

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


UpperCAmelCase_       :				Dict		   =      datasets.utils.logging.get_logger(__name__)






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




 def 					lowerCAmelCase_					(    self	:    str       ):







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




 def 					lowerCAmelCase_					(    self	:    Tuple				,			__A	:    Any				,			__A	:    Dict = None				,			):
  __A   : 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
  __A   : Union[str, 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       ):
     __A   : Any								= sqlalchemy.create_engine(config_kwargs["""con"""].split("""://"""       )[0] + """://"""       )
     __A   : str								= str(sql.compile(dialect=engine.dialect       )       )
     __A   : Dict								= 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}"""       )
  __A   : Dict								= config_kwargs["con"]
  if not isinstance(__A				,			__A       ):
   __A   : List[Any]								= 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 					lowerCAmelCase_					(    self	:    Tuple       ):
  __A   : Tuple								= {
      "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     lowerCamelCase_			(  datasets.ArrowBasedBuilder  ):
 _lowercase		:      List[Any]    				=							SqlConfig




 def 					lowerCAmelCase_					(    self	:    Union[str, Any]       ):
  return datasets.DatasetInfo(features=self.config.features       )




 def 					lowerCAmelCase_					(    self	:    Union[str, Any]				,			__A	:    List[str]       ):
  return [datasets.SplitGenerator(name=datasets.Split.TRAIN				,			gen_kwargs={}       )]




 def 					lowerCAmelCase_					(    self	:    Any				,			__A	:    List[Any]       ):







  if self.config.features is not None:
   __A   : int								= self.config.features.arrow_schema
   if all(not require_storage_cast(__A       ) for feature in self.config.features.values()       ):
    # cheaper cast
    __A   : Dict								= 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
    __A   : Dict								= table_cast(__A				,			__A       )
  return pa_table




 def 					lowerCAmelCase_					(    self	:    Tuple       ):
  __A   : str								= self.config.chunksize
  __A   : str								= pd.read_sql(
      self.config.sql				,			self.config.con				,			chunksize=__A				,			**self.config.pd_read_sql_kwargs       )
  __A   : Union[str, Any]								= [sql_reader] if chunksize is None else sql_reader
  for chunk_idx, df in enumerate(__A       ):
   __A   : Tuple								= pa.Table.from_pandas(__A       )
   yield chunk_idx, self._cast_table(__A       )








from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCAmelCase_       :				Optional[Any]		   =      logging.get_logger(__name__)

UpperCAmelCase_       :				List[str]		   =      {
    '''uw-madison/nystromformer-512''': '''https://huggingface.co/uw-madison/nystromformer-512/resolve/main/config.json''',
    # See all Nystromformer models at https://huggingface.co/models?filter=nystromformer
}






class     lowerCamelCase_			(  _lowercase  ):
	_lowercase		:      Optional[Any]    				=							'''nystromformer'''




	def __init__(    self	:    Optional[Any]				,			__A	:    Optional[Any]=3_0000				,			__A	:    Optional[int]=768				,			__A	:    List[str]=12				,			__A	:    str=12				,			__A	:    Tuple=3072				,			__A	:    List[Any]="gelu_new"				,			__A	:    Dict=0.1				,			__A	:    Optional[int]=0.1				,			__A	:    int=510				,			__A	:    Union[str, Any]=2				,			__A	:    Optional[int]=64				,			__A	:    str=64				,			__A	:    List[str]=65				,			__A	:    Tuple=False				,			__A	:    List[str]=0.0_2				,			__A	:    Tuple=1e-5				,			__A	:    List[str]=1				,			__A	:    int=0				,			__A	:    Tuple=2				,			**__A	:    Dict				,			):
		__A   : str								= vocab_size
		__A   : Union[str, Any]								= max_position_embeddings
		__A   : Optional[int]								= hidden_size
		__A   : Optional[int]								= num_hidden_layers
		__A   : List[Any]								= num_attention_heads
		__A   : Dict								= intermediate_size
		__A   : Union[str, Any]								= hidden_act
		__A   : int								= hidden_dropout_prob
		__A   : Tuple								= attention_probs_dropout_prob
		__A   : Optional[Any]								= initializer_range
		__A   : int								= type_vocab_size
		__A   : Optional[int]								= segment_means_seq_len
		__A   : List[str]								= num_landmarks
		__A   : Dict								= conv_kernel_size
		__A   : Union[str, Any]								= inv_coeff_init_option
		__A   : Union[str, Any]								= layer_norm_eps
		super().__init__(pad_token_id=__A				,			bos_token_id=__A				,			eos_token_id=__A				,			**__A       )





