




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

lowerCAmelCase							:			List[Any]     							=							{"""code_eval"""}
lowerCAmelCase							:			Tuple     							=							os.name == """nt"""

lowerCAmelCase							:			Optional[int]     							=							{"""bertscore""", """frugalscore""", """perplexity"""}
lowerCAmelCase							:			Union[str, Any]     							=							importlib.util.find_spec("""transformers""") is not None



def     a__ (				snake_case__				)						->					Union[str, Any]:
			@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__				)						->					Union[str, Any]:
			@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__				)						->					int:
			@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__ (				)						->					Dict:
			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     __magic_name__							(       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    _lowerCAmelCase				(       self		, _a ):





							"""simple docstring"""
							lowerCamelCase			      =					"""[...]"""
							lowerCamelCase			      =					importlib.import_module(
							    datasets.load.metric_module_factory(os.path.join("""metrics"""		, _a ) ).module_path )
							lowerCamelCase			      =					datasets.load.import_main_class(metric_module.__name__		, dataset=_a )
							# 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(_a		, metric_module.__name__ ):
										with self.use_local_metrics():
													try:
																lowerCamelCase			      =					doctest.testmod(_a		, verbose=_a		, raise_on_error=_a )
													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		, _a ):





							"""simple docstring"""
							lowerCamelCase			      =					"""[...]"""
							lowerCamelCase			      =					importlib.import_module(
							    datasets.load.metric_module_factory(os.path.join("""metrics"""		, _a ) ).module_path )
							# run doctest
							with self.use_local_metrics():
										lowerCamelCase			      =					doctest.testmod(_a		, verbose=_a		, raise_on_error=_a )
							self.assertEqual(results.failed		, 0 )
							self.assertGreater(results.attempted		, 1 )





				@contextmanager
				def    _lowerCAmelCase				(       self		, _a		, _a ):





							"""simple docstring"""
							if metric_name in self.INTENSIVE_CALLS_PATCHER:
										with self.INTENSIVE_CALLS_PATCHER[metric_name](_a ):
													yield
							else:
										yield





				@contextmanager
				def    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							def load_local_metric(_a		, *_a		, **_a ):
										return load_metric(os.path.join("""metrics"""		, _a )		, *_a		, **_a )

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





				@classmethod
				def    _lowerCAmelCase				(       cls		, _a ):





							"""simple docstring"""
							def wrapper(_a ):
										lowerCamelCase			      =					contextmanager(_a )
										lowerCamelCase			      =					patcher
										return patcher

							return wrapper




@LocalMetricTest.register_intensive_calls_patcher("""bleurt"""				)
def     a__ (				snake_case__				)						->					str:
			import tensorflow.compat.va as tf
			from bleurt.score import Predictor

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






			class     __magic_name__							(       UpperCAmelCase__				):






							'''simple docstring'''





							def    _lowerCAmelCase				(       self		, _a ):





										"""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:
						lowerCamelCase			      =					MockedPredictor()
						yield



@LocalMetricTest.register_intensive_calls_patcher("""bertscore"""				)
def     a__ (				snake_case__				)						->					Any:
			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:
						lowerCamelCase			      =					bert_cos_score_idf
						yield



@LocalMetricTest.register_intensive_calls_patcher("""comet"""				)
def     a__ (				snake_case__				)						->					Union[str, Any]:
			def load_from_checkpoint(snake_case__				):






						class     __magic_name__							:






										'''simple docstring'''





										def    _lowerCAmelCase				(       self		, _a		, *_a		, **_a ):





													"""simple docstring"""
													assert len(_a ) == 2
													lowerCamelCase			      =					[0.19, 0.92]
													return scores, sum(_a ) / len(_a )




						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     a__ (				)						->					int:
			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(snake_case__       , match=re.escape(snake_case__				)				):
						metric.compute(predictions=[]       , references=[]       , scheme=snake_case__				)









"""simple docstring"""




import unittest

import numpy as np

from transformers import RoFormerConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
					import jax.numpy as jnp

					from transformers.models.roformer.modeling_flax_roformer import (
					    FlaxRoFormerForMaskedLM,
					    FlaxRoFormerForMultipleChoice,
					    FlaxRoFormerForQuestionAnswering,
					    FlaxRoFormerForSequenceClassification,
					    FlaxRoFormerForTokenClassification,
					    FlaxRoFormerModel,
					)






class     __magic_name__							(       unittest.TestCase				):






				'''simple docstring'''





				def __init__(       self		, _a		, _a=13		, _a=7		, _a=True		, _a=True		, _a=True		, _a=True		, _a=99		, _a=32		, _a=5		, _a=4		, _a=37		, _a="gelu"		, _a=0.1		, _a=0.1		, _a=512		, _a=16		, _a=2		, _a=0.02		, _a=4		, ):





							"""simple docstring"""
							lowerCamelCase			      =					parent
							lowerCamelCase			      =					batch_size
							lowerCamelCase			      =					seq_length
							lowerCamelCase			      =					is_training
							lowerCamelCase			      =					use_attention_mask
							lowerCamelCase			      =					use_token_type_ids
							lowerCamelCase			      =					use_labels
							lowerCamelCase			      =					vocab_size
							lowerCamelCase			      =					hidden_size
							lowerCamelCase			      =					num_hidden_layers
							lowerCamelCase			      =					num_attention_heads
							lowerCamelCase			      =					intermediate_size
							lowerCamelCase			      =					hidden_act
							lowerCamelCase			      =					hidden_dropout_prob
							lowerCamelCase			      =					attention_probs_dropout_prob
							lowerCamelCase			      =					max_position_embeddings
							lowerCamelCase			      =					type_vocab_size
							lowerCamelCase			      =					type_sequence_label_size
							lowerCamelCase			      =					initializer_range
							lowerCamelCase			      =					num_choices





				def    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							lowerCamelCase			      =					ids_tensor([self.batch_size, self.seq_length]		, self.vocab_size )

							lowerCamelCase			      =					None
							if self.use_attention_mask:
										lowerCamelCase			      =					random_attention_mask([self.batch_size, self.seq_length] )

							lowerCamelCase			      =					None
							if self.use_token_type_ids:
										lowerCamelCase			      =					ids_tensor([self.batch_size, self.seq_length]		, self.type_vocab_size )

							lowerCamelCase			      =					RoFormerConfig(
							    vocab_size=self.vocab_size		, hidden_size=self.hidden_size		, num_hidden_layers=self.num_hidden_layers		, num_attention_heads=self.num_attention_heads		, intermediate_size=self.intermediate_size		, hidden_act=self.hidden_act		, hidden_dropout_prob=self.hidden_dropout_prob		, attention_probs_dropout_prob=self.attention_probs_dropout_prob		, max_position_embeddings=self.max_position_embeddings		, type_vocab_size=self.type_vocab_size		, is_decoder=_a		, initializer_range=self.initializer_range		, )

							return config, input_ids, token_type_ids, attention_mask





				def    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							lowerCamelCase			      =					self.prepare_config_and_inputs()
							lowerCamelCase      ,   lowerCamelCase      ,   lowerCamelCase      ,   lowerCamelCase			      =					config_and_inputs
							lowerCamelCase			      =					{"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": attention_mask}
							return config, inputs_dict






@require_flax
class     __magic_name__							(       UpperCAmelCase__						,					unittest.TestCase				):






				'''simple docstring'''
				__UpperCamelCase			  =						True

				__UpperCamelCase			  =						(
				    (
				        FlaxRoFormerModel,
				        FlaxRoFormerForMaskedLM,
				        FlaxRoFormerForSequenceClassification,
				        FlaxRoFormerForTokenClassification,
				        FlaxRoFormerForMultipleChoice,
				        FlaxRoFormerForQuestionAnswering,
				    )
				    if is_flax_available()
				    else ()
				)





				def    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							lowerCamelCase			      =					FlaxRoFormerModelTester(self )





				@slow
				def    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							for model_class_name in self.all_model_classes:
										lowerCamelCase			      =					model_class_name.from_pretrained("""junnyu/roformer_chinese_small"""		, from_pt=_a )
										lowerCamelCase			      =					model(np.ones((1, 1) ) )
										self.assertIsNotNone(_a )






@require_flax
class     __magic_name__							(       unittest.TestCase				):






				'''simple docstring'''





				@slow
				def    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							lowerCamelCase			      =					FlaxRoFormerForMaskedLM.from_pretrained("""junnyu/roformer_chinese_base""" )
							lowerCamelCase			      =					jnp.array([[0, 1, 2, 3, 4, 5]] )
							lowerCamelCase			      =					model(_a )[0]

							lowerCamelCase			      =					50_000

							lowerCamelCase			      =					(1, 6, vocab_size)
							self.assertEqual(output.shape		, _a )

							lowerCamelCase			      =					jnp.array(
							    [[[-0.1_205, -1.0_265, 0.2_922], [-1.5_134, 0.1_974, 0.1_519], [-5.0_135, -3.9_003, -0.8_404]]] )

							self.assertTrue(jnp.allclose(output[:, :3, :3]		, _a		, atol=1e-4 ) )



