





'''simple docstring'''






import argparse
import json
from pathlib import Path

import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform

from transformers import (
    BitConfig,
    ViTHybridConfig,
    ViTHybridForImageClassification,
    ViTHybridImageProcessor,
    ViTHybridModel,
)
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


logging.set_verbosity_info()
A_		:  Optional[int]		 =						logging.get_logger(__name__)



def        UpperCamelCase__		(						__magic_name__  :   Optional[Any]		,       __magic_name__  :   str=False      )				->			Tuple:
 '''simple docstring'''






 snake_case__     :					int    =  []

 # fmt: off
 # stem:
 rename_keys.append(("""cls_token""", """vit.embeddings.cls_token""")      )
 rename_keys.append(("""pos_embed""", """vit.embeddings.position_embeddings""")      )

 rename_keys.append(("""patch_embed.proj.weight""", """vit.embeddings.patch_embeddings.projection.weight""")      )
 rename_keys.append(("""patch_embed.proj.bias""", """vit.embeddings.patch_embeddings.projection.bias""")      )

 # backbone
 rename_keys.append(("""patch_embed.backbone.stem.conv.weight""", """vit.embeddings.patch_embeddings.backbone.bit.embedder.convolution.weight""")      )
 rename_keys.append(("""patch_embed.backbone.stem.norm.weight""", """vit.embeddings.patch_embeddings.backbone.bit.embedder.norm.weight""")      )
 rename_keys.append(("""patch_embed.backbone.stem.norm.bias""", """vit.embeddings.patch_embeddings.backbone.bit.embedder.norm.bias""")      )

 for stage_idx in range(len(config.backbone_config.depths      )      ):
  for layer_idx in range(config.backbone_config.depths[stage_idx]      ):
   rename_keys.append((f"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv1.weight", f"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv1.weight")      )
   rename_keys.append((f"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm1.weight", f"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm1.weight")      )
   rename_keys.append((f"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm1.bias", f"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm1.bias")      )
   rename_keys.append((f"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv2.weight", f"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv2.weight")      )
   rename_keys.append((f"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm2.weight", f"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm2.weight")      )
   rename_keys.append((f"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm2.bias", f"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm2.bias")      )
   rename_keys.append((f"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv3.weight", f"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv3.weight")      )
   rename_keys.append((f"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm3.weight", f"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm3.weight")      )
   rename_keys.append((f"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm3.bias", f"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm3.bias")      )

  rename_keys.append((f"patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.conv.weight", f"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.conv.weight")      )
  rename_keys.append((f"patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.norm.weight", f"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.norm.weight")      )
  rename_keys.append((f"patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.norm.bias", f"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.norm.bias")      )

 # transformer encoder
 for i in range(config.num_hidden_layers      ):
  # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
  rename_keys.append((f"blocks.{i}.norm1.weight", f"vit.encoder.layer.{i}.layernorm_before.weight")      )
  rename_keys.append((f"blocks.{i}.norm1.bias", f"vit.encoder.layer.{i}.layernorm_before.bias")      )
  rename_keys.append((f"blocks.{i}.attn.proj.weight", f"vit.encoder.layer.{i}.attention.output.dense.weight")      )
  rename_keys.append((f"blocks.{i}.attn.proj.bias", f"vit.encoder.layer.{i}.attention.output.dense.bias")      )
  rename_keys.append((f"blocks.{i}.norm2.weight", f"vit.encoder.layer.{i}.layernorm_after.weight")      )
  rename_keys.append((f"blocks.{i}.norm2.bias", f"vit.encoder.layer.{i}.layernorm_after.bias")      )
  rename_keys.append((f"blocks.{i}.mlp.fc1.weight", f"vit.encoder.layer.{i}.intermediate.dense.weight")      )
  rename_keys.append((f"blocks.{i}.mlp.fc1.bias", f"vit.encoder.layer.{i}.intermediate.dense.bias")      )
  rename_keys.append((f"blocks.{i}.mlp.fc2.weight", f"vit.encoder.layer.{i}.output.dense.weight")      )
  rename_keys.append((f"blocks.{i}.mlp.fc2.bias", f"vit.encoder.layer.{i}.output.dense.bias")      )

 if base_model:
  # layernorm + pooler
  rename_keys.extend(
      [
          ("""norm.weight""", """layernorm.weight"""),
          ("""norm.bias""", """layernorm.bias"""),
          ("""pre_logits.fc.weight""", """pooler.dense.weight"""),
          ("""pre_logits.fc.bias""", """pooler.dense.bias"""),
      ]      )

  # if just the base model, we should remove "vit" from all keys that start with "vit"
  snake_case__     :					List[Any]    =  [(pair[0], pair[1][4:]) if pair[1].startswith("""vit"""      ) else pair for pair in rename_keys]
 else:
  # layernorm + classification head
  rename_keys.extend(
      [
          ("""norm.weight""", """vit.layernorm.weight"""),
          ("""norm.bias""", """vit.layernorm.bias"""),
          ("""head.weight""", """classifier.weight"""),
          ("""head.bias""", """classifier.bias"""),
      ]      )
 # fmt: on

 return rename_keys



def        UpperCamelCase__		(						__magic_name__  :   Tuple		,       __magic_name__  :   int		,       __magic_name__  :   Tuple=False      )				->			str:
 '''simple docstring'''






 for i in range(config.num_hidden_layers      ):
  if base_model:
   snake_case__     :					int    =  """"""
  else:
   snake_case__     :					Dict    =  """vit."""
  # read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
  snake_case__     :					int    =  state_dict.pop(f"blocks.{i}.attn.qkv.weight"      )
  snake_case__     :					Union[str, Any]    =  state_dict.pop(f"blocks.{i}.attn.qkv.bias"      )
  # next, add query, keys and values (in that order) to the state dict
  snake_case__     :					Optional[int]    =  in_proj_weight[
      : config.hidden_size, :
  ]
  snake_case__     :					Optional[Any]    =  in_proj_bias[: config.hidden_size]
  snake_case__     :					List[Any]    =  in_proj_weight[
      config.hidden_size : config.hidden_size * 2, :
  ]
  snake_case__     :					List[Any]    =  in_proj_bias[
      config.hidden_size : config.hidden_size * 2
  ]
  snake_case__     :					List[Any]    =  in_proj_weight[
      -config.hidden_size :, :
  ]
  snake_case__     :					Optional[int]    =  in_proj_bias[-config.hidden_size :]



def        UpperCamelCase__		(						__magic_name__  :   Optional[Any]      )				->			List[str]:
 '''simple docstring'''






 snake_case__     :					str    =  ["""head.weight""", """head.bias"""]
 for k in ignore_keys:
  state_dict.pop(__magic_name__		,       __magic_name__      )



def        UpperCamelCase__		(						__magic_name__  :   List[str]		,       __magic_name__  :   Union[str, Any]		,       __magic_name__  :   str      )				->			Union[str, Any]:
 '''simple docstring'''






 snake_case__     :					List[str]    =  dct.pop(__magic_name__      )
 snake_case__     :					Dict    =  val



def        UpperCamelCase__		(						)				->			str:
 '''simple docstring'''






 snake_case__     :					Optional[int]    =  """http://images.cocodataset.org/val2017/000000039769.jpg"""
 snake_case__     :					Optional[int]    =  Image.open(requests.get(__magic_name__		,       stream=__magic_name__      ).raw      )
 return im



@torch.no_grad()
def        UpperCamelCase__		(						__magic_name__  :   List[Any]		,       __magic_name__  :   Union[str, Any]		,       __magic_name__  :   int=False      )				->			Optional[int]:
 '''simple docstring'''






 snake_case__     :					int    =  BitConfig(
     global_padding="""same"""		,       layer_type="""bottleneck"""		,       depths=(3, 4, 9)		,       out_features=["""stage3"""]		,       embedding_dynamic_padding=__magic_name__		,       )
 snake_case__     :					Optional[int]    =  ViTHybridConfig(backbone_config=__magic_name__		,       image_size=3_84		,       num_labels=10_00      )
 snake_case__     :					Union[str, Any]    =  False

 # load original model from timm
 snake_case__     :					List[Any]    =  timm.create_model(__magic_name__		,       pretrained=__magic_name__      )
 timm_model.eval()

 # load state_dict of original model, remove and rename some keys
 snake_case__     :					Optional[int]    =  timm_model.state_dict()
 if base_model:
  remove_classification_head_(__magic_name__      )
 snake_case__     :					int    =  create_rename_keys(__magic_name__		,       __magic_name__      )
 for src, dest in rename_keys:
  rename_key(__magic_name__		,       __magic_name__		,       __magic_name__      )
 read_in_q_k_v(__magic_name__		,       __magic_name__		,       __magic_name__      )

 snake_case__     :					str    =  """huggingface/label-files"""
 snake_case__     :					Union[str, Any]    =  """imagenet-1k-id2label.json"""
 snake_case__     :					Dict    =  json.load(open(hf_hub_download(__magic_name__		,       __magic_name__		,       repo_type="""dataset"""      )		,       """r"""      )      )
 snake_case__     :					List[Any]    =  {int(__magic_name__      ): v for k, v in idalabel.items()}
 snake_case__     :					int    =  idalabel
 snake_case__     :					str    =  {v: k for k, v in idalabel.items()}

 # load HuggingFace model
 if vit_name[-5:] == "in21k":
  snake_case__     :					str    =  ViTHybridModel(__magic_name__      ).eval()
 else:
  snake_case__     :					Union[str, Any]    =  ViTHybridForImageClassification(__magic_name__      ).eval()
 model.load_state_dict(__magic_name__      )

 # create image processor
 snake_case__     :					Optional[Any]    =  create_transform(**resolve_data_config({}		,       model=__magic_name__      )      )
 snake_case__     :					Union[str, Any]    =  transform.transforms

 snake_case__     :					Tuple    =  {
     """bilinear""": PILImageResampling.BILINEAR,
     """bicubic""": PILImageResampling.BICUBIC,
     """nearest""": PILImageResampling.NEAREST,
 }

 snake_case__     :					Any    =  ViTHybridImageProcessor(
     do_resize=__magic_name__		,       size={"""shortest_edge""": timm_transforms[0].size}		,       resample=pillow_resamplings[timm_transforms[0].interpolation.value]		,       do_center_crop=__magic_name__		,       crop_size={"""height""": timm_transforms[1].size[0], """width""": timm_transforms[1].size[1]}		,       do_normalize=__magic_name__		,       image_mean=timm_transforms[-1].mean.tolist()		,       image_std=timm_transforms[-1].std.tolist()		,       )

 snake_case__     :					Any    =  prepare_img()
 snake_case__     :					int    =  transform(__magic_name__      ).unsqueeze(0      )
 snake_case__     :					List[str]    =  processor(__magic_name__		,       return_tensors="""pt"""      ).pixel_values

 # verify pixel values
 assert torch.allclose(__magic_name__		,       __magic_name__      )

 # verify logits
 with torch.no_grad():
  snake_case__     :					Optional[Any]    =  model(__magic_name__      )
  snake_case__     :					Union[str, Any]    =  outputs.logits

 print("""Predicted class:"""		,       logits.argmax(-1      ).item()      )
 if base_model:
  snake_case__     :					Dict    =  timm_model.forward_features(__magic_name__      )
  assert timm_pooled_output.shape == outputs.pooler_output.shape
  assert torch.allclose(__magic_name__		,       outputs.pooler_output		,       atol=1E-3      )
 else:
  snake_case__     :					int    =  timm_model(__magic_name__      )
  assert timm_logits.shape == outputs.logits.shape
  assert torch.allclose(__magic_name__		,       outputs.logits		,       atol=1E-3      )
 print("""Looks ok!"""      )

 if pytorch_dump_folder_path is not None:
  Path(__magic_name__      ).mkdir(exist_ok=__magic_name__      )
  print(f"Saving model {vit_name} to {pytorch_dump_folder_path}"      )
  model.save_pretrained(__magic_name__      )
  print(f"Saving processor to {pytorch_dump_folder_path}"      )
  processor.save_pretrained(__magic_name__      )

 if push_to_hub:
  print(f"Pushing model and processor to the hub {vit_name}"      )
  model.push_to_hub(f"ybelkada/{vit_name}"      )
  processor.push_to_hub(f"ybelkada/{vit_name}"      )


if __name__ == "__main__":
    A_		:  List[str]		 =						argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        "--vit_name",
        default="vit_base_r50_s16_384",
        type=str,
        help="Name of the hybrid ViT timm model you'd like to convert.",
    )
    parser.add_argument(
        "--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
    )
    parser.add_argument(
        "--push_to_hub", action="store_true", help="Whether to upload the model to the HuggingFace hub."
    )

    A_		:  Union[str, Any]		 =						parser.parse_args()
    convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path, args.push_to_hub)












'''simple docstring'''







import argparse
import os

import torch

from diffusers import (
    CMStochasticIterativeScheduler,
    ConsistencyModelPipeline,
    UNetaDModel,
)


__lowerCAmelCase									=   {
    'sample_size': 32,
    'in_channels': 3,
    'out_channels': 3,
    'layers_per_block': 2,
    'num_class_embeds': 1_000,
    'block_out_channels': [32, 64],
    'attention_head_dim': 8,
    'down_block_types': [
        'ResnetDownsampleBlock2D',
        'AttnDownBlock2D',
    ],
    'up_block_types': [
        'AttnUpBlock2D',
        'ResnetUpsampleBlock2D',
    ],
    'resnet_time_scale_shift': 'scale_shift',
    'upsample_type': 'resnet',
    'downsample_type': 'resnet',
}

__lowerCAmelCase									=   {
    'sample_size': 64,
    'in_channels': 3,
    'out_channels': 3,
    'layers_per_block': 3,
    'num_class_embeds': 1_000,
    'block_out_channels': [192, 192 * 2, 192 * 3, 192 * 4],
    'attention_head_dim': 64,
    'down_block_types': [
        'ResnetDownsampleBlock2D',
        'AttnDownBlock2D',
        'AttnDownBlock2D',
        'AttnDownBlock2D',
    ],
    'up_block_types': [
        'AttnUpBlock2D',
        'AttnUpBlock2D',
        'AttnUpBlock2D',
        'ResnetUpsampleBlock2D',
    ],
    'resnet_time_scale_shift': 'scale_shift',
    'upsample_type': 'resnet',
    'downsample_type': 'resnet',
}

__lowerCAmelCase									=   {
    'sample_size': 256,
    'in_channels': 3,
    'out_channels': 3,
    'layers_per_block': 2,
    'num_class_embeds': None,
    'block_out_channels': [256, 256, 256 * 2, 256 * 2, 256 * 4, 256 * 4],
    'attention_head_dim': 64,
    'down_block_types': [
        'ResnetDownsampleBlock2D',
        'ResnetDownsampleBlock2D',
        'ResnetDownsampleBlock2D',
        'AttnDownBlock2D',
        'AttnDownBlock2D',
        'AttnDownBlock2D',
    ],
    'up_block_types': [
        'AttnUpBlock2D',
        'AttnUpBlock2D',
        'AttnUpBlock2D',
        'ResnetUpsampleBlock2D',
        'ResnetUpsampleBlock2D',
        'ResnetUpsampleBlock2D',
    ],
    'resnet_time_scale_shift': 'default',
    'upsample_type': 'resnet',
    'downsample_type': 'resnet',
}

__lowerCAmelCase									=   {
    'num_train_timesteps': 40,
    'sigma_min': 0.002,
    'sigma_max': 80.0,
}

__lowerCAmelCase									=   {
    'num_train_timesteps': 201,
    'sigma_min': 0.002,
    'sigma_max': 80.0,
}

__lowerCAmelCase									=   {
    'num_train_timesteps': 151,
    'sigma_min': 0.002,
    'sigma_max': 80.0,
}





def 		_UpperCAmelCase (				__A				: Tuple   ):
				if isinstance(__A    ,		__A   ):
								return v
				if v.lower() in ("yes", "true", "t", "y", "1"):
								return True
				elif v.lower() in ("no", "false", "f", "n", "0"):
								return False
				else:
								raise argparse.ArgumentTypeError('''boolean value expected'''   )





def 		_UpperCAmelCase (				__A				: str    ,		__A				: Tuple    ,		__A				: Optional[int]    ,		__A				: int    ,		__A				: Optional[Any]=False   ):
				a_		:	str									=   checkpoint[f'{old_prefix}.in_layers.0.weight']
				a_		:	Dict									=   checkpoint[f'{old_prefix}.in_layers.0.bias']
				a_		:	Optional[Any]									=   checkpoint[f'{old_prefix}.in_layers.2.weight']
				a_		:	Any									=   checkpoint[f'{old_prefix}.in_layers.2.bias']
				a_		:	Any									=   checkpoint[f'{old_prefix}.emb_layers.1.weight']
				a_		:	int									=   checkpoint[f'{old_prefix}.emb_layers.1.bias']
				a_		:	Union[str, Any]									=   checkpoint[f'{old_prefix}.out_layers.0.weight']
				a_		:	Optional[Any]									=   checkpoint[f'{old_prefix}.out_layers.0.bias']
				a_		:	Optional[Any]									=   checkpoint[f'{old_prefix}.out_layers.3.weight']
				a_		:	List[Any]									=   checkpoint[f'{old_prefix}.out_layers.3.bias']

				if has_skip:
								a_		:	int									=   checkpoint[f'{old_prefix}.skip_connection.weight']
								a_		:	List[Any]									=   checkpoint[f'{old_prefix}.skip_connection.bias']

				return new_checkpoint





def 		_UpperCAmelCase (				__A				: int    ,		__A				: int    ,		__A				: Union[str, Any]    ,		__A				: Optional[int]    ,		__A				: List[str]=None   ):
				a_					,      a_					,      a_		:	Any									=   checkpoint[f'{old_prefix}.qkv.weight'].chunk(3    ,		dim=0   )
				a_					,      a_					,      a_		:	List[Any]									=   checkpoint[f'{old_prefix}.qkv.bias'].chunk(3    ,		dim=0   )

				a_		:	List[str]									=   checkpoint[f'{old_prefix}.norm.weight']
				a_		:	Optional[Any]									=   checkpoint[f'{old_prefix}.norm.bias']

				a_		:	Any									=   weight_q.squeeze(-1   ).squeeze(-1   )
				a_		:	int									=   bias_q.squeeze(-1   ).squeeze(-1   )
				a_		:	Any									=   weight_k.squeeze(-1   ).squeeze(-1   )
				a_		:	Optional[int]									=   bias_k.squeeze(-1   ).squeeze(-1   )
				a_		:	str									=   weight_v.squeeze(-1   ).squeeze(-1   )
				a_		:	Optional[Any]									=   bias_v.squeeze(-1   ).squeeze(-1   )

				a_		:	Union[str, Any]									=   (
				    checkpoint[f'{old_prefix}.proj_out.weight'].squeeze(-1   ).squeeze(-1   )
				)
				a_		:	Any									=   checkpoint[f'{old_prefix}.proj_out.bias'].squeeze(-1   ).squeeze(-1   )

				return new_checkpoint





def 		_UpperCAmelCase (				__A				: str    ,		__A				: str   ):
				a_		:	int									=   torch.load(__A    ,		map_location='''cpu'''   )
				a_		:	Union[str, Any]									=   {}

				a_		:	int									=   checkpoint['''time_embed.0.weight''']
				a_		:	Tuple									=   checkpoint['''time_embed.0.bias''']
				a_		:	List[Any]									=   checkpoint['''time_embed.2.weight''']
				a_		:	Union[str, Any]									=   checkpoint['''time_embed.2.bias''']

				if unet_config["num_class_embeds"] is not None:
								a_		:	List[Any]									=   checkpoint['''label_emb.weight''']

				a_		:	Optional[int]									=   checkpoint['''input_blocks.0.0.weight''']
				a_		:	int									=   checkpoint['''input_blocks.0.0.bias''']

				a_		:	Optional[int]									=   unet_config['''down_block_types''']
				a_		:	Union[str, Any]									=   unet_config['''layers_per_block''']
				a_		:	Optional[Any]									=   unet_config['''attention_head_dim''']
				a_		:	str									=   unet_config['''block_out_channels''']
				a_		:	Any									=   1
				a_		:	Union[str, Any]									=   channels_list[0]

				for i, layer_type in enumerate(__A   ):
								a_		:	Any									=   channels_list[i]
								a_		:	Tuple									=   current_channels != prev_channels
								if layer_type == "ResnetDownsampleBlock2D":
												for j in range(__A   ):
																a_		:	Union[str, Any]									=   f'down_blocks.{i}.resnets.{j}'
																a_		:	int									=   f'input_blocks.{current_layer}.0'
																a_		:	List[Any]									=   True if j == 0 and downsample_block_has_skip else False
																a_		:	List[str]									=   convert_resnet(__A    ,		__A    ,		__A    ,		__A    ,		has_skip=__A   )
																current_layer += 1

								elif layer_type == "AttnDownBlock2D":
												for j in range(__A   ):
																a_		:	Union[str, Any]									=   f'down_blocks.{i}.resnets.{j}'
																a_		:	Optional[int]									=   f'input_blocks.{current_layer}.0'
																a_		:	Optional[int]									=   True if j == 0 and downsample_block_has_skip else False
																a_		:	Any									=   convert_resnet(__A    ,		__A    ,		__A    ,		__A    ,		has_skip=__A   )
																a_		:	Optional[int]									=   f'down_blocks.{i}.attentions.{j}'
																a_		:	List[Any]									=   f'input_blocks.{current_layer}.1'
																a_		:	int									=   convert_attention(
																    __A    ,		__A    ,		__A    ,		__A    ,		__A   )
																current_layer += 1

								if i != len(__A   ) - 1:
												a_		:	Dict									=   f'down_blocks.{i}.downsamplers.0'
												a_		:	Optional[int]									=   f'input_blocks.{current_layer}.0'
												a_		:	Union[str, Any]									=   convert_resnet(__A    ,		__A    ,		__A    ,		__A   )
												current_layer += 1

								a_		:	Any									=   current_channels

				# hardcoded the mid-block for now
				a_		:	Tuple									=   '''mid_block.resnets.0'''
				a_		:	int									=   '''middle_block.0'''
				a_		:	Tuple									=   convert_resnet(__A    ,		__A    ,		__A    ,		__A   )
				a_		:	List[Any]									=   '''mid_block.attentions.0'''
				a_		:	Optional[int]									=   '''middle_block.1'''
				a_		:	Optional[int]									=   convert_attention(__A    ,		__A    ,		__A    ,		__A    ,		__A   )
				a_		:	Any									=   '''mid_block.resnets.1'''
				a_		:	Tuple									=   '''middle_block.2'''
				a_		:	Tuple									=   convert_resnet(__A    ,		__A    ,		__A    ,		__A   )

				a_		:	str									=   0
				a_		:	Optional[int]									=   unet_config['''up_block_types''']

				for i, layer_type in enumerate(__A   ):
								if layer_type == "ResnetUpsampleBlock2D":
												for j in range(layers_per_block + 1   ):
																a_		:	Tuple									=   f'up_blocks.{i}.resnets.{j}'
																a_		:	Optional[int]									=   f'output_blocks.{current_layer}.0'
																a_		:	Optional[Any]									=   convert_resnet(__A    ,		__A    ,		__A    ,		__A    ,		has_skip=__A   )
																current_layer += 1

												if i != len(__A   ) - 1:
																a_		:	int									=   f'up_blocks.{i}.upsamplers.0'
																a_		:	Union[str, Any]									=   f'output_blocks.{current_layer-1}.1'
																a_		:	int									=   convert_resnet(__A    ,		__A    ,		__A    ,		__A   )
								elif layer_type == "AttnUpBlock2D":
												for j in range(layers_per_block + 1   ):
																a_		:	Optional[Any]									=   f'up_blocks.{i}.resnets.{j}'
																a_		:	str									=   f'output_blocks.{current_layer}.0'
																a_		:	List[Any]									=   convert_resnet(__A    ,		__A    ,		__A    ,		__A    ,		has_skip=__A   )
																a_		:	List[Any]									=   f'up_blocks.{i}.attentions.{j}'
																a_		:	List[str]									=   f'output_blocks.{current_layer}.1'
																a_		:	str									=   convert_attention(
																    __A    ,		__A    ,		__A    ,		__A    ,		__A   )
																current_layer += 1

												if i != len(__A   ) - 1:
																a_		:	Dict									=   f'up_blocks.{i}.upsamplers.0'
																a_		:	Any									=   f'output_blocks.{current_layer-1}.2'
																a_		:	int									=   convert_resnet(__A    ,		__A    ,		__A    ,		__A   )

				a_		:	Dict									=   checkpoint['''out.0.weight''']
				a_		:	int									=   checkpoint['''out.0.bias''']
				a_		:	int									=   checkpoint['''out.2.weight''']
				a_		:	str									=   checkpoint['''out.2.bias''']

				return new_checkpoint


if __name__ == "__main__":
		__lowerCAmelCase									=   argparse.ArgumentParser()

		parser.add_argument('--unet_path', default=None, type=str, required=True, help='Path to the unet.pt to convert.')
		parser.add_argument(
		    '--dump_path', default=None, type=str, required=True, help='Path to output the converted UNet model.'
		)
		parser.add_argument('--class_cond', default=True, type=str, help='Whether the model is class-conditional.')

		__lowerCAmelCase									=   parser.parse_args()
		__lowerCAmelCase									=   strabool(args.class_cond)

		__lowerCAmelCase									=   os.path.basename(args.unet_path)
		print(F"""Checkpoint: {ckpt_name}""")

		# Get U-Net config
		if "imagenet64" in ckpt_name:
				__lowerCAmelCase									=   IMAGENET_64_UNET_CONFIG
		elif "256" in ckpt_name and (("bedroom" in ckpt_name) or ("cat" in ckpt_name)):
				__lowerCAmelCase									=   LSUN_256_UNET_CONFIG
		elif "test" in ckpt_name:
				__lowerCAmelCase									=   TEST_UNET_CONFIG
		else:
				raise ValueError(F"""Checkpoint type {ckpt_name} is not currently supported.""")

		if not args.class_cond:
				__lowerCAmelCase									=   None

		__lowerCAmelCase									=   con_pt_to_diffuser(args.unet_path, unet_config)

		__lowerCAmelCase									=   UNetaDModel(**unet_config)
		image_unet.load_state_dict(converted_unet_ckpt)

		# Get scheduler config
		if "cd" in ckpt_name or "test" in ckpt_name:
				__lowerCAmelCase									=   CD_SCHEDULER_CONFIG
		elif "ct" in ckpt_name and "imagenet64" in ckpt_name:
				__lowerCAmelCase									=   CT_IMAGENET_64_SCHEDULER_CONFIG
		elif "ct" in ckpt_name and "256" in ckpt_name and (("bedroom" in ckpt_name) or ("cat" in ckpt_name)):
				__lowerCAmelCase									=   CT_LSUN_256_SCHEDULER_CONFIG
		else:
				raise ValueError(F"""Checkpoint type {ckpt_name} is not currently supported.""")

		__lowerCAmelCase									=   CMStochasticIterativeScheduler(**scheduler_config)

		__lowerCAmelCase									=   ConsistencyModelPipeline(unet=image_unet, scheduler=cm_scheduler)
		consistency_model.save_pretrained(args.dump_path)




