


'''simple docstring'''



import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    EulerAncestralDiscreteScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionInstructPixaPixPipeline,
    UNetaDConditionModel,
)
from diffusers.image_processor import VaeImageProcessor
from diffusers.utils import floats_tensor, load_image, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    IMAGE_TO_IMAGE_IMAGE_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()





class 							__UpperCamelCase    (		lowerCAmelCase_		,				lowerCAmelCase_		,				lowerCAmelCase_		,				unittest.TestCase ):
	A_           =   StableDiffusionInstructPixaPixPipeline
	A_           =   TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"height", "width", "cross_attention_kwargs"}
	A_           =   TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
	A_           =   IMAGE_TO_IMAGE_IMAGE_PARAMS
	A_           =   IMAGE_TO_IMAGE_IMAGE_PARAMS




	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						torch.manual_seed(0							)
						__a      :	str    =  UNetaDConditionModel(
						    block_out_channels=(32, 64)						,    layers_per_block=2						,    sample_size=32						,    in_channels=8						,    out_channels=4						,    down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')						,    up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D')						,    cross_attention_dim=32						,    )
						__a      :	Tuple    =  PNDMScheduler(skip_prk_steps=__a							)
						torch.manual_seed(0							)
						__a      :	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							)
						__a      :	str    =  CLIPTextConfig(
						    bos_token_id=0						,    eos_token_id=2						,    hidden_size=32						,    intermediate_size=37						,    layer_norm_eps=1E-0_5						,    num_attention_heads=4						,    num_hidden_layers=5						,    pad_token_id=1						,    vocab_size=1000						,    )
						__a      :	List[Any]    =  CLIPTextModel(__a							)
						__a      :	Dict    =  CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'							)

						__a      :	Optional[int]    =  {
						    'unet': unet,
						    'scheduler': scheduler,
						    'vae': vae,
						    'text_encoder': text_encoder,
						    'tokenizer': tokenizer,
						    'safety_checker': None,
						    'feature_extractor': None,
						}
						return components




	def 				__UpperCAmelCase							(       self						,    __a						,    __a=0							):

						'''simple docstring'''





						__a      :	Optional[Any]    =  floats_tensor((1, 3, 32, 32)						,    rng=random.Random(__a							)							).to(__a							)
						__a      :	List[Any]    =  image.cpu().permute(0						,    2						,    3						,    1							)[0]
						__a      :	str    =  Image.fromarray(np.uinta(__a							)							).convert('RGB'							)
						if str(__a							).startswith('mps'							):
											__a      :	Union[str, Any]    =  torch.manual_seed(__a							)
						else:
											__a      :	List[str]    =  torch.Generator(device=__a							).manual_seed(__a							)
						__a      :	Any    =  {
						    'prompt': 'A painting of a squirrel eating a burger',
						    'image': image,
						    'generator': generator,
						    'num_inference_steps': 2,
						    'guidance_scale': 6.0,
						    'image_guidance_scale': 1,
						    'output_type': 'numpy',
						}
						return inputs




	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						__a      :	List[str]    =  'cpu'  # ensure determinism for the device-dependent torch.Generator
						__a      :	str    =  self.get_dummy_components()
						__a      :	Any    =  StableDiffusionInstructPixaPixPipeline(**__a							)
						__a      :	Optional[Any]    =  sd_pipe.to(__a							)
						sd_pipe.set_progress_bar_config(disable=__a							)

						__a      :	List[str]    =  self.get_dummy_inputs(__a							)
						__a      :	Dict    =  sd_pipe(**__a							).images
						__a      :	Optional[Any]    =  image[0, -3:, -3:, -1]
						assert image.shape == (1, 32, 32, 3)
						__a      :	int    =  np.array([0.7526, 0.3750, 0.4547, 0.6117, 0.5866, 0.5016, 0.4327, 0.5642, 0.4815]							)

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




	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						__a      :	str    =  'cpu'  # ensure determinism for the device-dependent torch.Generator
						__a      :	Optional[int]    =  self.get_dummy_components()
						__a      :	Dict    =  StableDiffusionInstructPixaPixPipeline(**__a							)
						__a      :	int    =  sd_pipe.to(__a							)
						sd_pipe.set_progress_bar_config(disable=__a							)

						__a      :	str    =  self.get_dummy_inputs(__a							)
						__a      :	Tuple    =  'french fries'
						__a      :	Optional[Any]    =  sd_pipe(**__a						,    negative_prompt=__a							)
						__a      :	Any    =  output.images
						__a      :	Any    =  image[0, -3:, -3:, -1]

						assert image.shape == (1, 32, 32, 3)
						__a      :	Optional[int]    =  np.array([0.7511, 0.3642, 0.4553, 0.6236, 0.5797, 0.5013, 0.4343, 0.5611, 0.4831]							)

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




	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





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

						__a      :	str    =  self.get_dummy_inputs(__a							)
						__a      :	Union[str, Any]    =  [inputs['prompt']] * 2

						__a      :	List[Any]    =  np.array(inputs['image']							).astype(np.floataa							) / 255.0
						__a      :	Dict    =  torch.from_numpy(__a							).unsqueeze(0							).to(__a							)
						__a      :	List[str]    =  image / 2 + 0.5
						__a      :	Optional[Any]    =  image.permute(0						,    3						,    1						,    2							)
						__a      :	Any    =  image.repeat(2						,    1						,    1						,    1							)

						__a      :	str    =  sd_pipe(**__a							).images
						__a      :	int    =  image[-1, -3:, -3:, -1]

						assert image.shape == (2, 32, 32, 3)
						__a      :	str    =  np.array([0.5812, 0.5748, 0.5222, 0.5908, 0.5695, 0.7174, 0.6804, 0.5523, 0.5579]							)

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




	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						__a      :	Union[str, Any]    =  'cpu'  # ensure determinism for the device-dependent torch.Generator
						__a      :	Optional[int]    =  self.get_dummy_components()
						__a      :	Any    =  EulerAncestralDiscreteScheduler(
						    beta_start=0.00085						,    beta_end=0.012						,    beta_schedule='scaled_linear'							)
						__a      :	Optional[Any]    =  StableDiffusionInstructPixaPixPipeline(**__a							)
						__a      :	int    =  sd_pipe.to(__a							)
						sd_pipe.set_progress_bar_config(disable=__a							)

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

						__a      :	Optional[int]    =  [round(__a						,    4							) for x in image_slice.flatten().tolist()]
						print(','.join([str(__a							) for x in slice]							)							)

						assert image.shape == (1, 32, 32, 3)
						__a      :	List[Any]    =  np.array([0.7417, 0.3842, 0.4732, 0.5776, 0.5891, 0.5139, 0.4052, 0.5673, 0.4986]							)

						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							)





	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						__a      :	Optional[Any]    =  self.get_dummy_components()
						__a      :	Optional[int]    =  StableDiffusionInstructPixaPixPipeline(**__a							)
						__a      :	Optional[int]    =  VaeImageProcessor(do_resize=__a						,    do_normalize=__a							)
						__a      :	List[str]    =  pipe.to(__a							)
						pipe.set_progress_bar_config(disable=__a							)

						__a      :	Dict    =  pipe(**self.get_dummy_inputs_by_type(__a						,    input_image_type='pt'							)							)[0]

						__a      :	Union[str, Any]    =  components['vae']
						__a      :	Optional[int]    =  self.get_dummy_inputs_by_type(__a						,    input_image_type='pt'							)

						for image_param in self.image_latents_params:
											if image_param in inputs.keys():
																__a      :	List[Any]    =  vae.encode(inputs[image_param]							).latent_dist.mode()

						__a      :	Optional[int]    =  pipe(**__a							)[0]

						__a      :	Union[str, Any]    =  np.abs(out - out_latents_inputs							).max()
						self.assertLess(__a						,    1E-4						,    'passing latents as image input generate different result from passing image'							)





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




	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





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




	def 				__UpperCAmelCase							(       self						,    __a=0							):

						'''simple docstring'''





						__a      :	str    =  torch.manual_seed(__a							)
						__a      :	Tuple    =  load_image(
						    'https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_pix2pix/example.jpg'							)
						__a      :	Union[str, Any]    =  {
						    'prompt': 'turn him into a cyborg',
						    'image': image,
						    'generator': generator,
						    'num_inference_steps': 3,
						    'guidance_scale': 7.5,
						    'image_guidance_scale': 1.0,
						    'output_type': 'numpy',
						}
						return inputs




	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						__a      :	Optional[int]    =  StableDiffusionInstructPixaPixPipeline.from_pretrained(
						    'timbrooks/instruct-pix2pix'						,    safety_checker=__a							)
						pipe.to(__a							)
						pipe.set_progress_bar_config(disable=__a							)
						pipe.enable_attention_slicing()

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

						assert image.shape == (1, 512, 512, 3)
						__a      :	int    =  np.array([0.5902, 0.6015, 0.6027, 0.5983, 0.6092, 0.6061, 0.5765, 0.5785, 0.5555]							)

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




	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						__a      :	Dict    =  StableDiffusionInstructPixaPixPipeline.from_pretrained(
						    'timbrooks/instruct-pix2pix'						,    safety_checker=__a							)
						__a      :	List[Any]    =  LMSDiscreteScheduler.from_config(pipe.scheduler.config							)
						pipe.to(__a							)
						pipe.set_progress_bar_config(disable=__a							)
						pipe.enable_attention_slicing()

						__a      :	Optional[Any]    =  self.get_inputs()
						__a      :	Optional[int]    =  pipe(**__a							).images
						__a      :	Optional[int]    =  image[0, -3:, -3:, -1].flatten()

						assert image.shape == (1, 512, 512, 3)
						__a      :	int    =  np.array([0.6578, 0.6817, 0.6972, 0.6761, 0.6856, 0.6916, 0.6428, 0.6516, 0.6301]							)

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




	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						__a      :	Any    =  StableDiffusionInstructPixaPixPipeline.from_pretrained(
						    'timbrooks/instruct-pix2pix'						,    safety_checker=__a							)
						__a      :	List[str]    =  DDIMScheduler.from_config(pipe.scheduler.config							)
						pipe.to(__a							)
						pipe.set_progress_bar_config(disable=__a							)
						pipe.enable_attention_slicing()

						__a      :	str    =  self.get_inputs()
						__a      :	int    =  pipe(**__a							).images
						__a      :	Any    =  image[0, -3:, -3:, -1].flatten()

						assert image.shape == (1, 512, 512, 3)
						__a      :	Optional[Any]    =  np.array([0.3828, 0.3834, 0.3818, 0.3792, 0.3865, 0.3752, 0.3792, 0.3847, 0.3753]							)

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




	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						__a      :	List[Any]    =  0

						def callback_fn(__a						,    __a						,    __a							) -> None:
											__a      :	str    =  True
											nonlocal number_of_steps
											number_of_steps += 1
											if step == 1:
																__a      :	int    =  latents.detach().cpu().numpy()
																assert latents.shape == (1, 4, 64, 64)
																__a      :	Optional[int]    =  latents[0, -3:, -3:, -1]
																__a      :	List[Any]    =  np.array([-0.2463, -0.4644, -0.9756, 1.5176, 1.4414, 0.7866, 0.9897, 0.8521, 0.7983]							)

																assert np.abs(latents_slice.flatten() - expected_slice							).max() < 5E-2
											elif step == 2:
																__a      :	Optional[int]    =  latents.detach().cpu().numpy()
																assert latents.shape == (1, 4, 64, 64)
																__a      :	int    =  latents[0, -3:, -3:, -1]
																__a      :	Tuple    =  np.array([-0.2644, -0.4626, -0.9653, 1.5176, 1.4551, 0.7686, 0.9805, 0.8452, 0.8115]							)

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

						__a      :	Any    =  False

						__a      :	List[Any]    =  StableDiffusionInstructPixaPixPipeline.from_pretrained(
						    'timbrooks/instruct-pix2pix'						,    safety_checker=__a						,    torch_dtype=torch.floataa							)
						__a      :	str    =  pipe.to(__a							)
						pipe.set_progress_bar_config(disable=__a							)
						pipe.enable_attention_slicing()

						__a      :	Dict    =  self.get_inputs()
						pipe(**__a						,    callback=__a						,    callback_steps=1							)
						assert callback_fn.has_been_called
						assert number_of_steps == 3




	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





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

						__a      :	List[str]    =  StableDiffusionInstructPixaPixPipeline.from_pretrained(
						    'timbrooks/instruct-pix2pix'						,    safety_checker=__a						,    torch_dtype=torch.floataa							)
						__a      :	Any    =  pipe.to(__a							)
						pipe.set_progress_bar_config(disable=__a							)
						pipe.enable_attention_slicing(1							)
						pipe.enable_sequential_cpu_offload()

						__a      :	List[Any]    =  self.get_inputs()
						__a      :	Dict    =  pipe(**__a							)

						__a      :	List[str]    =  torch.cuda.max_memory_allocated()
						# make sure that less than 2.2 GB is allocated
						assert mem_bytes < 2.2 * 10**9





	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						__a      :	Tuple    =  self.get_inputs()
						# resize to resolution that is divisible by 8 but not 16 or 32
						__a      :	int    =  inputs['image'].resize((504, 504)							)

						__a      :	Dict    =  'timbrooks/instruct-pix2pix'
						__a      :	Tuple    =  StableDiffusionInstructPixaPixPipeline.from_pretrained(
						    __a						,    safety_checker=__a						,    )
						pipe.to(__a							)
						pipe.set_progress_bar_config(disable=__a							)
						pipe.enable_attention_slicing()

						__a      :	Dict    =  pipe(**__a							)
						__a      :	int    =  output.images[0]

						__a      :	Any    =  image[255:258, 383:386, -1]

						assert image.shape == (504, 504, 3)
						__a      :	Any    =  np.array([0.2726, 0.2529, 0.2664, 0.2655, 0.2641, 0.2642, 0.2591, 0.2649, 0.2590]							)

						assert np.abs(image_slice.flatten() - expected_slice							).max() < 5E-3







'''simple docstring'''



import numpy as np
import skfuzzy as fuzz

if __name__ == "__main__":
	# Create universe of discourse in Python using linspace ()
	__lowercase     :						int	=     np.linspace(start=0, stop=75, num=75, endpoint=True, retstep=False)

	# Create two fuzzy sets by defining any membership function
	# (trapmf(), gbellmf(), gaussmf(), etc).
	__lowercase     :						Any	=     [0, 25, 50]
	__lowercase     :						int	=     [25, 50, 75]
	__lowercase     :						List[str]	=     fuzz.membership.trimf(X, abca)
	__lowercase     :						Any	=     fuzz.membership.trimf(X, abca)

	# Compute the different operations using inbuilt functions.
	__lowercase     :						List[Any]	=     np.ones(75)
	__lowercase     :						Any	=     np.zeros((75,))
	# 1. Union = max(µA(x), µB(x))
	__lowercase     :						int	=     fuzz.fuzzy_or(X, young, X, middle_aged)[1]
	# 2. Intersection = min(µA(x), µB(x))
	__lowercase     :						int	=     fuzz.fuzzy_and(X, young, X, middle_aged)[1]
	# 3. Complement (A) = (1- min(µA(x))
	__lowercase     :						str	=     fuzz.fuzzy_not(young)
	# 4. Difference (A/B) = min(µA(x),(1- µB(x)))
	__lowercase     :						List[Any]	=     fuzz.fuzzy_and(X, young, X, fuzz.fuzzy_not(middle_aged)[1])[1]
	# 5. Algebraic Sum = [µA(x) + µB(x) – (µA(x) * µB(x))]
	__lowercase     :						Optional[Any]	=     young + middle_aged - (young * middle_aged)
	# 6. Algebraic Product = (µA(x) * µB(x))
	__lowercase     :						str	=     young * middle_aged
	# 7. Bounded Sum = min[1,(µA(x), µB(x))]
	__lowercase     :						Optional[Any]	=     fuzz.fuzzy_and(X, one, X, young + middle_aged)[1]
	# 8. Bounded difference = min[0,(µA(x), µB(x))]
	__lowercase     :						Union[str, Any]	=     fuzz.fuzzy_or(X, zero, X, young - middle_aged)[1]

	# max-min composition
	# max-product composition

	# Plot each set A, set B and each operation result using plot() and subplot().
	from matplotlib import pyplot as plt

	plt.figure()

	plt.subplot(4, 3, 1)
	plt.plot(X, young)
	plt.title('Young')
	plt.grid(True)

	plt.subplot(4, 3, 2)
	plt.plot(X, middle_aged)
	plt.title('Middle aged')
	plt.grid(True)

	plt.subplot(4, 3, 3)
	plt.plot(X, union)
	plt.title('union')
	plt.grid(True)

	plt.subplot(4, 3, 4)
	plt.plot(X, intersection)
	plt.title('intersection')
	plt.grid(True)

	plt.subplot(4, 3, 5)
	plt.plot(X, complement_a)
	plt.title('complement_a')
	plt.grid(True)

	plt.subplot(4, 3, 6)
	plt.plot(X, difference)
	plt.title('difference a/b')
	plt.grid(True)

	plt.subplot(4, 3, 7)
	plt.plot(X, alg_sum)
	plt.title('alg_sum')
	plt.grid(True)

	plt.subplot(4, 3, 8)
	plt.plot(X, alg_product)
	plt.title('alg_product')
	plt.grid(True)

	plt.subplot(4, 3, 9)
	plt.plot(X, bdd_sum)
	plt.title('bdd_sum')
	plt.grid(True)

	plt.subplot(4, 3, 10)
	plt.plot(X, bdd_difference)
	plt.title('bdd_difference')
	plt.grid(True)

	plt.subplots_adjust(hspace=0.5)
	plt.show()



