



'''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
__a          =		pytest.mark.integration


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

__a          =		{"code_eval"}
__a          =		os.name == "nt"

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





def 						__snake_case(				_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 						__snake_case(				_lowerCAmelCase		)		-> Optional[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 						__snake_case(				_lowerCAmelCase		)		-> Optional[Any]:
      @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 						__snake_case(				)		-> Dict:
      snake_case__  :    Union[str, Any]    						=      [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     UpperCAmelCase_       (       parameterized.TestCase	):
    """simple docstring"""







    lowercase					=		{}
    lowercase					=		None




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




    @contextmanager
    def lowerCamelCase       (    self					:     Optional[int] ,     snake_case_					:     Tuple ,     snake_case_					:     str						):
          if metric_name in self.INTENSIVE_CALLS_PATCHER:
                with self.INTENSIVE_CALLS_PATCHER[metric_name](snake_case_						):
                      yield
          else:
                yield




    @contextmanager
    def lowerCamelCase       (    self					:     str						):
          def load_local_metric(snake_case_					:     Optional[Any] ,     *snake_case_					:     str ,     **snake_case_					:     Tuple						):
                return load_metric(os.path.join("""metrics""" ,     snake_case_						) ,     *snake_case_ ,     **snake_case_						)

          with patch("""datasets.load_metric"""						) as mock_load_metric:
                snake_case__  :    List[Any]    						=      load_local_metric
                yield




    @classmethod
    def lowerCamelCase       (    cls					:     List[Any] ,     snake_case_					:     Optional[Any]						):
          def wrapper(snake_case_					:     List[Any]						):
                snake_case__  :    int    						=      contextmanager(snake_case_						)
                snake_case__  :    Optional[int]    						=      patcher
                return patcher

          return wrapper





@LocalMetricTest.register_intensive_calls_patcher("""bleurt"""		)
def 						__snake_case(				_lowerCAmelCase		)		-> List[Any]:
      import tensorflow.compat.va as tf
      from bleurt.score import Predictor

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







      class     UpperCAmelCase_       (       __SCREAMING_SNAKE_CASE	):
          """simple docstring"""







          def lowerCamelCase       (    self					:     str ,     snake_case_					:     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:
            snake_case__  :    List[str]    						=      MockedPredictor()
            yield





@LocalMetricTest.register_intensive_calls_patcher("""bertscore"""		)
def 						__snake_case(				_lowerCAmelCase		)		-> Tuple:
      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:
            snake_case__  :    List[Any]    						=      bert_cos_score_idf
            yield





@LocalMetricTest.register_intensive_calls_patcher("""comet"""		)
def 						__snake_case(				_lowerCAmelCase		)		-> List[Any]:
      def load_from_checkpoint(_lowerCAmelCase		):







            class     UpperCAmelCase_       :
                """simple docstring"""







                def lowerCamelCase       (    self					:     Tuple ,     snake_case_					:     Dict ,     *snake_case_					:     Any ,     **snake_case_					:     int						):
                      assert len(snake_case_						) == 2
                      snake_case__  :    Tuple    						=      [0.19, 0.92]
                      return scores, sum(snake_case_						) / len(snake_case_						)
            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:
            snake_case__  :    Optional[int]    						=      None
            with patch("""comet.load_from_checkpoint"""		) as mock_load_from_checkpoint:
                  snake_case__  :    Dict    						=      load_from_checkpoint
                  yield





def 						__snake_case(				)		-> Dict:
      snake_case__  :    int    						=      load_metric(os.path.join("""metrics"""     ,   """seqeval"""		)		)
      snake_case__  :    int    						=      """ERROR"""
      snake_case__  :    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


_lowerCamelCase					   =logging.get_logger(__name__)

_lowerCamelCase					   ={
    """microsoft/conditional-detr-resnet-50""": (
        """https://huggingface.co/microsoft/conditional-detr-resnet-50/resolve/main/config.json"""
    ),
}





class 							A__		(	__SCREAMING_SNAKE_CASE):
      _UpperCAmelCase				:    Union[str, Any]           =			"""conditional_detr"""
      _UpperCAmelCase				:    Optional[int]           =			["""past_key_values"""]
      _UpperCAmelCase				:    Optional[Any]           =			{
          """hidden_size""": """d_model""",
          """num_attention_heads""": """encoder_attention_heads""",
      }
      def __init__(      self    ,  __magic_name__=True    ,  __magic_name__=None    ,  __magic_name__=3    ,  __magic_name__=3_0_0    ,  __magic_name__=6    ,  __magic_name__=2_0_4_8    ,  __magic_name__=8    ,  __magic_name__=6    ,  __magic_name__=2_0_4_8    ,  __magic_name__=8    ,  __magic_name__=0.0    ,  __magic_name__=0.0    ,  __magic_name__=True    ,  __magic_name__="relu"    ,  __magic_name__=2_5_6    ,  __magic_name__=0.1    ,  __magic_name__=0.0    ,  __magic_name__=0.0    ,  __magic_name__=0.02    ,  __magic_name__=1.0    ,  __magic_name__=False    ,  __magic_name__="sine"    ,  __magic_name__="resnet50"    ,  __magic_name__=True    ,  __magic_name__=False    ,  __magic_name__=2    ,  __magic_name__=5    ,  __magic_name__=2    ,  __magic_name__=1    ,  __magic_name__=1    ,  __magic_name__=2    ,  __magic_name__=5    ,  __magic_name__=2    ,  __magic_name__=0.25    ,  **__magic_name__    ,  ):
       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."""      )
         lowerCamelCase		:	Optional[int]							=      CONFIG_MAPPING["""resnet"""](out_features=["""stage4"""]      )
        elif isinstance(__magic_name__    ,  __magic_name__      ):
         lowerCamelCase		:	List[Any]							=      backbone_config.get("""model_type"""      )
         lowerCamelCase		:	Dict							=      CONFIG_MAPPING[backbone_model_type]
         lowerCamelCase		:	str							=      config_class.from_dict(__magic_name__      )

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






      def 						UpperCamelCase__							(      self      ):
       lowerCamelCase		:	Optional[int]							=      copy.deepcopy(self.__dict__      )
       if self.backbone_config is not None:
        lowerCamelCase		:	Optional[int]							=      self.backbone_config.to_dict()
       lowerCamelCase		:	Optional[Any]							=      self.__class__.model_type
       return output





class 							A__		(	__SCREAMING_SNAKE_CASE):
      _UpperCAmelCase				:    Any           =			version.parse("""1.11""")
      @property
      def 						UpperCamelCase__							(      self      ):
       return OrderedDict(
           [
               ("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
               ("""pixel_mask""", {0: """batch"""}),
           ]      )
      @property
      def 						UpperCamelCase__							(      self      ):
       return 1e-5






      @property
      def 						UpperCamelCase__							(      self      ):
       return 1_2




