



import json
import os
import unittest

from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES, XLMTokenizer
from transformers.testing_utils import slow

from ...test_tokenization_common import TokenizerTesterMixin







class       __lowerCAmelCase   (      __SCREAMING_SNAKE_CASE    ,						unittest.TestCase						):
			UpperCamelCase        =					XLMTokenizer
			UpperCamelCase        =					False



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



						"""simple docstring"""






						super().setUp()

						# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
						_UpperCAmelCase			=       [
						    "l",
						    "o",
						    "w",
						    "e",
						    "r",
						    "s",
						    "t",
						    "i",
						    "d",
						    "n",
						    "w</w>",
						    "r</w>",
						    "t</w>",
						    "lo",
						    "low",
						    "er</w>",
						    "low</w>",
						    "lowest</w>",
						    "newer</w>",
						    "wider</w>",
						    "<unk>",
						]
						_UpperCAmelCase			=       dict(zip(_snake_case			,		range(len(_snake_case))))
						_UpperCAmelCase			=       ["l o 123", "lo w 1456", "e r</w> 1789", ""]

						_UpperCAmelCase			=       os.path.join(self.tmpdirname			,		VOCAB_FILES_NAMES['vocab_file'])
						_UpperCAmelCase			=       os.path.join(self.tmpdirname			,		VOCAB_FILES_NAMES['merges_file'])
						with open(self.vocab_file			,		'w') as fp:
									fp.write(json.dumps(_snake_case))
						with open(self.merges_file			,		'w') as fp:
									fp.write('\n'.join(_snake_case))



			def    _lowerCamelCase	(						self       :  Optional[int]			,		A       :  Any)       ->   List[Any]:



						"""simple docstring"""






						_UpperCAmelCase			=       "lower newer"
						_UpperCAmelCase			=       "lower newer"
						return input_text, output_text



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



						"""simple docstring"""






						_UpperCAmelCase			=       XLMTokenizer(self.vocab_file			,		self.merges_file)

						_UpperCAmelCase			=       "lower"
						_UpperCAmelCase			=       ["low", "er</w>"]
						_UpperCAmelCase			=       tokenizer.tokenize(_snake_case)
						self.assertListEqual(_snake_case			,		_snake_case)

						_UpperCAmelCase			=       tokens + ["<unk>"]
						_UpperCAmelCase			=       [14, 15, 20]
						self.assertListEqual(tokenizer.convert_tokens_to_ids(_snake_case)			,		_snake_case)



			@slow
			def    _lowerCamelCase	(						self       :  List[str])       ->   Optional[int]:



						"""simple docstring"""






						_UpperCAmelCase			=       XLMTokenizer.from_pretrained('xlm-mlm-en-2048')

						_UpperCAmelCase			=       tokenizer.encode('sequence builders'			,		add_special_tokens=_snake_case)
						_UpperCAmelCase			=       tokenizer.encode('multi-sequence build'			,		add_special_tokens=_snake_case)

						_UpperCAmelCase			=       tokenizer.build_inputs_with_special_tokens(_snake_case)
						_UpperCAmelCase			=       tokenizer.build_inputs_with_special_tokens(_snake_case			,		_snake_case)

						assert encoded_sentence == [0] + text + [1]
						assert encoded_pair == [0] + text + [1] + text_a + [1]





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
