




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       A      :
     """simple docstring"""

     def __init__(     self	: List[Any]						,	A__	: Any						,	A__	: Union[str, Any]=1_2						,	A__	: Tuple=7						,	A__	: List[str]=True						,	A__	: Optional[int]=True						,	A__	: str=True						,	A__	: Union[str, Any]=9_9						,	A__	: Any=3_2						,	A__	: Any=3_2						,	A__	: int=5						,	A__	: Union[str, Any]=4						,	A__	: str=3_7						,	A__	: Optional[Any]=0.1						,	A__	: Union[str, Any]=0.1						,	A__	: Dict=5_1_2						,	A__	: str=0.02						,	A__	: Optional[int]=0						,	A__	: int=None						,	)					->							Any:
           lowerCamelCase__   	=					parent
           lowerCamelCase__   	=					batch_size
           lowerCamelCase__   	=					seq_length
           lowerCamelCase__   	=					is_training
           lowerCamelCase__   	=					use_input_mask
           lowerCamelCase__   	=					use_labels
           lowerCamelCase__   	=					vocab_size
           lowerCamelCase__   	=					hidden_size
           lowerCamelCase__   	=					projection_dim
           lowerCamelCase__   	=					num_hidden_layers
           lowerCamelCase__   	=					num_attention_heads
           lowerCamelCase__   	=					intermediate_size
           lowerCamelCase__   	=					dropout
           lowerCamelCase__   	=					attention_dropout
           lowerCamelCase__   	=					max_position_embeddings
           lowerCamelCase__   	=					initializer_range
           lowerCamelCase__   	=					scope
           lowerCamelCase__   	=					bos_token_id




     def 			__snake_case (     self	: Dict      )					->							str:
           lowerCamelCase__   	=					ids_tensor([self.batch_size, self.seq_length]						,	self.vocab_size      )

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

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

           lowerCamelCase__   	=					self.get_config()

           return config, input_ids, input_mask




     def 			__snake_case (     self	: Any      )					->							Optional[Any]:
           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 			__snake_case (     self	: List[str]						,	A__	: Any						,	A__	: List[Any]						,	A__	: Tuple      )					->							Optional[Any]:
           lowerCamelCase__   	=					BlipTextModel(config=A__      )
           model.to(A__      )
           model.eval()
           with torch.no_grad():
                 lowerCamelCase__   	=					model(A__						,	attention_mask=A__      )
                 lowerCamelCase__   	=					model(A__      )
           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 			__snake_case (     self	: Any      )					->							Optional[Any]:
           lowerCamelCase__   	=					self.prepare_config_and_inputs()
           lowerCamelCase__	,		lowerCamelCase__	,		lowerCamelCase__   	=					config_and_inputs
           lowerCamelCase__   	=					{'input_ids': input_ids, 'attention_mask': input_mask}
           return config, inputs_dict






@require_torch
class       A      (		_lowerCAmelCase      ,    unittest.TestCase		):
     """simple docstring"""

     _snake_case:						int  =(BlipTextModel,) if is_torch_available() else ()
     _snake_case:						Dict  =False
     _snake_case:						Tuple  =False
     _snake_case:						Any  =False




     def 			__snake_case (     self	: Optional[int]      )					->							Any:
           lowerCamelCase__   	=					BlipTextModelTester(self      )
           lowerCamelCase__   	=					ConfigTester(self						,	config_class=A__						,	hidden_size=3_7      )




     def 			__snake_case (     self	: List[Any]      )					->							Union[str, Any]:
           self.config_tester.run_common_tests()




     def 			__snake_case (     self	: Optional[int]      )					->							Optional[Any]:
           lowerCamelCase__   	=					self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(*A__      )




     def 			__snake_case (     self	: Tuple      )					->							Any:
           pass




     def 			__snake_case (     self	: Optional[int]      )					->							List[str]:
           pass




     @unittest.skip(reason='Blip does not use inputs_embeds'      )
     def 			__snake_case (     self	: Any      )					->							List[str]:
           pass




     @unittest.skip(reason='BlipTextModel has no base class and is not available in MODEL_MAPPING'      )
     def 			__snake_case (     self	: Optional[Any]      )					->							Dict:
           pass




     @unittest.skip(reason='BlipTextModel has no base class and is not available in MODEL_MAPPING'      )
     def 			__snake_case (     self	: List[str]      )					->							Union[str, Any]:
           pass




     @slow
     def 			__snake_case (     self	: int      )					->							Any:
           for model_name in BLIP_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                 lowerCamelCase__   	=					BlipTextModel.from_pretrained(A__      )
                 self.assertIsNotNone(A__      )




     def 			__snake_case (     self	: List[str]      )					->							Dict:
           super().test_pt_tf_model_equivalence(allow_missing_keys=A__      )





import numpy as np
from cva import destroyAllWindows, imread, imshow, waitKey






class       A      :
     """simple docstring"""

     def __init__(     self	: int						,	A__	: List[Any]						,	A__	: int      )					->							List[str]:
           lowerCamelCase__   	=					0
           # max greyscale value for #FFFFFF
           lowerCamelCase__   	=					int(self.get_greyscale(2_5_5						,	2_5_5						,	2_5_5      )      )

           if not self.min_threshold < threshold < self.max_threshold:
                 lowerCamelCase__   	=					F"""Factor value should be from 0 to {self.max_threshold}"""
                 raise ValueError(A__      )

           lowerCamelCase__   	=					input_img
           lowerCamelCase__   	=					threshold
           lowerCamelCase__	,		lowerCamelCase__   	=					self.input_img.shape[1], self.input_img.shape[0]

           # error table size (+4 columns and +1 row) greater than input image because of
           # lack of if statements
           lowerCamelCase__   	=					[
               [0 for _ in range(self.height + 4      )] for __ in range(self.width + 1      )
           ]
           lowerCamelCase__   	=					np.ones((self.width, self.height, 3)						,	np.uinta      ) * 2_5_5




     @classmethod
     def 			__snake_case (     cls	: int						,	A__	: int						,	A__	: int						,	A__	: int      )					->							float:
           return 0.114 * blue + 0.587 * green + 0.2126 * red




     def 			__snake_case (     self	: List[Any]      )					->							None:
           for y in range(self.height      ):
                 for x in range(self.width      ):
                       lowerCamelCase__   	=					int(self.get_greyscale(*self.input_img[y][x]      )      )
                       if self.threshold > greyscale + self.error_table[y][x]:
                             lowerCamelCase__   	=					(0, 0, 0)
                             lowerCamelCase__   	=					greyscale + self.error_table[x][y]
                       else:
                             lowerCamelCase__   	=					(2_5_5, 2_5_5, 2_5_5)
                             lowerCamelCase__   	=					greyscale + self.error_table[x][y] - 2_5_5
                       self.error_table[y][x + 1] += int(8 / 3_2 * current_error      )
                       self.error_table[y][x + 2] += int(4 / 3_2 * current_error      )
                       self.error_table[y + 1][x] += int(8 / 3_2 * current_error      )
                       self.error_table[y + 1][x + 1] += int(4 / 3_2 * current_error      )
                       self.error_table[y + 1][x + 2] += int(2 / 3_2 * current_error      )
                       self.error_table[y + 1][x - 1] += int(4 / 3_2 * current_error      )
                       self.error_table[y + 1][x - 2] += int(2 / 3_2 * current_error      )


if __name__ == "__main__":
      # create Burke's instances with original images in greyscale
      __SCREAMING_SNAKE_CASE     : Optional[Any]      =[
          Burkes(imread("""image_data/lena.jpg""", 1), threshold)
          for threshold in (1, 126, 130, 140)
      ]

      for burkes in burkes_instances:
            burkes.process()

      for burkes in burkes_instances:
            imshow(
                F"""Original image with dithering threshold: {burkes.threshold}""",
                burkes.output_img,
            )

      waitKey(0)
      destroyAllWindows()