




'''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        lowercase_      :





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





     @property
     def 			_lowerCAmelCase      (     self     :					Union[str, Any]			):
           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     :					List[str]     ,							__lowerCamelCase     :					Optional[Any]=True     ,							__lowerCamelCase     :					str=False     ,							__lowerCamelCase     :					Optional[int]=False     ,							__lowerCamelCase     :					Dict=False     ,							):
           snake_case__       :   Union[str, Any]	      =    4
           snake_case__       :   Optional[Any]	      =    32
           snake_case__       :   Tuple	      =    (32, 32)

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

           if include_temb:
                 snake_case__       :   Tuple	      =    128
                 snake_case__       :   int	      =    randn_tensor((batch_size, temb_channels)     ,							generator=_lowerCAmelCase     ,							device=_lowerCAmelCase			)

           if include_res_hidden_states_tuple:
                 snake_case__       :   Any	      =    torch.manual_seed(1			)
                 snake_case__       :   List[str]	      =    (randn_tensor(_lowerCAmelCase     ,							generator=_lowerCAmelCase     ,							device=_lowerCAmelCase			),)

           if include_encoder_hidden_states:
                 snake_case__       :   str	      =    floats_tensor((batch_size, 32, 32)			).to(_lowerCAmelCase			)

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

           return dummy_input





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

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

           snake_case__       :   Dict	      =    self.dummy_input
           return init_dict, inputs_dict





     def 			_lowerCAmelCase      (     self     :					Dict     ,							__lowerCamelCase     :					Optional[Any]			):
           snake_case__      ,							snake_case__       :   Optional[int]	      =    self.prepare_init_args_and_inputs_for_common()
           snake_case__       :   Union[str, Any]	      =    self.block_class(**_lowerCAmelCase			)
           unet_block.to(_lowerCAmelCase			)
           unet_block.eval()

           with torch.no_grad():
                 snake_case__       :   List[str]	      =    unet_block(**_lowerCAmelCase			)

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

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

           snake_case__       :   int	      =    output[0, -1, -3:, -3:]
           snake_case__       :   int	      =    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     :					Any			):
           snake_case__      ,							snake_case__       :   Optional[Any]	      =    self.prepare_init_args_and_inputs_for_common()
           snake_case__       :   int	      =    self.block_class(**_lowerCAmelCase			)
           model.to(_lowerCAmelCase			)
           model.train()
           snake_case__       :   Optional[Any]	      =    model(**_lowerCAmelCase			)

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

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






import warnings

from ...utils import logging
from .image_processing_mobilevit import MobileViTImageProcessor


lowerCamelCase__					:  Optional[int]      							=				logging.get_logger(__name__)





class 			lowerCamelCase_				( _SCREAMING_SNAKE_CASE   ):


 '''simple docstring'''







 def __init__(				self    :						str							,							*_lowerCAmelCase    :						Tuple							,							**_lowerCAmelCase    :						int		):
       warnings.warn(
           'The class MobileViTFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'
           ' Please use MobileViTImageProcessor instead.'							,							_lowerCAmelCase							,							)
       super().__init__(*_lowerCAmelCase							,							**_lowerCAmelCase		)