
"""simple docstring"""




import argparse
import json

import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm.models import create_model

from transformers import (
    BeitImageProcessor,
    DataaVecVisionConfig,
    DataaVecVisionForImageClassification,
    DataaVecVisionModel,
)



def 				a__			(					lowerCAmelCase						:							Optional[int]							,     lowerCAmelCase						:							Optional[int]=False							,     lowerCAmelCase						:							Any=False							,     lowerCAmelCase						:							str="data2vec."		):






     '''simple docstring'''
     UpperCAmelCase__				:			int							=    "backbone." if is_semantic else ""

     UpperCAmelCase__				:			Union[str, Any]							=    []
     for i in range(config.num_hidden_layers		):
          # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
          rename_keys.append(
              (F"{prefix}blocks.{i}.norm1.weight", F"{hf_prefix}encoder.layer.{i}.layernorm_before.weight")		)
          rename_keys.append((F"{prefix}blocks.{i}.norm1.bias", F"{hf_prefix}encoder.layer.{i}.layernorm_before.bias")		)
          rename_keys.append(
              (F"{prefix}blocks.{i}.attn.proj.weight", F"{hf_prefix}encoder.layer.{i}.attention.output.dense.weight")		)
          rename_keys.append(
              (F"{prefix}blocks.{i}.attn.proj.bias", F"{hf_prefix}encoder.layer.{i}.attention.output.dense.bias")		)
          rename_keys.append(
              (F"{prefix}blocks.{i}.norm2.weight", F"{hf_prefix}encoder.layer.{i}.layernorm_after.weight")		)
          rename_keys.append((F"{prefix}blocks.{i}.norm2.bias", F"{hf_prefix}encoder.layer.{i}.layernorm_after.bias")		)
          rename_keys.append(
              (F"{prefix}blocks.{i}.mlp.fc1.weight", F"{hf_prefix}encoder.layer.{i}.intermediate.dense.weight")		)
          rename_keys.append(
              (F"{prefix}blocks.{i}.mlp.fc1.bias", F"{hf_prefix}encoder.layer.{i}.intermediate.dense.bias")		)
          rename_keys.append((F"{prefix}blocks.{i}.mlp.fc2.weight", F"{hf_prefix}encoder.layer.{i}.output.dense.weight")		)
          rename_keys.append((F"{prefix}blocks.{i}.mlp.fc2.bias", F"{hf_prefix}encoder.layer.{i}.output.dense.bias")		)

     # projection layer + position embeddings
     rename_keys.extend(
         [
             (F"{prefix}cls_token", F"{hf_prefix}embeddings.cls_token"),
             (F"{prefix}patch_embed.proj.weight", F"{hf_prefix}embeddings.patch_embeddings.projection.weight"),
             (F"{prefix}patch_embed.proj.bias", F"{hf_prefix}embeddings.patch_embeddings.projection.bias"),
         ]		)

     if has_lm_head:
          # mask token + shared relative position bias + layernorm
          rename_keys.extend(
              [
                  ("mask_token", F"{hf_prefix}embeddings.mask_token"),
                  (
                      "rel_pos_bias.relative_position_bias_table",
                      F"{hf_prefix}encoder.relative_position_bias.relative_position_bias_table",
                  ),
                  (
                      "rel_pos_bias.relative_position_index",
                      F"{hf_prefix}encoder.relative_position_bias.relative_position_index",
                  ),
                  ("norm.weight", "layernorm.weight"),
                  ("norm.bias", "layernorm.bias"),
              ]		)
     elif is_semantic:
          # semantic segmentation classification heads
          rename_keys.extend(
              [
                  ("decode_head.conv_seg.weight", "decode_head.classifier.weight"),
                  ("decode_head.conv_seg.bias", "decode_head.classifier.bias"),
                  ("auxiliary_head.conv_seg.weight", "auxiliary_head.classifier.weight"),
                  ("auxiliary_head.conv_seg.bias", "auxiliary_head.classifier.bias"),
              ]		)
     else:
          # layernorm + classification head
          rename_keys.extend(
              [
                  ("fc_norm.weight", F"{hf_prefix}pooler.layernorm.weight"),
                  ("fc_norm.bias", F"{hf_prefix}pooler.layernorm.bias"),
                  ("head.weight", "classifier.weight"),
                  ("head.bias", "classifier.bias"),
              ]		)

     return rename_keys



def 				a__			(					lowerCAmelCase						:							Any							,     lowerCAmelCase						:							Optional[int]							,     lowerCAmelCase						:							Optional[int]=False							,     lowerCAmelCase						:							List[str]=False							,     lowerCAmelCase						:							Optional[int]="data2vec_vision."		):






     '''simple docstring'''
     for i in range(config.num_hidden_layers		):
          UpperCAmelCase__				:			Union[str, Any]							=    "backbone." if is_semantic else ""
          # queries, keys and values
          UpperCAmelCase__				:			int							=    state_dict.pop(F"{prefix}blocks.{i}.attn.qkv.weight"		)
          UpperCAmelCase__				:			Optional[Any]							=    state_dict.pop(F"{prefix}blocks.{i}.attn.q_bias"		)
          UpperCAmelCase__				:			int							=    state_dict.pop(F"{prefix}blocks.{i}.attn.v_bias"		)

          UpperCAmelCase__				:			Optional[Any]							=    in_proj_weight[
              : config.hidden_size, :
          ]
          UpperCAmelCase__				:			List[str]							=    q_bias
          UpperCAmelCase__				:			List[Any]							=    in_proj_weight[
              config.hidden_size : config.hidden_size * 2, :
          ]
          UpperCAmelCase__				:			List[str]							=    in_proj_weight[
              -config.hidden_size :, :
          ]
          UpperCAmelCase__				:			str							=    v_bias

          # gamma_1 and gamma_2
          # we call them lambda because otherwise they are renamed when using .from_pretrained
          UpperCAmelCase__				:			Optional[int]							=    state_dict.pop(F"{prefix}blocks.{i}.gamma_1"		)
          UpperCAmelCase__				:			List[Any]							=    state_dict.pop(F"{prefix}blocks.{i}.gamma_2"		)

          UpperCAmelCase__				:			Dict							=    gamma_a
          UpperCAmelCase__				:			Any							=    gamma_a

          # relative_position bias table + index
          if not has_lm_head:
               # each layer has its own relative position bias
               UpperCAmelCase__				:			List[Any]							=    state_dict.pop(F"{prefix}blocks.{i}.attn.relative_position_bias_table"		)
               UpperCAmelCase__				:			Optional[int]							=    state_dict.pop(F"{prefix}blocks.{i}.attn.relative_position_index"		)

               UpperCAmelCase__				:			Dict							=    table
               UpperCAmelCase__				:			List[str]							=    index



def 				a__			(					):






     '''simple docstring'''
     UpperCAmelCase__				:			Optional[int]							=    argparse.ArgumentParser(
         "Convert Data2VecVision to HF for image classification and pretraining"							,     add_help=lowerCAmelCase		)
     parser.add_argument("--hf_checkpoint_name"							,     type=lowerCAmelCase		)
     parser.add_argument("--input_size"							,     default=224							,     type=lowerCAmelCase							,     help="images input size"		)
     parser.add_argument("--beit_checkpoint"							,     default=""							,     help="beit checkpoint"		)

     return parser.parse_args()



def 				a__			(					lowerCAmelCase						:							Optional[int]							,     lowerCAmelCase						:							Dict							,     lowerCAmelCase						:							Any		):






     '''simple docstring'''
     def load_state_dict(lowerCAmelCase						:							Union[str, Any]							,     lowerCAmelCase						:							List[str]							,     lowerCAmelCase						:							Tuple=""							,     lowerCAmelCase						:							List[Any]="relative_position_index"		):
          UpperCAmelCase__				:			str							=    []
          UpperCAmelCase__				:			List[str]							=    []
          UpperCAmelCase__				:			List[str]							=    []
          # copy state_dict so _load_from_state_dict can modify it
          UpperCAmelCase__				:			Optional[int]							=    getattr(lowerCAmelCase							,     "_metadata"							,     lowerCAmelCase		)
          UpperCAmelCase__				:			str							=    state_dict.copy()
          if metadata is not None:
               UpperCAmelCase__				:			str							=    metadata

          def load(lowerCAmelCase						:							Union[str, Any]							,     lowerCAmelCase						:							Union[str, Any]=""		):
               UpperCAmelCase__				:			str							=    {} if metadata is None else metadata.get(prefix[:-1]							,     {}		)
               module._load_from_state_dict(
                   lowerCAmelCase							,     lowerCAmelCase							,     lowerCAmelCase							,     lowerCAmelCase							,     lowerCAmelCase							,     lowerCAmelCase							,     lowerCAmelCase		)
               for name, child in module._modules.items():
                    if child is not None:
                         load(lowerCAmelCase							,     prefix + name + "."		)

          load(lowerCAmelCase							,     prefix=lowerCAmelCase		)

          UpperCAmelCase__				:			Optional[Any]							=    []
          UpperCAmelCase__				:			Any							=    []
          for key in missing_keys:
               UpperCAmelCase__				:			Optional[Any]							=    True
               for ignore_key in ignore_missing.split("|"		):
                    if ignore_key in key:
                         UpperCAmelCase__				:			List[str]							=    False
                         break
               if keep_flag:
                    warn_missing_keys.append(lowerCAmelCase		)
               else:
                    ignore_missing_keys.append(lowerCAmelCase		)

          UpperCAmelCase__				:			int							=    warn_missing_keys

          if len(lowerCAmelCase		) > 0:
               print(
                   "Weights of {} not initialized from pretrained model: {}".format(
                       model.__class__.__name__							,     lowerCAmelCase		)		)
          if len(lowerCAmelCase		) > 0:
               print("Weights from pretrained model not used in {}: {}".format(model.__class__.__name__							,     lowerCAmelCase		)		)
          if len(lowerCAmelCase		) > 0:
               print(
                   "Ignored weights of {} not initialized from pretrained model: {}".format(
                       model.__class__.__name__							,     lowerCAmelCase		)		)
          if len(lowerCAmelCase		) > 0:
               print("\n".join(lowerCAmelCase		)		)

     UpperCAmelCase__				:			Dict							=    {
         "pretrained": False,
         "use_shared_rel_pos_bias": True,
         "use_abs_pos_emb": False,
         "init_values": 0.1,
     }

     if is_finetuned:
          model_kwargs.update(
              {
                  "num_classes": 1000,
                  "use_mean_pooling": True,
                  "init_scale": 0.001,
                  "use_rel_pos_bias": True,
              }		)

     UpperCAmelCase__				:			List[Any]							=    create_model(
         "beit_large_patch16_224" if is_large else "beit_base_patch16_224"							,     **lowerCAmelCase							,     )
     UpperCAmelCase__				:			str							=    model.patch_embed.patch_size
     UpperCAmelCase__				:			List[str]							=    (args.input_size // patch_size[0], args.input_size // patch_size[1])
     UpperCAmelCase__				:			int							=    torch.load(args.beit_checkpoint							,     map_location="cpu"		)

     print(F"Load ckpt from {args.beit_checkpoint}"		)
     UpperCAmelCase__				:			List[str]							=    None
     for model_key in ("model", "module"):
          if model_key in checkpoint:
               UpperCAmelCase__				:			List[Any]							=    checkpoint[model_key]
               print(F"Load state_dict by model_key = {model_key}"		)
               break

     UpperCAmelCase__				:			Dict							=    list(checkpoint_model.keys()		)
     for key in all_keys:
          if "relative_position_index" in key:
               checkpoint_model.pop(lowerCAmelCase		)

          if "relative_position_bias_table" in key:
               UpperCAmelCase__				:			Dict							=    checkpoint_model[key]
               UpperCAmelCase__				,						UpperCAmelCase__				:			Optional[int]							=    rel_pos_bias.size()
               UpperCAmelCase__				,						UpperCAmelCase__				:			List[str]							=    model.state_dict()[key].size()
               UpperCAmelCase__				:			Tuple							=    model.patch_embed.patch_shape
               if dst_patch_shape[0] != dst_patch_shape[1]:
                    raise NotImplementedError()

     load_state_dict(lowerCAmelCase							,     lowerCAmelCase							,     prefix=""		)

     return model



def 				a__			(					):






     '''simple docstring'''
     UpperCAmelCase__				:			Tuple							=    get_args()

     UpperCAmelCase__				:			List[str]							=    "ft1k" in args.hf_checkpoint_name
     UpperCAmelCase__				:			str							=    "large" in args.hf_checkpoint_name

     if is_finetuned:
          # To convert Beit's data2vec_vision to HF you need to copy
          # https://github.com/facebookresearch/data2vec_vision/blob/main/beit/modeling_finetune.py
          # into this folder.
          import modeling_finetune  # noqa: F401
     else:
          # To convert Beit's data2vec_vision to HF you need to copy
          # https://github.com/facebookresearch/data2vec_vision/blob/main/beit/modeling_cyclical.py
          # into this folder
          # IMPORTANT: Note that for now we've only converted the down-stream
          # model and not the full pretrained model. This means for the integration
          # test you need to add a `return x` after the following line:
          # https://github.com/facebookresearch/data2vec_vision/blob/af9a36349aaed59ae66e69b5dabeef2d62fdc5da/beit/modeling_cyclical.py#L197
          # to make the integration test pass.
          import modeling_cyclical  # noqa: F401

     # 1. Create model config
     UpperCAmelCase__				:			Optional[Any]							=    DataaVecVisionConfig()
     if is_finetuned:
          UpperCAmelCase__				:			Union[str, Any]							=    True
          UpperCAmelCase__				:			int							=    False
          UpperCAmelCase__				:			List[str]							=    True
          UpperCAmelCase__				:			int							=    1000

          UpperCAmelCase__				:			int							=    "huggingface/label-files"
          UpperCAmelCase__				:			List[str]							=    "imagenet-1k-id2label.json"
          UpperCAmelCase__				:			str							=    json.load(open(hf_hub_download(lowerCAmelCase							,     lowerCAmelCase							,     repo_type="dataset"		)							,     "r"		)		)
          UpperCAmelCase__				:			Union[str, Any]							=    {int(lowerCAmelCase		): v for k, v in idalabel.items()}
          UpperCAmelCase__				:			Tuple							=    idalabel
          UpperCAmelCase__				:			Optional[int]							=    {v: k for k, v in idalabel.items()}
     else:
          UpperCAmelCase__				:			Union[str, Any]							=    False
          UpperCAmelCase__				:			Tuple							=    True
          UpperCAmelCase__				:			Tuple							=    False

     if is_large:
          UpperCAmelCase__				:			Any							=    1024
          UpperCAmelCase__				:			Dict							=    4096
          UpperCAmelCase__				:			str							=    24
          UpperCAmelCase__				:			Tuple							=    16

     # 2. Load Beit model
     UpperCAmelCase__				:			Tuple							=    load_beit_model(lowerCAmelCase							,     lowerCAmelCase							,     lowerCAmelCase		)
     orig_model.eval()

     # 3. Forward Beit model
     UpperCAmelCase__				:			Any							=    BeitImageProcessor(size=config.image_size							,     do_center_crop=lowerCAmelCase		)
     UpperCAmelCase__				:			List[str]							=    Image.open("../../../../tests/fixtures/tests_samples/COCO/000000039769.png"		)
     UpperCAmelCase__				:			int							=    image_processor(images=lowerCAmelCase							,     return_tensors="pt"		)
     UpperCAmelCase__				:			Tuple							=    encoding["pixel_values"]

     UpperCAmelCase__				:			Any							=    (pixel_values,) if is_finetuned else (pixel_values, None)
     with torch.no_grad():
          UpperCAmelCase__				:			Dict							=    orig_model(*lowerCAmelCase		)

     # 4. Load HF Data2VecVision model
     if is_finetuned:
          UpperCAmelCase__				:			str							=    DataaVecVisionForImageClassification(lowerCAmelCase		)
          hf_model.eval()
          UpperCAmelCase__				:			Any							=    False
          UpperCAmelCase__				:			List[str]							=    "data2vec_vision."
     else:
          UpperCAmelCase__				:			List[str]							=    DataaVecVisionModel(lowerCAmelCase		)
          hf_model.eval()
          UpperCAmelCase__				:			Any							=    True
          UpperCAmelCase__				:			List[str]							=    ""

     UpperCAmelCase__				:			Optional[int]							=    create_rename_keys(lowerCAmelCase							,     hf_prefix=lowerCAmelCase							,     has_lm_head=lowerCAmelCase		)
     UpperCAmelCase__				:			str							=    orig_model.state_dict()
     for src, dest in rename_keys:
          UpperCAmelCase__				:			Tuple							=    state_dict.pop(lowerCAmelCase		)
          UpperCAmelCase__				:			List[Any]							=    val

     read_in_q_k_v(lowerCAmelCase							,     lowerCAmelCase							,     hf_prefix=lowerCAmelCase							,     has_lm_head=lowerCAmelCase		)
     UpperCAmelCase__				,						UpperCAmelCase__				:			Tuple							=    hf_model.load_state_dict(lowerCAmelCase							,     strict=lowerCAmelCase		)
     print("HF missing"							,     lowerCAmelCase		)
     print("HF unexpected_keys"							,     lowerCAmelCase		)

     # 5. Forward HF Data2VecVision model
     with torch.no_grad():
          UpperCAmelCase__				:			Optional[int]							=    hf_model(lowerCAmelCase		)

     UpperCAmelCase__				:			List[str]							=    hf_model_output.logits if is_finetuned else hf_model_output.last_hidden_state

     # 6. Compare
     UpperCAmelCase__				:			str							=    torch.max(torch.abs(hf_output - orig_model_output		)		).item()

     print(F"max_absolute_diff = {max_absolute_diff}"		)
     UpperCAmelCase__				:			Union[str, Any]							=    torch.allclose(lowerCAmelCase							,     lowerCAmelCase							,     atol=1E-3		)
     print("Do both models output the same tensors?"							,     "🔥" if success else "💩"		)
     if not success:
          raise Exception("Something went wRoNg"		)

     # 7. Save
     print(F"Saving to {args.hf_checkpoint_name}"		)
     hf_model.save_pretrained(args.hf_checkpoint_name		)
     image_processor.save_pretrained(args.hf_checkpoint_name		)


if __name__ == "__main__":
    main()
    # Run the following to convert checkpoints
    #  python ./convert_data2vec_vision_original_pytorch_checkpoint_to_pytorch.py \
    #          --beit_checkpoint ./pretrained_base.pt \
    #          --hf_checkpoint_name "./data2vec-vision-base"
    #  python ./convert_data2vec_vision_original_pytorch_checkpoint_to_pytorch.py \
    #          --beit_checkpoint ./finetuned_base.pt \
    #          --hf_checkpoint_name "./data2vec-vision-base-ft1k"
    #  python ./convert_data2vec_vision_original_pytorch_checkpoint_to_pytorch.py \
    #          --beit_checkpoint ./pretrained_large.pt \
    #          --hf_checkpoint_name "./data2vec-vision-large"
    #  python ./convert_data2vec_vision_original_pytorch_checkpoint_to_pytorch.py \
    #          --beit_checkpoint ./finetuned_large.pt \
    #          --hf_checkpoint_name "./data2vec-vision-large-ft1k"





"""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 	_lowercase    (							lowerCAmelCase_					):


     '''simple docstring'''





     _A		    =				42







class 	_lowercase    (							lowerCAmelCase_						, lowerCAmelCase_					):


     '''simple docstring'''





     @register_to_config
     def __init__(   self			,      __UpperCamelCase = None			,      __UpperCamelCase = 3			,      __UpperCamelCase = 3			,      __UpperCamelCase = False			,      __UpperCamelCase = "positional"			,      __UpperCamelCase = 0			,      __UpperCamelCase = True			,      __UpperCamelCase = ("DownBlock2D", "AttnDownBlock2D", "AttnDownBlock2D", "AttnDownBlock2D")			,      __UpperCamelCase = ("AttnUpBlock2D", "AttnUpBlock2D", "AttnUpBlock2D", "UpBlock2D")			,      __UpperCamelCase = (2_24, 4_48, 6_72, 8_96)			,      __UpperCamelCase = 2			,      __UpperCamelCase = 1			,      __UpperCamelCase = 1			,      __UpperCamelCase = "conv"			,      __UpperCamelCase = "conv"			,      __UpperCamelCase = "silu"			,      __UpperCamelCase = 8			,      __UpperCamelCase = 32			,      __UpperCamelCase = 1E-5			,      __UpperCamelCase = "default"			,      __UpperCamelCase = True			,      __UpperCamelCase = None			,      __UpperCamelCase = None			,      )->       Any:
          super().__init__()

          UpperCAmelCase__				:			Optional[int]							=    sample_size
          UpperCAmelCase__				:			Any							=    block_out_channels[0] * 4

          # Check inputs
          if len(__UpperCamelCase	) != len(__UpperCamelCase	):
               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(__UpperCamelCase	) != len(__UpperCamelCase	):
               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
          UpperCAmelCase__				:			List[Any]							=    nn.Convad(__UpperCamelCase			,      block_out_channels[0]			,      kernel_size=3			,      padding=(1, 1)	)

          # time
          if time_embedding_type == "fourier":
               UpperCAmelCase__				:			int							=    GaussianFourierProjection(embedding_size=block_out_channels[0]			,      scale=16	)
               UpperCAmelCase__				:			Dict							=    2 * block_out_channels[0]
          elif time_embedding_type == "positional":
               UpperCAmelCase__				:			List[str]							=    Timesteps(block_out_channels[0]			,      __UpperCamelCase			,      __UpperCamelCase	)
               UpperCAmelCase__				:			Any							=    block_out_channels[0]

          UpperCAmelCase__				:			List[str]							=    TimestepEmbedding(__UpperCamelCase			,      __UpperCamelCase	)

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

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

          # down
          UpperCAmelCase__				:			Any							=    block_out_channels[0]
          for i, down_block_type in enumerate(__UpperCamelCase	):
               UpperCAmelCase__				:			List[Any]							=    output_channel
               UpperCAmelCase__				:			Optional[int]							=    block_out_channels[i]
               UpperCAmelCase__				:			Any							=    i == len(__UpperCamelCase	) - 1

               UpperCAmelCase__				:			List[Any]							=    get_down_block(
                   __UpperCamelCase			,      num_layers=__UpperCamelCase			,      in_channels=__UpperCamelCase			,      out_channels=__UpperCamelCase			,      temb_channels=__UpperCamelCase			,      add_downsample=not is_final_block			,      resnet_eps=__UpperCamelCase			,      resnet_act_fn=__UpperCamelCase			,      resnet_groups=__UpperCamelCase			,      attention_head_dim=attention_head_dim if attention_head_dim is not None else output_channel			,      downsample_padding=__UpperCamelCase			,      resnet_time_scale_shift=__UpperCamelCase			,      downsample_type=__UpperCamelCase			,      )
               self.down_blocks.append(__UpperCamelCase	)

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

          # up
          UpperCAmelCase__				:			Tuple							=    list(reversed(__UpperCamelCase	)	)
          UpperCAmelCase__				:			Optional[Any]							=    reversed_block_out_channels[0]
          for i, up_block_type in enumerate(__UpperCamelCase	):
               UpperCAmelCase__				:			int							=    output_channel
               UpperCAmelCase__				:			List[Any]							=    reversed_block_out_channels[i]
               UpperCAmelCase__				:			Dict							=    reversed_block_out_channels[min(i + 1			,      len(__UpperCamelCase	) - 1	)]

               UpperCAmelCase__				:			int							=    i == len(__UpperCamelCase	) - 1

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

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



     def 			lowerCAmelCase__							(   self			,      __UpperCamelCase			,      __UpperCamelCase			,      __UpperCamelCase = None			,      __UpperCamelCase = True			,      )->       Union[UNetaDOutput, Tuple]:
          # 0. center input if necessary
          if self.config.center_input_sample:
               UpperCAmelCase__				:			Any							=    2 * sample - 1.0

          # 1. time
          UpperCAmelCase__				:			Tuple							=    timestep
          if not torch.is_tensor(__UpperCamelCase	):
               UpperCAmelCase__				:			Optional[int]							=    torch.tensor([timesteps]			,      dtype=torch.long			,      device=sample.device	)
          elif torch.is_tensor(__UpperCamelCase	) and len(timesteps.shape	) == 0:
               UpperCAmelCase__				:			List[Any]							=    timesteps[None].to(sample.device	)

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

          UpperCAmelCase__				:			List[str]							=    self.time_proj(__UpperCamelCase	)

          # 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.
          UpperCAmelCase__				:			Dict							=    t_emb.to(dtype=self.dtype	)
          UpperCAmelCase__				:			Optional[int]							=    self.time_embedding(__UpperCamelCase	)

          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":
                    UpperCAmelCase__				:			List[Any]							=    self.time_proj(__UpperCamelCase	)

               UpperCAmelCase__				:			List[str]							=    self.class_embedding(__UpperCamelCase	).to(dtype=self.dtype	)
               UpperCAmelCase__				:			Optional[int]							=    emb + class_emb

          # 2. pre-process
          UpperCAmelCase__				:			List[str]							=    sample
          UpperCAmelCase__				:			str							=    self.conv_in(__UpperCamelCase	)

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

               down_block_res_samples += res_samples

          # 4. mid
          UpperCAmelCase__				:			Union[str, Any]							=    self.mid_block(__UpperCamelCase			,      __UpperCamelCase	)

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

               if hasattr(__UpperCamelCase			,      "skip_conv"	):
                    UpperCAmelCase__				,						UpperCAmelCase__				:			str							=    upsample_block(__UpperCamelCase			,      __UpperCamelCase			,      __UpperCamelCase			,      __UpperCamelCase	)
               else:
                    UpperCAmelCase__				:			Tuple							=    upsample_block(__UpperCamelCase			,      __UpperCamelCase			,      __UpperCamelCase	)

        # 6. post-process
          UpperCAmelCase__				:			List[Any]							=    self.conv_norm_out(__UpperCamelCase	)
          UpperCAmelCase__				:			Any							=    self.conv_act(__UpperCamelCase	)
          UpperCAmelCase__				:			Optional[Any]							=    self.conv_out(__UpperCamelCase	)

          if skip_sample is not None:
               sample += skip_sample

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

          if not return_dict:
               return (sample,)

          return UNetaDOutput(sample=__UpperCamelCase	)



