

'''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
lowerCAmelCase__						:       Optional[int]										=						pytest.mark.integration


lowerCAmelCase__						:       Tuple										=						{"""comet"""}
lowerCAmelCase__						:       Any										=						importlib.util.find_spec("""fairseq""") is not None

lowerCAmelCase__						:       Any										=						{"""code_eval"""}
lowerCAmelCase__						:       Optional[int]										=						os.name == """nt"""

lowerCAmelCase__						:       Tuple										=						{"""bertscore""", """frugalscore""", """perplexity"""}
lowerCAmelCase__						:       Dict										=						importlib.util.find_spec("""transformers""") is not None




def 	_a       (				__lowerCAmelCase		:      Any     ):

		"""simple docstring"""

		@wraps(__lowerCAmelCase     )
		def wrapper(self		:      List[Any]   ,							__lowerCAmelCase		:      List[Any]     ):
				if not _has_fairseq and metric_name in REQUIRE_FAIRSEQ:
						self.skipTest('''"test requires Fairseq"'''     )
				else:
						test_case(self   ,							__lowerCAmelCase     )

		return wrapper




def 	_a       (				__lowerCAmelCase		:      Any     ):

		"""simple docstring"""

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

		return wrapper




def 	_a       (				__lowerCAmelCase		:      Optional[int]     ):

		"""simple docstring"""

		@wraps(__lowerCAmelCase     )
		def wrapper(self		:      Union[str, Any]   ,							__lowerCAmelCase		:      Optional[Any]     ):
				if _on_windows and metric_name in UNSUPPORTED_ON_WINDOWS:
						self.skipTest('''"test not supported on Windows"'''     )
				else:
						test_case(self   ,							__lowerCAmelCase     )

		return wrapper




def 	_a       (				):

		"""simple docstring"""

		snake_case__						:       Optional[Any]       						=  [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(
    SCREAMING_SNAKE_CASE			,			SCREAMING_SNAKE_CASE			,			SCREAMING_SNAKE_CASE       )
@local
class     a			(				parameterized.TestCase       ):






						"""simple docstring"""




						__UpperCAmelCase			     =    {}
						__UpperCAmelCase			     =    None







						@pytest.mark.filterwarnings('''ignore:metric_module_factory is deprecated:FutureWarning'''  )
						@pytest.mark.filterwarnings('''ignore:load_metric is deprecated:FutureWarning'''  )
						def       __magic_name__							(							self					:      List[str]			,			snake_case_					:      List[Any]  ):



								'''simple docstring'''



								snake_case__						:       Optional[Any]       						=  '''[...]'''
								snake_case__						:       Optional[int]       						=  importlib.import_module(
								    datasets.load.metric_module_factory(os.path.join('''metrics'''			,			snake_case_  )  ).module_path  )
								snake_case__						:       Optional[Any]       						=  datasets.load.import_main_class(metric_module.__name__			,			dataset=snake_case_  )
								# check parameters
								snake_case__						:       Tuple       						=  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(snake_case_			,			metric_module.__name__  ):
										with self.use_local_metrics():
												try:
														snake_case__						:       List[Any]       						=  doctest.testmod(snake_case_			,			verbose=snake_case_			,			raise_on_error=snake_case_  )
												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       __magic_name__							(							self					:      Optional[int]			,			snake_case_					:      List[str]  ):



								'''simple docstring'''



								snake_case__						:       List[Any]       						=  '''[...]'''
								snake_case__						:       List[Any]       						=  importlib.import_module(
								    datasets.load.metric_module_factory(os.path.join('''metrics'''			,			snake_case_  )  ).module_path  )
								# run doctest
								with self.use_local_metrics():
										snake_case__						:       int       						=  doctest.testmod(snake_case_			,			verbose=snake_case_			,			raise_on_error=snake_case_  )
								self.assertEqual(results.failed			,			0  )
								self.assertGreater(results.attempted			,			1  )







						@contextmanager
						def       __magic_name__							(							self					:      List[Any]			,			snake_case_					:      Any			,			snake_case_					:      List[Any]  ):



								'''simple docstring'''



								if metric_name in self.INTENSIVE_CALLS_PATCHER:
										with self.INTENSIVE_CALLS_PATCHER[metric_name](snake_case_  ):
												yield
								else:
										yield







						@contextmanager
						def       __magic_name__							(							self					:      Optional[int]  ):



								'''simple docstring'''



								def load_local_metric(snake_case_					:      List[Any]			,			*snake_case_					:      int			,			**snake_case_					:      str  ):
										return load_metric(os.path.join('''metrics'''			,			snake_case_  )			,			*snake_case_			,			**snake_case_  )

								with patch('''datasets.load_metric'''  ) as mock_load_metric:
										snake_case__						:       Dict       						=  load_local_metric
										yield







						@classmethod
						def       __magic_name__							(							cls					:      Any			,			snake_case_					:      Any  ):



								'''simple docstring'''



								def wrapper(snake_case_					:      Optional[int]  ):
										snake_case__						:       str       						=  contextmanager(snake_case_  )
										snake_case__						:       str       						=  patcher
										return patcher

								return wrapper





@LocalMetricTest.register_intensive_calls_patcher('''bleurt'''     )
def 	_a       (				__lowerCAmelCase		:      str     ):

		"""simple docstring"""

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

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




		class     a			(				SCREAMING_SNAKE_CASE       ):






								"""simple docstring"""




								def       __magic_name__							(							self					:      int			,			snake_case_					:      Tuple  ):



										'''simple docstring'''



										assert len(input_dict['''input_ids''']  ) == 2
										return np.array([1.0_3, 1.0_4]  )

    # 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:
				snake_case__						:       Tuple       						=  MockedPredictor()
				yield




@LocalMetricTest.register_intensive_calls_patcher('''bertscore'''     )
def 	_a       (				__lowerCAmelCase		:      Optional[int]     ):

		"""simple docstring"""

		import torch

		def bert_cos_score_idf(__lowerCAmelCase		:      Dict   ,							__lowerCAmelCase		:      Tuple   ,							*__lowerCAmelCase		:      int   ,							**__lowerCAmelCase		:      Optional[int]     ):
				return torch.tensor([[1.0, 1.0, 1.0]] * len(__lowerCAmelCase     )     )

		# 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:
				snake_case__						:       str       						=  bert_cos_score_idf
				yield




@LocalMetricTest.register_intensive_calls_patcher('''comet'''     )
def 	_a       (				__lowerCAmelCase		:      str     ):

		"""simple docstring"""

		def load_from_checkpoint(__lowerCAmelCase		:      Union[str, Any]     ):




				class     a			:






										"""simple docstring"""




										def       __magic_name__							(							self					:      List[Any]			,			snake_case_					:      Tuple			,			*snake_case_					:      Any			,			**snake_case_					:      Union[str, Any]  ):



												'''simple docstring'''



												assert len(snake_case_  ) == 2
												snake_case__						:       Optional[int]       						=  [0.1_9, 0.9_2]
												return scores, sum(snake_case_  ) / len(snake_case_  )





				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:
				snake_case__						:       List[str]       						=  None
				with patch('''comet.load_from_checkpoint'''     ) as mock_load_from_checkpoint:
						snake_case__						:       Any       						=  load_from_checkpoint
						yield





def 	_a       (				):

		"""simple docstring"""

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






'''simple docstring'''


import warnings

from diffusers import StableDiffusionImgaImgPipeline  # noqa F401


warnings.warn(
    """The `image_to_image.py` script is outdated. Please use directly `from diffusers import"""
    """ StableDiffusionImg2ImgPipeline` instead."""
)



