

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					(   )   ->  int:
      SCREAMING_SNAKE_CASE       :					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."""  ,			)
      SCREAMING_SNAKE_CASE       :					Optional[int]        =      parser.parse_args()
      return args






def     __lowercase					(   _A  ,			_A  ,			_A	)   ->  Any:

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

      SCREAMING_SNAKE_CASE       :					int        =      imgs[0].size
      SCREAMING_SNAKE_CASE       :					str        =      Image.new("""RGB"""  ,			size=(cols * w, rows * h)	)
      SCREAMING_SNAKE_CASE       :					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					(   _A  ,			_A="robotic cat with wings"  ,			_A=7.5  ,			_A=50  ,			_A=1  ,			_A=42  ,			)   ->  Any:
      SCREAMING_SNAKE_CASE       :					Dict        =      torch.Generator(pipeline.device	).manual_seed(SCREAMING_SNAKE_CASE_	)
      SCREAMING_SNAKE_CASE       :					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
      SCREAMING_SNAKE_CASE       :					List[Any]        =      int(math.sqrt(SCREAMING_SNAKE_CASE_	)	)
      SCREAMING_SNAKE_CASE       :					Tuple        =      image_grid(SCREAMING_SNAKE_CASE_  ,			rows=_rows  ,			cols=num_images_per_prompt // _rows	)
      return grid, images


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

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

import numpy as np
from sympy import symbols

# Coefficient
# Speed of light (m/s)
_snake_case             =     2_99_79_24_58

# Symbols
_snake_case      ,       _snake_case      ,       _snake_case      ,       _snake_case             =     symbols('''ct x y z''')






def 		lowercase_(  SCREAMING_SNAKE_CASE_	):


  '''simple docstring'''

  if velocity > c:
    raise ValueError("Speed must not exceed light speed 299,792,458 [m/s]!"	)
  elif velocity < 1:
    # Usually the speed should be much higher than 1 (c order of magnitude)
    raise ValueError("Speed must be greater than or equal to 1!"	)

  return velocity / c






def 		lowercase_(  SCREAMING_SNAKE_CASE_	):


  '''simple docstring'''

  return 1 / sqrt(1 - beta(SCREAMING_SNAKE_CASE_	) ** 2	)






def 		lowercase_(  SCREAMING_SNAKE_CASE_	):


  '''simple docstring'''

  return np.array(
      [
          [gamma(SCREAMING_SNAKE_CASE_	), -gamma(SCREAMING_SNAKE_CASE_	) * beta(SCREAMING_SNAKE_CASE_	), 0, 0],
          [-gamma(SCREAMING_SNAKE_CASE_	) * beta(SCREAMING_SNAKE_CASE_	), gamma(SCREAMING_SNAKE_CASE_	), 0, 0],
          [0, 0, 1, 0],
          [0, 0, 0, 1],
      ]	)






def 		lowercase_(  SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_ = None	):


  '''simple docstring'''

  if event is None:
    lowerCamelCase :					Tuple    =				np.array([ct, x, y, z]	)  # Symbolic four vector
  else:
    event[0] *= c  # x0 is ct (speed of light * time)

  return transformation_matrix(SCREAMING_SNAKE_CASE_	) @ event


if __name__ == "__main__":
   import doctest

   doctest.testmod()

   # Example of symbolic vector:
   _snake_case             =     transform(29_97_92_45)
   print('''Example of four vector: ''')
   print(f'''ct\' = {four_vector[0]}''')
   print(f'''x\' = {four_vector[1]}''')
   print(f'''y\' = {four_vector[2]}''')
   print(f'''z\' = {four_vector[3]}''')

   # Substitute symbols with numerical values
   _snake_case             =     {ct: c, x: 1, y: 1, z: 1}
   _snake_case             =     [four_vector[i].subs(sub_dict) for i in range(4)]

   print(f'''\n{numerical_vector}''')



