



import contextlib
import importlib
import io
import unittest

import transformers

# Try to import everything from transformers to ensure every object can be loaded.
from transformers import *  # noqa F406
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, require_flax, require_tf, require_torch
from transformers.utils import ContextManagers, find_labels, is_flax_available, is_tf_available, is_torch_available


if is_torch_available():
	from transformers import BertForPreTraining, BertForQuestionAnswering, BertForSequenceClassification

if is_tf_available():
	from transformers import TFBertForPreTraining, TFBertForQuestionAnswering, TFBertForSequenceClassification

if is_flax_available():
	from transformers import FlaxBertForPreTraining, FlaxBertForQuestionAnswering, FlaxBertForSequenceClassification


_lowerCAmelCase	:   Any    =   DUMMY_UNKNOWN_IDENTIFIER
# An actual model hosted on huggingface.co

_lowerCAmelCase	:   str    =   "main"
# Default branch name
_lowerCAmelCase	:   Dict    =   "f2c752cfc5c0ab6f4bdec59acea69eefbee381c2"
# One particular commit (not the top of `main`)
_lowerCAmelCase	:   Optional[Any]    =   "aaaaaaa"
# This commit does not exist, so we should 404.

_lowerCAmelCase	:   Dict    =   "d9e9f15bc825e4b2c9249e9578f884bbcb5e3684"
# Sha-1 of config.json on the top of `main`, for checking purposes
_lowerCAmelCase	:   Tuple    =   "4b243c475af8d0a7754e87d7d096c92e5199ec2fe168a2ee7998e3b8e9bcb1d3"







@contextlib.contextmanager
def     UpperCAmelCase_		(					)	->     List[Any]:



				"""simple docstring"""





				print('Welcome!'					)
				yield
				print('Bye!'					)







@contextlib.contextmanager
def     UpperCAmelCase_		(					)	->     Dict:



				"""simple docstring"""





				print('Bonjour!'					)
				yield
				print('Au revoir!'					)


class 	__snake_case   ( unittest.TestCase ):
		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						# If the spec is missing, importlib would not be able to import the module dynamically.
						assert transformers.__spec__ is not None
						assert importlib.util.find_spec('transformers'						) is not None



class 	__snake_case   ( unittest.TestCase ):
		@unittest.mock.patch('sys.stdout'    ,new_callable=io.StringIO						)
		def 						SCREAMING_SNAKE_CASE_					(							self    ,a_						):







						"""simple docstring"""
						with ContextManagers([]						):
										print('Transformers are awesome!'						)
						# The print statement adds a new line at the end of the output
						self.assertEqual(mock_stdout.getvalue()    ,'Transformers are awesome!\n'						)
		@unittest.mock.patch('sys.stdout'    ,new_callable=io.StringIO						)
		def 						SCREAMING_SNAKE_CASE_					(							self    ,a_						):







						"""simple docstring"""
						with ContextManagers([context_en()]						):
										print('Transformers are awesome!'						)
						# The output should be wrapped with an English welcome and goodbye
						self.assertEqual(mock_stdout.getvalue()    ,'Welcome!\nTransformers are awesome!\nBye!\n'						)
		@unittest.mock.patch('sys.stdout'    ,new_callable=io.StringIO						)
		def 						SCREAMING_SNAKE_CASE_					(							self    ,a_						):







						"""simple docstring"""
						with ContextManagers([context_fr(), context_en()]						):
										print('Transformers are awesome!'						)
						# The output should be wrapped with an English and French welcome and goodbye
						self.assertEqual(mock_stdout.getvalue()    ,'Bonjour!\nWelcome!\nTransformers are awesome!\nBye!\nAu revoir!\n'						)
		@require_torch
		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						self.assertEqual(find_labels(a_						)    ,['labels']						)
						self.assertEqual(find_labels(a_						)    ,['labels', 'next_sentence_label']						)
						self.assertEqual(find_labels(a_						)    ,['start_positions', 'end_positions']						)


						class 	__snake_case   ( SCREAMING_SNAKE_CASE ):
								pass



						self.assertEqual(find_labels(a_						)    ,['labels']						)
		@require_tf
		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						self.assertEqual(find_labels(a_						)    ,['labels']						)
						self.assertEqual(find_labels(a_						)    ,['labels', 'next_sentence_label']						)
						self.assertEqual(find_labels(a_						)    ,['start_positions', 'end_positions']						)


						class 	__snake_case   ( SCREAMING_SNAKE_CASE ):
								pass



						self.assertEqual(find_labels(a_						)    ,['labels']						)



		@require_flax
		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						# Flax models don't have labels
						self.assertEqual(find_labels(a_						)    ,[]						)
						self.assertEqual(find_labels(a_						)    ,[]						)
						self.assertEqual(find_labels(a_						)    ,[]						)


						class 	__snake_case   ( SCREAMING_SNAKE_CASE ):
								pass



						self.assertEqual(find_labels(a_						)    ,[]						)





from itertools import product

from cva import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey
from numpy import dot, exp, mgrid, pi, ravel, square, uinta, zeros







def     UpperCAmelCase_		(					snake_case__    ,			snake_case__					)	->     Dict:



				"""simple docstring"""





				lowerCAmelCase__		  =  k_size // 2
				lowerCAmelCase__  ,   lowerCAmelCase__		  =  mgrid[0 - center : k_size - center, 0 - center : k_size - center]
				lowerCAmelCase__		  =  1 / (2 * pi * sigma) * exp(-(square(snake_case__					) + square(snake_case__					)) / (2 * square(snake_case__					))					)
				return g







def     UpperCAmelCase_		(					snake_case__    ,			snake_case__    ,			snake_case__					)	->     str:



				"""simple docstring"""





				lowerCAmelCase__  ,   lowerCAmelCase__		  =  image.shape[0], image.shape[1]
				# dst image height and width
				lowerCAmelCase__		  =  height - k_size + 1
				lowerCAmelCase__		  =  width - k_size + 1

				# im2col, turn the k_size*k_size pixels into a row and np.vstack all rows
				lowerCAmelCase__		  =  zeros((dst_height * dst_width, k_size * k_size)					)
				lowerCAmelCase__		  =  0
				for i, j in product(range(snake_case__					)    ,			range(snake_case__					)					):
								lowerCAmelCase__		  =  ravel(image[i : i + k_size, j : j + k_size]					)
								lowerCAmelCase__		  =  window
								row += 1

				#  turn the kernel into shape(k*k, 1)
				lowerCAmelCase__		  =  gen_gaussian_kernel(snake_case__    ,			snake_case__					)
				lowerCAmelCase__		  =  ravel(snake_case__					)

				# reshape and get the dst image
				lowerCAmelCase__		  =  dot(snake_case__    ,			snake_case__					).reshape(snake_case__    ,			snake_case__					).astype(snake_case__					)

				return dst


if __name__ == "__main__":
	# read original image
	_lowerCAmelCase	:   List[str]    =   imread(R"../image_data/lena.jpg")
	# turn image in gray scale value
	_lowerCAmelCase	:   Optional[Any]    =   cvtColor(img, COLOR_BGR2GRAY)

	# get values with two different mask size
	_lowerCAmelCase	:   Optional[int]    =   gaussian_filter(gray, 3, sigma=1)
	_lowerCAmelCase	:   Tuple    =   gaussian_filter(gray, 5, sigma=0.8)

	# show result images
	imshow("gaussian filter with 3x3 mask", gaussianaxa)
	imshow("gaussian filter with 5x5 mask", gaussianaxa)
	waitKey()
