



'''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       __magic_name__		:







							@property
							def 				__lowercase     (				self   :			Optional[Any]						):
									return self.get_dummy_input()







							@property
							def 				__lowercase     (				self   :			Dict						):
									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 				__lowercase     (				self   :			int      ,_UpperCAmelCase   :			int=True      ,_UpperCAmelCase   :			List[str]=False      ,_UpperCAmelCase   :			Optional[Any]=False      ,_UpperCAmelCase   :			int=False      ,):
									_a	: Dict								=				4
									_a	: Union[str, Any]								=				32
									_a	: int								=				(32, 32)

									_a	: Tuple								=				torch.manual_seed(0						)
									_a	: Dict								=				torch.device(_UpperCAmelCase						)
									_a	: str								=				(batch_size, num_channels) + sizes
									_a	: Tuple								=				randn_tensor(_UpperCAmelCase      ,generator=_UpperCAmelCase      ,device=_UpperCAmelCase						)
									_a	: Optional[int]								=				{'hidden_states': hidden_states}

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

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

									if include_encoder_hidden_states:
											_a	: Dict								=				floats_tensor((batch_size, 32, 32)						).to(_UpperCAmelCase						)

									if include_skip_sample:
											_a	: Union[str, Any]								=				randn_tensor(((batch_size, 3) + sizes)      ,generator=_UpperCAmelCase      ,device=_UpperCAmelCase						)

									return dummy_input







							def 				__lowercase     (				self   :			Union[str, Any]						):
									_a	: List[Any]								=				{
									    'in_channels': 32,
									    'out_channels': 32,
									    'temb_channels': 128,
									}
									if self.block_type == "up":
											_a	: Dict								=				32

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

									_a	: Any								=				self.dummy_input
									return init_dict, inputs_dict







							def 				__lowercase     (				self   :			Optional[Any]      ,_UpperCAmelCase   :			Optional[Any]						):
									_a			,   _a	: List[str]								=				self.prepare_init_args_and_inputs_for_common()
									_a	: Dict								=				self.block_class(**_UpperCAmelCase						)
									unet_block.to(_UpperCAmelCase						)
									unet_block.eval()

									with torch.no_grad():
											_a	: Dict								=				unet_block(**_UpperCAmelCase						)

									if isinstance(_UpperCAmelCase      ,_UpperCAmelCase						):
											_a	: List[Any]								=				output[0]

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

									_a	: Dict								=				output[0, -1, -3:, -3:]
									_a	: Optional[Any]								=				torch.tensor(_UpperCAmelCase						).to(_UpperCAmelCase						)
									assert torch_all_close(output_slice.flatten()      ,_UpperCAmelCase      ,atol=5E-3						)







							@unittest.skipIf(torch_device == 'mps'      ,'Training is not supported in mps'						)
							def 				__lowercase     (				self   :			int						):
									_a			,   _a	: Optional[int]								=				self.prepare_init_args_and_inputs_for_common()
									_a	: Optional[Any]								=				self.block_class(**_UpperCAmelCase						)
									model.to(_UpperCAmelCase						)
									model.train()
									_a	: Any								=				model(**_UpperCAmelCase						)

									if isinstance(_UpperCAmelCase      ,_UpperCAmelCase						):
											_a	: str								=				output[0]

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









'''simple docstring'''

from .configuration_bert_masked import MaskedBertConfig
from .modeling_bert_masked import (
    MaskedBertForMultipleChoice,
    MaskedBertForQuestionAnswering,
    MaskedBertForSequenceClassification,
    MaskedBertForTokenClassification,
    MaskedBertModel,
)
from .modules import *




