
from cva import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey
from numpy import array, dot, pad, ravel, uinta, zeros



def 						lowercase_			(   A__ ,		A__		)							->							Union[str, Any]:
    """simple docstring"""

    snake_case     ,							snake_case      =					image.shape
    snake_case      =					cols - block_size[1] + 1
    snake_case      =					rows - block_size[0] + 1
    snake_case      =					zeros((dst_height * dst_width, block_size[1] * block_size[0])		)
    snake_case      =					0
    for i in range(0 ,		A__		):
        for j in range(0 ,		A__		):
            snake_case      =					ravel(image[i : i + block_size[0], j : j + block_size[1]]		)
            snake_case      =					window
            row += 1

    return image_array



def 						lowercase_			(   A__ ,		A__		)							->							List[Any]:
    """simple docstring"""

    snake_case     ,							snake_case      =					image.shape[0], image.shape[1]
    snake_case      =					filter_kernel.shape[0]
    snake_case      =					k_size // 2
    # Pads image with the edge values of array.
    snake_case      =					pad(A__ ,		A__ ,		mode="edge"		)

    # im2col, turn the k_size*k_size pixels into a row and np.vstack all rows
    snake_case      =					imacol(A__ ,		(k_size, k_size)		)

    #  turn the kernel into shape(k*k, 1)
    snake_case      =					ravel(A__		)
    # reshape and get the dst image
    snake_case      =					dot(A__ ,		A__		).reshape(A__ ,		A__		)
    return dst


if __name__ == "__main__":
   # read original image
   _A  					= imread(R"../image_data/lena.jpg")
   # turn image in gray scale value
   _A  					= cvtColor(img, COLOR_BGR2GRAY)
   # Laplace operator
   _A  					= array([[0, 1, 0], [1, -4, 1], [0, 1, 0]])
   _A  					= img_convolve(gray, Laplace_kernel).astype(uinta)
   imshow("Laplacian", out)
   waitKey(0)



import os
import shutil
import unittest
from unittest.mock import patch

from transformers.testing_utils import CaptureStd, is_pt_tf_cross_test, require_torch







class      lowerCamelCase		(  unittest.TestCase  ):





     @patch("sys.argv"				,    ["fakeprogrampath", "env"]   )
     def 		UpperCAmelCase(self      :    Optional[Any]   )						->						Dict:
         # test transformers-cli env
         import transformers.commands.transformers_cli

         with CaptureStd() as cs:
             transformers.commands.transformers_cli.main()
         self.assertIn("Python version"				,    cs.out   )
         self.assertIn("Platform"				,    cs.out   )
         self.assertIn("Using distributed or parallel set-up in script?"				,    cs.out   )





     @is_pt_tf_cross_test
     @patch(
         "sys.argv"				,    ["fakeprogrampath", "pt-to-tf", "--model-name", "hf-internal-testing/tiny-random-gptj", "--no-pr"]   )
     def 		UpperCAmelCase(self      :    Any   )						->						int:
         import transformers.commands.transformers_cli

         shutil.rmtree("/tmp/hf-internal-testing/tiny-random-gptj"				,    ignore_errors=_A   )  # cleans potential past runs
         transformers.commands.transformers_cli.main()

         # The original repo has no TF weights -- if they exist, they were created by the CLI
         self.assertTrue(os.path.exists("/tmp/hf-internal-testing/tiny-random-gptj/tf_model.h5"   )   )





     @require_torch
     @patch("sys.argv"				,    ["fakeprogrampath", "download", "hf-internal-testing/tiny-random-gptj", "--cache-dir", "/tmp"]   )
     def 		UpperCAmelCase(self      :    Dict   )						->						List[Any]:
         import transformers.commands.transformers_cli

         # # remove any previously downloaded model to start clean
         shutil.rmtree("/tmp/models--hf-internal-testing--tiny-random-gptj"				,    ignore_errors=_A   )

         # run the command
         transformers.commands.transformers_cli.main()

         # check if the model files are downloaded correctly on /tmp/models--hf-internal-testing--tiny-random-gptj
         self.assertTrue(os.path.exists("/tmp/models--hf-internal-testing--tiny-random-gptj/blobs"   )   )
         self.assertTrue(os.path.exists("/tmp/models--hf-internal-testing--tiny-random-gptj/refs"   )   )
         self.assertTrue(os.path.exists("/tmp/models--hf-internal-testing--tiny-random-gptj/snapshots"   )   )





     @require_torch
     @patch(
         "sys.argv"				,    [
             "fakeprogrampath",
             "download",
             "hf-internal-testing/test_dynamic_model_with_tokenizer",
             "--trust-remote-code",
             "--cache-dir",
             "/tmp",
         ]				,    )
     def 		UpperCAmelCase(self      :    Any   )						->						List[str]:
         import transformers.commands.transformers_cli

         # # remove any previously downloaded model to start clean
         shutil.rmtree("/tmp/models--hf-internal-testing--test_dynamic_model_with_tokenizer"				,    ignore_errors=_A   )

         # run the command
         transformers.commands.transformers_cli.main()

         # check if the model files are downloaded correctly on /tmp/models--hf-internal-testing--test_dynamic_model_with_tokenizer
         self.assertTrue(os.path.exists("/tmp/models--hf-internal-testing--test_dynamic_model_with_tokenizer/blobs"   )   )
         self.assertTrue(os.path.exists("/tmp/models--hf-internal-testing--test_dynamic_model_with_tokenizer/refs"   )   )
         self.assertTrue(
             os.path.exists("/tmp/models--hf-internal-testing--test_dynamic_model_with_tokenizer/snapshots"   )   )

