




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_  						=       "stas/tiny-wmt19-en-ru"
class 				__a (			SCREAMING_SNAKE_CASE__							,					unittest.TestCase					):


     """simple docstring"""


     _A    :   Optional[int]     =  FSMTTokenizer
     _A    :   Dict     =  False




     def   __A    (     self      :			List[Any]						)		->       Any:


         '''simple docstring'''





         super().setUp()

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

         SCREAMING_SNAKE_CASE__       =["""en""", """ru"""]
         SCREAMING_SNAKE_CASE__       ={
             """langs""": self.langs,
             """src_vocab_size""": 1_0,
             """tgt_vocab_size""": 2_0,
         }

         SCREAMING_SNAKE_CASE__       =os.path.join(self.tmpdirname    ,VOCAB_FILES_NAMES["""src_vocab_file"""]						)
         SCREAMING_SNAKE_CASE__       =os.path.join(self.tmpdirname    ,VOCAB_FILES_NAMES["""tgt_vocab_file"""]						)
         SCREAMING_SNAKE_CASE__       =os.path.join(self.tmpdirname    ,"""tokenizer_config.json"""						)
         SCREAMING_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   __A    (     self      :			int						)		->       List[Any]:


         '''simple docstring'''





         return FSMTTokenizer.from_pretrained("""facebook/wmt19-ru-en"""						)




     @cached_property
     def   __A    (     self      :			int						)		->       Optional[Any]:


         '''simple docstring'''





         return FSMTTokenizer.from_pretrained("""facebook/wmt19-en-ru"""						)




     def   __A    (     self      :			Any						)		->       Dict:


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__       =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   __A    (     self      :			Any						)		->       Optional[int]:


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__       =FSMTTokenizer(self.langs    ,self.src_vocab_file    ,self.tgt_vocab_file    ,self.merges_file						)

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

         SCREAMING_SNAKE_CASE__       =tokens + ["""<unk>"""]
         SCREAMING_SNAKE_CASE__       =[1_4, 1_5, 2_0]
         self.assertListEqual(tokenizer.convert_tokens_to_ids(_UpperCamelCase						)    ,_UpperCamelCase						)




     @slow
     def   __A    (     self      :			Any						)		->       Dict:


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__       =self.tokenizer_ru_en

         SCREAMING_SNAKE_CASE__       =tokenizer.encode("""sequence builders"""    ,add_special_tokens=_UpperCamelCase						)
         SCREAMING_SNAKE_CASE__       =tokenizer.encode("""multi-sequence build"""    ,add_special_tokens=_UpperCamelCase						)

         SCREAMING_SNAKE_CASE__       =tokenizer.build_inputs_with_special_tokens(_UpperCamelCase						)
         SCREAMING_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   __A    (     self      :			Union[str, Any]						)		->       Optional[Any]:


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__       =self.tokenizer_en_ru
         SCREAMING_SNAKE_CASE__       =self.tokenizer_ru_en

         SCREAMING_SNAKE_CASE__       =[
             [
                 """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:
             SCREAMING_SNAKE_CASE__       =tokenizer_enc.encode(_UpperCamelCase    ,return_tensors=_UpperCamelCase						)
             self.assertListEqual(_UpperCamelCase    ,_UpperCamelCase						)

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




     @slow
     def   __A    (     self      :			Dict						)		->       str:


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__       =FSMTTokenizer.from_pretrained("""facebook/wmt19-ru-en"""    ,do_lower_case=_UpperCamelCase						)
         SCREAMING_SNAKE_CASE__       =tokenizer.tokenize("""USA is United States of America"""						)
         SCREAMING_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   __A    (     self      :			Union[str, Any]						)		->       Tuple:


         '''simple docstring'''





         pass




     @unittest.skip("""FSMTConfig.__init__  requires non-optional args"""						)
     def   __A    (     self      :			int						)		->       Tuple:


         '''simple docstring'''





         pass












import os
from shutil import copyfile
from typing import Optional, Tuple

from ...file_utils import is_sentencepiece_available
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging


if is_sentencepiece_available():
    from .tokenization_deberta_va import DebertaVaTokenizer
else:
    lowerCamelCase_  						=       None

lowerCamelCase_  						=       logging.get_logger(__name__)

lowerCamelCase_  						=       {"vocab_file": "spm.model", "tokenizer_file": "tokenizer.json"}

lowerCamelCase_  						=       {
    "vocab_file": {
        "microsoft/deberta-v2-xlarge": "https://huggingface.co/microsoft/deberta-v2-xlarge/resolve/main/spm.model",
        "microsoft/deberta-v2-xxlarge": "https://huggingface.co/microsoft/deberta-v2-xxlarge/resolve/main/spm.model",
        "microsoft/deberta-v2-xlarge-mnli": (
            "https://huggingface.co/microsoft/deberta-v2-xlarge-mnli/resolve/main/spm.model"
        ),
        "microsoft/deberta-v2-xxlarge-mnli": (
            "https://huggingface.co/microsoft/deberta-v2-xxlarge-mnli/resolve/main/spm.model"
        ),
    }
}

lowerCamelCase_  						=       {
    "microsoft/deberta-v2-xlarge": 512,
    "microsoft/deberta-v2-xxlarge": 512,
    "microsoft/deberta-v2-xlarge-mnli": 512,
    "microsoft/deberta-v2-xxlarge-mnli": 512,
}

lowerCamelCase_  						=       {
    "microsoft/deberta-v2-xlarge": {"do_lower_case": False},
    "microsoft/deberta-v2-xxlarge": {"do_lower_case": False},
    "microsoft/deberta-v2-xlarge-mnli": {"do_lower_case": False},
    "microsoft/deberta-v2-xxlarge-mnli": {"do_lower_case": False},
}
class 				__a (			__lowerCamelCase					):


     """simple docstring"""


     _A    :   str     =  VOCAB_FILES_NAMES
     _A    :   Union[str, Any]     =  PRETRAINED_VOCAB_FILES_MAP
     _A    :   Optional[int]     =  PRETRAINED_INIT_CONFIGURATION
     _A    :   Any     =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     _A    :   Optional[int]     =  DebertaVaTokenizer




     def __init__(     self      :			List[Any]    ,_UpperCamelCase      :			Tuple=None    ,_UpperCamelCase      :			str=None    ,_UpperCamelCase      :			Any=False    ,_UpperCamelCase      :			List[Any]=False    ,_UpperCamelCase      :			Any="[CLS]"    ,_UpperCamelCase      :			Optional[int]="[SEP]"    ,_UpperCamelCase      :			int="[UNK]"    ,_UpperCamelCase      :			int="[SEP]"    ,_UpperCamelCase      :			Tuple="[PAD]"    ,_UpperCamelCase      :			Optional[Any]="[CLS]"    ,_UpperCamelCase      :			Optional[int]="[MASK]"    ,**_UpperCamelCase      :			int    ,)		->       None:


         '''simple docstring'''





         super().__init__(
             _UpperCamelCase    ,tokenizer_file=_UpperCamelCase    ,do_lower_case=_UpperCamelCase    ,bos_token=_UpperCamelCase    ,eos_token=_UpperCamelCase    ,unk_token=_UpperCamelCase    ,sep_token=_UpperCamelCase    ,pad_token=_UpperCamelCase    ,cls_token=_UpperCamelCase    ,mask_token=_UpperCamelCase    ,split_by_punct=_UpperCamelCase    ,**_UpperCamelCase    ,)

         SCREAMING_SNAKE_CASE__       =do_lower_case
         SCREAMING_SNAKE_CASE__       =split_by_punct
         SCREAMING_SNAKE_CASE__       =vocab_file
         SCREAMING_SNAKE_CASE__       =False if not self.vocab_file else True




     def   __A    (     self      :			Optional[Any]    ,_UpperCamelCase      :			int    ,_UpperCamelCase      :			str=None						)		->       Dict:


         '''simple docstring'''





         if token_ids_a is None:
             return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
         SCREAMING_SNAKE_CASE__       =[self.cls_token_id]
         SCREAMING_SNAKE_CASE__       =[self.sep_token_id]
         return cls + token_ids_a + sep + token_ids_a + sep




     def   __A    (     self      :			List[str]    ,_UpperCamelCase      :			Any    ,_UpperCamelCase      :			List[Any]=None    ,_UpperCamelCase      :			Union[str, Any]=False						)		->       str:


         '''simple docstring'''





         if already_has_special_tokens:
             return super().get_special_tokens_mask(
                 token_ids_a=_UpperCamelCase    ,token_ids_a=_UpperCamelCase    ,already_has_special_tokens=_UpperCamelCase						)

         if token_ids_a is not None:
             return [1] + ([0] * len(_UpperCamelCase						)) + [1] + ([0] * len(_UpperCamelCase						)) + [1]
         return [1] + ([0] * len(_UpperCamelCase						)) + [1]




     def   __A    (     self      :			Union[str, Any]    ,_UpperCamelCase      :			List[str]    ,_UpperCamelCase      :			str=None						)		->       int:


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__       =[self.sep_token_id]
         SCREAMING_SNAKE_CASE__       =[self.cls_token_id]
         if token_ids_a is None:
             return len(cls + token_ids_a + sep						) * [0]
         return len(cls + token_ids_a + sep						) * [0] + len(token_ids_a + sep						) * [1]




     def   __A    (     self      :			int    ,_UpperCamelCase      :			str    ,_UpperCamelCase      :			Optional[str] = None						)		->       Tuple[str]:


         '''simple docstring'''





         if not self.can_save_slow_tokenizer:
             raise ValueError(
                 """Your fast tokenizer does not have the necessary information to save the vocabulary for a slow """
                 """tokenizer."""						)

         if not os.path.isdir(_UpperCamelCase						):
             logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""						)
             return
         SCREAMING_SNAKE_CASE__       =os.path.join(
             _UpperCamelCase    ,(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]						)

         if os.path.abspath(self.vocab_file						) != os.path.abspath(_UpperCamelCase						):
             copyfile(self.vocab_file    ,_UpperCamelCase						)

         return (out_vocab_file,)






