





import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow


if is_torch_available():
    import torch

    from transformers import XLMRobertaModel



@require_sentencepiece
@require_tokenizers
@require_torch
class  A__       (	unittest.TestCase      ):





      @slow
      def 	__UpperCamelCase(			self       ):
       '''simple docstring'''




       UpperCamelCase     : Dict	     =       XLMRobertaModel.from_pretrained("xlm-roberta-base"       )
       UpperCamelCase     : Any	     =       torch.tensor([[0, 581, 1_0269, 83, 9_9942, 136, 6_0742, 23, 70, 8_0583, 1_8276, 2]]       )
       # The dog is cute and lives in the garden house

       UpperCamelCase     : int	     =       torch.Size((1, 12, 768)       )  # batch_size, sequence_length, embedding_vector_dim
       UpperCamelCase     : int	     =       torch.tensor(
           [[-0.01_01, 0.12_18, -0.08_03, 0.08_01, 0.13_27, 0.07_76, -0.12_15, 0.23_83, 0.33_38, 0.31_06, 0.03_00, 0.02_52]]       )
       #  xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')
       #  xlmr.eval()
       #  expected_output_values_last_dim = xlmr.extract_features(input_ids[0])[:, :, -1]
       with torch.no_grad():
        UpperCamelCase     : Dict	     =       model(snake_case__       )["last_hidden_state"].detach()
       self.assertEqual(output.shape	,							snake_case__       )
       # compare the actual values for a slice of last dim
       self.assertTrue(torch.allclose(output[:, :, -1]	,							snake_case__	,							atol=1e-3       )       )





      @slow
      def 	__UpperCamelCase(			self       ):
       '''simple docstring'''




       UpperCamelCase     : Optional[Any]	     =       XLMRobertaModel.from_pretrained("xlm-roberta-large"       )
       UpperCamelCase     : Union[str, Any]	     =       torch.tensor([[0, 581, 1_0269, 83, 9_9942, 136, 6_0742, 23, 70, 8_0583, 1_8276, 2]]       )
       # The dog is cute and lives in the garden house

       UpperCamelCase     : Tuple	     =       torch.Size((1, 12, 1024)       )  # batch_size, sequence_length, embedding_vector_dim
       UpperCamelCase     : Tuple	     =       torch.tensor(
           [[-0.06_99, -0.03_18, 0.07_05, -0.12_41, 0.09_99, -0.05_20, 0.10_04, -0.18_38, -0.47_04, 0.14_37, 0.08_21, 0.01_26]]       )
       #  xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.large')
       #  xlmr.eval()
       #  expected_output_values_last_dim = xlmr.extract_features(input_ids[0])[:, :, -1]
       with torch.no_grad():
        UpperCamelCase     : int	     =       model(snake_case__       )["last_hidden_state"].detach()
       self.assertEqual(output.shape	,							snake_case__       )
       # compare the actual values for a slice of last dim
       self.assertTrue(torch.allclose(output[:, :, -1]	,							snake_case__	,							atol=1e-3       )       )







from pathlib import Path

import numpy as np
from PIL import Image



def 		A_    (					_lowerCAmelCase					)						->			np.ndarray:
 UpperCamelCase     ,   UpperCamelCase     ,   UpperCamelCase     : Optional[Any]	     =       rgb[:, :, 0], rgb[:, :, 1], rgb[:, :, 2]
 return 0.2_989 * r + 0.5_870 * g + 0.1_140 * b



def 		A_    (					_lowerCAmelCase					)						->			np.ndarray:
 return (gray > 127) & (gray <= 255)



def 		A_    (					_lowerCAmelCase							,       _lowerCAmelCase					)						->			np.ndarray:
 UpperCamelCase     : Dict	     =       np.zeros_like(_lowerCAmelCase					)
 UpperCamelCase     : List[str]	     =       np.zeros(
     (image.shape[0] + kernel.shape[0] - 1, image.shape[1] + kernel.shape[1] - 1)					)

 # Copy image to padded image
 UpperCamelCase     : Tuple	     =       image

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


if __name__ == "__main__":
    # read original image
    __lowerCamelCase	:					int			  =					Path(__file__).resolve().parent / """image_data""" / """lena.jpg"""
    __lowerCamelCase	:					Tuple			  =					np.array(Image.open(lena_path))
    # kernel to be applied
    __lowerCamelCase	:					Optional[Any]			  =					np.array([[0, 1, 0], [1, 1, 1], [0, 1, 0]])
    __lowerCamelCase	:					Union[str, Any]			  =					dilation(gray_to_binary(rgb_to_gray(lena)), structuring_element)
    # Save the output image
    __lowerCamelCase	:					Any			  =					Image.fromarray(output).convert("""RGB""")
    pil_img.save("""result_dilation.png""")
