
import os
import warnings
from typing import List, Optional

from ...tokenization_utils_base import BatchEncoding
from ...utils import logging
from .configuration_rag import RagConfig


lowercase  :					List[Any]						    =							logging.get_logger(__name__)

class __snake_case		:
    def __init__(  self		,snake_case		,snake_case      ):



           '''simple docstring'''







           lowercase							:     List[Any]       	=      question_encoder
           lowercase							:     Union[str, Any]       	=      generator
           lowercase							:     str       	=      self.question_encoder
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case      ):



           '''simple docstring'''







           if os.path.isfile(snake_case      ):
                  raise ValueError(f"Provided path ({save_directory}) should be a directory, not a file"      )
           os.makedirs(snake_case		,exist_ok=snake_case      )
           lowercase							:     Dict       	=      os.path.join(snake_case		,"""question_encoder_tokenizer"""      )
           lowercase							:     List[Any]       	=      os.path.join(snake_case		,"""generator_tokenizer"""      )
           self.question_encoder.save_pretrained(snake_case      )
           self.generator.save_pretrained(snake_case      )
    @classmethod
    def       _SCREAMING_SNAKE_CASE       (  cls		,snake_case		,**snake_case      ):



           '''simple docstring'''







           from ..auto.tokenization_auto import AutoTokenizer

           lowercase							:     List[str]       	=      kwargs.pop("""config"""		,snake_case      )

           if config is None:
                  lowercase							:     List[Any]       	=      RagConfig.from_pretrained(snake_case      )

           lowercase							:     str       	=      AutoTokenizer.from_pretrained(
               snake_case		,config=config.question_encoder		,subfolder="""question_encoder_tokenizer"""      )
           lowercase							:     Any       	=      AutoTokenizer.from_pretrained(
               snake_case		,config=config.generator		,subfolder="""generator_tokenizer"""      )
           return cls(question_encoder=snake_case		,generator=snake_case      )
    def __call__(  self		,*snake_case		,**snake_case      ):



           '''simple docstring'''







           return self.current_tokenizer(*snake_case		,**snake_case      )
    def       _SCREAMING_SNAKE_CASE       (  self		,*snake_case		,**snake_case      ):



           '''simple docstring'''







           return self.generator.batch_decode(*snake_case		,**snake_case      )
    def       _SCREAMING_SNAKE_CASE       (  self		,*snake_case		,**snake_case      ):



           '''simple docstring'''







           return self.generator.decode(*snake_case		,**snake_case      )
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           lowercase							:     Optional[Any]       	=      self.question_encoder
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           lowercase							:     Union[str, Any]       	=      self.generator
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case		,snake_case = None		,snake_case = None		,snake_case = None		,snake_case = "longest"		,snake_case = None		,snake_case = True		,**snake_case		,):



           '''simple docstring'''







           warnings.warn(
               """`prepare_seq2seq_batch` is deprecated and will be removed in version 5 of 🤗 Transformers. Use the """
               """regular `__call__` method to prepare your inputs and the tokenizer under the `with_target_tokenizer` """
               """context manager to prepare your targets. See the documentation of your specific tokenizer for more """
               """details"""		,snake_case		,)
           if max_length is None:
                  lowercase							:     Union[str, Any]       	=      self.current_tokenizer.model_max_length
           lowercase							:     Tuple       	=      self(
               snake_case		,add_special_tokens=snake_case		,return_tensors=snake_case		,max_length=snake_case		,padding=snake_case		,truncation=snake_case		,**snake_case		,)
           if tgt_texts is None:
                  return model_inputs
           # Process tgt_texts
           if max_target_length is None:
                  lowercase							:     Dict       	=      self.current_tokenizer.model_max_length
           lowercase							:     Tuple       	=      self(
               text_target=snake_case		,add_special_tokens=snake_case		,return_tensors=snake_case		,padding=snake_case		,max_length=snake_case		,truncation=snake_case		,**snake_case		,)
           lowercase							:     List[str]       	=      labels["""input_ids"""]
           return model_inputs


import contextlib
import os
import sqlitea

import pytest

from datasets import Dataset, Features, Value
from datasets.io.sql import SqlDatasetReader, SqlDatasetWriter

from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases, require_sqlalchemy







def 						_snake_case(  SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				) ->    Any:
       assert isinstance(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				)
       assert dataset.num_rows == 4
       assert dataset.num_columns == 3
       assert dataset.column_names == ["col_1", "col_2", "col_3"]
       for feature, expected_dtype in expected_features.items():
              assert dataset.features[feature].dtype == expected_dtype







@require_sqlalchemy
@pytest.mark.parametrize("""keep_in_memory"""					,	[False, True]				)
def 						_snake_case(  SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				) ->    Union[str, Any]:
       lowercase							:     Union[str, Any]       	=      tmp_path / """cache"""
       lowercase							:     Optional[Any]       	=      {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
       with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
              lowercase							:     Any       	=      SqlDatasetReader(
                  """dataset"""					,	"""sqlite:///""" + sqlite_path					,	cache_dir=SCREAMING_SNAKE_CASE__					,	keep_in_memory=SCREAMING_SNAKE_CASE__				).read()
       _check_sql_dataset(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				)







@require_sqlalchemy
@pytest.mark.parametrize(
    """features"""					,	[
        None,
        {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""},
        {"""col_1""": """string""", """col_2""": """string""", """col_3""": """string"""},
        {"""col_1""": """int32""", """col_2""": """int32""", """col_3""": """int32"""},
        {"""col_1""": """float32""", """col_2""": """float32""", """col_3""": """float32"""},
    ]					,	)
def 						_snake_case(  SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				) ->    Tuple:
       lowercase							:     Union[str, Any]       	=      tmp_path / """cache"""
       lowercase							:     int       	=      {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
       lowercase							:     str       	=      features.copy() if features else default_expected_features
       lowercase							:     Optional[Any]       	=      (
           Features({feature: Value(SCREAMING_SNAKE_CASE__				) for feature, dtype in features.items()}				) if features is not None else None
       )
       lowercase							:     Optional[int]       	=      SqlDatasetReader("""dataset"""					,	"""sqlite:///""" + sqlite_path					,	features=SCREAMING_SNAKE_CASE__					,	cache_dir=SCREAMING_SNAKE_CASE__				).read()
       _check_sql_dataset(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				)







def 						_snake_case(  SCREAMING_SNAKE_CASE__				) ->    List[Any]:
       with contextlib.closing(sqlitea.connect(SCREAMING_SNAKE_CASE__				)				) as con:
              lowercase							:     Optional[int]       	=      con.cursor()
              cur.execute("""SELECT * FROM dataset"""				)
              for row in cur:
                     yield row







@require_sqlalchemy
def 						_snake_case(  SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				) ->    int:
       lowercase							:     Any       	=      tmp_path / """cache"""
       lowercase							:     int       	=      os.path.join(SCREAMING_SNAKE_CASE__					,	"""tmp.sql"""				)
       lowercase							:     List[str]       	=      SqlDatasetReader("""dataset"""					,	"""sqlite:///""" + sqlite_path					,	cache_dir=SCREAMING_SNAKE_CASE__				).read()
       SqlDatasetWriter(SCREAMING_SNAKE_CASE__					,	"""dataset"""					,	"""sqlite:///""" + output_sqlite_path					,	num_proc=1				).write()

       lowercase							:     List[str]       	=      iter_sql_file(SCREAMING_SNAKE_CASE__				)
       lowercase							:     Tuple       	=      iter_sql_file(SCREAMING_SNAKE_CASE__				)

       for rowa, rowa in zip(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				):
              assert rowa == rowa







@require_sqlalchemy
def 						_snake_case(  SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				) ->    Dict:
       lowercase							:     Dict       	=      tmp_path / """cache"""
       lowercase							:     List[str]       	=      os.path.join(SCREAMING_SNAKE_CASE__					,	"""tmp.sql"""				)
       lowercase							:     List[str]       	=      SqlDatasetReader("""dataset"""					,	"""sqlite:///""" + sqlite_path					,	cache_dir=SCREAMING_SNAKE_CASE__				).read()
       SqlDatasetWriter(SCREAMING_SNAKE_CASE__					,	"""dataset"""					,	"""sqlite:///""" + output_sqlite_path					,	num_proc=2				).write()

       lowercase							:     Optional[int]       	=      iter_sql_file(SCREAMING_SNAKE_CASE__				)
       lowercase							:     List[Any]       	=      iter_sql_file(SCREAMING_SNAKE_CASE__				)

       for rowa, rowa in zip(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				):
              assert rowa == rowa







@require_sqlalchemy
def 						_snake_case(  SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				) ->    List[str]:
       lowercase							:     str       	=      tmp_path / """cache"""
       lowercase							:     Optional[int]       	=      os.path.join(SCREAMING_SNAKE_CASE__					,	"""tmp.sql"""				)
       lowercase							:     Optional[Any]       	=      SqlDatasetReader("""dataset"""					,	"""sqlite:///""" + sqlite_path					,	cache_dir=SCREAMING_SNAKE_CASE__				).read()
       with pytest.raises(SCREAMING_SNAKE_CASE__				):
              SqlDatasetWriter(SCREAMING_SNAKE_CASE__					,	"""dataset"""					,	"""sqlite:///""" + output_sqlite_path					,	num_proc=0				).write()
