






import os
import tempfile
import unittest

from transformers import FlaubertConfig, is_torch_available
from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
				import torch

				from transformers import (
				    FlaubertForMultipleChoice,
				    FlaubertForQuestionAnswering,
				    FlaubertForQuestionAnsweringSimple,
				    FlaubertForSequenceClassification,
				    FlaubertForTokenClassification,
				    FlaubertModel,
				    FlaubertWithLMHeadModel,
				)
				from transformers.models.flaubert.modeling_flaubert import FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST

class 		_snake_case	(	A__       ):






			def __init__(							self		,       a		,       a=13		,       a=7		,       a=True		,       a=True		,       a=True		,       a=True		,       a=True		,       a=False		,       a=False		,       a=False		,       a=2		,       a=99		,       a=0		,       a=32		,       a=5		,       a=4		,       a=0.1		,       a=0.1		,       a=512		,       a=12		,       a=2		,       a=0.02		,       a=3		,       a=4		,       a="last"		,       a=None		,       a=None		,       )   ->							Optional[Any]:
						SCREAMING_SNAKE_CASE							=			parent
						SCREAMING_SNAKE_CASE							=			batch_size
						SCREAMING_SNAKE_CASE							=			seq_length
						SCREAMING_SNAKE_CASE							=			is_training
						SCREAMING_SNAKE_CASE							=			use_input_lengths
						SCREAMING_SNAKE_CASE							=			use_token_type_ids
						SCREAMING_SNAKE_CASE							=			use_labels
						SCREAMING_SNAKE_CASE							=			gelu_activation
						SCREAMING_SNAKE_CASE							=			sinusoidal_embeddings
						SCREAMING_SNAKE_CASE							=			causal
						SCREAMING_SNAKE_CASE							=			asm
						SCREAMING_SNAKE_CASE							=			n_langs
						SCREAMING_SNAKE_CASE							=			vocab_size
						SCREAMING_SNAKE_CASE							=			n_special
						SCREAMING_SNAKE_CASE							=			hidden_size
						SCREAMING_SNAKE_CASE							=			num_hidden_layers
						SCREAMING_SNAKE_CASE							=			num_attention_heads
						SCREAMING_SNAKE_CASE							=			hidden_dropout_prob
						SCREAMING_SNAKE_CASE							=			attention_probs_dropout_prob
						SCREAMING_SNAKE_CASE							=			max_position_embeddings
						SCREAMING_SNAKE_CASE							=			type_vocab_size
						SCREAMING_SNAKE_CASE							=			type_sequence_label_size
						SCREAMING_SNAKE_CASE							=			initializer_range
						SCREAMING_SNAKE_CASE							=			num_labels
						SCREAMING_SNAKE_CASE							=			num_choices
						SCREAMING_SNAKE_CASE							=			summary_type
						SCREAMING_SNAKE_CASE							=			use_proj
						SCREAMING_SNAKE_CASE							=			scope






			def  SCREAMING_SNAKE_CASE__     (							self)   ->							Union[str, Any]:
						SCREAMING_SNAKE_CASE							=			ids_tensor([self.batch_size, self.seq_length]		,       self.vocab_size)
						SCREAMING_SNAKE_CASE							=			random_attention_mask([self.batch_size, self.seq_length])

						SCREAMING_SNAKE_CASE							=			None
						if self.use_input_lengths:
									SCREAMING_SNAKE_CASE							=			(
									    ids_tensor([self.batch_size]		,       vocab_size=2) + self.seq_length - 2
									)  # small variation of seq_length

						SCREAMING_SNAKE_CASE							=			None
						if self.use_token_type_ids:
									SCREAMING_SNAKE_CASE							=			ids_tensor([self.batch_size, self.seq_length]		,       self.n_langs)

						SCREAMING_SNAKE_CASE							=			None
						SCREAMING_SNAKE_CASE							=			None
						SCREAMING_SNAKE_CASE							=			None
						if self.use_labels:
									SCREAMING_SNAKE_CASE							=			ids_tensor([self.batch_size]		,       self.type_sequence_label_size)
									SCREAMING_SNAKE_CASE							=			ids_tensor([self.batch_size, self.seq_length]		,       self.num_labels)
									SCREAMING_SNAKE_CASE							=			ids_tensor([self.batch_size]		,       2).float()
									SCREAMING_SNAKE_CASE							=			ids_tensor([self.batch_size]		,       self.num_choices)

						SCREAMING_SNAKE_CASE							=			self.get_config()

						return (
						    config,
						    input_ids,
						    token_type_ids,
						    input_lengths,
						    sequence_labels,
						    token_labels,
						    is_impossible_labels,
						    choice_labels,
						    input_mask,
						)






			def  SCREAMING_SNAKE_CASE__     (							self)   ->							Union[str, Any]:
						return FlaubertConfig(
						    vocab_size=self.vocab_size		,       n_special=self.n_special		,       emb_dim=self.hidden_size		,       n_layers=self.num_hidden_layers		,       n_heads=self.num_attention_heads		,       dropout=self.hidden_dropout_prob		,       attention_dropout=self.attention_probs_dropout_prob		,       gelu_activation=self.gelu_activation		,       sinusoidal_embeddings=self.sinusoidal_embeddings		,       asm=self.asm		,       causal=self.causal		,       n_langs=self.n_langs		,       max_position_embeddings=self.max_position_embeddings		,       initializer_range=self.initializer_range		,       summary_type=self.summary_type		,       use_proj=self.use_proj		,       )






			def  SCREAMING_SNAKE_CASE__     (							self		,       a		,       a		,       a		,       a		,       a		,       a		,       a		,       a		,       a		,       )   ->							Dict:
						SCREAMING_SNAKE_CASE							=			FlaubertModel(config=a)
						model.to(a)
						model.eval()
						SCREAMING_SNAKE_CASE							=			model(a		,       lengths=a		,       langs=a)
						SCREAMING_SNAKE_CASE							=			model(a		,       langs=a)
						SCREAMING_SNAKE_CASE							=			model(a)
						self.parent.assertEqual(result.last_hidden_state.shape		,       (self.batch_size, self.seq_length, self.hidden_size))






			def  SCREAMING_SNAKE_CASE__     (							self		,       a		,       a		,       a		,       a		,       a		,       a		,       a		,       a		,       a		,       )   ->							str:
						SCREAMING_SNAKE_CASE							=			FlaubertWithLMHeadModel(a)
						model.to(a)
						model.eval()

						SCREAMING_SNAKE_CASE							=			model(a		,       token_type_ids=a		,       labels=a)
						self.parent.assertEqual(result.loss.shape		,       ())
						self.parent.assertEqual(result.logits.shape		,       (self.batch_size, self.seq_length, self.vocab_size))






			def  SCREAMING_SNAKE_CASE__     (							self		,       a		,       a		,       a		,       a		,       a		,       a		,       a		,       a		,       a		,       )   ->							Any:
						SCREAMING_SNAKE_CASE							=			FlaubertForQuestionAnsweringSimple(a)
						model.to(a)
						model.eval()

						SCREAMING_SNAKE_CASE							=			model(a)

						SCREAMING_SNAKE_CASE							=			model(a		,       start_positions=a		,       end_positions=a)
						self.parent.assertEqual(result.start_logits.shape		,       (self.batch_size, self.seq_length))
						self.parent.assertEqual(result.end_logits.shape		,       (self.batch_size, self.seq_length))






			def  SCREAMING_SNAKE_CASE__     (							self		,       a		,       a		,       a		,       a		,       a		,       a		,       a		,       a		,       a		,       )   ->							Dict:
						SCREAMING_SNAKE_CASE							=			FlaubertForQuestionAnswering(a)
						model.to(a)
						model.eval()

						SCREAMING_SNAKE_CASE							=			model(a)

						SCREAMING_SNAKE_CASE							=			model(
						    a		,       start_positions=a		,       end_positions=a		,       cls_index=a		,       is_impossible=a		,       p_mask=a		,       )

						SCREAMING_SNAKE_CASE							=			model(
						    a		,       start_positions=a		,       end_positions=a		,       cls_index=a		,       is_impossible=a		,       )

						((SCREAMING_SNAKE_CASE)					,    )							=			result_with_labels.to_tuple()

						SCREAMING_SNAKE_CASE							=			model(a		,       start_positions=a		,       end_positions=a)

						((SCREAMING_SNAKE_CASE)					,    )							=			result_with_labels.to_tuple()

						self.parent.assertEqual(result_with_labels.loss.shape		,       ())
						self.parent.assertEqual(result.start_top_log_probs.shape		,       (self.batch_size, model.config.start_n_top))
						self.parent.assertEqual(result.start_top_index.shape		,       (self.batch_size, model.config.start_n_top))
						self.parent.assertEqual(
						    result.end_top_log_probs.shape		,       (self.batch_size, model.config.start_n_top * model.config.end_n_top))
						self.parent.assertEqual(
						    result.end_top_index.shape		,       (self.batch_size, model.config.start_n_top * model.config.end_n_top))
						self.parent.assertEqual(result.cls_logits.shape		,       (self.batch_size,))






			def  SCREAMING_SNAKE_CASE__     (							self		,       a		,       a		,       a		,       a		,       a		,       a		,       a		,       a		,       a		,       )   ->							int:
						SCREAMING_SNAKE_CASE							=			FlaubertForSequenceClassification(a)
						model.to(a)
						model.eval()

						SCREAMING_SNAKE_CASE							=			model(a)
						SCREAMING_SNAKE_CASE							=			model(a		,       labels=a)

						self.parent.assertEqual(result.loss.shape		,       ())
						self.parent.assertEqual(result.logits.shape		,       (self.batch_size, self.type_sequence_label_size))






			def  SCREAMING_SNAKE_CASE__     (							self		,       a		,       a		,       a		,       a		,       a		,       a		,       a		,       a		,       a		,       )   ->							Any:
						SCREAMING_SNAKE_CASE							=			self.num_labels
						SCREAMING_SNAKE_CASE							=			FlaubertForTokenClassification(a)
						model.to(a)
						model.eval()

						SCREAMING_SNAKE_CASE							=			model(a		,       attention_mask=a		,       labels=a)
						self.parent.assertEqual(result.logits.shape		,       (self.batch_size, self.seq_length, self.num_labels))






			def  SCREAMING_SNAKE_CASE__     (							self		,       a		,       a		,       a		,       a		,       a		,       a		,       a		,       a		,       a		,       )   ->							Tuple:
						SCREAMING_SNAKE_CASE							=			self.num_choices
						SCREAMING_SNAKE_CASE							=			FlaubertForMultipleChoice(config=a)
						model.to(a)
						model.eval()
						SCREAMING_SNAKE_CASE							=			input_ids.unsqueeze(1).expand(-1		,       self.num_choices		,       -1).contiguous()
						SCREAMING_SNAKE_CASE							=			token_type_ids.unsqueeze(1).expand(-1		,       self.num_choices		,       -1).contiguous()
						SCREAMING_SNAKE_CASE							=			input_mask.unsqueeze(1).expand(-1		,       self.num_choices		,       -1).contiguous()
						SCREAMING_SNAKE_CASE							=			model(
						    a		,       attention_mask=a		,       token_type_ids=a		,       labels=a		,       )
						self.parent.assertEqual(result.logits.shape		,       (self.batch_size, self.num_choices))







			def  SCREAMING_SNAKE_CASE__     (							self)   ->							Tuple:
						SCREAMING_SNAKE_CASE							=			self.prepare_config_and_inputs()
						(
						    (
						    SCREAMING_SNAKE_CASE
						)					,    (
						    SCREAMING_SNAKE_CASE
						)					,    (
						    SCREAMING_SNAKE_CASE
						)					,    (
						    SCREAMING_SNAKE_CASE
						)					,    (
						    SCREAMING_SNAKE_CASE
						)					,    (
						    SCREAMING_SNAKE_CASE
						)					,    (
						    SCREAMING_SNAKE_CASE
						)					,    (
						    SCREAMING_SNAKE_CASE
						)					,    (
						    SCREAMING_SNAKE_CASE
						)					,    
						)							=			config_and_inputs
						SCREAMING_SNAKE_CASE							=			{
						    'input_ids': input_ids,
						    'token_type_ids': token_type_ids,
						    'lengths': input_lengths,
						    'attention_mask': input_mask,
						}
						return config, inputs_dict



@require_torch
class 		_snake_case	(	A__ ,     A__ ,     unittest.TestCase       ):
			_lowercase		:     Any        =				(
			    (
			        FlaubertModel,
			        FlaubertWithLMHeadModel,
			        FlaubertForQuestionAnswering,
			        FlaubertForQuestionAnsweringSimple,
			        FlaubertForSequenceClassification,
			        FlaubertForTokenClassification,
			        FlaubertForMultipleChoice,
			    )
			    if is_torch_available()
			    else ()
			)
			_lowercase		:     Any        =				(
			    {
			        '''feature-extraction''': FlaubertModel,
			        '''fill-mask''': FlaubertWithLMHeadModel,
			        '''question-answering''': FlaubertForQuestionAnsweringSimple,
			        '''text-classification''': FlaubertForSequenceClassification,
			        '''token-classification''': FlaubertForTokenClassification,
			        '''zero-shot''': FlaubertForSequenceClassification,
			    }
			    if is_torch_available()
			    else {}
			)






			def  SCREAMING_SNAKE_CASE__     (							self		,       a		,       a		,       a		,       a		,       a)   ->							Optional[int]:
						if (
						    pipeline_test_casse_name == "QAPipelineTests"
						    and tokenizer_name is not None
						    and not tokenizer_name.endswith('Fast')
						):
									# `QAPipelineTests` fails for a few models when the slower tokenizer are used.
									# (The slower tokenizers were never used for pipeline tests before the pipeline testing rework)
									# TODO: check (and possibly fix) the `QAPipelineTests` with slower tokenizer
									return True

						return False






			def  SCREAMING_SNAKE_CASE__     (							self		,       a		,       a		,       a=False)   ->							Any:
						SCREAMING_SNAKE_CASE							=			super()._prepare_for_class(a		,       a		,       return_labels=a)

						if return_labels:
									if model_class.__name__ == "FlaubertForQuestionAnswering":
												SCREAMING_SNAKE_CASE							=			torch.zeros(
												    self.model_tester.batch_size		,       dtype=torch.long		,       device=a)
												SCREAMING_SNAKE_CASE							=			torch.zeros(
												    self.model_tester.batch_size		,       dtype=torch.long		,       device=a)

						return inputs_dict






			def  SCREAMING_SNAKE_CASE__     (							self)   ->							Dict:
						SCREAMING_SNAKE_CASE							=			FlaubertModelTester(self)
						SCREAMING_SNAKE_CASE							=			ConfigTester(self		,       config_class=a		,       emb_dim=37)






			def  SCREAMING_SNAKE_CASE__     (							self)   ->							List[Any]:
						self.config_tester.run_common_tests()






			def  SCREAMING_SNAKE_CASE__     (							self)   ->							Any:
						SCREAMING_SNAKE_CASE							=			self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_flaubert_model(*a)






			def  SCREAMING_SNAKE_CASE__     (							self)   ->							Optional[Any]:
						SCREAMING_SNAKE_CASE							=			self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_flaubert_lm_head(*a)






			def  SCREAMING_SNAKE_CASE__     (							self)   ->							List[str]:
						SCREAMING_SNAKE_CASE							=			self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_flaubert_simple_qa(*a)






			def  SCREAMING_SNAKE_CASE__     (							self)   ->							List[str]:
						SCREAMING_SNAKE_CASE							=			self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_flaubert_qa(*a)






			def  SCREAMING_SNAKE_CASE__     (							self)   ->							Any:
						SCREAMING_SNAKE_CASE							=			self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_flaubert_sequence_classif(*a)






			def  SCREAMING_SNAKE_CASE__     (							self)   ->							Tuple:
						SCREAMING_SNAKE_CASE							=			self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_flaubert_token_classif(*a)






			def  SCREAMING_SNAKE_CASE__     (							self)   ->							List[str]:
						SCREAMING_SNAKE_CASE							=			self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_flaubert_multiple_choice(*a)






			@slow
			def  SCREAMING_SNAKE_CASE__     (							self)   ->							Tuple:
						for model_name in FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									SCREAMING_SNAKE_CASE							=			FlaubertModel.from_pretrained(a)
									self.assertIsNotNone(a)







			@slow
			@require_torch_gpu
			def  SCREAMING_SNAKE_CASE__     (							self)   ->							Tuple:
						SCREAMING_SNAKE_CASE					,    SCREAMING_SNAKE_CASE							=			self.model_tester.prepare_config_and_inputs_for_common()
						for model_class in self.all_model_classes:
									# FlauBertForMultipleChoice behaves incorrectly in JIT environments.
									if model_class == FlaubertForMultipleChoice:
												return

									SCREAMING_SNAKE_CASE							=			True
									SCREAMING_SNAKE_CASE							=			model_class(config=a)

									SCREAMING_SNAKE_CASE							=			self._prepare_for_class(a		,       a)
									SCREAMING_SNAKE_CASE							=			torch.jit.trace(
									    a		,       (inputs_dict['input_ids'].to('cpu'), inputs_dict['attention_mask'].to('cpu')))

									with tempfile.TemporaryDirectory() as tmp:
												torch.jit.save(a		,       os.path.join(a		,       'traced_model.pt'))
												SCREAMING_SNAKE_CASE							=			torch.jit.load(os.path.join(a		,       'traced_model.pt')		,       map_location=a)
												loaded(inputs_dict['input_ids'].to(a)		,       inputs_dict['attention_mask'].to(a))



@require_torch
class 		_snake_case	(	unittest.TestCase       ):






			@slow
			def  SCREAMING_SNAKE_CASE__     (							self)   ->							List[Any]:
						SCREAMING_SNAKE_CASE							=			FlaubertModel.from_pretrained('flaubert/flaubert_base_cased')
						SCREAMING_SNAKE_CASE							=			torch.tensor([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]])
						with torch.no_grad():
									SCREAMING_SNAKE_CASE							=			model(a)[0]
						SCREAMING_SNAKE_CASE							=			torch.Size((1, 11, 768))
						self.assertEqual(output.shape		,       a)
						SCREAMING_SNAKE_CASE							=			torch.tensor(
						    [[[-2.62_51, -1.42_98, -0.02_27], [-2.85_10, -1.63_87, 0.22_58], [-2.81_14, -1.18_32, -0.30_66]]])

						self.assertTrue(torch.allclose(output[:, :3, :3]		,       a		,       atol=1E-4))












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	(							snake_case__  : List[Any]						,snake_case__  : List[str]	):




     '''simple docstring'''






     __snake_case      :int 							=    []
     for part_id in partition_order:
          __snake_case      :int 							=    df.where(f'''SPARK_PARTITION_ID() = {part_id}'''	).collect()
          for row_idx, row in enumerate(snake_case__	):
               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	(							):




     '''simple docstring'''






     __snake_case      :List[str] 							=    pyspark.sql.SparkSession.builder.master("""local[*]"""	).appName("""pyspark"""	).getOrCreate()
     __snake_case      :Any 							=    spark.range(100	).repartition(1	)
     __snake_case      :int 							=    Spark(snake_case__	)
     # 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	(							):




     '''simple docstring'''






     __snake_case      :Tuple 							=    pyspark.sql.SparkSession.builder.master("""local[*]"""	).appName("""pyspark"""	).getOrCreate()
     __snake_case      :int 							=    spark.range(10	).repartition(2	)
     __snake_case      :str 							=    [1, 0]
     __snake_case      :List[Any] 							=    _generate_iterable_examples(snake_case__						,snake_case__	)  # Reverse the partitions.
     __snake_case      :Union[str, Any] 							=    _get_expected_row_ids_and_row_dicts_for_partition_order(snake_case__						,snake_case__	)

     for i, (row_id, row_dict) in enumerate(generate_fn()	):
          __snake_case							,			__snake_case      :Union[str, Any] 							=    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	(							):




     '''simple docstring'''






     __snake_case      :Union[str, Any] 							=    pyspark.sql.SparkSession.builder.master("""local[*]"""	).appName("""pyspark"""	).getOrCreate()
     __snake_case      :Tuple 							=    spark.range(10	).repartition(1	)
     __snake_case      :Dict 							=    SparkExamplesIterable(snake_case__	)
     assert it.n_shards == 1
     for i, (row_id, row_dict) in enumerate(snake_case__	):
          assert row_id == f'''0_{i}'''
          assert row_dict == {"id": i}






@require_not_windows
@require_dill_gt_0_3_2
def 	UpperCamelCase	(							):




     '''simple docstring'''






     __snake_case      :List[str] 							=    pyspark.sql.SparkSession.builder.master("""local[*]"""	).appName("""pyspark"""	).getOrCreate()
     __snake_case      :Union[str, Any] 							=    spark.range(30	).repartition(3	)
     # Mock the generator so that shuffle reverses the partition indices.
     with patch("""numpy.random.Generator"""	) as generator_mock:
          __snake_case      :Dict 							=    lambda snake_case__	: x.reverse()
          __snake_case      :int 							=    _get_expected_row_ids_and_row_dicts_for_partition_order(snake_case__						,[2, 1, 0]	)

          __snake_case      :Dict 							=    SparkExamplesIterable(snake_case__	).shuffle_data_sources(snake_case__	)
          assert shuffled_it.n_shards == 3
          for i, (row_id, row_dict) in enumerate(snake_case__	):
               __snake_case							,			__snake_case      :List[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 	UpperCamelCase	(							):




     '''simple docstring'''






     __snake_case      :Union[str, Any] 							=    pyspark.sql.SparkSession.builder.master("""local[*]"""	).appName("""pyspark"""	).getOrCreate()
     __snake_case      :Tuple 							=    spark.range(20	).repartition(4	)

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

     # Partitions 1 and 3
     __snake_case      :str 							=    SparkExamplesIterable(snake_case__	).shard_data_sources(worker_id=1						,num_workers=2	)
     assert shard_it_a.n_shards == 2
     __snake_case      :Optional[int] 							=    _get_expected_row_ids_and_row_dicts_for_partition_order(snake_case__						,[1, 3]	)
     for i, (row_id, row_dict) in enumerate(snake_case__	):
          __snake_case							,			__snake_case      :Dict 							=    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	(							):




     '''simple docstring'''






     __snake_case      :Union[str, Any] 							=    pyspark.sql.SparkSession.builder.master("""local[*]"""	).appName("""pyspark"""	).getOrCreate()
     __snake_case      :Tuple 							=    spark.range(100	).repartition(1	)
     __snake_case      :Dict 							=    Spark(snake_case__	)
     # 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


