



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						  =		pytest.mark.integration


_lowercase						  =		{'comet'}
_lowercase						  =		importlib.util.find_spec("fairseq") is not None

_lowercase						  =		{'code_eval'}
_lowercase						  =		os.name == 'nt'

_lowercase						  =		{'bertscore', 'frugalscore', 'perplexity'}
_lowercase						  =		importlib.util.find_spec("transformers") is not None



def 		lowerCAmelCase__    (					UpperCamelCase_				:       List[str]	)->  str:
    @wraps(_SCREAMING_SNAKE_CASE	)
    def wrapper(self				:       Union[str, Any] ,       UpperCamelCase_				:       Tuple	):
        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 		lowerCAmelCase__    (					UpperCamelCase_				:       Tuple	)->  Optional[int]:
    @wraps(_SCREAMING_SNAKE_CASE	)
    def wrapper(self				:       Tuple ,       UpperCamelCase_				:       str	):
        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 		lowerCAmelCase__    (					UpperCamelCase_				:       List[str]	)->  str:
    @wraps(_SCREAMING_SNAKE_CASE	)
    def wrapper(self				:       List[Any] ,       UpperCamelCase_				:       Any	):
        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 		lowerCAmelCase__    (					)->  List[str]:
    A__		 =					[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     _UpperCAmelCase			(       parameterized.TestCase     ):
     UpperCamelCase__					    =				{}
     UpperCamelCase__					    =				None




     @pytest.mark.filterwarnings('''ignore:metric_module_factory is deprecated:FutureWarning''')
     @pytest.mark.filterwarnings('''ignore:load_metric is deprecated:FutureWarning''')
     def 		snake_case_		(				self ,						a__):
         A__		 =					'''[...]'''
         A__		 =					importlib.import_module(
             datasets.load.metric_module_factory(os.path.join('''metrics''' ,						UpperCAmelCase_)).module_path)
         A__		 =					datasets.load.import_main_class(metric_module.__name__ ,						dataset=UpperCAmelCase_)
         # check parameters
         A__		 =					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(UpperCAmelCase_ ,						metric_module.__name__):
             with self.use_local_metrics():
                 try:
                     A__		 =					doctest.testmod(UpperCAmelCase_ ,						verbose=UpperCAmelCase_ ,						raise_on_error=UpperCAmelCase_)
                 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 ,						a__):
         A__		 =					'''[...]'''
         A__		 =					importlib.import_module(
             datasets.load.metric_module_factory(os.path.join('''metrics''' ,						UpperCAmelCase_)).module_path)
         # run doctest
         with self.use_local_metrics():
             A__		 =					doctest.testmod(UpperCAmelCase_ ,						verbose=UpperCAmelCase_ ,						raise_on_error=UpperCAmelCase_)
         self.assertEqual(results.failed ,						0)
         self.assertGreater(results.attempted ,						1)




     @contextmanager
     def 		snake_case_		(				self ,						a__ ,						a__):



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




     @contextmanager
     def 		snake_case_		(				self):



         def load_local_metric(a__ ,						*a__ ,						**a__):
             return load_metric(os.path.join('''metrics''' ,						UpperCAmelCase_) ,						*UpperCAmelCase_ ,						**UpperCAmelCase_)

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




     @classmethod
     def 		snake_case_		(				cls ,						a__):



         def wrapper(a__):
             A__		 =					contextmanager(UpperCAmelCase_)
             A__		 =					patcher
             return patcher

         return wrapper







@LocalMetricTest.register_intensive_calls_patcher('''bleurt'''	)
def 		lowerCAmelCase__    (					UpperCamelCase_				:       Dict	)->  str:
    import tensorflow.compat.va as tf
    from bleurt.score import Predictor

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



    class     _UpperCAmelCase			(       snake_case__     ):




         def 		snake_case_		(				self ,						a__):
             assert len(input_dict['''input_ids''']) == 2
             return np.array([1.0_3, 1.0_4])

    # 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:
        A__		 =					MockedPredictor()
        yield



@LocalMetricTest.register_intensive_calls_patcher('''bertscore'''	)
def 		lowerCAmelCase__    (					UpperCamelCase_				:       Any	)->  Optional[Any]:
    import torch

    def bert_cos_score_idf(UpperCamelCase_				:       str ,       UpperCamelCase_				:       Tuple ,       *UpperCamelCase_				:       List[Any] ,       **UpperCamelCase_				:       Union[str, Any]	):
        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:
        A__		 =					bert_cos_score_idf
        yield



@LocalMetricTest.register_intensive_calls_patcher('''comet'''	)
def 		lowerCAmelCase__    (					UpperCamelCase_				:       List[Any]	)->  Any:
    def load_from_checkpoint(UpperCamelCase_				:       List[Any]	):



        class     _UpperCAmelCase			:




             def 		snake_case_		(				self ,						a__ ,						*a__ ,						**a__):
                 assert len(UpperCAmelCase_) == 2
                 A__		 =					[0.1_9, 0.9_2]
                 return scores, sum(UpperCAmelCase_) / len(UpperCAmelCase_)







        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:
        A__		 =					None
        with patch('''comet.load_from_checkpoint'''	) as mock_load_from_checkpoint:
            A__		 =					load_from_checkpoint
            yield



def 		lowerCAmelCase__    (					)->  List[str]:
    A__		 =					load_metric(os.path.join('''metrics''' ,       '''seqeval'''	)	)
    A__		 =					'''ERROR'''
    A__		 =					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	)





from __future__ import annotations

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
   import numpy as np
   import tensorflow as tf

   from transformers import (
       TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
       FlaubertConfig,
       TFFlaubertForMultipleChoice,
       TFFlaubertForQuestionAnsweringSimple,
       TFFlaubertForSequenceClassification,
       TFFlaubertForTokenClassification,
       TFFlaubertModel,
       TFFlaubertWithLMHeadModel,
   )



class     _UpperCAmelCase			:




     def __init__(				self ,						a__ ,						):
         A__		 =					parent
         A__		 =					1_3
         A__		 =					7
         A__		 =					True
         A__		 =					True
         A__		 =					True
         A__		 =					True
         A__		 =					True
         A__		 =					False
         A__		 =					False
         A__		 =					False
         A__		 =					2
         A__		 =					9_9
         A__		 =					0
         A__		 =					3_2
         A__		 =					2
         A__		 =					4
         A__		 =					0.1
         A__		 =					0.1
         A__		 =					5_1_2
         A__		 =					1_6
         A__		 =					2
         A__		 =					0.0_2
         A__		 =					3
         A__		 =					4
         A__		 =					'''last'''
         A__		 =					True
         A__		 =					None
         A__		 =					0




     def 		snake_case_		(				self):
         A__		 =					ids_tensor([self.batch_size, self.seq_length] ,						self.vocab_size)
         A__		 =					random_attention_mask([self.batch_size, self.seq_length] ,						dtype=tf.floataa)

         A__		 =					None
         if self.use_input_lengths:
             A__		 =					(
                 ids_tensor([self.batch_size] ,						vocab_size=2) + self.seq_length - 2
             )  # small variation of seq_length

         A__		 =					None
         if self.use_token_type_ids:
             A__		 =					ids_tensor([self.batch_size, self.seq_length] ,						self.n_langs)

         A__		 =					None
         A__		 =					None
         A__		 =					None
         if self.use_labels:
             A__		 =					ids_tensor([self.batch_size] ,						self.type_sequence_label_size)
             A__		 =					ids_tensor([self.batch_size, self.seq_length] ,						self.num_labels)
             A__		 =					ids_tensor([self.batch_size] ,						2 ,						dtype=tf.floataa)
             A__		 =					ids_tensor([self.batch_size] ,						self.num_choices)

         A__		 =					FlaubertConfig(
             vocab_size=self.vocab_size ,						n_special=self.n_special ,						emb_dim=self.hidden_size ,						n_layers=self.num_hidden_layers ,						n_heads=self.num_attention_heads ,						dropout=self.hidden_dropout_prob ,						attention_dropout=self.attention_probs_dropout_prob ,						gelu_activation=self.gelu_activation ,						sinusoidal_embeddings=self.sinusoidal_embeddings ,						asm=self.asm ,						causal=self.causal ,						n_langs=self.n_langs ,						max_position_embeddings=self.max_position_embeddings ,						initializer_range=self.initializer_range ,						summary_type=self.summary_type ,						use_proj=self.use_proj ,						bos_token_id=self.bos_token_id ,						)

         return (
             config,
             input_ids,
             token_type_ids,
             input_lengths,
             sequence_labels,
             token_labels,
             is_impossible_labels,
             choice_labels,
             input_mask,
         )




     def 		snake_case_		(				self ,						a__ ,						a__ ,						a__ ,						a__ ,						a__ ,						a__ ,						a__ ,						a__ ,						a__ ,						):
         A__		 =					TFFlaubertModel(config=a__)
         A__		 =					{'''input_ids''': input_ids, '''lengths''': input_lengths, '''langs''': token_type_ids}
         A__		 =					model(a__)

         A__		 =					[input_ids, input_mask]
         A__		 =					model(a__)
         self.parent.assertEqual(result.last_hidden_state.shape ,						(self.batch_size, self.seq_length, self.hidden_size))




     def 		snake_case_		(				self ,						a__ ,						a__ ,						a__ ,						a__ ,						a__ ,						a__ ,						a__ ,						a__ ,						a__ ,						):
         A__		 =					TFFlaubertWithLMHeadModel(a__)

         A__		 =					{'''input_ids''': input_ids, '''lengths''': input_lengths, '''langs''': token_type_ids}
         A__		 =					model(a__)

         self.parent.assertEqual(result.logits.shape ,						(self.batch_size, self.seq_length, self.vocab_size))




     def 		snake_case_		(				self ,						a__ ,						a__ ,						a__ ,						a__ ,						a__ ,						a__ ,						a__ ,						a__ ,						a__ ,						):
         A__		 =					TFFlaubertForQuestionAnsweringSimple(a__)

         A__		 =					{'''input_ids''': input_ids, '''lengths''': input_lengths}

         A__		 =					model(a__)

         self.parent.assertEqual(result.start_logits.shape ,						(self.batch_size, self.seq_length))
         self.parent.assertEqual(result.end_logits.shape ,						(self.batch_size, self.seq_length))




     def 		snake_case_		(				self ,						a__ ,						a__ ,						a__ ,						a__ ,						a__ ,						a__ ,						a__ ,						a__ ,						a__ ,						):
         A__		 =					TFFlaubertForSequenceClassification(a__)

         A__		 =					{'''input_ids''': input_ids, '''lengths''': input_lengths}

         A__		 =					model(a__)

         self.parent.assertEqual(result.logits.shape ,						(self.batch_size, self.type_sequence_label_size))




     def 		snake_case_		(				self ,						a__ ,						a__ ,						a__ ,						a__ ,						a__ ,						a__ ,						a__ ,						a__ ,						a__ ,						):
         A__		 =					self.num_labels
         A__		 =					TFFlaubertForTokenClassification(config=a__)
         A__		 =					{'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
         A__		 =					model(a__)
         self.parent.assertEqual(result.logits.shape ,						(self.batch_size, self.seq_length, self.num_labels))




     def 		snake_case_		(				self ,						a__ ,						a__ ,						a__ ,						a__ ,						a__ ,						a__ ,						a__ ,						a__ ,						a__ ,						):
         A__		 =					self.num_choices
         A__		 =					TFFlaubertForMultipleChoice(config=a__)
         A__		 =					tf.tile(tf.expand_dims(a__ ,						1) ,						(1, self.num_choices, 1))
         A__		 =					tf.tile(tf.expand_dims(a__ ,						1) ,						(1, self.num_choices, 1))
         A__		 =					tf.tile(tf.expand_dims(a__ ,						1) ,						(1, self.num_choices, 1))
         A__		 =					{
             '''input_ids''': multiple_choice_inputs_ids,
             '''attention_mask''': multiple_choice_input_mask,
             '''token_type_ids''': multiple_choice_token_type_ids,
         }
         A__		 =					model(a__)
         self.parent.assertEqual(result.logits.shape ,						(self.batch_size, self.num_choices))




     def 		snake_case_		(				self):
         A__		 =					self.prepare_config_and_inputs()
         (
             (
             A__
         ) ,	(
             A__
         ) ,	(
             A__
         ) ,	(
             A__
         ) ,	(
             A__
         ) ,	(
             A__
         ) ,	(
             A__
         ) ,	(
             A__
         ) ,	(
             A__
         ) ,	
         )		 =					config_and_inputs
         A__		 =					{
             '''input_ids''': input_ids,
             '''token_type_ids''': token_type_ids,
             '''langs''': token_type_ids,
             '''lengths''': input_lengths,
         }
         return config, inputs_dict







@require_tf
class     _UpperCAmelCase			(       A__			,      A__			,      unittest.TestCase     ):
     UpperCamelCase__		    =				(
         (
             TFFlaubertModel,
             TFFlaubertWithLMHeadModel,
             TFFlaubertForSequenceClassification,
             TFFlaubertForQuestionAnsweringSimple,
             TFFlaubertForTokenClassification,
             TFFlaubertForMultipleChoice,
         )
         if is_tf_available()
         else ()
     )
     UpperCamelCase__		    =				(
         (TFFlaubertWithLMHeadModel,) if is_tf_available() else ()
     )  # TODO (PVP): Check other models whether language generation is also applicable
     UpperCamelCase__		    =				(
         {
             '''feature-extraction''': TFFlaubertModel,
             '''fill-mask''': TFFlaubertWithLMHeadModel,
             '''question-answering''': TFFlaubertForQuestionAnsweringSimple,
             '''text-classification''': TFFlaubertForSequenceClassification,
             '''token-classification''': TFFlaubertForTokenClassification,
             '''zero-shot''': TFFlaubertForSequenceClassification,
         }
         if is_tf_available()
         else {}
     )
     UpperCamelCase__		    =				False
     UpperCamelCase__		    =				False




     def 		snake_case_		(				self ,						a__ ,						a__ ,						a__ ,						a__ ,						a__):
         if (
             pipeline_test_casse_name == "QAPipelineTests"
             and tokenizer_name is not None
             and not tokenizer_name.endswith('''Fast''')
         ):
             # `QAPipelineTests` fails for a few models when the slower tokenizer are used.
             # (The slower tokenizers were never used for pipeline tests before the pipeline testing rework)
             # TODO: check (and possibly fix) the `QAPipelineTests` with slower tokenizer
             return True

         return False




     def 		snake_case_		(				self):
         A__		 =					TFFlaubertModelTester(self)
         A__		 =					ConfigTester(self ,						config_class=a__ ,						emb_dim=3_7)




     def 		snake_case_		(				self):
         self.config_tester.run_common_tests()




     def 		snake_case_		(				self):
         A__		 =					self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_flaubert_model(*a__)




     def 		snake_case_		(				self):
         A__		 =					self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_flaubert_lm_head(*a__)




     def 		snake_case_		(				self):
         A__		 =					self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_flaubert_qa(*a__)




     def 		snake_case_		(				self):
         A__		 =					self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_flaubert_sequence_classif(*a__)




     def 		snake_case_		(				self):
         A__		 =					self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_flaubert_for_token_classification(*a__)




     def 		snake_case_		(				self):
         A__		 =					self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_flaubert_for_multiple_choice(*a__)




     @slow
     def 		snake_case_		(				self):
         for model_name in TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
             A__		 =					TFFlaubertModel.from_pretrained(a__)
             self.assertIsNotNone(a__)







@require_tf
@require_sentencepiece
@require_tokenizers
class     _UpperCAmelCase			(       unittest.TestCase     ):




     @slow
     def 		snake_case_		(				self):
         A__		 =					TFFlaubertModel.from_pretrained('''jplu/tf-flaubert-small-cased''')

         A__		 =					tf.convert_to_tensor(
             [[0, 1_5_8, 7_3_5, 2_5_9_2, 1_4_2_4, 6_7_2_7, 8_2, 1]] ,						dtype=tf.intaa ,						)  # "J'aime flaubert !"

         A__		 =					model(a__)[0]
         A__		 =					tf.TensorShape((1, 8, 5_1_2))
         self.assertEqual(output.shape ,						a__)
         # compare the actual values for a slice.
         A__		 =					tf.convert_to_tensor(
             [
                 [
                     [-1.8_7_6_8_7_7_3, -1.5_6_6_5_5_5, 0.2_7_0_7_2_4_1_8],
                     [-1.6_9_2_0_0_3_8, -0.5_8_7_3_5_0_5, 1.9_3_2_9_5_9_9],
                     [-2.9_5_6_3_9_8_5, -1.6_9_9_3_8_3_5, 1.7_9_7_2_0_5_2],
                 ]
             ] ,						dtype=tf.floataa ,						)

         self.assertTrue(np.allclose(output[:, :3, :3].numpy() ,						expected_slice.numpy() ,						atol=1e-4))
