

"""simple docstring"""




from __future__ import annotations

from math import pi

# Define the Reduced Planck Constant ℏ (H bar), speed of light C, value of
# Pi and the function
A_							:			Dict											= 1.0_5457_1817E-34  # unit of ℏ : J * s

A_							:			Tuple											= 3E8  # unit of c : m * s^-1



def 	A       (     snake_case__		,				snake_case__		,				snake_case__    ):




			'''simple docstring'''


			if (force, area, distance).count(0    ) != 1:
						raise ValueError("""One and only one argument must be 0"""    )
			if force < 0:
						raise ValueError("""Magnitude of force can not be negative"""    )
			if distance < 0:
						raise ValueError("""Distance can not be negative"""    )
			if area < 0:
						raise ValueError("""Area can not be negative"""    )
			if force == 0:
						SCREAMING_SNAKE_CASE__								=      (REDUCED_PLANCK_CONSTANT * SPEED_OF_LIGHT * pi**2 * area) / (
						    2_40 * (distance) ** 4
						)
						return {"force": force}
			elif area == 0:
						SCREAMING_SNAKE_CASE__								=      (2_40 * force * (distance) ** 4) / (
						    REDUCED_PLANCK_CONSTANT * SPEED_OF_LIGHT * pi**2
						)
						return {"area": area}
			elif distance == 0:
						SCREAMING_SNAKE_CASE__								=      (
						    (REDUCED_PLANCK_CONSTANT * SPEED_OF_LIGHT * pi**2 * area) / (2_40 * force)
						) ** (1 / 4)
						return {"distance": distance}
			raise ValueError("""One and only one argument must be 0"""    )


# Run doctest
if __name__ == "__main__":
						import doctest

						doctest.testmod()





"""simple docstring"""




import doctest
import glob
import importlib
import inspect
import os
import re
from contextlib import contextmanager
from functools import wraps
from unittest.mock import patch

import numpy as np
import pytest
from absl.testing import parameterized

import datasets
from datasets import load_metric

from .utils import for_all_test_methods, local, slow


# mark all tests as integration
A_							:			Optional[Any]											= pytest.mark.integration


A_							:			Union[str, Any]											= {"comet"}
A_							:			str											= importlib.util.find_spec("fairseq") is not None

A_							:			Any											= {"code_eval"}
A_							:			Tuple											= os.name == "nt"

A_							:			List[Any]											= {"bertscore", "frugalscore", "perplexity"}
A_							:			Any											= importlib.util.find_spec("transformers") is not None

def 	A       (     snake_case__    ):




			'''simple docstring'''


			@wraps(snake_case__    )
			def wrapper(self		,				snake_case__    ):
						if not _has_fairseq and metric_name in REQUIRE_FAIRSEQ:
									self.skipTest("""\"test requires Fairseq\""""    )
						else:
									test_case(self		,				snake_case__    )

			return wrapper

def 	A       (     snake_case__    ):




			'''simple docstring'''


			@wraps(snake_case__    )
			def wrapper(self		,				snake_case__    ):
						if not _has_transformers and metric_name in REQUIRE_TRANSFORMERS:
									self.skipTest("""\"test requires transformers\""""    )
						else:
									test_case(self		,				snake_case__    )

			return wrapper

def 	A       (     snake_case__    ):




			'''simple docstring'''


			@wraps(snake_case__    )
			def wrapper(self		,				snake_case__    ):
						if _on_windows and metric_name in UNSUPPORTED_ON_WINDOWS:
									self.skipTest("""\"test not supported on Windows\""""    )
						else:
									test_case(self		,				snake_case__    )

			return wrapper

def 	A       (     ):




			'''simple docstring'''


			SCREAMING_SNAKE_CASE__								=      [metric_dir.split(os.sep    )[-2] for metric_dir in glob.glob("""./metrics/*/"""    )]
			return [{"testcase_name": x, "metric_name": x} for x in metrics if x != "gleu"]  # gleu is unfinished





@parameterized.named_parameters(get_local_metric_names()			)
@for_all_test_methods(
    A__			,A__			,A__			)
@local
class    lowerCamelCase						(parameterized.TestCase			):
			lowerCamelCase__   :		int							=							{}
			lowerCamelCase__   :		Dict							=							None

			@pytest.mark.filterwarnings("""ignore:metric_module_factory is deprecated:FutureWarning"""       )
			@pytest.mark.filterwarnings("""ignore:load_metric is deprecated:FutureWarning"""       )
			def 					SCREAMING_SNAKE_CASE				(					self				:						str    ,				__UpperCAmelCase				:						str       )   ->    Union[str, Any]:
						SCREAMING_SNAKE_CASE__								=      """[...]"""
						SCREAMING_SNAKE_CASE__								=      importlib.import_module(
						    datasets.load.metric_module_factory(os.path.join("""metrics"""    ,				__UpperCAmelCase       )       ).module_path       )
						SCREAMING_SNAKE_CASE__								=      datasets.load.import_main_class(metric_module.__name__    ,				dataset=__UpperCAmelCase       )
						# check parameters
						SCREAMING_SNAKE_CASE__								=      inspect.signature(metric._compute       ).parameters
						self.assertTrue(all(p.kind != p.VAR_KEYWORD for p in parameters.values()       )       )  # no **kwargs
						# run doctest
						with self.patch_intensive_calls(__UpperCAmelCase    ,				metric_module.__name__       ):
									with self.use_local_metrics():
												try:
															SCREAMING_SNAKE_CASE__								=      doctest.testmod(__UpperCAmelCase    ,				verbose=__UpperCAmelCase    ,				raise_on_error=__UpperCAmelCase       )
												except doctest.UnexpectedException as e:
															raise e.exc_info[1]  # raise the exception that doctest caught
						self.assertEqual(results.failed    ,				0       )
						self.assertGreater(results.attempted    ,				1       )

			@slow
			def 					SCREAMING_SNAKE_CASE				(					self				:						Optional[Any]    ,				__UpperCAmelCase				:						Optional[Any]       )   ->    str:
						SCREAMING_SNAKE_CASE__								=      """[...]"""
						SCREAMING_SNAKE_CASE__								=      importlib.import_module(
						    datasets.load.metric_module_factory(os.path.join("""metrics"""    ,				__UpperCAmelCase       )       ).module_path       )
						# run doctest
						with self.use_local_metrics():
									SCREAMING_SNAKE_CASE__								=      doctest.testmod(__UpperCAmelCase    ,				verbose=__UpperCAmelCase    ,				raise_on_error=__UpperCAmelCase       )
						self.assertEqual(results.failed    ,				0       )
						self.assertGreater(results.attempted    ,				1       )

			@contextmanager
			def 					SCREAMING_SNAKE_CASE				(					self				:						Any    ,				__UpperCAmelCase				:						List[str]    ,				__UpperCAmelCase				:						Optional[Any]       )   ->    str:
						if metric_name in self.INTENSIVE_CALLS_PATCHER:
									with self.INTENSIVE_CALLS_PATCHER[metric_name](__UpperCAmelCase       ):
												yield
						else:
									yield

			@contextmanager
			def 					SCREAMING_SNAKE_CASE				(					self				:						Optional[int]       )   ->    Union[str, Any]:
						def load_local_metric(__UpperCAmelCase				:						int    ,				*__UpperCAmelCase				:						int    ,				**__UpperCAmelCase				:						Optional[int]       ):
									return load_metric(os.path.join("""metrics"""    ,				__UpperCAmelCase       )    ,				*__UpperCAmelCase    ,				**__UpperCAmelCase       )

						with patch("""datasets.load_metric"""       ) as mock_load_metric:
									SCREAMING_SNAKE_CASE__								=      load_local_metric
									yield





			@classmethod
			def 					SCREAMING_SNAKE_CASE				(					cls				:						Dict    ,				__UpperCAmelCase				:						int       )   ->    Optional[Any]:
						def wrapper(__UpperCAmelCase				:						List[str]       ):
									SCREAMING_SNAKE_CASE__								=      contextmanager(__UpperCAmelCase       )
									SCREAMING_SNAKE_CASE__								=      patcher
									return patcher

						return wrapper

@LocalMetricTest.register_intensive_calls_patcher("""bleurt"""    )
def 	A       (     snake_case__    ):




			'''simple docstring'''


			import tensorflow.compat.va as tf
			from bleurt.score import Predictor

			tf.flags.DEFINE_string("""sv"""		,				""""""		,				""""""    )  # handle pytest cli flags





			class    lowerCamelCase						(A__			):

						def 					SCREAMING_SNAKE_CASE				(					self				:						Any    ,				__UpperCAmelCase				:						Union[str, Any]       )   ->    List[str]:
									assert len(input_dict["""input_ids"""]       ) == 2
									return np.array([1.03, 1.04]       )

    # mock predict_fn which is supposed to do a forward pass with a bleurt model
			with patch("""bleurt.score._create_predictor"""    ) as mock_create_predictor:
						SCREAMING_SNAKE_CASE__								=      MockedPredictor()
						yield

@LocalMetricTest.register_intensive_calls_patcher("""bertscore"""    )
def 	A       (     snake_case__    ):




			'''simple docstring'''


			import torch

			def bert_cos_score_idf(snake_case__		,				snake_case__		,				*snake_case__		,				**snake_case__    ):
						return torch.tensor([[1.0, 1.0, 1.0]] * len(snake_case__    )    )

			# mock get_model which is supposed to do download a bert model
			# mock bert_cos_score_idf which is supposed to do a forward pass with a bert model
			with patch("""bert_score.scorer.get_model"""    ), patch(
			    """bert_score.scorer.bert_cos_score_idf"""    ) as mock_bert_cos_score_idf:
						SCREAMING_SNAKE_CASE__								=      bert_cos_score_idf
						yield

@LocalMetricTest.register_intensive_calls_patcher("""comet"""    )
def 	A       (     snake_case__    ):




			'''simple docstring'''


			def load_from_checkpoint(snake_case__    ):





						class    lowerCamelCase						:

									def 					SCREAMING_SNAKE_CASE				(					self				:						List[str]    ,				__UpperCAmelCase				:						Any    ,				*__UpperCAmelCase				:						str    ,				**__UpperCAmelCase				:						List[Any]       )   ->    Optional[int]:
												assert len(__UpperCAmelCase       ) == 2
												SCREAMING_SNAKE_CASE__								=      [0.19, 0.92]
												return scores, sum(__UpperCAmelCase       ) / len(__UpperCAmelCase       )
						return Model()

			# mock load_from_checkpoint which is supposed to do download a bert model
			# mock load_from_checkpoint which is supposed to do download a bert model
			with patch("""comet.download_model"""    ) as mock_download_model:
						SCREAMING_SNAKE_CASE__								=      None
						with patch("""comet.load_from_checkpoint"""    ) as mock_load_from_checkpoint:
									SCREAMING_SNAKE_CASE__								=      load_from_checkpoint
									yield




def 	A       (     ):




			'''simple docstring'''


			SCREAMING_SNAKE_CASE__								=      load_metric(os.path.join("""metrics"""		,				"""seqeval"""    )    )
			SCREAMING_SNAKE_CASE__								=      """ERROR"""
			SCREAMING_SNAKE_CASE__								=      f"""Scheme should be one of [IOB1, IOB2, IOE1, IOE2, IOBES, BILOU], got {wrong_scheme}"""
			with pytest.raises(snake_case__		,				match=re.escape(snake_case__    )    ):
						metric.compute(predictions=[]		,				references=[]		,				scheme=snake_case__    )


