"""simple docstring"""
import json
import os
import unittest

from transformers.models.gptsan_japanese.tokenization_gptsan_japanese import (
    VOCAB_FILES_NAMES,
    GPTSanJapaneseTokenizer,
)
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin



@require_tokenizers
class      lowerCAmelCase_			(	UpperCamelCase__   ,			unittest.TestCase    ):



				"""simple docstring"""
				_lowerCAmelCase   :			List[Any]	     =  GPTSanJapaneseTokenizer
				_lowerCAmelCase   :			Optional[Any]	     =  False
				_lowerCAmelCase   :			List[str]	     =  {"""do_clean_text""": False, """add_prefix_space""": False}

				def 						snake_case       ( self			):






										"""simple docstring"""

										super().setUp()

										# fmt: off
										snake_case	    =	['こん', 'こんに', 'にちは', 'ばんは', '世界,㔺界', '、', '。', '<BR>', '<SP>', '<TAB>', '<URL>', '<EMAIL>', '<TEL>', '<DATE>', '<PRICE>', '<BLOCK>', '<KIGOU>', '<U2000U2BFF>', '<|emoji1|>', '<unk>', '<|bagoftoken|>', '<|endoftext|>']
										# fmt: on
										snake_case	    =	{'emoji': {'\ud83d\ude00': '<|emoji1|>'}, 'emoji_inv': {'<|emoji1|>': '\ud83d\ude00'}}  # 😀
										snake_case	    =	{'unk_token': '<unk>'}

										snake_case	    =	os.path.join(self.tmpdirname						,			VOCAB_FILES_NAMES['vocab_file']			)
										snake_case	    =	os.path.join(self.tmpdirname						,			VOCAB_FILES_NAMES['emoji_file']			)
										with open(self.vocab_file						,			'w'						,			encoding='utf-8'			) as vocab_writer:
																vocab_writer.write(''.join([x + '\n' for x in vocab_tokens]			)			)
										with open(self.emoji_file						,			'w'			) as emoji_writer:
																emoji_writer.write(json.dumps(__a			)			)

				def 						snake_case       ( self						,			**lowerCAmelCase			):






										"""simple docstring"""

										kwargs.update(self.special_tokens_map			)
										return GPTSanJapaneseTokenizer.from_pretrained(self.tmpdirname						,			**__a			)

				def 						snake_case       ( self						,			lowerCAmelCase			):






										"""simple docstring"""

										snake_case	    =	'こんにちは、世界。 \nこんばんは、㔺界。😀'
										snake_case	    =	'こんにちは、世界。 \nこんばんは、世界。😀'
										return input_text, output_text

				def 						snake_case       ( self						,			lowerCAmelCase			):






										"""simple docstring"""

										snake_case    ,snake_case	    =	self.get_input_output_texts(__a			)
										snake_case	    =	tokenizer.encode(__a						,			add_special_tokens=__a			)
										snake_case	    =	tokenizer.decode(__a						,			clean_up_tokenization_spaces=__a			)
										return text, ids

				def 						snake_case       ( self			):






										"""simple docstring"""

										pass  # TODO add if relevant

				def 						snake_case       ( self			):






										"""simple docstring"""

										pass  # TODO add if relevant

				def 						snake_case       ( self			):






										"""simple docstring"""

										pass  # TODO add if relevant

				def 						snake_case       ( self			):






										"""simple docstring"""

										snake_case	    =	self.get_tokenizer()

										# Testing tokenization
										snake_case	    =	'こんにちは、世界。　こんばんは、㔺界。'
										snake_case	    =	['こん', 'にちは', '、', '世界', '。', '<SP>', 'こん', 'ばんは', '、', '㔺界', '。']
										snake_case	    =	tokenizer.tokenize(__a			)
										self.assertListEqual(__a						,			__a			)

										# Testing conversion to ids without special tokens
										snake_case	    =	[0, 2, 5, 4, 6, 8, 0, 3, 5, 4, 6]
										snake_case	    =	tokenizer.convert_tokens_to_ids(__a			)
										self.assertListEqual(__a						,			__a			)

										# Testing conversion to ids with special tokens
										snake_case	    =	tokens + [tokenizer.unk_token]
										snake_case	    =	[0, 2, 5, 4, 6, 8, 0, 3, 5, 4, 6, 19]
										snake_case	    =	tokenizer.convert_tokens_to_ids(__a			)
										self.assertListEqual(__a						,			__a			)

				def 						snake_case       ( self			):






										"""simple docstring"""

										snake_case	    =	self.get_tokenizer()

										# Testing tokenization
										snake_case	    =	'こんにちは、<|bagoftoken|>世界。こんばんは、<|bagoftoken|>㔺界。'
										snake_case	    =	'こんにちは、、、、世界。こんばんは、、、、世界。'
										snake_case	    =	tokenizer.encode(__a			)
										snake_case	    =	tokenizer.decode(__a			)
										self.assertEqual(__a						,			__a			)

				@slow
				def 						snake_case       ( self			):






										"""simple docstring"""

										snake_case	    =	self.tokenizer_class.from_pretrained('Tanrei/GPTSAN-japanese'			)

										# Testing tokenization
										snake_case	    =	'こんにちは、世界。'
										snake_case	    =	'こんばんは、㔺界。😀'
										snake_case	    =	'こんにちは、世界。こんばんは、世界。😀'
										snake_case	    =	tokenizer.encode(prefix_text + input_text			)
										snake_case	    =	tokenizer.encode(''						,			prefix_text=prefix_text + input_text			)
										snake_case	    =	tokenizer.encode(__a						,			prefix_text=__a			)
										snake_case	    =	tokenizer.decode(__a			)
										snake_case	    =	tokenizer.decode(__a			)
										snake_case	    =	tokenizer.decode(__a			)
										self.assertEqual(__a						,			__a			)
										self.assertEqual(__a						,			__a			)
										self.assertEqual(__a						,			__a			)

				@slow
				def 						snake_case       ( self			):






										"""simple docstring"""

										snake_case	    =	self.tokenizer_class.from_pretrained('Tanrei/GPTSAN-japanese'			)

										# Testing tokenization
										snake_case	    =	'こんにちは、世界。'
										snake_case	    =	'こんばんは、㔺界。😀'

										snake_case	    =	len(tokenizer.encode(__a			)			) - 2
										snake_case	    =	len(tokenizer.encode(__a			)			) - 2

										snake_case	    =	[1] + [0] * (len_prefix + len_text + 1)
										snake_case	    =	[1] * (len_prefix + len_text + 1) + [0]
										snake_case	    =	[1] + [1] * (len_prefix) + [0] * (len_text + 1)

										snake_case	    =	tokenizer(prefix_text + input_text			).token_type_ids
										snake_case	    =	tokenizer(''						,			prefix_text=prefix_text + input_text			).token_type_ids
										snake_case	    =	tokenizer(__a						,			prefix_text=__a			).token_type_ids
										self.assertListEqual(__a						,			__a			)
										self.assertListEqual(__a						,			__a			)
										self.assertListEqual(__a						,			__a			)

				@slow
				def 						snake_case       ( self			):






										"""simple docstring"""

										snake_case	    =	self.tokenizer_class.from_pretrained('Tanrei/GPTSAN-japanese'			)

										snake_case	    =	tokenizer.encode('あンいワ'			)
										snake_case	    =	tokenizer.encode(''						,			prefix_text='あンいワ'			)
										snake_case	    =	tokenizer.encode('いワ'						,			prefix_text='あン'			)

										self.assertEqual(tokenizer.decode(__a			)						,			tokenizer.decode(__a			)			)
										self.assertEqual(tokenizer.decode(__a			)						,			tokenizer.decode(__a			)			)
										self.assertNotEqual(__a						,			__a			)
										self.assertNotEqual(__a						,			__a			)
										self.assertEqual(x_token_a[1]						,			x_token_a[-1]			)  # SEG token
										self.assertEqual(x_token_a[1]						,			x_token_a[3]			)  # SEG token

				@slow
				def 						snake_case       ( self			):






										"""simple docstring"""

										snake_case	    =	self.tokenizer_class.from_pretrained('Tanrei/GPTSAN-japanese'			)

										snake_case	    =	[['武田信玄', 'は、'], ['織田信長', 'の配下の、']]
										snake_case	    =	tokenizer(__a						,			padding=__a			)
										snake_case	    =	tokenizer.batch_encode_plus(__a						,			padding=__a			)

										# fmt: off
										snake_case	    =	[[3_59_93, 86_40, 2_59_48, 3_59_98, 3_06_47, 3_56_75, 3_59_99, 3_59_99], [3_59_93, 1_03_82, 98_68, 3_59_98, 3_06_46, 94_59, 3_06_46, 3_56_75]]
										snake_case	    =	[[1, 1, 1, 0, 0, 0, 0, 0], [1, 1, 1, 0, 0, 0, 0, 0]]
										snake_case	    =	[[1, 1, 1, 1, 1, 1, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1]]
										# fmt: on
										self.assertListEqual(x_token.input_ids						,			__a			)
										self.assertListEqual(x_token.token_type_ids						,			__a			)
										self.assertListEqual(x_token.attention_mask						,			__a			)
										self.assertListEqual(x_token_a.input_ids						,			__a			)
										self.assertListEqual(x_token_a.token_type_ids						,			__a			)
										self.assertListEqual(x_token_a.attention_mask						,			__a			)

				def 						snake_case       ( self			):






										"""simple docstring"""

										pass

				def 						snake_case       ( self			):






										"""simple docstring"""

										pass



'''simple docstring'''





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

import datasets


SCREAMING_SNAKE_CASE_:     Optional[Any]  ='\\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_:     Union[str, Any]  ='\\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_:     List[Any]  =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	):



    def 					_lowercase						(self    :    Optional[Any] ):
         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 					_lowercase						(self    :    Tuple   ,			__a    :    Optional[int]   ,			__a    :    List[Any] ):
         UpperCAmelCase_           =   0.0
         for i, j in zip(__a   ,			__a ):
              n_correct += 1.0 if math_equivalence.is_equiv(__a   ,			__a ) else 0.0
         UpperCAmelCase_           =   n_correct / len(__a )
         return {
             "accuracy": accuracy,
         }




