
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 			_lowerCAmelCase      (			__a      ,   unittest.TestCase ):
				_lowercase    	=KandinskyInpaintPipeline
				_lowercase    	=['''prompt''', '''image_embeds''', '''negative_image_embeds''', '''image''', '''mask_image''']
				_lowercase    	=[
				    '''prompt''',
				    '''negative_prompt''',
				    '''image_embeds''',
				    '''negative_image_embeds''',
				    '''image''',
				    '''mask_image''',
				]
				_lowercase    	=[
				    '''generator''',
				    '''height''',
				    '''width''',
				    '''latents''',
				    '''guidance_scale''',
				    '''negative_prompt''',
				    '''num_inference_steps''',
				    '''return_dict''',
				    '''guidance_scale''',
				    '''num_images_per_prompt''',
				    '''output_type''',
				    '''return_dict''',
				]
				_lowercase    	=False




				@property
				def   __a		(      self  )       ->   List[Any]:
							return 32




				@property
				def   __a		(      self  )       ->   str:
							return 32




				@property
				def   __a		(      self  )       ->   List[str]:
							return self.time_input_dim




				@property
				def   __a		(      self  )       ->   Any:
							return self.time_input_dim * 4




				@property
				def   __a		(      self  )       ->   Union[str, Any]:
							return 100




				@property
				def   __a		(      self  )       ->   Dict:
							lowerCAmelCase_      			=			XLMRobertaTokenizerFast.from_pretrained("YiYiXu/tiny-random-mclip-base"  )
							return tokenizer




				@property
				def   __a		(      self  )       ->   Optional[Any]:
							torch.manual_seed(0  )
							lowerCAmelCase_      			=			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       ,       )

							lowerCAmelCase_      			=			MultilingualCLIP(_UpperCamelCase  )
							lowerCAmelCase_      			=			text_encoder.eval()

							return text_encoder




				@property
				def   __a		(      self  )       ->   str:
							torch.manual_seed(0  )

							lowerCAmelCase_      			=			{
							    "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_      			=			UNetaDConditionModel(**_UpperCamelCase  )
							return model




				@property
				def   __a		(      self  )       ->   List[Any]:
							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   __a		(      self  )       ->   Tuple:
							torch.manual_seed(0  )
							lowerCAmelCase_      			=			VQModel(**self.dummy_movq_kwargs  )
							return model




				def   __a		(      self  )       ->   Tuple:
							lowerCAmelCase_      			=			self.dummy_text_encoder
							lowerCAmelCase_      			=			self.dummy_tokenizer
							lowerCAmelCase_      			=			self.dummy_unet
							lowerCAmelCase_      			=			self.dummy_movq

							lowerCAmelCase_      			=			DDIMScheduler(
							    num_train_timesteps=1_000       ,       beta_schedule="linear"       ,       beta_start=0.00085       ,       beta_end=0.012       ,       clip_sample=_UpperCamelCase       ,       set_alpha_to_one=_UpperCamelCase       ,       steps_offset=1       ,       prediction_type="epsilon"       ,       thresholding=_UpperCamelCase       ,       )

							lowerCAmelCase_      			=			{
							    "text_encoder": text_encoder,
							    "tokenizer": tokenizer,
							    "unet": unet,
							    "scheduler": scheduler,
							    "movq": movq,
							}

							return components




				def   __a		(      self       ,       _UpperCamelCase       ,       _UpperCamelCase=0  )       ->   Any:
							lowerCAmelCase_      			=			floats_tensor((1, self.cross_attention_dim)       ,       rng=random.Random(_UpperCamelCase  )  ).to(_UpperCamelCase  )
							lowerCAmelCase_      			=			floats_tensor((1, self.cross_attention_dim)       ,       rng=random.Random(seed + 1  )  ).to(_UpperCamelCase  )
							# create init_image
							lowerCAmelCase_      			=			floats_tensor((1, 3, 64, 64)       ,       rng=random.Random(_UpperCamelCase  )  ).to(_UpperCamelCase  )
							lowerCAmelCase_      			=			image.cpu().permute(0       ,       2       ,       3       ,       1  )[0]
							lowerCAmelCase_      			=			Image.fromarray(np.uinta(_UpperCamelCase  )  ).convert("RGB"  ).resize((256, 256)  )
							# create mask
							lowerCAmelCase_      			=			np.ones((64, 64)       ,       dtype=np.floataa  )
							lowerCAmelCase_      			=			0

							if str(_UpperCamelCase  ).startswith("mps"  ):
										lowerCAmelCase_      			=			torch.manual_seed(_UpperCamelCase  )
							else:
										lowerCAmelCase_      			=			torch.Generator(device=_UpperCamelCase  ).manual_seed(_UpperCamelCase  )
							lowerCAmelCase_      			=			{
							    "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   __a		(      self  )       ->   List[Any]:
							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.images

							lowerCAmelCase_      			=			pipe(
							    **self.get_dummy_inputs(_UpperCamelCase  )       ,       return_dict=_UpperCamelCase       ,       )[0]

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

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

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

							lowerCAmelCase_      			=			np.array(
							    [0.8326919, 0.73790467, 0.20918581, 0.9309612, 0.5511791, 0.43713328, 0.5513321, 0.49922934, 0.59497786]  )

							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   __a		(      self  )       ->   Dict:
							super().test_inference_batch_single_identical(expected_max_diff=3e-3  )





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




				def   __a		(      self  )       ->   List[str]:
							# clean up the VRAM after each test
							super().tearDown()
							gc.collect()
							torch.cuda.empty_cache()




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

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

							lowerCAmelCase_      			=			"a hat"

							lowerCAmelCase_      			=			KandinskyPriorPipeline.from_pretrained(
							    "kandinsky-community/kandinsky-2-1-prior"       ,       torch_dtype=torch.floataa  )
							pipe_prior.to(_UpperCamelCase  )

							lowerCAmelCase_      			=			KandinskyInpaintPipeline.from_pretrained(
							    "kandinsky-community/kandinsky-2-1-inpaint"       ,       torch_dtype=torch.floataa  )
							lowerCAmelCase_      			=			pipeline.to(_UpperCamelCase  )
							pipeline.set_progress_bar_config(disable=_UpperCamelCase  )

							lowerCAmelCase_      			=			torch.Generator(device="cpu"  ).manual_seed(0  )
							lowerCAmelCase_			,						lowerCAmelCase_      			=			pipe_prior(
							    _UpperCamelCase       ,       generator=_UpperCamelCase       ,       num_inference_steps=5       ,       negative_prompt=""       ,       ).to_tuple()

							lowerCAmelCase_      			=			pipeline(
							    _UpperCamelCase       ,       image=_UpperCamelCase       ,       mask_image=_UpperCamelCase       ,       image_embeds=_UpperCamelCase       ,       negative_image_embeds=_UpperCamelCase       ,       generator=_UpperCamelCase       ,       num_inference_steps=100       ,       height=768       ,       width=768       ,       output_type="np"       ,       )

							lowerCAmelCase_      			=			output.images[0]

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

							assert_mean_pixel_difference(_UpperCamelCase       ,       _UpperCamelCase  )





import math





class 			_lowerCAmelCase      :




				def __init__(      self       ,       _UpperCamelCase=0  )       ->   Tuple:  # a graph with Node 0,1,...,N-1
							lowerCAmelCase_      			=			n
							lowerCAmelCase_      			=			[
							    [math.inf for j in range(0       ,       _UpperCamelCase  )] for i in range(0       ,       _UpperCamelCase  )
							]  # adjacency matrix for weight
							lowerCAmelCase_      			=			[
							    [math.inf for j in range(0       ,       _UpperCamelCase  )] for i in range(0       ,       _UpperCamelCase  )
							]  # dp[i][j] stores minimum distance from i to j




				def   __a		(      self       ,       _UpperCamelCase       ,       _UpperCamelCase       ,       _UpperCamelCase  )       ->   List[Any]:
							lowerCAmelCase_      			=			w




				def   __a		(      self  )       ->   List[str]:
							for k in range(0       ,       self.n  ):
										for i in range(0       ,       self.n  ):
													for j in range(0       ,       self.n  ):
																lowerCAmelCase_      			=			min(self.dp[i][j]       ,       self.dp[i][k] + self.dp[k][j]  )




				def   __a		(      self       ,       _UpperCamelCase       ,       _UpperCamelCase  )       ->   Dict:
							return self.dp[u][v]


if __name__ == "__main__":
			_A     =  Graph(5)
			graph.add_edge(0, 2, 9)
			graph.add_edge(0, 4, 10)
			graph.add_edge(1, 3, 5)
			graph.add_edge(2, 3, 7)
			graph.add_edge(3, 0, 10)
			graph.add_edge(3, 1, 2)
			graph.add_edge(3, 2, 1)
			graph.add_edge(3, 4, 6)
			graph.add_edge(4, 1, 3)
			graph.add_edge(4, 2, 4)
			graph.add_edge(4, 3, 9)
			graph.floyd_warshall()
			graph.show_min(1, 4)
			graph.show_min(0, 3)



