


'''simple docstring'''




import json
import os
import tempfile
import unittest

from transformers.modelcard import ModelCard




class  _lowercase   (  unittest.TestCase       ):
   def a     (	self					:			Any			)   ->       List[Any]:
        __snake_case    			=	{
            'model_details': {
                'Organization': 'testing',
                'Model date': 'today',
                'Model version': 'v2.1, Developed by Test Corp in 2019.',
                'Architecture': 'Convolutional Neural Network.',
            },
            'metrics': 'BLEU and ROUGE-1',
            'evaluation_data': {
                'Datasets': {'BLEU': 'My-great-dataset-v1', 'ROUGE-1': 'My-short-dataset-v2.1'},
                'Preprocessing': 'See details on https://arxiv.org/pdf/1810.03993.pdf',
            },
            'training_data': {
                'Dataset': 'English Wikipedia dump dated 2018-12-01',
                'Preprocessing': (
                    'Using SentencePiece vocabulary of size 52k tokens. See details on'
                    ' https://arxiv.org/pdf/1810.03993.pdf'
                ),
            },
            'quantitative_analyses': {'BLEU': 55.1, 'ROUGE-1': 76},
        }
   def a     (	self					:			List[str]			)   ->       List[str]:
        __snake_case    			=	ModelCard.from_dict(self.inputs_dict			)
        self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_					,      'model_details'			)			)
        self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_					,      'intended_use'			)			)
        self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_					,      'factors'			)			)
        self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_					,      'metrics'			)			)
        self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_					,      'evaluation_data'			)			)
        self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_					,      'training_data'			)			)
        self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_					,      'quantitative_analyses'			)			)
        self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_					,      'ethical_considerations'			)			)
        self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_					,      'caveats_and_recommendations'			)			)
   def a     (	self					:			str			)   ->       str:
        __snake_case    			=	ModelCard.from_dict(self.inputs_dict			)
        __snake_case    			=	json.loads(modelcard.to_json_string()			)
        for key, value in self.inputs_dict.items():
             self.assertEqual(obj[key]					,      SCREAMING_SNAKE_CASE_			)
   def a     (	self					:			str			)   ->       Union[str, Any]:
        __snake_case    			=	ModelCard.from_dict(self.inputs_dict			)

        with tempfile.TemporaryDirectory() as tmpdirname:
             __snake_case    			=	os.path.join(SCREAMING_SNAKE_CASE_					,      'modelcard.json'			)
             model_card_first.to_json_file(SCREAMING_SNAKE_CASE_			)
             __snake_case    			=	ModelCard.from_json_file(SCREAMING_SNAKE_CASE_			)

        self.assertEqual(model_card_second.to_dict()					,      model_card_first.to_dict()			)





   def a     (	self					:			Optional[Any]			)   ->       Dict:
        __snake_case    			=	ModelCard.from_dict(self.inputs_dict			)

        with tempfile.TemporaryDirectory() as tmpdirname:
             model_card_first.save_pretrained(SCREAMING_SNAKE_CASE_			)
             __snake_case    			=	ModelCard.from_pretrained(SCREAMING_SNAKE_CASE_			)

        self.assertEqual(model_card_second.to_dict()					,      model_card_first.to_dict()			)








'''simple docstring'''




import numpy as np
from PIL import Image





def      _a		(lowercase__ :      np.array	) -> np.array:






     """simple docstring"""
     __snake_case			,					__snake_case			,					__snake_case    			=	rgb[:, :, 0], rgb[:, :, 1], rgb[:, :, 2]
     return 0.29_89 * r + 0.58_70 * g + 0.11_40 * b





def      _a		(lowercase__ :      np.array	) -> np.array:






     """simple docstring"""
     return (gray > 1_2_7) & (gray <= 2_5_5)





def      _a		(lowercase__ :      np.array       ,      lowercase__ :      np.array	) -> np.array:






     """simple docstring"""
     __snake_case    			=	np.zeros_like(lowercase__	)
     __snake_case    			=	np.zeros(
         (image.shape[0] + kernel.shape[0] - 1, image.shape[1] + kernel.shape[1] - 1)	)

     # Copy image to padded image
     __snake_case    			=	image

     # Iterate over image & apply kernel
     for x in range(image.shape[1]	):
          for y in range(image.shape[0]	):
               __snake_case    			=	(
                   kernel * image_padded[y : y + kernel.shape[0], x : x + kernel.shape[1]]
               ).sum()
               __snake_case    			=	int(summation == 5	)
     return output


# kernel to be applied
_a      :      Tuple       				=      np.array([[0, 1, 0], [1, 1, 1], [0, 1, 0]])

if __name__ == "__main__":
       # read original image
       _a      :      Any       				=      np.array(Image.open(R"..\image_data\lena.jpg"))
       # Apply erosion operation to a binary image
       _a      :      List[Any]       				=      erosion(grayabinary(rgbagray(image)), structuring_element)
       # Save the output image
       _a      :      int       				=      Image.fromarray(output).convert("RGB")
       pil_img.save("result_erosion.png")




