

'''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 	_a       (				):

		"""simple docstring"""

		snake_case__						:       str       						=  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=42   ,							help='''Seed for random process.'''   ,							)
		parser.add_argument(
		    '''-ci'''   ,							'''--cuda_id'''   ,							type=__lowerCAmelCase   ,							default=0   ,							help='''cuda_id.'''   ,							)
		snake_case__						:       Union[str, Any]       						=  parser.parse_args()
		return args




def 	_a       (				__lowerCAmelCase		:      str   ,							__lowerCAmelCase		:      List[Any]   ,							__lowerCAmelCase		:      Union[str, Any]     ):

		"""simple docstring"""

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

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

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




def 	_a       (				__lowerCAmelCase		:      Tuple   ,							__lowerCAmelCase		:      Any="robotic cat with wings"   ,							__lowerCAmelCase		:      Dict=7.5   ,							__lowerCAmelCase		:      List[Any]=50   ,							__lowerCAmelCase		:      Tuple=1   ,							__lowerCAmelCase		:      List[str]=42   ,							):

		"""simple docstring"""

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


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

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






'''simple docstring'''


import unittest

from diffusers import FlaxAutoencoderKL
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import require_flax

from .test_modeling_common_flax import FlaxModelTesterMixin


if is_flax_available():
					import jax




@require_flax
class     a			(				SCREAMING_SNAKE_CASE			,			unittest.TestCase       ):






						"""simple docstring"""




						__UpperCAmelCase			     =    FlaxAutoencoderKL







						@property
						def       __magic_name__							(							self					:      str  ):



								'''simple docstring'''



								snake_case__						:       Any       						=  4
								snake_case__						:       Optional[Any]       						=  3
								snake_case__						:       Optional[int]       						=  (3_2, 3_2)

								snake_case__						:       Optional[int]       						=  jax.random.PRNGKey(0  )
								snake_case__						:       Union[str, Any]       						=  jax.random.uniform(snake_case_			,			((batch_size, num_channels) + sizes)  )

								return {"sample": image, "prng_key": prng_key}







						def       __magic_name__							(							self					:      Union[str, Any]  ):



								'''simple docstring'''



								snake_case__						:       Any       						=  {
								    '''block_out_channels''': [3_2, 6_4],
								    '''in_channels''': 3,
								    '''out_channels''': 3,
								    '''down_block_types''': ['''DownEncoderBlock2D''', '''DownEncoderBlock2D'''],
								    '''up_block_types''': ['''UpDecoderBlock2D''', '''UpDecoderBlock2D'''],
								    '''latent_channels''': 4,
								}
								snake_case__						:       Dict       						=  self.dummy_input
								return init_dict, inputs_dict



