



'''simple docstring'''





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    ():



     """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=42    ,      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    ,      __A    ,      __A):



     """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    ,      __A="robotic cat with wings"    ,      __A=7.5    ,      __A=50    ,      __A=1    ,      __A=42    ,      ):



     """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


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_			,   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)))









'''simple docstring'''



from collections import UserDict
from typing import List, Union

from ..utils import (
    add_end_docstrings,
    is_tf_available,
    is_torch_available,
    is_vision_available,
    logging,
    requires_backends,
)
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_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING

if is_tf_available():
     from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING
     from ..tf_utils import stable_softmax

UpperCAmelCase_							:	str	     =		logging.get_logger(__name__)





@add_end_docstrings(__lowerCAmelCase)
class      lowerCAmelCase (			__lowerCAmelCase):


       def __init__( self     ,	**__SCREAMING_SNAKE_CASE	)   ->	Optional[int]:



            '''simple docstring'''




            super().__init__(**__SCREAMING_SNAKE_CASE	)

            requires_backends(self     ,	'''vision'''	)
            self.check_model_type(
                TF_MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING
                if self.framework == '''tf'''
                else MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING	)


       def __call__( self     ,	__SCREAMING_SNAKE_CASE     ,	**__SCREAMING_SNAKE_CASE	)   ->	List[Any]:



            '''simple docstring'''




            return super().__call__(__SCREAMING_SNAKE_CASE     ,	**__SCREAMING_SNAKE_CASE	)


       def    lowerCAmelCase   ( self     ,	**__SCREAMING_SNAKE_CASE	)   ->	Tuple:



            '''simple docstring'''




            __snake_case            =	{}
            if "candidate_labels" in kwargs:
                 __snake_case            =	kwargs['''candidate_labels''']
            if "hypothesis_template" in kwargs:
                 __snake_case            =	kwargs['''hypothesis_template''']

            return preprocess_params, {}, {}


       def    lowerCAmelCase   ( self     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE=None     ,	__SCREAMING_SNAKE_CASE="This is a photo of {}."	)   ->	Optional[Any]:



            '''simple docstring'''




            __snake_case            =	load_image(__SCREAMING_SNAKE_CASE	)
            __snake_case            =	self.image_processor(images=[image]     ,	return_tensors=self.framework	)
            __snake_case            =	candidate_labels
            __snake_case            =	[hypothesis_template.format(__SCREAMING_SNAKE_CASE	) for x in candidate_labels]
            __snake_case            =	self.tokenizer(__SCREAMING_SNAKE_CASE     ,	return_tensors=self.framework     ,	padding=__SCREAMING_SNAKE_CASE	)
            __snake_case            =	[text_inputs]
            return inputs


       def    lowerCAmelCase   ( self     ,	__SCREAMING_SNAKE_CASE	)   ->	Dict:



            '''simple docstring'''




            __snake_case            =	model_inputs.pop('''candidate_labels'''	)
            __snake_case            =	model_inputs.pop('''text_inputs'''	)
            if isinstance(text_inputs[0]     ,	__SCREAMING_SNAKE_CASE	):
                 __snake_case            =	text_inputs[0]
            else:
                 # Batching case.
                 __snake_case            =	text_inputs[0][0]

            __snake_case            =	self.model(**__SCREAMING_SNAKE_CASE     ,	**__SCREAMING_SNAKE_CASE	)

            __snake_case            =	{
                '''candidate_labels''': candidate_labels,
                '''logits''': outputs.logits_per_image,
            }
            return model_outputs




       def    lowerCAmelCase   ( self     ,	__SCREAMING_SNAKE_CASE	)   ->	str:



            '''simple docstring'''




            __snake_case            =	model_outputs.pop('''candidate_labels'''	)
            __snake_case            =	model_outputs['''logits'''][0]
            if self.framework == "pt":
                 __snake_case            =	logits.softmax(dim=-1	).squeeze(-1	)
                 __snake_case            =	probs.tolist()
                 if not isinstance(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE	):
                      __snake_case            =	[scores]
            elif self.framework == "tf":
                 __snake_case            =	stable_softmax(__SCREAMING_SNAKE_CASE     ,	axis=-1	)
                 __snake_case            =	probs.numpy().tolist()
            else:
                 raise ValueError(F'''Unsupported framework: {self.framework}'''	)

            __snake_case            =	[
                {'''score''': score, '''label''': candidate_label}
                for score, candidate_label in sorted(zip(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE	)     ,	key=lambda __SCREAMING_SNAKE_CASE	: -x[0]	)
            ]
            return result






