





'''simple docstring'''






import logging
import random

import ray

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


__UpperCamelCase      :   Optional[int]								=     logging.getLogger(__name__)



class  lowerCamelCase__				:


	"""simple docstring"""





	def __init__(					self			)       ->				Any:
					_A :				Union[str, Any]				=  False




	def 				_lowerCamelCase				(					self   ,     UpperCAmelCase__   ,     UpperCAmelCase__   ,     UpperCAmelCase__   ,     UpperCAmelCase__			)       ->				Any:
					if not self.initialized:
									_A :				Tuple				=  RagRetriever(
									    UpperCAmelCase__   ,     question_encoder_tokenizer=UpperCAmelCase__   ,     generator_tokenizer=UpperCAmelCase__   ,     index=UpperCAmelCase__   ,     init_retrieval=UpperCAmelCase__   ,     )
									_A :				Union[str, Any]				=  True




	def 				_lowerCamelCase				(					self			)       ->				List[str]:
					self.retriever.index.init_index()




	def 				_lowerCamelCase				(					self			)       ->				Optional[Any]:
					# delete the old self.retriever object before assigning the new index
					del self.retriever
					_A :				Union[str, Any]				=  False




	def 				_lowerCamelCase				(					self   ,     UpperCAmelCase__   ,     UpperCAmelCase__			)       ->				Optional[int]:
					_A	,       _A :				List[str]				=  self.retriever._main_retrieve(UpperCAmelCase__   ,     UpperCAmelCase__			)
					_A :				Any				=  self.retriever.index.get_doc_dicts(UpperCAmelCase__			)
					return doc_ids, retrieved_doc_embeds, doc_dicts



class  lowerCamelCase__				(						snake_case_						):


	"""simple docstring"""





	def __init__(					self   ,     UpperCAmelCase__   ,     UpperCAmelCase__   ,     UpperCAmelCase__   ,     UpperCAmelCase__   ,     UpperCAmelCase__=None			)       ->				List[Any]:
					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__   ,     )

					_A :				List[str]				=  retrieval_workers
					_A :				Optional[Any]				=  question_encoder_tokenizer
					_A :				List[Any]				=  generator_tokenizer
					if len(self.retrieval_workers			) > 0:
									ray.get(
									    [
									        worker.create_rag_retriever.remote(UpperCAmelCase__   ,     UpperCAmelCase__   ,     UpperCAmelCase__   ,     UpperCAmelCase__			)
									        for worker in self.retrieval_workers
									    ]			)




	def 				_lowerCamelCase				(					self			)       ->				List[Any]:
					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 				_lowerCamelCase				(					self   ,     UpperCAmelCase__   ,     UpperCAmelCase__			)       ->				Dict:
					if len(self.retrieval_workers			) > 0:
									# Select a random retrieval actor.
									_A :				Optional[Any]				=  self.retrieval_workers[random.randint(0   ,     len(self.retrieval_workers			) - 1			)]
									_A	,       _A	,       _A :				Optional[int]				=  ray.get(
									    random_worker.retrieve.remote(UpperCAmelCase__   ,     UpperCAmelCase__			)			)
					else:
									_A	,       _A :				Optional[Any]				=  self._main_retrieve(UpperCAmelCase__   ,     UpperCAmelCase__			)
									_A :				Tuple				=  self.index.get_doc_dicts(UpperCAmelCase__			)
					return retrieved_doc_embeds, doc_ids, doc_dicts




	@classmethod
	def 				_lowerCamelCase				(					cls   ,     UpperCAmelCase__   ,     UpperCAmelCase__=None   ,     **UpperCAmelCase__			)       ->				List[Any]:
					return super(UpperCAmelCase__   ,     cls			).get_tokenizers(UpperCAmelCase__   ,     UpperCAmelCase__   ,     **UpperCAmelCase__			)




	@classmethod
	def 				_lowerCamelCase				(					cls   ,     UpperCAmelCase__   ,     UpperCAmelCase__   ,     UpperCAmelCase__=None   ,     **UpperCAmelCase__			)       ->				Union[str, Any]:
					_A :				int				=  kwargs.pop('''config'''   ,     UpperCAmelCase__			) or RagConfig.from_pretrained(UpperCAmelCase__   ,     **UpperCAmelCase__			)
					_A :				List[Any]				=  RagTokenizer.from_pretrained(UpperCAmelCase__   ,     config=UpperCAmelCase__			)
					_A :				List[str]				=  rag_tokenizer.question_encoder
					_A :				List[str]				=  rag_tokenizer.generator

					if indexed_dataset is not None:
									_A :				Dict				=  '''custom'''
									_A :				Tuple				=  CustomHFIndex(config.retrieval_vector_size   ,     UpperCAmelCase__			)
					else:
									_A :				List[Any]				=  cls._build_index(UpperCAmelCase__			)

					return cls(
					    UpperCAmelCase__   ,     question_encoder_tokenizer=UpperCAmelCase__   ,     generator_tokenizer=UpperCAmelCase__   ,     retrieval_workers=UpperCAmelCase__   ,     index=UpperCAmelCase__   ,     )




	def 				_lowerCamelCase				(					self			)       ->				int:
					logger.info('''re-loading the new dataset with embeddings'''			)
					# access from the training loop

					ray.get([worker.clear_object.remote() for worker in self.retrieval_workers]			)

					# build the index object again
					_A :				Dict				=  self._build_index(self.config			)

					ray.get(
					    [
					        worker.create_rag_retriever.remote(
					            self.config   ,     self.question_encoder_tokenizer   ,     self.generator_tokenizer   ,     UpperCAmelCase__			)
					        for worker in self.retrieval_workers
					    ]			)







'''simple docstring'''






import textwrap

import pyarrow as pa
import pytest

from datasets import Features, Image
from datasets.packaged_modules.text.text import Text

from ..utils import require_pil





@pytest.fixture
def      lowercase  (  lowerCAmelCase :						Union[str, Any]):



				"""simple docstring"""


				_A :				int				=  tmp_path / '''text.txt'''
				_A :				int				=  textwrap.dedent(
				    '''\
        Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua.
        Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat.
        Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur.
        Excepteur sint occaecat cupidatat non proident, sunt in culpa qui officia deserunt mollit anim id est laborum.

        Second paragraph:
        Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua.
        Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat.
        Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur.
        Excepteur sint occaecat cupidatat non proident, sunt in culpa qui officia deserunt mollit anim id est laborum.
        ''')
				with open(lowerCAmelCase   , '''w'''   , encoding='''utf-8''') as f:
								f.write(lowerCAmelCase)
				return str(lowerCAmelCase)





@pytest.fixture
def      lowercase  (  lowerCAmelCase :						Optional[Any]   , lowerCAmelCase :						Tuple):



				"""simple docstring"""


				_A :				str				=  tmp_path / '''text_with_image.txt'''
				with open(lowerCAmelCase   , '''w'''   , encoding='''utf-8''') as f:
								f.write(lowerCAmelCase)
				return str(lowerCAmelCase)





@pytest.mark.parametrize('''keep_linebreaks'''   , [True, False])
def      lowercase  (  lowerCAmelCase :						Optional[Any]   , lowerCAmelCase :						Union[str, Any]):



				"""simple docstring"""


				with open(lowerCAmelCase   , encoding='''utf-8''') as f:
								_A :				Any				=  f.read().splitlines(keepends=lowerCAmelCase)
				_A :				Any				=  Text(keep_linebreaks=lowerCAmelCase   , encoding='''utf-8''')
				_A :				Optional[Any]				=  text._generate_tables([[text_file]])
				_A :				Dict				=  pa.concat_tables([table for _, table in generator]).to_pydict()['''text''']
				assert generated_content == expected_content





@require_pil
def      lowercase  (  lowerCAmelCase :						Dict):



				"""simple docstring"""


				with open(lowerCAmelCase   , encoding='''utf-8''') as f:
								_A :				Tuple				=  f.read().splitlines()[0]
				_A :				Optional[int]				=  Text(encoding='''utf-8'''   , features=Features({'''image''': Image()}))
				_A :				Any				=  text._generate_tables([[text_file_with_image]])
				_A :				str				=  pa.concat_tables([table for _, table in generator])
				assert pa_table.schema.field('''image''').type == Image()()
				_A :				List[str]				=  pa_table.to_pydict()['''image''']
				assert generated_content == [{"path": image_file, "bytes": None}]





@pytest.mark.parametrize('''sample_by'''   , ['''line''', '''paragraph''', '''document'''])
def      lowercase  (  lowerCAmelCase :						int   , lowerCAmelCase :						Optional[int]):



				"""simple docstring"""


				with open(lowerCAmelCase   , encoding='''utf-8''') as f:
								_A :				Any				=  f.read()
				if sample_by == "line":
								_A :				str				=  expected_content.splitlines()
				elif sample_by == "paragraph":
								_A :				str				=  expected_content.split('''\n\n''')
				elif sample_by == "document":
								_A :				Tuple				=  [expected_content]
				_A :				str				=  Text(sample_by=lowerCAmelCase   , encoding='''utf-8'''   , chunksize=100)
				_A :				Optional[Any]				=  text._generate_tables([[text_file]])
				_A :				int				=  pa.concat_tables([table for _, table in generator]).to_pydict()['''text''']
				assert generated_content == expected_content
