




"""simple docstring"""


import unittest

from transformers import CamembertTokenizer, CamembertTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import is_torch_available

from ...test_tokenization_common import TokenizerTesterMixin


A_						=				get_tests_dir('''fixtures/test_sentencepiece.model''')
A_						=				get_tests_dir('''fixtures/test_sentencepiece_bpe.model''')

A_						=				'''pt''' if is_torch_available() else '''tf'''






@require_sentencepiece
@require_tokenizers
class 					lowercase(     __a       ,			unittest.TestCase ):







 '''simple docstring'''





 lowercase__ 					=					CamembertTokenizer
 lowercase__ 					=					CamembertTokenizerFast
 lowercase__ 					=					True
 lowercase__ 					=					True


 def  UpperCamelCase_   (				self:		Optional[Any]		):




   '''simple docstring'''



   super().setUp()

   # We have a SentencePiece fixture for testing
   _snake_case							:		List[str]								=  CamembertTokenizer(a_		)
   tokenizer.save_pretrained(self.tmpdirname		)


 def  UpperCamelCase_   (				self:		Tuple		):




   '''simple docstring'''



   _snake_case							:		str								=  """<pad>"""
   _snake_case							:		int								=  1

   self.assertEqual(self.get_tokenizer()._convert_token_to_id(a_		),      a_		)
   self.assertEqual(self.get_tokenizer()._convert_id_to_token(a_		),      a_		)


 def  UpperCamelCase_   (				self:		List[Any]		):




   '''simple docstring'''



   _snake_case							:		Any								=  list(self.get_tokenizer().get_vocab().keys()		)

   self.assertEqual(vocab_keys[0],      """<s>NOTUSED"""		)
   self.assertEqual(vocab_keys[1],      """<pad>"""		)
   self.assertEqual(vocab_keys[-1],      """<mask>"""		)
   self.assertEqual(len(a_		),      1_004		)


 def  UpperCamelCase_   (				self:		List[str]		):




   '''simple docstring'''



   self.assertEqual(self.get_tokenizer().vocab_size,      1_005		)


 def  UpperCamelCase_   (				self:		int		):




   '''simple docstring'''



   _snake_case							:		List[Any]								=  CamembertTokenizer(a_		)
   tokenizer.save_pretrained(self.tmpdirname		)
   _snake_case							:		Dict								=  CamembertTokenizerFast.from_pretrained(self.tmpdirname		)

   _snake_case							:		Dict								=  """I was born in 92000, and this is falsé."""

   _snake_case							:		List[str]								=  tokenizer.encode(a_		)
   _snake_case							:		Optional[Any]								=  rust_tokenizer.encode(a_		)
   self.assertListEqual(a_,      a_		)

   _snake_case							:		int								=  tokenizer.encode(a_,      add_special_tokens=a_		)
   _snake_case							:		int								=  rust_tokenizer.encode(a_,      add_special_tokens=a_		)
   self.assertListEqual(a_,      a_		)

   # <unk> tokens are not the same for `rust` than for `slow`.
   # Because spm gives back raw token instead of `unk` in EncodeAsPieces
   # tokens = tokenizer.tokenize(sequence)
   _snake_case							:		Any								=  tokenizer.convert_ids_to_tokens(a_		)
   _snake_case							:		Optional[Any]								=  rust_tokenizer.tokenize(a_		)
   self.assertListEqual(a_,      a_		)


 def  UpperCamelCase_   (				self:		Optional[Any]		):




   '''simple docstring'''



   if not self.test_rust_tokenizer:
     return

   _snake_case							:		Tuple								=  self.get_tokenizer()
   _snake_case							:		Tuple								=  self.get_rust_tokenizer()

   _snake_case							:		List[Any]								=  """I was born in 92000, and this is falsé."""

   _snake_case							:		int								=  tokenizer.tokenize(a_		)
   _snake_case							:		Union[str, Any]								=  rust_tokenizer.tokenize(a_		)
   self.assertListEqual(a_,      a_		)

   _snake_case							:		Tuple								=  tokenizer.encode(a_,      add_special_tokens=a_		)
   _snake_case							:		Optional[Any]								=  rust_tokenizer.encode(a_,      add_special_tokens=a_		)
   self.assertListEqual(a_,      a_		)

   _snake_case							:		Any								=  self.get_rust_tokenizer()
   _snake_case							:		List[str]								=  tokenizer.encode(a_		)
   _snake_case							:		str								=  rust_tokenizer.encode(a_		)
   self.assertListEqual(a_,      a_		)


 @slow
 def  UpperCamelCase_   (				self:		Any		):




   '''simple docstring'''



   _snake_case							:		Optional[Any]								=  {"""input_ids""": [[5, 54, 7_196, 297, 30, 23, 776, 18, 11, 3_215, 3_705, 8_252, 22, 3_164, 1_181, 2_116, 29, 16, 813, 25, 791, 3_314, 20, 3_446, 38, 27_575, 120, 6, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [5, 468, 17, 11, 9_088, 20, 1_517, 8, 22_804, 18_818, 10, 38, 629, 607, 607, 142, 19, 7_196, 867, 56, 10_326, 24, 2_267, 20, 416, 5_072, 15_612, 233, 734, 7, 2_399, 27, 16, 3_015, 1_649, 7, 24, 20, 4_338, 2_399, 27, 13, 3_400, 14, 13, 6_189, 8, 930, 9, 6]], """attention_mask""": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}  # noqa: E501
   # fmt: on

   # camembert is a french model. So we also use french texts.
   _snake_case							:		List[str]								=  [
       """Le transformeur est un modèle d'apprentissage profond introduit en 2017, """
       """utilisé principalement dans le domaine du traitement automatique des langues (TAL).""",
       """À l'instar des réseaux de neurones récurrents (RNN), les transformeurs sont conçus """
       """pour gérer des données séquentielles, telles que le langage naturel, pour des tâches """
       """telles que la traduction et la synthèse de texte.""",
   ]

   self.tokenizer_integration_test_util(
       expected_encoding=a_,      model_name="""camembert-base""",      revision="""3a0641d9a1aeb7e848a74299e7e4c4bca216b4cf""",      sequences=a_,      )








"""simple docstring"""


import copy

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


A_						=				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 					lowercase(     __a ):







 '''simple docstring'''





 lowercase__ 					=					"rag"
 lowercase__ 					=					True


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




   '''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"
   _snake_case							:		Union[str, Any]								=  kwargs.pop("""question_encoder"""		)
   _snake_case							:		List[str]								=  question_encoder_config.pop("""model_type"""		)
   _snake_case							:		Union[str, Any]								=  kwargs.pop("""generator"""		)
   _snake_case							:		Any								=  decoder_config.pop("""model_type"""		)

   from ..auto.configuration_auto import AutoConfig

   _snake_case							:		Union[str, Any]								=  AutoConfig.for_model(a_,      **a_		)
   _snake_case							:		Optional[Any]								=  AutoConfig.for_model(a_,      **a_		)

   _snake_case							:		Any								=  reduce_loss
   _snake_case							:		Optional[int]								=  label_smoothing
   _snake_case							:		Dict								=  exclude_bos_score
   _snake_case							:		int								=  do_marginalize

   _snake_case							:		Optional[Any]								=  title_sep
   _snake_case							:		Any								=  doc_sep
   _snake_case							:		List[str]								=  n_docs
   _snake_case							:		Tuple								=  max_combined_length

   _snake_case							:		Optional[Any]								=  dataset
   _snake_case							:		Union[str, Any]								=  dataset_split
   _snake_case							:		Tuple								=  index_name

   _snake_case							:		Any								=  retrieval_vector_size
   _snake_case							:		Union[str, Any]								=  retrieval_batch_size
   _snake_case							:		str								=  passages_path
   _snake_case							:		Tuple								=  index_path
   _snake_case							:		List[Any]								=  use_dummy_dataset

   _snake_case							:		Optional[Any]								=  output_retrieved

   _snake_case							:		Tuple								=  do_deduplication

   _snake_case							:		Union[str, Any]								=  use_cache

   if self.forced_eos_token_id is None:
     _snake_case							:		Dict								=  getattr(self.generator,      """forced_eos_token_id""",      a_		)


 @classmethod
 def  UpperCamelCase_   (				cls:		Any,      a_:		PretrainedConfig,      a_:		PretrainedConfig,      **a_:		Optional[Any]		):




   '''simple docstring'''



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


 def  UpperCamelCase_   (				self:		Tuple		):




   '''simple docstring'''



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


