





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 			lowerCAmelCase__:






       '''simple docstring'''


       @property
       def     UpperCamelCase_			(      self							)			->     Dict:
            return self.get_dummy_input()




       @property
       def     UpperCamelCase_			(      self							)			->     int:
            if self.block_type == "down":
                 return (4, 3_2, 1_6, 1_6)
            elif self.block_type == "mid":
                 return (4, 3_2, 3_2, 3_2)
            elif self.block_type == "up":
                 return (4, 3_2, 6_4, 6_4)

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




       def     UpperCamelCase_			(      self    ,						__lowerCamelCase=True    ,						__lowerCamelCase=False    ,						__lowerCamelCase=False    ,						__lowerCamelCase=False    ,						)			->     Any:
            _SCREAMING_SNAKE_CASE       :	List[Any]						    =							4
            _SCREAMING_SNAKE_CASE       :	Dict						    =							3_2
            _SCREAMING_SNAKE_CASE       :	Optional[int]						    =							(3_2, 3_2)

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

            if include_temb:
                 _SCREAMING_SNAKE_CASE       :	List[Any]						    =							1_2_8
                 _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							randn_tensor((batch_size, temb_channels)    ,						generator=__lowerCamelCase    ,						device=__lowerCamelCase							)

            if include_res_hidden_states_tuple:
                 _SCREAMING_SNAKE_CASE       :	List[str]						    =							torch.manual_seed(1							)
                 _SCREAMING_SNAKE_CASE       :	List[str]						    =							(randn_tensor(__lowerCamelCase    ,						generator=__lowerCamelCase    ,						device=__lowerCamelCase							),)

            if include_encoder_hidden_states:
                 _SCREAMING_SNAKE_CASE       :	Any						    =							floats_tensor((batch_size, 3_2, 3_2)							).to(__lowerCamelCase							)

            if include_skip_sample:
                 _SCREAMING_SNAKE_CASE       :	List[str]						    =							randn_tensor(((batch_size, 3) + sizes)    ,						generator=__lowerCamelCase    ,						device=__lowerCamelCase							)

            return dummy_input




       def     UpperCamelCase_			(      self							)			->     Union[str, Any]:
            _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							{
                "in_channels": 3_2,
                "out_channels": 3_2,
                "temb_channels": 1_2_8,
            }
            if self.block_type == "up":
                 _SCREAMING_SNAKE_CASE       :	List[Any]						    =							3_2

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

            _SCREAMING_SNAKE_CASE       :	int						    =							self.dummy_input
            return init_dict, inputs_dict




       def     UpperCamelCase_			(      self    ,						__lowerCamelCase							)			->     str:
            _SCREAMING_SNAKE_CASE    ,			_SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							self.prepare_init_args_and_inputs_for_common()
            _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							self.block_class(**__lowerCamelCase							)
            unet_block.to(__lowerCamelCase							)
            unet_block.eval()

            with torch.no_grad():
                 _SCREAMING_SNAKE_CASE       :	List[str]						    =							unet_block(**__lowerCamelCase							)

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

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

            _SCREAMING_SNAKE_CASE       :	str						    =							output[0, -1, -3:, -3:]
            _SCREAMING_SNAKE_CASE       :	List[str]						    =							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     UpperCamelCase_			(      self							)			->     Dict:
            _SCREAMING_SNAKE_CASE    ,			_SCREAMING_SNAKE_CASE       :	Any						    =							self.prepare_init_args_and_inputs_for_common()
            _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							self.block_class(**__lowerCamelCase							)
            model.to(__lowerCamelCase							)
            model.train()
            _SCREAMING_SNAKE_CASE       :	Dict						    =							model(**__lowerCamelCase							)

            if isinstance(__lowerCamelCase    ,						__lowerCamelCase							):
                 _SCREAMING_SNAKE_CASE       :	str						    =							output[0]

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






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCamelCase__      ={
    'configuration_lilt': ['LILT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'LiltConfig'],
}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       UpperCamelCase__      =[
           'LILT_PRETRAINED_MODEL_ARCHIVE_LIST',
           'LiltForQuestionAnswering',
           'LiltForSequenceClassification',
           'LiltForTokenClassification',
           'LiltModel',
           'LiltPreTrainedModel',
       ]

if TYPE_CHECKING:
       from .configuration_lilt import LILT_PRETRAINED_CONFIG_ARCHIVE_MAP, LiltConfig

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_lilt import (
                  LILT_PRETRAINED_MODEL_ARCHIVE_LIST,
                  LiltForQuestionAnswering,
                  LiltForSequenceClassification,
                  LiltForTokenClassification,
                  LiltModel,
                  LiltPreTrainedModel,
              )

else:
       import sys

       UpperCamelCase__      =_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)