


'''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:
  lowerCamelCase_  				=						ksize + 1
 lowerCamelCase_  				=						np.zeros((ksize, ksize)      ,       dtype=np.floataa	)

 # each value
 for y in range(UpperCAmelCase_	):
  for x in range(UpperCAmelCase_	):
   # distance from center
   lowerCamelCase_  				=						x - ksize // 2
   lowerCamelCase_  				=						y - ksize // 2

   # degree to radiant
   lowerCamelCase_  				=						theta / 180 * np.pi
   lowerCamelCase_  				=						np.cos(_theta	)
   lowerCamelCase_  				=						np.sin(_theta	)

   # get kernel x
   lowerCamelCase_  				=						cos_theta * px + sin_theta * py

   # get kernel y
   lowerCamelCase_  				=						-sin_theta * px + cos_theta * py

   # fill kernel
   lowerCamelCase_  				=						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_						:       Dict      			=				imread("""../image_data/lena.jpg""")
     # turn image in gray scale value
     a_						:       Any      			=				cvtColor(img, COLOR_BGR2GRAY)

     # Apply multiple Kernel to detect edges
     a_						:       Optional[int]      			=				np.zeros(gray.shape[:2])
     for theta in [0, 30, 60, 90, 120, 150]:
          a_						:       List[Any]      			=				gabor_filter_kernel(10, 8, theta, 10, 0, 0)
          out += filteraD(gray, CV_8UC3, kernel_aa)
     a_						:       int      			=				out / out.max() * 255
     a_						:       Union[str, Any]      			=				out.astype(np.uinta)

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

     waitKey(0)







'''simple docstring'''

import unittest

import numpy as np

from transformers import DistilBertConfig, 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.distilbert.modeling_flax_distilbert import (
         FlaxDistilBertForMaskedLM,
         FlaxDistilBertForMultipleChoice,
         FlaxDistilBertForQuestionAnswering,
         FlaxDistilBertForSequenceClassification,
         FlaxDistilBertForTokenClassification,
         FlaxDistilBertModel,
     )





class   snake_case					(					unittest.TestCase    ):





  """simple docstring"""




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







   """simple docstring"""
   lowerCamelCase_  				=						parent
   lowerCamelCase_  				=						batch_size
   lowerCamelCase_  				=						seq_length
   lowerCamelCase_  				=						is_training
   lowerCamelCase_  				=						use_attention_mask
   lowerCamelCase_  				=						use_token_type_ids
   lowerCamelCase_  				=						use_labels
   lowerCamelCase_  				=						vocab_size
   lowerCamelCase_  				=						hidden_size
   lowerCamelCase_  				=						num_hidden_layers
   lowerCamelCase_  				=						num_attention_heads
   lowerCamelCase_  				=						intermediate_size
   lowerCamelCase_  				=						hidden_act
   lowerCamelCase_  				=						hidden_dropout_prob
   lowerCamelCase_  				=						attention_probs_dropout_prob
   lowerCamelCase_  				=						max_position_embeddings
   lowerCamelCase_  				=						type_vocab_size
   lowerCamelCase_  				=						type_sequence_label_size
   lowerCamelCase_  				=						initializer_range
   lowerCamelCase_  				=						num_choices






  def 						snake_case	(					self  ):







   """simple docstring"""
   lowerCamelCase_  				=						ids_tensor([self.batch_size, self.seq_length]    ,						self.vocab_size  )

   lowerCamelCase_  				=						None
   if self.use_attention_mask:
    lowerCamelCase_  				=						random_attention_mask([self.batch_size, self.seq_length]  )

   lowerCamelCase_  				=						DistilBertConfig(
       vocab_size=self.vocab_size    ,						dim=self.hidden_size    ,						n_layers=self.num_hidden_layers    ,						n_heads=self.num_attention_heads    ,						hidden_dim=self.intermediate_size    ,						hidden_act=self.hidden_act    ,						dropout=self.hidden_dropout_prob    ,						attention_dropout=self.attention_probs_dropout_prob    ,						max_position_embeddings=self.max_position_embeddings    ,						initializer_range=self.initializer_range    ,						tie_weights_=UpperCamelCase    ,						)

   return config, input_ids, attention_mask







  def 						snake_case	(					self  ):







   """simple docstring"""
   lowerCamelCase_  				=						self.prepare_config_and_inputs()
   lowerCamelCase_				,lowerCamelCase_				,lowerCamelCase_  				=						config_and_inputs
   lowerCamelCase_  				=						{"input_ids": input_ids, "attention_mask": attention_mask}
   return config, inputs_dict





@require_flax
class   snake_case					(					lowercase    ,		unittest.TestCase    ):





  """simple docstring"""




  _lowerCamelCase						=						(
      (
          FlaxDistilBertModel,
          FlaxDistilBertForMaskedLM,
          FlaxDistilBertForMultipleChoice,
          FlaxDistilBertForQuestionAnswering,
          FlaxDistilBertForSequenceClassification,
          FlaxDistilBertForTokenClassification,
          FlaxDistilBertForQuestionAnswering,
      )
      if is_flax_available()
      else ()
  )






  def 						snake_case	(					self  ):







   """simple docstring"""
   lowerCamelCase_  				=						FlaxDistilBertModelTester(self  )







  @slow
  def 						snake_case	(					self  ):







   """simple docstring"""
   for model_class_name in self.all_model_classes:
    lowerCamelCase_  				=						model_class_name.from_pretrained("distilbert-base-uncased"  )
    lowerCamelCase_  				=						model(np.ones((1, 1)  )  )
    self.assertIsNotNone(UpperCamelCase  )





@require_flax
class   snake_case					(					unittest.TestCase    ):





  """simple docstring"""




  @slow
  def 						snake_case	(					self  ):







   """simple docstring"""
   lowerCamelCase_  				=						FlaxDistilBertModel.from_pretrained("distilbert-base-uncased"  )
   lowerCamelCase_  				=						np.array([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]]  )
   lowerCamelCase_  				=						np.array([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]  )
   lowerCamelCase_  				=						model(UpperCamelCase    ,						attention_mask=UpperCamelCase  )[0]
   lowerCamelCase_  				=						(1, 11, 768)
   self.assertEqual(output.shape    ,						UpperCamelCase  )
   lowerCamelCase_  				=						np.array([[[-0.1_639, 0.3_299, 0.1_648], [-0.1_746, 0.3_289, 0.1_710], [-0.1_884, 0.3_357, 0.1_810]]]  )

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



