
"""simple docstring"""






import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast

from diffusers import DDIMScheduler, KandinskyInpaintPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()





class       snake_case_       (    UpperCamelCase__    ,							unittest.TestCase       ):



	"""simple docstring"""

	SCREAMING_SNAKE_CASE_:							Union[str, Any]   			=					KandinskyInpaintPipeline
	SCREAMING_SNAKE_CASE_:							Tuple   			=					["""prompt""", """image_embeds""", """negative_image_embeds""", """image""", """mask_image"""]
	SCREAMING_SNAKE_CASE_:							int   			=					[
	    """prompt""",
	    """negative_prompt""",
	    """image_embeds""",
	    """negative_image_embeds""",
	    """image""",
	    """mask_image""",
	]
	SCREAMING_SNAKE_CASE_:							Dict   			=					[
	    """generator""",
	    """height""",
	    """width""",
	    """latents""",
	    """guidance_scale""",
	    """negative_prompt""",
	    """num_inference_steps""",
	    """return_dict""",
	    """guidance_scale""",
	    """num_images_per_prompt""",
	    """output_type""",
	    """return_dict""",
	]
	SCREAMING_SNAKE_CASE_:							str   			=					False


	@property
	def       _UpperCAmelCase						(      self						):


					"""simple docstring"""

					return 32


	@property
	def       _UpperCAmelCase						(      self						):


					"""simple docstring"""

					return 32


	@property
	def       _UpperCAmelCase						(      self						):


					"""simple docstring"""

					return self.time_input_dim


	@property
	def       _UpperCAmelCase						(      self						):


					"""simple docstring"""

					return self.time_input_dim * 4


	@property
	def       _UpperCAmelCase						(      self						):


					"""simple docstring"""

					return 100


	@property
	def       _UpperCAmelCase						(      self						):


					"""simple docstring"""

					A__							 =       XLMRobertaTokenizerFast.from_pretrained('YiYiXu/tiny-random-mclip-base'						)
					return tokenizer


	@property
	def       _UpperCAmelCase						(      self						):


					"""simple docstring"""

					torch.manual_seed(0						)
					A__							 =       MCLIPConfig(
					    numDims=self.cross_attention_dim  ,					transformerDimensions=self.text_embedder_hidden_size  ,					hidden_size=self.text_embedder_hidden_size  ,					intermediate_size=37  ,					num_attention_heads=4  ,					num_hidden_layers=5  ,					vocab_size=1005  ,					)

					A__							 =       MultilingualCLIP(__A						)
					A__							 =       text_encoder.eval()

					return text_encoder


	@property
	def       _UpperCAmelCase						(      self						):


					"""simple docstring"""

					torch.manual_seed(0						)

					A__							 =       {
					    'in_channels': 9,
					    # Out channels is double in channels because predicts mean and variance
					    'out_channels': 8,
					    'addition_embed_type': 'text_image',
					    'down_block_types': ('ResnetDownsampleBlock2D', 'SimpleCrossAttnDownBlock2D'),
					    'up_block_types': ('SimpleCrossAttnUpBlock2D', 'ResnetUpsampleBlock2D'),
					    'mid_block_type': 'UNetMidBlock2DSimpleCrossAttn',
					    'block_out_channels': (self.block_out_channels_a, self.block_out_channels_a * 2),
					    'layers_per_block': 1,
					    'encoder_hid_dim': self.text_embedder_hidden_size,
					    'encoder_hid_dim_type': 'text_image_proj',
					    'cross_attention_dim': self.cross_attention_dim,
					    'attention_head_dim': 4,
					    'resnet_time_scale_shift': 'scale_shift',
					    'class_embed_type': None,
					}

					A__							 =       UNetaDConditionModel(**__A						)
					return model


	@property
	def       _UpperCAmelCase						(      self						):


					"""simple docstring"""

					return {
					    "block_out_channels": [32, 64],
					    "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
					    "in_channels": 3,
					    "latent_channels": 4,
					    "layers_per_block": 1,
					    "norm_num_groups": 8,
					    "norm_type": "spatial",
					    "num_vq_embeddings": 12,
					    "out_channels": 3,
					    "up_block_types": [
					        "AttnUpDecoderBlock2D",
					        "UpDecoderBlock2D",
					    ],
					    "vq_embed_dim": 4,
					}


	@property
	def       _UpperCAmelCase						(      self						):


					"""simple docstring"""

					torch.manual_seed(0						)
					A__							 =       VQModel(**self.dummy_movq_kwargs						)
					return model


	def       _UpperCAmelCase						(      self						):


					"""simple docstring"""

					A__							 =       self.dummy_text_encoder
					A__							 =       self.dummy_tokenizer
					A__							 =       self.dummy_unet
					A__							 =       self.dummy_movq

					A__							 =       DDIMScheduler(
					    num_train_timesteps=1000  ,					beta_schedule='linear'  ,					beta_start=0.0_0085  ,					beta_end=0.012  ,					clip_sample=__A  ,					set_alpha_to_one=__A  ,					steps_offset=1  ,					prediction_type='epsilon'  ,					thresholding=__A  ,					)

					A__							 =       {
					    'text_encoder': text_encoder,
					    'tokenizer': tokenizer,
					    'unet': unet,
					    'scheduler': scheduler,
					    'movq': movq,
					}

					return components


	def       _UpperCAmelCase						(      self  ,					__a  ,					__a=0						):


					"""simple docstring"""

					A__							 =       floats_tensor((1, self.cross_attention_dim)  ,					rng=random.Random(__A						)						).to(__A						)
					A__							 =       floats_tensor((1, self.cross_attention_dim)  ,					rng=random.Random(seed + 1						)						).to(__A						)
					# create init_image
					A__							 =       floats_tensor((1, 3, 64, 64)  ,					rng=random.Random(__A						)						).to(__A						)
					A__							 =       image.cpu().permute(0  ,					2  ,					3  ,					1						)[0]
					A__							 =       Image.fromarray(np.uinta(__A						)						).convert('RGB'						).resize((256, 256)						)
					# create mask
					A__							 =       np.ones((64, 64)  ,					dtype=np.floataa						)
					A__							 =       0

					if str(__A						).startswith('mps'						):
									A__							 =       torch.manual_seed(__A						)
					else:
									A__							 =       torch.Generator(device=__A						).manual_seed(__A						)
					A__							 =       {
					    'prompt': 'horse',
					    'image': init_image,
					    'mask_image': mask,
					    'image_embeds': image_embeds,
					    'negative_image_embeds': negative_image_embeds,
					    'generator': generator,
					    'height': 64,
					    'width': 64,
					    'num_inference_steps': 2,
					    'guidance_scale': 4.0,
					    'output_type': 'np',
					}
					return inputs


	def       _UpperCAmelCase						(      self						):


					"""simple docstring"""

					A__							 =       'cpu'

					A__							 =       self.get_dummy_components()

					A__							 =       self.pipeline_class(**__A						)
					A__							 =       pipe.to(__A						)

					pipe.set_progress_bar_config(disable=__A						)

					A__							 =       pipe(**self.get_dummy_inputs(__A						)						)
					A__							 =       output.images

					A__							 =       pipe(
					    **self.get_dummy_inputs(__A						)  ,					return_dict=__A  ,					)[0]

					A__							 =       image[0, -3:, -3:, -1]
					A__							 =       image_from_tuple[0, -3:, -3:, -1]

					print(f'''image.shape {image.shape}'''						)

					assert image.shape == (1, 64, 64, 3)

					A__							 =       np.array(
					    [0.832_6919, 0.7379_0467, 0.2091_8581, 0.930_9612, 0.551_1791, 0.4371_3328, 0.551_3321, 0.4992_2934, 0.5949_7786]						)

					assert (
					    np.abs(image_slice.flatten() - expected_slice						).max() < 1E-2
					), f''' expected_slice {expected_slice}, but got {image_slice.flatten()}'''
					assert (
					    np.abs(image_from_tuple_slice.flatten() - expected_slice						).max() < 1E-2
					), f''' expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}'''






	def       _UpperCAmelCase						(      self						):


					"""simple docstring"""

					super().test_inference_batch_single_identical(expected_max_diff=3E-3						)





@slow
@require_torch_gpu
class       snake_case_       (    unittest.TestCase       ):



	"""simple docstring"""

	def       _UpperCAmelCase						(      self						):


					"""simple docstring"""

					super().tearDown()
					gc.collect()
					torch.cuda.empty_cache()






	def       _UpperCAmelCase						(      self						):


					"""simple docstring"""

					A__							 =       load_numpy(
					    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
					    '/kandinsky/kandinsky_inpaint_cat_with_hat_fp16.npy'						)

					A__							 =       load_image(
					    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main' '/kandinsky/cat.png'						)
					A__							 =       np.ones((768, 768)  ,					dtype=np.floataa						)
					A__							 =       0

					A__							 =       'a hat'

					A__							 =       KandinskyPriorPipeline.from_pretrained(
					    'kandinsky-community/kandinsky-2-1-prior'  ,					torch_dtype=torch.floataa						)
					pipe_prior.to(__A						)

					A__							 =       KandinskyInpaintPipeline.from_pretrained(
					    'kandinsky-community/kandinsky-2-1-inpaint'  ,					torch_dtype=torch.floataa						)
					A__							 =       pipeline.to(__A						)
					pipeline.set_progress_bar_config(disable=__A						)

					A__							 =       torch.Generator(device='cpu'						).manual_seed(0						)
					A__ ,			A__							 =       pipe_prior(
					    __A  ,					generator=__A  ,					num_inference_steps=5  ,					negative_prompt=''  ,					).to_tuple()

					A__							 =       pipeline(
					    __A  ,					image=__A  ,					mask_image=__A  ,					image_embeds=__A  ,					negative_image_embeds=__A  ,					generator=__A  ,					num_inference_steps=100  ,					height=768  ,					width=768  ,					output_type='np'  ,					)

					A__							 =       output.images[0]

					assert image.shape == (768, 768, 3)

					assert_mean_pixel_difference(__A  ,					__A						)










"""simple docstring"""






from PIL import Image



def  __lowerCamelCase					(					lowerCAmelCase__				):
				A__ ,			A__							 =       image.size
				A__							 =       0
				A__							 =       image.load()
				for i in range(lowerCAmelCase__				):
								for j in range(lowerCAmelCase__				):
												A__							 =       pixels[j, i]
												mean += pixel
				mean //= width * height

				for j in range(lowerCAmelCase__				):
								for i in range(lowerCAmelCase__				):
												A__							 =       255 if pixels[i, j] > mean else 0
				return image


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE						:						Dict					   =       mean_threshold(Image.open('''path_to_image''').convert('''L'''))
					image.save('''output_image_path''')


