




import os




def 					__SCREAMING_SNAKE_CASE   (  )       ->	Any:

    """simple docstring"""


    __UpperCamelCase  		=						os.path.dirname(os.path.realpath(lowercase_      )      )
    __UpperCamelCase  		=						os.path.join(lowercase_					,     '''triangle.txt'''      )

    with open(lowercase_      ) as f:
        __UpperCamelCase  		=						f.readlines()

    __UpperCamelCase  		=						[]
    for line in triangle:
        __UpperCamelCase  		=						[]
        for number in line.strip().split(''' '''      ):
            numbers_from_line.append(int(lowercase_      )      )
        a.append(lowercase_      )

    for i in range(1					,     len(lowercase_      )      ):
        for j in range(len(a[i]      )      ):
            __UpperCamelCase  		=						a[i - 1][j] if j != len(a[i - 1]      ) else 0
            __UpperCamelCase  		=						a[i - 1][j - 1] if j > 0 else 0
            a[i][j] += max(lowercase_					,     lowercase_      )
    return max(a[-1]      )


if __name__ == "__main__":
 print(solution())












import functools
import operator

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


a_   		=     logging.get_logger(__name__)

a_   		=     {
    "microsoft/unispeech-large-1500h-cv": (
        "https://huggingface.co/microsoft/unispeech-large-1500h-cv/resolve/main/config.json"
    ),
    # See all UniSpeech models at https://huggingface.co/models?filter=unispeech
}

class  _lowerCamelCase						(			_SCREAMING_SNAKE_CASE   ):

      """simple docstring"""

      lowerCAmelCase__			: Dict    =					"unispeech"







      def __init__(     self					: str ,       snake_case					: Union[str, Any]=32 ,       snake_case					: Optional[Any]=768 ,       snake_case					: Dict=12 ,       snake_case					: Tuple=12 ,       snake_case					: Optional[Any]=3072 ,       snake_case					: Any="gelu" ,       snake_case					: Dict=0.1 ,       snake_case					: Tuple=0.1 ,       snake_case					: str=0.1 ,       snake_case					: Optional[int]=0.0 ,       snake_case					: Any=0.0 ,       snake_case					: Optional[Any]=0.1 ,       snake_case					: List[Any]=0.1 ,       snake_case					: Optional[int]=0.02 ,       snake_case					: List[str]=1E-5 ,       snake_case					: str="group" ,       snake_case					: List[Any]="gelu" ,       snake_case					: Union[str, Any]=(512, 512, 512, 512, 512, 512, 512) ,       snake_case					: List[Any]=(5, 2, 2, 2, 2, 2, 2) ,       snake_case					: Union[str, Any]=(10, 3, 3, 3, 3, 2, 2) ,       snake_case					: Tuple=False ,       snake_case					: Optional[int]=128 ,       snake_case					: List[str]=16 ,       snake_case					: List[str]=False ,       snake_case					: Dict=True ,       snake_case					: Optional[Any]=0.05 ,       snake_case					: Optional[Any]=10 ,       snake_case					: Union[str, Any]=2 ,       snake_case					: List[str]=0.0 ,       snake_case					: str=10 ,       snake_case					: int=0 ,       snake_case					: Tuple=320 ,       snake_case					: Any=2 ,       snake_case					: List[str]=0.1 ,       snake_case					: Optional[Any]=100 ,       snake_case					: List[Any]=256 ,       snake_case					: Union[str, Any]=256 ,       snake_case					: Any=0.1 ,       snake_case					: str="mean" ,       snake_case					: Union[str, Any]=False ,       snake_case					: str=False ,       snake_case					: Union[str, Any]=256 ,       snake_case					: Optional[Any]=80 ,       snake_case					: str=0 ,       snake_case					: int=1 ,       snake_case					: int=2 ,       snake_case					: Dict=0.5 ,       **snake_case					: Optional[int] ,       ):
          super().__init__(**snake_case ,       pad_token_id=snake_case ,       bos_token_id=snake_case ,       eos_token_id=snake_case				)
          __UpperCamelCase  		=						hidden_size
          __UpperCamelCase  		=						feat_extract_norm
          __UpperCamelCase  		=						feat_extract_activation
          __UpperCamelCase  		=						list(snake_case				)
          __UpperCamelCase  		=						list(snake_case				)
          __UpperCamelCase  		=						list(snake_case				)
          __UpperCamelCase  		=						conv_bias
          __UpperCamelCase  		=						num_conv_pos_embeddings
          __UpperCamelCase  		=						num_conv_pos_embedding_groups
          __UpperCamelCase  		=						len(self.conv_dim				)
          __UpperCamelCase  		=						num_hidden_layers
          __UpperCamelCase  		=						intermediate_size
          __UpperCamelCase  		=						hidden_act
          __UpperCamelCase  		=						num_attention_heads
          __UpperCamelCase  		=						hidden_dropout
          __UpperCamelCase  		=						attention_dropout
          __UpperCamelCase  		=						activation_dropout
          __UpperCamelCase  		=						feat_proj_dropout
          __UpperCamelCase  		=						final_dropout
          __UpperCamelCase  		=						layerdrop
          __UpperCamelCase  		=						layer_norm_eps
          __UpperCamelCase  		=						initializer_range
          __UpperCamelCase  		=						num_ctc_classes
          __UpperCamelCase  		=						vocab_size
          __UpperCamelCase  		=						do_stable_layer_norm
          __UpperCamelCase  		=						use_weighted_layer_sum
          __UpperCamelCase  		=						classifier_proj_size

          if (
              (len(self.conv_stride				) != self.num_feat_extract_layers)
              or (len(self.conv_kernel				) != self.num_feat_extract_layers)
              or (len(self.conv_dim				) != self.num_feat_extract_layers)
          ):
              raise ValueError(
                  '''Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` =='''
                  ''' `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) ='''
                  F" {len(self.conv_dim				)}`, `len(config.conv_stride) = {len(self.conv_stride				)}`,"
                  F" `len(config.conv_kernel) = {len(self.conv_kernel				)}`."				)

          # fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
          __UpperCamelCase  		=						apply_spec_augment
          __UpperCamelCase  		=						mask_time_prob
          __UpperCamelCase  		=						mask_time_length
          __UpperCamelCase  		=						mask_time_min_masks
          __UpperCamelCase  		=						mask_feature_prob
          __UpperCamelCase  		=						mask_feature_length
          __UpperCamelCase  		=						mask_feature_min_masks

          # parameters for pretraining with codevector quantized representations
          __UpperCamelCase  		=						num_codevectors_per_group
          __UpperCamelCase  		=						num_codevector_groups
          __UpperCamelCase  		=						contrastive_logits_temperature
          __UpperCamelCase  		=						feat_quantizer_dropout
          __UpperCamelCase  		=						num_negatives
          __UpperCamelCase  		=						codevector_dim
          __UpperCamelCase  		=						proj_codevector_dim
          __UpperCamelCase  		=						diversity_loss_weight

          # ctc loss
          __UpperCamelCase  		=						ctc_loss_reduction
          __UpperCamelCase  		=						ctc_zero_infinity

          # pretraining loss
          __UpperCamelCase  		=						replace_prob







      @property
      def       snake_case	(     self					: Dict				):
          return functools.reduce(operator.mul ,       self.conv_stride ,       1				)






