


import json
import os
import unittest

from transformers import DebertaTokenizer, DebertaTokenizerFast
from transformers.models.deberta.tokenization_deberta import VOCAB_FILES_NAMES
from transformers.testing_utils import slow

from ...test_tokenization_common import TokenizerTesterMixin


class 		_SCREAMING_SNAKE_CASE     (						A							,	unittest.TestCase     ):
     __SCREAMING_SNAKE_CASE     					=    DebertaTokenizer
     __SCREAMING_SNAKE_CASE     					=    True
     __SCREAMING_SNAKE_CASE     					=    DebertaTokenizerFast


     def        __snake_case(  self    ):
       super().setUp()

       # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
       _UpperCAmelCase		:							List[str]							=     [
           """l""",
           """o""",
           """w""",
           """e""",
           """r""",
           """s""",
           """t""",
           """i""",
           """d""",
           """n""",
           """\u0120""",
           """\u0120l""",
           """\u0120n""",
           """\u0120lo""",
           """\u0120low""",
           """er""",
           """\u0120lowest""",
           """\u0120newer""",
           """\u0120wider""",
           """[UNK]""",
       ]
       _UpperCAmelCase		:							List[str]							=     dict(zip(A_							,					range(len(A_    )    )    )    )
       _UpperCAmelCase		:							Any							=     ["""#version: 0.2""", """\u0120 l""", """\u0120l o""", """\u0120lo w""", """e r""", """"""]
       _UpperCAmelCase		:							Optional[Any]							=     {"""unk_token""": """[UNK]"""}

       _UpperCAmelCase		:							Any							=     os.path.join(self.tmpdirname							,					VOCAB_FILES_NAMES["""vocab_file"""]    )
       _UpperCAmelCase		:							Optional[int]							=     os.path.join(self.tmpdirname							,					VOCAB_FILES_NAMES["""merges_file"""]    )
       with open(self.vocab_file							,					"""w"""							,					encoding="""utf-8"""    ) as fp:
         fp.write(json.dumps(A_    ) + """\n"""    )
       with open(self.merges_file							,					"""w"""							,					encoding="""utf-8"""    ) as fp:
         fp.write("""\n""".join(A_    )    )


     def        __snake_case(  self							,					**A_    ):
       kwargs.update(self.special_tokens_map    )
       return self.tokenizer_class.from_pretrained(self.tmpdirname							,					**A_    )


     def        __snake_case(  self							,					A_    ):
       _UpperCAmelCase		:							Any							=     """lower newer"""
       _UpperCAmelCase		:							Optional[int]							=     """lower newer"""
       return input_text, output_text


     def        __snake_case(  self    ):
       _UpperCAmelCase		:							List[str]							=     self.get_tokenizer()
       _UpperCAmelCase		:							Union[str, Any]							=     """lower newer"""
       _UpperCAmelCase		:							str							=     ["""l""", """o""", """w""", """er""", """\u0120""", """n""", """e""", """w""", """er"""]
       _UpperCAmelCase		:							Optional[Any]							=     tokenizer.tokenize(A_    )
       self.assertListEqual(A_							,					A_    )

       _UpperCAmelCase		:							Optional[Any]							=     tokens + [tokenizer.unk_token]
       _UpperCAmelCase		:							List[str]							=     [0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
       self.assertListEqual(tokenizer.convert_tokens_to_ids(A_    )							,					A_    )


     def        __snake_case(  self    ):
       _UpperCAmelCase		:							List[str]							=     self.get_tokenizer()
       _UpperCAmelCase		:							List[Any]							=     tokenizer("""Hello"""							,					"""World"""    )
       _UpperCAmelCase		:							Union[str, Any]							=     [0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1]
       self.assertListEqual(tokd["""token_type_ids"""]							,					A_    )


     @slow
     def        __snake_case(  self    ):
       _UpperCAmelCase		:							str							=     self.tokenizer_class.from_pretrained("""microsoft/deberta-base"""    )

       _UpperCAmelCase		:							str							=     tokenizer.encode("""sequence builders"""							,					add_special_tokens=A_    )
       _UpperCAmelCase		:							Dict							=     tokenizer.encode("""multi-sequence build"""							,					add_special_tokens=A_    )

       _UpperCAmelCase		:							Dict							=     tokenizer.encode(
           """sequence builders"""							,					add_special_tokens=A_							,					add_prefix_space=A_    )
       _UpperCAmelCase		:							Any							=     tokenizer.encode(
           """sequence builders"""							,					"""multi-sequence build"""							,					add_special_tokens=A_							,					add_prefix_space=A_    )

       _UpperCAmelCase		:							Any							=     tokenizer.build_inputs_with_special_tokens(A_    )
       _UpperCAmelCase		:							Tuple							=     tokenizer.build_inputs_with_special_tokens(A_							,					A_    )

       assert encoded_sentence == encoded_text_from_decode
       assert encoded_pair == encoded_pair_from_decode




     @slow
     def        __snake_case(  self    ):
       _UpperCAmelCase		:							Optional[int]							=     [self.tokenizer_class]
       if self.test_rust_tokenizer:
         tokenizer_classes.append(self.rust_tokenizer_class    )

       for tokenizer_class in tokenizer_classes:
         _UpperCAmelCase		:							Optional[int]							=     tokenizer_class.from_pretrained("""microsoft/deberta-base"""    )

         _UpperCAmelCase		:							Tuple							=     [
             """ALBERT: A Lite BERT for Self-supervised Learning of Language Representations""",
             """ALBERT incorporates two parameter reduction techniques""",
             """The first one is a factorized embedding parameterization. By decomposing the large vocabulary"""
             """ embedding matrix into two small matrices, we separate the size of the hidden layers from the size of"""
             """ vocabulary embedding.""",
         ]

         _UpperCAmelCase		:							Any							=     tokenizer(A_							,					padding=A_    )
         _UpperCAmelCase		:							Optional[int]							=     [tokenizer.decode(A_							,					skip_special_tokens=A_    ) for seq in encoding["""input_ids"""]]

         # fmt: off
         _UpperCAmelCase		:							Optional[Any]							=     {
             """input_ids""": [
                 [1, 21_18, 1_11_26, 5_65, 35, 83, 2_51_91, 1_63, 1_88_54, 13, 1_21_56, 12, 1_61_01, 2_53_76, 1_38_07, 9, 2_22_05, 2_78_93, 16_35, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                 [1, 21_18, 1_11_26, 5_65, 2_45_36, 80, 4_37_97, 48_78, 73_73, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                 [1, 1_33, 78, 65, 16, 10, 37_24, 15_38, 3_31_83, 1_13_03, 4_37_97, 19_38, 4, 8_70, 2_41_65, 2_91_05, 5, 7_39, 3_26_44, 3_31_83, 1_13_03, 3_61_73, 88, 80, 6_50, 78_21, 4_59_40, 6, 52, 25_59, 5, 18_36, 9, 5, 73_97, 1_31_71, 31, 5, 18_36, 9, 3_26_44, 3_31_83, 1_13_03, 4, 2]
             ],
             """token_type_ids""": [
                 [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                 [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                 [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
             ],
             """attention_mask""": [
                 [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                 [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                 [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
             ]
         }
         # fmt: on

         _UpperCAmelCase		:							List[Any]							=     [
             """ALBERT: A Lite BERT for Self-supervised Learning of Language Representations""",
             """ALBERT incorporates two parameter reduction techniques""",
             """The first one is a factorized embedding parameterization. By decomposing the large vocabulary"""
             """ embedding matrix into two small matrices, we separate the size of the hidden layers from the size of"""
             """ vocabulary embedding.""",
         ]

         self.assertDictEqual(encoding.data							,					A_    )

         for expected, decoded in zip(A_							,					A_    ):
           self.assertEqual(A_							,					A_    )









from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


SCREAMING_SNAKE_CASE__					:     Optional[Any]       =     {'configuration_sew': ['SEW_PRETRAINED_CONFIG_ARCHIVE_MAP', 'SEWConfig']}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE__					:     List[str]       =     [
     'SEW_PRETRAINED_MODEL_ARCHIVE_LIST',
     'SEWForCTC',
     'SEWForSequenceClassification',
     'SEWModel',
     'SEWPreTrainedModel',
 ]

if TYPE_CHECKING:
 from .configuration_sew import SEW_PRETRAINED_CONFIG_ARCHIVE_MAP, SEWConfig

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_sew import (
      SEW_PRETRAINED_MODEL_ARCHIVE_LIST,
      SEWForCTC,
      SEWForSequenceClassification,
      SEWModel,
      SEWPreTrainedModel,
  )


else:
 import sys

 SCREAMING_SNAKE_CASE__					:     Tuple       =     _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





