
import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image

from diffusers import (
    DDIMScheduler,
    KandinskyVaaInpaintPipeline,
    KandinskyVaaPriorPipeline,
    UNetaDConditionModel,
    VQModel,
)
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		(       _a				,							unittest.TestCase							):
					__magic_name__    :				List[str]  						=						KandinskyVaaInpaintPipeline
					__magic_name__    :				List[str]  						=						["""image_embeds""", """negative_image_embeds""", """image""", """mask_image"""]
					__magic_name__    :				str  						=						[
					    """image_embeds""",
					    """negative_image_embeds""",
					    """image""",
					    """mask_image""",
					]
					__magic_name__    :				List[str]  						=						[
					    """generator""",
					    """height""",
					    """width""",
					    """latents""",
					    """guidance_scale""",
					    """num_inference_steps""",
					    """return_dict""",
					    """guidance_scale""",
					    """num_images_per_prompt""",
					    """output_type""",
					    """return_dict""",
					]
					__magic_name__    :				Tuple  						=						False


					@property
					def 							lowerCAmelCase_     (						self    )		->				Tuple:





									'''simple docstring'''
									return 3_2


					@property
					def 							lowerCAmelCase_     (						self    )		->				Union[str, Any]:





									'''simple docstring'''
									return 3_2


					@property
					def 							lowerCAmelCase_     (						self    )		->				List[str]:





									'''simple docstring'''
									return self.time_input_dim


					@property
					def 							lowerCAmelCase_     (						self    )		->				Any:





									'''simple docstring'''
									return self.time_input_dim * 4


					@property
					def 							lowerCAmelCase_     (						self    )		->				Tuple:





									'''simple docstring'''
									return 1_0_0


					@property
					def 							lowerCAmelCase_     (						self    )		->				Any:





									'''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''': '''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''': '''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 							lowerCAmelCase_     (						self    )		->				Tuple:





									'''simple docstring'''
									return {
									    "block_out_channels": [3_2, 6_4],
									    "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": 1_2,
									    "out_channels": 3,
									    "up_block_types": [
									        "AttnUpDecoderBlock2D",
									        "UpDecoderBlock2D",
									    ],
									    "vq_embed_dim": 4,
									}


					@property
					def 							lowerCAmelCase_     (						self    )		->				str:





									'''simple docstring'''
									torch.manual_seed(0    )
									A_													=	VQModel(**self.dummy_movq_kwargs    )
									return model


					def 							lowerCAmelCase_     (						self    )		->				Optional[int]:





									'''simple docstring'''
									A_													=	self.dummy_unet
									A_													=	self.dummy_movq

									A_													=	DDIMScheduler(
									    num_train_timesteps=1_0_0_0							,							beta_schedule='''linear'''							,							beta_start=0.0_00_85							,							beta_end=0.0_12							,							clip_sample=A_							,							set_alpha_to_one=A_							,							steps_offset=1							,							prediction_type='''epsilon'''							,							thresholding=A_							,							)

									A_													=	{
									    '''unet''': unet,
									    '''scheduler''': scheduler,
									    '''movq''': movq,
									}

									return components


					def 							lowerCAmelCase_     (						self							,							a__							,							a__=0    )		->				List[str]:





									'''simple docstring'''
									A_													=	floats_tensor((1, self.text_embedder_hidden_size)							,							rng=random.Random(A_    )    ).to(A_    )
									A_													=	floats_tensor((1, self.text_embedder_hidden_size)							,							rng=random.Random(seed + 1    )    ).to(
									    A_    )
									# create init_image
									A_													=	floats_tensor((1, 3, 6_4, 6_4)							,							rng=random.Random(A_    )    ).to(A_    )
									A_													=	image.cpu().permute(0							,							2							,							3							,							1    )[0]
									A_													=	Image.fromarray(np.uinta(A_    )    ).convert('''RGB'''    ).resize((2_5_6, 2_5_6)    )
									# create mask
									A_													=	np.ones((6_4, 6_4)							,							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_													=	{
									    '''image''': init_image,
									    '''mask_image''': mask,
									    '''image_embeds''': image_embeds,
									    '''negative_image_embeds''': negative_image_embeds,
									    '''generator''': generator,
									    '''height''': 6_4,
									    '''width''': 6_4,
									    '''num_inference_steps''': 2,
									    '''guidance_scale''': 4.0,
									    '''output_type''': '''np''',
									}
									return inputs


					def 							lowerCAmelCase_     (						self    )		->				Optional[int]:





									'''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, 6_4, 6_4, 3)

									A_													=	np.array(
									    [0.50_77_59_03, 0.49_52_71_95, 0.48_82_45_43, 0.50_19_22_37, 0.48_64_49_06, 0.49_37_38_14, 0.4_78_05_98, 0.47_23_48_27, 0.48_32_78_48]    )

									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:





									'''simple docstring'''
									super().test_inference_batch_single_identical(expected_max_diff=3E-3    )






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


					def 							lowerCAmelCase_     (						self    )		->				int:





									'''simple docstring'''
									super().tearDown()
									gc.collect()
									torch.cuda.empty_cache()


					def 							lowerCAmelCase_     (						self    )		->				str:





									'''simple docstring'''
									A_													=	load_numpy(
									    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
									    '''/kandinskyv22/kandinskyv22_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((7_6_8, 7_6_8)							,							dtype=np.floataa    )
									A_													=	0

									A_													=	'''a hat'''

									A_													=	KandinskyVaaPriorPipeline.from_pretrained(
									    '''kandinsky-community/kandinsky-2-2-prior'''							,							torch_dtype=torch.floataa    )
									pipe_prior.to(A_    )

									A_													=	KandinskyVaaInpaintPipeline.from_pretrained(
									    '''kandinsky-community/kandinsky-2-2-decoder-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(
									    image=A_							,							mask_image=A_							,							image_embeds=A_							,							negative_image_embeds=A_							,							generator=A_							,							num_inference_steps=1_0_0							,							height=7_6_8							,							width=7_6_8							,							output_type='''np'''							,							)

									A_													=	output.images[0]

									assert image.shape == (7_6_8, 7_6_8, 3)

									assert_mean_pixel_difference(A_							,							A_    )



"""simple docstring"""

from __future__ import annotations


def lowerCAmelCase_   (					UpperCamelCase__						:			list[float]					, UpperCamelCase__						:			Any			):






 """simple docstring"""







 print(f'''Vertex\tShortest Distance from vertex {src}'''			)
 for i, d in enumerate(UpperCamelCase__			):
  print(f'''{i}\t\t{d}'''			)


def lowerCAmelCase_   (					UpperCamelCase__						:			list[dict[str, int]]					, UpperCamelCase__						:			list[float]					, UpperCamelCase__						:			int			):






 """simple docstring"""







 for j in range(UpperCamelCase__			):
  __lowercase				,		__lowercase				,		__lowercase						=						(graph[j][k] for k in ["""src""", """dst""", """weight"""])
  if distance[u] != float("""inf"""			) and distance[u] + w < distance[v]:
   return True
 return False


def lowerCAmelCase_   (					UpperCamelCase__						:			list[dict[str, int]]					, UpperCamelCase__						:			int					, UpperCamelCase__						:			int					, UpperCamelCase__						:			int			):






 """simple docstring"""







 __lowercase						=						[float("""inf"""			)] * vertex_count
 __lowercase						=						0.0

 for _ in range(vertex_count - 1			):
  for j in range(UpperCamelCase__			):
   __lowercase				,		__lowercase				,		__lowercase						=						(graph[j][k] for k in ["""src""", """dst""", """weight"""])

   if distance[u] != float("""inf"""			) and distance[u] + w < distance[v]:
    __lowercase						=						distance[u] + w

 __lowercase						=						check_negative_cycle(UpperCamelCase__					, UpperCamelCase__					, UpperCamelCase__			)
 if negative_cycle_exists:
  raise Exception("""Negative cycle found"""			)

 return distance


if __name__ == "__main__":
       import doctest

       doctest.testmod()

       UpperCAmelCase__					=int(input("Enter number of vertices: ").strip())
       UpperCAmelCase__					=int(input("Enter number of edges: ").strip())

       UpperCAmelCase__					=[{} for _ in range(E)]

       for i in range(E):
              print("Edge ", i + 1)
              UpperCAmelCase__				,   UpperCAmelCase__				,   UpperCAmelCase__					=(
                  int(x)
                  for x in input("Enter source, destination, weight: ").strip().split(" ")
              )
              UpperCAmelCase__					={"src": src, "dst": dest, "weight": weight}

       UpperCAmelCase__					=int(input("\nEnter shortest path source:").strip())
       UpperCAmelCase__					=bellman_ford(graph, V, E, source)
       print_distance(shortest_distance, 0)
