






'''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
__A      :       Optional[int]      =	"""stas/tiny-wmt19-en-ru"""






class 					a__    (			lowerCAmelCase_ ,						unittest.TestCase						):
					'''simple docstring'''







					_snake_case             =  FSMTTokenizer
					_snake_case             =  False






					def     _UpperCamelCase   (self    :	Union[str, Any]					)  ->       List[str]:
											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(__SCREAMING_SNAKE_CASE				,				range(len(__SCREAMING_SNAKE_CASE					)					)					)					)
											__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(__SCREAMING_SNAKE_CASE					)					)
											with open(self.tgt_vocab_file				,				"w"					) as fp:
																	fp.write(json.dumps(__SCREAMING_SNAKE_CASE					)					)
											with open(self.merges_file				,				"w"					) as fp:
																	fp.write("\n".join(__SCREAMING_SNAKE_CASE					)					)
											with open(__SCREAMING_SNAKE_CASE				,				"w"					) as fp:
																	fp.write(json.dumps(__SCREAMING_SNAKE_CASE					)					)






					@cached_property
					def     _UpperCamelCase   (self    :	List[str]					)  ->       List[str]:
											return FSMTTokenizer.from_pretrained("facebook/wmt19-ru-en"					)






					@cached_property
					def     _UpperCamelCase   (self    :	Optional[int]					)  ->       Any:
											return FSMTTokenizer.from_pretrained("facebook/wmt19-en-ru"					)






					def     _UpperCamelCase   (self    :	int					)  ->       Dict:
											__A		   =      FSMTTokenizer.from_pretrained(__SCREAMING_SNAKE_CASE					)
											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     _UpperCamelCase   (self    :	Dict					)  ->       Any:
											__A		   =      FSMTTokenizer(self.langs				,				self.src_vocab_file				,				self.tgt_vocab_file				,				self.merges_file					)

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

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






					@slow
					def     _UpperCamelCase   (self    :	List[Any]					)  ->       int:
											__A		   =      self.tokenizer_ru_en

											__A		   =      tokenizer.encode("sequence builders"				,				add_special_tokens=__SCREAMING_SNAKE_CASE					)
											__A		   =      tokenizer.encode("multi-sequence build"				,				add_special_tokens=__SCREAMING_SNAKE_CASE					)

											__A		   =      tokenizer.build_inputs_with_special_tokens(__SCREAMING_SNAKE_CASE					)
											__A		   =      tokenizer.build_inputs_with_special_tokens(__SCREAMING_SNAKE_CASE				,				__SCREAMING_SNAKE_CASE					)

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






					@slow
					def     _UpperCamelCase   (self    :	Optional[Any]					)  ->       Any:
											__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(__SCREAMING_SNAKE_CASE				,				return_tensors=__SCREAMING_SNAKE_CASE					)
																	self.assertListEqual(__SCREAMING_SNAKE_CASE				,				__SCREAMING_SNAKE_CASE					)

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






					@slow
					def     _UpperCamelCase   (self    :	Optional[int]					)  ->       str:
											__A		   =      FSMTTokenizer.from_pretrained("facebook/wmt19-ru-en"				,				do_lower_case=__SCREAMING_SNAKE_CASE					)
											__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(__SCREAMING_SNAKE_CASE				,				__SCREAMING_SNAKE_CASE					)






					@unittest.skip("FSMTConfig.__init__  requires non-optional args"					)
					def     _UpperCamelCase   (self    :	Dict					)  ->       Optional[int]:
											pass






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










'''simple docstring'''







def 						_UpperCamelCase							(     snake_case__							,			snake_case__							):
      if n < 0:
            return 0
      # table[i] represents the number of ways to get to amount i
      __A		   =      [0] * (n + 1)

      # There is exactly 1 way to get to zero(You pick no coins).
      __A		   =      1

      # Pick all coins one by one and update table[] values
      # after the index greater than or equal to the value of the
      # picked coin
      for coin_val in s:
            for j in range(snake_case__							,			n + 1							):
                  table[j] += table[j - coin_val]

      return table[n]


if __name__ == "__main__":
       import doctest

       doctest.testmod()


