





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 			SCREAMING_SNAKE_CASE (			_UpperCAmelCase					)     ->	Optional[Any]:
    lowerCamelCase__       : Any   					=						[tensor.shape for tensor in tensor_list]
    return all(shape == shapes[0] for shape in shapes[1:]					)

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

 UpperCAmelCase__						=						True
 @property
 def 	A_      ( self    : Any      )     ->      Dict:
     lowerCamelCase__       : Any   					=						1
     lowerCamelCase__       : Optional[int]   					=						4
     lowerCamelCase__       : int   					=						(16, 16)

     lowerCamelCase__       : Any   					=						floats_tensor((batch_size, num_channels) + sizes     ,	rng=random.Random(0      )      ).to(UpperCAmelCase      )
     return image
 def 	A_      ( self    : Dict      )     ->      str:
     torch.manual_seed(0      )
     lowerCamelCase__       : Dict   					=						UNetaDConditionModel(
         act_fn='gelu'     ,	attention_head_dim=8     ,	norm_num_groups=UpperCAmelCase     ,	block_out_channels=[32, 32, 64, 64]     ,	time_cond_proj_dim=160     ,	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=UpperCAmelCase     ,	only_cross_attention=UpperCAmelCase     ,	out_channels=5     ,	resnet_time_scale_shift='scale_shift'     ,	time_embedding_type='fourier'     ,	timestep_post_act='gelu'     ,	up_block_types=('KCrossAttnUpBlock2D', 'KCrossAttnUpBlock2D', 'KCrossAttnUpBlock2D', 'KUpBlock2D')     ,	)
     lowerCamelCase__       : Any   					=						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     ,	)
     lowerCamelCase__       : Dict   					=						EulerDiscreteScheduler(prediction_type='sample'      )
     lowerCamelCase__       : Dict   					=						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     ,	hidden_act='quick_gelu'     ,	projection_dim=512     ,	)
     lowerCamelCase__       : Optional[Any]   					=						CLIPTextModel(UpperCAmelCase      )
     lowerCamelCase__       : Union[str, Any]   					=						CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'      )

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

     return components
 def 	A_      ( self    : Any     ,	UpperCAmelCase    : List[Any]     ,	UpperCAmelCase    : Optional[int]=0      )     ->      Optional[int]:
     if str(UpperCAmelCase      ).startswith('mps'      ):
         lowerCamelCase__       : Union[str, Any]   					=						torch.manual_seed(UpperCAmelCase      )
     else:
         lowerCamelCase__       : Tuple   					=						torch.Generator(device=UpperCAmelCase      ).manual_seed(UpperCAmelCase      )
     lowerCamelCase__       : List[str]   					=						{
         '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    : int      )     ->      Union[str, Any]:
     lowerCamelCase__       : List[Any]   					=						'cpu'

     lowerCamelCase__       : Optional[int]   					=						self.get_dummy_components()
     lowerCamelCase__       : Optional[int]   					=						self.pipeline_class(**UpperCAmelCase      )
     pipe.to(UpperCAmelCase      )
     pipe.set_progress_bar_config(disable=UpperCAmelCase      )

     lowerCamelCase__       : Dict   					=						self.get_dummy_inputs(UpperCAmelCase      )
     lowerCamelCase__       : Any   					=						pipe(**UpperCAmelCase      ).images
     lowerCamelCase__       : Union[str, Any]   					=						image[0, -3:, -3:, -1]

     self.assertEqual(image.shape     ,	(1, 256, 256, 3)      )
     lowerCamelCase__       : List[Any]   					=						np.array(
         [0.4_7_2_2_2_4_1_2, 0.4_1_9_2_1_6_3_3, 0.4_4_7_1_7_4_3_4, 0.4_6_8_7_4_1_9_2, 0.4_2_5_8_8_2_5_8, 0.4_6_1_5_0_7_2_6, 0.4_6_7_7_5_3_4, 0.4_5_5_8_3_8_3_2, 0.4_8_5_7_9_0_5_5]      )
     lowerCamelCase__       : int   					=						np.abs(image_slice.flatten() - expected_slice      ).max()
     self.assertLessEqual(UpperCAmelCase     ,	1e-3      )
 def 	A_      ( self    : List[str]      )     ->      Tuple:
     super().test_attention_slicing_forward_pass(expected_max_diff=7e-3      )
 def 	A_      ( self    : int      )     ->      Union[str, Any]:
     super().test_cpu_offload_forward_pass(expected_max_diff=3e-3      )
 def 	A_      ( self    : Tuple      )     ->      int:
     super().test_dict_tuple_outputs_equivalent(expected_max_difference=3e-3      )
 def 	A_      ( self    : List[Any]      )     ->      List[str]:
     super().test_inference_batch_single_identical(expected_max_diff=7e-3      )
 def 	A_      ( self    : Optional[Any]      )     ->      Dict:
     super().test_pt_np_pil_outputs_equivalent(expected_max_diff=3e-3      )
 def 	A_      ( self    : List[Any]      )     ->      str:
     super().test_save_load_local(expected_max_difference=3e-3      )
 def 	A_      ( self    : List[str]      )     ->      List[str]:
     super().test_save_load_optional_components(expected_max_difference=3e-3      )







 def 	A_      ( self    : List[Any]      )     ->      Optional[Any]:
     lowerCamelCase__       : List[Any]   					=						[
         'DDIMScheduler',
         'DDPMScheduler',
         'PNDMScheduler',
         'HeunDiscreteScheduler',
         'EulerAncestralDiscreteScheduler',
         'KDPM2DiscreteScheduler',
         'KDPM2AncestralDiscreteScheduler',
         'DPMSolverSDEScheduler',
     ]
     lowerCamelCase__       : List[Any]   					=						self.get_dummy_components()
     lowerCamelCase__       : str   					=						self.pipeline_class(**UpperCAmelCase      )

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

     pipe.to(UpperCAmelCase      )
     pipe.set_progress_bar_config(disable=UpperCAmelCase      )
     lowerCamelCase__       : Optional[int]   					=						self.get_dummy_inputs(UpperCAmelCase      )
     lowerCamelCase__       : Dict   					=						2

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

         lowerCamelCase__       : Tuple   					=						getattr(UpperCAmelCase     ,	scheduler_enum.name      )
         lowerCamelCase__       : Any   					=						scheduler_cls.from_config(pipe.scheduler.config      )
         lowerCamelCase__       : List[Any]   					=						pipe(**UpperCAmelCase      )[0]
         outputs.append(UpperCAmelCase      )

     assert check_same_shape(UpperCAmelCase      )


@require_torch_gpu
@slow
class   lowerCAmelCase				(   unittest.TestCase     ):
 def 	A_      ( self    : Dict      )     ->      Optional[Any]:
     super().tearDown()
     gc.collect()
     torch.cuda.empty_cache()
 def 	A_      ( self    : List[Any]      )     ->      Any:
     lowerCamelCase__       : Optional[int]   					=						torch.manual_seed(33      )

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

     lowerCamelCase__       : Optional[int]   					=						StableDiffusionLatentUpscalePipeline.from_pretrained(
         'stabilityai/sd-x2-latent-upscaler'     ,	torch_dtype=torch.floataa      )
     upscaler.to('cuda'      )

     lowerCamelCase__       : Optional[int]   					=						'a photo of an astronaut high resolution, unreal engine, ultra realistic'

     lowerCamelCase__       : List[str]   					=						pipe(UpperCAmelCase     ,	generator=UpperCAmelCase     ,	output_type='latent'      ).images

     lowerCamelCase__       : str   					=						upscaler(
         prompt=UpperCAmelCase     ,	image=UpperCAmelCase     ,	num_inference_steps=20     ,	guidance_scale=0     ,	generator=UpperCAmelCase     ,	output_type='np'     ,	).images[0]

     lowerCamelCase__       : List[str]   					=						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    : Dict      )     ->      int:
     lowerCamelCase__       : Optional[int]   					=						torch.manual_seed(33      )

     lowerCamelCase__       : Union[str, Any]   					=						StableDiffusionLatentUpscalePipeline.from_pretrained(
         'stabilityai/sd-x2-latent-upscaler'     ,	torch_dtype=torch.floataa      )
     upscaler.to('cuda'      )

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

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

     lowerCamelCase__       : Optional[int]   					=						upscaler(
         prompt=UpperCAmelCase     ,	image=UpperCAmelCase     ,	num_inference_steps=20     ,	guidance_scale=0     ,	generator=UpperCAmelCase     ,	output_type='np'     ,	).images[0]

     lowerCamelCase__       : Dict   					=						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












def 			SCREAMING_SNAKE_CASE (			_UpperCAmelCase		,      _UpperCAmelCase					)     ->	Union[str, Any]:
    lowerCamelCase__       : List[Any]   					=						[1]
    for i in range(2		,      _UpperCAmelCase					):
        factorials.append(factorials[-1] * i					)
    assert 0 <= k < factorials[-1] * n, "k out of bounds"

    lowerCamelCase__       : str   					=						[]
    lowerCamelCase__       : Optional[Any]   					=						list(range(_UpperCAmelCase					)					)

    # Find permutation
    while factorials:
        lowerCamelCase__       : List[str]   					=						factorials.pop()
        lowerCamelCase__					,				lowerCamelCase__       : Tuple   					=						divmod(_UpperCAmelCase		,      _UpperCAmelCase					)
        permutation.append(elements[number]					)
        elements.remove(elements[number]					)
    permutation.append(elements[0]					)

    return permutation


if __name__ == "__main__":
    import doctest

    doctest.testmod()





