






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 _UpperCAmelCase   (	a						:	Optional[Any] ):
							snake_case__	  =       [tensor.shape for tensor in tensor_list]
							return all(shape == shapes[0] for shape in shapes[1:] )


class    _lowerCAmelCase			(		UpperCAmelCase__     ,      UpperCAmelCase__     ,      UpperCAmelCase__     ,      unittest.TestCase		):



				"""simple docstring"""



				_lowercase				:   List[Any]      =  StableDiffusionLatentUpscalePipeline
				_lowercase				:   Optional[int]      =  TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {
				    '''height''',
				    '''width''',
				    '''cross_attention_kwargs''',
				    '''negative_prompt_embeds''',
				    '''prompt_embeds''',
				}
				_lowercase				:   Dict      =  PipelineTesterMixin.required_optional_params - {'''num_images_per_prompt'''}
				_lowercase				:   Optional[Any]      =  TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
				_lowercase				:   Dict      =  frozenset(
				    []		)  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
				_lowercase				:   List[Any]      =  frozenset([]		)

				_lowercase				:   Optional[Any]      =  True



				@property
				def 					__magic_name__							(      self    :    Any):






											'''simple docstring'''







											snake_case__	  =       1
											snake_case__	  =       4
											snake_case__	  =       (1_6, 1_6)

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



				def 					__magic_name__							(      self    :    Any):






											'''simple docstring'''







											torch.manual_seed(0)
											snake_case__	  =       UNetaDConditionModel(
											    act_fn="""gelu"""			,		attention_head_dim=8			,		norm_num_groups=__lowerCAmelCase			,		block_out_channels=[3_2, 3_2, 6_4, 6_4]			,		time_cond_proj_dim=1_6_0			,		conv_in_kernel=1			,		conv_out_kernel=1			,		cross_attention_dim=3_2			,		down_block_types=(
											        """KDownBlock2D""",
											        """KCrossAttnDownBlock2D""",
											        """KCrossAttnDownBlock2D""",
											        """KCrossAttnDownBlock2D""",
											    )			,		in_channels=8			,		mid_block_type=__lowerCAmelCase			,		only_cross_attention=__lowerCAmelCase			,		out_channels=5			,		resnet_time_scale_shift="""scale_shift"""			,		time_embedding_type="""fourier"""			,		timestep_post_act="""gelu"""			,		up_block_types=("""KCrossAttnUpBlock2D""", """KCrossAttnUpBlock2D""", """KCrossAttnUpBlock2D""", """KUpBlock2D""")			,		)
											snake_case__	  =       AutoencoderKL(
											    block_out_channels=[3_2, 3_2, 6_4, 6_4]			,		in_channels=3			,		out_channels=3			,		down_block_types=[
											        """DownEncoderBlock2D""",
											        """DownEncoderBlock2D""",
											        """DownEncoderBlock2D""",
											        """DownEncoderBlock2D""",
											    ]			,		up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D""", """UpDecoderBlock2D""", """UpDecoderBlock2D"""]			,		latent_channels=4			,		)
											snake_case__	  =       EulerDiscreteScheduler(prediction_type="""sample""")
											snake_case__	  =       CLIPTextConfig(
											    bos_token_id=0			,		eos_token_id=2			,		hidden_size=3_2			,		intermediate_size=3_7			,		layer_norm_eps=1E-05			,		num_attention_heads=4			,		num_hidden_layers=5			,		pad_token_id=1			,		vocab_size=1_0_0_0			,		hidden_act="""quick_gelu"""			,		projection_dim=5_1_2			,		)
											snake_case__	  =       CLIPTextModel(__lowerCAmelCase)
											snake_case__	  =       CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip""")

											snake_case__	  =       {
											    """unet""": model.eval(),
											    """vae""": vae.eval(),
											    """scheduler""": scheduler,
											    """text_encoder""": text_encoder,
											    """tokenizer""": tokenizer,
											}

											return components



				def 					__magic_name__							(      self    :    Union[str, Any]			,		UpperCamelCase__    :    int			,		UpperCamelCase__    :    Optional[Any]=0):






											'''simple docstring'''







											if str(__lowerCAmelCase).startswith("""mps"""):
																		snake_case__	  =       torch.manual_seed(__lowerCAmelCase)
											else:
																		snake_case__	  =       torch.Generator(device=__lowerCAmelCase).manual_seed(__lowerCAmelCase)
											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 					__magic_name__							(      self    :    Optional[int]):






											'''simple docstring'''







											snake_case__	  =       """cpu"""

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

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

											self.assertEqual(image.shape			,		(1, 2_5_6, 2_5_6, 3))
											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])
											snake_case__	  =       np.abs(image_slice.flatten() - expected_slice).max()
											self.assertLessEqual(__lowerCAmelCase			,		1E-3)



				def 					__magic_name__							(      self    :    Any):






											'''simple docstring'''







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



				def 					__magic_name__							(      self    :    List[str]):






											'''simple docstring'''







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



				def 					__magic_name__							(      self    :    str):






											'''simple docstring'''







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



				def 					__magic_name__							(      self    :    Dict):






											'''simple docstring'''







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



				def 					__magic_name__							(      self    :    Union[str, Any]):






											'''simple docstring'''







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



				def 					__magic_name__							(      self    :    Union[str, Any]):






											'''simple docstring'''







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



				def 					__magic_name__							(      self    :    int):






											'''simple docstring'''







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



				def 					__magic_name__							(      self    :    Optional[Any]):






											'''simple docstring'''







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

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

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

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

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

											assert check_same_shape(__lowerCAmelCase)


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



				"""simple docstring"""



				def 					__magic_name__							(      self    :    Optional[Any]):






											'''simple docstring'''







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



				def 					__magic_name__							(      self    :    Dict):






											'''simple docstring'''







											snake_case__	  =       torch.manual_seed(3_3)

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

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

											snake_case__	  =       """a photo of an astronaut high resolution, unreal engine, ultra realistic"""

											snake_case__	  =       pipe(__lowerCAmelCase			,		generator=__lowerCAmelCase			,		output_type="""latent""").images

											snake_case__	  =       upscaler(
											    prompt=__lowerCAmelCase			,		image=__lowerCAmelCase			,		num_inference_steps=2_0			,		guidance_scale=0			,		generator=__lowerCAmelCase			,		output_type="""np"""			,		).images[0]

											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 					__magic_name__							(      self    :    int):






											'''simple docstring'''







											snake_case__	  =       torch.manual_seed(3_3)

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

											snake_case__	  =       """the temple of fire by Ross Tran and Gerardo Dottori, oil on canvas"""

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

											snake_case__	  =       upscaler(
											    prompt=__lowerCAmelCase			,		image=__lowerCAmelCase			,		num_inference_steps=2_0			,		guidance_scale=0			,		generator=__lowerCAmelCase			,		output_type="""np"""			,		).images[0]

											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








from math import factorial

a__       =     {str(digit): factorial(digit) for digit in range(1_0)}

def _UpperCAmelCase   (	a						:	int ):
							if not isinstance(a  ,					a ):
														raise TypeError("""Parameter number must be int""" )

							if number < 0:
														raise ValueError("""Parameter number must be greater than or equal to 0""" )

							# Converts number in string to iterate on its digits and adds its factorial.
							return sum(DIGIT_FACTORIAL[digit] for digit in str(a ) )

def _UpperCAmelCase   (	a						:	int = 60  ,					a						:	int = 100_0000 ):

							if not isinstance(a  ,					a ) or not isinstance(a  ,					a ):
														raise TypeError("""Parameters chain_length and number_limit must be int""" )

							if chain_length <= 0 or number_limit <= 0:
														raise ValueError(
														    """Parameters chain_length and number_limit must be greater than 0""" )

							# the counter for the chains with the exact desired length
							snake_case__	  =       0
							# the cached sizes of the previous chains
							snake_case__	  =       {}

							for start_chain_element in range(1  ,					a ):
														# The temporary set will contain the elements of the chain
														snake_case__	  =       set()
														snake_case__	  =       0

														# Stop computing the chain when you find a cached size, a repeating item or the
														# length is greater then the desired one.
														snake_case__	  =       start_chain_element
														while (
														    chain_element not in chain_sets_lengths
														    and chain_element not in chain_set
														    and chain_set_length <= chain_length
														):
																					chain_set.add(a )
																					chain_set_length += 1
																					snake_case__	  =       digit_factorial_sum(a )

														if chain_element in chain_sets_lengths:
																					chain_set_length += chain_sets_lengths[chain_element]

														snake_case__	  =       chain_set_length

														# If chain contains the exact amount of elements increase the counter
														if chain_set_length == chain_length:
																					chains_counter += 1

							return chains_counter


if __name__ == "__main__":
	import doctest

	doctest.testmod()
	print(F'''{solution()}''')
