



'''simple docstring'''

from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__lowerCAmelCase						      =       {
    "configuration_deberta": ["DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP", "DebertaConfig", "DebertaOnnxConfig"],
    "tokenization_deberta": ["DebertaTokenizer"],
}

try:
       if not is_tokenizers_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __lowerCAmelCase						      =       ["DebertaTokenizerFast"]

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __lowerCAmelCase						      =       [
           "DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST",
           "DebertaForMaskedLM",
           "DebertaForQuestionAnswering",
           "DebertaForSequenceClassification",
           "DebertaForTokenClassification",
           "DebertaModel",
           "DebertaPreTrainedModel",
       ]

try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __lowerCAmelCase						      =       [
           "TF_DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST",
           "TFDebertaForMaskedLM",
           "TFDebertaForQuestionAnswering",
           "TFDebertaForSequenceClassification",
           "TFDebertaForTokenClassification",
           "TFDebertaModel",
           "TFDebertaPreTrainedModel",
       ]


if TYPE_CHECKING:
       from .configuration_deberta import DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, DebertaConfig, DebertaOnnxConfig
       from .tokenization_deberta import DebertaTokenizer

       try:
              if not is_tokenizers_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_deberta_fast import DebertaTokenizerFast

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_deberta import (
                  DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
                  DebertaForMaskedLM,
                  DebertaForQuestionAnswering,
                  DebertaForSequenceClassification,
                  DebertaForTokenClassification,
                  DebertaModel,
                  DebertaPreTrainedModel,
              )

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_deberta import (
                  TF_DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
                  TFDebertaForMaskedLM,
                  TFDebertaForQuestionAnswering,
                  TFDebertaForSequenceClassification,
                  TFDebertaForTokenClassification,
                  TFDebertaModel,
                  TFDebertaPreTrainedModel,
              )


else:
       import sys

       __lowerCAmelCase						      =       _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)









'''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						      =       pytest.mark.integration


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

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

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



def 					__lowerCamelCase					(				lowerCAmelCase_  )      ->				Any:
		@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 					__lowerCamelCase					(				lowerCAmelCase_  )      ->				Union[str, Any]:
		@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 					__lowerCamelCase					(				lowerCAmelCase_  )      ->				int:
		@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 					__lowerCamelCase					(				)      ->				Tuple:
		_a					:				Optional[int]				=							[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       ):
							lowerCAmelCase	:   List[str]           = {}
							lowerCAmelCase	:   Optional[int]           = None







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







							@contextmanager
							def 							__lowercase      (							self :	List[Any]     ,_UpperCAmelCase :	Dict     ,_UpperCAmelCase :	List[str]		):
									if metric_name in self.INTENSIVE_CALLS_PATCHER:
											with self.INTENSIVE_CALLS_PATCHER[metric_name](_UpperCAmelCase		):
													yield
									else:
											yield







							@contextmanager
							def 							__lowercase      (							self :	Optional[int]		):
									def load_local_metric(_UpperCAmelCase :	Tuple     ,*_UpperCAmelCase :	Dict     ,**_UpperCAmelCase :	Tuple		):
											return load_metric(os.path.join('metrics'     ,_UpperCAmelCase		)     ,*_UpperCAmelCase     ,**_UpperCAmelCase		)

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







							@classmethod
							def 							__lowercase      (							cls :	str     ,_UpperCAmelCase :	List[str]		):
									def wrapper(_UpperCAmelCase :	int		):
											_a					:				Optional[Any]				=							contextmanager(_UpperCAmelCase		)
											_a					:				Optional[int]				=							patcher
											return patcher

									return wrapper



@LocalMetricTest.register_intensive_calls_patcher('bleurt'  )
def 					__lowerCamelCase					(				lowerCAmelCase_  )      ->				List[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       ):







									def 							__lowercase      (							self :	int     ,_UpperCAmelCase :	Union[str, Any]		):
											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:
				_a					:				int				=							MockedPredictor()
				yield



@LocalMetricTest.register_intensive_calls_patcher('bertscore'  )
def 					__lowerCamelCase					(				lowerCAmelCase_  )      ->				Union[str, Any]:
		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:
				_a					:				Any				=							bert_cos_score_idf
				yield



@LocalMetricTest.register_intensive_calls_patcher('comet'  )
def 					__lowerCamelCase					(				lowerCAmelCase_  )      ->				Dict:
		def load_from_checkpoint(lowerCAmelCase_  ):

				class      __magic_name__						:







											def 							__lowercase      (							self :	str     ,_UpperCAmelCase :	Dict     ,*_UpperCAmelCase :	int     ,**_UpperCAmelCase :	str		):
													assert len(_UpperCAmelCase		) == 2
													_a					:				Dict				=							[0.19, 0.92]
													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					:				Any				=							None
				with patch('comet.load_from_checkpoint'  ) as mock_load_from_checkpoint:
						_a					:				Optional[Any]				=							load_from_checkpoint
						yield



def 					__lowerCamelCase					(				)      ->				Tuple:
		_a					:				Dict				=							load_metric(os.path.join('metrics'						,      'seqeval'  )  )
		_a					:				Optional[int]				=							'ERROR'
		_a					:				Optional[Any]				=							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_  )




