




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        __UpperCAmelCase						(							)       ->  Union[str, Any]:







			"""simple docstring"""





			SCREAMING_SNAKE_CASE_   : Optional[int]					  =      argparse.ArgumentParser()
			parser.add_argument(
			    '-m'  ,							'--pretrained_model_name_or_path'  ,							type=lowerCamelCase_  ,							default=lowerCamelCase_  ,							required=lowerCamelCase_  ,							help='Path to pretrained model or model identifier from huggingface.co/models.'  ,							)
			parser.add_argument(
			    '-c'  ,							'--caption'  ,							type=lowerCamelCase_  ,							default='robotic cat with wings'  ,							help='Text used to generate images.'  ,							)
			parser.add_argument(
			    '-n'  ,							'--images_num'  ,							type=lowerCamelCase_  ,							default=4  ,							help='How much images to generate.'  ,							)
			parser.add_argument(
			    '-s'  ,							'--seed'  ,							type=lowerCamelCase_  ,							default=42  ,							help='Seed for random process.'  ,							)
			parser.add_argument(
			    '-ci'  ,							'--cuda_id'  ,							type=lowerCamelCase_  ,							default=0  ,							help='cuda_id.'  ,							)
			SCREAMING_SNAKE_CASE_   : Any					  =      parser.parse_args()
			return args





def        __UpperCAmelCase						(							lowerCamelCase_ :     Tuple  ,							lowerCamelCase_ :     Tuple  ,							lowerCamelCase_ :     str  )       ->  Union[str, Any]:







			"""simple docstring"""





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

			SCREAMING_SNAKE_CASE_  ,    SCREAMING_SNAKE_CASE_   : Tuple					  =      imgs[0].size
			SCREAMING_SNAKE_CASE_   : int					  =      Image.new('RGB'  ,							size=(cols * w, rows * h)  )
			SCREAMING_SNAKE_CASE_  ,    SCREAMING_SNAKE_CASE_   : Optional[int]					  =      grid.size

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





def        __UpperCAmelCase						(							lowerCamelCase_ :     Dict  ,							lowerCamelCase_ :     Union[str, Any]="robotic cat with wings"  ,							lowerCamelCase_ :     Optional[int]=7.5  ,							lowerCamelCase_ :     Any=50  ,							lowerCamelCase_ :     List[str]=1  ,							lowerCamelCase_ :     Union[str, Any]=42  ,							)       ->  Union[str, Any]:







			"""simple docstring"""





			SCREAMING_SNAKE_CASE_   : Any					  =      torch.Generator(pipeline.device  ).manual_seed(lowerCamelCase_  )
			SCREAMING_SNAKE_CASE_   : List[Any]					  =      pipeline(
			    lowerCamelCase_  ,							guidance_scale=lowerCamelCase_  ,							num_inference_steps=lowerCamelCase_  ,							generator=lowerCamelCase_  ,							num_images_per_prompt=lowerCamelCase_  ,							).images
			SCREAMING_SNAKE_CASE_   : Dict					  =      int(math.sqrt(lowerCamelCase_  )  )
			SCREAMING_SNAKE_CASE_   : Optional[int]					  =      image_grid(lowerCamelCase_  ,							rows=_rows  ,							cols=num_images_per_prompt // _rows  )
			return grid, images


UpperCamelCase__ :   List[str]						=							parse_args()
# Load models and create wrapper for stable diffusion
UpperCamelCase__ :   Tuple						=							CLIPTokenizer.from_pretrained(args.pretrained_model_name_or_path, subfolder='''tokenizer''')
UpperCamelCase__ :   Dict						=							CLIPTextModel.from_pretrained(args.pretrained_model_name_or_path, subfolder='''text_encoder''')
UpperCamelCase__ :   Union[str, Any]						=							AutoencoderKL.from_pretrained(args.pretrained_model_name_or_path, subfolder='''vae''')
UpperCamelCase__ :   Any						=							UNetaDConditionModel.from_pretrained(args.pretrained_model_name_or_path, subfolder='''unet''')

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

from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
						from PIL import Image

						from ..image_utils import load_image

if is_torch_available():
						from ..models.auto.modeling_auto import MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING

UpperCamelCase__ :   List[str]						=							logging.get_logger(__name__)







@add_end_docstrings(lowerCamelCase_							)
class 				lowerCAmelCase_       (					lowerCamelCase_							):






				def __init__(			self      ,*snake_case__      ,**snake_case__ ):
							super().__init__(*snake_case__      ,**snake_case__ )
							self.check_model_type(snake_case__ )






				def 		snake_case	(			self      ,snake_case__=None      ,snake_case__=None      ,snake_case__=None      ,**snake_case__ ):
							SCREAMING_SNAKE_CASE_  ,    SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      {}, {}
							if padding is not None:
										SCREAMING_SNAKE_CASE_   : Any					  =      padding
							if truncation is not None:
										SCREAMING_SNAKE_CASE_   : Tuple					  =      truncation
							if top_k is not None:
										SCREAMING_SNAKE_CASE_   : int					  =      top_k
							return preprocess_params, {}, postprocess_params






				def __call__(			self      ,snake_case__      ,snake_case__ = None      ,**snake_case__ ):
							if isinstance(snake_case__      ,(Image.Image, str) ) and isinstance(snake_case__      ,snake_case__ ):
										SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      {'image': image, 'question': question}
							else:
										SCREAMING_SNAKE_CASE_   : Optional[int]					  =      image
							SCREAMING_SNAKE_CASE_   : List[Any]					  =      super().__call__(snake_case__      ,**snake_case__ )
							return results






				def 		snake_case	(			self      ,snake_case__      ,snake_case__=False      ,snake_case__=False ):
							SCREAMING_SNAKE_CASE_   : List[str]					  =      load_image(inputs['image'] )
							SCREAMING_SNAKE_CASE_   : Dict					  =      self.tokenizer(
							    inputs['question']      ,return_tensors=self.framework      ,padding=snake_case__      ,truncation=snake_case__ )
							SCREAMING_SNAKE_CASE_   : Any					  =      self.image_processor(images=snake_case__      ,return_tensors=self.framework )
							model_inputs.update(snake_case__ )
							return model_inputs






				def 		snake_case	(			self      ,snake_case__ ):
							SCREAMING_SNAKE_CASE_   : Optional[int]					  =      self.model(**snake_case__ )
							return model_outputs






				def 		snake_case	(			self      ,snake_case__      ,snake_case__=5 ):
							if top_k > self.model.config.num_labels:
										SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      self.model.config.num_labels

							if self.framework == "pt":
										SCREAMING_SNAKE_CASE_   : Any					  =      model_outputs.logits.sigmoid()[0]
										SCREAMING_SNAKE_CASE_  ,    SCREAMING_SNAKE_CASE_   : Dict					  =      probs.topk(snake_case__ )
							else:
										raise ValueError(F'Unsupported framework: {self.framework}' )

							SCREAMING_SNAKE_CASE_   : Optional[int]					  =      scores.tolist()
							SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      ids.tolist()
							return [{"score": score, "answer": self.model.config.idalabel[_id]} for score, _id in zip(snake_case__      ,snake_case__ )]



