






import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import BlenderbotSmallConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor


if is_flax_available():
   import os

   # The slow tests are often failing with OOM error on GPU
   # This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
   # but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
   UpperCAmelCase__						:Optional[int]          =			"""platform"""

   import jax
   import jax.numpy as jnp

   from transformers.models.blenderbot_small.modeling_flax_blenderbot_small import (
       FlaxBlenderbotSmallForConditionalGeneration,
       FlaxBlenderbotSmallModel,
       shift_tokens_right,
   )
def     __lowercase  (_lowercase,						_lowercase,						_lowercase=None,						_lowercase=None,						_lowercase=None,						_lowercase=None,						_lowercase=None,						_lowercase=None,						)   ->     Union[str, Any]:


     """simple docstring"""
     if attention_mask is None:
          __lowerCamelCase						:       List[str]				   =				np.where(input_ids != config.pad_token_id,						1,						0     )
     if decoder_attention_mask is None:
          __lowerCamelCase						:       Dict				   =				np.where(decoder_input_ids != config.pad_token_id,						1,						0     )
     if head_mask is None:
          __lowerCamelCase						:       Any				   =				np.ones((config.encoder_layers, config.encoder_attention_heads)     )
     if decoder_head_mask is None:
          __lowerCamelCase						:       Optional[Any]				   =				np.ones((config.decoder_layers, config.decoder_attention_heads)     )
     if cross_attn_head_mask is None:
          __lowerCamelCase						:       List[Any]				   =				np.ones((config.decoder_layers, config.decoder_attention_heads)     )
     return {
         "input_ids": input_ids,
         "decoder_input_ids": decoder_input_ids,
         "attention_mask": attention_mask,
         "decoder_attention_mask": attention_mask,
     }







class 	SCREAMING_SNAKE_CASE	:




       def __init__(					self      : Optional[int]	,    A__      : Any	,    A__      : int=13	,    A__      : List[Any]=7	,    A__      : Optional[int]=True	,    A__      : Tuple=False	,    A__      : str=99	,    A__      : int=16	,    A__      : Union[str, Any]=2	,    A__      : Optional[int]=4	,    A__      : Optional[int]=4	,    A__      : Dict="gelu"	,    A__      : Optional[Any]=0.1	,    A__      : int=0.1	,    A__      : str=32	,    A__      : List[str]=2	,    A__      : Optional[int]=1	,    A__      : Optional[Any]=0	,    A__      : Tuple=0.02	,    ):


            """simple docstring"""







            __lowerCamelCase						:       List[Any]				   =				parent
            __lowerCamelCase						:       Any				   =				batch_size
            __lowerCamelCase						:       int				   =				seq_length
            __lowerCamelCase						:       Optional[Any]				   =				is_training
            __lowerCamelCase						:       int				   =				use_labels
            __lowerCamelCase						:       Optional[int]				   =				vocab_size
            __lowerCamelCase						:       Optional[Any]				   =				hidden_size
            __lowerCamelCase						:       Optional[Any]				   =				num_hidden_layers
            __lowerCamelCase						:       List[str]				   =				num_attention_heads
            __lowerCamelCase						:       Any				   =				intermediate_size
            __lowerCamelCase						:       Dict				   =				hidden_act
            __lowerCamelCase						:       Dict				   =				hidden_dropout_prob
            __lowerCamelCase						:       Optional[Any]				   =				attention_probs_dropout_prob
            __lowerCamelCase						:       List[str]				   =				max_position_embeddings
            __lowerCamelCase						:       Union[str, Any]				   =				eos_token_id
            __lowerCamelCase						:       List[str]				   =				pad_token_id
            __lowerCamelCase						:       str				   =				bos_token_id
            __lowerCamelCase						:       Optional[Any]				   =				initializer_range




       def 					a_					(					self      : Dict					):


            """simple docstring"""







            __lowerCamelCase						:       List[Any]				   =				np.clip(ids_tensor([self.batch_size, self.seq_length - 1]	,    self.vocab_size					)	,    3	,    self.vocab_size					)
            __lowerCamelCase						:       Any				   =				np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)	,    dtype=np.intaa					))	,    -1					)

            __lowerCamelCase						:       Tuple				   =				shift_tokens_right(A__	,    1	,    2					)

            __lowerCamelCase						:       Union[str, Any]				   =				BlenderbotSmallConfig(
                vocab_size=self.vocab_size	,    d_model=self.hidden_size	,    encoder_layers=self.num_hidden_layers	,    decoder_layers=self.num_hidden_layers	,    encoder_attention_heads=self.num_attention_heads	,    decoder_attention_heads=self.num_attention_heads	,    encoder_ffn_dim=self.intermediate_size	,    decoder_ffn_dim=self.intermediate_size	,    dropout=self.hidden_dropout_prob	,    attention_dropout=self.attention_probs_dropout_prob	,    max_position_embeddings=self.max_position_embeddings	,    eos_token_id=self.eos_token_id	,    bos_token_id=self.bos_token_id	,    pad_token_id=self.pad_token_id	,    initializer_range=self.initializer_range	,    use_cache=A__	,    )
            __lowerCamelCase						:       Tuple				   =				prepare_blenderbot_inputs_dict(A__	,    A__	,    A__					)
            return config, inputs_dict




       def 					a_					(					self      : List[str]					):


            """simple docstring"""







            __lowerCamelCase						:       Optional[int]				   =				self.prepare_config_and_inputs()
            return config, inputs_dict




       def 					a_					(					self      : Tuple	,    A__      : str	,    A__      : int	,    A__      : int					):


            """simple docstring"""







            __lowerCamelCase						:       Tuple				   =				20
            __lowerCamelCase						:       str				   =				model_class_name(A__					)

            __lowerCamelCase						:       str				   =				model.encode(inputs_dict["""input_ids"""]					)

            __lowerCamelCase						:       List[str]				   =				(
                inputs_dict["""decoder_input_ids"""],
                inputs_dict["""decoder_attention_mask"""],
            )

            __lowerCamelCase						:       Optional[Any]				   =				model.init_cache(decoder_input_ids.shape[0]	,    A__	,    A__					)
            __lowerCamelCase						:       List[str]				   =				jnp.ones((decoder_input_ids.shape[0], max_decoder_length)	,    dtype="""i4"""					)

            __lowerCamelCase						:       Optional[int]				   =				jnp.broadcast_to(
                jnp.arange(decoder_input_ids.shape[-1] - 1					)[None, :]	,    (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)	,    )
            __lowerCamelCase						:       Tuple				   =				model.decode(
                decoder_input_ids[:, :-1]	,    A__	,    decoder_attention_mask=A__	,    past_key_values=A__	,    decoder_position_ids=A__	,    )

            __lowerCamelCase						:       int				   =				jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]	,    dtype="""i4"""					)
            __lowerCamelCase						:       Optional[int]				   =				model.decode(
                decoder_input_ids[:, -1:]	,    A__	,    decoder_attention_mask=A__	,    past_key_values=outputs_cache.past_key_values	,    decoder_position_ids=A__	,    )

            __lowerCamelCase						:       List[str]				   =				model.decode(A__	,    A__					)

            __lowerCamelCase						:       str				   =				np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])					)					)
            self.parent.assertTrue(diff < 1e-3	,    msg=f"Max diff is {diff}"					)




       def 					a_					(					self      : Any	,    A__      : Optional[int]	,    A__      : List[Any]	,    A__      : Optional[int]					):


            """simple docstring"""







            __lowerCamelCase						:       Union[str, Any]				   =				20
            __lowerCamelCase						:       Dict				   =				model_class_name(A__					)

            __lowerCamelCase						:       Any				   =				model.encode(inputs_dict["""input_ids"""]					)

            __lowerCamelCase						:       Dict				   =				(
                inputs_dict["""decoder_input_ids"""],
                inputs_dict["""decoder_attention_mask"""],
            )

            __lowerCamelCase						:       List[Any]				   =				jnp.concatenate(
                [
                    decoder_attention_mask,
                    jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])					),
                ]	,    axis=-1	,    )

            __lowerCamelCase						:       List[Any]				   =				model.init_cache(decoder_input_ids.shape[0]	,    A__	,    A__					)
            __lowerCamelCase						:       str				   =				jnp.broadcast_to(
                jnp.arange(decoder_input_ids.shape[-1] - 1					)[None, :]	,    (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)	,    )

            __lowerCamelCase						:       List[str]				   =				model.decode(
                decoder_input_ids[:, :-1]	,    A__	,    decoder_attention_mask=A__	,    past_key_values=A__	,    decoder_position_ids=A__	,    )
            __lowerCamelCase						:       str				   =				jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]	,    dtype="""i4"""					)
            __lowerCamelCase						:       Any				   =				model.decode(
                decoder_input_ids[:, -1:]	,    A__	,    past_key_values=outputs_cache.past_key_values	,    decoder_attention_mask=A__	,    decoder_position_ids=A__	,    )

            __lowerCamelCase						:       Union[str, Any]				   =				model.decode(A__	,    A__	,    decoder_attention_mask=A__					)

            __lowerCamelCase						:       Optional[int]				   =				np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])					)					)
            self.parent.assertTrue(diff < 1e-3	,    msg=f"Max diff is {diff}"					)







@require_flax
class 	SCREAMING_SNAKE_CASE	(      unittest.TestCase  ):
       snake_case__    :     List[str]   		=			9_9




       def 					a_					(					self      : int					):


            """simple docstring"""







            __lowerCamelCase						:       Dict				   =				np.array(
                [
                    [71, 82, 18, 33, 46, 91, 2],
                    [68, 34, 26, 58, 30, 82, 2],
                    [5, 97, 17, 39, 94, 40, 2],
                    [76, 83, 94, 25, 70, 78, 2],
                    [87, 59, 41, 35, 48, 66, 2],
                    [55, 13, 16, 58, 5, 2, 1],  # note padding
                    [64, 27, 31, 51, 12, 75, 2],
                    [52, 64, 86, 17, 83, 39, 2],
                    [48, 61, 9, 24, 71, 82, 2],
                    [26, 1, 60, 48, 22, 13, 2],
                    [21, 5, 62, 28, 14, 76, 2],
                    [45, 98, 37, 86, 59, 48, 2],
                    [70, 70, 50, 9, 28, 0, 2],
                ]	,    dtype=np.intaa	,    )

            __lowerCamelCase						:       Union[str, Any]				   =				input_ids.shape[0]
            __lowerCamelCase						:       Optional[int]				   =				BlenderbotSmallConfig(
                vocab_size=self.vocab_size	,    d_model=24	,    encoder_layers=2	,    decoder_layers=2	,    encoder_attention_heads=2	,    decoder_attention_heads=2	,    encoder_ffn_dim=32	,    decoder_ffn_dim=32	,    max_position_embeddings=48	,    eos_token_id=2	,    pad_token_id=1	,    bos_token_id=0	,    )
            return config, input_ids, batch_size




       def 					a_					(					self      : Optional[Any]					):


            """simple docstring"""







            __lowerCamelCase						:       Tuple				   =				self._get_config_and_data()
            __lowerCamelCase						:       Optional[int]				   =				FlaxBlenderbotSmallForConditionalGeneration(A__					)
            __lowerCamelCase						:       Tuple				   =				lm_model(input_ids=A__					)
            __lowerCamelCase						:       Union[str, Any]				   =				(batch_size, input_ids.shape[1], config.vocab_size)
            self.assertEqual(outputs["""logits"""].shape	,    A__					)




       def 					a_					(					self      : int					):


            """simple docstring"""







            __lowerCamelCase						:       Dict				   =				BlenderbotSmallConfig(
                vocab_size=self.vocab_size	,    d_model=14	,    encoder_layers=2	,    decoder_layers=2	,    encoder_attention_heads=2	,    decoder_attention_heads=2	,    encoder_ffn_dim=8	,    decoder_ffn_dim=8	,    max_position_embeddings=48	,    )
            __lowerCamelCase						:       Dict				   =				FlaxBlenderbotSmallForConditionalGeneration(A__					)
            __lowerCamelCase						:       int				   =				np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]]	,    dtype=np.intaa					)
            __lowerCamelCase						:       Any				   =				np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]]	,    dtype=np.intaa					)
            __lowerCamelCase						:       List[str]				   =				lm_model(input_ids=A__	,    decoder_input_ids=A__					)
            __lowerCamelCase						:       Any				   =				(*summary.shape, config.vocab_size)
            self.assertEqual(outputs["""logits"""].shape	,    A__					)




       def 					a_					(					self      : Union[str, Any]					):


            """simple docstring"""







            __lowerCamelCase						:       Optional[Any]				   =				np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]]	,    dtype=np.intaa					)
            __lowerCamelCase						:       Dict				   =				shift_tokens_right(A__	,    1	,    2					)
            __lowerCamelCase						:       List[Any]				   =				np.equal(A__	,    1					).astype(np.floataa					).sum()
            __lowerCamelCase						:       Any				   =				np.equal(A__	,    1					).astype(np.floataa					).sum()
            self.assertEqual(shifted.shape	,    input_ids.shape					)
            self.assertEqual(A__	,    n_pad_before - 1					)
            self.assertTrue(np.equal(shifted[:, 0]	,    2					).all()					)







@require_flax
class 	SCREAMING_SNAKE_CASE	(      lowerCAmelCase_    ,   unittest.TestCase    ,   lowerCAmelCase_  ):
       snake_case__    :     Optional[int]   		=			True
       snake_case__    :     int   		=			(
           (
               FlaxBlenderbotSmallModel,
               FlaxBlenderbotSmallForConditionalGeneration,
           )
           if is_flax_available()
           else ()
       )
       snake_case__    :     int   		=			(FlaxBlenderbotSmallForConditionalGeneration,) if is_flax_available() else ()




       def 					a_					(					self      : int					):


            """simple docstring"""







            __lowerCamelCase						:       Dict				   =				FlaxBlenderbotSmallModelTester(self					)




       def 					a_					(					self      : str					):


            """simple docstring"""







            __lowerCamelCase						:       Any				   =				self.model_tester.prepare_config_and_inputs()
            for model_class in self.all_model_classes:
                 self.model_tester.check_use_cache_forward(A__	,    A__	,    A__					)




       def 					a_					(					self      : Optional[int]					):


            """simple docstring"""







            __lowerCamelCase						:       str				   =				self.model_tester.prepare_config_and_inputs()
            for model_class in self.all_model_classes:
                 self.model_tester.check_use_cache_forward_with_attn_mask(A__	,    A__	,    A__					)




       def 					a_					(					self      : str					):


            """simple docstring"""







            __lowerCamelCase						:       Optional[int]				   =				self.model_tester.prepare_config_and_inputs_for_common()

            for model_class in self.all_model_classes:
                 with self.subTest(model_class.__name__					):
                      __lowerCamelCase						:       Optional[Any]				   =				self._prepare_for_class(A__	,    A__					)
                      __lowerCamelCase						:       str				   =				model_class(A__					)

                      @jax.jit
                      def encode_jitted(A__      : Union[str, Any]	,    A__      : int=None	,    **A__      : Any					):
                           return model.encode(input_ids=A__	,    attention_mask=A__					)

                      with self.subTest("""JIT Enabled"""					):
                           __lowerCamelCase						:       Dict				   =				encode_jitted(**A__					).to_tuple()

                      with self.subTest("""JIT Disabled"""					):
                           with jax.disable_jit():
                                __lowerCamelCase						:       Union[str, Any]				   =				encode_jitted(**A__					).to_tuple()

                      self.assertEqual(len(A__					)	,    len(A__					)					)
                      for jitted_output, output in zip(A__	,    A__					):
                           self.assertEqual(jitted_output.shape	,    output.shape					)




       def 					a_					(					self      : Any					):


            """simple docstring"""







            __lowerCamelCase						:       int				   =				self.model_tester.prepare_config_and_inputs_for_common()

            for model_class in self.all_model_classes:
                 with self.subTest(model_class.__name__					):
                      __lowerCamelCase						:       Any				   =				model_class(A__					)
                      __lowerCamelCase						:       List[str]				   =				model.encode(inputs_dict["""input_ids"""]	,    inputs_dict["""attention_mask"""]					)

                      __lowerCamelCase						:       int				   =				{
                          """decoder_input_ids""": inputs_dict["""decoder_input_ids"""],
                          """decoder_attention_mask""": inputs_dict["""decoder_attention_mask"""],
                          """encoder_outputs""": encoder_outputs,
                      }

                      @jax.jit
                      def decode_jitted(A__      : Tuple	,    A__      : int	,    A__      : Any					):
                           return model.decode(
                               decoder_input_ids=A__	,    decoder_attention_mask=A__	,    encoder_outputs=A__	,    )

                      with self.subTest("""JIT Enabled"""					):
                           __lowerCamelCase						:       Any				   =				decode_jitted(**A__					).to_tuple()

                      with self.subTest("""JIT Disabled"""					):
                           with jax.disable_jit():
                                __lowerCamelCase						:       Dict				   =				decode_jitted(**A__					).to_tuple()

                      self.assertEqual(len(A__					)	,    len(A__					)					)
                      for jitted_output, output in zip(A__	,    A__					):
                           self.assertEqual(jitted_output.shape	,    output.shape					)




       @slow
       def 					a_					(					self      : Dict					):


            """simple docstring"""







            for model_class_name in self.all_model_classes:
                 __lowerCamelCase						:       str				   =				model_class_name.from_pretrained("""facebook/blenderbot_small-90M"""					)
                 # FlaxBlenderbotForSequenceClassification expects eos token in input_ids
                 __lowerCamelCase						:       str				   =				np.ones((1, 1)					) * model.config.eos_token_id
                 __lowerCamelCase						:       Optional[int]				   =				model(A__					)
                 self.assertIsNotNone(A__					)











'''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     __lowercase  (_lowercase,						_lowercase     )   ->     List[str]:


     """simple docstring"""
     __lowerCamelCase						:       Union[str, Any]				   =				[]
     for part_id in partition_order:
          __lowerCamelCase						:       Union[str, Any]				   =				df.where(f"SPARK_PARTITION_ID() = {part_id}"     ).collect()
          for row_idx, row in enumerate(_lowercase     ):
               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     __lowercase  ()   ->     Optional[int]:


     """simple docstring"""
     __lowerCamelCase						:       int				   =				pyspark.sql.SparkSession.builder.master("""local[*]"""     ).appName("""pyspark"""     ).getOrCreate()
     __lowerCamelCase						:       List[str]				   =				spark.range(100     ).repartition(1     )
     __lowerCamelCase						:       str				   =				Spark(_lowercase     )
     # 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     __lowercase  ()   ->     Optional[Any]:


     """simple docstring"""
     __lowerCamelCase						:       Any				   =				pyspark.sql.SparkSession.builder.master("""local[*]"""     ).appName("""pyspark"""     ).getOrCreate()
     __lowerCamelCase						:       Dict				   =				spark.range(10     ).repartition(2     )
     __lowerCamelCase						:       List[Any]				   =				[1, 0]
     __lowerCamelCase						:       Any				   =				_generate_iterable_examples(_lowercase,						_lowercase     )  # Reverse the partitions.
     __lowerCamelCase						:       int				   =				_get_expected_row_ids_and_row_dicts_for_partition_order(_lowercase,						_lowercase     )

     for i, (row_id, row_dict) in enumerate(generate_fn()     ):
          __lowerCamelCase   ,      __lowerCamelCase						:       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     __lowercase  ()   ->     int:


     """simple docstring"""
     __lowerCamelCase						:       Dict				   =				pyspark.sql.SparkSession.builder.master("""local[*]"""     ).appName("""pyspark"""     ).getOrCreate()
     __lowerCamelCase						:       Union[str, Any]				   =				spark.range(10     ).repartition(1     )
     __lowerCamelCase						:       Dict				   =				SparkExamplesIterable(_lowercase     )
     assert it.n_shards == 1
     for i, (row_id, row_dict) in enumerate(_lowercase     ):
          assert row_id == f"0_{i}"
          assert row_dict == {"id": i}
@require_not_windows
@require_dill_gt_0_3_2
def     __lowercase  ()   ->     Tuple:


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

          __lowerCamelCase						:       Tuple				   =				SparkExamplesIterable(_lowercase     ).shuffle_data_sources(_lowercase     )
          assert shuffled_it.n_shards == 3
          for i, (row_id, row_dict) in enumerate(_lowercase     ):
               __lowerCamelCase   ,      __lowerCamelCase						:       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     __lowercase  ()   ->     str:


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

     # Partitions 0 and 2
     __lowerCamelCase						:       Union[str, Any]				   =				SparkExamplesIterable(_lowercase     ).shard_data_sources(worker_id=0,						num_workers=2     )
     assert shard_it_a.n_shards == 2
     __lowerCamelCase						:       Optional[int]				   =				_get_expected_row_ids_and_row_dicts_for_partition_order(_lowercase,						[0, 2]     )
     for i, (row_id, row_dict) in enumerate(_lowercase     ):
          __lowerCamelCase   ,      __lowerCamelCase						:       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
     __lowerCamelCase						:       List[str]				   =				SparkExamplesIterable(_lowercase     ).shard_data_sources(worker_id=1,						num_workers=2     )
     assert shard_it_a.n_shards == 2
     __lowerCamelCase						:       int				   =				_get_expected_row_ids_and_row_dicts_for_partition_order(_lowercase,						[1, 3]     )
     for i, (row_id, row_dict) in enumerate(_lowercase     ):
          __lowerCamelCase   ,      __lowerCamelCase						:       int				   =				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     __lowercase  ()   ->     int:


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



