






"""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       snake_case_       (    _lowerCamelCase    ,							_lowerCamelCase    ,							unittest.TestCase       ):



	"""simple docstring"""

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


	def       _UpperCAmelCase						(      self						):


					"""simple docstring"""

					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=1000  ,					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=1000  ,					)
					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						):


					"""simple docstring"""

					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						):


					"""simple docstring"""

					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						):


					"""simple docstring"""

					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						):


					"""simple docstring"""

					return super().test_save_load_local()


	@unittest.skip('non-deterministic pipeline'						)
	def       _UpperCAmelCase						(      self						):


					"""simple docstring"""

					return super().test_inference_batch_single_identical()


	@skip_mps
	def       _UpperCAmelCase						(      self						):


					"""simple docstring"""

					return super().test_dict_tuple_outputs_equivalent()


	@skip_mps
	def       _UpperCAmelCase						(      self						):


					"""simple docstring"""

					return super().test_save_load_optional_components()






	@skip_mps
	def       _UpperCAmelCase						(      self						):


					"""simple docstring"""

					return super().test_attention_slicing_forward_pass()





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



	"""simple docstring"""

	def       _UpperCAmelCase						(      self						):


					"""simple docstring"""

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


	def       _UpperCAmelCase						(      self						):


					"""simple docstring"""

					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						):


					"""simple docstring"""

					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"""






from __future__ import annotations



def  __lowerCamelCase					(					lowerCAmelCase__       ,lowerCAmelCase__ = None				):
				A__							 =       word_bank or []
				# create a table
				A__							 =       len(lowerCAmelCase__				) + 1

				A__							 =       []
				for _ in range(lowerCAmelCase__				):
								table.append([]				)
				# seed value
				A__							 =       [[]]  # because empty string has empty combination

				# iterate through the indices
				for i in range(lowerCAmelCase__				):
								# condition
								if table[i] != []:
												for word in word_bank:
																# slice condition
																if target[i : i + len(lowerCAmelCase__				)] == word:
																				A__							 =       [
																				    [word, *way] for way in table[i]
																				]
																				# adds the word to every combination the current position holds
																				# now,push that combination to the table[i+len(word)]
																				table[i + len(lowerCAmelCase__				)] += new_combinations

    # combinations are in reverse order so reverse for better output
				for combination in table[len(lowerCAmelCase__				)]:
								combination.reverse()

				return table[len(lowerCAmelCase__				)]


if __name__ == "__main__":
					print(all_construct('''jwajalapa''', ['''jwa''', '''j''', '''w''', '''a''', '''la''', '''lapa''']))
					print(all_construct('''rajamati''', ['''s''', '''raj''', '''amat''', '''raja''', '''ma''', '''i''', '''t''']))
					print(
					    all_construct(
					        '''hexagonosaurus''',
					        ['''h''', '''ex''', '''hex''', '''ag''', '''ago''', '''ru''', '''auru''', '''rus''', '''go''', '''no''', '''o''', '''s'''],
					    )
					)


