




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      (      _SCREAMING_SNAKE_CASE    )	-> Optional[Any]:
       lowerCamelCase   :      Tuple			 =					[tensor.shape for tensor in tensor_list]
       return all(shape == shapes[0] for shape in shapes[1:]    )




class 		UpperCamelCase__	(lowerCAmelCase__    ,				lowerCAmelCase__    ,				lowerCAmelCase__    ,				unittest.TestCase     ):

    '''simple docstring'''



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

    lowerCamelCase_      :	Tuple	     =      True







    @property
    def     _lowercase     (    self     )     ->      List[Any]:
           lowerCamelCase   :      Optional[int]			 =					1
           lowerCamelCase   :      List[str]			 =					4
           lowerCamelCase   :      List[str]			 =					(16, 16)

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







    def     _lowercase     (    self     )     ->      List[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   :      Tuple			 =					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   :      int			 =					EulerDiscreteScheduler(prediction_type="sample"     )
           lowerCamelCase   :      List[str]			 =					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   :      List[str]			 =					CLIPTextModel(UpperCamelCase__     )
           lowerCamelCase   :      List[str]			 =					CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"     )

           lowerCamelCase   :      Optional[Any]			 =					{
               "unet": model.eval(),
               "vae": vae.eval(),
               "scheduler": scheduler,
               "text_encoder": text_encoder,
               "tokenizer": tokenizer,
           }

           return components







    def     _lowercase     (    self     ,  UpperCamelCase__     ,  UpperCamelCase__=0     )     ->      Optional[Any]:
           if str(UpperCamelCase__     ).startswith("mps"     ):
                  lowerCamelCase   :      Any			 =					torch.manual_seed(UpperCamelCase__     )
           else:
                  lowerCamelCase   :      Optional[Any]			 =					torch.Generator(device=UpperCamelCase__     ).manual_seed(UpperCamelCase__     )
           lowerCamelCase   :      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     )     ->      List[Any]:
           lowerCamelCase   :      List[str]			 =					"cpu"

           lowerCamelCase   :      Any			 =					self.get_dummy_components()
           lowerCamelCase   :      Union[str, Any]			 =					self.pipeline_class(**UpperCamelCase__     )
           pipe.to(UpperCamelCase__     )
           pipe.set_progress_bar_config(disable=UpperCamelCase__     )

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

           self.assertEqual(image.shape     ,  (1, 256, 256, 3)     )
           lowerCamelCase   :      str			 =					np.array(
               [0.47222412, 0.41921633, 0.44717434, 0.46874192, 0.42588258, 0.46150726, 0.4677534, 0.45583832, 0.48579055]     )
           lowerCamelCase   :      List[Any]			 =					np.abs(image_slice.flatten() - expected_slice     ).max()
           self.assertLessEqual(UpperCamelCase__     ,  1e-3     )







    def     _lowercase     (    self     )     ->      str:
           super().test_attention_slicing_forward_pass(expected_max_diff=7e-3     )







    def     _lowercase     (    self     )     ->      Any:
           super().test_cpu_offload_forward_pass(expected_max_diff=3e-3     )







    def     _lowercase     (    self     )     ->      Union[str, Any]:
           super().test_dict_tuple_outputs_equivalent(expected_max_difference=3e-3     )







    def     _lowercase     (    self     )     ->      Tuple:
           super().test_inference_batch_single_identical(expected_max_diff=7e-3     )







    def     _lowercase     (    self     )     ->      Dict:
           super().test_pt_np_pil_outputs_equivalent(expected_max_diff=3e-3     )







    def     _lowercase     (    self     )     ->      Optional[int]:
           super().test_save_load_local(expected_max_difference=3e-3     )







    def     _lowercase     (    self     )     ->      Any:
           super().test_save_load_optional_components(expected_max_difference=3e-3     )







    def     _lowercase     (    self     )     ->      Dict:
           lowerCamelCase   :      List[str]			 =					[
               "DDIMScheduler",
               "DDPMScheduler",
               "PNDMScheduler",
               "HeunDiscreteScheduler",
               "EulerAncestralDiscreteScheduler",
               "KDPM2DiscreteScheduler",
               "KDPM2AncestralDiscreteScheduler",
               "DPMSolverSDEScheduler",
           ]
           lowerCamelCase   :      Optional[Any]			 =					self.get_dummy_components()
           lowerCamelCase   :      Tuple			 =					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[Any]			 =					self.get_dummy_inputs(UpperCamelCase__     )
           lowerCamelCase   :      Optional[int]			 =					2

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

                  lowerCamelCase   :      int			 =					getattr(UpperCamelCase__     ,  scheduler_enum.name     )
                  lowerCamelCase   :      Optional[Any]			 =					scheduler_cls.from_config(pipe.scheduler.config     )
                  lowerCamelCase   :      Optional[int]			 =					pipe(**UpperCamelCase__     )[0]
                  outputs.append(UpperCamelCase__     )

           assert check_same_shape(UpperCamelCase__     )




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

    '''simple docstring'''



    def     _lowercase     (    self     )     ->      List[Any]:
           super().tearDown()
           gc.collect()
           torch.cuda.empty_cache()







    def     _lowercase     (    self     )     ->      str:
           lowerCamelCase   :      int			 =					torch.manual_seed(33     )

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

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

           lowerCamelCase   :      List[Any]			 =					"a photo of an astronaut high resolution, unreal engine, ultra realistic"

           lowerCamelCase   :      int			 =					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   :      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     _lowercase     (    self     )     ->      Optional[int]:
           lowerCamelCase   :      int			 =					torch.manual_seed(33     )

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

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

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

           lowerCamelCase   :      str			 =					upscaler(
               prompt=UpperCamelCase__     ,  image=UpperCamelCase__     ,  num_inference_steps=20     ,  guidance_scale=0     ,  generator=UpperCamelCase__     ,  output_type="np"     ,  ).images[0]

           lowerCamelCase   :      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








import inspect
import warnings
from typing import Any, Dict, Optional, Union

from packaging import version







def 						_snake_case(  *SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__ = None					,	SCREAMING_SNAKE_CASE__=True					,	SCREAMING_SNAKE_CASE__=2				) ->    Optional[Any]:
       from .. import __version__

       lowercase							:     int       	=      take_from
       lowercase							:     Tuple       	=      ()
       if not isinstance(args[0]					,	SCREAMING_SNAKE_CASE__				):
              lowercase							:     Dict       	=      (args,)

       for attribute, version_name, message in args:
              if version.parse(version.parse(SCREAMING_SNAKE_CASE__				).base_version				) >= version.parse(SCREAMING_SNAKE_CASE__				):
                     raise ValueError(
                         f"The deprecation tuple {(attribute, version_name, message)} should be removed since diffusers'"
                         f" version {__version__} is >= {version_name}"				)

              lowercase							:     int       	=      None
              if isinstance(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				) and attribute in deprecated_kwargs:
                     values += (deprecated_kwargs.pop(SCREAMING_SNAKE_CASE__				),)
                     lowercase							:     Union[str, Any]       	=      f"The `{attribute}` argument is deprecated and will be removed in version {version_name}."
              elif hasattr(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				):
                     values += (getattr(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				),)
                     lowercase							:     int       	=      f"The `{attribute}` attribute is deprecated and will be removed in version {version_name}."
              elif deprecated_kwargs is None:
                     lowercase							:     Dict       	=      f"`{attribute}` is deprecated and will be removed in version {version_name}."

              if warning is not None:
                     lowercase							:     Dict       	=      warning + """ """ if standard_warn else """"""
                     warnings.warn(warning + message					,	SCREAMING_SNAKE_CASE__					,	stacklevel=SCREAMING_SNAKE_CASE__				)

       if isinstance(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				) and len(SCREAMING_SNAKE_CASE__				) > 0:
              lowercase							:     str       	=      inspect.getouterframes(inspect.currentframe()				)[1]
              lowercase							:     List[str]       	=      call_frame.filename
              lowercase							:     Tuple       	=      call_frame.lineno
              lowercase							:     List[str]       	=      call_frame.function
              lowercase	,			lowercase							:     Optional[Any]       	=      next(iter(deprecated_kwargs.items()				)				)
              raise TypeError(f"{function} in {filename} line {line_number-1} got an unexpected keyword argument `{key}`"				)

       if len(SCREAMING_SNAKE_CASE__				) == 0:
              return
       elif len(SCREAMING_SNAKE_CASE__				) == 1:
              return values[0]
       return values
