






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 							A_    (						__lowercase ,   unittest.TestCase			):






 '''simple docstring'''




 _SCREAMING_SNAKE_CASE							:			Optional[int] =					DebertaTokenizer
 _SCREAMING_SNAKE_CASE							:			Optional[int] =					True
 _SCREAMING_SNAKE_CASE							:			List[Any] =					DebertaTokenizerFast


 def  snake_case__				(	self) ->		Optional[int]:

     """simple docstring"""



     super().setUp()

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

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


 def  snake_case__				(	self							,  **_A) ->		List[Any]:

     """simple docstring"""



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


 def  snake_case__				(	self							,  _A) ->		str:

     """simple docstring"""



     _UpperCAmelCase				:      Union[str, Any]			  =				'''lower newer'''
     _UpperCAmelCase				:      Dict			  =				'''lower newer'''
     return input_text, output_text


 def  snake_case__				(	self) ->		Any:

     """simple docstring"""



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

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


 def  snake_case__				(	self) ->		Union[str, Any]:

     """simple docstring"""



     _UpperCAmelCase				:      int			  =				self.get_tokenizer()
     _UpperCAmelCase				:      Union[str, 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) ->		List[str]:

     """simple docstring"""



     _UpperCAmelCase				:      Union[str, Any]			  =				self.tokenizer_class.from_pretrained('''microsoft/deberta-base''')

     _UpperCAmelCase				:      int			  =				tokenizer.encode('''sequence builders'''							,  add_special_tokens=_A)
     _UpperCAmelCase				:      str			  =				tokenizer.encode('''multi-sequence build'''							,  add_special_tokens=_A)

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

     _UpperCAmelCase				:      Optional[int]			  =				tokenizer.build_inputs_with_special_tokens(_A)
     _UpperCAmelCase				:      Dict			  =				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) ->		int:

     """simple docstring"""



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

     for tokenizer_class in tokenizer_classes:
         _UpperCAmelCase				:      str			  =				tokenizer_class.from_pretrained('''microsoft/deberta-base''')

         _UpperCAmelCase				:      int			  =				[
             '''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				:      List[str]			  =				[tokenizer.decode(_A							,  skip_special_tokens=_A) for seq in encoding['''input_ids''']]

         # fmt: off
         _UpperCAmelCase				:      str			  =				{
             '''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

         _UpperCAmelCase				:      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)












import math_equivalence  # From: git+https://github.com/hendrycks/math.git

import datasets


SCREAMING_SNAKE_CASE 							=						'\\n@article{hendrycksmath2021,\n  title={Measuring Mathematical Problem Solving With the MATH Dataset},\n  author={Dan Hendrycks\n    and Collin Burns\n    and Saurav Kadavath\n    and Akul Arora\n    and Steven Basart\n    and Eric Tang\n    and Dawn Song\n    and Jacob Steinhardt},\n  journal={arXiv preprint arXiv:2103.03874},\n  year={2021}\n}\n'


SCREAMING_SNAKE_CASE 							=						'\\nThis metric is used to assess performance on the Mathematics Aptitude Test of Heuristics (MATH) dataset.\nIt first canonicalizes the inputs (e.g., converting "1/2" to "\\frac{1}{2}") and then computes accuracy.\n'


SCREAMING_SNAKE_CASE 							=						R'\nCalculates accuracy after canonicalizing inputs.\n\nArgs:\n    predictions: list of predictions to score. Each prediction\n        is a string that contains natural language and LaTex.\n    references: list of reference for each prediction. Each\n        reference is a string that contains natural language\n        and LaTex.\nReturns:\n    accuracy: accuracy after canonicalizing inputs\n        (e.g., converting "1/2" to "\\frac{1}{2}")\n\nExamples:\n    >>> metric = datasets.load_metric("competition_math")\n    >>> results = metric.compute(references=["\\frac{1}{2}"], predictions=["1/2"])\n    >>> print(results)\n    {\'accuracy\': 1.0}\n'
@datasets.utils.file_utils.add_end_docstrings(_DESCRIPTION ,   _KWARGS_DESCRIPTION			)
class 							A_    (						datasets.Metric			):






 '''simple docstring'''




 def  snake_case__				(	self) ->		Optional[Any]:

     """simple docstring"""



     return datasets.MetricInfo(
         description=_DESCRIPTION							,  citation=_CITATION							,  inputs_description=_KWARGS_DESCRIPTION							,  features=datasets.Features(
             {
                 '''predictions''': datasets.Value('''string'''),
                 '''references''': datasets.Value('''string'''),
             })							,  homepage='''https://github.com/hendrycks/math'''							,  codebase_urls=['''https://github.com/hendrycks/math''']							,  )


 def  snake_case__				(	self							,  _A							,  _A) ->		Tuple:

     """simple docstring"""



     _UpperCAmelCase				:      Tuple			  =				0.0
     for i, j in zip(_A							,  _A):
         n_correct += 1.0 if math_equivalence.is_equiv(_A							,  _A) else 0.0
     _UpperCAmelCase				:      Tuple			  =				n_correct / len(_A)
     return {
         "accuracy": accuracy,
     }




