





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  (	)      ->    Optional[Any]:

						"""simple docstring"""





						__SCREAMING_SNAKE_CASE:				int           =   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:				Tuple           =   parser.parse_args()
						return args






def 		lowerCAmelCase  (	UpperCamelCase__   :   Tuple       ,			UpperCamelCase__   :   Dict       ,			UpperCamelCase__   :   str					)      ->    int:

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

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






def 		lowerCAmelCase  (	UpperCamelCase__   :   Dict       ,			UpperCamelCase__   :   str="robotic cat with wings"       ,			UpperCamelCase__   :   str=7.5       ,			UpperCamelCase__   :   List[Any]=50       ,			UpperCamelCase__   :   Optional[Any]=1       ,			UpperCamelCase__   :   Any=42       ,			)      ->    List[str]:

						"""simple docstring"""





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


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

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

import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DDPMScheduler,
    PriorTransformer,
    StableUnCLIPPipeline,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.stable_unclip_image_normalizer import StableUnCLIPImageNormalizer
from diffusers.utils.testing_utils import enable_full_determinism, load_numpy, require_torch_gpu, slow, torch_device

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import (
    PipelineKarrasSchedulerTesterMixin,
    PipelineLatentTesterMixin,
    PipelineTesterMixin,
    assert_mean_pixel_difference,
)


enable_full_determinism()





class 				a			(      __lowercase   ,__lowercase   ,__lowercase   ,unittest.TestCase			):
				SCREAMING_SNAKE_CASE__     :   str		      =     StableUnCLIPPipeline
				SCREAMING_SNAKE_CASE__     :   str		      =     TEXT_TO_IMAGE_PARAMS
				SCREAMING_SNAKE_CASE__     :   int		      =     TEXT_TO_IMAGE_BATCH_PARAMS
				SCREAMING_SNAKE_CASE__     :   Dict		      =     TEXT_TO_IMAGE_IMAGE_PARAMS
				SCREAMING_SNAKE_CASE__     :   List[Any]		      =     TEXT_TO_IMAGE_IMAGE_PARAMS

				# TODO(will) Expected attn_bias.stride(1) == 0 to be true, but got false
				SCREAMING_SNAKE_CASE__     :   int		      =     False






				def 			snake_case_    (  self					):

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE:				Tuple           =   32
										__SCREAMING_SNAKE_CASE:				List[Any]           =   embedder_hidden_size

										# prior components

										torch.manual_seed(0					)
										__SCREAMING_SNAKE_CASE:				Dict           =   CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''					)

										torch.manual_seed(0					)
										__SCREAMING_SNAKE_CASE:				int           =   CLIPTextModelWithProjection(
										    CLIPTextConfig(
										        bos_token_id=0			, eos_token_id=2			, hidden_size=_lowerCAmelCase			, projection_dim=_lowerCAmelCase			, intermediate_size=37			, layer_norm_eps=1e-05			, num_attention_heads=4			, num_hidden_layers=5			, pad_token_id=1			, vocab_size=1000			, )					)

										torch.manual_seed(0					)
										__SCREAMING_SNAKE_CASE:				str           =   PriorTransformer(
										    num_attention_heads=2			, attention_head_dim=12			, embedding_dim=_lowerCAmelCase			, num_layers=1			, )

										torch.manual_seed(0					)
										__SCREAMING_SNAKE_CASE:				Optional[int]           =   DDPMScheduler(
										    variance_type='''fixed_small_log'''			, prediction_type='''sample'''			, num_train_timesteps=1000			, clip_sample=_lowerCAmelCase			, clip_sample_range=5.0			, beta_schedule='''squaredcos_cap_v2'''			, )

										# regular denoising components

										torch.manual_seed(0					)
										__SCREAMING_SNAKE_CASE:				List[Any]           =   StableUnCLIPImageNormalizer(embedding_dim=_lowerCAmelCase					)
										__SCREAMING_SNAKE_CASE:				Tuple           =   DDPMScheduler(beta_schedule='''squaredcos_cap_v2'''					)

										torch.manual_seed(0					)
										__SCREAMING_SNAKE_CASE:				str           =   CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''					)

										torch.manual_seed(0					)
										__SCREAMING_SNAKE_CASE:				Union[str, Any]           =   CLIPTextModel(
										    CLIPTextConfig(
										        bos_token_id=0			, eos_token_id=2			, hidden_size=_lowerCAmelCase			, projection_dim=32			, intermediate_size=37			, layer_norm_eps=1e-05			, num_attention_heads=4			, num_hidden_layers=5			, pad_token_id=1			, vocab_size=1000			, )					)

										torch.manual_seed(0					)
										__SCREAMING_SNAKE_CASE:				str           =   UNetaDConditionModel(
										    sample_size=32			, in_channels=4			, out_channels=4			, down_block_types=('''CrossAttnDownBlock2D''', '''DownBlock2D''')			, up_block_types=('''UpBlock2D''', '''CrossAttnUpBlock2D''')			, block_out_channels=(32, 64)			, attention_head_dim=(2, 4)			, class_embed_type='''projection'''			, projection_class_embeddings_input_dim=embedder_projection_dim * 2			, cross_attention_dim=_lowerCAmelCase			, layers_per_block=1			, upcast_attention=_lowerCAmelCase			, use_linear_projection=_lowerCAmelCase			, )

										torch.manual_seed(0					)
										__SCREAMING_SNAKE_CASE:				Optional[int]           =   DDIMScheduler(
										    beta_schedule='''scaled_linear'''			, beta_start=0.00085			, beta_end=0.012			, prediction_type='''v_prediction'''			, set_alpha_to_one=_lowerCAmelCase			, steps_offset=1			, )

										torch.manual_seed(0					)
										__SCREAMING_SNAKE_CASE:				int           =   AutoencoderKL()

										__SCREAMING_SNAKE_CASE:				Dict           =   {
										    # prior components
										    '''prior_tokenizer''': prior_tokenizer,
										    '''prior_text_encoder''': prior_text_encoder,
										    '''prior''': prior,
										    '''prior_scheduler''': prior_scheduler,
										    # image noising components
										    '''image_normalizer''': image_normalizer,
										    '''image_noising_scheduler''': image_noising_scheduler,
										    # regular denoising components
										    '''tokenizer''': tokenizer,
										    '''text_encoder''': text_encoder,
										    '''unet''': unet,
										    '''scheduler''': scheduler,
										    '''vae''': vae,
										}

										return components






				def 			snake_case_    (  self			, _lowerCAmelCase			, _lowerCAmelCase=0					):

										"""simple docstring"""




										if str(_lowerCAmelCase					).startswith('''mps'''					):
																__SCREAMING_SNAKE_CASE:				List[str]           =   torch.manual_seed(_lowerCAmelCase					)
										else:
																__SCREAMING_SNAKE_CASE:				List[str]           =   torch.Generator(device=_lowerCAmelCase					).manual_seed(_lowerCAmelCase					)
										__SCREAMING_SNAKE_CASE:				List[str]           =   {
										    '''prompt''': '''A painting of a squirrel eating a burger''',
										    '''generator''': generator,
										    '''num_inference_steps''': 2,
										    '''prior_num_inference_steps''': 2,
										    '''output_type''': '''numpy''',
										}
										return inputs






				def 			snake_case_    (  self					):

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE:				str           =   torch_device == '''cpu'''

										self._test_attention_slicing_forward_pass(test_max_difference=_lowerCAmelCase					)






				def 			snake_case_    (  self					):

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE:				List[str]           =   torch_device in ['''cpu''', '''mps''']

										self._test_inference_batch_single_identical(test_max_difference=_lowerCAmelCase					)





@slow
@require_torch_gpu
class 				a			(      unittest.TestCase			):






				def 			snake_case_    (  self					):

										"""simple docstring"""




										super().tearDown()
										gc.collect()
										torch.cuda.empty_cache()






				def 			snake_case_    (  self					):

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE:				int           =   load_numpy(
										    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_l_anime_turtle_fp16.npy'''					)

										__SCREAMING_SNAKE_CASE:				Optional[int]           =   StableUnCLIPPipeline.from_pretrained('''fusing/stable-unclip-2-1-l'''			, torch_dtype=torch.floataa					)
										pipe.to(_lowerCAmelCase					)
										pipe.set_progress_bar_config(disable=_lowerCAmelCase					)
										# stable unclip will oom when integration tests are run on a V100,
										# so turn on memory savings
										pipe.enable_attention_slicing()
										pipe.enable_sequential_cpu_offload()

										__SCREAMING_SNAKE_CASE:				Tuple           =   torch.Generator(device='''cpu'''					).manual_seed(0					)
										__SCREAMING_SNAKE_CASE:				List[str]           =   pipe('''anime turle'''			, generator=_lowerCAmelCase			, output_type='''np'''					)

										__SCREAMING_SNAKE_CASE:				Optional[int]           =   output.images[0]

										assert image.shape == (768, 768, 3)

										assert_mean_pixel_difference(_lowerCAmelCase			, _lowerCAmelCase					)






				def 			snake_case_    (  self					):

										"""simple docstring"""




										torch.cuda.empty_cache()
										torch.cuda.reset_max_memory_allocated()
										torch.cuda.reset_peak_memory_stats()

										__SCREAMING_SNAKE_CASE:				int           =   StableUnCLIPPipeline.from_pretrained('''fusing/stable-unclip-2-1-l'''			, torch_dtype=torch.floataa					)
										__SCREAMING_SNAKE_CASE:				Union[str, Any]           =   pipe.to(_lowerCAmelCase					)
										pipe.set_progress_bar_config(disable=_lowerCAmelCase					)
										pipe.enable_attention_slicing()
										pipe.enable_sequential_cpu_offload()

										__SCREAMING_SNAKE_CASE:				Dict           =   pipe(
										    '''anime turtle'''			, prior_num_inference_steps=2			, num_inference_steps=2			, output_type='''np'''			, )

										__SCREAMING_SNAKE_CASE:				Union[str, Any]           =   torch.cuda.max_memory_allocated()
										# make sure that less than 7 GB is allocated
										assert mem_bytes < 7 * 10**9



