
"""simple docstring"""



import unittest

import numpy as np
import torch
from torch import nn
from transformers import (
    CLIPImageProcessor,
    CLIPTextConfig,
    CLIPTextModelWithProjection,
    CLIPTokenizer,
    CLIPVisionConfig,
    CLIPVisionModelWithProjection,
)

from diffusers import KandinskyVaaPriorPipeline, PriorTransformer, UnCLIPScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import enable_full_determinism, skip_mps

from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()
class        _SCREAMING_SNAKE_CASE  (  UpperCAmelCase      ,				unittest.TestCase							):






				'''simple docstring'''



				SCREAMING_SNAKE_CASE_:							Dict							=					KandinskyVaaPriorPipeline
				SCREAMING_SNAKE_CASE_:							Tuple							=					["prompt"]
				SCREAMING_SNAKE_CASE_:							List[Any]							=					["prompt", "negative_prompt"]
				SCREAMING_SNAKE_CASE_:							Any							=					[
				    "num_images_per_prompt",
				    "generator",
				    "num_inference_steps",
				    "latents",
				    "negative_prompt",
				    "guidance_scale",
				    "output_type",
				    "return_dict",
				]
				SCREAMING_SNAKE_CASE_:							Optional[Any]							=					False


				@property
				def 				__lowerCamelCase	(					self	:			str  )		->  Union[str, Any]:







											"""simple docstring"""




											return 32


				@property
				def 				__lowerCamelCase	(					self	:			List[Any]  )		->  str:







											"""simple docstring"""




											return 32


				@property
				def 				__lowerCamelCase	(					self	:			int  )		->  Optional[Any]:







											"""simple docstring"""




											return self.time_input_dim


				@property
				def 				__lowerCamelCase	(					self	:			Optional[int]  )		->  int:







											"""simple docstring"""




											return self.time_input_dim * 4


				@property
				def 				__lowerCamelCase	(					self	:			Optional[int]  )		->  Dict:







											"""simple docstring"""




											return 100


				@property
				def 				__lowerCamelCase	(					self	:			Union[str, Any]  )		->  Optional[int]:







											"""simple docstring"""




											_lowerCAmelCase  	=			CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'  )
											return tokenizer


				@property
				def 				__lowerCamelCase	(					self	:			int  )		->  Tuple:







											"""simple docstring"""




											torch.manual_seed(0  )
											_lowerCAmelCase  	=			CLIPTextConfig(
											    bos_token_id=0				,     eos_token_id=2				,     hidden_size=self.text_embedder_hidden_size				,     projection_dim=self.text_embedder_hidden_size				,     intermediate_size=37				,     layer_norm_eps=1E-0_5				,     num_attention_heads=4				,     num_hidden_layers=5				,     pad_token_id=1				,     vocab_size=1_000				,     )
											return CLIPTextModelWithProjection(UpperCAmelCase_  )


				@property
				def 				__lowerCamelCase	(					self	:			Optional[int]  )		->  Optional[Any]:







											"""simple docstring"""




											torch.manual_seed(0  )

											_lowerCAmelCase  	=			{
											    'num_attention_heads': 2,
											    'attention_head_dim': 12,
											    'embedding_dim': self.text_embedder_hidden_size,
											    'num_layers': 1,
											}

											_lowerCAmelCase  	=			PriorTransformer(**UpperCAmelCase_  )
											# clip_std and clip_mean is initialized to be 0 so PriorTransformer.post_process_latents will always return 0 - set clip_std to be 1 so it won't return 0
											_lowerCAmelCase  	=			nn.Parameter(torch.ones(model.clip_std.shape  )  )
											return model


				@property
				def 				__lowerCamelCase	(					self	:			Tuple  )		->  Optional[int]:







											"""simple docstring"""




											torch.manual_seed(0  )
											_lowerCAmelCase  	=			CLIPVisionConfig(
											    hidden_size=self.text_embedder_hidden_size				,     image_size=224				,     projection_dim=self.text_embedder_hidden_size				,     intermediate_size=37				,     num_attention_heads=4				,     num_channels=3				,     num_hidden_layers=5				,     patch_size=14				,     )

											_lowerCAmelCase  	=			CLIPVisionModelWithProjection(UpperCAmelCase_  )
											return model


				@property
				def 				__lowerCamelCase	(					self	:			Tuple  )		->  List[Any]:







											"""simple docstring"""




											_lowerCAmelCase  	=			CLIPImageProcessor(
											    crop_size=224				,     do_center_crop=UpperCAmelCase_				,     do_normalize=UpperCAmelCase_				,     do_resize=UpperCAmelCase_				,     image_mean=[0.48145466, 0.4578275, 0.40821073]				,     image_std=[0.26862954, 0.26130258, 0.27577711]				,     resample=3				,     size=224				,     )

											return image_processor


				def 				__lowerCamelCase	(					self	:			Tuple  )		->  Dict:







											"""simple docstring"""




											_lowerCAmelCase  	=			self.dummy_prior
											_lowerCAmelCase  	=			self.dummy_image_encoder
											_lowerCAmelCase  	=			self.dummy_text_encoder
											_lowerCAmelCase  	=			self.dummy_tokenizer
											_lowerCAmelCase  	=			self.dummy_image_processor

											_lowerCAmelCase  	=			UnCLIPScheduler(
											    variance_type='fixed_small_log'				,     prediction_type='sample'				,     num_train_timesteps=1_000				,     clip_sample=UpperCAmelCase_				,     clip_sample_range=10.0				,     )

											_lowerCAmelCase  	=			{
											    'prior': prior,
											    'image_encoder': image_encoder,
											    'text_encoder': text_encoder,
											    'tokenizer': tokenizer,
											    'scheduler': scheduler,
											    'image_processor': image_processor,
											}

											return components


				def 				__lowerCamelCase	(					self	:			Dict				,     UpperCAmelCase_	:			Any				,     UpperCAmelCase_	:			str=0  )		->  List[str]:







											"""simple docstring"""




											if str(UpperCAmelCase_  ).startswith('mps'  ):
																		_lowerCAmelCase  	=			torch.manual_seed(UpperCAmelCase_  )
											else:
																		_lowerCAmelCase  	=			torch.Generator(device=UpperCAmelCase_  ).manual_seed(UpperCAmelCase_  )
											_lowerCAmelCase  	=			{
											    'prompt': 'horse',
											    'generator': generator,
											    'guidance_scale': 4.0,
											    'num_inference_steps': 2,
											    'output_type': 'np',
											}
											return inputs


				def 				__lowerCamelCase	(					self	:			Optional[int]  )		->  Tuple:







											"""simple docstring"""




											_lowerCAmelCase  	=			'cpu'

											_lowerCAmelCase  	=			self.get_dummy_components()

											_lowerCAmelCase  	=			self.pipeline_class(**UpperCAmelCase_  )
											_lowerCAmelCase  	=			pipe.to(UpperCAmelCase_  )

											pipe.set_progress_bar_config(disable=UpperCAmelCase_  )

											_lowerCAmelCase  	=			pipe(**self.get_dummy_inputs(UpperCAmelCase_  )  )
											_lowerCAmelCase  	=			output.image_embeds

											_lowerCAmelCase  	=			pipe(
											    **self.get_dummy_inputs(UpperCAmelCase_  )				,     return_dict=UpperCAmelCase_				,     )[0]

											_lowerCAmelCase  	=			image[0, -10:]
											_lowerCAmelCase  	=			image_from_tuple[0, -10:]

											assert image.shape == (1, 32)

											_lowerCAmelCase  	=			np.array(
											    [-0.0532, 1.7120, 0.3656, -1.0852, -0.8946, -1.1756, 0.4348, 0.2482, 0.5146, -0.1156]  )

											assert np.abs(image_slice.flatten() - expected_slice  ).max() < 1E-2
											assert np.abs(image_from_tuple_slice.flatten() - expected_slice  ).max() < 1E-2


				@skip_mps
				def 				__lowerCamelCase	(					self	:			List[Any]  )		->  Dict:







											"""simple docstring"""




											_lowerCAmelCase  	=			torch_device == 'cpu'
											_lowerCAmelCase  	=			True
											_lowerCAmelCase  	=			False

											self._test_inference_batch_single_identical(
											    test_max_difference=UpperCAmelCase_				,     relax_max_difference=UpperCAmelCase_				,     test_mean_pixel_difference=UpperCAmelCase_				,     )







				@skip_mps
				def 				__lowerCamelCase	(					self	:			Optional[Any]  )		->  Tuple:







											"""simple docstring"""




											_lowerCAmelCase  	=			torch_device == 'cpu'
											_lowerCAmelCase  	=			False

											self._test_attention_slicing_forward_pass(
											    test_max_difference=UpperCAmelCase_				,     test_mean_pixel_difference=UpperCAmelCase_				,     )








"""simple docstring"""



from pathlib import Path

import numpy as np
from PIL import Image






def 	__snake_case  (    SCREAMING_SNAKE_CASE:					np.ndarray						):






							"""simple docstring"""



							_lowerCAmelCase	,		_lowerCAmelCase	,		_lowerCAmelCase  	=			rgb[:, :, 0], rgb[:, :, 1], rgb[:, :, 2]
							return 0.29_89 * r + 0.58_70 * g + 0.11_40 * b






def 	__snake_case  (    SCREAMING_SNAKE_CASE:					np.ndarray						):






							"""simple docstring"""



							return (gray > 127) & (gray <= 255)






def 	__snake_case  (    SCREAMING_SNAKE_CASE:					np.ndarray     ,  SCREAMING_SNAKE_CASE:					np.ndarray						):






							"""simple docstring"""



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

							# Copy image to padded image
							_lowerCAmelCase  	=			image

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


if __name__ == "__main__":
	# read original image
	_snake_case          =						Path(__file__).resolve().parent / '''image_data''' / '''lena.jpg'''
	_snake_case          =						np.array(Image.open(lena_path))
	# kernel to be applied
	_snake_case          =						np.array([[0, 1, 0], [1, 1, 1], [0, 1, 0]])
	_snake_case          =						dilation(gray_to_binary(rgb_to_gray(lena)), structuring_element)
	# Save the output image
	_snake_case          =						Image.fromarray(output).convert('''RGB''')
	pil_img.save('''result_dilation.png''')






