'''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    :


  """simple docstring"""

  @property
  def 		lowerCamelCase__  (		self      :       int      ):







    '''simple docstring'''


    return self.get_dummy_input()




  @property
  def 		lowerCamelCase__  (		self      :       int      ):







    '''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      :       Tuple   ,				lowercase_      :       Dict=True   ,				lowercase_      :       Union[str, Any]=False   ,				lowercase_      :       List[Any]=False   ,				lowercase_      :       Tuple=False   ,				):







    '''simple docstring'''


    lowercase_     =       4
    lowercase_     =       32
    lowercase_     =       (32, 32)

    lowercase_     =       torch.manual_seed(0      )
    lowercase_     =       torch.device(lowercase_      )
    lowercase_     =       (batch_size, num_channels) + sizes
    lowercase_     =       randn_tensor(lowercase_   ,				generator=lowercase_   ,				device=lowercase_      )
    lowercase_     =       {"""hidden_states""": hidden_states}

    if include_temb:
      lowercase_     =       128
      lowercase_     =       randn_tensor((batch_size, temb_channels)   ,				generator=lowercase_   ,				device=lowercase_      )

    if include_res_hidden_states_tuple:
      lowercase_     =       torch.manual_seed(1      )
      lowercase_     =       (randn_tensor(lowercase_   ,				generator=lowercase_   ,				device=lowercase_      ),)

    if include_encoder_hidden_states:
      lowercase_     =       floats_tensor((batch_size, 32, 32)      ).to(lowercase_      )

    if include_skip_sample:
      lowercase_     =       randn_tensor(((batch_size, 3) + sizes)   ,				generator=lowercase_   ,				device=lowercase_      )

    return dummy_input




  def 		lowerCamelCase__  (		self      :       List[str]      ):







    '''simple docstring'''


    lowercase_     =       {
        """in_channels""": 32,
        """out_channels""": 32,
        """temb_channels""": 128,
    }
    if self.block_type == "up":
      lowercase_     =       32

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

    lowercase_     =       self.dummy_input
    return init_dict, inputs_dict




  def 		lowerCamelCase__  (		self      :       List[str]   ,				lowercase_      :       Optional[int]      ):







    '''simple docstring'''


    lowercase_     ,	lowercase_     =       self.prepare_init_args_and_inputs_for_common()
    lowercase_     =       self.block_class(**lowercase_      )
    unet_block.to(lowercase_      )
    unet_block.eval()

    with torch.no_grad():
      lowercase_     =       unet_block(**lowercase_      )

    if isinstance(lowercase_   ,				lowercase_      ):
      lowercase_     =       output[0]

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

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





  @unittest.skipIf(torch_device == """mps"""   ,				"""Training is not supported in mps"""      )
  def 		lowerCamelCase__  (		self      :       Union[str, Any]      ):







    '''simple docstring'''


    lowercase_     ,	lowercase_     =       self.prepare_init_args_and_inputs_for_common()
    lowercase_     =       self.block_class(**lowercase_      )
    model.to(lowercase_      )
    model.train()
    lowercase_     =       model(**lowercase_      )

    if isinstance(lowercase_   ,				lowercase_      ):
      lowercase_     =       output[0]

    lowercase_     =       torch.device(lowercase_      )
    lowercase_     =       randn_tensor(output.shape   ,				device=lowercase_      )
    lowercase_     =       torch.nn.functional.mse_loss(lowercase_   ,				lowercase_      )
    loss.backward()


'''simple docstring'''


import warnings

from ...utils import logging
from .image_processing_poolformer import PoolFormerImageProcessor


__snake_case						  =		logging.get_logger(__name__)





class       _a    (		__a      ):


  """simple docstring"""

  def __init__(		self      :       int   ,				*lowercase_      :       Tuple   ,				**lowercase_      :       List[Any]      ):







    '''simple docstring'''


    warnings.warn(
        """The class PoolFormerFeatureExtractor is deprecated and will be removed in version 5 of Transformers."""
        """ Please use PoolFormerImageProcessor instead."""   ,				lowercase_   ,				)
    super().__init__(*lowercase_   ,				**lowercase_      )

