


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 		lowercase_(  ):


  '''simple docstring'''

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






def 		lowercase_(  SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_	):


  '''simple docstring'''

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

  lowerCamelCase    ,	lowerCamelCase :					int    =				imgs[0].size
  lowerCamelCase :					str    =				Image.new("RGB"      ,						size=(cols * w, rows * h)	)
  lowerCamelCase    ,	lowerCamelCase :					Any    =				grid.size

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






def 		lowercase_(  SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_="robotic cat with wings"      ,						SCREAMING_SNAKE_CASE_=7.5      ,						SCREAMING_SNAKE_CASE_=50      ,						SCREAMING_SNAKE_CASE_=1      ,						SCREAMING_SNAKE_CASE_=42      ,						):


  '''simple docstring'''

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


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

_snake_case             =     StableDiffusionPipeline.from_pretrained(
    args.pretrained_model_name_or_path, text_encoder=text_encoder, vae=vae, unet=unet, tokenizer=tokenizer
)
_snake_case             =     lambda images, clip_input: (images, False)
if os.path.exists(os.path.join(args.pretrained_model_name_or_path, '''best_model.pt''')):
   _snake_case             =     load(args.pretrained_model_name_or_path, model=unet)
   unet.eval()
   setattr(pipeline, '''unet''', unet)
else:
   _snake_case             =     unet.to(torch.device('''cuda''', args.cuda_id))
_snake_case             =     pipeline.to(unet.device)
_snake_case      ,       _snake_case             =     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()))))
_snake_case             =     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_flax_available,
    is_tf_available,
    is_torch_available,
)


_snake_case             =     {
    '''configuration_vision_encoder_decoder''': ['''VisionEncoderDecoderConfig''', '''VisionEncoderDecoderOnnxConfig''']
}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _snake_case             =     ['''VisionEncoderDecoderModel''']

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _snake_case             =     ['''TFVisionEncoderDecoderModel''']

try:
   if not is_flax_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _snake_case             =     ['''FlaxVisionEncoderDecoderModel''']

if TYPE_CHECKING:
   from .configuration_vision_encoder_decoder import VisionEncoderDecoderConfig, VisionEncoderDecoderOnnxConfig

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_vision_encoder_decoder import VisionEncoderDecoderModel

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_vision_encoder_decoder import TFVisionEncoderDecoderModel

   try:
      if not is_flax_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_flax_vision_encoder_decoder import FlaxVisionEncoderDecoderModel

else:
   import sys

   _snake_case             =     _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)



