




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
__UpperCamelCase   :			Optional[Any]          = pytest.mark.integration


__UpperCamelCase   :			Optional[Any]          = {'comet'}
__UpperCamelCase   :			Any          = importlib.util.find_spec("fairseq") is not None

__UpperCamelCase   :			Optional[Any]          = {'code_eval'}
__UpperCamelCase   :			List[str]          = os.name == 'nt'

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





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

	return wrapper





def 						__A						(      __lowerCamelCase       ) ->			List[Any]:
	@wraps(__A       )
	def wrapper(self				,     __lowerCamelCase       ):
		if not _has_transformers and metric_name in REQUIRE_TRANSFORMERS:
			self.skipTest("""\"test requires transformers\""""       )
		else:
			test_case(self				,     __A       )

	return wrapper





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

	return wrapper





def 						__A						(      ) ->			List[Any]:
	a									=				[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(
    _lowerCamelCase ,			_lowerCamelCase ,			_lowerCamelCase					)
@local
class 						__lowerCAmelCase      (      parameterized.TestCase					):
							UpperCamelCase__   	=    {}
							UpperCamelCase__   	=    None
							@pytest.mark.filterwarnings("""ignore:metric_module_factory is deprecated:FutureWarning"""							)
							@pytest.mark.filterwarnings("""ignore:load_metric is deprecated:FutureWarning"""							)
							def  lowerCamelCase__				(		self		:List[str]						, __magic_name__		:Union[str, Any]							):







								'''simple docstring'''







								a									=				'[...]'
								a									=				importlib.import_module(
								    datasets.load.metric_module_factory(os.path.join("""metrics"""						, _SCREAMING_SNAKE_CASE							)							).module_path							)
								a									=				datasets.load.import_main_class(metric_module.__name__						, dataset=_SCREAMING_SNAKE_CASE							)
								# check parameters
								a									=				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(_SCREAMING_SNAKE_CASE						, metric_module.__name__							):
									with self.use_local_metrics():
										try:
											a									=				doctest.testmod(_SCREAMING_SNAKE_CASE						, verbose=_SCREAMING_SNAKE_CASE						, raise_on_error=_SCREAMING_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  lowerCamelCase__				(		self		:List[Any]						, __magic_name__		:int							):







								'''simple docstring'''







								a									=				'[...]'
								a									=				importlib.import_module(
								    datasets.load.metric_module_factory(os.path.join("""metrics"""						, _SCREAMING_SNAKE_CASE							)							).module_path							)
								# run doctest
								with self.use_local_metrics():
									a									=				doctest.testmod(_SCREAMING_SNAKE_CASE						, verbose=_SCREAMING_SNAKE_CASE						, raise_on_error=_SCREAMING_SNAKE_CASE							)
								self.assertEqual(results.failed						, 0							)
								self.assertGreater(results.attempted						, 1							)
							@contextmanager
							def  lowerCamelCase__				(		self		:Optional[Any]						, __magic_name__		:List[str]						, __magic_name__		:Optional[int]							):







								'''simple docstring'''







								if metric_name in self.INTENSIVE_CALLS_PATCHER:
									with self.INTENSIVE_CALLS_PATCHER[metric_name](_SCREAMING_SNAKE_CASE							):
										yield
								else:
									yield
							@contextmanager
							def  lowerCamelCase__				(		self		:str							):







								'''simple docstring'''







								def load_local_metric(__magic_name__		:int						, *__magic_name__		:Dict						, **__magic_name__		:int							):
									return load_metric(os.path.join("""metrics"""						, _SCREAMING_SNAKE_CASE							)						, *_SCREAMING_SNAKE_CASE						, **_SCREAMING_SNAKE_CASE							)

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





							@classmethod
							def  lowerCamelCase__				(		cls		:List[Any]						, __magic_name__		:List[Any]							):







								'''simple docstring'''







								def wrapper(__magic_name__		:Union[str, Any]							):
									a									=				contextmanager(_SCREAMING_SNAKE_CASE							)
									a									=				patcher
									return patcher

								return wrapper







@LocalMetricTest.register_intensive_calls_patcher("""bleurt"""       )
def 						__A						(      __lowerCamelCase       ) ->			Union[str, Any]:
	import tensorflow.compat.va as tf
	from bleurt.score import Predictor

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

	class 						__lowerCAmelCase      (      _lowerCamelCase					):
								def  lowerCamelCase__				(		self		:Optional[int]						, __magic_name__		:Union[str, Any]							):







									'''simple docstring'''







									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:
		a									=				MockedPredictor()
		yield





@LocalMetricTest.register_intensive_calls_patcher("""bertscore"""       )
def 						__A						(      __lowerCamelCase       ) ->			str:
	import torch

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

	# 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:
		a									=				bert_cos_score_idf
		yield





@LocalMetricTest.register_intensive_calls_patcher("""comet"""       )
def 						__A						(      __lowerCamelCase       ) ->			List[str]:
	def load_from_checkpoint(__lowerCamelCase       ):

		class 						__lowerCAmelCase      :
									def  lowerCamelCase__				(		self		:Union[str, Any]						, __magic_name__		:int						, *__magic_name__		:Tuple						, **__magic_name__		:List[str]							):







										'''simple docstring'''







										assert len(_SCREAMING_SNAKE_CASE							) == 2
										a									=				[0.19, 0.92]
										return scores, sum(_SCREAMING_SNAKE_CASE							) / len(_SCREAMING_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:
		a									=				None
		with patch("""comet.load_from_checkpoint"""       ) as mock_load_from_checkpoint:
			a									=				load_from_checkpoint
			yield





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






"""simple docstring"""

import gc
import unittest

import numpy as np
import torch

from diffusers import StableDiffusionKDiffusionPipeline
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu


enable_full_determinism()
@slow
@require_torch_gpu
class     lowerCAmelCase__						(	unittest.TestCase ):


	'''simple docstring'''







	def 			_lowerCAmelCase     (      self				:       List[Any]    )						->	str:





					"""simple docstring"""


					super().tearDown()
					gc.collect()
					torch.cuda.empty_cache()





	def 			_lowerCAmelCase     (      self				:       Any    )						->	str:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							int							=     StableDiffusionKDiffusionPipeline.from_pretrained('CompVis/stable-diffusion-v1-4'    )
					SCREAMING_SNAKE_CASE						:							Dict							=     sd_pipe.to(_SCREAMING_SNAKE_CASE    )
					sd_pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE    )

					sd_pipe.set_scheduler('sample_euler'    )

					SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     'A painting of a squirrel eating a burger'
					SCREAMING_SNAKE_CASE						:							Optional[Any]							=     torch.manual_seed(0    )
					SCREAMING_SNAKE_CASE						:							Optional[Any]							=     sd_pipe([prompt]     ,       generator=_SCREAMING_SNAKE_CASE     ,       guidance_scale=9.0     ,       num_inference_steps=20     ,       output_type='np'    )

					SCREAMING_SNAKE_CASE						:							str							=     output.images

					SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     image[0, -3:, -3:, -1]

					assert image.shape == (1, 512, 512, 3)
					SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     np.array([0.0_4_4_7, 0.0_4_9_2, 0.0_4_6_8, 0.0_4_0_8, 0.0_3_8_3, 0.0_4_0_8, 0.0_3_5_4, 0.0_3_8_0, 0.0_3_3_9]    )

					assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1E-2





	def 			_lowerCAmelCase     (      self				:       Dict    )						->	Optional[int]:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							Optional[Any]							=     StableDiffusionKDiffusionPipeline.from_pretrained('stabilityai/stable-diffusion-2-1-base'    )
					SCREAMING_SNAKE_CASE						:							List[Any]							=     sd_pipe.to(_SCREAMING_SNAKE_CASE    )
					sd_pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE    )

					sd_pipe.set_scheduler('sample_euler'    )

					SCREAMING_SNAKE_CASE						:							List[str]							=     'A painting of a squirrel eating a burger'
					SCREAMING_SNAKE_CASE						:							Optional[int]							=     torch.manual_seed(0    )
					SCREAMING_SNAKE_CASE						:							Dict							=     sd_pipe([prompt]     ,       generator=_SCREAMING_SNAKE_CASE     ,       guidance_scale=9.0     ,       num_inference_steps=20     ,       output_type='np'    )

					SCREAMING_SNAKE_CASE						:							Optional[int]							=     output.images

					SCREAMING_SNAKE_CASE						:							List[str]							=     image[0, -3:, -3:, -1]

					assert image.shape == (1, 512, 512, 3)
					SCREAMING_SNAKE_CASE						:							List[Any]							=     np.array([0.1_2_3_7, 0.1_3_2_0, 0.1_4_3_8, 0.1_3_5_9, 0.1_3_9_0, 0.1_1_3_2, 0.1_2_7_7, 0.1_1_7_5, 0.1_1_1_2]    )

					assert np.abs(image_slice.flatten() - expected_slice    ).max() < 5E-1







	def 			_lowerCAmelCase     (      self				:       Dict    )						->	Optional[Any]:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							Optional[int]							=     StableDiffusionKDiffusionPipeline.from_pretrained('stabilityai/stable-diffusion-2-1-base'    )
					SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     sd_pipe.to(_SCREAMING_SNAKE_CASE    )
					sd_pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE    )

					sd_pipe.set_scheduler('sample_dpmpp_2m'    )

					SCREAMING_SNAKE_CASE						:							Optional[int]							=     'A painting of a squirrel eating a burger'
					SCREAMING_SNAKE_CASE						:							str							=     torch.manual_seed(0    )
					SCREAMING_SNAKE_CASE						:							Optional[Any]							=     sd_pipe(
					    [prompt]     ,       generator=_SCREAMING_SNAKE_CASE     ,       guidance_scale=7.5     ,       num_inference_steps=15     ,       output_type='np'     ,       use_karras_sigmas=_SCREAMING_SNAKE_CASE     ,       )

					SCREAMING_SNAKE_CASE						:							int							=     output.images

					SCREAMING_SNAKE_CASE						:							List[Any]							=     image[0, -3:, -3:, -1]

					assert image.shape == (1, 512, 512, 3)
					SCREAMING_SNAKE_CASE						:							int							=     np.array(
					    [0.1_1_3_8_1_6_8_9, 0.1_2_1_1_2_9_2_1, 0.1_3_8_9_4_5_7, 0.1_2_5_4_9_6_0_6, 0.1_2_4_4_9_6_4, 0.1_0_8_3_1_5_1_7, 0.1_1_5_6_2_8_6_6, 0.1_0_8_6_7_8_1_6, 0.1_0_4_9_9_0_4_8]    )

					assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1E-2





