





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
lowercase_		:				Optional[int]				=						pytest.mark.integration


lowercase_		:				List[str]				=						{'''comet'''}
lowercase_		:				Optional[int]				=						importlib.util.find_spec('''fairseq''') is not None

lowercase_		:				Any				=						{'''code_eval'''}
lowercase_		:				List[Any]				=						os.name == '''nt'''

lowercase_		:				int				=						{'''bertscore''', '''frugalscore''', '''perplexity'''}
lowercase_		:				int				=						importlib.util.find_spec('''transformers''') is not None

def       A__(		__lowerCAmelCase ):
				@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       A__(		__lowerCAmelCase ):
				@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       A__(		__lowerCAmelCase ):
				@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       A__(		):
				_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(
    a_		,			a_		,			a_     )
@local
class 			lowercase				(							parameterized.TestCase     ):



						"""simple docstring"""

						_UpperCamelCase  :	List[Any]								=	{}
						_UpperCamelCase  :	Optional[Any]								=	None







						@pytest.mark.filterwarnings('ignore:metric_module_factory is deprecated:FutureWarning'					)
						@pytest.mark.filterwarnings('ignore:load_metric is deprecated:FutureWarning'					)
						def 		__UpperCAmelCase       (    self			:		int ,				lowerCamelCase_			:		Any					):





										'''simple docstring'''







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





										'''simple docstring'''







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







						@contextmanager
						def 		__UpperCAmelCase       (    self			:		Optional[Any] ,				lowerCamelCase_			:		str ,				lowerCamelCase_			:		Tuple					):





										'''simple docstring'''







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







						@contextmanager
						def 		__UpperCAmelCase       (    self			:		str					):





										'''simple docstring'''







										def load_local_metric(lowerCamelCase_			:		Union[str, Any] ,				*lowerCamelCase_			:		Tuple ,				**lowerCamelCase_			:		Optional[int]					):
														return load_metric(os.path.join('metrics' ,				lowerCamelCase_					) ,				*lowerCamelCase_ ,				**lowerCamelCase_					)

										with patch('datasets.load_metric'					) as mock_load_metric:
														_snake_case :			Any  						=   load_local_metric
														yield







						@classmethod
						def 		__UpperCAmelCase       (    cls			:		Union[str, Any] ,				lowerCamelCase_			:		Optional[Any]					):





										'''simple docstring'''







										def wrapper(lowerCamelCase_			:		Union[str, Any]					):
														_snake_case :			List[Any]  						=   contextmanager(lowerCamelCase_					)
														_snake_case :			List[Any]  						=   patcher
														return patcher

										return wrapper



@LocalMetricTest.register_intensive_calls_patcher('bleurt' )
def       A__(		__lowerCAmelCase ):
				import tensorflow.compat.va as tf
				from bleurt.score import Predictor

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


				class 			lowercase				(							a_     ):



										"""simple docstring"""

										def 		__UpperCAmelCase       (    self			:		str ,				lowerCamelCase_			:		Dict					):





														'''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:
								_snake_case :			Union[str, Any]  						=   MockedPredictor()
								yield

@LocalMetricTest.register_intensive_calls_patcher('bertscore' )
def       A__(		__lowerCAmelCase ):
				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:
								_snake_case :			Optional[int]  						=   bert_cos_score_idf
								yield

@LocalMetricTest.register_intensive_calls_patcher('comet' )
def       A__(		__lowerCAmelCase ):
				def load_from_checkpoint(__lowerCAmelCase ):


								class 			lowercase				:



														"""simple docstring"""

														def 		__UpperCAmelCase       (    self			:		Dict ,				lowerCamelCase_			:		Dict ,				*lowerCamelCase_			:		Optional[Any] ,				**lowerCamelCase_			:		Optional[Any]					):





																		'''simple docstring'''







																		assert len(lowerCamelCase_					) == 2
																		_snake_case :			int  						=   [0.19, 0.92]
																		return scores, sum(lowerCamelCase_					) / len(lowerCamelCase_					)



								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 :			int  						=   None
								with patch('comet.load_from_checkpoint' ) as mock_load_from_checkpoint:
												_snake_case :			Tuple  						=   load_from_checkpoint
												yield

def       A__(		):
				_snake_case :			Optional[int]  						=   load_metric(os.path.join('metrics'	,			'seqeval' ) )
				_snake_case :			List[str]  						=   'ERROR'
				_snake_case :			str  						=   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 )













import torch

from transformers import AutoModel


class 			lowercase				(							torch.nn.Module     ):



						"""simple docstring"""

						def __init__(    self			:		Tuple ,				lowerCamelCase_			:		Dict="sayef/fsner-bert-base-uncased"					):





										'''simple docstring'''







										super(lowerCamelCase_ ,				self					).__init__()

										_snake_case :			Optional[Any]  						=   AutoModel.from_pretrained(lowerCamelCase_ ,				return_dict=lowerCamelCase_					)
										_snake_case :			str  						=   torch.nn.CosineSimilarity(3 ,				1e-08					)
										_snake_case :			str  						=   torch.nn.Softmax(dim=1					)







						def 		__UpperCAmelCase       (    self			:		int ,				**lowerCamelCase_			:		List[str]					):





										'''simple docstring'''







										return self.bert(**lowerCamelCase_					).last_hidden_state







						def 		__UpperCAmelCase       (    self			:		List[Any] ,				lowerCamelCase_			:		Union[str, Any]					):





										'''simple docstring'''







										return token_embeddings.sum(2 ,				keepdim=lowerCamelCase_					)







						def 		__UpperCAmelCase       (    self			:		Dict ,				lowerCamelCase_			:		Dict ,				lowerCamelCase_			:		Tuple ,				lowerCamelCase_			:		Optional[Any]=1					):





										'''simple docstring'''







										return self.softmax(T * self.cos(lowerCamelCase_ ,				lowerCamelCase_					)					)







						def 		__UpperCAmelCase       (    self			:		Optional[Any] ,				lowerCamelCase_			:		Any ,				lowerCamelCase_			:		Union[str, Any]					):





										'''simple docstring'''







										_snake_case :			int  						=   W_supports['sizes'].tolist()
										_snake_case :			int  						=   W_supports['start_token_id'].item()
										_snake_case :			List[str]  						=   W_supports['end_token_id'].item()

										del W_supports["sizes"]
										del W_supports["start_token_id"]
										del W_supports["end_token_id"]

										_snake_case :			Optional[int]  						=   self.BERT(**lowerCamelCase_					)
										_snake_case :			Optional[Any]  						=   self.BERT(**lowerCamelCase_					)

										_snake_case :			Optional[int]  						=   None
										_snake_case :			Optional[int]  						=   None

										_snake_case :			List[str]  						=   W_supports['input_ids'] == start_token_id
										_snake_case :			Union[str, Any]  						=   W_supports['input_ids'] == end_token_id

										for i, size in enumerate(lowerCamelCase_					):
														if i == 0:
																		_snake_case :			str  						=   0
														else:
																		_snake_case :			Union[str, Any]  						=   support_sizes[i - 1]

														_snake_case :			Tuple  						=   S[s : s + size][start_token_masks[s : s + size]]
														_snake_case :			Optional[int]  						=   S[s : s + size][end_token_masks[s : s + size]]

														_snake_case :			Tuple  						=   torch.matmul(q[i] ,				s_start.T					).sum(1					).softmax(0					)
														_snake_case :			Union[str, Any]  						=   torch.matmul(q[i] ,				s_end.T					).sum(1					).softmax(0					)

														if p_starts is not None:
																		_snake_case :			Optional[Any]  						=   torch.vstack((p_starts, p_start)					)
																		_snake_case :			List[str]  						=   torch.vstack((p_ends, p_end)					)
														else:
																		_snake_case :			Union[str, Any]  						=   p_start
																		_snake_case :			Any  						=   p_end

										return p_starts, p_ends






