





from typing import Any, Callable, Dict, List, Optional, Union

import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    LMSDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionPipeline,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker


lowercase    : Any    	=  """CompVis/stable-diffusion-v1-1"""
lowercase    : Any    	=  """CompVis/stable-diffusion-v1-2"""
lowercase    : List[str]    	=  """CompVis/stable-diffusion-v1-3"""
lowercase    : str    	=  """CompVis/stable-diffusion-v1-4"""

class  __A(						__UpperCAmelCase ):

     def __init__(      self,       A,       A,       A,       A,       A,       A,       A,       A = True,       ):





            """simple docstring"""

            super()._init_()

            _UpperCamelCase              =						StableDiffusionPipeline.from_pretrained(A      )
            _UpperCamelCase              =						StableDiffusionPipeline.from_pretrained(A      )
            _UpperCamelCase              =						StableDiffusionPipeline.from_pretrained(A      )
            _UpperCamelCase              =						StableDiffusionPipeline(
                vae=A,       text_encoder=A,       tokenizer=A,       unet=A,       scheduler=A,       safety_checker=A,       feature_extractor=A,       requires_safety_checker=A,       )

            self.register_modules(pipelinea=self.pipea,       pipelinea=self.pipea,       pipelinea=self.pipea,       pipelinea=self.pipea      )

     @property
     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            return {k: getattr(self,       A      ) for k in self.config.keys() if not k.startswith('''_'''      )}

     def 	_UpperCamelCase     (      self,       A = "auto"      ):





            """simple docstring"""

            if slice_size == "auto":
                   # half the attention head size is usually a good trade-off between
                   # speed and memory
                   _UpperCamelCase              =						self.unet.config.attention_head_dim // 2
            self.unet.set_attention_slice(A      )

     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            self.enable_attention_slicing(A      )

     @torch.no_grad()
     def 	_UpperCamelCase     (      self,       A,       A = 512,       A = 512,       A = 50,       A = 7.5,       A = None,       A = 1,       A = 0.0,       A = None,       A = None,       A = "pil",       A = True,       A = None,       A = 1,       **A,       ):





            """simple docstring"""

            return self.pipea(
                prompt=A,       height=A,       width=A,       num_inference_steps=A,       guidance_scale=A,       negative_prompt=A,       num_images_per_prompt=A,       eta=A,       generator=A,       latents=A,       output_type=A,       return_dict=A,       callback=A,       callback_steps=A,       **A,       )

     @torch.no_grad()
     def 	_UpperCamelCase     (      self,       A,       A = 512,       A = 512,       A = 50,       A = 7.5,       A = None,       A = 1,       A = 0.0,       A = None,       A = None,       A = "pil",       A = True,       A = None,       A = 1,       **A,       ):





            """simple docstring"""

            return self.pipea(
                prompt=A,       height=A,       width=A,       num_inference_steps=A,       guidance_scale=A,       negative_prompt=A,       num_images_per_prompt=A,       eta=A,       generator=A,       latents=A,       output_type=A,       return_dict=A,       callback=A,       callback_steps=A,       **A,       )

     @torch.no_grad()
     def 	_UpperCamelCase     (      self,       A,       A = 512,       A = 512,       A = 50,       A = 7.5,       A = None,       A = 1,       A = 0.0,       A = None,       A = None,       A = "pil",       A = True,       A = None,       A = 1,       **A,       ):





            """simple docstring"""

            return self.pipea(
                prompt=A,       height=A,       width=A,       num_inference_steps=A,       guidance_scale=A,       negative_prompt=A,       num_images_per_prompt=A,       eta=A,       generator=A,       latents=A,       output_type=A,       return_dict=A,       callback=A,       callback_steps=A,       **A,       )

     @torch.no_grad()
     def 	_UpperCamelCase     (      self,       A,       A = 512,       A = 512,       A = 50,       A = 7.5,       A = None,       A = 1,       A = 0.0,       A = None,       A = None,       A = "pil",       A = True,       A = None,       A = 1,       **A,       ):





            """simple docstring"""

            return self.pipea(
                prompt=A,       height=A,       width=A,       num_inference_steps=A,       guidance_scale=A,       negative_prompt=A,       num_images_per_prompt=A,       eta=A,       generator=A,       latents=A,       output_type=A,       return_dict=A,       callback=A,       callback_steps=A,       **A,       )



     @torch.no_grad()
     def 	_UpperCamelCase     (      self,       A,       A = 512,       A = 512,       A = 50,       A = 7.5,       A = None,       A = 1,       A = 0.0,       A = None,       A = None,       A = "pil",       A = True,       A = None,       A = 1,       **A,       ):





            """simple docstring"""

            _UpperCamelCase              =						'''cuda''' if torch.cuda.is_available() else '''cpu'''
            self.to(A      )

            # Checks if the height and width are divisible by 8 or not
            if height % 8 != 0 or width % 8 != 0:
                   raise ValueError(F'''`height` and `width` must be divisible by 8 but are {height} and {width}.'''      )

            # Get first result from Stable Diffusion Checkpoint v1.1
            _UpperCamelCase              =						self.textaimg_sda_a(
                prompt=A,       height=A,       width=A,       num_inference_steps=A,       guidance_scale=A,       negative_prompt=A,       num_images_per_prompt=A,       eta=A,       generator=A,       latents=A,       output_type=A,       return_dict=A,       callback=A,       callback_steps=A,       **A,       )

            # Get first result from Stable Diffusion Checkpoint v1.2
            _UpperCamelCase              =						self.textaimg_sda_a(
                prompt=A,       height=A,       width=A,       num_inference_steps=A,       guidance_scale=A,       negative_prompt=A,       num_images_per_prompt=A,       eta=A,       generator=A,       latents=A,       output_type=A,       return_dict=A,       callback=A,       callback_steps=A,       **A,       )

            # Get first result from Stable Diffusion Checkpoint v1.3
            _UpperCamelCase              =						self.textaimg_sda_a(
                prompt=A,       height=A,       width=A,       num_inference_steps=A,       guidance_scale=A,       negative_prompt=A,       num_images_per_prompt=A,       eta=A,       generator=A,       latents=A,       output_type=A,       return_dict=A,       callback=A,       callback_steps=A,       **A,       )

            # Get first result from Stable Diffusion Checkpoint v1.4
            _UpperCamelCase              =						self.textaimg_sda_a(
                prompt=A,       height=A,       width=A,       num_inference_steps=A,       guidance_scale=A,       negative_prompt=A,       num_images_per_prompt=A,       eta=A,       generator=A,       latents=A,       output_type=A,       return_dict=A,       callback=A,       callback_steps=A,       **A,       )

            # Get all result images into a single list and pass it via StableDiffusionPipelineOutput for final result
            return StableDiffusionPipelineOutput([resa[0], resa[0], resa[0], resa[0]]      )













from __future__ import annotations

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

class  __A:

     def __init__(      self,       A,       A      ):





            """simple docstring"""

            _UpperCamelCase              =						graph
            # mapping node to its parent in resulting breadth first tree
            _UpperCamelCase              =						{}
            _UpperCamelCase              =						source_vertex

     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            _UpperCamelCase              =						{self.source_vertex}
            _UpperCamelCase              =						None
            _UpperCamelCase              =						[self.source_vertex]  # first in first out queue

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



     def 	_UpperCamelCase     (      self,       A      ):





            """simple docstring"""

            if target_vertex == self.source_vertex:
                   return self.source_vertex

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

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


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






