




import gc
import random
import unittest

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

import diffusers
from diffusers import (
    AutoencoderKL,
    EulerDiscreteScheduler,
    StableDiffusionLatentUpscalePipeline,
    StableDiffusionPipeline,
    UNetaDConditionModel,
)
from diffusers.schedulers import KarrasDiffusionSchedulers
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

from ..pipeline_params import TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()


def 					_a							(					lowerCAmelCase )->	Tuple:
		SCREAMING_SNAKE_CASE_					      =							[tensor.shape for tensor in tensor_list]
		return all(shape == shapes[0] for shape in shapes[1:] )



class    lowercase_		(SCREAMING_SNAKE_CASE__							,	SCREAMING_SNAKE_CASE__							,	SCREAMING_SNAKE_CASE__							,	unittest.TestCase ):
						lowerCAmelCase__			      =StableDiffusionLatentUpscalePipeline
						lowerCAmelCase__			      =TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {
						    "height",
						    "width",
						    "cross_attention_kwargs",
						    "negative_prompt_embeds",
						    "prompt_embeds",
						}
						lowerCAmelCase__			      =PipelineTesterMixin.required_optional_params - {"num_images_per_prompt"}
						lowerCAmelCase__			      =TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
						lowerCAmelCase__			      =frozenset(
						    [] )  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
						lowerCAmelCase__			      =frozenset([] )

						lowerCAmelCase__			      =True



						@property
						def 	__a					(			self	:   Dict  ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_					      =							1
								SCREAMING_SNAKE_CASE_					      =							4
								SCREAMING_SNAKE_CASE_					      =							(16, 16)

								SCREAMING_SNAKE_CASE_					      =							floats_tensor((batch_size, num_channels) + sizes					,							rng=random.Random(0  )  ).to(snake_case__  )
								return image



						def 	__a					(			self	:   Optional[Any]  ):

								"""simple docstring"""

								torch.manual_seed(0  )
								SCREAMING_SNAKE_CASE_					      =							UNetaDConditionModel(
								    act_fn='gelu'					,							attention_head_dim=8					,							norm_num_groups=snake_case__					,							block_out_channels=[32, 32, 64, 64]					,							time_cond_proj_dim=1_60					,							conv_in_kernel=1					,							conv_out_kernel=1					,							cross_attention_dim=32					,							down_block_types=(
								        'KDownBlock2D',
								        'KCrossAttnDownBlock2D',
								        'KCrossAttnDownBlock2D',
								        'KCrossAttnDownBlock2D',
								    )					,							in_channels=8					,							mid_block_type=snake_case__					,							only_cross_attention=snake_case__					,							out_channels=5					,							resnet_time_scale_shift='scale_shift'					,							time_embedding_type='fourier'					,							timestep_post_act='gelu'					,							up_block_types=('KCrossAttnUpBlock2D', 'KCrossAttnUpBlock2D', 'KCrossAttnUpBlock2D', 'KUpBlock2D')					,							)
								SCREAMING_SNAKE_CASE_					      =							AutoencoderKL(
								    block_out_channels=[32, 32, 64, 64]					,							in_channels=3					,							out_channels=3					,							down_block_types=[
								        'DownEncoderBlock2D',
								        'DownEncoderBlock2D',
								        'DownEncoderBlock2D',
								        'DownEncoderBlock2D',
								    ]					,							up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D', 'UpDecoderBlock2D', 'UpDecoderBlock2D']					,							latent_channels=4					,							)
								SCREAMING_SNAKE_CASE_					      =							EulerDiscreteScheduler(prediction_type='sample'  )
								SCREAMING_SNAKE_CASE_					      =							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					,							hidden_act='quick_gelu'					,							projection_dim=5_12					,							)
								SCREAMING_SNAKE_CASE_					      =							CLIPTextModel(snake_case__  )
								SCREAMING_SNAKE_CASE_					      =							CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'  )

								SCREAMING_SNAKE_CASE_					      =							{
								    'unet': model.eval(),
								    'vae': vae.eval(),
								    'scheduler': scheduler,
								    'text_encoder': text_encoder,
								    'tokenizer': tokenizer,
								}

								return components



						def 	__a					(			self	:   int					,							snake_case__	:   int					,							snake_case__	:   List[str]=0  ):

								"""simple docstring"""

								if str(snake_case__  ).startswith('mps'  ):
										SCREAMING_SNAKE_CASE_					      =							torch.manual_seed(snake_case__  )
								else:
										SCREAMING_SNAKE_CASE_					      =							torch.Generator(device=snake_case__  ).manual_seed(snake_case__  )
								SCREAMING_SNAKE_CASE_					      =							{
								    'prompt': 'A painting of a squirrel eating a burger',
								    'image': self.dummy_image.cpu(),
								    'generator': generator,
								    'num_inference_steps': 2,
								    'output_type': 'numpy',
								}
								return inputs



						def 	__a					(			self	:   List[str]  ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_					      =							'cpu'

								SCREAMING_SNAKE_CASE_					      =							self.get_dummy_components()
								SCREAMING_SNAKE_CASE_					      =							self.pipeline_class(**snake_case__  )
								pipe.to(snake_case__  )
								pipe.set_progress_bar_config(disable=snake_case__  )

								SCREAMING_SNAKE_CASE_					      =							self.get_dummy_inputs(snake_case__  )
								SCREAMING_SNAKE_CASE_					      =							pipe(**snake_case__  ).images
								SCREAMING_SNAKE_CASE_					      =							image[0, -3:, -3:, -1]

								self.assertEqual(image.shape					,							(1, 2_56, 2_56, 3)  )
								SCREAMING_SNAKE_CASE_					      =							np.array(
								    [0.47_22_24_12, 0.41_92_16_33, 0.44_71_74_34, 0.46_87_41_92, 0.42_58_82_58, 0.46_15_07_26, 0.4_67_75_34, 0.45_58_38_32, 0.48_57_90_55]  )
								SCREAMING_SNAKE_CASE_					      =							np.abs(image_slice.flatten() - expected_slice  ).max()
								self.assertLessEqual(snake_case__					,							1e-3  )



						def 	__a					(			self	:   Optional[int]  ):

								"""simple docstring"""

								super().test_attention_slicing_forward_pass(expected_max_diff=7e-3  )



						def 	__a					(			self	:   List[Any]  ):

								"""simple docstring"""

								super().test_cpu_offload_forward_pass(expected_max_diff=3e-3  )



						def 	__a					(			self	:   Optional[int]  ):

								"""simple docstring"""

								super().test_dict_tuple_outputs_equivalent(expected_max_difference=3e-3  )



						def 	__a					(			self	:   Dict  ):

								"""simple docstring"""

								super().test_inference_batch_single_identical(expected_max_diff=7e-3  )



						def 	__a					(			self	:   Optional[Any]  ):

								"""simple docstring"""

								super().test_pt_np_pil_outputs_equivalent(expected_max_diff=3e-3  )



						def 	__a					(			self	:   Any  ):

								"""simple docstring"""

								super().test_save_load_local(expected_max_difference=3e-3  )



						def 	__a					(			self	:   List[Any]  ):

								"""simple docstring"""

								super().test_save_load_optional_components(expected_max_difference=3e-3  )






						def 	__a					(			self	:   Dict  ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_					      =							[
								    'DDIMScheduler',
								    'DDPMScheduler',
								    'PNDMScheduler',
								    'HeunDiscreteScheduler',
								    'EulerAncestralDiscreteScheduler',
								    'KDPM2DiscreteScheduler',
								    'KDPM2AncestralDiscreteScheduler',
								    'DPMSolverSDEScheduler',
								]
								SCREAMING_SNAKE_CASE_					      =							self.get_dummy_components()
								SCREAMING_SNAKE_CASE_					      =							self.pipeline_class(**snake_case__  )

								# make sure that PNDM does not need warm-up
								pipe.scheduler.register_to_config(skip_prk_steps=snake_case__  )

								pipe.to(snake_case__  )
								pipe.set_progress_bar_config(disable=snake_case__  )
								SCREAMING_SNAKE_CASE_					      =							self.get_dummy_inputs(snake_case__  )
								SCREAMING_SNAKE_CASE_					      =							2

								SCREAMING_SNAKE_CASE_					      =							[]
								for scheduler_enum in KarrasDiffusionSchedulers:
										if scheduler_enum.name in skip_schedulers:
												# no sigma schedulers are not supported
												# no schedulers
												continue

										SCREAMING_SNAKE_CASE_					      =							getattr(snake_case__					,							scheduler_enum.name  )
										SCREAMING_SNAKE_CASE_					      =							scheduler_cls.from_config(pipe.scheduler.config  )
										SCREAMING_SNAKE_CASE_					      =							pipe(**snake_case__  )[0]
										outputs.append(snake_case__  )

								assert check_same_shape(snake_case__  )



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



						def 	__a					(			self	:   Tuple  ):

								"""simple docstring"""

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



						def 	__a					(			self	:   List[Any]  ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_					      =							torch.manual_seed(33  )

								SCREAMING_SNAKE_CASE_					      =							StableDiffusionPipeline.from_pretrained('CompVis/stable-diffusion-v1-4'					,							torch_dtype=torch.floataa  )
								pipe.to('cuda'  )

								SCREAMING_SNAKE_CASE_					      =							StableDiffusionLatentUpscalePipeline.from_pretrained(
								    'stabilityai/sd-x2-latent-upscaler'					,							torch_dtype=torch.floataa  )
								upscaler.to('cuda'  )

								SCREAMING_SNAKE_CASE_					      =							'a photo of an astronaut high resolution, unreal engine, ultra realistic'

								SCREAMING_SNAKE_CASE_					      =							pipe(snake_case__					,							generator=snake_case__					,							output_type='latent'  ).images

								SCREAMING_SNAKE_CASE_					      =							upscaler(
								    prompt=snake_case__					,							image=snake_case__					,							num_inference_steps=20					,							guidance_scale=0					,							generator=snake_case__					,							output_type='np'					,							).images[0]

								SCREAMING_SNAKE_CASE_					      =							load_numpy(
								    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/latent-upscaler/astronaut_1024.npy'  )
								assert np.abs((expected_image - image).mean()  ) < 5e-2






						def 	__a					(			self	:   List[str]  ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_					      =							torch.manual_seed(33  )

								SCREAMING_SNAKE_CASE_					      =							StableDiffusionLatentUpscalePipeline.from_pretrained(
								    'stabilityai/sd-x2-latent-upscaler'					,							torch_dtype=torch.floataa  )
								upscaler.to('cuda'  )

								SCREAMING_SNAKE_CASE_					      =							'the temple of fire by Ross Tran and Gerardo Dottori, oil on canvas'

								SCREAMING_SNAKE_CASE_					      =							load_image(
								    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/latent-upscaler/fire_temple_512.png'  )

								SCREAMING_SNAKE_CASE_					      =							upscaler(
								    prompt=snake_case__					,							image=snake_case__					,							num_inference_steps=20					,							guidance_scale=0					,							generator=snake_case__					,							output_type='np'					,							).images[0]

								SCREAMING_SNAKE_CASE_					      =							load_numpy(
								    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/latent-upscaler/fire_temple_1024.npy'  )
								assert np.abs((expected_image - image).max()  ) < 5e-2





SCREAMING_SNAKE_CASE:     dict[tuple[int, int, int], int]									= {}


def 					_a							(					lowerCAmelCase    ,						lowerCAmelCase    ,						lowerCAmelCase )->	int:

		# if we are absent twice, or late 3 consecutive days,
		# no further prize strings are possible
		if late == 3 or absent == 2:
				return 0

		# if we have no days left, and have not failed any other rules,
		# we have a prize string
		if days == 0:
				return 1

		# No easy solution, so now we need to do the recursive calculation

		# First, check if the combination is already in the cache, and
		# if yes, return the stored value from there since we already
		# know the number of possible prize strings from this point on
		SCREAMING_SNAKE_CASE_					      =							(days, absent, late)
		if key in cache:
				return cache[key]

		# now we calculate the three possible ways that can unfold from
		# this point on, depending on our attendance today

		# 1) if we are late (but not absent), the "absent" counter stays as
		# it is, but the "late" counter increases by one
		SCREAMING_SNAKE_CASE_					      =							_calculate(days - 1    ,						lowerCAmelCase    ,						late + 1 )

		# 2) if we are absent, the "absent" counter increases by 1, and the
		# "late" counter resets to 0
		SCREAMING_SNAKE_CASE_					      =							_calculate(days - 1    ,						absent + 1    ,						0 )

		# 3) if we are on time, this resets the "late" counter and keeps the
		# absent counter
		SCREAMING_SNAKE_CASE_					      =							_calculate(days - 1    ,						lowerCAmelCase    ,						0 )

		SCREAMING_SNAKE_CASE_					      =							state_late + state_absent + state_ontime

		SCREAMING_SNAKE_CASE_					      =							prizestrings
		return prizestrings


def 					_a							(					lowerCAmelCase = 30 )->	int:
		return _calculate(lowerCAmelCase    ,						absent=0    ,						late=0 )


if __name__ == "__main__":
						print(solution())