






"""simple docstring"""


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available, is_torch_available


_UpperCamelCase				:    List[Any]     =						{
    'configuration_xlm_prophetnet': ['XLM_PROPHETNET_PRETRAINED_CONFIG_ARCHIVE_MAP', 'XLMProphetNetConfig'],
}

try:
   if not is_sentencepiece_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _UpperCamelCase				:    Dict     =						['XLMProphetNetTokenizer']

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _UpperCamelCase				:    int     =						[
       'XLM_PROPHETNET_PRETRAINED_MODEL_ARCHIVE_LIST',
       'XLMProphetNetDecoder',
       'XLMProphetNetEncoder',
       'XLMProphetNetForCausalLM',
       'XLMProphetNetForConditionalGeneration',
       'XLMProphetNetModel',
       'XLMProphetNetPreTrainedModel',
   ]


if TYPE_CHECKING:
   from .configuration_xlm_prophetnet import XLM_PROPHETNET_PRETRAINED_CONFIG_ARCHIVE_MAP, XLMProphetNetConfig

   try:
      if not is_sentencepiece_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_xlm_prophetnet import XLMProphetNetTokenizer

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_xlm_prophetnet import (
          XLM_PROPHETNET_PRETRAINED_MODEL_ARCHIVE_LIST,
          XLMProphetNetDecoder,
          XLMProphetNetEncoder,
          XLMProphetNetForCausalLM,
          XLMProphetNetForConditionalGeneration,
          XLMProphetNetModel,
          XLMProphetNetPreTrainedModel,
      )

else:
   import sys

   _UpperCamelCase				:    Dict     =						_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)












"""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			)




