



import multiprocessing
from typing import TYPE_CHECKING, Optional, Union

from .. import Dataset, Features, config
from ..formatting import query_table
from ..packaged_modules.sql.sql import Sql
from ..utils import logging
from .abc import AbstractDatasetInputStream


if TYPE_CHECKING:
							import sqlitea

							import sqlalchemy


class      __lowerCAmelCase     (		UpperCAmelCase_      ):
					"""simple docstring"""






					def __init__(     self					:       str				,					_snake_case					:       Union[str, "sqlalchemy.sql.Selectable"]				,					_snake_case					:       Union[str, "sqlalchemy.engine.Connection", "sqlalchemy.engine.Engine", "sqlite3.Connection"]				,					_snake_case					:       Optional[Features] = None				,					_snake_case					:       str = None				,					_snake_case					:       bool = False				,					**_snake_case					:       Optional[Any]				,					):






								"""simple docstring"""


								super().__init__(features=_snake_case				,					cache_dir=_snake_case				,					keep_in_memory=_snake_case				,					**_snake_case					)
								A__       =    Sql(
								    cache_dir=_snake_case				,					features=_snake_case				,					sql=_snake_case				,					con=_snake_case				,					**_snake_case				,					)



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






								"""simple docstring"""


								A__       =    None
								A__       =    None
								A__       =    None
								A__       =    None

								self.builder.download_and_prepare(
								    download_config=_snake_case				,					download_mode=_snake_case				,					verification_mode=_snake_case				,					base_path=_snake_case				,					)

								# Build dataset for splits
								A__       =    self.builder.as_dataset(
								    split='train'				,					verification_mode=_snake_case				,					in_memory=self.keep_in_memory					)
								return dataset







class      __lowerCAmelCase     :
					"""simple docstring"""






					def __init__(     self					:       List[Any]				,					_snake_case					:       Dataset				,					_snake_case					:       str				,					_snake_case					:       Union[str, "sqlalchemy.engine.Connection", "sqlalchemy.engine.Engine", "sqlite3.Connection"]				,					_snake_case					:       Optional[int] = None				,					_snake_case					:       Optional[int] = None				,					**_snake_case					:       Any				,					):






								"""simple docstring"""


								if num_proc is not None and num_proc <= 0:
											raise ValueError(F'''num_proc {num_proc} must be an integer > 0.'''					)

								A__       =    dataset
								A__       =    name
								A__       =    con
								A__       =    batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
								A__       =    num_proc
								A__       =    to_sql_kwargs



					def     _a							(     self					:       Any					):






								"""simple docstring"""


								A__       =    self.to_sql_kwargs.pop('sql'				,					_snake_case					)
								A__       =    self.to_sql_kwargs.pop('con'				,					_snake_case					)
								A__       =    self.to_sql_kwargs.pop('index'				,					_snake_case					)

								A__       =    self._write(index=_snake_case				,					**self.to_sql_kwargs					)
								return written



					def     _a							(     self					:       Any				,					_snake_case					:       Union[str, Any]					):






								"""simple docstring"""


								A__     , A__     , A__       =    args
								A__       =    {**to_sql_kwargs, 'if_exists': 'append'} if offset > 0 else to_sql_kwargs
								A__       =    query_table(
								    table=self.dataset.data				,					key=slice(_snake_case				,					offset + self.batch_size					)				,					indices=self.dataset._indices				,					)
								A__       =    batch.to_pandas()
								A__       =    df.to_sql(self.name				,					self.con				,					index=_snake_case				,					**_snake_case					)
								return num_rows or len(_snake_case					)



					def     _a							(     self					:       Optional[int]				,					_snake_case					:       Dict				,					**_snake_case					:       Dict					):






								"""simple docstring"""


								A__       =    0

								if self.num_proc is None or self.num_proc == 1:
											for offset in logging.tqdm(
											    range(0				,					len(self.dataset					)				,					self.batch_size					)				,					unit='ba'				,					disable=not logging.is_progress_bar_enabled()				,					desc='Creating SQL from Arrow format'				,					):
														written += self._batch_sql((offset, index, to_sql_kwargs)					)
								else:
											A__     , A__       =    len(self.dataset					), self.batch_size
											with multiprocessing.Pool(self.num_proc					) as pool:
														for num_rows in logging.tqdm(
														    pool.imap(
														        self._batch_sql				,					[(offset, index, to_sql_kwargs) for offset in range(0				,					_snake_case				,					_snake_case					)]				,					)				,					total=(num_rows // batch_size) + 1 if num_rows % batch_size else num_rows // batch_size				,					unit='ba'				,					disable=not logging.is_progress_bar_enabled()				,					desc='Creating SQL from Arrow format'				,					):
																	written += num_rows

								return written









import random
import unittest

import torch

from diffusers import IFInpaintingPipeline
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_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin


@skip_mps
class      __lowerCAmelCase     (		UpperCAmelCase_							,     UpperCAmelCase_							,     unittest.TestCase      ):
					"""simple docstring"""






					A__				:		Any			       =    IFInpaintingPipeline
					A__				:		Dict			       =    TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {"width", "height"}
					A__				:		List[Any]			       =    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
					A__				:		Dict			       =    PipelineTesterMixin.required_optional_params - {"latents"}



					def     _a							(     self					:       Any					):






								"""simple docstring"""


								return self._get_dummy_components()



					def     _a							(     self					:       Optional[int]				,					_snake_case					:       Any				,					_snake_case					:       str=0					):






								"""simple docstring"""


								if str(_snake_case					).startswith('mps'					):
											A__       =    torch.manual_seed(_snake_case					)
								else:
											A__       =    torch.Generator(device=_snake_case					).manual_seed(_snake_case					)

								A__       =    floats_tensor((1, 3, 32, 32)				,					rng=random.Random(_snake_case					)					).to(_snake_case					)
								A__       =    floats_tensor((1, 3, 32, 32)				,					rng=random.Random(_snake_case					)					).to(_snake_case					)

								A__       =    {
								    'prompt': 'A painting of a squirrel eating a burger',
								    'image': image,
								    'mask_image': mask_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     _a							(     self					:       Dict					):






								"""simple docstring"""


								self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3					)



					def     _a							(     self					:       int					):






								"""simple docstring"""


								self._test_save_load_optional_components()



					@unittest.skipIf(torch_device != 'cuda'				,					reason='float16 requires CUDA'					)
					def     _a							(     self					:       Optional[int]					):






								"""simple docstring"""


								super().test_save_load_floataa(expected_max_diff=1E-1					)



					def     _a							(     self					:       List[str]					):






								"""simple docstring"""


								self._test_attention_slicing_forward_pass(expected_max_diff=1E-2					)



					def     _a							(     self					:       Dict					):






								"""simple docstring"""


								self._test_save_load_local()



					def     _a							(     self					:       Optional[int]					):






								"""simple docstring"""


								self._test_inference_batch_single_identical(
								    expected_max_diff=1E-2				,					)




