


import os

import pytest
import yaml

from datasets.features.features import Features, Value
from datasets.info import DatasetInfo, DatasetInfosDict


@pytest.mark.parametrize(
    "files"	,  [
        ["full:README.md", "dataset_infos.json"],
        ["empty:README.md", "dataset_infos.json"],
        ["dataset_infos.json"],
        ["full:README.md"],
    ]	,  )
def 				lowerCamelCase__	(      a	:   Optional[Any]	,  a	:   Tuple				)    ->	Any:





			"""simple docstring"""
			a__ :Optional[Any]						  =    tmp_path_factory.mktemp("dset_infos_dir"				)
			if "full:README.md" in files:
						with open(dataset_infos_dir / "README.md"	,  "w"				) as f:
									f.write("---\ndataset_info:\n  dataset_size: 42\n---"				)
			if "empty:README.md" in files:
						with open(dataset_infos_dir / "README.md"	,  "w"				) as f:
									f.write(""				)
    # we want to support dataset_infos.json for backward compatibility
			if "dataset_infos.json" in files:
						with open(dataset_infos_dir / "dataset_infos.json"	,  "w"				) as f:
									f.write("{\"default\": {\"dataset_size\": 42}}"				)
			a__ :List[Any]						  =    DatasetInfosDict.from_directory(a				)
			assert dataset_infos
			assert dataset_infos["default"].dataset_size == 42


@pytest.mark.parametrize(
    "dataset_info"	,  [
        DatasetInfo(),
        DatasetInfo(
            description="foo"	,  features=Features({"a": Value("int32"				)}				)	,  builder_name="builder"	,  config_name="config"	,  version="1.0.0"	,  splits=[{"name": "train"}]	,  download_size=42	,  ),
    ]	,  )
def 				lowerCamelCase__	(      a	:   List[Any]	,  a	:   DatasetInfo				)    ->	str:





			"""simple docstring"""
			a__ :Dict						  =    str(a				)
			dataset_info.write_to_directory(a				)
			a__ :Tuple						  =    DatasetInfo.from_directory(a				)
			assert dataset_info == reloaded
			assert os.path.exists(os.path.join(a	,  "dataset_info.json"				)				)


def 				lowerCamelCase__	(      )    ->	Optional[int]:





			"""simple docstring"""
			a__ :Dict						  =    DatasetInfo(
			    description="foo"	,  citation="bar"	,  homepage="https://foo.bar"	,  license="CC0"	,  features=Features({"a": Value("int32"				)}				)	,  post_processed={}	,  supervised_keys=()	,  task_templates=[]	,  builder_name="builder"	,  config_name="config"	,  version="1.0.0"	,  splits=[{"name": "train", "num_examples": 42}]	,  download_checksums={}	,  download_size=1_337	,  post_processing_size=442	,  dataset_size=1_234	,  size_in_bytes=1_337 + 442 + 1_234	,  )
			a__ :int						  =    dataset_info._to_yaml_dict()
			assert sorted(a				) == sorted(DatasetInfo._INCLUDED_INFO_IN_YAML				)
			for key in DatasetInfo._INCLUDED_INFO_IN_YAML:
						assert key in dataset_info_yaml_dict
						assert isinstance(dataset_info_yaml_dict[key]	,  (list, dict, int, str)				)
			a__ :List[Any]						  =    yaml.safe_dump(a				)
			a__ :Dict						  =    yaml.safe_load(a				)
			assert dataset_info_yaml_dict == reloaded


def 				lowerCamelCase__	(      )    ->	str:





			"""simple docstring"""
			a__ :Any						  =    DatasetInfo()
			a__ :Dict						  =    dataset_info._to_yaml_dict()
			assert dataset_info_yaml_dict == {}







@pytest.mark.parametrize(
    "dataset_infos_dict"	,  [
        DatasetInfosDict(),
        DatasetInfosDict({"default": DatasetInfo()}				),
        DatasetInfosDict({"my_config_name": DatasetInfo()}				),
        DatasetInfosDict(
            {
                "default": DatasetInfo(
                    description="foo"	,  features=Features({"a": Value("int32"				)}				)	,  builder_name="builder"	,  config_name="config"	,  version="1.0.0"	,  splits=[{"name": "train"}]	,  download_size=42	,  )
            }				),
        DatasetInfosDict(
            {
                "v1": DatasetInfo(dataset_size=42				),
                "v2": DatasetInfo(dataset_size=1_337				),
            }				),
    ]	,  )
def 				lowerCamelCase__	(      a	:   Dict	,  a	:   DatasetInfosDict				)    ->	Optional[Any]:





			"""simple docstring"""
			a__ :Dict						  =    str(a				)
			dataset_infos_dict.write_to_directory(a				)
			a__ :int						  =    DatasetInfosDict.from_directory(a				)

			# the config_name of the dataset_infos_dict take over the attribute
			for config_name, dataset_info in dataset_infos_dict.items():
						a__ :int						  =    config_name
						# the yaml representation doesn't include fields like description or citation
						# so we just test that we can recover what we can from the yaml
						a__ :Any						  =    DatasetInfo._from_yaml_dict(dataset_info._to_yaml_dict()				)
			assert dataset_infos_dict == reloaded

			if dataset_infos_dict:
						assert os.path.exists(os.path.join(a	,  "README.md"				)				)






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


snake_case__			 =						logging.get_logger(__name__)

snake_case__			 =						{
    '''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):
						lowerCamelCase_         =     'speech_to_text_2'
						lowerCamelCase_         =     ['past_key_values']
						lowerCamelCase_         =     {'num_attention_heads': 'decoder_attention_heads', 'hidden_size': 'd_model'}




						def __init__(	self						:   Dict   ,     __A						:   Union[str, Any]=10000   ,     __A						:   Tuple=6   ,     __A						:   Tuple=2048   ,     __A						:   Any=4   ,     __A						:   str=0.0   ,     __A						:   Tuple=True   ,     __A						:   List[Any]="relu"   ,     __A						:   Union[str, Any]=256   ,     __A						:   str=0.1   ,     __A						:   Optional[Any]=0.0   ,     __A						:   Tuple=0.0   ,     __A						:   Any=0.02   ,     __A						:   int=2   ,     __A						:   Union[str, Any]=True   ,     __A						:   Tuple=1   ,     __A						:   List[str]=0   ,     __A						:   List[str]=2   ,     __A						:   Union[str, Any]=1024   ,     **__A						:   Tuple   ,     )      ->List[str]:






									"""simple docstring"""






									a__ :Tuple						  =    vocab_size
									a__ :Dict						  =    d_model
									a__ :Optional[int]						  =    decoder_ffn_dim
									a__ :Optional[int]						  =    decoder_layers
									a__ :Optional[int]						  =    decoder_attention_heads
									a__ :Dict						  =    dropout
									a__ :Optional[Any]						  =    attention_dropout
									a__ :Any						  =    activation_dropout
									a__ :int						  =    activation_function
									a__ :Optional[int]						  =    init_std
									a__ :List[Any]						  =    decoder_layerdrop
									a__ :Optional[Any]						  =    use_cache
									a__ :List[str]						  =    decoder_layers
									a__ :int						  =    scale_embedding  # scale factor will be sqrt(d_model) if True
									a__ :Optional[int]						  =    max_target_positions

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


