

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									=  '''stas/tiny-wmt19-en-ru'''






class 				A_					(						snake_case__				,      unittest.TestCase   ):
     _lowercase     :	Any        =      FSMTTokenizer
     _lowercase     :	Optional[int]        =      False


     def     UpperCAmelCase    (				self     :						Any   )				->      int:
      super().setUp()

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

      __lowerCAmelCase:  Optional[Any]        = ['en', 'ru']
      __lowerCAmelCase:  Tuple        = {
          'langs': self.langs,
          'src_vocab_size': 1_0,
          'tgt_vocab_size': 2_0,
      }

      __lowerCAmelCase:  Optional[Any]        = os.path.join(self.tmpdirname     ,    VOCAB_FILES_NAMES['src_vocab_file']   )
      __lowerCAmelCase:  Tuple        = os.path.join(self.tmpdirname     ,    VOCAB_FILES_NAMES['tgt_vocab_file']   )
      __lowerCAmelCase:  Optional[int]        = os.path.join(self.tmpdirname     ,    'tokenizer_config.json'   )
      __lowerCAmelCase:  int        = 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     :						int   )				->      Dict:
      return FSMTTokenizer.from_pretrained('facebook/wmt19-ru-en'   )


     @cached_property
     def     UpperCAmelCase    (				self     :						Tuple   )				->      Optional[Any]:
      return FSMTTokenizer.from_pretrained('facebook/wmt19-en-ru'   )


     def     UpperCAmelCase    (				self     :						Optional[int]   )				->      Tuple:
      __lowerCAmelCase:  Optional[int]        = FSMTTokenizer.from_pretrained(UpperCAmelCase   )
      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     UpperCAmelCase    (				self     :						str   )				->      Optional[int]:
      __lowerCAmelCase:  int        = FSMTTokenizer(self.langs     ,    self.src_vocab_file     ,    self.tgt_vocab_file     ,    self.merges_file   )

      __lowerCAmelCase:  int        = 'lower'
      __lowerCAmelCase:  List[str]        = ['low', 'er</w>']
      __lowerCAmelCase:  str        = tokenizer.tokenize(UpperCAmelCase   )
      self.assertListEqual(UpperCAmelCase     ,    UpperCAmelCase   )

      __lowerCAmelCase:  Optional[Any]        = tokens + ['<unk>']
      __lowerCAmelCase:  Tuple        = [1_4, 1_5, 2_0]
      self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCAmelCase   )     ,    UpperCAmelCase   )


     @slow
     def     UpperCAmelCase    (				self     :						Optional[Any]   )				->      Optional[Any]:
      __lowerCAmelCase:  Tuple        = self.tokenizer_ru_en

      __lowerCAmelCase:  Dict        = tokenizer.encode('sequence builders'     ,    add_special_tokens=UpperCAmelCase   )
      __lowerCAmelCase:  str        = tokenizer.encode('multi-sequence build'     ,    add_special_tokens=UpperCAmelCase   )

      __lowerCAmelCase:  Tuple        = tokenizer.build_inputs_with_special_tokens(UpperCAmelCase   )
      __lowerCAmelCase:  List[Any]        = 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     :						int   )				->      List[str]:
      __lowerCAmelCase:  Union[str, Any]        = self.tokenizer_en_ru
      __lowerCAmelCase:  Union[str, Any]        = self.tokenizer_ru_en

      __lowerCAmelCase:  Tuple        = [
          [
              '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:
       __lowerCAmelCase:  Tuple        = tokenizer_enc.encode(UpperCAmelCase     ,    return_tensors=UpperCAmelCase   )
       self.assertListEqual(UpperCAmelCase     ,    UpperCAmelCase   )

       # and decode backward, using the reversed languages model
       __lowerCAmelCase:  Optional[Any]        = tokenizer_dec.decode(UpperCAmelCase     ,    skip_special_tokens=UpperCAmelCase   )
       self.assertEqual(UpperCAmelCase     ,    UpperCAmelCase   )


     @slow
     def     UpperCAmelCase    (				self     :						Union[str, Any]   )				->      List[Any]:
      __lowerCAmelCase:  Optional[Any]        = FSMTTokenizer.from_pretrained('facebook/wmt19-ru-en'     ,    do_lower_case=UpperCAmelCase   )
      __lowerCAmelCase:  Dict        = tokenizer.tokenize('USA is United States of America'   )
      __lowerCAmelCase:  int        = ['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     :						Optional[int]   )				->      int:
      pass


     @unittest.skip('FSMTConfig.__init__  requires non-optional args'   )
     def     UpperCAmelCase    (				self     :						Any   )				->      Tuple:
      pass













from __future__ import annotations


def __A       ( __lowerCAmelCase					,						__lowerCAmelCase					,						__lowerCAmelCase					,						)->    list[float | int]:





						"""simple docstring"""



						if not all((nth_term, start_term_a, common_ratio_r)      ):
												return []
						_UpperCAmelCase							   =     []
						_UpperCAmelCase							   =     1
						_UpperCAmelCase							   =     common_ratio_r
						for _ in range(int(__lowerCAmelCase      )      ):
												if not series:
																		series.append(__lowerCAmelCase      )
												else:
																		power += 1
																		series.append(float(start_term_a * multiple      )      )
																		_UpperCAmelCase							   =     pow(float(__lowerCAmelCase      )					,						__lowerCAmelCase      )
						return series


if __name__ == "__main__":
		import doctest

		doctest.testmod()

		_a       =    float(input('''Enter the last number (n term) of the Geometric Series'''))
		_a       =    float(input('''Enter the starting term (a) of the Geometric Series'''))
		_a       =    float(
		    input('''Enter the common ratio between two terms (r) of the Geometric Series''')
		)
		print('''Formula of Geometric Series => a + ar + ar^2 ... +ar^n''')
		print(geometric_series(nth_term, start_term_a, common_ratio_r))
