
'''simple docstring'''
import json
import os
import pickle
import shutil
import tempfile
from unittest import TestCase
from unittest.mock import patch

import numpy as np
from datasets import Dataset

from transformers import is_faiss_available
from transformers.models.bart.configuration_bart import BartConfig
from transformers.models.bart.tokenization_bart import BartTokenizer
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES as DPR_VOCAB_FILES_NAMES
from transformers.models.dpr.configuration_dpr import DPRConfig
from transformers.models.dpr.tokenization_dpr import DPRContextEncoderTokenizer, DPRQuestionEncoderTokenizer
from transformers.models.rag.configuration_rag import RagConfig
from transformers.models.rag.retrieval_rag import CustomHFIndex, RagRetriever
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES as BART_VOCAB_FILES_NAMES
from transformers.testing_utils import require_faiss, require_sentencepiece, require_tokenizers, require_torch


if is_faiss_available():
		import faiss







@require_faiss
class   a_ (      UpperCamelCase__					):




			def 					lowerCAmelCase__					(       self	):
				a_      				=						tempfile.mkdtemp()
				a_      				=						8

				# DPR tok
				a_      				=						[
				    """[UNK]""",
				    """[CLS]""",
				    """[SEP]""",
				    """[PAD]""",
				    """[MASK]""",
				    """want""",
				    """##want""",
				    """##ed""",
				    """wa""",
				    """un""",
				    """runn""",
				    """##ing""",
				    """,""",
				    """low""",
				    """lowest""",
				]
				a_      				=						os.path.join(self.tmpdirname					,   """dpr_tokenizer"""	)
				os.makedirs(UpperCAmelCase					,   exist_ok=UpperCAmelCase	)
				a_      				=						os.path.join(UpperCAmelCase					,   DPR_VOCAB_FILES_NAMES["""vocab_file"""]	)
				with open(self.vocab_file					,   """w"""					,   encoding="""utf-8"""	) as vocab_writer:
					vocab_writer.write("""""".join([x + """\n""" for x in vocab_tokens]	)	)

				# BART tok
				a_      				=						[
				    """l""",
				    """o""",
				    """w""",
				    """e""",
				    """r""",
				    """s""",
				    """t""",
				    """i""",
				    """d""",
				    """n""",
				    """\u0120""",
				    """\u0120l""",
				    """\u0120n""",
				    """\u0120lo""",
				    """\u0120low""",
				    """er""",
				    """\u0120lowest""",
				    """\u0120newer""",
				    """\u0120wider""",
				    """<unk>""",
				]
				a_      				=						dict(zip(UpperCAmelCase					,   range(len(UpperCAmelCase	)	)	)	)
				a_      				=						["""#version: 0.2""", """\u0120 l""", """\u0120l o""", """\u0120lo w""", """e r""", """"""]
				a_      				=						{"""unk_token""": """<unk>"""}

				a_      				=						os.path.join(self.tmpdirname					,   """bart_tokenizer"""	)
				os.makedirs(UpperCAmelCase					,   exist_ok=UpperCAmelCase	)
				a_      				=						os.path.join(UpperCAmelCase					,   BART_VOCAB_FILES_NAMES["""vocab_file"""]	)
				a_      				=						os.path.join(UpperCAmelCase					,   BART_VOCAB_FILES_NAMES["""merges_file"""]	)
				with open(self.vocab_file					,   """w"""					,   encoding="""utf-8"""	) as fp:
					fp.write(json.dumps(UpperCAmelCase	) + """\n"""	)
				with open(self.merges_file					,   """w"""					,   encoding="""utf-8"""	) as fp:
					fp.write("""\n""".join(UpperCAmelCase	)	)




			def 					lowerCAmelCase__					(       self	):
				return DPRQuestionEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname					,   """dpr_tokenizer"""	)	)




			def 					lowerCAmelCase__					(       self	):
				return DPRContextEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname					,   """dpr_tokenizer"""	)	)




			def 					lowerCAmelCase__					(       self	):
				return BartTokenizer.from_pretrained(os.path.join(self.tmpdirname					,   """bart_tokenizer"""	)	)




			def 					lowerCAmelCase__					(       self	):
				shutil.rmtree(self.tmpdirname	)




			def 					lowerCAmelCase__					(       self	):
				a_      				=						Dataset.from_dict(
				    {
				        """id""": ["""0""", """1"""],
				        """text""": ["""foo""", """bar"""],
				        """title""": ["""Foo""", """Bar"""],
				        """embeddings""": [np.ones(self.retrieval_vector_size	), 2 * np.ones(self.retrieval_vector_size	)],
				    }	)
				dataset.add_faiss_index("""embeddings"""					,   string_factory="""Flat"""					,   metric_type=faiss.METRIC_INNER_PRODUCT	)
				return dataset




			def 					lowerCAmelCase__					(       self	):
				a_      				=						self.get_dummy_dataset()
				a_      				=						RagConfig(
				    retrieval_vector_size=self.retrieval_vector_size					,   question_encoder=DPRConfig().to_dict()					,   generator=BartConfig().to_dict()					,   )
				with patch("""transformers.models.rag.retrieval_rag.load_dataset"""	) as mock_load_dataset:
					a_      				=						dataset
					a_      				=						RagRetriever(
					    UpperCAmelCase					,   question_encoder_tokenizer=self.get_dpr_tokenizer()					,   generator_tokenizer=self.get_bart_tokenizer()					,   )
				return retriever




			def 					lowerCAmelCase__					(       self					,   UpperCAmelCase	):
				a_      				=						self.get_dummy_dataset()
				a_      				=						RagConfig(
				    retrieval_vector_size=self.retrieval_vector_size					,   question_encoder=DPRConfig().to_dict()					,   generator=BartConfig().to_dict()					,   index_name="""custom"""					,   )
				if from_disk:
					a_      				=						os.path.join(self.tmpdirname					,   """dataset"""	)
					a_      				=						os.path.join(self.tmpdirname					,   """index.faiss"""	)
					dataset.get_index("""embeddings"""	).save(os.path.join(self.tmpdirname					,   """index.faiss"""	)	)
					dataset.drop_index("""embeddings"""	)
					dataset.save_to_disk(os.path.join(self.tmpdirname					,   """dataset"""	)	)
					del dataset
					a_      				=						RagRetriever(
					    UpperCAmelCase					,   question_encoder_tokenizer=self.get_dpr_tokenizer()					,   generator_tokenizer=self.get_bart_tokenizer()					,   )
				else:
					a_      				=						RagRetriever(
					    UpperCAmelCase					,   question_encoder_tokenizer=self.get_dpr_tokenizer()					,   generator_tokenizer=self.get_bart_tokenizer()					,   index=CustomHFIndex(config.retrieval_vector_size					,   UpperCAmelCase	)					,   )
				return retriever




			def 					lowerCAmelCase__					(       self	):
				a_      				=						Dataset.from_dict(
				    {
				        """id""": ["""0""", """1"""],
				        """text""": ["""foo""", """bar"""],
				        """title""": ["""Foo""", """Bar"""],
				        """embeddings""": [np.ones(self.retrieval_vector_size + 1	), 2 * np.ones(self.retrieval_vector_size + 1	)],
				    }	)
				dataset.add_faiss_index("""embeddings"""					,   string_factory="""Flat"""					,   metric_type=faiss.METRIC_INNER_PRODUCT	)

				a_      				=						os.path.join(self.tmpdirname					,   """hf_bert_base.hnswSQ8_correct_phi_128.c_index"""	)
				dataset.save_faiss_index("""embeddings"""					,   index_file_name + """.index.dpr"""	)
				pickle.dump(dataset["""id"""]					,   open(index_file_name + """.index_meta.dpr"""					,   """wb"""	)	)

				a_      				=						os.path.join(self.tmpdirname					,   """psgs_w100.tsv.pkl"""	)
				a_      				=						{sample["""id"""]: [sample["""text"""], sample["""title"""]] for sample in dataset}
				pickle.dump(UpperCAmelCase					,   open(UpperCAmelCase					,   """wb"""	)	)

				a_      				=						RagConfig(
				    retrieval_vector_size=self.retrieval_vector_size					,   question_encoder=DPRConfig().to_dict()					,   generator=BartConfig().to_dict()					,   index_name="""legacy"""					,   index_path=self.tmpdirname					,   )
				a_      				=						RagRetriever(
				    UpperCAmelCase					,   question_encoder_tokenizer=self.get_dpr_tokenizer()					,   generator_tokenizer=self.get_bart_tokenizer()	)
				return retriever




			def 					lowerCAmelCase__					(       self	):
				a_      				=						1
				a_      				=						self.get_dummy_canonical_hf_index_retriever()
				a_      				=						np.array(
				    [np.ones(self.retrieval_vector_size	), -np.ones(self.retrieval_vector_size	)]					,   dtype=np.floataa	)
				a_				,  a_				,  a_      				=						retriever.retrieve(UpperCAmelCase					,   n_docs=UpperCAmelCase	)
				self.assertEqual(retrieved_doc_embeds.shape					,   (2, n_docs, self.retrieval_vector_size)	)
				self.assertEqual(len(UpperCAmelCase	)					,   2	)
				self.assertEqual(sorted(doc_dicts[0]	)					,   ["""embeddings""", """id""", """text""", """title"""]	)
				self.assertEqual(len(doc_dicts[0]["""id"""]	)					,   UpperCAmelCase	)
				self.assertEqual(doc_dicts[0]["""id"""][0]					,   """1"""	)  # max inner product is reached with second doc
				self.assertEqual(doc_dicts[1]["""id"""][0]					,   """0"""	)  # max inner product is reached with first doc
				self.assertListEqual(doc_ids.tolist()					,   [[1], [0]]	)




			def 					lowerCAmelCase__					(       self	):
				a_      				=						self.get_dummy_canonical_hf_index_retriever()
				with tempfile.TemporaryDirectory() as tmp_dirname:
					with patch("""transformers.models.rag.retrieval_rag.load_dataset"""	) as mock_load_dataset:
						a_      				=						self.get_dummy_dataset()
						retriever.save_pretrained(UpperCAmelCase	)
						a_      				=						RagRetriever.from_pretrained(UpperCAmelCase	)
						self.assertIsInstance(UpperCAmelCase					,   UpperCAmelCase	)
						a_      				=						np.array(
						    [np.ones(self.retrieval_vector_size	), -np.ones(self.retrieval_vector_size	)]					,   dtype=np.floataa	)
						a_      				=						retriever.retrieve(UpperCAmelCase					,   n_docs=1	)
						self.assertTrue(out is not None	)




			def 					lowerCAmelCase__					(       self	):
				a_      				=						1
				a_      				=						self.get_dummy_custom_hf_index_retriever(from_disk=UpperCAmelCase	)
				a_      				=						np.array(
				    [np.ones(self.retrieval_vector_size	), -np.ones(self.retrieval_vector_size	)]					,   dtype=np.floataa	)
				a_				,  a_				,  a_      				=						retriever.retrieve(UpperCAmelCase					,   n_docs=UpperCAmelCase	)
				self.assertEqual(retrieved_doc_embeds.shape					,   (2, n_docs, self.retrieval_vector_size)	)
				self.assertEqual(len(UpperCAmelCase	)					,   2	)
				self.assertEqual(sorted(doc_dicts[0]	)					,   ["""embeddings""", """id""", """text""", """title"""]	)
				self.assertEqual(len(doc_dicts[0]["""id"""]	)					,   UpperCAmelCase	)
				self.assertEqual(doc_dicts[0]["""id"""][0]					,   """1"""	)  # max inner product is reached with second doc
				self.assertEqual(doc_dicts[1]["""id"""][0]					,   """0"""	)  # max inner product is reached with first doc
				self.assertListEqual(doc_ids.tolist()					,   [[1], [0]]	)




			def 					lowerCAmelCase__					(       self	):
				a_      				=						self.get_dummy_custom_hf_index_retriever(from_disk=UpperCAmelCase	)
				with tempfile.TemporaryDirectory() as tmp_dirname:
					retriever.save_pretrained(UpperCAmelCase	)
					a_      				=						RagRetriever.from_pretrained(UpperCAmelCase	)
					self.assertIsInstance(UpperCAmelCase					,   UpperCAmelCase	)
					a_      				=						np.array(
					    [np.ones(self.retrieval_vector_size	), -np.ones(self.retrieval_vector_size	)]					,   dtype=np.floataa	)
					a_      				=						retriever.retrieve(UpperCAmelCase					,   n_docs=1	)
					self.assertTrue(out is not None	)




			def 					lowerCAmelCase__					(       self	):
				a_      				=						1
				a_      				=						self.get_dummy_custom_hf_index_retriever(from_disk=UpperCAmelCase	)
				a_      				=						np.array(
				    [np.ones(self.retrieval_vector_size	), -np.ones(self.retrieval_vector_size	)]					,   dtype=np.floataa	)
				a_				,  a_				,  a_      				=						retriever.retrieve(UpperCAmelCase					,   n_docs=UpperCAmelCase	)
				self.assertEqual(retrieved_doc_embeds.shape					,   (2, n_docs, self.retrieval_vector_size)	)
				self.assertEqual(len(UpperCAmelCase	)					,   2	)
				self.assertEqual(sorted(doc_dicts[0]	)					,   ["""embeddings""", """id""", """text""", """title"""]	)
				self.assertEqual(len(doc_dicts[0]["""id"""]	)					,   UpperCAmelCase	)
				self.assertEqual(doc_dicts[0]["""id"""][0]					,   """1"""	)  # max inner product is reached with second doc
				self.assertEqual(doc_dicts[1]["""id"""][0]					,   """0"""	)  # max inner product is reached with first doc
				self.assertListEqual(doc_ids.tolist()					,   [[1], [0]]	)




			def 					lowerCAmelCase__					(       self	):
				a_      				=						self.get_dummy_custom_hf_index_retriever(from_disk=UpperCAmelCase	)
				with tempfile.TemporaryDirectory() as tmp_dirname:
					retriever.save_pretrained(UpperCAmelCase	)
					a_      				=						RagRetriever.from_pretrained(UpperCAmelCase	)
					self.assertIsInstance(UpperCAmelCase					,   UpperCAmelCase	)
					a_      				=						np.array(
					    [np.ones(self.retrieval_vector_size	), -np.ones(self.retrieval_vector_size	)]					,   dtype=np.floataa	)
					a_      				=						retriever.retrieve(UpperCAmelCase					,   n_docs=1	)
					self.assertTrue(out is not None	)




			def 					lowerCAmelCase__					(       self	):
				a_      				=						1
				a_      				=						self.get_dummy_legacy_index_retriever()
				a_      				=						np.array(
				    [np.ones(self.retrieval_vector_size	), -np.ones(self.retrieval_vector_size	)]					,   dtype=np.floataa	)
				a_				,  a_				,  a_      				=						retriever.retrieve(UpperCAmelCase					,   n_docs=UpperCAmelCase	)
				self.assertEqual(retrieved_doc_embeds.shape					,   (2, n_docs, self.retrieval_vector_size)	)
				self.assertEqual(len(UpperCAmelCase	)					,   2	)
				self.assertEqual(sorted(doc_dicts[0]	)					,   ["""text""", """title"""]	)
				self.assertEqual(len(doc_dicts[0]["""text"""]	)					,   UpperCAmelCase	)
				self.assertEqual(doc_dicts[0]["""text"""][0]					,   """bar"""	)  # max inner product is reached with second doc
				self.assertEqual(doc_dicts[1]["""text"""][0]					,   """foo"""	)  # max inner product is reached with first doc
				self.assertListEqual(doc_ids.tolist()					,   [[1], [0]]	)




			def 					lowerCAmelCase__					(       self	):
				a_      				=						self.get_dummy_legacy_index_retriever()
				with tempfile.TemporaryDirectory() as tmp_dirname:
					retriever.save_pretrained(UpperCAmelCase	)
					a_      				=						RagRetriever.from_pretrained(UpperCAmelCase	)
					self.assertIsInstance(UpperCAmelCase					,   UpperCAmelCase	)
					a_      				=						np.array(
					    [np.ones(self.retrieval_vector_size	), -np.ones(self.retrieval_vector_size	)]					,   dtype=np.floataa	)
					a_      				=						retriever.retrieve(UpperCAmelCase					,   n_docs=1	)
					self.assertTrue(out is not None	)




			@require_torch
			@require_tokenizers
			@require_sentencepiece
			def 					lowerCAmelCase__					(       self	):
				import torch

				a_      				=						1
				a_      				=						self.get_dummy_canonical_hf_index_retriever()
				a_      				=						[[5, 7], [10, 11]]
				a_      				=						np.array(
				    [np.ones(self.retrieval_vector_size	), -np.ones(self.retrieval_vector_size	)]					,   dtype=np.floataa	)
				a_      				=						retriever(UpperCAmelCase					,   UpperCAmelCase					,   prefix=retriever.config.generator.prefix					,   n_docs=UpperCAmelCase	)
				a_				,  a_				,  a_      				=						(
				    out["""context_input_ids"""],
				    out["""context_attention_mask"""],
				    out["""retrieved_doc_embeds"""],
				)
				self.assertEqual(retrieved_doc_embeds.shape					,   (2, n_docs, self.retrieval_vector_size)	)
				self.assertIsInstance(UpperCAmelCase					,   UpperCAmelCase	)
				self.assertIsInstance(UpperCAmelCase					,   UpperCAmelCase	)
				self.assertIsInstance(UpperCAmelCase					,   np.ndarray	)

				a_      				=						retriever(
				    UpperCAmelCase					,   UpperCAmelCase					,   prefix=retriever.config.generator.prefix					,   n_docs=UpperCAmelCase					,   return_tensors="""pt"""					,   )
				a_				,  a_				,  a_				,  a_      				=						(  # noqa: F841
				    out["""context_input_ids"""],
				    out["""context_attention_mask"""],
				    out["""retrieved_doc_embeds"""],
				    out["""doc_ids"""],
				)
				self.assertEqual(retrieved_doc_embeds.shape					,   (2, n_docs, self.retrieval_vector_size)	)
				self.assertIsInstance(UpperCAmelCase					,   torch.Tensor	)
				self.assertIsInstance(UpperCAmelCase					,   torch.Tensor	)
				self.assertIsInstance(UpperCAmelCase					,   torch.Tensor	)






			@require_torch
			@require_tokenizers
			@require_sentencepiece
			def 					lowerCAmelCase__					(       self	):
				a_      				=						self.get_dpr_ctx_encoder_tokenizer()
				a_      				=						1
				a_      				=						self.get_dummy_custom_hf_index_retriever(from_disk=UpperCAmelCase	)
				retriever.set_ctx_encoder_tokenizer(UpperCAmelCase	)

				a_      				=						[[5, 7], [10, 11]]
				a_      				=						np.array(
				    [np.ones(self.retrieval_vector_size	), -np.ones(self.retrieval_vector_size	)]					,   dtype=np.floataa	)
				a_      				=						retriever(UpperCAmelCase					,   UpperCAmelCase					,   prefix=retriever.config.generator.prefix					,   n_docs=UpperCAmelCase	)

				self.assertEqual(
				    len(UpperCAmelCase	)					,   6	)  # check whether the retriever output consist of 6 attributes including tokenized docs
				self.assertEqual(
				    all(k in out for k in ("""tokenized_doc_ids""", """tokenized_doc_attention_mask""")	)					,   UpperCAmelCase	)  # check for doc token related keys in dictionary.


'''simple docstring'''
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      UpperCamelCase_				(      A__  ,      A__						):
	a_      				=						[]
	for part_id in partition_order:
		a_      				=						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      UpperCamelCase_				(      ):
	a_      				=						pyspark.sql.SparkSession.builder.master("""local[*]"""						).appName("""pyspark"""						).getOrCreate()
	a_      				=						spark.range(1_00						).repartition(1						)
	a_      				=						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      UpperCamelCase_				(      ):
	a_      				=						pyspark.sql.SparkSession.builder.master("""local[*]"""						).appName("""pyspark"""						).getOrCreate()
	a_      				=						spark.range(10						).repartition(2						)
	a_      				=						[1, 0]
	a_      				=						_generate_iterable_examples(A__  ,      A__						)  # Reverse the partitions.
	a_      				=						_get_expected_row_ids_and_row_dicts_for_partition_order(A__  ,      A__						)

	for i, (row_id, row_dict) in enumerate(generate_fn()						):
		a_				,  a_      				=						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      UpperCamelCase_				(      ):
	a_      				=						pyspark.sql.SparkSession.builder.master("""local[*]"""						).appName("""pyspark"""						).getOrCreate()
	a_      				=						spark.range(10						).repartition(1						)
	a_      				=						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      UpperCamelCase_				(      ):
	a_      				=						pyspark.sql.SparkSession.builder.master("""local[*]"""						).appName("""pyspark"""						).getOrCreate()
	a_      				=						spark.range(30						).repartition(3						)
	# Mock the generator so that shuffle reverses the partition indices.
	with patch("""numpy.random.Generator"""						) as generator_mock:
		a_      				=						lambda A__						: x.reverse()
		a_      				=						_get_expected_row_ids_and_row_dicts_for_partition_order(A__  ,      [2, 1, 0]						)

		a_      				=						SparkExamplesIterable(A__						).shuffle_data_sources(A__						)
		assert shuffled_it.n_shards == 3
		for i, (row_id, row_dict) in enumerate(A__						):
			a_				,  a_      				=						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      UpperCamelCase_				(      ):
	a_      				=						pyspark.sql.SparkSession.builder.master("""local[*]"""						).appName("""pyspark"""						).getOrCreate()
	a_      				=						spark.range(20						).repartition(4						)

	# Partitions 0 and 2
	a_      				=						SparkExamplesIterable(A__						).shard_data_sources(worker_id=0  ,      num_workers=2						)
	assert shard_it_a.n_shards == 2
	a_      				=						_get_expected_row_ids_and_row_dicts_for_partition_order(A__  ,      [0, 2]						)
	for i, (row_id, row_dict) in enumerate(A__						):
		a_				,  a_      				=						expected_row_ids_and_row_dicts_a[i]
		assert row_id == expected_row_id
		assert row_dict == expected_row_dict

	# Partitions 1 and 3
	a_      				=						SparkExamplesIterable(A__						).shard_data_sources(worker_id=1  ,      num_workers=2						)
	assert shard_it_a.n_shards == 2
	a_      				=						_get_expected_row_ids_and_row_dicts_for_partition_order(A__  ,      [1, 3]						)
	for i, (row_id, row_dict) in enumerate(A__						):
		a_				,  a_      				=						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      UpperCamelCase_				(      ):
	a_      				=						pyspark.sql.SparkSession.builder.master("""local[*]"""						).appName("""pyspark"""						).getOrCreate()
	a_      				=						spark.range(1_00						).repartition(1						)
	a_      				=						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() == 1_00
