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


a  :      Optional[int]            =    logging.get_logger(__name__)

a  :      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 					_lowercase    (     UpperCAmelCase__			):






    '''simple docstring'''
    SCREAMING_SNAKE_CASE:				int          =    'speech_to_text'
    SCREAMING_SNAKE_CASE:				List[str]          =    ['past_key_values']
    SCREAMING_SNAKE_CASE:				Any          =    {'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}




    def __init__(    self       ,						lowerCamelCase__=10_000       ,						lowerCamelCase__=12       ,						lowerCamelCase__=2_048       ,						lowerCamelCase__=4       ,						lowerCamelCase__=6       ,						lowerCamelCase__=2_048       ,						lowerCamelCase__=4       ,						lowerCamelCase__=0.0       ,						lowerCamelCase__=0.0       ,						lowerCamelCase__=True       ,						lowerCamelCase__=True       ,						lowerCamelCase__="relu"       ,						lowerCamelCase__=256       ,						lowerCamelCase__=0.1       ,						lowerCamelCase__=0.0       ,						lowerCamelCase__=0.0       ,						lowerCamelCase__=0.0_2       ,						lowerCamelCase__=2       ,						lowerCamelCase__=True       ,						lowerCamelCase__=1       ,						lowerCamelCase__=0       ,						lowerCamelCase__=2       ,						lowerCamelCase__=6_000       ,						lowerCamelCase__=1_024       ,						lowerCamelCase__=2       ,						lowerCamelCase__=(5, 5)       ,						lowerCamelCase__=1_024       ,						lowerCamelCase__=80       ,						lowerCamelCase__=1       ,						**lowerCamelCase__       ,						):
       lowerCAmelCase_:							Tuple       =	vocab_size
       lowerCAmelCase_:							Optional[Any]       =	d_model
       lowerCAmelCase_:							int       =	encoder_ffn_dim
       lowerCAmelCase_:							List[Any]       =	encoder_layers
       lowerCAmelCase_:							Any       =	encoder_attention_heads
       lowerCAmelCase_:							Tuple       =	decoder_ffn_dim
       lowerCAmelCase_:							Tuple       =	decoder_layers
       lowerCAmelCase_:							Dict       =	decoder_attention_heads
       lowerCAmelCase_:							int       =	dropout
       lowerCAmelCase_:							Dict       =	attention_dropout
       lowerCAmelCase_:							Tuple       =	activation_dropout
       lowerCAmelCase_:							Tuple       =	activation_function
       lowerCAmelCase_:							Optional[Any]       =	init_std
       lowerCAmelCase_:							Optional[int]       =	encoder_layerdrop
       lowerCAmelCase_:							int       =	decoder_layerdrop
       lowerCAmelCase_:							List[str]       =	use_cache
       lowerCAmelCase_:							Optional[Any]       =	encoder_layers
       lowerCAmelCase_:							List[Any]       =	scale_embedding  # scale factor will be sqrt(d_model) if True
       lowerCAmelCase_:							List[Any]       =	max_source_positions
       lowerCAmelCase_:							List[Any]       =	max_target_positions
       lowerCAmelCase_:							Optional[int]       =	num_conv_layers
       lowerCAmelCase_:							str       =	list(lowerCamelCase__ )
       lowerCAmelCase_:							Optional[int]       =	conv_channels
       lowerCAmelCase_:							Tuple       =	input_feat_per_channel
       lowerCAmelCase_:							Dict       =	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__       ,						)
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__	(		lowercase							,				lowercase					):
   assert isinstance(lowercase							,				lowercase					)
   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__	(		lowercase							,				lowercase							,				lowercase							,				lowercase					):
   lowerCAmelCase_:							Any       =	tmp_path / "cache"
   lowerCAmelCase_:							int       =	{"col_1": "string", "col_2": "int64", "col_3": "float64"}
   with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
      lowerCAmelCase_:							Optional[int]       =	SqlDatasetReader(
          "dataset"							,				"sqlite:///" + sqlite_path							,				cache_dir=lowercase							,				keep_in_memory=lowercase					).read()
   _check_sql_dataset(lowercase							,				lowercase					)
@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__	(		lowercase							,				lowercase							,				lowercase							,				lowercase					):
   lowerCAmelCase_:							List[str]       =	tmp_path / "cache"
   lowerCAmelCase_:							int       =	{"col_1": "string", "col_2": "int64", "col_3": "float64"}
   lowerCAmelCase_:							List[str]       =	features.copy() if features else default_expected_features
   lowerCAmelCase_:							Any       =	(
       Features({feature: Value(lowercase					) for feature, dtype in features.items()}					) if features is not None else None
   )
   lowerCAmelCase_:							int       =	SqlDatasetReader("dataset"							,				"sqlite:///" + sqlite_path							,				features=lowercase							,				cache_dir=lowercase					).read()
   _check_sql_dataset(lowercase							,				lowercase					)
def 		snake_case__	(		lowercase					):
   with contextlib.closing(sqlitea.connect(lowercase					)					) as con:
      lowerCAmelCase_:							Any       =	con.cursor()
      cur.execute("SELECT * FROM dataset"					)
      for row in cur:
         yield row
@require_sqlalchemy
def 		snake_case__	(		lowercase							,				lowercase							,				lowercase					):
   lowerCAmelCase_:							Optional[int]       =	tmp_path / "cache"
   lowerCAmelCase_:							Optional[Any]       =	os.path.join(lowercase							,				"tmp.sql"					)
   lowerCAmelCase_:							str       =	SqlDatasetReader("dataset"							,				"sqlite:///" + sqlite_path							,				cache_dir=lowercase					).read()
   SqlDatasetWriter(lowercase							,				"dataset"							,				"sqlite:///" + output_sqlite_path							,				num_proc=1					).write()

   lowerCAmelCase_:							Union[str, Any]       =	iter_sql_file(lowercase					)
   lowerCAmelCase_:							str       =	iter_sql_file(lowercase					)

   for rowa, rowa in zip(lowercase							,				lowercase					):
      assert rowa == rowa
@require_sqlalchemy
def 		snake_case__	(		lowercase							,				lowercase							,				lowercase					):
   lowerCAmelCase_:							str       =	tmp_path / "cache"
   lowerCAmelCase_:							Optional[int]       =	os.path.join(lowercase							,				"tmp.sql"					)
   lowerCAmelCase_:							Optional[Any]       =	SqlDatasetReader("dataset"							,				"sqlite:///" + sqlite_path							,				cache_dir=lowercase					).read()
   SqlDatasetWriter(lowercase							,				"dataset"							,				"sqlite:///" + output_sqlite_path							,				num_proc=2					).write()

   lowerCAmelCase_:							Optional[Any]       =	iter_sql_file(lowercase					)
   lowerCAmelCase_:							Optional[int]       =	iter_sql_file(lowercase					)

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







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