



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				(      ):





					"""simple docstring"""

					_lowerCAmelCase						=    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=42	,						help="""Seed for random process."""	,						)
					parser.add_argument(
					    """-ci"""	,						"""--cuda_id"""	,						type=snake_case	,						default=0	,						help="""cuda_id."""	,						)
					_lowerCAmelCase						=    parser.parse_args()
					return args


def 		_UpperCAmelCase				(      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."""			)

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

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


def 		_UpperCAmelCase				(      snake_case	,						snake_case="robotic cat with wings"	,						snake_case=7.5	,						snake_case=50	,						snake_case=1	,						snake_case=42	,						):





					"""simple docstring"""

					_lowerCAmelCase						=    torch.Generator(pipeline.device			).manual_seed(snake_case			)
					_lowerCAmelCase						=    pipeline(
					    snake_case	,						guidance_scale=snake_case	,						num_inference_steps=snake_case	,						generator=snake_case	,						num_images_per_prompt=snake_case	,						).images
					_lowerCAmelCase						=    int(math.sqrt(snake_case			)			)
					_lowerCAmelCase						=    image_grid(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__      ,			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"""






import numpy as np

UpperCAmelCase    :     Optional[Any]   						=		[
    ['a', 'b', 'c', 'd', 'e'],
    ['f', 'g', 'h', 'i', 'k'],
    ['l', 'm', 'n', 'o', 'p'],
    ['q', 'r', 's', 't', 'u'],
    ['v', 'w', 'x', 'y', 'z'],
]





class      lowerCamelCase__   :







					"""simple docstring"""


					def __init__(       self    : Union[str, Any]  ):
												'''simple docstring'''






												__UpperCAmelCase   :						List[Any]      							=					np.array(UpperCamelCase  )






					def lowerCamelCase__							(       self    : int ,       UpperCamelCase    : str  ):
												'''simple docstring'''






												__UpperCAmelCase							,__UpperCAmelCase   :						Optional[Any]      							=					np.where(letter == self.SQUARE  )
												__UpperCAmelCase   :						Optional[Any]      							=					np.concatenate([indexa + 1, indexa + 1]  )
												return indexes






					def lowerCamelCase__							(       self    : Any ,       UpperCamelCase    : int ,       UpperCamelCase    : int  ):
												'''simple docstring'''






												__UpperCAmelCase   :						Tuple      							=					self.SQUARE[indexa - 1, indexa - 1]
												return letter






					def lowerCamelCase__							(       self    : Optional[int] ,       UpperCamelCase    : str  ):
												'''simple docstring'''






												__UpperCAmelCase   :						str      							=					message.lower()
												__UpperCAmelCase   :						List[Any]      							=					message.replace(""" """ ,       """"""  )
												__UpperCAmelCase   :						List[Any]      							=					message.replace("""j""" ,       """i"""  )

												__UpperCAmelCase   :						Optional[int]      							=					np.empty((2, len(UpperCamelCase  ))  )
												for letter_index in range(len(UpperCamelCase  )  ):
																			__UpperCAmelCase   :						List[Any]      							=					self.letter_to_numbers(message[letter_index]  )

																			__UpperCAmelCase   :						str      							=					numbers[0]
																			__UpperCAmelCase   :						int      							=					numbers[1]

												__UpperCAmelCase   :						Union[str, Any]      							=					first_step.reshape(2 * len(UpperCamelCase  )  )
												__UpperCAmelCase   :						Optional[Any]      							=					""""""
												for numbers_index in range(len(UpperCamelCase  )  ):
																			__UpperCAmelCase   :						Any      							=					int(second_step[numbers_index * 2]  )
																			__UpperCAmelCase   :						Any      							=					int(second_step[(numbers_index * 2) + 1]  )
																			__UpperCAmelCase   :						str      							=					self.numbers_to_letter(UpperCamelCase ,       UpperCamelCase  )
																			__UpperCAmelCase   :						Optional[Any]      							=					encoded_message + letter

												return encoded_message






					def lowerCamelCase__							(       self    : Optional[int] ,       UpperCamelCase    : str  ):
												'''simple docstring'''






												__UpperCAmelCase   :						Any      							=					message.lower()
												message.replace(""" """ ,       """"""  )
												__UpperCAmelCase   :						int      							=					np.empty(2 * len(UpperCamelCase  )  )
												for letter_index in range(len(UpperCamelCase  )  ):
																			__UpperCAmelCase   :						Any      							=					self.letter_to_numbers(message[letter_index]  )
																			__UpperCAmelCase   :						Any      							=					numbers[0]
																			__UpperCAmelCase   :						Dict      							=					numbers[1]

												__UpperCAmelCase   :						str      							=					first_step.reshape((2, len(UpperCamelCase  ))  )
												__UpperCAmelCase   :						Union[str, Any]      							=					""""""
												for numbers_index in range(len(UpperCamelCase  )  ):
																			__UpperCAmelCase   :						Optional[int]      							=					int(second_step[0, numbers_index]  )
																			__UpperCAmelCase   :						Tuple      							=					int(second_step[1, numbers_index]  )
																			__UpperCAmelCase   :						Tuple      							=					self.numbers_to_letter(UpperCamelCase ,       UpperCamelCase  )
																			__UpperCAmelCase   :						Any      							=					decoded_message + letter

												return decoded_message
