import unittest

import numpy as np
import torch

from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()







class __snake_case       (		unittest.TestCase	):




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



         """simple docstring"""






         torch.manual_seed(0   )
         SCREAMING_SNAKE_CASE__    							=   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 			__a		(					self    :       Dict   ):



         """simple docstring"""






         SCREAMING_SNAKE_CASE__    							=   self.dummy_uncond_unet
         SCREAMING_SNAKE_CASE__    							=   ScoreSdeVeScheduler()

         SCREAMING_SNAKE_CASE__    							=   ScoreSdeVePipeline(unet=lowercase__ ,			scheduler=lowercase__   )
         sde_ve.to(lowercase__   )
         sde_ve.set_progress_bar_config(disable=lowercase__   )

         SCREAMING_SNAKE_CASE__    							=   torch.manual_seed(0   )
         SCREAMING_SNAKE_CASE__    							=   sde_ve(num_inference_steps=2 ,			output_type="""numpy""" ,			generator=lowercase__   ).images

         SCREAMING_SNAKE_CASE__    							=   torch.manual_seed(0   )
         SCREAMING_SNAKE_CASE__    							=   sde_ve(num_inference_steps=2 ,			output_type="""numpy""" ,			generator=lowercase__ ,			return_dict=lowercase__   )[
             0
         ]

         SCREAMING_SNAKE_CASE__    							=   image[0, -3:, -3:, -1]
         SCREAMING_SNAKE_CASE__    							=   image_from_tuple[0, -3:, -3:, -1]

         assert image.shape == (1, 32, 32, 3)
         SCREAMING_SNAKE_CASE__    							=   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	):




   def 			__a		(					self    :       Optional[Any]   ):



         """simple docstring"""






         SCREAMING_SNAKE_CASE__    							=   "google/ncsnpp-church-256"
         SCREAMING_SNAKE_CASE__    							=   UNetaDModel.from_pretrained(lowercase__   )

         SCREAMING_SNAKE_CASE__    							=   ScoreSdeVeScheduler.from_pretrained(lowercase__   )

         SCREAMING_SNAKE_CASE__    							=   ScoreSdeVePipeline(unet=lowercase__ ,			scheduler=lowercase__   )
         sde_ve.to(lowercase__   )
         sde_ve.set_progress_bar_config(disable=lowercase__   )

         SCREAMING_SNAKE_CASE__    							=   torch.manual_seed(0   )
         SCREAMING_SNAKE_CASE__    							=   sde_ve(num_inference_steps=10 ,			output_type="""numpy""" ,			generator=lowercase__   ).images

         SCREAMING_SNAKE_CASE__    							=   image[0, -3:, -3:, -1]

         assert image.shape == (1, 2_56, 2_56, 3)

         SCREAMING_SNAKE_CASE__    							=   np.array([0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0]   )

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

from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__lowerCamelCase    :				List[Any]							     =	{
    '''configuration_informer''': [
        '''INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''InformerConfig''',
    ],
}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   __lowerCamelCase    :				Union[str, Any]							     =	[
       '''INFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''InformerForPrediction''',
       '''InformerModel''',
       '''InformerPreTrainedModel''',
   ]


if TYPE_CHECKING:
   from .configuration_informer import INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, InformerConfig

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_informer import (
          INFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
          InformerForPrediction,
          InformerModel,
          InformerPreTrainedModel,
      )

else:
   import sys

   __lowerCamelCase    :				Dict							     =	_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
