'''simple docstring'''


import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPImageProcessor, CLIPVisionConfig, CLIPVisionModelWithProjection

from diffusers import (
    AutoencoderKL,
    DPMSolverMultistepScheduler,
    PNDMScheduler,
    StableDiffusionImageVariationPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, nightly, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import IMAGE_VARIATION_BATCH_PARAMS, IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()






class 	__SCREAMING_SNAKE_CASE		(			lowerCamelCase_		,      lowerCamelCase_		,      lowerCamelCase_		,      unittest.TestCase			):







  '''simple docstring'''






  lowerCamelCase_						:Any		       =						StableDiffusionImageVariationPipeline
  lowerCamelCase_						:int		       =						IMAGE_VARIATION_PARAMS
  lowerCamelCase_						:Dict		       =						IMAGE_VARIATION_BATCH_PARAMS
  lowerCamelCase_						:Tuple		       =						frozenset([]			)
  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
  lowerCamelCase_						:Dict		       =						frozenset([]			)


  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       torch.manual_seed(0		)
       UpperCAmelCase_				:  str  = UNetaDConditionModel(
           block_out_channels=(3_2, 6_4) ,						layers_per_block=2 ,						sample_size=3_2 ,						in_channels=4 ,						out_channels=4 ,						down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D') ,						up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D') ,						cross_attention_dim=3_2 ,						)
       UpperCAmelCase_				:  Union[str, Any]  = PNDMScheduler(skip_prk_steps=snake_case_		)
       torch.manual_seed(0		)
       UpperCAmelCase_				:  str  = AutoencoderKL(
           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 ,						)
       torch.manual_seed(0		)
       UpperCAmelCase_				:  Dict  = CLIPVisionConfig(
           hidden_size=3_2 ,						projection_dim=3_2 ,						intermediate_size=3_7 ,						layer_norm_eps=1E-05 ,						num_attention_heads=4 ,						num_hidden_layers=5 ,						image_size=3_2 ,						patch_size=4 ,						)
       UpperCAmelCase_				:  Dict  = CLIPVisionModelWithProjection(snake_case_		)
       UpperCAmelCase_				:  Tuple  = CLIPImageProcessor(crop_size=3_2 ,						size=3_2		)

       UpperCAmelCase_				:  str  = {
           'unet': unet,
           'scheduler': scheduler,
           'vae': vae,
           'image_encoder': image_encoder,
           'feature_extractor': feature_extractor,
           'safety_checker': None,
       }
       return components


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




       '''simple docstring'''

       UpperCAmelCase_				:  Tuple  = floats_tensor((1, 3, 3_2, 3_2) ,						rng=random.Random(snake_case_		)		)
       UpperCAmelCase_				:  Optional[int]  = image.cpu().permute(0 ,						2 ,						3 ,						1		)[0]
       UpperCAmelCase_				:  List[str]  = Image.fromarray(np.uinta(snake_case_		)		).convert('RGB'		).resize((3_2, 3_2)		)
       if str(snake_case_		).startswith('mps'		):
            UpperCAmelCase_				:  Optional[Any]  = torch.manual_seed(snake_case_		)
       else:
            UpperCAmelCase_				:  str  = torch.Generator(device=snake_case_		).manual_seed(snake_case_		)
       UpperCAmelCase_				:  Tuple  = {
           'image': image,
           'generator': generator,
           'num_inference_steps': 2,
           'guidance_scale': 6.0,
           'output_type': 'numpy',
       }
       return inputs


  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       UpperCAmelCase_				:  Union[str, Any]  = 'cpu'  # ensure determinism for the device-dependent torch.Generator
       UpperCAmelCase_				:  Tuple  = self.get_dummy_components()
       UpperCAmelCase_				:  Optional[Any]  = StableDiffusionImageVariationPipeline(**snake_case_		)
       UpperCAmelCase_				:  List[Any]  = sd_pipe.to(snake_case_		)
       sd_pipe.set_progress_bar_config(disable=snake_case_		)

       UpperCAmelCase_				:  Any  = self.get_dummy_inputs(snake_case_		)
       UpperCAmelCase_				:  int  = sd_pipe(**snake_case_		).images
       UpperCAmelCase_				:  Optional[Any]  = image[0, -3:, -3:, -1]

       assert image.shape == (1, 6_4, 6_4, 3)
       UpperCAmelCase_				:  Tuple  = np.array([0.52_39, 0.57_23, 0.47_96, 0.50_49, 0.55_50, 0.46_85, 0.53_29, 0.48_91, 0.49_21]		)

       assert np.abs(image_slice.flatten() - expected_slice		).max() < 1E-3


  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       UpperCAmelCase_				:  int  = 'cpu'  # ensure determinism for the device-dependent torch.Generator
       UpperCAmelCase_				:  int  = self.get_dummy_components()
       UpperCAmelCase_				:  Tuple  = StableDiffusionImageVariationPipeline(**snake_case_		)
       UpperCAmelCase_				:  Any  = sd_pipe.to(snake_case_		)
       sd_pipe.set_progress_bar_config(disable=snake_case_		)

       UpperCAmelCase_				:  Dict  = self.get_dummy_inputs(snake_case_		)
       UpperCAmelCase_				:  Dict  = 2 * [inputs['image']]
       UpperCAmelCase_				:  str  = sd_pipe(**snake_case_		)

       UpperCAmelCase_				:  Union[str, Any]  = output.images

       UpperCAmelCase_				:  Optional[Any]  = image[-1, -3:, -3:, -1]

       assert image.shape == (2, 6_4, 6_4, 3)
       UpperCAmelCase_				:  Any  = np.array([0.68_92, 0.56_37, 0.58_36, 0.57_71, 0.62_54, 0.64_09, 0.55_80, 0.55_69, 0.52_89]		)

       assert np.abs(image_slice.flatten() - expected_slice		).max() < 1E-3






  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       super().test_inference_batch_single_identical(expected_max_diff=3E-3		)







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







  '''simple docstring'''






  def _UpperCamelCase	(					self		):




       '''simple docstring'''

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


  def _UpperCamelCase	(					self ,						snake_case_ ,						snake_case_="cpu" ,						snake_case_=torch.floataa ,						snake_case_=0		):




       '''simple docstring'''

       UpperCAmelCase_				:  List[str]  = torch.Generator(device=snake_case_		).manual_seed(snake_case_		)
       UpperCAmelCase_				:  str  = load_image(
           'https://huggingface.co/datasets/diffusers/test-arrays/resolve/main'
           '/stable_diffusion_imgvar/input_image_vermeer.png'		)
       UpperCAmelCase_				:  Optional[Any]  = np.random.RandomState(snake_case_		).standard_normal((1, 4, 6_4, 6_4)		)
       UpperCAmelCase_				:  Any  = torch.from_numpy(snake_case_		).to(device=snake_case_ ,						dtype=snake_case_		)
       UpperCAmelCase_				:  Union[str, Any]  = {
           'image': init_image,
           'latents': latents,
           'generator': generator,
           'num_inference_steps': 3,
           'guidance_scale': 7.5,
           'output_type': 'numpy',
       }
       return inputs


  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       UpperCAmelCase_				:  Any  = StableDiffusionImageVariationPipeline.from_pretrained(
           'lambdalabs/sd-image-variations-diffusers' ,						safety_checker=snake_case_		)
       UpperCAmelCase_				:  Tuple  = sd_pipe.to(snake_case_		)
       sd_pipe.set_progress_bar_config(disable=snake_case_		)

       UpperCAmelCase_				:  Optional[int]  = self.get_inputs(snake_case_		)
       UpperCAmelCase_				:  str  = sd_pipe(**snake_case_		).images
       UpperCAmelCase_				:  List[Any]  = image[0, -3:, -3:, -1].flatten()

       assert image.shape == (1, 5_1_2, 5_1_2, 3)
       UpperCAmelCase_				:  int  = np.array([0.8_44_91, 0.9_07_89, 0.7_57_08, 0.7_87_34, 0.8_34_85, 0.7_00_99, 0.6_69_38, 0.6_87_27, 0.6_13_79]		)
       assert np.abs(image_slice - expected_slice		).max() < 6E-3


  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       UpperCAmelCase_				:  List[str]  = 0

       def callback_fn(snake_case_ ,						snake_case_ ,						snake_case_		) -> None:
            UpperCAmelCase_				:  Dict  = True
            nonlocal number_of_steps
            number_of_steps += 1
            if step == 1:
                 UpperCAmelCase_				:  Optional[int]  = latents.detach().cpu().numpy()
                 assert latents.shape == (1, 4, 6_4, 6_4)
                 UpperCAmelCase_				:  str  = latents[0, -3:, -3:, -1]
                 UpperCAmelCase_				:  Tuple  = np.array(
                     [-0.16_21, 0.28_37, -0.79_79, -0.12_21, -1.30_57, 0.76_81, -2.11_91, 0.04_64, 1.63_09]		)

                 assert np.abs(latents_slice.flatten() - expected_slice		).max() < 5E-2
            elif step == 2:
                 UpperCAmelCase_				:  int  = latents.detach().cpu().numpy()
                 assert latents.shape == (1, 4, 6_4, 6_4)
                 UpperCAmelCase_				:  int  = latents[0, -3:, -3:, -1]
                 UpperCAmelCase_				:  List[str]  = np.array([0.62_99, 1.75_00, 1.19_92, -2.15_82, -1.89_94, 0.73_34, -0.70_90, 1.01_37, 1.52_73]		)

                 assert np.abs(latents_slice.flatten() - expected_slice		).max() < 5E-2

       UpperCAmelCase_				:  str  = False

       UpperCAmelCase_				:  Optional[Any]  = StableDiffusionImageVariationPipeline.from_pretrained(
           'fusing/sd-image-variations-diffusers' ,						safety_checker=snake_case_ ,						torch_dtype=torch.floataa ,						)
       pipe.to(snake_case_		)
       pipe.set_progress_bar_config(disable=snake_case_		)
       pipe.enable_attention_slicing()

       UpperCAmelCase_				:  Any  = self.get_inputs(snake_case_ ,						dtype=torch.floataa		)
       pipe(**snake_case_ ,						callback=snake_case_ ,						callback_steps=1		)
       assert callback_fn.has_been_called
       assert number_of_steps == inputs["num_inference_steps"]






  def _UpperCamelCase	(					self		):




       '''simple docstring'''

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

       UpperCAmelCase_				:  Tuple  = 'fusing/sd-image-variations-diffusers'
       UpperCAmelCase_				:  Tuple  = StableDiffusionImageVariationPipeline.from_pretrained(
           snake_case_ ,						safety_checker=snake_case_ ,						torch_dtype=torch.floataa		)
       UpperCAmelCase_				:  Union[str, Any]  = pipe.to(snake_case_		)
       pipe.set_progress_bar_config(disable=snake_case_		)
       pipe.enable_attention_slicing(1		)
       pipe.enable_sequential_cpu_offload()

       UpperCAmelCase_				:  Union[str, Any]  = self.get_inputs(snake_case_ ,						dtype=torch.floataa		)
       UpperCAmelCase_				:  int  = pipe(**snake_case_		)

       UpperCAmelCase_				:  str  = torch.cuda.max_memory_allocated()
       # make sure that less than 2.6 GB is allocated
       assert mem_bytes < 2.6 * 1_0**9







@nightly
@require_torch_gpu
class 	__SCREAMING_SNAKE_CASE		(			unittest.TestCase			):







  '''simple docstring'''






  def _UpperCamelCase	(					self		):




       '''simple docstring'''

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


  def _UpperCamelCase	(					self ,						snake_case_ ,						snake_case_="cpu" ,						snake_case_=torch.floataa ,						snake_case_=0		):




       '''simple docstring'''

       UpperCAmelCase_				:  List[str]  = torch.Generator(device=snake_case_		).manual_seed(snake_case_		)
       UpperCAmelCase_				:  Union[str, Any]  = load_image(
           'https://huggingface.co/datasets/diffusers/test-arrays/resolve/main'
           '/stable_diffusion_imgvar/input_image_vermeer.png'		)
       UpperCAmelCase_				:  List[Any]  = np.random.RandomState(snake_case_		).standard_normal((1, 4, 6_4, 6_4)		)
       UpperCAmelCase_				:  Optional[Any]  = torch.from_numpy(snake_case_		).to(device=snake_case_ ,						dtype=snake_case_		)
       UpperCAmelCase_				:  Dict  = {
           'image': init_image,
           'latents': latents,
           'generator': generator,
           'num_inference_steps': 5_0,
           'guidance_scale': 7.5,
           'output_type': 'numpy',
       }
       return inputs


  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       UpperCAmelCase_				:  Optional[int]  = StableDiffusionImageVariationPipeline.from_pretrained('fusing/sd-image-variations-diffusers'		)
       sd_pipe.to(snake_case_		)
       sd_pipe.set_progress_bar_config(disable=snake_case_		)

       UpperCAmelCase_				:  Optional[int]  = self.get_inputs(snake_case_		)
       UpperCAmelCase_				:  Any  = sd_pipe(**snake_case_		).images[0]

       UpperCAmelCase_				:  Any  = load_numpy(
           'https://huggingface.co/datasets/diffusers/test-arrays/resolve/main'
           '/stable_diffusion_imgvar/lambdalabs_variations_pndm.npy'		)
       UpperCAmelCase_				:  str  = np.abs(expected_image - image		).max()
       assert max_diff < 1E-3






  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       UpperCAmelCase_				:  Optional[int]  = StableDiffusionImageVariationPipeline.from_pretrained('fusing/sd-image-variations-diffusers'		)
       UpperCAmelCase_				:  str  = DPMSolverMultistepScheduler.from_config(sd_pipe.scheduler.config		)
       sd_pipe.to(snake_case_		)
       sd_pipe.set_progress_bar_config(disable=snake_case_		)

       UpperCAmelCase_				:  Tuple  = self.get_inputs(snake_case_		)
       UpperCAmelCase_				:  List[str]  = 2_5
       UpperCAmelCase_				:  str  = sd_pipe(**snake_case_		).images[0]

       UpperCAmelCase_				:  int  = load_numpy(
           'https://huggingface.co/datasets/diffusers/test-arrays/resolve/main'
           '/stable_diffusion_imgvar/lambdalabs_variations_dpm_multi.npy'		)
       UpperCAmelCase_				:  Tuple  = np.abs(expected_image - image		).max()
       assert max_diff < 1E-3











import json
import logging
import os
import subprocess
from argparse import ArgumentParser


__A       =logging.getLogger(__name__)

def 			a		( ):







		'''simple docstring'''





		__UpperCAmelCase       :	Dict						=      ArgumentParser()
		__UpperCAmelCase				,   __UpperCAmelCase       :	int						=      parser.parse_known_args()
		for arg in unknown:
				if arg.startswith(('''-''', '''--''')		):
						parser.add_argument(arg.split('''='''		)[0]		)

		return parser.parse_args()

def 			a		( ):







		'''simple docstring'''





		__UpperCAmelCase       :	List[Any]						=      parse_args()
		__UpperCAmelCase       :	Tuple						=      88_88
		__UpperCAmelCase       :	Any						=      int(os.environ['''SM_NUM_GPUS''']		)
		__UpperCAmelCase       :	Optional[int]						=      json.loads(os.environ['''SM_HOSTS''']		)
		__UpperCAmelCase       :	int						=      len(_UpperCAmelCase		)
		__UpperCAmelCase       :	Optional[Any]						=      os.environ['''SM_CURRENT_HOST''']
		__UpperCAmelCase       :	List[str]						=      hosts.index(_UpperCAmelCase		)
		__UpperCAmelCase       :	Dict						=      '''INFO'''

		if num_nodes > 1:
				__UpperCAmelCase       :	Tuple						=      f'python -m torch.distributed.launch \\n                --nnodes={num_nodes}  \\n                --node_rank={rank}  \\n                --nproc_per_node={num_gpus}  \\n                --master_addr={hosts[0]}  \\n                --master_port={port} \\n                ./run_glue.py \\n                {"".join(["nested fstring" for parameter,value in args.__dict__.items()]		)}'
		else:
				__UpperCAmelCase       :	Dict						=      f'python -m torch.distributed.launch \\n            --nproc_per_node={num_gpus}  \\n            ./run_glue.py \\n            {"".join(["nested fstring" for parameter,value in args.__dict__.items()]		)}'
		try:
				subprocess.run(_UpperCAmelCase					,  shell=_UpperCAmelCase		)
		except Exception as e:
				logger.info(_UpperCAmelCase		)


if __name__ == "__main__":
				main()





