



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
SCREAMING_SNAKE_CASE_:Optional[int]					=						pytest.mark.integration


SCREAMING_SNAKE_CASE_:Tuple					=						{"""comet"""}
SCREAMING_SNAKE_CASE_:List[Any]					=						importlib.util.find_spec("""fairseq""") is not None

SCREAMING_SNAKE_CASE_:List[str]					=						{"""code_eval"""}
SCREAMING_SNAKE_CASE_:Tuple					=						os.name == """nt"""

SCREAMING_SNAKE_CASE_:str					=						{"""bertscore""", """frugalscore""", """perplexity"""}
SCREAMING_SNAKE_CASE_:Any					=						importlib.util.find_spec("""transformers""") is not None




def      __UpperCamelCase     (  _lowerCAmelCase		)	->  Tuple:



    """simple docstring"""






    @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      __UpperCamelCase     (  _lowerCAmelCase		)	->  List[Any]:



    """simple docstring"""






    @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      __UpperCamelCase     (  _lowerCAmelCase		)	->  Any:



    """simple docstring"""






    @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      __UpperCamelCase     (  )	->  Optional[int]:



    """simple docstring"""






    A			:	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(
    SCREAMING_SNAKE_CASE__			,						SCREAMING_SNAKE_CASE__			,						SCREAMING_SNAKE_CASE__   )
@local
class SCREAMING_SNAKE_CASE__					(		parameterized.TestCase   ):




    '''simple docstring'''






    __lowerCamelCase      :			Any =				{}
    __lowerCamelCase      :			Any =				None






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






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






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

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






    @classmethod
    def       _lowerCAmelCase (	cls,		lowerCamelCase__		):
        def wrapper(lowerCamelCase__		):
            A			:	Any										=       contextmanager(lowerCamelCase__		)
            A			:	Dict										=       patcher
            return patcher

        return wrapper




@LocalMetricTest.register_intensive_calls_patcher("""bleurt"""		)
def      __UpperCamelCase     (  _lowerCAmelCase		)	->  int:



    """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__   ):




        '''simple docstring'''






        def       _lowerCAmelCase (	self,		lowerCamelCase__		):
            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			:	Tuple										=       MockedPredictor()
        yield




@LocalMetricTest.register_intensive_calls_patcher("""bertscore"""		)
def      __UpperCamelCase     (  _lowerCAmelCase		)	->  Any:



    """simple docstring"""






    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			:	Optional[int]										=       bert_cos_score_idf
        yield




@LocalMetricTest.register_intensive_calls_patcher("""comet"""		)
def      __UpperCamelCase     (  _lowerCAmelCase		)	->  Dict:



    """simple docstring"""






    def load_from_checkpoint(_lowerCAmelCase		):






        class SCREAMING_SNAKE_CASE__					:




            '''simple docstring'''






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




def      __UpperCamelCase     (  )	->  str:



    """simple docstring"""






    A			:	Union[str, Any]										=       load_metric(os.path.join("""metrics"""				,					"""seqeval"""		)		)
    A			:	List[str]										=       """ERROR"""
    A			:	List[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		)







import copy
from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


SCREAMING_SNAKE_CASE_:Optional[Any]					=						logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_:Union[str, Any]					=						{
    """microsoft/conditional-detr-resnet-50""": (
        """https://huggingface.co/microsoft/conditional-detr-resnet-50/resolve/main/config.json"""
    ),
}






class SCREAMING_SNAKE_CASE__					(		SCREAMING_SNAKE_CASE__   ):




    '''simple docstring'''






    __lowerCamelCase      :			Dict =				"conditional_detr"
    __lowerCamelCase      :			str =				["past_key_values"]
    __lowerCamelCase      :			str =				{
        "hidden_size": "d_model",
        "num_attention_heads": "encoder_attention_heads",
    }






    def __init__(	self,		lowerCamelCase__=True,		lowerCamelCase__=None,		lowerCamelCase__=3,		lowerCamelCase__=300,		lowerCamelCase__=6,		lowerCamelCase__=2048,		lowerCamelCase__=8,		lowerCamelCase__=6,		lowerCamelCase__=2048,		lowerCamelCase__=8,		lowerCamelCase__=0.0,		lowerCamelCase__=0.0,		lowerCamelCase__=True,		lowerCamelCase__="relu",		lowerCamelCase__=256,		lowerCamelCase__=0.1,		lowerCamelCase__=0.0,		lowerCamelCase__=0.0,		lowerCamelCase__=0.02,		lowerCamelCase__=1.0,		lowerCamelCase__=False,		lowerCamelCase__="sine",		lowerCamelCase__="resnet50",		lowerCamelCase__=True,		lowerCamelCase__=False,		lowerCamelCase__=2,		lowerCamelCase__=5,		lowerCamelCase__=2,		lowerCamelCase__=1,		lowerCamelCase__=1,		lowerCamelCase__=2,		lowerCamelCase__=5,		lowerCamelCase__=2,		lowerCamelCase__=0.25,		**lowerCamelCase__,		):
        if backbone_config is not None and use_timm_backbone:
            raise ValueError("""You can't specify both `backbone_config` and `use_timm_backbone`."""		)

        if not use_timm_backbone:
            if backbone_config is None:
                logger.info("""`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone."""		)
                A			:	List[Any]										=       CONFIG_MAPPING["""resnet"""](out_features=["""stage4"""]		)
            elif isinstance(lowerCamelCase__,		lowerCamelCase__		):
                A			:	Any										=       backbone_config.get("""model_type"""		)
                A			:	Optional[Any]										=       CONFIG_MAPPING[backbone_model_type]
                A			:	Tuple										=       config_class.from_dict(lowerCamelCase__		)

        A			:	Dict										=       use_timm_backbone
        A			:	int										=       backbone_config
        A			:	Union[str, Any]										=       num_channels
        A			:	Optional[Any]										=       num_queries
        A			:	Union[str, Any]										=       d_model
        A			:	str										=       encoder_ffn_dim
        A			:	List[Any]										=       encoder_layers
        A			:	Tuple										=       encoder_attention_heads
        A			:	Union[str, Any]										=       decoder_ffn_dim
        A			:	Tuple										=       decoder_layers
        A			:	int										=       decoder_attention_heads
        A			:	Union[str, Any]										=       dropout
        A			:	List[str]										=       attention_dropout
        A			:	Optional[int]										=       activation_dropout
        A			:	Optional[Any]										=       activation_function
        A			:	Any										=       init_std
        A			:	List[Any]										=       init_xavier_std
        A			:	Any										=       encoder_layerdrop
        A			:	List[str]										=       decoder_layerdrop
        A			:	int										=       encoder_layers
        A			:	Union[str, Any]										=       auxiliary_loss
        A			:	Union[str, Any]										=       position_embedding_type
        A			:	Tuple										=       backbone
        A			:	Dict										=       use_pretrained_backbone
        A			:	int										=       dilation
        # Hungarian matcher
        A			:	List[Any]										=       class_cost
        A			:	List[Any]										=       bbox_cost
        A			:	int										=       giou_cost
        # Loss coefficients
        A			:	List[Any]										=       mask_loss_coefficient
        A			:	Any										=       dice_loss_coefficient
        A			:	int										=       cls_loss_coefficient
        A			:	Tuple										=       bbox_loss_coefficient
        A			:	List[Any]										=       giou_loss_coefficient
        A			:	int										=       focal_alpha
        super().__init__(is_encoder_decoder=lowerCamelCase__,		**lowerCamelCase__		)






    @property
    def       _lowerCAmelCase (	self		):
        return self.encoder_attention_heads






    @property
    def       _lowerCAmelCase (	self		):
        return self.d_model






    def       _lowerCAmelCase (	self		):
        A			:	Dict										=       copy.deepcopy(self.__dict__		)
        if self.backbone_config is not None:
            A			:	List[Any]										=       self.backbone_config.to_dict()
        A			:	List[str]										=       self.__class__.model_type
        return output






class SCREAMING_SNAKE_CASE__					(		SCREAMING_SNAKE_CASE__   ):




    '''simple docstring'''






    __lowerCamelCase      :			Tuple =				version.parse("1.11"   )






    @property
    def       _lowerCAmelCase (	self		):
        return OrderedDict(
            [
                ("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
                ("""pixel_mask""", {0: """batch"""}),
            ]		)






    @property
    def       _lowerCAmelCase (	self		):
        return 1e-5






    @property
    def       _lowerCAmelCase (	self		):
        return 12


