'''simple docstring'''





import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import CONFIG_MAPPING


SCREAMING_SNAKE_CASE :	List[Any]							     =		logging.get_logger(__name__)
class    A_ (       a_ ):
  _SCREAMING_SNAKE_CASE    				=			"""upernet"""

  def __init__(		self :      Any     ,							__SCREAMING_SNAKE_CASE :      Optional[Any]=None     ,							__SCREAMING_SNAKE_CASE :      Dict=5_12     ,							__SCREAMING_SNAKE_CASE :      Optional[Any]=0.02     ,							__SCREAMING_SNAKE_CASE :      List[Any]=[1, 2, 3, 6]     ,							__SCREAMING_SNAKE_CASE :      Dict=True     ,							__SCREAMING_SNAKE_CASE :      str=0.4     ,							__SCREAMING_SNAKE_CASE :      Union[str, Any]=3_84     ,							__SCREAMING_SNAKE_CASE :      Optional[Any]=2_56     ,							__SCREAMING_SNAKE_CASE :      Optional[int]=1     ,							__SCREAMING_SNAKE_CASE :      Any=False     ,							__SCREAMING_SNAKE_CASE :      Dict=2_55     ,							**__SCREAMING_SNAKE_CASE :      Optional[Any]     ,							):
   super().__init__(**__A       )

   if backbone_config is None:
    logger.info("`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone."       )
    __a       =  CONFIG_MAPPING["resnet"](out_features=["stage1", "stage2", "stage3", "stage4"]       )
   elif isinstance(__A     ,							__A       ):
    __a       =  backbone_config.get("model_type"       )
    __a       =  CONFIG_MAPPING[backbone_model_type]
    __a       =  config_class.from_dict(__A       )

   __a       =  backbone_config
   __a       =  hidden_size
   __a       =  initializer_range
   __a       =  pool_scales
   __a       =  use_auxiliary_head
   __a       =  auxiliary_loss_weight
   __a       =  auxiliary_in_channels
   __a       =  auxiliary_channels
   __a       =  auxiliary_num_convs
   __a       =  auxiliary_concat_input
   __a       =  loss_ignore_index

  def 		_UpperCAmelCase   (		self :      Optional[int]       ):
   __a       =  copy.deepcopy(self.__dict__       )
   __a       =  self.backbone_config.to_dict()
   __a       =  self.__class__.model_type
   return output



import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel

from diffusers import DDIMScheduler, LDMPipeline, UNetaDModel, VQModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()
class    A_ (       unittest.TestCase ):

  @property
  def 		_UpperCAmelCase   (		self :      str       ):
   torch.manual_seed(0       )
   __a       =  UNetaDModel(
       block_out_channels=(32, 64)     ,							layers_per_block=2     ,							sample_size=32     ,							in_channels=3     ,							out_channels=3     ,							down_block_types=("DownBlock2D", "AttnDownBlock2D")     ,							up_block_types=("AttnUpBlock2D", "UpBlock2D")     ,							)
   return model

  @property
  def 		_UpperCAmelCase   (		self :      Dict       ):
   torch.manual_seed(0       )
   __a       =  VQModel(
       block_out_channels=[32, 64]     ,							in_channels=3     ,							out_channels=3     ,							down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"]     ,							up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"]     ,							latent_channels=3     ,							)
   return model

  @property
  def 		_UpperCAmelCase   (		self :      Optional[Any]       ):
   torch.manual_seed(0       )
   __a       =  CLIPTextConfig(
       bos_token_id=0     ,							eos_token_id=2     ,							hidden_size=32     ,							intermediate_size=37     ,							layer_norm_eps=1E-05     ,							num_attention_heads=4     ,							num_hidden_layers=5     ,							pad_token_id=1     ,							vocab_size=10_00     ,							)
   return CLIPTextModel(__SCREAMING_SNAKE_CASE       )

  def 		_UpperCAmelCase   (		self :      Dict       ):
   __a       =  self.dummy_uncond_unet
   __a       =  DDIMScheduler()
   __a       =  self.dummy_vq_model

   __a       =  LDMPipeline(unet=__SCREAMING_SNAKE_CASE     ,							vqvae=__SCREAMING_SNAKE_CASE     ,							scheduler=__SCREAMING_SNAKE_CASE       )
   ldm.to(__SCREAMING_SNAKE_CASE       )
   ldm.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE       )

   __a       =  torch.manual_seed(0       )
   __a       =  ldm(generator=__SCREAMING_SNAKE_CASE     ,							num_inference_steps=2     ,							output_type="numpy"       ).images

   __a       =  torch.manual_seed(0       )
   __a       =  ldm(generator=__SCREAMING_SNAKE_CASE     ,							num_inference_steps=2     ,							output_type="numpy"     ,							return_dict=__SCREAMING_SNAKE_CASE       )[0]

   __a       =  image[0, -3:, -3:, -1]
   __a       =  image_from_tuple[0, -3:, -3:, -1]

   assert image.shape == (1, 64, 64, 3)
   __a       =  np.array([0.85_12, 0.8_18, 0.64_11, 0.68_08, 0.44_65, 0.56_18, 0.46, 0.62_31, 0.51_72]       )
   __a       =  1E-2 if torch_device != "mps" else 3E-2

   assert np.abs(image_slice.flatten() - expected_slice       ).max() < tolerance
   assert np.abs(image_from_tuple_slice.flatten() - expected_slice       ).max() < tolerance
@slow
@require_torch
class    A_ (       unittest.TestCase ):

  def 		_UpperCAmelCase   (		self :      str       ):
   __a       =  LDMPipeline.from_pretrained("CompVis/ldm-celebahq-256"       )
   ldm.to(__SCREAMING_SNAKE_CASE       )
   ldm.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE       )

   __a       =  torch.manual_seed(0       )
   __a       =  ldm(generator=__SCREAMING_SNAKE_CASE     ,							num_inference_steps=5     ,							output_type="numpy"       ).images

   __a       =  image[0, -3:, -3:, -1]

   assert image.shape == (1, 2_56, 2_56, 3)
   __a       =  np.array([0.43_99, 0.4_49_75, 0.4_68_25, 0.4_74, 0.43_59, 0.45_81, 0.4_50_95, 0.43_41, 0.44_47]       )
   __a       =  1E-2 if torch_device != "mps" else 3E-2

   assert np.abs(image_slice.flatten() - expected_slice       ).max() < tolerance


