




import numpy as np

from transformers import BatchFeature
from transformers.testing_utils import require_tf, require_torch

from .test_feature_extraction_common import FeatureExtractionSavingTestMixin

class     __a(	_a  ):






    """simple docstring"""





    lowerCAmelCase         = None
    lowerCAmelCase         = None





    @property
    def 				a__ ( self    )					->						Optional[Any]:
         return self.feat_extract_tester.prepare_feat_extract_dict()





    def 				a__ ( self    )					->						List[Any]:
         UpperCAmelCase_     :				Optional[int]  =     self.feature_extraction_class(**self.feat_extract_dict    )
         self.assertTrue(hasattr(__lowerCAmelCase  ,'''feature_size'''    )    )
         self.assertTrue(hasattr(__lowerCAmelCase  ,'''sampling_rate'''    )    )
         self.assertTrue(hasattr(__lowerCAmelCase  ,'''padding_value'''    )    )





    def 				a__ ( self    )					->						str:
         UpperCAmelCase_     :				List[Any]  =     self.feat_extract_tester.prepare_inputs_for_common()
         UpperCAmelCase_     :				Union[str, Any]  =     self.feature_extraction_class(**self.feat_extract_dict    )
         UpperCAmelCase_     :				Dict  =     feat_extract.model_input_names[0]

         UpperCAmelCase_     :				Optional[int]  =     BatchFeature({input_name: speech_inputs}    )

         self.assertTrue(all(len(__lowerCAmelCase    ) == len(__lowerCAmelCase    ) for x, y in zip(__lowerCAmelCase  ,processed_features[input_name]    )    )    )

         UpperCAmelCase_     :				str  =     self.feat_extract_tester.prepare_inputs_for_common(equal_length=__lowerCAmelCase    )
         UpperCAmelCase_     :				Dict  =     BatchFeature({input_name: speech_inputs}  ,tensor_type='''np'''    )

         UpperCAmelCase_     :				Dict  =     processed_features[input_name]

         if len(batch_features_input.shape    ) < 3:
              UpperCAmelCase_     :				Optional[int]  =     batch_features_input[:, :, None]

         self.assertTrue(
             batch_features_input.shape
             == (self.feat_extract_tester.batch_size, len(speech_inputs[0]    ), self.feat_extract_tester.feature_size)    )





    @require_torch
    def 				a__ ( self    )					->						List[str]:
         UpperCAmelCase_     :				Union[str, Any]  =     self.feat_extract_tester.prepare_inputs_for_common(equal_length=__lowerCAmelCase    )
         UpperCAmelCase_     :				Optional[int]  =     self.feature_extraction_class(**self.feat_extract_dict    )
         UpperCAmelCase_     :				Union[str, Any]  =     feat_extract.model_input_names[0]

         UpperCAmelCase_     :				List[str]  =     BatchFeature({input_name: speech_inputs}  ,tensor_type='''pt'''    )

         UpperCAmelCase_     :				Any  =     processed_features[input_name]

         if len(batch_features_input.shape    ) < 3:
              UpperCAmelCase_     :				Any  =     batch_features_input[:, :, None]

         self.assertTrue(
             batch_features_input.shape
             == (self.feat_extract_tester.batch_size, len(speech_inputs[0]    ), self.feat_extract_tester.feature_size)    )





    @require_tf
    def 				a__ ( self    )					->						Any:
         UpperCAmelCase_     :				Tuple  =     self.feat_extract_tester.prepare_inputs_for_common(equal_length=__lowerCAmelCase    )
         UpperCAmelCase_     :				Union[str, Any]  =     self.feature_extraction_class(**self.feat_extract_dict    )
         UpperCAmelCase_     :				str  =     feat_extract.model_input_names[0]

         UpperCAmelCase_     :				Dict  =     BatchFeature({input_name: speech_inputs}  ,tensor_type='''tf'''    )

         UpperCAmelCase_     :				Optional[Any]  =     processed_features[input_name]

         if len(batch_features_input.shape    ) < 3:
              UpperCAmelCase_     :				int  =     batch_features_input[:, :, None]

         self.assertTrue(
             batch_features_input.shape
             == (self.feat_extract_tester.batch_size, len(speech_inputs[0]    ), self.feat_extract_tester.feature_size)    )





    def 				a__ ( self  ,_SCREAMING_SNAKE_CASE=False    )					->						int:



         def _inputs_have_equal_length(_SCREAMING_SNAKE_CASE    ):
              UpperCAmelCase_     :				str  =     len(input[0]    )
              for input_slice in input[1:]:
                   if len(__lowerCAmelCase    ) != length:
                        return False
              return True

         def _inputs_are_equal(_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE    ):
              if len(__lowerCAmelCase    ) != len(__lowerCAmelCase    ):
                   return False

              for input_slice_a, input_slice_a in zip(__lowerCAmelCase  ,__lowerCAmelCase    ):
                   if not np.allclose(np.asarray(__lowerCAmelCase    )  ,np.asarray(__lowerCAmelCase    )  ,atol=1e-3    ):
                        return False
              return True

         UpperCAmelCase_     :				Tuple  =     self.feature_extraction_class(**self.feat_extract_dict    )
         UpperCAmelCase_     :				Union[str, Any]  =     self.feat_extract_tester.prepare_inputs_for_common(numpify=__lowerCAmelCase    )
         UpperCAmelCase_     :				int  =     feat_extract.model_input_names[0]

         UpperCAmelCase_     :				Tuple  =     BatchFeature({input_name: speech_inputs}    )

         UpperCAmelCase_     :				Any  =     self.feat_extract_tester.seq_length_diff
         UpperCAmelCase_     :				Union[str, Any]  =     self.feat_extract_tester.max_seq_length + pad_diff
         UpperCAmelCase_     :				Optional[int]  =     self.feat_extract_tester.min_seq_length
         UpperCAmelCase_     :				List[Any]  =     self.feat_extract_tester.batch_size
         UpperCAmelCase_     :				Union[str, Any]  =     self.feat_extract_tester.feature_size

         # test padding for List[int] + numpy
         UpperCAmelCase_     :				Union[str, Any]  =     feat_extract.pad(__lowerCAmelCase  ,padding=__lowerCAmelCase    )
         UpperCAmelCase_     :				str  =     input_a[input_name]

         UpperCAmelCase_     :				int  =     feat_extract.pad(__lowerCAmelCase  ,padding='''longest'''    )
         UpperCAmelCase_     :				Dict  =     input_a[input_name]

         UpperCAmelCase_     :				List[Any]  =     feat_extract.pad(__lowerCAmelCase  ,padding='''max_length'''  ,max_length=len(speech_inputs[-1]    )    )
         UpperCAmelCase_     :				Any  =     input_a[input_name]

         UpperCAmelCase_     :				str  =     feat_extract.pad(__lowerCAmelCase  ,padding='''longest'''  ,return_tensors='''np'''    )
         UpperCAmelCase_     :				Union[str, Any]  =     input_a[input_name]

         # max_length parameter has to be provided when setting `padding="max_length"`
         with self.assertRaises(__lowerCAmelCase    ):
              feat_extract.pad(__lowerCAmelCase  ,padding='''max_length'''    )[input_name]

         UpperCAmelCase_     :				Tuple  =     feat_extract.pad(
             __lowerCAmelCase  ,padding='''max_length'''  ,max_length=__lowerCAmelCase  ,return_tensors='''np'''    )
         UpperCAmelCase_     :				Tuple  =     input_a[input_name]

         self.assertFalse(_inputs_have_equal_length(__lowerCAmelCase    )    )
         self.assertTrue(_inputs_have_equal_length(__lowerCAmelCase    )    )
         self.assertTrue(_inputs_have_equal_length(__lowerCAmelCase    )    )
         self.assertTrue(_inputs_are_equal(__lowerCAmelCase  ,__lowerCAmelCase    )    )
         self.assertTrue(len(input_a[0]    ) == pad_min_length    )
         self.assertTrue(len(input_a[1]    ) == pad_min_length + pad_diff    )
         self.assertTrue(input_a.shape[:2] == (batch_size, len(input_a[0]    ))    )
         self.assertTrue(input_a.shape[:2] == (batch_size, pad_max_length)    )

         if feature_size > 1:
              self.assertTrue(input_a.shape[2] == input_a.shape[2] == feature_size    )

         # test padding for `pad_to_multiple_of` for List[int] + numpy
         UpperCAmelCase_     :				int  =     feat_extract.pad(__lowerCAmelCase  ,pad_to_multiple_of=10    )
         UpperCAmelCase_     :				Tuple  =     input_a[input_name]

         UpperCAmelCase_     :				List[Any]  =     feat_extract.pad(__lowerCAmelCase  ,padding='''longest'''  ,pad_to_multiple_of=10    )
         UpperCAmelCase_     :				Optional[Any]  =     input_a[input_name]

         UpperCAmelCase_     :				Tuple  =     feat_extract.pad(
             __lowerCAmelCase  ,padding='''max_length'''  ,pad_to_multiple_of=10  ,max_length=__lowerCAmelCase    )
         UpperCAmelCase_     :				Any  =     input_a[input_name]

         UpperCAmelCase_     :				Any  =     feat_extract.pad(
             __lowerCAmelCase  ,padding='''max_length'''  ,pad_to_multiple_of=10  ,max_length=__lowerCAmelCase  ,return_tensors='''np'''  ,)
         UpperCAmelCase_     :				Tuple  =     input_a[input_name]

         self.assertTrue(all(len(__lowerCAmelCase    ) % 10 == 0 for x in input_a    )    )
         self.assertTrue(_inputs_are_equal(__lowerCAmelCase  ,__lowerCAmelCase    )    )

         UpperCAmelCase_     :				List[Any]  =     pad_max_length if pad_max_length % 10 == 0 else (pad_max_length // 10 + 1) * 10
         self.assertTrue(all(len(__lowerCAmelCase    ) == expected_mult_pad_length for x in input_a    )    )
         self.assertEqual(input_a.shape[:2]  ,(batch_size, expected_mult_pad_length)    )

         if feature_size > 1:
              self.assertTrue(input_a.shape[2] == feature_size    )

         # Check padding value is correct
         UpperCAmelCase_     :				Optional[int]  =     (np.ones(self.feat_extract_tester.feature_size    ) * feat_extract.padding_value).sum()
         self.assertTrue(
             abs(np.asarray(input_a[0]    )[pad_min_length:].sum() - padding_vector_sum * (pad_max_length - pad_min_length)    )
             < 1e-3    )
         self.assertTrue(
             abs(
                 np.asarray(input_a[1]    )[pad_min_length + pad_diff :].sum()
                 - padding_vector_sum * (pad_max_length - pad_min_length - pad_diff)    )
             < 1e-3    )
         self.assertTrue(
             abs(
                 np.asarray(input_a[2]    )[pad_min_length + 2 * pad_diff :].sum()
                 - padding_vector_sum * (pad_max_length - pad_min_length - 2 * pad_diff)    )
             < 1e-3    )
         self.assertTrue(
             abs(input_a[0, pad_min_length:].sum() - padding_vector_sum * (pad_max_length - pad_min_length)    ) < 1e-3    )
         self.assertTrue(
             abs(input_a[0, pad_min_length:].sum() - padding_vector_sum * (expected_mult_pad_length - pad_min_length)    )
             < 1e-3    )





    def 				a__ ( self  ,_SCREAMING_SNAKE_CASE=False    )					->						Optional[Any]:



         def _inputs_have_equal_length(_SCREAMING_SNAKE_CASE    ):
              UpperCAmelCase_     :				List[Any]  =     len(input[0]    )
              for input_slice in input[1:]:
                   if len(__lowerCAmelCase    ) != length:
                        return False
              return True

         def _inputs_are_equal(_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE    ):
              if len(__lowerCAmelCase    ) != len(__lowerCAmelCase    ):
                   return False

              for input_slice_a, input_slice_a in zip(__lowerCAmelCase  ,__lowerCAmelCase    ):
                   if not np.allclose(np.asarray(__lowerCAmelCase    )  ,np.asarray(__lowerCAmelCase    )  ,atol=1e-3    ):
                        return False
              return True

         UpperCAmelCase_     :				List[str]  =     self.feature_extraction_class(**self.feat_extract_dict    )
         UpperCAmelCase_     :				Union[str, Any]  =     self.feat_extract_tester.prepare_inputs_for_common(numpify=__lowerCAmelCase    )
         UpperCAmelCase_     :				Dict  =     feat_extract.model_input_names[0]

         UpperCAmelCase_     :				Optional[Any]  =     BatchFeature({input_name: speech_inputs}    )

         # truncate to smallest
         UpperCAmelCase_     :				Union[str, Any]  =     feat_extract.pad(
             __lowerCAmelCase  ,padding='''max_length'''  ,max_length=len(speech_inputs[0]    )  ,truncation=__lowerCAmelCase    )
         UpperCAmelCase_     :				Optional[int]  =     input_a[input_name]

         UpperCAmelCase_     :				List[Any]  =     feat_extract.pad(__lowerCAmelCase  ,padding='''max_length'''  ,max_length=len(speech_inputs[0]    )    )
         UpperCAmelCase_     :				int  =     input_a[input_name]

         self.assertTrue(_inputs_have_equal_length(__lowerCAmelCase    )    )
         self.assertFalse(_inputs_have_equal_length(__lowerCAmelCase    )    )

         # truncate to smallest with np
         UpperCAmelCase_     :				List[str]  =     feat_extract.pad(
             __lowerCAmelCase  ,padding='''max_length'''  ,max_length=len(speech_inputs[0]    )  ,return_tensors='''np'''  ,truncation=__lowerCAmelCase  ,)
         UpperCAmelCase_     :				Any  =     input_a[input_name]

         UpperCAmelCase_     :				str  =     feat_extract.pad(
             __lowerCAmelCase  ,padding='''max_length'''  ,max_length=len(speech_inputs[0]    )  ,return_tensors='''np'''    )
         UpperCAmelCase_     :				str  =     input_a[input_name]

         self.assertTrue(_inputs_have_equal_length(__lowerCAmelCase    )    )
         self.assertTrue(input_a.shape[1] == len(speech_inputs[0]    )    )

         # since truncation forces padding to be smaller than longest input
         # function can't return `np.ndarray`, but has to return list
         self.assertFalse(_inputs_have_equal_length(__lowerCAmelCase    )    )

         # truncate to middle
         UpperCAmelCase_     :				Optional[int]  =     feat_extract.pad(
             __lowerCAmelCase  ,padding='''max_length'''  ,max_length=len(speech_inputs[1]    )  ,truncation=__lowerCAmelCase  ,return_tensors='''np'''  ,)
         UpperCAmelCase_     :				Union[str, Any]  =     input_a[input_name]

         UpperCAmelCase_     :				Dict  =     feat_extract.pad(
             __lowerCAmelCase  ,padding='''max_length'''  ,max_length=len(speech_inputs[1]    )  ,truncation=__lowerCAmelCase    )
         UpperCAmelCase_     :				Optional[Any]  =     input_a[input_name]

         UpperCAmelCase_     :				Optional[int]  =     feat_extract.pad(
             __lowerCAmelCase  ,padding='''max_length'''  ,max_length=len(speech_inputs[1]    )  ,return_tensors='''np'''    )
         UpperCAmelCase_     :				Optional[int]  =     input_a[input_name]

         self.assertTrue(input_a.shape[1] == len(speech_inputs[1]    )    )
         self.assertTrue(_inputs_have_equal_length(__lowerCAmelCase    )    )
         self.assertTrue(_inputs_have_equal_length(__lowerCAmelCase    )    )
         self.assertTrue(_inputs_are_equal(__lowerCAmelCase  ,__lowerCAmelCase    )    )

         # since truncation forces padding to be smaller than longest input
         # function can't return `np.ndarray`, but has to return list
         self.assertFalse(_inputs_have_equal_length(__lowerCAmelCase    )    )
         self.assertTrue(len(input_a[-1]    ) == len(speech_inputs[-1]    )    )

         # padding has to be max_length when setting `truncation=True`
         with self.assertRaises(__lowerCAmelCase    ):
              feat_extract.pad(__lowerCAmelCase  ,truncation=__lowerCAmelCase    )[input_name]

         # padding has to be max_length when setting `truncation=True`
         with self.assertRaises(__lowerCAmelCase    ):
              feat_extract.pad(__lowerCAmelCase  ,padding='''longest'''  ,truncation=__lowerCAmelCase    )[input_name]

         # padding has to be max_length when setting `truncation=True`
         with self.assertRaises(__lowerCAmelCase    ):
              feat_extract.pad(__lowerCAmelCase  ,padding='''longest'''  ,truncation=__lowerCAmelCase    )[input_name]

         # max_length parameter has to be provided when setting `truncation=True` and padding="max_length"
         with self.assertRaises(__lowerCAmelCase    ):
              feat_extract.pad(__lowerCAmelCase  ,padding='''max_length'''  ,truncation=__lowerCAmelCase    )[input_name]

         # test truncation for `pad_to_multiple_of` for List[int] + numpy
         UpperCAmelCase_     :				Dict  =     12
         UpperCAmelCase_     :				Dict  =     feat_extract.pad(
             __lowerCAmelCase  ,padding='''max_length'''  ,max_length=len(speech_inputs[0]    )  ,pad_to_multiple_of=__lowerCAmelCase  ,truncation=__lowerCAmelCase  ,)
         UpperCAmelCase_     :				Any  =     input_a[input_name]

         UpperCAmelCase_     :				Tuple  =     feat_extract.pad(
             __lowerCAmelCase  ,padding='''max_length'''  ,max_length=len(speech_inputs[0]    )  ,pad_to_multiple_of=__lowerCAmelCase  ,)
         UpperCAmelCase_     :				Tuple  =     input_a[input_name]

         # retrieve expected_length as multiple of pad_to_multiple_of
         UpperCAmelCase_     :				Dict  =     len(speech_inputs[0]    )
         if expected_length % pad_to_multiple_of != 0:
              UpperCAmelCase_     :				Optional[int]  =     ((len(speech_inputs[0]    ) // pad_to_multiple_of) + 1) * pad_to_multiple_of

         self.assertTrue(len(input_a[0]    ) == expected_length    )
         self.assertTrue(_inputs_have_equal_length(__lowerCAmelCase    )    )
         self.assertFalse(_inputs_have_equal_length(__lowerCAmelCase    )    )





    def 				a__ ( self    )					->						List[str]:
         self._check_padding(numpify=__lowerCAmelCase    )





    def 				a__ ( self    )					->						Tuple:
         self._check_padding(numpify=__lowerCAmelCase    )





    def 				a__ ( self    )					->						str:
         self._check_truncation(numpify=__lowerCAmelCase    )





    def 				a__ ( self    )					->						str:
         self._check_truncation(numpify=__lowerCAmelCase    )





    @require_torch
    def 				a__ ( self    )					->						List[str]:
         UpperCAmelCase_     :				List[str]  =     self.feature_extraction_class(**self.feat_extract_dict    )
         UpperCAmelCase_     :				str  =     self.feat_extract_tester.prepare_inputs_for_common()
         UpperCAmelCase_     :				Optional[Any]  =     feat_extract.model_input_names[0]

         UpperCAmelCase_     :				Union[str, Any]  =     BatchFeature({input_name: speech_inputs}    )

         UpperCAmelCase_     :				Tuple  =     feat_extract.pad(__lowerCAmelCase  ,padding='''longest'''  ,return_tensors='''np'''    )[input_name]
         UpperCAmelCase_     :				List[str]  =     feat_extract.pad(__lowerCAmelCase  ,padding='''longest'''  ,return_tensors='''pt'''    )[input_name]

         self.assertTrue(abs(input_np.astype(np.floataa    ).sum() - input_pt.numpy().astype(np.floataa    ).sum()    ) < 1e-2    )





    @require_tf
    def 				a__ ( self    )					->						Dict:
         UpperCAmelCase_     :				Dict  =     self.feature_extraction_class(**self.feat_extract_dict    )
         UpperCAmelCase_     :				Optional[int]  =     self.feat_extract_tester.prepare_inputs_for_common()
         UpperCAmelCase_     :				Optional[Any]  =     feat_extract.model_input_names[0]

         UpperCAmelCase_     :				str  =     BatchFeature({input_name: speech_inputs}    )

         UpperCAmelCase_     :				List[str]  =     feat_extract.pad(__lowerCAmelCase  ,padding='''longest'''  ,return_tensors='''np'''    )[input_name]
         UpperCAmelCase_     :				Dict  =     feat_extract.pad(__lowerCAmelCase  ,padding='''longest'''  ,return_tensors='''tf'''    )[input_name]

         self.assertTrue(abs(input_np.astype(np.floataa    ).sum() - input_tf.numpy().astype(np.floataa    ).sum()    ) < 1e-2    )





    def 				a__ ( self    )					->						List[Any]:
         UpperCAmelCase_     :				List[str]  =     self.feat_extract_dict
         UpperCAmelCase_     :				Dict  =     True
         UpperCAmelCase_     :				Dict  =     self.feature_extraction_class(**__lowerCAmelCase    )
         UpperCAmelCase_     :				Union[str, Any]  =     self.feat_extract_tester.prepare_inputs_for_common()
         UpperCAmelCase_     :				Tuple  =     [len(__lowerCAmelCase    ) for x in speech_inputs]
         UpperCAmelCase_     :				Optional[Any]  =     feat_extract.model_input_names[0]

         UpperCAmelCase_     :				Tuple  =     BatchFeature({input_name: speech_inputs}    )

         UpperCAmelCase_     :				str  =     feat_extract.pad(__lowerCAmelCase  ,padding='''longest'''  ,return_tensors='''np'''    )
         self.assertIn('''attention_mask'''  ,__lowerCAmelCase    )
         self.assertListEqual(list(processed.attention_mask.shape    )  ,list(processed[input_name].shape[:2]    )    )
         self.assertListEqual(processed.attention_mask.sum(-1    ).tolist()  ,__lowerCAmelCase    )





    def 				a__ ( self    )					->						Optional[int]:
         UpperCAmelCase_     :				Tuple  =     self.feat_extract_dict
         UpperCAmelCase_     :				List[Any]  =     True
         UpperCAmelCase_     :				Tuple  =     self.feature_extraction_class(**__lowerCAmelCase    )
         UpperCAmelCase_     :				Union[str, Any]  =     self.feat_extract_tester.prepare_inputs_for_common()
         UpperCAmelCase_     :				Union[str, Any]  =     [len(__lowerCAmelCase    ) for x in speech_inputs]
         UpperCAmelCase_     :				Optional[int]  =     feat_extract.model_input_names[0]

         UpperCAmelCase_     :				Tuple  =     BatchFeature({input_name: speech_inputs}    )
         UpperCAmelCase_     :				str  =     min(__lowerCAmelCase    )

         UpperCAmelCase_     :				List[str]  =     feat_extract.pad(
             __lowerCAmelCase  ,padding='''max_length'''  ,max_length=__lowerCAmelCase  ,truncation=__lowerCAmelCase  ,return_tensors='''np'''    )
         self.assertIn('''attention_mask'''  ,__lowerCAmelCase    )
         self.assertListEqual(
             list(processed_pad.attention_mask.shape    )  ,[processed_pad[input_name].shape[0], max_length]    )
         self.assertListEqual(
             processed_pad.attention_mask[:, :max_length].sum(-1    ).tolist()  ,[max_length for x in speech_inputs]    )
"""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 					_A		(			lowerCAmelCase      ):

      def __init__(		self						,			__lowerCAmelCase						,			__lowerCAmelCase						,			__lowerCAmelCase = None						,			__lowerCAmelCase = None						,			__lowerCAmelCase = False						,			**__lowerCAmelCase						,			):







            """simple docstring"""



            super().__init__(features=__lowerCAmelCase						,			cache_dir=__lowerCAmelCase						,			keep_in_memory=__lowerCAmelCase						,			**__lowerCAmelCase		)
            lowercase								=      Sql(
                cache_dir=__lowerCAmelCase						,			features=__lowerCAmelCase						,			sql=__lowerCAmelCase						,			con=__lowerCAmelCase						,			**__lowerCAmelCase						,			)







      def 							A__			(		self		):







            """simple docstring"""



            lowercase								=      None
            lowercase								=      None
            lowercase								=      None
            lowercase								=      None

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

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






class 					_A		:

      def __init__(		self						,			__lowerCAmelCase						,			__lowerCAmelCase						,			__lowerCAmelCase						,			__lowerCAmelCase = None						,			__lowerCAmelCase = None						,			**__lowerCAmelCase						,			):







            """simple docstring"""



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

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

      def 							A__			(		self		):







            """simple docstring"""



            lowercase								=      self.to_sql_kwargs.pop("""sql"""						,			__lowerCAmelCase		)
            lowercase								=      self.to_sql_kwargs.pop("""con"""						,			__lowerCAmelCase		)
            lowercase								=      self.to_sql_kwargs.pop("""index"""						,			__lowerCAmelCase		)

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

      def 							A__			(		self						,			__lowerCAmelCase		):







            """simple docstring"""



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







      def 							A__			(		self						,			__lowerCAmelCase						,			**__lowerCAmelCase		):







            """simple docstring"""



            lowercase								=      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:
                  lowercase				,				lowercase								=      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



