

'''simple docstring'''




import sys
from random import randint



class        lowercase_	:

  '''simple docstring'''





  def __init__(     self   , a_   , a_     )							->				str:


    """simple docstring"""
    UpperCAmelCase													=	key
    UpperCAmelCase													=	freq





  def __str__(     self     )							->				Optional[int]:


    """simple docstring"""
    return F'''Node(key={self.key}, freq={self.freq})'''




def       lowerCamelCase__		(						SCREAMING_SNAKE_CASE      :   List[str]       ,   SCREAMING_SNAKE_CASE      :   List[Any]       ,   SCREAMING_SNAKE_CASE      :   Optional[int]       ,   SCREAMING_SNAKE_CASE      :   str       ,   SCREAMING_SNAKE_CASE      :   Optional[int]       ,   SCREAMING_SNAKE_CASE      :   Any						):
  if i > j or i < 0 or j > len(SCREAMING_SNAKE_CASE						) - 1:
    return

  UpperCAmelCase													=	root[i][j]
  if parent == -1:  # root does not have a parent
    print(f'''{key[node]} is the root of the binary search tree.'''						)
  elif is_left:
    print(f'''{key[node]} is the left child of key {parent}.'''						)
  else:
    print(f'''{key[node]} is the right child of key {parent}.'''						)

  print_binary_search_tree(SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE       ,   node - 1       ,   key[node]       ,   SCREAMING_SNAKE_CASE						)
  print_binary_search_tree(SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE       ,   node + 1       ,   SCREAMING_SNAKE_CASE       ,   key[node]       ,   SCREAMING_SNAKE_CASE						)


def       lowerCamelCase__		(						SCREAMING_SNAKE_CASE      :   str						):
  nodes.sort(key=lambda SCREAMING_SNAKE_CASE						: node.key						)

  UpperCAmelCase													=	len(SCREAMING_SNAKE_CASE						)

  UpperCAmelCase													=	[nodes[i].key for i in range(SCREAMING_SNAKE_CASE						)]
  UpperCAmelCase													=	[nodes[i].freq for i in range(SCREAMING_SNAKE_CASE						)]

  # This 2D array stores the overall tree cost (which's as minimized as possible);
  # for a single key, cost is equal to frequency of the key.
  UpperCAmelCase													=	[[freqs[i] if i == j else 0 for j in range(SCREAMING_SNAKE_CASE						)] for i in range(SCREAMING_SNAKE_CASE						)]
  # sum[i][j] stores the sum of key frequencies between i and j inclusive in nodes
  # array
  UpperCAmelCase													=	[[freqs[i] if i == j else 0 for j in range(SCREAMING_SNAKE_CASE						)] for i in range(SCREAMING_SNAKE_CASE						)]
  # stores tree roots that will be used later for constructing binary search tree
  UpperCAmelCase													=	[[i if i == j else 0 for j in range(SCREAMING_SNAKE_CASE						)] for i in range(SCREAMING_SNAKE_CASE						)]

  for interval_length in range(2       ,   n + 1						):
    for i in range(n - interval_length + 1						):
      UpperCAmelCase													=	i + interval_length - 1

      UpperCAmelCase													=	sys.maxsize  # set the value to "infinity"
      UpperCAmelCase													=	total[i][j - 1] + freqs[j]

      # Apply Knuth's optimization
      # Loop without optimization: for r in range(i, j + 1):
      for r in range(root[i][j - 1]       ,   root[i + 1][j] + 1						):  # r is a temporal root
        UpperCAmelCase													=	dp[i][r - 1] if r != i else 0  # optimal cost for left subtree
        UpperCAmelCase													=	dp[r + 1][j] if r != j else 0  # optimal cost for right subtree
        UpperCAmelCase													=	left + total[i][j] + right

        if dp[i][j] > cost:
          UpperCAmelCase													=	cost
          UpperCAmelCase													=	r

  print('Binary search tree nodes:'						)
  for node in nodes:
    print(SCREAMING_SNAKE_CASE						)

  print(f'''\nThe cost of optimal BST for given tree nodes is {dp[0][n - 1]}.'''						)
  print_binary_search_tree(SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE       ,   0       ,   n - 1       ,   -1       ,   SCREAMING_SNAKE_CASE						)


def       lowerCamelCase__		(						):
  # A sample binary search tree
  UpperCAmelCase													=	[Node(SCREAMING_SNAKE_CASE       ,   randint(1       ,   50						)						) for i in range(10       ,   0       ,   -1						)]
  find_optimal_binary_search_tree(SCREAMING_SNAKE_CASE						)


if __name__ == "__main__":
  main()






'''simple docstring'''




from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_a				: Optional[int] 							=						logging.get_logger(__name__)

_a				: Dict 							=						{
    'mnaylor/mega-base-wikitext': 'https://huggingface.co/mnaylor/mega-base-wikitext/resolve/main/config.json',
}



class        lowercase_	(		a ):

  '''simple docstring'''





  __lowerCAmelCase					: List[str]      =   "mega"
  def __init__(     self   , a_=3_0_5_2_2   , a_=1_2_8   , a_=4   , a_=2_5_6   , a_=1_6   , a_=True   , a_=6_4   , a_=False   , a_=-1   , a_=None   , a_=True   , a_="scalenorm"   , a_=True   , a_="silu"   , a_="softmax"   , a_=0.1   , a_=0.1   , a_=0.1   , a_=False   , a_=True   , a_=2_5_6   , a_=True   , a_=0.1   , a_=2_0_4_8   , a_=False   , a_=2   , a_=0.02   , a_=0.2   , a_=0.02   , a_=1.0   , a_=1   , a_=0   , a_=2   , a_="rotary"   , a_=None   , a_=True   , a_=True   , **a_   , )							->				Any:


    """simple docstring"""
    super().__init__(pad_token_id=a_   , bos_token_id=a_   , eos_token_id=a_   , **a_     )

    UpperCAmelCase													=	vocab_size
    UpperCAmelCase													=	hidden_size
    UpperCAmelCase													=	num_hidden_layers
    UpperCAmelCase													=	activation
    UpperCAmelCase													=	attention_activation
    UpperCAmelCase													=	intermediate_size
    UpperCAmelCase													=	ema_projection_size
    UpperCAmelCase													=	bidirectional
    UpperCAmelCase													=	shared_representation_size
    UpperCAmelCase													=	use_chunking
    UpperCAmelCase													=	chunk_size
    UpperCAmelCase													=	truncation
    UpperCAmelCase													=	normalize_before_mega
    UpperCAmelCase													=	normalization_type
    UpperCAmelCase													=	norm_affine
    UpperCAmelCase													=	dropout_prob
    UpperCAmelCase													=	hidden_dropout_prob
    UpperCAmelCase													=	attention_probs_dropout_prob
    UpperCAmelCase													=	use_feature_dropout
    UpperCAmelCase													=	use_normalized_ffn
    UpperCAmelCase													=	nffn_hidden_size
    UpperCAmelCase													=	normalize_before_ffn
    UpperCAmelCase													=	nffn_activation_dropout_prob
    UpperCAmelCase													=	max_positions
    UpperCAmelCase													=	add_token_type_embeddings
    UpperCAmelCase													=	type_vocab_size
    UpperCAmelCase													=	initializer_range
    UpperCAmelCase													=	ema_delta_alpha_range
    UpperCAmelCase													=	ema_beta_range
    UpperCAmelCase													=	ema_gamma_omega_range
    UpperCAmelCase													=	relative_positional_bias
    UpperCAmelCase													=	use_cache
    UpperCAmelCase													=	classifier_dropout
    UpperCAmelCase													=	add_lm_hidden_dense_layer
    UpperCAmelCase													=	1  # not used but required by Hugging Face




class        lowercase_	(		a ):

  '''simple docstring'''





  @property
  def 						snake_case_			(     self     )							->				Mapping[str, Mapping[int, str]]:


    """simple docstring"""
    if self.task == "multiple-choice":
      UpperCAmelCase													=	{0: 'batch', 1: 'choice', 2: 'sequence'}
    else:
      UpperCAmelCase													=	{0: 'batch', 1: 'sequence'}
    return OrderedDict(
        [
            ('input_ids', dynamic_axis),
            ('attention_mask', dynamic_axis),
        ]     )


