





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 		lowerCAmelCase_    (  __UpperCAmelCase:			Union[str, Any]      )      ->	Dict:
    UpperCamelCase__							:				str          =      [tensor.shape for tensor in tensor_list]
    return all(shape == shapes[0] for shape in shapes[1:]      )






class     lowercase__       (	__lowerCamelCase			,				__lowerCamelCase			,				__lowerCamelCase			,				unittest.TestCase				):

   '''simple docstring'''


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

   a						:							Dict   		=		True




   @property
   def       UpperCamelCase__  (    self							) ->      Dict:






       """simple docstring"""
       UpperCamelCase__							:				List[str]          =      1
       UpperCamelCase__							:				List[Any]          =      4
       UpperCamelCase__							:				List[Any]          =      (16, 16)

       UpperCamelCase__							:				Dict          =      floats_tensor((batch_size, num_channels) + sizes,		rng=random.Random(0							)							).to(__magic_name__							)
       return image




   def       UpperCamelCase__  (    self							) ->      Optional[Any]:






       """simple docstring"""
       torch.manual_seed(0							)
       UpperCamelCase__							:				Optional[Any]          =      UNetaDConditionModel(
           act_fn='''gelu''',		attention_head_dim=8,		norm_num_groups=__magic_name__,		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=__magic_name__,		only_cross_attention=__magic_name__,		out_channels=5,		resnet_time_scale_shift='''scale_shift''',		time_embedding_type='''fourier''',		timestep_post_act='''gelu''',		up_block_types=('''KCrossAttnUpBlock2D''', '''KCrossAttnUpBlock2D''', '''KCrossAttnUpBlock2D''', '''KUpBlock2D'''),		)
       UpperCamelCase__							:				Union[str, 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,		)
       UpperCamelCase__							:				Dict          =      EulerDiscreteScheduler(prediction_type='''sample'''							)
       UpperCamelCase__							:				Union[str, Any]          =      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,		)
       UpperCamelCase__							:				Optional[int]          =      CLIPTextModel(__magic_name__							)
       UpperCamelCase__							:				Union[str, Any]          =      CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''							)

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

       return components




   def       UpperCamelCase__  (    self,		__magic_name__,		__magic_name__=0							) ->      Dict:






       """simple docstring"""
       if str(__magic_name__							).startswith('''mps'''							):
           UpperCamelCase__							:				Optional[int]          =      torch.manual_seed(__magic_name__							)
       else:
           UpperCamelCase__							:				Any          =      torch.Generator(device=__magic_name__							).manual_seed(__magic_name__							)
       UpperCamelCase__							:				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       UpperCamelCase__  (    self							) ->      int:






       """simple docstring"""
       UpperCamelCase__							:				Union[str, Any]          =      '''cpu'''

       UpperCamelCase__							:				List[str]          =      self.get_dummy_components()
       UpperCamelCase__							:				List[str]          =      self.pipeline_class(**__magic_name__							)
       pipe.to(__magic_name__							)
       pipe.set_progress_bar_config(disable=__magic_name__							)

       UpperCamelCase__							:				Any          =      self.get_dummy_inputs(__magic_name__							)
       UpperCamelCase__							:				str          =      pipe(**__magic_name__							).images
       UpperCamelCase__							:				int          =      image[0, -3:, -3:, -1]

       self.assertEqual(image.shape,		(1, 256, 256, 3)							)
       UpperCamelCase__							:				int          =      np.array(
           [0.4722_2412, 0.4192_1633, 0.4471_7434, 0.4687_4192, 0.4258_8258, 0.4615_0726, 0.467_7534, 0.4558_3832, 0.4857_9055]							)
       UpperCamelCase__							:				List[str]          =      np.abs(image_slice.flatten() - expected_slice							).max()
       self.assertLessEqual(__magic_name__,		1E-3							)




   def       UpperCamelCase__  (    self							) ->      int:






       """simple docstring"""
       super().test_attention_slicing_forward_pass(expected_max_diff=7E-3							)




   def       UpperCamelCase__  (    self							) ->      Tuple:






       """simple docstring"""
       super().test_cpu_offload_forward_pass(expected_max_diff=3E-3							)




   def       UpperCamelCase__  (    self							) ->      Optional[Any]:






       """simple docstring"""
       super().test_dict_tuple_outputs_equivalent(expected_max_difference=3E-3							)




   def       UpperCamelCase__  (    self							) ->      Dict:






       """simple docstring"""
       super().test_inference_batch_single_identical(expected_max_diff=7E-3							)




   def       UpperCamelCase__  (    self							) ->      List[str]:






       """simple docstring"""
       super().test_pt_np_pil_outputs_equivalent(expected_max_diff=3E-3							)




   def       UpperCamelCase__  (    self							) ->      Dict:






       """simple docstring"""
       super().test_save_load_local(expected_max_difference=3E-3							)




   def       UpperCamelCase__  (    self							) ->      Any:






       """simple docstring"""
       super().test_save_load_optional_components(expected_max_difference=3E-3							)




   def       UpperCamelCase__  (    self							) ->      Dict:






       """simple docstring"""
       UpperCamelCase__							:				Dict          =      [
           '''DDIMScheduler''',
           '''DDPMScheduler''',
           '''PNDMScheduler''',
           '''HeunDiscreteScheduler''',
           '''EulerAncestralDiscreteScheduler''',
           '''KDPM2DiscreteScheduler''',
           '''KDPM2AncestralDiscreteScheduler''',
           '''DPMSolverSDEScheduler''',
       ]
       UpperCamelCase__							:				Tuple          =      self.get_dummy_components()
       UpperCamelCase__							:				str          =      self.pipeline_class(**__magic_name__							)

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

       pipe.to(__magic_name__							)
       pipe.set_progress_bar_config(disable=__magic_name__							)
       UpperCamelCase__							:				Dict          =      self.get_dummy_inputs(__magic_name__							)
       UpperCamelCase__							:				List[str]          =      2

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

           UpperCamelCase__							:				Any          =      getattr(__magic_name__,		scheduler_enum.name							)
           UpperCamelCase__							:				List[Any]          =      scheduler_cls.from_config(pipe.scheduler.config							)
           UpperCamelCase__							:				str          =      pipe(**__magic_name__							)[0]
           outputs.append(__magic_name__							)

       assert check_same_shape(__magic_name__							)






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

   '''simple docstring'''


   def       UpperCamelCase__  (    self							) ->      Dict:






       """simple docstring"""
       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()




   def       UpperCamelCase__  (    self							) ->      Optional[int]:






       """simple docstring"""
       UpperCamelCase__							:				Optional[int]          =      torch.manual_seed(33							)

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

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

       UpperCamelCase__							:				str          =      '''a photo of an astronaut high resolution, unreal engine, ultra realistic'''

       UpperCamelCase__							:				str          =      pipe(__magic_name__,		generator=__magic_name__,		output_type='''latent'''							).images

       UpperCamelCase__							:				Optional[Any]          =      upscaler(
           prompt=__magic_name__,		image=__magic_name__,		num_inference_steps=20,		guidance_scale=0,		generator=__magic_name__,		output_type='''np''',		).images[0]

       UpperCamelCase__							:				Any          =      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       UpperCamelCase__  (    self							) ->      str:






       """simple docstring"""
       UpperCamelCase__							:				Union[str, Any]          =      torch.manual_seed(33							)

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

       UpperCamelCase__							:				Union[str, Any]          =      '''the temple of fire by Ross Tran and Gerardo Dottori, oil on canvas'''

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

       UpperCamelCase__							:				List[Any]          =      upscaler(
           prompt=__magic_name__,		image=__magic_name__,		num_inference_steps=20,		guidance_scale=0,		generator=__magic_name__,		output_type='''np''',		).images[0]

       UpperCamelCase__							:				List[Any]          =      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 		lowerCAmelCase_    (  __UpperCAmelCase:			int = 100_0000      )      ->	int:
    UpperCamelCase__							:				str          =      limit + 1
    UpperCamelCase__							:				List[str]          =      [0] * limit
    for first_term in range(1		,   __UpperCAmelCase      ):
        for n in range(__UpperCAmelCase		,   __UpperCAmelCase		,   __UpperCAmelCase      ):
            UpperCamelCase__							:				str          =      first_term + n / first_term
            if common_difference % 4:  # d must be divisble by 4
                continue
            else:
                common_difference /= 4
                if (
                    first_term > common_difference
                    and first_term < 4 * common_difference
                ):  # since x,y,z are positive integers
                    frequency[n] += 1  # so z>0 and a>d ,also 4d<a

    UpperCamelCase__							:				Any          =      sum(1 for x in frequency[1:limit] if x == 10      )

    return count


if __name__ == "__main__":
  print(F'''{solution() = }''')

