


'''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      UpperCAmelCase_						(_UpperCAmelCase      ,	_UpperCAmelCase      ,	_UpperCAmelCase      ,	unittest.TestCase						):





    """simple docstring"""






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







    def    lowercase_       (							self			)       ->  Union[str, Any]:
         torch.manual_seed(0			)
         __lowerCamelCase     :			List[str]              =	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    ,  )
         __lowerCamelCase     :			Optional[int]              =	PNDMScheduler(skip_prk_steps=SCREAMING_SNAKE_CASE_			)
         torch.manual_seed(0			)
         __lowerCamelCase     :			Optional[int]              =	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			)
         __lowerCamelCase     :			Optional[int]              =	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    ,  )
         __lowerCamelCase     :			List[str]              =	CLIPVisionModelWithProjection(SCREAMING_SNAKE_CASE_			)
         __lowerCamelCase     :			Tuple              =	CLIPImageProcessor(crop_size=32    ,  size=32			)

         __lowerCamelCase     :			List[str]              =	{
             'unet': unet,
             'scheduler': scheduler,
             'vae': vae,
             'image_encoder': image_encoder,
             'feature_extractor': feature_extractor,
             'safety_checker': None,
         }
         return components







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







    def    lowercase_       (							self			)       ->  Optional[int]:
         __lowerCamelCase     :			str              =	'cpu'  # ensure determinism for the device-dependent torch.Generator
         __lowerCamelCase     :			Dict              =	self.get_dummy_components()
         __lowerCamelCase     :			List[str]              =	StableDiffusionImageVariationPipeline(**SCREAMING_SNAKE_CASE_			)
         __lowerCamelCase     :			int              =	sd_pipe.to(SCREAMING_SNAKE_CASE_			)
         sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_			)

         __lowerCamelCase     :			List[str]              =	self.get_dummy_inputs(SCREAMING_SNAKE_CASE_			)
         __lowerCamelCase     :			Optional[int]              =	sd_pipe(**SCREAMING_SNAKE_CASE_			).images
         __lowerCamelCase     :			int              =	image[0, -3:, -3:, -1]

         assert image.shape == (1, 64, 64, 3)
         __lowerCamelCase     :			List[Any]              =	np.array([0.5_2_3_9, 0.5_7_2_3, 0.4_7_9_6, 0.5_0_4_9, 0.5_5_5_0, 0.4_6_8_5, 0.5_3_2_9, 0.4_8_9_1, 0.4_9_2_1]			)

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







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

         __lowerCamelCase     :			Tuple              =	self.get_dummy_inputs(SCREAMING_SNAKE_CASE_			)
         __lowerCamelCase     :			Dict              =	2 * [inputs['image']]
         __lowerCamelCase     :			int              =	sd_pipe(**SCREAMING_SNAKE_CASE_			)

         __lowerCamelCase     :			int              =	output.images

         __lowerCamelCase     :			Dict              =	image[-1, -3:, -3:, -1]

         assert image.shape == (2, 64, 64, 3)
         __lowerCamelCase     :			int              =	np.array([0.6_8_9_2, 0.5_6_3_7, 0.5_8_3_6, 0.5_7_7_1, 0.6_2_5_4, 0.6_4_0_9, 0.5_5_8_0, 0.5_5_6_9, 0.5_2_8_9]			)

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







    def    lowercase_       (							self			)       ->  str:
         super().test_inference_batch_single_identical(expected_max_diff=3E-3			)







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





    """simple docstring"""






    def    lowercase_       (							self			)       ->  str:
         super().tearDown()
         gc.collect()
         torch.cuda.empty_cache()







    def    lowercase_       (							self    ,  SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_="cpu"    ,  SCREAMING_SNAKE_CASE_=torch.floataa    ,  SCREAMING_SNAKE_CASE_=0			)       ->  Dict:
         __lowerCamelCase     :			Optional[Any]              =	torch.Generator(device=SCREAMING_SNAKE_CASE_			).manual_seed(SCREAMING_SNAKE_CASE_			)
         __lowerCamelCase     :			int              =	load_image(
             'https://huggingface.co/datasets/diffusers/test-arrays/resolve/main'
             '/stable_diffusion_imgvar/input_image_vermeer.png'			)
         __lowerCamelCase     :			Union[str, Any]              =	np.random.RandomState(SCREAMING_SNAKE_CASE_			).standard_normal((1, 4, 64, 64)			)
         __lowerCamelCase     :			List[str]              =	torch.from_numpy(SCREAMING_SNAKE_CASE_			).to(device=SCREAMING_SNAKE_CASE_    ,  dtype=SCREAMING_SNAKE_CASE_			)
         __lowerCamelCase     :			str              =	{
             'image': init_image,
             'latents': latents,
             'generator': generator,
             'num_inference_steps': 3,
             'guidance_scale': 7.5,
             'output_type': 'numpy',
         }
         return inputs







    def    lowercase_       (							self			)       ->  Optional[int]:
         __lowerCamelCase     :			Dict              =	StableDiffusionImageVariationPipeline.from_pretrained(
             'lambdalabs/sd-image-variations-diffusers'    ,  safety_checker=SCREAMING_SNAKE_CASE_			)
         __lowerCamelCase     :			int              =	sd_pipe.to(SCREAMING_SNAKE_CASE_			)
         sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_			)

         __lowerCamelCase     :			Optional[Any]              =	self.get_inputs(SCREAMING_SNAKE_CASE_			)
         __lowerCamelCase     :			Union[str, Any]              =	sd_pipe(**SCREAMING_SNAKE_CASE_			).images
         __lowerCamelCase     :			str              =	image[0, -3:, -3:, -1].flatten()

         assert image.shape == (1, 5_12, 5_12, 3)
         __lowerCamelCase     :			Dict              =	np.array([0.8_4_4_9_1, 0.9_0_7_8_9, 0.7_5_7_0_8, 0.7_8_7_3_4, 0.8_3_4_8_5, 0.7_0_0_9_9, 0.6_6_9_3_8, 0.6_8_7_2_7, 0.6_1_3_7_9]			)
         assert np.abs(image_slice - expected_slice			).max() < 6E-3







    def    lowercase_       (							self			)       ->  Optional[int]:
         __lowerCamelCase     :			Optional[Any]              =	0

         def callback_fn(SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_			) -> None:
              __lowerCamelCase     :			List[Any]              =	True
              nonlocal number_of_steps
              number_of_steps += 1
              if step == 1:
                   __lowerCamelCase     :			List[Any]              =	latents.detach().cpu().numpy()
                   assert latents.shape == (1, 4, 64, 64)
                   __lowerCamelCase     :			Tuple              =	latents[0, -3:, -3:, -1]
                   __lowerCamelCase     :			Any              =	np.array(
                       [-0.1_6_2_1, 0.2_8_3_7, -0.7_9_7_9, -0.1_2_2_1, -1.3_0_5_7, 0.7_6_8_1, -2.1_1_9_1, 0.0_4_6_4, 1.6_3_0_9]			)

                   assert np.abs(latents_slice.flatten() - expected_slice			).max() < 5E-2
              elif step == 2:
                   __lowerCamelCase     :			int              =	latents.detach().cpu().numpy()
                   assert latents.shape == (1, 4, 64, 64)
                   __lowerCamelCase     :			Union[str, Any]              =	latents[0, -3:, -3:, -1]
                   __lowerCamelCase     :			Tuple              =	np.array([0.6_2_9_9, 1.7_5_0_0, 1.1_9_9_2, -2.1_5_8_2, -1.8_9_9_4, 0.7_3_3_4, -0.7_0_9_0, 1.0_1_3_7, 1.5_2_7_3]			)

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

         __lowerCamelCase     :			Tuple              =	False

         __lowerCamelCase     :			int              =	StableDiffusionImageVariationPipeline.from_pretrained(
             'fusing/sd-image-variations-diffusers'    ,  safety_checker=SCREAMING_SNAKE_CASE_    ,  torch_dtype=torch.floataa    ,  )
         pipe.to(SCREAMING_SNAKE_CASE_			)
         pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_			)
         pipe.enable_attention_slicing()

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







    def    lowercase_       (							self			)       ->  str:
         torch.cuda.empty_cache()
         torch.cuda.reset_max_memory_allocated()
         torch.cuda.reset_peak_memory_stats()

         __lowerCamelCase     :			Any              =	'fusing/sd-image-variations-diffusers'
         __lowerCamelCase     :			int              =	StableDiffusionImageVariationPipeline.from_pretrained(
             SCREAMING_SNAKE_CASE_    ,  safety_checker=SCREAMING_SNAKE_CASE_    ,  torch_dtype=torch.floataa			)
         __lowerCamelCase     :			int              =	pipe.to(SCREAMING_SNAKE_CASE_			)
         pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_			)
         pipe.enable_attention_slicing(1			)
         pipe.enable_sequential_cpu_offload()

         __lowerCamelCase     :			Optional[Any]              =	self.get_inputs(SCREAMING_SNAKE_CASE_    ,  dtype=torch.floataa			)
         __lowerCamelCase     :			Optional[Any]              =	pipe(**SCREAMING_SNAKE_CASE_			)

         __lowerCamelCase     :			Optional[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      UpperCAmelCase_						(unittest.TestCase						):





    """simple docstring"""






    def    lowercase_       (							self			)       ->  List[str]:
         super().tearDown()
         gc.collect()
         torch.cuda.empty_cache()







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







    def    lowercase_       (							self			)       ->  Dict:
         __lowerCamelCase     :			Dict              =	StableDiffusionImageVariationPipeline.from_pretrained('fusing/sd-image-variations-diffusers'			)
         sd_pipe.to(SCREAMING_SNAKE_CASE_			)
         sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_			)

         __lowerCamelCase     :			Dict              =	self.get_inputs(SCREAMING_SNAKE_CASE_			)
         __lowerCamelCase     :			str              =	sd_pipe(**SCREAMING_SNAKE_CASE_			).images[0]

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







    def    lowercase_       (							self			)       ->  Union[str, Any]:
         __lowerCamelCase     :			int              =	StableDiffusionImageVariationPipeline.from_pretrained('fusing/sd-image-variations-diffusers'			)
         __lowerCamelCase     :			Optional[Any]              =	DPMSolverMultistepScheduler.from_config(sd_pipe.scheduler.config			)
         sd_pipe.to(SCREAMING_SNAKE_CASE_			)
         sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_			)

         __lowerCamelCase     :			Tuple              =	self.get_inputs(SCREAMING_SNAKE_CASE_			)
         __lowerCamelCase     :			Optional[int]              =	25
         __lowerCamelCase     :			str              =	sd_pipe(**SCREAMING_SNAKE_CASE_			).images[0]

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










'''simple docstring'''



A__	:      List[Any]				=						[0.3_3, 0.4_4, 0.5_5, 0.4_4, 0.3_3]
A__	:      Dict				=						3
A__	:      Union[str, Any]				=						5

# Buffer Space (with Parameters Space)
A__	:      list[float]				=						[]
A__	:      list[float]				=						[]

# Machine Time
A__	:      Dict				=						0



def     UpperCAmelCase__      (			UpperCAmelCase_					:       List[str]							)  ->	str:
     global buffer_space, params_space, machine_time, K, m, t

     # Choosing Dynamical Systems (All)
     for key, value in enumerate(UpperCAmelCase_							):
          # Evolution Parameter
          __lowerCamelCase     :			List[str]              =	float(seed / value							)

          # Control Theory: Orbit Change
          __lowerCamelCase     :			Optional[int]              =	(buffer_space[(key + 1) % m] + e) % 1

          # Control Theory: Trajectory Change
          __lowerCamelCase     :			str              =	(params_space[key] + e) % 1 + 3

          # Modification (Transition Function) - Jumps
          __lowerCamelCase     :			List[Any]              =	round(float(r * value * (1 - value)							)   ,					10							)
          __lowerCamelCase     :			Tuple              =	r  # Saving to Parameters Space

     # Logistic Map
     assert max(UpperCAmelCase_							) < 1
     assert max(UpperCAmelCase_							) < 4

     # Machine Time
     machine_time += 1



def     UpperCAmelCase__      (			)  ->	Optional[int]:
     global buffer_space, params_space, machine_time, K, m, t

     # PRNG (Xorshift by George Marsaglia)
     def xorshift(UpperCAmelCase_					:       Any   ,					UpperCAmelCase_					:       Tuple							):
          x ^= y >> 13
          y ^= x << 17
          x ^= y >> 5
          return x

     # Choosing Dynamical Systems (Increment)
     __lowerCamelCase     :			List[Any]              =	machine_time % m

     # Evolution (Time Length)
     for _ in range(0   ,					UpperCAmelCase_							):
          # Variables (Position + Parameters)
          __lowerCamelCase     :			Union[str, Any]              =	params_space[key]
          __lowerCamelCase     :			Optional[Any]              =	buffer_space[key]

          # Modification (Transition Function) - Flow
          __lowerCamelCase     :			str              =	round(float(r * value * (1 - value)							)   ,					10							)
          __lowerCamelCase     :			Dict              =	(machine_time * 0.01 + r * 1.01) % 1 + 3

     # Choosing Chaotic Data
     __lowerCamelCase     :			List[Any]              =	int(buffer_space[(key + 2) % m] * (10**10)							)
     __lowerCamelCase     :			List[str]              =	int(buffer_space[(key - 2) % m] * (10**10)							)

     # Machine Time
     machine_time += 1

     return xorshift(UpperCAmelCase_   ,					UpperCAmelCase_							) % 0xff_ff_ff_ff



def     UpperCAmelCase__      (			)  ->	Any:
     global buffer_space, params_space, machine_time, K, m, t

     __lowerCamelCase     :			Union[str, Any]              =	K
     __lowerCamelCase     :			Optional[int]              =	[0] * m
     __lowerCamelCase     :			Tuple              =	0


if __name__ == "__main__":
      # Initialization
      reset()

      # Pushing Data (Input)
      import random

      A__	:      Optional[Any]				=						random.sample(range(0XFFFFFFFF), 100)
      for chunk in message:
            push(chunk)

      # for controlling
      A__	:      Tuple				=						""""""

      # Pulling Data (Output)
      while inp in ("e", "E"):
            print(f'''{format(pull(), '#04x')}''')
            print(buffer_space)
            print(params_space)
            A__	:      Optional[Any]				=						input("""(e)exit? """).strip()






