
"""simple docstring"""






import pytest

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



@pytest.mark.parametrize("dataset_size"			,   [None, 4_0_0 * 2**2_0, 6_0_0 * 2**2_0]      )
@pytest.mark.parametrize("input_in_memory_max_size"			,   ["default", 0, 1_0_0 * 2**2_0, 9_0_0 * 2**2_0]      )
def      lowerCAmelCase__			(		__magic_name__			,   __magic_name__			,   __magic_name__      )						->str:
			if input_in_memory_max_size != "default":
						monkeypatch.setattr(datasets.config			,   "IN_MEMORY_MAX_SIZE"			,   __snake_case      )
			__lowercase       =       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:
						__lowercase       =       dataset_size < in_memory_max_size
			else:
						__lowercase       =       False
			__lowercase       =       is_small_dataset(__snake_case      )
			assert result == expected






'''simple docstring'''

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


A_     :						Optional[Any]    							={
    '''configuration_squeezebert''': [
        '''SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''SqueezeBertConfig''',
        '''SqueezeBertOnnxConfig''',
    ],
    '''tokenization_squeezebert''': ['''SqueezeBertTokenizer'''],
}

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 A_     :						Dict    							=['''SqueezeBertTokenizerFast''']

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 A_     :						Optional[int]    							=[
     '''SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''SqueezeBertForMaskedLM''',
     '''SqueezeBertForMultipleChoice''',
     '''SqueezeBertForQuestionAnswering''',
     '''SqueezeBertForSequenceClassification''',
     '''SqueezeBertForTokenClassification''',
     '''SqueezeBertModel''',
     '''SqueezeBertModule''',
     '''SqueezeBertPreTrainedModel''',
 ]


if TYPE_CHECKING:
 from .configuration_squeezebert import (
     SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
     SqueezeBertConfig,
     SqueezeBertOnnxConfig,
 )
 from .tokenization_squeezebert import SqueezeBertTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_squeezebert_fast import SqueezeBertTokenizerFast

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_squeezebert import (
      SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
      SqueezeBertForMaskedLM,
      SqueezeBertForMultipleChoice,
      SqueezeBertForQuestionAnswering,
      SqueezeBertForSequenceClassification,
      SqueezeBertForTokenClassification,
      SqueezeBertModel,
      SqueezeBertModule,
      SqueezeBertPreTrainedModel,
  )

else:
 import sys

 A_     :						List[str]    							=_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)





