



'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging


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

__SCREAMING_SNAKE_CASE			:   int							   ={
    '''facebook/nllb-moe-54B''': '''https://huggingface.co/facebook/nllb-moe-54b/resolve/main/config.json''',
}


class 	SCREAMING_SNAKE_CASE__					( _lowercase       ):
	"""simple docstring"""


	A__     :     List[str]    =					'''nllb-moe'''
	A__     :     Any    =					['''past_key_values''']
	A__     :     List[str]    =					{'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}


	def __init__(		self       , A=12_81_12       , A=10_24       , A=12       , A=40_96       , A=16       , A=12       , A=40_96       , A=16       , A=0.05       , A=0.05       , A=True       , A=True       , A="relu"       , A=10_24       , A=0.1       , A=0.1       , A=0.0       , A=0.02       , A=2       , A=True       , A=False       , A="float32"       , A=False       , A=1_28       , A=64       , A=4       , A=4       , A=0.001       , A=0.001       , A="all"       , A=False       , A=False       , A=1.0       , A=0.2       , A=1       , A=0       , A=2       , A=False       , **A       , )			->				Dict:
				A:   str								= vocab_size
				A:   Optional[int]								= max_position_embeddings
				A:   Optional[Any]								= d_model
				A:   str								= encoder_ffn_dim
				A:   Tuple								= encoder_layers
				A:   Dict								= encoder_attention_heads
				A:   int								= decoder_ffn_dim
				A:   List[str]								= decoder_layers
				A:   int								= decoder_attention_heads
				A:   Tuple								= dropout
				A:   Union[str, Any]								= attention_dropout
				A:   Union[str, Any]								= activation_dropout
				A:   Optional[Any]								= activation_function
				A:   List[str]								= init_std
				A:   List[Any]								= encoder_layerdrop
				A:   str								= decoder_layerdrop
				A:   Optional[int]								= use_cache
				A:   Any								= encoder_layers
				A:   List[Any]								= scale_embedding  # scale factor will be sqrt(d_model) if True
				A:   Optional[int]								= router_z_loss_coef
				A:   Dict								= router_aux_loss_coef
				A:   Dict								= decoder_sparse_step
				A:   Tuple								= encoder_sparse_step
				A:   Optional[int]								= num_experts
				A:   int								= expert_capacity
				A:   Tuple								= router_bias
				if router_dtype not in ["float32", "float16", "bfloat16"]:
							raise ValueError(f'`router_dtype` must be one of \'float32\', \'float16\' or \'bfloat16\', got {router_dtype}'      )
				A:   int								= router_dtype

				A:   Optional[Any]								= router_ignore_padding_tokens
				A:   List[str]								= batch_prioritized_routing
				A:   List[Any]								= second_expert_policy
				A:   Optional[Any]								= normalize_router_prob_before_dropping
				A:   Dict								= moe_eval_capacity_token_fraction
				A:   List[str]								= moe_token_dropout
				A:   List[str]								= output_router_logits
				super().__init__(
				    pad_token_id=_snake_case       , bos_token_id=_snake_case       , eos_token_id=_snake_case       , is_encoder_decoder=_snake_case       , decoder_start_token_id=_snake_case       , **_snake_case       , )







"""simple docstring"""





from ..utils import is_flax_available, is_torch_available


if is_torch_available():
		from .autoencoder_kl import AutoencoderKL
		from .controlnet import ControlNetModel
		from .dual_transformer_ad import DualTransformeraDModel
		from .modeling_utils import ModelMixin
		from .prior_transformer import PriorTransformer
		from .ta_film_transformer import TaFilmDecoder
		from .transformer_ad import TransformeraDModel
		from .unet_ad import UNetaDModel
		from .unet_ad import UNetaDModel
		from .unet_ad_condition import UNetaDConditionModel
		from .unet_ad_condition import UNetaDConditionModel
		from .vq_model import VQModel

if is_flax_available():
		from .controlnet_flax import FlaxControlNetModel
		from .unet_ad_condition_flax import FlaxUNetaDConditionModel
		from .vae_flax import FlaxAutoencoderKL




