'''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
__snake_case						  =		pytest.mark.integration


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

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

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

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

  return wrapper

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

  return wrapper

def   A_      (       SCREAMING_SNAKE_CASE_						)			->List[str]:
  @wraps(SCREAMING_SNAKE_CASE_						)
  def wrapper(self ,	SCREAMING_SNAKE_CASE_						):
    if _on_windows and metric_name in UNSUPPORTED_ON_WINDOWS:
      self.skipTest("""\"test not supported on Windows\""""						)
    else:
      test_case(self ,	SCREAMING_SNAKE_CASE_						)

  return wrapper

def   A_      (       )			->Any:
  lowercase_     =       [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(
    __a ,				__a ,				__a      )
@local
class       _a    (		parameterized.TestCase      ):


  """simple docstring"""

  A_    							=						{}
  A_    							=						None




  @pytest.mark.filterwarnings("""ignore:metric_module_factory is deprecated:FutureWarning"""      )
  @pytest.mark.filterwarnings("""ignore:load_metric is deprecated:FutureWarning"""      )
  def 		lowerCamelCase__  (		self      :       Dict   ,				lowercase_      :       Any      ):







    '''simple docstring'''


    lowercase_     =       """[...]"""
    lowercase_     =       importlib.import_module(
        datasets.load.metric_module_factory(os.path.join("""metrics"""   ,				lowercase_      )      ).module_path      )
    lowercase_     =       datasets.load.import_main_class(metric_module.__name__   ,				dataset=lowercase_      )
    # check parameters
    lowercase_     =       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:
          lowercase_     =       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 		lowerCamelCase__  (		self      :       Dict   ,				lowercase_      :       str      ):







    '''simple docstring'''


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




  @contextmanager
  def 		lowerCamelCase__  (		self      :       int   ,				lowercase_      :       Any   ,				lowercase_      :       List[Any]      ):







    '''simple docstring'''


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




  @contextmanager
  def 		lowerCamelCase__  (		self      :       Optional[int]      ):







    '''simple docstring'''


    def load_local_metric(lowercase_      :       Dict   ,				*lowercase_      :       Dict   ,				**lowercase_      :       Any      ):
      return load_metric(os.path.join("""metrics"""   ,				lowercase_      )   ,				*lowercase_   ,				**lowercase_      )

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





  @classmethod
  def 		lowerCamelCase__  (		cls      :       Optional[int]   ,				lowercase_      :       Dict      ):







    '''simple docstring'''


    def wrapper(lowercase_      :       int      ):
      lowercase_     =       contextmanager(lowercase_      )
      lowercase_     =       patcher
      return patcher

    return wrapper






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

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





  class       _a    (		__a      ):


    """simple docstring"""

    def 		lowerCamelCase__  (		self      :       Dict   ,				lowercase_      :       Union[str, 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:
    lowercase_     =       MockedPredictor()
    yield

@LocalMetricTest.register_intensive_calls_patcher("""bertscore"""						)
def   A_      (       SCREAMING_SNAKE_CASE_						)			->Optional[int]:
  import torch

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

  # 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:
    lowercase_     =       bert_cos_score_idf
    yield

@LocalMetricTest.register_intensive_calls_patcher("""comet"""						)
def   A_      (       SCREAMING_SNAKE_CASE_						)			->Union[str, Any]:
  def load_from_checkpoint(SCREAMING_SNAKE_CASE_						):





    class       _a    :


      """simple docstring"""

      def 		lowerCamelCase__  (		self      :       Any   ,				lowercase_      :       Optional[int]   ,				*lowercase_      :       str   ,				**lowercase_      :       Dict      ):







        '''simple docstring'''


        assert len(lowercase_      ) == 2
        lowercase_     =       [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:
    lowercase_     =       None
    with patch("""comet.load_from_checkpoint"""						) as mock_load_from_checkpoint:
      lowercase_     =       load_from_checkpoint
      yield




def   A_      (       )			->List[Any]:
  lowercase_     =       load_metric(os.path.join("""metrics""" ,	"""seqeval"""						)						)
  lowercase_     =       """ERROR"""
  lowercase_     =       f"""Scheme should be one of [IOB1, IOB2, IOE1, IOE2, IOBES, BILOU], got {wrong_scheme}"""
  with pytest.raises(SCREAMING_SNAKE_CASE_ ,	match=re.escape(SCREAMING_SNAKE_CASE_						)						):
    metric.compute(predictions=[] ,	references=[] ,	scheme=SCREAMING_SNAKE_CASE_						)


'''simple docstring'''


from ...configuration_utils import PretrainedConfig
from ...utils import logging


__snake_case						  =		logging.get_logger(__name__)

__snake_case						  =		{
    """uw-madison/mra-base-512-4""": """https://huggingface.co/uw-madison/mra-base-512-4/resolve/main/config.json""",
}





class       _a    (		__a      ):


  """simple docstring"""

  A_    							=						'''mra'''




  def __init__(		self      :       Any   ,				lowercase_      :       List[str]=50_265   ,				lowercase_      :       Tuple=768   ,				lowercase_      :       Optional[Any]=12   ,				lowercase_      :       str=12   ,				lowercase_      :       Optional[int]=3_072   ,				lowercase_      :       Union[str, Any]="gelu"   ,				lowercase_      :       str=0.1   ,				lowercase_      :       Tuple=0.1   ,				lowercase_      :       str=512   ,				lowercase_      :       Optional[int]=1   ,				lowercase_      :       Tuple=0.0_2   ,				lowercase_      :       List[Any]=1e-5   ,				lowercase_      :       Optional[Any]="absolute"   ,				lowercase_      :       int=4   ,				lowercase_      :       int="full"   ,				lowercase_      :       Tuple=0   ,				lowercase_      :       str=0   ,				lowercase_      :       List[str]=1   ,				lowercase_      :       Tuple=0   ,				lowercase_      :       Optional[Any]=2   ,				**lowercase_      :       List[str]   ,				):







    '''simple docstring'''


    super().__init__(pad_token_id=lowercase_   ,				bos_token_id=lowercase_   ,				eos_token_id=lowercase_   ,				**lowercase_      )

    lowercase_     =       vocab_size
    lowercase_     =       max_position_embeddings
    lowercase_     =       hidden_size
    lowercase_     =       num_hidden_layers
    lowercase_     =       num_attention_heads
    lowercase_     =       intermediate_size
    lowercase_     =       hidden_act
    lowercase_     =       hidden_dropout_prob
    lowercase_     =       attention_probs_dropout_prob
    lowercase_     =       initializer_range
    lowercase_     =       type_vocab_size
    lowercase_     =       layer_norm_eps
    lowercase_     =       position_embedding_type
    lowercase_     =       block_per_row
    lowercase_     =       approx_mode
    lowercase_     =       initial_prior_first_n_blocks
    lowercase_     =       initial_prior_diagonal_n_blocks

