


import unittest

import numpy as np

from transformers import AlbertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
		import jax.numpy as jnp

		from transformers.models.albert.modeling_flax_albert import (
		    FlaxAlbertForMaskedLM,
		    FlaxAlbertForMultipleChoice,
		    FlaxAlbertForPreTraining,
		    FlaxAlbertForQuestionAnswering,
		    FlaxAlbertForSequenceClassification,
		    FlaxAlbertForTokenClassification,
		    FlaxAlbertModel,
		)



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






							def __init__(     self ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE=13 ,					__SCREAMING_SNAKE_CASE=7 ,					__SCREAMING_SNAKE_CASE=True ,					__SCREAMING_SNAKE_CASE=True ,					__SCREAMING_SNAKE_CASE=True ,					__SCREAMING_SNAKE_CASE=True ,					__SCREAMING_SNAKE_CASE=99 ,					__SCREAMING_SNAKE_CASE=32 ,					__SCREAMING_SNAKE_CASE=5 ,					__SCREAMING_SNAKE_CASE=4 ,					__SCREAMING_SNAKE_CASE=37 ,					__SCREAMING_SNAKE_CASE="gelu" ,					__SCREAMING_SNAKE_CASE=0.1 ,					__SCREAMING_SNAKE_CASE=0.1 ,					__SCREAMING_SNAKE_CASE=512 ,					__SCREAMING_SNAKE_CASE=16 ,					__SCREAMING_SNAKE_CASE=2 ,					__SCREAMING_SNAKE_CASE=0.02 ,					__SCREAMING_SNAKE_CASE=4 ,					):




														"""simple docstring"""




														UpperCamelCase    :	Union[str, Any]   	=		parent
														UpperCamelCase    :	Optional[int]   	=		batch_size
														UpperCamelCase    :	List[Any]   	=		seq_length
														UpperCamelCase    :	str   	=		is_training
														UpperCamelCase    :	Dict   	=		use_attention_mask
														UpperCamelCase    :	List[str]   	=		use_token_type_ids
														UpperCamelCase    :	Tuple   	=		use_labels
														UpperCamelCase    :	List[str]   	=		vocab_size
														UpperCamelCase    :	Optional[Any]   	=		hidden_size
														UpperCamelCase    :	Optional[Any]   	=		num_hidden_layers
														UpperCamelCase    :	List[Any]   	=		num_attention_heads
														UpperCamelCase    :	Dict   	=		intermediate_size
														UpperCamelCase    :	int   	=		hidden_act
														UpperCamelCase    :	Optional[int]   	=		hidden_dropout_prob
														UpperCamelCase    :	str   	=		attention_probs_dropout_prob
														UpperCamelCase    :	Tuple   	=		max_position_embeddings
														UpperCamelCase    :	Dict   	=		type_vocab_size
														UpperCamelCase    :	Optional[Any]   	=		type_sequence_label_size
														UpperCamelCase    :	Any   	=		initializer_range
														UpperCamelCase    :	Union[str, Any]   	=		num_choices






							def  _lowercase   (     self						):




														"""simple docstring"""




														UpperCamelCase    :	List[Any]   	=		ids_tensor([self.batch_size, self.seq_length] ,					self.vocab_size						)

														UpperCamelCase    :	Dict   	=		None
														if self.use_attention_mask:
																					UpperCamelCase    :	List[str]   	=		random_attention_mask([self.batch_size, self.seq_length]						)

														UpperCamelCase    :	Optional[int]   	=		None
														if self.use_token_type_ids:
																					UpperCamelCase    :	Tuple   	=		ids_tensor([self.batch_size, self.seq_length] ,					self.type_vocab_size						)

														UpperCamelCase    :	Optional[Any]   	=		AlbertConfig(
														    vocab_size=self.vocab_size ,					hidden_size=self.hidden_size ,					num_hidden_layers=self.num_hidden_layers ,					num_attention_heads=self.num_attention_heads ,					intermediate_size=self.intermediate_size ,					hidden_act=self.hidden_act ,					hidden_dropout_prob=self.hidden_dropout_prob ,					attention_probs_dropout_prob=self.attention_probs_dropout_prob ,					max_position_embeddings=self.max_position_embeddings ,					type_vocab_size=self.type_vocab_size ,					is_decoder=__lowerCamelCase ,					initializer_range=self.initializer_range ,					)

														return config, input_ids, token_type_ids, attention_mask






							def  _lowercase   (     self						):




														"""simple docstring"""




														UpperCamelCase    :	int   	=		self.prepare_config_and_inputs()
														UpperCamelCase    :	List[Any]   	=		config_and_inputs
														UpperCamelCase    :	List[str]   	=		{"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": attention_mask}
														return config, inputs_dict




@require_flax
class    UpperCAmelCase_	(							lowercase__,     unittest.TestCase):
							'''simple docstring'''
							__UpperCamelCase      :     Union[str, Any]							=					(
							    (
							        FlaxAlbertModel,
							        FlaxAlbertForPreTraining,
							        FlaxAlbertForMaskedLM,
							        FlaxAlbertForMultipleChoice,
							        FlaxAlbertForQuestionAnswering,
							        FlaxAlbertForSequenceClassification,
							        FlaxAlbertForTokenClassification,
							        FlaxAlbertForQuestionAnswering,
							    )
							    if is_flax_available()
							    else ()
							)






							def  _lowercase   (     self						):




														"""simple docstring"""




														UpperCamelCase    :	Optional[int]   	=		FlaxAlbertModelTester(self						)






							@slow
							def  _lowercase   (     self						):




														"""simple docstring"""




														for model_class_name in self.all_model_classes:
																					UpperCamelCase    :	Union[str, Any]   	=		model_class_name.from_pretrained('''albert-base-v2'''						)
																					UpperCamelCase    :	Dict   	=		model(np.ones((1, 1)						)						)
																					self.assertIsNotNone(__lowerCamelCase						)




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






							@slow
							def  _lowercase   (     self						):




														"""simple docstring"""




														UpperCamelCase    :	Optional[int]   	=		FlaxAlbertModel.from_pretrained('''albert-base-v2'''						)
														UpperCamelCase    :	Optional[int]   	=		np.array([[0, 345, 232, 328, 740, 140, 1_695, 69, 6_078, 1_588, 2]]						)
														UpperCamelCase    :	List[str]   	=		np.array([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]						)
														UpperCamelCase    :	Union[str, Any]   	=		model(__lowerCamelCase ,					attention_mask=__lowerCamelCase						)[0]
														UpperCamelCase    :	Tuple   	=		(1, 11, 768)
														self.assertEqual(output.shape ,					__lowerCamelCase						)
														UpperCamelCase    :	Any   	=		np.array(
														    [[[-0.6_513, 1.5_035, -0.2_766], [-0.6_515, 1.5_046, -0.2_780], [-0.6_512, 1.5_049, -0.2_784]]]						)

														self.assertTrue(jnp.allclose(output[:, 1:4, 1:4] ,					__lowerCamelCase ,					atol=1e-4						)						)





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()

