


"""simple docstring"""







import gc
import random
import unittest

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

from diffusers import AutoencoderKL, CycleDiffusionPipeline, DDIMScheduler, UNetaDConditionModel
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps

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


enable_full_determinism()




class  UpperCamelCase_	( UpperCamelCase			,  UpperCamelCase			,  unittest.TestCase):

	"""simple docstring"""





	snake_case__				:  Any    					=			CycleDiffusionPipeline
	snake_case__				:  Dict    					=			TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {
	    "negative_prompt",
	    "height",
	    "width",
	    "negative_prompt_embeds",
	}
	snake_case__				:  List[Any]    					=			PipelineTesterMixin.required_optional_params - {"latents"}
	snake_case__				:  Any    					=			TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({"source_prompt"})
	snake_case__				:  Union[str, Any]    					=			IMAGE_TO_IMAGE_IMAGE_PARAMS
	snake_case__				:  str    					=			IMAGE_TO_IMAGE_IMAGE_PARAMS




	def   UpperCAmelCase_      ( self			:				Tuple   )       ->	str:
						torch.manual_seed(0   )
						__SCREAMING_SNAKE_CASE						   =				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			,			)
						__SCREAMING_SNAKE_CASE						   =				DDIMScheduler(
						    beta_start=0.00_085			,			beta_end=0.012			,			beta_schedule="scaled_linear"			,			num_train_timesteps=1_0_0_0			,			clip_sample=UpperCAmelCase__			,			set_alpha_to_one=UpperCAmelCase__			,			)
						torch.manual_seed(0   )
						__SCREAMING_SNAKE_CASE						   =				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   )
						__SCREAMING_SNAKE_CASE						   =				CLIPTextConfig(
						    bos_token_id=0			,			eos_token_id=2			,			hidden_size=3_2			,			intermediate_size=3_7			,			layer_norm_eps=1E-05			,			num_attention_heads=4			,			num_hidden_layers=5			,			pad_token_id=1			,			vocab_size=1_0_0_0			,			)
						__SCREAMING_SNAKE_CASE						   =				CLIPTextModel(UpperCAmelCase__   )
						__SCREAMING_SNAKE_CASE						   =				CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"   )

						__SCREAMING_SNAKE_CASE						   =				{
						    "unet": unet,
						    "scheduler": scheduler,
						    "vae": vae,
						    "text_encoder": text_encoder,
						    "tokenizer": tokenizer,
						    "safety_checker": None,
						    "feature_extractor": None,
						}
						return components




	def   UpperCAmelCase_      ( self			:				Optional[int]			,			UpperCAmelCase__			:				List[str]			,			UpperCAmelCase__			:				Optional[int]=0   )       ->	Any:
						__SCREAMING_SNAKE_CASE						   =				floats_tensor((1, 3, 3_2, 3_2)			,			rng=random.Random(UpperCAmelCase__   )   ).to(UpperCAmelCase__   )
						__SCREAMING_SNAKE_CASE						   =				image / 2 + 0.5
						if str(UpperCAmelCase__   ).startswith("mps"   ):
											__SCREAMING_SNAKE_CASE						   =				torch.manual_seed(UpperCAmelCase__   )
						else:
											__SCREAMING_SNAKE_CASE						   =				torch.Generator(device=UpperCAmelCase__   ).manual_seed(UpperCAmelCase__   )
						__SCREAMING_SNAKE_CASE						   =				{
						    "prompt": "An astronaut riding an elephant",
						    "source_prompt": "An astronaut riding a horse",
						    "image": image,
						    "generator": generator,
						    "num_inference_steps": 2,
						    "eta": 0.1,
						    "strength": 0.8,
						    "guidance_scale": 3,
						    "source_guidance_scale": 1,
						    "output_type": "numpy",
						}
						return inputs




	def   UpperCAmelCase_      ( self			:				Tuple   )       ->	List[Any]:
						__SCREAMING_SNAKE_CASE						   =				"cpu"  # ensure determinism for the device-dependent torch.Generator

						__SCREAMING_SNAKE_CASE						   =				self.get_dummy_components()
						__SCREAMING_SNAKE_CASE						   =				CycleDiffusionPipeline(**UpperCAmelCase__   )
						__SCREAMING_SNAKE_CASE						   =				pipe.to(UpperCAmelCase__   )
						pipe.set_progress_bar_config(disable=UpperCAmelCase__   )

						__SCREAMING_SNAKE_CASE						   =				self.get_dummy_inputs(UpperCAmelCase__   )
						__SCREAMING_SNAKE_CASE						   =				pipe(**UpperCAmelCase__   )
						__SCREAMING_SNAKE_CASE						   =				output.images

						__SCREAMING_SNAKE_CASE						   =				images[0, -3:, -3:, -1]

						assert images.shape == (1, 3_2, 3_2, 3)
						__SCREAMING_SNAKE_CASE						   =				np.array([0.4_459, 0.4_943, 0.4_544, 0.6_643, 0.5_474, 0.4_327, 0.5_701, 0.5_959, 0.5_179]   )

						assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1E-2




	@unittest.skipIf(torch_device != "cuda"			,			"This test requires a GPU"   )
	def   UpperCAmelCase_      ( self			:				Union[str, Any]   )       ->	Tuple:
						__SCREAMING_SNAKE_CASE						   =				self.get_dummy_components()
						for name, module in components.items():
											if hasattr(UpperCAmelCase__			,			"half"   ):
																__SCREAMING_SNAKE_CASE						   =				module.half()
						__SCREAMING_SNAKE_CASE						   =				CycleDiffusionPipeline(**UpperCAmelCase__   )
						__SCREAMING_SNAKE_CASE						   =				pipe.to(UpperCAmelCase__   )
						pipe.set_progress_bar_config(disable=UpperCAmelCase__   )

						__SCREAMING_SNAKE_CASE						   =				self.get_dummy_inputs(UpperCAmelCase__   )
						__SCREAMING_SNAKE_CASE						   =				pipe(**UpperCAmelCase__   )
						__SCREAMING_SNAKE_CASE						   =				output.images

						__SCREAMING_SNAKE_CASE						   =				images[0, -3:, -3:, -1]

						assert images.shape == (1, 3_2, 3_2, 3)
						__SCREAMING_SNAKE_CASE						   =				np.array([0.3_506, 0.4_543, 0.446, 0.4_575, 0.5_195, 0.4_155, 0.5_273, 0.518, 0.4_116]   )

						assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1E-2




	@skip_mps
	def   UpperCAmelCase_      ( self			:				int   )       ->	Tuple:
						return super().test_save_load_local()




	@unittest.skip("non-deterministic pipeline"   )
	def   UpperCAmelCase_      ( self			:				List[str]   )       ->	Union[str, Any]:
						return super().test_inference_batch_single_identical()




	@skip_mps
	def   UpperCAmelCase_      ( self			:				Optional[Any]   )       ->	Tuple:
						return super().test_dict_tuple_outputs_equivalent()




	@skip_mps
	def   UpperCAmelCase_      ( self			:				Dict   )       ->	List[Any]:
						return super().test_save_load_optional_components()




	@skip_mps
	def   UpperCAmelCase_      ( self			:				int   )       ->	str:
						return super().test_attention_slicing_forward_pass()




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

	"""simple docstring"""





	def   UpperCAmelCase_      ( self			:				Tuple   )       ->	List[Any]:
						# clean up the VRAM after each test
						super().tearDown()
						gc.collect()
						torch.cuda.empty_cache()




	def   UpperCAmelCase_      ( self			:				Optional[int]   )       ->	Tuple:
						__SCREAMING_SNAKE_CASE						   =				load_image(
						    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
						    "/cycle-diffusion/black_colored_car.png"   )
						__SCREAMING_SNAKE_CASE						   =				load_numpy(
						    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car_fp16.npy"   )
						__SCREAMING_SNAKE_CASE						   =				init_image.resize((5_1_2, 5_1_2)   )

						__SCREAMING_SNAKE_CASE						   =				"CompVis/stable-diffusion-v1-4"
						__SCREAMING_SNAKE_CASE						   =				DDIMScheduler.from_pretrained(UpperCAmelCase__			,			subfolder="scheduler"   )
						__SCREAMING_SNAKE_CASE						   =				CycleDiffusionPipeline.from_pretrained(
						    UpperCAmelCase__			,			scheduler=UpperCAmelCase__			,			safety_checker=UpperCAmelCase__			,			torch_dtype=torch.floataa			,			revision="fp16"   )

						pipe.to(UpperCAmelCase__   )
						pipe.set_progress_bar_config(disable=UpperCAmelCase__   )
						pipe.enable_attention_slicing()

						__SCREAMING_SNAKE_CASE						   =				"A black colored car"
						__SCREAMING_SNAKE_CASE						   =				"A blue colored car"

						__SCREAMING_SNAKE_CASE						   =				torch.manual_seed(0   )
						__SCREAMING_SNAKE_CASE						   =				pipe(
						    prompt=UpperCAmelCase__			,			source_prompt=UpperCAmelCase__			,			image=UpperCAmelCase__			,			num_inference_steps=1_0_0			,			eta=0.1			,			strength=0.85			,			guidance_scale=3			,			source_guidance_scale=1			,			generator=UpperCAmelCase__			,			output_type="np"			,			)
						__SCREAMING_SNAKE_CASE						   =				output.images

						# the values aren't exactly equal, but the images look the same visually
						assert np.abs(image - expected_image   ).max() < 5E-1




	def   UpperCAmelCase_      ( self			:				List[Any]   )       ->	List[Any]:
						__SCREAMING_SNAKE_CASE						   =				load_image(
						    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
						    "/cycle-diffusion/black_colored_car.png"   )
						__SCREAMING_SNAKE_CASE						   =				load_numpy(
						    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car.npy"   )
						__SCREAMING_SNAKE_CASE						   =				init_image.resize((5_1_2, 5_1_2)   )

						__SCREAMING_SNAKE_CASE						   =				"CompVis/stable-diffusion-v1-4"
						__SCREAMING_SNAKE_CASE						   =				DDIMScheduler.from_pretrained(UpperCAmelCase__			,			subfolder="scheduler"   )
						__SCREAMING_SNAKE_CASE						   =				CycleDiffusionPipeline.from_pretrained(UpperCAmelCase__			,			scheduler=UpperCAmelCase__			,			safety_checker=UpperCAmelCase__   )

						pipe.to(UpperCAmelCase__   )
						pipe.set_progress_bar_config(disable=UpperCAmelCase__   )
						pipe.enable_attention_slicing()

						__SCREAMING_SNAKE_CASE						   =				"A black colored car"
						__SCREAMING_SNAKE_CASE						   =				"A blue colored car"

						__SCREAMING_SNAKE_CASE						   =				torch.manual_seed(0   )
						__SCREAMING_SNAKE_CASE						   =				pipe(
						    prompt=UpperCAmelCase__			,			source_prompt=UpperCAmelCase__			,			image=UpperCAmelCase__			,			num_inference_steps=1_0_0			,			eta=0.1			,			strength=0.85			,			guidance_scale=3			,			source_guidance_scale=1			,			generator=UpperCAmelCase__			,			output_type="np"			,			)
						__SCREAMING_SNAKE_CASE						   =				output.images

						assert np.abs(image - expected_image   ).max() < 2E-2




"""simple docstring"""







def    UpperCAmelCase__       (lowerCAmelCase_   ):




					'''simple docstring'''


					if upper_limit < 0:
										raise ValueError("Limit for the Catalan sequence must be ≥ 0"   )

					__SCREAMING_SNAKE_CASE						   =				[0] * (upper_limit + 1)

					# Base case: C(0) = C(1) = 1
					__SCREAMING_SNAKE_CASE						   =				1
					if upper_limit > 0:
										__SCREAMING_SNAKE_CASE						   =				1

					# Recurrence relation: C(i) = sum(C(j).C(i-j-1)), from j = 0 to i
					for i in range(2  ,						upper_limit + 1   ):
										for j in range(lowerCAmelCase_   ):
															catalan_list[i] += catalan_list[j] * catalan_list[i - j - 1]

					return catalan_list


if __name__ == "__main__":
		print('''\n********* Catalan Numbers Using Dynamic Programming ************\n''')
		print('''\n*** Enter -1 at any time to quit ***''')
		print('''\nEnter the upper limit (≥ 0) for the Catalan number sequence: ''', end='''''')
		try:
				while True:
						a__				:							List[str]						=     int(input().strip())
						if N < 0:
								print('''\n********* Goodbye!! ************''')
								break
						else:
								print(F"The Catalan numbers from 0 through {N} are:")
								print(catalan_numbers(N))
								print('''Try another upper limit for the sequence: ''', end='''''')
		except (NameError, ValueError):
				print('''\n********* Invalid input, goodbye! ************\n''')

		import doctest

		doctest.testmod()
