


import unittest

from transformers import AutoConfig, AutoTokenizer, BertConfig, TensorType, is_flax_available
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, require_flax, slow


if is_flax_available():
  import jax

  from transformers.models.auto.modeling_flax_auto import FlaxAutoModel
  from transformers.models.bert.modeling_flax_bert import FlaxBertModel
  from transformers.models.roberta.modeling_flax_roberta import FlaxRobertaModel



@require_flax
class    UpperCAmelCase_	(							unittest.TestCase):
       '''simple docstring'''






       @slow
       def  _lowercase   (     self						):




              """simple docstring"""




              for model_name in ["bert-base-cased", "bert-large-uncased"]:
                     with self.subTest(__SCREAMING_SNAKE_CASE						):
                            UpperCamelCase    :	Tuple   	=		AutoConfig.from_pretrained(__SCREAMING_SNAKE_CASE						)
                            self.assertIsNotNone(__SCREAMING_SNAKE_CASE						)
                            self.assertIsInstance(__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE						)

                            UpperCamelCase    :	List[str]   	=		FlaxAutoModel.from_pretrained(__SCREAMING_SNAKE_CASE						)
                            self.assertIsNotNone(__SCREAMING_SNAKE_CASE						)
                            self.assertIsInstance(__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE						)






       @slow
       def  _lowercase   (     self						):




              """simple docstring"""




              for model_name in ["roberta-base", "roberta-large"]:
                     with self.subTest(__SCREAMING_SNAKE_CASE						):
                            UpperCamelCase    :	Any   	=		AutoConfig.from_pretrained(__SCREAMING_SNAKE_CASE						)
                            self.assertIsNotNone(__SCREAMING_SNAKE_CASE						)
                            self.assertIsInstance(__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE						)

                            UpperCamelCase    :	Union[str, Any]   	=		FlaxAutoModel.from_pretrained(__SCREAMING_SNAKE_CASE						)
                            self.assertIsNotNone(__SCREAMING_SNAKE_CASE						)
                            self.assertIsInstance(__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE						)






       @slow
       def  _lowercase   (     self						):




              """simple docstring"""




              for model_name in ["bert-base-cased", "bert-large-uncased"]:
                     UpperCamelCase    :	Union[str, Any]   	=		AutoTokenizer.from_pretrained(__SCREAMING_SNAKE_CASE						)
                     UpperCamelCase    :	List[Any]   	=		FlaxBertModel.from_pretrained(__SCREAMING_SNAKE_CASE						)
                     UpperCamelCase    :	Union[str, Any]   	=		tokenizer('''Do you support jax jitted function?''' ,					return_tensors=TensorType.JAX						)

                     @jax.jit
                     def eval(**__SCREAMING_SNAKE_CASE						):
                            return model(**__SCREAMING_SNAKE_CASE						)

                     eval(**__SCREAMING_SNAKE_CASE						).block_until_ready()






       @slow
       def  _lowercase   (     self						):




              """simple docstring"""




              for model_name in ["roberta-base", "roberta-large"]:
                     UpperCamelCase    :	Any   	=		AutoTokenizer.from_pretrained(__SCREAMING_SNAKE_CASE						)
                     UpperCamelCase    :	List[str]   	=		FlaxRobertaModel.from_pretrained(__SCREAMING_SNAKE_CASE						)
                     UpperCamelCase    :	Optional[int]   	=		tokenizer('''Do you support jax jitted function?''' ,					return_tensors=TensorType.JAX						)

                     @jax.jit
                     def eval(**__SCREAMING_SNAKE_CASE						):
                            return model(**__SCREAMING_SNAKE_CASE						)

                     eval(**__SCREAMING_SNAKE_CASE						).block_until_ready()






       def  _lowercase   (     self						):




              """simple docstring"""




              with self.assertRaisesRegex(
                  __SCREAMING_SNAKE_CASE ,					'''bert-base is not a local folder and is not a valid model identifier'''						):
                     UpperCamelCase    :	str   	=		FlaxAutoModel.from_pretrained('''bert-base'''						)






       def  _lowercase   (     self						):




              """simple docstring"""




              with self.assertRaisesRegex(
                  __SCREAMING_SNAKE_CASE ,					R'''aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)'''						):
                     UpperCamelCase    :	Any   	=		FlaxAutoModel.from_pretrained(__SCREAMING_SNAKE_CASE ,					revision='''aaaaaa'''						)






       def  _lowercase   (     self						):




              """simple docstring"""




              with self.assertRaisesRegex(
                  __SCREAMING_SNAKE_CASE ,					'''hf-internal-testing/config-no-model does not appear to have a file named flax_model.msgpack''' ,					):
                     UpperCamelCase    :	Optional[int]   	=		FlaxAutoModel.from_pretrained('''hf-internal-testing/config-no-model'''						)






       def  _lowercase   (     self						):




              """simple docstring"""




              with self.assertRaisesRegex(__SCREAMING_SNAKE_CASE ,					'''Use `from_pt=True` to load this model'''						):
                     UpperCamelCase    :	str   	=		FlaxAutoModel.from_pretrained('''hf-internal-testing/tiny-bert-pt-only'''						)





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      a	(							SCREAMING_SNAKE_CASE_      :		List[Any]							,			SCREAMING_SNAKE_CASE_      :		List[str]       ):






       """simple docstring"""





       UpperCamelCase    :	List[str]   	=		k_size // 2
       UpperCamelCase	, UpperCamelCase    :	Optional[int]   	=		mgrid[0 - center : k_size - center, 0 - center : k_size - center]
       UpperCamelCase    :	Dict   	=		1 / (2 * pi * sigma) * exp(-(square(SCREAMING_SNAKE_CASE_       ) + square(SCREAMING_SNAKE_CASE_       )) / (2 * square(SCREAMING_SNAKE_CASE_       ))       )
       return g


def      a	(							SCREAMING_SNAKE_CASE_      :		Dict							,			SCREAMING_SNAKE_CASE_      :		Optional[int]							,			SCREAMING_SNAKE_CASE_      :		int       ):






       """simple docstring"""





       UpperCamelCase	, UpperCamelCase    :	Tuple   	=		image.shape[0], image.shape[1]
       # dst image height and width
       UpperCamelCase    :	str   	=		height - k_size + 1
       UpperCamelCase    :	Optional[int]   	=		width - k_size + 1

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

       #  turn the kernel into shape(k*k, 1)
       UpperCamelCase    :	Optional[int]   	=		gen_gaussian_kernel(SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_       )
       UpperCamelCase    :	List[Any]   	=		ravel(SCREAMING_SNAKE_CASE_       )

       # reshape and get the dst image
       UpperCamelCase    :	Optional[int]   	=		dot(SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_       ).reshape(SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_       ).astype(SCREAMING_SNAKE_CASE_       )

       return dst


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

  # get values with two different mask size
  __UpperCAmelCase : Optional[int] 			=				gaussian_filter(gray, 3, sigma=1)
  __UpperCAmelCase : List[Any] 			=				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()

