


'''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_  (      __a ):






      def __init__(   self							:       Optional[int]		,		_A							:       Union[str, "sqlalchemy.sql.Selectable"]		,		_A							:       Union[str, "sqlalchemy.engine.Connection", "sqlalchemy.engine.Engine", "sqlite3.Connection"]		,		_A							:       Optional[Features] = None		,		_A							:       str = None		,		_A							:       bool = False		,		**_A							:       Dict		,		):


          '''simple docstring'''


          super().__init__(features=_A		,		cache_dir=_A		,		keep_in_memory=_A		,		**_A					)
          UpperCAmelCase__     :						int						=							Sql(
              cache_dir=_A		,		features=_A		,		sql=_A		,		con=_A		,		**_A		,		)






      def lowercase_				(   self							:       List[str]					):


          '''simple docstring'''


          UpperCAmelCase__     :						Union[str, Any]						=							None
          UpperCAmelCase__     :						Tuple						=							None
          UpperCAmelCase__     :						List[str]						=							None
          UpperCAmelCase__     :						List[str]						=							None

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

          # Build dataset for splits
          UpperCAmelCase__     :						Optional[int]						=							self.builder.as_dataset(
              split='''train'''		,		verification_mode=_A		,		in_memory=self.keep_in_memory					)
          return dataset




class      lowerCamelCase_  :






      def __init__(   self							:       Any		,		_A							:       Dataset		,		_A							:       str		,		_A							:       Union[str, "sqlalchemy.engine.Connection", "sqlalchemy.engine.Engine", "sqlite3.Connection"]		,		_A							:       Optional[int] = None		,		_A							:       Optional[int] = None		,		**_A							:       Tuple		,		):


          '''simple docstring'''


          if num_proc is not None and num_proc <= 0:
              raise ValueError(f"""num_proc {num_proc} must be an integer > 0."""					)

          UpperCAmelCase__     :						Any						=							dataset
          UpperCAmelCase__     :						int						=							name
          UpperCAmelCase__     :						Union[str, Any]						=							con
          UpperCAmelCase__     :						Any						=							batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
          UpperCAmelCase__     :						int						=							num_proc
          UpperCAmelCase__     :						Optional[int]						=							to_sql_kwargs






      def lowercase_				(   self							:       int					):


          '''simple docstring'''


          UpperCAmelCase__     :						int						=							self.to_sql_kwargs.pop('''sql'''		,		_A					)
          UpperCAmelCase__     :						int						=							self.to_sql_kwargs.pop('''con'''		,		_A					)
          UpperCAmelCase__     :						List[Any]						=							self.to_sql_kwargs.pop('''index'''		,		_A					)

          UpperCAmelCase__     :						Optional[int]						=							self._write(index=_A		,		**self.to_sql_kwargs					)
          return written






      def lowercase_				(   self							:       Dict		,		_A							:       str					):


          '''simple docstring'''


          UpperCAmelCase__		,		UpperCAmelCase__		,		UpperCAmelCase__     :						int						=							args
          UpperCAmelCase__     :						int						=							{**to_sql_kwargs, '''if_exists''': '''append'''} if offset > 0 else to_sql_kwargs
          UpperCAmelCase__     :						Union[str, Any]						=							query_table(
              table=self.dataset.data		,		key=slice(_A		,		offset + self.batch_size					)		,		indices=self.dataset._indices		,		)
          UpperCAmelCase__     :						Tuple						=							batch.to_pandas()
          UpperCAmelCase__     :						Tuple						=							df.to_sql(self.name		,		self.con		,		index=_A		,		**_A					)
          return num_rows or len(_A					)






      def lowercase_				(   self							:       Optional[Any]		,		_A							:       Optional[int]		,		**_A							:       Tuple					):


          '''simple docstring'''


          UpperCAmelCase__     :						Any						=							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:
              UpperCAmelCase__		,		UpperCAmelCase__     :						Optional[int]						=							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		,		_A		,		_A					)]		,		)		,		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




import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device


if is_torch_available():
	from transformers import AutoModelForSeqaSeqLM, AutoTokenizer




@require_torch
@require_sentencepiece
@require_tokenizers
class       lowercase_					(unittest.TestCase   ):





				@slow
				def 						__UpperCamelCase    (				self)  -> Optional[int]:
											a__      =AutoModelForSeqaSeqLM.from_pretrained('google/mt5-small'       ,  return_dict=lowercase_).to(lowercase_)
											a__      =AutoTokenizer.from_pretrained('google/mt5-small')

											a__      =tokenizer('Hello there'       ,  return_tensors='pt').input_ids
											a__      =tokenizer('Hi I am'       ,  return_tensors='pt').input_ids

											a__      =model(input_ids.to(lowercase_)       ,  labels=labels.to(lowercase_)).loss
											a__      =-(labels.shape[-1] * loss.item())

											a__      =-84.91_27
											self.assertTrue(abs(mtf_score - EXPECTED_SCORE) < 1e-4)

