



import cva
import numpy as np

from digital_image_processing.filters.convolve import img_convolve
from digital_image_processing.filters.sobel_filter import sobel_filter

UpperCAmelCase :				Tuple							=180



def  _lowerCAmelCase     (_lowerCAmelCase ,						_lowerCAmelCase):
					UpperCamelCase_			= k_size // 2
					UpperCamelCase_	,     UpperCamelCase_			= np.mgrid[0 - center : k_size - center, 0 - center : k_size - center]
					UpperCamelCase_			= (
					    1
					    / (2 * np.pi * sigma)
					    * np.exp(-(np.square(_lowerCAmelCase) + np.square(_lowerCAmelCase)) / (2 * np.square(_lowerCAmelCase)))
					)
					return g



def  _lowerCAmelCase     (_lowerCAmelCase ,						_lowerCAmelCase ,						_lowerCAmelCase):
					UpperCamelCase_			= np.zeros(_lowerCAmelCase)

					for row in range(1 ,						image_shape[0] - 1):
										for col in range(1 ,						image_shape[1] - 1):
															UpperCamelCase_			= gradient_direction[row, col]

															if (
															    0 <= direction < PI / 8
															    or 15 * PI / 8 <= direction <= 2 * PI
															    or 7 * PI / 8 <= direction <= 9 * PI / 8
															):
																				UpperCamelCase_			= sobel_grad[row, col - 1]
																				UpperCamelCase_			= sobel_grad[row, col + 1]
																				if sobel_grad[row, col] >= w and sobel_grad[row, col] >= e:
																									UpperCamelCase_			= sobel_grad[row, col]

															elif (
															    PI / 8 <= direction < 3 * PI / 8
															    or 9 * PI / 8 <= direction < 11 * PI / 8
															):
																				UpperCamelCase_			= sobel_grad[row + 1, col - 1]
																				UpperCamelCase_			= sobel_grad[row - 1, col + 1]
																				if sobel_grad[row, col] >= sw and sobel_grad[row, col] >= ne:
																									UpperCamelCase_			= sobel_grad[row, col]

															elif (
															    3 * PI / 8 <= direction < 5 * PI / 8
															    or 11 * PI / 8 <= direction < 13 * PI / 8
															):
																				UpperCamelCase_			= sobel_grad[row - 1, col]
																				UpperCamelCase_			= sobel_grad[row + 1, col]
																				if sobel_grad[row, col] >= n and sobel_grad[row, col] >= s:
																									UpperCamelCase_			= sobel_grad[row, col]

															elif (
															    5 * PI / 8 <= direction < 7 * PI / 8
															    or 13 * PI / 8 <= direction < 15 * PI / 8
															):
																				UpperCamelCase_			= sobel_grad[row - 1, col - 1]
																				UpperCamelCase_			= sobel_grad[row + 1, col + 1]
																				if sobel_grad[row, col] >= nw and sobel_grad[row, col] >= se:
																									UpperCamelCase_			= sobel_grad[row, col]

					return destination



def  _lowerCAmelCase     (_lowerCAmelCase ,						_lowerCAmelCase ,						_lowerCAmelCase ,						_lowerCAmelCase ,						_lowerCAmelCase ,						_lowerCAmelCase):
					for row in range(1 ,						image_shape[0] - 1):
										for col in range(1 ,						image_shape[1] - 1):
															if destination[row, col] >= threshold_high:
																				UpperCamelCase_			= strong
															elif destination[row, col] <= threshold_low:
																				UpperCamelCase_			= 0
															else:
																				UpperCamelCase_			= weak



def  _lowerCAmelCase     (_lowerCAmelCase ,						_lowerCAmelCase ,						_lowerCAmelCase ,						_lowerCAmelCase):
					for row in range(1 ,						image_shape[0]):
										for col in range(1 ,						image_shape[1]):
															if destination[row, col] == weak:
																				if 2_55 in (
																				    destination[row, col + 1],
																				    destination[row, col - 1],
																				    destination[row - 1, col],
																				    destination[row + 1, col],
																				    destination[row - 1, col - 1],
																				    destination[row + 1, col - 1],
																				    destination[row - 1, col + 1],
																				    destination[row + 1, col + 1],
																				):
																									UpperCamelCase_			= strong
																				else:
																									UpperCamelCase_			= 0



def  _lowerCAmelCase     (_lowerCAmelCase ,						_lowerCAmelCase=15 ,						_lowerCAmelCase=30 ,						_lowerCAmelCase=1_28 ,						_lowerCAmelCase=2_55):
					# gaussian_filter
					UpperCamelCase_			= img_convolve(_lowerCAmelCase ,						gen_gaussian_kernel(9 ,						sigma=1.4))
					# get the gradient and degree by sobel_filter
					UpperCamelCase_	,     UpperCamelCase_			= sobel_filter(_lowerCAmelCase)
					UpperCamelCase_			= PI + np.radadeg(_lowerCAmelCase)

					UpperCamelCase_			= suppress_non_maximum(image.shape ,						_lowerCAmelCase ,						_lowerCAmelCase)

					detect_high_low_threshold(
					    image.shape ,						_lowerCAmelCase ,						_lowerCAmelCase ,						_lowerCAmelCase ,						_lowerCAmelCase ,						_lowerCAmelCase)

					track_edge(image.shape ,						_lowerCAmelCase ,						_lowerCAmelCase ,						_lowerCAmelCase)

					return destination


if __name__ == "__main__":
	# read original image in gray mode
	UpperCAmelCase :				Any							=cva.imread(r"""../image_data/lena.jpg""", 0)
	# canny edge detection
	UpperCAmelCase :				str							=canny(lena)
	cva.imshow("""canny""", canny_destination)
	cva.waitKey(0)





import torch
from transformers import AutoModelForSequenceClassification, AutoTokenizer, Trainer, TrainingArguments

import datasets


__lowerCamelCase								=      '''\
@article{eddine2021frugalscore,
  title={FrugalScore: Learning Cheaper, Lighter and Faster Evaluation Metrics for Automatic Text Generation},
  author={Eddine, Moussa Kamal and Shang, Guokan and Tixier, Antoine J-P and Vazirgiannis, Michalis},
  journal={arXiv preprint arXiv:2110.08559},
  year={2021}
}
'''

__lowerCamelCase								=      '''\
FrugalScore is a reference-based metric for NLG models evaluation. It is based on a distillation approach that allows to learn a fixed, low cost version of any expensive NLG metric, while retaining most of its original performance.
'''


__lowerCamelCase								=      '''
Calculates how good are predictions given some references, using certain scores.
Args:
    predictions (list of str): list of predictions to score. Each predictions
        should be a string.
    references (list of str): list of reference for each prediction. Each
        reference should be a string.
    batch_size (int): the batch size for predictions.
    max_length (int): maximum sequence length.
    device (str): either gpu or cpu
Returns:
    scores (list of int): list of scores.
Examples:
    >>> frugalscore = datasets.load_metric("frugalscore")
    >>> results = frugalscore.compute(predictions=[\'hello there\', \'huggingface\'], references=[\'hello world\', \'hugging face\'])
    >>> print([round(s, 3) for s in results["scores"]])
    [0.631, 0.645]
'''


@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION			,			_KWARGS_DESCRIPTION	)
class 	snake_case_			(datasets.Metric	):




       """simple docstring"""






       def 			A_ (  self):

             """simple docstring"""





             return datasets.MetricInfo(
                 description=_DESCRIPTION  ,citation=_CITATION  ,inputs_description=_KWARGS_DESCRIPTION  ,features=datasets.Features(
                     {
                         "predictions": datasets.Value("string"),
                         "references": datasets.Value("string"),
                     })  ,homepage="https://github.com/moussaKam/FrugalScore"  ,)



       def 			A_ (  self  ,lowercase):

             """simple docstring"""





             if self.config_name == "default":
                   UpperCAmelCase_	:						str					   =		"moussaKam/frugalscore_tiny_bert-base_bert-score"
             else:
                   UpperCAmelCase_	:						Optional[int]					   =		self.config_name
             UpperCAmelCase_	:						Dict					   =		AutoModelForSequenceClassification.from_pretrained(lowercase)
             UpperCAmelCase_	:						str					   =		AutoTokenizer.from_pretrained(lowercase)



       def 			A_ (  self  ,lowercase  ,lowercase  ,lowercase=32  ,lowercase=128  ,lowercase=None  ,):

             """simple docstring"""





             assert len(lowercase) == len(
                 lowercase), "predictions and references should have the same number of sentences."
             if device is not None:
                   assert device in ["gpu", "cpu"], "device should be either gpu or cpu."
             else:
                   UpperCAmelCase_	:						Union[str, Any]					   =		"gpu" if torch.cuda.is_available() else "cpu"
             UpperCAmelCase_	:						Optional[Any]					   =		TrainingArguments(
                 "trainer"  ,fpaa=(device == "gpu")  ,per_device_eval_batch_size=lowercase  ,report_to="all"  ,no_cuda=(device == "cpu")  ,log_level="warning"  ,)
             UpperCAmelCase_	:						List[str]					   =		{"sentence1": predictions, "sentence2": references}
             UpperCAmelCase_	:						List[Any]					   =		datasets.Dataset.from_dict(lowercase)

             def tokenize_function(lowercase):
                   return self.tokenizer(
                       data["sentence1"]  ,data["sentence2"]  ,max_length=lowercase  ,truncation=lowercase  ,padding=lowercase)

             UpperCAmelCase_	:						int					   =		raw_datasets.map(lowercase  ,batched=lowercase)
             tokenized_datasets.remove_columns(["sentence1", "sentence2"])
             UpperCAmelCase_	:						Union[str, Any]					   =		Trainer(self.model  ,lowercase  ,tokenizer=self.tokenizer)
             UpperCAmelCase_	:						Tuple					   =		trainer.predict(lowercase)
             return {"scores": list(predictions.predictions.squeeze(-1))}
