

"""simple docstring"""




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 	UpperCAmelCase__   (				A__	,    A__	,    A__   )      ->      List[Any]:




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







"""simple docstring"""




from typing import TYPE_CHECKING

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


SCREAMING_SNAKE_CASE_     :			List[Any]	  =			{
    '''configuration_rag''': ['''RagConfig'''],
    '''retrieval_rag''': ['''RagRetriever'''],
    '''tokenization_rag''': ['''RagTokenizer'''],
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     SCREAMING_SNAKE_CASE_     :			Union[str, Any]	  =			[
         '''RagModel''',
         '''RagPreTrainedModel''',
         '''RagSequenceForGeneration''',
         '''RagTokenForGeneration''',
     ]

try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     SCREAMING_SNAKE_CASE_     :			str	  =			[
         '''TFRagModel''',
         '''TFRagPreTrainedModel''',
         '''TFRagSequenceForGeneration''',
         '''TFRagTokenForGeneration''',
     ]


if TYPE_CHECKING:
     from .configuration_rag import RagConfig
     from .retrieval_rag import RagRetriever
     from .tokenization_rag import RagTokenizer

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_rag import RagModel, RagPreTrainedModel, RagSequenceForGeneration, RagTokenForGeneration

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_rag import (
              TFRagModel,
              TFRagPreTrainedModel,
              TFRagSequenceForGeneration,
              TFRagTokenForGeneration,
          )

else:
     import sys

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




