






"""simple docstring"""






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		,			lowercase_  :						Union[str, "sqlalchemy.sql.Selectable"]		,			lowercase_  :						Union[str, "sqlalchemy.engine.Connection", "sqlalchemy.engine.Engine", "sqlite3.Connection"]		,			lowercase_  :						Optional[Features] = None		,			lowercase_  :						str = None		,			lowercase_  :						bool = False		,			**lowercase_  :						Tuple		,			):




          '''simple docstring'''


          super().__init__(features=lowercase_		,			cache_dir=lowercase_		,			keep_in_memory=lowercase_		,			**lowercase_)
          SCREAMING_SNAKE_CASE_   :						List[Any]							=	Sql(
              cache_dir=lowercase_		,			features=lowercase_		,			sql=lowercase_		,			con=lowercase_		,			**lowercase_		,			)



      def _SCREAMING_SNAKE_CASE				(		self  :						Any):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						str							=	None
          SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	None
          SCREAMING_SNAKE_CASE_   :						List[Any]							=	None
          SCREAMING_SNAKE_CASE_   :						str							=	None

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

          # Build dataset for splits
          SCREAMING_SNAKE_CASE_   :						List[str]							=	self.builder.as_dataset(
              split='''train'''		,			verification_mode=lowercase_		,			in_memory=self.keep_in_memory)
          return dataset




class 							lowerCAmelCase__       :






      '''simple docstring'''

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




          '''simple docstring'''


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

          SCREAMING_SNAKE_CASE_   :						Optional[int]							=	dataset
          SCREAMING_SNAKE_CASE_   :						List[str]							=	name
          SCREAMING_SNAKE_CASE_   :						str							=	con
          SCREAMING_SNAKE_CASE_   :						List[Any]							=	batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
          SCREAMING_SNAKE_CASE_   :						Optional[int]							=	num_proc
          SCREAMING_SNAKE_CASE_   :						List[Any]							=	to_sql_kwargs
      def _SCREAMING_SNAKE_CASE				(		self  :						str):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						str							=	self.to_sql_kwargs.pop('''sql'''		,			lowercase_)
          SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	self.to_sql_kwargs.pop('''con'''		,			lowercase_)
          SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	self.to_sql_kwargs.pop('''index'''		,			lowercase_)

          SCREAMING_SNAKE_CASE_   :						List[str]							=	self._write(index=lowercase_		,			**self.to_sql_kwargs)
          return written
      def _SCREAMING_SNAKE_CASE				(		self  :						int		,			lowercase_  :						Optional[Any]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_   :						Tuple							=	args
          SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	{**to_sql_kwargs, '''if_exists''': '''append'''} if offset > 0 else to_sql_kwargs
          SCREAMING_SNAKE_CASE_   :						str							=	query_table(
              table=self.dataset.data		,			key=slice(lowercase_		,			offset + self.batch_size)		,			indices=self.dataset._indices		,			)
          SCREAMING_SNAKE_CASE_   :						int							=	batch.to_pandas()
          SCREAMING_SNAKE_CASE_   :						str							=	df.to_sql(self.name		,			self.con		,			index=lowercase_		,			**lowercase_)
          return num_rows or len(lowercase_)



      def _SCREAMING_SNAKE_CASE				(		self  :						List[Any]		,			lowercase_  :						Tuple		,			**lowercase_  :						Optional[int]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						Any							=	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:
              SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	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		,			lowercase_		,			lowercase_)]		,			)		,			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










"""simple docstring"""






import unittest

import numpy as np
import torch

from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()




class 							lowerCAmelCase__       (    unittest.TestCase				):






      '''simple docstring'''

      @property
      def _SCREAMING_SNAKE_CASE				(		self  :						Tuple):




          '''simple docstring'''


          torch.manual_seed(0)
          SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	UNetaDModel(
              block_out_channels=(32, 64)		,			layers_per_block=2		,			sample_size=32		,			in_channels=3		,			out_channels=3		,			down_block_types=('''DownBlock2D''', '''AttnDownBlock2D''')		,			up_block_types=('''AttnUpBlock2D''', '''UpBlock2D''')		,			)
          return model



      def _SCREAMING_SNAKE_CASE				(		self  :						Optional[Any]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						int							=	self.dummy_uncond_unet
          SCREAMING_SNAKE_CASE_   :						int							=	ScoreSdeVeScheduler()

          SCREAMING_SNAKE_CASE_   :						List[str]							=	ScoreSdeVePipeline(unet=lowercase_		,			scheduler=lowercase_)
          sde_ve.to(lowercase_)
          sde_ve.set_progress_bar_config(disable=lowercase_)

          SCREAMING_SNAKE_CASE_   :						Tuple							=	torch.manual_seed(0)
          SCREAMING_SNAKE_CASE_   :						Dict							=	sde_ve(num_inference_steps=2		,			output_type='''numpy'''		,			generator=lowercase_).images

          SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	torch.manual_seed(0)
          SCREAMING_SNAKE_CASE_   :						Optional[int]							=	sde_ve(num_inference_steps=2		,			output_type='''numpy'''		,			generator=lowercase_		,			return_dict=lowercase_)[
              0
          ]

          SCREAMING_SNAKE_CASE_   :						Dict							=	image[0, -3:, -3:, -1]
          SCREAMING_SNAKE_CASE_   :						List[Any]							=	image_from_tuple[0, -3:, -3:, -1]

          assert image.shape == (1, 32, 32, 3)
          SCREAMING_SNAKE_CASE_   :						str							=	np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0])

          assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
          assert np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1e-2




@slow
@require_torch
class 							lowerCAmelCase__       (    unittest.TestCase				):






      '''simple docstring'''

      def _SCREAMING_SNAKE_CASE				(		self  :						Dict):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	'''google/ncsnpp-church-256'''
          SCREAMING_SNAKE_CASE_   :						str							=	UNetaDModel.from_pretrained(lowercase_)

          SCREAMING_SNAKE_CASE_   :						Any							=	ScoreSdeVeScheduler.from_pretrained(lowercase_)

          SCREAMING_SNAKE_CASE_   :						Any							=	ScoreSdeVePipeline(unet=lowercase_		,			scheduler=lowercase_)
          sde_ve.to(lowercase_)
          sde_ve.set_progress_bar_config(disable=lowercase_)

          SCREAMING_SNAKE_CASE_   :						Optional[int]							=	torch.manual_seed(0)
          SCREAMING_SNAKE_CASE_   :						List[str]							=	sde_ve(num_inference_steps=10		,			output_type='''numpy'''		,			generator=lowercase_).images

          SCREAMING_SNAKE_CASE_   :						Any							=	image[0, -3:, -3:, -1]

          assert image.shape == (1, 256, 256, 3)

          SCREAMING_SNAKE_CASE_   :						int							=	np.array([0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0])

          assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2


