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      :				Any     			=datasets.utils.logging.get_logger(__name__)




@dataclass
class 						__snake_case						(			datasets.BuilderConfig		):





      '''simple docstring'''






      _snake_case     		=							None
      _snake_case     		=							None
      _snake_case     		=							None
      _snake_case     		=							True
      _snake_case     		=							None
      _snake_case     		=							None
      _snake_case     		=							None
      _snake_case     		=							10_000
      _snake_case     		=							None



      def 						_SCREAMING_SNAKE_CASE     (       self  :					Union[str, Any])					->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 						_SCREAMING_SNAKE_CASE     (       self  :					List[str]     ,	_UpperCamelCase  :					Union[str, Any]     ,	_UpperCamelCase  :					List[str] = None     ,	)					->Optional[Any]:


          """simple docstring"""


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

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

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




      @property
      def 						_SCREAMING_SNAKE_CASE     (       self  :					Any)					->Any:


          """simple docstring"""


          _lowerCamelCase  :		List[str]			    =    {
              '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		):





      '''simple docstring'''






      _snake_case     		=							SqlConfig



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


          """simple docstring"""


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



      def 						_SCREAMING_SNAKE_CASE     (       self  :					str     ,	_UpperCamelCase  :					Any)					->Any:


          """simple docstring"""


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



      def 						_SCREAMING_SNAKE_CASE     (       self  :					Tuple     ,	_UpperCamelCase  :					int)					->Optional[Any]:


          """simple docstring"""


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




      def 						_SCREAMING_SNAKE_CASE     (       self  :					int)					->List[str]:


          """simple docstring"""


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





"""simple docstring"""







def 						_lowercase				(  _SCREAMING_SNAKE_CASE				:				list[int]						)    ->     int:







     '''simple docstring'''


     if not isinstance(_SCREAMING_SNAKE_CASE  ,       _SCREAMING_SNAKE_CASE						) or not all(
         isinstance(_SCREAMING_SNAKE_CASE  ,       _SCREAMING_SNAKE_CASE						) and item >= 0 for item in citations						):
          raise ValueError('The citations should be a list of non negative integers.'						)

     citations.sort()
     __A					:					Any								=		len(_SCREAMING_SNAKE_CASE						)

     for i in range(_SCREAMING_SNAKE_CASE						):
          if citations[len_citations - 1 - i] <= i:
               return i

     return len_citations


if __name__ == "__main__":
       import doctest

       doctest.testmod()



