
"""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				(  _a      ,      _a      ,      unittest.TestCase  ):




	'''simple docstring'''





	snake_case_    =					CycleDiffusionPipeline
	snake_case_    =					TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {
	    "negative_prompt",
	    "height",
	    "width",
	    "negative_prompt_embeds",
	}
	snake_case_    =					PipelineTesterMixin.required_optional_params - {"latents"}
	snake_case_    =					TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({"source_prompt"}  )
	snake_case_    =					IMAGE_TO_IMAGE_IMAGE_PARAMS
	snake_case_    =					IMAGE_TO_IMAGE_IMAGE_PARAMS






	def     UpperCamelCase_  (	self :			Dict			):
					torch.manual_seed(0			)
					__A             =			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							,)
					__A             =			DDIMScheduler(
					    beta_start=0.0_00_85							,beta_end=0.0_12							,beta_schedule="scaled_linear"							,num_train_timesteps=10_00							,clip_sample=snake_case_							,set_alpha_to_one=snake_case_							,)
					torch.manual_seed(0			)
					__A             =			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             =			CLIPTextConfig(
					    bos_token_id=0							,eos_token_id=2							,hidden_size=32							,intermediate_size=37							,layer_norm_eps=1E-05							,num_attention_heads=4							,num_hidden_layers=5							,pad_token_id=1							,vocab_size=10_00							,)
					__A             =			CLIPTextModel(snake_case_			)
					__A             =			CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"			)

					__A             =			{
					    """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]							,A :			Any							,A :			str=0			):
					__A             =			floats_tensor((1, 3, 32, 32)							,rng=random.Random(snake_case_			)			).to(snake_case_			)
					__A             =			image / 2 + 0.5
					if str(snake_case_			).startswith("mps"			):
									__A             =			torch.manual_seed(snake_case_			)
					else:
									__A             =			torch.Generator(device=snake_case_			).manual_seed(snake_case_			)
					__A             =			{
					    """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			):
					__A             =			"""cpu"""  # ensure determinism for the device-dependent torch.Generator

					__A             =			self.get_dummy_components()
					__A             =			CycleDiffusionPipeline(**snake_case_			)
					__A             =			pipe.to(snake_case_			)
					pipe.set_progress_bar_config(disable=snake_case_			)

					__A             =			self.get_dummy_inputs(snake_case_			)
					__A             =			pipe(**snake_case_			)
					__A             =			output.images

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

					assert images.shape == (1, 32, 32, 3)
					__A             =			np.array([0.44_59, 0.49_43, 0.45_44, 0.66_43, 0.54_74, 0.43_27, 0.57_01, 0.59_59, 0.51_79]			)

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






	@unittest.skipIf(torch_device != "cuda"							,"This test requires a GPU"			)
	def     UpperCamelCase_  (	self :			List[str]			):
					__A             =			self.get_dummy_components()
					for name, module in components.items():
									if hasattr(snake_case_							,"half"			):
													__A             =			module.half()
					__A             =			CycleDiffusionPipeline(**snake_case_			)
					__A             =			pipe.to(snake_case_			)
					pipe.set_progress_bar_config(disable=snake_case_			)

					__A             =			self.get_dummy_inputs(snake_case_			)
					__A             =			pipe(**snake_case_			)
					__A             =			output.images

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

					assert images.shape == (1, 32, 32, 3)
					__A             =			np.array([0.35_06, 0.45_43, 0.4_46, 0.45_75, 0.51_95, 0.41_55, 0.52_73, 0.5_18, 0.41_16]			)

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






	@skip_mps
	def     UpperCamelCase_  (	self :			List[Any]			):
					return super().test_save_load_local()






	@unittest.skip("non-deterministic pipeline"			)
	def     UpperCamelCase_  (	self :			int			):
					return super().test_inference_batch_single_identical()






	@skip_mps
	def     UpperCamelCase_  (	self :			Dict			):
					return super().test_dict_tuple_outputs_equivalent()






	@skip_mps
	def     UpperCamelCase_  (	self :			int			):
					return super().test_save_load_optional_components()







	@skip_mps
	def     UpperCamelCase_  (	self :			Optional[int]			):
					return super().test_attention_slicing_forward_pass()





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




	'''simple docstring'''





	def     UpperCamelCase_  (	self :			Dict			):
					# clean up the VRAM after each test
					super().tearDown()
					gc.collect()
					torch.cuda.empty_cache()






	def     UpperCamelCase_  (	self :			str			):
					__A             =			load_image(
					    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
					    "/cycle-diffusion/black_colored_car.png"			)
					__A             =			load_numpy(
					    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car_fp16.npy"			)
					__A             =			init_image.resize((5_12, 5_12)			)

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

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

					__A             =			"""A black colored car"""
					__A             =			"""A blue colored car"""

					__A             =			torch.manual_seed(0			)
					__A             =			pipe(
					    prompt=snake_case_							,source_prompt=snake_case_							,image=snake_case_							,num_inference_steps=1_00							,eta=0.1							,strength=0.85							,guidance_scale=3							,source_guidance_scale=1							,generator=snake_case_							,output_type="np"							,)
					__A             =			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 :			int			):
					__A             =			load_image(
					    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
					    "/cycle-diffusion/black_colored_car.png"			)
					__A             =			load_numpy(
					    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car.npy"			)
					__A             =			init_image.resize((5_12, 5_12)			)

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

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

					__A             =			"""A black colored car"""
					__A             =			"""A blue colored car"""

					__A             =			torch.manual_seed(0			)
					__A             =			pipe(
					    prompt=snake_case_							,source_prompt=snake_case_							,image=snake_case_							,num_inference_steps=1_00							,eta=0.1							,strength=0.85							,guidance_scale=3							,source_guidance_scale=1							,generator=snake_case_							,output_type="np"							,)
					__A             =			output.images

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


from __future__ import annotations


def 						UpperCAmelCase      (     a_ = 4						)		->     list[list[int]]:







				"""simple docstring"""



				__A             =			abs(a_						) or 4
				return [[1 + x + y * row_size for x in range(a_						)] for y in range(a_						)]


def 						UpperCAmelCase      (     a_						)		->     list[list[int]]:







				"""simple docstring"""



				return reverse_row(transpose(a_						)						)
				# OR.. transpose(reverse_column(matrix))


def 						UpperCAmelCase      (     a_						)		->     list[list[int]]:







				"""simple docstring"""



				return reverse_row(reverse_column(a_						)						)
				# OR.. reverse_column(reverse_row(matrix))


def 						UpperCAmelCase      (     a_						)		->     list[list[int]]:







				"""simple docstring"""



				return reverse_column(transpose(a_						)						)
				# OR.. transpose(reverse_row(matrix))


def 						UpperCAmelCase      (     a_						)		->     list[list[int]]:







				"""simple docstring"""



				__A             =			[list(a_						) for x in zip(*a_						)]
				return matrix


def 						UpperCAmelCase      (     a_						)		->     list[list[int]]:







				"""simple docstring"""



				__A             =			matrix[::-1]
				return matrix


def 						UpperCAmelCase      (     a_						)		->     list[list[int]]:







				"""simple docstring"""



				__A             =			[x[::-1] for x in matrix]
				return matrix


def 						UpperCAmelCase      (     a_						)		->     None:







				"""simple docstring"""



				for i in matrix:
								print(*a_						)


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE							:Union[str, Any]										=    make_matrix()
					print('\norigin:\n')
					print_matrix(matrix)
					print('\nrotate 90 counterclockwise:\n')
					print_matrix(rotate_aa(matrix))

					SCREAMING_SNAKE_CASE							:Tuple										=    make_matrix()
					print('\norigin:\n')
					print_matrix(matrix)
					print('\nrotate 180:\n')
					print_matrix(rotate_aaa(matrix))

					SCREAMING_SNAKE_CASE							:Union[str, Any]										=    make_matrix()
					print('\norigin:\n')
					print_matrix(matrix)
					print('\nrotate 270 counterclockwise:\n')
					print_matrix(rotate_aaa(matrix))
