


"""simple docstring"""






import pytest

from datasets.utils.sharding import _distribute_shards, _number_of_shards_in_gen_kwargs, _split_gen_kwargs
@pytest.mark.parametrize(
    """kwargs, expected"""       ,			[
        ({"""num_shards""": 0, """max_num_jobs""": 1}, []),
        ({"""num_shards""": 1_0, """max_num_jobs""": 1}, [range(1_0							)]),
        ({"""num_shards""": 1_0, """max_num_jobs""": 1_0}, [range(__lowerCamelCase       ,			i + 1							) for i in range(1_0							)]),
        ({"""num_shards""": 1, """max_num_jobs""": 1_0}, [range(1							)]),
        ({"""num_shards""": 1_0, """max_num_jobs""": 3}, [range(0       ,			4							), range(4       ,			7							), range(7       ,			1_0							)]),
        ({"""num_shards""": 3, """max_num_jobs""": 1_0}, [range(0       ,			1							), range(1       ,			2							), range(2       ,			3							)]),
    ]       ,			)
def      lowerCamelCase       (     _UpperCamelCase		:      Optional[Any]       ,			_UpperCamelCase		:      Any							)		->       Dict:

							'''simple docstring'''



							__UpperCAmelCase   :						Union[str, Any]      							=					_distribute_shards(**__lowerCamelCase							)
							assert out == expected
@pytest.mark.parametrize(
    """gen_kwargs, max_num_jobs, expected"""       ,			[
        ({"""foo""": 0}, 1_0, [{"""foo""": 0}]),
        ({"""shards""": [0, 1, 2, 3]}, 1, [{"""shards""": [0, 1, 2, 3]}]),
        ({"""shards""": [0, 1, 2, 3]}, 4, [{"""shards""": [0]}, {"""shards""": [1]}, {"""shards""": [2]}, {"""shards""": [3]}]),
        ({"""shards""": [0, 1]}, 4, [{"""shards""": [0]}, {"""shards""": [1]}]),
        ({"""shards""": [0, 1, 2, 3]}, 2, [{"""shards""": [0, 1]}, {"""shards""": [2, 3]}]),
    ]       ,			)
def      lowerCamelCase       (     _UpperCamelCase		:      Dict       ,			_UpperCamelCase		:      str       ,			_UpperCamelCase		:      Dict							)		->       List[str]:

							'''simple docstring'''



							__UpperCAmelCase   :						str      							=					_split_gen_kwargs(__lowerCamelCase       ,			__lowerCamelCase							)
							assert out == expected
@pytest.mark.parametrize(
    """gen_kwargs, expected"""       ,			[
        ({"""foo""": 0}, 1),
        ({"""shards""": [0]}, 1),
        ({"""shards""": [0, 1, 2, 3]}, 4),
        ({"""shards""": [0, 1, 2, 3], """foo""": 0}, 4),
        ({"""shards""": [0, 1, 2, 3], """other""": (0, 1)}, 4),
        ({"""shards""": [0, 1, 2, 3], """shards2""": [0, 1]}, RuntimeError),
    ]       ,			)
def      lowerCamelCase       (     _UpperCamelCase		:      Union[str, Any]       ,			_UpperCamelCase		:      Optional[int]							)		->       List[str]:

							'''simple docstring'''



							if expected is RuntimeError:
														with pytest.raises(__lowerCamelCase							):
																					_number_of_shards_in_gen_kwargs(__lowerCamelCase							)
							else:
														__UpperCAmelCase   :						Optional[Any]      							=					_number_of_shards_in_gen_kwargs(__lowerCamelCase							)
														assert out == expected


from typing import TYPE_CHECKING

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


__lowerCamelCase							=       {
    """configuration_biogpt""": ["""BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """BioGptConfig"""],
    """tokenization_biogpt""": ["""BioGptTokenizer"""],
}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   __lowerCamelCase							=       [
       """BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST""",
       """BioGptForCausalLM""",
       """BioGptForTokenClassification""",
       """BioGptForSequenceClassification""",
       """BioGptModel""",
       """BioGptPreTrainedModel""",
   ]


if TYPE_CHECKING:
   from .configuration_biogpt import BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP, BioGptConfig
   from .tokenization_biogpt import BioGptTokenizer

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_biogpt import (
          BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST,
          BioGptForCausalLM,
          BioGptForSequenceClassification,
          BioGptForTokenClassification,
          BioGptModel,
          BioGptPreTrainedModel,
      )


else:
   import sys

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





