





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


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

lowercase__						:      Any	=    {'code_eval'}
lowercase__						:      Optional[Any]	=    os.name == 'nt'

lowercase__						:      Optional[int]	=    {'bertscore', 'frugalscore', 'perplexity'}
lowercase__						:      List[str]	=    importlib.util.find_spec('transformers') is not None







def  a__    (    lowercase :					Optional[int]				)		->		Union[str, Any]:





     """simple docstring"""

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

     return wrapper







def  a__    (    lowercase :					Union[str, Any]				)		->		List[Any]:





     """simple docstring"""

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

     return wrapper







def  a__    (    lowercase :					Dict				)		->		Any:





     """simple docstring"""

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

     return wrapper







def  a__    (    )		->		Any:





     """simple docstring"""

     _UpperCamelCase       =	[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(
    __magic_name__						,						__magic_name__						,						__magic_name__			)
@local
class     __lowerCAmelCase					(   parameterized.TestCase			):






       """simple docstring"""
       _snake_case     :					Optional[Any]						    =   {}
       _snake_case     :					str						    =   None





       @pytest.mark.filterwarnings('''ignore:metric_module_factory is deprecated:FutureWarning'''					)
       @pytest.mark.filterwarnings('''ignore:load_metric is deprecated:FutureWarning'''					)
       def snake_case__						(							self       :		Tuple	,			lowerCAmelCase__       :		Dict					)   ->					int:

            '''simple docstring'''




            _UpperCamelCase       =	'''[...]'''
            _UpperCamelCase       =	importlib.import_module(
                datasets.load.metric_module_factory(os.path.join('''metrics'''	,			lowerCAmelCase__					)					).module_path					)
            _UpperCamelCase       =	datasets.load.import_main_class(metric_module.__name__	,			dataset=lowerCAmelCase__					)
            # check parameters
            _UpperCamelCase       =	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:
                           _UpperCamelCase       =	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 snake_case__						(							self       :		Optional[int]	,			lowerCAmelCase__       :		str					)   ->					Any:

            '''simple docstring'''




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





       @contextmanager
       def snake_case__						(							self       :		int	,			lowerCAmelCase__       :		Union[str, Any]	,			lowerCAmelCase__       :		Optional[int]					)   ->					Dict:

            '''simple docstring'''




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





       @contextmanager
       def snake_case__						(							self       :		int					)   ->					str:

            '''simple docstring'''




            def load_local_metric(lowerCAmelCase__       :		Dict	,			*lowerCAmelCase__       :		str	,			**lowerCAmelCase__       :		str					):
                 return load_metric(os.path.join('''metrics'''	,			lowerCAmelCase__					)	,			*lowerCAmelCase__	,			**lowerCAmelCase__					)

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







       @classmethod
       def snake_case__						(							cls       :		List[Any]	,			lowerCAmelCase__       :		Union[str, Any]					)   ->					Any:

            '''simple docstring'''




            def wrapper(lowerCAmelCase__       :		Tuple					):
                 _UpperCamelCase       =	contextmanager(lowerCAmelCase__					)
                 _UpperCamelCase       =	patcher
                 return patcher

            return wrapper







@LocalMetricTest.register_intensive_calls_patcher('''bleurt'''				)
def  a__    (    lowercase :					int				)		->		Tuple:





     """simple docstring"""

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

     tf.flags.DEFINE_string('''sv''', '''''', ''''''				)  # handle pytest cli flags
     class     __lowerCAmelCase					(   __magic_name__			):






            """simple docstring"""





            def snake_case__						(							self       :		List[str]	,			lowerCAmelCase__       :		str					)   ->					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:
          _UpperCamelCase       =	MockedPredictor()
          yield







@LocalMetricTest.register_intensive_calls_patcher('''bertscore'''				)
def  a__    (    lowercase :					Optional[Any]				)		->		List[str]:





     """simple docstring"""

     import torch

     def bert_cos_score_idf(lowercase :					int, lowercase :					Optional[int], *lowercase :					List[str], **lowercase :					str				):
          return torch.tensor([[1.0, 1.0, 1.0]] * len(lowercase				)				)

     # 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:
          _UpperCamelCase       =	bert_cos_score_idf
          yield







@LocalMetricTest.register_intensive_calls_patcher('''comet'''				)
def  a__    (    lowercase :					List[str]				)		->		Union[str, Any]:





     """simple docstring"""

     def load_from_checkpoint(lowercase :					Optional[int]				):
          class     __lowerCAmelCase					:






                 """simple docstring"""





                 def snake_case__						(							self       :		Tuple	,			lowerCAmelCase__       :		List[str]	,			*lowerCAmelCase__       :		List[str]	,			**lowerCAmelCase__       :		Optional[int]					)   ->					Union[str, Any]:

                      '''simple docstring'''




                      assert len(lowerCAmelCase__					) == 2
                      _UpperCamelCase       =	[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:
          _UpperCamelCase       =	None
          with patch('''comet.load_from_checkpoint'''				) as mock_load_from_checkpoint:
               _UpperCamelCase       =	load_from_checkpoint
               yield







def  a__    (    )		->		Optional[Any]:





     """simple docstring"""

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










'''simple docstring'''

import unittest

from transformers import CamembertTokenizer, CamembertTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import is_torch_available

from ...test_tokenization_common import TokenizerTesterMixin


lowercase__						:      Optional[Any]	=    get_tests_dir('fixtures/test_sentencepiece.model')
lowercase__						:      Any	=    get_tests_dir('fixtures/test_sentencepiece_bpe.model')

lowercase__						:      Tuple	=    'pt' if is_torch_available() else 'tf'
@require_sentencepiece
@require_tokenizers
class     __lowerCAmelCase					(   __magic_name__						,						unittest.TestCase			):






       """simple docstring"""
       _snake_case     :					Union[str, Any]						    =   CamembertTokenizer
       _snake_case     :					str						    =   CamembertTokenizerFast
       _snake_case     :					int						    =   True
       _snake_case     :					List[str]						    =   True





       def snake_case__						(							self       :		Dict					)   ->					Any:

            '''simple docstring'''




            super().setUp()

            # We have a SentencePiece fixture for testing
            _UpperCamelCase       =	CamembertTokenizer(lowerCAmelCase__					)
            tokenizer.save_pretrained(self.tmpdirname					)





       def snake_case__						(							self       :		Optional[int]					)   ->					List[Any]:

            '''simple docstring'''




            _UpperCamelCase       =	'''<pad>'''
            _UpperCamelCase       =	1

            self.assertEqual(self.get_tokenizer()._convert_token_to_id(lowerCAmelCase__					)	,			lowerCAmelCase__					)
            self.assertEqual(self.get_tokenizer()._convert_id_to_token(lowerCAmelCase__					)	,			lowerCAmelCase__					)





       def snake_case__						(							self       :		Dict					)   ->					List[Any]:

            '''simple docstring'''




            _UpperCamelCase       =	list(self.get_tokenizer().get_vocab().keys()					)

            self.assertEqual(vocab_keys[0]	,			'''<s>NOTUSED'''					)
            self.assertEqual(vocab_keys[1]	,			'''<pad>'''					)
            self.assertEqual(vocab_keys[-1]	,			'''<mask>'''					)
            self.assertEqual(len(lowerCAmelCase__					)	,			1004					)





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

            '''simple docstring'''




            self.assertEqual(self.get_tokenizer().vocab_size	,			1005					)





       def snake_case__						(							self       :		int					)   ->					Tuple:

            '''simple docstring'''




            _UpperCamelCase       =	CamembertTokenizer(lowerCAmelCase__					)
            tokenizer.save_pretrained(self.tmpdirname					)
            _UpperCamelCase       =	CamembertTokenizerFast.from_pretrained(self.tmpdirname					)

            _UpperCamelCase       =	'''I was born in 92000, and this is falsé.'''

            _UpperCamelCase       =	tokenizer.encode(lowerCAmelCase__					)
            _UpperCamelCase       =	rust_tokenizer.encode(lowerCAmelCase__					)
            self.assertListEqual(lowerCAmelCase__	,			lowerCAmelCase__					)

            _UpperCamelCase       =	tokenizer.encode(lowerCAmelCase__	,			add_special_tokens=lowerCAmelCase__					)
            _UpperCamelCase       =	rust_tokenizer.encode(lowerCAmelCase__	,			add_special_tokens=lowerCAmelCase__					)
            self.assertListEqual(lowerCAmelCase__	,			lowerCAmelCase__					)

            # <unk> tokens are not the same for `rust` than for `slow`.
            # Because spm gives back raw token instead of `unk` in EncodeAsPieces
            # tokens = tokenizer.tokenize(sequence)
            _UpperCamelCase       =	tokenizer.convert_ids_to_tokens(lowerCAmelCase__					)
            _UpperCamelCase       =	rust_tokenizer.tokenize(lowerCAmelCase__					)
            self.assertListEqual(lowerCAmelCase__	,			lowerCAmelCase__					)





       def snake_case__						(							self       :		Optional[int]					)   ->					List[Any]:

            '''simple docstring'''




            if not self.test_rust_tokenizer:
                 return

            _UpperCamelCase       =	self.get_tokenizer()
            _UpperCamelCase       =	self.get_rust_tokenizer()

            _UpperCamelCase       =	'''I was born in 92000, and this is falsé.'''

            _UpperCamelCase       =	tokenizer.tokenize(lowerCAmelCase__					)
            _UpperCamelCase       =	rust_tokenizer.tokenize(lowerCAmelCase__					)
            self.assertListEqual(lowerCAmelCase__	,			lowerCAmelCase__					)

            _UpperCamelCase       =	tokenizer.encode(lowerCAmelCase__	,			add_special_tokens=lowerCAmelCase__					)
            _UpperCamelCase       =	rust_tokenizer.encode(lowerCAmelCase__	,			add_special_tokens=lowerCAmelCase__					)
            self.assertListEqual(lowerCAmelCase__	,			lowerCAmelCase__					)

            _UpperCamelCase       =	self.get_rust_tokenizer()
            _UpperCamelCase       =	tokenizer.encode(lowerCAmelCase__					)
            _UpperCamelCase       =	rust_tokenizer.encode(lowerCAmelCase__					)
            self.assertListEqual(lowerCAmelCase__	,			lowerCAmelCase__					)







       @slow
       def snake_case__						(							self       :		Any					)   ->					Optional[Any]:

            '''simple docstring'''




            _UpperCamelCase       =	{'''input_ids''': [[5, 54, 7196, 297, 30, 23, 776, 18, 11, 3215, 3705, 8252, 22, 3164, 1181, 2116, 29, 16, 813, 25, 791, 3314, 20, 3446, 38, 27575, 120, 6, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [5, 468, 17, 11, 9088, 20, 1517, 8, 22804, 18818, 10, 38, 629, 607, 607, 142, 19, 7196, 867, 56, 10326, 24, 2267, 20, 416, 5072, 15612, 233, 734, 7, 2399, 27, 16, 3015, 1649, 7, 24, 20, 4338, 2399, 27, 13, 3400, 14, 13, 6189, 8, 930, 9, 6]], '''attention_mask''': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}  # noqa: E501
            # fmt: on

            # camembert is a french model. So we also use french texts.
            _UpperCamelCase       =	[
                '''Le transformeur est un modèle d\'apprentissage profond introduit en 2017, '''
                '''utilisé principalement dans le domaine du traitement automatique des langues (TAL).''',
                '''À l\'instar des réseaux de neurones récurrents (RNN), les transformeurs sont conçus '''
                '''pour gérer des données séquentielles, telles que le langage naturel, pour des tâches '''
                '''telles que la traduction et la synthèse de texte.''',
            ]

            self.tokenizer_integration_test_util(
                expected_encoding=lowerCAmelCase__	,			model_name='''camembert-base'''	,			revision='''3a0641d9a1aeb7e848a74299e7e4c4bca216b4cf'''	,			sequences=lowerCAmelCase__	,			)



