


import warnings

from ...utils import is_sklearn_available, requires_backends


if is_sklearn_available():
  from scipy.stats import pearsonr, spearmanr
  from sklearn.metrics import fa_score, matthews_corrcoef


__snake_case             =   (
    """This metric will be removed from the library soon, metrics should be handled with the 🤗 Evaluate """
    """library. You can have a look at this example script for pointers: """
    """https://github.com/huggingface/transformers/blob/main/examples/pytorch/text-classification/run_glue.py"""
)







def   __lowerCAmelCase						(						lowercase  :  Optional[Any]						,							lowercase  :  Any			)     ->						Optional[Any]:


    """simple docstring"""





    warnings.warn(a__						,							a__			)
    requires_backends(a__						,							"sklearn"			)
    return (preds == labels).mean()







def   __lowerCAmelCase						(						lowercase  :  Union[str, Any]						,							lowercase  :  Optional[int]			)     ->						Dict:


    """simple docstring"""





    warnings.warn(a__						,							a__			)
    requires_backends(a__						,							"sklearn"			)
    snake_case   :				Optional[Any]				 =   simple_accuracy(a__						,							a__			)
    snake_case   :				List[str]				 =   fa_score(y_true=a__						,							y_pred=a__			)
    return {
        "acc": acc,
        "f1": fa,
        "acc_and_f1": (acc + fa) / 2,
    }







def   __lowerCAmelCase						(						lowercase  :  List[Any]						,							lowercase  :  Optional[int]			)     ->						List[Any]:


    """simple docstring"""





    warnings.warn(a__						,							a__			)
    requires_backends(a__						,							"sklearn"			)
    snake_case   :				Any				 =   pearsonr(a__						,							a__			)[0]
    snake_case   :				List[str]				 =   spearmanr(a__						,							a__			)[0]
    return {
        "pearson": pearson_corr,
        "spearmanr": spearman_corr,
        "corr": (pearson_corr + spearman_corr) / 2,
    }







def   __lowerCAmelCase						(						lowercase  :  Any						,							lowercase  :  Any						,							lowercase  :  Optional[Any]			)     ->						List[str]:


    """simple docstring"""





    warnings.warn(a__						,							a__			)
    requires_backends(a__						,							"sklearn"			)
    assert len(a__			) == len(a__			), F'Predictions and labels have mismatched lengths {len(a__			)} and {len(a__			)}'
    if task_name == "cola":
        return {"mcc": matthews_corrcoef(a__						,							a__			)}
    elif task_name == "sst-2":
        return {"acc": simple_accuracy(a__						,							a__			)}
    elif task_name == "mrpc":
        return acc_and_fa(a__						,							a__			)
    elif task_name == "sts-b":
        return pearson_and_spearman(a__						,							a__			)
    elif task_name == "qqp":
        return acc_and_fa(a__						,							a__			)
    elif task_name == "mnli":
        return {"mnli/acc": simple_accuracy(a__						,							a__			)}
    elif task_name == "mnli-mm":
        return {"mnli-mm/acc": simple_accuracy(a__						,							a__			)}
    elif task_name == "qnli":
        return {"acc": simple_accuracy(a__						,							a__			)}
    elif task_name == "rte":
        return {"acc": simple_accuracy(a__						,							a__			)}
    elif task_name == "wnli":
        return {"acc": simple_accuracy(a__						,							a__			)}
    elif task_name == "hans":
        return {"acc": simple_accuracy(a__						,							a__			)}
    else:
        raise KeyError(a__			)







def   __lowerCAmelCase						(						lowercase  :  Dict						,							lowercase  :  Any						,							lowercase  :  Any			)     ->						str:


    """simple docstring"""





    warnings.warn(a__						,							a__			)
    requires_backends(a__						,							"sklearn"			)
    if len(a__			) != len(a__			):
        raise ValueError(F'Predictions and labels have mismatched lengths {len(a__			)} and {len(a__			)}'			)
    if task_name == "xnli":
        return {"acc": simple_accuracy(a__						,							a__			)}
    else:
        raise KeyError(a__			)









"""simple docstring"""



import json
import os
import unittest

from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin







@require_tokenizers
class _lowerCAmelCase    (				snake_case_   ,							unittest.TestCase     ):
   __UpperCAmelCase			:	List[Any]							  =  MgpstrTokenizer
   __UpperCAmelCase			:	Optional[int]							  =  False
   __UpperCAmelCase			:	Optional[int]							  =  {}
   __UpperCAmelCase			:	List[str]							  =  False







   def 			lowerCamelCase		(     self						)     ->		int:

       '''simple docstring'''




       super().setUp()

       # fmt: off
       snake_case   :				List[str]				 =   ["[GO]", "[s]", "0", "1", "2", "3", "4", "5", "6", "7", "8", "9", "a", "b", "c", "d", "e", "f", "g", "h", "i", "j", "k", "l", "m", "n", "o", "p", "q", "r", "s", "t", "u", "v", "w", "x", "y", "z"]
       # fmt: on
       snake_case   :				Union[str, Any]				 =   dict(zip(UpperCamelCase__						,     range(len(UpperCamelCase__						)						)						)						)

       snake_case   :				int				 =   os.path.join(self.tmpdirname						,     VOCAB_FILES_NAMES["vocab_file"]						)
       with open(self.vocab_file						,     "w"						,     encoding="utf-8"						) as fp:
           fp.write(json.dumps(UpperCamelCase__						) + "\n"						)







   def 			lowerCamelCase		(     self						,     **UpperCamelCase__						)     ->		List[str]:

       '''simple docstring'''




       return MgpstrTokenizer.from_pretrained(self.tmpdirname						,     **UpperCamelCase__						)







   def 			lowerCamelCase		(     self						,     UpperCamelCase__						)     ->		List[Any]:

       '''simple docstring'''




       snake_case   :				int				 =   "tester"
       snake_case   :				List[str]				 =   "tester"
       return input_text, output_text







   @unittest.skip("MGP-STR always lower cases letters."						)
   def 			lowerCamelCase		(     self						)     ->		str:

       '''simple docstring'''




       pass







   def 			lowerCamelCase		(     self						)     ->		Tuple:

       '''simple docstring'''




       snake_case   :				int				 =   self.get_tokenizers(do_lower_case=UpperCamelCase__						)
       for tokenizer in tokenizers:
           with self.subTest(F'{tokenizer.__class__.__name__}'						):
               snake_case   :				List[Any]				 =   "[SPECIAL_TOKEN]"

               tokenizer.add_special_tokens({"cls_token": special_token}						)
               snake_case   :				str				 =   tokenizer.encode([special_token]						,     add_special_tokens=UpperCamelCase__						)
               self.assertEqual(len(UpperCamelCase__						)						,     1						)

               snake_case   :				List[Any]				 =   tokenizer.decode(UpperCamelCase__						,     skip_special_tokens=UpperCamelCase__						)
               self.assertTrue(special_token not in decoded						)







   def 			lowerCamelCase		(     self						)     ->		List[Any]:

       '''simple docstring'''




       snake_case   :				List[str]				 =   self.get_tokenizers()
       for tokenizer in tokenizers:
           with self.subTest(F'{tokenizer.__class__.__name__}'						):
               snake_case ,snake_case   :				Union[str, Any]				 =   self.get_input_output_texts(UpperCamelCase__						)

               snake_case   :				Dict				 =   tokenizer.tokenize(UpperCamelCase__						)
               snake_case   :				Tuple				 =   tokenizer.convert_tokens_to_ids(UpperCamelCase__						)
               snake_case   :				List[Any]				 =   tokenizer.encode(UpperCamelCase__						,     add_special_tokens=UpperCamelCase__						)
               self.assertListEqual(UpperCamelCase__						,     UpperCamelCase__						)

               snake_case   :				Dict				 =   tokenizer.convert_ids_to_tokens(UpperCamelCase__						)
               self.assertNotEqual(len(UpperCamelCase__						)						,     0						)
               snake_case   :				Tuple				 =   tokenizer.decode(UpperCamelCase__						)
               self.assertIsInstance(UpperCamelCase__						,     UpperCamelCase__						)

               self.assertEqual(text_a.replace(" "						,     ""						)						,     UpperCamelCase__						)







   @unittest.skip("MGP-STR tokenizer only handles one sequence."						)
   def 			lowerCamelCase		(     self						)     ->		List[Any]:

       '''simple docstring'''




       pass







   @unittest.skip("inputs cannot be pretokenized in MgpstrTokenizer"						)
   def 			lowerCamelCase		(     self						)     ->		Tuple:

       '''simple docstring'''




       pass





