


"""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 					__lowerCAmelCase					(   __SCREAMING_SNAKE_CASE     ,			__SCREAMING_SNAKE_CASE     ,			unittest.TestCase       ):


		'''simple docstring'''



		__UpperCAmelCase		:			Optional[int]										=				CycleDiffusionPipeline
		__UpperCAmelCase		:			Any										=				TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {
		    'negative_prompt',
		    'height',
		    'width',
		    'negative_prompt_embeds',
		}
		__UpperCAmelCase		:			int										=				PipelineTesterMixin.required_optional_params - {'latents'}
		__UpperCAmelCase		:			Any										=				TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({'source_prompt'}       )
		__UpperCAmelCase		:			Tuple										=				IMAGE_TO_IMAGE_IMAGE_PARAMS
		__UpperCAmelCase		:			int										=				IMAGE_TO_IMAGE_IMAGE_PARAMS

		def 			__UpperCAmelCase       (  self      ):
				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_0085							, beta_end=0.012							, beta_schedule='''scaled_linear'''							, num_train_timesteps=1_000							, clip_sample=_a							, set_alpha_to_one=_a							, )
				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=1_000							, )
				__a           =			CLIPTextModel(_a      )
				__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							, _a							, _a=0      ):
				__a           =			floats_tensor((1, 3, 32, 32)							, rng=random.Random(_a      )      ).to(_a      )
				__a           =			image / 2 + 0.5
				if str(_a      ).startswith('''mps'''      ):
						__a           =			torch.manual_seed(_a      )
				else:
						__a           =			torch.Generator(device=_a      ).manual_seed(_a      )
				__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      ):
				__a           =			'''cpu'''  # ensure determinism for the device-dependent torch.Generator

				__a           =			self.get_dummy_components()
				__a           =			CycleDiffusionPipeline(**_a      )
				__a           =			pipe.to(_a      )
				pipe.set_progress_bar_config(disable=_a      )

				__a           =			self.get_dummy_inputs(_a      )
				__a           =			pipe(**_a      )
				__a           =			output.images

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

				assert images.shape == (1, 32, 32, 3)
				__a           =			np.array([0.4459, 0.4943, 0.4544, 0.6643, 0.5474, 0.4327, 0.5701, 0.5959, 0.5179]      )

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

		@unittest.skipIf(torch_device != '''cuda'''							, '''This test requires a GPU'''      )
		def 			__UpperCAmelCase       (  self      ):
				__a           =			self.get_dummy_components()
				for name, module in components.items():
						if hasattr(_a							, '''half'''      ):
								__a           =			module.half()
				__a           =			CycleDiffusionPipeline(**_a      )
				__a           =			pipe.to(_a      )
				pipe.set_progress_bar_config(disable=_a      )

				__a           =			self.get_dummy_inputs(_a      )
				__a           =			pipe(**_a      )
				__a           =			output.images

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

				assert images.shape == (1, 32, 32, 3)
				__a           =			np.array([0.3506, 0.4543, 0.446, 0.4575, 0.5195, 0.4155, 0.5273, 0.518, 0.4116]      )

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

		@skip_mps
		def 			__UpperCAmelCase       (  self      ):
				return super().test_save_load_local()

		@unittest.skip('''non-deterministic pipeline'''      )
		def 			__UpperCAmelCase       (  self      ):
				return super().test_inference_batch_single_identical()

		@skip_mps
		def 			__UpperCAmelCase       (  self      ):
				return super().test_dict_tuple_outputs_equivalent()

		@skip_mps
		def 			__UpperCAmelCase       (  self      ):
				return super().test_save_load_optional_components()





		@skip_mps
		def 			__UpperCAmelCase       (  self      ):
				return super().test_attention_slicing_forward_pass()







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


		'''simple docstring'''



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

		def 			__UpperCAmelCase       (  self      ):
				__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((512, 512)      )

				__a           =			'''CompVis/stable-diffusion-v1-4'''
				__a           =			DDIMScheduler.from_pretrained(_a							, subfolder='''scheduler'''      )
				__a           =			CycleDiffusionPipeline.from_pretrained(
				    _a							, scheduler=_a							, safety_checker=_a							, torch_dtype=torch.floataa							, revision='''fp16'''      )

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

				__a           =			'''A black colored car'''
				__a           =			'''A blue colored car'''

				__a           =			torch.manual_seed(0      )
				__a           =			pipe(
				    prompt=_a							, source_prompt=_a							, image=_a							, num_inference_steps=100							, eta=0.1							, strength=0.85							, guidance_scale=3							, source_guidance_scale=1							, generator=_a							, 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      ):
				__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((512, 512)      )

				__a           =			'''CompVis/stable-diffusion-v1-4'''
				__a           =			DDIMScheduler.from_pretrained(_a							, subfolder='''scheduler'''      )
				__a           =			CycleDiffusionPipeline.from_pretrained(_a							, scheduler=_a							, safety_checker=_a      )

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

				__a           =			'''A black colored car'''
				__a           =			'''A blue colored car'''

				__a           =			torch.manual_seed(0      )
				__a           =			pipe(
				    prompt=_a							, source_prompt=_a							, image=_a							, num_inference_steps=100							, eta=0.1							, strength=0.85							, guidance_scale=3							, source_guidance_scale=1							, generator=_a							, output_type='''np'''							, )
				__a           =			output.images

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










"""simple docstring"""




def        lowercase							(		lowerCAmelCase__ :     int    ,					lowerCAmelCase__ :     list			) ->      List[Any]:
		_enforce_args(lowerCAmelCase__    ,					lowerCAmelCase__			)
		if n == 0:
				return 0
		__a           =			float('''-inf'''			)
		for i in range(1    ,					n + 1			):
				__a           =			max(
				    lowerCAmelCase__    ,					prices[i - 1] + naive_cut_rod_recursive(n - i    ,					lowerCAmelCase__			)			)

		return max_revue

def        lowercase							(		lowerCAmelCase__ :     int    ,					lowerCAmelCase__ :     list			) ->      List[str]:
		_enforce_args(lowerCAmelCase__    ,					lowerCAmelCase__			)
		__a           =			[float('''-inf'''			) for _ in range(n + 1			)]
		return _top_down_cut_rod_recursive(lowerCAmelCase__    ,					lowerCAmelCase__    ,					lowerCAmelCase__			)

def        lowercase							(		lowerCAmelCase__ :     int    ,					lowerCAmelCase__ :     list    ,					lowerCAmelCase__ :     list			) ->      Union[str, Any]:
		if max_rev[n] >= 0:
				return max_rev[n]
		elif n == 0:
				return 0
		else:
				__a           =			float('''-inf'''			)
				for i in range(1    ,					n + 1			):
						__a           =			max(
						    lowerCAmelCase__    ,					prices[i - 1] + _top_down_cut_rod_recursive(n - i    ,					lowerCAmelCase__    ,					lowerCAmelCase__			)    ,					)

				__a           =			max_revenue

		return max_rev[n]

def        lowercase							(		lowerCAmelCase__ :     int    ,					lowerCAmelCase__ :     list			) ->      Dict:
		_enforce_args(lowerCAmelCase__    ,					lowerCAmelCase__			)

		# length(max_rev) = n + 1, to accommodate for the revenue obtainable from a rod of
		# length 0.
		__a           =			[float('''-inf'''			) for _ in range(n + 1			)]
		__a           =			0

		for i in range(1    ,					n + 1			):
				__a           =			max_rev[i]
				for j in range(1    ,					i + 1			):
						__a           =			max(lowerCAmelCase__    ,					prices[j - 1] + max_rev[i - j]			)

				__a           =			max_revenue_i

		return max_rev[n]

def        lowercase							(		lowerCAmelCase__ :     int    ,					lowerCAmelCase__ :     list			) ->      str:
		if n < 0:
				__a           =			f'''n must be greater than or equal to 0. Got n = {n}'''
				raise ValueError(lowerCAmelCase__			)

		if n > len(lowerCAmelCase__			):
				__a           =			(
				    '''Each integral piece of rod must have a corresponding price. '''
				    f'''Got n = {n} but length of prices = {len(lowerCAmelCase__			)}'''
				)
				raise ValueError(lowerCAmelCase__			)

def        lowercase							(		) ->      int:
		__a           =			[6, 10, 12, 15, 20, 23]
		__a           =			len(lowerCAmelCase__			)

		# the best revenue comes from cutting the rod into 6 pieces, each
		# of length 1 resulting in a revenue of 6 * 6 = 36.
		__a           =			36

		__a           =			top_down_cut_rod(lowerCAmelCase__    ,					lowerCAmelCase__			)
		__a           =			bottom_up_cut_rod(lowerCAmelCase__    ,					lowerCAmelCase__			)
		__a           =			naive_cut_rod_recursive(lowerCAmelCase__    ,					lowerCAmelCase__			)

		assert expected_max_revenue == max_rev_top_down
		assert max_rev_top_down == max_rev_bottom_up
		assert max_rev_bottom_up == max_rev_naive


if __name__ == "__main__":
				main()






