



"""simple docstring"""

# Lint as: python3
import os
import re
import urllib.parse
from pathlib import Path
from typing import Callable, List, Optional, Union
from zipfile import ZipFile

from ..utils.file_utils import cached_path, hf_github_url
from ..utils.logging import get_logger
from ..utils.version import Version


A_						:    Tuple				=      get_logger(__name__)
class     lowerCAmelCase__						:


	'''simple docstring'''







	_SCREAMING_SNAKE_CASE	:		List[str]  						= '''dummy_data'''
	_SCREAMING_SNAKE_CASE	:		Optional[Any]  						= '''datasets'''
	_SCREAMING_SNAKE_CASE	:		Union[str, Any]  						= False





	def __init__(      self				:       List[str]     ,       _SCREAMING_SNAKE_CASE				:       str     ,       _SCREAMING_SNAKE_CASE				:       str     ,       _SCREAMING_SNAKE_CASE				:       Union[Version, str]     ,       _SCREAMING_SNAKE_CASE				:       Optional[str] = None     ,       _SCREAMING_SNAKE_CASE				:       bool = False     ,       _SCREAMING_SNAKE_CASE				:       bool = True     ,       _SCREAMING_SNAKE_CASE				:       Optional[List[Callable]] = None     ,       )						->	Union[str, Any]:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							List[Any]							=     0
					SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     dataset_name
					SCREAMING_SNAKE_CASE						:							List[Any]							=     cache_dir
					SCREAMING_SNAKE_CASE						:							List[str]							=     use_local_dummy_data
					SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     config
					# download_callbacks take a single url as input
					SCREAMING_SNAKE_CASE						:							List[Callable]							=     download_callbacks or []
					# if False, it doesn't load existing files and it returns the paths of the dummy files relative
					# to the dummy_data zip file root
					SCREAMING_SNAKE_CASE						:							Optional[Any]							=     load_existing_dummy_data

					# TODO(PVP, QL) might need to make this more general
					SCREAMING_SNAKE_CASE						:							Tuple							=     str(_SCREAMING_SNAKE_CASE    )
					# to be downloaded
					SCREAMING_SNAKE_CASE						:							int							=     None
					SCREAMING_SNAKE_CASE						:							Dict							=     None





	@property
	def 			_lowerCAmelCase     (      self				:       int    )						->	Optional[int]:





					"""simple docstring"""


					if self._dummy_file is None:
									SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     self.download_dummy_data()
					return self._dummy_file





	@property
	def 			_lowerCAmelCase     (      self				:       Optional[Any]    )						->	Tuple:





					"""simple docstring"""


					if self.config is not None:
									# structure is dummy / config_name / version_name
									return os.path.join('dummy'     ,       self.config.name     ,       self.version_name    )
					# structure is dummy / version_name
					return os.path.join('dummy'     ,       self.version_name    )





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





					"""simple docstring"""


					return os.path.join(self.dummy_data_folder     ,       'dummy_data.zip'    )





	def 			_lowerCAmelCase     (      self				:       Any    )						->	Optional[Any]:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							List[str]							=     (
					    self.local_path_to_dummy_data if self.use_local_dummy_data is True else self.github_path_to_dummy_data
					)

					SCREAMING_SNAKE_CASE						:							str							=     cached_path(
					    _SCREAMING_SNAKE_CASE     ,       cache_dir=self.cache_dir     ,       extract_compressed_file=_SCREAMING_SNAKE_CASE     ,       force_extract=_SCREAMING_SNAKE_CASE    )

					return os.path.join(_SCREAMING_SNAKE_CASE     ,       self.dummy_file_name    )





	@property
	def 			_lowerCAmelCase     (      self				:       Optional[Any]    )						->	Optional[int]:





					"""simple docstring"""


					return os.path.join(self.datasets_scripts_dir     ,       self.dataset_name     ,       self.dummy_zip_file    )





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





					"""simple docstring"""


					if self._bucket_url is None:
									SCREAMING_SNAKE_CASE						:							Dict							=     hf_github_url(self.dataset_name     ,       self.dummy_zip_file.replace(os.sep     ,       '/'    )    )
					return self._bucket_url





	@property
	def 			_lowerCAmelCase     (      self				:       Dict    )						->	List[Any]:





					"""simple docstring"""


					if os.path.isdir(self.dummy_file    ):
									return self.dummy_file
					# else cut off path to file -> example `xsum`.
					return "/".join(self.dummy_file.replace(os.sep     ,       '/'    ).split('/'    )[:-1]    )





	def 			_lowerCAmelCase     (      self				:       Tuple     ,       _SCREAMING_SNAKE_CASE				:       Tuple     ,       *_SCREAMING_SNAKE_CASE				:       Union[str, Any]    )						->	Any:





					"""simple docstring"""


					if self.load_existing_dummy_data:
									# dummy data is downloaded and tested
									SCREAMING_SNAKE_CASE						:							Dict							=     self.dummy_file
					else:
									# dummy data cannot be downloaded and only the path to dummy file is returned
									SCREAMING_SNAKE_CASE						:							List[str]							=     self.dummy_file_name

					# special case when data_url is a dict
					if isinstance(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE    ):
									return self.create_dummy_data_dict(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE    )
					elif isinstance(_SCREAMING_SNAKE_CASE     ,       (list, tuple)    ):
									return self.create_dummy_data_list(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE    )
					else:
									return self.create_dummy_data_single(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE    )





	def 			_lowerCAmelCase     (      self				:       Any     ,       _SCREAMING_SNAKE_CASE				:       Union[str, Any]     ,       *_SCREAMING_SNAKE_CASE				:       Tuple    )						->	Dict:





					"""simple docstring"""


					return self.download_and_extract(_SCREAMING_SNAKE_CASE    )





	def 			_lowerCAmelCase     (      self				:       Optional[Any]     ,       _SCREAMING_SNAKE_CASE				:       Optional[int]     ,       _SCREAMING_SNAKE_CASE				:       Dict    )						->	Dict:





					"""simple docstring"""


					return self.download_and_extract(_SCREAMING_SNAKE_CASE    )





	def 			_lowerCAmelCase     (      self				:       Dict     ,       _SCREAMING_SNAKE_CASE				:       str     ,       *_SCREAMING_SNAKE_CASE				:       Dict     ,       **_SCREAMING_SNAKE_CASE				:       Optional[Any]    )						->	Dict:





					"""simple docstring"""


					return path





	def 			_lowerCAmelCase     (      self				:       Optional[Any]    )						->	Optional[int]:





					"""simple docstring"""


					return {}





	def 			_lowerCAmelCase     (      self				:       List[Any]     ,       _SCREAMING_SNAKE_CASE				:       Any     ,       _SCREAMING_SNAKE_CASE				:       Optional[Any]    )						->	Tuple:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							Tuple							=     {}
					for key, single_urls in data_url.items():
									for download_callback in self.download_callbacks:
													if isinstance(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE    ):
																	for single_url in single_urls:
																					download_callback(_SCREAMING_SNAKE_CASE    )
													else:
																	SCREAMING_SNAKE_CASE						:							int							=     single_urls
																	download_callback(_SCREAMING_SNAKE_CASE    )
            # we force the name of each key to be the last file / folder name of the url path
            # if the url has arguments, we need to encode them with urllib.parse.quote_plus
									if isinstance(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE    ):
													SCREAMING_SNAKE_CASE						:							Optional[int]							=     [os.path.join(_SCREAMING_SNAKE_CASE     ,       urllib.parse.quote_plus(Path(_SCREAMING_SNAKE_CASE    ).name    )    ) for x in single_urls]
									else:
													SCREAMING_SNAKE_CASE						:							Optional[int]							=     single_urls
													SCREAMING_SNAKE_CASE						:							List[str]							=     os.path.join(_SCREAMING_SNAKE_CASE     ,       urllib.parse.quote_plus(Path(_SCREAMING_SNAKE_CASE    ).name    )    )
									SCREAMING_SNAKE_CASE						:							Optional[int]							=     value

					# make sure that values are unique
					if all(isinstance(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE    ) for i in dummy_data_dict.values()    ) and len(set(dummy_data_dict.values()    )    ) < len(
					    dummy_data_dict.values()    ):
									# append key to value to make its name unique
									SCREAMING_SNAKE_CASE						:							List[Any]							=     {key: value + key for key, value in dummy_data_dict.items()}

					return dummy_data_dict





	def 			_lowerCAmelCase     (      self				:       Dict     ,       _SCREAMING_SNAKE_CASE				:       Dict     ,       _SCREAMING_SNAKE_CASE				:       Union[str, Any]    )						->	Union[str, Any]:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							Optional[int]							=     []
					# trick: if there are many shards named like `data.txt-000001-of-00300`, only use the first one
					SCREAMING_SNAKE_CASE						:							Optional[int]							=     all(bool(re.findall('[0-9]{3,}-of-[0-9]{3,}'     ,       _SCREAMING_SNAKE_CASE    )    ) for url in data_url    )
					SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     all(
					    url.startswith('https://ftp.ncbi.nlm.nih.gov/pubmed/baseline/pubmed'    ) for url in data_url    )
					if data_url and (is_tf_records or is_pubmed_records):
									SCREAMING_SNAKE_CASE						:							Any							=     [data_url[0]] * len(_SCREAMING_SNAKE_CASE    )
					for single_url in data_url:
									for download_callback in self.download_callbacks:
													download_callback(_SCREAMING_SNAKE_CASE    )
									# we force the name of each key to be the last file / folder name of the url path
									# if the url has arguments, we need to encode them with urllib.parse.quote_plus
									SCREAMING_SNAKE_CASE						:							Optional[int]							=     os.path.join(_SCREAMING_SNAKE_CASE     ,       urllib.parse.quote_plus(single_url.split('/'    )[-1]    )    )
									dummy_data_list.append(_SCREAMING_SNAKE_CASE    )
					return dummy_data_list





	def 			_lowerCAmelCase     (      self				:       Tuple     ,       _SCREAMING_SNAKE_CASE				:       Any     ,       _SCREAMING_SNAKE_CASE				:       Optional[int]    )						->	Any:





					"""simple docstring"""


					for download_callback in self.download_callbacks:
									download_callback(_SCREAMING_SNAKE_CASE    )
					# we force the name of each key to be the last file / folder name of the url path
					# if the url has arguments, we need to encode them with urllib.parse.quote_plus
					SCREAMING_SNAKE_CASE						:							List[str]							=     os.path.join(_SCREAMING_SNAKE_CASE     ,       urllib.parse.quote_plus(data_url.split('/'    )[-1]    )    )
					if os.path.exists(_SCREAMING_SNAKE_CASE    ) or not self.load_existing_dummy_data:
									return value
					else:
									# Backward compatibility, maybe deprecate at one point.
									# For many datasets with single url calls to dl_manager.download_and_extract,
									# the dummy_data.zip file is actually the zipped downloaded file
									# while now we expected the dummy_data.zip file to be a directory containing
									# the downloaded file.
									return path_to_dummy_data





	def 			_lowerCAmelCase     (      self				:       List[str]    )						->	Union[str, Any]:





					"""simple docstring"""


					pass





	def 			_lowerCAmelCase     (      self				:       Optional[int]    )						->	List[str]:





					"""simple docstring"""


					pass





	def 			_lowerCAmelCase     (      self				:       Optional[int]     ,       _SCREAMING_SNAKE_CASE				:       Any    )						->	Union[str, Any]:





					"""simple docstring"""


					def _iter_archive_members(_SCREAMING_SNAKE_CASE				:       Tuple    ):
									# this preserves the order of the members inside the ZIP archive
									SCREAMING_SNAKE_CASE						:							Optional[Any]							=     Path(self.dummy_file    ).parent
									SCREAMING_SNAKE_CASE						:							Tuple							=     path.relative_to(_SCREAMING_SNAKE_CASE    )
									with ZipFile(self.local_path_to_dummy_data    ) as zip_file:
													SCREAMING_SNAKE_CASE						:							Tuple							=     zip_file.namelist()
									for member in members:
													if member.startswith(relative_path.as_posix()    ):
																	yield dummy_parent_path.joinpath(_SCREAMING_SNAKE_CASE    )

					SCREAMING_SNAKE_CASE						:							Any							=     Path(_SCREAMING_SNAKE_CASE    )
					SCREAMING_SNAKE_CASE						:							Optional[Any]							=     _iter_archive_members(_SCREAMING_SNAKE_CASE    ) if self.use_local_dummy_data else path.rglob('*'    )
					for file_path in file_paths:
									if file_path.is_file() and not file_path.name.startswith(('.', '__')    ):
													yield file_path.relative_to(_SCREAMING_SNAKE_CASE    ).as_posix(), file_path.open('rb'    )







	def 			_lowerCAmelCase     (      self				:       str     ,       _SCREAMING_SNAKE_CASE				:       Tuple    )						->	Tuple:





					"""simple docstring"""


					if not isinstance(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE    ):
									SCREAMING_SNAKE_CASE						:							Optional[Any]							=     [paths]
					for path in paths:
									if os.path.isfile(_SCREAMING_SNAKE_CASE    ):
													if os.path.basename(_SCREAMING_SNAKE_CASE    ).startswith(('.', '__')    ):
																	return
													yield path
									else:
													for dirpath, dirnames, filenames in os.walk(_SCREAMING_SNAKE_CASE    ):
																	if os.path.basename(_SCREAMING_SNAKE_CASE    ).startswith(('.', '__')    ):
																					continue
																	dirnames.sort()
																	for filename in sorted(_SCREAMING_SNAKE_CASE    ):
																					if filename.startswith(('.', '__')    ):
																									continue
																					yield os.path.join(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE    )










"""simple docstring"""

import argparse

import intel_extension_for_pytorch as ipex
import torch

from diffusers import DPMSolverMultistepScheduler, StableDiffusionPipeline


A_						:    List[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')
A_						:    Tuple				=      parser.parse_args()


A_						:    Optional[int]				=      'cpu'
A_						:    Dict				=      'a lovely <dicoo> in red dress and hat, in the snowly and brightly night, with many brighly buildings'

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

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

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

# compute
A_						:    Union[str, Any]				=      666
A_						:    Optional[Any]				=      torch.Generator(device).manual_seed(seed)
A_						:    Union[str, Any]				=      {'generator': generator}
if args.steps is not None:
							A_						:    int				=      args.steps

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

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





