
"""simple docstring"""


import logging
import random

import ray

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


A_    							=					logging.getLogger(__name__)




class  __lowerCamelCase  :




				def __init__(				self   ):
							lowerCamelCase_					=  False




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





				def        UpperCAmelCase__						(				self							,						UpperCAmelCase							,						UpperCAmelCase   ):
							lowerCamelCase_		, lowerCamelCase_					=  self.retriever._main_retrieve(UpperCAmelCase							,						UpperCAmelCase   )
							return doc_ids, retrieved_doc_embeds




class  __lowerCamelCase  ( lowerCAmelCase			):




				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        UpperCAmelCase__						(				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        UpperCAmelCase__						(				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        UpperCAmelCase__						(				cls							,						UpperCAmelCase							,						UpperCAmelCase=None							,						**UpperCAmelCase   ):
							return super(UpperCAmelCase							,						cls   ).get_tokenizers(UpperCAmelCase							,						UpperCAmelCase							,						**UpperCAmelCase   )





				@classmethod
				def        UpperCAmelCase__						(				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							,						)





"""simple docstring"""


import os
from collections import namedtuple

import pytest

from datasets import ClassLabel, Features, Sequence, Value
from datasets.commands.test import TestCommand
from datasets.info import DatasetInfo, DatasetInfosDict


A_    							=					namedtuple(
    """_TestCommandArgs""",
    [
        """dataset""",
        """name""",
        """cache_dir""",
        """data_dir""",
        """all_configs""",
        """save_infos""",
        """ignore_verifications""",
        """force_redownload""",
        """clear_cache""",
    ],
    defaults=[None, None, None, False, False, False, False, False],
)






def  lowercase						(	lowerCAmelCase__	,lowerCAmelCase__						):
			return (abs(source - target						) / target) < 0.01






@pytest.mark.integration
def  lowercase						(	lowerCAmelCase__						):
			lowerCamelCase_					=  _TestCommandArgs(dataset=lowerCAmelCase__	,all_configs=lowerCAmelCase__	,save_infos=lowerCAmelCase__						)
			lowerCamelCase_					=  TestCommand(*lowerCAmelCase__						)
			test_command.run()
			lowerCamelCase_					=  os.path.join(lowerCAmelCase__	,'''README.md'''						)
			assert os.path.exists(lowerCAmelCase__						)
			lowerCamelCase_					=  DatasetInfosDict.from_directory(lowerCAmelCase__						)
			lowerCamelCase_					=  DatasetInfosDict(
			    {
			        '''default''': DatasetInfo(
			            features=Features(
			                {
			                    '''tokens''': Sequence(Value('''string'''						)						),
			                    '''ner_tags''': Sequence(
			                        ClassLabel(names=['''O''', '''B-PER''', '''I-PER''', '''B-ORG''', '''I-ORG''', '''B-LOC''', '''I-LOC''']						)						),
			                    '''langs''': Sequence(Value('''string'''						)						),
			                    '''spans''': Sequence(Value('''string'''						)						),
			                }						)	,splits=[
			                {
			                    '''name''': '''train''',
			                    '''num_bytes''': 2_351_563,
			                    '''num_examples''': 10_000,
			                },
			                {
			                    '''name''': '''validation''',
			                    '''num_bytes''': 238_418,
			                    '''num_examples''': 1_000,
			                },
			            ]	,download_size=3_940_680	,dataset_size=2_589_981	,)
			    }						)
			assert dataset_infos.keys() == expected_dataset_infos.keys()
			for key in DatasetInfo._INCLUDED_INFO_IN_YAML:
						lowerCamelCase_		, lowerCamelCase_					=  getattr(dataset_infos['''default''']	,lowerCAmelCase__						), getattr(expected_dataset_infos['''default''']	,lowerCAmelCase__						)
						if key == "num_bytes":
									assert is_apercent_close(lowerCAmelCase__	,lowerCAmelCase__						)
						elif key == "splits":
									assert list(lowerCAmelCase__						) == list(lowerCAmelCase__						)
									for split in result:
												assert result[split].name == expected[split].name
												assert result[split].num_examples == expected[split].num_examples
												assert is_apercent_close(result[split].num_bytes	,expected[split].num_bytes						)
						else:
									result == expected



