




"""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					(				A__				,    unittest.TestCase							):

				'''simple docstring'''




				UpperCamelCase__		  =KandinskyInpaintPipeline
				UpperCamelCase__		  =["""prompt""", """image_embeds""", """negative_image_embeds""", """image""", """mask_image"""]
				UpperCamelCase__		  =[
				    """prompt""",
				    """negative_prompt""",
				    """image_embeds""",
				    """negative_image_embeds""",
				    """image""",
				    """mask_image""",
				]
				UpperCamelCase__		  =[
				    """generator""",
				    """height""",
				    """width""",
				    """latents""",
				    """guidance_scale""",
				    """negative_prompt""",
				    """num_inference_steps""",
				    """return_dict""",
				    """guidance_scale""",
				    """num_images_per_prompt""",
				    """output_type""",
				    """return_dict""",
				]
				UpperCamelCase__		  =False





				@property
				def 					snake_case_			(				self   :	Optional[Any]  ):
								return 32





				@property
				def 					snake_case_			(				self   :	List[Any]  ):
								return 32





				@property
				def 					snake_case_			(				self   :	List[Any]  ):
								return self.time_input_dim





				@property
				def 					snake_case_			(				self   :	Dict  ):
								return self.time_input_dim * 4





				@property
				def 					snake_case_			(				self   :	int  ):
								return 100





				@property
				def 					snake_case_			(				self   :	Any  ):
								UpperCAmelCase_       :List[Any]         =   XLMRobertaTokenizerFast.from_pretrained('''YiYiXu/tiny-random-mclip-base'''  )
								return tokenizer





				@property
				def 					snake_case_			(				self   :	Any  ):
								torch.manual_seed(0  )
								UpperCAmelCase_       :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=1_005       ,				)

								UpperCAmelCase_       :Union[str, Any]         =   MultilingualCLIP(__A  )
								UpperCAmelCase_       :List[str]         =   text_encoder.eval()

								return text_encoder





				@property
				def 					snake_case_			(				self   :	Tuple  ):
								torch.manual_seed(0  )

								UpperCAmelCase_       :Optional[int]         =   {
								    '''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,
								}

								UpperCAmelCase_       :int         =   UNetaDConditionModel(**__A  )
								return model





				@property
				def 					snake_case_			(				self   :	List[str]  ):
								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 					snake_case_			(				self   :	Union[str, Any]  ):
								torch.manual_seed(0  )
								UpperCAmelCase_       :Tuple         =   VQModel(**self.dummy_movq_kwargs  )
								return model





				def 					snake_case_			(				self   :	Tuple  ):
								UpperCAmelCase_       :Optional[Any]         =   self.dummy_text_encoder
								UpperCAmelCase_       :Optional[Any]         =   self.dummy_tokenizer
								UpperCAmelCase_       :Dict         =   self.dummy_unet
								UpperCAmelCase_       :Any         =   self.dummy_movq

								UpperCAmelCase_       :Optional[int]         =   DDIMScheduler(
								    num_train_timesteps=1_000       ,				beta_schedule='''linear'''       ,				beta_start=0.00_085       ,				beta_end=0.012       ,				clip_sample=__A       ,				set_alpha_to_one=__A       ,				steps_offset=1       ,				prediction_type='''epsilon'''       ,				thresholding=__A       ,				)

								UpperCAmelCase_       :Optional[Any]         =   {
								    '''text_encoder''': text_encoder,
								    '''tokenizer''': tokenizer,
								    '''unet''': unet,
								    '''scheduler''': scheduler,
								    '''movq''': movq,
								}

								return components





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

								if str(__A  ).startswith('''mps'''  ):
												UpperCAmelCase_       :Optional[int]         =   torch.manual_seed(__A  )
								else:
												UpperCAmelCase_       :Any         =   torch.Generator(device=__A  ).manual_seed(__A  )
								UpperCAmelCase_       :List[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 					snake_case_			(				self   :	Optional[Any]  ):
								UpperCAmelCase_       :int         =   '''cpu'''

								UpperCAmelCase_       :List[str]         =   self.get_dummy_components()

								UpperCAmelCase_       :Optional[int]         =   self.pipeline_class(**__A  )
								UpperCAmelCase_       :List[str]         =   pipe.to(__A  )

								pipe.set_progress_bar_config(disable=__A  )

								UpperCAmelCase_       :int         =   pipe(**self.get_dummy_inputs(__A  )  )
								UpperCAmelCase_       :List[Any]         =   output.images

								UpperCAmelCase_       :str         =   pipe(
								    **self.get_dummy_inputs(__A  )       ,				return_dict=__A       ,				)[0]

								UpperCAmelCase_       :Tuple         =   image[0, -3:, -3:, -1]
								UpperCAmelCase_       :Optional[int]         =   image_from_tuple[0, -3:, -3:, -1]

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

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

								UpperCAmelCase_       :List[Any]         =   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 					snake_case_			(				self   :	List[Any]  ):
								super().test_inference_batch_single_identical(expected_max_diff=3e-3  )



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

				'''simple docstring'''




				def 					snake_case_			(				self   :	Optional[int]  ):
								# clean up the VRAM after each test
								super().tearDown()
								gc.collect()
								torch.cuda.empty_cache()





				def 					snake_case_			(				self   :	List[Any]  ):
								UpperCAmelCase_       :Any         =   load_numpy(
								    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
								    '''/kandinsky/kandinsky_inpaint_cat_with_hat_fp16.npy'''  )

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

								UpperCAmelCase_       :int         =   '''a hat'''

								UpperCAmelCase_       :List[Any]         =   KandinskyPriorPipeline.from_pretrained(
								    '''kandinsky-community/kandinsky-2-1-prior'''       ,				torch_dtype=torch.floataa  )
								pipe_prior.to(__A  )

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

								UpperCAmelCase_       :List[str]         =   torch.Generator(device='''cpu'''  ).manual_seed(0  )
								UpperCAmelCase_       :Dict         =   pipe_prior(
								    __A       ,				generator=__A       ,				num_inference_steps=5       ,				negative_prompt=''''''       ,				).to_tuple()

								UpperCAmelCase_       :List[Any]         =   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'''       ,				)

								UpperCAmelCase_       :List[str]         =   output.images[0]

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

								assert_mean_pixel_difference(__A       ,				__A  )




def 							SCREAMING_SNAKE_CASE_	(					UpperCAmelCase_	:     list				)			->						float:
       SCREAMING_SNAKE_CASE_							:				Dict		=0
       while len(UpperCAmelCase_				) > 1:
              SCREAMING_SNAKE_CASE_							:				Tuple		=0
              # Consider two files with minimum cost to be merged
              for _ in range(2				):
                     SCREAMING_SNAKE_CASE_							:				int		=files.index(min(UpperCAmelCase_				)				)
                     temp += files[min_index]
                     files.pop(UpperCAmelCase_				)
              files.append(UpperCAmelCase_				)
              optimal_merge_cost += temp
       return optimal_merge_cost


if __name__ == "__main__":
 import doctest

 doctest.testmod()
