




import pytest

from datasets import inspect_metric, list_metrics, load_metric



@pytest.fixture
def 						lowerCAmelCase		(		_lowerCAmelCase							:   Optional[int]	)     ->   str:




 """simple docstring"""







 monkeypatch.setattr("datasets.utils.deprecation_utils._emitted_deprecation_warnings"						,							set()	)



@pytest.fixture
def 						lowerCAmelCase		(		_lowerCAmelCase							:   List[Any]	)     ->   Union[str, Any]:




 """simple docstring"""







 class 			_UpperCamelCase      :
       def __init__(			self   :Optional[int] ,      lowerCamelCase   :Union[str, Any]      )     ->			List[Any]:
        UpperCAmelCase__							 =						metric_id



 class 			_UpperCamelCase      :
       UpperCAmelCase_        =		[MetricMock(_SCREAMING_SNAKE_CASE		) for metric_id in ["accuracy", "mse", "precision", "codeparrot/apps_metric"]]
       def 	UpperCAmelCase_						(			self   :Optional[int]      )     ->			Optional[int]:
        return self._metrics



 monkeypatch.setattr("datasets.inspect.huggingface_hub"						,							HfhMock()	)



@pytest.mark.parametrize(
    "func, args"						,							[(load_metric, ("metrics/mse",)), (list_metrics, ()), (inspect_metric, ("metrics/mse", "tmp_path"))]	)
def 						lowerCAmelCase		(		_lowerCAmelCase							:   Any						,							_lowerCAmelCase							:   Optional[Any]						,							_lowerCAmelCase							:   int						,							_lowerCAmelCase							:   Optional[int]						,							_lowerCAmelCase							:   List[str]	)     ->   str:




 """simple docstring"""







 if "tmp_path" in args:
  UpperCAmelCase__							 =						tuple(arg if arg != "tmp_path" else tmp_path for arg in args	)
 with pytest.warns(_lowerCAmelCase						,							match="https://huggingface.co/docs/evaluate"	):
  func(*_lowerCAmelCase	)











from typing import TYPE_CHECKING

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


_lowerCAmelCase						:   Optional[int]		=   {
    "configuration_gpt_neo": ["GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPTNeoConfig", "GPTNeoOnnxConfig"],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _lowerCAmelCase						:   List[str]		=   [
      "GPT_NEO_PRETRAINED_MODEL_ARCHIVE_LIST",
      "GPTNeoForCausalLM",
      "GPTNeoForQuestionAnswering",
      "GPTNeoForSequenceClassification",
      "GPTNeoForTokenClassification",
      "GPTNeoModel",
      "GPTNeoPreTrainedModel",
      "load_tf_weights_in_gpt_neo",
  ]

try:
  if not is_flax_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _lowerCAmelCase						:   Tuple		=   [
      "FlaxGPTNeoForCausalLM",
      "FlaxGPTNeoModel",
      "FlaxGPTNeoPreTrainedModel",
  ]


if TYPE_CHECKING:
  from .configuration_gpt_neo import GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoConfig, GPTNeoOnnxConfig

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_gpt_neo import (
        GPT_NEO_PRETRAINED_MODEL_ARCHIVE_LIST,
        GPTNeoForCausalLM,
        GPTNeoForQuestionAnswering,
        GPTNeoForSequenceClassification,
        GPTNeoForTokenClassification,
        GPTNeoModel,
        GPTNeoPreTrainedModel,
        load_tf_weights_in_gpt_neo,
    )

  try:
    if not is_flax_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_flax_gpt_neo import FlaxGPTNeoForCausalLM, FlaxGPTNeoModel, FlaxGPTNeoPreTrainedModel


else:
  import sys

  _lowerCAmelCase						:   Union[str, Any]		=   _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





