'''simple docstring'''





import sys
from random import randint




class 							UpperCAmelCase :


 def __init__(			self       :  Dict ,					__lowerCamelCase       :  str ,					__lowerCamelCase       :  Dict   ):
    UpperCAmelCase__  :Tuple            =			key
    UpperCAmelCase__  :Union[str, Any]            =			freq





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


def 					a__    (				UpperCamelCase_	:			Union[str, Any],      UpperCamelCase_	:			Union[str, Any],      UpperCamelCase_	:			Optional[Any],      UpperCamelCase_	:			int,      UpperCamelCase_	:			Dict,      UpperCamelCase_	:			List[str]	):






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

   UpperCAmelCase__  :Optional[int]            =			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(UpperCamelCase__,      UpperCamelCase__,      UpperCamelCase__,      node - 1,      key[node],      UpperCamelCase__	)
   print_binary_search_tree(UpperCamelCase__,      UpperCamelCase__,      node + 1,      UpperCamelCase__,      key[node],      UpperCamelCase__	)


def 					a__    (				UpperCamelCase_	:			Tuple	):
   nodes.sort(key=lambda UpperCamelCase_	: node.key	)

   UpperCAmelCase__  :Union[str, Any]            =			len(UpperCamelCase__	)

   UpperCAmelCase__  :List[str]            =			[nodes[i].key for i in range(UpperCamelCase__	)]
   UpperCAmelCase__  :Any            =			[nodes[i].freq for i in range(UpperCamelCase__	)]

   # 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__  :Any            =			[[freqs[i] if i == j else 0 for j in range(UpperCamelCase__	)] for i in range(UpperCamelCase__	)]
   # sum[i][j] stores the sum of key frequencies between i and j inclusive in nodes
   # array
   UpperCAmelCase__  :List[Any]            =			[[freqs[i] if i == j else 0 for j in range(UpperCamelCase__	)] for i in range(UpperCamelCase__	)]
   # stores tree roots that will be used later for constructing binary search tree
   UpperCAmelCase__  :Union[str, Any]            =			[[i if i == j else 0 for j in range(UpperCamelCase__	)] for i in range(UpperCamelCase__	)]

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

         UpperCAmelCase__  :Any            =			sys.maxsize  # set the value to "infinity"
         UpperCAmelCase__  :Optional[int]            =			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__  :Optional[int]            =			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__  :Optional[Any]            =			left + total[i][j] + right

            if dp[i][j] > cost:
               UpperCAmelCase__  :Tuple            =			cost
               UpperCAmelCase__  :Optional[Any]            =			r

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

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


def 					a__    (				):
   UpperCAmelCase__  :Tuple            =			[Node(UpperCamelCase__,      randint(1,      50	)	) for i in range(10,      0,      -1	)]
   find_optimal_binary_search_tree(UpperCamelCase__	)


if __name__ == "__main__":
    main()







'''simple docstring'''





import json
import os
import unittest

from transformers.models.fsmt.tokenization_fsmt import VOCAB_FILES_NAMES, FSMTTokenizer
from transformers.testing_utils import slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


# using a different tiny model than the one used for default params defined in init to ensure proper testing
__lowerCamelCase       				=	'''stas/tiny-wmt19-en-ru'''




class 							UpperCAmelCase ( _snake_case	,   unittest.TestCase	):
 UpperCAmelCase				=				FSMTTokenizer
 UpperCAmelCase				=				False


 def 					__SCREAMING_SNAKE_CASE				(			self       :  Union[str, Any]   ):
    super().setUp()

    # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
    UpperCAmelCase__  :Any            =			[
        '''l''',
        '''o''',
        '''w''',
        '''e''',
        '''r''',
        '''s''',
        '''t''',
        '''i''',
        '''d''',
        '''n''',
        '''w</w>''',
        '''r</w>''',
        '''t</w>''',
        '''lo''',
        '''low''',
        '''er</w>''',
        '''low</w>''',
        '''lowest</w>''',
        '''newer</w>''',
        '''wider</w>''',
        '''<unk>''',
    ]
    UpperCAmelCase__  :Dict            =			dict(zip(__lowerCamelCase ,					range(len(__lowerCamelCase   )   )   )   )
    UpperCAmelCase__  :Dict            =			['''l o 123''', '''lo w 1456''', '''e r</w> 1789''', '''''']

    UpperCAmelCase__  :Dict            =			['''en''', '''ru''']
    UpperCAmelCase__  :Tuple            =			{
        '''langs''': self.langs,
        '''src_vocab_size''': 1_0,
        '''tgt_vocab_size''': 2_0,
    }

    UpperCAmelCase__  :Any            =			os.path.join(self.tmpdirname ,					VOCAB_FILES_NAMES['''src_vocab_file''']   )
    UpperCAmelCase__  :Any            =			os.path.join(self.tmpdirname ,					VOCAB_FILES_NAMES['''tgt_vocab_file''']   )
    UpperCAmelCase__  :Dict            =			os.path.join(self.tmpdirname ,					'''tokenizer_config.json'''   )
    UpperCAmelCase__  :Union[str, Any]            =			os.path.join(self.tmpdirname ,					VOCAB_FILES_NAMES['''merges_file''']   )
    with open(self.src_vocab_file ,					'''w'''   ) as fp:
       fp.write(json.dumps(__lowerCamelCase   )   )
    with open(self.tgt_vocab_file ,					'''w'''   ) as fp:
       fp.write(json.dumps(__lowerCamelCase   )   )
    with open(self.merges_file ,					'''w'''   ) as fp:
       fp.write('''\n'''.join(__lowerCamelCase   )   )
    with open(__lowerCamelCase ,					'''w'''   ) as fp:
       fp.write(json.dumps(__lowerCamelCase   )   )


 @cached_property
 def 					__SCREAMING_SNAKE_CASE				(			self       :  List[Any]   ):
    return FSMTTokenizer.from_pretrained('''facebook/wmt19-ru-en'''   )


 @cached_property
 def 					__SCREAMING_SNAKE_CASE				(			self       :  Tuple   ):
    return FSMTTokenizer.from_pretrained('''facebook/wmt19-en-ru'''   )


 def 					__SCREAMING_SNAKE_CASE				(			self       :  Optional[Any]   ):
    UpperCAmelCase__  :int            =			FSMTTokenizer.from_pretrained(__lowerCamelCase   )
    self.assertListEqual([tokenizer.src_lang, tokenizer.tgt_lang] ,					['''en''', '''ru''']   )
    self.assertEqual(tokenizer.src_vocab_size ,					2_1   )
    self.assertEqual(tokenizer.tgt_vocab_size ,					2_1   )


 def 					__SCREAMING_SNAKE_CASE				(			self       :  Optional[int]   ):
    UpperCAmelCase__  :Tuple            =			FSMTTokenizer(self.langs ,					self.src_vocab_file ,					self.tgt_vocab_file ,					self.merges_file   )

    UpperCAmelCase__  :Any            =			'''lower'''
    UpperCAmelCase__  :List[Any]            =			['''low''', '''er</w>''']
    UpperCAmelCase__  :Union[str, Any]            =			tokenizer.tokenize(__lowerCamelCase   )
    self.assertListEqual(__lowerCamelCase ,					__lowerCamelCase   )

    UpperCAmelCase__  :Dict            =			tokens + ['''<unk>''']
    UpperCAmelCase__  :Union[str, Any]            =			[1_4, 1_5, 2_0]
    self.assertListEqual(tokenizer.convert_tokens_to_ids(__lowerCamelCase   ) ,					__lowerCamelCase   )


 @slow
 def 					__SCREAMING_SNAKE_CASE				(			self       :  Any   ):
    UpperCAmelCase__  :str            =			self.tokenizer_ru_en

    UpperCAmelCase__  :Union[str, Any]            =			tokenizer.encode('''sequence builders''' ,					add_special_tokens=__lowerCamelCase   )
    UpperCAmelCase__  :Dict            =			tokenizer.encode('''multi-sequence build''' ,					add_special_tokens=__lowerCamelCase   )

    UpperCAmelCase__  :Any            =			tokenizer.build_inputs_with_special_tokens(__lowerCamelCase   )
    UpperCAmelCase__  :Union[str, Any]            =			tokenizer.build_inputs_with_special_tokens(__lowerCamelCase ,					__lowerCamelCase   )

    assert encoded_sentence == text + [2]
    assert encoded_pair == text + [2] + text_a + [2]


 @slow
 def 					__SCREAMING_SNAKE_CASE				(			self       :  Union[str, Any]   ):
    UpperCAmelCase__  :List[str]            =			self.tokenizer_en_ru
    UpperCAmelCase__  :Union[str, Any]            =			self.tokenizer_ru_en

    UpperCAmelCase__  :Any            =			[
        [
            '''Here\'s a little song I wrote. Don\'t worry, be happy.''',
            [2_4_7_0, 3_9, 1_1, 2_3_4_9, 7_2_2_2, 7_0, 5_9_7_9, 7, 8_4_5_0, 1_0_5_0, 1_3_1_6_0, 5, 2_6, 6_4_4_5, 7, 2],
        ],
        ['''This is it. No more. I\'m done!''', [1_3_2, 2_1, 3_7, 7, 1_4_3_4, 8_6, 7, 7_0, 6_4_7_6, 1_3_0_5, 4_2_7, 2]],
    ]

    # if data needs to be recreated or added, run:
    # import torch
    # model = torch.hub.load("pytorch/fairseq", "transformer.wmt19.en-ru", checkpoint_file="model4.pt", tokenizer="moses", bpe="fastbpe")
    # for src_text, _ in targets: print(f"""[\n"{src_text}",\n {model.encode(src_text).tolist()}\n],""")

    for src_text, tgt_input_ids in targets:
       UpperCAmelCase__  :List[Any]            =			tokenizer_enc.encode(__lowerCamelCase ,					return_tensors=__lowerCamelCase   )
       self.assertListEqual(__lowerCamelCase ,					__lowerCamelCase   )

       # and decode backward, using the reversed languages model
       UpperCAmelCase__  :str            =			tokenizer_dec.decode(__lowerCamelCase ,					skip_special_tokens=__lowerCamelCase   )
       self.assertEqual(__lowerCamelCase ,					__lowerCamelCase   )


 @slow
 def 					__SCREAMING_SNAKE_CASE				(			self       :  Union[str, Any]   ):
    UpperCAmelCase__  :Optional[Any]            =			FSMTTokenizer.from_pretrained('''facebook/wmt19-ru-en''' ,					do_lower_case=__lowerCamelCase   )
    UpperCAmelCase__  :str            =			tokenizer.tokenize('''USA is United States of America'''   )
    UpperCAmelCase__  :str            =			['''us''', '''a</w>''', '''is</w>''', '''un''', '''i''', '''ted</w>''', '''st''', '''ates</w>''', '''of</w>''', '''am''', '''er''', '''ica</w>''']
    self.assertListEqual(__lowerCamelCase ,					__lowerCamelCase   )


 @unittest.skip('''FSMTConfig.__init__  requires non-optional args'''   )
 def 					__SCREAMING_SNAKE_CASE				(			self       :  List[str]   ):
    pass





 @unittest.skip('''FSMTConfig.__init__  requires non-optional args'''   )
 def 					__SCREAMING_SNAKE_CASE				(			self       :  Dict   ):
    pass



