


import inspect
import unittest

import numpy as np

from tests.test_modeling_common import floats_tensor
from transformers import MaskaFormerConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
		import torch

		from transformers import MaskaFormerForUniversalSegmentation, MaskaFormerModel

		if is_vision_available():
				from transformers import MaskaFormerImageProcessor

if is_vision_available():
		from PIL import Image




class 		a__    :





							'''simple docstring'''






							def __init__(		self ,   lowerCamelCase_ ,   lowerCamelCase_=2 ,   lowerCamelCase_=True ,   lowerCamelCase_=False ,   lowerCamelCase_=10 ,   lowerCamelCase_=3 ,   lowerCamelCase_=32 * 8 ,   lowerCamelCase_=32 * 8 ,   lowerCamelCase_=4 ,   lowerCamelCase_=64 ,   )	->							str:
														lowerCAmelCase__	       =     parent
														lowerCAmelCase__	       =     batch_size
														lowerCAmelCase__	       =     is_training
														lowerCAmelCase__	       =     use_auxiliary_loss
														lowerCAmelCase__	       =     num_queries
														lowerCAmelCase__	       =     num_channels
														lowerCAmelCase__	       =     min_size
														lowerCAmelCase__	       =     max_size
														lowerCAmelCase__	       =     num_labels
														lowerCAmelCase__	       =     hidden_dim
														lowerCAmelCase__	       =     hidden_dim







							def   __SCREAMING_SNAKE_CASE							(		self       )	->							Any:
														lowerCAmelCase__	       =     floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size]       ).to(
														    _snake_case       )

														lowerCAmelCase__	       =     torch.ones([self.batch_size, self.min_size, self.max_size] ,   device=_snake_case       )

														lowerCAmelCase__	       =     (
														    torch.rand([self.batch_size, self.num_labels, self.min_size, self.max_size] ,   device=_snake_case       ) > 0.5
														).float()
														lowerCAmelCase__	       =     (torch.rand((self.batch_size, self.num_labels) ,   device=_snake_case       ) > 0.5).long()

														lowerCAmelCase__	       =     self.get_config()
														return config, pixel_values, pixel_mask, mask_labels, class_labels







							def   __SCREAMING_SNAKE_CASE							(		self       )	->							List[str]:
														lowerCAmelCase__	       =     MaskaFormerConfig(
														    hidden_size=self.hidden_dim ,   )
														lowerCAmelCase__	       =     self.num_queries
														lowerCAmelCase__	       =     self.num_labels

														lowerCAmelCase__	       =     [1, 1, 1, 1]
														lowerCAmelCase__	       =     self.num_channels

														lowerCAmelCase__	       =     64
														lowerCAmelCase__	       =     1_28
														lowerCAmelCase__	       =     self.hidden_dim
														lowerCAmelCase__	       =     self.hidden_dim
														lowerCAmelCase__	       =     self.hidden_dim
														return config







							def   __SCREAMING_SNAKE_CASE							(		self       )	->							List[Any]:
														lowerCAmelCase__       , lowerCAmelCase__       , lowerCAmelCase__       , lowerCAmelCase__       , lowerCAmelCase__	       =     self.prepare_config_and_inputs()
														lowerCAmelCase__	       =     {'''pixel_values''': pixel_values, '''pixel_mask''': pixel_mask}
														return config, inputs_dict







							def   __SCREAMING_SNAKE_CASE							(		self ,   lowerCamelCase_ ,   lowerCamelCase_       )	->							str:
														lowerCAmelCase__	       =     output.encoder_hidden_states
														lowerCAmelCase__	       =     output.pixel_decoder_hidden_states
														lowerCAmelCase__	       =     output.transformer_decoder_hidden_states

														self.parent.assertTrue(len(_snake_case       ) ,   len(config.backbone_config.depths       )       )
														self.parent.assertTrue(len(_snake_case       ) ,   len(config.backbone_config.depths       )       )
														self.parent.assertTrue(len(_snake_case       ) ,   config.decoder_layers       )







							def   __SCREAMING_SNAKE_CASE							(		self ,   lowerCamelCase_ ,   lowerCamelCase_ ,   lowerCamelCase_ ,   lowerCamelCase_=False       )	->							Any:




														with torch.no_grad():
																					lowerCAmelCase__	       =     MaskaFormerModel(config=_snake_case       )
																					model.to(_snake_case       )
																					model.eval()

																					lowerCAmelCase__	       =     model(pixel_values=_snake_case ,   pixel_mask=_snake_case       )
																					lowerCAmelCase__	       =     model(_snake_case ,   output_hidden_states=_snake_case       )

														self.parent.assertEqual(
														    output.transformer_decoder_last_hidden_state.shape ,   (self.batch_size, self.num_queries, self.hidden_dim) ,   )
														# let's ensure the other two hidden state exists
														self.parent.assertTrue(output.pixel_decoder_last_hidden_state is not None       )
														self.parent.assertTrue(output.encoder_last_hidden_state is not None       )

														if output_hidden_states:
																					self.check_output_hidden_state(_snake_case ,   _snake_case       )







							def   __SCREAMING_SNAKE_CASE							(		self ,   lowerCamelCase_ ,   lowerCamelCase_ ,   lowerCamelCase_ ,   lowerCamelCase_ ,   lowerCamelCase_       )	->							Union[str, Any]:
														lowerCAmelCase__	       =     MaskaFormerForUniversalSegmentation(config=_snake_case       )
														model.to(_snake_case       )
														model.eval()

														def comm_check_on_output(lowerCamelCase_       ):
																					# let's still check that all the required stuff is there
																					self.parent.assertTrue(result.transformer_decoder_last_hidden_state is not None       )
																					self.parent.assertTrue(result.pixel_decoder_last_hidden_state is not None       )
																					self.parent.assertTrue(result.encoder_last_hidden_state is not None       )
																					# okay, now we need to check the logits shape
																					# due to the encoder compression, masks have a //4 spatial size
																					self.parent.assertEqual(
																					    result.masks_queries_logits.shape ,   (self.batch_size, self.num_queries, self.min_size // 4, self.max_size // 4) ,   )
																					# + 1 for null class
																					self.parent.assertEqual(
																					    result.class_queries_logits.shape ,   (self.batch_size, self.num_queries, self.num_labels + 1)       )

														with torch.no_grad():
																					lowerCAmelCase__	       =     model(pixel_values=_snake_case ,   pixel_mask=_snake_case       )
																					lowerCAmelCase__	       =     model(_snake_case       )

																					comm_check_on_output(_snake_case       )

																					lowerCAmelCase__	       =     model(
																					    pixel_values=_snake_case ,   pixel_mask=_snake_case ,   mask_labels=_snake_case ,   class_labels=_snake_case       )

														comm_check_on_output(_snake_case       )

														self.parent.assertTrue(result.loss is not None       )
														self.parent.assertEqual(result.loss.shape ,   torch.Size([1]       )       )




@require_torch
class 		a__    (							a__    ,    a__    ,    unittest.TestCase  ):





							'''simple docstring'''






							lowercase__      :       int     						=				(MaskaFormerModel, MaskaFormerForUniversalSegmentation) if is_torch_available() else ()
							lowercase__      :       Tuple     						=				{"feature-extraction": MaskaFormerModel} if is_torch_available() else {}

							lowercase__      :       List[Any]     						=				False
							lowercase__      :       List[Any]     						=				False
							lowercase__      :       Union[str, Any]     						=				False
							lowercase__      :       Any     						=				False







							def   __SCREAMING_SNAKE_CASE							(		self       )	->							Dict:
														lowerCAmelCase__	       =     MaskaFormerModelTester(self       )
														lowerCAmelCase__	       =     ConfigTester(self ,   config_class=_snake_case ,   has_text_modality=_snake_case       )







							def   __SCREAMING_SNAKE_CASE							(		self       )	->							Optional[int]:
														self.config_tester.run_common_tests()







							def   __SCREAMING_SNAKE_CASE							(		self       )	->							List[str]:
														lowerCAmelCase__       , lowerCAmelCase__	       =     self.model_tester.prepare_config_and_inputs_for_common()
														self.model_tester.create_and_check_maskaformer_model(_snake_case ,   **_snake_case ,   output_hidden_states=_snake_case       )







							def   __SCREAMING_SNAKE_CASE							(		self       )	->							List[Any]:
														lowerCAmelCase__	       =     self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_maskaformer_instance_segmentation_head_model(*_snake_case       )







							@unittest.skip(reason='''Mask2Former does not use inputs_embeds'''       )
							def   __SCREAMING_SNAKE_CASE							(		self       )	->							Union[str, Any]:
														pass







							@unittest.skip(reason='''Mask2Former does not have a get_input_embeddings method'''       )
							def   __SCREAMING_SNAKE_CASE							(		self       )	->							List[Any]:
														pass







							@unittest.skip(reason='''Mask2Former is not a generative model'''       )
							def   __SCREAMING_SNAKE_CASE							(		self       )	->							List[Any]:
														pass







							@unittest.skip(reason='''Mask2Former does not use token embeddings'''       )
							def   __SCREAMING_SNAKE_CASE							(		self       )	->							List[str]:
														pass







							@require_torch_multi_gpu
							@unittest.skip(
							    reason='''Mask2Former has some layers using `add_module` which doesn\'t work well with `nn.DataParallel`'''       )
							def   __SCREAMING_SNAKE_CASE							(		self       )	->							List[Any]:
														pass







							@unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.'''       )
							def   __SCREAMING_SNAKE_CASE							(		self       )	->							Optional[int]:
														pass







							def   __SCREAMING_SNAKE_CASE							(		self       )	->							str:
														lowerCAmelCase__       , lowerCAmelCase__	       =     self.model_tester.prepare_config_and_inputs_for_common()

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

																					lowerCAmelCase__	       =     ['''pixel_values''']
																					self.assertListEqual(arg_names[:1] ,   _snake_case       )







							@slow
							def   __SCREAMING_SNAKE_CASE							(		self       )	->							Any:




														for model_name in ["facebook/mask2former-swin-small-coco-instance"]:
																					lowerCAmelCase__	       =     MaskaFormerModel.from_pretrained(_snake_case       )
																					self.assertIsNotNone(_snake_case       )







							def   __SCREAMING_SNAKE_CASE							(		self       )	->							Union[str, Any]:
														lowerCAmelCase__	       =     (self.model_tester.min_size,) * 2
														lowerCAmelCase__	       =     {
														    '''pixel_values''': torch.randn((2, 3, *size) ,   device=_snake_case       ),
														    '''mask_labels''': torch.randn((2, 10, *size) ,   device=_snake_case       ),
														    '''class_labels''': torch.zeros(2 ,   10 ,   device=_snake_case       ).long(),
														}
														lowerCAmelCase__	       =     self.model_tester.get_config()

														lowerCAmelCase__	       =     MaskaFormerForUniversalSegmentation(_snake_case       ).to(_snake_case       )
														lowerCAmelCase__	       =     model(**_snake_case       )
														self.assertTrue(outputs.loss is not None       )







							def   __SCREAMING_SNAKE_CASE							(		self       )	->							Optional[Any]:
														lowerCAmelCase__       , lowerCAmelCase__	       =     self.model_tester.prepare_config_and_inputs_for_common()
														self.model_tester.create_and_check_maskaformer_model(_snake_case ,   **_snake_case ,   output_hidden_states=_snake_case       )







							def   __SCREAMING_SNAKE_CASE							(		self       )	->							Tuple:
														lowerCAmelCase__       , lowerCAmelCase__	       =     self.model_tester.prepare_config_and_inputs_for_common()

														for model_class in self.all_model_classes:
																					lowerCAmelCase__	       =     model_class(_snake_case       ).to(_snake_case       )
																					lowerCAmelCase__	       =     model(**_snake_case ,   output_attentions=_snake_case       )
																					self.assertTrue(outputs.attentions is not None       )







							def   __SCREAMING_SNAKE_CASE							(		self       )	->							str:




														if not self.model_tester.is_training:
																					return

														lowerCAmelCase__	       =     self.all_model_classes[1]
														lowerCAmelCase__       , lowerCAmelCase__       , lowerCAmelCase__       , lowerCAmelCase__       , lowerCAmelCase__	       =     self.model_tester.prepare_config_and_inputs()

														lowerCAmelCase__	       =     model_class(_snake_case       )
														model.to(_snake_case       )
														model.train()

														lowerCAmelCase__	       =     model(_snake_case ,   mask_labels=_snake_case ,   class_labels=_snake_case       ).loss
														loss.backward()







							def   __SCREAMING_SNAKE_CASE							(		self       )	->							Union[str, Any]:
														lowerCAmelCase__	       =     self.all_model_classes[1]
														lowerCAmelCase__       , lowerCAmelCase__       , lowerCAmelCase__       , lowerCAmelCase__       , lowerCAmelCase__	       =     self.model_tester.prepare_config_and_inputs()
														lowerCAmelCase__	       =     True
														lowerCAmelCase__	       =     True

														lowerCAmelCase__	       =     model_class(_snake_case       ).to(_snake_case       )
														model.train()

														lowerCAmelCase__	       =     model(_snake_case ,   mask_labels=_snake_case ,   class_labels=_snake_case       )

														lowerCAmelCase__	       =     outputs.encoder_hidden_states[0]
														encoder_hidden_states.retain_grad()

														lowerCAmelCase__	       =     outputs.pixel_decoder_hidden_states[0]
														pixel_decoder_hidden_states.retain_grad()

														lowerCAmelCase__	       =     outputs.transformer_decoder_hidden_states[0]
														transformer_decoder_hidden_states.retain_grad()

														lowerCAmelCase__	       =     outputs.attentions[0]
														attentions.retain_grad()

														outputs.loss.backward(retain_graph=_snake_case       )

														self.assertIsNotNone(encoder_hidden_states.grad       )
														self.assertIsNotNone(pixel_decoder_hidden_states.grad       )
														self.assertIsNotNone(transformer_decoder_hidden_states.grad       )
														self.assertIsNotNone(attentions.grad       )

__UpperCAmelCase       =							1e-4


def  _snake_case       (					)						->   int:
							lowerCAmelCase__	       =     Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''	)
							return image






@require_vision
@slow
class 		a__    (							unittest.TestCase  ):





							'''simple docstring'''






							@cached_property
							def   __SCREAMING_SNAKE_CASE							(		self       )	->							Dict:
														return "facebook/mask2former-swin-small-coco-instance"







							@cached_property
							def   __SCREAMING_SNAKE_CASE							(		self       )	->							Dict:
														return MaskaFormerImageProcessor.from_pretrained(self.model_checkpoints       ) if is_vision_available() else None







							def   __SCREAMING_SNAKE_CASE							(		self       )	->							str:
														lowerCAmelCase__	       =     MaskaFormerModel.from_pretrained(self.model_checkpoints       ).to(_snake_case       )
														lowerCAmelCase__	       =     self.default_image_processor
														lowerCAmelCase__	       =     prepare_img()
														lowerCAmelCase__	       =     image_processor(_snake_case ,   return_tensors='''pt'''       ).to(_snake_case       )
														lowerCAmelCase__	       =     inputs['''pixel_values'''].shape
														# check size is divisible by 32
														self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0       )
														# check size
														self.assertEqual(_snake_case ,   (1, 3, 3_84, 3_84)       )

														with torch.no_grad():
																					lowerCAmelCase__	       =     model(**_snake_case       )

														lowerCAmelCase__	       =     torch.tensor(
														    [[-0.2_790, -1.0_717, -1.1_668], [-0.5_128, -0.3_128, -0.4_987], [-0.5_832, 0.1_971, -0.0_197]]       ).to(_snake_case       )
														self.assertTrue(
														    torch.allclose(
														        outputs.encoder_last_hidden_state[0, 0, :3, :3] ,   _snake_case ,   atol=_snake_case       )       )

														lowerCAmelCase__	       =     torch.tensor(
														    [[0.8_973, 1.1_847, 1.1_776], [1.1_934, 1.5_040, 1.5_128], [1.1_153, 1.4_486, 1.4_951]]       ).to(_snake_case       )
														self.assertTrue(
														    torch.allclose(
														        outputs.pixel_decoder_last_hidden_state[0, 0, :3, :3] ,   _snake_case ,   atol=_snake_case       )       )

														lowerCAmelCase__	       =     torch.tensor(
														    [[2.1_152, 1.7_000, -0.8_603], [1.5_808, 1.8_004, -0.9_353], [1.6_043, 1.7_495, -0.5_999]]       ).to(_snake_case       )
														self.assertTrue(
														    torch.allclose(
														        outputs.transformer_decoder_last_hidden_state[0, :3, :3] ,   _snake_case ,   atol=_snake_case       )       )







							def   __SCREAMING_SNAKE_CASE							(		self       )	->							List[str]:
														lowerCAmelCase__	       =     MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints       ).to(_snake_case       ).eval()
														lowerCAmelCase__	       =     self.default_image_processor
														lowerCAmelCase__	       =     prepare_img()
														lowerCAmelCase__	       =     image_processor(_snake_case ,   return_tensors='''pt'''       ).to(_snake_case       )
														lowerCAmelCase__	       =     inputs['''pixel_values'''].shape
														# check size is divisible by 32
														self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0       )
														# check size
														self.assertEqual(_snake_case ,   (1, 3, 3_84, 3_84)       )

														with torch.no_grad():
																					lowerCAmelCase__	       =     model(**_snake_case       )
														# masks_queries_logits
														lowerCAmelCase__	       =     outputs.masks_queries_logits
														self.assertEqual(
														    masks_queries_logits.shape ,   (1, model.config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4)       )
														lowerCAmelCase__	       =     [
														    [-8.7_839, -9.0_056, -8.8_121],
														    [-7.4_104, -7.0_313, -6.5_401],
														    [-6.6_105, -6.3_427, -6.4_675],
														]
														lowerCAmelCase__	       =     torch.tensor(_snake_case       ).to(_snake_case       )
														self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3] ,   _snake_case ,   atol=_snake_case       )       )
														# class_queries_logits
														lowerCAmelCase__	       =     outputs.class_queries_logits
														self.assertEqual(class_queries_logits.shape ,   (1, model.config.num_queries, model.config.num_labels + 1)       )
														lowerCAmelCase__	       =     torch.tensor(
														    [
														        [1.8_324, -8.0_835, -4.1_922],
														        [0.8_450, -9.0_050, -3.6_053],
														        [0.3_045, -7.7_293, -3.0_275],
														    ]       ).to(_snake_case       )
														self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3] ,   _snake_case ,   atol=_snake_case       )       )







							def   __SCREAMING_SNAKE_CASE							(		self       )	->							int:
														lowerCAmelCase__	       =     MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints       ).to(_snake_case       ).eval()
														lowerCAmelCase__	       =     self.default_image_processor

														lowerCAmelCase__	       =     image_processor(
														    [np.zeros((3, 8_00, 13_33)       ), np.zeros((3, 8_00, 13_33)       )] ,   segmentation_maps=[np.zeros((3_84, 3_84)       ).astype(np.floataa       ), np.zeros((3_84, 3_84)       ).astype(np.floataa       )] ,   return_tensors='''pt''' ,   )

														lowerCAmelCase__	       =     inputs['''pixel_values'''].to(_snake_case       )
														lowerCAmelCase__	       =     [el.to(_snake_case       ) for el in inputs['''mask_labels''']]
														lowerCAmelCase__	       =     [el.to(_snake_case       ) for el in inputs['''class_labels''']]

														with torch.no_grad():
																					lowerCAmelCase__	       =     model(**_snake_case       )

														self.assertTrue(outputs.loss is not None       )



'''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 get_down_block, get_mid_block, get_out_block, get_up_block




@dataclass
class 		a__    (							a__  ):





							'''simple docstring'''






							lowercase__      :       torch.FloatTensor




class 		a__    (							a__    ,    a__  ):





							'''simple docstring'''






							@register_to_config
							def __init__(		self ,   lowerCamelCase_ = 6_55_36 ,   lowerCamelCase_ = None ,   lowerCamelCase_ = 2 ,   lowerCamelCase_ = 2 ,   lowerCamelCase_ = 0 ,   lowerCamelCase_ = "fourier" ,   lowerCamelCase_ = True ,   lowerCamelCase_ = False ,   lowerCamelCase_ = 0.0 ,   lowerCamelCase_ = ("DownBlock1DNoSkip", "DownBlock1D", "AttnDownBlock1D") ,   lowerCamelCase_ = ("AttnUpBlock1D", "UpBlock1D", "UpBlock1DNoSkip") ,   lowerCamelCase_ = "UNetMidBlock1D" ,   lowerCamelCase_ = None ,   lowerCamelCase_ = (32, 32, 64) ,   lowerCamelCase_ = None ,   lowerCamelCase_ = 8 ,   lowerCamelCase_ = 1 ,   lowerCamelCase_ = False ,   )	->							Optional[int]:
														super().__init__()
														lowerCAmelCase__	       =     sample_size

														# time
														if time_embedding_type == "fourier":
																					lowerCAmelCase__	       =     GaussianFourierProjection(
																					    embedding_size=8 ,   set_W_to_weight=lowerCamelCase_ ,   log=lowerCamelCase_ ,   flip_sin_to_cos=lowerCamelCase_       )
																					lowerCAmelCase__	       =     2 * block_out_channels[0]
														elif time_embedding_type == "positional":
																					lowerCAmelCase__	       =     Timesteps(
																					    block_out_channels[0] ,   flip_sin_to_cos=lowerCamelCase_ ,   downscale_freq_shift=lowerCamelCase_       )
																					lowerCAmelCase__	       =     block_out_channels[0]

														if use_timestep_embedding:
																					lowerCAmelCase__	       =     block_out_channels[0] * 4
																					lowerCAmelCase__	       =     TimestepEmbedding(
																					    in_channels=lowerCamelCase_ ,   time_embed_dim=lowerCamelCase_ ,   act_fn=lowerCamelCase_ ,   out_dim=block_out_channels[0] ,   )

														lowerCAmelCase__	       =     nn.ModuleList([]       )
														lowerCAmelCase__	       =     None
														lowerCAmelCase__	       =     nn.ModuleList([]       )
														lowerCAmelCase__	       =     None

														# down
														lowerCAmelCase__	       =     in_channels
														for i, down_block_type in enumerate(lowerCamelCase_       ):
																					lowerCAmelCase__	       =     output_channel
																					lowerCAmelCase__	       =     block_out_channels[i]

																					if i == 0:
																												input_channel += extra_in_channels

																					lowerCAmelCase__	       =     i == len(lowerCamelCase_       ) - 1

																					lowerCAmelCase__	       =     get_down_block(
																					    lowerCamelCase_ ,   num_layers=lowerCamelCase_ ,   in_channels=lowerCamelCase_ ,   out_channels=lowerCamelCase_ ,   temb_channels=block_out_channels[0] ,   add_downsample=not is_final_block or downsample_each_block ,   )
																					self.down_blocks.append(lowerCamelCase_       )

														# mid
														lowerCAmelCase__	       =     get_mid_block(
														    lowerCamelCase_ ,   in_channels=block_out_channels[-1] ,   mid_channels=block_out_channels[-1] ,   out_channels=block_out_channels[-1] ,   embed_dim=block_out_channels[0] ,   num_layers=lowerCamelCase_ ,   add_downsample=lowerCamelCase_ ,   )

														# up
														lowerCAmelCase__	       =     list(reversed(lowerCamelCase_       )       )
														lowerCAmelCase__	       =     reversed_block_out_channels[0]
														if out_block_type is None:
																					lowerCAmelCase__	       =     out_channels
														else:
																					lowerCAmelCase__	       =     block_out_channels[0]

														for i, up_block_type in enumerate(lowerCamelCase_       ):
																					lowerCAmelCase__	       =     output_channel
																					lowerCAmelCase__	       =     (
																					    reversed_block_out_channels[i + 1] if i < len(lowerCamelCase_       ) - 1 else final_upsample_channels
																					)

																					lowerCAmelCase__	       =     i == len(lowerCamelCase_       ) - 1

																					lowerCAmelCase__	       =     get_up_block(
																					    lowerCamelCase_ ,   num_layers=lowerCamelCase_ ,   in_channels=lowerCamelCase_ ,   out_channels=lowerCamelCase_ ,   temb_channels=block_out_channels[0] ,   add_upsample=not is_final_block ,   )
																					self.up_blocks.append(lowerCamelCase_       )
																					lowerCAmelCase__	       =     output_channel

														# out
														lowerCAmelCase__	       =     norm_num_groups if norm_num_groups is not None else min(block_out_channels[0] // 4 ,   32       )
														lowerCAmelCase__	       =     get_out_block(
														    out_block_type=lowerCamelCase_ ,   num_groups_out=lowerCamelCase_ ,   embed_dim=block_out_channels[0] ,   out_channels=lowerCamelCase_ ,   act_fn=lowerCamelCase_ ,   fc_dim=block_out_channels[-1] // 4 ,   )







							def   __SCREAMING_SNAKE_CASE							(		self ,   lowerCamelCase_ ,   lowerCamelCase_ ,   lowerCamelCase_ = True ,   )	->							Union[UNetaDOutput, Tuple]:
														lowerCAmelCase__	       =     timestep
														if not torch.is_tensor(lowerCamelCase_       ):
																					lowerCAmelCase__	       =     torch.tensor([timesteps] ,   dtype=torch.long ,   device=sample.device       )
														elif torch.is_tensor(lowerCamelCase_       ) and len(timesteps.shape       ) == 0:
																					lowerCAmelCase__	       =     timesteps[None].to(sample.device       )

														lowerCAmelCase__	       =     self.time_proj(lowerCamelCase_       )
														if self.config.use_timestep_embedding:
																					lowerCAmelCase__	       =     self.time_mlp(lowerCamelCase_       )
														else:
																					lowerCAmelCase__	       =     timestep_embed[..., None]
																					lowerCAmelCase__	       =     timestep_embed.repeat([1, 1, sample.shape[2]]       ).to(sample.dtype       )
																					lowerCAmelCase__	       =     timestep_embed.broadcast_to((sample.shape[:1] + timestep_embed.shape[1:])       )

														# 2. down
														lowerCAmelCase__	       =     ()
														for downsample_block in self.down_blocks:
																					lowerCAmelCase__       , lowerCAmelCase__	       =     downsample_block(hidden_states=lowerCamelCase_ ,   temb=lowerCamelCase_       )
																					down_block_res_samples += res_samples

														# 3. mid
														if self.mid_block:
																					lowerCAmelCase__	       =     self.mid_block(lowerCamelCase_ ,   lowerCamelCase_       )

														# 4. up
														for i, upsample_block in enumerate(self.up_blocks       ):
																					lowerCAmelCase__	       =     down_block_res_samples[-1:]
																					lowerCAmelCase__	       =     down_block_res_samples[:-1]
																					lowerCAmelCase__	       =     upsample_block(lowerCamelCase_ ,   res_hidden_states_tuple=lowerCamelCase_ ,   temb=lowerCamelCase_       )

														# 5. post-process
														if self.out_block:
																					lowerCAmelCase__	       =     self.out_block(lowerCamelCase_ ,   lowerCamelCase_       )

														if not return_dict:
																					return (sample,)

														return UNetaDOutput(sample=lowerCamelCase_       )