




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_ : int    		=       pytest.mark.integration


snake_case_ : str    		=       {"comet"}
snake_case_ : Optional[Any]    		=       importlib.util.find_spec("fairseq") is not None

snake_case_ : List[Any]    		=       {"code_eval"}
snake_case_ : str    		=       os.name == "nt"

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


def    A						(__A   :    Dict       )   ->      List[Any]:







     """simple docstring"""


     @wraps(__A       )
     def wrapper(self   :    Optional[int]   ,				__A   :    Tuple       ):
          if not _has_fairseq and metric_name in REQUIRE_FAIRSEQ:
               self.skipTest('''"test requires Fairseq"'''       )
          else:
               test_case(self   ,				__A       )

     return wrapper


def    A						(__A   :    Tuple       )   ->      List[str]:







     """simple docstring"""


     @wraps(__A       )
     def wrapper(self   :    str   ,				__A   :    int       ):
          if not _has_transformers and metric_name in REQUIRE_TRANSFORMERS:
               self.skipTest('''"test requires transformers"'''       )
          else:
               test_case(self   ,				__A       )

     return wrapper


def    A						(__A   :    int       )   ->      List[Any]:







     """simple docstring"""


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

     return wrapper


def    A						()   ->      int:







     """simple docstring"""


     UpperCAmelCase_          =							[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 __snake_case			(			parameterized.TestCase		):
   UpperCAmelCase__  :    List[Any]          =  {}
   UpperCAmelCase__  :    List[str]          =  None







   @pytest.mark.filterwarnings('''ignore:metric_module_factory is deprecated:FutureWarning''')
   @pytest.mark.filterwarnings('''ignore:load_metric is deprecated:FutureWarning''')
   def lowerCamelCase						(      self		:						Union[str, Any]					,						_snake_case		:						str):

        """simple docstring"""


        UpperCAmelCase_          =							'''[...]'''
        UpperCAmelCase_          =							importlib.import_module(
            datasets.load.metric_module_factory(os.path.join('''metrics'''					,						_snake_case)).module_path)
        UpperCAmelCase_          =							datasets.load.import_main_class(metric_module.__name__					,						dataset=_snake_case)
        # check parameters
        UpperCAmelCase_          =							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:
                       UpperCAmelCase_          =							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		:						Union[str, Any]					,						_snake_case		:						List[Any]):

        """simple docstring"""


        UpperCAmelCase_          =							'''[...]'''
        UpperCAmelCase_          =							importlib.import_module(
            datasets.load.metric_module_factory(os.path.join('''metrics'''					,						_snake_case)).module_path)
        # run doctest
        with self.use_local_metrics():
             UpperCAmelCase_          =							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		:						List[Any]					,						_snake_case		:						Optional[Any]					,						_snake_case		:						List[str]):

        """simple docstring"""


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







   @contextmanager
   def lowerCamelCase						(      self		:						Optional[Any]):

        """simple docstring"""


        def load_local_metric(_snake_case		:						List[str]					,						*_snake_case		:						Union[str, Any]					,						**_snake_case		:						List[Any]):
             return load_metric(os.path.join('''metrics'''					,						_snake_case)					,						*_snake_case					,						**_snake_case)

        with patch('''datasets.load_metric''') as mock_load_metric:
             UpperCAmelCase_          =							load_local_metric
             yield







   @classmethod
   def lowerCamelCase						(      cls		:						List[str]					,						_snake_case		:						List[Any]):

        """simple docstring"""


        def wrapper(_snake_case		:						Optional[Any]):
             UpperCAmelCase_          =							contextmanager(_snake_case)
             UpperCAmelCase_          =							patcher
             return patcher

        return wrapper







@LocalMetricTest.register_intensive_calls_patcher('''bleurt'''       )
def    A						(__A   :    Any       )   ->      List[str]:







     """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			(			a		):







        def lowerCamelCase						(      self		:						int					,						_snake_case		:						Optional[int]):

             """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:
          UpperCAmelCase_          =							MockedPredictor()
          yield


@LocalMetricTest.register_intensive_calls_patcher('''bertscore'''       )
def    A						(__A   :    int       )   ->      Tuple:







     """simple docstring"""


     import torch

     def bert_cos_score_idf(__A   :    str   ,				__A   :    Optional[Any]   ,				*__A   :    List[Any]   ,				**__A   :    List[str]       ):
          return torch.tensor([[1.0, 1.0, 1.0]] * len(__A       )       )

     # 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:
          UpperCAmelCase_          =							bert_cos_score_idf
          yield


@LocalMetricTest.register_intensive_calls_patcher('''comet'''       )
def    A						(__A   :    List[str]       )   ->      Optional[Any]:







     """simple docstring"""


     def load_from_checkpoint(__A   :    str       ):







          class __snake_case			:







             def lowerCamelCase						(      self		:						Any					,						_snake_case		:						Optional[int]					,						*_snake_case		:						str					,						**_snake_case		:						Optional[int]):

                  """simple docstring"""


                  assert len(_snake_case) == 2
                  UpperCAmelCase_          =							[0.1_9, 0.9_2]
                  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:
          UpperCAmelCase_          =							None
          with patch('''comet.load_from_checkpoint'''       ) as mock_load_from_checkpoint:
               UpperCAmelCase_          =							load_from_checkpoint
               yield


def    A						()   ->      int:







     """simple docstring"""


     UpperCAmelCase_          =							load_metric(os.path.join('''metrics'''   ,				'''seqeval'''       )       )
     UpperCAmelCase_          =							'''ERROR'''
     UpperCAmelCase_          =							F"""Scheme should be one of [IOB1, IOB2, IOE1, IOE2, IOBES, BILOU], got {wrong_scheme}"""
     with pytest.raises(__A   ,				match=re.escape(__A       )       ):
          metric.compute(predictions=[]   ,				references=[]   ,				scheme=__A       )







"""simple docstring"""






import argparse
import logging
import sys
from unittest.mock import patch

import run_glue_deebert

from transformers.testing_utils import TestCasePlus, get_gpu_count, require_torch_non_multi_gpu, slow


logging.basicConfig(level=logging.DEBUG)

lowerCAmelCase__  :  Optional[Any]						     =	logging.getLogger()



def   a_						(					):
				UpperCAmelCase__             =							argparse.ArgumentParser()
				parser.add_argument('-f'       )
				UpperCAmelCase__             =							parser.parse_args()
				return args.f







class 				snake_case       ( __UpperCAmelCase				):




					"""simple docstring"""

					def 		__lowerCAmelCase						(				self   :	int			):
									UpperCAmelCase__             =							logging.StreamHandler(sys.stdout			)
									logger.addHandler(lowerCamelCase__			)







					def 		__lowerCAmelCase						(				self   :	Optional[Any]    ,lowerCamelCase__   :	Union[str, Any]			):
									UpperCAmelCase__             =							get_gpu_count()

									if n_gpu > 1:
													pass
													# XXX: doesn't quite work with n_gpu > 1 https://github.com/huggingface/transformers/issues/10560
													# script = f"{self.examples_dir_str}/research_projects/deebert/run_glue_deebert.py"
													# distributed_args = f"-m torch.distributed.launch --nproc_per_node={n_gpu} {script}".split()
													# cmd = [sys.executable] + distributed_args + args
													# execute_subprocess_async(cmd, env=self.get_env())
													# XXX: test the results - need to save them first into .json file
									else:
													args.insert(0    ,'run_glue_deebert.py'			)
													with patch.object(lowerCamelCase__    ,'argv'    ,lowerCamelCase__			):
																	UpperCAmelCase__             =							run_glue_deebert.main()
																	for value in result.values():
																					self.assertGreaterEqual(lowerCamelCase__    ,0.6_6_6			)







					@slow
					@require_torch_non_multi_gpu
					def 		__lowerCAmelCase						(				self   :	List[str]			):
									UpperCAmelCase__             =							'\n            --model_type roberta\n            --model_name_or_path roberta-base\n            --task_name MRPC\n            --do_train\n            --do_eval\n            --do_lower_case\n            --data_dir ./tests/fixtures/tests_samples/MRPC/\n            --max_seq_length 128\n            --per_gpu_eval_batch_size=1\n            --per_gpu_train_batch_size=8\n            --learning_rate 2e-4\n            --num_train_epochs 3\n            --overwrite_output_dir\n            --seed 42\n            --output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage\n            --plot_data_dir ./examples/deebert/results/\n            --save_steps 0\n            --overwrite_cache\n            --eval_after_first_stage\n            '.split()
									self.run_and_check(lowerCamelCase__			)

									UpperCAmelCase__             =							'\n            --model_type roberta\n            --model_name_or_path ./examples/deebert/saved_models/roberta-base/MRPC/two_stage\n            --task_name MRPC\n            --do_eval\n            --do_lower_case\n            --data_dir ./tests/fixtures/tests_samples/MRPC/\n            --output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage\n            --plot_data_dir ./examples/deebert/results/\n            --max_seq_length 128\n            --eval_each_highway\n            --eval_highway\n            --overwrite_cache\n            --per_gpu_eval_batch_size=1\n            '.split()
									self.run_and_check(lowerCamelCase__			)

									UpperCAmelCase__             =							'\n            --model_type roberta\n            --model_name_or_path ./examples/deebert/saved_models/roberta-base/MRPC/two_stage\n            --task_name MRPC\n            --do_eval\n            --do_lower_case\n            --data_dir ./tests/fixtures/tests_samples/MRPC/\n            --output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage\n            --plot_data_dir ./examples/deebert/results/\n            --max_seq_length 128\n            --early_exit_entropy 0.1\n            --eval_highway\n            --overwrite_cache\n            --per_gpu_eval_batch_size=1\n            '.split()
									self.run_and_check(lowerCamelCase__			)



