


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


SCREAMING_SNAKE_CASE :    Tuple									=				logging.get_logger(__name__)

SCREAMING_SNAKE_CASE :    Tuple									=				{
    """facebook/nllb-moe-54B""": """https://huggingface.co/facebook/nllb-moe-54b/resolve/main/config.json""",
}



class 			_UpperCAmelCase (							__snake_case				):






		'''simple docstring'''

		lowerCamelCase__				='nllb-moe'
		lowerCamelCase__				=['past_key_values']
		lowerCamelCase__				={'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_				,       ):





			'''simple docstring'''

			__snake_case	:						Optional[Any]						=				vocab_size
			__snake_case	:						int						=				max_position_embeddings
			__snake_case	:						int						=				d_model
			__snake_case	:						List[str]						=				encoder_ffn_dim
			__snake_case	:						Optional[Any]						=				encoder_layers
			__snake_case	:						Dict						=				encoder_attention_heads
			__snake_case	:						str						=				decoder_ffn_dim
			__snake_case	:						Dict						=				decoder_layers
			__snake_case	:						Union[str, Any]						=				decoder_attention_heads
			__snake_case	:						Optional[Any]						=				dropout
			__snake_case	:						Union[str, Any]						=				attention_dropout
			__snake_case	:						Union[str, Any]						=				activation_dropout
			__snake_case	:						List[Any]						=				activation_function
			__snake_case	:						int						=				init_std
			__snake_case	:						List[Any]						=				encoder_layerdrop
			__snake_case	:						Optional[Any]						=				decoder_layerdrop
			__snake_case	:						Tuple						=				use_cache
			__snake_case	:						Dict						=				encoder_layers
			__snake_case	:						List[str]						=				scale_embedding  # scale factor will be sqrt(d_model) if True
			__snake_case	:						List[str]						=				router_z_loss_coef
			__snake_case	:						int						=				router_aux_loss_coef
			__snake_case	:						Union[str, Any]						=				decoder_sparse_step
			__snake_case	:						Tuple						=				encoder_sparse_step
			__snake_case	:						int						=				num_experts
			__snake_case	:						List[Any]						=				expert_capacity
			__snake_case	:						int						=				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}"""					)
			__snake_case	:						Optional[int]						=				router_dtype

			__snake_case	:						Tuple						=				router_ignore_padding_tokens
			__snake_case	:						List[Any]						=				batch_prioritized_routing
			__snake_case	:						Optional[int]						=				second_expert_policy
			__snake_case	:						Any						=				normalize_router_prob_before_dropping
			__snake_case	:						List[Any]						=				moe_eval_capacity_token_fraction
			__snake_case	:						Optional[int]						=				moe_token_dropout
			__snake_case	:						int						=				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_				,       )







from __future__ import annotations

import typing
from collections import Counter


def     __lowerCAmelCase			(			a__  )       ->  typing.Counter[int]:
				__a										=    Counter()
				for base in range(1						,    max_perimeter + 1  ):
								for perpendicular in range(a__						,    max_perimeter + 1  ):
												__a										=    (base * base + perpendicular * perpendicular) ** 0.5
												if hypotenuse == int(a__  ):
																__a										=    int(base + perpendicular + hypotenuse  )
																if perimeter > max_perimeter:
																				continue
																triplets[perimeter] += 1
				return triplets


def     __lowerCAmelCase			(			a__ = 1000  )       ->  int:
				__a										=    pythagorean_triple(a__  )
				return triplets.most_common(1  )[0][0]


if __name__ == "__main__":
					print(F"Perimeter {solution()} has maximum solutions")