


'''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 ):






      @property
      def lowercase_				(   self							:       List[str]					):


          '''simple docstring'''


          torch.manual_seed(0					)
          UpperCAmelCase__     :						Tuple						=							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							:       List[str]					):


          '''simple docstring'''


          UpperCAmelCase__     :						Any						=							self.dummy_uncond_unet
          UpperCAmelCase__     :						str						=							ScoreSdeVeScheduler()

          UpperCAmelCase__     :						Tuple						=							ScoreSdeVePipeline(unet=_A		,		scheduler=_A					)
          sde_ve.to(_A					)
          sde_ve.set_progress_bar_config(disable=_A					)

          UpperCAmelCase__     :						int						=							torch.manual_seed(0					)
          UpperCAmelCase__     :						Any						=							sde_ve(num_inference_steps=2		,		output_type='''numpy'''		,		generator=_A					).images

          UpperCAmelCase__     :						Tuple						=							torch.manual_seed(0					)
          UpperCAmelCase__     :						Dict						=							sde_ve(num_inference_steps=2		,		output_type='''numpy'''		,		generator=_A		,		return_dict=_A					)[
              0
          ]

          UpperCAmelCase__     :						Optional[Any]						=							image[0, -3:, -3:, -1]
          UpperCAmelCase__     :						List[Any]						=							image_from_tuple[0, -3:, -3:, -1]

          assert image.shape == (1, 32, 32, 3)
          UpperCAmelCase__     :						Union[str, 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 ):






      def lowercase_				(   self							:       Optional[int]					):


          '''simple docstring'''


          UpperCAmelCase__     :						Optional[int]						=							'''google/ncsnpp-church-256'''
          UpperCAmelCase__     :						Dict						=							UNetaDModel.from_pretrained(_A					)

          UpperCAmelCase__     :						Optional[Any]						=							ScoreSdeVeScheduler.from_pretrained(_A					)

          UpperCAmelCase__     :						int						=							ScoreSdeVePipeline(unet=_A		,		scheduler=_A					)
          sde_ve.to(_A					)
          sde_ve.set_progress_bar_config(disable=_A					)

          UpperCAmelCase__     :						Any						=							torch.manual_seed(0					)
          UpperCAmelCase__     :						int						=							sde_ve(num_inference_steps=10		,		output_type='''numpy'''		,		generator=_A					).images

          UpperCAmelCase__     :						Any						=							image[0, -3:, -3:, -1]

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

          UpperCAmelCase__     :						List[str]						=							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

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


_lowerCamelCase      :					Tuple					    =      {
    '''configuration_squeezebert''': [
        '''SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''SqueezeBertConfig''',
        '''SqueezeBertOnnxConfig''',
    ],
    '''tokenization_squeezebert''': ['''SqueezeBertTokenizer'''],
}

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _lowerCamelCase      :					List[str]					    =      ['''SqueezeBertTokenizerFast''']

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _lowerCamelCase      :					Tuple					    =      [
       '''SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''SqueezeBertForMaskedLM''',
       '''SqueezeBertForMultipleChoice''',
       '''SqueezeBertForQuestionAnswering''',
       '''SqueezeBertForSequenceClassification''',
       '''SqueezeBertForTokenClassification''',
       '''SqueezeBertModel''',
       '''SqueezeBertModule''',
       '''SqueezeBertPreTrainedModel''',
   ]


if TYPE_CHECKING:
   from .configuration_squeezebert import (
       SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
       SqueezeBertConfig,
       SqueezeBertOnnxConfig,
   )
   from .tokenization_squeezebert import SqueezeBertTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_squeezebert_fast import SqueezeBertTokenizerFast

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_squeezebert import (
          SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
          SqueezeBertForMaskedLM,
          SqueezeBertForMultipleChoice,
          SqueezeBertForQuestionAnswering,
          SqueezeBertForSequenceClassification,
          SqueezeBertForTokenClassification,
          SqueezeBertModel,
          SqueezeBertModule,
          SqueezeBertPreTrainedModel,
      )

else:
   import sys

   _lowerCamelCase      :					List[str]					    =      _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)


