'''simple docstring'''

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 			__UpperCamelCase       (   UpperCAmelCase		,     UpperCAmelCase     ):
 assert isinstance(snake_case__		,     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 			__UpperCamelCase       (   UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase     ):
 lowercase__				:    Optional[int]  =					tmp_path / 'cache'
 lowercase__				:    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__				:    Tuple  =					SqlDatasetReader(
      '''dataset'''		,     '''sqlite:///''' + sqlite_path		,     cache_dir=snake_case__		,     keep_in_memory=snake_case__     ).read()
 _check_sql_dataset(snake_case__		,     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 			__UpperCamelCase       (   UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase     ):
 lowercase__				:    str  =					tmp_path / 'cache'
 lowercase__				:    Dict  =					{'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'}
 lowercase__				:    Optional[int]  =					features.copy() if features else default_expected_features
 lowercase__				:    Union[str, Any]  =					(
     Features({feature: Value(snake_case__     ) for feature, dtype in features.items()}     ) if features is not None else None
 )
 lowercase__				:    int  =					SqlDatasetReader('''dataset'''		,     '''sqlite:///''' + sqlite_path		,     features=snake_case__		,     cache_dir=snake_case__     ).read()
 _check_sql_dataset(snake_case__		,     snake_case__     )
def 			__UpperCamelCase       (   UpperCAmelCase     ):
 with contextlib.closing(sqlitea.connect(snake_case__     )     ) as con:
  lowercase__				:    List[str]  =					con.cursor()
  cur.execute('''SELECT * FROM dataset'''     )
  for row in cur:
   yield row
@require_sqlalchemy
def 			__UpperCamelCase       (   UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase     ):
 lowercase__				:    Any  =					tmp_path / 'cache'
 lowercase__				:    Any  =					os.path.join(snake_case__		,     '''tmp.sql'''     )
 lowercase__				:    Tuple  =					SqlDatasetReader('''dataset'''		,     '''sqlite:///''' + sqlite_path		,     cache_dir=snake_case__     ).read()
 SqlDatasetWriter(snake_case__		,     '''dataset'''		,     '''sqlite:///''' + output_sqlite_path		,     num_proc=1     ).write()

 lowercase__				:    Dict  =					iter_sql_file(snake_case__     )
 lowercase__				:    Optional[int]  =					iter_sql_file(snake_case__     )

 for rowa, rowa in zip(snake_case__		,     snake_case__     ):
  assert rowa == rowa
@require_sqlalchemy
def 			__UpperCamelCase       (   UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase     ):
 lowercase__				:    List[str]  =					tmp_path / 'cache'
 lowercase__				:    List[Any]  =					os.path.join(snake_case__		,     '''tmp.sql'''     )
 lowercase__				:    Union[str, Any]  =					SqlDatasetReader('''dataset'''		,     '''sqlite:///''' + sqlite_path		,     cache_dir=snake_case__     ).read()
 SqlDatasetWriter(snake_case__		,     '''dataset'''		,     '''sqlite:///''' + output_sqlite_path		,     num_proc=2     ).write()

 lowercase__				:    Tuple  =					iter_sql_file(snake_case__     )
 lowercase__				:    List[str]  =					iter_sql_file(snake_case__     )

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





@require_sqlalchemy
def 			__UpperCamelCase       (   UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase     ):
 lowercase__				:    List[Any]  =					tmp_path / 'cache'
 lowercase__				:    Tuple  =					os.path.join(snake_case__		,     '''tmp.sql'''     )
 lowercase__				:    int  =					SqlDatasetReader('''dataset'''		,     '''sqlite:///''' + sqlite_path		,     cache_dir=snake_case__     ).read()
 with pytest.raises(snake_case__     ):
  SqlDatasetWriter(snake_case__		,     '''dataset'''		,     '''sqlite:///''' + output_sqlite_path		,     num_proc=0     ).write()






from ...configuration_utils import PretrainedConfig
from ...utils import logging


__UpperCAmelCase          =       logging.get_logger(__name__)

__UpperCAmelCase          =       {
    '''facebook/s2t-wav2vec2-large-en-de''': (
        '''https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/config.json'''
    ),
    # See all Speech2Text models at https://huggingface.co/models?filter=speech2text2
}



class 				lowerCAmelCase_		(  a__							):
       UpperCAmelCase__				:					Optional[Any]        =  "speech_to_text_2"
       UpperCAmelCase__				:					List[Any]        =  ["past_key_values"]
       UpperCAmelCase__				:					Any        =  {"num_attention_heads": "decoder_attention_heads", "hidden_size": "d_model"}
       def __init__(			self,			SCREAMING_SNAKE_CASE_=1_0000,			SCREAMING_SNAKE_CASE_=6,			SCREAMING_SNAKE_CASE_=2048,			SCREAMING_SNAKE_CASE_=4,			SCREAMING_SNAKE_CASE_=0.0,			SCREAMING_SNAKE_CASE_=True,			SCREAMING_SNAKE_CASE_="relu",			SCREAMING_SNAKE_CASE_=256,			SCREAMING_SNAKE_CASE_=0.1,			SCREAMING_SNAKE_CASE_=0.0,			SCREAMING_SNAKE_CASE_=0.0,			SCREAMING_SNAKE_CASE_=0.02,			SCREAMING_SNAKE_CASE_=2,			SCREAMING_SNAKE_CASE_=True,			SCREAMING_SNAKE_CASE_=1,			SCREAMING_SNAKE_CASE_=0,			SCREAMING_SNAKE_CASE_=2,			SCREAMING_SNAKE_CASE_=1024,			**SCREAMING_SNAKE_CASE_,			)     ->		int:
          UpperCamelCase    :  Optional[int]							=	vocab_size
          UpperCamelCase    :  List[str]							=	d_model
          UpperCamelCase    :  List[str]							=	decoder_ffn_dim
          UpperCamelCase    :  Optional[Any]							=	decoder_layers
          UpperCamelCase    :  Any							=	decoder_attention_heads
          UpperCamelCase    :  Tuple							=	dropout
          UpperCamelCase    :  str							=	attention_dropout
          UpperCamelCase    :  str							=	activation_dropout
          UpperCamelCase    :  Union[str, Any]							=	activation_function
          UpperCamelCase    :  Optional[int]							=	init_std
          UpperCamelCase    :  Tuple							=	decoder_layerdrop
          UpperCamelCase    :  Dict							=	use_cache
          UpperCamelCase    :  Any							=	decoder_layers
          UpperCamelCase    :  Union[str, Any]							=	scale_embedding  # scale factor will be sqrt(d_model) if True
          UpperCamelCase    :  Union[str, Any]							=	max_target_positions

          super().__init__(
              pad_token_id=SCREAMING_SNAKE_CASE_,			bos_token_id=SCREAMING_SNAKE_CASE_,			eos_token_id=SCREAMING_SNAKE_CASE_,			decoder_start_token_id=SCREAMING_SNAKE_CASE_,			**SCREAMING_SNAKE_CASE_,			)



