
'''simple docstring'''







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      :    Dict ):






   """simple docstring"""

   _a				:	Union[str, Any]			=   [tensor.shape for tensor in tensor_list]
   return all(shape == shapes[0] for shape in shapes[1:] )







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





  """simple docstring"""

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

  __UpperCAmelCase				:				Tuple   =					True


  @property
  def   __lowercase		(		self      :     List[Any]       ):


     '''simple docstring'''






     _a				:	Dict			=   1
     _a				:	int			=   4
     _a				:	Union[str, Any]			=   (16, 16)

     _a				:	List[str]			=   floats_tensor((batch_size, num_channels) + sizes							,rng=random.Random(0       )       ).to(_a       )
     return image


  def   __lowercase		(		self      :     List[str]       ):


     '''simple docstring'''






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

     _a				:	List[Any]			=   {
         'unet': model.eval(),
         'vae': vae.eval(),
         'scheduler': scheduler,
         'text_encoder': text_encoder,
         'tokenizer': tokenizer,
     }

     return components


  def   __lowercase		(		self      :     List[str]							,_a      :     int							,_a      :     List[str]=0       ):


     '''simple docstring'''






     if str(_a       ).startswith('mps'       ):
        _a				:	List[Any]			=   torch.manual_seed(_a       )
     else:
        _a				:	Dict			=   torch.Generator(device=_a       ).manual_seed(_a       )
     _a				:	int			=   {
         '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   __lowercase		(		self      :     int       ):


     '''simple docstring'''






     _a				:	str			=   'cpu'

     _a				:	List[Any]			=   self.get_dummy_components()
     _a				:	Any			=   self.pipeline_class(**_a       )
     pipe.to(_a       )
     pipe.set_progress_bar_config(disable=_a       )

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

     self.assertEqual(image.shape							,(1, 256, 256, 3)       )
     _a				:	Union[str, Any]			=   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]       )
     _a				:	int			=   np.abs(image_slice.flatten() - expected_slice       ).max()
     self.assertLessEqual(_a							,1E-3       )


  def   __lowercase		(		self      :     Tuple       ):


     '''simple docstring'''






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


  def   __lowercase		(		self      :     List[Any]       ):


     '''simple docstring'''






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


  def   __lowercase		(		self      :     Optional[Any]       ):


     '''simple docstring'''






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


  def   __lowercase		(		self      :     Optional[Any]       ):


     '''simple docstring'''






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


  def   __lowercase		(		self      :     Optional[Any]       ):


     '''simple docstring'''






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


  def   __lowercase		(		self      :     int       ):


     '''simple docstring'''






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


  def   __lowercase		(		self      :     str       ):


     '''simple docstring'''






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


  def   __lowercase		(		self      :     Optional[Any]       ):


     '''simple docstring'''






     _a				:	List[Any]			=   [
         'DDIMScheduler',
         'DDPMScheduler',
         'PNDMScheduler',
         'HeunDiscreteScheduler',
         'EulerAncestralDiscreteScheduler',
         'KDPM2DiscreteScheduler',
         'KDPM2AncestralDiscreteScheduler',
         'DPMSolverSDEScheduler',
     ]
     _a				:	Optional[int]			=   self.get_dummy_components()
     _a				:	Optional[int]			=   self.pipeline_class(**_a       )

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

     pipe.to(_a       )
     pipe.set_progress_bar_config(disable=_a       )
     _a				:	Union[str, Any]			=   self.get_dummy_inputs(_a       )
     _a				:	Tuple			=   2

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

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

     assert check_same_shape(_a       )







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





  """simple docstring"""

  def   __lowercase		(		self      :     str       ):


     '''simple docstring'''






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


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


     '''simple docstring'''






     _a				:	Any			=   torch.manual_seed(33       )

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

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

     _a				:	Tuple			=   'a photo of an astronaut high resolution, unreal engine, ultra realistic'

     _a				:	Union[str, Any]			=   pipe(_a							,generator=_a							,output_type='latent'       ).images

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

     _a				:	Tuple			=   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   __lowercase		(		self      :     Union[str, Any]       ):


     '''simple docstring'''






     _a				:	Optional[int]			=   torch.manual_seed(33       )

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

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

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

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

     _a				:	Tuple			=   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





'''simple docstring'''







import unittest

import numpy as np
import torch

from diffusers import PNDMPipeline, PNDMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()







class UpperCAmelCase__	( unittest.TestCase	):





  """simple docstring"""

  @property
  def   __lowercase		(		self      :     List[str]       ):


     '''simple docstring'''






     torch.manual_seed(0       )
     _a				:	int			=   UNetaDModel(
         block_out_channels=(32, 64)							,layers_per_block=2							,sample_size=32							,in_channels=3							,out_channels=3							,down_block_types=('DownBlock2D', 'AttnDownBlock2D')							,up_block_types=('AttnUpBlock2D', 'UpBlock2D')							,)
     return model


  def   __lowercase		(		self      :     Optional[Any]       ):


     '''simple docstring'''






     _a				:	str			=   self.dummy_uncond_unet
     _a				:	int			=   PNDMScheduler()

     _a				:	str			=   PNDMPipeline(unet=_a							,scheduler=_a       )
     pndm.to(_a       )
     pndm.set_progress_bar_config(disable=_a       )

     _a				:	Optional[int]			=   torch.manual_seed(0       )
     _a				:	Optional[Any]			=   pndm(generator=_a							,num_inference_steps=20							,output_type='numpy'       ).images

     _a				:	List[str]			=   torch.manual_seed(0       )
     _a				:	Any			=   pndm(generator=_a							,num_inference_steps=20							,output_type='numpy'							,return_dict=_a       )[0]

     _a				:	List[Any]			=   image[0, -3:, -3:, -1]
     _a				:	Any			=   image_from_tuple[0, -3:, -3:, -1]

     assert image.shape == (1, 32, 32, 3)
     _a				:	List[Any]			=   np.array([1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0]       )

     assert np.abs(image_slice.flatten() - expected_slice       ).max() < 1E-2
     assert np.abs(image_from_tuple_slice.flatten() - expected_slice       ).max() < 1E-2







@slow
@require_torch
class UpperCAmelCase__	( unittest.TestCase	):





  """simple docstring"""

  def   __lowercase		(		self      :     Tuple       ):


     '''simple docstring'''






     _a				:	List[str]			=   'google/ddpm-cifar10-32'

     _a				:	str			=   UNetaDModel.from_pretrained(_a       )
     _a				:	Union[str, Any]			=   PNDMScheduler()

     _a				:	Tuple			=   PNDMPipeline(unet=_a							,scheduler=_a       )
     pndm.to(_a       )
     pndm.set_progress_bar_config(disable=_a       )
     _a				:	str			=   torch.manual_seed(0       )
     _a				:	Optional[Any]			=   pndm(generator=_a							,output_type='numpy'       ).images

     _a				:	Dict			=   image[0, -3:, -3:, -1]

     assert image.shape == (1, 32, 32, 3)
     _a				:	Tuple			=   np.array([0.1564, 0.1_4645, 0.1406, 0.1_4715, 0.1_2425, 0.1_4045, 0.1_3115, 0.1_2175, 0.125]       )

     assert np.abs(image_slice.flatten() - expected_slice       ).max() < 1E-2



