

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


__UpperCAmelCase      = logging.get_logger(__name__)

__UpperCAmelCase      = {
    """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 SCREAMING_SNAKE_CASE	(  a_ ):
  """simple docstring"""






  lowerCamelCase    :			Optional[Any]						      ="nystromformer"







  def __init__(			self :    List[str]       ,							lowerCAmelCase :    List[Any]=3_00_00       ,							lowerCAmelCase :    Union[str, Any]=7_68       ,							lowerCAmelCase :    int=12       ,							lowerCAmelCase :    str=12       ,							lowerCAmelCase :    Tuple=30_72       ,							lowerCAmelCase :    Optional[Any]="gelu_new"       ,							lowerCAmelCase :    Any=0.1       ,							lowerCAmelCase :    Tuple=0.1       ,							lowerCAmelCase :    Tuple=5_10       ,							lowerCAmelCase :    int=2       ,							lowerCAmelCase :    Any=64       ,							lowerCAmelCase :    Dict=64       ,							lowerCAmelCase :    List[str]=65       ,							lowerCAmelCase :    Union[str, Any]=False       ,							lowerCAmelCase :    int=0.02       ,							lowerCAmelCase :    int=1e-5       ,							lowerCAmelCase :    Dict=1       ,							lowerCAmelCase :    str=0       ,							lowerCAmelCase :    List[Any]=2       ,							**lowerCAmelCase :    int       ,							)							->	List[Any]:






     """simple docstring"""







     __lowerCAmelCase  :			Optional[Any]					    =						vocab_size
     __lowerCAmelCase  :			Any					    =						max_position_embeddings
     __lowerCAmelCase  :			List[Any]					    =						hidden_size
     __lowerCAmelCase  :			Dict					    =						num_hidden_layers
     __lowerCAmelCase  :			Union[str, Any]					    =						num_attention_heads
     __lowerCAmelCase  :			int					    =						intermediate_size
     __lowerCAmelCase  :			str					    =						hidden_act
     __lowerCAmelCase  :			Any					    =						hidden_dropout_prob
     __lowerCAmelCase  :			str					    =						attention_probs_dropout_prob
     __lowerCAmelCase  :			List[str]					    =						initializer_range
     __lowerCAmelCase  :			List[str]					    =						type_vocab_size
     __lowerCAmelCase  :			Union[str, Any]					    =						segment_means_seq_len
     __lowerCAmelCase  :			Tuple					    =						num_landmarks
     __lowerCAmelCase  :			Dict					    =						conv_kernel_size
     __lowerCAmelCase  :			Tuple					    =						inv_coeff_init_option
     __lowerCAmelCase  :			List[Any]					    =						layer_norm_eps
     super().__init__(pad_token_id=lowerCAmelCase       ,							bos_token_id=lowerCAmelCase       ,							eos_token_id=lowerCAmelCase       ,							**lowerCAmelCase    )










"""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


_UpperCamelCase				:    Optional[Any]     =						datasets.utils.logging.get_logger(__name__)






@dataclass
class 						snake_case  (  datasets.BuilderConfig       ):
    __magic_name__         =     None
    __magic_name__         =     None
    __magic_name__         =     None
    __magic_name__         =     True
    __magic_name__         =     None
    __magic_name__         =     None
    __magic_name__         =     None
    __magic_name__         =     1_0000
    __magic_name__         =     None





    def 				lowerCamelCase__      (							self	:					Tuple			):


      '''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 				lowerCamelCase__      (							self	:					int				,		A	:					dict				,		A	:					Optional[datasets.Features] = None				,		):


      '''simple docstring'''




      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     :			List[str]				=   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     :			Union[str, Any]				=   sqlalchemy.create_engine(config_kwargs['con'].split('://'			)[0] + '://'			)
            a     :			List[Any]				=   str(sql.compile(dialect=engine.dialect			)			)
            a     :			str				=   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     :			List[str]				=   config_kwargs['con']
      if not isinstance(A				,		A			):
        a     :			Optional[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	:					Optional[int]			):


      '''simple docstring'''




      a     :			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 						snake_case  (  datasets.ArrowBasedBuilder       ):
    __magic_name__         =     SqlConfig





    def 				lowerCamelCase__      (							self	:					Any			):


      '''simple docstring'''




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





    def 				lowerCamelCase__      (							self	:					Dict				,		A	:					Optional[int]			):


      '''simple docstring'''




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





    def 				lowerCamelCase__      (							self	:					Union[str, Any]				,		A	:					pa.Table			):


      '''simple docstring'''




      if self.config.features is not None:
        a     :			List[Any]				=   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     :			Union[str, Any]				=   table_cast(A				,		A			)
      return pa_table





    def 				lowerCamelCase__      (							self	:					Dict			):


      '''simple docstring'''




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




