



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


					'''simple docstring'''





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



					def 		snake_case	(	self							:  int					)-> Optional[Any]:
								torch.manual_seed(0					)
								lowerCamelCase__		:		Union[str, Any]		     =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,  )
								lowerCamelCase__		:		Any		     =DDIMScheduler(
								    beta_start=0.00_085,  beta_end=0.012,  beta_schedule='''scaled_linear''',  num_train_timesteps=1000,  clip_sample=lowerCamelCase,  set_alpha_to_one=lowerCamelCase,  )
								torch.manual_seed(0					)
								lowerCamelCase__		:		List[Any]		     =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					)
								lowerCamelCase__		:		str		     =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,  )
								lowerCamelCase__		:		Dict		     =CLIPTextModel(lowerCamelCase					)
								lowerCamelCase__		:		Tuple		     =CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''					)

								lowerCamelCase__		:		List[str]		     ={
								    '''unet''': unet,
								    '''scheduler''': scheduler,
								    '''vae''': vae,
								    '''text_encoder''': text_encoder,
								    '''tokenizer''': tokenizer,
								    '''safety_checker''': None,
								    '''feature_extractor''': None,
								}
								return components



					def 		snake_case	(	self							:  Optional[int],  lowerCamelCase							:  str,  lowerCamelCase							:  List[Any]=0					)-> List[str]:
								lowerCamelCase__		:		Any		     =floats_tensor((1, 3, 32, 32),  rng=random.Random(lowerCamelCase					)					).to(lowerCamelCase					)
								lowerCamelCase__		:		Tuple		     =image / 2 + 0.5
								if str(lowerCamelCase					).startswith('''mps'''					):
											lowerCamelCase__		:		Tuple		     =torch.manual_seed(lowerCamelCase					)
								else:
											lowerCamelCase__		:		List[str]		     =torch.Generator(device=lowerCamelCase					).manual_seed(lowerCamelCase					)
								lowerCamelCase__		:		List[Any]		     ={
								    '''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 		snake_case	(	self							:  Union[str, Any]					)-> List[Any]:
								lowerCamelCase__		:		Any		     ='''cpu'''  # ensure determinism for the device-dependent torch.Generator

								lowerCamelCase__		:		Any		     =self.get_dummy_components()
								lowerCamelCase__		:		Tuple		     =CycleDiffusionPipeline(**lowerCamelCase					)
								lowerCamelCase__		:		Tuple		     =pipe.to(lowerCamelCase					)
								pipe.set_progress_bar_config(disable=lowerCamelCase					)

								lowerCamelCase__		:		Optional[Any]		     =self.get_dummy_inputs(lowerCamelCase					)
								lowerCamelCase__		:		Any		     =pipe(**lowerCamelCase					)
								lowerCamelCase__		:		str		     =output.images

								lowerCamelCase__		:		List[Any]		     =images[0, -3:, -3:, -1]

								assert images.shape == (1, 32, 32, 3)
								lowerCamelCase__		:		Optional[int]		     =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 		snake_case	(	self							:  int					)-> Optional[Any]:
								lowerCamelCase__		:		Any		     =self.get_dummy_components()
								for name, module in components.items():
											if hasattr(lowerCamelCase,  '''half'''					):
														lowerCamelCase__		:		Any		     =module.half()
								lowerCamelCase__		:		Any		     =CycleDiffusionPipeline(**lowerCamelCase					)
								lowerCamelCase__		:		Any		     =pipe.to(lowerCamelCase					)
								pipe.set_progress_bar_config(disable=lowerCamelCase					)

								lowerCamelCase__		:		Dict		     =self.get_dummy_inputs(lowerCamelCase					)
								lowerCamelCase__		:		Dict		     =pipe(**lowerCamelCase					)
								lowerCamelCase__		:		List[Any]		     =output.images

								lowerCamelCase__		:		List[Any]		     =images[0, -3:, -3:, -1]

								assert images.shape == (1, 32, 32, 3)
								lowerCamelCase__		:		Optional[int]		     =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 		snake_case	(	self							:  List[str]					)-> Any:
								return super().test_save_load_local()



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



					@skip_mps
					def 		snake_case	(	self							:  Dict					)-> List[str]:
								return super().test_dict_tuple_outputs_equivalent()



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




					@skip_mps
					def 		snake_case	(	self							:  Tuple					)-> Any:
								return super().test_attention_slicing_forward_pass()





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


					'''simple docstring'''





					def 		snake_case	(	self							:  Union[str, Any]					)-> int:
								# clean up the VRAM after each test
								super().tearDown()
								gc.collect()
								torch.cuda.empty_cache()



					def 		snake_case	(	self							:  int					)-> Tuple:
								lowerCamelCase__		:		Optional[Any]		     =load_image(
								    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
								    '''/cycle-diffusion/black_colored_car.png'''					)
								lowerCamelCase__		:		int		     =load_numpy(
								    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car_fp16.npy'''					)
								lowerCamelCase__		:		int		     =init_image.resize((512, 512)					)

								lowerCamelCase__		:		Union[str, Any]		     ='''CompVis/stable-diffusion-v1-4'''
								lowerCamelCase__		:		Tuple		     =DDIMScheduler.from_pretrained(lowerCamelCase,  subfolder='''scheduler'''					)
								lowerCamelCase__		:		str		     =CycleDiffusionPipeline.from_pretrained(
								    lowerCamelCase,  scheduler=lowerCamelCase,  safety_checker=lowerCamelCase,  torch_dtype=torch.floataa,  revision='''fp16'''					)

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

								lowerCamelCase__		:		Dict		     ='''A black colored car'''
								lowerCamelCase__		:		int		     ='''A blue colored car'''

								lowerCamelCase__		:		Optional[Any]		     =torch.manual_seed(0					)
								lowerCamelCase__		:		Union[str, Any]		     =pipe(
								    prompt=lowerCamelCase,  source_prompt=lowerCamelCase,  image=lowerCamelCase,  num_inference_steps=100,  eta=0.1,  strength=0.85,  guidance_scale=3,  source_guidance_scale=1,  generator=lowerCamelCase,  output_type='''np''',  )
								lowerCamelCase__		:		List[str]		     =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 		snake_case	(	self							:  Union[str, Any]					)-> str:
								lowerCamelCase__		:		List[str]		     =load_image(
								    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
								    '''/cycle-diffusion/black_colored_car.png'''					)
								lowerCamelCase__		:		Any		     =load_numpy(
								    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car.npy'''					)
								lowerCamelCase__		:		Union[str, Any]		     =init_image.resize((512, 512)					)

								lowerCamelCase__		:		List[str]		     ='''CompVis/stable-diffusion-v1-4'''
								lowerCamelCase__		:		List[str]		     =DDIMScheduler.from_pretrained(lowerCamelCase,  subfolder='''scheduler'''					)
								lowerCamelCase__		:		Optional[int]		     =CycleDiffusionPipeline.from_pretrained(lowerCamelCase,  scheduler=lowerCamelCase,  safety_checker=lowerCamelCase					)

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

								lowerCamelCase__		:		str		     ='''A black colored car'''
								lowerCamelCase__		:		Optional[int]		     ='''A blue colored car'''

								lowerCamelCase__		:		Optional[int]		     =torch.manual_seed(0					)
								lowerCamelCase__		:		Tuple		     =pipe(
								    prompt=lowerCamelCase,  source_prompt=lowerCamelCase,  image=lowerCamelCase,  num_inference_steps=100,  eta=0.1,  strength=0.85,  guidance_scale=3,  source_guidance_scale=1,  generator=lowerCamelCase,  output_type='''np''',  )
								lowerCamelCase__		:		List[Any]		     =output.images

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









"""simple docstring"""






from collections import defaultdict

class 	__SCREAMING_SNAKE_CASE     :


					'''simple docstring'''





					def __init__(	self							:  Union[str, Any],  lowerCamelCase							:  List[Any],  lowerCamelCase							:  List[str]					)-> Optional[int]:
								lowerCamelCase__		:		List[Any]		     =total  # total no of tasks (N)

								# DP table will have a dimension of (2^M)*N
								# initially all values are set to -1
								lowerCamelCase__		:		Optional[Any]		     =[
								    [-1 for i in range(total + 1					)] for j in range(2 ** len(lowerCamelCase					)					)
								]

								lowerCamelCase__		:		Any		     =defaultdict(lowerCamelCase					)  # stores the list of persons for each task

								# final_mask is used to check if all persons are included by setting all bits
								# to 1
								lowerCamelCase__		:		List[Any]		     =(1 << len(lowerCamelCase					)) - 1



					def 		snake_case	(	self							:  int,  lowerCamelCase							:  str,  lowerCamelCase							:  Any					)-> Any:
								# if mask == self.finalmask all persons are distributed tasks, return 1
								if mask == self.final_mask:
											return 1

								# if not everyone gets the task and no more tasks are available, return 0
								if task_no > self.total_tasks:
											return 0

								# if case already considered
								if self.dp[mask][task_no] != -1:
											return self.dp[mask][task_no]

								# Number of ways when we don't this task in the arrangement
								lowerCamelCase__		:		Optional[int]		     =self.count_ways_until(lowerCamelCase,  task_no + 1					)

								# now assign the tasks one by one to all possible persons and recursively
								# assign for the remaining tasks.
								if task_no in self.task:
											for p in self.task[task_no]:
														# if p is already given a task
														if mask & (1 << p):
																	continue

														# assign this task to p and change the mask value. And recursively
														# assign tasks with the new mask value.
														total_ways_util += self.count_ways_until(mask | (1 << p),  task_no + 1					)

        # save the value.
								lowerCamelCase__		:		int		     =total_ways_util

								return self.dp[mask][task_no]




					def 		snake_case	(	self							:  Dict,  lowerCamelCase							:  Dict					)-> int:
								# Store the list of persons for each task
								for i in range(len(lowerCamelCase					)					):
											for j in task_performed[i]:
														self.task[j].append(lowerCamelCase					)

        # call the function to fill the DP table, final answer is stored in dp[0][1]
								return self.count_ways_until(0,  1					)


if __name__ == "__main__":
		_lowercase			:			Tuple										=						5  # total no of tasks (the value of N)

		# the list of tasks that can be done by M persons.
		_lowercase			:			Dict										=						[[1, 3, 4], [1, 2, 5], [3, 4]]
		print(
		    AssignmentUsingBitmask(task_performed, total_tasks).count_no_of_ways(
		        task_performed
		    )
		)




