





'''simple docstring'''



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




def    _lowerCAmelCase						(				lowerCamelCase_			:  Optional[Any]		,       lowerCamelCase_			:  str		,       lowerCamelCase_			:  str				):
			if not len(lowerCamelCase_				) == rows * cols:
						raise ValueError('''The specified number of rows and columns are not correct.'''				)

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

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




def    _lowerCAmelCase						(				lowerCamelCase_			:  List[str]		,       lowerCamelCase_			:  str="robotic cat with wings"		,       lowerCamelCase_			:  Dict=7.5		,       lowerCamelCase_			:  Tuple=5_0		,       lowerCamelCase_			:  Tuple=1		,       lowerCamelCase_			:  str=4_2		,       ):
			__lowercase										=							torch.Generator(pipeline.device				).manual_seed(lowerCamelCase_				)
			__lowercase										=							pipeline(
			    lowerCamelCase_		,       guidance_scale=lowerCamelCase_		,       num_inference_steps=lowerCamelCase_		,       generator=lowerCamelCase_		,       num_images_per_prompt=lowerCamelCase_		,       ).images
			__lowercase										=							int(math.sqrt(lowerCamelCase_				)				)
			__lowercase										=							image_grid(lowerCamelCase_		,       rows=_rows		,       cols=num_images_per_prompt // _rows				)
			return grid, images


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

_SCREAMING_SNAKE_CASE					=    StableDiffusionPipeline.from_pretrained(
    args.pretrained_model_name_or_path, text_encoder=text_encoder, vae=vae, unet=unet, tokenizer=tokenizer
)
_SCREAMING_SNAKE_CASE					=    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					=    load(args.pretrained_model_name_or_path, model=unet)
	unet.eval()
	setattr(pipeline, '''unet''', unet)
else:
	_SCREAMING_SNAKE_CASE					=    unet.to(torch.device('''cuda''', args.cuda_id))
_SCREAMING_SNAKE_CASE					=    pipeline.to(unet.device)
_SCREAMING_SNAKE_CASE					,	_SCREAMING_SNAKE_CASE					=    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					=    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'''



from typing import List, Optional, Union

import numpy as np
import PIL.Image

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
    ChannelDimension,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


_SCREAMING_SNAKE_CASE					=    logging.get_logger(__name__)






class 							__lowercase       (      lowerCAmelCase__						):







	'''simple docstring'''

	a    :				int											=       ["pixel_values"]


	def __init__(self     ,_lowerCamelCase = True     ,_lowerCamelCase = 32     ,_lowerCamelCase=PILImageResampling.BILINEAR     ,_lowerCamelCase = True     ,**_lowerCamelCase     ,)							->							None:







				'''simple docstring'''





				__lowercase										=							do_resize
				__lowercase										=							do_rescale
				__lowercase										=							size_divisor
				__lowercase										=							resample
				super().__init__(**_lowerCamelCase )


	def 						_UpperCAmelCase     (self     ,_lowerCamelCase     ,_lowerCamelCase     ,_lowerCamelCase     ,_lowerCamelCase = None     ,**_lowerCamelCase )							->							np.ndarray:







				'''simple docstring'''





				__lowercase						,		__lowercase										=							get_image_size(_lowerCamelCase )
				# Rounds the height and width down to the closest multiple of size_divisor
				__lowercase										=							height // size_divisor * size_divisor
				__lowercase										=							width // size_divisor * size_divisor
				__lowercase										=							resize(_lowerCamelCase     ,(new_h, new_w)     ,resample=_lowerCamelCase     ,data_format=_lowerCamelCase     ,**_lowerCamelCase )
				return image


	def 						_UpperCAmelCase     (self     ,_lowerCamelCase     ,_lowerCamelCase     ,_lowerCamelCase = None     ,**_lowerCamelCase )							->							np.ndarray:







				'''simple docstring'''





				return rescale(image=_lowerCamelCase     ,scale=_lowerCamelCase     ,data_format=_lowerCamelCase     ,**_lowerCamelCase )


	def 						_UpperCAmelCase     (self     ,_lowerCamelCase     ,_lowerCamelCase = None     ,_lowerCamelCase = None     ,_lowerCamelCase=None     ,_lowerCamelCase = None     ,_lowerCamelCase = None     ,_lowerCamelCase = ChannelDimension.FIRST     ,**_lowerCamelCase     ,)							->							BatchFeature:







				'''simple docstring'''





				__lowercase										=							do_resize if do_resize is not None else self.do_resize
				__lowercase										=							do_rescale if do_rescale is not None else self.do_rescale
				__lowercase										=							size_divisor if size_divisor is not None else self.size_divisor
				__lowercase										=							resample if resample is not None else self.resample

				if do_resize and size_divisor is None:
							raise ValueError('''size_divisor is required for resizing''' )

				__lowercase										=							make_list_of_images(_lowerCamelCase )

				if not valid_images(_lowerCamelCase ):
							raise ValueError('''Invalid image(s)''' )

				# All transformations expect numpy arrays.
				__lowercase										=							[to_numpy_array(_lowerCamelCase ) for img in images]

				if do_resize:
							__lowercase										=							[self.resize(_lowerCamelCase     ,size_divisor=_lowerCamelCase     ,resample=_lowerCamelCase ) for image in images]

				if do_rescale:
							__lowercase										=							[self.rescale(_lowerCamelCase     ,scale=1 / 255 ) for image in images]

				__lowercase										=							[to_channel_dimension_format(_lowerCamelCase     ,_lowerCamelCase ) for image in images]

				__lowercase										=							{'''pixel_values''': images}
				return BatchFeature(data=_lowerCamelCase     ,tensor_type=_lowerCamelCase )





