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


_UpperCAmelCase :			Union[str, Any]              =	logging.get_logger(__name__)

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


class     lowercase (		_SCREAMING_SNAKE_CASE       ):
		__lowercase	:	Optional[int]     					=    "nllb-moe"
		__lowercase	:	Dict     					=    ["past_key_values"]
		__lowercase	:	Optional[Any]     					=    {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}



		def __init__(			self    ,				A_=128_112    ,				A_=1_024    ,				A_=12    ,				A_=4_096    ,				A_=16    ,				A_=12    ,				A_=4_096    ,				A_=16    ,				A_=0.05    ,				A_=0.05    ,				A_=True    ,				A_=True    ,				A_="relu"    ,				A_=1_024    ,				A_=0.1    ,				A_=0.1    ,				A_=0.0    ,				A_=0.02    ,				A_=2    ,				A_=True    ,				A_=False    ,				A_="float32"    ,				A_=False    ,				A_=128    ,				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_    ,				)  -> List[Any]:
			"""simple docstring"""
			UpperCamelCase          =					vocab_size
			UpperCamelCase          =					max_position_embeddings
			UpperCamelCase          =					d_model
			UpperCamelCase          =					encoder_ffn_dim
			UpperCamelCase          =					encoder_layers
			UpperCamelCase          =					encoder_attention_heads
			UpperCamelCase          =					decoder_ffn_dim
			UpperCamelCase          =					decoder_layers
			UpperCamelCase          =					decoder_attention_heads
			UpperCamelCase          =					dropout
			UpperCamelCase          =					attention_dropout
			UpperCamelCase          =					activation_dropout
			UpperCamelCase          =					activation_function
			UpperCamelCase          =					init_std
			UpperCamelCase          =					encoder_layerdrop
			UpperCamelCase          =					decoder_layerdrop
			UpperCamelCase          =					use_cache
			UpperCamelCase          =					encoder_layers
			UpperCamelCase          =					scale_embedding  # scale factor will be sqrt(d_model) if True
			UpperCamelCase          =					router_z_loss_coef
			UpperCamelCase          =					router_aux_loss_coef
			UpperCamelCase          =					decoder_sparse_step
			UpperCamelCase          =					encoder_sparse_step
			UpperCamelCase          =					num_experts
			UpperCamelCase          =					expert_capacity
			UpperCamelCase          =					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}'''	)
			UpperCamelCase          =					router_dtype

			UpperCamelCase          =					router_ignore_padding_tokens
			UpperCamelCase          =					batch_prioritized_routing
			UpperCamelCase          =					second_expert_policy
			UpperCamelCase          =					normalize_router_prob_before_dropping
			UpperCamelCase          =					moe_eval_capacity_token_fraction
			UpperCamelCase          =					moe_token_dropout
			UpperCamelCase          =					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_    ,				)


import pprint

import requests

_UpperCAmelCase :			Union[str, Any]              =	"https://zenquotes.io/api"


def 				A	( )       ->  list:
		'''simple docstring'''

		return requests.get(API_ENDPOINT_URL + '/today'			).json()


def 				A	( )       ->  list:
		'''simple docstring'''

		return requests.get(API_ENDPOINT_URL + '/random'			).json()


if __name__ == "__main__":
					_UpperCAmelCase :			str              =	random_quotes()
					pprint.pprint(response)
