

from __future__ import annotations

import unittest

import numpy as np

from transformers import BlipTextConfig
from transformers.testing_utils import require_tf, slow
from transformers.utils import is_tf_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask


if is_tf_available():
     import tensorflow as tf

     from transformers import TFBlipTextModel
     from transformers.models.blip.modeling_tf_blip import TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST




class 					_SCREAMING_SNAKE_CASE  :
      def __init__(			self		,	lowerCamelCase		,	lowerCamelCase=12		,	lowerCamelCase=7		,	lowerCamelCase=True		,	lowerCamelCase=True		,	lowerCamelCase=True		,	lowerCamelCase=99		,	lowerCamelCase=32		,	lowerCamelCase=32		,	lowerCamelCase=2		,	lowerCamelCase=4		,	lowerCamelCase=37		,	lowerCamelCase=0.1		,	lowerCamelCase=0.1		,	lowerCamelCase=5_12		,	lowerCamelCase=0.0_2		,	lowerCamelCase=0		,	lowerCamelCase=None		,	):
          snake_case__						=				parent
          snake_case__						=				batch_size
          snake_case__						=				seq_length
          snake_case__						=				is_training
          snake_case__						=				use_input_mask
          snake_case__						=				use_labels
          snake_case__						=				vocab_size
          snake_case__						=				hidden_size
          snake_case__						=				projection_dim
          snake_case__						=				num_hidden_layers
          snake_case__						=				num_attention_heads
          snake_case__						=				intermediate_size
          snake_case__						=				dropout
          snake_case__						=				attention_dropout
          snake_case__						=				max_position_embeddings
          snake_case__						=				initializer_range
          snake_case__						=				scope
          snake_case__						=				bos_token_id
      def A_			(			self ):
          snake_case__						=				ids_tensor([self.batch_size, self.seq_length]		,	self.vocab_size )

          snake_case__						=				None
          if self.use_input_mask:
              snake_case__						=				random_attention_mask([self.batch_size, self.seq_length] )

          if input_mask is not None:
              snake_case__						=				input_mask.numpy()
              snake_case__						,					snake_case__						=				input_mask.shape
              snake_case__						=				np.random.randint(1		,	seq_length - 1		,	size=(batch_size,) )
              for batch_idx, start_index in enumerate(lowerCamelCase ):
                  snake_case__						=				1
                  snake_case__						=				0

          snake_case__						=				self.get_config()

          return config, input_ids, tf.convert_to_tensor(lowerCamelCase )
      def A_			(			self ):
          return BlipTextConfig(
              vocab_size=self.vocab_size		,	hidden_size=self.hidden_size		,	projection_dim=self.projection_dim		,	num_hidden_layers=self.num_hidden_layers		,	num_attention_heads=self.num_attention_heads		,	intermediate_size=self.intermediate_size		,	dropout=self.dropout		,	attention_dropout=self.attention_dropout		,	max_position_embeddings=self.max_position_embeddings		,	initializer_range=self.initializer_range		,	bos_token_id=self.bos_token_id		,	)
      def A_			(			self		,	lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase ):
          snake_case__						=				TFBlipTextModel(config=lowerCamelCase )
          snake_case__						=				model(lowerCamelCase		,	attention_mask=lowerCamelCase		,	training=lowerCamelCase )
          snake_case__						=				model(lowerCamelCase		,	training=lowerCamelCase )
          self.parent.assertEqual(result.last_hidden_state.shape		,	(self.batch_size, self.seq_length, self.hidden_size) )
          self.parent.assertEqual(result.pooler_output.shape		,	(self.batch_size, self.hidden_size) )



      def A_			(			self ):
          snake_case__						=				self.prepare_config_and_inputs()
          snake_case__						,					snake_case__						,					snake_case__						=				config_and_inputs
          snake_case__						=				{"input_ids": input_ids, "attention_mask": input_mask}
          return config, inputs_dict





@require_tf
class 					_SCREAMING_SNAKE_CASE  (      __UpperCamelCase       ,					unittest.TestCase ):
      _A						:		Dict    	=     (TFBlipTextModel,) if is_tf_available() else ()
      _A						:		int    	=     False
      _A						:		Tuple    	=     False
      _A						:		int    	=     False
      def A_			(			self ):
          snake_case__						=				BlipTextModelTester(self )
          snake_case__						=				ConfigTester(self		,	config_class=lowerCamelCase		,	hidden_size=37 )
      def A_			(			self ):
          self.config_tester.run_common_tests()
      def A_			(			self ):
          snake_case__						=				self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*lowerCamelCase )
      def A_			(			self ):
          pass
      def A_			(			self ):
          pass
      @unittest.skip(reason="Blip does not use inputs_embeds" )
      def A_			(			self ):
          pass
      @unittest.skip(reason="BlipTextModel has no base class and is not available in MODEL_MAPPING" )
      def A_			(			self ):
          pass
      @unittest.skip(reason="BlipTextModel has no base class and is not available in MODEL_MAPPING" )
      def A_			(			self ):
          pass
      @slow
      def A_			(			self ):
          for model_name in TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              snake_case__						=				TFBlipTextModel.from_pretrained(lowerCamelCase )
              self.assertIsNotNone(lowerCamelCase )



      def A_			(			self		,	lowerCamelCase=True ):
          super().test_pt_tf_model_equivalence(allow_missing_keys=lowerCamelCase )





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





def 	SCREAMING_SNAKE_CASE__    (      __lowerCAmelCase    ,		__lowerCAmelCase    ,		__lowerCAmelCase    ,		__lowerCAmelCase    ,		__lowerCAmelCase    ,		__lowerCAmelCase							):

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

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

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

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

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

            # fill kernel
            snake_case__						=				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
     __magic_name__            =  imread('''../image_data/lena.jpg''')
     # turn image in gray scale value
     __magic_name__            =  cvtColor(img, COLOR_BGR2GRAY)

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

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

     waitKey(0)



