




'''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
UpperCamelCase_    	=				pytest.mark.integration


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

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

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




def   lowercase__(			__UpperCamelCase:					Tuple    ):

      """simple docstring"""






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

      return wrapper




def   lowercase__(			__UpperCamelCase:					str    ):

      """simple docstring"""






      @wraps(__UpperCamelCase    )
      def wrapper(self:					Any      ,__UpperCamelCase:					Optional[int]    ):
            if not _has_transformers and metric_name in REQUIRE_TRANSFORMERS:
                  self.skipTest('"test requires transformers"'    )
            else:
                  test_case(self      ,__UpperCamelCase    )

      return wrapper




def   lowercase__(			__UpperCamelCase:					Optional[int]    ):

      """simple docstring"""






      @wraps(__UpperCamelCase    )
      def wrapper(self:					Optional[int]      ,__UpperCamelCase:					Tuple    ):
            if _on_windows and metric_name in UNSUPPORTED_ON_WINDOWS:
                  self.skipTest('"test not supported on Windows"'    )
            else:
                  test_case(self      ,__UpperCamelCase    )

      return wrapper




def   lowercase__(			):

      """simple docstring"""






      SCREAMING_SNAKE_CASE   :      Optional[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 ):





       '''simple docstring'''






       A						:				Any        =						{}
       A						:				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,							A  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      Any    					=						'[...]'
             SCREAMING_SNAKE_CASE   :      List[Any]    					=						importlib.import_module(
                 datasets.load.metric_module_factory(os.path.join('metrics',							A  )  ).module_path  )
             SCREAMING_SNAKE_CASE   :      int    					=						datasets.load.import_main_class(metric_module.__name__,							dataset=A  )
             # check parameters
             SCREAMING_SNAKE_CASE   :      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(A,							metric_module.__name__  ):
                   with self.use_local_metrics():
                         try:
                               SCREAMING_SNAKE_CASE   :      Any    					=						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        UpperCamelCase_							(      self,							A  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						'[...]'
             SCREAMING_SNAKE_CASE   :      str    					=						importlib.import_module(
                 datasets.load.metric_module_factory(os.path.join('metrics',							A  )  ).module_path  )
             # run doctest
             with self.use_local_metrics():
                   SCREAMING_SNAKE_CASE   :      Tuple    					=						doctest.testmod(A,							verbose=A,							raise_on_error=A  )
             self.assertEqual(results.failed,							0  )
             self.assertGreater(results.attempted,							1  )







       @contextmanager
       def        UpperCamelCase_							(      self,							A,							A  ):







             '''simple docstring'''







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







       @contextmanager
       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             def load_local_metric(A,							*A,							**A  ):
                   return load_metric(os.path.join('metrics',							A  ),							*A,							**A  )

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







       @classmethod
       def        UpperCamelCase_							(      cls,							A  ):







             '''simple docstring'''







             def wrapper(A  ):
                   SCREAMING_SNAKE_CASE   :      str    					=						contextmanager(A  )
                   SCREAMING_SNAKE_CASE   :      int    					=						patcher
                   return patcher

             return wrapper




@LocalMetricTest.register_intensive_calls_patcher('bleurt'    )
def   lowercase__(			__UpperCamelCase:					Union[str, Any]    ):

      """simple docstring"""






      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 ):





             '''simple docstring'''






             def        UpperCamelCase_							(      self,							A  ):







                   '''simple docstring'''







                   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:
            SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						MockedPredictor()
            yield




@LocalMetricTest.register_intensive_calls_patcher('bertscore'    )
def   lowercase__(			__UpperCamelCase:					Union[str, Any]    ):

      """simple docstring"""






      import torch

      def bert_cos_score_idf(__UpperCamelCase:					int      ,__UpperCamelCase:					List[Any]      ,*__UpperCamelCase:					Optional[Any]      ,**__UpperCamelCase:					Dict    ):
            return torch.tensor([[1.0, 1.0, 1.0]] * len(__UpperCamelCase    )    )

      # 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:
            SCREAMING_SNAKE_CASE   :      str    					=						bert_cos_score_idf
            yield




@LocalMetricTest.register_intensive_calls_patcher('comet'    )
def   lowercase__(			__UpperCamelCase:					Dict    ):

      """simple docstring"""






      def load_from_checkpoint(__UpperCamelCase:					Any    ):






            class 							_a				:





                   '''simple docstring'''






                   def        UpperCamelCase_							(      self,							A,							*A,							**A  ):







                         '''simple docstring'''







                         assert len(A  ) == 2
                         SCREAMING_SNAKE_CASE   :      Optional[int]    					=						[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:
            SCREAMING_SNAKE_CASE   :      Any    					=						None
            with patch('comet.load_from_checkpoint'    ) as mock_load_from_checkpoint:
                  SCREAMING_SNAKE_CASE   :      Dict    					=						load_from_checkpoint
                  yield





def   lowercase__(			):

      """simple docstring"""






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








'''simple docstring'''



import warnings

from transformers import AutoTokenizer
from transformers.utils import is_torch_available
from transformers.utils.generic import ExplicitEnum

from ...processing_utils import ProcessorMixin


if is_torch_available():
      import torch






class 							_a				(			SCREAMING_SNAKE_CASE ):





       '''simple docstring'''






       A						:				Dict        =						'''char'''
       A						:				Any        =						'''bpe'''
       A						:				Dict        =						'''wp'''

UpperCamelCase_    	=				(DecodeType.CHARACTER, DecodeType.BPE, DecodeType.WORDPIECE)






class 							_a				(			SCREAMING_SNAKE_CASE ):





       '''simple docstring'''






       A						:				List[Any]        =						['''image_processor''', '''char_tokenizer''']
       A						:				int        =						'''ViTImageProcessor'''
       A						:				List[str]        =						'''MgpstrTokenizer'''







       def __init__(      self,							A=None,							A=None,							**A  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      Optional[int]    					=						None
             if "feature_extractor" in kwargs:
                   warnings.warn(
                       'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
                       ' instead.',							A,							)
                   SCREAMING_SNAKE_CASE   :      str    					=						kwargs.pop('feature_extractor'  )

             SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						image_processor if image_processor is not None else feature_extractor
             if image_processor is None:
                   raise ValueError('You need to specify an `image_processor`.'  )
             if tokenizer is None:
                   raise ValueError('You need to specify a `tokenizer`.'  )

             SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						tokenizer
             SCREAMING_SNAKE_CASE   :      Optional[int]    					=						AutoTokenizer.from_pretrained('gpt2'  )
             SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						AutoTokenizer.from_pretrained('bert-base-uncased'  )

             super().__init__(A,							A  )







       def __call__(      self,							A=None,							A=None,							A=None,							**A  ):







             '''simple docstring'''







             if images is None and text is None:
                   raise ValueError('You need to specify either an `images` or `text` input to process.'  )

             if images is not None:
                   SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						self.image_processor(A,							return_tensors=A,							**A  )
             if text is not None:
                   SCREAMING_SNAKE_CASE   :      int    					=						self.char_tokenizer(A,							return_tensors=A,							**A  )

             if text is None:
                   return inputs
             elif images is None:
                   return encodings
             else:
                   SCREAMING_SNAKE_CASE   :      Any    					=						encodings['input_ids']
                   return inputs







       def        UpperCamelCase_							(      self,							A  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE					,    SCREAMING_SNAKE_CASE					,    SCREAMING_SNAKE_CASE   :      int    					=						sequences
             SCREAMING_SNAKE_CASE   :      List[str]    					=						char_preds.size(0  )

             SCREAMING_SNAKE_CASE					,    SCREAMING_SNAKE_CASE   :      Dict    					=						self._decode_helper(A,							'char'  )
             SCREAMING_SNAKE_CASE					,    SCREAMING_SNAKE_CASE   :      int    					=						self._decode_helper(A,							'bpe'  )
             SCREAMING_SNAKE_CASE					,    SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						self._decode_helper(A,							'wp'  )

             SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						[]
             SCREAMING_SNAKE_CASE   :      Tuple    					=						[]
             for i in range(A  ):
                   SCREAMING_SNAKE_CASE   :      str    					=						[char_scores[i], bpe_scores[i], wp_scores[i]]
                   SCREAMING_SNAKE_CASE   :      Dict    					=						[char_strs[i], bpe_strs[i], wp_strs[i]]
                   SCREAMING_SNAKE_CASE   :      List[str]    					=						scores.index(max(A  )  )
                   final_strs.append(strs[max_score_index]  )
                   final_scores.append(scores[max_score_index]  )

             SCREAMING_SNAKE_CASE   :      List[Any]    					=						{}
             SCREAMING_SNAKE_CASE   :      int    					=						final_strs
             SCREAMING_SNAKE_CASE   :      Any    					=						final_scores
             SCREAMING_SNAKE_CASE   :      Dict    					=						char_strs
             SCREAMING_SNAKE_CASE   :      Any    					=						bpe_strs
             SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						wp_strs
             return out







       def        UpperCamelCase_							(      self,							A,							A  ):







             '''simple docstring'''







             if format == DecodeType.CHARACTER:
                   SCREAMING_SNAKE_CASE   :      List[Any]    					=						self.char_decode
                   SCREAMING_SNAKE_CASE   :      Optional[int]    					=						1
                   SCREAMING_SNAKE_CASE   :      str    					=						'[s]'
             elif format == DecodeType.BPE:
                   SCREAMING_SNAKE_CASE   :      str    					=						self.bpe_decode
                   SCREAMING_SNAKE_CASE   :      str    					=						2
                   SCREAMING_SNAKE_CASE   :      List[str]    					=						'#'
             elif format == DecodeType.WORDPIECE:
                   SCREAMING_SNAKE_CASE   :      Any    					=						self.wp_decode
                   SCREAMING_SNAKE_CASE   :      Tuple    					=						102
                   SCREAMING_SNAKE_CASE   :      List[Any]    					=						'[SEP]'
             else:
                   raise ValueError(F"Format {format} is not supported."  )

             SCREAMING_SNAKE_CASE					,    SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						[], []
             SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						pred_logits.size(0  )
             SCREAMING_SNAKE_CASE   :      Any    					=						pred_logits.size(1  )
             SCREAMING_SNAKE_CASE					,    SCREAMING_SNAKE_CASE   :      List[Any]    					=						pred_logits.topk(1,							dim=-1,							largest=A,							sorted=A  )
             SCREAMING_SNAKE_CASE   :      Optional[int]    					=						preds_index.view(-1,							A  )[:, 1:]
             SCREAMING_SNAKE_CASE   :      List[Any]    					=						decoder(A  )
             SCREAMING_SNAKE_CASE					,    SCREAMING_SNAKE_CASE   :      Optional[int]    					=						torch.nn.functional.softmax(A,							dim=2  ).max(dim=2  )
             SCREAMING_SNAKE_CASE   :      Dict    					=						preds_max_prob[:, 1:]

             for index in range(A  ):
                   SCREAMING_SNAKE_CASE   :      Optional[int]    					=						preds_str[index].find(A  )
                   SCREAMING_SNAKE_CASE   :      List[Any]    					=						preds_str[index][:pred_eos]
                   SCREAMING_SNAKE_CASE   :      Dict    					=						preds_index[index].cpu().tolist()
                   SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						pred_index.index(A  ) if eos_token in pred_index else -1
                   SCREAMING_SNAKE_CASE   :      Optional[int]    					=						preds_max_prob[index][: pred_eos_index + 1]
                   SCREAMING_SNAKE_CASE   :      Optional[int]    					=						pred_max_prob.cumprod(dim=0  )[-1] if pred_max_prob.nelement() != 0 else 0.0
                   dec_strs.append(A  )
                   conf_scores.append(A  )

             return dec_strs, conf_scores







       def        UpperCamelCase_							(      self,							A  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						[seq.replace(' ',							''  ) for seq in self.char_tokenizer.batch_decode(A  )]
             return decode_strs







       def        UpperCamelCase_							(      self,							A  ):







             '''simple docstring'''







             return self.bpe_tokenizer.batch_decode(A  )







       def        UpperCamelCase_							(      self,							A  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      List[str]    					=						[seq.replace(' ',							''  ) for seq in self.wp_tokenizer.batch_decode(A  )]
             return decode_strs


