


import argparse
import os
import re


_SCREAMING_SNAKE_CASE     :					Any      =       'src/transformers/models/auto'


# re pattern that matches mapping introductions:
#    SUPER_MODEL_MAPPING_NAMES = OrderedDict or SUPER_MODEL_MAPPING = OrderedDict
_SCREAMING_SNAKE_CASE     :					Any      =       re.compile(R"[A-Z_]+_MAPPING(\s+|_[A-Z_]+\s+)=\s+OrderedDict")
# re pattern that matches identifiers in mappings
_SCREAMING_SNAKE_CASE     :					List[str]      =       re.compile(R"\s*\(\s*\"(\S[^\"]+)\"")
def      UpperCAmelCase__		(UpperCamelCase_				,UpperCamelCase_ = False							):



     """simple docstring"""






     with open(UpperCamelCase_				,'''r'''				,encoding='''utf-8'''							) as f:
          snake_case											=							f.read()

     snake_case											=							content.split('''\n'''							)
     snake_case											=							[]
     snake_case											=							0
     while line_idx < len(UpperCamelCase_							):
          if _re_intro_mapping.search(lines[line_idx]							) is not None:
               snake_case											=							len(re.search(r'''^(\s*)\S'''				,lines[line_idx]							).groups()[0]							) + 8
               # Start of a new mapping!
               while not lines[line_idx].startswith(''' ''' * indent + '''('''							):
                    new_lines.append(lines[line_idx]							)
                    line_idx += 1

               snake_case											=							[]
               while lines[line_idx].strip() != "]":
                    # Blocks either fit in one line or not
                    if lines[line_idx].strip() == "(":
                         snake_case											=							line_idx
                         while not lines[line_idx].startswith(''' ''' * indent + ''')'''							):
                              line_idx += 1
                         blocks.append('''\n'''.join(lines[start_idx : line_idx + 1]							)							)
                    else:
                         blocks.append(lines[line_idx]							)
                    line_idx += 1

               # Sort blocks by their identifiers
               snake_case											=							sorted(UpperCamelCase_				,key=lambda UpperCamelCase_							: _re_identifier.search(UpperCamelCase_							).groups()[0]							)
               new_lines += blocks
          else:
               new_lines.append(lines[line_idx]							)
               line_idx += 1

     if overwrite:
          with open(UpperCamelCase_				,'''w'''				,encoding='''utf-8'''							) as f:
               f.write('''\n'''.join(UpperCamelCase_							)							)
     elif "\n".join(UpperCamelCase_							) != content:
          return True
def      UpperCAmelCase__		(UpperCamelCase_ = False							):



     """simple docstring"""






     snake_case											=							[os.path.join(UpperCamelCase_				,UpperCamelCase_							) for f in os.listdir(UpperCamelCase_							) if f.endswith('''.py'''							)]
     snake_case											=							[sort_auto_mapping(UpperCamelCase_				,overwrite=UpperCamelCase_							) for fname in fnames]

     if not overwrite and any(UpperCamelCase_							):
          snake_case											=							[f for f, d in zip(UpperCamelCase_				,UpperCamelCase_							) if d]
          raise ValueError(
              F'''The following files have auto mappings that need sorting: {", ".join(UpperCamelCase_							)}. Run `make style` to fix'''
              ''' this.'''							)


if __name__ == "__main__":
 _SCREAMING_SNAKE_CASE     :					Optional[int]      =       argparse.ArgumentParser()
 parser.add_argument("--check_only", action="store_true", help="Whether to only check or fix style.")
 _SCREAMING_SNAKE_CASE     :					Any      =       parser.parse_args()

 sort_all_auto_mappings(not args.check_only)











'''simple docstring'''

import argparse

import intel_extension_for_pytorch as ipex
import torch

from diffusers import DPMSolverMultistepScheduler, StableDiffusionPipeline


lowercase__		:     int			=							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__		:     Union[str, Any]			=							parser.parse_args()


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

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

# to channels last
lowercase__		:     Any			=							pipe.unet.to(memory_format=torch.channels_last)
lowercase__		:     Dict			=							pipe.vae.to(memory_format=torch.channels_last)
lowercase__		:     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[str]			=							torch.randn(2, 4, 64, 64)
lowercase__		:     int			=							torch.rand(1) * 9_99
lowercase__		:     Optional[int]			=							torch.randn(2, 77, 7_68)
lowercase__		:     List[str]			=							(sample, timestep, encoder_hidden_status)
try:
   lowercase__		:     Optional[Any]			=							ipex.optimize(pipe.unet.eval(), dtype=torch.bfloataa, inplace=True, sample_input=input_example)
except Exception:
   lowercase__		:     Any			=							ipex.optimize(pipe.unet.eval(), dtype=torch.bfloataa, inplace=True)
lowercase__		:     Tuple			=							ipex.optimize(pipe.vae.eval(), dtype=torch.bfloataa, inplace=True)
lowercase__		:     Any			=							ipex.optimize(pipe.text_encoder.eval(), dtype=torch.bfloataa, inplace=True)
if pipe.requires_safety_checker:
   lowercase__		:     Union[str, Any]			=							ipex.optimize(pipe.safety_checker.eval(), dtype=torch.bfloataa, inplace=True)

# compute
lowercase__		:     Optional[Any]			=							6_66
lowercase__		:     Tuple			=							torch.Generator(device).manual_seed(seed)
lowercase__		:     Dict			=							{'generator': generator}
if args.steps is not None:
   lowercase__		:     int			=							args.steps

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

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



