'''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


snake_case__	:     int      =    datasets.utils.logging.get_logger(__name__)






@dataclass
class 	__SCREAMING_SNAKE_CASE		(			datasets.BuilderConfig			):







  '''simple docstring'''






  lowerCamelCase_						:Union[str, "sqlalchemy.sql.Selectable"]		       =						None
  lowerCamelCase_						:Union[str, "sqlalchemy.engine.Connection", "sqlalchemy.engine.Engine", "sqlite3.Connection"]		       =						None
  lowerCamelCase_						:Optional[Union[str, List[str]]]		       =						None
  lowerCamelCase_						:bool		       =						True
  lowerCamelCase_						:Optional[Union[List, Tuple, Dict]]		       =						None
  lowerCamelCase_						:Optional[Union[List, Dict]]		       =						None
  lowerCamelCase_						:Optional[List[str]]		       =						None
  lowerCamelCase_						:Optional[int]		       =						1_0000
  lowerCamelCase_						:Optional[datasets.Features]		       =						None


  def _UpperCamelCase	(					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 _UpperCamelCase	(					self ,						snake_case_ ,						snake_case_ = None ,						):




       '''simple docstring'''

       UpperCAmelCase_				:  List[Any]  = 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
       UpperCAmelCase_				:  str  = config_kwargs['sql']
       if not isinstance(snake_case_ ,						snake_case_		):
            if datasets.config.SQLALCHEMY_AVAILABLE and "sqlalchemy" in sys.modules:
                 import sqlalchemy

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

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






  @property
  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       UpperCAmelCase_				:  List[Any]  = {
           '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 	__SCREAMING_SNAKE_CASE		(			datasets.ArrowBasedBuilder			):







  '''simple docstring'''






  lowerCamelCase_						:Any		       =						SqlConfig


  def _UpperCamelCase	(					self		):




       '''simple docstring'''

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


  def _UpperCamelCase	(					self ,						snake_case_		):




       '''simple docstring'''

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


  def _UpperCamelCase	(					self ,						snake_case_		):




       '''simple docstring'''

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






  def _UpperCamelCase	(					self		):




       '''simple docstring'''

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










"""simple docstring"""






import torch

from diffusers import DDIMScheduler

from .test_schedulers import SchedulerCommonTest


class      __lowerCamelCase	(       a__					):

     '''simple docstring'''
     A_							:						Tuple     =		(DDIMScheduler,)
     A_							:						Tuple     =		(('eta', 0.0), ('num_inference_steps', 50))


     def 				_UpperCAmelCase   (   self     ,					**__UpperCAmelCase				)							->				str:
          _a     						=					{
              '''num_train_timesteps''': 1000,
              '''beta_start''': 0.0001,
              '''beta_end''': 0.02,
              '''beta_schedule''': '''linear''',
              '''clip_sample''': True,
          }

          config.update(**__UpperCAmelCase				)
          return config


     def 				_UpperCAmelCase   (   self     ,					**__UpperCAmelCase				)							->				List[Any]:
          _a     						=					self.scheduler_classes[0]
          _a     						=					self.get_scheduler_config(**__UpperCAmelCase				)
          _a     						=					scheduler_class(**__UpperCAmelCase				)

          _a						,   _a     						=					10, 0.0

          _a     						=					self.dummy_model()
          _a     						=					self.dummy_sample_deter

          scheduler.set_timesteps(__UpperCAmelCase				)

          for t in scheduler.timesteps:
               _a     						=					model(__UpperCAmelCase     ,					__UpperCAmelCase				)
               _a     						=					scheduler.step(__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase				).prev_sample

          return sample


     def 				_UpperCAmelCase   (   self				)							->				str:
          for timesteps in [100, 500, 1000]:
               self.check_over_configs(num_train_timesteps=__UpperCAmelCase				)


     def 				_UpperCAmelCase   (   self				)							->				Dict:
          for steps_offset in [0, 1]:
               self.check_over_configs(steps_offset=__UpperCAmelCase				)

          _a     						=					self.scheduler_classes[0]
          _a     						=					self.get_scheduler_config(steps_offset=1				)
          _a     						=					scheduler_class(**__UpperCAmelCase				)
          scheduler.set_timesteps(5				)
          assert torch.equal(scheduler.timesteps     ,					torch.LongTensor([801, 601, 401, 201, 1]				)				)


     def 				_UpperCAmelCase   (   self				)							->				int:
          for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1]     ,					[0.002, 0.02, 0.2, 2]				):
               self.check_over_configs(beta_start=__UpperCAmelCase     ,					beta_end=__UpperCAmelCase				)


     def 				_UpperCAmelCase   (   self				)							->				Union[str, Any]:
          for schedule in ["linear", "squaredcos_cap_v2"]:
               self.check_over_configs(beta_schedule=__UpperCAmelCase				)


     def 				_UpperCAmelCase   (   self				)							->				List[str]:
          for prediction_type in ["epsilon", "v_prediction"]:
               self.check_over_configs(prediction_type=__UpperCAmelCase				)


     def 				_UpperCAmelCase   (   self				)							->				int:
          for clip_sample in [True, False]:
               self.check_over_configs(clip_sample=__UpperCAmelCase				)


     def 				_UpperCAmelCase   (   self				)							->				Union[str, Any]:
          for timestep_spacing in ["trailing", "leading"]:
               self.check_over_configs(timestep_spacing=__UpperCAmelCase				)


     def 				_UpperCAmelCase   (   self				)							->				List[str]:
          for rescale_betas_zero_snr in [True, False]:
               self.check_over_configs(rescale_betas_zero_snr=__UpperCAmelCase				)


     def 				_UpperCAmelCase   (   self				)							->				List[str]:
          self.check_over_configs(thresholding=__UpperCAmelCase				)
          for threshold in [0.5, 1.0, 2.0]:
               for prediction_type in ["epsilon", "v_prediction"]:
                    self.check_over_configs(
                        thresholding=__UpperCAmelCase     ,					prediction_type=__UpperCAmelCase     ,					sample_max_value=__UpperCAmelCase     ,					)


     def 				_UpperCAmelCase   (   self				)							->				int:
          for t in [1, 10, 49]:
               self.check_over_forward(time_step=__UpperCAmelCase				)


     def 				_UpperCAmelCase   (   self				)							->				Tuple:
          for t, num_inference_steps in zip([1, 10, 50]     ,					[10, 50, 500]				):
               self.check_over_forward(time_step=__UpperCAmelCase     ,					num_inference_steps=__UpperCAmelCase				)


     def 				_UpperCAmelCase   (   self				)							->				Optional[Any]:
          for t, eta in zip([1, 10, 49]     ,					[0.0, 0.5, 1.0]				):
               self.check_over_forward(time_step=__UpperCAmelCase     ,					eta=__UpperCAmelCase				)


     def 				_UpperCAmelCase   (   self				)							->				int:
          _a     						=					self.scheduler_classes[0]
          _a     						=					self.get_scheduler_config()
          _a     						=					scheduler_class(**__UpperCAmelCase				)

          assert torch.sum(torch.abs(scheduler._get_variance(0     ,					0				) - 0.0				)				) < 1e-5
          assert torch.sum(torch.abs(scheduler._get_variance(420     ,					400				) - 0.14771				)				) < 1e-5
          assert torch.sum(torch.abs(scheduler._get_variance(980     ,					960				) - 0.32460				)				) < 1e-5
          assert torch.sum(torch.abs(scheduler._get_variance(0     ,					0				) - 0.0				)				) < 1e-5
          assert torch.sum(torch.abs(scheduler._get_variance(487     ,					486				) - 0.00979				)				) < 1e-5
          assert torch.sum(torch.abs(scheduler._get_variance(999     ,					998				) - 0.02				)				) < 1e-5


     def 				_UpperCAmelCase   (   self				)							->				Optional[int]:
          _a     						=					self.full_loop()

          _a     						=					torch.sum(torch.abs(__UpperCAmelCase				)				)
          _a     						=					torch.mean(torch.abs(__UpperCAmelCase				)				)

          assert abs(result_sum.item() - 172.0067				) < 1e-2
          assert abs(result_mean.item() - 0.223967				) < 1e-3


     def 				_UpperCAmelCase   (   self				)							->				Any:
          _a     						=					self.full_loop(prediction_type='''v_prediction'''				)

          _a     						=					torch.sum(torch.abs(__UpperCAmelCase				)				)
          _a     						=					torch.mean(torch.abs(__UpperCAmelCase				)				)

          assert abs(result_sum.item() - 52.5302				) < 1e-2
          assert abs(result_mean.item() - 0.0684				) < 1e-3


     def 				_UpperCAmelCase   (   self				)							->				List[str]:
          # We specify different beta, so that the first alpha is 0.99
          _a     						=					self.full_loop(set_alpha_to_one=__UpperCAmelCase     ,					beta_start=0.01				)
          _a     						=					torch.sum(torch.abs(__UpperCAmelCase				)				)
          _a     						=					torch.mean(torch.abs(__UpperCAmelCase				)				)

          assert abs(result_sum.item() - 149.8295				) < 1e-2
          assert abs(result_mean.item() - 0.1951				) < 1e-3





     def 				_UpperCAmelCase   (   self				)							->				int:
          # We specify different beta, so that the first alpha is 0.99
          _a     						=					self.full_loop(set_alpha_to_one=__UpperCAmelCase     ,					beta_start=0.01				)
          _a     						=					torch.sum(torch.abs(__UpperCAmelCase				)				)
          _a     						=					torch.mean(torch.abs(__UpperCAmelCase				)				)

          assert abs(result_sum.item() - 149.0784				) < 1e-2
          assert abs(result_mean.item() - 0.1941				) < 1e-3