import argparse

import intel_extension_for_pytorch as ipex
import torch

from diffusers import DPMSolverMultistepScheduler, StableDiffusionPipeline


_lowercase							:      Any      							=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							:      List[str]      							=parser.parse_args()


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

_lowercase							:      Tuple      							='''path-to-your-trained-model'''
_lowercase							:      int      							=StableDiffusionPipeline.from_pretrained(model_id)
if args.dpm:
    _lowercase							:      List[str]      							=DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
_lowercase							:      List[Any]      							=pipe.to(device)

# to channels last
_lowercase							:      int      							=pipe.unet.to(memory_format=torch.channels_last)
_lowercase							:      Union[str, Any]      							=pipe.vae.to(memory_format=torch.channels_last)
_lowercase							:      Union[str, 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[Any]      							=torch.randn(2, 4, 6_4, 6_4)
_lowercase							:      Tuple      							=torch.rand(1) * 9_9_9
_lowercase							:      str      							=torch.randn(2, 7_7, 7_6_8)
_lowercase							:      List[str]      							=(sample, timestep, encoder_hidden_status)
try:
    _lowercase							:      Dict      							=ipex.optimize(pipe.unet.eval(), dtype=torch.bfloataa, inplace=True, sample_input=input_example)
except Exception:
    _lowercase							:      str      							=ipex.optimize(pipe.unet.eval(), dtype=torch.bfloataa, inplace=True)
_lowercase							:      Tuple      							=ipex.optimize(pipe.vae.eval(), dtype=torch.bfloataa, inplace=True)
_lowercase							:      Union[str, Any]      							=ipex.optimize(pipe.text_encoder.eval(), dtype=torch.bfloataa, inplace=True)
if pipe.requires_safety_checker:
    _lowercase							:      str      							=ipex.optimize(pipe.safety_checker.eval(), dtype=torch.bfloataa, inplace=True)

# compute
_lowercase							:      Dict      							=6_6_6
_lowercase							:      List[str]      							=torch.Generator(device).manual_seed(seed)
_lowercase							:      Any      							={'''generator''': generator}
if args.steps is not None:
    _lowercase							:      Tuple      							=args.steps

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

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







import heapq



def A__  (			lowercase:     dict					)				->		set[int]:
       A						:     list[list]         =[]

       # for each node and his adjacency list add them and the rank of the node to queue
       # using heapq module the queue will be filled like a Priority Queue
       # heapq works with a min priority queue, so I used -1*len(v) to build it
       for key, value in graph.items():
              # O(log(n))
              heapq.heappush(lowercase,		[-1 * len(lowercase					), (key, value)]					)

       # chosen_vertices = set of chosen vertices
       A						:     Dict         =set()

       # while queue isn't empty and there are still edges
       #   (queue[0][0] is the rank of the node with max rank)
       while queue and queue[0][0] != 0:
              # extract vertex with max rank from queue and add it to chosen_vertices
              A						:     List[str]         =heapq.heappop(lowercase					)[1][0]
              chosen_vertices.add(lowercase					)

              # Remove all arcs adjacent to argmax
              for elem in queue:
                     # if v haven't adjacent node, skip
                     if elem[0] == 0:
                            continue
                     # if argmax is reachable from elem
                     # remove argmax from elem's adjacent list and update his rank
                     if argmax in elem[1][1]:
                            A						:     str         =elem[1][1].index(lowercase					)
                            del elem[1][1][index]
                            elem[0] += 1
        # re-order the queue
              heapq.heapify(lowercase					)
       return chosen_vertices


if __name__ == "__main__":
    import doctest

    doctest.testmod()

    _lowercase							:      List[Any]      							={0: [1, 3], 1: [0, 3], 2: [0, 3, 4], 3: [0, 1, 2], 4: [2, 3]}
    print(f'''Minimum vertex cover:\n{greedy_min_vertex_cover(graph)}''')






