'''simple docstring'''




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 	lowerCamelCase_	(unittest.TestCase    ):
      '''simple docstring'''

      @property
      def      _A	(       self						:				List[str]       ):
             torch.manual_seed(0       )
             _UpperCAmelCase     :  List[Any]							      =    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						:				int       ):
             _UpperCAmelCase     :  Optional[Any]							      =    self.dummy_uncond_unet
             _UpperCAmelCase     :  Any							      =    ScoreSdeVeScheduler()

             _UpperCAmelCase     :  str							      =    ScoreSdeVePipeline(unet=A						,					scheduler=A       )
             sde_ve.to(A       )
             sde_ve.set_progress_bar_config(disable=A       )

             _UpperCAmelCase     :  List[str]							      =    torch.manual_seed(0       )
             _UpperCAmelCase     :  List[str]							      =    sde_ve(num_inference_steps=2						,					output_type="numpy"						,					generator=A       ).images

             _UpperCAmelCase     :  Optional[int]							      =    torch.manual_seed(0       )
             _UpperCAmelCase     :  List[Any]							      =    sde_ve(num_inference_steps=2						,					output_type="numpy"						,					generator=A						,					return_dict=A       )[
                 0
             ]

             _UpperCAmelCase     :  Tuple							      =    image[0, -3:, -3:, -1]
             _UpperCAmelCase     :  Optional[Any]							      =    image_from_tuple[0, -3:, -3:, -1]

             assert image.shape == (1, 32, 32, 3)
             _UpperCAmelCase     :  List[Any]							      =    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 	lowerCamelCase_	(unittest.TestCase    ):
      '''simple docstring'''

      def      _A	(       self						:				int       ):
             _UpperCAmelCase     :  int							      =    "google/ncsnpp-church-256"
             _UpperCAmelCase     :  Any							      =    UNetaDModel.from_pretrained(A       )

             _UpperCAmelCase     :  List[Any]							      =    ScoreSdeVeScheduler.from_pretrained(A       )

             _UpperCAmelCase     :  Union[str, Any]							      =    ScoreSdeVePipeline(unet=A						,					scheduler=A       )
             sde_ve.to(A       )
             sde_ve.set_progress_bar_config(disable=A       )

             _UpperCAmelCase     :  int							      =    torch.manual_seed(0       )
             _UpperCAmelCase     :  Tuple							      =    sde_ve(num_inference_steps=10						,					output_type="numpy"						,					generator=A       ).images

             _UpperCAmelCase     :  int							      =    image[0, -3:, -3:, -1]

             assert image.shape == (1, 256, 256, 3)

             _UpperCAmelCase     :  int							      =    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






'''simple docstring'''


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_lowerCamelCase	:       Tuple      	=	{"configuration_ibert": ["IBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "IBertConfig", "IBertOnnxConfig"]}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _lowerCamelCase	:       Dict      	=	[
       "IBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
       "IBertForMaskedLM",
       "IBertForMultipleChoice",
       "IBertForQuestionAnswering",
       "IBertForSequenceClassification",
       "IBertForTokenClassification",
       "IBertModel",
       "IBertPreTrainedModel",
   ]

if TYPE_CHECKING:
   from .configuration_ibert import IBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, IBertConfig, IBertOnnxConfig

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_ibert import (
          IBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
          IBertForMaskedLM,
          IBertForMultipleChoice,
          IBertForQuestionAnswering,
          IBertForSequenceClassification,
          IBertForTokenClassification,
          IBertModel,
          IBertPreTrainedModel,
      )

else:
   import sys

   _lowerCamelCase	:       Union[str, Any]      	=	_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

