






from pathlib import PurePosixPath
from typing import Optional

import fsspec
from fsspec import AbstractFileSystem
from huggingface_hub.hf_api import DatasetInfo

from ..utils.file_utils import get_authentication_headers_for_url
from ..utils.hub import hf_hub_url


class SCREAMING_SNAKE_CASE  (		snake_case__							):






			"""simple docstring"""




			__A						  =  """"""
			__A						  =  """hf-legacy"""  # "hf://"" is reserved for hffs


			def __init__(    self ,				__UpperCamelCase = None ,				__UpperCamelCase = None ,				**__UpperCamelCase ,				):

				"""simple docstring"""


				super().__init__(self ,				**UpperCAmelCase_							)
				snake_case_      		= repo_info
				snake_case_      		= token
				snake_case_      		= None


			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				if self.dir_cache is None:
					snake_case_      		= {}
					for hf_file in self.repo_info.siblings:
						# TODO(QL): add sizes
						snake_case_      		= {
						    'name': hf_file.rfilename,
						    'size': None,
						    'type': 'file',
						}
						self.dir_cache.update(
						    {
						        str(UpperCAmelCase_							): {'name': str(UpperCAmelCase_							), 'size': None, 'type': 'directory'}
						        for d in list(PurePosixPath(hf_file.rfilename							).parents							)[:-1]
						    }							)


			def 			__lowerCAmelCase (    self ,				__UpperCamelCase ,				__UpperCamelCase = "rb" ,				**__UpperCamelCase ,				):

				"""simple docstring"""


				if not isinstance(self.repo_info ,				UpperCAmelCase_							):
					raise NotImplementedError(f"""Open is only implemented for dataset repositories, but got {self.repo_info}"""							)
				snake_case_      		= hf_hub_url(self.repo_info.id ,				UpperCAmelCase_ ,				revision=self.repo_info.sha							)
				return fsspec.open(
				    UpperCAmelCase_ ,				mode=UpperCAmelCase_ ,				headers=get_authentication_headers_for_url(UpperCAmelCase_ ,				use_auth_token=self.token							) ,				client_kwargs={'trust_env': True} ,				).open()


			def 			__lowerCAmelCase (    self ,				__UpperCamelCase ,				**__UpperCamelCase							):

				"""simple docstring"""


				self._get_dirs()
				snake_case_      		= self._strip_protocol(UpperCAmelCase_							)
				if path in self.dir_cache:
					return self.dir_cache[path]
				else:
					raise FileNotFoundError(UpperCAmelCase_							)







			def 			__lowerCAmelCase (    self ,				__UpperCamelCase ,				__UpperCamelCase=False ,				**__UpperCamelCase							):

				"""simple docstring"""


				self._get_dirs()
				snake_case_      		= PurePosixPath(path.strip('/'							)							)
				snake_case_      		= {}
				for p, f in self.dir_cache.items():
					snake_case_      		= PurePosixPath(p.strip('/'							)							)
					snake_case_      		= p.parent
					if root == path:
						snake_case_      		= f
				snake_case_      		= list(paths.values()							)
				if detail:
					return out
				else:
					return sorted(f['name'] for f in out							)



import argparse

import intel_extension_for_pytorch as ipex
import torch

from diffusers import DPMSolverMultistepScheduler, StableDiffusionPipeline


A											=     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')
A											=     parser.parse_args()


A											=     'cpu'
A											=     'a lovely <dicoo> in red dress and hat, in the snowly and brightly night, with many brighly buildings'

A											=     'path-to-your-trained-model'
A											=     StableDiffusionPipeline.from_pretrained(model_id)
if args.dpm:
		A											=     DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
A											=     pipe.to(device)

# to channels last
A											=     pipe.unet.to(memory_format=torch.channels_last)
A											=     pipe.vae.to(memory_format=torch.channels_last)
A											=     pipe.text_encoder.to(memory_format=torch.channels_last)
if pipe.requires_safety_checker:
		A											=     pipe.safety_checker.to(memory_format=torch.channels_last)

# optimize with ipex
A											=     torch.randn(2, 4, 64, 64)
A											=     torch.rand(1) * 999
A											=     torch.randn(2, 77, 768)
A											=     (sample, timestep, encoder_hidden_status)
try:
		A											=     ipex.optimize(pipe.unet.eval(), dtype=torch.bfloataa, inplace=True, sample_input=input_example)
except Exception:
		A											=     ipex.optimize(pipe.unet.eval(), dtype=torch.bfloataa, inplace=True)
A											=     ipex.optimize(pipe.vae.eval(), dtype=torch.bfloataa, inplace=True)
A											=     ipex.optimize(pipe.text_encoder.eval(), dtype=torch.bfloataa, inplace=True)
if pipe.requires_safety_checker:
		A											=     ipex.optimize(pipe.safety_checker.eval(), dtype=torch.bfloataa, inplace=True)

# compute
A											=     666
A											=     torch.Generator(device).manual_seed(seed)
A											=     {'generator': generator}
if args.steps is not None:
		A											=     args.steps

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

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