





import pytest

from datasets import inspect_metric, list_metrics, load_metric





@pytest.fixture
def lowerCAmelCase__(__snake_case							)       ->   str:







	'''simple docstring'''







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





@pytest.fixture
def lowerCAmelCase__(__snake_case							)       ->   Any:







	'''simple docstring'''







	class       __A					:

				'''simple docstring'''







				def __init__(					self ,						__lowerCAmelCase							):



					'''simple docstring'''







					lowerCamelCase__              =	metric_id






	class       __A					:

				'''simple docstring'''







				lowerCAmelCase_							  =		[MetricMock(_A				) for metric_id in ["""accuracy""", """mse""", """precision""", """codeparrot/apps_metric"""]]





				def 	__lowerCamelCase       (					self							):



					'''simple docstring'''







					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__(__snake_case ,__snake_case ,__snake_case ,__snake_case ,__snake_case							)       ->   List[Any]:







	'''simple docstring'''







	if "tmp_path" in args:
		lowerCamelCase__              =	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


_a						=   {
    "configuration_gpt_neo": ["GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPTNeoConfig", "GPTNeoOnnxConfig"],
}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_a						=   [
			    "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:
			_a						=   [
			    "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

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



