
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 UpperCAmelCase   (						SCREAMING_SNAKE_CASE__							,							unittest.TestCase   ):





   '''simple docstring'''
   __UpperCamelCase		:    Optional[Any] 				=    DebertaTokenizer
   __UpperCamelCase		:    Optional[int] 				=    True
   __UpperCamelCase		:    Optional[Any] 				=    DebertaTokenizerFast


   def  __magic_name__				(					self     :							Any     ):

      """simple docstring"""
      super().setUp()

      # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
      _A:  List[str]					=       [
          '''l''',
          '''o''',
          '''w''',
          '''e''',
          '''r''',
          '''s''',
          '''t''',
          '''i''',
          '''d''',
          '''n''',
          '''\u0120''',
          '''\u0120l''',
          '''\u0120n''',
          '''\u0120lo''',
          '''\u0120low''',
          '''er''',
          '''\u0120lowest''',
          '''\u0120newer''',
          '''\u0120wider''',
          '''[UNK]''',
      ]
      _A:  Dict					=       dict(zip(lowerCAmelCase_			,			range(len(lowerCAmelCase_     )     )     )     )
      _A:  List[Any]					=       ['''#version: 0.2''', '''\u0120 l''', '''\u0120l o''', '''\u0120lo w''', '''e r''', '''''']
      _A:  str					=       {'''unk_token''': '''[UNK]'''}

      _A:  List[str]					=       os.path.join(self.tmpdirname			,			VOCAB_FILES_NAMES['''vocab_file''']     )
      _A:  Optional[Any]					=       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(lowerCAmelCase_     ) + '''\n'''     )
      with open(self.merges_file			,			'''w'''			,			encoding='''utf-8'''     ) as fp:
         fp.write('''\n'''.join(lowerCAmelCase_     )     )


   def  __magic_name__				(					self     :							Any			,			**lowerCAmelCase_     :							int     ):

      """simple docstring"""
      kwargs.update(self.special_tokens_map     )
      return self.tokenizer_class.from_pretrained(self.tmpdirname			,			**lowerCAmelCase_     )


   def  __magic_name__				(					self     :							str			,			lowerCAmelCase_     :							Union[str, Any]     ):

      """simple docstring"""
      _A:  str					=       '''lower newer'''
      _A:  Dict					=       '''lower newer'''
      return input_text, output_text


   def  __magic_name__				(					self     :							Tuple     ):

      """simple docstring"""
      _A:  str					=       self.get_tokenizer()
      _A:  List[str]					=       '''lower newer'''
      _A:  str					=       ['''l''', '''o''', '''w''', '''er''', '''\u0120''', '''n''', '''e''', '''w''', '''er''']
      _A:  str					=       tokenizer.tokenize(lowerCAmelCase_     )
      self.assertListEqual(lowerCAmelCase_			,			lowerCAmelCase_     )

      _A:  Optional[int]					=       tokens + [tokenizer.unk_token]
      _A:  List[Any]					=       [0, 1, 2, 1_5, 1_0, 9, 3, 2, 1_5, 1_9]
      self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCAmelCase_     )			,			lowerCAmelCase_     )


   def  __magic_name__				(					self     :							List[Any]     ):

      """simple docstring"""
      _A:  Any					=       self.get_tokenizer()
      _A:  Optional[int]					=       tokenizer('''Hello'''			,			'''World'''     )
      _A:  int					=       [0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1]
      self.assertListEqual(tokd['''token_type_ids''']			,			lowerCAmelCase_     )


   @slow
   def  __magic_name__				(					self     :							Dict     ):

      """simple docstring"""
      _A:  List[str]					=       self.tokenizer_class.from_pretrained('''microsoft/deberta-base'''     )

      _A:  Dict					=       tokenizer.encode('''sequence builders'''			,			add_special_tokens=lowerCAmelCase_     )
      _A:  List[str]					=       tokenizer.encode('''multi-sequence build'''			,			add_special_tokens=lowerCAmelCase_     )

      _A:  Optional[int]					=       tokenizer.encode(
          '''sequence builders'''			,			add_special_tokens=lowerCAmelCase_			,			add_prefix_space=lowerCAmelCase_     )
      _A:  Union[str, Any]					=       tokenizer.encode(
          '''sequence builders'''			,			'''multi-sequence build'''			,			add_special_tokens=lowerCAmelCase_			,			add_prefix_space=lowerCAmelCase_     )

      _A:  Any					=       tokenizer.build_inputs_with_special_tokens(lowerCAmelCase_     )
      _A:  str					=       tokenizer.build_inputs_with_special_tokens(lowerCAmelCase_			,			lowerCAmelCase_     )

      assert encoded_sentence == encoded_text_from_decode
      assert encoded_pair == encoded_pair_from_decode


   @slow
   def  __magic_name__				(					self     :							str     ):

      """simple docstring"""
      _A:  List[str]					=       [self.tokenizer_class]
      if self.test_rust_tokenizer:
         tokenizer_classes.append(self.rust_tokenizer_class     )

      for tokenizer_class in tokenizer_classes:
         _A:  Union[str, Any]					=       tokenizer_class.from_pretrained('''microsoft/deberta-base'''     )

         _A:  Dict					=       [
             '''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.''',
         ]

         _A:  Tuple					=       tokenizer(lowerCAmelCase_			,			padding=lowerCAmelCase_     )
         _A:  Any					=       [tokenizer.decode(lowerCAmelCase_			,			skip_special_tokens=lowerCAmelCase_     ) for seq in encoding['''input_ids''']]

         # fmt: off
         _A:  Any					=       {
             '''input_ids''': [
                 [1, 2_1_1_8, 1_1_1_2_6, 5_6_5, 3_5, 8_3, 2_5_1_9_1, 1_6_3, 1_8_8_5_4, 1_3, 1_2_1_5_6, 1_2, 1_6_1_0_1, 2_5_3_7_6, 1_3_8_0_7, 9, 2_2_2_0_5, 2_7_8_9_3, 1_6_3_5, 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, 2_1_1_8, 1_1_1_2_6, 5_6_5, 2_4_5_3_6, 8_0, 4_3_7_9_7, 4_8_7_8, 7_3_7_3, 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_3_3, 7_8, 6_5, 1_6, 1_0, 3_7_2_4, 1_5_3_8, 3_3_1_8_3, 1_1_3_0_3, 4_3_7_9_7, 1_9_3_8, 4, 8_7_0, 2_4_1_6_5, 2_9_1_0_5, 5, 7_3_9, 3_2_6_4_4, 3_3_1_8_3, 1_1_3_0_3, 3_6_1_7_3, 8_8, 8_0, 6_5_0, 7_8_2_1, 4_5_9_4_0, 6, 5_2, 2_5_5_9, 5, 1_8_3_6, 9, 5, 7_3_9_7, 1_3_1_7_1, 3_1, 5, 1_8_3_6, 9, 3_2_6_4_4, 3_3_1_8_3, 1_1_3_0_3, 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

         _A:  Dict					=       [
             '''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			,			lowerCAmelCase_     )

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





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


UpperCAmelCase__		:     Any     			=      {'configuration_swin': ['SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'SwinConfig', 'SwinOnnxConfig']}


try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   UpperCAmelCase__		:     Union[str, Any]     			=      [
       'SWIN_PRETRAINED_MODEL_ARCHIVE_LIST',
       'SwinForImageClassification',
       'SwinForMaskedImageModeling',
       'SwinModel',
       'SwinPreTrainedModel',
       'SwinBackbone',
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   UpperCAmelCase__		:     str     			=      [
       'TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST',
       'TFSwinForImageClassification',
       'TFSwinForMaskedImageModeling',
       'TFSwinModel',
       'TFSwinPreTrainedModel',
   ]

if TYPE_CHECKING:
   from .configuration_swin import SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP, SwinConfig, SwinOnnxConfig

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_swin import (
          SWIN_PRETRAINED_MODEL_ARCHIVE_LIST,
          SwinBackbone,
          SwinForImageClassification,
          SwinForMaskedImageModeling,
          SwinModel,
          SwinPreTrainedModel,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_swin import (
          TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFSwinForImageClassification,
          TFSwinForMaskedImageModeling,
          TFSwinModel,
          TFSwinPreTrainedModel,
      )

else:
   import sys

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



