






"""simple docstring"""




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCamelCase											=     {"""configuration_sew_d""": ["""SEW_D_PRETRAINED_CONFIG_ARCHIVE_MAP""", """SEWDConfig"""]}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCamelCase											=     [
         """SEW_D_PRETRAINED_MODEL_ARCHIVE_LIST""",
         """SEWDForCTC""",
         """SEWDForSequenceClassification""",
         """SEWDModel""",
         """SEWDPreTrainedModel""",
     ]

if TYPE_CHECKING:
     from .configuration_sew_d import SEW_D_PRETRAINED_CONFIG_ARCHIVE_MAP, SEWDConfig

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_sew_d import (
              SEW_D_PRETRAINED_MODEL_ARCHIVE_LIST,
              SEWDForCTC,
              SEWDForSequenceClassification,
              SEWDModel,
              SEWDPreTrainedModel,
          )


else:
     import sys

     UpperCamelCase											=     _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)








'''simple docstring'''






import os
import unittest

from transformers.models.bertweet.tokenization_bertweet import VOCAB_FILES_NAMES, BertweetTokenizer

from ...test_tokenization_common import TokenizerTesterMixin



class    A						(					SCREAMING_SNAKE_CASE__     ,				unittest.TestCase						):




				'''simple docstring'''





				A       =							BertweetTokenizer
				A       =							False
				def 			a_			(self     )      ->			Any:
							super().setUp()

							# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
							__UpperCamelCase :     str										=  ["I", "m", "V@@", "R@@", "r", "e@@"]
							__UpperCamelCase :     int										=  dict(zip(_UpperCAmelCase					,    range(len(_UpperCAmelCase     )     )     )     )
							__UpperCamelCase :     List[Any]										=  ["#version: 0.2", "a m</w>"]
							__UpperCamelCase :     str										=  {"unk_token": "<unk>"}

							__UpperCamelCase :     Any										=  os.path.join(self.tmpdirname					,    VOCAB_FILES_NAMES["vocab_file"]     )
							__UpperCamelCase :     Tuple										=  os.path.join(self.tmpdirname					,    VOCAB_FILES_NAMES["merges_file"]     )
							with open(self.vocab_file					,    "w"					,    encoding="utf-8"     ) as fp:
										for token in vocab_tokens:
													fp.write(f"{token} {vocab_tokens[token]}\n"     )
							with open(self.merges_file					,    "w"					,    encoding="utf-8"     ) as fp:
										fp.write("\n".join(_UpperCAmelCase     )     )
				def 			a_			(self					,    **_UpperCAmelCase     )      ->			str:
							kwargs.update(self.special_tokens_map     )
							return BertweetTokenizer.from_pretrained(self.tmpdirname					,    **_UpperCAmelCase     )
				def 			a_			(self					,    _UpperCAmelCase     )      ->			Dict:
							__UpperCamelCase :     Dict										=  "I am VinAI Research"
							__UpperCamelCase :     Any										=  "I <unk> m V<unk> <unk> <unk> I Re<unk> e<unk> <unk> <unk> <unk>"
							return input_text, output_text





				def 			a_			(self     )      ->			str:
							__UpperCamelCase :     Tuple										=  BertweetTokenizer(self.vocab_file					,    self.merges_file					,    **self.special_tokens_map     )
							__UpperCamelCase :     Union[str, Any]										=  "I am VinAI Research"
							__UpperCamelCase :     List[str]										=  "I a@@ m V@@ i@@ n@@ A@@ I R@@ e@@ s@@ e@@ a@@ r@@ c@@ h".split()
							__UpperCamelCase :     Optional[int]										=  tokenizer.tokenize(_UpperCAmelCase     )
							self.assertListEqual(_UpperCAmelCase					,    _UpperCAmelCase     )

							__UpperCamelCase :     Optional[Any]										=  tokens + [tokenizer.unk_token]

							__UpperCamelCase :     Any										=  [4, 3, 5, 6, 3, 3, 3, 4, 7, 9, 3, 9, 3, 3, 3, 3, 3]
							self.assertListEqual(tokenizer.convert_tokens_to_ids(_UpperCAmelCase     )					,    _UpperCAmelCase     )



