






SCREAMING_SNAKE_CASE__      :			Optional[int]							=		"""ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/"""

def      __lowercase					( snake_case ):







				"""simple docstring"""






				if not isinstance(snake_case,  snake_case ):
								__magic_name__		:Optional[Any]        =      f'''a bytes-like object is required, not \'{data.__class__.__name__}\''''
								raise TypeError(snake_case )

				__magic_name__		:Optional[Any]        =      ''''''.join(bin(snake_case )[2:].zfill(8 ) for byte in data )

				__magic_name__		:Tuple        =      len(snake_case ) % 6 != 0

				if padding_needed:
								# The padding that will be added later
								__magic_name__		:str        =      B'''=''' * ((6 - len(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(snake_case ) % 6)
				else:
								__magic_name__		:Union[str, Any]        =      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(snake_case ),  6 ) ).encode()
				    + padding
				)

def      __lowercase					( snake_case ):







				"""simple docstring"""






				if not isinstance(snake_case,  snake_case ) and not isinstance(snake_case,  snake_case ):
								__magic_name__		:List[Any]        =      (
								    '''argument should be a bytes-like object or ASCII string, '''
								    f'''not \'{encoded_data.__class__.__name__}\''''
								)
								raise TypeError(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(snake_case,  snake_case ):
								try:
												__magic_name__		:Union[str, Any]        =      encoded_data.decode('''utf-8''' )
								except UnicodeDecodeError:
												raise ValueError('''base64 encoded data should only contain ASCII characters''' )

				__magic_name__		:Any        =      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(snake_case ) % 4 == 0 and padding < 3, "Incorrect padding"

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

								__magic_name__		:List[Any]        =      ''''''.join(
								    bin(B64_CHARSET.index(snake_case ) )[2:].zfill(6 ) for char in encoded_data )[: -padding * 2]
				else:
								__magic_name__		:Any        =      ''''''.join(
								    bin(B64_CHARSET.index(snake_case ) )[2:].zfill(6 ) for char in encoded_data )

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

				return bytes(snake_case )


if __name__ == "__main__":
					import doctest

					doctest.testmod()














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"""






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

def      __lowercase					( snake_case,  snake_case,  snake_case ):







				"""simple docstring"""






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

				__magic_name__     ,				__magic_name__		:Tuple        =      imgs[0].size
				__magic_name__		:List[Any]        =      Image.new('''RGB''',  size=(cols * w, rows * h) )
				__magic_name__     ,				__magic_name__		:List[Any]        =      grid.size

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

def      __lowercase					( snake_case,  snake_case="robotic cat with wings",  snake_case=7.5,  snake_case=5_0,  snake_case=1,  snake_case=4_2,  ):







				"""simple docstring"""






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


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

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






