import unittest

import numpy as np

from transformers import BlipTextConfig
from transformers.testing_utils import require_torch, slow, torch_device
from transformers.utils import is_torch_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask


if is_torch_available():
      import torch

      from transformers import BlipTextModel
      from transformers.models.blip.modeling_blip import BLIP_PRETRAINED_MODEL_ARCHIVE_LIST





class   lowerCAmelCase    :



     def __init__(       self					,   lowerCAmelCase__					,   lowerCAmelCase__=12					,   lowerCAmelCase__=7					,   lowerCAmelCase__=True					,   lowerCAmelCase__=True					,   lowerCAmelCase__=True					,   lowerCAmelCase__=99					,   lowerCAmelCase__=32					,   lowerCAmelCase__=32					,   lowerCAmelCase__=5					,   lowerCAmelCase__=4					,   lowerCAmelCase__=37					,   lowerCAmelCase__=0.1					,   lowerCAmelCase__=0.1					,   lowerCAmelCase__=512					,   lowerCAmelCase__=0.02					,   lowerCAmelCase__=0					,   lowerCAmelCase__=None					,   ):
            _A=						parent
            _A=						batch_size
            _A=						seq_length
            _A=						is_training
            _A=						use_input_mask
            _A=						use_labels
            _A=						vocab_size
            _A=						hidden_size
            _A=						projection_dim
            _A=						num_hidden_layers
            _A=						num_attention_heads
            _A=						intermediate_size
            _A=						dropout
            _A=						attention_dropout
            _A=						max_position_embeddings
            _A=						initializer_range
            _A=						scope
            _A=						bos_token_id



     def       a__	(       self							):
            _A=						ids_tensor([self.batch_size, self.seq_length]					,   self.vocab_size							)

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

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

            _A=						self.get_config()

            return config, input_ids, input_mask



     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__							):
            _A=						BlipTextModel(config=lowerCAmelCase__							)
            model.to(lowerCAmelCase__							)
            model.eval()
            with torch.no_grad():
                   _A=						model(lowerCAmelCase__					,   attention_mask=lowerCAmelCase__							)
                   _A=						model(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							):
            _A=						self.prepare_config_and_inputs()
            _A,      _A,      _A=						config_and_inputs
            _A=						{'input_ids': input_ids, 'attention_mask': input_mask}
            return config, inputs_dict





@require_torch
class   lowerCAmelCase    (       _a      ,		unittest.TestCase      ):
     _SCREAMING_SNAKE_CASE  :  List[str]		=(BlipTextModel,) if is_torch_available() else ()
     _SCREAMING_SNAKE_CASE  :  Any		=False
     _SCREAMING_SNAKE_CASE  :  int		=False
     _SCREAMING_SNAKE_CASE  :  Any		=False



     def       a__	(       self							):
            _A=						BlipTextModelTester(self							)
            _A=						ConfigTester(self					,   config_class=lowerCAmelCase__					,   hidden_size=37							)



     def       a__	(       self							):
            self.config_tester.run_common_tests()



     def       a__	(       self							):
            _A=						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 BLIP_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                   _A=						BlipTextModel.from_pretrained(lowerCAmelCase__							)
                   self.assertIsNotNone(lowerCAmelCase__							)





     def       a__	(       self							):
            super().test_pt_tf_model_equivalence(allow_missing_keys=lowerCAmelCase__							)
import numpy as np
from PIL import Image

def 				UpperCamelCase					(     lowerCAmelCase_ )		->    np.array:




       '''simple docstring'''






       _A,      _A,      _A=						rgb[:, :, 0], rgb[:, :, 1], rgb[:, :, 2]
       return 0.2_989 * r + 0.5_870 * g + 0.1_140 * b

def 				UpperCamelCase					(     lowerCAmelCase_ )		->    np.array:




       '''simple docstring'''






       return (gray > 1_27) & (gray <= 2_55)

def 				UpperCamelCase					(     lowerCAmelCase_							,       lowerCAmelCase_ )		->    np.array:




       '''simple docstring'''






       _A=						np.zeros_like(lowerCAmelCase_ )
       _A=						np.zeros(
           (image.shape[0] + kernel.shape[0] - 1, image.shape[1] + kernel.shape[1] - 1) )

       # Copy image to padded image
       _A=						image

       # Iterate over image & apply kernel
       for x in range(image.shape[1] ):
              for y in range(image.shape[0] ):
                     _A=						(
                         kernel * image_padded[y : y + kernel.shape[0], x : x + kernel.shape[1]]
                     ).sum()
                     _A=						int(summation == 5 )
       return output


# kernel to be applied
UpperCAmelCase_    		=				np.array([[0, 1, 0], [1, 1, 1], [0, 1, 0]])

if __name__ == "__main__":
      # read original image
      UpperCAmelCase_    		=				np.array(Image.open(R'''..\image_data\lena.jpg'''))
      # Apply erosion operation to a binary image
      UpperCAmelCase_    		=				erosion(grayabinary(rgbagray(image)), structuring_element)
      # Save the output image
      UpperCAmelCase_    		=				Image.fromarray(output).convert('''RGB''')
      pil_img.save('''result_erosion.png''')