




"""simple docstring"""

from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import TensorType, is_torch_available, logging


__lowerCamelCase								=						logging.get_logger(__name__)

__lowerCamelCase								=						{
    "facebook/mbart-large-cc25": "https://huggingface.co/facebook/mbart-large-cc25/resolve/main/config.json",
    # See all MBART models at https://huggingface.co/models?filter=mbart
}



class  _snake_case				(							A__       ):

				'''simple docstring'''




				UpperCamelCase__		     ="""mbart"""
				UpperCamelCase__		     =["""past_key_values"""]
				UpperCamelCase__		     ={"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}





				def __init__(							self      :  Union[str, Any]					,							snake_case      :  Optional[int]=50_265					,							snake_case      :  List[str]=1_024					,							snake_case      :  List[str]=12					,							snake_case      :  Tuple=4_096					,							snake_case      :  Tuple=16					,							snake_case      :  Dict=12					,							snake_case      :  List[str]=4_096					,							snake_case      :  int=16					,							snake_case      :  int=0.0					,							snake_case      :  Tuple=0.0					,							snake_case      :  Dict=True					,							snake_case      :  Optional[Any]=True					,							snake_case      :  str="gelu"					,							snake_case      :  Optional[int]=1_024					,							snake_case      :  Any=0.1					,							snake_case      :  str=0.0					,							snake_case      :  Tuple=0.0					,							snake_case      :  List[Any]=0.02					,							snake_case      :  List[Any]=0.0					,							snake_case      :  List[Any]=False					,							snake_case      :  List[Any]=1					,							snake_case      :  Tuple=0					,							snake_case      :  List[Any]=2					,							snake_case      :  Any=2					,							**snake_case      :  Tuple					,							):
								UpperCAmelCase_					:str      			=      vocab_size
								UpperCAmelCase_					:Any      			=      max_position_embeddings
								UpperCAmelCase_					:Tuple      			=      d_model
								UpperCAmelCase_					:Union[str, Any]      			=      encoder_ffn_dim
								UpperCAmelCase_					:Optional[Any]      			=      encoder_layers
								UpperCAmelCase_					:Tuple      			=      encoder_attention_heads
								UpperCAmelCase_					:List[Any]      			=      decoder_ffn_dim
								UpperCAmelCase_					:Optional[Any]      			=      decoder_layers
								UpperCAmelCase_					:int      			=      decoder_attention_heads
								UpperCAmelCase_					:Optional[int]      			=      dropout
								UpperCAmelCase_					:List[Any]      			=      attention_dropout
								UpperCAmelCase_					:str      			=      activation_dropout
								UpperCAmelCase_					:Any      			=      activation_function
								UpperCAmelCase_					:Optional[int]      			=      init_std
								UpperCAmelCase_					:Optional[int]      			=      encoder_layerdrop
								UpperCAmelCase_					:List[Any]      			=      decoder_layerdrop
								UpperCAmelCase_					:Dict      			=      classifier_dropout
								UpperCAmelCase_					:int      			=      use_cache
								UpperCAmelCase_					:Optional[Any]      			=      encoder_layers
								UpperCAmelCase_					:List[Any]      			=      scale_embedding  # scale factor will be sqrt(d_model) if True
								super().__init__(
								    pad_token_id=snake_case					,							bos_token_id=snake_case					,							eos_token_id=snake_case					,							is_encoder_decoder=snake_case					,							forced_eos_token_id=snake_case					,							**snake_case					,							)



class  _snake_case				(							A__       ):

				'''simple docstring'''




				@property
				def 				snake_case_ (							self      :  int     ):
								if self.task in ["default", "seq2seq-lm"]:
												UpperCAmelCase_					:List[Any]      			=      OrderedDict(
												    [
												        ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
												        ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
												    ]     )

												if self.use_past:
																UpperCAmelCase_					:Optional[int]      			=      {0: '''batch'''}
																UpperCAmelCase_					:Tuple      			=      {0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
												else:
																UpperCAmelCase_					:List[str]      			=      {0: '''batch''', 1: '''decoder_sequence'''}
																UpperCAmelCase_					:Dict      			=      {0: '''batch''', 1: '''decoder_sequence'''}

												if self.use_past:
																self.fill_with_past_key_values_(snake_case					,							direction='''inputs'''     )
								elif self.task == "causal-lm":
												# TODO: figure this case out.
												UpperCAmelCase_					:Tuple      			=      OrderedDict(
												    [
												        ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
												        ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
												    ]     )
												if self.use_past:
																UpperCAmelCase_			,UpperCAmelCase_					:str      			=      self.num_layers
																for i in range(snake_case     ):
																				UpperCAmelCase_					:List[str]      			=      {0: '''batch''', 2: '''past_sequence + sequence'''}
																				UpperCAmelCase_					:List[Any]      			=      {0: '''batch''', 2: '''past_sequence + sequence'''}
								else:
												UpperCAmelCase_					:int      			=      OrderedDict(
												    [
												        ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
												        ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
												        ('''decoder_input_ids''', {0: '''batch''', 1: '''decoder_sequence'''}),
												        ('''decoder_attention_mask''', {0: '''batch''', 1: '''decoder_sequence'''}),
												    ]     )

								return common_inputs





				@property
				def 				snake_case_ (							self      :  Union[str, Any]     ):
								if self.task in ["default", "seq2seq-lm"]:
												UpperCAmelCase_					:int      			=      super().outputs
								else:
												UpperCAmelCase_					:Optional[int]      			=      super(snake_case					,							self     ).outputs
												if self.use_past:
																UpperCAmelCase_			,UpperCAmelCase_					:List[str]      			=      self.num_layers
																for i in range(snake_case     ):
																				UpperCAmelCase_					:Dict      			=      {0: '''batch''', 2: '''past_sequence + sequence'''}
																				UpperCAmelCase_					:Tuple      			=      {0: '''batch''', 2: '''past_sequence + sequence'''}
								return common_outputs





				def 				snake_case_ (							self      :  Union[str, Any]					,							snake_case      :  PreTrainedTokenizer					,							snake_case      :  int = -1					,							snake_case      :  int = -1					,							snake_case      :  bool = False					,							snake_case      :  Optional[TensorType] = None					,							):
								UpperCAmelCase_					:Dict      			=      self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
								    snake_case					,							snake_case					,							snake_case					,							snake_case					,							snake_case     )

								# Generate decoder inputs
								UpperCAmelCase_					:Optional[int]      			=      seq_length if not self.use_past else 1
								UpperCAmelCase_					:str      			=      self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
								    snake_case					,							snake_case					,							snake_case					,							snake_case					,							snake_case     )
								UpperCAmelCase_					:Any      			=      {f'decoder_{name}': tensor for name, tensor in decoder_inputs.items()}
								UpperCAmelCase_					:Any      			=      dict(**snake_case					,							**snake_case     )

								if self.use_past:
												if not is_torch_available():
																raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.'''     )
												else:
																import torch
												UpperCAmelCase_			,UpperCAmelCase_					:int      			=      common_inputs['''input_ids'''].shape
												UpperCAmelCase_					:str      			=      common_inputs['''decoder_input_ids'''].shape[1]
												UpperCAmelCase_			,UpperCAmelCase_					:Tuple      			=      self.num_attention_heads
												UpperCAmelCase_					:str      			=      (
												    batch,
												    num_encoder_attention_heads,
												    encoder_seq_length,
												    self._config.hidden_size // num_encoder_attention_heads,
												)
												UpperCAmelCase_					:Optional[int]      			=      decoder_seq_length + 3
												UpperCAmelCase_					:int      			=      (
												    batch,
												    num_decoder_attention_heads,
												    decoder_past_length,
												    self._config.hidden_size // num_decoder_attention_heads,
												)

												UpperCAmelCase_					:Union[str, Any]      			=      torch.cat(
												    [common_inputs['''decoder_attention_mask'''], torch.ones(snake_case					,							snake_case     )]					,							dim=1     )

												UpperCAmelCase_					:Union[str, Any]      			=      []
												# If the number of encoder and decoder layers are present in the model configuration, both are considered
												UpperCAmelCase_			,UpperCAmelCase_					:List[Any]      			=      self.num_layers
												UpperCAmelCase_					:Any      			=      min(snake_case					,							snake_case     )
												UpperCAmelCase_					:Optional[Any]      			=      max(snake_case					,							snake_case     ) - min_num_layers
												UpperCAmelCase_					:List[Any]      			=      '''encoder''' if num_encoder_layers > num_decoder_layers else '''decoder'''

												for _ in range(snake_case     ):
																common_inputs["past_key_values"].append(
																    (
																        torch.zeros(snake_case     ),
																        torch.zeros(snake_case     ),
																        torch.zeros(snake_case     ),
																        torch.zeros(snake_case     ),
																    )     )
												# TODO: test this.
												UpperCAmelCase_					:int      			=      encoder_shape if remaining_side_name == '''encoder''' else decoder_shape
												for _ in range(snake_case					,							snake_case     ):
																common_inputs["past_key_values"].append((torch.zeros(snake_case     ), torch.zeros(snake_case     ))     )
								return common_inputs





				def 				snake_case_ (							self      :  Tuple					,							snake_case      :  PreTrainedTokenizer					,							snake_case      :  int = -1					,							snake_case      :  int = -1					,							snake_case      :  bool = False					,							snake_case      :  Optional[TensorType] = None					,							):
								UpperCAmelCase_					:List[Any]      			=      self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
								    snake_case					,							snake_case					,							snake_case					,							snake_case					,							snake_case     )

								if self.use_past:
												if not is_torch_available():
																raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.'''     )
												else:
																import torch
												UpperCAmelCase_			,UpperCAmelCase_					:Union[str, Any]      			=      common_inputs['''input_ids'''].shape
												# Not using the same length for past_key_values
												UpperCAmelCase_					:Dict      			=      seqlen + 2
												UpperCAmelCase_			,UpperCAmelCase_					:Optional[int]      			=      self.num_layers
												UpperCAmelCase_			,UpperCAmelCase_					:Any      			=      self.num_attention_heads
												UpperCAmelCase_					:Tuple      			=      (
												    batch,
												    num_encoder_attention_heads,
												    past_key_values_length,
												    self._config.hidden_size // num_encoder_attention_heads,
												)

												UpperCAmelCase_					:List[str]      			=      common_inputs['''attention_mask'''].dtype
												UpperCAmelCase_					:Optional[Any]      			=      torch.cat(
												    [common_inputs['''attention_mask'''], torch.ones(snake_case					,							snake_case					,							dtype=snake_case     )]					,							dim=1     )
												UpperCAmelCase_					:Union[str, Any]      			=      [
												    (torch.zeros(snake_case     ), torch.zeros(snake_case     )) for _ in range(snake_case     )
												]
								return common_inputs





				def 				snake_case_ (							self      :  Tuple					,							snake_case      :  PreTrainedTokenizer					,							snake_case      :  int = -1					,							snake_case      :  int = -1					,							snake_case      :  bool = False					,							snake_case      :  Optional[TensorType] = None					,							):
								# Copied from OnnxConfig.generate_dummy_inputs
								# Did not use super(OnnxConfigWithPast, self).generate_dummy_inputs for code clarity.
								# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
								UpperCAmelCase_					:Optional[Any]      			=      compute_effective_axis_dimension(
								    snake_case					,							fixed_dimension=OnnxConfig.default_fixed_batch					,							num_token_to_add=0     )

								# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
								UpperCAmelCase_					:Tuple      			=      tokenizer.num_special_tokens_to_add(snake_case     )
								UpperCAmelCase_					:Optional[Any]      			=      compute_effective_axis_dimension(
								    snake_case					,							fixed_dimension=OnnxConfig.default_fixed_sequence					,							num_token_to_add=snake_case     )

								# Generate dummy inputs according to compute batch and sequence
								UpperCAmelCase_					:List[Any]      			=      [''' '''.join([tokenizer.unk_token]     ) * seq_length] * batch_size
								UpperCAmelCase_					:Tuple      			=      dict(tokenizer(snake_case					,							return_tensors=snake_case     )     )
								return common_inputs





				def 				snake_case_ (							self      :  Dict					,							snake_case      :  PreTrainedTokenizer					,							snake_case      :  int = -1					,							snake_case      :  int = -1					,							snake_case      :  bool = False					,							snake_case      :  Optional[TensorType] = None					,							):
								if self.task in ["default", "seq2seq-lm"]:
												UpperCAmelCase_					:Tuple      			=      self._generate_dummy_inputs_for_default_and_seqaseq_lm(
												    snake_case					,							batch_size=snake_case					,							seq_length=snake_case					,							is_pair=snake_case					,							framework=snake_case     )

								elif self.task == "causal-lm":
												UpperCAmelCase_					:Any      			=      self._generate_dummy_inputs_for_causal_lm(
												    snake_case					,							batch_size=snake_case					,							seq_length=snake_case					,							is_pair=snake_case					,							framework=snake_case     )
								else:
												UpperCAmelCase_					:List[Any]      			=      self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
												    snake_case					,							batch_size=snake_case					,							seq_length=snake_case					,							is_pair=snake_case					,							framework=snake_case     )

								return common_inputs





				def 				snake_case_ (							self      :  List[Any]					,							snake_case      :  int					,							snake_case      :  Dict					,							snake_case      :  Optional[int]					,							snake_case      :  Optional[int]     ):
								if self.task in ["default", "seq2seq-lm"]:
												UpperCAmelCase_					:Optional[Any]      			=      super()._flatten_past_key_values_(snake_case					,							snake_case					,							snake_case					,							snake_case     )
								else:
												UpperCAmelCase_					:Optional[Any]      			=      super(snake_case					,							self     )._flatten_past_key_values_(
												    snake_case					,							snake_case					,							snake_case					,							snake_case     )








"""simple docstring"""

# 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  _snake_case				:

				'''simple docstring'''




				def __init__(							self      :  str					,							snake_case      :  Optional[Any]					,							snake_case      :  str     ):
								UpperCAmelCase_					:Union[str, Any]      			=      key
								UpperCAmelCase_					:str      			=      freq





				def __str__(							self      :  Optional[Any]     ):
								return f'Node(key={self.key}, freq={self.freq})'



def 				a (						__snake_case     :				int,    __snake_case     :				Optional[int],    __snake_case     :				Tuple,    __snake_case     :				Any,    __snake_case     :				Dict,    __snake_case     :				List[str]  ):



				'''simple docstring'''





				if i > j or i < 0 or j > len(__snake_case  ) - 1:
								return

				UpperCAmelCase_					:List[str]      			=      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(__snake_case,    __snake_case,    __snake_case,    node - 1,    key[node],    __snake_case  )
				print_binary_search_tree(__snake_case,    __snake_case,    node + 1,    __snake_case,    key[node],    __snake_case  )



def 				a (						__snake_case     :				Optional[Any]  ):



				'''simple docstring'''





				nodes.sort(key=lambda __snake_case  : node.key  )

				UpperCAmelCase_					:List[str]      			=      len(__snake_case  )

				UpperCAmelCase_					:Any      			=      [nodes[i].key for i in range(__snake_case  )]
				UpperCAmelCase_					:int      			=      [nodes[i].freq for i in range(__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_					:Dict      			=      [[freqs[i] if i == j else 0 for j in range(__snake_case  )] for i in range(__snake_case  )]
				# sum[i][j] stores the sum of key frequencies between i and j inclusive in nodes
				# array
				UpperCAmelCase_					:Optional[Any]      			=      [[freqs[i] if i == j else 0 for j in range(__snake_case  )] for i in range(__snake_case  )]
				# stores tree roots that will be used later for constructing binary search tree
				UpperCAmelCase_					:int      			=      [[i if i == j else 0 for j in range(__snake_case  )] for i in range(__snake_case  )]

				for interval_length in range(2,    n + 1  ):
								for i in range(n - interval_length + 1  ):
												UpperCAmelCase_					:Union[str, Any]      			=      i + interval_length - 1

												UpperCAmelCase_					:Optional[int]      			=      sys.maxsize  # set the value to "infinity"
												UpperCAmelCase_					:List[Any]      			=      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_					:Any      			=      dp[i][r - 1] if r != i else 0  # optimal cost for left subtree
																UpperCAmelCase_					:Optional[int]      			=      dp[r + 1][j] if r != j else 0  # optimal cost for right subtree
																UpperCAmelCase_					:Union[str, Any]      			=      left + total[i][j] + right

																if dp[i][j] > cost:
																				UpperCAmelCase_					:Optional[Any]      			=      cost
																				UpperCAmelCase_					:Optional[int]      			=      r

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

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



def 				a (						):



				'''simple docstring'''





				UpperCAmelCase_					:Union[str, Any]      			=      [Node(__snake_case,    randint(1,    50  )  ) for i in range(10,    0,    -1  )]
				find_optimal_binary_search_tree(__snake_case  )


if __name__ == "__main__":
						main()


