






'''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_				: Tuple    	=							pytest.mark.integration


lowerCamelCase_				: Any    	=							{'''comet'''}
lowerCamelCase_				: Optional[Any]    	=							importlib.util.find_spec('''fairseq''') is not None

lowerCamelCase_				: Union[str, Any]    	=							{'''code_eval'''}
lowerCamelCase_				: Dict    	=							os.name == '''nt'''

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







def 	__magic_name__( _A					):
						'''simple docstring'''





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

						return wrapper







def 	__magic_name__( _A					):
						'''simple docstring'''





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

						return wrapper







def 	__magic_name__( _A					):
						'''simple docstring'''





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

						return wrapper







def 	__magic_name__( ):
						'''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(
    SCREAMING_SNAKE_CASE       ,SCREAMING_SNAKE_CASE       ,SCREAMING_SNAKE_CASE	)
@local
class    _SCREAMING_SNAKE_CASE							(		parameterized.TestCase	):
			__a							:							str					     =					{}
			__a							:							int					     =					None






			@pytest.mark.filterwarnings("""ignore:metric_module_factory is deprecated:FutureWarning"""			)
			@pytest.mark.filterwarnings("""ignore:load_metric is deprecated:FutureWarning"""			)
			def 				A      (   self							:						Optional[Any] ,      lowercase							:						Optional[Any]			)							->    List[Any]:

									'''simple docstring'''







									UpperCamelCase__         = """[...]"""
									UpperCamelCase__         = importlib.import_module(
									    datasets.load.metric_module_factory(os.path.join("""metrics""" ,      lowercase			)			).module_path			)
									UpperCamelCase__         = datasets.load.import_main_class(metric_module.__name__ ,      dataset=lowercase			)
									# 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(lowercase ,      metric_module.__name__			):
															with self.use_local_metrics():
																					try:
																											UpperCamelCase__         = doctest.testmod(lowercase ,      verbose=lowercase ,      raise_on_error=lowercase			)
																					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 				A      (   self							:						Optional[Any] ,      lowercase							:						Dict			)							->    Union[str, Any]:

									'''simple docstring'''







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






			@contextmanager
			def 				A      (   self							:						Optional[Any] ,      lowercase							:						List[Any] ,      lowercase							:						int			)							->    Any:

									'''simple docstring'''







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






			@contextmanager
			def 				A      (   self							:						List[str]			)							->    Optional[Any]:

									'''simple docstring'''







									def load_local_metric(lowercase							:						Tuple ,      *lowercase							:						List[Any] ,      **lowercase							:						Union[str, Any]			):
															return load_metric(os.path.join("""metrics""" ,      lowercase			) ,      *lowercase ,      **lowercase			)

									with patch("""datasets.load_metric"""			) as mock_load_metric:
															UpperCamelCase__         = load_local_metric
															yield







			@classmethod
			def 				A      (   cls							:						Tuple ,      lowercase							:						int			)							->    int:

									'''simple docstring'''







									def wrapper(lowercase							:						Tuple			):
															UpperCamelCase__         = contextmanager(lowercase			)
															UpperCamelCase__         = patcher
															return patcher

									return wrapper







@LocalMetricTest.register_intensive_calls_patcher("""bleurt"""					)
def 	__magic_name__( _A					):
						'''simple docstring'''





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

						tf.flags.DEFINE_string("""sv"""						,      """"""						,      """"""					)  # handle pytest cli flags
						class    _SCREAMING_SNAKE_CASE							(		SCREAMING_SNAKE_CASE	):






									def 				A      (   self							:						Optional[int] ,      lowercase							:						Optional[Any]			)							->    List[Any]:

															'''simple docstring'''







															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:
												UpperCamelCase__         = MockedPredictor()
												yield







@LocalMetricTest.register_intensive_calls_patcher("""bertscore"""					)
def 	__magic_name__( _A					):
						'''simple docstring'''





						import torch

						def bert_cos_score_idf(_A						,      _A						,      *_A						,      **_A					):
												return torch.tensor([[1.0, 1.0, 1.0]] * len(_A					)					)

						# 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 	__magic_name__( _A					):
						'''simple docstring'''





						def load_from_checkpoint(_A					):
												class    _SCREAMING_SNAKE_CASE							:






															def 				A      (   self							:						Union[str, Any] ,      lowercase							:						Union[str, Any] ,      *lowercase							:						str ,      **lowercase							:						Union[str, Any]			)							->    Tuple:

																					'''simple docstring'''







																					assert len(lowercase			) == 2
																					UpperCamelCase__         = [0.1_9, 0.9_2]
																					return scores, sum(lowercase			) / len(lowercase			)
												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 	__magic_name__( ):
						'''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(_A						,      match=re.escape(_A					)					):
												metric.compute(predictions=[]						,      references=[]						,      scheme=_A					)








'''simple docstring'''


from typing import TYPE_CHECKING

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


lowerCamelCase_				: Tuple    	=							{
    '''configuration_layoutlmv3''': [
        '''LAYOUTLMV3_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''LayoutLMv3Config''',
        '''LayoutLMv3OnnxConfig''',
    ],
    '''processing_layoutlmv3''': ['''LayoutLMv3Processor'''],
    '''tokenization_layoutlmv3''': ['''LayoutLMv3Tokenizer'''],
}

try:
					if not is_tokenizers_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowerCamelCase_				: Tuple    	=							['''LayoutLMv3TokenizerFast''']

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowerCamelCase_				: Union[str, Any]    	=							[
					    '''LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST''',
					    '''LayoutLMv3ForQuestionAnswering''',
					    '''LayoutLMv3ForSequenceClassification''',
					    '''LayoutLMv3ForTokenClassification''',
					    '''LayoutLMv3Model''',
					    '''LayoutLMv3PreTrainedModel''',
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowerCamelCase_				: Optional[Any]    	=							[
					    '''TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST''',
					    '''TFLayoutLMv3ForQuestionAnswering''',
					    '''TFLayoutLMv3ForSequenceClassification''',
					    '''TFLayoutLMv3ForTokenClassification''',
					    '''TFLayoutLMv3Model''',
					    '''TFLayoutLMv3PreTrainedModel''',
					]

try:
					if not is_vision_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowerCamelCase_				: Optional[Any]    	=							['''LayoutLMv3FeatureExtractor''']
					lowerCamelCase_				: Dict    	=							['''LayoutLMv3ImageProcessor''']


if TYPE_CHECKING:
					from .configuration_layoutlmva import (
					    LAYOUTLMV3_PRETRAINED_CONFIG_ARCHIVE_MAP,
					    LayoutLMvaConfig,
					    LayoutLMvaOnnxConfig,
					)
					from .processing_layoutlmva import LayoutLMvaProcessor
					from .tokenization_layoutlmva import LayoutLMvaTokenizer

					try:
										if not is_tokenizers_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_layoutlmva_fast import LayoutLMvaTokenizerFast

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_layoutlmva import (
										    LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST,
										    LayoutLMvaForQuestionAnswering,
										    LayoutLMvaForSequenceClassification,
										    LayoutLMvaForTokenClassification,
										    LayoutLMvaModel,
										    LayoutLMvaPreTrainedModel,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_layoutlmva import (
										    TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFLayoutLMvaForQuestionAnswering,
										    TFLayoutLMvaForSequenceClassification,
										    TFLayoutLMvaForTokenClassification,
										    TFLayoutLMvaModel,
										    TFLayoutLMvaPreTrainedModel,
										)

					try:
										if not is_vision_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .feature_extraction_layoutlmva import LayoutLMvaFeatureExtractor
										from .image_processing_layoutlmva import LayoutLMvaImageProcessor

else:
					import sys

					lowerCamelCase_				: Optional[Any]    	=							_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
