




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		:				List[Any]       			=		pytest.mark.integration


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

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

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




def 			__magic_name__				( A		:					Union[str, Any]   ):






			'''simple docstring'''







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

			return wrapper




def 			__magic_name__				( A		:					Union[str, Any]   ):






			'''simple docstring'''







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

			return wrapper




def 			__magic_name__				( A		:					Tuple   ):






			'''simple docstring'''







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

			return wrapper




def 			__magic_name__				( ):






			'''simple docstring'''







			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(
    _UpperCamelCase    ,       _UpperCamelCase    ,       _UpperCamelCase   )
@local
class     snake_case__							(parameterized.TestCase   ):
				"""simple docstring"""



				SCREAMING_SNAKE_CASE_       :							int							 =					{}
				SCREAMING_SNAKE_CASE_       :							List[Any]							 =					None





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





				@contextmanager
				def 							__UpperCAmelCase							(      self				:     Union[str, Any]					,      __lowerCamelCase				:     List[Any]					,      __lowerCamelCase				:     Dict							)					->     Optional[Any]:






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





				@contextmanager
				def 							__UpperCAmelCase							(      self				:     int							)					->     Tuple:






							def load_local_metric(__lowerCamelCase				:     Optional[Any]					,      *__lowerCamelCase				:     str					,      **__lowerCamelCase				:     int							):
										return load_metric(os.path.join("metrics"					,      __lowerCamelCase							)					,      *__lowerCamelCase					,      **__lowerCamelCase							)

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





				@classmethod
				def 							__UpperCAmelCase							(      cls				:     Optional[Any]					,      __lowerCamelCase				:     Tuple							)					->     Union[str, Any]:






							def wrapper(__lowerCamelCase				:     List[str]							):
										a    =			contextmanager(__lowerCamelCase							)
										a    =			patcher
										return patcher

							return wrapper




@LocalMetricTest.register_intensive_calls_patcher("bleurt"   )
def 			__magic_name__				( A		:					List[Any]   ):






			'''simple docstring'''







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

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




			class     snake_case__							(_UpperCamelCase   ):
							"""simple docstring"""



							def 							__UpperCAmelCase							(      self				:     List[str]					,      __lowerCamelCase				:     Tuple							)					->     List[str]:
										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    =			MockedPredictor()
						yield




@LocalMetricTest.register_intensive_calls_patcher("bertscore"   )
def 			__magic_name__				( A		:					Dict   ):






			'''simple docstring'''







			import torch

			def bert_cos_score_idf(A		:					int,  A		:					Tuple,  *A		:					List[str],  **A		:					int   ):
						return torch.tensor([[1.0, 1.0, 1.0]] * len(_UpperCamelCase   )   )

			# 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 			__magic_name__				( A		:					Optional[int]   ):






			'''simple docstring'''







			def load_from_checkpoint(A		:					Optional[Any]   ):




						class     snake_case__							:
										"""simple docstring"""



										def 							__UpperCAmelCase							(      self				:     Optional[int]					,      __lowerCamelCase				:     List[Any]					,      *__lowerCamelCase				:     Any					,      **__lowerCamelCase				:     Tuple							)					->     Optional[int]:
													assert len(__lowerCamelCase							) == 2
													a    =			[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:
						a    =			None
						with patch("comet.load_from_checkpoint"   ) as mock_load_from_checkpoint:
									a    =			load_from_checkpoint
									yield






def 			__magic_name__				( ):






			'''simple docstring'''







			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(_UpperCamelCase,  match=re.escape(_UpperCamelCase   )   ):
						metric.compute(predictions=[],  references=[],  scheme=_UpperCamelCase   )










"""simple docstring"""






from typing import TYPE_CHECKING

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


UpperCAmelCase    :     Any   						=		{
    'configuration_blenderbot': [
        'BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'BlenderbotConfig',
        'BlenderbotOnnxConfig',
    ],
    'tokenization_blenderbot': ['BlenderbotTokenizer'],
}

try:
							if not is_tokenizers_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCAmelCase    :     Optional[int]   						=		['BlenderbotTokenizerFast']

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCAmelCase    :     Optional[Any]   						=		[
							    'BLENDERBOT_PRETRAINED_MODEL_ARCHIVE_LIST',
							    'BlenderbotForCausalLM',
							    'BlenderbotForConditionalGeneration',
							    'BlenderbotModel',
							    'BlenderbotPreTrainedModel',
							]


try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCAmelCase    :     Optional[int]   						=		[
							    'TFBlenderbotForConditionalGeneration',
							    'TFBlenderbotModel',
							    'TFBlenderbotPreTrainedModel',
							]


try:
							if not is_flax_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCAmelCase    :     Optional[int]   						=		[
							    'FlaxBlenderbotForConditionalGeneration',
							    'FlaxBlenderbotModel',
							    'FlaxBlenderbotPreTrainedModel',
							]


if TYPE_CHECKING:
							from .configuration_blenderbot import (
							    BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_MAP,
							    BlenderbotConfig,
							    BlenderbotOnnxConfig,
							)
							from .tokenization_blenderbot import BlenderbotTokenizer

							try:
														if not is_tokenizers_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_blenderbot_fast import BlenderbotTokenizerFast

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_blenderbot import (
														    BLENDERBOT_PRETRAINED_MODEL_ARCHIVE_LIST,
														    BlenderbotForCausalLM,
														    BlenderbotForConditionalGeneration,
														    BlenderbotModel,
														    BlenderbotPreTrainedModel,
														)

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_blenderbot import (
														    TFBlenderbotForConditionalGeneration,
														    TFBlenderbotModel,
														    TFBlenderbotPreTrainedModel,
														)

							try:
														if not is_flax_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_flax_blenderbot import (
														    FlaxBlenderbotForConditionalGeneration,
														    FlaxBlenderbotModel,
														    FlaxBlenderbotPreTrainedModel,
														)

else:
							import sys

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