import pytest

import datasets.config
from datasets.utils.info_utils import is_small_dataset



@pytest.mark.parametrize("""dataset_size"""				,  [None, 4_00 * 2**20, 6_00 * 2**20]			)
@pytest.mark.parametrize("""input_in_memory_max_size"""				,  ["""default""", 0, 1_00 * 2**20, 9_00 * 2**20]			)
def 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: List[str]				,  __UpperCamelCase					: List[Any]				,  __UpperCamelCase					: List[Any]			)							->      List[str]:







  """simple docstring"""





  if input_in_memory_max_size != "default":
    monkeypatch.setattr(datasets.config				,  """IN_MEMORY_MAX_SIZE"""				,  __UpperCamelCase			)
  SCREAMING_SNAKE_CASE__    							=   datasets.config.IN_MEMORY_MAX_SIZE
  if input_in_memory_max_size == "default":
    assert in_memory_max_size == 0
  else:
    assert in_memory_max_size == input_in_memory_max_size
  if dataset_size and in_memory_max_size:
    SCREAMING_SNAKE_CASE__    							=   dataset_size < in_memory_max_size
  else:
    SCREAMING_SNAKE_CASE__    							=   False
  SCREAMING_SNAKE_CASE__    							=   is_small_dataset(__UpperCamelCase			)
  assert result == expected

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

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







@dataclass(frozen=lowerCamelCase_	)
class __snake_case       (		lowerCamelCase_	):
   # `task` is not a ClassVar since we want it to be part of the `asdict` output for JSON serialization
   lowerCAmelCase_														=							field(default="text-classification"     ,	metadata={"include_in_asdict_even_if_is_default": True}	)
   lowerCAmelCase_														=							Features({"text": Value("string"	)}	)
   lowerCAmelCase_														=							Features({"labels": ClassLabel}	)
   lowerCAmelCase_														=							"text"
   lowerCAmelCase_														=							"labels"




   def 			__a		(					self    :       Dict ,			_lowercase    :       List[Any]   ):



     """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] ,			_lowercase   ):
       raise ValueError(f"""Column {self.label_column} is not a ClassLabel."""   )
     SCREAMING_SNAKE_CASE__    							=   copy.deepcopy(self   )
     SCREAMING_SNAKE_CASE__    							=   self.label_schema.copy()
     SCREAMING_SNAKE_CASE__    							=   features[self.label_column]
     SCREAMING_SNAKE_CASE__    							=   label_schema
     return task_template




   @property
   def 			__a		(					self    :       List[Any]   ):



     """simple docstring"""






     return {
         self.text_column: "text",
         self.label_column: "labels",
     }
