"""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 					__A						(	self						):
       return self.get_dummy_input()






     @property
     def 					__A						(	self						):
       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 					__A						(	self       ,  a__=True       ,  a__=False       ,  a__=False       ,  a__=False       ,  ):
       _lowerCAmelCase		: Any						    =  4
       _lowerCAmelCase		: Optional[Any]						    =  32
       _lowerCAmelCase		: Optional[Any]						    =  (32, 32)

       _lowerCAmelCase		: List[Any]						    =  torch.manual_seed(0						)
       _lowerCAmelCase		: Tuple						    =  torch.device(a__						)
       _lowerCAmelCase		: Any						    =  (batch_size, num_channels) + sizes
       _lowerCAmelCase		: Any						    =  randn_tensor(a__       ,  generator=a__       ,  device=a__						)
       _lowerCAmelCase		: Union[str, Any]						    =  {"""hidden_states""": hidden_states}

       if include_temb:
         _lowerCAmelCase		: Optional[Any]						    =  128
         _lowerCAmelCase		: Optional[Any]						    =  randn_tensor((batch_size, temb_channels)       ,  generator=a__       ,  device=a__						)

       if include_res_hidden_states_tuple:
         _lowerCAmelCase		: Optional[Any]						    =  torch.manual_seed(1						)
         _lowerCAmelCase		: Optional[int]						    =  (randn_tensor(a__       ,  generator=a__       ,  device=a__						),)

       if include_encoder_hidden_states:
         _lowerCAmelCase		: List[Any]						    =  floats_tensor((batch_size, 32, 32)						).to(a__						)

       if include_skip_sample:
         _lowerCAmelCase		: Optional[int]						    =  randn_tensor(((batch_size, 3) + sizes)       ,  generator=a__       ,  device=a__						)

       return dummy_input






     def 					__A						(	self						):
       _lowerCAmelCase		: Any						    =  {
           """in_channels""": 32,
           """out_channels""": 32,
           """temb_channels""": 128,
       }
       if self.block_type == "up":
         _lowerCAmelCase		: Optional[Any]						    =  32

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

       _lowerCAmelCase		: Optional[int]						    =  self.dummy_input
       return init_dict, inputs_dict






     def 					__A						(	self       ,  a__						):
       _lowerCAmelCase    ,			_lowerCAmelCase		: Optional[int]						    =  self.prepare_init_args_and_inputs_for_common()
       _lowerCAmelCase		: str						    =  self.block_class(**a__						)
       unet_block.to(a__						)
       unet_block.eval()

       with torch.no_grad():
         _lowerCAmelCase		: Any						    =  unet_block(**a__						)

       if isinstance(a__       ,  a__						):
         _lowerCAmelCase		: Tuple						    =  output[0]

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

       _lowerCAmelCase		: List[Any]						    =  output[0, -1, -3:, -3:]
       _lowerCAmelCase		: Any						    =  torch.tensor(a__						).to(a__						)
       assert torch_all_close(output_slice.flatten()       ,  a__       ,  atol=5e-3						)






     @unittest.skipIf(torch_device == """mps"""       ,  """Training is not supported in mps"""						)
     def 					__A						(	self						):
       _lowerCAmelCase    ,			_lowerCAmelCase		: int						    =  self.prepare_init_args_and_inputs_for_common()
       _lowerCAmelCase		: Any						    =  self.block_class(**a__						)
       model.to(a__						)
       model.train()
       _lowerCAmelCase		: List[str]						    =  model(**a__						)

       if isinstance(a__       ,  a__						):
         _lowerCAmelCase		: int						    =  output[0]

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





"""simple docstring"""




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available


_a   :	Union[str, Any] 					=     {}

try:
     if not is_sentencepiece_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _a   :	Tuple 					=     ['GPTSw3Tokenizer']


if TYPE_CHECKING:
     try:
          if not is_sentencepiece_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_gpt_swa import GPTSwaTokenizer

else:
     import sys

     _a   :	Optional[int] 					=     _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




