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


_lowerCAmelCase       =		logging.get_logger(__name__)

_lowerCAmelCase       =		{
    "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_     (       __lowercase ):
							UpperCAmelCase				   =     "speech_to_text_2"
							UpperCAmelCase				   =     ["past_key_values"]
							UpperCAmelCase				   =     {"num_attention_heads": "decoder_attention_heads", "hidden_size": "d_model"}







							def __init__(   self      :     Dict ,			_A      :     Dict=1_0000 ,			_A      :     Any=6 ,			_A      :     Optional[int]=2048 ,			_A      :     str=4 ,			_A      :     int=0.0 ,			_A      :     Tuple=True ,			_A      :     Dict="relu" ,			_A      :     Optional[Any]=256 ,			_A      :     str=0.1 ,			_A      :     List[Any]=0.0 ,			_A      :     Tuple=0.0 ,			_A      :     Optional[int]=0.02 ,			_A      :     Optional[Any]=2 ,			_A      :     Optional[Any]=True ,			_A      :     Optional[int]=1 ,			_A      :     Optional[int]=0 ,			_A      :     List[Any]=2 ,			_A      :     str=1024 ,			**_A      :     str ,			):
												_UpperCamelCase			     =     vocab_size
												_UpperCamelCase			     =     d_model
												_UpperCamelCase			     =     decoder_ffn_dim
												_UpperCamelCase			     =     decoder_layers
												_UpperCamelCase			     =     decoder_attention_heads
												_UpperCamelCase			     =     dropout
												_UpperCamelCase			     =     attention_dropout
												_UpperCamelCase			     =     activation_dropout
												_UpperCamelCase			     =     activation_function
												_UpperCamelCase			     =     init_std
												_UpperCamelCase			     =     decoder_layerdrop
												_UpperCamelCase			     =     use_cache
												_UpperCamelCase			     =     decoder_layers
												_UpperCamelCase			     =     scale_embedding  # scale factor will be sqrt(d_model) if True
												_UpperCamelCase			     =     max_target_positions

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








import warnings

from ...utils import logging
from .image_processing_beit import BeitImageProcessor


UpperCamelCase    							=      logging.get_logger(__name__)







class 		_A						( UpperCAmelCase_ ):

  def __init__(    self						:	str   ,				*lowerCamelCase__						:	Optional[int]   ,				**lowerCamelCase__						:	Any			):
     """simple docstring"""

     warnings.warn(
         """The class BeitFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"""
         """ use BeitImageProcessor instead."""   ,				lowerCamelCase__   ,				)
     super().__init__(*lowerCamelCase__   ,				**lowerCamelCase__			)



