


import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import Audio, ClassLabel, Features
from .base import TaskTemplate





@dataclass(frozen=__UpperCamelCase      )
class   __a			(    __UpperCamelCase      ):
 __lowercase :							str        = field(default='audio-classification'       ,       metadata={'include_in_asdict_even_if_is_default': True}      )
 __lowercase :							ClassVar[Features]        = Features({'audio': Audio()}      )
 __lowercase :							ClassVar[Features]        = Features({'labels': ClassLabel}      )
 __lowercase :							str        = "audio"
 __lowercase :							str        = "labels"
 def 		SCREAMING_SNAKE_CASE__   (   self				, lowerCAmelCase__ )					->				int:




        '''simple docstring'''






        if self.label_column not in features:
               raise ValueError(F'Column {self.label_column} is not present in features.' )
        if not isinstance(features[self.label_column]				, SCREAMING_SNAKE_CASE__ ):
               raise ValueError(F'Column {self.label_column} is not a ClassLabel.' )
        lowercase__:   str  			=   copy.deepcopy(self )
        lowercase__:   Tuple  			=   self.label_schema.copy()
        lowercase__:   Optional[Any]  			=   features[self.label_column]
        lowercase__:   int  			=   label_schema
        return task_template
 @property
 def 		SCREAMING_SNAKE_CASE__   (   self )					->				Dict[str, str]:




        '''simple docstring'''
        return {
            self.audio_column: "audio",
            self.label_column: "labels",
        }









"""simple docstring"""
import pytest

from datasets.splits import SplitDict, SplitInfo
from datasets.utils.py_utils import asdict



@pytest.mark.parametrize(
    '''split_dict''' ,   [
        SplitDict(),
        SplitDict({'''train''': SplitInfo(name='''train''' ,   num_bytes=13_37 ,   num_examples=42 ,   dataset_name='''my_dataset'''							)}							),
        SplitDict({'''train''': SplitInfo(name='''train''' ,   num_bytes=13_37 ,   num_examples=42							)}							),
        SplitDict({'''train''': SplitInfo()}							),
    ] ,   )
def      __lowerCamelCase   (							a_     :		SplitDict							)			->      Optional[int]:
      __SCREAMING_SNAKE_CASE   :Tuple						=  split_dict._to_yaml_list()
      assert len(a_							) == len(a_							)
      __SCREAMING_SNAKE_CASE   :Union[str, Any]						=  SplitDict._from_yaml_list(a_							)
      for split_name, split_info in split_dict.items():
            # dataset_name field is deprecated, and is therefore not part of the YAML dump
            __SCREAMING_SNAKE_CASE   :int						=  None
            # the split name of split_dict takes over the name of the split info object
            __SCREAMING_SNAKE_CASE   :Union[str, Any]						=  split_name
      assert split_dict == reloaded







@pytest.mark.parametrize(
    '''split_info''' ,   [SplitInfo(), SplitInfo(dataset_name=a_							), SplitInfo(dataset_name='''my_dataset'''							)]							)
def      __lowerCamelCase   (							a_     :		Dict							)			->      Any:
      # For backward compatibility, we need asdict(split_dict) to return split info dictrionaries with the "dataset_name"
      # field even if it's deprecated. This way old versionso of `datasets` can still reload dataset_infos.json files
      __SCREAMING_SNAKE_CASE   :Union[str, Any]						=  asdict(SplitDict({'''train''': split_info}							)							)
      assert "dataset_name" in split_dict_asdict["train"]
      assert split_dict_asdict["train"]["dataset_name"] == split_info.dataset_name