





import logging
import random

import ray

from transformers import RagConfig, RagRetriever, RagTokenizer
from transformers.models.rag.retrieval_rag import CustomHFIndex


UpperCAmelCase__						:			List[str]									=logging.getLogger(__name__)

class       __A     :


				def __init__(		self							):
								lowerCamelCase	 =False


				def _snake_case       (		self   , UpperCAmelCase_   , UpperCAmelCase_   , UpperCAmelCase_   , UpperCAmelCase_							):
								if not self.initialized:
												lowerCamelCase	 =RagRetriever(
												    UpperCAmelCase_   , question_encoder_tokenizer=UpperCAmelCase_   , generator_tokenizer=UpperCAmelCase_   , index=UpperCAmelCase_   , init_retrieval=UpperCAmelCase_   , )
												lowerCamelCase	 =True


				def _snake_case       (		self							):
								self.retriever.index.init_index()


				def _snake_case       (		self   , UpperCAmelCase_   , UpperCAmelCase_							):
								lowerCamelCase ,  lowerCamelCase	 =self.retriever._main_retrieve(UpperCAmelCase_   , UpperCAmelCase_							)
								return doc_ids, retrieved_doc_embeds




class       __A     (						a    ):


				def __init__(		self   , UpperCAmelCase_   , UpperCAmelCase_   , UpperCAmelCase_   , UpperCAmelCase_   , UpperCAmelCase_=None							):
								if index is not None and index.is_initialized() and len(UpperCAmelCase_							) > 0:
												raise ValueError(
												    """When using Ray for distributed fine-tuning, """
												    """you'll need to provide the paths instead, """
												    """as the dataset and the index are loaded """
												    """separately. More info in examples/rag/use_own_knowledge_dataset.py """							)
								super().__init__(
								    UpperCAmelCase_   , question_encoder_tokenizer=UpperCAmelCase_   , generator_tokenizer=UpperCAmelCase_   , index=UpperCAmelCase_   , init_retrieval=UpperCAmelCase_   , )
								lowerCamelCase	 =retrieval_workers
								if len(self.retrieval_workers							) > 0:
												ray.get(
												    [
												        worker.create_rag_retriever.remote(UpperCAmelCase_   , UpperCAmelCase_   , UpperCAmelCase_   , UpperCAmelCase_							)
												        for worker in self.retrieval_workers
												    ]							)


				def _snake_case       (		self							):
								logger.info("""initializing retrieval"""							)

								if len(self.retrieval_workers							) > 0:
												ray.get([worker.init_retrieval.remote() for worker in self.retrieval_workers]							)
								else:
												# Non-distributed training. Load index into this same process.
												self.index.init_index()


				def _snake_case       (		self   , UpperCAmelCase_   , UpperCAmelCase_							):
								if len(self.retrieval_workers							) > 0:
												# Select a random retrieval actor.
												lowerCamelCase	 =self.retrieval_workers[random.randint(0   , len(self.retrieval_workers							) - 1							)]
												lowerCamelCase ,  lowerCamelCase	 =ray.get(random_worker.retrieve.remote(UpperCAmelCase_   , UpperCAmelCase_							)							)
								else:
												lowerCamelCase ,  lowerCamelCase	 =self._main_retrieve(UpperCAmelCase_   , UpperCAmelCase_							)
								return retrieved_doc_embeds, doc_ids, self.index.get_doc_dicts(UpperCAmelCase_							)


				@classmethod
				def _snake_case       (		cls   , UpperCAmelCase_   , UpperCAmelCase_=None   , **UpperCAmelCase_							):
								return super(UpperCAmelCase_   , cls							).get_tokenizers(UpperCAmelCase_   , UpperCAmelCase_   , **UpperCAmelCase_							)


				@classmethod
				def _snake_case       (		cls   , UpperCAmelCase_   , UpperCAmelCase_   , UpperCAmelCase_=None   , **UpperCAmelCase_							):
								lowerCamelCase	 =kwargs.pop("""config"""   , UpperCAmelCase_							) or RagConfig.from_pretrained(UpperCAmelCase_   , **UpperCAmelCase_							)
								lowerCamelCase	 =RagTokenizer.from_pretrained(UpperCAmelCase_   , config=UpperCAmelCase_							)
								lowerCamelCase	 =rag_tokenizer.question_encoder
								lowerCamelCase	 =rag_tokenizer.generator
								if indexed_dataset is not None:
												lowerCamelCase	 ="""custom"""
												lowerCamelCase	 =CustomHFIndex(config.retrieval_vector_size   , UpperCAmelCase_							)
								else:
												lowerCamelCase	 =cls._build_index(UpperCAmelCase_							)
								return cls(
								    UpperCAmelCase_   , question_encoder_tokenizer=UpperCAmelCase_   , generator_tokenizer=UpperCAmelCase_   , retrieval_workers=UpperCAmelCase_   , index=UpperCAmelCase_   , )












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

from transformers import MaMaaaTokenizer, is_torch_available
from transformers.testing_utils import (
    get_tests_dir,
    nested_simplify,
    require_sentencepiece,
    require_tokenizers,
    require_torch,
    slow,
)
from transformers.utils import is_sentencepiece_available


if is_sentencepiece_available():
							from transformers.models.mam_aaa.tokenization_mam_aaa import VOCAB_FILES_NAMES, save_json

from ...test_tokenization_common import TokenizerTesterMixin


if is_sentencepiece_available():
							UpperCAmelCase__						:			Union[str, Any]									=get_tests_dir('''fixtures/test_sentencepiece.model''')


if is_torch_available():
							from transformers.models.mam_aaa.modeling_mam_aaa import shift_tokens_right

UpperCAmelCase__						:			Union[str, Any]									=12_80_22
UpperCAmelCase__						:			Optional[Any]									=12_80_28

@require_sentencepiece
class       __A     (						a    ,					unittest.TestCase    ):
				__A      =				MaMaaaTokenizer
				__A      =				False
				__A      =				False
				__A      =				True


				def _snake_case       (		self							):
								super().setUp()

								lowerCamelCase	 =["""</s>""", """<unk>""", """▁This""", """▁is""", """▁a""", """▁t""", """est""", """\u0120""", """<pad>"""]
								lowerCamelCase	 =dict(zip(UpperCAmelCase_   , range(len(UpperCAmelCase_							)							)							)							)
								lowerCamelCase	 =Path(self.tmpdirname							)
								save_json(UpperCAmelCase_   , save_dir / VOCAB_FILES_NAMES["""vocab_file"""]							)
								if not (save_dir / VOCAB_FILES_NAMES["spm_file"]).exists():
												copyfile(UpperCAmelCase_   , save_dir / VOCAB_FILES_NAMES["""spm_file"""]							)

								lowerCamelCase	 =MaMaaaTokenizer.from_pretrained(self.tmpdirname							)
								tokenizer.save_pretrained(self.tmpdirname							)


				def _snake_case       (		self   , **UpperCAmelCase_							):
								return MaMaaaTokenizer.from_pretrained(self.tmpdirname   , **UpperCAmelCase_							)


				def _snake_case       (		self   , UpperCAmelCase_							):
								return (
								    "This is a test",
								    "This is a test",
								)


				def _snake_case       (		self							):
								lowerCamelCase	 ="""</s>"""
								lowerCamelCase	 =0

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


				def _snake_case       (		self							):
								lowerCamelCase	 =self.get_tokenizer()
								lowerCamelCase	 =list(tokenizer.get_vocab().keys()							)

								self.assertEqual(vocab_keys[0]   , """</s>"""							)
								self.assertEqual(vocab_keys[1]   , """<unk>"""							)
								self.assertEqual(vocab_keys[-1]   , """<s>"""							)
								self.assertEqual(len(UpperCAmelCase_							)   , tokenizer.vocab_size + len(tokenizer.get_added_vocab()							)							)


				@unittest.skip("""Skip this test while all models are still to be uploaded."""							)
				def _snake_case       (		self							):
								pass


				def _snake_case       (		self							):
								lowerCamelCase	 =self.get_tokenizer()

								lowerCamelCase	 =tokenizer.tokenize("""This is a test"""							)
								self.assertListEqual(UpperCAmelCase_   , ["""▁This""", """▁is""", """▁a""", """▁t""", """est"""]							)

								self.assertListEqual(
								    tokenizer.convert_tokens_to_ids(UpperCAmelCase_							)   , [2, 3, 4, 5, 6]   , )

								lowerCamelCase	 =tokenizer.convert_ids_to_tokens([2, 3, 4, 5, 6]							)
								self.assertListEqual(UpperCAmelCase_   , ["""▁This""", """▁is""", """▁a""", """▁t""", """est"""]							)

								lowerCamelCase	 =tokenizer.convert_tokens_to_string(UpperCAmelCase_							)
								self.assertEqual(UpperCAmelCase_   , """This is a test"""							)


				@slow
				def _snake_case       (		self							):
								# fmt: off
								lowerCamelCase	 ={"""input_ids""": [[128022, 110108, 397, 11, 38272, 2247, 124811, 285, 18105, 1586, 207, 7, 39534, 4428, 397, 1019, 18105, 1586, 207, 7, 41337, 16786, 241, 7, 20214, 17, 125690, 10398, 7, 44378, 58069, 68342, 7798, 7343, 11, 299, 33310, 4, 158, 37350, 94077, 4569, 299, 33310, 90, 4, 52840, 290, 4, 31270, 112, 299, 682, 4, 52840, 39953, 14079, 193, 52519, 90894, 17894, 120697, 11, 40445, 551, 17, 1019, 52519, 90894, 17756, 963, 11, 40445, 480, 17, 9792, 1120, 5173, 1393, 6240, 16786, 241, 120996, 28, 1245, 1393, 118240, 11123, 1019, 93612, 2691, 10618, 98058, 120409, 1928, 279, 4, 40683, 367, 178, 207, 1019, 103, 103121, 506, 65296, 5, 2], [128022, 21217, 367, 117, 125450, 128, 719, 7, 7308, 40, 93612, 12669, 1116, 16704, 71, 17785, 3699, 15592, 35, 144, 9584, 241, 11943, 713, 950, 799, 2247, 88427, 150, 149, 118813, 120706, 1019, 106906, 81518, 28, 1224, 22799, 397, 5, 2, 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], [128022, 1658, 123311, 5155, 5578, 4722, 279, 14947, 2366, 1120, 1197, 14, 1348, 9232, 5, 2, 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]], """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, 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], [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, 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=UpperCAmelCase_   , model_name="""facebook/m2m100_418M"""   , revision="""c168bae485c864188cf9aa0e4108b0b6934dc91e"""   , )




@require_torch
@require_sentencepiece
@require_tokenizers
class       __A     (						unittest.TestCase    ):
				__A      =				"""facebook/m2m100_418M"""
				__A      =				[
				    """In my opinion, there are two levels of response from the French government.""",
				    """NSA Affair Emphasizes Complete Lack of Debate on Intelligence""",
				]
				__A      =				[
				    """Selon moi, il y a deux niveaux de réponse de la part du gouvernement français.""",
				    """L'affaire NSA souligne l'absence totale de débat sur le renseignement""",
				]

				# fmt: off
				__A      =				[EN_CODE, 5_93, 19_49, 11_57_81, 4, 7_15_86, 42_34, 6_06_33, 12_62_33, 4_32, 12_38_08, 1_55_92, 11_97, 11_71_32, 12_06_18, 5, 2]


				@classmethod
				def _snake_case       (		cls							):
								lowerCamelCase	 =MaMaaaTokenizer.from_pretrained(
								    cls.checkpoint_name   , src_lang="""en"""   , tgt_lang="""fr"""							)
								lowerCamelCase	 =1
								return cls


				def _snake_case       (		self							):
								self.assertEqual(self.tokenizer.get_lang_id("""ar"""							)   , 128006							)
								self.assertEqual(self.tokenizer.get_lang_id("""en"""							)   , 128022							)
								self.assertEqual(self.tokenizer.get_lang_id("""ro"""							)   , 128076							)
								self.assertEqual(self.tokenizer.get_lang_id("""mr"""							)   , 128063							)


				def _snake_case       (		self							):
								lowerCamelCase	 =self.tokenizer.get_vocab()
								self.assertEqual(len(UpperCAmelCase_							)   , self.tokenizer.vocab_size							)
								self.assertEqual(vocab["""<unk>"""]   , 3							)
								self.assertIn(self.tokenizer.get_lang_token("""en"""							)   , UpperCAmelCase_							)


				def _snake_case       (		self							):
								lowerCamelCase	 ="""en"""
								lowerCamelCase	 =self.tokenizer.batch_encode_plus(self.src_text							).input_ids[0]
								self.assertListEqual(self.expected_src_tokens   , UpperCAmelCase_							)


				def _snake_case       (		self							):
								self.assertIn(UpperCAmelCase_   , self.tokenizer.all_special_ids							)
								# fmt: off
								lowerCamelCase	 =[FR_CODE, 5364, 82, 8642, 4, 294, 47, 8, 14028, 136, 3286, 9706, 6, 90797, 6, 144012, 162, 88128, 30061, 5, 2]
								# fmt: on
								lowerCamelCase	 =self.tokenizer.decode(UpperCAmelCase_   , skip_special_tokens=UpperCAmelCase_							)
								lowerCamelCase	 =self.tokenizer.decode(generated_ids[1:]   , skip_special_tokens=UpperCAmelCase_							)
								self.assertEqual(UpperCAmelCase_   , UpperCAmelCase_							)
								self.assertNotIn(self.tokenizer.eos_token   , UpperCAmelCase_							)


				def _snake_case       (		self							):
								lowerCamelCase	 =tempfile.mkdtemp()
								lowerCamelCase	 =self.tokenizer.lang_token_to_id
								self.tokenizer.save_pretrained(UpperCAmelCase_							)
								lowerCamelCase	 =MaMaaaTokenizer.from_pretrained(UpperCAmelCase_							)
								self.assertDictEqual(new_tok.lang_token_to_id   , UpperCAmelCase_							)


				@require_torch
				def _snake_case       (		self							):
								lowerCamelCase	 ="""en"""
								lowerCamelCase	 ="""fr"""

								lowerCamelCase	 =self.tokenizer(self.src_text   , text_target=self.tgt_text   , padding=UpperCAmelCase_   , return_tensors="""pt"""							)

								lowerCamelCase	 =shift_tokens_right(
								    batch["""labels"""]   , self.tokenizer.pad_token_id   , self.tokenizer.eos_token_id							)

								for k in batch:
												lowerCamelCase	 =batch[k].tolist()
								# batch = {k: v.tolist() for k,v in batch.items()}
								# fairseq batch: https://gist.github.com/sshleifer/cba08bc2109361a74ac3760a7e30e4f4
								# batch.decoder_inputs_ids[0][0] ==
								assert batch.input_ids[1][0] == EN_CODE
								assert batch.input_ids[1][-1] == 2
								assert batch.labels[1][0] == FR_CODE
								assert batch.labels[1][-1] == 2
								assert batch.decoder_input_ids[1][:2] == [2, FR_CODE]


				@require_torch
				def _snake_case       (		self							):
								lowerCamelCase	 ="""mr"""
								self.assertListEqual(self.tokenizer.prefix_tokens   , [self.tokenizer.get_lang_id("""mr"""							)]							)
								self.assertListEqual(self.tokenizer.suffix_tokens   , [self.tokenizer.eos_token_id]							)

								lowerCamelCase	 ="""zh"""
								self.assertListEqual(self.tokenizer.prefix_tokens   , [self.tokenizer.get_lang_id("""zh"""							)]							)
								self.assertListEqual(self.tokenizer.suffix_tokens   , [self.tokenizer.eos_token_id]							)


				@require_torch
				def _snake_case       (		self							):
								lowerCamelCase	 ="""mr"""
								self.tokenizer._switch_to_target_mode()
								self.assertListEqual(self.tokenizer.prefix_tokens   , [self.tokenizer.get_lang_id("""mr"""							)]							)
								self.assertListEqual(self.tokenizer.suffix_tokens   , [self.tokenizer.eos_token_id]							)
								self.tokenizer._switch_to_input_mode()
								self.assertListEqual(self.tokenizer.prefix_tokens   , [self.tokenizer.get_lang_id(self.tokenizer.src_lang							)]							)

								lowerCamelCase	 ="""zh"""
								self.tokenizer._switch_to_target_mode()
								self.assertListEqual(self.tokenizer.prefix_tokens   , [self.tokenizer.get_lang_id("""zh"""							)]							)
								self.assertListEqual(self.tokenizer.suffix_tokens   , [self.tokenizer.eos_token_id]							)
								self.tokenizer._switch_to_input_mode()
								self.assertListEqual(self.tokenizer.prefix_tokens   , [self.tokenizer.get_lang_id(self.tokenizer.src_lang							)]							)


				@require_torch
				def _snake_case       (		self							):
								lowerCamelCase	 =self.tokenizer._build_translation_inputs("""A test"""   , return_tensors="""pt"""   , src_lang="""en"""   , tgt_lang="""ar"""							)

								self.assertEqual(
								    nested_simplify(UpperCAmelCase_							)   , {
								        # en_XX, A, test, EOS
								        """input_ids""": [[128022, 58, 4183, 2]],
								        """attention_mask""": [[1, 1, 1, 1]],
								        # ar_AR
								        """forced_bos_token_id""": 128006,
								    }   , )





