

'''simple docstring'''

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


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

SCREAMING_SNAKE_CASE							:				str     	=			{
    "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    snake_case     (      UpperCAmelCase_		):






				"""simple docstring"""
				_a    	=     '''speech_to_text'''
				_a    	=     ['''past_key_values''']
				_a    	=     {'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}



				def __init__(    self,     _lowercase=10000,     _lowercase=12,     _lowercase=2048,     _lowercase=4,     _lowercase=6,     _lowercase=2048,     _lowercase=4,     _lowercase=0.0,     _lowercase=0.0,     _lowercase=True,     _lowercase=True,     _lowercase="relu",     _lowercase=256,     _lowercase=0.1,     _lowercase=0.0,     _lowercase=0.0,     _lowercase=0.02,     _lowercase=2,     _lowercase=True,     _lowercase=1,     _lowercase=0,     _lowercase=2,     _lowercase=6000,     _lowercase=1024,     _lowercase=2,     _lowercase=(5, 5),     _lowercase=1024,     _lowercase=80,     _lowercase=1,     **_lowercase,     )     ->							str:
											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__,     )













import os
from collections import namedtuple

import pytest

from datasets import ClassLabel, Features, Sequence, Value
from datasets.commands.test import TestCommand
from datasets.info import DatasetInfo, DatasetInfosDict


UpperCamelCase    							=      namedtuple(
    '_TestCommandArgs',
    [
        'dataset',
        'name',
        'cache_dir',
        'data_dir',
        'all_configs',
        'save_infos',
        'ignore_verifications',
        'force_redownload',
        'clear_cache',
    ],
    defaults=[None, None, None, False, False, False, False, False],
)


def 				__lowerCamelCase     (							__lowerCAmelCase							:					List[Any]					,							__lowerCAmelCase							:					Any							)     ->  Optional[int]:
   return (abs(source - target							) / target) < 0.01



@pytest.mark.integration
def 				__lowerCamelCase     (							__lowerCAmelCase							:					str							)     ->  Union[str, Any]:
   __UpperCamelCase	:			Dict					=					_TestCommandArgs(dataset=__lowerCAmelCase					,							all_configs=__lowerCAmelCase					,							save_infos=__lowerCAmelCase							)
   __UpperCamelCase	:			List[Any]					=					TestCommand(*__lowerCAmelCase							)
   test_command.run()
   __UpperCamelCase	:			Any					=					os.path.join(__lowerCAmelCase					,							"""README.md"""							)
   assert os.path.exists(__lowerCAmelCase							)
   __UpperCamelCase	:			Optional[int]					=					DatasetInfosDict.from_directory(__lowerCAmelCase							)
   __UpperCamelCase	:			List[Any]					=					DatasetInfosDict(
       {
           """default""": DatasetInfo(
               features=Features(
                   {
                       """tokens""": Sequence(Value("""string"""							)							),
                       """ner_tags""": Sequence(
                           ClassLabel(names=["""O""", """B-PER""", """I-PER""", """B-ORG""", """I-ORG""", """B-LOC""", """I-LOC"""]							)							),
                       """langs""": Sequence(Value("""string"""							)							),
                       """spans""": Sequence(Value("""string"""							)							),
                   }							)					,							splits=[
                   {
                       """name""": """train""",
                       """num_bytes""": 2351563,
                       """num_examples""": 10000,
                   },
                   {
                       """name""": """validation""",
                       """num_bytes""": 238418,
                       """num_examples""": 1000,
                   },
               ]					,							download_size=3940680					,							dataset_size=2589981					,							)
       }							)
   assert dataset_infos.keys() == expected_dataset_infos.keys()
   for key in DatasetInfo._INCLUDED_INFO_IN_YAML:
      __UpperCamelCase	,     __UpperCamelCase	:			Union[str, Any]					=					getattr(dataset_infos["""default"""]					,							__lowerCAmelCase							), getattr(expected_dataset_infos["""default"""]					,							__lowerCAmelCase							)
      if key == "num_bytes":
         assert is_apercent_close(__lowerCAmelCase					,							__lowerCAmelCase							)
      elif key == "splits":
         assert list(__lowerCAmelCase							) == list(__lowerCAmelCase							)
         for split in result:
            assert result[split].name == expected[split].name
            assert result[split].num_examples == expected[split].num_examples
            assert is_apercent_close(result[split].num_bytes					,							expected[split].num_bytes							)
      else:
         result == expected



