
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     _a					(						lowerCamelCase							):
 @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     _a					(						lowerCamelCase							):
 @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     _a					(						lowerCamelCase							):
 @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     _a					(						):
 lowerCamelCase		:	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 							A__		(	parameterized.TestCase):
      _UpperCAmelCase				:    Optional[Any]           =			{}
      _UpperCAmelCase				:    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    ,  __magic_name__      ):
       lowerCamelCase		:	List[Any]							=      """[...]"""
       lowerCamelCase		:	Any							=      importlib.import_module(
           datasets.load.metric_module_factory(os.path.join("""metrics"""    ,  __magic_name__      )      ).module_path      )
       lowerCamelCase		:	Optional[Any]							=      datasets.load.import_main_class(metric_module.__name__    ,  dataset=__magic_name__      )
       # check parameters
       lowerCamelCase		:	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(__magic_name__    ,  metric_module.__name__      ):
        with self.use_local_metrics():
         try:
          lowerCamelCase		:	List[str]							=      doctest.testmod(__magic_name__    ,  verbose=__magic_name__    ,  raise_on_error=__magic_name__      )
         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    ,  __magic_name__      ):
       lowerCamelCase		:	str							=      """[...]"""
       lowerCamelCase		:	int							=      importlib.import_module(
           datasets.load.metric_module_factory(os.path.join("""metrics"""    ,  __magic_name__      )      ).module_path      )
       # run doctest
       with self.use_local_metrics():
        lowerCamelCase		:	str							=      doctest.testmod(__magic_name__    ,  verbose=__magic_name__    ,  raise_on_error=__magic_name__      )
       self.assertEqual(results.failed    ,  0      )
       self.assertGreater(results.attempted    ,  1      )
      @contextmanager
      def 						UpperCamelCase__							(      self    ,  __magic_name__    ,  __magic_name__      ):
       if metric_name in self.INTENSIVE_CALLS_PATCHER:
        with self.INTENSIVE_CALLS_PATCHER[metric_name](__magic_name__      ):
         yield
       else:
        yield
      @contextmanager
      def 						UpperCamelCase__							(      self      ):
       def load_local_metric(__magic_name__    ,  *__magic_name__    ,  **__magic_name__      ):
        return load_metric(os.path.join("""metrics"""    ,  __magic_name__      )    ,  *__magic_name__    ,  **__magic_name__      )

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






      @classmethod
      def 						UpperCamelCase__							(      cls    ,  __magic_name__      ):
       def wrapper(__magic_name__      ):
        lowerCamelCase		:	int							=      contextmanager(__magic_name__      )
        lowerCamelCase		:	Optional[int]							=      patcher
        return patcher

       return wrapper





@LocalMetricTest.register_intensive_calls_patcher("""bleurt"""							)
def     _a					(						lowerCamelCase							):
 import tensorflow.compat.va as tf
 from bleurt.score import Predictor

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





 class 							A__		(	__SCREAMING_SNAKE_CASE):
       def 						UpperCamelCase__							(      self    ,  __magic_name__      ):
        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:
  lowerCamelCase		:	List[str]							=      MockedPredictor()
  yield





@LocalMetricTest.register_intensive_calls_patcher("""bertscore"""							)
def     _a					(						lowerCamelCase							):
 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:
  lowerCamelCase		:	List[Any]							=      bert_cos_score_idf
  yield





@LocalMetricTest.register_intensive_calls_patcher("""comet"""							)
def     _a					(						lowerCamelCase							):
 def load_from_checkpoint(lowerCamelCase							):





  class 							A__		:
        def 						UpperCamelCase__							(      self    ,  __magic_name__    ,  *__magic_name__    ,  **__magic_name__      ):
         assert len(__magic_name__      ) == 2
         lowerCamelCase		:	Tuple							=      [0.19, 0.92]
         return scores, sum(__magic_name__      ) / len(__magic_name__      )


  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:
  lowerCamelCase		:	Optional[int]							=      None
  with patch("""comet.load_from_checkpoint"""							) as mock_load_from_checkpoint:
   lowerCamelCase		:	Dict							=      load_from_checkpoint
   yield





def     _a					(						):
 lowerCamelCase		:	int							=      load_metric(os.path.join("""metrics""",	"""seqeval"""							)							)
 lowerCamelCase		:	int							=      """ERROR"""
 lowerCamelCase		:	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 warnings
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


_lowerCamelCase					   =logging.get_logger(__name__)

_lowerCamelCase					   ={
    """nvidia/segformer-b0-finetuned-ade-512-512""": (
        """https://huggingface.co/nvidia/segformer-b0-finetuned-ade-512-512/resolve/main/config.json"""
    ),
    # See all SegFormer models at https://huggingface.co/models?filter=segformer
}





class 							A__		(	__SCREAMING_SNAKE_CASE):
      _UpperCAmelCase				:    Dict           =			"""segformer"""
      def __init__(      self    ,  __magic_name__=3    ,  __magic_name__=4    ,  __magic_name__=[2, 2, 2, 2]    ,  __magic_name__=[8, 4, 2, 1]    ,  __magic_name__=[3_2, 6_4, 1_6_0, 2_5_6]    ,  __magic_name__=[7, 3, 3, 3]    ,  __magic_name__=[4, 2, 2, 2]    ,  __magic_name__=[1, 2, 5, 8]    ,  __magic_name__=[4, 4, 4, 4]    ,  __magic_name__="gelu"    ,  __magic_name__=0.0    ,  __magic_name__=0.0    ,  __magic_name__=0.1    ,  __magic_name__=0.02    ,  __magic_name__=0.1    ,  __magic_name__=1e-6    ,  __magic_name__=2_5_6    ,  __magic_name__=2_5_5    ,  **__magic_name__    ,  ):
       super().__init__(**__magic_name__      )

       if "reshape_last_stage" in kwargs and kwargs["reshape_last_stage"] is False:
        warnings.warn(
            """Reshape_last_stage is set to False in this config. This argument is deprecated and will soon be"""
            """ removed, as the behaviour will default to that of reshape_last_stage = True."""    ,  __magic_name__    ,  )

       lowerCamelCase		:	Optional[Any]							=      num_channels
       lowerCamelCase		:	str							=      num_encoder_blocks
       lowerCamelCase		:	Any							=      depths
       lowerCamelCase		:	List[Any]							=      sr_ratios
       lowerCamelCase		:	int							=      hidden_sizes
       lowerCamelCase		:	Union[str, Any]							=      patch_sizes
       lowerCamelCase		:	Optional[Any]							=      strides
       lowerCamelCase		:	Dict							=      mlp_ratios
       lowerCamelCase		:	str							=      num_attention_heads
       lowerCamelCase		:	Any							=      hidden_act
       lowerCamelCase		:	Tuple							=      hidden_dropout_prob
       lowerCamelCase		:	Union[str, Any]							=      attention_probs_dropout_prob
       lowerCamelCase		:	Dict							=      classifier_dropout_prob
       lowerCamelCase		:	Tuple							=      initializer_range
       lowerCamelCase		:	Dict							=      drop_path_rate
       lowerCamelCase		:	List[str]							=      layer_norm_eps
       lowerCamelCase		:	Any							=      decoder_hidden_size
       lowerCamelCase		:	str							=      kwargs.get("""reshape_last_stage"""    ,  __magic_name__      )
       lowerCamelCase		:	Dict							=      semantic_loss_ignore_index





class 							A__		(	__SCREAMING_SNAKE_CASE):
      _UpperCAmelCase				:    str           =			version.parse("""1.11""")
      @property
      def 						UpperCamelCase__							(      self      ):
       return OrderedDict(
           [
               ("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
           ]      )
      @property
      def 						UpperCamelCase__							(      self      ):
       return 1e-4






      @property
      def 						UpperCamelCase__							(      self      ):
       return 1_2




