



import os
import posixpath
import uuid
from dataclasses import dataclass
from typing import TYPE_CHECKING, Iterable, List, Optional, Tuple, Union

import numpy as np
import pyarrow as pa

import datasets
from datasets.arrow_writer import ArrowWriter, ParquetWriter
from datasets.config import MAX_SHARD_SIZE
from datasets.filesystems import (
    is_remote_filesystem,
    rename,
)
from datasets.iterable_dataset import _BaseExamplesIterable
from datasets.utils.py_utils import convert_file_size_to_int


_lowercase: Optional[int]      			=		datasets.utils.logging.get_logger(__name__)

if TYPE_CHECKING:
				import pyspark



@dataclass
class 	_lowercase							(   datasets.BuilderConfig	):





	"""simple docstring"""
	__A							=							None






def     a(      A       :    Dict ,     A       :    Any ,     )		->       List[str]:
					"""simple docstring"""





					import pyspark

					def generate_fn():
										a					     =   df.select("*" ,     pyspark.sql.functions.spark_partition_id().alias("part_id"       )       )
										for partition_id in partition_order:
															a					     =   df_with_partition_id.select("*"       ).where(f'''part_id = {partition_id}'''       ).drop("part_id"       )
															a					     =   partition_df.collect()
															a					     =   0
															for row in rows:
																				yield f'''{partition_id}_{row_id}''', row.asDict()
																				row_id += 1

					return generate_fn



class 	_lowercase							(   _BaseExamplesIterable	):





	"""simple docstring"""




	def __init__(self		,       lowerCamelCase_		,       lowerCamelCase_=None		,       ):
						"""simple docstring"""

						a					     =   df
						a					     =   partition_order or range(self.df.rdd.getNumPartitions()		)
						a					     =   _generate_iterable_examples(self.df		,       self.partition_order		)




	def __iter__(self		):
						"""simple docstring"""

						yield from self.generate_examples_fn()




	def      UpperCamelCase_       (self		,       lowerCamelCase_		):
						"""simple docstring"""

						a					     =   list(range(self.df.rdd.getNumPartitions()		)		)
						generator.shuffle(lowerCamelCase_		)
						return SparkExamplesIterable(self.df		,       partition_order=lowerCamelCase_		)




	def      UpperCamelCase_       (self		,       lowerCamelCase_		,       lowerCamelCase_		):
						"""simple docstring"""

						a					     =   self.split_shard_indices_by_worker(lowerCamelCase_		,       lowerCamelCase_		)
						return SparkExamplesIterable(self.df		,       partition_order=lowerCamelCase_		)




	@property
	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						return len(self.partition_order		)




class 	_lowercase							(   datasets.DatasetBuilder	):





	"""simple docstring"""
	__A							=							SparkConfig




	def __init__(self		,       lowerCamelCase_		,       lowerCamelCase_ = None		,       lowerCamelCase_ = None		,       **lowerCamelCase_		,       ):
						"""simple docstring"""

						import pyspark

						a					     =   pyspark.sql.SparkSession.builder.getOrCreate()
						a					     =   df
						a					     =   working_dir

						super().__init__(
						    cache_dir=lowerCamelCase_		,       config_name=str(self.df.semanticHash()		)		,       **lowerCamelCase_		,       )




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						def create_cache_and_write_probe(lowerCamelCase_		):
											# makedirs with exist_ok will recursively create the directory. It will not throw an error if directories
											# already exist.
											os.makedirs(self._cache_dir		,       exist_ok=lowerCamelCase_		)
											a					     =   os.path.join(self._cache_dir		,       "fs_test" + uuid.uuida().hex		)
											# Opening the file in append mode will create a new file unless it already exists, in which case it will not
											# change the file contents.
											open(lowerCamelCase_		,       "a"		)
											return [probe_file]

						if self._spark.conf.get("spark.master"		,       ""		).startswith("local"		):
											return

						# If the cluster is multi-node, make sure that the user provided a cache_dir and that it is on an NFS
						# accessible to the driver.
						# TODO: Stream batches to the driver using ArrowCollectSerializer instead of throwing an error.
						if self._cache_dir:
											a					     =   (
											    self._spark.sparkContext.parallelize(range(1		)		,       1		).mapPartitions(lowerCamelCase_		).collect()
											)
											if os.path.isfile(probe[0]		):
																return

						raise ValueError(
						    "When using Dataset.from_spark on a multi-node cluster, the driver and all workers should be able to access cache_dir"		)




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						return datasets.DatasetInfo(features=self.config.features		)




	def      UpperCamelCase_       (self		,       lowerCamelCase_		):
						"""simple docstring"""

						return [datasets.SplitGenerator(name=datasets.Split.TRAIN		)]




	def      UpperCamelCase_       (self		,       lowerCamelCase_		):
						"""simple docstring"""

						import pyspark

						def get_arrow_batch_size(lowerCamelCase_		):
											for batch in it:
																yield pa.RecordBatch.from_pydict({"batch_bytes": [batch.nbytes]}		)

						a					     =   self.df.count()
						a					     =   df_num_rows if df_num_rows <= 100 else 100
						# Approximate the size of each row (in Arrow format) by averaging over a max-100-row sample.
						a					     =   (
						    self.df.limit(lowerCamelCase_		)
						    .repartition(1		)
						    .mapInArrow(lowerCamelCase_		,       "batch_bytes: long"		)
						    .agg(pyspark.sql.functions.sum("batch_bytes"		).alias("sample_bytes"		)		)
						    .collect()[0]
						    .sample_bytes
						    / sample_num_rows
						)
						a					     =   approx_bytes_per_row * df_num_rows
						if approx_total_size > max_shard_size:
											# Make sure there is at least one row per partition.
											a					     =   min(lowerCamelCase_		,       int(approx_total_size / max_shard_size		)		)
											a					     =   self.df.repartition(lowerCamelCase_		)




	def      UpperCamelCase_       (self		,       lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		,       ):
						"""simple docstring"""

						import pyspark

						a					     =   ParquetWriter if file_format == """parquet""" else ArrowWriter
						a					     =   os.path.join(self._working_dir		,       os.path.basename(lowerCamelCase_		)		) if self._working_dir else fpath
						a					     =   file_format == """parquet"""

						# Define these so that we don't reference self in write_arrow, which will result in a pickling error due to
						# pickling the SparkContext.
						a					     =   self.config.features
						a					     =   self._writer_batch_size
						a					     =   self._fs.storage_options

						def write_arrow(lowerCamelCase_		):
											# Within the same SparkContext, no two task attempts will share the same attempt ID.
											a					     =   pyspark.TaskContext().taskAttemptId()
											a					     =   next(lowerCamelCase_		,       lowerCamelCase_		)
											if first_batch is None:
																# Some partitions might not receive any data.
																return pa.RecordBatch.from_arrays(
																    [[task_id], [0], [0]]		,       names=["task_id", "num_examples", "num_bytes"]		,       )
											a					     =   0
											a					     =   writer_class(
											    features=lowerCamelCase_		,       path=working_fpath.replace("SSSSS"		,       F'''{shard_id:05d}'''		).replace("TTTTT"		,       F'''{task_id:05d}'''		)		,       writer_batch_size=lowerCamelCase_		,       storage_options=lowerCamelCase_		,       embed_local_files=lowerCamelCase_		,       )
											a					     =   pa.Table.from_batches([first_batch]		)
											writer.write_table(lowerCamelCase_		)
											for batch in it:
																if max_shard_size is not None and writer._num_bytes >= max_shard_size:
																					a					     =   writer.finalize()
																					writer.close()
																					yield pa.RecordBatch.from_arrays(
																					    [[task_id], [num_examples], [num_bytes]]		,       names=["task_id", "num_examples", "num_bytes"]		,       )
																					shard_id += 1
																					a					     =   writer_class(
																					    features=writer._features		,       path=working_fpath.replace("SSSSS"		,       F'''{shard_id:05d}'''		).replace("TTTTT"		,       F'''{task_id:05d}'''		)		,       writer_batch_size=lowerCamelCase_		,       storage_options=lowerCamelCase_		,       embed_local_files=lowerCamelCase_		,       )
																a					     =   pa.Table.from_batches([batch]		)
																writer.write_table(lowerCamelCase_		)

											if writer._num_bytes > 0:
																a					     =   writer.finalize()
																writer.close()
																yield pa.RecordBatch.from_arrays(
																    [[task_id], [num_examples], [num_bytes]]		,       names=["task_id", "num_examples", "num_bytes"]		,       )

											if working_fpath != fpath:
																for file in os.listdir(os.path.dirname(lowerCamelCase_		)		):
																					a					     =   os.path.join(os.path.dirname(lowerCamelCase_		)		,       os.path.basename(lowerCamelCase_		)		)
																					shutil.move(lowerCamelCase_		,       lowerCamelCase_		)

						a					     =   (
						    self.df.mapInArrow(lowerCamelCase_		,       "task_id: long, num_examples: long, num_bytes: long"		)
						    .groupBy("task_id"		)
						    .agg(
						        pyspark.sql.functions.sum("num_examples"		).alias("total_num_examples"		)		,       pyspark.sql.functions.sum("num_bytes"		).alias("total_num_bytes"		)		,       pyspark.sql.functions.count("num_bytes"		).alias("num_shards"		)		,       pyspark.sql.functions.collect_list("num_examples"		).alias("shard_lengths"		)		,       )
						    .collect()
						)
						for row in stats:
											yield row.task_id, (row.total_num_examples, row.total_num_bytes, row.num_shards, row.shard_lengths)




	def      UpperCamelCase_       (self		,       lowerCamelCase_		,       lowerCamelCase_ = "arrow"		,       lowerCamelCase_ = None		,       lowerCamelCase_ = None		,       **lowerCamelCase_		,       ):
						"""simple docstring"""

						self._validate_cache_dir()

						a					     =   convert_file_size_to_int(max_shard_size or MAX_SHARD_SIZE		)
						self._repartition_df_if_needed(lowerCamelCase_		)
						a					     =   not is_remote_filesystem(self._fs		)
						a					     =   os.path.join if is_local else posixpath.join

						a					     =   """-TTTTT-SSSSS-of-NNNNN"""
						a					     =   F'''{self.name}-{split_generator.name}{SUFFIX}.{file_format}'''
						a					     =   path_join(self._output_dir		,       lowerCamelCase_		)

						a					     =   0
						a					     =   0
						a					     =   0
						a					     =   []
						a					     =   []

						for task_id, content in self._prepare_split_single(lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		):
											(
											    a     
											)					     =   content
											if num_bytes > 0:
																total_num_examples += num_examples
																total_num_bytes += num_bytes
																total_shards += num_shards
																task_id_and_num_shards.append((task_id, num_shards)		)
																all_shard_lengths.extend(lowerCamelCase_		)

						a					     =   total_num_examples
						a					     =   total_num_bytes

						# should rename everything at the end
						logger.debug(F'''Renaming {total_shards} shards.'''		)
						if total_shards > 1:
											a					     =   all_shard_lengths

											# Define fs outside of _rename_shard so that we don't reference self in the function, which will result in a
											# pickling error due to pickling the SparkContext.
											a					     =   self._fs

											# use the -SSSSS-of-NNNNN pattern
											def _rename_shard(
											    lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		,       ):
																rename(
																    lowerCamelCase_		,       fpath.replace("SSSSS"		,       F'''{shard_id:05d}'''		).replace("TTTTT"		,       F'''{task_id:05d}'''		)		,       fpath.replace("TTTTT-SSSSS"		,       F'''{global_shard_id:05d}'''		).replace("NNNNN"		,       F'''{total_shards:05d}'''		)		,       )

											a					     =   []
											a					     =   0
											for i in range(len(lowerCamelCase_		)		):
																a					     =   task_id_and_num_shards[i]
																for shard_id in range(lowerCamelCase_		):
																					args.append([task_id, shard_id, global_shard_id]		)
																					global_shard_id += 1
											self._spark.sparkContext.parallelize(lowerCamelCase_		,       len(lowerCamelCase_		)		).map(lambda lowerCamelCase_		: _rename_shard(*lowerCamelCase_		)		).collect()
						else:
											# don't use any pattern
											a					     =   0
											a					     =   task_id_and_num_shards[0][0]
											self._rename(
											    fpath.replace("SSSSS"		,       F'''{shard_id:05d}'''		).replace("TTTTT"		,       F'''{task_id:05d}'''		)		,       fpath.replace(lowerCamelCase_		,       ""		)		,       )




	def      UpperCamelCase_       (self		,       lowerCamelCase_		,       ):
						"""simple docstring"""

						return SparkExamplesIterable(self.df		)





'''simple docstring'''




import gc
import random
import tempfile
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMInverseScheduler,
    DDIMScheduler,
    DPMSolverMultistepInverseScheduler,
    DPMSolverMultistepScheduler,
    StableDiffusionDiffEditPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import load_image, slow
from diffusers.utils.testing_utils import enable_full_determinism, floats_tensor, require_torch_gpu, torch_device

from ..pipeline_params import TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()

class 			UpperCamelCase_ (							__magic_name__      ,     __magic_name__      ,     unittest.TestCase			):
	lowercase          = StableDiffusionDiffEditPipeline
	lowercase          = TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {'height', 'width', 'image'} | {'image_latents'}
	lowercase          = TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS - {'image'} | {'image_latents'}
	lowercase          = frozenset(
	    []			)  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
	lowercase          = frozenset([]			)





	def  _lowercase(  self    )			->			Optional[int]:
								torch.manual_seed(0    )
								UpperCAmelCase     :		Dict							     =		UNetaDConditionModel(
								    block_out_channels=(32, 64)		,		layers_per_block=2		,		sample_size=32		,		in_channels=4		,		out_channels=4		,		down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""")		,		up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""")		,		cross_attention_dim=32		,		attention_head_dim=(2, 4)		,		use_linear_projection=A		,		)
								UpperCAmelCase     :		int							     =		DDIMScheduler(
								    beta_start=0.0_0_0_8_5		,		beta_end=0.0_1_2		,		beta_schedule="""scaled_linear"""		,		clip_sample=A		,		set_alpha_to_one=A		,		)
								UpperCAmelCase     :		List[Any]							     =		DDIMInverseScheduler(
								    beta_start=0.0_0_0_8_5		,		beta_end=0.0_1_2		,		beta_schedule="""scaled_linear"""		,		clip_sample=A		,		set_alpha_to_zero=A		,		)
								torch.manual_seed(0    )
								UpperCAmelCase     :		int							     =		AutoencoderKL(
								    block_out_channels=[32, 64]		,		in_channels=3		,		out_channels=3		,		down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""]		,		up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""]		,		latent_channels=4		,		sample_size=128		,		)
								torch.manual_seed(0    )
								UpperCAmelCase     :		Optional[int]							     =		CLIPTextConfig(
								    bos_token_id=0		,		eos_token_id=2		,		hidden_size=32		,		intermediate_size=37		,		layer_norm_eps=1e-05		,		num_attention_heads=4		,		num_hidden_layers=5		,		pad_token_id=1		,		vocab_size=1000		,		hidden_act="""gelu"""		,		projection_dim=512		,		)
								UpperCAmelCase     :		Optional[Any]							     =		CLIPTextModel(A    )
								UpperCAmelCase     :		List[str]							     =		CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""    )

								UpperCAmelCase     :		int							     =		{
								    """unet""": unet,
								    """scheduler""": scheduler,
								    """inverse_scheduler""": inverse_scheduler,
								    """vae""": vae,
								    """text_encoder""": text_encoder,
								    """tokenizer""": tokenizer,
								    """safety_checker""": None,
								    """feature_extractor""": None,
								}

								return components





	def  _lowercase(  self		,		A		,		A=0    )			->			Optional[Any]:
								UpperCAmelCase     :		Any							     =		floats_tensor((1, 16, 16)		,		rng=random.Random(A    )    ).to(A    )
								UpperCAmelCase     :		Any							     =		floats_tensor((1, 2, 4, 16, 16)		,		rng=random.Random(A    )    ).to(A    )
								if str(A    ).startswith("""mps"""    ):
															UpperCAmelCase     :		List[Any]							     =		torch.manual_seed(A    )
								else:
															UpperCAmelCase     :		List[Any]							     =		torch.Generator(device=A    ).manual_seed(A    )
								UpperCAmelCase     :		int							     =		{
								    """prompt""": """a dog and a newt""",
								    """mask_image""": mask,
								    """image_latents""": latents,
								    """generator""": generator,
								    """num_inference_steps""": 2,
								    """inpaint_strength""": 1.0,
								    """guidance_scale""": 6.0,
								    """output_type""": """numpy""",
								}

								return inputs





	def  _lowercase(  self		,		A		,		A=0    )			->			Optional[int]:
								UpperCAmelCase     :		Optional[Any]							     =		floats_tensor((1, 3, 32, 32)		,		rng=random.Random(A    )    ).to(A    )
								UpperCAmelCase     :		Union[str, Any]							     =		image.cpu().permute(0		,		2		,		3		,		1    )[0]
								UpperCAmelCase     :		int							     =		Image.fromarray(np.uinta(A    )    ).convert("""RGB"""    )
								if str(A    ).startswith("""mps"""    ):
															UpperCAmelCase     :		Optional[int]							     =		torch.manual_seed(A    )
								else:
															UpperCAmelCase     :		Optional[Any]							     =		torch.Generator(device=A    ).manual_seed(A    )
								UpperCAmelCase     :		Any							     =		{
								    """image""": image,
								    """source_prompt""": """a cat and a frog""",
								    """target_prompt""": """a dog and a newt""",
								    """generator""": generator,
								    """num_inference_steps""": 2,
								    """num_maps_per_mask""": 2,
								    """mask_encode_strength""": 1.0,
								    """guidance_scale""": 6.0,
								    """output_type""": """numpy""",
								}

								return inputs





	def  _lowercase(  self		,		A		,		A=0    )			->			str:
								UpperCAmelCase     :		int							     =		floats_tensor((1, 3, 32, 32)		,		rng=random.Random(A    )    ).to(A    )
								UpperCAmelCase     :		str							     =		image.cpu().permute(0		,		2		,		3		,		1    )[0]
								UpperCAmelCase     :		int							     =		Image.fromarray(np.uinta(A    )    ).convert("""RGB"""    )
								if str(A    ).startswith("""mps"""    ):
															UpperCAmelCase     :		Optional[int]							     =		torch.manual_seed(A    )
								else:
															UpperCAmelCase     :		Optional[int]							     =		torch.Generator(device=A    ).manual_seed(A    )
								UpperCAmelCase     :		str							     =		{
								    """image""": image,
								    """prompt""": """a cat and a frog""",
								    """generator""": generator,
								    """num_inference_steps""": 2,
								    """inpaint_strength""": 1.0,
								    """guidance_scale""": 6.0,
								    """decode_latents""": True,
								    """output_type""": """numpy""",
								}
								return inputs





	def  _lowercase(  self    )			->			List[Any]:
								if not hasattr(self.pipeline_class		,		"""_optional_components"""    ):
															return

								UpperCAmelCase     :		Dict							     =		self.get_dummy_components()
								UpperCAmelCase     :		int							     =		self.pipeline_class(**A    )
								pipe.to(A    )
								pipe.set_progress_bar_config(disable=A    )

								# set all optional components to None and update pipeline config accordingly
								for optional_component in pipe._optional_components:
															setattr(A		,		A		,		A    )
								pipe.register_modules(**{optional_component: None for optional_component in pipe._optional_components}    )

								UpperCAmelCase     :		Any							     =		self.get_dummy_inputs(A    )
								UpperCAmelCase     :		Optional[Any]							     =		pipe(**A    )[0]

								with tempfile.TemporaryDirectory() as tmpdir:
															pipe.save_pretrained(A    )
															UpperCAmelCase     :		Dict							     =		self.pipeline_class.from_pretrained(A    )
															pipe_loaded.to(A    )
															pipe_loaded.set_progress_bar_config(disable=A    )

								for optional_component in pipe._optional_components:
															self.assertTrue(
															    getattr(A		,		A    ) is None		,		f'''`{optional_component}` did not stay set to None after loading.'''		,		)

								UpperCAmelCase     :		Union[str, Any]							     =		self.get_dummy_inputs(A    )
								UpperCAmelCase     :		Tuple							     =		pipe_loaded(**A    )[0]

								UpperCAmelCase     :		Optional[Any]							     =		np.abs(output - output_loaded    ).max()
								self.assertLess(A		,		1e-4    )





	def  _lowercase(  self    )			->			Optional[int]:
								UpperCAmelCase     :		Optional[int]							     =		"""cpu"""

								UpperCAmelCase     :		Optional[Any]							     =		self.get_dummy_components()
								UpperCAmelCase     :		List[str]							     =		self.pipeline_class(**A    )
								pipe.to(A    )
								pipe.set_progress_bar_config(disable=A    )

								UpperCAmelCase     :		Union[str, Any]							     =		self.get_dummy_mask_inputs(A    )
								UpperCAmelCase     :		List[Any]							     =		pipe.generate_mask(**A    )
								UpperCAmelCase     :		Dict							     =		mask[0, -3:, -3:]

								self.assertEqual(mask.shape		,		(1, 16, 16)    )
								UpperCAmelCase     :		Optional[int]							     =		np.array([0] * 9    )
								UpperCAmelCase     :		Union[str, Any]							     =		np.abs(mask_slice.flatten() - expected_slice    ).max()
								self.assertLessEqual(A		,		1e-3    )
								self.assertEqual(mask[0, -3, -4]		,		0    )





	def  _lowercase(  self    )			->			Optional[Any]:
								UpperCAmelCase     :		Optional[Any]							     =		"""cpu"""

								UpperCAmelCase     :		List[str]							     =		self.get_dummy_components()
								UpperCAmelCase     :		Optional[Any]							     =		self.pipeline_class(**A    )
								pipe.to(A    )
								pipe.set_progress_bar_config(disable=A    )

								UpperCAmelCase     :		Optional[int]							     =		self.get_dummy_inversion_inputs(A    )
								UpperCAmelCase     :		List[str]							     =		pipe.invert(**A    ).images
								UpperCAmelCase     :		Union[str, Any]							     =		image[0, -1, -3:, -3:]

								self.assertEqual(image.shape		,		(2, 32, 32, 3)    )
								UpperCAmelCase     :		Dict							     =		np.array(
								    [0.5_1_5_0, 0.5_1_3_4, 0.5_0_4_3, 0.5_3_7_6, 0.4_6_9_4, 0.5_1_0_5_0, 0.5_0_1_5, 0.4_4_0_7, 0.4_7_9_9]		,		)
								UpperCAmelCase     :		Dict							     =		np.abs(image_slice.flatten() - expected_slice    ).max()
								self.assertLessEqual(A		,		1e-3    )





	def  _lowercase(  self    )			->			Dict:
								super().test_inference_batch_single_identical(expected_max_diff=5e-3    )






	def  _lowercase(  self    )			->			int:
								UpperCAmelCase     :		List[Any]							     =		"""cpu"""

								UpperCAmelCase     :		int							     =		self.get_dummy_components()

								UpperCAmelCase     :		List[Any]							     =		{"""beta_start""": 0.0_0_0_8_5, """beta_end""": 0.0_1_2, """beta_schedule""": """scaled_linear"""}
								UpperCAmelCase     :		int							     =		DPMSolverMultistepScheduler(**A    )
								UpperCAmelCase     :		int							     =		DPMSolverMultistepInverseScheduler(**A    )

								UpperCAmelCase     :		List[str]							     =		self.pipeline_class(**A    )
								pipe.to(A    )
								pipe.set_progress_bar_config(disable=A    )

								UpperCAmelCase     :		Tuple							     =		self.get_dummy_inversion_inputs(A    )
								UpperCAmelCase     :		Any							     =		pipe.invert(**A    ).images
								UpperCAmelCase     :		Dict							     =		image[0, -1, -3:, -3:]

								self.assertEqual(image.shape		,		(2, 32, 32, 3)    )
								UpperCAmelCase     :		Any							     =		np.array(
								    [0.5_1_5_0, 0.5_1_3_4, 0.5_0_4_3, 0.5_3_7_6, 0.4_6_9_4, 0.5_1_0_5_0, 0.5_0_1_5, 0.4_4_0_7, 0.4_7_9_9]		,		)
								UpperCAmelCase     :		Optional[int]							     =		np.abs(image_slice.flatten() - expected_slice    ).max()
								self.assertLessEqual(A		,		1e-3    )




@require_torch_gpu
@slow
class 			UpperCamelCase_ (							unittest.TestCase			):





	def  _lowercase(  self    )			->			int:
								super().tearDown()
								gc.collect()
								torch.cuda.empty_cache()





	@classmethod
	def  _lowercase(  cls    )			->			Dict:
								UpperCAmelCase     :		Tuple							     =		load_image(
								    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/diffedit/fruit.png"""    )

								UpperCAmelCase     :		Union[str, Any]							     =		raw_image.convert("""RGB"""    ).resize((768, 768)    )

								UpperCAmelCase     :		List[str]							     =		raw_image





	def  _lowercase(  self    )			->			Optional[Any]:
								UpperCAmelCase     :		Dict							     =		torch.manual_seed(0    )

								UpperCAmelCase     :		int							     =		StableDiffusionDiffEditPipeline.from_pretrained(
								    """stabilityai/stable-diffusion-2-1"""		,		safety_checker=A		,		torch_dtype=torch.floataa    )
								UpperCAmelCase     :		Optional[int]							     =		DDIMScheduler.from_config(pipe.scheduler.config    )
								UpperCAmelCase     :		Optional[int]							     =		DDIMInverseScheduler.from_config(pipe.scheduler.config    )
								pipe.enable_model_cpu_offload()
								pipe.set_progress_bar_config(disable=A    )

								UpperCAmelCase     :		Tuple							     =		"""a bowl of fruit"""
								UpperCAmelCase     :		List[Any]							     =		"""a bowl of pears"""

								UpperCAmelCase     :		str							     =		pipe.generate_mask(
								    image=self.raw_image		,		source_prompt=A		,		target_prompt=A		,		generator=A		,		)

								UpperCAmelCase     :		Tuple							     =		pipe.invert(
								    prompt=A		,		image=self.raw_image		,		inpaint_strength=0.7		,		generator=A    ).latents

								UpperCAmelCase     :		Any							     =		pipe(
								    prompt=A		,		mask_image=A		,		image_latents=A		,		generator=A		,		negative_prompt=A		,		inpaint_strength=0.7		,		output_type="""numpy"""		,		).images[0]

								UpperCAmelCase     :		List[str]							     =		(
								    np.array(
								        load_image(
								            """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
								            """/diffedit/pears.png"""    ).resize((768, 768)    )    )
								    / 255
								)
								assert np.abs((expected_image - image).max()    ) < 5e-1






	def  _lowercase(  self    )			->			Optional[Any]:
								UpperCAmelCase     :		Optional[Any]							     =		torch.manual_seed(0    )

								UpperCAmelCase     :		Union[str, Any]							     =		StableDiffusionDiffEditPipeline.from_pretrained(
								    """stabilityai/stable-diffusion-2-1"""		,		safety_checker=A		,		torch_dtype=torch.floataa    )
								UpperCAmelCase     :		List[str]							     =		DPMSolverMultistepScheduler.from_config(pipe.scheduler.config    )
								UpperCAmelCase     :		Any							     =		DPMSolverMultistepInverseScheduler.from_config(pipe.scheduler.config    )
								pipe.enable_model_cpu_offload()
								pipe.set_progress_bar_config(disable=A    )

								UpperCAmelCase     :		int							     =		"""a bowl of fruit"""
								UpperCAmelCase     :		int							     =		"""a bowl of pears"""

								UpperCAmelCase     :		str							     =		pipe.generate_mask(
								    image=self.raw_image		,		source_prompt=A		,		target_prompt=A		,		generator=A		,		)

								UpperCAmelCase     :		Any							     =		pipe.invert(
								    prompt=A		,		image=self.raw_image		,		inpaint_strength=0.7		,		generator=A		,		num_inference_steps=25		,		).latents

								UpperCAmelCase     :		str							     =		pipe(
								    prompt=A		,		mask_image=A		,		image_latents=A		,		generator=A		,		negative_prompt=A		,		inpaint_strength=0.7		,		num_inference_steps=25		,		output_type="""numpy"""		,		).images[0]

								UpperCAmelCase     :		Tuple							     =		(
								    np.array(
								        load_image(
								            """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
								            """/diffedit/pears.png"""    ).resize((768, 768)    )    )
								    / 255
								)
								assert np.abs((expected_image - image).max()    ) < 5e-1

