


from collections import OrderedDict
from typing import Mapping

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


__lowerCamelCase   	=  logging.get_logger(__name__)

__lowerCamelCase   	=  {
    '''flaubert/flaubert_small_cased''': '''https://huggingface.co/flaubert/flaubert_small_cased/resolve/main/config.json''',
    '''flaubert/flaubert_base_uncased''': '''https://huggingface.co/flaubert/flaubert_base_uncased/resolve/main/config.json''',
    '''flaubert/flaubert_base_cased''': '''https://huggingface.co/flaubert/flaubert_base_cased/resolve/main/config.json''',
    '''flaubert/flaubert_large_cased''': '''https://huggingface.co/flaubert/flaubert_large_cased/resolve/main/config.json''',
}


class 	a__						(    lowerCAmelCase_     ):
       lowerCamelCase__:  str				     =  """flaubert"""
       lowerCamelCase__:  str				     =  {
           """hidden_size""": """emb_dim""",
           """num_attention_heads""": """n_heads""",
           """num_hidden_layers""": """n_layers""",
           """n_words""": """vocab_size""",  # For backward compatibility
       }

       def __init__( self   :  List[Any]   ,				lowerCamelCase_   :  str=False   ,				lowerCamelCase_   :  List[Any]=0.0   ,				lowerCamelCase_   :  int=3_0_1_4_5   ,				lowerCamelCase_   :  str=2_0_4_8   ,				lowerCamelCase_   :  Dict=1_2   ,				lowerCamelCase_   :  Any=1_6   ,				lowerCamelCase_   :  Optional[int]=0.1   ,				lowerCamelCase_   :  Tuple=0.1   ,				lowerCamelCase_   :  List[str]=True   ,				lowerCamelCase_   :  str=False   ,				lowerCamelCase_   :  int=False   ,				lowerCamelCase_   :  Optional[int]=False   ,				lowerCamelCase_   :  Tuple=1   ,				lowerCamelCase_   :  Union[str, Any]=True   ,				lowerCamelCase_   :  str=5_1_2   ,				lowerCamelCase_   :  Optional[Any]=2_0_4_8**-0.5   ,				lowerCamelCase_   :  Optional[int]=1E-12   ,				lowerCamelCase_   :  int=0.0_2   ,				lowerCamelCase_   :  int=0   ,				lowerCamelCase_   :  Optional[int]=1   ,				lowerCamelCase_   :  Dict=2   ,				lowerCamelCase_   :  int=3   ,				lowerCamelCase_   :  str=5   ,				lowerCamelCase_   :  Optional[int]=True   ,				lowerCamelCase_   :  Union[str, Any]="first"   ,				lowerCamelCase_   :  int=True   ,				lowerCamelCase_   :  Dict=None   ,				lowerCamelCase_   :  Union[str, Any]=True   ,				lowerCamelCase_   :  str=0.1   ,				lowerCamelCase_   :  str=5   ,				lowerCamelCase_   :  Any=5   ,				lowerCamelCase_   :  int=0   ,				lowerCamelCase_   :  Any=0   ,				lowerCamelCase_   :  Tuple=2   ,				lowerCamelCase_   :  Optional[Any]=0   ,				**lowerCamelCase_   :  int   ,				):
            a_     :  Dict				      =     pre_norm
            a_     :  List[str]				      =     layerdrop
            a_     :  Any				      =     vocab_size
            a_     :  Tuple				      =     emb_dim
            a_     :  str				      =     n_layers
            a_     :  Tuple				      =     n_heads
            a_     :  List[Any]				      =     dropout
            a_     :  str				      =     attention_dropout
            a_     :  Dict				      =     gelu_activation
            a_     :  Optional[Any]				      =     sinusoidal_embeddings
            a_     :  List[str]				      =     causal
            a_     :  Union[str, Any]				      =     asm
            a_     :  int				      =     n_langs
            a_     :  int				      =     use_lang_emb
            a_     :  int				      =     layer_norm_eps
            a_     :  Dict				      =     bos_index
            a_     :  int				      =     eos_index
            a_     :  Dict				      =     pad_index
            a_     :  Dict				      =     unk_index
            a_     :  Tuple				      =     mask_index
            a_     :  Tuple				      =     is_encoder
            a_     :  Dict				      =     max_position_embeddings
            a_     :  Union[str, Any]				      =     embed_init_std
            a_     :  List[str]				      =     init_std
            a_     :  Union[str, Any]				      =     summary_type
            a_     :  Tuple				      =     summary_use_proj
            a_     :  int				      =     summary_activation
            a_     :  Union[str, Any]				      =     summary_proj_to_labels
            a_     :  List[str]				      =     summary_first_dropout
            a_     :  Optional[int]				      =     start_n_top
            a_     :  List[Any]				      =     end_n_top
            a_     :  Dict				      =     mask_token_id
            a_     :  List[Any]				      =     lang_id

            if "n_words" in kwargs:
                 a_     :  Any				      =     kwargs["""n_words"""]

            super().__init__(pad_token_id=lowerCamelCase_   ,				bos_token_id=lowerCamelCase_   ,				**lowerCamelCase_     )


class 	a__						(    lowerCAmelCase_     ):

       @property
       def     UpperCAmelCase( self   :  Tuple     ):
            if self.task == "multiple-choice":
                 a_     :  Optional[Any]				      =     {0: """batch""", 1: """choice""", 2: """sequence"""}
            else:
                 a_     :  List[Any]				      =     {0: """batch""", 1: """sequence"""}
            return OrderedDict(
                [
                    ("""input_ids""", dynamic_axis),
                    ("""attention_mask""", dynamic_axis),
                ]     )






# This Python program implements an optimal binary search tree (abbreviated BST)
# building dynamic programming algorithm that delivers O(n^2) performance.
#
# The goal of the optimal BST problem is to build a low-cost BST for a
# given set of nodes, each with its own key and frequency. The frequency
# of the node is defined as how many time the node is being searched.
# The search cost of binary search tree is given by this formula:
#
# cost(1, n) = sum{i = 1 to n}((depth(node_i) + 1) * node_i_freq)
#
# where n is number of nodes in the BST. The characteristic of low-cost
# BSTs is having a faster overall search time than other implementations.
# The reason for their fast search time is that the nodes with high
# frequencies will be placed near the root of the tree while the nodes
# with low frequencies will be placed near the leaves of the tree thus
# reducing search time in the most frequent instances.
import sys
from random import randint







class  UpperCamelCase_      :


  """simple docstring"""



  def __init__(						self     :					Any  ,	A_     :					Union[str, Any]  ,	A_     :					Optional[Any]						)       ->			Union[str, Any]:
      SCREAMING_SNAKE_CASE__         =    key
      SCREAMING_SNAKE_CASE__         =    freq







  def __str__(						self     :					Union[str, Any]						)       ->			List[str]:
      return f"Node(key={self.key}, freq={self.freq})"





def 				__a							(       __A			,							__A			,							__A			,							__A			,							__A			,							__A						):
    if i > j or i < 0 or j > len(__A						) - 1:
        return

    SCREAMING_SNAKE_CASE__         =    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(__A			,							__A			,							__A			,							node - 1			,							key[node]			,							__A						)
    print_binary_search_tree(__A			,							__A			,							node + 1			,							__A			,							key[node]			,							__A						)





def 				__a							(       __A						):
    nodes.sort(key=lambda __A						: node.key						)

    SCREAMING_SNAKE_CASE__         =    len(__A						)

    SCREAMING_SNAKE_CASE__         =    [nodes[i].key for i in range(__A						)]
    SCREAMING_SNAKE_CASE__         =    [nodes[i].freq for i in range(__A						)]

    # 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.
    SCREAMING_SNAKE_CASE__         =    [[freqs[i] if i == j else 0 for j in range(__A						)] for i in range(__A						)]
    # sum[i][j] stores the sum of key frequencies between i and j inclusive in nodes
    # array
    SCREAMING_SNAKE_CASE__         =    [[freqs[i] if i == j else 0 for j in range(__A						)] for i in range(__A						)]
    # stores tree roots that will be used later for constructing binary search tree
    SCREAMING_SNAKE_CASE__         =    [[i if i == j else 0 for j in range(__A						)] for i in range(__A						)]

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

            SCREAMING_SNAKE_CASE__         =    sys.maxsize  # set the value to "infinity"
            SCREAMING_SNAKE_CASE__         =    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
                SCREAMING_SNAKE_CASE__         =    dp[i][r - 1] if r != i else 0  # optimal cost for left subtree
                SCREAMING_SNAKE_CASE__         =    dp[r + 1][j] if r != j else 0  # optimal cost for right subtree
                SCREAMING_SNAKE_CASE__         =    left + total[i][j] + right

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

    print("Binary search tree nodes:"						)
    for node in nodes:
        print(__A						)

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





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


if __name__ == "__main__":
  main()


