



"""simple docstring"""

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 (    _a					,  unittest.TestCase):
 _lowerCAmelCase     :    Dict	      =	DebertaTokenizer
 _lowerCAmelCase     :    Optional[int]	      =	True
 _lowerCAmelCase     :    Optional[Any]	      =	DebertaTokenizerFast

 def 		_snake_case      (  self	: Any       ):
     super().setUp()

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

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

 def 		_snake_case      (  self	: str						,  **lowercase_	: Any       ):
     kwargs.update(self.special_tokens_map       )
     return self.tokenizer_class.from_pretrained(self.tmpdirname						,  **snake_case_       )

 def 		_snake_case      (  self	: Optional[int]						,  lowercase_	: Any       ):
     snake_case_				:  List[str]       =			'''lower newer'''
     snake_case_				:  str       =			'''lower newer'''
     return input_text, output_text

 def 		_snake_case      (  self	: Any       ):
     snake_case_				:  str       =			self.get_tokenizer()
     snake_case_				:  Union[str, Any]       =			'''lower newer'''
     snake_case_				:  Dict       =			['''l''', '''o''', '''w''', '''er''', '''\u0120''', '''n''', '''e''', '''w''', '''er''']
     snake_case_				:  List[str]       =			tokenizer.tokenize(snake_case_       )
     self.assertListEqual(snake_case_						,  snake_case_       )

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

 def 		_snake_case      (  self	: int       ):
     snake_case_				:  Union[str, Any]       =			self.get_tokenizer()
     snake_case_				:  str       =			tokenizer('''Hello'''						,  '''World'''       )
     snake_case_				:  Optional[Any]       =			[0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1]
     self.assertListEqual(tokd['''token_type_ids''']						,  snake_case_       )

 @slow
 def 		_snake_case      (  self	: int       ):
     snake_case_				:  List[str]       =			self.tokenizer_class.from_pretrained('''microsoft/deberta-base'''       )

     snake_case_				:  Dict       =			tokenizer.encode('''sequence builders'''						,  add_special_tokens=snake_case_       )
     snake_case_				:  Optional[Any]       =			tokenizer.encode('''multi-sequence build'''						,  add_special_tokens=snake_case_       )

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

     snake_case_				:  Tuple       =			tokenizer.build_inputs_with_special_tokens(snake_case_       )
     snake_case_				:  List[str]       =			tokenizer.build_inputs_with_special_tokens(snake_case_						,  snake_case_       )

     assert encoded_sentence == encoded_text_from_decode
     assert encoded_pair == encoded_pair_from_decode






 @slow
 def 		_snake_case      (  self	: Dict       ):
     snake_case_				:  List[str]       =			[self.tokenizer_class]
     if self.test_rust_tokenizer:
         tokenizer_classes.append(self.rust_tokenizer_class       )

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

         snake_case_				:  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.''',
         ]

         snake_case_				:  Optional[Any]       =			tokenizer(snake_case_						,  padding=snake_case_       )
         snake_case_				:  Dict       =			[tokenizer.decode(snake_case_						,  skip_special_tokens=snake_case_       ) for seq in encoding['''input_ids''']]

         # fmt: off
         snake_case_				:  Optional[int]       =			{
             '''input_ids''': [
                 [1, 2118, 11126, 565, 35, 83, 25191, 163, 18854, 13, 12156, 12, 16101, 25376, 13807, 9, 22205, 27893, 1635, 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, 2118, 11126, 565, 24536, 80, 43797, 4878, 7373, 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, 133, 78, 65, 16, 10, 3724, 1538, 33183, 11303, 43797, 1938, 4, 870, 24165, 29105, 5, 739, 32644, 33183, 11303, 36173, 88, 80, 650, 7821, 45940, 6, 52, 2559, 5, 1836, 9, 5, 7397, 13171, 31, 5, 1836, 9, 32644, 33183, 11303, 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

         snake_case_				:  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.''',
         ]

         self.assertDictEqual(encoding.data						,  snake_case_       )

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










"""simple docstring"""

lowercase__					:						Union[str, Any]												=			{
    '''Pillow''': '''Pillow''',
    '''accelerate''': '''accelerate>=0.11.0''',
    '''compel''': '''compel==0.1.8''',
    '''black''': '''black~=23.1''',
    '''datasets''': '''datasets''',
    '''filelock''': '''filelock''',
    '''flax''': '''flax>=0.4.1''',
    '''hf-doc-builder''': '''hf-doc-builder>=0.3.0''',
    '''huggingface-hub''': '''huggingface-hub>=0.13.2''',
    '''requests-mock''': '''requests-mock==1.10.0''',
    '''importlib_metadata''': '''importlib_metadata''',
    '''invisible-watermark''': '''invisible-watermark''',
    '''isort''': '''isort>=5.5.4''',
    '''jax''': '''jax>=0.2.8,!=0.3.2''',
    '''jaxlib''': '''jaxlib>=0.1.65''',
    '''Jinja2''': '''Jinja2''',
    '''k-diffusion''': '''k-diffusion>=0.0.12''',
    '''torchsde''': '''torchsde''',
    '''note_seq''': '''note_seq''',
    '''librosa''': '''librosa''',
    '''numpy''': '''numpy''',
    '''omegaconf''': '''omegaconf''',
    '''parameterized''': '''parameterized''',
    '''protobuf''': '''protobuf>=3.20.3,<4''',
    '''pytest''': '''pytest''',
    '''pytest-timeout''': '''pytest-timeout''',
    '''pytest-xdist''': '''pytest-xdist''',
    '''ruff''': '''ruff>=0.0.241''',
    '''safetensors''': '''safetensors''',
    '''sentencepiece''': '''sentencepiece>=0.1.91,!=0.1.92''',
    '''scipy''': '''scipy''',
    '''onnx''': '''onnx''',
    '''regex''': '''regex!=2019.12.17''',
    '''requests''': '''requests''',
    '''tensorboard''': '''tensorboard''',
    '''torch''': '''torch>=1.4''',
    '''torchvision''': '''torchvision''',
    '''transformers''': '''transformers>=4.25.1''',
    '''urllib3''': '''urllib3<=2.0.0''',
}





