

import pytest

from datasets import inspect_metric, list_metrics, load_metric

@pytest.fixture
def 						UpperCamelCase (lowercase_:			Any )					->				List[Any]:
	monkeypatch.setattr("""datasets.utils.deprecation_utils._emitted_deprecation_warnings"""   ,				set() )

@pytest.fixture
def 						UpperCamelCase (lowercase_:			str )					->				Union[str, Any]:



	class    _a						:


						'''simple docstring'''






						def __init__(			self   ,	A__      ):
							A__							:   Optional[int]					       =		metric_id



	class    _a						:


						'''simple docstring'''






						UpperCAmelCase__:       Optional[int]  							=	[MetricMock(__magic_name__    ) for metric_id in ['''accuracy''', '''mse''', '''precision''', '''codeparrot/apps_metric''']]






						def   __A      (			self      ):
							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 						UpperCamelCase (lowercase_:			Optional[int]   ,				lowercase_:			List[str]   ,				lowercase_:			int   ,				lowercase_:			Optional[Any]   ,				lowercase_:			Tuple )					->				Union[str, Any]:
	if "tmp_path" in args:
		A__							:   Any					       =		tuple(arg if arg != """tmp_path""" else tmp_path for arg in args )
	with pytest.warns(lowercase_   ,				match="""https://huggingface.co/docs/evaluate""" ):
		func(*lowercase_ )






import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device


if is_torch_available():
	from transformers import AutoModelForSeqaSeqLM, AutoTokenizer



@require_torch
@require_sentencepiece
@require_tokenizers
class    _a						(unittest.TestCase    ):


					'''simple docstring'''






					@slow
					def   __A      (			self      ):
						A__							:   Optional[Any]					       =		AutoModelForSeqaSeqLM.from_pretrained("""google/mt5-small"""   ,	return_dict=A__      ).to(A__      )
						A__							:   str					       =		AutoTokenizer.from_pretrained("""google/mt5-small"""      )

						A__							:   int					       =		tokenizer("""Hello there"""   ,	return_tensors="""pt"""      ).input_ids
						A__							:   List[Any]					       =		tokenizer("""Hi I am"""   ,	return_tensors="""pt"""      ).input_ids

						A__							:   Union[str, Any]					       =		model(input_ids.to(A__      )   ,	labels=labels.to(A__      )      ).loss
						A__							:   Union[str, Any]					       =		-(labels.shape[-1] * loss.item())

						A__							:   Any					       =		-8_4.9_1_2_7
						self.assertTrue(abs(mtf_score - EXPECTED_SCORE      ) < 1e-4      )



