"""simple docstring"""






import pytest

import datasets


# Import fixture modules as plugins
_A        =     ['''tests.fixtures.files''', '''tests.fixtures.hub''', '''tests.fixtures.fsspec''']




def     SCREAMING_SNAKE_CASE       (	__UpperCAmelCase	,       __UpperCAmelCase							)				-> str:
				# Mark tests as "unit" by default if not marked as "integration" (or already marked as "unit")
				for item in items:
								if any(marker in item.keywords for marker in ["integration", "unit"]							):
												continue
								item.add_marker(pytest.mark.unit							)




def     SCREAMING_SNAKE_CASE       (	__UpperCAmelCase							)				-> Optional[Any]:
				config.addinivalue_line("markers"	,       "torchaudio_latest: mark test to run with torchaudio>=0.12"							)




@pytest.fixture(autouse=__snake_case							)
def     SCREAMING_SNAKE_CASE       (	__UpperCAmelCase	,       __UpperCAmelCase							)				-> Optional[Any]:
				# test_hf_cache_home = tmp_path_factory.mktemp("cache")  # TODO: why a cache dir per test function does not work?
				SCREAMING_SNAKE_CASE__			=					tmp_path_factory.getbasetemp() / "cache"
				SCREAMING_SNAKE_CASE__			=					test_hf_cache_home / "datasets"
				SCREAMING_SNAKE_CASE__			=					test_hf_cache_home / "metrics"
				SCREAMING_SNAKE_CASE__			=					test_hf_cache_home / "modules"
				monkeypatch.setattr("datasets.config.HF_DATASETS_CACHE"	,       str(__snake_case							)							)
				monkeypatch.setattr("datasets.config.HF_METRICS_CACHE"	,       str(__snake_case							)							)
				monkeypatch.setattr("datasets.config.HF_MODULES_CACHE"	,       str(__snake_case							)							)
				SCREAMING_SNAKE_CASE__			=					test_hf_datasets_cache / "downloads"
				monkeypatch.setattr("datasets.config.DOWNLOADED_DATASETS_PATH"	,       str(__snake_case							)							)
				SCREAMING_SNAKE_CASE__			=					test_hf_datasets_cache / "downloads" / "extracted"
				monkeypatch.setattr("datasets.config.EXTRACTED_DATASETS_PATH"	,       str(__snake_case							)							)




@pytest.fixture(autouse=__snake_case	,       scope="session"							)
def     SCREAMING_SNAKE_CASE       (	)				-> int:
				datasets.disable_progress_bar()




@pytest.fixture(autouse=__snake_case							)
def     SCREAMING_SNAKE_CASE       (	__UpperCAmelCase							)				-> Optional[Any]:
				# don't take tests into account when counting downloads
				monkeypatch.setattr("datasets.config.HF_UPDATE_DOWNLOAD_COUNTS"	,       __snake_case							)




@pytest.fixture
def     SCREAMING_SNAKE_CASE       (	__UpperCAmelCase							)				-> List[Any]:
				# Required to suppress RemovedIn20Warning when feature(s) are not compatible with SQLAlchemy 2.0
				# To be removed once SQLAlchemy 2.0 supported
				monkeypatch.setattr("sqlalchemy.util.deprecations.SILENCE_UBER_WARNING"	,       __snake_case							)












from typing import TYPE_CHECKING

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


__a:     int   					=					{
    '''configuration_mvp''': ['''MVP_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MvpConfig''', '''MvpOnnxConfig'''],
    '''tokenization_mvp''': ['''MvpTokenizer'''],
}

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

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __a:     Tuple   					=					[
      '''MVP_PRETRAINED_MODEL_ARCHIVE_LIST''',
      '''MvpForCausalLM''',
      '''MvpForConditionalGeneration''',
      '''MvpForQuestionAnswering''',
      '''MvpForSequenceClassification''',
      '''MvpModel''',
      '''MvpPreTrainedModel''',
  ]

if TYPE_CHECKING:
  from .configuration_mvp import MVP_PRETRAINED_CONFIG_ARCHIVE_MAP, MvpConfig, MvpOnnxConfig
  from .tokenization_mvp import MvpTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_mvp_fast import MvpTokenizerFast

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_mvp import (
        MVP_PRETRAINED_MODEL_ARCHIVE_LIST,
        MvpForCausalLM,
        MvpForConditionalGeneration,
        MvpForQuestionAnswering,
        MvpForSequenceClassification,
        MvpModel,
        MvpPreTrainedModel,
    )

else:
  import sys

  __a:     Optional[Any]   					=					_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)