





'''simple docstring'''


from __future__ import annotations

from collections import deque
from collections.abc import Iterator
from dataclasses import dataclass






@dataclass
class     lowerCAmelCase__ :

       '''simple docstring'''
       _lowerCamelCase								=42
       _lowerCamelCase								=42






class     lowerCAmelCase__ :

       '''simple docstring'''

       def __init__( self					:				Union[str, Any]       ,     a__					:				int						):
              UpperCAmelCase  =						[[] for _ in range(a__						)]
              UpperCAmelCase  =						size

       def __getitem__( self					:				Any       ,     a__					:				int						):
              return iter(self._graph[vertex]						)

       @property
       def 			__snake_case      ( self					:				List[str]						):
              return self._size

       def 			__snake_case      ( self					:				Optional[Any]       ,     a__					:				int       ,     a__					:				int       ,     a__					:				int						):
              if weight not in (0, 1):
                     raise ValueError('''Edge weight must be either 0 or 1.'''						)

              if to_vertex < 0 or to_vertex >= self.size:
                     raise ValueError('''Vertex indexes must be in [0; size).'''						)

              self._graph[from_vertex].append(Edge(a__       ,     a__						)						)



       def 			__snake_case      ( self					:				Dict       ,     a__					:				int       ,     a__					:				int						):
              UpperCAmelCase  =						deque([start_vertex]						)
              UpperCAmelCase  =						[None] * self.size
              UpperCAmelCase  =						0

              while queue:
                     UpperCAmelCase  =						queue.popleft()
                     UpperCAmelCase  =						distances[current_vertex]
                     if current_distance is None:
                            continue

                     for edge in self[current_vertex]:
                            UpperCAmelCase  =						current_distance + edge.weight
                            UpperCAmelCase  =						distances[edge.destination_vertex]
                            if (
                                isinstance(a__       ,     a__						)
                                and new_distance >= dest_vertex_distance
                            ):
                                   continue
                            UpperCAmelCase  =						new_distance
                            if edge.weight == 0:
                                   queue.appendleft(edge.destination_vertex						)
                            else:
                                   queue.append(edge.destination_vertex						)

              if distances[finish_vertex] is None:
                     raise ValueError('''No path from start_vertex to finish_vertex.'''						)

              return distances[finish_vertex]


if __name__ == "__main__":
    import doctest

    doctest.testmod()









'''simple docstring'''

import argparse

import intel_extension_for_pytorch as ipex
import torch

from diffusers import DPMSolverMultistepScheduler, StableDiffusionPipeline


lowercase__		:     int			=							argparse.ArgumentParser('Stable Diffusion script with intel optimization', add_help=False)
parser.add_argument('--dpm', action='store_true', help='Enable DPMSolver or not')
parser.add_argument('--steps', default=None, type=int, help='Num inference steps')
lowercase__		:     Union[str, Any]			=							parser.parse_args()


lowercase__		:     Union[str, Any]			=							'cpu'
lowercase__		:     Tuple			=							'a lovely <dicoo> in red dress and hat, in the snowly and brightly night, with many brighly buildings'

lowercase__		:     List[Any]			=							'path-to-your-trained-model'
lowercase__		:     Optional[Any]			=							StableDiffusionPipeline.from_pretrained(model_id)
if args.dpm:
   lowercase__		:     Union[str, Any]			=							DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
lowercase__		:     Dict			=							pipe.to(device)

# to channels last
lowercase__		:     Any			=							pipe.unet.to(memory_format=torch.channels_last)
lowercase__		:     Dict			=							pipe.vae.to(memory_format=torch.channels_last)
lowercase__		:     Any			=							pipe.text_encoder.to(memory_format=torch.channels_last)
if pipe.requires_safety_checker:
   lowercase__		:     Union[str, Any]			=							pipe.safety_checker.to(memory_format=torch.channels_last)

# optimize with ipex
lowercase__		:     List[str]			=							torch.randn(2, 4, 64, 64)
lowercase__		:     int			=							torch.rand(1) * 9_99
lowercase__		:     Optional[int]			=							torch.randn(2, 77, 7_68)
lowercase__		:     List[str]			=							(sample, timestep, encoder_hidden_status)
try:
   lowercase__		:     Optional[Any]			=							ipex.optimize(pipe.unet.eval(), dtype=torch.bfloataa, inplace=True, sample_input=input_example)
except Exception:
   lowercase__		:     Any			=							ipex.optimize(pipe.unet.eval(), dtype=torch.bfloataa, inplace=True)
lowercase__		:     Tuple			=							ipex.optimize(pipe.vae.eval(), dtype=torch.bfloataa, inplace=True)
lowercase__		:     Any			=							ipex.optimize(pipe.text_encoder.eval(), dtype=torch.bfloataa, inplace=True)
if pipe.requires_safety_checker:
   lowercase__		:     Union[str, Any]			=							ipex.optimize(pipe.safety_checker.eval(), dtype=torch.bfloataa, inplace=True)

# compute
lowercase__		:     Optional[Any]			=							6_66
lowercase__		:     Tuple			=							torch.Generator(device).manual_seed(seed)
lowercase__		:     Dict			=							{'generator': generator}
if args.steps is not None:
   lowercase__		:     int			=							args.steps

with torch.cpu.amp.autocast(enabled=True, dtype=torch.bfloataa):
   lowercase__		:     Dict			=							pipe(prompt, **generate_kwargs).images[0]

# save image
image.save('generated.png')



