






"""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    lowercase__			(							lowerCamelCase_,   unittest.TestCase   ):
			_UpperCAmelCase		:Union[str, Any]   			= KandinskyInpaintPipeline
			_UpperCAmelCase		:Any   			= ["prompt", "image_embeds", "negative_image_embeds", "image", "mask_image"]
			_UpperCAmelCase		:List[str]   			= [
			    "prompt",
			    "negative_prompt",
			    "image_embeds",
			    "negative_image_embeds",
			    "image",
			    "mask_image",
			]
			_UpperCAmelCase		:List[str]   			= [
			    "generator",
			    "height",
			    "width",
			    "latents",
			    "guidance_scale",
			    "negative_prompt",
			    "num_inference_steps",
			    "return_dict",
			    "guidance_scale",
			    "num_images_per_prompt",
			    "output_type",
			    "return_dict",
			]
			_UpperCAmelCase		:Optional[int]   			= False


			@property
			def 	UpperCAmelCase__						(      self     :  Tuple					):
										return 32


			@property
			def 	UpperCAmelCase__						(      self     :  Optional[int]					):
										return 32


			@property
			def 	UpperCAmelCase__						(      self     :  Optional[Any]					):
										return self.time_input_dim


			@property
			def 	UpperCAmelCase__						(      self     :  Optional[Any]					):
										return self.time_input_dim * 4


			@property
			def 	UpperCAmelCase__						(      self     :  List[str]					):
										return 100


			@property
			def 	UpperCAmelCase__						(      self     :  str					):
										lowerCamelCase_       :							Union[str, Any]									=XLMRobertaTokenizerFast.from_pretrained("YiYiXu/tiny-random-mclip-base"					)
										return tokenizer


			@property
			def 	UpperCAmelCase__						(      self     :  Union[str, Any]					):
										torch.manual_seed(0					)
										lowerCamelCase_       :							Optional[Any]									=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   ,       )

										lowerCamelCase_       :							int									=MultilingualCLIP(__SCREAMING_SNAKE_CASE					)
										lowerCamelCase_       :							Union[str, Any]									=text_encoder.eval()

										return text_encoder


			@property
			def 	UpperCAmelCase__						(      self     :  Dict					):
										torch.manual_seed(0					)

										lowerCamelCase_       :							Any									={
										    '''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,
										}

										lowerCamelCase_       :							int									=UNetaDConditionModel(**__SCREAMING_SNAKE_CASE					)
										return model


			@property
			def 	UpperCAmelCase__						(      self     :  Tuple					):
										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     :  Optional[Any]					):
										torch.manual_seed(0					)
										lowerCamelCase_       :							Tuple									=VQModel(**self.dummy_movq_kwargs					)
										return model


			def 	UpperCAmelCase__						(      self     :  List[str]					):
										lowerCamelCase_       :							int									=self.dummy_text_encoder
										lowerCamelCase_       :							int									=self.dummy_tokenizer
										lowerCamelCase_       :							Tuple									=self.dummy_unet
										lowerCamelCase_       :							List[Any]									=self.dummy_movq

										lowerCamelCase_       :							Dict									=DDIMScheduler(
										    num_train_timesteps=1000   ,       beta_schedule="linear"   ,       beta_start=0.00_085   ,       beta_end=0.012   ,       clip_sample=__SCREAMING_SNAKE_CASE   ,       set_alpha_to_one=__SCREAMING_SNAKE_CASE   ,       steps_offset=1   ,       prediction_type="epsilon"   ,       thresholding=__SCREAMING_SNAKE_CASE   ,       )

										lowerCamelCase_       :							List[Any]									={
										    '''text_encoder''': text_encoder,
										    '''tokenizer''': tokenizer,
										    '''unet''': unet,
										    '''scheduler''': scheduler,
										    '''movq''': movq,
										}

										return components


			def 	UpperCAmelCase__						(      self     :  Optional[Any]   ,       snake_case__     :  Any   ,       snake_case__     :  str=0					):
										lowerCamelCase_       :							List[str]									=floats_tensor((1, self.cross_attention_dim)   ,       rng=random.Random(__SCREAMING_SNAKE_CASE					)					).to(__SCREAMING_SNAKE_CASE					)
										lowerCamelCase_       :							List[str]									=floats_tensor((1, self.cross_attention_dim)   ,       rng=random.Random(seed + 1					)					).to(__SCREAMING_SNAKE_CASE					)
										# create init_image
										lowerCamelCase_       :							Optional[int]									=floats_tensor((1, 3, 64, 64)   ,       rng=random.Random(__SCREAMING_SNAKE_CASE					)					).to(__SCREAMING_SNAKE_CASE					)
										lowerCamelCase_       :							Union[str, Any]									=image.cpu().permute(0   ,       2   ,       3   ,       1					)[0]
										lowerCamelCase_       :							List[Any]									=Image.fromarray(np.uinta(__SCREAMING_SNAKE_CASE					)					).convert("RGB"					).resize((256, 256)					)
										# create mask
										lowerCamelCase_       :							Tuple									=np.ones((64, 64)   ,       dtype=np.floataa					)
										lowerCamelCase_       :							Optional[int]									=0

										if str(__SCREAMING_SNAKE_CASE					).startswith("mps"					):
																	lowerCamelCase_       :							str									=torch.manual_seed(__SCREAMING_SNAKE_CASE					)
										else:
																	lowerCamelCase_       :							str									=torch.Generator(device=__SCREAMING_SNAKE_CASE					).manual_seed(__SCREAMING_SNAKE_CASE					)
										lowerCamelCase_       :							Optional[Any]									={
										    '''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     :  str					):
										lowerCamelCase_       :							List[Any]									='''cpu'''

										lowerCamelCase_       :							Dict									=self.get_dummy_components()

										lowerCamelCase_       :							List[str]									=self.pipeline_class(**__SCREAMING_SNAKE_CASE					)
										lowerCamelCase_       :							Optional[Any]									=pipe.to(__SCREAMING_SNAKE_CASE					)

										pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE					)

										lowerCamelCase_       :							Union[str, Any]									=pipe(**self.get_dummy_inputs(__SCREAMING_SNAKE_CASE					)					)
										lowerCamelCase_       :							List[Any]									=output.images

										lowerCamelCase_       :							str									=pipe(
										    **self.get_dummy_inputs(__SCREAMING_SNAKE_CASE					)   ,       return_dict=__SCREAMING_SNAKE_CASE   ,       )[0]

										lowerCamelCase_       :							Optional[Any]									=image[0, -3:, -3:, -1]
										lowerCamelCase_       :							Tuple									=image_from_tuple[0, -3:, -3:, -1]

										print(F"""image.shape {image.shape}"""					)

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

										lowerCamelCase_       :							Optional[int]									=np.array(
										    [0.8_326_919, 0.73_790_467, 0.20_918_581, 0.9_309_612, 0.5_511_791, 0.43_713_328, 0.5_513_321, 0.49_922_934, 0.59_497_786]					)

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







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


			def 	UpperCAmelCase__						(      self     :  str					):
										super().tearDown()
										gc.collect()
										torch.cuda.empty_cache()


			def 	UpperCAmelCase__						(      self     :  str					):
										lowerCamelCase_       :							Optional[int]									=load_numpy(
										    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
										    "/kandinsky/kandinsky_inpaint_cat_with_hat_fp16.npy"					)

										lowerCamelCase_       :							Dict									=load_image(
										    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main" "/kandinsky/cat.png"					)
										lowerCamelCase_       :							str									=np.ones((768, 768)   ,       dtype=np.floataa					)
										lowerCamelCase_       :							List[Any]									=0

										lowerCamelCase_       :							int									='''a hat'''

										lowerCamelCase_       :							int									=KandinskyPriorPipeline.from_pretrained(
										    "kandinsky-community/kandinsky-2-1-prior"   ,       torch_dtype=torch.floataa					)
										pipe_prior.to(__SCREAMING_SNAKE_CASE					)

										lowerCamelCase_       :							Optional[Any]									=KandinskyInpaintPipeline.from_pretrained(
										    "kandinsky-community/kandinsky-2-1-inpaint"   ,       torch_dtype=torch.floataa					)
										lowerCamelCase_       :							Dict									=pipeline.to(__SCREAMING_SNAKE_CASE					)
										pipeline.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE					)

										lowerCamelCase_       :							Optional[Any]									=torch.Generator(device="cpu"					).manual_seed(0					)
										lowerCamelCase_       :							Dict									=pipe_prior(
										    __SCREAMING_SNAKE_CASE   ,       generator=__SCREAMING_SNAKE_CASE   ,       num_inference_steps=5   ,       negative_prompt=""   ,       ).to_tuple()

										lowerCamelCase_       :							Dict									=pipeline(
										    __SCREAMING_SNAKE_CASE   ,       image=__SCREAMING_SNAKE_CASE   ,       mask_image=__SCREAMING_SNAKE_CASE   ,       image_embeds=__SCREAMING_SNAKE_CASE   ,       negative_image_embeds=__SCREAMING_SNAKE_CASE   ,       generator=__SCREAMING_SNAKE_CASE   ,       num_inference_steps=100   ,       height=768   ,       width=768   ,       output_type="np"   ,       )

										lowerCamelCase_       :							Tuple									=output.images[0]

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

										assert_mean_pixel_difference(__SCREAMING_SNAKE_CASE   ,       __SCREAMING_SNAKE_CASE					)





'''simple docstring'''







def 					snake_case_		(				__SCREAMING_SNAKE_CASE					: int							):



						"""simple docstring"""





						lowercase_	:     Optional[int]													=       int(__SCREAMING_SNAKE_CASE							)
						if decimal in (0, 1):  # Exit cases for the recursion
												return str(__SCREAMING_SNAKE_CASE							)
						lowercase_						, lowercase_	:     List[str]													=       divmod(__SCREAMING_SNAKE_CASE      ,      2							)
						return binary_recursive(__SCREAMING_SNAKE_CASE							) + str(__SCREAMING_SNAKE_CASE							)
def 					snake_case_		(				__SCREAMING_SNAKE_CASE					: str							):



						"""simple docstring"""





						lowercase_	:     str													=       str(__SCREAMING_SNAKE_CASE							).strip()
						if not number:
												raise ValueError('''No input value was provided'''							)
						lowercase_	:     Optional[int]													=       '''-''' if number.startswith('''-'''							) else ''''''
						lowercase_	:     Union[str, Any]													=       number.lstrip('''-'''							)
						if not number.isnumeric():
												raise ValueError('''Input value is not an integer'''							)
						return F'''{negative}0b{binary_recursive(int(__SCREAMING_SNAKE_CASE							)							)}'''


if __name__ == "__main__":
							from doctest import testmod

							testmod()



