






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
lowercase__					:		Tuple	  =						pytest.mark.integration


lowercase__					:		Union[str, Any]	  =						{'comet'}
lowercase__					:		Tuple	  =						importlib.util.find_spec("""fairseq""") is not None

lowercase__					:		Optional[int]	  =						{'code_eval'}
lowercase__					:		Union[str, Any]	  =						os.name == 'nt'

lowercase__					:		int	  =						{'bertscore', 'frugalscore', 'perplexity'}
lowercase__					:		str	  =						importlib.util.find_spec("""transformers""") is not None
def      a_ (      _A  )			->			List[str]:







				"""simple docstring"""
				@wraps(lowercase_  )
				def wrapper(self      ,   _A  ):
								if not _has_fairseq and metric_name in REQUIRE_FAIRSEQ:
												self.skipTest('\"test requires Fairseq\"'  )
								else:
												test_case(self      ,   lowercase_  )

				return wrapper
def      a_ (      _A  )			->			Optional[int]:







				"""simple docstring"""
				@wraps(lowercase_  )
				def wrapper(self      ,   _A  ):
								if not _has_transformers and metric_name in REQUIRE_TRANSFORMERS:
												self.skipTest('\"test requires transformers\"'  )
								else:
												test_case(self      ,   lowercase_  )

				return wrapper
def      a_ (      _A  )			->			Tuple:







				"""simple docstring"""
				@wraps(lowercase_  )
				def wrapper(self      ,   _A  ):
								if _on_windows and metric_name in UNSUPPORTED_ON_WINDOWS:
												self.skipTest('\"test not supported on Windows\"'  )
								else:
												test_case(self      ,   lowercase_  )

				return wrapper
def      a_ (      )			->			Optional[Any]:







				"""simple docstring"""
				snake_case__									=     [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   __SCREAMING_SNAKE_CASE(	parameterized.TestCase	):
							_UpperCAmelCase     =       {}
							_UpperCAmelCase     =       None
							@pytest.mark.filterwarnings('ignore:metric_module_factory is deprecated:FutureWarning'						)
							@pytest.mark.filterwarnings('ignore:load_metric is deprecated:FutureWarning'						)
							def lowerCAmelCase_ (     self:      List[Any]       ,	UpperCamelCase:      Any						)		->		int:
											snake_case__									=     """[...]"""
											snake_case__									=     importlib.import_module(
											    datasets.load.metric_module_factory(os.path.join('metrics'       ,	A__						)						).module_path						)
											snake_case__									=     datasets.load.import_main_class(metric_module.__name__       ,	dataset=A__						)
											# check parameters
											snake_case__									=     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(A__       ,	metric_module.__name__						):
															with self.use_local_metrics():
																			try:
																							snake_case__									=     doctest.testmod(A__       ,	verbose=A__       ,	raise_on_error=A__						)
																			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:      Tuple       ,	UpperCamelCase:      Tuple						)		->		List[str]:
											snake_case__									=     """[...]"""
											snake_case__									=     importlib.import_module(
											    datasets.load.metric_module_factory(os.path.join('metrics'       ,	A__						)						).module_path						)
											# run doctest
											with self.use_local_metrics():
															snake_case__									=     doctest.testmod(A__       ,	verbose=A__       ,	raise_on_error=A__						)
											self.assertEqual(results.failed       ,	0						)
											self.assertGreater(results.attempted       ,	1						)
							@contextmanager
							def lowerCAmelCase_ (     self:      List[str]       ,	UpperCamelCase:      Any       ,	UpperCamelCase:      List[Any]						)		->		Tuple:
											if metric_name in self.INTENSIVE_CALLS_PATCHER:
															with self.INTENSIVE_CALLS_PATCHER[metric_name](A__						):
																			yield
											else:
															yield
							@contextmanager
							def lowerCAmelCase_ (     self:      Optional[int]						)		->		Dict:
											def load_local_metric(UpperCamelCase:      str       ,	*UpperCamelCase:      Union[str, Any]       ,	**UpperCamelCase:      int						):
															return load_metric(os.path.join('metrics'       ,	A__						)       ,	*A__       ,	**A__						)

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


							@classmethod
							def lowerCAmelCase_ (     cls:      Union[str, Any]       ,	UpperCamelCase:      str						)		->		List[str]:
											def wrapper(UpperCamelCase:      List[Any]						):
															snake_case__									=     contextmanager(A__						)
															snake_case__									=     patcher
															return patcher

											return wrapper


@LocalMetricTest.register_intensive_calls_patcher('bleurt'  )
def      a_ (      _A  )			->			int:







				"""simple docstring"""
				import tensorflow.compat.va as tf
				from bleurt.score import Predictor

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


				class   __SCREAMING_SNAKE_CASE(	a_	):
											def lowerCAmelCase_ (     self:      Any       ,	UpperCamelCase:      int						)		->		Optional[Any]:
															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__									=     MockedPredictor()
								yield
@LocalMetricTest.register_intensive_calls_patcher('bertscore'  )
def      a_ (      _A  )			->			List[Any]:







				"""simple docstring"""
				import torch

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

				# 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__									=     bert_cos_score_idf
								yield
@LocalMetricTest.register_intensive_calls_patcher('comet'  )
def      a_ (      _A  )			->			Optional[Any]:







				"""simple docstring"""
				def load_from_checkpoint(_A  ):


								class   __SCREAMING_SNAKE_CASE:
															def lowerCAmelCase_ (     self:      List[str]       ,	UpperCamelCase:      List[str]       ,	*UpperCamelCase:      str       ,	**UpperCamelCase:      int						)		->		Union[str, Any]:
																			assert len(A__						) == 2
																			snake_case__									=     [0.19, 0.92]
																			return scores, sum(A__						) / len(A__						)


								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__									=     None
								with patch('comet.load_from_checkpoint'  ) as mock_load_from_checkpoint:
												snake_case__									=     load_from_checkpoint
												yield


def      a_ (      )			->			List[str]:







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












from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


__UpperCamelCase					:		Any	  =						logging.get_logger(__name__)

__UpperCamelCase					:		int	  =						{
    """junnyu/roformer_chinese_small""": """https://huggingface.co/junnyu/roformer_chinese_small/resolve/main/config.json""",
    """junnyu/roformer_chinese_base""": """https://huggingface.co/junnyu/roformer_chinese_base/resolve/main/config.json""",
    """junnyu/roformer_chinese_char_small""": (
        """https://huggingface.co/junnyu/roformer_chinese_char_small/resolve/main/config.json"""
    ),
    """junnyu/roformer_chinese_char_base""": (
        """https://huggingface.co/junnyu/roformer_chinese_char_base/resolve/main/config.json"""
    ),
    """junnyu/roformer_small_discriminator""": (
        """https://huggingface.co/junnyu/roformer_small_discriminator/resolve/main/config.json"""
    ),
    """junnyu/roformer_small_generator""": (
        """https://huggingface.co/junnyu/roformer_small_generator/resolve/main/config.json"""
    ),
    # See all RoFormer models at https://huggingface.co/models?filter=roformer
}


class   __SCREAMING_SNAKE_CASE(	a_	):
							_UpperCAmelCase     =       "roformer"
							def __init__(     self:      Tuple       ,	UpperCamelCase:      Optional[Any]=5_00_00       ,	UpperCamelCase:      str=None       ,	UpperCamelCase:      Any=7_68       ,	UpperCamelCase:      Dict=12       ,	UpperCamelCase:      List[Any]=12       ,	UpperCamelCase:      List[str]=30_72       ,	UpperCamelCase:      int="gelu"       ,	UpperCamelCase:      str=0.1       ,	UpperCamelCase:      Union[str, Any]=0.1       ,	UpperCamelCase:      Any=15_36       ,	UpperCamelCase:      Dict=2       ,	UpperCamelCase:      Dict=0.02       ,	UpperCamelCase:      List[str]=1e-12       ,	UpperCamelCase:      int=0       ,	UpperCamelCase:      Any=False       ,	UpperCamelCase:      int=True       ,	**UpperCamelCase:      List[Any]       ,	)		->		List[str]:
											super().__init__(pad_token_id=UpperCamelCase       ,	**UpperCamelCase						)

											snake_case__									=     vocab_size
											snake_case__									=     hidden_size if embedding_size is None else embedding_size
											snake_case__									=     hidden_size
											snake_case__									=     num_hidden_layers
											snake_case__									=     num_attention_heads
											snake_case__									=     hidden_act
											snake_case__									=     intermediate_size
											snake_case__									=     hidden_dropout_prob
											snake_case__									=     attention_probs_dropout_prob
											snake_case__									=     max_position_embeddings
											snake_case__									=     type_vocab_size
											snake_case__									=     initializer_range
											snake_case__									=     layer_norm_eps
											snake_case__									=     rotary_value
											snake_case__									=     use_cache


class   __SCREAMING_SNAKE_CASE(	a_	):
							@property
							def lowerCAmelCase_ (     self:      Any						)		->		Mapping[str, Mapping[int, str]]:
											if self.task == "multiple-choice":
															snake_case__									=     {0: 'batch', 1: 'choice', 2: 'sequence'}
											else:
															snake_case__									=     {0: 'batch', 1: 'sequence'}
											snake_case__									=     {0: 'batch', 1: 'sequence'}
											return OrderedDict(
											    [
											        ('input_ids', dynamic_axis),
											        ('attention_mask', dynamic_axis),
											        ('token_type_ids', dynamic_axis),
											    ]						)




