code: 
  
  
  
  
  
  '''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							:			int				="stas/tiny-wmt19-en-ru"
  
  
  
  
  
  
  class      UpperCAmelCase			( UpperCamelCase__    , unittest.TestCase       ):
   __lowercase     	=       FSMTTokenizer
   __lowercase     	=       False
   def   UpperCAmelCase_							(       self   :Dict							)->							Any:
       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(lowercase_		, range(len(lowercase_							)							)							)							)
       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(lowercase_							)							)
       with open(self.tgt_vocab_file		, "w"							) as fp:
           fp.write(json.dumps(lowercase_							)							)
       with open(self.merges_file		, "w"							) as fp:
           fp.write("\n".join(lowercase_							)							)
       with open(lowercase_		, "w"							) as fp:
           fp.write(json.dumps(lowercase_							)							)
   @cached_property
   def   UpperCAmelCase_							(       self   :List[Any]							)->							Tuple:
       return FSMTTokenizer.from_pretrained("facebook/wmt19-ru-en"							)
   @cached_property
   def   UpperCAmelCase_							(       self   :str							)->							List[Any]:
       return FSMTTokenizer.from_pretrained("facebook/wmt19-en-ru"							)
   def   UpperCAmelCase_							(       self   :int							)->							str:
       A__       =    FSMTTokenizer.from_pretrained(lowercase_							)
       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   :Tuple							)->							Union[str, 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(lowercase_							)
       self.assertListEqual(lowercase_		, lowercase_							)
  
       A__       =    tokens + ["<unk>"]
       A__       =    [14, 15, 20]
       self.assertListEqual(tokenizer.convert_tokens_to_ids(lowercase_							)		, lowercase_							)
   @slow
   def   UpperCAmelCase_							(       self   :Optional[Any]							)->							Optional[Any]:
       A__       =    self.tokenizer_ru_en
  
       A__       =    tokenizer.encode("sequence builders"		, add_special_tokens=lowercase_							)
       A__       =    tokenizer.encode("multi-sequence build"		, add_special_tokens=lowercase_							)
  
       A__       =    tokenizer.build_inputs_with_special_tokens(lowercase_							)
       A__       =    tokenizer.build_inputs_with_special_tokens(lowercase_		, lowercase_							)
  
       assert encoded_sentence == text + [2]
       assert encoded_pair == text + [2] + text_a + [2]
   @slow
   def   UpperCAmelCase_							(       self   :Dict							)->							Optional[int]:
       A__       =    self.tokenizer_en_ru
       A__       =    self.tokenizer_ru_en
  
       A__       =    [
           [
               "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:
           A__       =    tokenizer_enc.encode(lowercase_		, return_tensors=lowercase_							)
           self.assertListEqual(lowercase_		, lowercase_							)
  
           # and decode backward, using the reversed languages model
           A__       =    tokenizer_dec.decode(lowercase_		, skip_special_tokens=lowercase_							)
           self.assertEqual(lowercase_		, lowercase_							)
   @slow
   def   UpperCAmelCase_							(       self   :int							)->							int:
       A__       =    FSMTTokenizer.from_pretrained("facebook/wmt19-ru-en"		, do_lower_case=lowercase_							)
       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(lowercase_		, lowercase_							)
   @unittest.skip("FSMTConfig.__init__  requires non-optional args"							)
   def   UpperCAmelCase_							(       self   :Tuple							)->							Union[str, Any]:
       pass
  
  
  
   @unittest.skip("FSMTConfig.__init__  requires non-optional args"							)
   def   UpperCAmelCase_							(       self   :Any							)->							Tuple:
       pass
  
  
  
  
style_context: 
  
  
  
  
  
  '''simple docstring'''
  
  import torch
  
  from transformers import AutoTokenizer
  
  
  
  
  
  
  class      UpperCAmelCase			( UpperCamelCase__       ):
   def __init__(       self   :Any		, lowercase_   :Dict							)->							Any:
       A__       =    AutoTokenizer.from_pretrained(lowercase_							)
   def   UpperCAmelCase_							(       self   :Tuple		, lowercase_   :int							)->							Optional[int]:
  
       if isinstance(lowercase_		, lowercase_							) and all(isinstance(_x		, lowercase_							) for _x in x							):
           A__       =    None
           for l in x:
               A__       =    self.tokenizer(
                   lowercase_		, padding="max_length"		, max_length=3_84		, truncation=lowercase_		, return_tensors="pt"		, )
               A__       =    torch.tensor([len(lowercase_							)]							)
               if d is not None:
                   for k in d.keys():
                       A__       =    torch.cat((d[k], t[k])		, 0							)
               else:
                   A__       =    t
  
           A__       =    torch.tensor(self.tokenizer.convert_tokens_to_ids("[E]"							)							)
           A__       =    torch.tensor(self.tokenizer.convert_tokens_to_ids("[/E]"							)							)
  
       elif isinstance(lowercase_		, lowercase_							) and all(isinstance(_x		, lowercase_							) for _x in x							):
           A__       =    self.tokenizer(
               lowercase_		, padding="max_length"		, max_length=3_84		, truncation=lowercase_		, return_tensors="pt"		, )
  
       else:
           raise Exception(
               "Type of parameter x was not recognized! Only `list of strings` for query or `list of lists of"
               " strings` for supports are supported."							)
  
       return d
  
  
  
   def   UpperCAmelCase_							(       self   :Union[str, Any]		, lowercase_   :List[str]		, lowercase_   :str		, lowercase_   :str		, lowercase_   :List[Any]		, lowercase_   :Dict=0.7_0							)->							int:
       A__       =    []
       for idx in range(len(W_query["input_ids"]							)							):
           A__       =    A__       =    range(p_start.shape[1]							)
  
           A__       =    []
           for start_id in start_indexes:
               for end_id in end_indexes:
                   if start_id < end_id:
                       output.append(
                           (
                               start_id,
                               end_id,
                               p_start[idx][start_id].item(),
                               p_end[idx][end_id].item(),
                           )							)
  
           output.sort(key=lambda lowercase_							: (tup[2] * tup[3])		, reverse=lowercase_							)
           A__       =    []
           for k in range(len(lowercase_							)							):
               if output[k][2] * output[k][3] >= thresh:
                   A__,			A__       =    output[k][0], output[k][1]
                   A__       =    self.tokenizer.decode(W_query["input_ids"][idx][c_start_pos:c_end_pos]							)
                   temp.append((decoded, output[k][2] * output[k][3])							)
  
           final_outputs.append(lowercase_							)
  
       return final_outputs
  
  
  
  