"""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_:       List[str]			     =       "stas/tiny-wmt19-en-ru"






class  a__     ( _a       ,       unittest.TestCase						):
   snake_case_				=   FSMTTokenizer
   snake_case_				=   False


   def 						snake_case__						(  self	):
        '''simple docstring'''



        super().setUp()

        # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
        lowercase__								=				[
            "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>",
        ]
        lowercase__								=				dict(zip(_UpperCAmelCase,					range(len(_UpperCAmelCase	)	)	)	)
        lowercase__								=				["l o 123", "lo w 1456", "e r</w> 1789", ""]

        lowercase__								=				["en", "ru"]
        lowercase__								=				{
            "langs": self.langs,
            "src_vocab_size": 10,
            "tgt_vocab_size": 20,
        }

        lowercase__								=				os.path.join(self.tmpdirname,					VOCAB_FILES_NAMES["src_vocab_file"]	)
        lowercase__								=				os.path.join(self.tmpdirname,					VOCAB_FILES_NAMES["tgt_vocab_file"]	)
        lowercase__								=				os.path.join(self.tmpdirname,					"tokenizer_config.json"	)
        lowercase__								=				os.path.join(self.tmpdirname,					VOCAB_FILES_NAMES["merges_file"]	)
        with open(self.src_vocab_file,					"w"	) as fp:
             fp.write(json.dumps(_UpperCAmelCase	)	)
        with open(self.tgt_vocab_file,					"w"	) as fp:
             fp.write(json.dumps(_UpperCAmelCase	)	)
        with open(self.merges_file,					"w"	) as fp:
             fp.write("\n".join(_UpperCAmelCase	)	)
        with open(_UpperCAmelCase,					"w"	) as fp:
             fp.write(json.dumps(_UpperCAmelCase	)	)


   @cached_property
   def 						snake_case__						(  self	):
        '''simple docstring'''



        return FSMTTokenizer.from_pretrained("facebook/wmt19-ru-en"	)


   @cached_property
   def 						snake_case__						(  self	):
        '''simple docstring'''



        return FSMTTokenizer.from_pretrained("facebook/wmt19-en-ru"	)


   def 						snake_case__						(  self	):
        '''simple docstring'''



        lowercase__								=				FSMTTokenizer.from_pretrained(_UpperCAmelCase	)
        self.assertListEqual([tokenizer.src_lang, tokenizer.tgt_lang],					["en", "ru"]	)
        self.assertEqual(tokenizer.src_vocab_size,					21	)
        self.assertEqual(tokenizer.tgt_vocab_size,					21	)


   def 						snake_case__						(  self	):
        '''simple docstring'''



        lowercase__								=				FSMTTokenizer(self.langs,					self.src_vocab_file,					self.tgt_vocab_file,					self.merges_file	)

        lowercase__								=				"lower"
        lowercase__								=				["low", "er</w>"]
        lowercase__								=				tokenizer.tokenize(_UpperCAmelCase	)
        self.assertListEqual(_UpperCAmelCase,					_UpperCAmelCase	)

        lowercase__								=				tokens + ["<unk>"]
        lowercase__								=				[14, 15, 20]
        self.assertListEqual(tokenizer.convert_tokens_to_ids(_UpperCAmelCase	),					_UpperCAmelCase	)


   @slow
   def 						snake_case__						(  self	):
        '''simple docstring'''



        lowercase__								=				self.tokenizer_ru_en

        lowercase__								=				tokenizer.encode("sequence builders",					add_special_tokens=_UpperCAmelCase	)
        lowercase__								=				tokenizer.encode("multi-sequence build",					add_special_tokens=_UpperCAmelCase	)

        lowercase__								=				tokenizer.build_inputs_with_special_tokens(_UpperCAmelCase	)
        lowercase__								=				tokenizer.build_inputs_with_special_tokens(_UpperCAmelCase,					_UpperCAmelCase	)

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


   @slow
   def 						snake_case__						(  self	):
        '''simple docstring'''



        lowercase__								=				self.tokenizer_en_ru
        lowercase__								=				self.tokenizer_ru_en

        lowercase__								=				[
            [
                "Here's a little song I wrote. Don't worry, be happy.",
                [2470, 39, 11, 2349, 7222, 70, 5979, 7, 8450, 1050, 1_3160, 5, 26, 6445, 7, 2],
            ],
            ["This is it. No more. I'm done!", [132, 21, 37, 7, 1434, 86, 7, 70, 6476, 1305, 427, 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:
             lowercase__								=				tokenizer_enc.encode(_UpperCAmelCase,					return_tensors=_UpperCAmelCase	)
             self.assertListEqual(_UpperCAmelCase,					_UpperCAmelCase	)

             # and decode backward, using the reversed languages model
             lowercase__								=				tokenizer_dec.decode(_UpperCAmelCase,					skip_special_tokens=_UpperCAmelCase	)
             self.assertEqual(_UpperCAmelCase,					_UpperCAmelCase	)


   @slow
   def 						snake_case__						(  self	):
        '''simple docstring'''



        lowercase__								=				FSMTTokenizer.from_pretrained("facebook/wmt19-ru-en",					do_lower_case=_UpperCAmelCase	)
        lowercase__								=				tokenizer.tokenize("USA is United States of America"	)
        lowercase__								=				["us", "a</w>", "is</w>", "un", "i", "ted</w>", "st", "ates</w>", "of</w>", "am", "er", "ica</w>"]
        self.assertListEqual(_UpperCAmelCase,					_UpperCAmelCase	)


   @unittest.skip("FSMTConfig.__init__  requires non-optional args"	)
   def 						snake_case__						(  self	):
        '''simple docstring'''



        pass


   @unittest.skip("FSMTConfig.__init__  requires non-optional args"	)
   def 						snake_case__						(  self	):
        '''simple docstring'''



        pass





"""simple docstring"""


import qiskit
from qiskit.providers import Backend







def        __a (		A     ,					A   ):






     '''simple docstring'''



     lowercase__			,    lowercase__								=				bin(A   )[2:], bin(A   )[2:]  # Remove leading '0b'

     # Ensure that both strings are of the same length
     if len(A   ) > len(A   ):
          lowercase__								=				y.zfill(len(A   )   )
     else:
          lowercase__								=				x.zfill(len(A   )   )

     # We need (3 * number of bits in the larger number)+1 qBits
     # The second parameter is the number of classical registers, to measure the result
     lowercase__								=				qiskit.QuantumCircuit((len(A   ) * 3) + 1     ,					len(A   ) + 1   )

     # We are essentially "not-ing" the bits that are 1
     # Reversed because it's easier to perform ops on more significant bits
     for i in range(len(A   )   ):
          if x[::-1][i] == "1":
               circuit.x(A   )
     for j in range(len(A   )   ):
          if y[::-1][j] == "1":
               circuit.x(len(A   ) + j   )

     return circuit, x, y







def        __a (		A     ,					A     ,					A     ,					A     ,					A     ,					):






     '''simple docstring'''



     circuit.ccx(A     ,					A     ,					A   )
     circuit.cx(A     ,					A   )
     circuit.ccx(A     ,					A     ,					A   )
     circuit.cx(A     ,					A   )
     circuit.cx(A     ,					A   )







def        __a (		A     ,					A     ,					A = qiskit.Aer.get_backend("aer_simulator"   )     ,					):






     '''simple docstring'''



     if vala < 0 or vala < 0:
          raise ValueError("Both Integers must be positive!"   )

     # Store the Integers
     lowercase__			,    lowercase__			,    lowercase__								=				store_two_classics(A     ,					A   )
     for i in range(len(A   )   ):
          full_adder(A     ,					A     ,					len(A   ) + i     ,					len(A   ) + len(A   ) + i     ,					len(A   ) + len(A   ) + i + 1   )
          circuit.barrier()  # Optional, just for aesthetics

     # Measure the resultant qBits
     for i in range(len(A   ) + 1   ):
          circuit.measure([(len(A   ) * 2) + i]     ,					[i]   )

     lowercase__								=				qiskit.execute(A     ,					A     ,					shots=1   ).result()

     # The result is in binary. Convert it back to int
     return int(list(res.get_counts()   )[0]     ,					2   )


if __name__ == "__main__":
 import doctest

 doctest.testmod()




