





import argparse
import os
import re

import packaging.version


lowercase_		:					Tuple      						=    '''examples/'''
lowercase_		:					Optional[Any]      						=    {
    '''examples''': (re.compile(r'''^check_min_version\("[^"]+"\)\s*$''', re.MULTILINE), '''check_min_version("VERSION")\n'''),
    '''init''': (re.compile(r'''^__version__\s+=\s+"([^"]+)"\s*$''', re.MULTILINE), '''__version__ = "VERSION"\n'''),
    '''setup''': (re.compile(r'''^(\s*)version\s*=\s*"[^"]+",''', re.MULTILINE), r'''\1version="VERSION",'''),
    '''doc''': (re.compile(r'''^(\s*)release\s*=\s*"[^"]+"$''', re.MULTILINE), '''release = "VERSION"\n'''),
}
lowercase_		:					Optional[Any]      						=    {
    '''init''': '''src/transformers/__init__.py''',
    '''setup''': '''setup.py''',
}
lowercase_		:					Optional[Any]      						=    '''README.md'''

def    A__(		__lowerCAmelCase						,      __lowerCAmelCase						,      __lowerCAmelCase			):
				with open(_lowerCAmelCase						,      'r'						,      encoding='utf-8'						,      newline='\n'			) as f:
								_snake_case			:      Union[str, Any]	    = f.read()
				_snake_case			:      Tuple	    = REPLACE_PATTERNS[pattern]
				_snake_case			:      Optional[int]	    = replace.replace('VERSION'						,      _lowerCAmelCase			)
				_snake_case			:      Optional[int]	    = re_pattern.sub(_lowerCAmelCase						,      _lowerCAmelCase			)
				with open(_lowerCAmelCase						,      'w'						,      encoding='utf-8'						,      newline='\n'			) as f:
								f.write(_lowerCAmelCase			)

def    A__(		__lowerCAmelCase			):
				for folder, directories, fnames in os.walk(_lowerCAmelCase			):
								# Removing some of the folders with non-actively maintained examples from the walk
								if "research_projects" in directories:
												directories.remove('research_projects'			)
								if "legacy" in directories:
												directories.remove('legacy'			)
								for fname in fnames:
												if fname.endswith('.py'			):
																update_version_in_file(os.path.join(_lowerCAmelCase						,      _lowerCAmelCase			)						,      _lowerCAmelCase						,      pattern='examples'			)

def    A__(		__lowerCAmelCase						,      __lowerCAmelCase=False			):
				for pattern, fname in REPLACE_FILES.items():
								update_version_in_file(_lowerCAmelCase						,      _lowerCAmelCase						,      _lowerCAmelCase			)
				if not patch:
								update_version_in_examples(_lowerCAmelCase			)

def    A__(		):
				_snake_case			:      Dict	    = "🤗 Transformers currently provides the following architectures"
				_snake_case			:      int	    = "1. Want to contribute a new model?"
				with open(_lowerCAmelCase						,      'r'						,      encoding='utf-8'						,      newline='\n'			) as f:
								_snake_case			:      Optional[int]	    = f.readlines()

				# Find the start of the list.
				_snake_case			:      Union[str, Any]	    = 0
				while not lines[start_index].startswith(_start_prompt			):
								start_index += 1
				start_index += 1

				_snake_case			:      List[Any]	    = start_index
				# Update the lines in the model list.
				while not lines[index].startswith(_end_prompt			):
								if lines[index].startswith('1.'			):
												_snake_case			:      List[Any]	    = lines[index].replace(
												    'https://huggingface.co/docs/transformers/main/model_doc'						,      'https://huggingface.co/docs/transformers/model_doc'						,      )
								index += 1

				with open(_lowerCAmelCase						,      'w'						,      encoding='utf-8'						,      newline='\n'			) as f:
								f.writelines(_lowerCAmelCase			)

def    A__(		):
				with open(REPLACE_FILES['init']						,      'r'			) as f:
								_snake_case			:      Dict	    = f.read()
				_snake_case			:      Any	    = REPLACE_PATTERNS["init"][0].search(_lowerCAmelCase			).groups()[0]
				return packaging.version.parse(_lowerCAmelCase			)

def    A__(		__lowerCAmelCase=False			):
				_snake_case			:      Any	    = get_version()
				if patch and default_version.is_devrelease:
								raise ValueError('Can\'t create a patch version from the dev branch, checkout a released version!'			)
				if default_version.is_devrelease:
								_snake_case			:      Optional[int]	    = default_version.base_version
				elif patch:
								_snake_case			:      Dict	    = F'''{default_version.major}.{default_version.minor}.{default_version.micro + 1}'''
				else:
								_snake_case			:      Dict	    = F'''{default_version.major}.{default_version.minor + 1}.0'''

				# Now let's ask nicely if that's the right one.
				_snake_case			:      Any	    = input(F'''Which version are you releasing? [{default_version}]'''			)
				if len(_lowerCAmelCase			) == 0:
								_snake_case			:      Any	    = default_version

				print(F'''Updating version to {version}.'''			)
				global_version_update(_lowerCAmelCase						,      patch=_lowerCAmelCase			)
				if not patch:
								print('Cleaning main README, don\'t forget to run `make fix-copies`.'			)
								clean_main_ref_in_model_list()

def    A__(		):
				_snake_case			:      List[str]	    = get_version()
				_snake_case			:      Optional[Any]	    = F'''{current_version.major}.{current_version.minor + 1}.0.dev0'''
				_snake_case			:      List[str]	    = current_version.base_version

				# Check with the user we got that right.
				_snake_case			:      Union[str, Any]	    = input(F'''Which version are we developing now? [{dev_version}]'''			)
				if len(_lowerCAmelCase			) == 0:
								_snake_case			:      Optional[int]	    = dev_version

				print(F'''Updating version to {version}.'''			)
				global_version_update(_lowerCAmelCase			)
				print('Cleaning main README, don\'t forget to run `make fix-copies`.'			)
				clean_main_ref_in_model_list()


if __name__ == "__main__":
		lowercase_		:					str      						=    argparse.ArgumentParser()
		parser.add_argument('''--post_release''', action='''store_true''', help='''Whether this is pre or post release.''')
		parser.add_argument('''--patch''', action='''store_true''', help='''Whether or not this is a patch release.''')
		lowercase_		:					Union[str, Any]      						=    parser.parse_args()
		if not args.post_release:
				pre_release_work(patch=args.patch)
		elif args.patch:
				print('''Nothing to do after a patch :-)''')
		else:
				post_release_work()













import argparse

import intel_extension_for_pytorch as ipex
import torch

from diffusers import DPMSolverMultistepScheduler, StableDiffusionPipeline


_lowerCAmelCase				:   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''')
_lowerCAmelCase				:   Tuple						       =						parser.parse_args()


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

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

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

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

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

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

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