



"""simple docstring"""
import numpy as np
from cva import COLOR_BGR2GRAY, CV_8UC3, cvtColor, filteraD, imread, imshow, waitKey

def       _snake_case						(						UpperCamelCase						:		int			,		UpperCamelCase						:		int			,		UpperCamelCase						:		int			,		UpperCamelCase						:		int			,		UpperCamelCase						:		int			,		UpperCamelCase						:		int						):

 # prepare kernel
 # the kernel size have to be odd
 if (ksize % 2) == 0:
  UpperCAmelCase	:					int	=	ksize + 1
 UpperCAmelCase	:					Any	=	np.zeros((ksize, ksize)			,		dtype=np.floataa						)

 # each value
 for y in range(UpperCamelCase						):
  for x in range(UpperCamelCase						):
   # distance from center
   UpperCAmelCase	:					int	=	x - ksize // 2
   UpperCAmelCase	:					Optional[int]	=	y - ksize // 2

   # degree to radiant
   UpperCAmelCase	:					List[str]	=	theta / 180 * np.pi
   UpperCAmelCase	:					str	=	np.cos(_theta						)
   UpperCAmelCase	:					Optional[Any]	=	np.sin(_theta						)

   # get kernel x
   UpperCAmelCase	:					List[Any]	=	cos_theta * px + sin_theta * py

   # get kernel y
   UpperCAmelCase	:					Any	=	-sin_theta * px + cos_theta * py

   # fill kernel
   UpperCAmelCase	:					Any	=	np.exp(
       -(_x**2 + gamma**2 * _y**2) / (2 * sigma**2)						) * np.cos(2 * np.pi * _x / lambd + psi						)

 return gabor


if __name__ == "__main__":
      import doctest

      doctest.testmod()
      # read original image
      A:   Any								=		imread("../image_data/lena.jpg")
      # turn image in gray scale value
      A:   Optional[int]								=		cvtColor(img, COLOR_BGR2GRAY)

      # Apply multiple Kernel to detect edges
      A:   List[str]								=		np.zeros(gray.shape[:2])
      for theta in [0, 3_0, 6_0, 9_0, 1_2_0, 1_5_0]:
            A:   List[str]								=		gabor_filter_kernel(1_0, 8, theta, 1_0, 0, 0)
            out += filteraD(gray, CV_8UC3, kernel_aa)
      A:   str								=		out / out.max() * 2_5_5
      A:   List[Any]								=		out.astype(np.uinta)

      imshow("Original", gray)
      imshow("Gabor filter with 20x20 mask and 6 directions", out)

      waitKey(0)


'''simple docstring'''







import subprocess
import sys

from transformers import BertConfig, BertModel, BertTokenizer, pipeline
from transformers.testing_utils import TestCasePlus, require_torch


class 						__UpperCAmelCase      (					_lowerCamelCase						):


							@require_torch
							def  lowerCamelCase				(       self  ):





								"""simple docstring"""






								_snake_case   						=		'\nfrom transformers import BertConfig, BertModel, BertTokenizer, pipeline\n        '

								_snake_case   						=		'\nmname = "hf-internal-testing/tiny-random-bert"\nBertConfig.from_pretrained(mname)\nBertModel.from_pretrained(mname)\nBertTokenizer.from_pretrained(mname)\npipe = pipeline(task="fill-mask", model=mname)\nprint("success")\n        '

								_snake_case   						=		'\nimport socket\ndef offline_socket(*args, **kwargs): raise RuntimeError("Offline mode is enabled, we shouldn\'t access internet")\nsocket.socket = offline_socket\n        '

								# Force fetching the files so that we can use the cache
								_snake_case   						=		'hf-internal-testing/tiny-random-bert'
								BertConfig.from_pretrained(lowerCAmelCase_  )
								BertModel.from_pretrained(lowerCAmelCase_  )
								BertTokenizer.from_pretrained(lowerCAmelCase_  )
								pipeline(task='fill-mask'    ,   model=lowerCAmelCase_  )

								# baseline - just load from_pretrained with normal network
								_snake_case   						=		[sys.executable, '-c', '\n'.join([load, run, mock]  )]

								# should succeed
								_snake_case   						=		self.get_env()
								# should succeed as TRANSFORMERS_OFFLINE=1 tells it to use local files
								_snake_case   						=		'1'
								_snake_case   						=		subprocess.run(lowerCAmelCase_    ,   env=lowerCAmelCase_    ,   check=lowerCAmelCase_    ,   capture_output=lowerCAmelCase_  )
								self.assertEqual(result.returncode    ,   0    ,   result.stderr  )
								self.assertIn('success'    ,   result.stdout.decode()  )


							@require_torch
							def  lowerCamelCase				(       self  ):





								"""simple docstring"""






								_snake_case   						=		'\nfrom transformers import BertConfig, BertModel, BertTokenizer, pipeline\n        '

								_snake_case   						=		'\nmname = "hf-internal-testing/tiny-random-bert"\nBertConfig.from_pretrained(mname)\nBertModel.from_pretrained(mname)\nBertTokenizer.from_pretrained(mname)\npipe = pipeline(task="fill-mask", model=mname)\nprint("success")\n        '

								_snake_case   						=		'\nimport socket\ndef offline_socket(*args, **kwargs): raise socket.error("Faking flaky internet")\nsocket.socket = offline_socket\n        '

								# Force fetching the files so that we can use the cache
								_snake_case   						=		'hf-internal-testing/tiny-random-bert'
								BertConfig.from_pretrained(lowerCAmelCase_  )
								BertModel.from_pretrained(lowerCAmelCase_  )
								BertTokenizer.from_pretrained(lowerCAmelCase_  )
								pipeline(task='fill-mask'    ,   model=lowerCAmelCase_  )

								# baseline - just load from_pretrained with normal network
								_snake_case   						=		[sys.executable, '-c', '\n'.join([load, run, mock]  )]

								# should succeed
								_snake_case   						=		self.get_env()
								_snake_case   						=		subprocess.run(lowerCAmelCase_    ,   env=lowerCAmelCase_    ,   check=lowerCAmelCase_    ,   capture_output=lowerCAmelCase_  )
								self.assertEqual(result.returncode    ,   0    ,   result.stderr  )
								self.assertIn('success'    ,   result.stdout.decode()  )


							@require_torch
							def  lowerCamelCase				(       self  ):





								"""simple docstring"""






								_snake_case   						=		'\nfrom transformers import BertConfig, BertModel, BertTokenizer\n        '

								_snake_case   						=		'\nmname = "hf-internal-testing/tiny-random-bert-sharded"\nBertConfig.from_pretrained(mname)\nBertModel.from_pretrained(mname)\nprint("success")\n        '

								_snake_case   						=		'\nimport socket\ndef offline_socket(*args, **kwargs): raise ValueError("Offline mode is enabled")\nsocket.socket = offline_socket\n        '

								# baseline - just load from_pretrained with normal network
								_snake_case   						=		[sys.executable, '-c', '\n'.join([load, run]  )]

								# should succeed
								_snake_case   						=		self.get_env()
								_snake_case   						=		subprocess.run(lowerCAmelCase_    ,   env=lowerCAmelCase_    ,   check=lowerCAmelCase_    ,   capture_output=lowerCAmelCase_  )
								self.assertEqual(result.returncode    ,   0    ,   result.stderr  )
								self.assertIn('success'    ,   result.stdout.decode()  )

								# next emulate no network
								_snake_case   						=		[sys.executable, '-c', '\n'.join([load, mock, run]  )]

								# Doesn't fail anymore since the model is in the cache due to other tests, so commenting this.
								# env["TRANSFORMERS_OFFLINE"] = "0"
								# result = subprocess.run(cmd, env=env, check=False, capture_output=True)
								# self.assertEqual(result.returncode, 1, result.stderr)

								# should succeed as TRANSFORMERS_OFFLINE=1 tells it to use local files
								_snake_case   						=		'1'
								_snake_case   						=		subprocess.run(lowerCAmelCase_    ,   env=lowerCAmelCase_    ,   check=lowerCAmelCase_    ,   capture_output=lowerCAmelCase_  )
								self.assertEqual(result.returncode    ,   0    ,   result.stderr  )
								self.assertIn('success'    ,   result.stdout.decode()  )


							@require_torch
							def  lowerCamelCase				(       self  ):





								"""simple docstring"""






								_snake_case   						=		'\nfrom transformers import pipeline\n        '
								_snake_case   						=		'\nmname = "hf-internal-testing/tiny-random-bert"\npipe = pipeline(model=mname)\n        '

								_snake_case   						=		'\nimport socket\ndef offline_socket(*args, **kwargs): raise socket.error("Offline mode is enabled")\nsocket.socket = offline_socket\n        '
								_snake_case   						=		self.get_env()
								_snake_case   						=		'1'
								_snake_case   						=		[sys.executable, '-c', '\n'.join([load, mock, run]  )]
								_snake_case   						=		subprocess.run(lowerCAmelCase_    ,   env=lowerCAmelCase_    ,   check=lowerCAmelCase_    ,   capture_output=lowerCAmelCase_  )
								self.assertEqual(result.returncode    ,   1    ,   result.stderr  )
								self.assertIn(
								    'You cannot infer task automatically within `pipeline` when using offline mode'    ,   result.stderr.decode().replace('\n'    ,   ''  )    ,   )




							@require_torch
							def  lowerCamelCase				(       self  ):





								"""simple docstring"""






								_snake_case   						=		'\nfrom transformers import AutoModel\n        '
								_snake_case   						=		'\nmname = "hf-internal-testing/test_dynamic_model"\nAutoModel.from_pretrained(mname, trust_remote_code=True)\nprint("success")\n        '

								# baseline - just load from_pretrained with normal network
								_snake_case   						=		[sys.executable, '-c', '\n'.join([load, run]  )]

								# should succeed
								_snake_case   						=		self.get_env()
								_snake_case   						=		subprocess.run(lowerCAmelCase_    ,   env=lowerCAmelCase_    ,   check=lowerCAmelCase_    ,   capture_output=lowerCAmelCase_  )
								self.assertEqual(result.returncode    ,   0    ,   result.stderr  )
								self.assertIn('success'    ,   result.stdout.decode()  )

								# should succeed as TRANSFORMERS_OFFLINE=1 tells it to use local files
								_snake_case   						=		'1'
								_snake_case   						=		subprocess.run(lowerCAmelCase_    ,   env=lowerCAmelCase_    ,   check=lowerCAmelCase_    ,   capture_output=lowerCAmelCase_  )
								self.assertEqual(result.returncode    ,   0    ,   result.stderr  )
								self.assertIn('success'    ,   result.stdout.decode()  )

