
"""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 							__magic_name__     (		lowercase__      ,		unittest.TestCase   ):
					_SCREAMING_SNAKE_CASE       :					List[str]     							=   KandinskyInpaintPipeline
					_SCREAMING_SNAKE_CASE       :					List[str]     							=   ['prompt', 'image_embeds', 'negative_image_embeds', 'image', 'mask_image']
					_SCREAMING_SNAKE_CASE       :					Optional[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       :					List[Any]     							=   False





					@property
					def 							lowerCAmelCase      (				self   :							List[str]    ):
												return 32





					@property
					def 							lowerCAmelCase      (				self   :							str    ):
												return 32





					@property
					def 							lowerCAmelCase      (				self   :							int    ):
												return self.time_input_dim





					@property
					def 							lowerCAmelCase      (				self   :							List[Any]    ):
												return self.time_input_dim * 4





					@property
					def 							lowerCAmelCase      (				self   :							Any    ):
												return 100





					@property
					def 							lowerCAmelCase      (				self   :							Any    ):
												__snake_case     			=       XLMRobertaTokenizerFast.from_pretrained("YiYiXu/tiny-random-mclip-base"    )
												return tokenizer





					@property
					def 							lowerCAmelCase      (				self   :							Dict    ):
												torch.manual_seed(0    )
												__snake_case     			=       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							,						)

												__snake_case     			=       MultilingualCLIP(snake_case_    )
												__snake_case     			=       text_encoder.eval()

												return text_encoder





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

												__snake_case     			=       {
												    "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,
												}

												__snake_case     			=       UNetaDConditionModel(**snake_case_    )
												return model





					@property
					def 							lowerCAmelCase      (				self   :							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 							lowerCAmelCase      (				self   :							List[Any]    ):
												torch.manual_seed(0    )
												__snake_case     			=       VQModel(**self.dummy_movq_kwargs    )
												return model





					def 							lowerCAmelCase      (				self   :							Dict    ):
												__snake_case     			=       self.dummy_text_encoder
												__snake_case     			=       self.dummy_tokenizer
												__snake_case     			=       self.dummy_unet
												__snake_case     			=       self.dummy_movq

												__snake_case     			=       DDIMScheduler(
												    num_train_timesteps=1000							,						beta_schedule="linear"							,						beta_start=0.00085							,						beta_end=0.012							,						clip_sample=snake_case_							,						set_alpha_to_one=snake_case_							,						steps_offset=1							,						prediction_type="epsilon"							,						thresholding=snake_case_							,						)

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

												return components





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

												if str(snake_case_    ).startswith("mps"    ):
																			__snake_case     			=       torch.manual_seed(snake_case_    )
												else:
																			__snake_case     			=       torch.Generator(device=snake_case_    ).manual_seed(snake_case_    )
												__snake_case     			=       {
												    "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 							lowerCAmelCase      (				self   :							Union[str, Any]    ):
												__snake_case     			=       "cpu"

												__snake_case     			=       self.get_dummy_components()

												__snake_case     			=       self.pipeline_class(**snake_case_    )
												__snake_case     			=       pipe.to(snake_case_    )

												pipe.set_progress_bar_config(disable=snake_case_    )

												__snake_case     			=       pipe(**self.get_dummy_inputs(snake_case_    )    )
												__snake_case     			=       output.images

												__snake_case     			=       pipe(
												    **self.get_dummy_inputs(snake_case_    )							,						return_dict=snake_case_							,						)[0]

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

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

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

												__snake_case     			=       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 							lowerCAmelCase      (				self   :							Any    ):
												super().test_inference_batch_single_identical(expected_max_diff=3e-3    )







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





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





					def 							lowerCAmelCase      (				self   :							int    ):
												__snake_case     			=       load_numpy(
												    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
												    "/kandinsky/kandinsky_inpaint_cat_with_hat_fp16.npy"    )

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

												__snake_case     			=       "a hat"

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

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

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

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

												__snake_case     			=       output.images[0]

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

												assert_mean_pixel_difference(snake_case_							,						snake_case_    )


"""simple docstring"""







from __future__ import annotations

_SCREAMING_SNAKE_CASE	       =      {
    """A""": ["""B""", """C""", """E"""],
    """B""": ["""A""", """D""", """E"""],
    """C""": ["""A""", """F""", """G"""],
    """D""": ["""B"""],
    """E""": ["""A""", """B""", """D"""],
    """F""": ["""C"""],
    """G""": ["""C"""],
}







class 							__magic_name__     :





					def __init__(				self   :							Any							,						snake_case_   :							dict[str, list[str]]							,						snake_case_   :							str    ):
												__snake_case     			=       graph
												# mapping node to its parent in resulting breadth first tree
												__snake_case     			=       {}
												__snake_case     			=       source_vertex





					def 							lowerCAmelCase      (				self   :							Union[str, Any]    ):
												__snake_case     			=       {self.source_vertex}
												__snake_case     			=       None
												__snake_case     			=       [self.source_vertex]  # first in first out queue

												while queue:
																			__snake_case     			=       queue.pop(0    )
																			for adjacent_vertex in self.graph[vertex]:
																										if adjacent_vertex not in visited:
																																	visited.add(snake_case_    )
																																	__snake_case     			=       vertex
																																	queue.append(snake_case_    )





					def 							lowerCAmelCase      (				self   :							Dict							,						snake_case_   :							str    ):
												if target_vertex == self.source_vertex:
																			return self.source_vertex

												__snake_case     			=       self.parent.get(snake_case_    )
												if target_vertex_parent is None:
																			__snake_case     			=       (
																			    F'''No path from vertex: {self.source_vertex} to vertex: {target_vertex}'''
																			)
																			raise ValueError(snake_case_    )

												return self.shortest_path(snake_case_    ) + F'''->{target_vertex}'''


if __name__ == "__main__":
		_SCREAMING_SNAKE_CASE	       =      Graph(graph, """G""")
		g.breath_first_search()
		print(g.shortest_path("""D"""))
		print(g.shortest_path("""G"""))
		print(g.shortest_path("""Foo"""))
