

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


_UpperCAmelCase		:       List[str]						    =	(
    """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     (			UpperCamelCase__       ,							UpperCamelCase__				):







							'''simple docstring'''






							warnings.warn(UpperCamelCase__       ,							UpperCamelCase__				)
							requires_backends(UpperCamelCase__       ,							'sklearn'				)
							return (preds == labels).mean()





def  __lowerCamelCase     (			UpperCamelCase__       ,							UpperCamelCase__				):







							'''simple docstring'''






							warnings.warn(UpperCamelCase__       ,							UpperCamelCase__				)
							requires_backends(UpperCamelCase__       ,							'sklearn'				)
							snake_case_          =   simple_accuracy(UpperCamelCase__       ,							UpperCamelCase__				)
							snake_case_          =   fa_score(y_true=UpperCamelCase__       ,							y_pred=UpperCamelCase__				)
							return {
							    "acc": acc,
							    "f1": fa,
							    "acc_and_f1": (acc + fa) / 2,
							}





def  __lowerCamelCase     (			UpperCamelCase__       ,							UpperCamelCase__				):







							'''simple docstring'''






							warnings.warn(UpperCamelCase__       ,							UpperCamelCase__				)
							requires_backends(UpperCamelCase__       ,							'sklearn'				)
							snake_case_          =   pearsonr(UpperCamelCase__       ,							UpperCamelCase__				)[0]
							snake_case_          =   spearmanr(UpperCamelCase__       ,							UpperCamelCase__				)[0]
							return {
							    "pearson": pearson_corr,
							    "spearmanr": spearman_corr,
							    "corr": (pearson_corr + spearman_corr) / 2,
							}





def  __lowerCamelCase     (			UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__				):







							'''simple docstring'''






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





def  __lowerCamelCase     (			UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__				):







							'''simple docstring'''






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







import unittest

from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device


if is_torch_available():
					import torch

					from transformers import AutoModelForImageClassification

if is_vision_available():
					from transformers import AutoImageProcessor







@require_torch
@require_vision
class 			lowercase    (	unittest.TestCase      ):




				@slow
				def    a							( self	):
											snake_case_          =   AutoImageProcessor.from_pretrained('microsoft/dit-base-finetuned-rvlcdip'	)
											snake_case_          =   AutoModelForImageClassification.from_pretrained('microsoft/dit-base-finetuned-rvlcdip'	)
											model.to(snake_case	)

											from datasets import load_dataset

											snake_case_          =   load_dataset('nielsr/rvlcdip-demo'	)

											snake_case_          =   dataset['train'][0]['image'].convert('RGB'	)

											snake_case_          =   image_processor(snake_case				,       return_tensors='pt'	).to(snake_case	)

											# forward pass
											with torch.no_grad():
																		snake_case_          =   model(**snake_case	)
																		snake_case_          =   outputs.logits

											snake_case_          =   torch.Size((1, 16)	)
											self.assertEqual(logits.shape				,       snake_case	)

											snake_case_          =   torch.tensor(
											    [-0.41_58, -0.40_92, -0.43_47]				,       device=snake_case				,       dtype=torch.float				,       )
											self.assertTrue(torch.allclose(logits[0, :3]				,       snake_case				,       atol=1e-4	)	)




