


"""simple docstring"""


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


A     :   List[str]			    =			logging.get_logger(__name__)

A     :   int			    =			{
    "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   _UpperCamelCase  (   lowerCAmelCase__		):
							'''simple docstring'''







							__UpperCAmelCase						:    Optional[int]				      ="""speech_to_text_2"""
							__UpperCAmelCase						:    Any				      =["""past_key_values"""]
							__UpperCAmelCase						:    Optional[Any]				      ={"""num_attention_heads""": """decoder_attention_heads""", """hidden_size""": """d_model"""}

							def __init__(     self    ,   __a=1_00_00    ,   __a=6    ,   __a=20_48    ,   __a=4    ,   __a=0.0    ,   __a=True    ,   __a="relu"    ,   __a=2_56    ,   __a=0.1    ,   __a=0.0    ,   __a=0.0    ,   __a=0.0_2    ,   __a=2    ,   __a=True    ,   __a=1    ,   __a=0    ,   __a=2    ,   __a=10_24    ,   **__a    ,   ):
										__lowerCAmelCase				    =			vocab_size
										__lowerCAmelCase				    =			d_model
										__lowerCAmelCase				    =			decoder_ffn_dim
										__lowerCAmelCase				    =			decoder_layers
										__lowerCAmelCase				    =			decoder_attention_heads
										__lowerCAmelCase				    =			dropout
										__lowerCAmelCase				    =			attention_dropout
										__lowerCAmelCase				    =			activation_dropout
										__lowerCAmelCase				    =			activation_function
										__lowerCAmelCase				    =			init_std
										__lowerCAmelCase				    =			decoder_layerdrop
										__lowerCAmelCase				    =			use_cache
										__lowerCAmelCase				    =			decoder_layers
										__lowerCAmelCase				    =			scale_embedding  # scale factor will be sqrt(d_model) if True
										__lowerCAmelCase				    =			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"""


import logging
from dataclasses import dataclass, field
from typing import Optional

from seqaseq_trainer import arg_to_scheduler

from transformers import TrainingArguments


A     :   Dict			    =			logging.getLogger(__name__)
@dataclass
class   _UpperCamelCase  (   lowerCAmelCase__		):
							'''simple docstring'''







							__UpperCAmelCase						:    Optional[float]				      =field(
							    default=0.0       ,metadata={"""help""": """The label smoothing epsilon to apply (if not zero)."""}		)
							__UpperCAmelCase						:    bool				      =field(default=lowerCAmelCase__       ,metadata={"""help""": """Whether to SortishSamler or not."""}		)
							__UpperCAmelCase						:    bool				      =field(
							    default=lowerCAmelCase__       ,metadata={"""help""": """Whether to use generate to calculate generative metrics (ROUGE, BLEU)."""}		)
							__UpperCAmelCase						:    bool				      =field(default=lowerCAmelCase__       ,metadata={"""help""": """whether to use adafactor"""}		)
							__UpperCAmelCase						:    Optional[float]				      =field(
							    default=lowerCAmelCase__       ,metadata={"""help""": """Encoder layer dropout probability. Goes into model.config."""}		)
							__UpperCAmelCase						:    Optional[float]				      =field(
							    default=lowerCAmelCase__       ,metadata={"""help""": """Decoder layer dropout probability. Goes into model.config."""}		)
							__UpperCAmelCase						:    Optional[float]				      =field(default=lowerCAmelCase__       ,metadata={"""help""": """Dropout probability. Goes into model.config."""}		)
							__UpperCAmelCase						:    Optional[float]				      =field(
							    default=lowerCAmelCase__       ,metadata={"""help""": """Attention dropout probability. Goes into model.config."""}		)
							__UpperCAmelCase						:    Optional[str]				      =field(
							    default="""linear"""       ,metadata={"""help""": F'''Which lr scheduler to use. Selected in {sorted(arg_to_scheduler.keys()		)}'''}       ,)




