


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							(		)    ->       List[str]:



       '''simple docstring'''





       UpperCAmelCase__					    =  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=4_2			,    help="Seed for random process."			,    )
       parser.add_argument(
           "-ci"			,    "--cuda_id"			,    type=SCREAMING_SNAKE_CASE__			,    default=0			,    help="cuda_id."			,    )
       UpperCAmelCase__					    =  parser.parse_args()
       return args





def __UpperCAmelCase							(		__A			,    __A			,    __A      )    ->       Optional[int]:



       '''simple docstring'''





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

       UpperCAmelCase__    ,      UpperCAmelCase__					    =  imgs[0].size
       UpperCAmelCase__					    =  Image.new("RGB"			,    size=(cols * w, rows * h)      )
       UpperCAmelCase__    ,      UpperCAmelCase__					    =  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 __UpperCAmelCase							(		__A			,    __A="robotic cat with wings"			,    __A=7.5			,    __A=5_0			,    __A=1			,    __A=4_2			,    )    ->       Any:



       '''simple docstring'''





       UpperCAmelCase__					    =  torch.Generator(pipeline.device      ).manual_seed(SCREAMING_SNAKE_CASE__      )
       UpperCAmelCase__					    =  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
       UpperCAmelCase__					    =  int(math.sqrt(SCREAMING_SNAKE_CASE__      )      )
       UpperCAmelCase__					    =  image_grid(SCREAMING_SNAKE_CASE__			,    rows=_rows			,    cols=num_images_per_prompt // _rows      )
       return grid, images


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

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



__UpperCamelCase   :							List[Any]         =   """ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/"""



def  __UpperCAmelCase						(					SCREAMING_SNAKE_CASE__:						bytes     )	->      bytes:

   """simple docstring"""
   # Make sure the supplied data is a bytes-like object
   if not isinstance(SCREAMING_SNAKE_CASE__,       SCREAMING_SNAKE_CASE__     ):
      __a					  =     f"""a bytes-like object is required, not '{data.__class__.__name__}'"""
      raise TypeError(SCREAMING_SNAKE_CASE__     )

   __a					  =     ''.join(bin(SCREAMING_SNAKE_CASE__     )[2:].zfill(8     ) for byte in data     )

   __a					  =     len(SCREAMING_SNAKE_CASE__     ) % 6 != 0

   if padding_needed:
      # The padding that will be added later
      __a					  =     b'=' * ((6 - len(SCREAMING_SNAKE_CASE__     ) % 6) // 2)

      # Append binary_stream with arbitrary binary digits (0's by default) to make its
      # length a multiple of 6.
      binary_stream += "0" * (6 - len(SCREAMING_SNAKE_CASE__     ) % 6)
   else:
      __a					  =     b''

   # Encode every 6 binary digits to their corresponding Base64 character
   return (
       "".join(
           B64_CHARSET[int(binary_stream[index : index + 6],       2     )]
           for index in range(0,       len(SCREAMING_SNAKE_CASE__     ),       6     )     ).encode()
       + padding
   )



def  __UpperCAmelCase						(					SCREAMING_SNAKE_CASE__:						str     )	->      bytes:

   """simple docstring"""
   # Make sure encoded_data is either a string or a bytes-like object
   if not isinstance(SCREAMING_SNAKE_CASE__,       SCREAMING_SNAKE_CASE__     ) and not isinstance(SCREAMING_SNAKE_CASE__,       SCREAMING_SNAKE_CASE__     ):
      __a					  =     (
          'argument should be a bytes-like object or ASCII string, '
          f"""not '{encoded_data.__class__.__name__}'"""
      )
      raise TypeError(SCREAMING_SNAKE_CASE__     )

   # In case encoded_data is a bytes-like object, make sure it contains only
   # ASCII characters so we convert it to a string object
   if isinstance(SCREAMING_SNAKE_CASE__,       SCREAMING_SNAKE_CASE__     ):
      try:
         __a					  =     encoded_data.decode('utf-8'     )
      except UnicodeDecodeError:
         raise ValueError('base64 encoded data should only contain ASCII characters'     )

   __a					  =     encoded_data.count('='     )

   # Check if the encoded string contains non base64 characters
   if padding:
      assert all(
          char in B64_CHARSET for char in encoded_data[:-padding]     ), "Invalid base64 character(s) found."
   else:
      assert all(
          char in B64_CHARSET for char in encoded_data     ), "Invalid base64 character(s) found."

   # Check the padding
   assert len(SCREAMING_SNAKE_CASE__     ) % 4 == 0 and padding < 3, "Incorrect padding"

   if padding:
      # Remove padding if there is one
      __a					  =     encoded_data[:-padding]

      __a					  =     ''.join(
          bin(B64_CHARSET.index(SCREAMING_SNAKE_CASE__     )     )[2:].zfill(6     ) for char in encoded_data     )[: -padding * 2]
   else:
      __a					  =     ''.join(
          bin(B64_CHARSET.index(SCREAMING_SNAKE_CASE__     )     )[2:].zfill(6     ) for char in encoded_data     )

   __a					  =     [
       int(binary_stream[index : index + 8],       2     )
       for index in range(0,       len(SCREAMING_SNAKE_CASE__     ),       8     )
   ]

   return bytes(SCREAMING_SNAKE_CASE__     )


if __name__ == "__main__":
 import doctest

 doctest.testmod()