_STREAM						) as s:
												return s.connect_ex(("""localhost""", port)						) == 0
