




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						:     Dict      ,      __UpperCAmelCase						:     List[Any]							) ->			Dict:
      assert isinstance(__UpperCAmelCase      ,      __UpperCAmelCase							)
      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						:     Dict      ,      __UpperCAmelCase						:     Dict      ,      __UpperCAmelCase						:     Optional[int]      ,      __UpperCAmelCase						:     List[Any]							) ->			str:
      SCREAMING_SNAKE_CASE_     						=      tmp_path / 'cache'
      SCREAMING_SNAKE_CASE_     						=      {'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'}
      with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
            SCREAMING_SNAKE_CASE_     						=      SqlDatasetReader(
                'dataset'      ,      'sqlite:///' + sqlite_path      ,      cache_dir=__UpperCAmelCase      ,      keep_in_memory=__UpperCAmelCase							).read()
      _check_sql_dataset(__UpperCAmelCase      ,      __UpperCAmelCase							)

@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						:     int      ,      __UpperCAmelCase						:     str      ,      __UpperCAmelCase						:     Optional[Any]      ,      __UpperCAmelCase						:     Optional[int]							) ->			Tuple:
      SCREAMING_SNAKE_CASE_     						=      tmp_path / 'cache'
      SCREAMING_SNAKE_CASE_     						=      {'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'}
      SCREAMING_SNAKE_CASE_     						=      features.copy() if features else default_expected_features
      SCREAMING_SNAKE_CASE_     						=      (
          Features({feature: Value(__UpperCAmelCase							) for feature, dtype in features.items()}							) if features is not None else None
      )
      SCREAMING_SNAKE_CASE_     						=      SqlDatasetReader('dataset'      ,      'sqlite:///' + sqlite_path      ,      features=__UpperCAmelCase      ,      cache_dir=__UpperCAmelCase							).read()
      _check_sql_dataset(__UpperCAmelCase      ,      __UpperCAmelCase							)

def  UpperCAmelCase_							(				__UpperCAmelCase						:     List[str]							) ->			Union[str, Any]:
      with contextlib.closing(sqlitea.connect(__UpperCAmelCase							)							) as con:
            SCREAMING_SNAKE_CASE_     						=      con.cursor()
            cur.execute('SELECT * FROM dataset'							)
            for row in cur:
                  yield row

@require_sqlalchemy
def  UpperCAmelCase_							(				__UpperCAmelCase						:     List[Any]      ,      __UpperCAmelCase						:     Dict      ,      __UpperCAmelCase						:     List[Any]							) ->			Optional[Any]:
      SCREAMING_SNAKE_CASE_     						=      tmp_path / 'cache'
      SCREAMING_SNAKE_CASE_     						=      os.path.join(__UpperCAmelCase      ,      'tmp.sql'							)
      SCREAMING_SNAKE_CASE_     						=      SqlDatasetReader('dataset'      ,      'sqlite:///' + sqlite_path      ,      cache_dir=__UpperCAmelCase							).read()
      SqlDatasetWriter(__UpperCAmelCase      ,      'dataset'      ,      'sqlite:///' + output_sqlite_path      ,      num_proc=1							).write()

      SCREAMING_SNAKE_CASE_     						=      iter_sql_file(__UpperCAmelCase							)
      SCREAMING_SNAKE_CASE_     						=      iter_sql_file(__UpperCAmelCase							)

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

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

      SCREAMING_SNAKE_CASE_     						=      iter_sql_file(__UpperCAmelCase							)
      SCREAMING_SNAKE_CASE_     						=      iter_sql_file(__UpperCAmelCase							)

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





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





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


lowerCamelCase__			:       Dict         =							logging.get_logger(__name__)

lowerCamelCase__			:       Any         =							{
    'facebook/s2t-small-librispeech-asr': (
        'https://huggingface.co/facebook/s2t-small-librispeech-asr/resolve/main/config.json'
    ),
    # See all Speech2Text models at https://huggingface.co/models?filter=speech_to_text
}





class 	lowerCamelCase_							(				_SCREAMING_SNAKE_CASE ):


 '''simple docstring'''







 lowercase_       =   "speech_to_text"
 lowercase_       =   ["past_key_values"]
 lowercase_       =   {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}



 def __init__(							self  :		Union[str, Any]    ,    _lowerCAmelCase  :		Optional[int]=10_000    ,    _lowerCAmelCase  :		str=12    ,    _lowerCAmelCase  :		Tuple=2_048    ,    _lowerCAmelCase  :		str=4    ,    _lowerCAmelCase  :		Tuple=6    ,    _lowerCAmelCase  :		Optional[int]=2_048    ,    _lowerCAmelCase  :		Dict=4    ,    _lowerCAmelCase  :		List[Any]=0.0    ,    _lowerCAmelCase  :		int=0.0    ,    _lowerCAmelCase  :		Optional[int]=True    ,    _lowerCAmelCase  :		Union[str, Any]=True    ,    _lowerCAmelCase  :		Any="relu"    ,    _lowerCAmelCase  :		Any=256    ,    _lowerCAmelCase  :		List[str]=0.1    ,    _lowerCAmelCase  :		Optional[Any]=0.0    ,    _lowerCAmelCase  :		Optional[int]=0.0    ,    _lowerCAmelCase  :		Tuple=0.02    ,    _lowerCAmelCase  :		List[str]=2    ,    _lowerCAmelCase  :		Tuple=True    ,    _lowerCAmelCase  :		List[str]=1    ,    _lowerCAmelCase  :		Dict=0    ,    _lowerCAmelCase  :		Tuple=2    ,    _lowerCAmelCase  :		Optional[int]=6_000    ,    _lowerCAmelCase  :		Tuple=1_024    ,    _lowerCAmelCase  :		Any=2    ,    _lowerCAmelCase  :		str=(5, 5)    ,    _lowerCAmelCase  :		Optional[int]=1_024    ,    _lowerCAmelCase  :		List[Any]=80    ,    _lowerCAmelCase  :		List[Any]=1    ,    **_lowerCAmelCase  :		List[Any]    ,    ):
       SCREAMING_SNAKE_CASE_     						=      vocab_size
       SCREAMING_SNAKE_CASE_     						=      d_model
       SCREAMING_SNAKE_CASE_     						=      encoder_ffn_dim
       SCREAMING_SNAKE_CASE_     						=      encoder_layers
       SCREAMING_SNAKE_CASE_     						=      encoder_attention_heads
       SCREAMING_SNAKE_CASE_     						=      decoder_ffn_dim
       SCREAMING_SNAKE_CASE_     						=      decoder_layers
       SCREAMING_SNAKE_CASE_     						=      decoder_attention_heads
       SCREAMING_SNAKE_CASE_     						=      dropout
       SCREAMING_SNAKE_CASE_     						=      attention_dropout
       SCREAMING_SNAKE_CASE_     						=      activation_dropout
       SCREAMING_SNAKE_CASE_     						=      activation_function
       SCREAMING_SNAKE_CASE_     						=      init_std
       SCREAMING_SNAKE_CASE_     						=      encoder_layerdrop
       SCREAMING_SNAKE_CASE_     						=      decoder_layerdrop
       SCREAMING_SNAKE_CASE_     						=      use_cache
       SCREAMING_SNAKE_CASE_     						=      encoder_layers
       SCREAMING_SNAKE_CASE_     						=      scale_embedding  # scale factor will be sqrt(d_model) if True
       SCREAMING_SNAKE_CASE_     						=      max_source_positions
       SCREAMING_SNAKE_CASE_     						=      max_target_positions
       SCREAMING_SNAKE_CASE_     						=      num_conv_layers
       SCREAMING_SNAKE_CASE_     						=      list(_lowerCAmelCase						)
       SCREAMING_SNAKE_CASE_     						=      conv_channels
       SCREAMING_SNAKE_CASE_     						=      input_feat_per_channel
       SCREAMING_SNAKE_CASE_     						=      input_channels

       if len(self.conv_kernel_sizes						) != self.num_conv_layers:
             raise ValueError(
                 'Configuration for convolutional module is incorrect. '
                 'It is required that `len(config.conv_kernel_sizes)` == `config.num_conv_layers` '
                 F"but is `len(config.conv_kernel_sizes) = {len(self.conv_kernel_sizes						)}`, "
                 F"`config.num_conv_layers = {self.num_conv_layers}`."						)

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