



import pytest

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





@pytest.mark.parametrize('dataset_size'							,       [None, 400 * 2**20, 600 * 2**20]   )
@pytest.mark.parametrize('input_in_memory_max_size'							,       ['default', 0, 100 * 2**20, 900 * 2**20]   )
def 							A     (					_UpperCAmelCase    :       str							,       _UpperCAmelCase    :       int							,       _UpperCAmelCase    :       int   )    ->			Optional[Any]:







			'''simple docstring'''
			if input_in_memory_max_size != "default":
						monkeypatch.setattr(datasets.config							,       'IN_MEMORY_MAX_SIZE'							,       _UpperCAmelCase   )
			_UpperCAmelCase			=       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:
						_UpperCAmelCase			=       dataset_size < in_memory_max_size
			else:
						_UpperCAmelCase			=       False
			_UpperCAmelCase			=       is_small_dataset(_UpperCAmelCase   )
			assert result == expected





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


UpperCAmelCase__       =	{
    "configuration_mega": ["MEGA_PRETRAINED_CONFIG_ARCHIVE_MAP", "MegaConfig", "MegaOnnxConfig"],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCAmelCase__       =	[
							    "MEGA_PRETRAINED_MODEL_ARCHIVE_LIST",
							    "MegaForCausalLM",
							    "MegaForMaskedLM",
							    "MegaForMultipleChoice",
							    "MegaForQuestionAnswering",
							    "MegaForSequenceClassification",
							    "MegaForTokenClassification",
							    "MegaModel",
							    "MegaPreTrainedModel",
							]

if TYPE_CHECKING:
							from .configuration_mega import MEGA_PRETRAINED_CONFIG_ARCHIVE_MAP, MegaConfig, MegaOnnxConfig

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_mega import (
														    MEGA_PRETRAINED_MODEL_ARCHIVE_LIST,
														    MegaForCausalLM,
														    MegaForMaskedLM,
														    MegaForMultipleChoice,
														    MegaForQuestionAnswering,
														    MegaForSequenceClassification,
														    MegaForTokenClassification,
														    MegaModel,
														    MegaPreTrainedModel,
														)

else:
							import sys

							UpperCAmelCase__       =	_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
