


import contextlib
import os
import sqlitea

import pytest

from datasets import Dataset, Features, Value
from datasets.io.sql import SqlDatasetReader, SqlDatasetWriter

from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases, require_sqlalchemy
def 							__lowerCamelCase		(					UpperCAmelCase_		:						List[str]					,    UpperCAmelCase_		:						List[Any]		):




      """simple docstring"""





      assert isinstance(UpperCAmelCase_					,    UpperCAmelCase_		)
      assert dataset.num_rows == 4
      assert dataset.num_columns == 3
      assert dataset.column_names == ["col_1", "col_2", "col_3"]
      for feature, expected_dtype in expected_features.items():
            assert dataset.features[feature].dtype == expected_dtype
@require_sqlalchemy
@pytest.mark.parametrize('''keep_in_memory'''					,    [False, True]		)
def 							__lowerCamelCase		(					UpperCAmelCase_		:						Optional[Any]					,    UpperCAmelCase_		:						str					,    UpperCAmelCase_		:						Tuple					,    UpperCAmelCase_		:						Optional[Any]		):




      """simple docstring"""





      a		:List[str]									=			tmp_path / '''cache'''
      a		:int									=			{'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
      with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
            a		:Tuple									=			SqlDatasetReader(
                '''dataset'''					,    '''sqlite:///''' + sqlite_path					,    cache_dir=UpperCAmelCase_					,    keep_in_memory=UpperCAmelCase_		).read()
      _check_sql_dataset(UpperCAmelCase_					,    UpperCAmelCase_		)
@require_sqlalchemy
@pytest.mark.parametrize(
    '''features'''					,    [
        None,
        {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''},
        {'''col_1''': '''string''', '''col_2''': '''string''', '''col_3''': '''string'''},
        {'''col_1''': '''int32''', '''col_2''': '''int32''', '''col_3''': '''int32'''},
        {'''col_1''': '''float32''', '''col_2''': '''float32''', '''col_3''': '''float32'''},
    ]					,    )
def 							__lowerCamelCase		(					UpperCAmelCase_		:						int					,    UpperCAmelCase_		:						str					,    UpperCAmelCase_		:						Tuple					,    UpperCAmelCase_		:						Tuple		):




      """simple docstring"""





      a		:int									=			tmp_path / '''cache'''
      a		:Optional[int]									=			{'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
      a		:Any									=			features.copy() if features else default_expected_features
      a		:int									=			(
          Features({feature: Value(UpperCAmelCase_		) for feature, dtype in features.items()}		) if features is not None else None
      )
      a		:Tuple									=			SqlDatasetReader('''dataset'''					,    '''sqlite:///''' + sqlite_path					,    features=UpperCAmelCase_					,    cache_dir=UpperCAmelCase_		).read()
      _check_sql_dataset(UpperCAmelCase_					,    UpperCAmelCase_		)
def 							__lowerCamelCase		(					UpperCAmelCase_		:						int		):




      """simple docstring"""





      with contextlib.closing(sqlitea.connect(UpperCAmelCase_		)		) as con:
            a		:Optional[Any]									=			con.cursor()
            cur.execute('''SELECT * FROM dataset'''		)
            for row in cur:
                  yield row
@require_sqlalchemy
def 							__lowerCamelCase		(					UpperCAmelCase_		:						List[Any]					,    UpperCAmelCase_		:						Any					,    UpperCAmelCase_		:						Any		):




      """simple docstring"""





      a		:Optional[Any]									=			tmp_path / '''cache'''
      a		:Any									=			os.path.join(UpperCAmelCase_					,    '''tmp.sql'''		)
      a		:Union[str, Any]									=			SqlDatasetReader('''dataset'''					,    '''sqlite:///''' + sqlite_path					,    cache_dir=UpperCAmelCase_		).read()
      SqlDatasetWriter(UpperCAmelCase_					,    '''dataset'''					,    '''sqlite:///''' + output_sqlite_path					,    num_proc=1		).write()

      a		:Dict									=			iter_sql_file(UpperCAmelCase_		)
      a		:Optional[Any]									=			iter_sql_file(UpperCAmelCase_		)

      for rowa, rowa in zip(UpperCAmelCase_					,    UpperCAmelCase_		):
            assert rowa == rowa
@require_sqlalchemy
def 							__lowerCamelCase		(					UpperCAmelCase_		:						str					,    UpperCAmelCase_		:						Dict					,    UpperCAmelCase_		:						Any		):




      """simple docstring"""





      a		:Optional[Any]									=			tmp_path / '''cache'''
      a		:List[Any]									=			os.path.join(UpperCAmelCase_					,    '''tmp.sql'''		)
      a		:Dict									=			SqlDatasetReader('''dataset'''					,    '''sqlite:///''' + sqlite_path					,    cache_dir=UpperCAmelCase_		).read()
      SqlDatasetWriter(UpperCAmelCase_					,    '''dataset'''					,    '''sqlite:///''' + output_sqlite_path					,    num_proc=2		).write()

      a		:List[Any]									=			iter_sql_file(UpperCAmelCase_		)
      a		:List[str]									=			iter_sql_file(UpperCAmelCase_		)

      for rowa, rowa in zip(UpperCAmelCase_					,    UpperCAmelCase_		):
            assert rowa == rowa






@require_sqlalchemy
def 							__lowerCamelCase		(					UpperCAmelCase_		:						Union[str, Any]					,    UpperCAmelCase_		:						List[str]					,    UpperCAmelCase_		:						List[str]		):




      """simple docstring"""





      a		:Optional[Any]									=			tmp_path / '''cache'''
      a		:List[Any]									=			os.path.join(UpperCAmelCase_					,    '''tmp.sql'''		)
      a		:Any									=			SqlDatasetReader('''dataset'''					,    '''sqlite:///''' + sqlite_path					,    cache_dir=UpperCAmelCase_		).read()
      with pytest.raises(UpperCAmelCase_		):
            SqlDatasetWriter(UpperCAmelCase_					,    '''dataset'''					,    '''sqlite:///''' + output_sqlite_path					,    num_proc=0		).write()









import unittest

import numpy as np
import torch

from diffusers import PNDMPipeline, PNDMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()







class 						_snake_case    ( unittest.TestCase				):




 @property
 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       torch.manual_seed(0       )
       a		:List[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       ):
       a		:Union[str, Any]									=			self.dummy_uncond_unet
       a		:Any									=			PNDMScheduler()

       a		:Any									=			PNDMPipeline(unet=_lowerCamelCase   ,    scheduler=_lowerCamelCase       )
       pndm.to(_lowerCamelCase       )
       pndm.set_progress_bar_config(disable=_lowerCamelCase       )

       a		:Any									=			torch.manual_seed(0       )
       a		:List[str]									=			pndm(generator=_lowerCamelCase   ,    num_inference_steps=20   ,    output_type='''numpy'''       ).images

       a		:int									=			torch.manual_seed(0       )
       a		:str									=			pndm(generator=_lowerCamelCase   ,    num_inference_steps=20   ,    output_type='''numpy'''   ,    return_dict=_lowerCamelCase       )[0]

       a		:int									=			image[0, -3:, -3:, -1]
       a		:List[str]									=			image_from_tuple[0, -3:, -3:, -1]

       assert image.shape == (1, 32, 32, 3)
       a		:str									=			np.array([1.0, 1.0, 0.0, 1.0, 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 						_snake_case    ( unittest.TestCase				):




 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       a		:int									=			'''google/ddpm-cifar10-32'''

       a		:str									=			UNetaDModel.from_pretrained(_lowerCamelCase       )
       a		:Optional[Any]									=			PNDMScheduler()

       a		:Union[str, Any]									=			PNDMPipeline(unet=_lowerCamelCase   ,    scheduler=_lowerCamelCase       )
       pndm.to(_lowerCamelCase       )
       pndm.set_progress_bar_config(disable=_lowerCamelCase       )
       a		:Dict									=			torch.manual_seed(0       )
       a		:List[str]									=			pndm(generator=_lowerCamelCase   ,    output_type='''numpy'''       ).images

       a		:List[Any]									=			image[0, -3:, -3:, -1]

       assert image.shape == (1, 32, 32, 3)
       a		:int									=			np.array([0.1564, 0.1_4645, 0.1406, 0.1_4715, 0.1_2425, 0.1_4045, 0.1_3115, 0.1_2175, 0.125]       )

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





