






'''simple docstring'''







import random
import unittest

import torch

from diffusers import IFImgaImgSuperResolutionPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device

from ..pipeline_params import TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin





@skip_mps
class 	SCREAMING_SNAKE_CASE					(					_UpperCAmelCase	,   _UpperCAmelCase	,   unittest.TestCase       ):
			snake_case__  =				IFImgaImgSuperResolutionPipeline
			snake_case__  =				TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"width", "height"}
			snake_case__  =				TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({"original_image"}       )
			snake_case__  =				PipelineTesterMixin.required_optional_params - {"latents"}






			def 			SCREAMING_SNAKE_CASE      ( self      :			List[str]					)			->			str:
							return self._get_superresolution_dummy_components()






			def 			SCREAMING_SNAKE_CASE      ( self      :			List[Any]  ,						__SCREAMING_SNAKE_CASE      :			Optional[Any]  ,						__SCREAMING_SNAKE_CASE      :			Tuple=0					)			->			Union[str, Any]:






							if str(lowercase__					).startswith('''mps'''					):
											a_		:  Union[str, Any]											=      torch.manual_seed(lowercase__					)
							else:
											a_		:  Any											=      torch.Generator(device=lowercase__					).manual_seed(lowercase__					)

							a_		:  List[Any]											=      floats_tensor((1, 3, 32, 32)  ,						rng=random.Random(lowercase__					)					).to(lowercase__					)
							a_		:  Optional[int]											=      floats_tensor((1, 3, 16, 16)  ,						rng=random.Random(lowercase__					)					).to(lowercase__					)

							a_		:  Optional[Any]											=      {
							    "prompt": "A painting of a squirrel eating a burger",
							    "image": image,
							    "original_image": original_image,
							    "generator": generator,
							    "num_inference_steps": 2,
							    "output_type": "numpy",
							}

							return inputs






			@unittest.skipIf(
			    torch_device != '''cuda''' or not is_xformers_available()  ,						reason='''XFormers attention is only available with CUDA and `xformers` installed'''  ,						)
			def 			SCREAMING_SNAKE_CASE      ( self      :			List[Any]					)			->			str:
							self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3					)






			def 			SCREAMING_SNAKE_CASE      ( self      :			List[str]					)			->			Union[str, Any]:
							self._test_save_load_optional_components()






			@unittest.skipIf(torch_device != '''cuda'''  ,						reason='''float16 requires CUDA'''					)
			def 			SCREAMING_SNAKE_CASE      ( self      :			Union[str, Any]					)			->			Dict:
							super().test_save_load_floataa(expected_max_diff=1e-1					)






			def 			SCREAMING_SNAKE_CASE      ( self      :			Union[str, Any]					)			->			Dict:
							self._test_attention_slicing_forward_pass(expected_max_diff=1e-2					)






			def 			SCREAMING_SNAKE_CASE      ( self      :			int					)			->			Union[str, Any]:
							self._test_save_load_local()







			def 			SCREAMING_SNAKE_CASE      ( self      :			Dict					)			->			Any:
							self._test_inference_batch_single_identical(
							    expected_max_diff=1e-2  ,						)












'''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							:   List[str]						,		__A							:   List[Any]      ):
				a_		:  Any											=      []
				for part_id in partition_order:
								a_		:  str											=      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_		:  List[str]											=      pyspark.sql.SparkSession.builder.master('''local[*]'''      ).appName('''pyspark'''      ).getOrCreate()
				a_		:  Union[str, Any]											=      spark.range(1_00      ).repartition(1      )
				a_		:  Any											=      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_		:  List[Any]											=      pyspark.sql.SparkSession.builder.master('''local[*]'''      ).appName('''pyspark'''      ).getOrCreate()
				a_		:  int											=      spark.range(10      ).repartition(2      )
				a_		:  Tuple											=      [1, 0]
				a_		:  List[str]											=      _generate_iterable_examples(__A						,		__A      )  # Reverse the partitions.
				a_		:  int											=      _get_expected_row_ids_and_row_dicts_for_partition_order(__A						,		__A      )

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

								a_		:  str											=      SparkExamplesIterable(__A      ).shuffle_data_sources(__A      )
								assert shuffled_it.n_shards == 3
								for i, (row_id, row_dict) in enumerate(__A      ):
												a_				,			a_		:  Optional[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   (							):
				a_		:  int											=      pyspark.sql.SparkSession.builder.master('''local[*]'''      ).appName('''pyspark'''      ).getOrCreate()
				a_		:  List[str]											=      spark.range(20      ).repartition(4      )

				# Partitions 0 and 2
				a_		:  Dict											=      SparkExamplesIterable(__A      ).shard_data_sources(worker_id=0						,		num_workers=2      )
				assert shard_it_a.n_shards == 2
				a_		:  Optional[Any]											=      _get_expected_row_ids_and_row_dicts_for_partition_order(__A						,		[0, 2]      )
				for i, (row_id, row_dict) in enumerate(__A      ):
								a_				,			a_		:  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
				a_		:  List[Any]											=      SparkExamplesIterable(__A      ).shard_data_sources(worker_id=1						,		num_workers=2      )
				assert shard_it_a.n_shards == 2
				a_		:  Optional[int]											=      _get_expected_row_ids_and_row_dicts_for_partition_order(__A						,		[1, 3]      )
				for i, (row_id, row_dict) in enumerate(__A      ):
								a_				,			a_		:  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 		_UpperCAmelCase   (							):
				a_		:  Any											=      pyspark.sql.SparkSession.builder.master('''local[*]'''      ).appName('''pyspark'''      ).getOrCreate()
				a_		:  List[Any]											=      spark.range(1_00      ).repartition(1      )
				a_		:  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() == 1_00




