





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     (					_snake_case	,unittest.TestCase   ):
       _UpperCAmelCase :    int					       =       DebertaTokenizer
       _UpperCAmelCase :    Tuple					       =       True
       _UpperCAmelCase :    Tuple					       =       DebertaTokenizerFast







       def 		__lowerCamelCase       ( self	:      Dict     )  ->Dict:
          super().setUp()

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

          lowerCamelCase__  :		str     =				os.path.join(self.tmpdirname						,   VOCAB_FILES_NAMES['''vocab_file''']     )
          lowerCamelCase__  :		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 		__lowerCamelCase       ( self	:      Any						,   **A	:      List[Any]     )  ->List[str]:
          kwargs.update(self.special_tokens_map     )
          return self.tokenizer_class.from_pretrained(self.tmpdirname						,   **__lowerCamelCase     )







       def 		__lowerCamelCase       ( self	:      List[str]						,   A	:      Tuple     )  ->Dict:
          lowerCamelCase__  :		List[str]     =				'''lower newer'''
          lowerCamelCase__  :		str     =				'''lower newer'''
          return input_text, output_text







       def 		__lowerCamelCase       ( self	:      List[str]     )  ->int:
          lowerCamelCase__  :		Optional[Any]     =				self.get_tokenizer()
          lowerCamelCase__  :		List[str]     =				'''lower newer'''
          lowerCamelCase__  :		str     =				['''l''', '''o''', '''w''', '''er''', '''\u0120''', '''n''', '''e''', '''w''', '''er''']
          lowerCamelCase__  :		int     =				tokenizer.tokenize(__lowerCamelCase     )
          self.assertListEqual(__lowerCamelCase						,   __lowerCamelCase     )

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







       def 		__lowerCamelCase       ( self	:      Optional[int]     )  ->List[str]:
          lowerCamelCase__  :		Tuple     =				self.get_tokenizer()
          lowerCamelCase__  :		Dict     =				tokenizer('''Hello'''						,   '''World'''     )
          lowerCamelCase__  :		Any     =				[0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1]
          self.assertListEqual(tokd['''token_type_ids''']						,   __lowerCamelCase     )







       @slow
       def 		__lowerCamelCase       ( self	:      Union[str, Any]     )  ->str:
          lowerCamelCase__  :		Any     =				self.tokenizer_class.from_pretrained('''microsoft/deberta-base'''     )

          lowerCamelCase__  :		Any     =				tokenizer.encode('''sequence builders'''						,   add_special_tokens=__lowerCamelCase     )
          lowerCamelCase__  :		Union[str, Any]     =				tokenizer.encode('''multi-sequence build'''						,   add_special_tokens=__lowerCamelCase     )

          lowerCamelCase__  :		Optional[Any]     =				tokenizer.encode(
              '''sequence builders'''						,   add_special_tokens=__lowerCamelCase						,   add_prefix_space=__lowerCamelCase     )
          lowerCamelCase__  :		Optional[int]     =				tokenizer.encode(
              '''sequence builders'''						,   '''multi-sequence build'''						,   add_special_tokens=__lowerCamelCase						,   add_prefix_space=__lowerCamelCase     )

          lowerCamelCase__  :		Optional[int]     =				tokenizer.build_inputs_with_special_tokens(__lowerCamelCase     )
          lowerCamelCase__  :		Optional[int]     =				tokenizer.build_inputs_with_special_tokens(__lowerCamelCase						,   __lowerCamelCase     )

          assert encoded_sentence == encoded_text_from_decode
          assert encoded_pair == encoded_pair_from_decode







       @slow
       def 		__lowerCamelCase       ( self	:      str     )  ->Dict:
          lowerCamelCase__  :		str     =				[self.tokenizer_class]
          if self.test_rust_tokenizer:
             tokenizer_classes.append(self.rust_tokenizer_class     )

          for tokenizer_class in tokenizer_classes:
             lowerCamelCase__  :		List[Any]     =				tokenizer_class.from_pretrained('''microsoft/deberta-base'''     )

             lowerCamelCase__  :		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.''',
             ]

             lowerCamelCase__  :		Optional[int]     =				tokenizer(__lowerCamelCase						,   padding=__lowerCamelCase     )
             lowerCamelCase__  :		List[str]     =				[tokenizer.decode(__lowerCamelCase						,   skip_special_tokens=__lowerCamelCase     ) for seq in encoding['''input_ids''']]

             # fmt: off
             lowerCamelCase__  :		List[str]     =				{
                 '''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

             lowerCamelCase__  :		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						,   __lowerCamelCase     )

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







'''simple docstring'''





from typing import TYPE_CHECKING

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


__lowerCamelCase												=						{
    '''configuration_maskformer''': ['''MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MaskFormerConfig'''],
    '''configuration_maskformer_swin''': ['''MaskFormerSwinConfig'''],
}

try:
    if not is_vision_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __lowerCamelCase												=						['''MaskFormerFeatureExtractor''']
    __lowerCamelCase												=						['''MaskFormerImageProcessor''']


try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __lowerCamelCase												=						[
        '''MASKFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
        '''MaskFormerForInstanceSegmentation''',
        '''MaskFormerModel''',
        '''MaskFormerPreTrainedModel''',
    ]
    __lowerCamelCase												=						[
        '''MaskFormerSwinBackbone''',
        '''MaskFormerSwinModel''',
        '''MaskFormerSwinPreTrainedModel''',
    ]

if TYPE_CHECKING:
    from .configuration_maskformer import MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, MaskFormerConfig
    from .configuration_maskformer_swin import MaskFormerSwinConfig

    try:
        if not is_vision_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .feature_extraction_maskformer import MaskFormerFeatureExtractor
        from .image_processing_maskformer import MaskFormerImageProcessor
    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_maskformer import (
            MASKFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
            MaskFormerForInstanceSegmentation,
            MaskFormerModel,
            MaskFormerPreTrainedModel,
        )
        from .modeling_maskformer_swin import (
            MaskFormerSwinBackbone,
            MaskFormerSwinModel,
            MaskFormerSwinPreTrainedModel,
        )


else:
    import sys

    __lowerCamelCase												=						_LazyModule(__name__, globals()['''__file__'''], _import_structure)



