





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 					lowercase__    (					lowercase_			)					->		Any:




      """simple docstring"""

      _UpperCamelCase      :	Optional[int]						=       [tensor.shape for tensor in tensor_list]
      return all(shape == shapes[0] for shape in shapes[1:]			)



class      __SCREAMING_SNAKE_CASE    (						_UpperCamelCase		,  _UpperCamelCase		,  _UpperCamelCase		,  unittest.TestCase   ):







  '''simple docstring'''


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

  SCREAMING_SNAKE_CASE__							:Tuple	     =					True

  @property
  def    __SCREAMING_SNAKE_CASE				(		self   :       Dict	)		->				Tuple:
        _UpperCamelCase      :	Union[str, Any]						=       1
        _UpperCamelCase      :	Union[str, Any]						=       4
        _UpperCamelCase      :	Union[str, Any]						=       (16, 16)

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

  def    __SCREAMING_SNAKE_CASE				(		self   :       Dict	)		->				Any:
        torch.manual_seed(0	)
        _UpperCamelCase      :	List[str]						=       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")					,   )
        _UpperCamelCase      :	int						=       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      :	Optional[Any]						=       EulerDiscreteScheduler(prediction_type="sample"	)
        _UpperCamelCase      :	Dict						=       CLIPTextConfig(
            bos_token_id=0					,   eos_token_id=2					,   hidden_size=32					,   intermediate_size=37					,   layer_norm_eps=1e-0_5					,   num_attention_heads=4					,   num_hidden_layers=5					,   pad_token_id=1					,   vocab_size=1000					,   hidden_act="quick_gelu"					,   projection_dim=512					,   )
        _UpperCamelCase      :	List[str]						=       CLIPTextModel(__a	)
        _UpperCamelCase      :	List[Any]						=       CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"	)

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

        return components

  def    __SCREAMING_SNAKE_CASE				(		self   :       Optional[Any]					,   __a   :       int					,   __a   :       Tuple=0	)		->				Dict:
        if str(__a	).startswith("mps"	):
              _UpperCamelCase      :	Dict						=       torch.manual_seed(__a	)
        else:
              _UpperCamelCase      :	int						=       torch.Generator(device=__a	).manual_seed(__a	)
        _UpperCamelCase      :	Any						=       {
            "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    __SCREAMING_SNAKE_CASE				(		self   :       List[str]	)		->				int:
        _UpperCamelCase      :	Tuple						=       "cpu"

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

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

        self.assertEqual(image.shape					,   (1, 256, 256, 3)	)
        _UpperCamelCase      :	Optional[int]						=       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]	)
        _UpperCamelCase      :	Optional[Any]						=       np.abs(image_slice.flatten() - expected_slice	).max()
        self.assertLessEqual(__a					,   1e-3	)

  def    __SCREAMING_SNAKE_CASE				(		self   :       Any	)		->				Dict:
        super().test_attention_slicing_forward_pass(expected_max_diff=7e-3	)

  def    __SCREAMING_SNAKE_CASE				(		self   :       Optional[Any]	)		->				Union[str, Any]:
        super().test_cpu_offload_forward_pass(expected_max_diff=3e-3	)

  def    __SCREAMING_SNAKE_CASE				(		self   :       Any	)		->				str:
        super().test_dict_tuple_outputs_equivalent(expected_max_difference=3e-3	)

  def    __SCREAMING_SNAKE_CASE				(		self   :       List[Any]	)		->				Optional[Any]:
        super().test_inference_batch_single_identical(expected_max_diff=7e-3	)

  def    __SCREAMING_SNAKE_CASE				(		self   :       Dict	)		->				List[Any]:
        super().test_pt_np_pil_outputs_equivalent(expected_max_diff=3e-3	)

  def    __SCREAMING_SNAKE_CASE				(		self   :       Tuple	)		->				Dict:
        super().test_save_load_local(expected_max_difference=3e-3	)

  def    __SCREAMING_SNAKE_CASE				(		self   :       str	)		->				Dict:
        super().test_save_load_optional_components(expected_max_difference=3e-3	)


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

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

              _UpperCamelCase      :	Tuple						=       getattr(__a					,   scheduler_enum.name	)
              _UpperCamelCase      :	Optional[int]						=       scheduler_cls.from_config(pipe.scheduler.config	)
              _UpperCamelCase      :	List[str]						=       pipe(**__a	)[0]
              outputs.append(__a	)

        assert check_same_shape(__a	)





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







  '''simple docstring'''


  def    __SCREAMING_SNAKE_CASE				(		self   :       Optional[Any]	)		->				Optional[int]:
        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()

  def    __SCREAMING_SNAKE_CASE				(		self   :       int	)		->				List[Any]:
        _UpperCamelCase      :	List[Any]						=       torch.manual_seed(33	)

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

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

        _UpperCamelCase      :	Any						=       "a photo of an astronaut high resolution, unreal engine, ultra realistic"

        _UpperCamelCase      :	Optional[int]						=       pipe(__a					,   generator=__a					,   output_type="latent"	).images

        _UpperCamelCase      :	Any						=       upscaler(
            prompt=__a					,   image=__a					,   num_inference_steps=20					,   guidance_scale=0					,   generator=__a					,   output_type="np"					,   ).images[0]

        _UpperCamelCase      :	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    __SCREAMING_SNAKE_CASE				(		self   :       Any	)		->				Optional[Any]:
        _UpperCamelCase      :	int						=       torch.manual_seed(33	)

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

        _UpperCamelCase      :	Optional[int]						=       "the temple of fire by Ross Tran and Gerardo Dottori, oil on canvas"

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

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

        _UpperCamelCase      :	Optional[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







"""simple docstring"""


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


lowerCamelCase__				  =   {
    "configuration_mask2former": [
        "MASK2FORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "Mask2FormerConfig",
    ],
}

try:
  if not is_vision_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowerCamelCase__				  =   ["Mask2FormerImageProcessor"]

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowerCamelCase__				  =   [
      "MASK2FORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
      "Mask2FormerForUniversalSegmentation",
      "Mask2FormerModel",
      "Mask2FormerPreTrainedModel",
  ]

if TYPE_CHECKING:
  from .configuration_maskaformer import MASK2FORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, MaskaFormerConfig

  try:
    if not is_vision_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .image_processing_maskaformer import MaskaFormerImageProcessor

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_maskaformer import (
        MASK2FORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
        MaskaFormerForUniversalSegmentation,
        MaskaFormerModel,
        MaskaFormerPreTrainedModel,
    )


else:
  import sys

  lowerCamelCase__				  =   _LazyModule(__name__, globals()["__file__"], _import_structure)
