






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			(		)							->  Union[str, Any]:






						"""simple docstring"""





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

def        __lowerCamelCase			(		snake_case__       ,snake_case__       ,snake_case__		)							->  List[str]:






						"""simple docstring"""





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

						_SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE	      =  imgs[0].size
						_SCREAMING_SNAKE_CASE	      =  Image.new("""RGB"""       ,size=(cols * w, rows * h)		)
						_SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE	      =  grid.size

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

def        __lowerCamelCase			(		snake_case__       ,snake_case__="robotic cat with wings"       ,snake_case__=7.5       ,snake_case__=50       ,snake_case__=1       ,snake_case__=42       ,)							->  str:






						"""simple docstring"""





						_SCREAMING_SNAKE_CASE	      =  torch.Generator(pipeline.device		).manual_seed(__UpperCamelCase		)
						_SCREAMING_SNAKE_CASE	      =  pipeline(
						    __UpperCamelCase       ,guidance_scale=__UpperCamelCase       ,num_inference_steps=__UpperCamelCase       ,generator=__UpperCamelCase       ,num_images_per_prompt=__UpperCamelCase       ,).images
						_SCREAMING_SNAKE_CASE	      =  int(math.sqrt(__UpperCamelCase		)		)
						_SCREAMING_SNAKE_CASE	      =  image_grid(__UpperCamelCase       ,rows=_rows       ,cols=num_images_per_prompt // _rows		)
						return grid, images


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

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


import re

from flax.core.frozen_dict import freeze
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.experimental import PartitionSpec as P


# Sentinels
__lowerCamelCase			: List[str]							   =		object()

# For specifying empty leaf dict `{}`
__lowerCamelCase			: Optional[int]							   =		object()



def 	__SCREAMING_SNAKE_CASE  (					__UpperCamelCase    :       List[Any] ,     __UpperCamelCase    :       List[str]	)							->					Optional[Any]:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__										=      tuple((re.compile(x + """$"""	) for x in qs)	)
  for i in range(len(__UpperCamelCase	) - len(__UpperCamelCase	) + 1	):
    SCREAMING_SNAKE_CASE__										=      [x.match(__UpperCamelCase	) for x, y in zip(__UpperCamelCase ,     ks[i:]	)]
    if matches and all(__UpperCamelCase	):
      return True
  return False



def 	__SCREAMING_SNAKE_CASE  (					__UpperCamelCase    :       Optional[int]	)							->					List[str]:







  """simple docstring"""





  def replace(__UpperCamelCase    :       Tuple ,     __UpperCamelCase    :       Any	):
    for rule, replacement in rules:
      if _match(__UpperCamelCase ,     __UpperCamelCase	):
        return replacement
    return val

  return replace



def 	__SCREAMING_SNAKE_CASE  (					)							->					List[Any]:







  """simple docstring"""





  return [
      # embeddings
      (("transformer", "wpe", "embedding"), P("""mp""" ,     __UpperCamelCase	)),
      (("transformer", "wte", "embedding"), P("""mp""" ,     __UpperCamelCase	)),
      # atention
      (("attention", "(q_proj|k_proj|v_proj)", "kernel"), P(__UpperCamelCase ,     """mp"""	)),
      (("attention", "out_proj", "kernel"), P("""mp""" ,     __UpperCamelCase	)),
      (("attention", "out_proj", "bias"), None),
      # mlp
      (("mlp", "c_fc", "kernel"), P(__UpperCamelCase ,     """mp"""	)),
      (("mlp", "c_fc", "bias"), P("""mp"""	)),
      (("mlp", "c_proj", "kernel"), P("""mp""" ,     __UpperCamelCase	)),
      (("mlp", "c_proj", "bias"), None),
      # layer norms
      ((r"ln_\d+", "bias"), None),
      ((r"\d+", r"ln_\d+", "scale"), None),
      (("ln_f", "bias"), None),
      (("ln_f", "scale"), None),
  ]






def 	__SCREAMING_SNAKE_CASE  (					__UpperCamelCase    :       Union[str, Any]	)							->					Dict:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__										=      _get_partition_rules()
  SCREAMING_SNAKE_CASE__										=      _replacement_rules(__UpperCamelCase	)
  SCREAMING_SNAKE_CASE__										=      {k: _unmatched for k in flatten_dict(__UpperCamelCase	)}
  SCREAMING_SNAKE_CASE__										=      {k: replace(__UpperCamelCase ,     __UpperCamelCase	) for k, v in initd.items()}
  assert _unmatched not in result.values(), "Incomplete partition spec."
  return freeze(unflatten_dict(__UpperCamelCase	)	)
