



"""simple docstring"""






import sys
from dataclasses import dataclass
from typing import TYPE_CHECKING, Dict, List, Optional, Tuple, Union

import pandas as pd
import pyarrow as pa

import datasets
import datasets.config
from datasets.features.features import require_storage_cast
from datasets.table import table_cast


if TYPE_CHECKING:
    import sqlitea

    import sqlalchemy


lowerCamelCase				:     Optional[Any]								=  datasets.utils.logging.get_logger(__name__)




@dataclass
class    __snake_case(					datasets.BuilderConfig       ):
    _A       = None
    _A       = None
    _A       = None
    _A       = True
    _A       = None
    _A       = None
    _A       = None
    _A       = 1_00_00
    _A       = None




    def   A   (   self			):





      '''simple docstring'''



      if self.sql is None:
        raise ValueError('''sql must be specified'''			)
      if self.con is None:
        raise ValueError('''con must be specified'''			)




    def   A   (   self    ,							A_    ,							A_ = None    ,							):





      '''simple docstring'''



      _SCREAMING_SNAKE_CASE        =			config_kwargs.copy()
      # We need to stringify the Selectable object to make its hash deterministic

      # The process of stringifying is explained here: http://docs.sqlalchemy.org/en/latest/faq/sqlexpressions.html
      _SCREAMING_SNAKE_CASE        =			config_kwargs['''sql''']
      if not isinstance(A_    ,							A_			):
        if datasets.config.SQLALCHEMY_AVAILABLE and "sqlalchemy" in sys.modules:
          import sqlalchemy

          if isinstance(A_    ,							sqlalchemy.sql.Selectable			):
            _SCREAMING_SNAKE_CASE        =			sqlalchemy.create_engine(config_kwargs['''con'''].split('''://'''			)[0] + '''://'''			)
            _SCREAMING_SNAKE_CASE        =			str(sql.compile(dialect=engine.dialect			)			)
            _SCREAMING_SNAKE_CASE        =			sql_str
          else:
            raise TypeError(
                F'''Supported types for \'sql\' are string and sqlalchemy.sql.Selectable but got {type(A_			)}: {sql}'''			)
        else:
          raise TypeError(
              F'''Supported types for \'sql\' are string and sqlalchemy.sql.Selectable but got {type(A_			)}: {sql}'''			)
      _SCREAMING_SNAKE_CASE        =			config_kwargs['''con''']
      if not isinstance(A_    ,							A_			):
        _SCREAMING_SNAKE_CASE        =			id(A_			)
        logger.info(
            F'''SQL connection \'con\' of type {type(A_			)} couldn\'t be hashed properly. To enable hashing, specify \'con\' as URI string instead.'''			)

      return super().create_config_id(A_    ,							custom_features=A_			)




    @property
    def   A   (   self			):





      '''simple docstring'''



      _SCREAMING_SNAKE_CASE        =			{
          '''index_col''': self.index_col,
          '''columns''': self.columns,
          '''params''': self.params,
          '''coerce_float''': self.coerce_float,
          '''parse_dates''': self.parse_dates,
      }
      return pd_read_sql_kwargs




class    __snake_case(					datasets.ArrowBasedBuilder       ):
    _A       = SqlConfig




    def   A   (   self			):





      '''simple docstring'''



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




    def   A   (   self    ,							A_			):





      '''simple docstring'''



      return [datasets.SplitGenerator(name=datasets.Split.TRAIN    ,							gen_kwargs={}			)]




    def   A   (   self    ,							A_			):





      '''simple docstring'''



      if self.config.features is not None:
        _SCREAMING_SNAKE_CASE        =			self.config.features.arrow_schema
        if all(not require_storage_cast(A_			) for feature in self.config.features.values()			):
          # cheaper cast
          _SCREAMING_SNAKE_CASE        =			pa.Table.from_arrays([pa_table[field.name] for field in schema]    ,							schema=A_			)
        else:
          # more expensive cast; allows str <-> int/float or str to Audio for example
          _SCREAMING_SNAKE_CASE        =			table_cast(A_    ,							A_			)
      return pa_table




    def   A   (   self			):





      '''simple docstring'''



      _SCREAMING_SNAKE_CASE        =			self.config.chunksize
      _SCREAMING_SNAKE_CASE        =			pd.read_sql(
          self.config.sql    ,							self.config.con    ,							chunksize=A_    ,							**self.config.pd_read_sql_kwargs			)
      _SCREAMING_SNAKE_CASE        =			[sql_reader] if chunksize is None else sql_reader
      for chunk_idx, df in enumerate(A_			):
        _SCREAMING_SNAKE_CASE        =			pa.Table.from_pandas(A_			)
        yield chunk_idx, self._cast_table(A_			)





"""simple docstring"""






import unittest

import numpy as np
import torch

from diffusers import DDPMPipeline, DDPMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow, torch_device


enable_full_determinism()




class    __snake_case(					unittest.TestCase       ):




    @property
    def   A   (   self			):





      '''simple docstring'''



      torch.manual_seed(0			)
      _SCREAMING_SNAKE_CASE        =			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   A   (   self			):





      '''simple docstring'''



      _SCREAMING_SNAKE_CASE        =			'''cpu'''
      _SCREAMING_SNAKE_CASE        =			self.dummy_uncond_unet
      _SCREAMING_SNAKE_CASE        =			DDPMScheduler()

      _SCREAMING_SNAKE_CASE        =			DDPMPipeline(unet=A_    ,							scheduler=A_			)
      ddpm.to(A_			)
      ddpm.set_progress_bar_config(disable=A_			)

      _SCREAMING_SNAKE_CASE        =			torch.Generator(device=A_			).manual_seed(0			)
      _SCREAMING_SNAKE_CASE        =			ddpm(generator=A_    ,							num_inference_steps=2    ,							output_type='''numpy'''			).images

      _SCREAMING_SNAKE_CASE        =			torch.Generator(device=A_			).manual_seed(0			)
      _SCREAMING_SNAKE_CASE        =			ddpm(generator=A_    ,							num_inference_steps=2    ,							output_type='''numpy'''    ,							return_dict=A_			)[0]

      _SCREAMING_SNAKE_CASE        =			image[0, -3:, -3:, -1]
      _SCREAMING_SNAKE_CASE        =			image_from_tuple[0, -3:, -3:, -1]

      assert image.shape == (1, 32, 32, 3)
      _SCREAMING_SNAKE_CASE        =			np.array(
          [9.956e-01, 5.785e-01, 4.675e-01, 9.930e-01, 0.0, 1.000, 1.199e-03, 2.648e-04, 5.101e-04]			)

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




    def   A   (   self			):





      '''simple docstring'''



      _SCREAMING_SNAKE_CASE        =			self.dummy_uncond_unet
      _SCREAMING_SNAKE_CASE        =			DDPMScheduler(prediction_type='''sample'''			)

      _SCREAMING_SNAKE_CASE        =			DDPMPipeline(unet=A_    ,							scheduler=A_			)
      ddpm.to(A_			)
      ddpm.set_progress_bar_config(disable=A_			)

      _SCREAMING_SNAKE_CASE        =			torch.manual_seed(0			)
      _SCREAMING_SNAKE_CASE        =			ddpm(generator=A_    ,							num_inference_steps=2    ,							output_type='''numpy'''			).images

      _SCREAMING_SNAKE_CASE        =			torch.manual_seed(0			)
      _SCREAMING_SNAKE_CASE        =			ddpm(generator=A_    ,							num_inference_steps=2    ,							output_type='''numpy'''			)[0]

      _SCREAMING_SNAKE_CASE        =			image[0, -3:, -3:, -1]
      _SCREAMING_SNAKE_CASE        =			image_eps[0, -3:, -3:, -1]

      assert image.shape == (1, 32, 32, 3)
      _SCREAMING_SNAKE_CASE        =			1e-2 if torch_device != '''mps''' else 3e-2
      assert np.abs(image_slice.flatten() - image_eps_slice.flatten()			).max() < tolerance




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




    def   A   (   self			):





      '''simple docstring'''



      _SCREAMING_SNAKE_CASE        =			'''google/ddpm-cifar10-32'''

      _SCREAMING_SNAKE_CASE        =			UNetaDModel.from_pretrained(A_			)
      _SCREAMING_SNAKE_CASE        =			DDPMScheduler.from_pretrained(A_			)

      _SCREAMING_SNAKE_CASE        =			DDPMPipeline(unet=A_    ,							scheduler=A_			)
      ddpm.to(A_			)
      ddpm.set_progress_bar_config(disable=A_			)

      _SCREAMING_SNAKE_CASE        =			torch.manual_seed(0			)
      _SCREAMING_SNAKE_CASE        =			ddpm(generator=A_    ,							output_type='''numpy'''			).images

      _SCREAMING_SNAKE_CASE        =			image[0, -3:, -3:, -1]

      assert image.shape == (1, 32, 32, 3)
      _SCREAMING_SNAKE_CASE        =			np.array([0.4_200, 0.3_588, 0.1_939, 0.3_847, 0.3_382, 0.2_647, 0.4_155, 0.3_582, 0.3_385]			)
      assert np.abs(image_slice.flatten() - expected_slice			).max() < 1e-2
