



"""simple docstring"""






import enum
import inspect
from typing import Iterable, List, Optional, Tuple, Union

class 			__SCREAMING_SNAKE_CASE      ( enum.Enum  ):


					'''simple docstring'''





					_a       =				'timm'
					_a       =				'transformers'






def 	snake_case__  (						__lowerCamelCase							:       Optional[Iterable[str]]				,						__lowerCamelCase							:       Optional[Iterable[int]]				,						__lowerCamelCase							:       Optional[Iterable[str]]      ):
			"""simple docstring"""
			if stage_names is None:
						raise ValueError('''Stage_names must be set for transformers backbones'''      )

			if out_features is not None:
						if not isinstance(__lowerCamelCase				,						(list,)      ):
									raise ValueError(f'''out_features must be a list {type(__lowerCamelCase      )}'''      )
						if any(feat not in stage_names for feat in out_features      ):
									raise ValueError(f'''out_features must be a subset of stage_names: {stage_names} got {out_features}'''      )

			if out_indices is not None:
						if not isinstance(__lowerCamelCase				,						(list, tuple)      ):
									raise ValueError(f'''out_indices must be a list or tuple, got {type(__lowerCamelCase      )}'''      )
						if any(idx >= len(__lowerCamelCase      ) for idx in out_indices      ):
									raise ValueError('''out_indices must be valid indices for stage_names {stage_names}, got {out_indices}'''      )

			if out_features is not None and out_indices is not None:
						if len(__lowerCamelCase      ) != len(__lowerCamelCase      ):
									raise ValueError('''out_features and out_indices should have the same length if both are set'''      )
						if out_features != [stage_names[idx] for idx in out_indices]:
									raise ValueError('''out_features and out_indices should correspond to the same stages if both are set'''      )






def 	snake_case__  (						__lowerCamelCase							:       Optional[List[str]]				,						__lowerCamelCase							:       Optional[Union[List[int], Tuple[int]]]				,						__lowerCamelCase							:       List[str]				,						):
			"""simple docstring"""
			if out_indices is None and out_features is None:
						lowerCamelCase__						:						Union[str, Any]						      =[len(__lowerCamelCase      ) - 1]
						lowerCamelCase__						:						Any						      =[stage_names[-1]]
			elif out_indices is None and out_features is not None:
						lowerCamelCase__						:						List[Any]						      =[stage_names.index(__lowerCamelCase      ) for layer in out_features]
			elif out_features is None and out_indices is not None:
						lowerCamelCase__						:						Tuple						      =[stage_names[idx] for idx in out_indices]
			return out_features, out_indices






def 	snake_case__  (						__lowerCamelCase							:       Optional[List[str]]				,						__lowerCamelCase							:       Optional[Union[List[int], Tuple[int]]]				,						__lowerCamelCase							:       List[str]				,						):
			"""simple docstring"""
			verify_out_features_out_indices(out_features=__lowerCamelCase				,						out_indices=__lowerCamelCase				,						stage_names=__lowerCamelCase      )
			lowerCamelCase__      ,  lowerCamelCase__						:						Dict						      =_align_output_features_output_indices(
			    out_features=__lowerCamelCase				,						out_indices=__lowerCamelCase				,						stage_names=__lowerCamelCase      )
			# Verify that the aligned out_features and out_indices are valid
			verify_out_features_out_indices(out_features=__lowerCamelCase				,						out_indices=__lowerCamelCase				,						stage_names=__lowerCamelCase      )
			return output_features, output_indices

class 			__SCREAMING_SNAKE_CASE      :


					'''simple docstring'''





					_a       =				None



					def 						snake_case			(			self				:    Dict,    lowerCamelCase				:    Optional[int]	)->   None:
								if getattr(self,    '''_backbone''',    lowerCamelCase	) is None:
											raise ValueError('''self._backbone must be set before calling _init_timm_backbone'''	)

								# These will diagree with the defaults for the transformers models e.g. for resnet50
								# the transformer model has out_features = ['stem', 'stage1', 'stage2', 'stage3', 'stage4']
								# the timm model has out_features = ['act', 'layer1', 'layer2', 'layer3', 'layer4']
								lowerCamelCase__						:						List[str]						      =[stage['''module'''] for stage in self._backbone.feature_info.info]
								lowerCamelCase__						:						Dict						      =[stage['''num_chs'''] for stage in self._backbone.feature_info.info]
								lowerCamelCase__						:						Any						      =self._backbone.feature_info.out_indices
								lowerCamelCase__						:						Union[str, Any]						      =self._backbone.feature_info.module_name()

								# We verify the out indices and out features are valid
								verify_out_features_out_indices(
								    out_features=lowerCamelCase,    out_indices=lowerCamelCase,    stage_names=self.stage_names	)
								lowerCamelCase__      ,  lowerCamelCase__						:						List[Any]						      =out_features, out_indices



					def 						snake_case			(			self				:    Optional[Any],    lowerCamelCase				:    List[Any]	)->   None:
								lowerCamelCase__						:						List[Any]						      =getattr(lowerCamelCase,    '''stage_names'''	)
								lowerCamelCase__						:						Any						      =getattr(lowerCamelCase,    '''out_features''',    lowerCamelCase	)
								lowerCamelCase__						:						Union[str, Any]						      =getattr(lowerCamelCase,    '''out_indices''',    lowerCamelCase	)

								lowerCamelCase__						:						Tuple						      =stage_names
								lowerCamelCase__      ,  lowerCamelCase__						:						Any						      =get_aligned_output_features_output_indices(
								    out_features=lowerCamelCase,    out_indices=lowerCamelCase,    stage_names=lowerCamelCase	)
								# Number of channels for each stage. This is set in the transformer backbone model init
								lowerCamelCase__						:						Union[str, Any]						      =None



					def 						snake_case			(			self				:    List[str],    lowerCamelCase				:    str	)->   None:
								lowerCamelCase__						:						List[str]						      =config

								lowerCamelCase__						:						List[str]						      =getattr(lowerCamelCase,    '''use_timm_backbone''',    lowerCamelCase	)
								lowerCamelCase__						:						Tuple						      =BackboneType.TIMM if self.use_timm_backbone else BackboneType.TRANSFORMERS

								if self.backbone_type == BackboneType.TIMM:
											self._init_timm_backbone(lowerCamelCase	)
								elif self.backbone_type == BackboneType.TRANSFORMERS:
											self._init_transformers_backbone(lowerCamelCase	)
								else:
											raise ValueError(F'''backbone_type {self.backbone_type} not supported.'''	)



					@property
					def 						snake_case			(			self				:    Union[str, Any]	)->   Optional[int]:
								return self._out_features



					@out_features.setter
					def 						snake_case			(			self				:    Optional[int],    lowerCamelCase				:    List[str]	)->   Optional[Any]:
								lowerCamelCase__      ,  lowerCamelCase__						:						Optional[Any]						      =get_aligned_output_features_output_indices(
								    out_features=lowerCamelCase,    out_indices=lowerCamelCase,    stage_names=self.stage_names	)



					@property
					def 						snake_case			(			self				:    Dict	)->   Tuple:
								return self._out_indices



					@out_indices.setter
					def 						snake_case			(			self				:    List[str],    lowerCamelCase				:    Union[Tuple[int], List[int]]	)->   List[Any]:
								lowerCamelCase__      ,  lowerCamelCase__						:						Dict						      =get_aligned_output_features_output_indices(
								    out_features=lowerCamelCase,    out_indices=lowerCamelCase,    stage_names=self.stage_names	)



					@property
					def 						snake_case			(			self				:    List[Any]	)->   List[Any]:
								# the current backbones will output the number of channels for each stage
								# even if that stage is not in the out_features list.
								return {stage: self.num_features[i] for i, stage in enumerate(self.stage_names	)}



					@property
					def 						snake_case			(			self				:    int	)->   Optional[int]:
								return [self.out_feature_channels[name] for name in self.out_features]



					def 						snake_case			(			self				:    List[str],    *lowerCamelCase				:    Tuple,    **lowerCamelCase				:    Dict	)->   int:
								lowerCamelCase__						:						Optional[Any]						      =dict(inspect.signature(self.forward	).parameters	)
								lowerCamelCase__						:						Optional[int]						      ={k: v for k, v in kwargs.items() if k in signature}
								return self(*lowerCamelCase,    **lowerCamelCase	)



					def 						snake_case			(			self				:    Any,    lowerCamelCase				:    Optional[Any],    lowerCamelCase				:    Optional[bool] = None,    lowerCamelCase				:    Optional[bool] = None,    lowerCamelCase				:    Optional[bool] = None,    )->   Optional[int]:
								raise NotImplementedError('''This method should be implemented by the derived class.'''	)




					def 						snake_case			(			self				:    Union[str, Any]	)->   Tuple:
								lowerCamelCase__						:						Any						      =super().to_dict()
								lowerCamelCase__						:						Optional[Any]						      =output.pop('''_out_features'''	)
								lowerCamelCase__						:						List[str]						      =output.pop('''_out_indices'''	)
								return output





class 			__SCREAMING_SNAKE_CASE      :


					'''simple docstring'''





					@property
					def 						snake_case			(			self				:    Optional[int]	)->   int:
								return self._out_features



					@out_features.setter
					def 						snake_case			(			self				:    Tuple,    lowerCamelCase				:    List[str]	)->   Any:
								lowerCamelCase__      ,  lowerCamelCase__						:						Any						      =get_aligned_output_features_output_indices(
								    out_features=lowerCamelCase,    out_indices=lowerCamelCase,    stage_names=self.stage_names	)



					@property
					def 						snake_case			(			self				:    List[Any]	)->   Tuple:
								return self._out_indices



					@out_indices.setter
					def 						snake_case			(			self				:    Dict,    lowerCamelCase				:    Union[Tuple[int], List[int]]	)->   List[Any]:
								lowerCamelCase__      ,  lowerCamelCase__						:						str						      =get_aligned_output_features_output_indices(
								    out_features=lowerCamelCase,    out_indices=lowerCamelCase,    stage_names=self.stage_names	)




					def 						snake_case			(			self				:    Any	)->   int:
								lowerCamelCase__						:						int						      =super().to_dict()
								lowerCamelCase__						:						List[str]						      =output.pop('''_out_features'''	)
								lowerCamelCase__						:						Optional[Any]						      =output.pop('''_out_indices'''	)
								return output







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


lowercase_									=    datasets.utils.logging.get_logger(__name__)


@dataclass
class 					A		(				datasets.BuilderConfig					):







    """simple docstring"""





    lowerCamelCase						      =				None
    lowerCamelCase						      =				None
    lowerCamelCase						      =				None
    lowerCamelCase						      =				True
    lowerCamelCase						      =				None
    lowerCamelCase						      =				None
    lowerCamelCase						      =				None
    lowerCamelCase						      =				1_00_00
    lowerCamelCase						      =				None


    def 		snake_case__	( self					:			Tuple						)->       Optional[int]:





        '''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 		snake_case__	( self					:			Dict,lowercase_					:			dict,lowercase_					:			Optional[datasets.Features] = None,)->       str:





        '''simple docstring'''







        A__        =  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
        A__        =  config_kwargs['sql']
        if not isinstance(lowercase_,lowercase_						):
            if datasets.config.SQLALCHEMY_AVAILABLE and "sqlalchemy" in sys.modules:
                import sqlalchemy

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

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


    @property
    def 		snake_case__	( self					:			Union[str, Any]						)->       Any:





        '''simple docstring'''







        A__        =  {
            '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					):







    """simple docstring"""





    lowerCamelCase						      =				SqlConfig


    def 		snake_case__	( self					:			Any						)->       Any:





        '''simple docstring'''







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


    def 		snake_case__	( self					:			Tuple,lowercase_					:			List[Any]						)->       Union[str, Any]:





        '''simple docstring'''







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


    def 		snake_case__	( self					:			Any,lowercase_					:			pa.Table						)->       pa.Table:





        '''simple docstring'''







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


    def 		snake_case__	( self					:			List[str]						)->       Union[str, Any]:





        '''simple docstring'''







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

