


'''simple docstring'''






import unittest

import numpy as np
import torch

from diffusers import KarrasVePipeline, KarrasVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()






class        __snake_case	(       unittest.TestCase):







    """simple docstring"""



    @property
    def __lowercase       (       self   :  List[str]		)       ->  int:
      torch.manual_seed(0		)
      lowerCAmelCase_      :				List[str]      =       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   :  int		)       ->  str:
      lowerCAmelCase_      :				Any      =       self.dummy_uncond_unet
      lowerCAmelCase_      :				str      =       KarrasVeScheduler()

      lowerCAmelCase_      :				List[str]      =       KarrasVePipeline(unet=lowerCamelCase   ,						scheduler=lowerCamelCase		)
      pipe.to(lowerCamelCase		)
      pipe.set_progress_bar_config(disable=lowerCamelCase		)

      lowerCAmelCase_      :				Optional[Any]      =       torch.manual_seed(0		)
      lowerCAmelCase_      :				Optional[Any]      =       pipe(num_inference_steps=2   ,						generator=lowerCamelCase   ,						output_type="""numpy"""		).images

      lowerCAmelCase_      :				Tuple      =       torch.manual_seed(0		)
      lowerCAmelCase_      :				List[Any]      =       pipe(num_inference_steps=2   ,						generator=lowerCamelCase   ,						output_type="""numpy"""   ,						return_dict=lowerCamelCase		)[0]

      lowerCAmelCase_      :				Dict      =       image[0, -3:, -3:, -1]
      lowerCAmelCase_      :				List[str]      =       image_from_tuple[0, -3:, -3:, -1]

      assert image.shape == (1, 32, 32, 3)
      lowerCAmelCase_      :				Tuple      =       np.array([0.0, 1.0, 0.0, 0.0, 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        __snake_case	(       unittest.TestCase):







    """simple docstring"""



    def __lowercase       (       self   :  Optional[int]		)       ->  List[Any]:
      lowerCAmelCase_      :				Optional[Any]      =       """google/ncsnpp-celebahq-256"""
      lowerCAmelCase_      :				str      =       UNetaDModel.from_pretrained(lowerCamelCase		)
      lowerCAmelCase_      :				List[Any]      =       KarrasVeScheduler()

      lowerCAmelCase_      :				Tuple      =       KarrasVePipeline(unet=lowerCamelCase   ,						scheduler=lowerCamelCase		)
      pipe.to(lowerCamelCase		)
      pipe.set_progress_bar_config(disable=lowerCamelCase		)

      lowerCAmelCase_      :				Tuple      =       torch.manual_seed(0		)
      lowerCAmelCase_      :				List[Any]      =       pipe(num_inference_steps=20   ,						generator=lowerCamelCase   ,						output_type="""numpy"""		).images

      lowerCAmelCase_      :				Optional[int]      =       image[0, -3:, -3:, -1]
      assert image.shape == (1, 2_56, 2_56, 3)
      lowerCAmelCase_      :				Union[str, Any]      =       np.array([0.578, 0.5_811, 0.5_924, 0.5_809, 0.587, 0.5_886, 0.5_861, 0.5_802, 0.586]		)

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







'''simple docstring'''






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
    is_vision_available,
)


__A :  List[str]    							=				{
    "configuration_owlvit": [
        "OWLVIT_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "OwlViTConfig",
        "OwlViTOnnxConfig",
        "OwlViTTextConfig",
        "OwlViTVisionConfig",
    ],
    "processing_owlvit": ["OwlViTProcessor"],
}


try:
  if not is_vision_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __A :  Optional[int]    							=				["OwlViTFeatureExtractor"]
  __A :  str    							=				["OwlViTImageProcessor"]

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __A :  Tuple    							=				[
      "OWLVIT_PRETRAINED_MODEL_ARCHIVE_LIST",
      "OwlViTModel",
      "OwlViTPreTrainedModel",
      "OwlViTTextModel",
      "OwlViTVisionModel",
      "OwlViTForObjectDetection",
  ]

if TYPE_CHECKING:
  from .configuration_owlvit import (
      OWLVIT_PRETRAINED_CONFIG_ARCHIVE_MAP,
      OwlViTConfig,
      OwlViTOnnxConfig,
      OwlViTTextConfig,
      OwlViTVisionConfig,
  )
  from .processing_owlvit import OwlViTProcessor

  try:
    if not is_vision_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .feature_extraction_owlvit import OwlViTFeatureExtractor
    from .image_processing_owlvit import OwlViTImageProcessor

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_owlvit import (
        OWLVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
        OwlViTForObjectDetection,
        OwlViTModel,
        OwlViTPreTrainedModel,
        OwlViTTextModel,
        OwlViTVisionModel,
    )

else:
  import sys

  __A :  List[Any]    							=				_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



