





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   (				)						->							List[Any]:






		"""simple docstring"""

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







def   __lowerCamelCase   (				__a  :Union[str, Any]       ,	__a  :str       ,	__a  :Tuple       )						->							Any:






		"""simple docstring"""

		if not len(__a       ) == rows * cols:
				raise ValueError("""The specified number of rows and columns are not correct."""       )

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

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







def   __lowerCamelCase   (				__a  :str       ,	__a  :Dict="robotic cat with wings"       ,	__a  :List[Any]=7.5       ,	__a  :List[str]=5_0       ,	__a  :Dict=1       ,	__a  :Dict=4_2       ,	)						->							int:






		"""simple docstring"""

		A__     =  torch.Generator(pipeline.device       ).manual_seed(__a       )
		A__     =  pipeline(
		    __a       ,	guidance_scale=__a       ,	num_inference_steps=__a       ,	generator=__a       ,	num_images_per_prompt=__a       ,	).images
		A__     =  int(math.sqrt(__a       )       )
		A__     =  image_grid(__a       ,	rows=_rows       ,	cols=num_images_per_prompt // _rows       )
		return grid, images


A       :   int					= parse_args()
# Load models and create wrapper for stable diffusion
A       :   Any					= CLIPTokenizer.from_pretrained(args.pretrained_model_name_or_path, subfolder='''tokenizer''')
A       :   Any					= CLIPTextModel.from_pretrained(args.pretrained_model_name_or_path, subfolder='''text_encoder''')
A       :   Optional[Any]					= AutoencoderKL.from_pretrained(args.pretrained_model_name_or_path, subfolder='''vae''')
A       :   int					= UNetaDConditionModel.from_pretrained(args.pretrained_model_name_or_path, subfolder='''unet''')

A       :   Dict					= StableDiffusionPipeline.from_pretrained(
    args.pretrained_model_name_or_path, text_encoder=text_encoder, vae=vae, unet=unet, tokenizer=tokenizer
)
A       :   Union[str, Any]					= lambda images, clip_input: (images, False)
if os.path.exists(os.path.join(args.pretrained_model_name_or_path, '''best_model.pt''')):
					A       :   Optional[int]					= load(args.pretrained_model_name_or_path, model=unet)
					unet.eval()
					setattr(pipeline, '''unet''', unet)
else:
					A       :   Tuple					= unet.to(torch.device('''cuda''', args.cuda_id))
A       :   str					= pipeline.to(unet.device)
A,      A       :   List[str]					= 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()))))
A       :   Optional[Any]					= 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)))










from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
    is_vision_available,
)


A       :   Dict					= {'''processing_layoutxlm''': ['''LayoutXLMProcessor''']}

try:
					if not is_sentencepiece_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					A       :   Dict					= ['''LayoutXLMTokenizer''']

try:
					if not is_tokenizers_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					A       :   Dict					= ['''LayoutXLMTokenizerFast''']

if TYPE_CHECKING:
					from .processing_layoutxlm import LayoutXLMProcessor

					try:
										if not is_sentencepiece_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_layoutxlm import LayoutXLMTokenizer

					try:
										if not is_tokenizers_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_layoutxlm_fast import LayoutXLMTokenizerFast

else:
					import sys

					A       :   int					= _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)



