





'''simple docstring'''

from collections import Counter
from pathlib import Path
from typing import Optional, Tuple

import yaml


class 					a_     (				yaml.SafeLoader       ):

    '''simple docstring'''


    def 			snake_case_(		self ,		A       )		->   Optional[int]:
        _SCREAMING_SNAKE_CASE									=			[self.constructed_objects[key_node] for key_node, _ in node.value]
        _SCREAMING_SNAKE_CASE									=			[tuple(A       ) if isinstance(A ,		A       ) else key for key in keys]
        _SCREAMING_SNAKE_CASE									=			Counter(A       )
        _SCREAMING_SNAKE_CASE									=			[key for key in counter if counter[key] > 1]
        if duplicate_keys:
            raise TypeError(f'Got duplicate yaml keys: {duplicate_keys}'       )





    def 			snake_case_(		self ,		A ,		A=False       )		->   List[str]:
        _SCREAMING_SNAKE_CASE									=			super().construct_mapping(A ,		deep=A       )
        self._check_no_duplicates_on_constructed_node(A       )
        return mapping





def lowerCamelCase						(     __lowerCamelCase :    str    )						->Tuple[Optional[str], str]:
    _SCREAMING_SNAKE_CASE									=			list(readme_content.splitlines()    )
    if full_content and full_content[0] == "---" and "---" in full_content[1:]:
        _SCREAMING_SNAKE_CASE									=			full_content[1:].index("""---"""    ) + 1
        _SCREAMING_SNAKE_CASE									=			"""\n""".join(full_content[1:sep_idx]    )
        return yamlblock, "\n".join(full_content[sep_idx + 1 :]    )

    return None, "\n".join(__lowerCamelCase    )


class 					a_     (				snake_case_       ):

    '''simple docstring'''
    # class attributes
    UpperCamelCase     = {'''train_eval_index'''}  # train-eval-index in the YAML metadata


    @classmethod
    def 			snake_case_(		cls ,		A       )		->   "DatasetMetadata":
        with open(A ,		encoding="""utf-8"""       ) as readme_file:
            _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE									=			_split_yaml_from_readme(readme_file.read()       )
        if yaml_string is not None:
            return cls.from_yaml_string(A       )
        else:
            return cls()


    def 			snake_case_(		self ,		A       )		->   Dict:
        if path.exists():
            with open(A ,		encoding="""utf-8"""       ) as readme_file:
                _SCREAMING_SNAKE_CASE									=			readme_file.read()
        else:
            _SCREAMING_SNAKE_CASE									=			None
        _SCREAMING_SNAKE_CASE									=			self._to_readme(A       )
        with open(A ,		"""w""" ,		encoding="""utf-8"""       ) as readme_file:
            readme_file.write(A       )


    def 			snake_case_(		self ,		A = None       )		->   str:
        if readme_content is not None:
            _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE									=			_split_yaml_from_readme(A       )
            _SCREAMING_SNAKE_CASE									=			"""---\n""" + self.to_yaml_string() + """---\n""" + content
        else:
            _SCREAMING_SNAKE_CASE									=			"""---\n""" + self.to_yaml_string() + """---\n"""
        return full_content


    @classmethod
    def 			snake_case_(		cls ,		A       )		->   "DatasetMetadata":
        _SCREAMING_SNAKE_CASE									=			yaml.load(A ,		Loader=_NoDuplicateSafeLoader       ) or {}

        # Convert the YAML keys to DatasetMetadata fields
        _SCREAMING_SNAKE_CASE									=			{
            (key.replace("""-""" ,		"""_"""       ) if key.replace("""-""" ,		"""_"""       ) in cls._FIELDS_WITH_DASHES else key): value
            for key, value in metadata_dict.items()
        }
        return cls(**A       )





    def 			snake_case_(		self       )		->   str:
        return yaml.safe_dump(
            {
                (key.replace("""_""" ,		"""-"""       ) if key in self._FIELDS_WITH_DASHES else key): value
                for key, value in self.items()
            } ,		sort_keys=A ,		allow_unicode=A ,		encoding="""utf-8""" ,		).decode("""utf-8"""       )




lowercase_					= {
    """image-classification""": [],
    """translation""": [],
    """image-segmentation""": [],
    """fill-mask""": [],
    """automatic-speech-recognition""": [],
    """token-classification""": [],
    """sentence-similarity""": [],
    """audio-classification""": [],
    """question-answering""": [],
    """summarization""": [],
    """zero-shot-classification""": [],
    """table-to-text""": [],
    """feature-extraction""": [],
    """other""": [],
    """multiple-choice""": [],
    """text-classification""": [],
    """text-to-image""": [],
    """text2text-generation""": [],
    """zero-shot-image-classification""": [],
    """tabular-classification""": [],
    """tabular-regression""": [],
    """image-to-image""": [],
    """tabular-to-text""": [],
    """unconditional-image-generation""": [],
    """text-retrieval""": [],
    """text-to-speech""": [],
    """object-detection""": [],
    """audio-to-audio""": [],
    """text-generation""": [],
    """conversational""": [],
    """table-question-answering""": [],
    """visual-question-answering""": [],
    """image-to-text""": [],
    """reinforcement-learning""": [],
    """voice-activity-detection""": [],
    """time-series-forecasting""": [],
    """document-question-answering""": [],
}


if __name__ == "__main__":
      from argparse import ArgumentParser

      lowercase_					= ArgumentParser(usage="""Validate the yaml metadata block of a README.md file.""")
      ap.add_argument("""readme_filepath""")
      lowercase_					= ap.parse_args()

      lowercase_					= Path(args.readme_filepath)
      lowercase_					= DatasetMetadata.from_readme(readme_filepath)
      print(dataset_metadata)
      dataset_metadata.to_readme(readme_filepath)










'''simple docstring'''

from unittest import TestCase

from datasets import Sequence, Value
from datasets.arrow_dataset import Dataset


class 					a_     (				snake_case_       ):

    '''simple docstring'''


    def 			snake_case_(		self       )		->   Tuple:
        return [
            {"col_1": 3, "col_2": "a"},
            {"col_1": 2, "col_2": "b"},
            {"col_1": 1, "col_2": "c"},
            {"col_1": 0, "col_2": "d"},
        ]


    def 			snake_case_(		self       )		->   Optional[int]:
        _SCREAMING_SNAKE_CASE									=			{"""col_1""": [3, 2, 1, 0], """col_2""": ["""a""", """b""", """c""", """d"""]}
        return Dataset.from_dict(A       )


    def 			snake_case_(		self       )		->   str:
        _SCREAMING_SNAKE_CASE									=			self._create_example_records()
        _SCREAMING_SNAKE_CASE									=			Dataset.from_list(A       )
        self.assertListEqual(dset.column_names ,		["""col_1""", """col_2"""]       )
        for i, r in enumerate(A       ):
            self.assertDictEqual(A ,		example_records[i]       )


    def 			snake_case_(		self       )		->   str:
        _SCREAMING_SNAKE_CASE									=			self._create_example_records()
        _SCREAMING_SNAKE_CASE									=			Dataset.from_list(A       )
        _SCREAMING_SNAKE_CASE									=			Dataset.from_dict({k: [r[k] for r in example_records] for k in example_records[0]}       )
        self.assertEqual(dset.info ,		dset_from_dict.info       )


    def 			snake_case_(		self       )		->   Union[str, Any]:  # checks what happens with missing columns
        _SCREAMING_SNAKE_CASE									=			[{"""col_1""": 1}, {"""col_2""": """x"""}]
        _SCREAMING_SNAKE_CASE									=			Dataset.from_list(A       )
        self.assertDictEqual(dset[0] ,		{"""col_1""": 1}       )
        self.assertDictEqual(dset[1] ,		{"""col_1""": None}       )  # NB: first record is used for columns


    def 			snake_case_(		self       )		->   Optional[Any]:  # checks if the type can be inferred from the second record
        _SCREAMING_SNAKE_CASE									=			[{"""col_1""": []}, {"""col_1""": [1, 2]}]
        _SCREAMING_SNAKE_CASE									=			Dataset.from_list(A       )
        self.assertEqual(dset.info.features["""col_1"""] ,		Sequence(Value("""int64"""       )       )       )





    def 			snake_case_(		self       )		->   str:
        _SCREAMING_SNAKE_CASE									=			Dataset.from_list([]       )
        self.assertEqual(len(A       ) ,		0       )
        self.assertListEqual(dset.column_names ,		[]       )



