


import re
import tempfile
import unittest
from pathlib import Path

from datasets.utils.metadata import DatasetMetadata
def 							UpperCamelCase_  (				__snake_case			:						str     )->      str:




						'''simple docstring'''
						__magic_name__ 		=			min(re.search('^ +'	,			__snake_case     ).end() if t.startswith(' '     ) else 0 for t in string.splitlines()     )
						return "\n".join([line[indent_level:] for line in string.splitlines() if indent_level < len(__snake_case     )]     )


UpperCamelCase :				List[Any]						    =       '''\
---
language:
- zh
- en
task_ids:
- sentiment-classification
---
# Begin of markdown

Some cool dataset card
'''

UpperCamelCase :				Optional[Any]						    =       '''\
---
---
# Begin of markdown

Some cool dataset card
'''


UpperCamelCase :				Tuple						    =       '''\
# Begin of markdown

Some cool dataset card
'''




class     __snake_case							(      unittest.TestCase):
					def   a (	self					:	List[Any]     )       ->				List[str]:



											'''simple docstring'''





											with tempfile.TemporaryDirectory() as tmp_dir:
																	__magic_name__ 		=			Path(_lowerCAmelCase     ) / 'README.md'
																	with open(_lowerCAmelCase  ,			'w+'     ) as readme_file:
																							readme_file.write(_lowerCAmelCase     )
																	__magic_name__ 		=			DatasetMetadata.from_readme(_lowerCAmelCase     )
																	self.assertDictEqual(_lowerCAmelCase  ,			{'language': ['zh', 'en'], 'task_ids': ['sentiment-classification']}     )

																	with open(_lowerCAmelCase  ,			'w+'     ) as readme_file:
																							readme_file.write(_lowerCAmelCase     )
																	__magic_name__ 		=			DatasetMetadata.from_readme(_lowerCAmelCase     )
																	self.assertDictEqual(_lowerCAmelCase  ,			{}     )

																	with open(_lowerCAmelCase  ,			'w+'     ) as readme_file:
																							readme_file.write(_lowerCAmelCase     )
																	__magic_name__ 		=			DatasetMetadata.from_readme(_lowerCAmelCase     )
																	self.assertEqual(_lowerCAmelCase  ,			{}     )







					def   a (	self					:	Optional[int]     )       ->				Any:



											'''simple docstring'''





											__magic_name__ 		=			_dedent(
											    '\\n            annotations_creators:\n            - found\n            language_creators:\n            - found\n            language:\n            - en\n            license:\n            - unknown\n            multilinguality:\n            - monolingual\n            pretty_name: Test Dataset\n            size_categories:\n            - 10K<n<100K\n            source_datasets:\n            - extended|other-yahoo-webscope-l6\n            task_categories:\n            - question-answering\n            task_ids:\n            - open-domain-qa\n            '     )
											assert DatasetMetadata.from_yaml_string(_lowerCAmelCase     )

											__magic_name__ 		=			_dedent(
											    '\\n            annotations_creators:\n            - found\n            language:\n            - en\n            license:\n            - unknown\n            multilinguality:\n            - monolingual\n            pretty_name: Test Dataset\n            size_categories:\n            - 10K<n<100K\n            source_datasets:\n            - extended|other-yahoo-webscope-l6\n            task_categories:\n            - question-answering\n            task_ids:\n            - open-domain-qa\n            task_ids:\n            - open-domain-qa\n            '     )
											with self.assertRaises(_lowerCAmelCase     ):
																	DatasetMetadata.from_yaml_string(_lowerCAmelCase     )

											__magic_name__ 		=			_dedent(
											    '\\n            annotations_creators:\n            - found\n            language_creators:\n            - found\n            language:\n            - en\n            license:\n            - unknown\n            multilinguality:\n            - monolingual\n            pretty_name: Test Dataset\n            size_categories:\n            - 10K<n<100K\n            source_datasets:\n            - extended|other-yahoo-webscope-l6\n            task_categories:\n            - text-classification\n            task_ids:\n            - multi-class-classification\n            paperswithcode_id:\n            - squad\n            configs:\n            - en\n            train-eval-index:\n            - config: en\n              task: text-classification\n              task_id: multi_class_classification\n              splits:\n                train_split: train\n                eval_split: test\n              col_mapping:\n                text: text\n                label: target\n              metrics:\n                - type: accuracy\n                  name: Accuracy\n            extra_gated_prompt: |\n              By clicking on “Access repository” below, you also agree to ImageNet Terms of Access:\n              [RESEARCHER_FULLNAME] (the "Researcher") has requested permission to use the ImageNet database (the "Database") at Princeton University and Stanford University. In exchange for such permission, Researcher hereby agrees to the following terms and conditions:\n              1. Researcher shall use the Database only for non-commercial research and educational purposes.\n            extra_gated_fields:\n              Company: text\n              Country: text\n              I agree to use this model for non-commerical use ONLY: checkbox\n            '     )
											assert DatasetMetadata.from_yaml_string(_lowerCAmelCase     )





import os
import tempfile
import unittest
from typing import List

from transformers.models.esm.tokenization_esm import VOCAB_FILES_NAMES, EsmTokenizer
from transformers.testing_utils import require_tokenizers
from transformers.tokenization_utils import PreTrainedTokenizer
from transformers.tokenization_utils_base import PreTrainedTokenizerBase

@require_tokenizers
class     __snake_case							(      unittest.TestCase):
					SCREAMING_SNAKE_CASE_		:					Tuple 				= EsmTokenizer
					def   a (	self					:	List[Any]     )       ->				Dict:



											'''simple docstring'''





											super().setUp()
											__magic_name__ 		=			tempfile.mkdtemp()
											# fmt: off
											__magic_name__ 		=			["<cls>", "<pad>", "<eos>", "<unk>", "L", "A", "G", "V", "S", "E", "R", "T", "I", "D", "P", "K", "Q", "N", "F", "Y", "M", "H", "W", "C", "X", "B", "U", "Z", "O", ".", "-", "<null_1>", "<mask>"]  # noqa: E501
											# fmt: on
											__magic_name__ 		=			os.path.join(self.tmpdirname  ,			VOCAB_FILES_NAMES['vocab_file']     )
											with open(self.vocab_file  ,			'w'  ,			encoding='utf-8'     ) as vocab_writer:
																	vocab_writer.write(''.join([x + '\n' for x in vocab_tokens]     )     )
					def   a (	self					:	Optional[int]  ,			**_lowerCAmelCase					:	int     )       ->				List[PreTrainedTokenizerBase]:



											'''simple docstring'''





											return [self.get_tokenizer(**_lowerCAmelCase     )]
					def   a (	self					:	List[str]  ,			**_lowerCAmelCase					:	int     )       ->				PreTrainedTokenizer:



											'''simple docstring'''





											return self.tokenizer_class.from_pretrained(self.tmpdirname  ,			**_lowerCAmelCase     )
					def   a (	self					:	int     )       ->				int:



											'''simple docstring'''





											__magic_name__ 		=			self.tokenizer_class(self.vocab_file     )

											__magic_name__ 		=			tokenizer.tokenize('LAGVS'     )
											self.assertListEqual(_lowerCAmelCase  ,			['L', 'A', 'G', 'V', 'S']     )
											self.assertListEqual(tokenizer.convert_tokens_to_ids(_lowerCAmelCase     )  ,			[4, 5, 6, 7, 8]     )
					def   a (	self					:	str     )       ->				str:



											'''simple docstring'''





											__magic_name__ 		=			self.tokenizer_class(self.vocab_file     )

											__magic_name__ 		=			'LAGVS'
											self.assertListEqual(tokenizer.encode(_lowerCAmelCase     )  ,			[0, 4, 5, 6, 7, 8, 2]     )
					def   a (	self					:	int     )       ->				List[Any]:



											'''simple docstring'''





											__magic_name__ 		=			self.tokenizer_class(self.vocab_file     )

											__magic_name__ 		=			['LAGVS', 'WCB']
											__magic_name__ 		=			tokenizer(_lowerCAmelCase  ,			padding=_lowerCAmelCase     )['input_ids']

											self.assertListEqual(_lowerCAmelCase  ,			[[0, 4, 5, 6, 7, 8, 2], [0, 2_2, 2_3, 2_5, 2]]     )
					def   a (	self					:	Dict     )       ->				List[Any]:



											'''simple docstring'''





											__magic_name__ 		=			self.tokenizer_class(self.vocab_file     )

											__magic_name__ 		=			['LAGVS', 'WCB']
											__magic_name__ 		=			tokenizer(_lowerCAmelCase  ,			padding=_lowerCAmelCase     )['input_ids']

											self.assertListEqual(_lowerCAmelCase  ,			[[0, 4, 5, 6, 7, 8, 2], [0, 2_2, 2_3, 2_5, 2, 1, 1]]     )







					def   a (	self					:	Optional[int]     )       ->				str:



											'''simple docstring'''





											__magic_name__ 		=			self.get_tokenizers(fast=_lowerCAmelCase     )
											for tokenizer in tokenizers:
																	with self.subTest(F'''{tokenizer.__class__.__name__}'''     ):
																							__magic_name__ 		=			'<unk>'
																							__magic_name__ 		=			'<mask>'

																							__magic_name__ 		=			tokenizer.tokenize(_lowerCAmelCase     )
																							__magic_name__ 		=			tokenizer.tokenize(_lowerCAmelCase     )

																							self.assertEqual(len(_lowerCAmelCase     )  ,			1     )
																							self.assertEqual(len(_lowerCAmelCase     )  ,			1     )
																							self.assertEqual(token_a[0]  ,			_lowerCAmelCase     )
																							self.assertEqual(token_a[0]  ,			_lowerCAmelCase     )

