





import os
import re
import shutil
import sys
import tempfile
import unittest

import black


_UpperCAmelCase       : Dict			     =       os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, """utils"""))

import check_copies  # noqa: E402


# This is the reference code that will be used in the tests.
# If DDPMSchedulerOutput is changed in scheduling_ddpm.py, this code needs to be manually updated.
_UpperCAmelCase       : Tuple			     =       """    \"\"\"
    Output class for the scheduler's step function output.

    Args:
        prev_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):
            Computed sample (x_{t-1}) of previous timestep. `prev_sample` should be used as next model input in the
            denoising loop.
        pred_original_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):
            The predicted denoised sample (x_{0}) based on the model output from the current timestep.
            `pred_original_sample` can be used to preview progress or for guidance.
    \"\"\"

    prev_sample: torch.FloatTensor
    pred_original_sample: Optional[torch.FloatTensor] = None
"""

class      lowerCAmelCase							(      unittest.TestCase    ):
 def   A_			(   self						:   str			)    ->			Optional[Any]:
     lowerCamelCase__					:   Dict      				=	tempfile.mkdtemp()
     os.makedirs(os.path.join(self.diffusers_dir    ,  'schedulers/'			)			)
     lowerCamelCase__					:   Optional[Any]      				=	self.diffusers_dir
     shutil.copy(
         os.path.join(UpperCAmelCase    ,  'src/diffusers/schedulers/scheduling_ddpm.py'			)    ,  os.path.join(self.diffusers_dir    ,  'schedulers/scheduling_ddpm.py'			)    ,  )
 def   A_			(   self						:   Optional[Any]			)    ->			str:
     lowerCamelCase__					:   Optional[int]      				=	'src/diffusers'
     shutil.rmtree(self.diffusers_dir			)
 def   A_			(   self						:   Optional[Any]    ,  UpperCAmelCase						:   Optional[int]    ,  UpperCAmelCase						:   Optional[int]    ,  UpperCAmelCase						:   List[Any]    ,  UpperCAmelCase						:   Optional[int]=None			)    ->			List[str]:
     lowerCamelCase__					:   Optional[int]      				=	comment + F"""\nclass {class_name}(nn.Module):\n""" + class_code
     if overwrite_result is not None:
         lowerCamelCase__					:   int      				=	comment + F"""\nclass {class_name}(nn.Module):\n""" + overwrite_result
     lowerCamelCase__					:   Optional[Any]      				=	black.Mode(target_versions={black.TargetVersion.PYaa}    ,  line_length=119			)
     lowerCamelCase__					:   Optional[int]      				=	black.format_str(UpperCAmelCase    ,  mode=UpperCAmelCase			)
     lowerCamelCase__					:   Tuple      				=	os.path.join(self.diffusers_dir    ,  'new_code.py'			)
     with open(UpperCAmelCase    ,  'w'    ,  newline='\n'			) as f:
         f.write(UpperCAmelCase			)
     if overwrite_result is None:
         self.assertTrue(len(check_copies.is_copy_consistent(UpperCAmelCase			)			) == 0			)
     else:
         check_copies.is_copy_consistent(f.name    ,  overwrite=UpperCAmelCase			)
         with open(UpperCAmelCase    ,  'r'			) as f:
             self.assertTrue(f.read()    ,  UpperCAmelCase			)
 def   A_			(   self						:   str			)    ->			Tuple:
     lowerCamelCase__					:   int      				=	check_copies.find_code_in_diffusers('schedulers.scheduling_ddpm.DDPMSchedulerOutput'			)
     self.assertEqual(UpperCAmelCase    ,  UpperCAmelCase			)







 def   A_			(   self						:   Optional[Any]			)    ->			str:
     # Base copy consistency
     self.check_copy_consistency(
         '# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput'    ,  'DDPMSchedulerOutput'    ,  REFERENCE_CODE + '\n'    ,  )

     # With no empty line at the end
     self.check_copy_consistency(
         '# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput'    ,  'DDPMSchedulerOutput'    ,  UpperCAmelCase    ,  )

     # Copy consistency with rename
     self.check_copy_consistency(
         '# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->Test'    ,  'TestSchedulerOutput'    ,  re.sub('DDPM'    ,  'Test'    ,  UpperCAmelCase			)    ,  )

     # Copy consistency with a really long name
     lowerCamelCase__					:   str      				=	'TestClassWithAReallyLongNameBecauseSomePeopleLikeThatForSomeReason'
     self.check_copy_consistency(
         F"""# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->{long_class_name}"""    ,  F"""{long_class_name}SchedulerOutput"""    ,  re.sub('Bert'    ,  UpperCAmelCase    ,  UpperCAmelCase			)    ,  )

     # Copy consistency with overwrite
     self.check_copy_consistency(
         '# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->Test'    ,  'TestSchedulerOutput'    ,  UpperCAmelCase    ,  overwrite_result=re.sub('DDPM'    ,  'Test'    ,  UpperCAmelCase			)    ,  )












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  SCREAMING_SNAKE_CASE   ( _UpperCAmelCase		,			_UpperCAmelCase				)    ->  Any:
    lowerCamelCase__					:   Any      				=	[]
    for part_id in partition_order:
        lowerCamelCase__					:   List[str]      				=	df.where(F"""SPARK_PARTITION_ID() = {part_id}"""				).collect()
        for row_idx, row in enumerate(_UpperCAmelCase				):
            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  SCREAMING_SNAKE_CASE   ( )    ->  List[Any]:
    lowerCamelCase__					:   List[str]      				=	pyspark.sql.SparkSession.builder.master('local[*]'				).appName('pyspark'				).getOrCreate()
    lowerCamelCase__					:   Any      				=	spark.range(100				).repartition(1				)
    lowerCamelCase__					:   str      				=	Spark(_UpperCAmelCase				)
    # 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  SCREAMING_SNAKE_CASE   ( )    ->  Tuple:
    lowerCamelCase__					:   Any      				=	pyspark.sql.SparkSession.builder.master('local[*]'				).appName('pyspark'				).getOrCreate()
    lowerCamelCase__					:   Optional[int]      				=	spark.range(10				).repartition(2				)
    lowerCamelCase__					:   Dict      				=	[1, 0]
    lowerCamelCase__					:   str      				=	_generate_iterable_examples(_UpperCAmelCase		,			_UpperCAmelCase				)  # Reverse the partitions.
    lowerCamelCase__					:   Any      				=	_get_expected_row_ids_and_row_dicts_for_partition_order(_UpperCAmelCase		,			_UpperCAmelCase				)

    for i, (row_id, row_dict) in enumerate(generate_fn()				):
        lowerCamelCase__				,							lowerCamelCase__					:   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  SCREAMING_SNAKE_CASE   ( )    ->  Optional[Any]:
    lowerCamelCase__					:   Dict      				=	pyspark.sql.SparkSession.builder.master('local[*]'				).appName('pyspark'				).getOrCreate()
    lowerCamelCase__					:   Dict      				=	spark.range(10				).repartition(1				)
    lowerCamelCase__					:   List[str]      				=	SparkExamplesIterable(_UpperCAmelCase				)
    assert it.n_shards == 1
    for i, (row_id, row_dict) in enumerate(_UpperCAmelCase				):
        assert row_id == F"""0_{i}"""
        assert row_dict == {"id": i}


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

        lowerCamelCase__					:   Tuple      				=	SparkExamplesIterable(_UpperCAmelCase				).shuffle_data_sources(_UpperCAmelCase				)
        assert shuffled_it.n_shards == 3
        for i, (row_id, row_dict) in enumerate(_UpperCAmelCase				):
            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  SCREAMING_SNAKE_CASE   ( )    ->  Optional[Any]:
    lowerCamelCase__					:   Optional[Any]      				=	pyspark.sql.SparkSession.builder.master('local[*]'				).appName('pyspark'				).getOrCreate()
    lowerCamelCase__					:   Tuple      				=	spark.range(20				).repartition(4				)

    # Partitions 0 and 2
    lowerCamelCase__					:   Optional[Any]      				=	SparkExamplesIterable(_UpperCAmelCase				).shard_data_sources(worker_id=0		,			num_workers=2				)
    assert shard_it_a.n_shards == 2
    lowerCamelCase__					:   List[str]      				=	_get_expected_row_ids_and_row_dicts_for_partition_order(_UpperCAmelCase		,			[0, 2]				)
    for i, (row_id, row_dict) in enumerate(_UpperCAmelCase				):
        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__					:   Dict      				=	SparkExamplesIterable(_UpperCAmelCase				).shard_data_sources(worker_id=1		,			num_workers=2				)
    assert shard_it_a.n_shards == 2
    lowerCamelCase__					:   Optional[int]      				=	_get_expected_row_ids_and_row_dicts_for_partition_order(_UpperCAmelCase		,			[1, 3]				)
    for i, (row_id, row_dict) in enumerate(_UpperCAmelCase				):
        lowerCamelCase__				,							lowerCamelCase__					:   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  SCREAMING_SNAKE_CASE   ( )    ->  Dict:
    lowerCamelCase__					:   int      				=	pyspark.sql.SparkSession.builder.master('local[*]'				).appName('pyspark'				).getOrCreate()
    lowerCamelCase__					:   str      				=	spark.range(100				).repartition(1				)
    lowerCamelCase__					:   Dict      				=	Spark(_UpperCAmelCase				)
    # 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





