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										=						pytest.mark.integration


_SCREAMING_SNAKE_CASE										=						{'comet'}
_SCREAMING_SNAKE_CASE										=						importlib.util.find_spec('fairseq') is not None

_SCREAMING_SNAKE_CASE										=						{'code_eval'}
_SCREAMING_SNAKE_CASE										=						os.name == 'nt'

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






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

       return wrapper






def  snake_case  (       snake_case__			:Dict)						->   List[str]:
       @wraps(snake_case__)
       def wrapper(self			:List[str]   ,	snake_case__			:Union[str, Any]):
              if not _has_transformers and metric_name in REQUIRE_TRANSFORMERS:
                     self.skipTest("""\"test requires transformers\"""")
              else:
                     test_case(self   ,	snake_case__)

       return wrapper






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

       return wrapper






def  snake_case  (       )						->   List[str]:
       _A        =  [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(
    __lowerCAmelCase       ,			__lowerCAmelCase       ,			__lowerCAmelCase       )
@local
class 				a							(			parameterized.TestCase       ):







 """simple docstring"""






 lowerCamelCase	:Dict    			=		{}
 lowerCamelCase	:Any    			=		None



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



 @contextmanager
 def      UpperCAmelCase							(					self		,				lowerCAmelCase_		,				lowerCAmelCase_							)  ->					Dict:
        if metric_name in self.INTENSIVE_CALLS_PATCHER:
               with self.INTENSIVE_CALLS_PATCHER[metric_name](lowerCAmelCase_							):
                      yield
        else:
               yield



 @contextmanager
 def      UpperCAmelCase							(					self							)  ->					List[Any]:
        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        =  load_local_metric
               yield



 @classmethod
 def      UpperCAmelCase							(					cls		,				lowerCAmelCase_							)  ->					int:
        def wrapper(lowerCAmelCase_							):
               _A        =  contextmanager(lowerCAmelCase_							)
               _A        =  patcher
               return patcher

        return wrapper






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

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



       class 				a							(			__lowerCAmelCase       ):







        """simple docstring"""






        def      UpperCAmelCase							(					self		,				lowerCAmelCase_							)  ->					Optional[int]:
               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        =  MockedPredictor()
              yield






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

       def bert_cos_score_idf(snake_case__			:List[str]   ,	snake_case__			:Dict   ,	*snake_case__			:Optional[Any]   ,	**snake_case__			:str):
              return torch.tensor([[1.0, 1.0, 1.0]] * len(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:
              _A        =  bert_cos_score_idf
              yield






@LocalMetricTest.register_intensive_calls_patcher("""comet""")
def  snake_case  (       snake_case__			:List[str])						->   Tuple:
       def load_from_checkpoint(snake_case__			:List[Any]):



              class 				a							:







               """simple docstring"""






               def      UpperCAmelCase							(					self		,				lowerCAmelCase_		,				*lowerCAmelCase_		,				**lowerCAmelCase_							)  ->					Any:
                      assert len(lowerCAmelCase_							) == 2
                      _A        =  [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        =  None
              with patch("""comet.load_from_checkpoint""") as mock_load_from_checkpoint:
                     _A        =  load_from_checkpoint
                     yield







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





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


_SCREAMING_SNAKE_CASE										=						logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE										=						{
    'microsoft/biogpt': 'https://huggingface.co/microsoft/biogpt/resolve/main/config.json',
    # See all BioGPT models at https://huggingface.co/models?filter=biogpt
}



class 				a							(			__lowerCAmelCase       ):







 """simple docstring"""






 lowerCamelCase	:Any    			=		'''biogpt'''



 def __init__(					self		,				lowerCAmelCase_=4_23_84		,				lowerCAmelCase_=10_24		,				lowerCAmelCase_=24		,				lowerCAmelCase_=16		,				lowerCAmelCase_=40_96		,				lowerCAmelCase_="gelu"		,				lowerCAmelCase_=0.1		,				lowerCAmelCase_=0.1		,				lowerCAmelCase_=10_24		,				lowerCAmelCase_=0.02		,				lowerCAmelCase_=1E-12		,				lowerCAmelCase_=True		,				lowerCAmelCase_=True		,				lowerCAmelCase_=0.0		,				lowerCAmelCase_=0.0		,				lowerCAmelCase_=1		,				lowerCAmelCase_=0		,				lowerCAmelCase_=2		,				**lowerCAmelCase_		,				)  ->					Tuple:
        _A        =  vocab_size
        _A        =  max_position_embeddings
        _A        =  hidden_size
        _A        =  num_hidden_layers
        _A        =  num_attention_heads
        _A        =  intermediate_size
        _A        =  hidden_act
        _A        =  hidden_dropout_prob
        _A        =  attention_probs_dropout_prob
        _A        =  initializer_range
        _A        =  layer_norm_eps
        _A        =  scale_embedding
        _A        =  use_cache
        _A        =  layerdrop
        _A        =  activation_dropout
        super().__init__(pad_token_id=lowerCAmelCase_		,				bos_token_id=lowerCAmelCase_		,				eos_token_id=lowerCAmelCase_		,				**lowerCAmelCase_							)




