




"""simple docstring"""

import argparse
import inspect
import logging
from typing import Tuple

import numpy as np
import torch

from transformers import (
    AutoTokenizer,
    BloomForCausalLM,
    BloomTokenizerFast,
    CTRLLMHeadModel,
    CTRLTokenizer,
    GenerationMixin,
    GPTaLMHeadModel,
    GPTaTokenizer,
    GPTJForCausalLM,
    LlamaForCausalLM,
    LlamaTokenizer,
    OpenAIGPTLMHeadModel,
    OpenAIGPTTokenizer,
    OPTForCausalLM,
    TransfoXLLMHeadModel,
    TransfoXLTokenizer,
    XLMTokenizer,
    XLMWithLMHeadModel,
    XLNetLMHeadModel,
    XLNetTokenizer,
)
from transformers.modeling_outputs import CausalLMOutputWithPast


logging.basicConfig(
    format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
    datefmt="%m/%d/%Y %H:%M:%S",
    level=logging.INFO,
)
__lowerCamelCase								=						logging.getLogger(__name__)

__lowerCamelCase								=						int(1_00_00)  # Hardcoded max length to avoid infinite loop

__lowerCamelCase								=						{
    "gpt2": (GPTaLMHeadModel, GPTaTokenizer),
    "ctrl": (CTRLLMHeadModel, CTRLTokenizer),
    "openai-gpt": (OpenAIGPTLMHeadModel, OpenAIGPTTokenizer),
    "xlnet": (XLNetLMHeadModel, XLNetTokenizer),
    "transfo-xl": (TransfoXLLMHeadModel, TransfoXLTokenizer),
    "xlm": (XLMWithLMHeadModel, XLMTokenizer),
    "gptj": (GPTJForCausalLM, AutoTokenizer),
    "bloom": (BloomForCausalLM, BloomTokenizerFast),
    "llama": (LlamaForCausalLM, LlamaTokenizer),
    "opt": (OPTForCausalLM, GPTaTokenizer),
}

# Padding text to help Transformer-XL and XLNet with short prompts as proposed by Aman Rusia
# in https://github.com/rusiaaman/XLNet-gen#methodology
# and https://medium.com/@amanrusia/xlnet-speaks-comparison-to-gpt-2-ea1a4e9ba39e
__lowerCamelCase								=						"In 1991, the remains of Russian Tsar Nicholas II and his family\n(except for Alexei and Maria) are discovered.\nThe voice of Nicholas's young son, Tsarevich Alexei Nikolaevich, narrates the\nremainder of the story. 1883 Western Siberia,\na young Grigori Rasputin is asked by his father and a group of men to perform magic.\nRasputin has a vision and denounces one of the men as a horse thief. Although his\nfather initially slaps him for making such an accusation, Rasputin watches as the\nman is chased outside and beaten. Twenty years later, Rasputin sees a vision of\nthe Virgin Mary, prompting him to become a priest. Rasputin quickly becomes famous,\nwith people, even a bishop, begging for his blessing. <eod> </s> <eos>"



def 				a (						__snake_case     :				Tuple  ):



				'''simple docstring'''





				np.random.seed(args.seed  )
				torch.manual_seed(args.seed  )
				if args.n_gpu > 0:
								torch.cuda.manual_seed_all(args.seed  )



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



				'''simple docstring'''





				if args.temperature > 0.7:
								logger.info('''CTRL typically works better with lower temperatures (and lower top_k).'''  )

				UpperCAmelCase_					:str      			=      tokenizer.encode(__snake_case,    add_special_tokens=__snake_case  )
				if not any(encoded_prompt[0] == x for x in tokenizer.control_codes.values()  ):
								logger.info('''WARNING! You are not starting your generation from a control code so you won\'t get good results'''  )
				return prompt_text



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



				'''simple docstring'''





				UpperCAmelCase_					:Any      			=      hasattr(model.config,    '''use_lang_emb'''  ) and model.config.use_lang_emb
				if hasattr(model.config,    '''lang2id'''  ) and use_lang_emb:
								UpperCAmelCase_					:Any      			=      model.config.langaid.keys()
								if args.xlm_language in available_languages:
												UpperCAmelCase_					:List[str]      			=      args.xlm_language
								else:
												UpperCAmelCase_					:Dict      			=      None
												while language not in available_languages:
																UpperCAmelCase_					:Optional[int]      			=      input('''Using XLM. Select language in ''' + str(list(__snake_case  )  ) + ''' >>> '''  )

								UpperCAmelCase_					:Union[str, Any]      			=      model.config.langaid[language]
								# kwargs["language"] = tokenizer.lang2id[language]

				# TODO fix mask_token_id setup when configurations will be synchronized between models and tokenizers
				# XLM masked-language modeling (MLM) models need masked token
				# is_xlm_mlm = "mlm" in args.model_name_or_path
				# if is_xlm_mlm:
				#     kwargs["mask_token_id"] = tokenizer.mask_token_id

				return prompt_text



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



				'''simple docstring'''





				UpperCAmelCase_					:Any      			=      args.prefix if args.prefix else args.padding_text if args.padding_text else PREFIX
				UpperCAmelCase_					:Tuple      			=      prefix + prompt_text
				return prompt_text



def 				a (						__snake_case     :				int,    __snake_case     :				int,    __snake_case     :				List[str],    __snake_case     :				Union[str, Any]  ):



				'''simple docstring'''





				UpperCAmelCase_					:str      			=      args.prefix if args.prefix else args.padding_text if args.padding_text else PREFIX
				UpperCAmelCase_					:Optional[int]      			=      prefix + prompt_text
				return prompt_text


__lowerCamelCase								=						{
    "ctrl": prepare_ctrl_input,
    "xlm": prepare_xlm_input,
    "xlnet": prepare_xlnet_input,
    "transfo-xl": prepare_transfoxl_input,
}



def 				a (						__snake_case     :				Union[str, Any],    __snake_case     :				List[str]  ):



				'''simple docstring'''





				if length < 0 and max_sequence_length > 0:
								UpperCAmelCase_					:str      			=      max_sequence_length
				elif 0 < max_sequence_length < length:
								UpperCAmelCase_					:Any      			=      max_sequence_length  # No generation bigger than model size
				elif length < 0:
								UpperCAmelCase_					:List[Any]      			=      MAX_LENGTH  # avoid infinite loop
				return length



def 				a (						__snake_case     :				Union[str, Any]  ):



				'''simple docstring'''





				UpperCAmelCase_					:str      			=      None
				if hasattr(__snake_case,    '''hidden_size'''  ):
								UpperCAmelCase_					:Optional[Any]      			=      model_config.hidden_size
				elif hasattr(__snake_case,    '''n_embed'''  ):
								UpperCAmelCase_					:List[str]      			=      model_config.n_embed
				elif hasattr(__snake_case,    '''n_embd'''  ):
								UpperCAmelCase_					:Dict      			=      model_config.n_embd

				UpperCAmelCase_					:Optional[int]      			=      None
				if hasattr(__snake_case,    '''num_attention_heads'''  ):
								UpperCAmelCase_					:Union[str, Any]      			=      model_config.num_attention_heads
				elif hasattr(__snake_case,    '''n_head'''  ):
								UpperCAmelCase_					:Tuple      			=      model_config.n_head

				if embedding_size is None or num_head is None or num_head == 0:
								raise ValueError('''Check the model config'''  )

				UpperCAmelCase_					:Dict      			=      int(embedding_size / num_head  )
				if hasattr(__snake_case,    '''n_layer'''  ):
								UpperCAmelCase_					:Dict      			=      model_config.n_layer
				elif hasattr(__snake_case,    '''num_hidden_layers'''  ):
								UpperCAmelCase_					:int      			=      model_config.num_hidden_layers
				else:
								raise ValueError('''Number of hidden layers couldn\'t be determined from the model config'''  )

				return num_layer, num_head, num_embedding_size_per_head



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



				'''simple docstring'''





				UpperCAmelCase_					:List[Any]      			=      sparse_model_config(model.config  )
				if model.config.model_type == "bloom":
								UpperCAmelCase_					:List[str]      			=      tuple(
								    (
								        torch.empty(int(num_attention_heads * batch_size  ),    __snake_case,    __snake_case  )
								        .to(model.dtype  )
								        .to(model.device  ),
								        torch.empty(int(num_attention_heads * batch_size  ),    __snake_case,    __snake_case  )
								        .to(model.dtype  )
								        .to(model.device  ),
								    )
								    for _ in range(__snake_case  )  )
				else:
								UpperCAmelCase_					:Union[str, Any]      			=      tuple(
								    (
								        torch.empty(__snake_case,    __snake_case,    __snake_case,    __snake_case  )
								        .to(model.dtype  )
								        .to(model.device  ),
								        torch.empty(__snake_case,    __snake_case,    __snake_case,    __snake_case  )
								        .to(model.dtype  )
								        .to(model.device  ),
								    )
								    for _ in range(__snake_case  )  )
				return past_key_values



def 				a (						__snake_case     :				Union[str, Any],    __snake_case     :				int,    __snake_case     :				List[str]  ):



				'''simple docstring'''





				UpperCAmelCase_					:str      			=      len(__snake_case  )
				UpperCAmelCase_					:List[str]      			=      tokenizer.batch_encode_plus(__snake_case,    return_tensors='''pt'''  )
				UpperCAmelCase_					:int      			=      dummy_input.to(model.device  )
				if model.config.use_cache:
								UpperCAmelCase_					:Union[str, Any]      			=      generate_past_key_values(__snake_case,    __snake_case,    1  )
				UpperCAmelCase_					:Tuple      			=      torch.cat(
				    [
				        torch.zeros(dummy_input['''attention_mask'''].shape[0],    1  )
				        .to(dummy_input['''attention_mask'''].dtype  )
				        .to(model.device  ),
				        dummy_input['''attention_mask'''],
				    ],    -1,    )
				return dummy_input



class  _snake_case				(							A__       ):

				'''simple docstring'''




				UpperCamelCase__		     =("""_optimized""", """_default""")





				def __init__(							self      :  int					,							snake_case      :  List[str]					,							snake_case      :  Dict     ):
								UpperCAmelCase_					:List[str]      			=      optimized
								UpperCAmelCase_					:Any      			=      default





				def __call__(							self      :  Optional[Any]					,							*snake_case      :  List[Any]					,							**snake_case      :  Optional[int]     ):
								if kwargs["past_key_values"] is None and self._default.config.use_cache:
												UpperCAmelCase_					:Any      			=      generate_past_key_values(self._default					,							kwargs['''input_ids'''].shape[0]					,							0     )
								kwargs.pop('''position_ids'''					,							snake_case     )
								for k in list(kwargs.keys()     ):
												if kwargs[k] is None or isinstance(kwargs[k]					,							snake_case     ):
																kwargs.pop(snake_case     )
								UpperCAmelCase_					:str      			=      self._optimized(**snake_case     )
								UpperCAmelCase_					:str      			=      outputs[0]
								UpperCAmelCase_					:List[Any]      			=      outputs[1]
								UpperCAmelCase_					:List[Any]      			=      CausalLMOutputWithPast(
								    loss=snake_case					,							logits=snake_case					,							past_key_values=snake_case					,							hidden_states=snake_case					,							attentions=snake_case					,							)
								return fixed_output





				def __getattr__(							self      :  Dict					,							snake_case      :  List[str]     ):
								return getattr(self._default					,							snake_case     )





				def 				snake_case_ (							self      :  Union[str, Any]					,							snake_case      :  Optional[int]					,							snake_case      :  int=None					,							snake_case      :  List[str]=None					,							snake_case      :  List[str]=None					,							**snake_case      :  str     ):
								return self._default.prepare_inputs_for_generation(
								    snake_case					,							past_key_values=snake_case					,							inputs_embeds=snake_case					,							use_cache=snake_case					,							**snake_case     )





				def 				snake_case_ (							self      :  int					,							snake_case      :  Tuple[Tuple[torch.Tensor]]					,							snake_case      :  torch.Tensor     ):
								return self._default._reorder_cache(snake_case					,							snake_case     )



def 				a (						):



				'''simple docstring'''





				UpperCAmelCase_					:Dict      			=      argparse.ArgumentParser()
				parser.add_argument(
				    '''--model_type''',    default=__snake_case,    type=__snake_case,    required=__snake_case,    help='''Model type selected in the list: ''' + ''', '''.join(MODEL_CLASSES.keys()  ),    )
				parser.add_argument(
				    '''--model_name_or_path''',    default=__snake_case,    type=__snake_case,    required=__snake_case,    help='''Path to pre-trained model or shortcut name selected in the list: ''' + ''', '''.join(MODEL_CLASSES.keys()  ),    )

				parser.add_argument('''--prompt''',    type=__snake_case,    default=''''''  )
				parser.add_argument('''--length''',    type=__snake_case,    default=20  )
				parser.add_argument('''--stop_token''',    type=__snake_case,    default=__snake_case,    help='''Token at which text generation is stopped'''  )

				parser.add_argument(
				    '''--temperature''',    type=__snake_case,    default=1.0,    help='''temperature of 1.0 has no effect, lower tend toward greedy sampling''',    )
				parser.add_argument(
				    '''--repetition_penalty''',    type=__snake_case,    default=1.0,    help='''primarily useful for CTRL model; in that case, use 1.2'''  )
				parser.add_argument('''--k''',    type=__snake_case,    default=0  )
				parser.add_argument('''--p''',    type=__snake_case,    default=0.9  )

				parser.add_argument('''--prefix''',    type=__snake_case,    default='''''',    help='''Text added prior to input.'''  )
				parser.add_argument('''--padding_text''',    type=__snake_case,    default='''''',    help='''Deprecated, the use of `--prefix` is preferred.'''  )
				parser.add_argument('''--xlm_language''',    type=__snake_case,    default='''''',    help='''Optional language when used with the XLM model.'''  )

				parser.add_argument('''--seed''',    type=__snake_case,    default=42,    help='''random seed for initialization'''  )
				parser.add_argument('''--no_cuda''',    action='''store_true''',    help='''Avoid using CUDA when available'''  )
				parser.add_argument('''--num_return_sequences''',    type=__snake_case,    default=1,    help='''The number of samples to generate.'''  )
				parser.add_argument(
				    '''--fp16''',    action='''store_true''',    help='''Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit''',    )
				parser.add_argument('''--jit''',    action='''store_true''',    help='''Whether or not to use jit trace to accelerate inference'''  )
				UpperCAmelCase_					:Optional[Any]      			=      parser.parse_args()

				UpperCAmelCase_					:Union[str, Any]      			=      torch.device('''cuda''' if torch.cuda.is_available() and not args.no_cuda else '''cpu'''  )
				UpperCAmelCase_					:Optional[int]      			=      0 if args.no_cuda else torch.cuda.device_count()

				logger.warning(f'device: {args.device}, n_gpu: {args.n_gpu}, 16-bits training: {args.fpaa}'  )

				set_seed(__snake_case  )

				# Initialize the model and tokenizer
				try:
								UpperCAmelCase_					:Union[str, Any]      			=      args.model_type.lower()
								UpperCAmelCase_					:List[Any]      			=      MODEL_CLASSES[args.model_type]
				except KeyError:
								raise KeyError('''the model {} you specified is not supported. You are welcome to add it and open a PR :)'''  )

				UpperCAmelCase_					:List[str]      			=      tokenizer_class.from_pretrained(args.model_name_or_path  )
				if tokenizer.pad_token is None:
								UpperCAmelCase_					:Tuple      			=      tokenizer.eos_token
				UpperCAmelCase_					:Any      			=      model_class.from_pretrained(args.model_name_or_path  )
				model.to(args.device  )

				if args.fpaa:
								model.half()
				UpperCAmelCase_					:Optional[Any]      			=      getattr(model.config,    '''max_position_embeddings''',    0  )
				UpperCAmelCase_					:Tuple      			=      adjust_length_to_model(args.length,    max_sequence_length=__snake_case  )
				logger.info(__snake_case  )

				UpperCAmelCase_					:List[str]      			=      args.prompt if args.prompt else input('''Model prompt >>> '''  )

				# Different models need different input formatting and/or extra arguments
				UpperCAmelCase_					:Dict      			=      args.model_type in PREPROCESSING_FUNCTIONS.keys()
				if requires_preprocessing:
								UpperCAmelCase_					:Optional[int]      			=      PREPROCESSING_FUNCTIONS.get(args.model_type  )
								UpperCAmelCase_					:Any      			=      prepare_input(__snake_case,    __snake_case,    __snake_case,    __snake_case  )

								if model.__class__.__name__ in ["TransfoXLLMHeadModel"]:
												UpperCAmelCase_					:List[Any]      			=      {'''add_space_before_punct_symbol''': True}
								else:
												UpperCAmelCase_					:Dict      			=      {}

								UpperCAmelCase_					:List[Any]      			=      tokenizer.encode(
								    __snake_case,    add_special_tokens=__snake_case,    return_tensors='''pt''',    **__snake_case  )
				else:
								UpperCAmelCase_					:Any      			=      args.prefix if args.prefix else args.padding_text
								UpperCAmelCase_					:Optional[Any]      			=      tokenizer.encode(prefix + prompt_text,    add_special_tokens=__snake_case,    return_tensors='''pt'''  )
				UpperCAmelCase_					:Optional[Any]      			=      encoded_prompt.to(args.device  )

				if encoded_prompt.size()[-1] == 0:
								UpperCAmelCase_					:Dict      			=      None
				else:
								UpperCAmelCase_					:Optional[Any]      			=      encoded_prompt

				if args.jit:
								UpperCAmelCase_					:int      			=      ['''enable jit''']
								UpperCAmelCase_					:Union[str, Any]      			=      prepare_jit_inputs(__snake_case,    __snake_case,    __snake_case  )
								torch._C._jit_set_texpr_fuser_enabled(__snake_case  )
								UpperCAmelCase_					:Any      			=      False
								if hasattr(__snake_case,    '''forward'''  ):
												UpperCAmelCase_					:Optional[Any]      			=      inspect.signature(model.forward  )
								else:
												UpperCAmelCase_					:Optional[int]      			=      inspect.signature(model.__call__  )
								UpperCAmelCase_					:List[str]      			=      tuple(jit_inputs[key] for key in sig.parameters if jit_inputs.get(__snake_case,    __snake_case  ) is not None  )
								UpperCAmelCase_					:Optional[int]      			=      torch.jit.trace(__snake_case,    __snake_case,    strict=__snake_case  )
								UpperCAmelCase_					:int      			=      torch.jit.freeze(traced_model.eval()  )
								traced_model(*__snake_case  )
								traced_model(*__snake_case  )

								UpperCAmelCase_					:str      			=      _ModelFallbackWrapper(__snake_case,    __snake_case  )

				UpperCAmelCase_					:Optional[int]      			=      model.generate(
				    input_ids=__snake_case,    max_length=args.length + len(encoded_prompt[0]  ),    temperature=args.temperature,    top_k=args.k,    top_p=args.p,    repetition_penalty=args.repetition_penalty,    do_sample=__snake_case,    num_return_sequences=args.num_return_sequences,    )

				# Remove the batch dimension when returning multiple sequences
				if len(output_sequences.shape  ) > 2:
								output_sequences.squeeze_()

				UpperCAmelCase_					:Tuple      			=      []

				for generated_sequence_idx, generated_sequence in enumerate(__snake_case  ):
								print(f'=== GENERATED SEQUENCE {generated_sequence_idx + 1} ==='  )
								UpperCAmelCase_					:Optional[int]      			=      generated_sequence.tolist()

								# Decode text
								UpperCAmelCase_					:List[Any]      			=      tokenizer.decode(__snake_case,    clean_up_tokenization_spaces=__snake_case  )

								# Remove all text after the stop token
								UpperCAmelCase_					:Any      			=      text[: text.find(args.stop_token  ) if args.stop_token else None]

								# Add the prompt at the beginning of the sequence. Remove the excess text that was used for pre-processing
								UpperCAmelCase_					:Union[str, Any]      			=      (
								    prompt_text + text[len(tokenizer.decode(encoded_prompt[0],    clean_up_tokenization_spaces=__snake_case  )  ) :]
								)

								generated_sequences.append(__snake_case  )
								print(__snake_case  )

				return generated_sequences


if __name__ == "__main__":
						main()








"""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()


