"""simple docstring"""




import logging
import random

import ray

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


_a      :		Any           =   logging.getLogger(__name__)
class 						__A							:






     def __init__(		self  ):
       _lowerCAmelCase						:       Tuple  			=     False






     def     __A  (		self				,     a__				,     a__				,     a__				,     a__  ):
       if not self.initialized:
         _lowerCAmelCase						:       Any  			=     RagRetriever(
             a__				,     question_encoder_tokenizer=a__				,     generator_tokenizer=a__				,     index=a__				,     init_retrieval=a__				,     )
         _lowerCAmelCase						:       Optional[int]  			=     True






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






     def     __A  (		self				,     a__				,     a__  ):
       _lowerCAmelCase			,	_lowerCAmelCase						:       Any  			=     self.retriever._main_retrieve(a__				,     a__  )
       return doc_ids, retrieved_doc_embeds







class 						__A							( SCREAMING_SNAKE_CASE_		):






     def __init__(		self				,     a__				,     a__				,     a__				,     a__				,     a__=None  ):
       if index is not None and index.is_initialized() and len(a__  ) > 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__(
           a__				,     question_encoder_tokenizer=a__				,     generator_tokenizer=a__				,     index=a__				,     init_retrieval=a__				,     )
       _lowerCAmelCase						:       Union[str, Any]  			=     retrieval_workers
       if len(self.retrieval_workers  ) > 0:
         ray.get(
             [
                 worker.create_rag_retriever.remote(a__				,     a__				,     a__				,     a__  )
                 for worker in self.retrieval_workers
             ]  )






     def     __A  (		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     __A  (		self				,     a__				,     a__  ):
       if len(self.retrieval_workers  ) > 0:
         # Select a random retrieval actor.
         _lowerCAmelCase						:       List[str]  			=     self.retrieval_workers[random.randint(0				,     len(self.retrieval_workers  ) - 1  )]
         _lowerCAmelCase			,	_lowerCAmelCase						:       Tuple  			=     ray.get(random_worker.retrieve.remote(a__				,     a__  )  )
       else:
         _lowerCAmelCase			,	_lowerCAmelCase						:       Any  			=     self._main_retrieve(a__				,     a__  )
       return retrieved_doc_embeds, doc_ids, self.index.get_doc_dicts(a__  )






     @classmethod
     def     __A  (		cls				,     a__				,     a__=None				,     **a__  ):
       return super(a__				,     cls  ).get_tokenizers(a__				,     a__				,     **a__  )






     @classmethod
     def     __A  (		cls				,     a__				,     a__				,     a__=None				,     **a__  ):
       _lowerCAmelCase						:       str  			=     kwargs.pop("""config"""				,     a__  ) or RagConfig.from_pretrained(a__				,     **a__  )
       _lowerCAmelCase						:       Tuple  			=     RagTokenizer.from_pretrained(a__				,     config=a__  )
       _lowerCAmelCase						:       str  			=     rag_tokenizer.question_encoder
       _lowerCAmelCase						:       Union[str, Any]  			=     rag_tokenizer.generator
       if indexed_dataset is not None:
         _lowerCAmelCase						:       Any  			=     """custom"""
         _lowerCAmelCase						:       Any  			=     CustomHFIndex(config.retrieval_vector_size				,     a__  )
       else:
         _lowerCAmelCase						:       Union[str, Any]  			=     cls._build_index(a__  )
       return cls(
           a__				,     question_encoder_tokenizer=a__				,     generator_tokenizer=a__				,     retrieval_workers=a__				,     index=a__				,     )





"""simple docstring"""




import itertools
from dataclasses import dataclass
from typing import List, Optional

import pyarrow as pa
import pyarrow.parquet as pq

import datasets
from datasets.table import table_cast


_a      :		Dict           =   datasets.utils.logging.get_logger(__name__)
@dataclass
class 						__A							( datasets.BuilderConfig		):
     _UpperCamelCase							:			int							=		10_000
     _UpperCamelCase							:			Optional[List[str]]							=		None
     _UpperCamelCase							:			Optional[datasets.Features]							=		None







class 						__A							( datasets.ArrowBasedBuilder		):
     _UpperCamelCase							:			List[str]							=		ParquetConfig






     def     __A  (		self  ):
       return datasets.DatasetInfo(features=self.config.features  )






     def     __A  (		self				,     a__  ):
       if not self.config.data_files:
         raise ValueError(F"At least one data file must be specified, but got data_files={self.config.data_files}"  )
       _lowerCAmelCase						:       Optional[Any]  			=     dl_manager.download_and_extract(self.config.data_files  )
       if isinstance(a__				,     (str, list, tuple)  ):
         _lowerCAmelCase						:       Any  			=     data_files
         if isinstance(a__				,     a__  ):
           _lowerCAmelCase						:       Tuple  			=     [files]
         # Use `dl_manager.iter_files` to skip hidden files in an extracted archive
         _lowerCAmelCase						:       Any  			=     [dl_manager.iter_files(a__  ) for file in files]
         return [datasets.SplitGenerator(name=datasets.Split.TRAIN				,     gen_kwargs={"""files""": files}  )]
       _lowerCAmelCase						:       Optional[Any]  			=     []
       for split_name, files in data_files.items():
         if isinstance(a__				,     a__  ):
           _lowerCAmelCase						:       Dict  			=     [files]
         # Use `dl_manager.iter_files` to skip hidden files in an extracted archive
         _lowerCAmelCase						:       Tuple  			=     [dl_manager.iter_files(a__  ) for file in files]
         # Infer features is they are stoed in the arrow schema
         if self.info.features is None:
           for file in itertools.chain.from_iterable(a__  ):
             with open(a__				,     """rb"""  ) as f:
               _lowerCAmelCase						:       Optional[Any]  			=     datasets.Features.from_arrow_schema(pq.read_schema(a__  )  )
             break
         splits.append(datasets.SplitGenerator(name=a__				,     gen_kwargs={"""files""": files}  )  )
       return splits






     def     __A  (		self				,     a__  ):
       if self.info.features is not None:
         # more expensive cast to support nested features with keys in a different order
         # allows str <-> int/float or str to Audio for example
         _lowerCAmelCase						:       Optional[int]  			=     table_cast(a__				,     self.info.features.arrow_schema  )
       return pa_table






     def     __A  (		self				,     a__  ):
       _lowerCAmelCase						:       Optional[int]  			=     self.info.features.arrow_schema if self.info.features is not None else None
       if self.info.features is not None and self.config.columns is not None:
         if sorted(field.name for field in schema  ) != sorted(self.config.columns  ):
           raise ValueError(
               F"Tried to load parquet data with columns '{self.config.columns}' with mismatching features '{self.info.features}'"  )
       for file_idx, file in enumerate(itertools.chain.from_iterable(a__  )  ):
         with open(a__				,     """rb"""  ) as f:
           _lowerCAmelCase						:       Tuple  			=     pq.ParquetFile(a__  )
           try:
             for batch_idx, record_batch in enumerate(
                 parquet_file.iter_batches(batch_size=self.config.batch_size				,     columns=self.config.columns  )  ):
               _lowerCAmelCase						:       Any  			=     pa.Table.from_batches([record_batch]  )
               # Uncomment for debugging (will print the Arrow table size and elements)
               # logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}")
               # logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))
               yield F"{file_idx}_{batch_idx}", self._cast_table(a__  )
           except ValueError as e:
             logger.error(F"Failed to read file '{file}' with error {type(a__  )}: {e}"  )
             raise




