



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


UpperCAmelCase__       =	get_tests_dir("fixtures/test_sentencepiece.model")
UpperCAmelCase__       =	get_tests_dir("fixtures/test_sentencepiece_bpe.model")

UpperCAmelCase__       =	"pt" if is_torch_available() else "tf"







@require_sentencepiece
@require_tokenizers
class       __lowerCAmelCase   (      A    ,						unittest.TestCase						):
			UpperCamelCase        =					CamembertTokenizer
			UpperCamelCase        =					CamembertTokenizerFast
			UpperCamelCase        =					True
			UpperCamelCase        =					True



			def    _lowerCamelCase	(						self       :  List[Any])       ->   List[str]:



						"""simple docstring"""






						super().setUp()

						# We have a SentencePiece fixture for testing
						_UpperCAmelCase			=       CamembertTokenizer(A)
						tokenizer.save_pretrained(self.tmpdirname)



			def    _lowerCamelCase	(						self       :  List[Any])       ->   Dict:



						"""simple docstring"""






						_UpperCAmelCase			=       '<pad>'
						_UpperCAmelCase			=       1

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



			def    _lowerCamelCase	(						self       :  str)       ->   Dict:



						"""simple docstring"""






						_UpperCAmelCase			=       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)			,		10_04)



			def    _lowerCamelCase	(						self       :  str)       ->   str:



						"""simple docstring"""






						self.assertEqual(self.get_tokenizer().vocab_size			,		10_05)



			def    _lowerCamelCase	(						self       :  Union[str, Any])       ->   Optional[Any]:



						"""simple docstring"""






						_UpperCAmelCase			=       CamembertTokenizer(A)
						tokenizer.save_pretrained(self.tmpdirname)
						_UpperCAmelCase			=       CamembertTokenizerFast.from_pretrained(self.tmpdirname)

						_UpperCAmelCase			=       'I was born in 92000, and this is falsé.'

						_UpperCAmelCase			=       tokenizer.encode(A)
						_UpperCAmelCase			=       rust_tokenizer.encode(A)
						self.assertListEqual(A			,		A)

						_UpperCAmelCase			=       tokenizer.encode(A			,		add_special_tokens=A)
						_UpperCAmelCase			=       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)
						_UpperCAmelCase			=       tokenizer.convert_ids_to_tokens(A)
						_UpperCAmelCase			=       rust_tokenizer.tokenize(A)
						self.assertListEqual(A			,		A)



			def    _lowerCamelCase	(						self       :  Optional[int])       ->   Union[str, Any]:



						"""simple docstring"""






						if not self.test_rust_tokenizer:
									return

						_UpperCAmelCase			=       self.get_tokenizer()
						_UpperCAmelCase			=       self.get_rust_tokenizer()

						_UpperCAmelCase			=       'I was born in 92000, and this is falsé.'

						_UpperCAmelCase			=       tokenizer.tokenize(A)
						_UpperCAmelCase			=       rust_tokenizer.tokenize(A)
						self.assertListEqual(A			,		A)

						_UpperCAmelCase			=       tokenizer.encode(A			,		add_special_tokens=A)
						_UpperCAmelCase			=       rust_tokenizer.encode(A			,		add_special_tokens=A)
						self.assertListEqual(A			,		A)

						_UpperCAmelCase			=       self.get_rust_tokenizer()
						_UpperCAmelCase			=       tokenizer.encode(A)
						_UpperCAmelCase			=       rust_tokenizer.encode(A)
						self.assertListEqual(A			,		A)



			@slow
			def    _lowerCamelCase	(						self       :  Optional[Any])       ->   Optional[Any]:



						"""simple docstring"""






						_UpperCAmelCase			=       {'input_ids': [[5, 54, 71_96, 2_97, 30, 23, 7_76, 18, 11, 32_15, 37_05, 82_52, 22, 31_64, 11_81, 21_16, 29, 16, 8_13, 25, 7_91, 33_14, 20, 34_46, 38, 2_75_75, 1_20, 6, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [5, 4_68, 17, 11, 90_88, 20, 15_17, 8, 2_28_04, 1_88_18, 10, 38, 6_29, 6_07, 6_07, 1_42, 19, 71_96, 8_67, 56, 1_03_26, 24, 22_67, 20, 4_16, 50_72, 1_56_12, 2_33, 7_34, 7, 23_99, 27, 16, 30_15, 16_49, 7, 24, 20, 43_38, 23_99, 27, 13, 34_00, 14, 13, 61_89, 8, 9_30, 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.
						_UpperCAmelCase			=       [
						    '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			,		)





import copy

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


UpperCAmelCase__       =	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(A						)
class       __lowerCAmelCase   (      A						):
			UpperCamelCase        =					'''rag'''
			UpperCamelCase        =					True



			def __init__(						self       :  str			,		A       :  int=None			,		A       :  Dict=True			,		A       :  int=None			,		A       :  Any=None			,		A       :  Any=None			,		A       :  Any=None			,		A       :  int=None			,		A       :  str=" / "			,		A       :  int=" // "			,		A       :  Any=5			,		A       :  Optional[Any]=3_00			,		A       :  Optional[Any]=7_68			,		A       :  Optional[int]=8			,		A       :  Union[str, Any]="wiki_dpr"			,		A       :  int="train"			,		A       :  Tuple="compressed"			,		A       :  str=None			,		A       :  Any=None			,		A       :  Tuple=False			,		A       :  List[str]=False			,		A       :  Tuple=0.0			,		A       :  Any=True			,		A       :  int=False			,		A       :  Dict=False			,		A       :  List[str]=False			,		A       :  Optional[Any]=True			,		A       :  Tuple=None			,		**A       :  Optional[Any]			,		)       ->   Optional[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"
						_UpperCAmelCase			=       kwargs.pop('question_encoder')
						_UpperCAmelCase			=       question_encoder_config.pop('model_type')
						_UpperCAmelCase			=       kwargs.pop('generator')
						_UpperCAmelCase			=       decoder_config.pop('model_type')

						from ..auto.configuration_auto import AutoConfig

						_UpperCAmelCase			=       AutoConfig.for_model(A			,		**A)
						_UpperCAmelCase			=       AutoConfig.for_model(A			,		**A)

						_UpperCAmelCase			=       reduce_loss
						_UpperCAmelCase			=       label_smoothing
						_UpperCAmelCase			=       exclude_bos_score
						_UpperCAmelCase			=       do_marginalize

						_UpperCAmelCase			=       title_sep
						_UpperCAmelCase			=       doc_sep
						_UpperCAmelCase			=       n_docs
						_UpperCAmelCase			=       max_combined_length

						_UpperCAmelCase			=       dataset
						_UpperCAmelCase			=       dataset_split
						_UpperCAmelCase			=       index_name

						_UpperCAmelCase			=       retrieval_vector_size
						_UpperCAmelCase			=       retrieval_batch_size
						_UpperCAmelCase			=       passages_path
						_UpperCAmelCase			=       index_path
						_UpperCAmelCase			=       use_dummy_dataset

						_UpperCAmelCase			=       output_retrieved

						_UpperCAmelCase			=       do_deduplication

						_UpperCAmelCase			=       use_cache

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



			@classmethod
			def    _lowerCamelCase	(						cls       :  Union[str, Any]			,		A       :  PretrainedConfig			,		A       :  PretrainedConfig			,		**A       :  Optional[Any])       ->   PretrainedConfig:



						"""simple docstring"""






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



			def    _lowerCamelCase	(						self       :  Dict)       ->   Optional[Any]:



						"""simple docstring"""






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