




import pytest

import datasets


# Import fixture modules as plugins
snake_case_ : Tuple    		=       ["tests.fixtures.files", "tests.fixtures.hub", "tests.fixtures.fsspec"]


def    A						(__A   :    Optional[int]   ,				__A   :    Any       )   ->      Optional[int]:







     """simple docstring"""


     for item in items:
          if any(marker in item.keywords for marker in ['''integration''', '''unit''']       ):
               continue
          item.add_marker(pytest.mark.unit       )


def    A						(__A   :    Optional[Any]       )   ->      Optional[int]:







     """simple docstring"""


     config.addinivalue_line('''markers'''   ,				'''torchaudio_latest: mark test to run with torchaudio>=0.12'''       )


@pytest.fixture(autouse=__A       )
def    A						(__A   :    Optional[int]   ,				__A   :    Dict       )   ->      List[Any]:







     """simple docstring"""


     UpperCAmelCase_          =							tmp_path_factory.getbasetemp() / '''cache'''
     UpperCAmelCase_          =							test_hf_cache_home / '''datasets'''
     UpperCAmelCase_          =							test_hf_cache_home / '''metrics'''
     UpperCAmelCase_          =							test_hf_cache_home / '''modules'''
     monkeypatch.setattr('''datasets.config.HF_DATASETS_CACHE'''   ,				str(__A       )       )
     monkeypatch.setattr('''datasets.config.HF_METRICS_CACHE'''   ,				str(__A       )       )
     monkeypatch.setattr('''datasets.config.HF_MODULES_CACHE'''   ,				str(__A       )       )
     UpperCAmelCase_          =							test_hf_datasets_cache / '''downloads'''
     monkeypatch.setattr('''datasets.config.DOWNLOADED_DATASETS_PATH'''   ,				str(__A       )       )
     UpperCAmelCase_          =							test_hf_datasets_cache / '''downloads''' / '''extracted'''
     monkeypatch.setattr('''datasets.config.EXTRACTED_DATASETS_PATH'''   ,				str(__A       )       )


@pytest.fixture(autouse=__A   ,				scope='''session'''       )
def    A						()   ->      Dict:







     """simple docstring"""


     datasets.disable_progress_bar()


@pytest.fixture(autouse=__A       )
def    A						(__A   :    Tuple       )   ->      str:







     """simple docstring"""


     monkeypatch.setattr('''datasets.config.HF_UPDATE_DOWNLOAD_COUNTS'''   ,				__A       )


@pytest.fixture
def    A						(__A   :    Union[str, Any]       )   ->      int:







     """simple docstring"""


     monkeypatch.setattr('''sqlalchemy.util.deprecations.SILENCE_UBER_WARNING'''   ,				__A       )












from typing import TYPE_CHECKING

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


snake_case_ : Union[str, Any]    		=       {"configuration_gpt_neox": ["GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPTNeoXConfig"]}

try:
    if not is_tokenizers_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    snake_case_ : Dict    		=       ["GPTNeoXTokenizerFast"]

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    snake_case_ : str    		=       [
        "GPT_NEOX_PRETRAINED_MODEL_ARCHIVE_LIST",
        "GPTNeoXForCausalLM",
        "GPTNeoXForQuestionAnswering",
        "GPTNeoXForSequenceClassification",
        "GPTNeoXForTokenClassification",
        "GPTNeoXLayer",
        "GPTNeoXModel",
        "GPTNeoXPreTrainedModel",
    ]


if TYPE_CHECKING:
    from .configuration_gpt_neox import GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoXConfig

    try:
        if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_gpt_neox_fast import GPTNeoXTokenizerFast

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_gpt_neox import (
            GPT_NEOX_PRETRAINED_MODEL_ARCHIVE_LIST,
            GPTNeoXForCausalLM,
            GPTNeoXForQuestionAnswering,
            GPTNeoXForSequenceClassification,
            GPTNeoXForTokenClassification,
            GPTNeoXLayer,
            GPTNeoXModel,
            GPTNeoXPreTrainedModel,
        )


else:
    import sys

    snake_case_ : Optional[int]    		=       _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)






