





'''simple docstring'''


import multiprocessing
from typing import TYPE_CHECKING, Optional, Union

from .. import Dataset, Features, config
from ..formatting import query_table
from ..packaged_modules.sql.sql import Sql
from ..utils import logging
from .abc import AbstractDatasetInputStream


if TYPE_CHECKING:
       import sqlitea

       import sqlalchemy

class    lowerCAmelCase_		(		__magic_name__		):




 def __init__(	self			,     _lowerCAmelCase			,     _lowerCAmelCase			,     _lowerCAmelCase = None			,     _lowerCAmelCase = None			,     _lowerCAmelCase = False			,     **_lowerCAmelCase			,     )    ->							Optional[int]:
     super().__init__(features=_lowerCAmelCase			,     cache_dir=_lowerCAmelCase			,     keep_in_memory=_lowerCAmelCase			,     **_lowerCAmelCase		)
     _lowerCAmelCase				=      Sql(
         cache_dir=_lowerCAmelCase			,     features=_lowerCAmelCase			,     sql=_lowerCAmelCase			,     con=_lowerCAmelCase			,     **_lowerCAmelCase			,     )





 def 							_snake_case   (	self		)    ->							List[str]:
     _lowerCAmelCase				=      None
     _lowerCAmelCase				=      None
     _lowerCAmelCase				=      None
     _lowerCAmelCase				=      None

     self.builder.download_and_prepare(
         download_config=_lowerCAmelCase			,     download_mode=_lowerCAmelCase			,     verification_mode=_lowerCAmelCase			,     base_path=_lowerCAmelCase			,     )

     # Build dataset for splits
     _lowerCAmelCase				=      self.builder.as_dataset(
         split="train"			,     verification_mode=_lowerCAmelCase			,     in_memory=self.keep_in_memory		)
     return dataset







class    lowerCAmelCase_		:




 def __init__(	self			,     _lowerCAmelCase			,     _lowerCAmelCase			,     _lowerCAmelCase			,     _lowerCAmelCase = None			,     _lowerCAmelCase = None			,     **_lowerCAmelCase			,     )    ->							Optional[Any]:
     if num_proc is not None and num_proc <= 0:
         raise ValueError(f'''num_proc {num_proc} must be an integer > 0.'''		)

     _lowerCAmelCase				=      dataset
     _lowerCAmelCase				=      name
     _lowerCAmelCase				=      con
     _lowerCAmelCase				=      batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
     _lowerCAmelCase				=      num_proc
     _lowerCAmelCase				=      to_sql_kwargs




 def 							_snake_case   (	self		)    ->							int:
     _lowerCAmelCase				=      self.to_sql_kwargs.pop("sql"			,     _lowerCAmelCase		)
     _lowerCAmelCase				=      self.to_sql_kwargs.pop("con"			,     _lowerCAmelCase		)
     _lowerCAmelCase				=      self.to_sql_kwargs.pop("index"			,     _lowerCAmelCase		)

     _lowerCAmelCase				=      self._write(index=_lowerCAmelCase			,     **self.to_sql_kwargs		)
     return written




 def 							_snake_case   (	self			,     _lowerCAmelCase		)    ->							Any:
     _lowerCAmelCase			,			_lowerCAmelCase			,			_lowerCAmelCase				=      args
     _lowerCAmelCase				=      {**to_sql_kwargs, "if_exists": "append"} if offset > 0 else to_sql_kwargs
     _lowerCAmelCase				=      query_table(
         table=self.dataset.data			,     key=slice(_lowerCAmelCase			,     offset + self.batch_size		)			,     indices=self.dataset._indices			,     )
     _lowerCAmelCase				=      batch.to_pandas()
     _lowerCAmelCase				=      df.to_sql(self.name			,     self.con			,     index=_lowerCAmelCase			,     **_lowerCAmelCase		)
     return num_rows or len(_lowerCAmelCase		)





 def 							_snake_case   (	self			,     _lowerCAmelCase			,     **_lowerCAmelCase		)    ->							int:
     _lowerCAmelCase				=      0

     if self.num_proc is None or self.num_proc == 1:
         for offset in logging.tqdm(
             range(0			,     len(self.dataset		)			,     self.batch_size		)			,     unit="ba"			,     disable=not logging.is_progress_bar_enabled()			,     desc="Creating SQL from Arrow format"			,     ):
             written += self._batch_sql((offset, index, to_sql_kwargs)		)
     else:
         _lowerCAmelCase			,			_lowerCAmelCase				=      len(self.dataset		), self.batch_size
         with multiprocessing.Pool(self.num_proc		) as pool:
             for num_rows in logging.tqdm(
                 pool.imap(
                     self._batch_sql			,     [(offset, index, to_sql_kwargs) for offset in range(0			,     _lowerCAmelCase			,     _lowerCAmelCase		)]			,     )			,     total=(num_rows // batch_size) + 1 if num_rows % batch_size else num_rows // batch_size			,     unit="ba"			,     disable=not logging.is_progress_bar_enabled()			,     desc="Creating SQL from Arrow format"			,     ):
                 written += num_rows

     return written









'''simple docstring'''


import jax.numpy as jnp

from ...utils import logging
from ..ta.modeling_flax_ta import FlaxTaEncoderModel, FlaxTaForConditionalGeneration, FlaxTaModel
from .configuration_mta import MTaConfig


_SCREAMING_SNAKE_CASE	 =							logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE	 =							"T5Config"


def 	__a(SCREAMING_SNAKE_CASE_  :					jnp.array		,       SCREAMING_SNAKE_CASE_  :					int		,       SCREAMING_SNAKE_CASE_  :					int    ):



    '''simple docstring'''





    _lowerCAmelCase				=      jnp.zeros_like(SCREAMING_SNAKE_CASE_    )
    _lowerCAmelCase				=      shifted_input_ids.at[:, 1:].set(input_ids[:, :-1]    )
    _lowerCAmelCase				=      shifted_input_ids.at[:, 0].set(SCREAMING_SNAKE_CASE_    )

    _lowerCAmelCase				=      jnp.where(shifted_input_ids == -100		,       SCREAMING_SNAKE_CASE_		,       SCREAMING_SNAKE_CASE_    )
    return shifted_input_ids

class    lowerCAmelCase_		(		__magic_name__		):
 __lowerCamelCase						:							List[str]         =					"mt5"
 __lowerCamelCase						:							Any         =					MTaConfig







class    lowerCAmelCase_		(		__magic_name__		):
 __lowerCamelCase						:							List[str]         =					"mt5"
 __lowerCamelCase						:							Dict         =					MTaConfig







class    lowerCAmelCase_		(		__magic_name__		):
 __lowerCamelCase						:							Optional[Any]         =					"mt5"
 __lowerCamelCase						:							str         =					MTaConfig


