
import argparse
import math
import os

import torch
from neural_compressor.utils.pytorch import load
from PIL import Image
from transformers import CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, StableDiffusionPipeline, UNetaDConditionModel



def   __lowerCAmelCase    (				)	->     int:
							lowerCamelCase_					=       argparse.ArgumentParser()
							parser.add_argument(
							    """-m"""      ,					"""--pretrained_model_name_or_path"""      ,					type=_lowercase      ,					default=_lowercase      ,					required=_lowercase      ,					help="""Path to pretrained model or model identifier from huggingface.co/models."""      ,					)
							parser.add_argument(
							    """-c"""      ,					"""--caption"""      ,					type=_lowercase      ,					default="""robotic cat with wings"""      ,					help="""Text used to generate images."""      ,					)
							parser.add_argument(
							    """-n"""      ,					"""--images_num"""      ,					type=_lowercase      ,					default=4      ,					help="""How much images to generate."""      ,					)
							parser.add_argument(
							    """-s"""      ,					"""--seed"""      ,					type=_lowercase      ,					default=4_2      ,					help="""Seed for random process."""      ,					)
							parser.add_argument(
							    """-ci"""      ,					"""--cuda_id"""      ,					type=_lowercase      ,					default=0      ,					help="""cuda_id."""      ,					)
							lowerCamelCase_					=       parser.parse_args()
							return args



def   __lowerCAmelCase    (				UpperCAmelCase__					:   Union[str, Any]      ,					UpperCAmelCase__					:   Optional[int]      ,					UpperCAmelCase__					:   Union[str, Any]							)	->     str:
							if not len(_lowercase							) == rows * cols:
														raise ValueError("""The specified number of rows and columns are not correct."""							)

							lowerCamelCase_					=       imgs[0].size
							lowerCamelCase_					=       Image.new("""RGB"""      ,					size=(cols * w, rows * h)							)
							lowerCamelCase_					=       grid.size

							for i, img in enumerate(_lowercase							):
														grid.paste(_lowercase      ,					box=(i % cols * w, i // cols * h)							)
							return grid



def   __lowerCAmelCase    (				UpperCAmelCase__					:   Optional[int]      ,					UpperCAmelCase__					:   Tuple="robotic cat with wings"      ,					UpperCAmelCase__					:   Optional[Any]=7.5      ,					UpperCAmelCase__					:   int=5_0      ,					UpperCAmelCase__					:   str=1      ,					UpperCAmelCase__					:   Any=4_2      ,					)	->     str:
							lowerCamelCase_					=       torch.Generator(pipeline.device							).manual_seed(_lowercase							)
							lowerCamelCase_					=       pipeline(
							    _lowercase      ,					guidance_scale=_lowercase      ,					num_inference_steps=_lowercase      ,					generator=_lowercase      ,					num_images_per_prompt=_lowercase      ,					).images
							lowerCamelCase_					=       int(math.sqrt(_lowercase							)							)
							lowerCamelCase_					=       image_grid(_lowercase      ,					rows=_rows      ,					cols=num_images_per_prompt // _rows							)
							return grid, images


lowercase   =	parse_args()
# Load models and create wrapper for stable diffusion
lowercase   =	CLIPTokenizer.from_pretrained(args.pretrained_model_name_or_path, subfolder='''tokenizer''')
lowercase   =	CLIPTextModel.from_pretrained(args.pretrained_model_name_or_path, subfolder='''text_encoder''')
lowercase   =	AutoencoderKL.from_pretrained(args.pretrained_model_name_or_path, subfolder='''vae''')
lowercase   =	UNetaDConditionModel.from_pretrained(args.pretrained_model_name_or_path, subfolder='''unet''')

lowercase   =	StableDiffusionPipeline.from_pretrained(
    args.pretrained_model_name_or_path, text_encoder=text_encoder, vae=vae, unet=unet, tokenizer=tokenizer
)
lowercase   =	lambda images, clip_input: (images, False)
if os.path.exists(os.path.join(args.pretrained_model_name_or_path, '''best_model.pt''')):
		lowercase   =	load(args.pretrained_model_name_or_path, model=unet)
		unet.eval()
		setattr(pipeline, '''unet''', unet)
else:
		lowercase   =	unet.to(torch.device('''cuda''', args.cuda_id))
lowercase   =	pipeline.to(unet.device)
lowercase   =	generate_images(pipeline, prompt=args.caption, num_images_per_prompt=args.images_num, seed=args.seed)
grid.save(os.path.join(args.pretrained_model_name_or_path, '''{}.png'''.format('''_'''.join(args.caption.split()))))
lowercase   =	os.path.join(args.pretrained_model_name_or_path, '''_'''.join(args.caption.split()))
os.makedirs(dirname, exist_ok=True)
for idx, image in enumerate(images):
		image.save(os.path.join(dirname, '''{}.png'''.format(idx + 1)))












import argparse
import os
import re


__UpperCamelCase:	Any = """src/transformers/models/auto"""


# re pattern that matches mapping introductions:
#    SUPER_MODEL_MAPPING_NAMES = OrderedDict or SUPER_MODEL_MAPPING = OrderedDict
__UpperCamelCase:	Dict = re.compile(r"""[A-Z_]+_MAPPING(\s+|_[A-Z_]+\s+)=\s+OrderedDict""")
# re pattern that matches identifiers in mappings
__UpperCamelCase:	List[str] = re.compile(r"""\s*\(\s*\"(\S[^\"]+)\"""")


def       SCREAMING_SNAKE_CASE__  (       _lowercase     : int   ,    _lowercase     : bool = False			)   ->							List[str]:




		'''simple docstring'''
		with open(_lowercase   ,    'r'   ,    encoding='utf-8'			) as f:
				lowercase__					:      Union[str, Any]      =		f.read()

		lowercase__					:      Optional[Any]      =		content.split('\n'			)
		lowercase__					:      Optional[int]      =		[]
		lowercase__					:      int      =		0
		while line_idx < len(_lowercase			):
				if _re_intro_mapping.search(lines[line_idx]			) is not None:
						lowercase__					:      Tuple      =		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

						lowercase__					:      Tuple      =		[]
						while lines[line_idx].strip() != "]":
								# Blocks either fit in one line or not
								if lines[line_idx].strip() == "(":
										lowercase__					:      Any      =		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
						lowercase__					:      List[str]      =		sorted(_lowercase   ,    key=lambda _lowercase			: _re_identifier.search(_lowercase			).groups()[0]			)
						new_lines += blocks
				else:
						new_lines.append(lines[line_idx]			)
						line_idx += 1

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


def       SCREAMING_SNAKE_CASE__  (       _lowercase     : bool = False			)   ->							List[Any]:




		'''simple docstring'''
		lowercase__					:      List[Any]      =		[os.path.join(_lowercase   ,    _lowercase			) for f in os.listdir(_lowercase			) if f.endswith('.py'			)]
		lowercase__					:      str      =		[sort_auto_mapping(_lowercase   ,    overwrite=_lowercase			) for fname in fnames]

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


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

			sort_all_auto_mappings(not args.check_only)


