

'''simple docstring'''


import inspect
import unittest

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

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


if is_torch_available():
						import torch
						from torch import nn

						from transformers import YolosForObjectDetection, YolosModel
						from transformers.models.yolos.modeling_yolos import YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
						from PIL import Image

						from transformers import AutoImageProcessor



class 					lowercase						:


							def __init__(			self    ,			_snake_case    ,			_snake_case=13    ,			_snake_case=[30, 30]    ,			_snake_case=2    ,			_snake_case=3    ,			_snake_case=True    ,			_snake_case=True    ,			_snake_case=32    ,			_snake_case=5    ,			_snake_case=4    ,			_snake_case=37    ,			_snake_case="gelu"    ,			_snake_case=0.1    ,			_snake_case=0.1    ,			_snake_case=10    ,			_snake_case=0.02    ,			_snake_case=3    ,			_snake_case=None    ,			_snake_case=8    ,			_snake_case=10    ,			)							->				Optional[Any]:
								UpperCAmelCase_		:      Dict       	=					parent
								UpperCAmelCase_		:      Union[str, Any]       	=					batch_size
								UpperCAmelCase_		:      Tuple       	=					image_size
								UpperCAmelCase_		:      int       	=					patch_size
								UpperCAmelCase_		:      Dict       	=					num_channels
								UpperCAmelCase_		:      Dict       	=					is_training
								UpperCAmelCase_		:      int       	=					use_labels
								UpperCAmelCase_		:      Dict       	=					hidden_size
								UpperCAmelCase_		:      Tuple       	=					num_hidden_layers
								UpperCAmelCase_		:      Dict       	=					num_attention_heads
								UpperCAmelCase_		:      Union[str, Any]       	=					intermediate_size
								UpperCAmelCase_		:      Optional[int]       	=					hidden_act
								UpperCAmelCase_		:      Any       	=					hidden_dropout_prob
								UpperCAmelCase_		:      Optional[Any]       	=					attention_probs_dropout_prob
								UpperCAmelCase_		:      Optional[Any]       	=					type_sequence_label_size
								UpperCAmelCase_		:      Optional[int]       	=					initializer_range
								UpperCAmelCase_		:      Optional[Any]       	=					num_labels
								UpperCAmelCase_		:      Union[str, Any]       	=					scope
								UpperCAmelCase_		:      Tuple       	=					n_targets
								UpperCAmelCase_		:      List[Any]       	=					num_detection_tokens
								# we set the expected sequence length (which is used in several tests)
								# expected sequence length = num_patches + 1 (we add 1 for the [CLS] token) + num_detection_tokens
								UpperCAmelCase_		:      Tuple       	=					(image_size[1] // patch_size) * (image_size[0] // patch_size)
								UpperCAmelCase_		:      Optional[int]       	=					num_patches + 1 + self.num_detection_tokens


							def 						_snake_case							(			self)							->				Optional[int]:
								UpperCAmelCase_		:      Union[str, Any]       	=					floats_tensor([self.batch_size, self.num_channels, self.image_size[0], self.image_size[1]])

								UpperCAmelCase_		:      List[Any]       	=					None
								if self.use_labels:
									# labels is a list of Dict (each Dict being the labels for a given example in the batch)
									UpperCAmelCase_		:      int       	=					[]
									for i in range(self.batch_size):
										UpperCAmelCase_		:      Optional[Any]       	=					{}
										UpperCAmelCase_		:      Tuple       	=					torch.randint(
										    high=self.num_labels    ,			size=(self.n_targets,)    ,			device=_snake_case)
										UpperCAmelCase_		:      List[str]       	=					torch.rand(self.n_targets    ,			4    ,			device=_snake_case)
										labels.append(_snake_case)

								UpperCAmelCase_		:      List[Any]       	=					self.get_config()

								return config, pixel_values, labels


							def 						_snake_case							(			self)							->				Optional[Any]:
								return YolosConfig(
								    image_size=self.image_size    ,			patch_size=self.patch_size    ,			num_channels=self.num_channels    ,			hidden_size=self.hidden_size    ,			num_hidden_layers=self.num_hidden_layers    ,			num_attention_heads=self.num_attention_heads    ,			intermediate_size=self.intermediate_size    ,			hidden_act=self.hidden_act    ,			hidden_dropout_prob=self.hidden_dropout_prob    ,			attention_probs_dropout_prob=self.attention_probs_dropout_prob    ,			is_decoder=_snake_case    ,			initializer_range=self.initializer_range    ,			num_detection_tokens=self.num_detection_tokens    ,			num_labels=self.num_labels    ,			)


							def 						_snake_case							(			self    ,			_snake_case    ,			_snake_case    ,			_snake_case)							->				List[Any]:
								UpperCAmelCase_		:      int       	=					YolosModel(config=_snake_case)
								model.to(_snake_case)
								model.eval()
								UpperCAmelCase_		:      List[str]       	=					model(_snake_case)
								self.parent.assertEqual(
								    result.last_hidden_state.shape    ,			(self.batch_size, self.expected_seq_len, self.hidden_size))


							def 						_snake_case							(			self    ,			_snake_case    ,			_snake_case    ,			_snake_case)							->				Any:
								UpperCAmelCase_		:      Any       	=					YolosForObjectDetection(_snake_case)
								model.to(_snake_case)
								model.eval()

								UpperCAmelCase_		:      List[str]       	=					model(pixel_values=_snake_case)
								UpperCAmelCase_		:      List[str]       	=					model(_snake_case)

								self.parent.assertEqual(result.logits.shape    ,			(self.batch_size, self.num_detection_tokens, self.num_labels + 1))
								self.parent.assertEqual(result.pred_boxes.shape    ,			(self.batch_size, self.num_detection_tokens, 4))

								UpperCAmelCase_		:      Tuple       	=					model(pixel_values=_snake_case    ,			labels=_snake_case)

								self.parent.assertEqual(result.loss.shape    ,			())
								self.parent.assertEqual(result.logits.shape    ,			(self.batch_size, self.num_detection_tokens, self.num_labels + 1))
								self.parent.assertEqual(result.pred_boxes.shape    ,			(self.batch_size, self.num_detection_tokens, 4))





							def 						_snake_case							(			self)							->				str:
								UpperCAmelCase_		:      Any       	=					self.prepare_config_and_inputs()
								UpperCAmelCase_	,    UpperCAmelCase_	,    UpperCAmelCase_		:      List[str]       	=					config_and_inputs
								UpperCAmelCase_		:      Optional[int]       	=					{'pixel_values': pixel_values}
								return config, inputs_dict



@require_torch
class 					lowercase						(  a_,					a_,					unittest.TestCase ):
							_lowerCamelCase		:      Union[str, Any]=   (YolosModel, YolosForObjectDetection) if is_torch_available() else ()
							_lowerCamelCase		:      Dict=   (
							    {"feature-extraction": YolosModel, "object-detection": YolosForObjectDetection} if is_torch_available() else {}
							)

							_lowerCamelCase		:      str=   False
							_lowerCamelCase		:      Optional[Any]=   False
							_lowerCamelCase		:      Tuple=   False
							_lowerCamelCase		:      str=   False


							def 						_snake_case							(			self    ,			_snake_case    ,			_snake_case    ,			_snake_case=False)							->				str:
								UpperCAmelCase_		:      Optional[int]       	=					super()._prepare_for_class(_snake_case    ,			_snake_case    ,			return_labels=_snake_case)

								if return_labels:
									if model_class.__name__ == "YolosForObjectDetection":
										UpperCAmelCase_		:      Optional[int]       	=					[]
										for i in range(self.model_tester.batch_size):
											UpperCAmelCase_		:      List[str]       	=					{}
											UpperCAmelCase_		:      Tuple       	=					torch.ones(
											    size=(self.model_tester.n_targets,)    ,			device=_snake_case    ,			dtype=torch.long)
											UpperCAmelCase_		:      str       	=					torch.ones(
											    self.model_tester.n_targets    ,			4    ,			device=_snake_case    ,			dtype=torch.float)
											labels.append(_snake_case)
										UpperCAmelCase_		:      Union[str, Any]       	=					labels

								return inputs_dict


							def 						_snake_case							(			self)							->				Any:
								UpperCAmelCase_		:      Dict       	=					YolosModelTester(self)
								UpperCAmelCase_		:      Optional[int]       	=					ConfigTester(self    ,			config_class=_snake_case    ,			has_text_modality=_snake_case    ,			hidden_size=37)


							def 						_snake_case							(			self)							->				str:
								self.config_tester.run_common_tests()


							def 						_snake_case							(			self)							->				List[str]:
								# YOLOS does not use inputs_embeds
								pass


							def 						_snake_case							(			self)							->				Tuple:
								UpperCAmelCase_	,    UpperCAmelCase_		:      Tuple       	=					self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
									UpperCAmelCase_		:      Optional[Any]       	=					model_class(_snake_case)
									self.assertIsInstance(model.get_input_embeddings()    ,			(nn.Module))
									UpperCAmelCase_		:      Dict       	=					model.get_output_embeddings()
									self.assertTrue(x is None or isinstance(_snake_case    ,			nn.Linear))


							def 						_snake_case							(			self)							->				Dict:
								UpperCAmelCase_	,    UpperCAmelCase_		:      Tuple       	=					self.model_tester.prepare_config_and_inputs_for_common()

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

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


							def 						_snake_case							(			self)							->				Tuple:
								UpperCAmelCase_		:      Tuple       	=					self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*_snake_case)


							def 						_snake_case							(			self)							->				Any:
								UpperCAmelCase_	,    UpperCAmelCase_		:      Any       	=					self.model_tester.prepare_config_and_inputs_for_common()
								UpperCAmelCase_		:      Tuple       	=					True

								# in YOLOS, the seq_len is different
								UpperCAmelCase_		:      str       	=					self.model_tester.expected_seq_len
								for model_class in self.all_model_classes:
									UpperCAmelCase_		:      Dict       	=					True
									UpperCAmelCase_		:      str       	=					False
									UpperCAmelCase_		:      str       	=					True
									UpperCAmelCase_		:      Union[str, Any]       	=					model_class(_snake_case)
									model.to(_snake_case)
									model.eval()
									with torch.no_grad():
										UpperCAmelCase_		:      Optional[int]       	=					model(**self._prepare_for_class(_snake_case    ,			_snake_case))
									UpperCAmelCase_		:      Tuple       	=					outputs.attentions
									self.assertEqual(len(_snake_case)    ,			self.model_tester.num_hidden_layers)

									# check that output_attentions also work using config
									del inputs_dict["output_attentions"]
									UpperCAmelCase_		:      List[Any]       	=					True
									UpperCAmelCase_		:      Any       	=					model_class(_snake_case)
									model.to(_snake_case)
									model.eval()
									with torch.no_grad():
										UpperCAmelCase_		:      Union[str, Any]       	=					model(**self._prepare_for_class(_snake_case    ,			_snake_case))
									UpperCAmelCase_		:      List[str]       	=					outputs.attentions
									self.assertEqual(len(_snake_case)    ,			self.model_tester.num_hidden_layers)

									self.assertListEqual(
									    list(attentions[0].shape[-3:])    ,			[self.model_tester.num_attention_heads, seq_len, seq_len]    ,			)
									UpperCAmelCase_		:      Union[str, Any]       	=					len(_snake_case)

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

									UpperCAmelCase_		:      Dict       	=					1
									self.assertEqual(out_len + added_hidden_states    ,			len(_snake_case))

									UpperCAmelCase_		:      Union[str, Any]       	=					outputs.attentions

									self.assertEqual(len(_snake_case)    ,			self.model_tester.num_hidden_layers)
									self.assertListEqual(
									    list(self_attentions[0].shape[-3:])    ,			[self.model_tester.num_attention_heads, seq_len, seq_len]    ,			)


							def 						_snake_case							(			self)							->				Optional[int]:
								def check_hidden_states_output(_snake_case    ,			_snake_case    ,			_snake_case):
									UpperCAmelCase_		:      int       	=					model_class(_snake_case)
									model.to(_snake_case)
									model.eval()

									with torch.no_grad():
										UpperCAmelCase_		:      Any       	=					model(**self._prepare_for_class(_snake_case    ,			_snake_case))

									UpperCAmelCase_		:      Tuple       	=					outputs.hidden_states

									UpperCAmelCase_		:      Dict       	=					getattr(
									    self.model_tester    ,			'expected_num_hidden_layers'    ,			self.model_tester.num_hidden_layers + 1)
									self.assertEqual(len(_snake_case)    ,			_snake_case)

									# YOLOS has a different seq_length
									UpperCAmelCase_		:      Any       	=					self.model_tester.expected_seq_len

									self.assertListEqual(
									    list(hidden_states[0].shape[-2:])    ,			[seq_length, self.model_tester.hidden_size]    ,			)

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

								for model_class in self.all_model_classes:
									UpperCAmelCase_		:      Dict       	=					True
									check_hidden_states_output(_snake_case    ,			_snake_case    ,			_snake_case)

									# check that output_hidden_states also work using config
									del inputs_dict["output_hidden_states"]
									UpperCAmelCase_		:      Tuple       	=					True

									check_hidden_states_output(_snake_case    ,			_snake_case    ,			_snake_case)


							def 						_snake_case							(			self)							->				List[Any]:
								UpperCAmelCase_		:      List[str]       	=					self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_object_detection(*_snake_case)





							@slow
							def 						_snake_case							(			self)							->				Optional[int]:
								for model_name in YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									UpperCAmelCase_		:      Optional[int]       	=					YolosModel.from_pretrained(_snake_case)
									self.assertIsNotNone(_snake_case)







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







@require_torch
@require_vision
class 					lowercase						(  unittest.TestCase ):


							@cached_property
							def 						_snake_case							(			self)							->				List[str]:
								return AutoImageProcessor.from_pretrained('hustvl/yolos-small') if is_vision_available() else None





							@slow
							def 						_snake_case							(			self)							->				Tuple:
								UpperCAmelCase_		:      Optional[int]       	=					YolosForObjectDetection.from_pretrained('hustvl/yolos-small').to(_snake_case)

								UpperCAmelCase_		:      Any       	=					self.default_image_processor
								UpperCAmelCase_		:      Optional[int]       	=					prepare_img()
								UpperCAmelCase_		:      Dict       	=					image_processor(images=_snake_case    ,			return_tensors='pt').to(_snake_case)

								# forward pass
								with torch.no_grad():
									UpperCAmelCase_		:      str       	=					model(inputs.pixel_values)

								# verify outputs
								UpperCAmelCase_		:      Optional[Any]       	=					torch.Size((1, 100, 92))
								self.assertEqual(outputs.logits.shape    ,			_snake_case)

								UpperCAmelCase_		:      Tuple       	=					torch.tensor(
								    [[-24.0_248, -10.3_024, -14.8_290], [-42.0_392, -16.8_200, -27.4_334], [-27.2_743, -11.8_154, -18.7_148]]    ,			device=_snake_case    ,			)
								UpperCAmelCase_		:      Optional[Any]       	=					torch.tensor(
								    [[0.2_559, 0.5_455, 0.4_706], [0.2_989, 0.7_279, 0.1_875], [0.7_732, 0.4_017, 0.4_462]]    ,			device=_snake_case)
								self.assertTrue(torch.allclose(outputs.logits[0, :3, :3]    ,			_snake_case    ,			atol=1e-4))
								self.assertTrue(torch.allclose(outputs.pred_boxes[0, :3, :3]    ,			_snake_case    ,			atol=1e-4))

								# verify postprocessing
								UpperCAmelCase_		:      Union[str, Any]       	=					image_processor.post_process_object_detection(
								    _snake_case    ,			threshold=0.3    ,			target_sizes=[image.size[::-1]])[0]
								UpperCAmelCase_		:      Tuple       	=					torch.tensor([0.9_994, 0.9_790, 0.9_964, 0.9_972, 0.9_861]).to(_snake_case)
								UpperCAmelCase_		:      Dict       	=					[75, 75, 17, 63, 17]
								UpperCAmelCase_		:      List[str]       	=					torch.tensor([335.0_609, 79.3_848, 375.4_216, 187.2_495]).to(_snake_case)

								self.assertEqual(len(results['scores'])    ,			5)
								self.assertTrue(torch.allclose(results['scores']    ,			_snake_case    ,			atol=1e-4))
								self.assertSequenceEqual(results['labels'].tolist()    ,			_snake_case)
								self.assertTrue(torch.allclose(results['boxes'][0, :]    ,			_snake_case))









'''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 					lowercase						(  a_ ):
							_lowerCamelCase		:      torch.FloatTensor



class 					lowercase						(  a_,					a_ ):


							@register_to_config
							def __init__(			self    ,			_snake_case = 6_5536    ,			_snake_case = None    ,			_snake_case = 2    ,			_snake_case = 2    ,			_snake_case = 0    ,			_snake_case = "fourier"    ,			_snake_case = True    ,			_snake_case = False    ,			_snake_case = 0.0    ,			_snake_case = ("DownBlock1DNoSkip", "DownBlock1D", "AttnDownBlock1D")    ,			_snake_case = ("AttnUpBlock1D", "UpBlock1D", "UpBlock1DNoSkip")    ,			_snake_case = "UNetMidBlock1D"    ,			_snake_case = None    ,			_snake_case = (32, 32, 64)    ,			_snake_case = None    ,			_snake_case = 8    ,			_snake_case = 1    ,			_snake_case = False    ,			)							->				List[str]:
								super().__init__()
								UpperCAmelCase_		:      Optional[Any]       	=					sample_size

								# time
								if time_embedding_type == "fourier":
									UpperCAmelCase_		:      Tuple       	=					GaussianFourierProjection(
									    embedding_size=8    ,			set_W_to_weight=_snake_case    ,			log=_snake_case    ,			flip_sin_to_cos=_snake_case)
									UpperCAmelCase_		:      int       	=					2 * block_out_channels[0]
								elif time_embedding_type == "positional":
									UpperCAmelCase_		:      Optional[Any]       	=					Timesteps(
									    block_out_channels[0]    ,			flip_sin_to_cos=_snake_case    ,			downscale_freq_shift=_snake_case)
									UpperCAmelCase_		:      List[Any]       	=					block_out_channels[0]

								if use_timestep_embedding:
									UpperCAmelCase_		:      Dict       	=					block_out_channels[0] * 4
									UpperCAmelCase_		:      List[Any]       	=					TimestepEmbedding(
									    in_channels=_snake_case    ,			time_embed_dim=_snake_case    ,			act_fn=_snake_case    ,			out_dim=block_out_channels[0]    ,			)

								UpperCAmelCase_		:      int       	=					nn.ModuleList([])
								UpperCAmelCase_		:      Optional[int]       	=					None
								UpperCAmelCase_		:      Optional[int]       	=					nn.ModuleList([])
								UpperCAmelCase_		:      Any       	=					None

								# down
								UpperCAmelCase_		:      Dict       	=					in_channels
								for i, down_block_type in enumerate(_snake_case):
									UpperCAmelCase_		:      int       	=					output_channel
									UpperCAmelCase_		:      Optional[int]       	=					block_out_channels[i]

									if i == 0:
										input_channel += extra_in_channels

									UpperCAmelCase_		:      int       	=					i == len(_snake_case) - 1

									UpperCAmelCase_		:      Any       	=					get_down_block(
									    _snake_case    ,			num_layers=_snake_case    ,			in_channels=_snake_case    ,			out_channels=_snake_case    ,			temb_channels=block_out_channels[0]    ,			add_downsample=not is_final_block or downsample_each_block    ,			)
									self.down_blocks.append(_snake_case)

								# mid
								UpperCAmelCase_		:      Optional[int]       	=					get_mid_block(
								    _snake_case    ,			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=_snake_case    ,			add_downsample=_snake_case    ,			)

								# up
								UpperCAmelCase_		:      Union[str, Any]       	=					list(reversed(_snake_case))
								UpperCAmelCase_		:      Optional[int]       	=					reversed_block_out_channels[0]
								if out_block_type is None:
									UpperCAmelCase_		:      Tuple       	=					out_channels
								else:
									UpperCAmelCase_		:      int       	=					block_out_channels[0]

								for i, up_block_type in enumerate(_snake_case):
									UpperCAmelCase_		:      Dict       	=					output_channel
									UpperCAmelCase_		:      Optional[Any]       	=					(
									    reversed_block_out_channels[i + 1] if i < len(_snake_case) - 1 else final_upsample_channels
									)

									UpperCAmelCase_		:      str       	=					i == len(_snake_case) - 1

									UpperCAmelCase_		:      Union[str, Any]       	=					get_up_block(
									    _snake_case    ,			num_layers=_snake_case    ,			in_channels=_snake_case    ,			out_channels=_snake_case    ,			temb_channels=block_out_channels[0]    ,			add_upsample=not is_final_block    ,			)
									self.up_blocks.append(_snake_case)
									UpperCAmelCase_		:      Dict       	=					output_channel

								# out
								UpperCAmelCase_		:      Union[str, Any]       	=					norm_num_groups if norm_num_groups is not None else min(block_out_channels[0] // 4    ,			32)
								UpperCAmelCase_		:      Any       	=					get_out_block(
								    out_block_type=_snake_case    ,			num_groups_out=_snake_case    ,			embed_dim=block_out_channels[0]    ,			out_channels=_snake_case    ,			act_fn=_snake_case    ,			fc_dim=block_out_channels[-1] // 4    ,			)





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

								UpperCAmelCase_		:      Any       	=					self.time_proj(_snake_case)
								if self.config.use_timestep_embedding:
									UpperCAmelCase_		:      int       	=					self.time_mlp(_snake_case)
								else:
									UpperCAmelCase_		:      int       	=					timestep_embed[..., None]
									UpperCAmelCase_		:      List[Any]       	=					timestep_embed.repeat([1, 1, sample.shape[2]]).to(sample.dtype)
									UpperCAmelCase_		:      int       	=					timestep_embed.broadcast_to((sample.shape[:1] + timestep_embed.shape[1:]))

								# 2. down
								UpperCAmelCase_		:      Optional[Any]       	=					()
								for downsample_block in self.down_blocks:
									UpperCAmelCase_	,    UpperCAmelCase_		:      Optional[int]       	=					downsample_block(hidden_states=_snake_case    ,			temb=_snake_case)
									down_block_res_samples += res_samples

								# 3. mid
								if self.mid_block:
									UpperCAmelCase_		:      List[Any]       	=					self.mid_block(_snake_case    ,			_snake_case)

								# 4. up
								for i, upsample_block in enumerate(self.up_blocks):
									UpperCAmelCase_		:      int       	=					down_block_res_samples[-1:]
									UpperCAmelCase_		:      Tuple       	=					down_block_res_samples[:-1]
									UpperCAmelCase_		:      List[Any]       	=					upsample_block(_snake_case    ,			res_hidden_states_tuple=_snake_case    ,			temb=_snake_case)

								# 5. post-process
								if self.out_block:
									UpperCAmelCase_		:      Optional[Any]       	=					self.out_block(_snake_case    ,			_snake_case)

								if not return_dict:
									return (sample,)

								return UNetaDOutput(sample=_snake_case)






