


'''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(__a      )
class 			lowerCamelCase_   (	__a      ):
      lowerCAmelCase__		     =     'rag'
      lowerCAmelCase__		     =     True






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


          '''simple docstring'''


          super().__init__(
              bos_token_id=_A						,						pad_token_id=_A						,						eos_token_id=_A						,						decoder_start_token_id=_A						,						forced_eos_token_id=_A						,						is_encoder_decoder=_A						,						prefix=_A						,						vocab_size=_A						,						**_A						,						)
          assert (
              "question_encoder" in kwargs and "generator" in kwargs
          ), "Config has to be initialized with question_encoder and generator config"
          UpperCAmelCase__			:		Dict													=				kwargs.pop('''question_encoder''' )
          UpperCAmelCase__			:		str													=				question_encoder_config.pop('''model_type''' )
          UpperCAmelCase__			:		List[Any]													=				kwargs.pop('''generator''' )
          UpperCAmelCase__			:		Dict													=				decoder_config.pop('''model_type''' )

          from ..auto.configuration_auto import AutoConfig

          UpperCAmelCase__			:		Any													=				AutoConfig.for_model(_A						,						**_A )
          UpperCAmelCase__			:		Optional[Any]													=				AutoConfig.for_model(_A						,						**_A )

          UpperCAmelCase__			:		int													=				reduce_loss
          UpperCAmelCase__			:		List[str]													=				label_smoothing
          UpperCAmelCase__			:		Optional[Any]													=				exclude_bos_score
          UpperCAmelCase__			:		List[Any]													=				do_marginalize

          UpperCAmelCase__			:		List[Any]													=				title_sep
          UpperCAmelCase__			:		List[str]													=				doc_sep
          UpperCAmelCase__			:		Optional[int]													=				n_docs
          UpperCAmelCase__			:		str													=				max_combined_length

          UpperCAmelCase__			:		Optional[Any]													=				dataset
          UpperCAmelCase__			:		Tuple													=				dataset_split
          UpperCAmelCase__			:		Dict													=				index_name

          UpperCAmelCase__			:		str													=				retrieval_vector_size
          UpperCAmelCase__			:		List[str]													=				retrieval_batch_size
          UpperCAmelCase__			:		Any													=				passages_path
          UpperCAmelCase__			:		Optional[int]													=				index_path
          UpperCAmelCase__			:		Union[str, Any]													=				use_dummy_dataset

          UpperCAmelCase__			:		str													=				output_retrieved

          UpperCAmelCase__			:		Optional[Any]													=				do_deduplication

          UpperCAmelCase__			:		Optional[Any]													=				use_cache

          if self.forced_eos_token_id is None:
              UpperCAmelCase__			:		Optional[Any]													=				getattr(self.generator						,						'''forced_eos_token_id'''						,						_A )






      @classmethod
      def lowercase_							(					cls				:    str						,						_A				:    PretrainedConfig						,						_A				:    PretrainedConfig						,						**_A				:    Dict ):


          '''simple docstring'''


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






      def lowercase_							(					self				:    Dict ):


          '''simple docstring'''


          UpperCAmelCase__			:		int													=				copy.deepcopy(self.__dict__ )
          UpperCAmelCase__			:		Tuple													=				self.question_encoder.to_dict()
          UpperCAmelCase__			:		List[str]													=				self.generator.to_dict()
          UpperCAmelCase__			:		Dict													=				self.__class__.model_type
          return output




'''simple docstring'''
import os
from collections import namedtuple

import pytest

from datasets import ClassLabel, Features, Sequence, Value
from datasets.commands.test import TestCommand
from datasets.info import DatasetInfo, DatasetInfosDict


UpperCamelCase__    					=      namedtuple(
    '''_TestCommandArgs''',
    [
        '''dataset''',
        '''name''',
        '''cache_dir''',
        '''data_dir''',
        '''all_configs''',
        '''save_infos''',
        '''ignore_verifications''',
        '''force_redownload''',
        '''clear_cache''',
    ],
    defaults=[None, None, None, False, False, False, False, False],
)





def 					a__       (					lowerCAmelCase__  ,					lowerCAmelCase__						)    ->	Optional[int]:
    return (abs(source - target						) / target) < 0.0_1







@pytest.mark.integration
def 					a__       (					lowerCAmelCase__						)    ->	Optional[int]:
    UpperCAmelCase__			:		str													=				_TestCommandArgs(dataset=lowerCAmelCase__  ,					all_configs=lowerCAmelCase__  ,					save_infos=lowerCAmelCase__						)
    UpperCAmelCase__			:		Tuple													=				TestCommand(*lowerCAmelCase__						)
    test_command.run()
    UpperCAmelCase__			:		List[Any]													=				os.path.join(lowerCAmelCase__  ,					'''README.md'''						)
    assert os.path.exists(lowerCAmelCase__						)
    UpperCAmelCase__			:		List[str]													=				DatasetInfosDict.from_directory(lowerCAmelCase__						)
    UpperCAmelCase__			:		Any													=				DatasetInfosDict(
        {
            '''default''': DatasetInfo(
                features=Features(
                    {
                        '''tokens''': Sequence(Value('''string'''						)						),
                        '''ner_tags''': Sequence(
                            ClassLabel(names=['''O''', '''B-PER''', '''I-PER''', '''B-ORG''', '''I-ORG''', '''B-LOC''', '''I-LOC''']						)						),
                        '''langs''': Sequence(Value('''string'''						)						),
                        '''spans''': Sequence(Value('''string'''						)						),
                    }						)  ,					splits=[
                    {
                        '''name''': '''train''',
                        '''num_bytes''': 2_35_15_63,
                        '''num_examples''': 1_00_00,
                    },
                    {
                        '''name''': '''validation''',
                        '''num_bytes''': 23_84_18,
                        '''num_examples''': 10_00,
                    },
                ]  ,					download_size=3_94_06_80  ,					dataset_size=2_58_99_81  ,					)
        }						)
    assert dataset_infos.keys() == expected_dataset_infos.keys()
    for key in DatasetInfo._INCLUDED_INFO_IN_YAML:
        UpperCAmelCase__						,						UpperCAmelCase__			:		str													=				getattr(dataset_infos['''default''']  ,					lowerCAmelCase__						), getattr(expected_dataset_infos['''default''']  ,					lowerCAmelCase__						)
        if key == "num_bytes":
            assert is_apercent_close(lowerCAmelCase__  ,					lowerCAmelCase__						)
        elif key == "splits":
            assert list(lowerCAmelCase__						) == list(lowerCAmelCase__						)
            for split in result:
                assert result[split].name == expected[split].name
                assert result[split].num_examples == expected[split].num_examples
                assert is_apercent_close(result[split].num_bytes  ,					expected[split].num_bytes						)
        else:
            result == expected
