"""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    :							Union[str, Any]													=						pytest.mark.integration


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

__lowerCAmelCase    :							Dict													=						{"code_eval"}
__lowerCAmelCase    :							List[str]													=						os.name == "nt"

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







def 							_UpperCAmelCase		(       lowerCamelCase__     ):





		"""simple docstring"""




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

		return wrapper







def 							_UpperCAmelCase		(       lowerCamelCase__     ):





		"""simple docstring"""




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

		return wrapper







def 							_UpperCAmelCase		(       lowerCamelCase__     ):





		"""simple docstring"""




		@wraps(lowerCamelCase__     )
		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			,  lowerCamelCase__     )

		return wrapper







def 							_UpperCAmelCase		(       ):





		"""simple docstring"""




		lowerCAmelCase__        =   [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(
    __UpperCamelCase ,		__UpperCamelCase ,		__UpperCamelCase			)
@local
class       a_      (     parameterized.TestCase			):
					UpperCamelCase_ :	Union[str, Any]			=    {}
					UpperCamelCase_ :	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  ,						snake_case__						:     Union[str, Any]     ):
							lowerCAmelCase__        =   """[...]"""
							lowerCAmelCase__        =   importlib.import_module(
							    datasets.load.metric_module_factory(os.path.join("""metrics"""  ,						snake_case__     )     ).module_path     )
							lowerCAmelCase__        =   datasets.load.import_main_class(metric_module.__name__  ,						dataset=snake_case__     )
							# check parameters
							lowerCAmelCase__        =   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:
													lowerCAmelCase__        =   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 						_SCREAMING_SNAKE_CASE						(	self						:     Dict  ,						snake_case__						:     int     ):
							lowerCAmelCase__        =   """[...]"""
							lowerCAmelCase__        =   importlib.import_module(
							    datasets.load.metric_module_factory(os.path.join("""metrics"""  ,						snake_case__     )     ).module_path     )
							# run doctest
							with self.use_local_metrics():
									lowerCAmelCase__        =   doctest.testmod(snake_case__  ,						verbose=snake_case__  ,						raise_on_error=snake_case__     )
							self.assertEqual(results.failed  ,						0     )
							self.assertGreater(results.attempted  ,						1     )




					@contextmanager
					def 						_SCREAMING_SNAKE_CASE						(	self						:     Dict  ,						snake_case__						:     Optional[Any]  ,						snake_case__						:     int     ):
							if metric_name in self.INTENSIVE_CALLS_PATCHER:
									with self.INTENSIVE_CALLS_PATCHER[metric_name](snake_case__     ):
											yield
							else:
									yield




					@contextmanager
					def 						_SCREAMING_SNAKE_CASE						(	self						:     Tuple     ):
							def load_local_metric(snake_case__						:     Dict  ,						*snake_case__						:     List[str]  ,						**snake_case__						:     int     ):
									return load_metric(os.path.join("""metrics"""  ,						snake_case__     )  ,						*snake_case__  ,						**snake_case__     )

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




					@classmethod
					def 						_SCREAMING_SNAKE_CASE						(	cls						:     Union[str, Any]  ,						snake_case__						:     int     ):
							def wrapper(snake_case__						:     str     ):
									lowerCAmelCase__        =   contextmanager(snake_case__     )
									lowerCAmelCase__        =   patcher
									return patcher

							return wrapper







@LocalMetricTest.register_intensive_calls_patcher("""bleurt"""     )
def 							_UpperCAmelCase		(       lowerCamelCase__     ):





		"""simple docstring"""




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

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

		class       a_      (     __UpperCamelCase			):




							def 						_SCREAMING_SNAKE_CASE						(	self						:     Optional[Any]  ,						snake_case__						:     Any     ):
									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:
				lowerCAmelCase__        =   MockedPredictor()
				yield







@LocalMetricTest.register_intensive_calls_patcher("""bertscore"""     )
def 							_UpperCAmelCase		(       lowerCamelCase__     ):





		"""simple docstring"""




		import torch

		def bert_cos_score_idf(lowerCamelCase__			,  lowerCamelCase__			,  *lowerCamelCase__			,  **lowerCamelCase__     ):
				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:
				lowerCAmelCase__        =   bert_cos_score_idf
				yield







@LocalMetricTest.register_intensive_calls_patcher("""comet"""     )
def 							_UpperCAmelCase		(       lowerCamelCase__     ):





		"""simple docstring"""




		def load_from_checkpoint(lowerCamelCase__     ):

				class       a_      :




									def 						_SCREAMING_SNAKE_CASE						(	self						:     Any  ,						snake_case__						:     List[str]  ,						*snake_case__						:     Union[str, Any]  ,						**snake_case__						:     List[str]     ):
											assert len(snake_case__     ) == 2
											lowerCAmelCase__        =   [0.19, 0.92]
											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:
				lowerCAmelCase__        =   None
				with patch("""comet.load_from_checkpoint"""     ) as mock_load_from_checkpoint:
						lowerCAmelCase__        =   load_from_checkpoint
						yield







def 							_UpperCAmelCase		(       ):





		"""simple docstring"""




		lowerCAmelCase__        =   load_metric(os.path.join("""metrics"""			,  """seqeval"""     )     )
		lowerCAmelCase__        =   """ERROR"""
		lowerCAmelCase__        =   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 io
import json
import unittest

from parameterized import parameterized

from transformers import FSMTForConditionalGeneration, FSMTTokenizer
from transformers.testing_utils import get_tests_dir, require_torch, slow, torch_device
from utils import calculate_bleu


__lowerCAmelCase    :							Union[str, Any]													=						get_tests_dir() + "/test_data/fsmt/fsmt_val_data.json"
with io.open(filename, "r", encoding="utf-8") as f:
			__lowerCAmelCase    :							Optional[int]													=						json.load(f)

@require_torch
class       a_      (     unittest.TestCase			):




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Optional[Any]  ,						snake_case__						:     Dict     ):
							return FSMTTokenizer.from_pretrained(snake_case__     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     List[str]  ,						snake_case__						:     Any     ):
							lowerCAmelCase__        =   FSMTForConditionalGeneration.from_pretrained(snake_case__     ).to(snake_case__     )
							if torch_device == "cuda":
									model.half()
							return model




					@parameterized.expand(
					    [
					        ["""en-ru""", 26.0],
					        ["""ru-en""", 22.0],
					        ["""en-de""", 22.0],
					        ["""de-en""", 29.0],
					    ]     )
					@slow
					def 						_SCREAMING_SNAKE_CASE						(	self						:     Dict  ,						snake_case__						:     Any  ,						snake_case__						:     int     ):
							# note: this test is not testing the best performance since it only evals a small batch
							# but it should be enough to detect a regression in the output quality
							lowerCAmelCase__        =   F"""facebook/wmt19-{pair}"""
							lowerCAmelCase__        =   self.get_tokenizer(snake_case__     )
							lowerCAmelCase__        =   self.get_model(snake_case__     )

							lowerCAmelCase__        =   bleu_data[pair]["""src"""]
							lowerCAmelCase__        =   bleu_data[pair]["""tgt"""]

							lowerCAmelCase__        =   tokenizer(snake_case__  ,						return_tensors="""pt"""  ,						truncation=snake_case__  ,						padding="""longest"""     ).to(snake_case__     )
							lowerCAmelCase__        =   model.generate(
							    input_ids=batch.input_ids  ,						num_beams=8  ,						)
							lowerCAmelCase__        =   tokenizer.batch_decode(
							    snake_case__  ,						skip_special_tokens=snake_case__  ,						clean_up_tokenization_spaces=snake_case__     )
							lowerCAmelCase__        =   calculate_bleu(snake_case__  ,						snake_case__     )
							print(snake_case__     )
							self.assertGreaterEqual(scores["""bleu"""]  ,						snake_case__     )




