





'''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










'''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



