'''simple docstring'''




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


_A								=   logging.get_logger(__name__)

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


class       UpperCAmelCase__  (       _snake_case			):






   """simple docstring"""
   A							:       Union[str, Any]     =    '''nllb-moe'''
   A							:       Dict     =    ['''past_key_values''']
   A							:       Tuple     =    {'''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=0.0_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       ,				)    ->     str:
        lowercase_					:       Any     				=					vocab_size
        lowercase_					:       List[Any]     				=					max_position_embeddings
        lowercase_					:       str     				=					d_model
        lowercase_					:       int     				=					encoder_ffn_dim
        lowercase_					:       List[str]     				=					encoder_layers
        lowercase_					:       List[Any]     				=					encoder_attention_heads
        lowercase_					:       List[Any]     				=					decoder_ffn_dim
        lowercase_					:       List[Any]     				=					decoder_layers
        lowercase_					:       int     				=					decoder_attention_heads
        lowercase_					:       List[Any]     				=					dropout
        lowercase_					:       str     				=					attention_dropout
        lowercase_					:       str     				=					activation_dropout
        lowercase_					:       Any     				=					activation_function
        lowercase_					:       Dict     				=					init_std
        lowercase_					:       str     				=					encoder_layerdrop
        lowercase_					:       Optional[Any]     				=					decoder_layerdrop
        lowercase_					:       Any     				=					use_cache
        lowercase_					:       str     				=					encoder_layers
        lowercase_					:       int     				=					scale_embedding  # scale factor will be sqrt(d_model) if True
        lowercase_					:       Optional[Any]     				=					router_z_loss_coef
        lowercase_					:       int     				=					router_aux_loss_coef
        lowercase_					:       Union[str, Any]     				=					decoder_sparse_step
        lowercase_					:       Optional[int]     				=					encoder_sparse_step
        lowercase_					:       int     				=					num_experts
        lowercase_					:       Tuple     				=					expert_capacity
        lowercase_					:       Any     				=					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}'''				)
        lowercase_					:       Dict     				=					router_dtype

        lowercase_					:       str     				=					router_ignore_padding_tokens
        lowercase_					:       Dict     				=					batch_prioritized_routing
        lowercase_					:       str     				=					second_expert_policy
        lowercase_					:       Optional[int]     				=					normalize_router_prob_before_dropping
        lowercase_					:       int     				=					moe_eval_capacity_token_fraction
        lowercase_					:       List[Any]     				=					moe_token_dropout
        lowercase_					:       Optional[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       ,				)





'''simple docstring'''




from __future__ import annotations

def _UpperCamelCase   (       SCREAMING_SNAKE_CASE_							):

     # preprocessing the first row
     for i in range(1  ,       len(matrix[0]							)							):
          matrix[0][i] += matrix[0][i - 1]

     # preprocessing the first column
     for i in range(1  ,       len(SCREAMING_SNAKE_CASE_							)							):
          matrix[i][0] += matrix[i - 1][0]

     # updating the path cost for current position
     for i in range(1  ,       len(SCREAMING_SNAKE_CASE_							)							):
          for j in range(1  ,       len(matrix[0]							)							):
               matrix[i][j] += min(matrix[i - 1][j]  ,       matrix[i][j - 1]							)

     return matrix[-1][-1]


if __name__ == "__main__":
 import doctest

 doctest.testmod()




