
"""simple docstring"""






import unittest
from typing import Tuple

import torch

from diffusers.utils import floats_tensor, randn_tensor, torch_all_close, torch_device
from diffusers.utils.testing_utils import require_torch





@require_torch
class 				a__					:



     @property
     def  lowerCamelCase_						(							self :Optional[int]   ):







      '''simple docstring'''



      return self.get_dummy_input()



     @property
     def  lowerCamelCase_						(							self :Union[str, Any]   ):







      '''simple docstring'''



      if self.block_type == "down":
       return (4, 32, 16, 16)
      elif self.block_type == "mid":
       return (4, 32, 32, 32)
      elif self.block_type == "up":
       return (4, 32, 64, 64)

      raise ValueError(f'''\'{self.block_type}\' is not a supported block_type. Set it to \'up\', \'mid\', or \'down\'.'''   )



     def  lowerCamelCase_						(							self :Any      ,  _lowerCamelCase :Optional[int]=True      ,  _lowerCamelCase :int=False      ,  _lowerCamelCase :str=False      ,  _lowerCamelCase :List[str]=False      ,  ):







      '''simple docstring'''



      UpperCamelCase_     :					Tuple   	=4
      UpperCamelCase_     :					Optional[Any]   	=32
      UpperCamelCase_     :					int   	=(32, 32)

      UpperCamelCase_     :					Dict   	=torch.manual_seed(0   )
      UpperCamelCase_     :					List[str]   	=torch.device(__lowerCamelCase   )
      UpperCamelCase_     :					Dict   	=(batch_size, num_channels) + sizes
      UpperCamelCase_     :					List[Any]   	=randn_tensor(__lowerCamelCase      ,  generator=__lowerCamelCase      ,  device=__lowerCamelCase   )
      UpperCamelCase_     :					Any   	={'hidden_states': hidden_states}

      if include_temb:
       UpperCamelCase_     :					Optional[int]   	=128
       UpperCamelCase_     :					Union[str, Any]   	=randn_tensor((batch_size, temb_channels)      ,  generator=__lowerCamelCase      ,  device=__lowerCamelCase   )

      if include_res_hidden_states_tuple:
       UpperCamelCase_     :					Union[str, Any]   	=torch.manual_seed(1   )
       UpperCamelCase_     :					Dict   	=(randn_tensor(__lowerCamelCase      ,  generator=__lowerCamelCase      ,  device=__lowerCamelCase   ),)

      if include_encoder_hidden_states:
       UpperCamelCase_     :					Any   	=floats_tensor((batch_size, 32, 32)   ).to(__lowerCamelCase   )

      if include_skip_sample:
       UpperCamelCase_     :					Optional[Any]   	=randn_tensor(((batch_size, 3) + sizes)      ,  generator=__lowerCamelCase      ,  device=__lowerCamelCase   )

      return dummy_input



     def  lowerCamelCase_						(							self :List[str]   ):







      '''simple docstring'''



      UpperCamelCase_     :					List[Any]   	={
          'in_channels': 32,
          'out_channels': 32,
          'temb_channels': 128,
      }
      if self.block_type == "up":
       UpperCamelCase_     :					Dict   	=32

      if self.block_type == "mid":
       init_dict.pop('out_channels'   )

      UpperCamelCase_     :					Union[str, Any]   	=self.dummy_input
      return init_dict, inputs_dict



     def  lowerCamelCase_						(							self :Dict      ,  _lowerCamelCase :Dict   ):







      '''simple docstring'''



      UpperCamelCase_	,     UpperCamelCase_     :					Optional[Any]   	=self.prepare_init_args_and_inputs_for_common()
      UpperCamelCase_     :					int   	=self.block_class(**__lowerCamelCase   )
      unet_block.to(__lowerCamelCase   )
      unet_block.eval()

      with torch.no_grad():
       UpperCamelCase_     :					Any   	=unet_block(**__lowerCamelCase   )

      if isinstance(__lowerCamelCase      ,  __lowerCamelCase   ):
       UpperCamelCase_     :					Tuple   	=output[0]

      self.assertEqual(output.shape      ,  self.output_shape   )

      UpperCamelCase_     :					List[str]   	=output[0, -1, -3:, -3:]
      UpperCamelCase_     :					Tuple   	=torch.tensor(__lowerCamelCase   ).to(__lowerCamelCase   )
      assert torch_all_close(output_slice.flatten()      ,  __lowerCamelCase      ,  atol=5E-3   )




     @unittest.skipIf(torch_device == 'mps'      ,  'Training is not supported in mps'   )
     def  lowerCamelCase_						(							self :int   ):







      '''simple docstring'''



      UpperCamelCase_	,     UpperCamelCase_     :					int   	=self.prepare_init_args_and_inputs_for_common()
      UpperCamelCase_     :					List[str]   	=self.block_class(**__lowerCamelCase   )
      model.to(__lowerCamelCase   )
      model.train()
      UpperCamelCase_     :					str   	=model(**__lowerCamelCase   )

      if isinstance(__lowerCamelCase      ,  __lowerCamelCase   ):
       UpperCamelCase_     :					Optional[int]   	=output[0]

      UpperCamelCase_     :					Union[str, Any]   	=torch.device(__lowerCamelCase   )
      UpperCamelCase_     :					str   	=randn_tensor(output.shape      ,  device=__lowerCamelCase   )
      UpperCamelCase_     :					Dict   	=torch.nn.functional.mse_loss(__lowerCamelCase      ,  __lowerCamelCase   )
      loss.backward()






from typing import TYPE_CHECKING

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


__magic_name__  :  Optional[int]					=       {
    """configuration_biogpt""": ["""BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """BioGptConfig"""],
    """tokenization_biogpt""": ["""BioGptTokenizer"""],
}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __magic_name__  :  int					=       [
          """BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """BioGptForCausalLM""",
          """BioGptForTokenClassification""",
          """BioGptForSequenceClassification""",
          """BioGptModel""",
          """BioGptPreTrainedModel""",
      ]


if TYPE_CHECKING:
      from .configuration_biogpt import BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP, BioGptConfig
      from .tokenization_biogpt import BioGptTokenizer

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_biogpt import (
                BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST,
                BioGptForCausalLM,
                BioGptForSequenceClassification,
                BioGptForTokenClassification,
                BioGptModel,
                BioGptPreTrainedModel,
            )


else:
      import sys

      __magic_name__  :  int					=       _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)

