



"""simple docstring"""






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


__snake_case				:  Dict =       logging.get_logger(__name__)

__snake_case				:  str =       {
    'google/switch-base-8': 'https://huggingface.co/google/switch-base-8/blob/main/config.json',
}



class      A__  (    __SCREAMING_SNAKE_CASE				):




	'''simple docstring'''





	SCREAMING_SNAKE_CASE        =      'switch_transformers'
	SCREAMING_SNAKE_CASE        =      ['past_key_values']
	SCREAMING_SNAKE_CASE        =      {'hidden_size': 'd_model', 'num_attention_heads': 'num_heads', 'num_hidden_layers': 'num_layers'}

	def __init__(	self:						Dict			,     _SCREAMING_SNAKE_CASE:						Optional[int]=3_2128			,     _SCREAMING_SNAKE_CASE:						str=768			,     _SCREAMING_SNAKE_CASE:						Dict=64			,     _SCREAMING_SNAKE_CASE:						Dict=2048			,     _SCREAMING_SNAKE_CASE:						List[Any]=64			,     _SCREAMING_SNAKE_CASE:						Tuple=12			,     _SCREAMING_SNAKE_CASE:						str=3			,     _SCREAMING_SNAKE_CASE:						Union[str, Any]=12			,     _SCREAMING_SNAKE_CASE:						Any=3			,     _SCREAMING_SNAKE_CASE:						Union[str, Any]=12			,     _SCREAMING_SNAKE_CASE:						str=8			,     _SCREAMING_SNAKE_CASE:						Tuple=False			,     _SCREAMING_SNAKE_CASE:						Optional[Any]=0.01			,     _SCREAMING_SNAKE_CASE:						Union[str, Any]="float32"			,     _SCREAMING_SNAKE_CASE:						Optional[Any]=False			,     _SCREAMING_SNAKE_CASE:						Optional[Any]=32			,     _SCREAMING_SNAKE_CASE:						Any=128			,     _SCREAMING_SNAKE_CASE:						int=0.1			,     _SCREAMING_SNAKE_CASE:						Dict=1e-6			,     _SCREAMING_SNAKE_CASE:						Union[str, Any]=0.001			,     _SCREAMING_SNAKE_CASE:						Tuple=0.001			,     _SCREAMING_SNAKE_CASE:						Tuple=1.0			,     _SCREAMING_SNAKE_CASE:						Optional[Any]="relu"			,     _SCREAMING_SNAKE_CASE:						int=True			,     _SCREAMING_SNAKE_CASE:						Optional[Any]=False			,     _SCREAMING_SNAKE_CASE:						Optional[Any]=True			,     _SCREAMING_SNAKE_CASE:						str=0			,     _SCREAMING_SNAKE_CASE:						Dict=1			,     **_SCREAMING_SNAKE_CASE:						Union[str, Any]			,     )							->			Dict:

				"""simple docstring"""

				__lowerCAmelCase :    str				=			vocab_size
				__lowerCAmelCase :    int				=			d_model
				__lowerCAmelCase :    List[str]				=			d_kv
				__lowerCAmelCase :    List[str]				=			d_ff

				__lowerCAmelCase :    str				=			num_sparse_encoder_layers

				__lowerCAmelCase :    Union[str, Any]				=			num_layers
				__lowerCAmelCase :    List[Any]				=			(
				    num_decoder_layers if num_decoder_layers is not None else self.num_layers
				)  # default = symmetry
				__lowerCAmelCase :    str				=			num_sparse_decoder_layers

				# This tells us, each how many encoder layer we'll have to set a sparse layer.
				if self.num_sparse_encoder_layers > 0:
							__lowerCAmelCase :    Any				=			self.num_layers // self.num_sparse_encoder_layers
				else:
							__lowerCAmelCase :    int				=			self.num_layers  # HACK: this will create 0 sparse layers

				# This tells us, each how many encoder layer we'll have to set a sparse layer.
				if self.num_sparse_decoder_layers > 0:
							__lowerCAmelCase :    Tuple				=			self.num_decoder_layers // self.num_sparse_decoder_layers
				else:
							__lowerCAmelCase :    Optional[Any]				=			self.num_decoder_layers  # HACK: this will create 0 sparse layers

				__lowerCAmelCase :    int				=			num_heads
				__lowerCAmelCase :    Tuple				=			num_experts
				__lowerCAmelCase :    Optional[Any]				=			expert_capacity
				__lowerCAmelCase :    Optional[int]				=			router_bias
				__lowerCAmelCase :    Dict				=			router_jitter_noise
				if router_dtype not in ["float32", "float16", "bfloat16"]:
							raise ValueError(F"""`router_dtype` must be one of 'float32', 'float16' or 'bfloat16', got {router_dtype}""")
				__lowerCAmelCase :    List[Any]				=			router_dtype

				__lowerCAmelCase :    int				=			router_ignore_padding_tokens
				__lowerCAmelCase :    Optional[int]				=			relative_attention_num_buckets
				__lowerCAmelCase :    Optional[Any]				=			relative_attention_max_distance

				__lowerCAmelCase :    Tuple				=			dropout_rate
				__lowerCAmelCase :    Union[str, Any]				=			layer_norm_epsilon
				__lowerCAmelCase :    Dict				=			initializer_factor
				__lowerCAmelCase :    int				=			feed_forward_proj
				__lowerCAmelCase :    Dict				=			use_cache
				__lowerCAmelCase :    List[str]				=			add_router_probs

				__lowerCAmelCase :    Tuple				=			router_z_loss_coef
				__lowerCAmelCase :    Any				=			router_aux_loss_coef

				__lowerCAmelCase :    Any				=			self.feed_forward_proj.split("-")
				__lowerCAmelCase :    Dict				=			act_info[-1]
				__lowerCAmelCase :    Dict				=			act_info[0] == "gated"

				if len(_SCREAMING_SNAKE_CASE) > 1 and act_info[0] != "gated" or len(_SCREAMING_SNAKE_CASE) > 2:
							raise ValueError(
							    F"""`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer."""
							    "Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. "
							    "'gated-gelu' or 'relu'")

				# for backwards compatibility
				if feed_forward_proj == "gated-gelu":
							__lowerCAmelCase :    int				=			"gelu_new"

				super().__init__(
				    pad_token_id=_SCREAMING_SNAKE_CASE			,     eos_token_id=_SCREAMING_SNAKE_CASE			,     is_encoder_decoder=_SCREAMING_SNAKE_CASE			,     **_SCREAMING_SNAKE_CASE			,     )




"""simple docstring"""






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


__snake_case				:  Tuple =       logging.get_logger(__name__)

__snake_case				:  Any =       {
    'facebook/xglm-564M': 'https://huggingface.co/facebook/xglm-564M/resolve/main/config.json',
    # See all XGLM models at https://huggingface.co/models?filter=xglm
}



class      A__  (    __SCREAMING_SNAKE_CASE				):




	'''simple docstring'''





	SCREAMING_SNAKE_CASE        =      'xglm'
	SCREAMING_SNAKE_CASE        =      ['past_key_values']

	SCREAMING_SNAKE_CASE        =      {
	    'num_attention_heads': 'attention_heads',
	    'hidden_size': 'd_model',
	    'num_hidden_layers': 'num_layers',
	}

	def __init__(	self:						Optional[Any]			,     _SCREAMING_SNAKE_CASE:						List[Any]=25_6008			,     _SCREAMING_SNAKE_CASE:						Dict=2048			,     _SCREAMING_SNAKE_CASE:						int=1024			,     _SCREAMING_SNAKE_CASE:						Dict=4096			,     _SCREAMING_SNAKE_CASE:						Optional[Any]=24			,     _SCREAMING_SNAKE_CASE:						int=16			,     _SCREAMING_SNAKE_CASE:						List[str]="gelu"			,     _SCREAMING_SNAKE_CASE:						Optional[Any]=0.1			,     _SCREAMING_SNAKE_CASE:						int=0.1			,     _SCREAMING_SNAKE_CASE:						List[Any]=0.0			,     _SCREAMING_SNAKE_CASE:						Union[str, Any]=0.0			,     _SCREAMING_SNAKE_CASE:						Any=0.02			,     _SCREAMING_SNAKE_CASE:						Any=True			,     _SCREAMING_SNAKE_CASE:						Tuple=True			,     _SCREAMING_SNAKE_CASE:						Any=2			,     _SCREAMING_SNAKE_CASE:						str=1			,     _SCREAMING_SNAKE_CASE:						Dict=0			,     _SCREAMING_SNAKE_CASE:						Dict=2			,     **_SCREAMING_SNAKE_CASE:						List[Any]			,     )							->			List[str]:

				"""simple docstring"""

				__lowerCAmelCase :    Optional[int]				=			vocab_size
				__lowerCAmelCase :    int				=			max_position_embeddings
				__lowerCAmelCase :    Optional[Any]				=			d_model
				__lowerCAmelCase :    List[Any]				=			ffn_dim
				__lowerCAmelCase :    int				=			num_layers
				__lowerCAmelCase :    Any				=			attention_heads
				__lowerCAmelCase :    int				=			activation_function
				__lowerCAmelCase :    List[Any]				=			dropout
				__lowerCAmelCase :    Optional[int]				=			attention_dropout
				__lowerCAmelCase :    Optional[int]				=			activation_dropout
				__lowerCAmelCase :    Optional[int]				=			layerdrop
				__lowerCAmelCase :    Optional[int]				=			init_std
				__lowerCAmelCase :    Tuple				=			scale_embedding  # scale factor will be sqrt(d_model) if True
				__lowerCAmelCase :    Dict				=			use_cache

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