
'''simple docstring'''

import argparse
import json
import os

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import ConvNextImageProcessor, ConvNextVaConfig, ConvNextVaForImageClassification
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


logging.set_verbosity_info()
UpperCAmelCase__				:	Dict					=						logging.get_logger(__name__)







def 					A       ( UpperCamelCase_   :       Dict )   ->    List[str]:







			'''simple docstring'''



			lowerCAmelCase__									=							ConvNextVaConfig()

			if "atto" in checkpoint_url:
						lowerCAmelCase__									=							[2, 2, 6, 2]
						lowerCAmelCase__									=							[40, 80, 1_60, 3_20]
			if "femto" in checkpoint_url:
						lowerCAmelCase__									=							[2, 2, 6, 2]
						lowerCAmelCase__									=							[48, 96, 1_92, 3_84]
			if "pico" in checkpoint_url:
						lowerCAmelCase__									=							[2, 2, 6, 2]
						lowerCAmelCase__									=							[64, 1_28, 2_56, 5_12]
			if "nano" in checkpoint_url:
						lowerCAmelCase__									=							[2, 2, 8, 2]
						lowerCAmelCase__									=							[80, 1_60, 3_20, 6_40]
			if "tiny" in checkpoint_url:
						lowerCAmelCase__									=							[3, 3, 9, 3]
						lowerCAmelCase__									=							[96, 1_92, 3_84, 7_68]
			if "base" in checkpoint_url:
						lowerCAmelCase__									=							[3, 3, 27, 3]
						lowerCAmelCase__									=							[1_28, 2_56, 5_12, 10_24]
			if "large" in checkpoint_url:
						lowerCAmelCase__									=							[3, 3, 27, 3]
						lowerCAmelCase__									=							[1_92, 3_84, 7_68, 15_36]
			if "huge" in checkpoint_url:
						lowerCAmelCase__									=							[3, 3, 27, 3]
						lowerCAmelCase__									=							[3_52, 7_04, 14_08, 28_16]

			lowerCAmelCase__									=							10_00
			lowerCAmelCase__									=							"imagenet-1k-id2label.json"
			lowerCAmelCase__									=							(1, 10_00)

			lowerCAmelCase__									=							"huggingface/label-files"
			lowerCAmelCase__									=							num_labels
			lowerCAmelCase__									=							json.load(open(hf_hub_download(UpperCamelCase_  ,		UpperCamelCase_  ,		repo_type="dataset" )  ,		"r" ) )
			lowerCAmelCase__									=							{int(UpperCamelCase_ ): v for k, v in idalabel.items()}

			lowerCAmelCase__									=							idalabel
			lowerCAmelCase__									=							{v: k for k, v in idalabel.items()}
			lowerCAmelCase__									=							hidden_sizes
			lowerCAmelCase__									=							depths

			return config, expected_shape







def 					A       ( UpperCamelCase_   :       Tuple )   ->    int:







			'''simple docstring'''



			if "downsample_layers.0.0" in name:
						lowerCAmelCase__									=							name.replace("downsample_layers.0.0"  ,		"embeddings.patch_embeddings" )
			if "downsample_layers.0.1" in name:
						lowerCAmelCase__									=							name.replace("downsample_layers.0.1"  ,		"embeddings.norm" )  # we rename to layernorm later on
			if "downsample_layers.1.0" in name:
						lowerCAmelCase__									=							name.replace("downsample_layers.1.0"  ,		"stages.1.downsampling_layer.0" )
			if "downsample_layers.1.1" in name:
						lowerCAmelCase__									=							name.replace("downsample_layers.1.1"  ,		"stages.1.downsampling_layer.1" )
			if "downsample_layers.2.0" in name:
						lowerCAmelCase__									=							name.replace("downsample_layers.2.0"  ,		"stages.2.downsampling_layer.0" )
			if "downsample_layers.2.1" in name:
						lowerCAmelCase__									=							name.replace("downsample_layers.2.1"  ,		"stages.2.downsampling_layer.1" )
			if "downsample_layers.3.0" in name:
						lowerCAmelCase__									=							name.replace("downsample_layers.3.0"  ,		"stages.3.downsampling_layer.0" )
			if "downsample_layers.3.1" in name:
						lowerCAmelCase__									=							name.replace("downsample_layers.3.1"  ,		"stages.3.downsampling_layer.1" )
			if "stages" in name and "downsampling_layer" not in name:
						# stages.0.0. for instance should be renamed to stages.0.layers.0.
						lowerCAmelCase__									=							name[: len("stages.0" )] + ".layers" + name[len("stages.0" ) :]
			if "gamma" in name:
						lowerCAmelCase__									=							name.replace("gamma"  ,		"weight" )
			if "beta" in name:
						lowerCAmelCase__									=							name.replace("beta"  ,		"bias" )
			if "stages" in name:
						lowerCAmelCase__									=							name.replace("stages"  ,		"encoder.stages" )
			if "norm" in name:
						lowerCAmelCase__									=							name.replace("norm"  ,		"layernorm" )
			if "head" in name:
						lowerCAmelCase__									=							name.replace("head"  ,		"classifier" )

			return name







def 					A       ( )   ->    Any:







			'''simple docstring'''



			lowerCAmelCase__									=							"http://images.cocodataset.org/val2017/000000039769.jpg"
			lowerCAmelCase__									=							Image.open(requests.get(UpperCamelCase_  ,		stream=UpperCamelCase_ ).raw )
			return im







def 					A       ( UpperCamelCase_   :       Any )   ->    Tuple:







			'''simple docstring'''



			if "224" in checkpoint_url:
						lowerCAmelCase__									=							2_24
						lowerCAmelCase__									=							2_24 / 2_56
			elif "384" in checkpoint_url:
						lowerCAmelCase__									=							3_84
						lowerCAmelCase__									=							None
			else:
						lowerCAmelCase__									=							5_12
						lowerCAmelCase__									=							None

			return ConvNextImageProcessor(
			    size=UpperCamelCase_  ,		crop_pct=UpperCamelCase_  ,		image_mean=[0.485, 0.456, 0.406]  ,		image_std=[0.229, 0.224, 0.225]  ,		resample=PILImageResampling.BICUBIC  ,		)







@torch.no_grad()
def 					A       ( UpperCamelCase_   :       str  ,		UpperCamelCase_   :       List[Any]  ,		UpperCamelCase_   :       List[Any]  ,		UpperCamelCase_   :       Optional[Any] )   ->    str:







			'''simple docstring'''



			print("Downloading original model from checkpoint..." )
			# define ConvNeXTV2 configuration based on URL
			lowerCAmelCase__		,lowerCAmelCase__									=							get_convnextva_config(UpperCamelCase_ )
			# load original state_dict from URL
			lowerCAmelCase__									=							torch.hub.load_state_dict_from_url(UpperCamelCase_ )["model"]

			print("Converting model parameters..." )
			# rename keys
			for key in state_dict.copy().keys():
						lowerCAmelCase__									=							state_dict.pop(UpperCamelCase_ )
						lowerCAmelCase__									=							val
			# add prefix to all keys expect classifier head
			for key in state_dict.copy().keys():
						lowerCAmelCase__									=							state_dict.pop(UpperCamelCase_ )
						if not key.startswith("classifier" ):
									lowerCAmelCase__									=							"convnextv2." + key
						lowerCAmelCase__									=							val

			# load HuggingFace model
			lowerCAmelCase__									=							ConvNextVaForImageClassification(UpperCamelCase_ )
			model.load_state_dict(UpperCamelCase_ )
			model.eval()

			# Check outputs on an image, prepared by ConvNextImageProcessor
			lowerCAmelCase__									=							convert_preprocessor(UpperCamelCase_ )
			lowerCAmelCase__									=							preprocessor(images=prepare_img()  ,		return_tensors="pt" )
			lowerCAmelCase__									=							model(**UpperCamelCase_ ).logits

			# note: the logits below were obtained without center cropping
			if checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_atto_1k_224_ema.pt":
						lowerCAmelCase__									=							torch.tensor([-0.3_930, 0.1_747, -0.5_246, 0.4_177, 0.4_295] )
			elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_femto_1k_224_ema.pt":
						lowerCAmelCase__									=							torch.tensor([-0.1_727, -0.5_341, -0.7_818, -0.4_745, -0.6_566] )
			elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_pico_1k_224_ema.pt":
						lowerCAmelCase__									=							torch.tensor([-0.0_333, 0.1_563, -0.9_137, 0.1_054, 0.0_381] )
			elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_nano_1k_224_ema.pt":
						lowerCAmelCase__									=							torch.tensor([-0.1_744, -0.1_555, -0.0_713, 0.0_950, -0.1_431] )
			elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_tiny_1k_224_ema.pt":
						lowerCAmelCase__									=							torch.tensor([0.9_996, 0.1_966, -0.4_386, -0.3_472, 0.6_661] )
			elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_base_1k_224_ema.pt":
						lowerCAmelCase__									=							torch.tensor([-0.2_553, -0.6_708, -0.1_359, 0.2_518, -0.2_488] )
			elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_large_1k_224_ema.pt":
						lowerCAmelCase__									=							torch.tensor([-0.0_673, -0.5_627, -0.3_753, -0.2_722, 0.0_178] )
			elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_huge_1k_224_ema.pt":
						lowerCAmelCase__									=							torch.tensor([-0.6_377, -0.7_458, -0.2_150, 0.1_184, -0.0_597] )
			elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_nano_22k_224_ema.pt":
						lowerCAmelCase__									=							torch.tensor([1.0_799, 0.2_322, -0.8_860, 1.0_219, 0.6_231] )
			elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_nano_22k_384_ema.pt":
						lowerCAmelCase__									=							torch.tensor([0.3_766, 0.4_917, -1.1_426, 0.9_942, 0.6_024] )
			elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_tiny_22k_224_ema.pt":
						lowerCAmelCase__									=							torch.tensor([0.4_220, -0.6_919, -0.4_317, -0.2_881, -0.6_609] )
			elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_tiny_22k_384_ema.pt":
						lowerCAmelCase__									=							torch.tensor([0.1_082, -0.8_286, -0.5_095, 0.4_681, -0.8_085] )
			elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_base_22k_224_ema.pt":
						lowerCAmelCase__									=							torch.tensor([-0.2_419, -0.6_221, 0.2_176, -0.0_980, -0.7_527] )
			elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_base_22k_384_ema.pt":
						lowerCAmelCase__									=							torch.tensor([0.0_391, -0.4_371, 0.3_786, 0.1_251, -0.2_784] )
			elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_large_22k_224_ema.pt":
						lowerCAmelCase__									=							torch.tensor([-0.0_504, 0.5_636, -0.1_729, -0.6_507, -0.3_949] )
			elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_large_22k_384_ema.pt":
						lowerCAmelCase__									=							torch.tensor([0.3_560, 0.9_486, 0.3_149, -0.2_667, -0.5_138] )
			elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_huge_22k_384_ema.pt":
						lowerCAmelCase__									=							torch.tensor([-0.2_469, -0.4_550, -0.5_853, -0.0_810, 0.0_309] )
			elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_huge_22k_512_ema.pt":
						lowerCAmelCase__									=							torch.tensor([-0.3_090, 0.0_802, -0.0_682, -0.1_979, -0.2_826] )
			else:
						raise ValueError(F"""Unknown URL: {checkpoint_url}""" )

			assert torch.allclose(logits[0, :5]  ,		UpperCamelCase_  ,		atol=1E-3 )
			assert logits.shape == expected_shape
			print("Model outputs match the original results!" )

			if save_model:
						print("Saving model to local..." )
						# Create folder to save model
						if not os.path.isdir(UpperCamelCase_ ):
									os.mkdir(UpperCamelCase_ )

						model.save_pretrained(UpperCamelCase_ )
						preprocessor.save_pretrained(UpperCamelCase_ )

			lowerCAmelCase__									=							"convnextv2"
			if "atto" in checkpoint_url:
						model_name += "-atto"
			if "femto" in checkpoint_url:
						model_name += "-femto"
			if "pico" in checkpoint_url:
						model_name += "-pico"
			if "nano" in checkpoint_url:
						model_name += "-nano"
			elif "tiny" in checkpoint_url:
						model_name += "-tiny"
			elif "base" in checkpoint_url:
						model_name += "-base"
			elif "large" in checkpoint_url:
						model_name += "-large"
			elif "huge" in checkpoint_url:
						model_name += "-huge"
			if "22k" in checkpoint_url and "1k" not in checkpoint_url:
						model_name += "-22k"
			elif "22k" in checkpoint_url and "1k" in checkpoint_url:
						model_name += "-22k-1k"
			elif "1k" in checkpoint_url:
						model_name += "-1k"
			if "224" in checkpoint_url:
						model_name += "-224"
			elif "384" in checkpoint_url:
						model_name += "-384"
			elif "512" in checkpoint_url:
						model_name += "-512"

			if push_to_hub:
						print(F"""Pushing {model_name} to the hub...""" )
						model.push_to_hub(UpperCamelCase_ )
						preprocessor.push_to_hub(UpperCamelCase_ )


if __name__ == "__main__":
	UpperCAmelCase__				:	Optional[Any]					=						argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    "--checkpoint_url",
	    default="https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_atto_1k_224_ema.pt",
	    type=str,
	    help="URL of the original ConvNeXTV2 checkpoint you'd like to convert.",
	)
	parser.add_argument(
	    "--pytorch_dump_folder_path",
	    default="model",
	    type=str,
	    help="Path to the output PyTorch model directory.",
	)
	parser.add_argument("--save_model", action="store_true", help="Save model to local")
	parser.add_argument("--push_to_hub", action="store_true", help="Push model and image preprocessor to the hub")

	UpperCAmelCase__				:	List[str]					=						parser.parse_args()
	convert_convnextva_checkpoint(
	    args.checkpoint_url, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub
	)







'''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 				A       (		SCREAMING_SNAKE_CASE__      ):
	snake_case__ :torch.FloatTensor






class 				A       (		SCREAMING_SNAKE_CASE__					, SCREAMING_SNAKE_CASE__      ):





	@register_to_config
	def __init__(   self     :    Tuple ,							__magic_name__     :    Optional[Union[int, Tuple[int, int]]] = None ,							__magic_name__     :    int = 3 ,							__magic_name__     :    int = 3 ,							__magic_name__     :    bool = False ,							__magic_name__     :    str = "positional" ,							__magic_name__     :    int = 0 ,							__magic_name__     :    bool = True ,							__magic_name__     :    Tuple[str] = ("DownBlock2D", "AttnDownBlock2D", "AttnDownBlock2D", "AttnDownBlock2D") ,							__magic_name__     :    Tuple[str] = ("AttnUpBlock2D", "AttnUpBlock2D", "AttnUpBlock2D", "UpBlock2D") ,							__magic_name__     :    Tuple[int] = (224, 448, 672, 896) ,							__magic_name__     :    int = 2 ,							__magic_name__     :    float = 1 ,							__magic_name__     :    int = 1 ,							__magic_name__     :    str = "conv" ,							__magic_name__     :    str = "conv" ,							__magic_name__     :    str = "silu" ,							__magic_name__     :    Optional[int] = 8 ,							__magic_name__     :    int = 32 ,							__magic_name__     :    float = 1E-5 ,							__magic_name__     :    str = "default" ,							__magic_name__     :    bool = True ,							__magic_name__     :    Optional[str] = None ,							__magic_name__     :    Optional[int] = None ,							):


				"""simple docstring"""

				super().__init__()

				lowerCAmelCase__									=							sample_size
				lowerCAmelCase__									=							block_out_channels[0] * 4

				# Check inputs
				if len(__magic_name__							) != len(__magic_name__							):
							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(__magic_name__							) != len(__magic_name__							):
							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
				lowerCAmelCase__									=							nn.Convad(__magic_name__ ,							block_out_channels[0] ,							kernel_size=3 ,							padding=(1, 1)							)

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

				lowerCAmelCase__									=							TimestepEmbedding(__magic_name__ ,							__magic_name__							)

				# class embedding
				if class_embed_type is None and num_class_embeds is not None:
							lowerCAmelCase__									=							nn.Embedding(__magic_name__ ,							__magic_name__							)
				elif class_embed_type == "timestep":
							lowerCAmelCase__									=							TimestepEmbedding(__magic_name__ ,							__magic_name__							)
				elif class_embed_type == "identity":
							lowerCAmelCase__									=							nn.Identity(__magic_name__ ,							__magic_name__							)
				else:
							lowerCAmelCase__									=							None

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

				# down
				lowerCAmelCase__									=							block_out_channels[0]
				for i, down_block_type in enumerate(__magic_name__							):
							lowerCAmelCase__									=							output_channel
							lowerCAmelCase__									=							block_out_channels[i]
							lowerCAmelCase__									=							i == len(__magic_name__							) - 1

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

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

				# up
				lowerCAmelCase__									=							list(reversed(__magic_name__							)							)
				lowerCAmelCase__									=							reversed_block_out_channels[0]
				for i, up_block_type in enumerate(__magic_name__							):
							lowerCAmelCase__									=							output_channel
							lowerCAmelCase__									=							reversed_block_out_channels[i]
							lowerCAmelCase__									=							reversed_block_out_channels[min(i + 1 ,							len(__magic_name__							) - 1							)]

							lowerCAmelCase__									=							i == len(__magic_name__							) - 1

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

				# out
				lowerCAmelCase__									=							norm_num_groups if norm_num_groups is not None else min(block_out_channels[0] // 4 ,							32							)
				lowerCAmelCase__									=							nn.GroupNorm(num_channels=block_out_channels[0] ,							num_groups=__magic_name__ ,							eps=__magic_name__							)
				lowerCAmelCase__									=							nn.SiLU()
				lowerCAmelCase__									=							nn.Convad(block_out_channels[0] ,							__magic_name__ ,							kernel_size=3 ,							padding=1							)







	def 							__SCREAMING_SNAKE_CASE					(   self     :    Optional[Any] ,							__magic_name__     :    torch.FloatTensor ,							__magic_name__     :    Union[torch.Tensor, float, int] ,							__magic_name__     :    Optional[torch.Tensor] = None ,							__magic_name__     :    bool = True ,							):


				"""simple docstring"""

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

				# 1. time
				lowerCAmelCase__									=							timestep
				if not torch.is_tensor(__magic_name__							):
							lowerCAmelCase__									=							torch.tensor([timesteps] ,							dtype=torch.long ,							device=sample.device							)
				elif torch.is_tensor(__magic_name__							) and len(timesteps.shape							) == 0:
							lowerCAmelCase__									=							timesteps[None].to(sample.device							)

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

				lowerCAmelCase__									=							self.time_proj(__magic_name__							)

				# 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.
				lowerCAmelCase__									=							t_emb.to(dtype=self.dtype							)
				lowerCAmelCase__									=							self.time_embedding(__magic_name__							)

				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":
										lowerCAmelCase__									=							self.time_proj(__magic_name__							)

							lowerCAmelCase__									=							self.class_embedding(__magic_name__							).to(dtype=self.dtype							)
							lowerCAmelCase__									=							emb + class_emb

				# 2. pre-process
				lowerCAmelCase__									=							sample
				lowerCAmelCase__									=							self.conv_in(__magic_name__							)

				# 3. down
				lowerCAmelCase__									=							(sample,)
				for downsample_block in self.down_blocks:
							if hasattr(__magic_name__ ,							"skip_conv"							):
										lowerCAmelCase__		,lowerCAmelCase__		,lowerCAmelCase__									=							downsample_block(
										    hidden_states=__magic_name__ ,							temb=__magic_name__ ,							skip_sample=__magic_name__							)
							else:
										lowerCAmelCase__		,lowerCAmelCase__									=							downsample_block(hidden_states=__magic_name__ ,							temb=__magic_name__							)

							down_block_res_samples += res_samples

				# 4. mid
				lowerCAmelCase__									=							self.mid_block(__magic_name__ ,							__magic_name__							)

				# 5. up
				lowerCAmelCase__									=							None
				for upsample_block in self.up_blocks:
							lowerCAmelCase__									=							down_block_res_samples[-len(upsample_block.resnets							) :]
							lowerCAmelCase__									=							down_block_res_samples[: -len(upsample_block.resnets							)]

							if hasattr(__magic_name__ ,							"skip_conv"							):
										lowerCAmelCase__		,lowerCAmelCase__									=							upsample_block(__magic_name__ ,							__magic_name__ ,							__magic_name__ ,							__magic_name__							)
							else:
										lowerCAmelCase__									=							upsample_block(__magic_name__ ,							__magic_name__ ,							__magic_name__							)

        # 6. post-process
				lowerCAmelCase__									=							self.conv_norm_out(__magic_name__							)
				lowerCAmelCase__									=							self.conv_act(__magic_name__							)
				lowerCAmelCase__									=							self.conv_out(__magic_name__							)

				if skip_sample is not None:
							sample += skip_sample

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

				if not return_dict:
							return (sample,)

				return UNetaDOutput(sample=__magic_name__							)





