


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 A  (       unittest.TestCase					):






      @patch('sys.argv'      ,       ['fakeprogrampath', 'env']			)
      def 	lowerCamelCase	( self						:					Optional[Any]			) ->						str:
             """simple docstring"""




             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 	lowerCamelCase	( self						:					Union[str, Any]			) ->						Any:
             """simple docstring"""




             import transformers.commands.transformers_cli

             shutil.rmtree('/tmp/hf-internal-testing/tiny-random-gptj'      ,       ignore_errors=lowercase_			)  # 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 	lowerCamelCase	( self						:					List[str]			) ->						Union[str, Any]:
             """simple docstring"""




             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=lowercase_			)

             # 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 	lowerCamelCase	( self						:					Optional[int]			) ->						Dict:
             """simple docstring"""




             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=lowercase_			)

             # 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'			)			)






import numpy as np
from cva import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey

from digital_image_processing.filters.convolve import img_convolve




def      a_				(   SCREAMING_SNAKE_CASE__ :				Optional[Any]					):
       '''simple docstring'''





       _lowerCamelCase	:		List[str]          =np.array([[-1, 0, 1], [-2, 0, 2], [-1, 0, 1]]					)
       _lowerCamelCase	:		Dict          =np.array([[1, 2, 1], [0, 0, 0], [-1, -2, -1]]					)

       _lowerCamelCase	:		Optional[Any]          =np.abs(img_convolve(SCREAMING_SNAKE_CASE__		,      SCREAMING_SNAKE_CASE__					)					)
       _lowerCamelCase	:		Optional[int]          =np.abs(img_convolve(SCREAMING_SNAKE_CASE__		,      SCREAMING_SNAKE_CASE__					)					)
       # modify the pix within [0, 255]
       _lowerCamelCase	:		Optional[int]          =dst_x * 255 / np.max(SCREAMING_SNAKE_CASE__					)
       _lowerCamelCase	:		Any          =dst_y * 255 / np.max(SCREAMING_SNAKE_CASE__					)

       _lowerCamelCase	:		Tuple          =np.sqrt((np.square(SCREAMING_SNAKE_CASE__					)) + (np.square(SCREAMING_SNAKE_CASE__					))					)
       _lowerCamelCase	:		int          =dst_xy * 255 / np.max(SCREAMING_SNAKE_CASE__					)
       _lowerCamelCase	:		int          =dst_xy.astype(np.uinta					)

       _lowerCamelCase	:		Optional[int]          =np.arctana(SCREAMING_SNAKE_CASE__		,      SCREAMING_SNAKE_CASE__					)
       return dst, theta


if __name__ == "__main__":
 # read original image
 lowerCamelCase						 =	imread('../image_data/lena.jpg')
 # turn image in gray scale value
 lowerCamelCase						 =	cvtColor(img, COLOR_BGR2GRAY)

 lowerCamelCase ,    lowerCamelCase						 =	sobel_filter(gray)

 # show result images
 imshow('sobel filter', sobel_grad)
 imshow('sobel theta', sobel_theta)
 waitKey(0)


