


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
UpperCAmelCase			=    """stas/tiny-wmt19-en-ru"""







class lowerCAmelCase_						( lowerCamelCase__					,						unittest.TestCase       ):

 '''simple docstring'''

 __snake_case												=  FSMTTokenizer
 __snake_case												=  False



 def 						UpperCamelCase__     (	self					):
       super().setUp()

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

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

       snake_case_       =      os.path.join(self.tmpdirname		,      VOCAB_FILES_NAMES['''src_vocab_file''']					)
       snake_case_       =      os.path.join(self.tmpdirname		,      VOCAB_FILES_NAMES['''tgt_vocab_file''']					)
       snake_case_       =      os.path.join(self.tmpdirname		,      '''tokenizer_config.json'''					)
       snake_case_       =      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 						UpperCamelCase__     (	self					):
       return FSMTTokenizer.from_pretrained('''facebook/wmt19-ru-en'''					)



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



 def 						UpperCamelCase__     (	self					):
       snake_case_       =      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 						UpperCamelCase__     (	self					):
       snake_case_       =      FSMTTokenizer(self.langs		,      self.src_vocab_file		,      self.tgt_vocab_file		,      self.merges_file					)

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

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



 @slow
 def 						UpperCamelCase__     (	self					):
       snake_case_       =      self.tokenizer_ru_en

       snake_case_       =      tokenizer.encode('''sequence builders'''		,      add_special_tokens=_UpperCAmelCase					)
       snake_case_       =      tokenizer.encode('''multi-sequence build'''		,      add_special_tokens=_UpperCAmelCase					)

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

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



 @slow
 def 						UpperCamelCase__     (	self					):
       snake_case_       =      self.tokenizer_en_ru
       snake_case_       =      self.tokenizer_ru_en

       snake_case_       =      [
           [
               '''Here\'s a little song I wrote. Don\'t worry, be happy.''',
               [24_70, 39, 11, 23_49, 72_22, 70, 59_79, 7, 84_50, 10_50, 1_31_60, 5, 26, 64_45, 7, 2],
           ],
           ['''This is it. No more. I\'m done!''', [1_32, 21, 37, 7, 14_34, 86, 7, 70, 64_76, 13_05, 4_27, 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:
             snake_case_       =      tokenizer_enc.encode(_UpperCAmelCase		,      return_tensors=_UpperCAmelCase					)
             self.assertListEqual(_UpperCAmelCase		,      _UpperCAmelCase					)

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



 @slow
 def 						UpperCamelCase__     (	self					):
       snake_case_       =      FSMTTokenizer.from_pretrained('''facebook/wmt19-ru-en'''		,      do_lower_case=_UpperCAmelCase					)
       snake_case_       =      tokenizer.tokenize('''USA is United States of America'''					)
       snake_case_       =      ['''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 						UpperCamelCase__     (	self					):
       pass





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



def        __lowerCAmelCase    (SCREAMING_SNAKE_CASE					)->  int:




      """simple docstring"""

      if number != int(SCREAMING_SNAKE_CASE					):
            raise ValueError('''factorial() only accepts integral values'''					)
      if number < 0:
            raise ValueError('''factorial() not defined for negative values'''					)
      snake_case_       =      1
      for i in range(1					,					number + 1					):
            value *= i
      return value






def        __lowerCAmelCase    (SCREAMING_SNAKE_CASE					)->  int:




      """simple docstring"""

      if not isinstance(SCREAMING_SNAKE_CASE					,					SCREAMING_SNAKE_CASE					):
            raise ValueError('''factorial() only accepts integral values'''					)
      if n < 0:
            raise ValueError('''factorial() not defined for negative values'''					)
      return 1 if n == 0 or n == 1 else n * factorial(n - 1					)


if __name__ == "__main__":
   import doctest

   doctest.testmod()

   UpperCAmelCase			=    int(input("""Enter a positive integer: """).strip() or 0)
   print(f'''factorial{n} is {factorial(n)}''')