




'''simple docstring'''



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 				_a							(       _UpperCAmelCase      ):





       '''simple docstring'''






       A		:							str								=		'''speech_to_text_2'''
       A		:							List[str]								=		['''past_key_values''']
       A		:							Optional[Any]								=		{'''num_attention_heads''': '''decoder_attention_heads''', '''hidden_size''': '''d_model'''}







       def __init__(	self,				A=10_000,				A=6,				A=2_048,				A=4,				A=0.0,				A=True,				A="relu",				A=256,				A=0.1,				A=0.0,				A=0.0,				A=0.02,				A=2,				A=True,				A=1,				A=0,				A=2,				A=1_024,				**A,				):







             '''simple docstring'''







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

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









"""simple docstring"""





# This is the module that test_patching.py uses to test patch_submodule()

import os  # noqa: this is just for tests
import os as renamed_os  # noqa: this is just for tests
from os import path  # noqa: this is just for tests
from os import path as renamed_path  # noqa: this is just for tests
from os.path import join  # noqa: this is just for tests
from os.path import join as renamed_join  # noqa: this is just for tests


SCREAMING_SNAKE_CASE__					=   open  # noqa: we just need to have a builtin inside this module to test it properly






