




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


lowercase__      :			Tuple           =					logging.get_logger(__name__)

lowercase__      :			Dict           =					{
    "facebook/opt-125m": "https://huggingface.co/facebook/opt-125m/blob/main/config.json",
    "facebook/opt-350m": "https://huggingface.co/facebook/opt-350m/blob/main/config.json",
    "facebook/opt-1.3b": "https://huggingface.co/facebook/opt-1.3b/blob/main/config.json",
    "facebook/opt-2.7b": "https://huggingface.co/facebook/opt-2.7b/blob/main/config.json",
    "facebook/opt-6.7b": "https://huggingface.co/facebook/opt-6.7b/blob/main/config.json",
    "facebook/opt-13b": "https://huggingface.co/facebook/opt-13b/blob/main/config.json",
}




class 			a__       (					UpperCamelCase__      ):
     a		:			List[str]							=	"""opt"""
     a		:			Optional[int]							=	["""past_key_values"""]







     def __init__(	self  ,		A=50272  ,		A=768  ,		A=12  ,		A=3072  ,		A=2048  ,		A=True  ,		A=False  ,		A=None  ,		A=0.1  ,		A=0.0  ,		A=12  ,		A="relu"  ,		A=0.0  ,		A=0.0_2  ,		A=True  ,		A=1  ,		A=2  ,		A=2  ,		A=True  ,		A=True  ,		**A  ,		)  ->    List[str]:



          '''simple docstring'''



          super().__init__(
              pad_token_id=A  ,		bos_token_id=A  ,		eos_token_id=A  ,		**A  ,		)
          a      =      vocab_size
          a      =      max_position_embeddings
          a      =      num_attention_heads
          a      =      word_embed_proj_dim if word_embed_proj_dim is not None else hidden_size
          a      =      ffn_dim
          a      =      hidden_size
          a      =      num_hidden_layers
          a      =      dropout
          a      =      attention_dropout
          a      =      activation_function
          a      =      init_std
          a      =      layerdrop
          a      =      use_cache
          a      =      do_layer_norm_before
          # We keep these variables at `True` for backward compatibility.
          a      =      enable_bias
          a      =      layer_norm_elementwise_affine

          # Note that the only purpose of `_remove_final_layer_norm` is to keep backward compatibility
          # with checkpoints that have been fine-tuned before transformers v4.20.1
          # see https://github.com/facebookresearch/metaseq/pull/164
          a      =      _remove_final_layer_norm







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


lowercase__      :			Optional[Any]           =					logging.get_logger(__name__)

lowercase__      :			Optional[int]           =					{
    "uw-madison/yoso-4096": "https://huggingface.co/uw-madison/yoso-4096/resolve/main/config.json",
    # See all YOSO models at https://huggingface.co/models?filter=yoso
}




class 			a__       (					UpperCamelCase__      ):
     a		:			Dict							=	"""yoso"""







     def __init__(	self  ,		A=50265  ,		A=768  ,		A=12  ,		A=12  ,		A=3072  ,		A="gelu"  ,		A=0.1  ,		A=0.1  ,		A=4096  ,		A=1  ,		A=0.0_2  ,		A=1e-12  ,		A="absolute"  ,		A=True  ,		A=9  ,		A=64  ,		A=None  ,		A=True  ,		A=True  ,		A=1  ,		A=0  ,		A=2  ,		**A  ,		)  ->    Dict:



          '''simple docstring'''



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

          a      =      vocab_size
          a      =      max_position_embeddings
          a      =      hidden_size
          a      =      num_hidden_layers
          a      =      num_attention_heads
          a      =      intermediate_size
          a      =      hidden_act
          a      =      hidden_dropout_prob
          a      =      attention_probs_dropout_prob
          a      =      initializer_range
          a      =      type_vocab_size
          a      =      layer_norm_eps
          a      =      position_embedding_type
          a      =      use_expectation
          a      =      hash_code_len
          a      =      num_hash
          a      =      conv_window
          a      =      use_fast_hash
          a      =      lsh_backward

