




import tempfile
import unittest
from pathlib import Path
from shutil import copyfile

from transformers import BatchEncoding, MarianTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, slow
from transformers.utils import is_sentencepiece_available, is_tf_available, is_torch_available


if is_sentencepiece_available():
  from transformers.models.marian.tokenization_marian import VOCAB_FILES_NAMES, save_json

from ...test_tokenization_common import TokenizerTesterMixin


lowerCamelCase    :		str			   =       get_tests_dir("fixtures/test_sentencepiece.model")

lowerCamelCase    :		Dict			   =       {"target_lang": "fi", "source_lang": "en"}
lowerCamelCase    :		List[Any]			   =       ">>zh<<"
lowerCamelCase    :		Tuple			   =       "Helsinki-NLP/"

if is_torch_available():
  lowerCamelCase    :		Optional[Any]			   =       "pt"
elif is_tf_available():
  lowerCamelCase    :		List[str]			   =       "tf"
else:
  lowerCamelCase    :		Any			   =       "jax"







@require_sentencepiece
class   A(						UpperCamelCase							,				unittest.TestCase       ):



 '''simple docstring'''


 UpperCamelCase						       =						MarianTokenizer
 UpperCamelCase						       =						False
 UpperCamelCase						       =						True

 def     a__			(    self :  Optional[int]  )       ->     List[str]:

       """simple docstring"""






       super().setUp()
       lowerCamelCase_							=   ['</s>', '<unk>', '▁This', '▁is', '▁a', '▁t', 'est', '\u0120', '<pad>']
       lowerCamelCase_							=   dict(zip(A_						,							range(len(A_  )  )  )  )
       lowerCamelCase_							=   Path(self.tmpdirname  )
       save_json(A_						,							save_dir / VOCAB_FILES_NAMES['vocab']  )
       save_json(A_						,							save_dir / VOCAB_FILES_NAMES['tokenizer_config_file']  )
       if not (save_dir / VOCAB_FILES_NAMES["source_spm"]).exists():
             copyfile(A_						,							save_dir / VOCAB_FILES_NAMES['source_spm']  )
             copyfile(A_						,							save_dir / VOCAB_FILES_NAMES['target_spm']  )

       lowerCamelCase_							=   MarianTokenizer.from_pretrained(self.tmpdirname  )
       tokenizer.save_pretrained(self.tmpdirname  )

 def     a__			(    self :  Any						,							**A_ :  Optional[Any]  )       ->     MarianTokenizer:

       """simple docstring"""






       return MarianTokenizer.from_pretrained(self.tmpdirname						,							**A_  )

 def     a__			(    self :  Dict						,							A_ :  Dict  )       ->     Optional[int]:

       """simple docstring"""






       return (
           "This is a test",
           "This is a test",
       )

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

       """simple docstring"""






       lowerCamelCase_							=   '</s>'
       lowerCamelCase_							=   0

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

 def     a__			(    self :  Optional[int]  )       ->     List[str]:

       """simple docstring"""






       lowerCamelCase_							=   list(self.get_tokenizer().get_vocab().keys()  )

       self.assertEqual(vocab_keys[0]						,							'</s>'  )
       self.assertEqual(vocab_keys[1]						,							'<unk>'  )
       self.assertEqual(vocab_keys[-1]						,							'<pad>'  )
       self.assertEqual(len(A_  )						,							9  )

 def     a__			(    self :  int  )       ->     Optional[Any]:

       """simple docstring"""






       self.assertEqual(self.get_tokenizer().vocab_size						,							9  )

 def     a__			(    self :  int  )       ->     int:

       """simple docstring"""






       lowerCamelCase_							=   MarianTokenizer.from_pretrained(f"""{ORG_NAME}opus-mt-en-de"""  )
       lowerCamelCase_							=   en_de_tokenizer(['I am a small frog']						,							return_tensors=A_  )
       self.assertIsInstance(A_						,							A_  )
       lowerCamelCase_							=   [38, 121, 14, 697, 38848, 0]
       self.assertListEqual(A_						,							batch.input_ids[0]  )

       lowerCamelCase_							=   tempfile.mkdtemp()
       en_de_tokenizer.save_pretrained(A_  )
       lowerCamelCase_							=   [x.name for x in Path(A_  ).glob('*'  )]
       self.assertIn('source.spm'						,							A_  )
       MarianTokenizer.from_pretrained(A_  )

 def     a__			(    self :  Dict  )       ->     Any:

       """simple docstring"""






       lowerCamelCase_							=   self.get_tokenizer()

       lowerCamelCase_							=   tok(
           ['I am a small frog' * 1000, 'I am a small frog']						,							padding=A_						,							truncation=A_						,							return_tensors=A_  )
       self.assertIsInstance(A_						,							A_  )
       self.assertEqual(batch.input_ids.shape						,							(2, 512)  )

 def     a__			(    self :  Dict  )       ->     str:

       """simple docstring"""






       lowerCamelCase_							=   self.get_tokenizer()
       lowerCamelCase_							=   tok(['I am a tiny frog', 'I am a small frog']						,							padding=A_						,							return_tensors=A_  )
       self.assertIsInstance(A_						,							A_  )
       self.assertEqual(batch_smaller.input_ids.shape						,							(2, 10)  )

 @slow
 def     a__			(    self :  int  )       ->     str:

       """simple docstring"""






       lowerCamelCase_							=   {'input_ids': [[43495, 462, 20, 42164, 1369, 52, 464, 132, 1703, 492, 13, 7491, 38999, 6, 8, 464, 132, 1703, 492, 13, 4669, 37867, 13, 7525, 27, 1593, 988, 13, 33972, 7029, 6, 20, 8251, 383, 2, 270, 5866, 3788, 2, 2353, 8251, 12338, 2, 13958, 387, 2, 3629, 6953, 188, 2900, 2, 13958, 8011, 11501, 23, 8460, 4073, 34009, 20, 435, 11439, 27, 8, 8460, 4073, 6004, 20, 9988, 375, 27, 33, 266, 1945, 1076, 1350, 37867, 3288, 5, 577, 1076, 4374, 8, 5082, 5, 26453, 257, 556, 403, 2, 242, 132, 383, 316, 492, 8, 10767, 6, 316, 304, 4239, 3, 0], [148, 15722, 19, 1839, 12, 1350, 13, 22327, 5082, 5418, 47567, 35938, 59, 318, 19552, 108, 2183, 54, 14976, 4835, 32, 547, 1114, 8, 315, 2417, 5, 92, 19088, 3, 0, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100], [36, 6395, 12570, 39147, 11597, 6, 266, 4, 45405, 7296, 3, 0, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100]], '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, 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, 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, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
       # fmt: on

       self.tokenizer_integration_test_util(
           expected_encoding=A_						,							model_name='Helsinki-NLP/opus-mt-en-de'						,							revision='1a8c2263da11e68e50938f97e10cd57820bd504c'						,							decode_kwargs={'use_source_tokenizer': True}						,							)





 def     a__			(    self :  Any  )       ->     int:

       """simple docstring"""






       lowerCamelCase_							=   MarianTokenizer.from_pretrained('hf-internal-testing/test-marian-two-vocabs'  )

       lowerCamelCase_							=   'Tämä on testi'
       lowerCamelCase_							=   'This is a test'

       lowerCamelCase_							=   [76, 7, 2047, 2]
       lowerCamelCase_							=   [69, 12, 11, 940, 2]

       lowerCamelCase_							=   tokenizer(A_  ).input_ids
       self.assertListEqual(A_						,							A_  )

       lowerCamelCase_							=   tokenizer(text_target=A_  ).input_ids
       self.assertListEqual(A_						,							A_  )

       lowerCamelCase_							=   tokenizer.decode(A_						,							skip_special_tokens=A_  )
       self.assertEqual(A_						,							A_  )









import copy

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


lowerCamelCase    :		Any			   =       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(UpperCamelCase       )
class   A(						UpperCamelCase       ):



 '''simple docstring'''


 UpperCamelCase						       =						'''rag'''
 UpperCamelCase						       =						True

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

       """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"
       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(A_						,							**A_  )
       lowerCamelCase_							=   AutoConfig.for_model(A_						,							**A_  )

       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'						,							A_  )

 @classmethod
 def     a__			(    cls :  str						,							A_ :  PretrainedConfig						,							A_ :  PretrainedConfig						,							**A_ :  List[str]  )       ->     PretrainedConfig:

       """simple docstring"""






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





 def     a__			(    self :  Tuple  )       ->     int:

       """simple docstring"""






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



