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 __snake_case       (		lowerCamelCase_     ,	unittest.TestCase	):
   lowerCAmelCase_														=							DebertaTokenizer
   lowerCAmelCase_														=							True
   lowerCAmelCase_														=							DebertaTokenizerFast




   def 			__a		(					self    :       int   ):



     """simple docstring"""






     super().setUp()

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

     SCREAMING_SNAKE_CASE__    							=   os.path.join(self.tmpdirname ,			VOCAB_FILES_NAMES["""vocab_file"""]   )
     SCREAMING_SNAKE_CASE__    							=   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(_lowercase   ) + """\n"""   )
     with open(self.merges_file ,			"""w""" ,			encoding="""utf-8"""   ) as fp:
       fp.write("""\n""".join(_lowercase   )   )




   def 			__a		(					self    :       List[Any] ,			**_lowercase    :       Any   ):



     """simple docstring"""






     kwargs.update(self.special_tokens_map   )
     return self.tokenizer_class.from_pretrained(self.tmpdirname ,			**_lowercase   )




   def 			__a		(					self    :       Optional[int] ,			_lowercase    :       Optional[Any]   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   """lower newer"""
     SCREAMING_SNAKE_CASE__    							=   """lower newer"""
     return input_text, output_text




   def 			__a		(					self    :       List[Any]   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   self.get_tokenizer()
     SCREAMING_SNAKE_CASE__    							=   """lower newer"""
     SCREAMING_SNAKE_CASE__    							=   ["""l""", """o""", """w""", """er""", """\u0120""", """n""", """e""", """w""", """er"""]
     SCREAMING_SNAKE_CASE__    							=   tokenizer.tokenize(_lowercase   )
     self.assertListEqual(_lowercase ,			_lowercase   )

     SCREAMING_SNAKE_CASE__    							=   tokens + [tokenizer.unk_token]
     SCREAMING_SNAKE_CASE__    							=   [0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
     self.assertListEqual(tokenizer.convert_tokens_to_ids(_lowercase   ) ,			_lowercase   )




   def 			__a		(					self    :       Optional[Any]   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   self.get_tokenizer()
     SCREAMING_SNAKE_CASE__    							=   tokenizer("""Hello""" ,			"""World"""   )
     SCREAMING_SNAKE_CASE__    							=   [0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1]
     self.assertListEqual(tokd["""token_type_ids"""] ,			_lowercase   )




   @slow
   def 			__a		(					self    :       int   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   self.tokenizer_class.from_pretrained("""microsoft/deberta-base"""   )

     SCREAMING_SNAKE_CASE__    							=   tokenizer.encode("""sequence builders""" ,			add_special_tokens=_lowercase   )
     SCREAMING_SNAKE_CASE__    							=   tokenizer.encode("""multi-sequence build""" ,			add_special_tokens=_lowercase   )

     SCREAMING_SNAKE_CASE__    							=   tokenizer.encode(
         """sequence builders""" ,			add_special_tokens=_lowercase ,			add_prefix_space=_lowercase   )
     SCREAMING_SNAKE_CASE__    							=   tokenizer.encode(
         """sequence builders""" ,			"""multi-sequence build""" ,			add_special_tokens=_lowercase ,			add_prefix_space=_lowercase   )

     SCREAMING_SNAKE_CASE__    							=   tokenizer.build_inputs_with_special_tokens(_lowercase   )
     SCREAMING_SNAKE_CASE__    							=   tokenizer.build_inputs_with_special_tokens(_lowercase ,			_lowercase   )

     assert encoded_sentence == encoded_text_from_decode
     assert encoded_pair == encoded_pair_from_decode




   @slow
   def 			__a		(					self    :       Optional[int]   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   [self.tokenizer_class]
     if self.test_rust_tokenizer:
       tokenizer_classes.append(self.rust_tokenizer_class   )

     for tokenizer_class in tokenizer_classes:
       SCREAMING_SNAKE_CASE__    							=   tokenizer_class.from_pretrained("""microsoft/deberta-base"""   )

       SCREAMING_SNAKE_CASE__    							=   [
           """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.""",
       ]

       SCREAMING_SNAKE_CASE__    							=   tokenizer(_lowercase ,			padding=_lowercase   )
       SCREAMING_SNAKE_CASE__    							=   [tokenizer.decode(_lowercase ,			skip_special_tokens=_lowercase   ) for seq in encoding["""input_ids"""]]

       # fmt: off
       SCREAMING_SNAKE_CASE__    							=   {
           """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

       SCREAMING_SNAKE_CASE__    							=   [
           """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 ,			_lowercase   )

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

import enum
import shutil
import sys


__lowerCamelCase     ,		__lowerCamelCase    :				Optional[Any]							     =	shutil.get_terminal_size()

__lowerCamelCase    :				Any							     =	{'''UP''': '''A''', '''DOWN''': '''B''', '''RIGHT''': '''C''', '''LEFT''': '''D'''}







class __snake_case       (		enum.Enum	):
   lowerCAmelCase_														=							0
   lowerCAmelCase_														=							1





def 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: Optional[int]				,  __UpperCamelCase					: List[str]=""			)							->      int:







  """simple docstring"""





  sys.stdout.write(str(__UpperCamelCase			) + end			)
  sys.stdout.flush()



def 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: Optional[int]				,  __UpperCamelCase					: List[str]				,  __UpperCamelCase					: Optional[Any]=""			)							->      Dict:







  """simple docstring"""





  forceWrite(f"""\u001b[{color}m{content}\u001b[0m"""				,  __UpperCamelCase			)



def 			__SCREAMING_SNAKE_CASE						(      )							->      Optional[Any]:







  """simple docstring"""





  forceWrite("""\r"""			)



def 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: int				,  __UpperCamelCase					: str			)							->      str:







  """simple docstring"""





  forceWrite(f"""\033[{num_lines}{CURSOR_TO_CHAR[direction.upper()]}"""			)



def 			__SCREAMING_SNAKE_CASE						(      )							->      Dict:







  """simple docstring"""





  forceWrite(""" """ * TERMINAL_WIDTH			)
  reset_cursor()






def 			__SCREAMING_SNAKE_CASE						(      )							->      int:







  """simple docstring"""





  reset_cursor()
  forceWrite("""-""" * TERMINAL_WIDTH			)
