


'''simple docstring'''


import unittest

from transformers import BarthezTokenizer, BarthezTokenizerFast, BatchEncoding
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow

from ...test_tokenization_common import TokenizerTesterMixin





@require_tokenizers
@require_sentencepiece
@slow  # see https://github.com/huggingface/transformers/issues/11457
class     lowercase__  (						_snake_case     ,     unittest.TestCase					):





							'''simple docstring'''


							A_							:      Optional[Any]      	=  BarthezTokenizer
							A_							:      Optional[Any]      	=  BarthezTokenizerFast
							A_							:      str      	=  True
							A_							:      str      	=  True






							def UpperCAmelCase_	(    self  ):
											super().setUp()

											_SCREAMING_SNAKE_CASE :       Any     		=      BarthezTokenizerFast.from_pretrained("""moussaKam/mbarthez"""  )
											tokenizer.save_pretrained(self.tmpdirname  )
											tokenizer.save_pretrained(self.tmpdirname    ,					legacy_format=__snake_case  )
											_SCREAMING_SNAKE_CASE :       List[Any]     		=      tokenizer






							def UpperCAmelCase_	(    self  ):
											_SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      """<pad>"""
											_SCREAMING_SNAKE_CASE :       Dict     		=      1

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






							def UpperCAmelCase_	(    self  ):
											_SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      list(self.get_tokenizer().get_vocab().keys()  )

											self.assertEqual(vocab_keys[0]    ,					"""<s>"""  )
											self.assertEqual(vocab_keys[1]    ,					"""<pad>"""  )
											self.assertEqual(vocab_keys[-1]    ,					"""<mask>"""  )
											self.assertEqual(len(__snake_case  )    ,					10_1122  )






							def UpperCAmelCase_	(    self  ):
											self.assertEqual(self.get_tokenizer().vocab_size    ,					10_1122  )






							@require_torch
							def UpperCAmelCase_	(    self  ):
											_SCREAMING_SNAKE_CASE :       Any     		=      ["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
											_SCREAMING_SNAKE_CASE :       Optional[Any]     		=      [0, 57, 3018, 7_0307, 91, 2]

											_SCREAMING_SNAKE_CASE :       Dict     		=      self.tokenizer(
											    __snake_case    ,					max_length=len(__snake_case  )    ,					padding=__snake_case    ,					truncation=__snake_case    ,					return_tensors="""pt"""  )
											self.assertIsInstance(__snake_case    ,					__snake_case  )

											self.assertEqual((2, 6)    ,					batch.input_ids.shape  )
											self.assertEqual((2, 6)    ,					batch.attention_mask.shape  )
											_SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      batch.input_ids.tolist()[0]
											self.assertListEqual(__snake_case    ,					__snake_case  )






							def UpperCAmelCase_	(    self  ):
											if not self.test_rust_tokenizer:
															return

											_SCREAMING_SNAKE_CASE :       Optional[int]     		=      self.get_tokenizer()
											_SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      self.get_rust_tokenizer()

											_SCREAMING_SNAKE_CASE :       Any     		=      """I was born in 92000, and this is falsé."""

											_SCREAMING_SNAKE_CASE :       Any     		=      tokenizer.tokenize(__snake_case  )
											_SCREAMING_SNAKE_CASE :       Dict     		=      rust_tokenizer.tokenize(__snake_case  )
											self.assertListEqual(__snake_case    ,					__snake_case  )

											_SCREAMING_SNAKE_CASE :       Any     		=      tokenizer.encode(__snake_case    ,					add_special_tokens=__snake_case  )
											_SCREAMING_SNAKE_CASE :       int     		=      rust_tokenizer.encode(__snake_case    ,					add_special_tokens=__snake_case  )
											self.assertListEqual(__snake_case    ,					__snake_case  )

											_SCREAMING_SNAKE_CASE :       int     		=      self.get_rust_tokenizer()
											_SCREAMING_SNAKE_CASE :       Optional[Any]     		=      tokenizer.encode(__snake_case  )
											_SCREAMING_SNAKE_CASE :       int     		=      rust_tokenizer.encode(__snake_case  )
											self.assertListEqual(__snake_case    ,					__snake_case  )






							@slow
							def UpperCAmelCase_	(    self  ):
											# fmt: off
											_SCREAMING_SNAKE_CASE :       Tuple     		=      {"""input_ids""": [[0, 490, 1_4328, 4507, 354, 47, 4_3669, 95, 25, 7_8117, 2_0215, 1_9779, 190, 22, 400, 4, 3_5343, 8_0310, 603, 86, 2_4937, 105, 3_3438, 9_4762, 196, 3_9642, 7, 15, 1_5933, 173, 2, 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, 1_0534, 87, 25, 66, 3358, 196, 5_5289, 8, 8_2961, 81, 2204, 7_5203, 7, 15, 763, 1_2956, 216, 178, 1_4328, 9595, 1377, 6_9693, 7, 448, 7_1021, 196, 1_8106, 1437, 1_3974, 108, 9083, 4, 4_9315, 7, 39, 86, 1326, 2793, 4_6333, 4, 448, 196, 7_4588, 7, 4_9315, 7, 39, 21, 822, 3_8470, 74, 21, 6_6723, 6_2480, 8, 2_2050, 5, 2]], """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, 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, 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, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}  # noqa: E501
											# fmt: on

											# moussaKam/mbarthez is a french model. So we also use french texts.
											_SCREAMING_SNAKE_CASE :       str     		=      [
											    """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=__snake_case    ,					model_name="""moussaKam/mbarthez"""    ,					revision="""c2e4ecbca5e3cd2c37fe1ac285ca4fbdf1366fb6"""    ,					sequences=__snake_case    ,					)








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


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

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

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







def 			snake_case_   (  SCREAMING_SNAKE_CASE__     ):







				"""simple docstring"""





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

				return wrapper







def 			snake_case_   (  SCREAMING_SNAKE_CASE__     ):







				"""simple docstring"""





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

				return wrapper







def 			snake_case_   (  SCREAMING_SNAKE_CASE__     ):







				"""simple docstring"""





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

				return wrapper







def 			snake_case_   (  ):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE :       Dict     		=      [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(
    _snake_case     ,     _snake_case     ,     _snake_case					)
@local
class     lowercase__  (						parameterized.TestCase					):





							'''simple docstring'''


							A_							:      Optional[int]      	=  {}
							A_							:      Union[str, Any]      	=  None






							@pytest.mark.filterwarnings("""ignore:metric_module_factory is deprecated:FutureWarning"""  )
							@pytest.mark.filterwarnings("""ignore:load_metric is deprecated:FutureWarning"""  )
							def UpperCAmelCase_	(    self    ,					__snake_case  ):
											_SCREAMING_SNAKE_CASE :       str     		=      """[...]"""
											_SCREAMING_SNAKE_CASE :       Any     		=      importlib.import_module(
											    datasets.load.metric_module_factory(os.path.join("""metrics"""    ,					__snake_case  )  ).module_path  )
											_SCREAMING_SNAKE_CASE :       Optional[int]     		=      datasets.load.import_main_class(metric_module.__name__    ,					dataset=__snake_case  )
											# check parameters
											_SCREAMING_SNAKE_CASE :       Tuple     		=      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:
																							_SCREAMING_SNAKE_CASE :       int     		=      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 UpperCAmelCase_	(    self    ,					__snake_case  ):
											_SCREAMING_SNAKE_CASE :       List[Any]     		=      """[...]"""
											_SCREAMING_SNAKE_CASE :       Optional[Any]     		=      importlib.import_module(
											    datasets.load.metric_module_factory(os.path.join("""metrics"""    ,					__snake_case  )  ).module_path  )
											# run doctest
											with self.use_local_metrics():
															_SCREAMING_SNAKE_CASE :       List[str]     		=      doctest.testmod(__snake_case    ,					verbose=__snake_case    ,					raise_on_error=__snake_case  )
											self.assertEqual(results.failed    ,					0  )
											self.assertGreater(results.attempted    ,					1  )






							@contextmanager
							def UpperCAmelCase_	(    self    ,					__snake_case    ,					__snake_case  ):
											if metric_name in self.INTENSIVE_CALLS_PATCHER:
															with self.INTENSIVE_CALLS_PATCHER[metric_name](__snake_case  ):
																			yield
											else:
															yield






							@contextmanager
							def UpperCAmelCase_	(    self  ):
											def load_local_metric(__snake_case    ,					*__snake_case    ,					**__snake_case  ):
															return load_metric(os.path.join("""metrics"""    ,					__snake_case  )    ,					*__snake_case    ,					**__snake_case  )

											with patch("""datasets.load_metric"""  ) as mock_load_metric:
															_SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      load_local_metric
															yield






							@classmethod
							def UpperCAmelCase_	(    cls    ,					__snake_case  ):
											def wrapper(__snake_case  ):
															_SCREAMING_SNAKE_CASE :       Any     		=      contextmanager(__snake_case  )
															_SCREAMING_SNAKE_CASE :       int     		=      patcher
															return patcher

											return wrapper







@LocalMetricTest.register_intensive_calls_patcher("""bleurt"""     )
def 			snake_case_   (  SCREAMING_SNAKE_CASE__     ):







				"""simple docstring"""





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

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





				class     lowercase__  (						_snake_case					):





											'''simple docstring'''


											def UpperCAmelCase_	(    self    ,					__snake_case  ):
															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:
								_SCREAMING_SNAKE_CASE :       Any     		=      MockedPredictor()
								yield







@LocalMetricTest.register_intensive_calls_patcher("""bertscore"""     )
def 			snake_case_   (  SCREAMING_SNAKE_CASE__     ):







				"""simple docstring"""





				import torch

				def bert_cos_score_idf(SCREAMING_SNAKE_CASE__							,      SCREAMING_SNAKE_CASE__							,      *SCREAMING_SNAKE_CASE__							,      **SCREAMING_SNAKE_CASE__     ):
								return torch.tensor([[1.0, 1.0, 1.0]] * len(SCREAMING_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:
								_SCREAMING_SNAKE_CASE :       Any     		=      bert_cos_score_idf
								yield







@LocalMetricTest.register_intensive_calls_patcher("""comet"""     )
def 			snake_case_   (  SCREAMING_SNAKE_CASE__     ):







				"""simple docstring"""





				def load_from_checkpoint(SCREAMING_SNAKE_CASE__     ):





								class     lowercase__  :





															'''simple docstring'''


															def UpperCAmelCase_	(    self    ,					__snake_case    ,					*__snake_case    ,					**__snake_case  ):
																			assert len(__snake_case  ) == 2
																			_SCREAMING_SNAKE_CASE :       Dict     		=      [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:
								_SCREAMING_SNAKE_CASE :       Any     		=      None
								with patch("""comet.load_from_checkpoint"""     ) as mock_load_from_checkpoint:
												_SCREAMING_SNAKE_CASE :       List[str]     		=      load_from_checkpoint
												yield







def 			snake_case_   (  ):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      load_metric(os.path.join("""metrics"""							,      """seqeval"""     )     )
				_SCREAMING_SNAKE_CASE :       List[str]     		=      """ERROR"""
				_SCREAMING_SNAKE_CASE :       Tuple     		=      f"""Scheme should be one of [IOB1, IOB2, IOE1, IOE2, IOBES, BILOU], got {wrong_scheme}"""
				with pytest.raises(SCREAMING_SNAKE_CASE__							,      match=re.escape(SCREAMING_SNAKE_CASE__     )     ):
								metric.compute(predictions=[]							,      references=[]							,      scheme=SCREAMING_SNAKE_CASE__     )




