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 			__snake_case   (     lowerCamelCase__					,   lowerCamelCase__					,   lowerCamelCase__					,   unittest.TestCase   ):
	__lowerCamelCase    :   Union[str, Any]     =      StableDiffusionImageVariationPipeline
	__lowerCamelCase    :   Optional[Any]     =      IMAGE_VARIATION_PARAMS
	__lowerCamelCase    :   Tuple     =      IMAGE_VARIATION_BATCH_PARAMS
	__lowerCamelCase    :   List[Any]     =      frozenset([]   )
	# TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
	__lowerCamelCase    :   Optional[int]     =      frozenset([]   )
	def 	UpperCAmelCase__       (  self						)	->							Tuple:
							'''simple docstring'''





							torch.manual_seed(0						)
							UpperCAmelCase    : Any      =UNetaDConditionModel(
							    block_out_channels=(32, 64)		,   layers_per_block=2		,   sample_size=32		,   in_channels=4		,   out_channels=4		,   down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')		,   up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')		,   cross_attention_dim=32		,   )
							UpperCAmelCase    : List[str]      =PNDMScheduler(skip_prk_steps=snake_case__						)
							torch.manual_seed(0						)
							UpperCAmelCase    : Any      =AutoencoderKL(
							    block_out_channels=[32, 64]		,   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=32		,   projection_dim=32		,   intermediate_size=37		,   layer_norm_eps=1e-05		,   num_attention_heads=4		,   num_hidden_layers=5		,   image_size=32		,   patch_size=4		,   )
							UpperCAmelCase    : Tuple      =CLIPVisionModelWithProjection(snake_case__						)
							UpperCAmelCase    : Any      =CLIPImageProcessor(crop_size=32		,   size=32						)

							UpperCAmelCase    : Union[str, Any]      ={
							    '''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						)	->							Any:
							'''simple docstring'''





							UpperCAmelCase    : Optional[int]      =floats_tensor((1, 3, 32, 32)		,   rng=random.Random(snake_case__						)						)
							UpperCAmelCase    : Any      =image.cpu().permute(0		,   2		,   3		,   1						)[0]
							UpperCAmelCase    : Optional[int]      =Image.fromarray(np.uinta(snake_case__						)						).convert('''RGB'''						).resize((32, 32)						)
							if str(snake_case__						).startswith('''mps'''						):
													UpperCAmelCase    : Optional[Any]      =torch.manual_seed(snake_case__						)
							else:
													UpperCAmelCase    : List[str]      =torch.Generator(device=snake_case__						).manual_seed(snake_case__						)
							UpperCAmelCase    : Optional[Any]      ={
							    '''image''': image,
							    '''generator''': generator,
							    '''num_inference_steps''': 2,
							    '''guidance_scale''': 6.0,
							    '''output_type''': '''numpy''',
							}
							return inputs
	def 	UpperCAmelCase__       (  self						)	->							List[str]:
							'''simple docstring'''





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

							UpperCAmelCase    : List[Any]      =self.get_dummy_inputs(snake_case__						)
							UpperCAmelCase    : Tuple      =sd_pipe(**snake_case__						).images
							UpperCAmelCase    : List[Any]      =image[0, -3:, -3:, -1]

							assert image.shape == (1, 64, 64, 3)
							UpperCAmelCase    : str      =np.array([0.5239, 0.5723, 0.4796, 0.5049, 0.5550, 0.4685, 0.5329, 0.4891, 0.4921]						)

							assert np.abs(image_slice.flatten() - expected_slice						).max() < 1e-3
	def 	UpperCAmelCase__       (  self						)	->							Tuple:
							'''simple docstring'''





							UpperCAmelCase    : List[Any]      ='''cpu'''  # ensure determinism for the device-dependent torch.Generator
							UpperCAmelCase    : Tuple      =self.get_dummy_components()
							UpperCAmelCase    : Optional[Any]      =StableDiffusionImageVariationPipeline(**snake_case__						)
							UpperCAmelCase    : Dict      =sd_pipe.to(snake_case__						)
							sd_pipe.set_progress_bar_config(disable=snake_case__						)

							UpperCAmelCase    : Union[str, Any]      =self.get_dummy_inputs(snake_case__						)
							UpperCAmelCase    : List[Any]      =2 * [inputs['''image''']]
							UpperCAmelCase    : int      =sd_pipe(**snake_case__						)

							UpperCAmelCase    : Optional[int]      =output.images

							UpperCAmelCase    : Optional[int]      =image[-1, -3:, -3:, -1]

							assert image.shape == (2, 64, 64, 3)
							UpperCAmelCase    : Union[str, Any]      =np.array([0.6892, 0.5637, 0.5836, 0.5771, 0.6254, 0.6409, 0.5580, 0.5569, 0.5289]						)

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






	def 	UpperCAmelCase__       (  self						)	->							List[str]:
							'''simple docstring'''





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







@slow
@require_torch_gpu
class 			__snake_case   (     unittest.TestCase   ):
	def 	UpperCAmelCase__       (  self						)	->							Any:
							'''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						)	->							Union[str, Any]:
							'''simple docstring'''





							UpperCAmelCase    : Optional[int]      =torch.Generator(device=snake_case__						).manual_seed(snake_case__						)
							UpperCAmelCase    : Dict      =load_image(
							    '''https://huggingface.co/datasets/diffusers/test-arrays/resolve/main'''
							    '''/stable_diffusion_imgvar/input_image_vermeer.png'''						)
							UpperCAmelCase    : str      =np.random.RandomState(snake_case__						).standard_normal((1, 4, 64, 64)						)
							UpperCAmelCase    : Any      =torch.from_numpy(snake_case__						).to(device=snake_case__		,   dtype=snake_case__						)
							UpperCAmelCase    : int      ={
							    '''image''': init_image,
							    '''latents''': latents,
							    '''generator''': generator,
							    '''num_inference_steps''': 3,
							    '''guidance_scale''': 7.5,
							    '''output_type''': '''numpy''',
							}
							return inputs
	def 	UpperCAmelCase__       (  self						)	->							int:
							'''simple docstring'''





							UpperCAmelCase    : str      =StableDiffusionImageVariationPipeline.from_pretrained(
							    '''lambdalabs/sd-image-variations-diffusers'''		,   safety_checker=snake_case__						)
							UpperCAmelCase    : Any      =sd_pipe.to(snake_case__						)
							sd_pipe.set_progress_bar_config(disable=snake_case__						)

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

							assert image.shape == (1, 512, 512, 3)
							UpperCAmelCase    : Dict      =np.array([0.8_4491, 0.9_0789, 0.7_5708, 0.7_8734, 0.8_3485, 0.7_0099, 0.6_6938, 0.6_8727, 0.6_1379]						)
							assert np.abs(image_slice - expected_slice						).max() < 6e-3
	def 	UpperCAmelCase__       (  self						)	->							Dict:
							'''simple docstring'''





							UpperCAmelCase    : List[Any]      =0

							def callback_fn(snake_case__		,   snake_case__		,   snake_case__						) -> None:
													UpperCAmelCase    : int      =True
													nonlocal number_of_steps
													number_of_steps += 1
													if step == 1:
																			UpperCAmelCase    : List[Any]      =latents.detach().cpu().numpy()
																			assert latents.shape == (1, 4, 64, 64)
																			UpperCAmelCase    : Any      =latents[0, -3:, -3:, -1]
																			UpperCAmelCase    : Any      =np.array(
																			    [-0.1621, 0.2837, -0.7979, -0.1221, -1.3057, 0.7681, -2.1191, 0.0464, 1.6309]						)

																			assert np.abs(latents_slice.flatten() - expected_slice						).max() < 5e-2
													elif step == 2:
																			UpperCAmelCase    : Any      =latents.detach().cpu().numpy()
																			assert latents.shape == (1, 4, 64, 64)
																			UpperCAmelCase    : Optional[Any]      =latents[0, -3:, -3:, -1]
																			UpperCAmelCase    : Any      =np.array([0.6299, 1.7500, 1.1992, -2.1582, -1.8994, 0.7334, -0.7090, 1.0137, 1.5273]						)

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

							UpperCAmelCase    : str      =False

							UpperCAmelCase    : Union[str, 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    : List[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						)	->							str:
							'''simple docstring'''





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

							UpperCAmelCase    : int      ='''fusing/sd-image-variations-diffusers'''
							UpperCAmelCase    : Optional[Any]      =StableDiffusionImageVariationPipeline.from_pretrained(
							    snake_case__		,   safety_checker=snake_case__		,   torch_dtype=torch.floataa						)
							UpperCAmelCase    : str      =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    : List[Any]      =pipe(**snake_case__						)

							UpperCAmelCase    : Union[str, Any]      =torch.cuda.max_memory_allocated()
							# make sure that less than 2.6 GB is allocated
							assert mem_bytes < 2.6 * 10**9







@nightly
@require_torch_gpu
class 			__snake_case   (     unittest.TestCase   ):
	def 	UpperCAmelCase__       (  self						)	->							List[Any]:
							'''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						)	->							Any:
							'''simple docstring'''





							UpperCAmelCase    : Optional[Any]      =torch.Generator(device=snake_case__						).manual_seed(snake_case__						)
							UpperCAmelCase    : Tuple      =load_image(
							    '''https://huggingface.co/datasets/diffusers/test-arrays/resolve/main'''
							    '''/stable_diffusion_imgvar/input_image_vermeer.png'''						)
							UpperCAmelCase    : int      =np.random.RandomState(snake_case__						).standard_normal((1, 4, 64, 64)						)
							UpperCAmelCase    : Optional[Any]      =torch.from_numpy(snake_case__						).to(device=snake_case__		,   dtype=snake_case__						)
							UpperCAmelCase    : str      ={
							    '''image''': init_image,
							    '''latents''': latents,
							    '''generator''': generator,
							    '''num_inference_steps''': 50,
							    '''guidance_scale''': 7.5,
							    '''output_type''': '''numpy''',
							}
							return inputs
	def 	UpperCAmelCase__       (  self						)	->							List[Any]:
							'''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    : Union[str, Any]      =self.get_inputs(snake_case__						)
							UpperCAmelCase    : Optional[int]      =sd_pipe(**snake_case__						).images[0]

							UpperCAmelCase    : Dict      =load_numpy(
							    '''https://huggingface.co/datasets/diffusers/test-arrays/resolve/main'''
							    '''/stable_diffusion_imgvar/lambdalabs_variations_pndm.npy'''						)
							UpperCAmelCase    : Optional[int]      =np.abs(expected_image - image						).max()
							assert max_diff < 1e-3






	def 	UpperCAmelCase__       (  self						)	->							List[Any]:
							'''simple docstring'''





							UpperCAmelCase    : Dict      =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    : List[Any]      =self.get_inputs(snake_case__						)
							UpperCAmelCase    : Optional[Any]      =25
							UpperCAmelCase    : Tuple      =sd_pipe(**snake_case__						).images[0]

							UpperCAmelCase    : List[str]      =load_numpy(
							    '''https://huggingface.co/datasets/diffusers/test-arrays/resolve/main'''
							    '''/stable_diffusion_imgvar/lambdalabs_variations_dpm_multi.npy'''						)
							UpperCAmelCase    : int      =np.abs(expected_image - image						).max()
							assert max_diff < 1e-3

__snake_case			      =    [[0, 1, 1], [0, 0, 1], [1, 0, 0]]







class 			__snake_case   :
 def __init__(  self		,   snake_case__						)	->							Any:
       '''simple docstring'''





       UpperCAmelCase    : List[Any]      =name
       UpperCAmelCase    : Tuple      =[]
       UpperCAmelCase    : str      =[]
 def 	UpperCAmelCase__       (  self		,   snake_case__						)	->							int:
       '''simple docstring'''





       self.inbound.append(snake_case__						)
 def 	UpperCAmelCase__       (  self		,   snake_case__						)	->							Dict:
       '''simple docstring'''





       self.outbound.append(snake_case__						)






 def __repr__(  self						)	->							Union[str, Any]:
       '''simple docstring'''





       return f'''<node={self.name} inbound={self.inbound} outbound={self.outbound}>'''





def   lowerCAmelCase_ (				__lowerCAmelCase		,					__lowerCAmelCase=3		,					__lowerCAmelCase=0.85						)->  Any:






      '''simple docstring'''





      UpperCAmelCase    : List[Any]      ={}
      for node in nodes:
            UpperCAmelCase    : str      =1

      UpperCAmelCase    : str      ={}
      for node in nodes:
            UpperCAmelCase    : int      =len(node.outbound						)

      for i in range(__lowerCAmelCase						):
            print(f'''======= Iteration {i + 1} ======='''						)
            for _, node in enumerate(__lowerCAmelCase						):
                  UpperCAmelCase    : Optional[int]      =(1 - d) + d * sum(
                      ranks[ib] / outbounds[ib] for ib in node.inbound						)
            print(__lowerCAmelCase						)





def   lowerCAmelCase_ (				)->  Union[str, Any]:






      '''simple docstring'''





      UpperCAmelCase    : Union[str, Any]      =list(input('''Enter Names of the Nodes: '''						).split()						)

      UpperCAmelCase    : str      =[Node(__lowerCAmelCase						) for name in names]

      for ri, row in enumerate(__lowerCAmelCase						):
            for ci, col in enumerate(__lowerCAmelCase						):
                  if col == 1:
                        nodes[ci].add_inbound(names[ri]						)
                        nodes[ri].add_outbound(names[ci]						)

      print('''======= Nodes ======='''						)
      for node in nodes:
            print(__lowerCAmelCase						)

      page_rank(__lowerCAmelCase						)


if __name__ == "__main__":
     main()
