




from __future__ import annotations

from cmath import sqrt







def 			_a   (		SCREAMING_SNAKE_CASE_							:       int   ,   SCREAMING_SNAKE_CASE_							:       int   ,   SCREAMING_SNAKE_CASE_							:       int  ):

					if a == 0:
										raise ValueError("Coefficient 'a' must not be zero."  )
					__lowerCAmelCase            = b * b - 4 * a * c

					__lowerCAmelCase            = (-b + sqrt(SCREAMING_SNAKE_CASE_  )) / (2 * a)
					__lowerCAmelCase            = (-b - sqrt(SCREAMING_SNAKE_CASE_  )) / (2 * a)

					return (
					    root_a.real if not root_a.imag else root_a,
					    root_a.real if not root_a.imag else root_a,
					)







def 			_a   (		):
					__lowerCAmelCase     ,							__lowerCAmelCase            = quadratic_roots(a=5   ,   b=6   ,   c=1  )
					print(F"""The solutions are: {solutiona} and {solutiona}"""  )


if __name__ == "__main__":
					main()










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


UpperCamelCase__					    =    logging.get_logger(__name__)

UpperCamelCase__					    =    {
    """facebook/nllb-moe-54B""": """https://huggingface.co/facebook/nllb-moe-54b/resolve/main/config.json""",
}
class     a__					(      snake_case__   ):
						_a							:					Tuple      =					"""nllb-moe"""
						_a							:					Dict      =					["""past_key_values"""]
						_a							:					Optional[int]      =					{"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}






						def __init__(     self	,    _A=1_2_8_1_1_2	,    _A=1_0_2_4	,    _A=1_2	,    _A=4_0_9_6	,    _A=1_6	,    _A=1_2	,    _A=4_0_9_6	,    _A=1_6	,    _A=0.05	,    _A=0.05	,    _A=True	,    _A=True	,    _A="relu"	,    _A=1_0_2_4	,    _A=0.1	,    _A=0.1	,    _A=0.0	,    _A=0.02	,    _A=2	,    _A=True	,    _A=False	,    _A="float32"	,    _A=False	,    _A=1_2_8	,    _A=6_4	,    _A=4	,    _A=4	,    _A=0.0_01	,    _A=0.0_01	,    _A="all"	,    _A=False	,    _A=False	,    _A=1.0	,    _A=0.2	,    _A=1	,    _A=0	,    _A=2	,    _A=False	,    **_A	,    ):




											"""simple docstring"""



											__lowerCAmelCase            = vocab_size
											__lowerCAmelCase            = max_position_embeddings
											__lowerCAmelCase            = d_model
											__lowerCAmelCase            = encoder_ffn_dim
											__lowerCAmelCase            = encoder_layers
											__lowerCAmelCase            = encoder_attention_heads
											__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            = encoder_layerdrop
											__lowerCAmelCase            = decoder_layerdrop
											__lowerCAmelCase            = use_cache
											__lowerCAmelCase            = encoder_layers
											__lowerCAmelCase            = scale_embedding  # scale factor will be sqrt(d_model) if True
											__lowerCAmelCase            = router_z_loss_coef
											__lowerCAmelCase            = router_aux_loss_coef
											__lowerCAmelCase            = decoder_sparse_step
											__lowerCAmelCase            = encoder_sparse_step
											__lowerCAmelCase            = num_experts
											__lowerCAmelCase            = expert_capacity
											__lowerCAmelCase            = 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}"""    )
											__lowerCAmelCase            = router_dtype

											__lowerCAmelCase            = router_ignore_padding_tokens
											__lowerCAmelCase            = batch_prioritized_routing
											__lowerCAmelCase            = second_expert_policy
											__lowerCAmelCase            = normalize_router_prob_before_dropping
											__lowerCAmelCase            = moe_eval_capacity_token_fraction
											__lowerCAmelCase            = moe_token_dropout
											__lowerCAmelCase            = output_router_logits
											super().__init__(
											    pad_token_id=_A	,    bos_token_id=_A	,    eos_token_id=_A	,    is_encoder_decoder=_A	,    decoder_start_token_id=_A	,    **_A	,    )




