

from unittest.mock import patch

import pyspark

from datasets.packaged_modules.spark.spark import (
    Spark,
    SparkExamplesIterable,
    _generate_iterable_examples,
)

from ..utils import (
    require_dill_gt_0_3_2,
    require_not_windows,
)

def        lowerCamelCase__       (    _a							,	_a):
	SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				[]
	for part_id in partition_order:
		SCREAMING_SNAKE_CASE      :					int    =				df.where(f"SPARK_PARTITION_ID() = {part_id}").collect()
		for row_idx, row in enumerate(_a):
			expected_row_ids_and_row_dicts.append((f"{part_id}_{row_idx}", row.asDict()))
	return expected_row_ids_and_row_dicts

@require_not_windows
@require_dill_gt_0_3_2
def        lowerCamelCase__       (    ):
	SCREAMING_SNAKE_CASE      :					str    =				pyspark.sql.SparkSession.builder.master("local[*]").appName("pyspark").getOrCreate()
	SCREAMING_SNAKE_CASE      :					Dict    =				spark.range(100).repartition(1)
	SCREAMING_SNAKE_CASE      :					List[str]    =				Spark(_a)
	# The id ints will be converted to Pyarrow int64s, so each row will be 8 bytes. Setting a max_shard_size of 16 means
	# that each partition can hold 2 rows.
	spark_builder._repartition_df_if_needed(max_shard_size=16)
	# Given that the dataframe has 100 rows and each partition has 2 rows, we expect 50 partitions.
	assert spark_builder.df.rdd.getNumPartitions() == 50

@require_not_windows
@require_dill_gt_0_3_2
def        lowerCamelCase__       (    ):
	SCREAMING_SNAKE_CASE      :					Optional[Any]    =				pyspark.sql.SparkSession.builder.master("local[*]").appName("pyspark").getOrCreate()
	SCREAMING_SNAKE_CASE      :					str    =				spark.range(10).repartition(2)
	SCREAMING_SNAKE_CASE      :					List[str]    =				[1, 0]
	SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				_generate_iterable_examples(_a							,	_a)  # Reverse the partitions.
	SCREAMING_SNAKE_CASE      :					Optional[int]    =				_get_expected_row_ids_and_row_dicts_for_partition_order(_a							,	_a)

	for i, (row_id, row_dict) in enumerate(generate_fn()):
		SCREAMING_SNAKE_CASE   ,SCREAMING_SNAKE_CASE      :					str    =				expected_row_ids_and_row_dicts[i]
		assert row_id == expected_row_id
		assert row_dict == expected_row_dict

@require_not_windows
@require_dill_gt_0_3_2
def        lowerCamelCase__       (    ):
	SCREAMING_SNAKE_CASE      :					Optional[int]    =				pyspark.sql.SparkSession.builder.master("local[*]").appName("pyspark").getOrCreate()
	SCREAMING_SNAKE_CASE      :					Optional[Any]    =				spark.range(10).repartition(1)
	SCREAMING_SNAKE_CASE      :					str    =				SparkExamplesIterable(_a)
	assert it.n_shards == 1
	for i, (row_id, row_dict) in enumerate(_a):
		assert row_id == f"0_{i}"
		assert row_dict == {"id": i}

@require_not_windows
@require_dill_gt_0_3_2
def        lowerCamelCase__       (    ):
	SCREAMING_SNAKE_CASE      :					Optional[Any]    =				pyspark.sql.SparkSession.builder.master("local[*]").appName("pyspark").getOrCreate()
	SCREAMING_SNAKE_CASE      :					Dict    =				spark.range(30).repartition(3)
	# Mock the generator so that shuffle reverses the partition indices.
	with patch("numpy.random.Generator") as generator_mock:
		SCREAMING_SNAKE_CASE      :					str    =				lambda _a: x.reverse()
		SCREAMING_SNAKE_CASE      :					Any    =				_get_expected_row_ids_and_row_dicts_for_partition_order(_a							,	[2, 1, 0])

		SCREAMING_SNAKE_CASE      :					Any    =				SparkExamplesIterable(_a).shuffle_data_sources(_a)
		assert shuffled_it.n_shards == 3
		for i, (row_id, row_dict) in enumerate(_a):
			SCREAMING_SNAKE_CASE   ,SCREAMING_SNAKE_CASE      :					Tuple    =				expected_row_ids_and_row_dicts[i]
			assert row_id == expected_row_id
			assert row_dict == expected_row_dict

@require_not_windows
@require_dill_gt_0_3_2
def        lowerCamelCase__       (    ):
	SCREAMING_SNAKE_CASE      :					List[Any]    =				pyspark.sql.SparkSession.builder.master("local[*]").appName("pyspark").getOrCreate()
	SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				spark.range(20).repartition(4)

	# Partitions 0 and 2
	SCREAMING_SNAKE_CASE      :					Tuple    =				SparkExamplesIterable(_a).shard_data_sources(worker_id=0							,	num_workers=2)
	assert shard_it_a.n_shards == 2
	SCREAMING_SNAKE_CASE      :					List[Any]    =				_get_expected_row_ids_and_row_dicts_for_partition_order(_a							,	[0, 2])
	for i, (row_id, row_dict) in enumerate(_a):
		SCREAMING_SNAKE_CASE   ,SCREAMING_SNAKE_CASE      :					int    =				expected_row_ids_and_row_dicts_a[i]
		assert row_id == expected_row_id
		assert row_dict == expected_row_dict

	# Partitions 1 and 3
	SCREAMING_SNAKE_CASE      :					Optional[Any]    =				SparkExamplesIterable(_a).shard_data_sources(worker_id=1							,	num_workers=2)
	assert shard_it_a.n_shards == 2
	SCREAMING_SNAKE_CASE      :					Optional[int]    =				_get_expected_row_ids_and_row_dicts_for_partition_order(_a							,	[1, 3])
	for i, (row_id, row_dict) in enumerate(_a):
		SCREAMING_SNAKE_CASE   ,SCREAMING_SNAKE_CASE      :					Optional[Any]    =				expected_row_ids_and_row_dicts_a[i]
		assert row_id == expected_row_id
		assert row_dict == expected_row_dict


@require_not_windows
@require_dill_gt_0_3_2
def        lowerCamelCase__       (    ):
	SCREAMING_SNAKE_CASE      :					List[Any]    =				pyspark.sql.SparkSession.builder.master("local[*]").appName("pyspark").getOrCreate()
	SCREAMING_SNAKE_CASE      :					Optional[Any]    =				spark.range(100).repartition(1)
	SCREAMING_SNAKE_CASE      :					Optional[Any]    =				Spark(_a)
	# Choose a small max_shard_size for maximum partitioning.
	spark_builder._repartition_df_if_needed(max_shard_size=1)
	# The new number of partitions should not be greater than the number of rows.
	assert spark_builder.df.rdd.getNumPartitions() == 100
"""simple docstring"""






import logging
import random

import ray

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


__lowerCAmelCase    :       int					     =logging.getLogger(__name__)





class  _A						:

      def __init__(						self						):







            """simple docstring"""



            lowercase														=	False

      def 				A__       (						self		,       __lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase						):







            """simple docstring"""



            if not self.initialized:
                  lowercase														=	RagRetriever(
                      __lowerCAmelCase		,       question_encoder_tokenizer=__lowerCAmelCase		,       generator_tokenizer=__lowerCAmelCase		,       index=__lowerCAmelCase		,       init_retrieval=__lowerCAmelCase		,       )
                  lowercase														=	True

      def 				A__       (						self						):







            """simple docstring"""



            self.retriever.index.init_index()







      def 				A__       (						self		,       __lowerCAmelCase		,       __lowerCAmelCase						):







            """simple docstring"""



            lowercase							,							lowercase														=	self.retriever._main_retrieve(__lowerCAmelCase		,       __lowerCAmelCase						)
            return doc_ids, retrieved_doc_embeds






class  _A						(       lowerCAmelCase	):

      def __init__(						self		,       __lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase=None						):







            """simple docstring"""



            if index is not None and index.is_initialized() and len(__lowerCAmelCase						) > 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__(
                __lowerCAmelCase		,       question_encoder_tokenizer=__lowerCAmelCase		,       generator_tokenizer=__lowerCAmelCase		,       index=__lowerCAmelCase		,       init_retrieval=__lowerCAmelCase		,       )
            lowercase														=	retrieval_workers
            if len(self.retrieval_workers						) > 0:
                  ray.get(
                      [
                          worker.create_rag_retriever.remote(__lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase						)
                          for worker in self.retrieval_workers
                      ]						)

      def 				A__       (						self						):







            """simple docstring"""



            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		,       __lowerCAmelCase		,       __lowerCAmelCase						):







            """simple docstring"""



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

      @classmethod
      def 				A__       (						cls		,       __lowerCAmelCase		,       __lowerCAmelCase=None		,       **__lowerCAmelCase						):







            """simple docstring"""



            return super(__lowerCAmelCase		,       cls						).get_tokenizers(__lowerCAmelCase		,       __lowerCAmelCase		,       **__lowerCAmelCase						)







      @classmethod
      def 				A__       (						cls		,       __lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase=None		,       **__lowerCAmelCase						):







            """simple docstring"""



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



