




'''simple docstring'''





from dataclasses import dataclass
from typing import Optional, Tuple, Union

import torch
import torch.nn as nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .embeddings import GaussianFourierProjection, TimestepEmbedding, Timesteps
from .modeling_utils import ModelMixin
from .unet_ad_blocks import UNetMidBlockaD, get_down_block, get_up_block




@dataclass
class      UpperCamelCase       (	snake_case__       ):
  a     :torch.FloatTensor






class      UpperCamelCase       (	snake_case__      ,		snake_case__       ):



  @register_to_config
  def __init__(			self		,     __lowerCAmelCase = None		,     __lowerCAmelCase = 3		,     __lowerCAmelCase = 3		,     __lowerCAmelCase = False		,     __lowerCAmelCase = "positional"		,     __lowerCAmelCase = 0		,     __lowerCAmelCase = True		,     __lowerCAmelCase = ("DownBlock2D", "AttnDownBlock2D", "AttnDownBlock2D", "AttnDownBlock2D")		,     __lowerCAmelCase = ("AttnUpBlock2D", "AttnUpBlock2D", "AttnUpBlock2D", "UpBlock2D")		,     __lowerCAmelCase = (224, 448, 672, 896)		,     __lowerCAmelCase = 2		,     __lowerCAmelCase = 1		,     __lowerCAmelCase = 1		,     __lowerCAmelCase = "conv"		,     __lowerCAmelCase = "conv"		,     __lowerCAmelCase = "silu"		,     __lowerCAmelCase = 8		,     __lowerCAmelCase = 32		,     __lowerCAmelCase = 1e-5		,     __lowerCAmelCase = "default"		,     __lowerCAmelCase = True		,     __lowerCAmelCase = None		,     __lowerCAmelCase = None		,     ):







      '''simple docstring'''


      super().__init__()

      _a				:							Tuple							       =      sample_size
      _a				:							List[str]							       =      block_out_channels[0] * 4

      # Check inputs
      if len(__lowerCAmelCase ) != len(__lowerCAmelCase ):
          raise ValueError(
              f'''Must provide the same number of `down_block_types` as `up_block_types`. `down_block_types`: {down_block_types}. `up_block_types`: {up_block_types}.''' )

      if len(__lowerCAmelCase ) != len(__lowerCAmelCase ):
          raise ValueError(
              f'''Must provide the same number of `block_out_channels` as `down_block_types`. `block_out_channels`: {block_out_channels}. `down_block_types`: {down_block_types}.''' )

      # input
      _a				:							str							       =      nn.Convad(__lowerCAmelCase		,     block_out_channels[0]		,     kernel_size=3		,     padding=(1, 1) )

      # time
      if time_embedding_type == "fourier":
          _a				:							Any							       =      GaussianFourierProjection(embedding_size=block_out_channels[0]		,     scale=16 )
          _a				:							str							       =      2 * block_out_channels[0]
      elif time_embedding_type == "positional":
          _a				:							Any							       =      Timesteps(block_out_channels[0]		,     __lowerCAmelCase		,     __lowerCAmelCase )
          _a				:							Any							       =      block_out_channels[0]

      _a				:							Optional[Any]							       =      TimestepEmbedding(__lowerCAmelCase		,     __lowerCAmelCase )

      # class embedding
      if class_embed_type is None and num_class_embeds is not None:
          _a				:							Optional[Any]							       =      nn.Embedding(__lowerCAmelCase		,     __lowerCAmelCase )
      elif class_embed_type == "timestep":
          _a				:							Any							       =      TimestepEmbedding(__lowerCAmelCase		,     __lowerCAmelCase )
      elif class_embed_type == "identity":
          _a				:							str							       =      nn.Identity(__lowerCAmelCase		,     __lowerCAmelCase )
      else:
          _a				:							Dict							       =      None

      _a				:							List[Any]							       =      nn.ModuleList([] )
      _a				:							str							       =      None
      _a				:							Tuple							       =      nn.ModuleList([] )

      # down
      _a				:							Any							       =      block_out_channels[0]
      for i, down_block_type in enumerate(__lowerCAmelCase ):
          _a				:							List[str]							       =      output_channel
          _a				:							List[Any]							       =      block_out_channels[i]
          _a				:							Tuple							       =      i == len(__lowerCAmelCase ) - 1

          _a				:							int							       =      get_down_block(
              __lowerCAmelCase		,     num_layers=__lowerCAmelCase		,     in_channels=__lowerCAmelCase		,     out_channels=__lowerCAmelCase		,     temb_channels=__lowerCAmelCase		,     add_downsample=not is_final_block		,     resnet_eps=__lowerCAmelCase		,     resnet_act_fn=__lowerCAmelCase		,     resnet_groups=__lowerCAmelCase		,     attention_head_dim=attention_head_dim if attention_head_dim is not None else output_channel		,     downsample_padding=__lowerCAmelCase		,     resnet_time_scale_shift=__lowerCAmelCase		,     downsample_type=__lowerCAmelCase		,     )
          self.down_blocks.append(__lowerCAmelCase )

      # mid
      _a				:							str							       =      UNetMidBlockaD(
          in_channels=block_out_channels[-1]		,     temb_channels=__lowerCAmelCase		,     resnet_eps=__lowerCAmelCase		,     resnet_act_fn=__lowerCAmelCase		,     output_scale_factor=__lowerCAmelCase		,     resnet_time_scale_shift=__lowerCAmelCase		,     attention_head_dim=attention_head_dim if attention_head_dim is not None else block_out_channels[-1]		,     resnet_groups=__lowerCAmelCase		,     add_attention=__lowerCAmelCase		,     )

      # up
      _a				:							List[Any]							       =      list(reversed(__lowerCAmelCase ) )
      _a				:							Any							       =      reversed_block_out_channels[0]
      for i, up_block_type in enumerate(__lowerCAmelCase ):
          _a				:							str							       =      output_channel
          _a				:							List[Any]							       =      reversed_block_out_channels[i]
          _a				:							Optional[Any]							       =      reversed_block_out_channels[min(i + 1		,     len(__lowerCAmelCase ) - 1 )]

          _a				:							Optional[int]							       =      i == len(__lowerCAmelCase ) - 1

          _a				:							Dict							       =      get_up_block(
              __lowerCAmelCase		,     num_layers=layers_per_block + 1		,     in_channels=__lowerCAmelCase		,     out_channels=__lowerCAmelCase		,     prev_output_channel=__lowerCAmelCase		,     temb_channels=__lowerCAmelCase		,     add_upsample=not is_final_block		,     resnet_eps=__lowerCAmelCase		,     resnet_act_fn=__lowerCAmelCase		,     resnet_groups=__lowerCAmelCase		,     attention_head_dim=attention_head_dim if attention_head_dim is not None else output_channel		,     resnet_time_scale_shift=__lowerCAmelCase		,     upsample_type=__lowerCAmelCase		,     )
          self.up_blocks.append(__lowerCAmelCase )
          _a				:							str							       =      output_channel

      # out
      _a				:							List[Any]							       =      norm_num_groups if norm_num_groups is not None else min(block_out_channels[0] // 4		,     32 )
      _a				:							List[Any]							       =      nn.GroupNorm(num_channels=block_out_channels[0]		,     num_groups=__lowerCAmelCase		,     eps=__lowerCAmelCase )
      _a				:							Dict							       =      nn.SiLU()
      _a				:							Optional[int]							       =      nn.Convad(block_out_channels[0]		,     __lowerCAmelCase		,     kernel_size=3		,     padding=1 )



  def     a_							(			self		,     __lowerCAmelCase		,     __lowerCAmelCase		,     __lowerCAmelCase = None		,     __lowerCAmelCase = True		,     ):







      '''simple docstring'''


      if self.config.center_input_sample:
          _a				:							str							       =      2 * sample - 1.0

      # 1. time
      _a				:							Dict							       =      timestep
      if not torch.is_tensor(__lowerCAmelCase ):
          _a				:							Union[str, Any]							       =      torch.tensor([timesteps]		,     dtype=torch.long		,     device=sample.device )
      elif torch.is_tensor(__lowerCAmelCase ) and len(timesteps.shape ) == 0:
          _a				:							str							       =      timesteps[None].to(sample.device )

      # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
      _a				:							Union[str, Any]							       =      timesteps * torch.ones(sample.shape[0]		,     dtype=timesteps.dtype		,     device=timesteps.device )

      _a				:							Dict							       =      self.time_proj(__lowerCAmelCase )

      # timesteps does not contain any weights and will always return f32 tensors
      # but time_embedding might actually be running in fp16. so we need to cast here.
      # there might be better ways to encapsulate this.
      _a				:							Optional[Any]							       =      t_emb.to(dtype=self.dtype )
      _a				:							List[str]							       =      self.time_embedding(__lowerCAmelCase )

      if self.class_embedding is not None:
          if class_labels is None:
              raise ValueError("""class_labels should be provided when doing class conditioning""" )

          if self.config.class_embed_type == "timestep":
              _a				:							Any							       =      self.time_proj(__lowerCAmelCase )

          _a				:							Any							       =      self.class_embedding(__lowerCAmelCase ).to(dtype=self.dtype )
          _a				:							List[Any]							       =      emb + class_emb

      # 2. pre-process
      _a				:							str							       =      sample
      _a				:							str							       =      self.conv_in(__lowerCAmelCase )

      # 3. down
      _a				:							Optional[Any]							       =      (sample,)
      for downsample_block in self.down_blocks:
          if hasattr(__lowerCAmelCase		,     """skip_conv""" ):
              _a       , _a       , _a				:							Optional[int]							       =      downsample_block(
                  hidden_states=__lowerCAmelCase		,     temb=__lowerCAmelCase		,     skip_sample=__lowerCAmelCase )
          else:
              _a       , _a				:							Union[str, Any]							       =      downsample_block(hidden_states=__lowerCAmelCase		,     temb=__lowerCAmelCase )

          down_block_res_samples += res_samples

      # 4. mid
      _a				:							Dict							       =      self.mid_block(__lowerCAmelCase		,     __lowerCAmelCase )

      # 5. up
      _a				:							List[Any]							       =      None
      for upsample_block in self.up_blocks:
          _a				:							Optional[Any]							       =      down_block_res_samples[-len(upsample_block.resnets ) :]
          _a				:							Union[str, Any]							       =      down_block_res_samples[: -len(upsample_block.resnets )]

          if hasattr(__lowerCAmelCase		,     """skip_conv""" ):
              _a       , _a				:							List[str]							       =      upsample_block(__lowerCAmelCase		,     __lowerCAmelCase		,     __lowerCAmelCase		,     __lowerCAmelCase )
          else:
              _a				:							Tuple							       =      upsample_block(__lowerCAmelCase		,     __lowerCAmelCase		,     __lowerCAmelCase )

        # 6. post-process
      _a				:							List[Any]							       =      self.conv_norm_out(__lowerCAmelCase )
      _a				:							Optional[int]							       =      self.conv_act(__lowerCAmelCase )
      _a				:							Any							       =      self.conv_out(__lowerCAmelCase )

      if skip_sample is not None:
          sample += skip_sample

      if self.config.time_embedding_type == "fourier":
          _a				:							Optional[Any]							       =      timesteps.reshape((sample.shape[0], *([1] * len(sample.shape[1:] ))) )
          _a				:							Dict							       =      sample / timesteps

      if not return_dict:
          return (sample,)

      return UNetaDOutput(sample=__lowerCAmelCase )








'''simple docstring'''





import inspect
import unittest

from transformers import SwinaSRConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
  import torch
  from torch import nn

  from transformers import SwinaSRForImageSuperResolution, SwinaSRModel
  from transformers.models.swinasr.modeling_swinasr import SWIN2SR_PRETRAINED_MODEL_ARCHIVE_LIST

if is_vision_available():
  from PIL import Image

  from transformers import SwinaSRImageProcessor




class      UpperCamelCase       :



  def __init__(			self		,     __lowerCAmelCase		,     __lowerCAmelCase=13		,     __lowerCAmelCase=32		,     __lowerCAmelCase=1		,     __lowerCAmelCase=3		,     __lowerCAmelCase=16		,     __lowerCAmelCase=[1, 2, 1]		,     __lowerCAmelCase=[2, 2, 4]		,     __lowerCAmelCase=2		,     __lowerCAmelCase=2.0		,     __lowerCAmelCase=True		,     __lowerCAmelCase=0.0		,     __lowerCAmelCase=0.0		,     __lowerCAmelCase=0.1		,     __lowerCAmelCase="gelu"		,     __lowerCAmelCase=False		,     __lowerCAmelCase=True		,     __lowerCAmelCase=0.02		,     __lowerCAmelCase=1e-5		,     __lowerCAmelCase=True		,     __lowerCAmelCase=None		,     __lowerCAmelCase=False		,     __lowerCAmelCase=2		,     ):







      '''simple docstring'''


      _a				:							str							       =      parent
      _a				:							str							       =      batch_size
      _a				:							int							       =      image_size
      _a				:							int							       =      patch_size
      _a				:							Any							       =      num_channels
      _a				:							Optional[Any]							       =      embed_dim
      _a				:							List[Any]							       =      depths
      _a				:							Union[str, Any]							       =      num_heads
      _a				:							List[Any]							       =      window_size
      _a				:							Optional[Any]							       =      mlp_ratio
      _a				:							Dict							       =      qkv_bias
      _a				:							Optional[int]							       =      hidden_dropout_prob
      _a				:							List[Any]							       =      attention_probs_dropout_prob
      _a				:							List[str]							       =      drop_path_rate
      _a				:							str							       =      hidden_act
      _a				:							Optional[int]							       =      use_absolute_embeddings
      _a				:							List[str]							       =      patch_norm
      _a				:							List[str]							       =      layer_norm_eps
      _a				:							Any							       =      initializer_range
      _a				:							Any							       =      is_training
      _a				:							Any							       =      scope
      _a				:							int							       =      use_labels
      _a				:							Union[str, Any]							       =      upscale

      # here we set some attributes to make tests pass
      _a				:							List[str]							       =      len(__lowerCAmelCase )
      _a				:							str							       =      embed_dim
      _a				:							Optional[int]							       =      (image_size // patch_size) ** 2



  def     a_							(			self ):







      '''simple docstring'''


      _a				:							str							       =      floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )

      _a				:							Optional[int]							       =      None
      if self.use_labels:
          _a				:							str							       =      ids_tensor([self.batch_size]		,     self.type_sequence_label_size )

      _a				:							str							       =      self.get_config()

      return config, pixel_values, labels



  def     a_							(			self ):







      '''simple docstring'''


      return SwinaSRConfig(
          image_size=self.image_size		,     patch_size=self.patch_size		,     num_channels=self.num_channels		,     embed_dim=self.embed_dim		,     depths=self.depths		,     num_heads=self.num_heads		,     window_size=self.window_size		,     mlp_ratio=self.mlp_ratio		,     qkv_bias=self.qkv_bias		,     hidden_dropout_prob=self.hidden_dropout_prob		,     attention_probs_dropout_prob=self.attention_probs_dropout_prob		,     drop_path_rate=self.drop_path_rate		,     hidden_act=self.hidden_act		,     use_absolute_embeddings=self.use_absolute_embeddings		,     path_norm=self.patch_norm		,     layer_norm_eps=self.layer_norm_eps		,     initializer_range=self.initializer_range		,     upscale=self.upscale		,     )



  def     a_							(			self		,     __lowerCAmelCase		,     __lowerCAmelCase		,     __lowerCAmelCase ):







      '''simple docstring'''


      _a				:							Union[str, Any]							       =      SwinaSRModel(config=__lowerCAmelCase )
      model.to(__lowerCAmelCase )
      model.eval()
      _a				:							List[str]							       =      model(__lowerCAmelCase )

      self.parent.assertEqual(
          result.last_hidden_state.shape		,     (self.batch_size, self.embed_dim, self.image_size, self.image_size) )



  def     a_							(			self		,     __lowerCAmelCase		,     __lowerCAmelCase		,     __lowerCAmelCase ):







      '''simple docstring'''


      _a				:							Dict							       =      SwinaSRForImageSuperResolution(__lowerCAmelCase )
      model.to(__lowerCAmelCase )
      model.eval()
      _a				:							Tuple							       =      model(__lowerCAmelCase )

      _a				:							Dict							       =      self.image_size * self.upscale
      self.parent.assertEqual(
          result.reconstruction.shape		,     (self.batch_size, self.num_channels, expected_image_size, expected_image_size) )



  def     a_							(			self ):







      '''simple docstring'''


      _a				:							List[Any]							       =      self.prepare_config_and_inputs()
      _a       , _a       , _a				:							int							       =      config_and_inputs
      _a				:							Optional[int]							       =      {"""pixel_values""": pixel_values}
      return config, inputs_dict






@require_torch
class      UpperCamelCase       (	snake_case__      ,		snake_case__      ,		unittest.TestCase       ):
  a     :Dict          =					(SwinaSRModel, SwinaSRForImageSuperResolution) if is_torch_available() else ()
  a     :List[str]          =					{'feature-extraction': SwinaSRModel} if is_torch_available() else {}

  a     :List[Any]          =					False
  a     :Tuple          =					False
  a     :Any          =					False
  a     :Union[str, Any]          =					False
  a     :Union[str, Any]          =					False



  def     a_							(			self ):







      '''simple docstring'''


      _a				:							Optional[Any]							       =      SwinaSRModelTester(self )
      _a				:							Optional[int]							       =      ConfigTester(self		,     config_class=__lowerCAmelCase		,     embed_dim=37 )



  def     a_							(			self ):







      '''simple docstring'''


      self.config_tester.create_and_test_config_to_json_string()
      self.config_tester.create_and_test_config_to_json_file()
      self.config_tester.create_and_test_config_from_and_save_pretrained()
      self.config_tester.create_and_test_config_with_num_labels()
      self.config_tester.check_config_can_be_init_without_params()
      self.config_tester.check_config_arguments_init()



  def     a_							(			self ):







      '''simple docstring'''


      _a				:							int							       =      self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*__lowerCAmelCase )



  def     a_							(			self ):







      '''simple docstring'''


      _a				:							Tuple							       =      self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_image_super_resolution(*__lowerCAmelCase )



  @unittest.skip(reason="""Got `CUDA error: misaligned address` with PyTorch 2.0.0.""" )
  def     a_							(			self ):







      '''simple docstring'''


      pass



  @unittest.skip(reason="""Swin2SR does not use inputs_embeds""" )
  def     a_							(			self ):







      '''simple docstring'''


      pass



  @unittest.skip(reason="""Swin2SR does not support training yet""" )
  def     a_							(			self ):







      '''simple docstring'''


      pass



  @unittest.skip(reason="""Swin2SR does not support training yet""" )
  def     a_							(			self ):







      '''simple docstring'''


      pass



  def     a_							(			self ):







      '''simple docstring'''


      _a       , _a				:							int							       =      self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
          _a				:							Union[str, Any]							       =      model_class(__lowerCAmelCase )
          self.assertIsInstance(model.get_input_embeddings()		,     (nn.Module) )
          _a				:							Optional[int]							       =      model.get_output_embeddings()
          self.assertTrue(x is None or isinstance(__lowerCAmelCase		,     nn.Linear ) )



  def     a_							(			self ):







      '''simple docstring'''


      _a       , _a				:							Tuple							       =      self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
          _a				:							str							       =      model_class(__lowerCAmelCase )
          _a				:							Tuple							       =      inspect.signature(model.forward )
          # signature.parameters is an OrderedDict => so arg_names order is deterministic
          _a				:							int							       =      [*signature.parameters.keys()]

          _a				:							List[Any]							       =      ["""pixel_values"""]
          self.assertListEqual(arg_names[:1]		,     __lowerCAmelCase )



  @slow
  def     a_							(			self ):







      '''simple docstring'''


      for model_name in SWIN2SR_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
          _a				:							List[str]							       =      SwinaSRModel.from_pretrained(__lowerCAmelCase )
          self.assertIsNotNone(__lowerCAmelCase )



  def     a_							(			self ):







      '''simple docstring'''


      _a       , _a				:							str							       =      self.model_tester.prepare_config_and_inputs_for_common()

      _a				:							Union[str, Any]							       =      _config_zero_init(__lowerCAmelCase )
      for model_class in self.all_model_classes:
          _a				:							Any							       =      model_class(config=__lowerCAmelCase )
          for name, param in model.named_parameters():
              if "logit_scale" in name:
                  continue
              if param.requires_grad:
                  self.assertIn(
                      ((param.data.mean() * 1e9).round() / 1e9).item()		,     [0.0, 1.0]		,     msg=f'''Parameter {name} of model {model_class} seems not properly initialized'''		,     )



  def     a_							(			self ):







      '''simple docstring'''


      _a       , _a				:							Dict							       =      self.model_tester.prepare_config_and_inputs_for_common()
      _a				:							List[str]							       =      True

      for model_class in self.all_model_classes:
          _a				:							Dict							       =      True
          _a				:							Optional[int]							       =      False
          _a				:							Any							       =      True
          _a				:							Dict							       =      model_class(__lowerCAmelCase )
          model.to(__lowerCAmelCase )
          model.eval()
          with torch.no_grad():
              _a				:							Optional[Any]							       =      model(**self._prepare_for_class(__lowerCAmelCase		,     __lowerCAmelCase ) )
          _a				:							Optional[int]							       =      outputs.attentions
          _a				:							Tuple							       =      len(self.model_tester.depths )
          self.assertEqual(len(__lowerCAmelCase )		,     __lowerCAmelCase )

          # check that output_attentions also work using config
          del inputs_dict["output_attentions"]
          _a				:							List[str]							       =      True
          _a				:							str							       =      config.window_size**2
          _a				:							Union[str, Any]							       =      model_class(__lowerCAmelCase )
          model.to(__lowerCAmelCase )
          model.eval()
          with torch.no_grad():
              _a				:							Any							       =      model(**self._prepare_for_class(__lowerCAmelCase		,     __lowerCAmelCase ) )
          _a				:							Optional[int]							       =      outputs.attentions
          self.assertEqual(len(__lowerCAmelCase )		,     __lowerCAmelCase )

          self.assertListEqual(
              list(attentions[0].shape[-3:] )		,     [self.model_tester.num_heads[0], window_size_squared, window_size_squared]		,     )
          _a				:							Dict							       =      len(__lowerCAmelCase )

          # Check attention is always last and order is fine
          _a				:							Tuple							       =      True
          _a				:							Any							       =      True
          _a				:							str							       =      model_class(__lowerCAmelCase )
          model.to(__lowerCAmelCase )
          model.eval()
          with torch.no_grad():
              _a				:							Optional[Any]							       =      model(**self._prepare_for_class(__lowerCAmelCase		,     __lowerCAmelCase ) )

          self.assertEqual(out_len + 1		,     len(__lowerCAmelCase ) )

          _a				:							List[str]							       =      outputs.attentions

          self.assertEqual(len(__lowerCAmelCase )		,     __lowerCAmelCase )

          self.assertListEqual(
              list(self_attentions[0].shape[-3:] )		,     [self.model_tester.num_heads[0], window_size_squared, window_size_squared]		,     )






@require_vision
@require_torch
@slow
class      UpperCamelCase       (	unittest.TestCase       ):



  def     a_							(			self ):







      '''simple docstring'''


      _a				:							int							       =      SwinaSRImageProcessor()
      _a				:							Optional[Any]							       =      SwinaSRForImageSuperResolution.from_pretrained("""caidas/swin2SR-classical-sr-x2-64""" ).to(__lowerCAmelCase )

      _a				:							Tuple							       =      Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
      _a				:							Optional[int]							       =      processor(images=__lowerCAmelCase		,     return_tensors="""pt""" ).to(__lowerCAmelCase )

      # forward pass
      with torch.no_grad():
          _a				:							str							       =      model(**__lowerCAmelCase )

      # verify the logits
      _a				:							List[Any]							       =      torch.Size([1, 3, 976, 1296] )
      self.assertEqual(outputs.reconstruction.shape		,     __lowerCAmelCase )
      _a				:							List[str]							       =      torch.tensor(
          [[0.5458, 0.5546, 0.5638], [0.5526, 0.5565, 0.5651], [0.5396, 0.5426, 0.5621]] ).to(__lowerCAmelCase )
      self.assertTrue(torch.allclose(outputs.reconstruction[0, 0, :3, :3]		,     __lowerCAmelCase		,     atol=1e-4 ) )


