


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		=						datasets.utils.logging.get_logger(__name__)





@dataclass
class    _a				(							datasets.BuilderConfig):
	_a    :    str        =				None
	_a    :    Dict        =				None
	_a    :    List[Any]        =				None
	_a    :    str        =				True
	_a    :    List[Any]        =				None
	_a    :    Optional[Any]        =				None
	_a    :    List[Any]        =				None
	_a    :    Optional[int]        =				10000
	_a    :    str        =				None




	def 				UpperCAmelCase__(   self		:						Tuple						)->			Optional[Any]:



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




	def 				UpperCAmelCase__(   self		:						int						,   _SCREAMING_SNAKE_CASE		:						Union[str, Any]						,   _SCREAMING_SNAKE_CASE		:						List[str] = None						,   )->			Optional[int]:
			lowerCAmelCase__  :     List[str]       	=   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
			lowerCAmelCase__  :     Optional[Any]       	=   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						):
									lowerCAmelCase__  :     List[Any]       	=   sqlalchemy.create_engine(config_kwargs['''con'''].split('''://'''						)[0] + '''://'''						)
									lowerCAmelCase__  :     Dict       	=   str(sql.compile(dialect=engine.dialect						)						)
									lowerCAmelCase__  :     Tuple       	=   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}'						)
			lowerCAmelCase__  :     Tuple       	=   config_kwargs['''con''']
			if not isinstance(A_						,   A_						):
					lowerCAmelCase__  :     Tuple       	=   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 				UpperCAmelCase__(   self		:						List[str]						)->			Union[str, Any]:
			lowerCAmelCase__  :     int       	=   {
			    '''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    _a				(							datasets.ArrowBasedBuilder):
	_a    :    Optional[Any]        =				SqlConfig




	def 				UpperCAmelCase__(   self		:						List[Any]						)->			Union[str, Any]:
			return datasets.DatasetInfo(features=self.config.features						)




	def 				UpperCAmelCase__(   self		:						Optional[int]						,   _SCREAMING_SNAKE_CASE		:						List[Any]						)->			str:
			return [datasets.SplitGenerator(name=datasets.Split.TRAIN						,   gen_kwargs={}						)]




	def 				UpperCAmelCase__(   self		:						Union[str, Any]						,   _SCREAMING_SNAKE_CASE		:						Optional[int]						)->			Optional[Any]:



			if self.config.features is not None:
					lowerCAmelCase__  :     Optional[Any]       	=   self.config.features.arrow_schema
					if all(not require_storage_cast(A_						) for feature in self.config.features.values()						):
							# cheaper cast
							lowerCAmelCase__  :     Any       	=   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
							lowerCAmelCase__  :     Union[str, Any]       	=   table_cast(A_						,   A_						)
			return pa_table




	def 				UpperCAmelCase__(   self		:						Any						)->			Optional[Any]:
			lowerCAmelCase__  :     Tuple       	=   self.config.chunksize
			lowerCAmelCase__  :     int       	=   pd.read_sql(
			    self.config.sql						,   self.config.con						,   chunksize=A_						,   **self.config.pd_read_sql_kwargs						)
			lowerCAmelCase__  :     Dict       	=   [sql_reader] if chunksize is None else sql_reader
			for chunk_idx, df in enumerate(A_						):
					lowerCAmelCase__  :     int       	=   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
