




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
lowercase__      :			Optional[Any]           =					"stas/tiny-wmt19-en-ru"




class 			a__       (					UpperCamelCase__   ,					unittest.TestCase      ):
     a		:			Optional[Any]							=	FSMTTokenizer
     a		:			Optional[int]							=	False







     def 			lowerCAmelCase_				(	self )  ->    str:



          '''simple docstring'''



          super().setUp()

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

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

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







     @cached_property
     def 			lowerCAmelCase_				(	self )  ->    int:



          '''simple docstring'''



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







     @cached_property
     def 			lowerCAmelCase_				(	self )  ->    Optional[Any]:



          '''simple docstring'''



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







     def 			lowerCAmelCase_				(	self )  ->    List[Any]:



          '''simple docstring'''



          a      =      FSMTTokenizer.from_pretrained(A )
          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 			lowerCAmelCase_				(	self )  ->    int:



          '''simple docstring'''



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

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

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







     @slow
     def 			lowerCAmelCase_				(	self )  ->    Any:



          '''simple docstring'''



          a      =      self.tokenizer_ru_en

          a      =      tokenizer.encode("sequence builders"  ,		add_special_tokens=A )
          a      =      tokenizer.encode("multi-sequence build"  ,		add_special_tokens=A )

          a      =      tokenizer.build_inputs_with_special_tokens(A )
          a      =      tokenizer.build_inputs_with_special_tokens(A  ,		A )

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







     @slow
     def 			lowerCAmelCase_				(	self )  ->    Any:



          '''simple docstring'''



          a      =      self.tokenizer_en_ru
          a      =      self.tokenizer_ru_en

          a      =      [
              [
                  "Here's a little song I wrote. Don't worry, be happy.",
                  [2470, 39, 11, 2349, 7222, 70, 5979, 7, 8450, 1050, 13160, 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:
               a      =      tokenizer_enc.encode(A  ,		return_tensors=A )
               self.assertListEqual(A  ,		A )

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







     @slow
     def 			lowerCAmelCase_				(	self )  ->    Union[str, Any]:



          '''simple docstring'''



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







     @unittest.skip("FSMTConfig.__init__  requires non-optional args" )
     def 			lowerCAmelCase_				(	self )  ->    Union[str, Any]:



          '''simple docstring'''



          pass







     @unittest.skip("FSMTConfig.__init__  requires non-optional args" )
     def 			lowerCAmelCase_				(	self )  ->    Any:



          '''simple docstring'''



          pass







import doctest

import projectq
from projectq.ops import H, Measure

def 					SCREAMING_SNAKE_CASE							(   __UpperCamelCase)			->     int:
     a      =      quantum_engine.allocate_qubit()
     H | qubit
     Measure | qubit
     return int(__UpperCamelCase)


if __name__ == "__main__":
   doctest.testmod()

   # initialises a new quantum backend
   lowercase__      :			Optional[int]           =					projectq.MainEngine()

   # Generate a list of 10 random numbers
   lowercase__      :			List[str]           =					[get_random_number(quantum_engine) for _ in range(10)]

   # Flushes the quantum engine from memory
   quantum_engine.flush()

   print("Random numbers", random_numbers_list)

